diff options
| -rw-r--r-- | python/dpd/ExtractStatistic.py | 8 | ||||
| -rw-r--r-- | python/dpd/GlobalConfig.py | 9 | ||||
| -rw-r--r-- | python/dpd/Model_AM.py | 119 | ||||
| -rw-r--r-- | python/dpd/Model_PM.py | 121 | ||||
| -rw-r--r-- | python/dpd/Model_Poly.py | 146 | ||||
| -rw-r--r-- | python/dpd/RX_Agc.py | 18 | ||||
| -rwxr-xr-x | python/dpdce.py | 41 | ||||
| -rw-r--r-- | python/gui/static/js/odr-predistortion.js | 10 | ||||
| -rw-r--r-- | python/gui/templates/predistortion.html | 5 |
9 files changed, 190 insertions, 287 deletions
diff --git a/python/dpd/ExtractStatistic.py b/python/dpd/ExtractStatistic.py index 1aa4391..a23fa1a 100644 --- a/python/dpd/ExtractStatistic.py +++ b/python/dpd/ExtractStatistic.py @@ -38,7 +38,7 @@ class ExtractStatistic: """Calculate a low variance RX value for equally spaced tx values of a predefined range""" - def __init__(self, c): + def __init__(self, c, peak_amplitude): self.c = c self._plot_data = None @@ -47,7 +47,7 @@ class ExtractStatistic: 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.tx_boundaries = np.linspace(0.0, peak_amplitude, c.ES_n_bins + 1) self.n_per_bin = c.ES_n_per_bin # List of rx values for each bin @@ -60,6 +60,10 @@ class ExtractStatistic: for i in range(c.ES_n_bins): self.tx_values_lists.append([]) + def get_bin_info(self): + return "Binning: {} bins used for amplitudes between {} and {}".format( + len(self.tx_boundaries), np.min(self.tx_boundaries), np.max(self.tx_boundaries)) + def plot(self, plot_path, title): if self._plot_data is not None: tx_values, rx_values, phase_diffs_values, phase_diffs_values_lists = self._plot_data diff --git a/python/dpd/GlobalConfig.py b/python/dpd/GlobalConfig.py index 99280f2..632a63b 100644 --- a/python/dpd/GlobalConfig.py +++ b/python/dpd/GlobalConfig.py @@ -26,6 +26,8 @@ class GlobalConfig: self.T_U = oversample * 2048 # Inverse of carrier spacing self.T_C = oversample * 504 # Duration of cyclic prefix + self.median_to_peak = 12 # Estimated value for a DAB OFDM signal + # Frequency Domain # example: np.delete(fft[3328:4865], 768) self.FFT_delta = 1536 # Number of carrier frequencies @@ -40,10 +42,8 @@ class GlobalConfig: self.phase_offset_per_sample = 1. / self.sample_rate * 2 * np.pi * 1000 # Constants for ExtractStatistic - self.ES_plot = plot - self.ES_start = 0.0 self.ES_end = 1.0 - self.ES_n_bins = 64 # Number of bins between ES_start and ES_end + self.ES_n_bins = 64 self.ES_n_per_bin = 128 # Number of measurements pre bin # Constants for Measure_Shoulder @@ -68,9 +68,6 @@ class GlobalConfig: # Constants for MER self.MER_plot = plot - # Constants for Model - self.MDL_plot = plot - # Constants for Model_PM # Set all phase offsets to zero for TX amplitude < MPM_tx_min self.MPM_tx_min = 0.1 diff --git a/python/dpd/Model_AM.py b/python/dpd/Model_AM.py deleted file mode 100644 index b07a5a5..0000000 --- a/python/dpd/Model_AM.py +++ /dev/null @@ -1,119 +0,0 @@ -# -*- coding: utf-8 -*- -# -# DPD Computation 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 -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_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, rcond=None)[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: - """Calculates new coefficients using the measurement and the previous - coefficients""" - - def __init__(self, c, learning_rate_am=1): - self.c = c - self.learning_rate_am = learning_rate_am - self._plot_data = None - - def plot(self, plot_location, title): - if self._plot_data is not None: - tx_dpd, rx_received, coefs_am, coefs_am_new = self._plot_data - - 