Move python stuff to folder

3 files changed, 0 insertions, 512 deletions

diff --git a/gui/dpd/Align.py b/gui/dpd/Align.py
deleted file mode 100644
index 1634ec8..0000000
--- a/gui/dpd/Align.py
+++ /dev/null
@@ -1,166 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# DPD Computation Engine, utility to do subsample alignment.
-#
-#   Copyright (c) 2017 Andreas Steger
-#   Copyright (c) 2018 Matthias P. Braendli
-#
-#    http://www.opendigitalradio.org
-#
-#   This file is part of ODR-DabMod.
-#
-#   ODR-DabMod is free software: you can redistribute it and/or modify
-#   it under the terms of the GNU General Public License as
-#   published by the Free Software Foundation, either version 3 of the
-#   License, or (at your option) any later version.
-#
-#   ODR-DabMod 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
-#   GNU General Public License for more details.
-#
-#   You should have received a copy of the GNU General Public License
-#   along with ODR-DabMod.  If not, see <http://www.gnu.org/licenses/>.
-import datetime
-import os
-import numpy as np
-from scipy import optimize
-import matplotlib.pyplot as plt
-
-def gen_omega(length):
-    if (length % 2) == 1:
-        raise ValueError("Needs an even length array.")
-
-    halflength = int(length / 2)
-    factor = 2.0 * np.pi / length
-
-    omega = np.zeros(length, dtype=np.float)
-    for i in range(halflength):
-        omega[i] = factor * i
-
-    for i in range(halflength, length):
-        omega[i] = factor * (i - length)
-
-    return omega
-
-
-def subsample_align(sig, ref_sig, plot_location=None):
-    """Do subsample alignment for sig relative to the reference signal
-    ref_sig. The delay between the two must be less than sample
-
-    Returns the aligned signal"""
-
-    n = len(sig)
-    if (n % 2) == 1:
-        raise ValueError("Needs an even length signal.")
-    halflen = int(n / 2)
-
-    fft_sig = np.fft.fft(sig)
-
-    omega = gen_omega(n)
-
-    def correlate_for_delay(tau):
-        # A subsample offset between two signals corresponds, in the frequency
-        # domain, to a linearly increasing phase shift, whose slope
-        # corresponds to the delay.
-        #
-        # Here, we build this phase shift in rotate_vec, and multiply it with
-        # our signal.
-
-        rotate_vec = np.exp(1j * tau * omega)
-        # zero-frequency is rotate_vec[0], so rotate_vec[N/2] is the
-        # bin corresponding to the [-1, 1, -1, 1, ...] time signal, which
-        # is both the maximum positive and negative frequency.
-        # I don't remember why we handle it differently.
-        rotate_vec[halflen] = np.cos(np.pi * tau)
-
-        corr_sig = np.fft.ifft(rotate_vec * fft_sig)
-
-        return -np.abs(np.sum(np.conj(corr_sig) * ref_sig))
-
-    optim_result = optimize.minimize_scalar(correlate_for_delay, bounds=(-1, 1), method='bounded',
-                                            options={'disp': True})
-
-    if optim_result.success:
-        best_tau = optim_result.x
-
-        if plot_location is not None:
-            corr = np.vectorize(correlate_for_delay)
-            ixs = np.linspace(-1, 1, 100)
-            taus = corr(ixs)
-
-            dt = datetime.datetime.now().isoformat()
-            tau_path = (plot_location + "/" + dt + "_tau.png")
-            plt.plot(ixs, taus)
-            plt.title("Subsample correlation, minimum is best: {}".format(best_tau))
-            plt.savefig(tau_path)
-            plt.close()
-
-        # Prepare rotate_vec = fft_sig with rotated phase
-        rotate_vec = np.exp(1j * best_tau * omega)
-        rotate_vec[halflen] = np.cos(np.pi * best_tau)
-        return np.fft.ifft(rotate_vec * fft_sig).astype(np.complex64)
-    else:
-        # print("Could not optimize: " + optim_result.message)
-        return np.zeros(0, dtype=np.complex64)
-
-def phase_align(sig, ref_sig, plot_location=None):
-    """Do phase alignment for sig relative to the reference signal
-    ref_sig.
