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-rw-r--r--dpd/src/Adapt.py2
-rw-r--r--dpd/src/Const.py81
-rw-r--r--dpd/src/Dab_Util.py44
-rw-r--r--dpd/src/ExtractStatistic.py78
-rw-r--r--dpd/src/Measure_Shoulders.py4
-rw-r--r--dpd/src/Model_AM.py48
-rw-r--r--dpd/src/RX_Agc.py (renamed from dpd/src/Agc.py)0
-rw-r--r--dpd/src/phase_align.py2
-rw-r--r--dpd/src/test_dab_Util.py2
9 files changed, 132 insertions, 129 deletions
diff --git a/dpd/src/Adapt.py b/dpd/src/Adapt.py
index cf8b6f5..1ab7710 100644
--- a/dpd/src/Adapt.py
+++ b/dpd/src/Adapt.py
@@ -219,7 +219,7 @@ class Adapt:
"txgain": self.get_txgain(),
"rxgain": self.get_rxgain(),
"digital_gain": self.get_digital_gain(),
- "predistorter": self.get_predistorter
+ "predistorter": self.get_predistorter()
}
with open(path, "w") as f:
pickle.dump(d, f)
diff --git a/dpd/src/Const.py b/dpd/src/Const.py
index ebdfeb2..390dfbb 100644
--- a/dpd/src/Const.py
+++ b/dpd/src/Const.py
@@ -1,16 +1,21 @@
-# DAB Frame constants
-# Sources:
-# - etsi_EN_300_401_v010401p p145
-# - Measured with USRP B200
+# -*- coding: utf-8 -*-
+#
+# DPD Calculation Engine, constants
+#
+# Source for DAB standard: etsi_EN_300_401_v010401p p145
+#
+# http://www.opendigitalradio.org
+# Licence: The MIT License, see notice at the end of this file
import numpy as np
+
class Const:
def __init__(self, sample_rate, target_median, plot):
+ assert sample_rate == 8192000 # By now only constants for 8192000
self.sample_rate = sample_rate
- self.tx_gain_max = 89
-
+ # DAB frame
# Time domain
self.T_F = sample_rate / 2048000 * 196608 # Transmission frame duration
self.T_NULL = sample_rate / 2048000 * 2656 # Null symbol duration
@@ -20,18 +25,10 @@ class Const:
# Frequency Domain
# example: np.delete(fft[3328:4865], 768)
- self.FFT_delete = 768
self.FFT_delta = 1536 # Number of carrier frequencies
- if sample_rate == 2048000:
- self.FFT_start = 256
- self.FFT_end = 1793
- elif sample_rate == 8192000:
- self.FFT_start = 3328
- self.FFT_end = 4865
- else:
- raise RuntimeError("Sample Rate '{}' not supported".format(
- sample_rate
- ))
+ self.FFT_delete = 768
+ self.FFT_start = 3328
+ self.FFT_end = 4865
# Calculate sample offset from phase rotation
# time per sample = 1 / sample_rate
@@ -43,42 +40,44 @@ class Const:
self.ES_plot = plot
self.ES_start = 0.0
self.ES_end = 1.0
- self.ES_n_bins = 64
- self.ES_n_per_bin = 128
-
- # Constants for TX_Agc
- self.TAGC_max_txgain = 89
- self.TAGC_tx_median_target = target_median
- self.TAGC_tx_median_max = self.TAGC_tx_median_target*1.4
- self.TAGC_tx_median_min = self.TAGC_tx_median_target/1.4
-
- self.RAGC_min_rxgain = 25
- self.RAGC_rx_median_target = self.TAGC_tx_median_target
-
- # Constants for Model
- self.MDL_plot = True or plot # Override default
-
- # Constants for MER
- self.MER_plot = plot
-
- # Constants for Model_PM
- self.MPM_tx_min = 0.1
+ self.ES_n_bins = 64 # Number of bins between ES_start and ES_end