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) - - 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, rx_est, - label="Estimated TX", - alpha=0.3, - color="gray") - ax.plot(tx_range_new, rx_est_new, - label="New Estimated TX", - color="red") - ax.scatter(tx_dpd, rx_received, - label="Binned Data", - color="blue", - s=1) - ax.set_title("Model_AM {}".format(title)) - ax.set_xlabel("TX Amplitude") - ax.set_ylabel("RX Amplitude") - ax.set_xlim(-0.5, 1.5) - ax.legend(loc=4) - - fig.tight_layout() - fig.savefig(plot_location) - plt.close(fig) - - def get_next_coefs(self, tx_dpd, rx_received, coefs_am): - """Calculate the next AM/AM coefficients using the extracted - statistic of TX and RX amplitude""" - check_input_get_next_coefs(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) - - self._plot_data = (tx_dpd, rx_received, coefs_am, coefs_am_new) - - return coefs_am_new - -# The MIT License (MIT) -# -# Copyright (c) 2017 Andreas Steger -# Copyright (c) 2018 Matthias P. Braendli -# -# 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/python/dpd/Model_PM.py b/python/dpd/Model_PM.py deleted file mode 100644 index 40fa1d4..0000000 --- a/python/dpd/Model_PM.py +++ /dev/null @@ -1,121 +0,0 @@ -# -*- coding: utf-8 -*- -# -# DPD Computation 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 -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, phase_diff): - is_npfloat32(tx_dpd) - is_npfloat32(phase_diff) - - -class Model_PM: - """Calculates new coefficients using the measurement and the previous - coefficients""" - - def __init__(self, c, learning_rate_pm=1): - self.c = c - self.learning_rate_pm = learning_rate_pm - self._plot_data = None - - def plot(self, plot_location, title): - if self._plot_data is not None: - tx_dpd, phase_diff, coefs_pm, coefs_pm_new = self._plot_data - - 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) - - 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=1) - ax.set_title("Model_PM {}".format(title)) - ax.set_xlabel("TX Amplitude") - ax.set_ylabel("Phase DIff") - ax.legend(loc=4) - - fig.tight_layout() - fig.savefig(plot_location) - plt.close(fig) - - def _discard_small_values(self, tx_dpd, phase_diff): - """ Assumes that the phase for small tx amplitudes is zero""" - mask = tx_dpd < self.c.MPM_tx_min - phase_diff[mask] = 0 - return tx_dpd, phase_diff - - 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, rcond=None)[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): - """Calculate the next AM/PM coefficients using the extracted - statistic of TX amplitude and phase difference""" - tx_dpd, phase_diff = self._discard_small_values(tx_dpd, phase_diff) - check_input_get_next_coefs(tx_dpd, phase_diff) - - coefs_pm_new = self.fit_poly(tx_dpd, phase_diff) - - coefs_pm_new = coefs_pm + self.learning_rate_pm * (coefs_pm_new - coefs_pm) - self._plot_data = (tx_dpd, phase_diff, coefs_pm, coefs_pm_new) - - return coefs_pm_new - -# The MIT License (MIT) -# -# Copyright (c) 2017 Andreas Steger -# Copyright (c) 2018 Matthias P. Braendli -# -# 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/python/dpd/Model_Poly.py b/python/dpd/Model_Poly.py index ca39492..5722531 100644 --- a/python/dpd/Model_Poly.py +++ b/python/dpd/Model_Poly.py @@ -8,15 +8,13 @@ import os import logging import numpy as np +import matplotlib.pyplot as plt -import dpd.Model_AM as Model_AM -import dpd.Model_PM as Model_PM - - -def assert_np_float32(x): - assert isinstance(x, np.ndarray) - assert x.dtype == np.float32 - assert x.flags.contiguous +def assert_np_float32(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_abs, rx_abs, phase_diff): @@ -44,12 +42,73 @@ class Poly: self.reset_coefs() - self.model_am = Model_AM.Model_AM(c) - self.model_pm = Model_PM.Model_PM(c) - def plot(self, am_plot_location, pm_plot_location, title): - self.model_am.plot(am_plot_location, title) - self.model_pm.plot(pm_plot_location, title) + if self._am_plot_data is not None: + tx_dpd, rx_received, coefs_am, coefs_am_new = self._am_plot_data + + tx_range, rx_est = self._am_calc_line(coefs_am, 0, 0.6) + tx_range_new, rx_est_new = self._am_calc_line(coefs_am_new, 0, 0.6) + + 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, rx_est, + label="Estimated TX", + alpha=0.3, + color="gray") + ax.plot(tx_range_new, rx_est_new, + label="New Estimated TX", + color="red") + ax.scatter(tx_dpd, rx_received, + label="Binned Data", + color="blue", + s=1) + ax.set_title("Model AM {}".format(title)) + ax.set_xlabel("TX Amplitude") + ax.set_ylabel("RX Amplitude") + ax.set_xlim(-0.5, 1.5) + ax.legend(loc=4) + + fig.tight_layout() + fig.savefig(am_plot_location) + plt.close(fig) + + if self._pm_plot_data is not None: + tx_dpd, phase_diff, coefs_pm, coefs_pm_new = self._pm_plot_data + + tx_range, phase_diff_est = self._pm_calc_line(coefs_pm, 0, 0.6) + tx_range_new, phase_diff_est_new = self._pm_calc_line(coefs_pm_new, 0, 0.6) + + 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=1) + ax.set_title("Model PM {}".format(title)) + ax.set_xlabel("TX Amplitude") + ax.set_ylabel("Phase DIff") + ax.legend(loc=4) + + fig.tight_layout() + fig.savefig(pm_plot_location) + plt.close(fig) def reset_coefs(self): self.coefs_am = np.zeros(5, dtype=np.float32) @@ -65,12 +124,8 @@ class Poly: """ _check_input_get_next_coefs(tx_abs, rx_abs, phase_diff) - if not lr is None: - self.model_am.learning_rate_am = lr - self.model_pm.learning_rate_pm = lr - - coefs_am_new = self.model_am.get_next_coefs(tx_abs, rx_abs, self.coefs_am) - coefs_pm_new = self.model_pm.get_next_coefs(tx_abs, phase_diff, self.coefs_pm) + coefs_am_new = self._am_get_next_coefs(tx_abs, rx_abs, self.coefs_am) + coefs_pm_new = self._pm_get_next_coefs(tx_abs, phase_diff, self.coefs_pm) self.coefs_am = self.coefs_am + (coefs_am_new - self.coefs_am) * self.learning_rate_am self.coefs_pm = self.coefs_pm + (coefs_pm_new - self.coefs_pm) * self.learning_rate_pm @@ -78,9 +133,62 @@ class Poly: def get_dpd_data(self): return "poly", self.coefs_am, self.coefs_pm + def _am_calc_line(self, coefs, min_amp, max_amp): + rx_range = np.linspace(min_amp, max_amp) + tx_est = np.sum(self._am_poly(rx_range) * coefs, axis=1) + return tx_est, rx_range + + def _am_poly(self, sig): + return np.array([sig ** i for i in range(1, 6)]).T + + def _am_fit_poly(self, tx_abs, rx_abs): + return np.linalg.lstsq(self._am_poly(rx_abs), tx_abs, rcond=None)[0] + + def _am_get_next_coefs(self, tx_dpd, rx_received, coefs_am): + """Calculate the next AM/AM coefficients using the extracted + statistic of TX and RX amplitude""" + + coefs_am_new = self._am_fit_poly(tx_dpd, rx_received) + coefs_am_new = coefs_am + \ + self.learning_rate_am * (coefs_am_new - coefs_am) + + self._am_plot_data = (tx_dpd, rx_received, coefs_am, coefs_am_new) + + return coefs_am_new + + def _pm_poly(self, sig): + return np.array([sig ** i for i in range(0, 5)]).T + + def _pm_calc_line(self, coefs, min_amp, max_amp): + tx_range = np.linspace(min_amp, max_amp) + phase_diff = np.sum(self._pm_poly(tx_range) * coefs, axis=1) + return tx_range, phase_diff + + def _discard_small_values(self, tx_dpd, phase_diff): + """ Assumes that the phase for small tx amplitudes