-
-    Returns the aligned signal"""
-
-    angle_diff = (np.angle(sig) - np.angle(ref_sig)) % (2. * np.pi)
-
-    real_diffs = np.cos(angle_diff)
-    imag_diffs = np.sin(angle_diff)
-
-    if plot_location is not None:
-        dt = datetime.datetime.now().isoformat()
-        fig_path = plot_location + "/" + dt + "_phase_align.png"
-
-        plt.subplot(511)
-        plt.hist(angle_diff, bins=60, label="Angle Diff")
-        plt.xlabel("Angle")
-        plt.ylabel("Count")
-        plt.legend(loc=4)
-
-        plt.subplot(512)
-        plt.hist(real_diffs, bins=60, label="Real Diff")
-        plt.xlabel("Real Part")
-        plt.ylabel("Count")
-        plt.legend(loc=4)
-
-        plt.subplot(513)
-        plt.hist(imag_diffs, bins=60, label="Imaginary Diff")
-        plt.xlabel("Imaginary Part")
-        plt.ylabel("Count")
-        plt.legend(loc=4)
-
-        plt.subplot(514)
-        plt.plot(np.angle(ref_sig[:128]), label="ref_sig")
-        plt.plot(np.angle(sig[:128]), label="sig")
-        plt.xlabel("Angle")
-        plt.ylabel("Sample")
-        plt.legend(loc=4)
-
-    real_diff = np.median(real_diffs)
-    imag_diff = np.median(imag_diffs)
-
-    angle = np.angle(real_diff + 1j * imag_diff)
-
-    #logging.debug( "Compensating phase by {} rad, {} degree. real median {}, imag median {}".format( angle, angle*180./np.pi, real_diff, imag_diff))
-    sig = sig * np.exp(1j * -angle)
-
-    if plot_location is not None:
-        plt.subplot(515)
-        plt.plot(np.angle(ref_sig[:128]), label="ref_sig")
-        plt.plot(np.angle(sig[:128]), label="sig")
-        plt.xlabel("Angle")
-        plt.ylabel("Sample")
-        plt.legend(loc=4)
-        plt.tight_layout()
-        plt.savefig(fig_path)
-        plt.close()
-
-    return sig
diff --git a/gui/dpd/Capture.py b/gui/dpd/Capture.py
deleted file mode 100644
index 7d95f90..0000000
--- a/gui/dpd/Capture.py
+++ /dev/null
@@ -1,253 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# DPD Computation Engine, Capture TX signal and RX feedback using ODR-DabMod's
-# DPD Server.
-#
-#   Copyright (c) 2017 Andreas Steger
-#   Copyright (c) 2018 Matthias P. Braendli
-#
-#    http://www.opendigitalradio.org
-#
-#   This file is part of ODR-DabMod.
-#
-#   ODR-DabMod is free software: you can redistribute it and/or modify
-#   it under the terms of the GNU General Public License as
-#   published by the Free Software Foundation, either version 3 of the
-#   License, or (at your option) any later version.
-#
-#   ODR-DabMod 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
-#   GNU General Public License for more details.
-#
-#   You should have received a copy of the GNU General Public License
-#   along with ODR-DabMod.  If not, see <http://www.gnu.org/licenses/>.