+ self.ES_n_per_bin = 128 # Number of measurements pre bin
# Constants for Measure_Shoulder
self.MS_enable = False
self.MS_plot = plot
- assert sample_rate==8192000
- meas_offset = 976 # Offset from center frequency to measure shoulder [kHz]
- meas_width = 100 # Size of frequency delta to measure shoulder [kHz]
+
+ meas_offset = 976 # Offset from center frequency to measure shoulder [kHz]
+ meas_width = 100 # Size of frequency delta to measure shoulder [kHz]
shoulder_offset_edge = np.abs(meas_offset - self.FFT_delta)
self.MS_shoulder_left_start = self.FFT_start - shoulder_offset_edge - meas_width / 2
self.MS_shoulder_left_end = self.FFT_start - shoulder_offset_edge + meas_width / 2
self.MS_shoulder_right_start = self.FFT_end + shoulder_offset_edge - meas_width / 2
self.MS_shoulder_right_end = self.FFT_end + shoulder_offset_edge + meas_width / 2
- self.MS_peak_start = self.FFT_start + 100 # Ignore region near edges
+ self.MS_peak_start = self.FFT_start + 100 # Ignore region near edges
self.MS_peak_end = self.FFT_end - 100
self.MS_FFT_size = 8192
self.MS_averaging_size = 4 * self.MS_FFT_size
self.MS_n_averaging = 40
self.MS_n_proc = 4
+
+ # Constants for MER
+ self.MER_plot = plot
+
+ # Constants for Model
+ self.MDL_plot = True or plot # Override default
+
+ # Constants for Model_PM
+ # Set all phase offsets to zero for TX amplitude < MPM_tx_min
+ self.MPM_tx_min = 0.1
+
+ # Constants for TX_Agc
+ self.TAGC_max_txgain = 89 # USRP specific
+ self.TAGC_tx_median_target = target_median
+ self.TAGC_tx_median_max = self.TAGC_tx_median_target * 1.4
+ self.TAGC_tx_median_min = self.TAGC_tx_median_target / 1.4
+
+ # Constants for RX_AGC
+ self.RAGC_min_rxgain = 25 # USRP specific
+ self.RAGC_rx_median_target = self.TAGC_tx_median_target
diff --git a/dpd/src/Dab_Util.py b/dpd/src/Dab_Util.py
index 37be5db..27a31ef 100644
--- a/dpd/src/Dab_Util.py
+++ b/dpd/src/Dab_Util.py
@@ -8,30 +8,40 @@
import datetime
import os
import logging
+
logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename)
import numpy as np
-import scipy
import matplotlib
+
matplotlib.use('agg')
import matplotlib.pyplot as plt
import src.subsample_align as sa
import src.phase_align as pa
from scipy import signal
+
+def fromfile(filename, offset=0, length=None):
+ if length is None:
+ return np.memmap(filename, dtype=np.complex64, mode='r', offset=64 / 8 * offset)
+ else:
+ return np.memmap(filename, dtype=np.complex64, mode='r', offset=64 / 8 * offset, shape=length)
+
+
class Dab_Util:
"""Collection of methods that can be applied to an array
complex IQ samples of a DAB signal
"""
+
def __init__(self, sample_rate, plot=False):
"""
:param sample_rate: sample rate [sample/sec] to use for calculations
"""
self.sample_rate = sample_rate
- self.dab_bandwidth = 1536000 #Bandwidth of a dab signal
- self.frame_ms = 96 #Duration of a Dab frame
