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Diffstat (limited to 'dpd/src/ExtractStatistic.py')
| -rw-r--r-- | dpd/src/ExtractStatistic.py | 196 |
1 files changed, 0 insertions, 196 deletions
diff --git a/dpd/src/ExtractStatistic.py b/dpd/src/ExtractStatistic.py deleted file mode 100644 index 639513a..0000000 --- a/dpd/src/ExtractStatistic.py +++ /dev/null @@ -1,196 +0,0 @@ -# -*- coding: utf-8 -*- -# -# DPD Computation Engine, -# Extract statistic from received TX and RX 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 matplotlib.pyplot as plt -import datetime -import os -import logging - - -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" - - -def _phase_diff_value_per_bin(phase_diffs_values_lists): - phase_list = [] - for values in phase_diffs_values_lists: - mean = np.mean(values) if len(values) > 0 else np.nan - phase_list.append(mean) - return phase_list - - -class ExtractStatistic: - """Calculate a low variance RX value for equally spaced tx values - of a predefined range""" - - def __init__(self, c): - self.c = c - - # Number of measurements used to extract the statistic - self.n_meas = 0 - - # Boundaries for the bins - 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 - - # List of rx values for each bin - self.rx_values_lists = [] - for i in range(c.ES_n_bins): - self.rx_values_lists.append([]) - - # List of tx values for each bin - self.tx_values_lists = [] - for i in range(c.ES_n_bins): - self.tx_values_lists.append([]) - - self.plot = c.ES_plot - - def _plot_and_log(self, tx_values, rx_values, phase_diffs_values, phase_diffs_values_lists): - if self.plot and self.c.plot_location is not None: - dt = datetime.datetime.now().isoformat() - fig_path = self.c.plot_location + "/" + 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(tx_values, rx_values, - label="Estimated Values", - color="red") - for i, tx_value in enumerate(tx_values): - rx_values_list = self.rx_values_lists[i] - ax.scatter(np.ones(len(rx_values_list)) * tx_value, - np.abs(rx_values_list), - 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(tx_values, np.rad2deg(phase_diffs_values), - label="Estimated Values", - color="red") - for i, tx_value in enumerate(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(tx_values): - rx_values_list = self.rx_values_lists[i] - num.append(len(rx_values_list)) - 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) - - def _rx_value_per_bin(self): - rx_values = [] - for values in self.rx_values_lists: - mean = np.mean(np.abs(values)) if len(values) > 0 else np.nan - rx_values.append(mean) - 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 extract(self, tx_dpd, rx): - """Extract information from a new measurement and store them - in member variables.""" - _check_input_extract(tx_dpd, rx) - self.n_meas += 1 - - 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]) - - rx_values = self._rx_value_per_bin() - tx_values = self._tx_value_per_bin() - - n_per_bin = np.array([len(values) for values in self.rx_values_lists]) - # Index of first not filled bin, assumes that never all bins are filled - idx_end = np.argmin(n_per_bin == self.c.ES_n_per_bin) - - phase_diffs_values_lists = self._phase_diff_list_per_bin() - phase_diffs_values = _phase_diff_value_per_bin(phase_diffs_values_lists) - - self._plot_and_log(tx_values, rx_values, phase_diffs_values, phase_diffs_values_lists) - - tx_values_crop = np.array(tx_values, dtype=np.float32)[:idx_end] - rx_values_crop = np.array(rx_values, dtype=np.float32)[:idx_end] - phase_diffs_values_crop = np.array(phase_diffs_values, dtype=np.float32)[:idx_end] - return tx_values_crop, rx_values_crop, phase_diffs_values_crop, 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. |