-
-import socket
-import struct
-import os.path
-import logging
-import numpy as np
-from scipy import signal
-import matplotlib
-matplotlib.use('Agg')
-import matplotlib.pyplot as plt
-import io
-
-from . import Align as sa
-
-def correlation_coefficient(sig_tx, sig_rx):
-    return np.corrcoef(sig_tx, sig_rx)[0, 1]
-
-def align_samples(sig_tx, sig_rx):
-    """
-    Returns an aligned version of sig_tx and sig_rx by cropping, subsample alignment and
-    correct phase offset
-    """
-
-    # Coarse sample-level alignment
-    c = np.abs(signal.correlate(sig_rx, sig_tx))
-    off_meas = np.argmax(c) - sig_tx.shape[0] + 1
-    off = int(abs(off_meas))
-
-    if off_meas > 0:
-        sig_tx = sig_tx[:-off]
-        sig_rx = sig_rx[off:]
-    elif off_meas < 0:
-        sig_tx = sig_tx[off:]
-        sig_rx = sig_rx[:-off]
-
-    if off % 2 == 1:
-        sig_tx = sig_tx[:-1]
-        sig_rx = sig_rx[:-1]
-
-    # Fine subsample alignment and phase offset
-    sig_rx = sa.subsample_align(sig_rx, sig_tx)
-    sig_rx = sa.phase_align(sig_rx, sig_tx)
-    return sig_tx, sig_rx, abs(off_meas)
-
-class Capture:
-    """Capture samples from ODR-DabMod"""
-    def __init__(self, samplerate, port, num_samples_to_request, plot_dir):
-        self.samplerate = samplerate
-        self.sizeof_sample = 8 # complex floats
-        self.port = port
-        self.num_samples_to_request = num_samples_to_request
-        self.plot_dir = plot_dir
-
-        # Before we run the samples through the model, we want to accumulate
-        # them into bins depending on their amplitude, and keep only n_per_bin
-        # samples to avoid that the polynomial gets overfitted in the low-amplitude
-        # part, which is less interesting than the high-amplitude part, where
-        # non-linearities become apparent.
-        self.binning_n_bins = 64  # Number of bins between binning_start and binning_end
-        self.binning_n_per_bin = 128  # Number of measurements pre bin
-
-        self.rx_normalisation = 1.0
-
-        self.clear_accumulated()
-
-    def clear_accumulated(self):
-        self.binning_start = 0.0
-        self.binning_end = 1.0
-
-        # axis 0: bins
-        # axis 1: 0=tx, 1=rx
-        self.accumulated_bins = [np.zeros((0, 2), dtype=np.complex64) for i in range(self.binning_n_bins)]
-
-    def _recv_exact(self, sock, num_bytes):
-        """Receive an exact number of bytes from a socket. This is
-        a wrapper around sock.recv() that can return less than the number
-        of requested bytes.
-
-        Args:
-            sock (socket): Socket to receive data from.
-            num_bytes (int): Number of bytes that will be returned.
-        """
-        bufs = []
-        while num_bytes > 0:
-            b = sock.recv(num_bytes)
-            if len(b) == 0:
-                break
-            num_bytes -= len(b)
-            bufs.append(b)
-        return b''.join(bufs)
-
-    def receive_tcp(self):
-        s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-        s.settimeout(4)
-        s.connect(('localhost', self.port))
-
-        logging.debug("Send version")
-        s.sendall(b"\x01")
-
-        logging.debug("Send request for {} samples".format(self.num_samples_to_request))
-        s.sendall(struct.pack("=I", self.num_samples_to_request))
-
-        logging.debug("Wait for TX metadata")
-        num_samps, tx_second, tx_pps = struct.unpack("=III", self._recv_exact(s, 12))
-        tx_ts = tx_second + tx_pps / 16384000.0
-
-        if num_samps > 0:
-            logging.debug("Receiving {} TX samples".format(num_samps))
-            txframe_bytes = self._recv_exact(s, num_samps * self.sizeof_sample)
-            txframe = np.fromstring(txframe_bytes, dtype=np.complex64)
-        else:
-            txframe = np.array([], dtype=np.complex64)
-
-        logging.debug("Wait for RX metadata")
-        rx_second, rx_pps = struct.unpack("=II", self._recv_exact(s, 8))
-        rx_ts = rx_second + rx_pps / 16384000.0
-
-        if num_samps > 0:
-            logging.debug("Receiving {} RX samples".format(num_samps))
-            rxframe_bytes = self._recv_exact(s, num_samps * self.sizeof_sample)
-            rxframe = np.fromstring(rxframe_bytes, dtype=np.complex64)
-        else:
-            rxframe = np.array([], dtype=np.complex64)
-
-        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
