diff options
| -rw-r--r-- | dpd/dpd.ini | 8 | ||||
| -rwxr-xr-x | dpd/main.py | 100 | ||||
| -rw-r--r-- | dpd/poly.coef | 18 | ||||
| -rw-r--r-- | dpd/src/Agc.py | 2 | ||||
| -rw-r--r-- | dpd/src/Dab_Util.py | 10 | ||||
| -rw-r--r-- | dpd/src/ExtractStatistic.py | 201 | ||||
| -rw-r--r-- | dpd/src/MER.py | 2 | ||||
| -rw-r--r-- | dpd/src/Measure.py | 4 | ||||
| -rw-r--r-- | dpd/src/Model.py | 389 | ||||
| -rw-r--r-- | dpd/src/Model_AM.py | 115 | ||||
| -rw-r--r-- | dpd/src/Model_PM.py | 118 | ||||
| -rw-r--r-- | dpd/src/Model_Poly.py | 99 | ||||
| -rw-r--r-- | dpd/src/Symbol_align.py | 3 | ||||
| -rw-r--r-- | dpd/src/const.py | 6 | ||||
| -rw-r--r-- | dpd/src/phase_align.py | 2 | ||||
| -rwxr-xr-x | dpd/src/subsample_align.py | 2 |
16 files changed, 635 insertions, 444 deletions
diff --git a/dpd/dpd.ini b/dpd/dpd.ini index 9a76393..26a53c5 100644 --- a/dpd/dpd.ini +++ b/dpd/dpd.ini @@ -10,8 +10,8 @@ filelog=1 filename=/tmp/dabmod.log [input] -transport=file -source=/home/bram/dab/mmbtools-aux/eti/buddard.eti +transport=tcp +source=localhost:9200 [modulator] gainmode=var @@ -31,7 +31,7 @@ polycoeffile=dpd/poly.coef [output] # to prepare a file for the dpd/iq_file_server.py script, # use output=file -output=file +output=uhd [fileoutput] filename=dpd.iq @@ -40,7 +40,7 @@ filename=dpd.iq device= master_clock_rate=32768000 type=b200 -txgain=15 +txgain=55 channel=13C refclk_source=internal pps_source=none diff --git a/dpd/main.py b/dpd/main.py index de3453e..084ccd5 100755 --- a/dpd/main.py +++ b/dpd/main.py @@ -42,6 +42,7 @@ import numpy as np import traceback import src.Measure as Measure import src.Model as Model +import src.ExtractStatistic as ExtractStatistic import src.Adapt as Adapt import src.Agc as Agc import src.TX_Agc as TX_Agc @@ -52,19 +53,19 @@ import argparse parser = argparse.ArgumentParser( description="DPD Computation Engine for ODR-DabMod") -parser.add_argument('--port', default='50055', +parser.add_argument('--port', default=50055, type=int, help='port of DPD server to connect to (default: 50055)', required=False) -parser.add_argument('--rc-port', default='9400', +parser.add_argument('--rc-port', default=9400, type=int, help='port of ODR-DabMod ZMQ Remote Control to connect to (default: 9400)', required=False) -parser.add_argument('--samplerate', default='8192000', +parser.add_argument('--samplerate', default=8192000, type=int, help='Sample rate', required=False) parser.add_argument('--coefs', default='poly.coef', help='File with DPD coefficients, which will be read by ODR-DabMod', required=False) -parser.add_argument('--txgain', default=71, +parser.add_argument('--txgain', default=73, help='TX Gain', required=False, type=int) @@ -76,10 +77,10 @@ parser.add_argument('--digital_gain', default=1, help='Digital Gain', required=False, type=float) -parser.add_argument('--samps', default='81920', +parser.add_argument('--samps', default='81920', type=int, help='Number of samples to request from ODR-DabMod', required=False) -parser.add_argument('-i', '--iterations', default='1', +parser.add_argument('-i', '--iterations', default=1, type=int, help='Number of iterations to run', required=False) parser.add_argument('-L', '--lut', @@ -88,29 +89,29 @@ parser.add_argument('-L', '--lut', cli_args = parser.parse_args() -port = int(cli_args.port) -port_rc = int(cli_args.rc_port) +port = cli_args.port +port_rc = cli_args.rc_port coef_path = cli_args.coefs digital_gain = cli_args.digital_gain txgain = cli_args.txgain rxgain = cli_args.rxgain -num_req = int(cli_args.samps) -samplerate = int(cli_args.samplerate) -num_iter = int(cli_args.iterations) +num_req = cli_args.samps +samplerate = cli_args.samplerate +num_iter = cli_args.iterations SA = src.Symbol_align.Symbol_align(samplerate) MER = src.MER.MER(samplerate) c = src.const.const(samplerate) meas = Measure.Measure(samplerate, port, num_req) - +extStat = ExtractStatistic.ExtractStatistic(c, plot=True) adapt = Adapt.Adapt(port_rc, coef_path) dpddata = adapt.get_predistorter() if cli_args.lut: - model = Model.LutModel(c, SA, MER, plot=True) + model = Model.Lut(c, plot=True) else: - model = Model.PolyModel(c, SA, MER, None, None, plot=True) + model = Model.Poly(c, plot=True) adapt.set_predistorter(model.get_dpd_data()) adapt.set_digital_gain(digital_gain) adapt.set_txgain(txgain) @@ -120,7 +121,7 @@ tx_gain = adapt.get_txgain() rx_gain = adapt.get_rxgain() digital_gain = adapt.get_digital_gain() -dpddata = adapt.get_coefs() +dpddata = adapt.get_predistorter() if dpddata[0] == "poly": coefs_am = dpddata[1] coefs_pm = dpddata[2] @@ -148,23 +149,62 @@ tx_agc = TX_Agc.TX_Agc(adapt) agc = Agc.Agc(meas, adapt) agc.run() -for i in range(num_iter): +state = "measure" +i = 0 +while i < num_iter: try: - txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median = meas.get_samples() - logging.debug("tx_ts {}, rx_ts {}".format(tx_ts, rx_ts)) - assert tx_ts - rx_ts < 1e-5, "Time stamps do not match." - - if tx_agc.adapt_if_necessary(txframe_aligned): - continue - - model.train(txframe_aligned, rxframe_aligned) - adapt.set_predistorter(model.get_dpd_data()) + # Measure + if state == "measure": + txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median = meas.get_samples() + tx, rx, phase_diff, n_per_bin = extStat.extract(txframe_aligned, rxframe_aligned) + n_use = int(len(n_per_bin) * 0.6) + tx = tx[:n_use] + rx = rx[:n_use] + phase_diff = phase_diff[:n_use] + if all(c.ES_n_per_bin == np.array(n_per_bin)[0:n_use]): + state = "model" + else: + state = "measure" + + # Model + elif state == "model": + dpddata = model_poly.get_dpd_data(tx, rx, phase_diff) + del extStat + extStat = ExtractStatistic.ExtractStatistic(c, plot=True) + state = "adapt" + + # Adapt + elif state == "adapt": + adapt.set_predistorter(dpddata) + state = "report" + i += 1 + + # Report + elif state == "report": + try: + off = SA.calc_offset(txframe_aligned) + tx_mer = MER.calc_mer(txframe_aligned[off:off+c.T_U], debug=True) + rx_mer = MER.calc_mer(rxframe_aligned[off:off+c.T_U], debug=True) + mse = np.mean(np.abs((txframe_aligned - rxframe_aligned)**2)) + + if dpddata[0] == "poly": + coefs_am = dpddata[1] + coefs_pm = dpddata[2] + logging.info("It {}: TX_MER {}, RX_MER {}," \ + " MSE {}, coefs_am {}, coefs_pm {}". + format(i, tx_mer, rx_mer, mse, coefs_am, coefs_pm)) + if dpddata[0] == "lut": + scalefactor = dpddata[1] + lut = dpddata[2] + logging.info("It {}: TX_MER {}, RX_MER {}," \ + " MSE {}, LUT scalefactor {}, LUT {}". + format(i, tx_mer, rx_mer, mse, scalefactor, lut)) + state = "measure" + except: + logging.warning("Iteration {}: Report failed.".format(i)) + logging.warning(traceback.format_exc()) + state = "measure" - off = SA.calc_offset(txframe_aligned) - tx_mer = MER.calc_mer(txframe_aligned[off:off + c.T_U]) - rx_mer = MER.calc_mer(rxframe_aligned[off:off + c.T_U], debug=True) - logging.info("MER with lag in it. {}: TX {}, RX {}". - format(i, tx_mer, rx_mer)) except Exception as e: logging.warning("Iteration {} failed.".format(i)) logging.warning(traceback.format_exc()) diff --git a/dpd/poly.coef b/dpd/poly.coef index 913507a..248d316 100644 --- a/dpd/poly.coef +++ b/dpd/poly.coef @@ -1,12 +1,12 @@ 1 5 1.0 -0 -0 -0 -0 -0 -0 -0 -0 -0 +0.0 +0.0 +0.0 +0.0 +0.0 +0.0 +0.0 +0.0 +0.0 diff --git a/dpd/src/Agc.py b/dpd/src/Agc.py index 978b607..b83c91e 100644 --- a/dpd/src/Agc.py +++ b/dpd/src/Agc.py @@ -139,7 +139,7 @@ class Agc: fig.tight_layout() fig.savefig(fig_path) - fig.clf() + plt.close(fig) # The MIT License (MIT) diff --git a/dpd/src/Dab_Util.py b/dpd/src/Dab_Util.py index e3dbfe3..37be5db 100644 --- a/dpd/src/Dab_Util.py +++ b/dpd/src/Dab_Util.py @@ -49,7 +49,7 @@ class Dab_Util: plt.plot(c, label="corr") plt.legend() plt.savefig(corr_path) - plt.clf() + plt.close() return np.argmax(c) - off + 1 @@ -118,7 +118,7 @@ class Dab_Util: fig.tight_layout() fig.savefig(fig_path) - fig.clf() + plt.close(fig) off_meas = self.lag_upsampling(sig_rx, sig_tx, n_up=1) off = int(abs(off_meas)) @@ -161,7 +161,7 @@ class Dab_Util: fig.tight_layout() fig.savefig(fig_path) - fig.clf() + plt.close(fig) sig_rx = sa.subsample_align(sig_rx, sig_tx) @@ -185,7 +185,7 @@ class Dab_Util: fig.tight_layout() fig.savefig(fig_path) - fig.clf() + plt.close(fig) sig_rx = pa.phase_align(sig_rx, sig_tx) @@ -209,7 +209,7 @@ class Dab_Util: fig.tight_layout() fig.savefig(fig_path) - fig.clf() + plt.close(fig) logging.debug("Sig1_cut: %d %s, Sig2_cut: %d %s, off: %d" % (len(sig_tx), sig_tx.dtype, len(sig_rx), sig_rx.dtype, off)) return sig_tx, sig_rx diff --git a/dpd/src/ExtractStatistic.py b/dpd/src/ExtractStatistic.py new file mode 100644 index 0000000..897ec0a --- /dev/null +++ b/dpd/src/ExtractStatistic.py @@ -0,0 +1,201 @@ +# -*- coding: utf-8 -*- +# +# DPD Calculation Engine, +# Extract statistic from data to use in Model +# +# http://www.opendigitalradio.org +# Licence: The MIT License, see notice at the end of this file + +import numpy as np +import pickle +import matplotlib.pyplot as