diff options
Diffstat (limited to 'dpd/src/ExtractStatistic.py')
| -rw-r--r-- | dpd/src/ExtractStatistic.py | 78 |
1 files changed, 36 insertions, 42 deletions
diff --git a/dpd/src/ExtractStatistic.py b/dpd/src/ExtractStatistic.py index 9df85bc..8ea849b 100644 --- a/dpd/src/ExtractStatistic.py +++ b/dpd/src/ExtractStatistic.py @@ -1,13 +1,12 @@ # -*- coding: utf-8 -*- # # DPD Calculation Engine, -# Extract statistic from data to use in Model +# 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 pickle import matplotlib.pyplot as plt import datetime @@ -30,6 +29,14 @@ def _check_input_extract(tx_dpd, 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""" @@ -37,31 +44,27 @@ class ExtractStatistic: 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.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 = c.ES_plot - def _plot_and_log(self): + def _plot_and_log(self, tx_values, rx_values, phase_diffs_values, phase_diffs_values_lists): 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" @@ -72,13 +75,13 @@ class ExtractStatistic: i_sub += 1 ax = plt.subplot(sub_rows, sub_cols, i_sub) - ax.plot(self.tx_values, self.rx_values, + ax.plot(tx_values, 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), + 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") @@ -90,10 +93,10 @@ class ExtractStatistic: i_sub += 1 ax = plt.subplot(sub_rows, sub_cols, i_sub) - ax.plot(self.tx_values, np.rad2deg(phase_diffs_values), + ax.plot(tx_values, np.rad2deg(phase_diffs_values), label="Estimated Values", color="red") - for i, tx_value in enumerate(self.tx_values): + 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), @@ -101,14 +104,14 @@ class ExtractStatistic: color="black") ax.set_xlabel("TX Amplitude") ax.set_ylabel("Phase Difference") - ax.set_ylim(-60,60) + 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)) + 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) @@ -120,9 +123,6 @@ class ExtractStatistic: 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: @@ -145,14 +145,9 @@ class ExtractStatistic: 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: - mean = np.mean(values) if len(values) > 0 else np.nan - phase_list.append(mean) - return phase_list - 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 @@ -165,23 +160,22 @@ class ExtractStatistic: 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() + 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) - # 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) + 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) - return np.array(self.tx_values, dtype=np.float32)[:idx_end], \ - np.array(self.rx_values, dtype=np.float32)[:idx_end], \ - np.array(phase_diffs_values, dtype=np.float32)[:idx_end], \ - n_per_bin + 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) # |