-            logging.debug('txframe: min {}, max {}, median {}'.format(
-                          np.min(np.abs(txframe)),
-                          np.max(np.abs(txframe)),
-                          np.median(np.abs(txframe))))
-
-            logging.debug('rxframe: min {}, max {}, median {}'.format(
-                          np.min(np.abs(rxframe)),
-                          np.max(np.abs(rxframe)),
-                          np.median(np.abs(rxframe))))
-
-        logging.debug("Disconnecting")
-        s.close()
-
-        return txframe, tx_ts, rxframe, rx_ts
-
-    def _plot_spectrum(self, signal, filename, title):
-        fig = plt.figure()
-        ax = plt.subplot(1, 1, 1)
-
-        fft = np.fft.fftshift(np.fft.fft(signal))
-        fft_db = 20 * np.log10(np.abs(fft))
-
-        ax.plot(fft_db)
-        ax.set_title(title)
-        fig.tight_layout()
-        fig.savefig(os.path.join(self.plot_dir, filename))
-        plt.close(fig)
-
-    def calibrate(self):
-        txframe, tx_ts, rxframe, rx_ts = self.receive_tcp()
-
-        # Normalize received signal with sent signal
-        tx_median = np.median(np.abs(txframe))
-        rx_median = np.median(np.abs(rxframe))
-        self.rx_normalisation = tx_median / rx_median
-
-        rxframe = rxframe * self.rx_normalisation
-        txframe_aligned, rxframe_aligned, coarse_offset = align_samples(txframe, rxframe)
-
-        self._plot_spectrum(rxframe[:8192], "rxframe.png", "RX Frame")
-        self._plot_spectrum(txframe[:8192], "txframe.png", "RX Frame")
-
-        return tx_ts, tx_median, rx_ts, rx_median, np.abs(coarse_offset), correlation_coefficient(txframe_aligned, rxframe_aligned)
-
-    def get_samples(self):
-        """Connect to ODR-DabMod, retrieve TX and RX samples, load
-        into numpy arrays, and return a tuple
-        (txframe_aligned, tx_ts, tx_median, rxframe_aligned, rx_ts, rx_median)
-        """
-
-        txframe, tx_ts, rxframe, rx_ts = self.receive_tcp()
-
-        # Normalize received signal with calibrated normalisation
-        rxframe = rxframe * self.rx_normalisation
-        txframe_aligned, rxframe_aligned, coarse_offset = align_samples(txframe, rxframe)
-        self._bin_and_accumulate(txframe_aligned, rxframe_aligned)
-        return txframe_aligned, tx_ts, tx_median, rxframe_aligned, rx_ts, rx_median
-
-    def bin_histogram(self):
-        return [b.shape[0] for b in self.accumulated_bins]
-
-    def pointcloud_png(self):
-        fig = plt.figure()
-        ax = plt.subplot(1, 1, 1)
-        for b in self.accumulated_bins:
-            if b:
-                ax.scatter(
-                        np.abs(b[0]),
-                        np.abs(b[1]),
-                        s=0.1,
-                        color="black")
-        ax.set_title("Captured and Binned Samples")
-        ax.set_xlabel("TX Amplitude")
-        ax.set_ylabel("RX Amplitude")
-        ax.set_ylim(0, 0.8)
-        ax.set_xlim(0, 1.1)
-        ax.legend(loc=4)
-        fig.tight_layout()
-        fig.savefig(os.path.join(self.plot_dir, "pointcloud.png"))
-        plt.close(fig)
-
-    def _bin_and_accumulate(self, txframe, rxframe):
-        """Bin the samples and extend the accumulated samples"""
-
-        bin_edges = np.linspace(self.binning_start, self.binning_end, self.binning_n_bins)
-
-        minsize = self.num_samples_to_request
-
-        for i, (tx_start, tx_end) in enumerate(zip(bin_edges, bin_edges[1:])):
-            txframe_abs = np.abs(txframe)
-            indices = np.bitwise_and(tx_start < txframe_abs, txframe_abs <= tx_end)
-            txsamples = np.asmatrix(txframe[indices])
-            rxsamples = np.asmatrix(rxframe[indices])
-            binned_sample_pairs = np.concatenate((txsamples, rxsamples)).T
-
-            missing_in_bin = self.binning_n_per_bin - self.accumulated_bins[i].shape[0]
-            num_to_append = min(missing_in_bin, binned_sample_pairs.shape[0])
-            print("Handling bin {} {}-{}, {} available, {} missing".format(i, tx_start, tx_end, binned_sample_pairs.shape[0], missing_in_bin))
-            if num_to_append:
-                print("Appending {} to bin {} with shape {}".format(num_to_append, i, self.accumulated_bins[i].shape))
-
-                self.accumulated_bins[i] = np.concatenate((self.accumulated_bins[i], binned_sample_pairs[:num_to_append,...]))
