1 files changed, 158 insertions, 0 deletions
diff --git a/python/dpd/src/Measure_Shoulders.py b/python/dpd/src/Measure_Shoulders.py
new file mode 100644
index 0000000..fd90050
--- /dev/null
+++ b/python/dpd/src/Measure_Shoulders.py
@@ -0,0 +1,158 @@
+# -*- coding: utf-8 -*-
+#
+# DPD Computation 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
+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_Shoulders:
+    """Calculate difference between the DAB signal and the shoulder hight in the
+    power spectrum"""
+
+    def __init__(self, c):
+        self.c = c
+        self.plot = c.MS_plot
+
+    def _plot(self, signal):
+        if self.c.plot_location is None:
+            return
+
+        dt = datetime.datetime.now().isoformat()
+        fig_path = self.c.plot_location + "/" + dt + "_sync_subsample_aligned.png"
+
+        fft = calc_fft_db(signal, 100, self.c)
+        peak, idxs_peak = _calc_peak(fft, self.c)
+        shoulder, idxs_sh = _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(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):
+        if not self.c.MS_enable:
+            logging.info("Shoulder Measurement disabled via Const.py")
+            return 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)
+
+        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.