Cleanup

1 files changed, 0 insertions, 165 deletions

diff --git a/dpd/src/Agc.py b/dpd/src/Agc.py
deleted file mode 100644
index 670fbbb..0000000
--- a/dpd/src/Agc.py
+++ /dev/null
@@ -1,165 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Automatic Gain Control
-#
-# http://www.opendigitalradio.org
-# Licence: The MIT License, see notice at the end of this file
-
-import datetime
-import os
-import logging
-import time
-logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename)
-
-import numpy as np
-import matplotlib
-matplotlib.use('agg')
-import matplotlib.pyplot as plt
-
-import src.Adapt as Adapt
-import src.Measure as Measure
-
-class Agc:
-    """The goal of the automatic gain control is to set the 
-    RX gain to a value at which all received amplitudes can 
-    be detected. This means that the maximum possible amplitude 
-    should be quantized at the highest possible digital value.
-
-    A problem we have to face, is that the estimation of the 
-    maximum amplitude by applying the max() function is very 
-    unstable. This is due to the maximum’s rareness. Therefore 
-    we estimate a far more robust value, such as the median, 
-    and then approximate the maximum amplitude from it.
-
-    Given this, we tune the RX gain in such a way, that the 
-    received signal fulfills our desired property, of having 
-    all amplitudes properly quantized."""
-
-    def __init__(self, measure, adapt, c):
-        assert isinstance(measure, Measure.Measure)
-        assert isinstance(adapt, Adapt.Adapt)
-        self.measure = measure
-        self.adapt = adapt
-        self.min_rxgain = c.RAGC_min_rxgain
-        self.rxgain = self.min_rxgain
-        self.peak_to_median = 1./c.RAGC_rx_median_target
-
-    def run(self):
-        self.adapt.set_rxgain(self.rxgain)
-
-        for i in range(2):
-            # Measure
-            txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median= \
-                self.measure.get_samples()
-
-            # Estimate Maximum
-            rx_peak = self.peak_to_median * rx_median
-            correction_factor = 20*np.log10(1/rx_peak)
-            self.rxgain = self.rxgain + correction_factor
-
-            assert self.min_rxgain <= self.rxgain, ("Desired RX Gain is {} which is smaller than the minimum of {}".format(
-                self.rxgain, self.min_rxgain))
-
-            logging.info("RX Median {:1.4f}, estimated peak {:1.4f}, correction factor {:1.4f}, new RX gain {:1.4f}".format(
-            rx_median, rx_peak, correction_factor, self.rxgain
-            ))
-
-            self.adapt.set_rxgain(self.rxgain)
-            time.sleep(0.5)
-
-    def plot_estimates(self):
-        """Plots the estimate of for Max, Median, Mean for different
-        number of samples."""
-        self.adapt.set_rxgain(self.min_rxgain)
-        time.sleep(1)
-
-        dt = datetime.datetime.now().isoformat()
-        fig_path = logging_path + "/" + dt + "_agc.svg"
-        fig, axs = plt.subplots(2, 2, figsize=(3*6,1*6))
-        axs = axs.ravel()
-
-        for j in range(5):
-            txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median =\
-                self.measure.get_samples()
-
-            rxframe_aligned_abs = np.abs(rxframe_aligned)
-
-            x = np.arange(100, rxframe_aligned_abs.shape[0], dtype=int)
-            rx_max_until = []
-            rx_median_until = []
-            rx_mean_until = []
-            for i in x:
-                rx_max_until.append(np.max(rxframe_aligned_abs[:i]))
-                rx_median_until.append(np.median(rxframe_aligned_abs[:i]))
-                rx_mean_until.append(np.mean(rxframe_aligned_abs[:i]))
-
-            axs[0].plot(x,
-                    rx_max_until,
-                    label="Run {}".format(j+1),
-                    color=matplotlib.colors.hsv_to_rgb((1./(j+1.),0.8,0.7)),
-                    linestyle="-", linewidth=0.25)
-            axs[0].set_xlabel("Samples")
-            axs[0].set_ylabel("Amplitude")
-            axs[0].set_title("Estimation for Maximum RX Amplitude")
-            axs[0].legend()
-
-            axs[1].plot(x,
-                    rx_median_until,
-                    label="Run {}".format(j+1),
-                    color=matplotlib.colors.hsv_to_rgb((1./(j+1.),0.9,0.7)),
-                    linestyle="-", linewidth=0.25)
-            axs[1].set_xlabel("Samples")
-            axs[1].set_ylabel("Amplitude")
-            axs[1].set_title("Estimation for Median RX Amplitude")
-            axs[1].legend()
-            ylim_1 = axs[1].get_ylim()
-
-            axs[2].plot(x,
-                    rx_mean_until,
-                    label="Run {}".format(j+1),
-                    color=matplotlib.colors.hsv_to_rgb((1./(j+1.),0.9,0.7)),
-                    linestyle="-", linewidth=0.25)
-            axs[2].set_xlabel("Samples")
-            axs[2].set_ylabel("Amplitude")
-            axs[2].set_title("Estimation for Mean RX Amplitude")
-            ylim_2 = axs[2].get_ylim()
-            axs[2].legend()
-
-            axs[1].set_ylim(min(ylim_1[0], ylim_2[0]),
-                            max(ylim_1[1], ylim_2[1]))
-
-            fig.tight_layout()
-            fig.savefig(fig_path)
-
-        axs[3].hist(rxframe_aligned_abs, bins=60)
-        axs[3].set_xlabel("Amplitude")
-        axs[3].set_ylabel("Frequency")
-        axs[3].set_title("Histogram of Amplitudes")
-        axs[3].legend()
-
-        fig.tight_layout()
-        fig.savefig(fig_path)
-        plt.close(fig)
-
-
-# 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.