Add Model_PM

4 files changed, 125 insertions, 2 deletions

diff --git a/dpd/src/ExtractStatistic.py b/dpd/src/ExtractStatistic.py
index 6139e1d..897ec0a 100644
--- a/dpd/src/ExtractStatistic.py
+++ b/dpd/src/ExtractStatistic.py
@@ -169,8 +169,13 @@ class ExtractStatistic:
 
         n_per_bin = [len(values) for values in self.rx_values_lists]
 
+        # 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)
+
         return np.array(self.tx_values,  dtype=np.float32), \
                np.array(self.rx_values, dtype=np.float32), \
+               np.array(phase_diffs_values, dtype=np.float32), \
                n_per_bin
 
 # The MIT License (MIT)
diff --git a/dpd/src/Model_AM.py b/dpd/src/Model_AM.py
index bdf55c6..5c307ef 100644
--- a/dpd/src/Model_AM.py
+++ b/dpd/src/Model_AM.py
@@ -22,7 +22,7 @@ def check_input_get_next_coefs(tx_dpd, rx_received):
                         x.flags.contiguous)
     assert is_float32(tx_dpd), \
            "tx_dpd is not float32 but {}".format(tx_dpd[0].dtype)
-    assert is_float32(tx_dpd), \
+    assert is_float32(rx_received), \
             "rx_received is not float32 but {}".format(tx_dpd[0].dtype)
 
 
diff --git a/dpd/src/Model_PM.py b/dpd/src/Model_PM.py
new file mode 100644
index 0000000..6639382
--- /dev/null
+++ b/dpd/src/Model_PM.py
@@ -0,0 +1,118 @@
+# -*- coding: utf-8 -*-
+#
+# DPD Calculation Engine, model implementation for Amplitude and not Phase
+#
+# http://www.opendigitalradio.org
+# Licence: The MIT License, see notice at the end of this file
+
+import datetime
+import os
+import logging
+
+logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename)
+
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import linear_model
+
+
+def check_input_get_next_coefs(tx_dpd, phase_diff):
+    is_float32 = lambda x: (isinstance(x, np.ndarray) and
+                        x.dtype == np.float32 and
+                        x.flags.contiguous)
+    assert is_float32(tx_dpd), \
+           "tx_dpd is not float32 but {}".format(tx_dpd[0].dtype)
+    assert is_float32(phase_diff), \
+            "phase_diff is not float32 but {}".format(tx_dpd[0].dtype)
+    assert tx_dpd.shape == phase_diff.shape, \
+        "tx_dpd.shape {}, phase_diff.shape {}".format(
+        tx_dpd.shape, phase_diff.shape)
+
+
+class Model_PM:
+    """Calculates new coefficients using the measurement and the previous
+    coefficients"""
+
+    def __init__(self,
+                 c,
+                 learning_rate_pm=0.1,
+                 plot=False):
+        self.c = c
+
+        self.learning_rate_pm = learning_rate_pm
+        self.plot = plot
+
+    def _plot(self, tx_dpd, phase_diff, coefs_pm, coefs_pm_new):
+        if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot:
+            tx_range, phase_diff_est = self.calc_line(coefs_pm, 0, 0.6)
+            tx_range_new, phase_diff_est_new = self.calc_line(coefs_pm_new, 0, 0.6)
+
+            dt = datetime.datetime.now().isoformat()
+            fig_path = logging_path + "/" + dt + "_Model_PM.svg"
+            sub_rows = 1
+            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_range, phase_diff_est,
+                    label="Estimated Phase Diff",
+                    alpha=0.3,
+                    color="gray")
+            ax.plot(tx_range_new, phase_diff_est_new,
+                    label="New Estimated Phase Diff",
+                    color="red")
+            ax.scatter(tx_dpd, phase_diff,
+                       label="Binned Data",
+                       color="blue",
+                       s=0.1)
+            ax.set_title("Model_PM")
+            ax.set_xlabel("TX Amplitude")
+            ax.set_ylabel("Phase DIff")
+            ax.legend(loc=4)
+
+            fig.tight_layout()
+            fig.savefig(fig_path)
+            plt.close(fig)
+
+    def poly(self, sig):
+        return np.array([sig ** i for i in range(0, 5)]).T
+
+    def fit_poly(self, tx_abs, phase_diff):
+        return np.linalg.lstsq(self.poly(tx_abs), phase_diff)[0]
+
+    def calc_line(self, coefs, min_amp, max_amp):
+        tx_range = np.linspace(min_amp, max_amp)
+        phase_diff = np.sum(self.poly(tx_range) * coefs, axis=1)
+        return tx_range, phase_diff
+
+    def get_next_coefs(self, tx_dpd, phase_diff, coefs_pm):
+        check_input_get_next_coefs(tx_dpd, phase_diff)
+
+        coefs_pm_new = self.fit_poly(tx_dpd, phase_diff)
+        self._plot(tx_dpd, phase_diff, coefs_pm, coefs_pm_new)
+
+        return coefs_pm_new
+
+# 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.
diff --git a/dpd/src/const.py b/dpd/src/const.py
index 6cf1537..75ff819 100644
--- a/dpd/src/const.py
+++ b/dpd/src/const.py
@@ -41,4 +41,4 @@ class const:
         self.ES_start = 0.0
         self.ES_end = 1.0
         self.ES_n_bins = 64
-        self.ES_n_per_bin = 1024
+        self.ES_n_per_bin = 256