plt + +import datetime +import os +import logging + +logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename) + + +def _check_input_extract(tx_dpd, rx_received): + # Check data type + assert tx_dpd[0].dtype == np.complex64, \ + "tx_dpd is not complex64 but {}".format(tx_dpd[0].dtype) + assert rx_received[0].dtype == np.complex64, \ + "rx_received is not complex64 but {}".format(rx_received[0].dtype) + # Check if signals have same normalization + normalization_error = np.abs(np.median(np.abs(tx_dpd)) - + np.median(np.abs(rx_received))) / ( + np.median(np.abs(tx_dpd)) + np.median(np.abs(rx_received))) + assert normalization_error < 0.01, "Non normalized signals" + + +class ExtractStatistic: + """Calculate a low variance RX value for equally spaced tx values + of a predefined range""" + + def __init__(self, + c, + plot=False): + self.c = c + + self.tx_boundaries = np.linspace(c.ES_start, c.ES_end, c.ES_n_bins + 1) + self.n_per_bin = c.ES_n_per_bin + + self.rx_values_lists = [] + for i in range(c.ES_n_bins): + self.rx_values_lists.append([]) + + self.tx_values_lists = [] + for i in range(c.ES_n_bins): + self.tx_values_lists.append([]) + + self.tx_values = self._tx_value_per_bin() + + self.rx_values = [] + for i in range(c.ES_n_bins): + self.rx_values.append(None) + + self.plot = plot + + def _plot_and_log(self): + if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot: + phase_diffs_values_lists = self._phase_diff_list_per_bin() + phase_diffs_values = self._phase_diff_value_per_bin(phase_diffs_values_lists) + + dt = datetime.datetime.now().isoformat() + fig_path = logging_path + "/" + dt + "_ExtractStatistic.png" + sub_rows = 3 + sub_cols = 1 + fig = plt.figure(figsize=(sub_cols * 6, sub_rows / 2. * 6)) + i_sub = 0 + + i_sub += 1 + ax = plt.subplot(sub_rows, sub_cols, i_sub) + ax.plot(self.tx_values, self.rx_values, + label="Estimated Values", + color="red") + for i, tx_value in enumerate(self.tx_values): + rx_values = self.rx_values_lists[i] + ax.scatter(np.ones(len(rx_values)) * tx_value, + np.abs(rx_values), + s=0.1, + color="black") + ax.set_title("Extracted Statistic") + 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) + + i_sub += 1 + ax = plt.subplot(sub_rows, sub_cols, i_sub) + ax.plot(self.tx_values, np.rad2deg(phase_diffs_values), + label="Estimated Values", + color="red") + for i, tx_value in enumerate(self.tx_values): + phase_diff = phase_diffs_values_lists[i] + ax.scatter(np.ones(len(phase_diff)) * tx_value, + np.rad2deg(phase_diff), + s=0.1, + color="black") + ax.set_xlabel("TX Amplitude") + ax.set_ylabel("Phase Difference") + ax.set_ylim(-60,60) + ax.set_xlim(0, 1.1) + ax.legend(loc=4) + + num = [] + for i, tx_value in enumerate(self.tx_values): + rx_values = self.rx_values_lists[i] + num.append(len(rx_values)) + i_sub += 1 + ax = plt.subplot(sub_rows, sub_cols, i_sub) + ax.plot(num) + ax.set_xlabel("TX Amplitude") + ax.set_ylabel("Number of Samples") + ax.set_ylim(0, self.n_per_bin * 1.2) + + fig.tight_layout() + fig.savefig(fig_path) + plt.close(fig) + + pickle.dump(self.rx_values_lists, open("/tmp/rx_values", "wb")) + pickle.dump(self.tx_values, open("/tmp/tx_values", "wb")) + + def _rx_value_per_bin(self): + rx_values = [] + for values in self.rx_values_lists: + rx_values.append(np.mean(np.abs(values))) + return rx_values + + def _tx_value_per_bin(self): + tx_values = [] + for start, end in zip(self.tx_boundaries, self.tx_boundaries[1:]): + tx_values.append(np.mean((start, end))) + return tx_values + + def _phase_diff_list_per_bin(self): + phase_values_lists = [] + for tx_list, rx_list in zip(self.tx_values_lists, self.rx_values_lists): + phase_diffs = [] + for tx, rx in zip(tx_list, rx_list): + phase_diffs.append(np.angle(rx * tx.conjugate())) + phase_values_lists.append(phase_diffs) + return phase_values_lists + + def _phase_diff_value_per_bin(self, phase_diffs_values_lists): + phase_list = [] + for values in phase_diffs_values_lists: + phase_list.append(np.mean(values)) + return phase_list + + def extract(self, tx_dpd, rx): + _check_input_extract(tx_dpd, rx) + + tx_abs = np.abs(tx_dpd) + for i, (tx_start, tx_end) in enumerate(zip(self.tx_boundaries, self.tx_boundaries[1:])): + mask = (tx_abs > tx_start) & (tx_abs < tx_end) + n_add = max(0, self.n_per_bin - len(self.rx_values_lists[i])) + self.rx_values_lists[i] += \ + list(rx[mask][:n_add]) + self.tx_values_lists[i] += \ + list(tx_dpd[mask][:n_add]) + + self.rx_values = self._rx_value_per_bin() + self.tx_values = self._tx_value_per_bin() + + self._plot_and_log() + + n_per_bin = [len(values) for values in self.rx_values_lists] + + # TODO cleanup + phase_diffs_values_lists = self._phase_diff_list_per_bin() + phase_diffs_values = self._phase_diff_value_per_bin(phase_diffs_values_lists) + + return np.array(self.tx_values, dtype=np.float32), \ + np.array(self.rx_values, dtype=np.float32), \ + np.array(phase_diffs_values, dtype=np.float32), \ + n_per_bin + +# 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. diff --git a/dpd/src/MER.py b/dpd/src/MER.py index 00fcc23..4f2918e 100644 --- a/dpd/src/MER.py +++ b/dpd/src/MER.py @@ -106,7 +106,7 @@ class MER: plt.tight_layout() plt.savefig(fig_path) plt.show() - plt.clf() + plt.close() MER_res = 20 * np.log10(np.mean([10 ** (MER / 20) for MER in MERs])) return MER_res diff --git a/dpd/src/Measure.py b/dpd/src/Measure.py index 2450b8a..7a9246c 100644 --- a/dpd/src/Measure.py +++ b/dpd/src/Measure.py @@ -104,10 +104,6 @@ class Measure: txframe, tx_ts, rxframe, rx_ts = self.receive_tcp() - if logging.getLogger().getEffectiveLevel() == logging.DEBUG: - dt = datetime.datetime.now().isoformat() - txframe.tofile(logging_path + "/txframe_" + dt + ".iq") - # Normalize received signal with sent signal rx_median = np.median(np.abs(rxframe)) rxframe = rxframe / rx_median * np.median(np.abs(txframe)) diff --git a/dpd/src/Model.py b/dpd/src/Model.py index a23f0ce..7ce6171 100644 --- a/dpd/src/Model.py +++ b/dpd/src/Model.py @@ -1,388 +1,3 @@ # -*- coding: utf-8 -*- -# -# DPD Calculation Engine, model implementation. -# -# http://www.opendigitalradio.org -# Licence: The MIT License, see notice at the end of this file - -import datetime -import os -import logging - -logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename) - -import numpy as np -import matplotlib.pyplot as plt -from sklearn import linear_model - -class PolyModel: - """Calculates new coefficients using the measurement and the old - coefficients""" - - def __init__(self, - c, - SA, - MER, - coefs_am, - coefs_pm, - learning_rate_am=1., - learning_rate_pm=1., - plot=False): - logging.debug("Initialising Poly Model") - self.c = c - self.SA = SA - self.MER = MER - - self.learning_rate_am = learning_rate_am - self.learning_rate_pm = learning_rate_pm - - if coefs_am is None: - self.coefs_am = [1.0, 0, 0, 0, 0] - else: - self.coefs_am = coefs_am - self.coefs_am_history = [coefs_am, ] - self.mses_am = [] - self.errs_am = [] - - self.tx_mers = [] - self.rx_mers = [] - - if coefs_pm is None: - self.coefs_pm = [0, 0, 0, 0, 0] - else: - self.coefs_pm = coefs_pm - self.coefs_pm_history = [coefs_pm, ] - self.errs_pm = [] - - self.plot = plot - - def train(self, tx_dpd, rx_received): - """Give new training data to the model""" - # Check data type - assert tx_dpd[0].dtype == np.complex64, \ - "tx_dpd is not complex64 but {}".format(tx_dpd[0].dtype) - assert rx_received[0].dtype == np.complex64, \ - "rx_received is not complex64 but {}".format(rx_received[0].dtype) - # Check if signals have same normalization - normalization_error = np.abs(np.median(np.abs(tx_dpd)) - - np.median(np.abs(rx_received))) / ( - np.median(np.abs(tx_dpd)) + np.median(np.abs(rx_received))) - assert normalization_error < 0.01, "Non normalized signals" - - tx_choice, rx_choice = self._sample_uniformly(tx_dpd, rx_received) - new_coefs_am = self._next_am_coefficent(tx_choice, rx_choice) - new_coefs_pm, phase_diff_choice = self._next_pm_coefficent(tx_choice, rx_choice) - - logging.debug('txframe: min {:.2f}, max {:.2f}, ' \ - 'median {:.2f}; rxframe: min {:.2f}, max {:.2f}, ' \ - 'median {:.2f}; new coefs_am {};' \ - 'new_coefs_pm {}'.format( - np.min(np.abs(tx_dpd)), - np.max(np.abs(tx_dpd)), - np.median(np.abs(tx_dpd)), - np.min(np.abs(rx_choice)), - np.max(np.abs(rx_choice)), - np.median(np.abs(rx_choice)), - new_coefs_am, - new_coefs_pm)) - - if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot: - off = self.SA.calc_offset(tx_dpd) - tx_mer = self.MER.calc_mer(tx_dpd[off:off + self.c.T_U]) - rx_mer = self.MER.calc_mer(rx_received[off:off + self.c.T_U], debug=True) - self.tx_mers.append(tx_mer) - self.rx_mers.append(rx_mer) - - if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot: - dt = datetime.datetime.now().isoformat() - fig_path = logging_path + "/" + dt + "_Model.svg" - - fig = plt.figure(figsize=(2 * 6, 2 * 6)) - - i_sub = 1 - - ax = plt.subplot(4, 2, i_sub) - i_sub += 1 - ax.plot(np.abs(tx_dpd[:128]), - label="TX