# Source code for deltasigma._bplogsmooth

# -*- coding: utf-8 -*-
# _bplogsmooth.py
# Module providing the bplogsmooth 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 bplogsmooth() function, which smooths an fft and
converts it to dB.
"""

from __future__ import division

import numpy as np
from numpy.linalg import norm

from ._dbp import dbp
from ._utils import carray, mround

[docs]def bplogsmooth(X, tbin, f0):
"""Smooth the FFT and convert it to dB.

Use 8 bins from the bin corresponding to f0 to tbin and again as far.
Thereafter increase bin sizes by a factor of 1.1, staying less than 2^10.
For tbin, group the bins together.

Use this for nice double-sided log-log plots.

.. note:: tbin is assumed to be in the upper sideband!

.. seealso:: :func:logsmooth

"""
X = carray(X).squeeze()
if len(X.shape) > 1:
raise ValueError("The X vector is not unidimensional: " + str(X.shape))

N = X.shape
N2 = int(np.floor(N/2))
tbin = int(tbin)
n = 8

bin0 = int(mround(f0*N))
assert tbin > bin0 # we said upper sideband!
bin1 = ((tbin - bin0) % n) + bin0
bind = bin1 - bin0
usb1 = np.concatenate((np.arange(bin1, tbin+1, n),
np.arange(tbin+3, tbin+bind+1, 8)
))
m = usb1[-1] + n
while m + n/2. < N/2.:
usb1 = np.concatenate((usb1, np.array((m,))))
n = mround(min(n*1.1, 2**10))
m = m + int(n)
usb2 = np.concatenate((usb1[1:]-1, np.array((N2,))))

n = 8
lsb2 = np.arange(bin1, bin1 - 2*bind + 1, -n) - 1
m = lsb2[-1] - n
while m - n/2. > 1:
lsb2 = np.concatenate((lsb2, np.array((m,))))
n = mround(min(n*1.1, 2**10))
m = m - int(n)
lsb1 = np.concatenate((lsb2[1:] + 1, np.ones((1,))))

startbin = np.concatenate((lsb1[::-1], usb1)) - 1
stopbin = np.concatenate((lsb2[::-1], usb2)) - 1

f = ((startbin + stopbin)/2.)/N - f0
p = np.zeros(f.shape)
for i in range(f.shape):
p[i] = dbp(
norm(X[startbin[i]:stopbin[i] + 1]**2. /
(stopbin[i] - startbin[i] + 1.),
ord=1)
)
return f, p