+ self.dab_bandwidth = 1536000 # Bandwidth of a dab signal
+ self.frame_ms = 96 # Duration of a Dab frame
- self.plot=plot
+ self.plot = plot
def lag(self, sig_orig, sig_rec):
"""
@@ -56,10 +66,10 @@ class Dab_Util:
def lag_upsampling(self, sig_orig, sig_rec, n_up):
if n_up != 1:
sig_orig_up = signal.resample(sig_orig, sig_orig.shape[0] * n_up)
- sig_rec_up = signal.resample(sig_rec, sig_rec.shape[0] * n_up)
+ sig_rec_up = signal.resample(sig_rec, sig_rec.shape[0] * n_up)
else:
sig_orig_up = sig_orig
- sig_rec_up = sig_rec
+ sig_rec_up = sig_rec
l = self.lag(sig_orig_up, sig_rec_up)
l_orig = float(l) / n_up
return l_orig
@@ -69,7 +79,7 @@ class Dab_Util:
Returns an aligned version of sig_tx and sig_rx by cropping and subsample alignment
Using upsampling
"""
- assert(sig_tx.shape[0] == sig_rx.shape[0])
+ assert (sig_tx.shape[0] == sig_rx.shape[0])
if sig_tx.shape[0] % 2 == 1:
sig_tx = sig_tx[:-1]
@@ -124,11 +134,11 @@ class Dab_Util:
off = int(abs(off_meas))
logging.debug("sig_tx_orig: {} {}, sig_rx_orig: {} {}, offset {}".format(
- len(sig_tx),
- sig_tx.dtype,
- len(sig_rx),
- sig_rx.dtype,
- off_meas))
+ len(sig_tx),
+ sig_tx.dtype,
+ len(sig_rx),
+ sig_rx.dtype,
+ off_meas))
if off_meas > 0:
sig_tx = sig_tx[:-off]
@@ -211,16 +221,10 @@ class Dab_Util:
fig.savefig(fig_path)
plt.close(fig)
- logging.debug("Sig1_cut: %d %s, Sig2_cut: %d %s, off: %d" % (len(sig_tx), sig_tx.dtype, len(sig_rx), sig_rx.dtype, off))
+ logging.debug(
+ "Sig1_cut: %d %s, Sig2_cut: %d %s, off: %d" % (len(sig_tx), sig_tx.dtype, len(sig_rx), sig_rx.dtype, off))
return sig_tx, sig_rx
- def fromfile(self, filename, offset=0, length=None):
- if length is None:
- return np.memmap(filename, dtype=np.complex64, mode='r', offset=64/8*offset)
- else:
- return np.memmap(filename, dtype=np.complex64, mode='r', offset=64/8*offset, shape=length)
-
-
# The MIT License (MIT)
#
# Copyright (c) 2017 Andreas Steger
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)
#
diff --git a/dpd/src/Measure_Shoulders.py b/dpd/src/Measure_Shoulders.py
index 710e800..7d48a2b 100644
--- a/dpd/src/Measure_Shoulders.py
+++ b/dpd/src/Measure_Shoulders.py
@@ -101,6 +101,10 @@ class Measure_Shoulders:
plt.close(fig)
def average_shoulders(self, signal, n_avg=None):
+ if not self.c.MS_enable:
+ logging.debug("Shoulder Measurement disabled")
+ return None
+
assert signal.shape[0] > 4 * self.c.MS_FFT_size
if n_avg is None: n_avg = self.c.MS_averaging_size
diff --git a/dpd/src/Model_AM.py b/dpd/src/Model_AM.py
index 85f6495..cdc3de1 100644
--- a/dpd/src/Model_AM.py
+++ b/dpd/src/Model_AM.py
@@ -15,14 +15,30 @@ import numpy as np
import matplotlib.pyplot as plt
+def is_npfloat32(array):
+ assert isinstance(array, np.ndarray), type(array)
+ assert array.dtype == np.float32, array.dtype
+ assert array.flags.contiguous
+ assert not any(np.isnan(array))
+
+
def check_input_get_next_coefs(tx_dpd, rx_received):