sent", - linestyle=":") - ax.plot(np.abs(rx_received[:128]), - label="RX received", - color="red") - ax.set_title("Synchronized Signals of Iteration {}" - .format(len(self.coefs_am_history))) - ax.set_xlabel("Samples") - ax.set_ylabel("Amplitude") - ax.text(0, 0, "TX (max {:01.3f}, mean {:01.3f}, " \ - "median {:01.3f})".format( - np.max(np.abs(tx_dpd)), - np.mean(np.abs(tx_dpd)), - np.median(np.abs(tx_dpd)) - ), size=8) - ax.legend(loc=4) - - ax = plt.subplot(4, 2, i_sub) - i_sub += 1 - ccdf_min, ccdf_max = 0, 1 - tx_hist, ccdf_edges = np.histogram(np.abs(tx_dpd), - bins=60, - range=(ccdf_min, ccdf_max)) - tx_hist_normalized = tx_hist.astype(float) / np.sum(tx_hist) - ccdf = 1.0 - np.cumsum(tx_hist_normalized) - ax.semilogy(ccdf_edges[:-1], ccdf, label="CCDF") - ax.semilogy(ccdf_edges[:-1], - tx_hist_normalized, - label="Histogram", - drawstyle='steps') - ax.legend(loc=4) - ax.set_ylim(1e-5, 2) - ax.set_title("Complementary Cumulative Distribution Function") - ax.set_xlabel("TX Amplitude") - ax.set_ylabel("Ratio of Samples larger than x") - - ax = plt.subplot(4, 2, i_sub) - i_sub += 1 - ax.semilogy(np.array(self.mses_am) + 1e-10, label="log(MSE)") - ax.semilogy(np.array(self.errs_am) + 1e-10, label="log(ERR)") - ax.legend(loc=4) - ax.set_title("MSE History") - ax.set_xlabel("Iterations") - ax.set_ylabel("MSE") - - ax = plt.subplot(4, 2, i_sub) - i_sub += 1 - ax.plot(self.tx_mers, label="TX MER") - ax.plot(self.rx_mers, label="RX MER") - ax.legend(loc=4) - ax.set_title("MER History") - ax.set_xlabel("Iterations") - ax.set_ylabel("MER") - - ax = plt.subplot(4, 2, i_sub) - rx_range = np.linspace(0, 1, num=100, dtype=np.complex64) - rx_range_dpd = self._dpd_amplitude(rx_range)[0] - rx_range_dpd_new = self._dpd_amplitude(rx_range, new_coefs_am)[0] - i_sub += 1 - ax.scatter( - np.abs(tx_choice), - np.abs(rx_choice), - s=0.1) - ax.plot(rx_range_dpd / self.coefs_am[0], rx_range, linewidth=0.25, label="current") - ax.plot(rx_range_dpd_new / self.coefs_am[0], rx_range, linewidth=0.25, label="next") - ax.set_ylim(0, 1) - ax.set_xlim(0, 1) - ax.legend() - ax.set_title("Amplifier Characteristic") - ax.set_xlabel("TX Amplitude") - ax.set_ylabel("RX Amplitude") - - ax = plt.subplot(4, 2, i_sub) - i_sub += 1 - coefs_am_history = np.array(self.coefs_am_history) - for idx, coef_hist in enumerate(coefs_am_history.T): - ax.plot(coef_hist, - label="Coef {}".format(idx), - linewidth=0.5) - ax.legend(loc=4) - ax.set_title("AM/AM Coefficient History") - ax.set_xlabel("Iterations") - ax.set_ylabel("Coefficient Value") - - phase_range = np.linspace(0, 1, num=100, dtype=np.complex64) - phase_range_dpd = self._dpd_phase(phase_range)[0] - phase_range_dpd_new = self._dpd_phase(phase_range, - coefs=new_coefs_pm)[0] - ax = plt.subplot(4, 2, i_sub) - i_sub += 1 - ax.scatter( - np.abs(tx_choice), - np.rad2deg(phase_diff_choice), - s=0.1) - ax.plot( - np.abs(phase_range), - np.rad2deg(phase_range_dpd), - linewidth=0.25, - label="current") - ax.plot( - np.abs(phase_range), - np.rad2deg(phase_range_dpd_new), - linewidth=0.25, - label="next") - ax.set_ylim(-60, 60) - ax.set_xlim(0, 1) - ax.legend() - ax.set_title("Amplifier Characteristic") - ax.set_xlabel("TX Amplitude") - ax.set_ylabel("Phase Difference") - - ax = plt.subplot(4, 2, i_sub) - i_sub += 1 - coefs_pm_history = np.array(self.coefs_pm_history) - for idx, coef_phase_hist in enumerate(coefs_pm_history.T): - ax.plot(coef_phase_hist, - label="Coef {}".format(idx), - linewidth=0.5) - ax.legend(loc=4) - ax.set_title("AM/PM Coefficient History") - ax.set_xlabel("Iterations") - ax.set_ylabel("Coefficient Value") - - fig.tight_layout() - fig.savefig(fig_path) - fig.clf() - - self.coefs_am = new_coefs_am - self.coefs_am_history.append(self.coefs_am) - self.coefs_pm = new_coefs_pm - self.coefs_pm_history.append(self.coefs_pm) - - def get_dpd_data(self): - return "poly", self.coefs_am, self.coefs_pm - - def _sample_uniformly(self, tx_dpd, rx_received, n_bins=5): - """This function returns tx and rx samples in a way - that the tx amplitudes have an approximate uniform - distribution with respect to the tx_dpd amplitudes""" - mask = np.logical_and((np.abs(tx_dpd) > 0.01), (np.abs(rx_received) > 0.01)) - tx_dpd = tx_dpd[mask] - rx_received = rx_received[mask] - - txframe_aligned_abs = np.abs(tx_dpd) - ccdf_min = 0 - ccdf_max = np.max(txframe_aligned_abs) - tx_hist, ccdf_edges = np.histogram(txframe_aligned_abs, - bins=n_bins, - range=(ccdf_min, ccdf_max)) - n_choise = np.min(tx_hist) - tx_choice = np.zeros(n_choise * n_bins, dtype=np.complex64) - rx_choice = np.zeros(n_choise * n_bins, dtype=np.complex64) - - for idx, bin in enumerate(tx_hist): - indices = np.where((txframe_aligned_abs >= ccdf_edges[idx]) & - (txframe_aligned_abs <= ccdf_edges[idx + 1]))[0] - indices_choise = np.random.choice(indices, - n_choise, - replace=False) - rx_choice[idx * n_choise:(idx + 1) * n_choise] = \ - rx_received[indices_choise] - tx_choice[idx * n_choise:(idx + 1) * n_choise] = \ - tx_dpd[indices_choise] - - assert isinstance(rx_choice[0], np.complex64), \ - "rx_choice is not complex64 but {}".format(rx_choice[0].dtype) - assert isinstance(tx_choice[0], np.complex64), \ - "tx_choice is not complex64 but {}".format(tx_choice[0].dtype) - - return tx_choice, rx_choice - - def _dpd_amplitude(self, sig, coefs=None): - if coefs is None: - coefs = self.coefs_am - assert isinstance(sig[0], np.complex64), "Sig is not complex64 but {}".format(sig[0].dtype) - sig_abs = np.abs(sig) - A_sig = np.vstack([np.ones(sig_abs.shape), - sig_abs ** 1, - sig_abs ** 2, - sig_abs ** 3, - sig_abs ** 4, - ]).T - sig_dpd = sig * np.sum(A_sig * coefs, axis=1) - return sig_dpd, A_sig - - def _dpd_phase(self, sig, coefs=None): - if coefs is None: - coefs = self.coefs_pm - assert isinstance(sig[0], np.complex64), "Sig is not complex64 but {}".format(sig[0].dtype) - sig_abs = np.abs(sig) - A_phase = np.vstack([np.ones(sig_abs.shape), - sig_abs ** 1, - sig_abs ** 2, - sig_abs ** 3, - sig_abs ** 4, - ]).T - phase_diff_est = np.sum(A_phase * coefs, axis=1) - return phase_diff_est, A_phase - - def _next_am_coefficent(self, tx_choice, rx_choice): - """Calculate new coefficients for AM/AM correction""" - rx_dpd, rx_A = self._dpd_amplitude(rx_choice) - rx_dpd = rx_dpd * ( - np.median(np.abs(tx_choice)) / - np.median(np.abs(rx_dpd))) - err = np.abs(rx_dpd) - np.abs(tx_choice) - mse = np.mean(np.abs((rx_dpd - tx_choice) ** 2)) - self.mses_am.append(mse) - self.errs_am.append(np.mean(err**2)) - - reg = linear_model.Ridge(alpha=0.00001) - reg.fit(rx_A, err) - a_delta = reg.coef_ - new_coefs_am = self.coefs_am - self.learning_rate_am * a_delta - new_coefs_am = new_coefs_am * (self.coefs_am[0] / new_coefs_am[0]) - return new_coefs_am - - def _next_pm_coefficent(self, tx_choice, rx_choice): - """Calculate new coefficients for AM/PM correction - Assuming deviations smaller than pi/2""" - phase_diff_choice = np.angle( - (rx_choice * tx_choice.conjugate()) / - (np.abs(rx_choice) * np.abs(tx_choice)) - ) - plt.hist(phase_diff_choice) - plt.savefig('/tmp/hist_' + str(np.random.randint(0,1000)) + '.svg') - plt.clf() - phase_diff_est, phase_A = self._dpd_phase(rx_choice) - err_phase = phase_diff_est - phase_diff_choice - self.errs_pm.append(np.mean(np.abs(err_phase ** 2))) - - reg = linear_model.Ridge(alpha=0.00001) - reg.fit(phase_A, err_phase) - p_delta = reg.coef_ - new_coefs_pm = self.coefs_pm - self.learning_rate_pm * p_delta - - return new_coefs_pm, phase_diff_choice - -class LutModel: - """Implements a model that calculates lookup table coefficients""" - - def __init__(self, - c, - SA, - MER, - learning_rate=1., - plot=False): - logging.debug("Initialising LUT Model") - self.c = c - self.SA = SA - self.MER = MER - self.learning_rate = learning_rate - self.plot = plot - - def train(self, tx_dpd, rx_received): - pass - - def get_dpd_data(self): - return ("lut", np.ones(32, dtype=np.complex64)) - -# The MIT License (MIT) -# -# Copyright (c) 2017 Andreas Steger -# Copyright (c) 2017 Matthias P. Braendli -# -# 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. +from src.Model_Poly import Poly +from src.Model_Lut import Lut diff --git a/dpd/src/Model_AM.py b/dpd/src/Model_AM.py new file mode 100644 index 0000000..ef6cc6c --- /dev/null +++ b/dpd/src/Model_AM.py @@ -0,0 +1,115 @@ +# -*- coding: utf-8 -*- +# +# DPD Calculation Engine, model implementation for Amplitude and not Phase +# +# http://www.opendigitalradio.org +# Licence: The MIT License, see notice at the end of this file + +import datetime +import os +import logging + +logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename) + +import numpy as np +import matplotlib.pyplot as plt +from sklearn import linear_model + + +def check_input_get_next_coefs(tx_dpd, rx_received): + is_float32 = lambda x: (isinstance(x, np.ndarray) and + x.dtype == np.float32 and + x.flags.contiguous) + assert is_float32(tx_dpd), \ + "tx_dpd is not float32 but {}".format(tx_dpd[0].dtype) + assert