def generate_exponential_data(num_frames, num_measurements, scale):
prior_pops = ensemble_fitter.get_prior_pops(num_frames)
predictions = np.random.exponential(scale, size=(num_frames, num_measurements))
return prior_pops, predictions
def generate_gaussian_data(num_frames, num_measurements):
prior_pops = ensemble_fitter.get_prior_pops(num_frames)
predictions = np.random.normal(size=(num_frames, num_measurements))
return prior_pops, predictions
def generate_uniform_data(num_frames, num_measurements):
prior_pops = ensemble_fitter.get_prior_pops(num_frames)
predictions = np.random.uniform(0.0, 1.0, size=(num_frames, num_measurements))
return prior_pops, predictions
