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# -*- coding: utf-8 -*-
#
# DPD Calculation Engine, calculate peak to shoulder difference
#
# http://www.opendigitalradio.org
# Licence: The MIT License, see notice at the end of this file
import datetime
import os
import logging
import multiprocessing
logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename)
import numpy as np
import matplotlib.pyplot as plt
def plt_next_axis(sub_rows, sub_cols, i_sub):
i_sub += 1
ax = plt.subplot(sub_rows, sub_cols, i_sub)
return i_sub, ax
def plt_annotate(ax, x,y,title=None,legend_loc=None):
ax.set_xlabel(x)
ax.set_ylabel(y)
if title is not None: ax.set_title(title)
if legend_loc is not None: ax.legend(loc=legend_loc)
def calc_fft_db(signal, offset, c):
fft = np.fft.fftshift(np.fft.fft(signal[offset:offset + c.MS_FFT_size]))
fft_db = 20 * np.log10(np.abs(fft))
return fft_db
def _calc_peak(fft, c):
assert fft.shape == (c.MS_FFT_size,), fft.shape
idxs = (c.MS_peak_start, c.MS_peak_end)
peak = np.mean(fft[idxs[0]:idxs[1]])
return peak, idxs
def _calc_shoulder_hight(fft_db, c):
assert fft_db.shape == (c.MS_FFT_size,), fft_db.shape
idxs_left = (c.MS_shoulder_left_start, c.MS_shoulder_left_end)
idxs_right = (c.MS_shoulder_right_start, c.MS_shoulder_right_end)
shoulder_left = np.mean(fft_db[idxs_left[0]:idxs_left[1]])
shoulder_right = np.mean(fft_db[idxs_right[0]:idxs_right[1]])
shoulder = np.mean((shoulder_left, shoulder_right))
return shoulder, (idxs_left, idxs_right)
def calc_shoulder(fft, c):
peak = _calc_peak(fft, c)[0]
shoulder = _calc_shoulder_hight(fft, c)[0]
assert (peak >= shoulder), (peak, shoulder)
return peak, shoulder
def shoulder_from_sig_offset(arg):
signal, offset, c = arg
fft_db = calc_fft_db(signal, offset, c)
peak, shoulder = calc_shoulder(fft_db, c)
return peak-shoulder, peak, shoulder
class Measure_Shoulder:
"""Calculate difference between the DAB signal and the shoulder hight in the
power spectrum"""
def __init__(self,
c,
plot=False):
self.c = c
self.plot = c.MS_plot
def _plot(self, signal):
dt = datetime.datetime.now().isoformat()
fig_path = logging_path + "/" + dt + "_sync_subsample_aligned.svg"
fft = calc_fft_db(signal, 100)
peak, idxs_peak = self._calc_peak(fft)
shoulder, idxs_sh = self._calc_shoulder_hight(fft, self.c)
sub_rows = 1
sub_cols = 1
fig = plt.figure(figsize=(sub_cols * 6, sub_rows / 2. * 6))
i_sub = 0
i_sub, ax = plt_next_axis(sub_rows, sub_cols, i_sub)
ax.scatter(np.arange(fft.shape[0]), fft, s=0.1,
label="FFT",
color="red")
ax.plot(idxs_peak, (peak, peak))
ax.plot(idxs_sh[0], (shoulder, shoulder), color='blue')
ax.plot(idxs_sh[1], (shoulder, shoulder), color='blue')
plt_annotate(ax, "Frequency", "Magnitude [dB]", None, 4)
ax.text(100, -17, str(self.calc_shoulder(fft, self.c)))
ax.set_ylim(-20, 30)
fig.tight_layout()
fig.savefig(fig_path)
plt.close(fig)
def average_shoulders(self, signal, n_avg=None):
assert signal.shape[0] > 4 * self.c.MS_FFT_size
if n_avg is None: n_avg = self.c.MS_averaging_size
off_min = 0
off_max = signal.shape[0] - self.c.MS_FFT_size
offsets = np.linspace(off_min, off_max, num=n_avg, dtype=int)
shoulders = []
args = zip(
[signal, ] * offsets.shape[0],
offsets,
[self.c, ] * offsets.shape[0]
)
pool = multiprocessing.Pool(self.c.MS_n_proc)
res = pool.map(shoulder_from_sig_offset, args)
shoulders_diff, shoulders, peaks = zip(*res)
if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot:
self._plot(signal)
return np.mean(shoulders_diff), np.mean(shoulders), np.mean(peaks)
# The MIT License (MIT)
#
# Copyright (c) 2017 Andreas Steger
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
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