Source code for deltasigma._bilogplot

# -*- coding: utf-8 -*-
# Module providing the bilogplot 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.

"""Module providing the bilogplot() function

from __future__ import division

from warnings import warn

import numpy as np
import pylab as plt
from numpy.linalg import norm

from ._dbp import dbp
from ._utils import carray

[docs]def bilogplot(V, f0, fbin, x, y, **fmt): """Plot the spectrum of a band-pass modulator in dB. The plot is a logarithmic plot, centered in 0, corresponding to f0, extending to negative frequencies, with respect to the center frequencies and to positive frequencies. The plot employs a logarithmic x-axis transform far from the origin and a linear one close to it, allowing the x-axis to reach zero and extend to negative values as well. .. note:: This is implemented in a slightly different way from The MATLAB Delta Sigma Toolbox, where all values below ``xmin`` are clipped and the scale is always logarithmic. It our implementation, no clipping is done and below ``xmin`` the data is simply plotted with a linear scale. For this reason slightly different plots may be generated. **Parameters:** V : 1d-ndarray or sequence Hann-windowed FFT f0 : int Bin number of center frequency fbin : int Bin number of test tone x : 3-elements sequence-like x is a sequence of three *positive* floats: ``xmin``, ``xmax_left``, ``xmax_right``. ``xmin`` is the minimum value of the logarithmic plot range. ``xmax_left`` is the length of the plotting interval on the left (negative) side, ``xmax_right`` is its respective on the right (positive) side. y : 3-elements sequence-like y is a sequence of three floats: ``ymin``, ``ymax``, ``dy``. ``ymin`` is the minimum value of the y-axis, ``ymax`` its maximum value and ``dy`` is the ticks spacing. .. note:: The MATLAB Delta Sigma toolbox allows for a fourth option ``y_skip``, which is the ``incr`` value passed to MATLAB's ``axisLabels``. No such thing is supported here. A warning is issued if ``len(v) == 4``. Additional keyword parameters ``**fmt`` will be passed to matplotlib's ``semilogx()``. The FFT is smoothed before plotting and converted to dB. See :func:`logsmooth` for details regarding the algorithm used. **Returns:** *None* .. plot:: from __future__ import division from deltasigma import synthesizeNTF, simulateDSM from deltasigma import calculateSNR, ds_hann, bilogplot import pylab as plt import numpy as np f0 = 1./8 OSR = 64 order = 8 N = 8192 H = synthesizeNTF(order, OSR, 1, 1.5, f0) fB = int(np.ceil(N/(2. * OSR))) ftest = int(np.round(f0*N + 1./3 * fB)) u = 0.5*np.sin(2*np.pi*ftest/N*np.arange(N)) v, xn, xmax, y = simulateDSM(u, H) spec = np.fft.fft(v*ds_hann(N))/(N/4) X = spec[:N/2 + 1] plt.figure() bilogplot(X, f0*N, ftest, (.03, .3, .3), (-140, 0, 10)) """ V = carray(V) if len(V.shape) > 1: if > max(V.shape): raise ValueError("The input value V should have only one" + " non-unitary dimension.") V = V.squeeze() Xl = V[f0::-1] Xr = V[f0:] N = V.shape[0] - 1 fbin = abs(fbin - f0) fl, pl = _logsmooth2(Xl, fbin) fr, pr = _logsmooth2(Xr, fbin) p = np.concatenate((pl[::-1], pr)) f = np.concatenate((-fl[::-1], fr)) plt.plot(f, p, **fmt) plt.xscale('symlog', linthreshx=x[0], subsx=np.logspace(10**int(np.ceil(np.log10(x[0]))), 10**int(1+np.ceil(np.log10(max(x[2], x[1]))))) ) ax = plt.gca() ax.set_xlim([-x[1], x[2]]) ax.set_ylim([y[0], y[1]]) plt.grid(True) ytix = range(y[0], y[1] + 1, y[2]) ax.yaxis.set_ticks(ytix) # we do not support axis labels # set_(gca,'YTickLabel', axisLabels(ytix, y[3])) # if len(y) == 4 and not y[3] is None: warn("Specifying y_skip is not currently supported and " + "it will be ignored. Sorry!") return
def _logsmooth2(X, inBin, nbin=8): """Smooth the fft, X, and convert it to dB. Use ``nbin`` bins from 0 to 3*inBin, thereafter increase bin sizes by a factor of 1.1, staying less than 2^10. For the 3 sets of bins inBin+[0:2], 2*inBin+[0:2], and 3*inBin+[0:2], don't do averaging. This way, the noise BW and the scaling of the tone and its harmonics are unchanged. Unfortunately, harmonics above the third appear smaller than they really are because their energy is averaged over several bins. """ N = max(X.shape) n = nbin f1 = int((inBin - 1) % n) + 1 startbin = np.concatenate((np.arange(f1, inBin, n), np.arange(inBin, inBin + 3), np.arange(inBin + 3, 2*inBin, n), 2*inBin + np.arange(0, 3), np.arange(2*inBin + 3, 3*inBin, n), 3*inBin + np.arange(0, 3))) m = startbin[-1] + n while m < N: startbin = np.concatenate((startbin, np.array((m,)))) n = min(n*1.1, 2**10) m = np.round(m + n) stopbin = np.concatenate((startbin[1:] - 1, np.array((N,)))) f = ((startbin + stopbin)/2. - 1)/N p = np.zeros(f.shape) for i in range(max(f.shape)): p[i] = 10*np.log10(norm(X[(startbin[i] - 1):stopbin[i]])**2 / (stopbin[i] - startbin[i] + 1)) return f, p