is zero""" + mask = tx_dpd < self.c.MPM_tx_min + phase_diff[mask] = 0 + return tx_dpd, phase_diff + + def _pm_fit_poly(self, tx_abs, phase_diff): + return np.linalg.lstsq(self._pm_poly(tx_abs), phase_diff, rcond=None)[0] + + def _pm_get_next_coefs(self, tx_dpd, phase_diff, coefs_pm): + """Calculate the next AM/PM coefficients using the extracted + statistic of TX amplitude and phase difference""" + tx_dpd, phase_diff = self._discard_small_values(tx_dpd, phase_diff) + + coefs_pm_new = self._pm_fit_poly(tx_dpd, phase_diff) + + coefs_pm_new = coefs_pm + self.learning_rate_pm * (coefs_pm_new - coefs_pm) + self._pm_plot_data = (tx_dpd, phase_diff, coefs_pm, coefs_pm_new) + + return coefs_pm_new + # The MIT License (MIT) # # Copyright (c) 2017 Andreas Steger +# Copyright (c) 2018 Matthias P. Brandli # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal diff --git a/python/dpd/RX_Agc.py b/python/dpd/RX_Agc.py index 4700e68..911f3c9 100644 --- a/python/dpd/RX_Agc.py +++ b/python/dpd/RX_Agc.py @@ -19,19 +19,19 @@ import dpd.Adapt as Adapt import dpd.Measure as Measure class Agc: - """The goal of the automatic gain control is to set the - RX gain to a value at which all received amplitudes can - be detected. This means that the maximum possible amplitude + """The goal of the automatic gain control is to set the + RX gain to a value at which all received amplitudes can + be detected. This means that the maximum possible amplitude should be quantized at the highest possible digital value. - A problem we have to face, is that the estimation of the - maximum amplitude by applying the max() function is very - unstable. This is due to the maximum’s rareness. Therefore - we estimate a far more robust value, such as the median, + A problem we have to face, is that the estimation of the + maximum amplitude by applying the max() function is very + unstable. This is due to the maximum’s rareness. Therefore + we estimate a far more robust value, such as the median, and then approximate the maximum amplitude from it. - Given this, we tune the RX gain in such a way, that the - received signal fulfills our desired property, of having + Given this, we tune the RX gain in such a way, that the + received signal fulfills our desired property, of having all amplitudes properly quantized.""" def __init__(self, measure, adapt, c): diff --git a/python/dpdce.py b/python/dpdce.py index e601d9c..18e628b 100755 --- a/python/dpdce.py +++ b/python/dpdce.py @@ -85,6 +85,7 @@ from lib import yamlrpc import numpy as np import traceback import os.path +import glob from threading import Thread, Lock from queue import Queue from dpd.Model import Poly @@ -156,8 +157,19 @@ command_queue = Queue(maxsize=1) # Automatic Gain Control for the RX gain agc = Agc(meas, adapt, c) +def clear_pngs(results): + results['statplot'] = None + results['amplot'] = None + results['pmplot'] = None + pngs = glob.glob(os.path.join(plot_path, "*.png")) + for png in pngs: + try: + os.remove(png) + except: + results['summary'] += ["failed to delete " + png] + def engine_worker(): - extStat = ExtractStatistic(c) + extStat = None try: while True: cmd = command_queue.get() @@ -168,12 +180,13 @@ def engine_worker(): with lock: results['state'] = 'RX Gain Calibration' results['stateprogress'] = 0 + clear_pngs(results) summary = [] N_ITER = 5 for i in range(N_ITER): agc_success, agc_summary = agc.run() - summary += ["calibration run {}:".format(i)] + agc_summary.split("\n") + summary += ["Iteration {}:".format(i)] + agc_summary.split("\n") with lock: results['stateprogress'] = int((i + 1) * 100/N_ITER) @@ -191,14 +204,16 @@ def engine_worker(): results['rx_median'] = float(rx_median) results['state'] = 'Idle' results['stateprogress'] = 100 - results['summary'] = ["Calibration was done:"] + summary + results['summary'] = summary + ["Calibration done"] elif cmd == "reset": with lock: internal_data['n_runs'] = 0 results['state'] = 'Idle' results['stateprogress'] = 0 results['summary'] = ["Reset"] - extStat = ExtractStatistic(c) + clear_pngs(results) + extStat = None + model.reset_coefs() elif cmd == "trigger_run": with lock: results['state'] = 'Capture + Model' @@ -208,12 +223,17 @@ def engine_worker(): while True: # Get Samples and check gain txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median, tx_median = meas.get_samples() - # TODO Check TX median + + if extStat is None: + # At first run, we must decide how to create the bins + peak_estimated = tx_median * c.median_to_peak + extStat = ExtractStatistic(c, peak_estimated) with lock: results['stateprogress'] += 5 results['summary'] = ["Captured {} samples".format(len(txframe_aligned)), - "TX/RX median: {} / {}".format(tx_median, rx_median)] + "TX/RX median: {} / {}".format(tx_median, rx_median), + extStat.get_bin_info()] # Extract usable data from measurement tx, rx, phase_diff, n_per_bin = extStat.extract(txframe_aligned, rxframe_aligned) @@ -240,7 +260,7 @@ def engine_worker(): else: with lock: results['state'] = 'Capture + Model' - results['stateprogress'] = 60 + results['stateprogress'] = 80 results['summary'] += ["Training model"] model.train(tx, rx, phase_diff, lr=Heuristics.get_learning_rate(n_runs)) @@ -257,7 +277,7 @@ def engine_worker(): results['amplot'] = "dpd/" + am_plot_file results['pmplot'] = "dpd/" + pm_plot_file results['state'] = 'Capture + Model' - results['stateprogress'] = 70 + results['stateprogress'] = 85 results['summary'] += ["Getting DPD data"] dpddata = model.get_dpd_data() @@ -266,16 +286,15 @@ def engine_worker(): internal_data['n_runs'] = 0 results['state'] = 'Capture + Model' - results['stateprogress'] = 80 + results['stateprogress'] = 90 results['summary'] += ["Reset statistics"] - extStat = ExtractStatistic(c) + extStat = None with lock: results['state'] = 'Idle' results['stateprogress'] = 100 results['summary'] += ["New DPD coefficients calculated"] - finally: with lock: results['state'] = 'Terminated' diff --git a/python/gui/static/js/odr-predistortion.js b/python/gui/static/js/odr-predistortion.js index ff82142..59dcd82 100644 --- a/python/gui/static/js/odr-predistortion.js +++ b/python/gui/static/js/odr-predistortion.js @@ -39,13 +39,23 @@ function resultrefresh() { if (data['statplot']) { $('#dpdcapturestats').attr('src', data['statplot']); } + else { + $('#dpdcapturestats').attr('src', ""); + } if (data['amplot']) { $('#dpdamplot').attr('src', data['amplot']); } + else { + $('#dpdamplot').attr('src', ""); + } + if (data['pmplot']) { $('#dpdpmplot').attr('src', data['pmplot']); } + else { + $('#dpdpmplot').attr('src', ""); + } }); jqxhr.always(function() { diff --git a/python/gui/templates/predistortion.html b/python/gui/templates/predistortion.html index e21c688..d953dff 100644 --- a/python/gui/templates/predistortion.html +++ b/python/gui/templates/predistortion.html @@ -42,6 +42,11 @@ <div class="panel-heading">Capture Statistics</div> <div class="panel-body"> <img id="dpdcapturestats" /> + </div> + </div> + <div class="panel panel-default"> + <div class="panel-heading">AM/AM and AM/PM Model</div> + <div class="panel-body"> <img id="dpdamplot" /> <img id="dpdpmplot" /> </div> |