# -*- 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
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."""
        if self.c.plot_location is None:
            return

        self.adapt.set_rxgain(self.min_rxgain)
        time.sleep(1)

        dt = datetime.datetime.now().isoformat()
        fig_path = self.c.plot_location + "/" + dt + "_agc.png"
        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.