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-rw-r--r--dpd/src/ExtractStatistic.py78
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)
#