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Diffstat (limited to 'dpd/src/ExtractStatistic.py')
| -rw-r--r-- | dpd/src/ExtractStatistic.py | 158 |
1 files changed, 158 insertions, 0 deletions
diff --git a/dpd/src/ExtractStatistic.py b/dpd/src/ExtractStatistic.py new file mode 100644 index 0000000..8ae48ac --- /dev/null +++ b/dpd/src/ExtractStatistic.py @@ -0,0 +1,158 @@ +# -*- 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: + dt = datetime.datetime.now().isoformat() + fig_path = logging_path + "/" + dt + "_ExtractStatistic.png" + sub_rows = 2 + 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) + + 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) + fig.clf() + + 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 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] + + return np.array(self.tx_values, dtype=np.float32), np.array(self.rx_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. |