is_float32(rx_received), \ + "rx_received is not float32 but {}".format(tx_dpd[0].dtype) + + +class Model_AM: + """Calculates new coefficients using the measurement and the previous + coefficients""" + + def __init__(self, + c, + learning_rate_am=0.1, + plot=False): + self.c = c + + self.learning_rate_am = learning_rate_am + self.plot = plot + + def _plot(self, tx_dpd, rx_received, coefs_am, coefs_am_new): + if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot: + tx_range, rx_est = self.calc_line(coefs_am, 0, 0.6) + tx_range_new, rx_est_new = self.calc_line(coefs_am_new, 0, 0.6) + + dt = datetime.datetime.now().isoformat() + fig_path = logging_path + "/" + dt + "_Model_AM.svg" + sub_rows = 1 + sub_cols = 1 + fig = plt.figure(figsize=(sub_cols * 6, sub_rows / 2. * 6)) + i_sub = 0 + + i_sub += 1 + ax = plt.subplot(sub_rows, sub_cols, i_sub) + ax.plot(tx_range, rx_est, + label="Estimated TX", + alpha=0.3, + color="gray") + ax.plot(tx_range_new, rx_est_new, + label="New Estimated TX", + color="red") + ax.scatter(tx_dpd, rx_received, + label="Binned Data", + color="blue", + s=0.1) + ax.set_title("Model_AM") + ax.set_xlabel("TX Amplitude") + ax.set_ylabel("RX Amplitude") + ax.legend(loc=4) + + fig.tight_layout() + fig.savefig(fig_path) + plt.close(fig) + + def poly(self, sig): + return np.array([sig ** i for i in range(1, 6)]).T + + def fit_poly(self, tx_abs, rx_abs): + return np.linalg.lstsq(self.poly(rx_abs), tx_abs)[0] + + def calc_line(self, coefs, min_amp, max_amp): + rx_range = np.linspace(min_amp, max_amp) + tx_est = np.sum(self.poly(rx_range) * coefs, axis=1) + return tx_est, rx_range + + def get_next_coefs(self, tx_dpd, rx_received, coefs_am): + check_input_get_next_coefs(tx_dpd, rx_received) + + coefs_am_new = self.fit_poly(tx_dpd, rx_received) + self._plot(tx_dpd, rx_received, coefs_am, coefs_am_new) + + return coefs_am_new + +# 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. diff --git a/dpd/src/Model_PM.py b/dpd/src/Model_PM.py new file mode 100644 index 0000000..6639382 --- /dev/null +++ b/dpd/src/Model_PM.py @@ -0,0 +1,118 @@ +# -*- coding: utf-8 -*- +# +# DPD Calculation Engine, model implementation for Amplitude and not Phase +# +# http://www.opendigitalradio.org +# Licence: The MIT License, see notice at the end of this file + +import datetime +import os +import logging + +logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename) + +import numpy as np +import matplotlib.pyplot as plt +from sklearn import linear_model + + +def check_input_get_next_coefs(tx_dpd, phase_diff): + is_float32 = lambda x: (isinstance(x, np.ndarray) and + x.dtype == np.float32 and + x.flags.contiguous) + assert is_float32(tx_dpd), \ + "tx_dpd is not float32 but {}".format(tx_dpd[0].dtype) + assert is_float32(phase_diff), \ + "phase_diff is not float32 but {}".format(tx_dpd[0].dtype) + assert tx_dpd.shape == phase_diff.shape, \ + "tx_dpd.shape {}, phase_diff.shape {}".format( + tx_dpd.shape, phase_diff.shape) + + +class Model_PM: + """Calculates new coefficients using the measurement and the previous + coefficients""" + + def __init__(self, + c, + learning_rate_pm=0.1, + plot=False): + self.c = c + + self.learning_rate_pm = learning_rate_pm + self.plot = plot + + def _plot(self, tx_dpd, phase_diff, coefs_pm, coefs_pm_new): + if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot: + tx_range, phase_diff_est = self.calc_line(coefs_pm, 0, 0.6) + tx_range_new, phase_diff_est_new = self.calc_line(coefs_pm_new, 0, 0.6) + + dt = datetime.datetime.now().isoformat() + fig_path = logging_path + "/" + dt + "_Model_PM.svg" + sub_rows = 1 + sub_cols = 1 + fig = plt.figure(figsize=(sub_cols * 6, sub_rows / 2. * 6)) + i_sub = 0 + + i_sub += 1 + ax = plt.subplot(sub_rows, sub_cols, i_sub) + ax.plot(tx_range, phase_diff_est, + label="Estimated Phase Diff", + alpha=0.3, + color="gray") + ax.plot(tx_range_new, phase_diff_est_new, + label="New Estimated Phase Diff", + color="red") + ax.scatter(tx_dpd, phase_diff, + label="Binned Data", + color="blue", + s=0.1) + ax.set_title("Model_PM") + ax.set_xlabel("TX Amplitude") + ax.set_ylabel("Phase DIff") + ax.legend(loc=4) + + fig.tight_layout() + fig.savefig(fig_path) + plt.close(fig) + + def poly(self, sig): + return np.array([sig ** i for i in range(0, 5)]).T + + def fit_poly(self, tx_abs, phase_diff): + return np.linalg.lstsq(self.poly(tx_abs), phase_diff)[0] + + def calc_line(self, coefs, min_amp, max_amp): + tx_range = np.linspace(min_amp, max_amp) + phase_diff = np.sum(self.poly(tx_range) * coefs, axis=1) + return tx_range, phase_diff + + def get_next_coefs(self, tx_dpd, phase_diff, coefs_pm): + check_input_get_next_coefs(tx_dpd, phase_diff) + + coefs_pm_new = self.fit_poly(tx_dpd, phase_diff) + self._plot(tx_dpd, phase_diff, coefs_pm, coefs_pm_new) + + return coefs_pm_new + +# 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. diff --git a/dpd/src/Model_Poly.py b/dpd/src/Model_Poly.py new file mode 100644 index 0000000..f6c024c --- /dev/null +++ b/dpd/src/Model_Poly.py @@ -0,0 +1,99 @@ +# -*- coding: utf-8 -*- +# +# DPD Calculation Engine, model implementation using polynomial +# +# http://www.opendigitalradio.org +# Licence: The MIT License, see notice at the end of this file + +import datetime +import os +import logging + +logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename) + +import numpy as np +import matplotlib.pyplot as plt +from sklearn import linear_model + +import src.Model_AM as Model_AM +import src.Model_PM as Model_PM + + +def assert_np_float32(x): + assert isinstance(x, np.ndarray) + assert x.dtype == np.float32 + assert x.flags.contiguous + +def _check_input_get_next_coefs(tx_abs, rx_abs, phase_diff): + assert_np_float32(tx_abs) + assert_np_float32(rx_abs) + assert_np_float32(phase_diff) + + assert tx_abs.shape == rx_abs.shape, \ + "tx_abs.shape {}, rx_abs.shape {}".format( + tx_abs.shape, rx_abs.shape) + assert tx_abs.shape == phase_diff.shape, \ + "tx_abs.shape {}, phase_diff.shape {}".format( + tx_abs.shape, phase_diff.shape) + + +class Poly: + """Calculates new coefficients using the measurement and the previous + coefficients""" + + def __init__(self, + c, + learning_rate_am=1.0, + learning_rate_pm=1.0, + plot=False): + self.c = c + + self.learning_rate_am = learning_rate_am + self.learning_rate_pm = learning_rate_pm + + self.reset_coefs() + + self.model_am = Model_AM.Model_AM(c, plot=True) + self.model_pm = Model_PM.Model_PM(c, plot=True) + + self.plot = plot + + def reset_coefs(self): + self.coefs_am = np.zeros(5, dtype=np.float32) + self.coefs_am[0] = 1 + self.coefs_pm = np.zeros(5, dtype=np.float32) + return self.coefs_am, self.coefs_pm + + def train(self, tx_abs, rx_abs, phase_diff): + _check_input_get_next_coefs(tx_abs, rx_abs, phase_diff) + + coefs_am_new = self.model_am.get_next_coefs(tx_abs, rx_abs, self.coefs_am) + coefs_pm_new = self.model_pm.get_next_coefs(tx_abs, phase_diff, self.coefs_pm) + + self.coefs_am = self.coefs_am + (coefs_am_new - self.coefs_am) * self.learning_rate_am + self.coefs_pm = self.coefs_pm + (coefs_pm_new - self.coefs_pm) * self.learning_rate_pm + + def get_dpd_data(self): + return "poly", self.coefs_am, self.coefs_pm + +# 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. diff --git a/dpd/src/Symbol_align.py b/dpd/src/Symbol_align.py index 05a9049..6c814a8 100644 --- a/dpd/src/Symbol_align.py +++ b/dpd/src/Symbol_align.py @@ -147,7 +147,8 @@ class Symbol_align: delta_sample_int = np.round(delta_sample).astype(int) error = np.abs(delta_sample_int - delta_sample) if error > 0.1: - raise RuntimeError("Could not calculate sample offset") + raise RuntimeError("Could not calculate " \ + "sample offset. Error {}".format(error)) return delta_sample_int def calc_offset(self, tx): diff --git a/dpd/src/const.py b/dpd/src/const.py index 1011c6c..75ff819 100644 --- a/dpd/src/const.py +++ b/dpd/src/const.py @@ -36,3 +36,9 @@ class const: # frequency per bin = 1kHz # phase difference per sample offset = delta_t * 2 * pi * delta_freq self.phase_offset_per_sample = 1. / sample_rate * 2 * np.pi * 1000 + + # Constants for ExtractStatistic + self.ES_start = 0.0 + self.ES_end = 1.0 + self.ES_n_bins = 64 + self.ES_n_per_bin = 256 diff --git a/dpd/src/phase_align.py b/dpd/src/phase_align.py index 7f82392..7317d70 100644 --- a/dpd/src/phase_align.py +++ b/dpd/src/phase_align.py @@ -75,7 +75,7 @@ def phase_align(sig, ref_sig, plot=False): plt.legend(loc=4) plt.tight_layout() plt.savefig(fig_path) - plt.clf() + plt.close() return sig diff --git a/dpd/src/subsample_align.py b/dpd/src/subsample_align.py index 6d1cd2a..b0cbe88 100755 --- a/dpd/src/subsample_align.py +++ b/dpd/src/subsample_align.py @@ -78,7 +78,7 @@ def subsample_align(sig, ref_sig, plot=False): plt.plot(ixs, taus) plt.title("Subsample correlation, minimum is best: {}".format(best_tau)) plt.savefig(tau_path) - plt.clf() + plt.close() # Prepare rotate_vec = fft_sig with rotated phase rotate_vec = np.exp(1j * best_tau * omega) |