# Source code for deltasigma._bilogplot

# -*- coding: utf-8 -*-
# _bilogplot.py
# 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
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# 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 np.prod(V.shape) > 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