Source code for deltasigma._ds_quantize

# -*- coding: utf-8 -*-
# This module provides the ds_quantize function.
# Copyright 2013 Giuseppe Venturini
# This file is part of python-deltasigma.
# python-deltasigma is a 1:1 Python replacement of Richard Schreier's
# MATLAB delta sigma toolbox (aka "delsigma"), upon which it is heavily based.
# The delta sigma toolbox is (c) 2009, Richard Schreier.
# python-deltasigma is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# LICENSE file for the licensing terms.

"""This module provides the ds_quantize() function, used to quantize signals
according to a user-specified quantizer characteristic.

import numpy as np

[docs]def ds_quantize(y, n=2): """Quantize ``y`` Quantize a vector :math:`y` to: * an odd integer in :math:`[-n+1, n-1]`, if :math:`n` is even, or * an even integer in :math:`[-n+1, n-1]`, if :math:`n` is odd. The quantizer implementation details are repeated here from its documentation for the user's convenience: The quantizer is ideal, producing integer outputs centered about zero. Quantizers with an even number of levels are of the mid-rise type and produce outputs which are odd integers. Quantizers with an odd number of levels are of the mid-tread type and produce outputs which are even integers. This definition gives the same step height for both mid-rise and mid-tread quantizers. .. image:: ../doc/_static/quantizer_model.png :align: center :alt: Quantizer model **Parameters:** n : int or ndarray, optional The number of levels in the quantizer. If ``n`` is an integer, then all rows of y are fed to the same quantizer. If ``n`` is a column vector, each of its elements specifies how to quantize the rows of ``y``. **Returns:** v : ndarray The quantized vector. .. seealso:: :func:`bquantize`, :func:`bunquantize` """ assert (np.round(n, 0) == n).all() # did we get an int or an array of int? if not isinstance(n, np.ndarray): n = n * np.ones(y.shape) # we got an int else: assert len(n.shape) == 1 or 1 in n.shape n = (np.ones((max(n.shape), y.shape[1])).T * n).T i = (n % 2 == 0) v = np.zeros(y.shape) v[i] = 2 * np.floor(0.5 * y[i]) + 1 # mid-rise quantizer v[~i] = 2 * np.floor(0.5 * (y[~i] + 1)) # mid-tread quantizer # Limit the output L = n - 1 for m in (-1, 1): i = (m * v > L) if i.any(): v[i] = m * L[i] return v