- 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(rx_received), \
- "rx_received is not float32 but {}".format(tx_dpd[0].dtype)
+ is_npfloat32(tx_dpd)
+ is_npfloat32(rx_received)
+
+
+def poly(sig):
+ return np.array([sig ** i for i in range(1, 6)]).T
+
+
+def fit_poly(tx_abs, rx_abs):
+ return np.linalg.lstsq(poly(rx_abs), tx_abs)[0]
+
+
+def calc_line(coefs, min_amp, max_amp):
+ rx_range = np.linspace(min_amp, max_amp)
+ tx_est = np.sum(poly(rx_range) * coefs, axis=1)
+ return tx_est, rx_range
class Model_AM:
@@ -40,8 +56,8 @@ class Model_AM:
def _plot(self, tx_dpd, rx_received, coefs_am, coefs_am_new):
if logging.getLogger().getEffectiveLevel() == logging.DEBUG and self.plot:
- tx_range, rx_est = self.calc_line(coefs_am, 0, 0.6)
- tx_range_new, rx_est_new = self.calc_line(coefs_am_new, 0, 0.6)
+ tx_range, rx_est = calc_line(coefs_am, 0, 0.6)
+ tx_range_new, rx_est_new = calc_line(coefs_am_new, 0, 0.6)
dt = datetime.datetime.now().isoformat()
fig_path = logging_path + "/" + dt + "_Model_AM.svg"
@@ -66,7 +82,6 @@ class Model_AM:
ax.set_title("Model_AM")
ax.set_xlabel("TX Amplitude")
ax.set_ylabel("RX Amplitude")
- xlim = ax.get_xlim()
ax.set_xlim(-0.5, 1.5)
ax.legend(loc=4)
@@ -74,21 +89,10 @@ class Model_AM:
fig.savefig(fig_path)
plt.close(fig)
- def poly(self, sig):
- return np.array([sig ** i for i in range(1, 6)]).T
-
- def fit_poly(self, tx_abs, rx_abs):
- return np.linalg.lstsq(self.poly(rx_abs), tx_abs)[0]
-
- def calc_line(self, coefs, min_amp, max_amp):
- rx_range = np.linspace(min_amp, max_amp)
- tx_est = np.sum(self.poly(rx_range) * coefs, axis=1)
- return tx_est, rx_range
-
def get_next_coefs(self, tx_dpd, rx_received, coefs_am):
check_input_get_next_coefs(tx_dpd, rx_received)
- coefs_am_new = self.fit_poly(tx_dpd, rx_received)
+ coefs_am_new = fit_poly(tx_dpd, rx_received)
coefs_am_new = coefs_am + \
self.learning_rate_am * (coefs_am_new - coefs_am)
diff --git a/dpd/src/Agc.py b/dpd/src/RX_Agc.py
index 670fbbb..670fbbb 100644
--- a/dpd/src/Agc.py
+++ b/dpd/src/RX_Agc.py
diff --git a/dpd/src/phase_align.py b/dpd/src/phase_align.py
index 7317d70..0d662ec 100644
--- a/dpd/src/phase_align.py
+++ b/dpd/src/phase_align.py
@@ -10,8 +10,6 @@ import logging
logging_path = os.path.dirname(logging.getLoggerClass().root.handlers[0].baseFilename)
import numpy as np
-from scipy import signal, optimize
-import sys
import matplotlib.pyplot as plt
diff --git a/dpd/src/test_dab_Util.py b/dpd/src/test_dab_Util.py
index ec15586..86ee2d8 100644
--- a/dpd/src/test_dab_Util.py
+++ b/dpd/src/test_dab_Util.py
@@ -16,7 +16,7 @@ class TestDab_Util(TestCase):
def test_subsample_align(self, sample_orig=r'../test_data/orig_rough_aligned.dat',
sample_rec =r'../test_data/recored_rough_aligned.dat',
length = 10240, max_size = 1000000):
- du = DU.Dab_Util(8196000)
+ du = DU
res1 = []
res2 = []
for i in range(10):
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-15 09:42:18 +0000