-                print("{} now has shape {}".format(i, self.accumulated_bins[i].shape))
-
diff --git a/gui/dpd/__init__.py b/gui/dpd/__init__.py
deleted file mode 100644
index 9009436..0000000
--- a/gui/dpd/__init__.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# DPD Computation Engine module
-#
-#   Copyright (c) 2017 Andreas Steger
-#   Copyright (c) 2018 Matthias P. Braendli
-#
-#    http://www.opendigitalradio.org
-#
-#   This file is part of ODR-DabMod.
-#
-#   ODR-DabMod is free software: you can redistribute it and/or modify
-#   it under the terms of the GNU General Public License as
-#   published by the Free Software Foundation, either version 3 of the
-#   License, or (at your option) any later version.
-#
-#   ODR-DabMod 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
-#   GNU General Public License for more details.
-#
-#   You should have received a copy of the GNU General Public License
-#   along with ODR-DabMod.  If not, see <http://www.gnu.org/licenses/>.
-
-from . import Capture
-import numpy as np
-
-class DPD:
-    def __init__(self, plot_dir, samplerate=8192000):
-        self.samplerate = samplerate
-
-        oversample = int(self.samplerate / 2048000)
-        self.T_F = oversample * 196608  # Transmission frame duration
-        self.T_NULL = oversample * 2656  # Null symbol duration
-        self.T_S = oversample * 2552  # Duration of OFDM symbols of indices l = 1, 2, 3,... L;
-        self.T_U = oversample * 2048  # Inverse of carrier spacing
-        self.T_C = oversample * 504  # Duration of cyclic prefix
-
-        self.last_capture_info = {}
-
-        port = 50055
-        samples_to_capture = 81920
-        self.capture = Capture.Capture(self.samplerate, port, samples_to_capture, plot_dir)
-
-    def status(self):
-        r = {}
-        r['histogram'] = self.capture.bin_histogram()
-        r['capture'] = self.last_capture_info
-        return r
-
-    def pointcloud_png(self):
-        return self.capture.pointcloud_png()
-
-    def clear_accumulated(self):
-        return self.capture.clear_accumulated()
-
-    def capture_calibration(self):
-        tx_ts, tx_median, rx_ts, rx_median, coarse_offset, correlation_coefficient = self.capture.calibrate()
-        result = {'status': "ok"}
-        result['tx_median'] = "{:.2f}dB".format(20*np.log10(tx_median))
-        result['rx_median'] = "{:.2f}dB".format(20*np.log10(rx_median))
-        result['tx_ts'] = tx_ts
-        result['rx_ts'] = rx_ts
-        result['coarse_offset'] = int(coarse_offset)
-        result['correlation'] = float(correlation_coefficient)
-        return result
-
-    def capture_samples(self):
-        """Captures samples and store them in the accumulated samples,
-        returns a dict with some info"""
-        result = {}
-        try:
-            txframe_aligned, tx_ts, tx_median, rxframe_aligned, rx_ts, rx_median = self.capture.get_samples()
-            result['status'] = "ok"
-            result['length'] = len(txframe_aligned)
-            result['tx_median'] = float(tx_median)
-            result['rx_median'] = float(rx_median)
-            result['tx_ts'] = tx_ts
-            result['rx_ts'] = rx_ts
-        except ValueError as e:
-            result['status'] = "Capture failed: {}".format(e)
-
-        self.last_capture_info = result
-
-        # tx, rx, phase_diff, n_per_bin = extStat.extract(txframe_aligned, rxframe_aligned)
-        # off = SA.calc_offset(txframe_aligned)
-        # print("off {}".format(off))
-        # tx_mer = MER.calc_mer(txframe_aligned[off:off + c.T_U], debug_name='TX')
-        # print("tx_mer {}".format(tx_mer))
-        # rx_mer = MER.calc_mer(rxframe_aligned[off:off + c.T_U], debug_name='RX')
-        # print("rx_mer {}".format(rx_mer))
-        # mse = np.mean(np.abs((txframe_aligned - rxframe_aligned) ** 2))
-        # print("mse {}".format(mse))