Add ExtractStatistic to condense infromation from several measurements

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.