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| -rw-r--r-- | COPYING | 9 | ||||
| -rw-r--r-- | README.md | 8 | ||||
| -rwxr-xr-x | bin/dlog-viewer | 307 |
3 files changed, 324 insertions, 0 deletions
@@ -0,0 +1,9 @@ +Copyright (C) 2020 Daniel Friesel <daniel.friesel@uos.de> + +Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/README.md b/README.md new file mode 100644 index 0000000..567edcf --- /dev/null +++ b/README.md @@ -0,0 +1,8 @@ +# dlog-viewer – Viewer and Exporter for Keysight dlog Files + +dlog-viewer loads voltage, current, and/or power measurements from .dlog files +produced by devices such as the Keysight N6705B DC Power Analyzer. +Measurements can be exported to CSV or plotted on-screen. + +This program is not affiliated with Keysight and has not been thoroughly +tested yet. Use at your own risk. diff --git a/bin/dlog-viewer b/bin/dlog-viewer new file mode 100755 index 0000000..98773ae --- /dev/null +++ b/bin/dlog-viewer @@ -0,0 +1,307 @@ +#!/usr/bin/env python3 +# vim:tabstop=4:softtabstop=4:shiftwidth=4:textwidth=160:smarttab:expandtab + +"""dlog-viewer - View and Convert Keysight .dlog Files + +USAGE + +dlog-viewer [--csv-export <file.csv>] [--plot] [--stat] <file.dlog> + +DESCRIPTION + +dlog-viewer loads voltage, current, and/or power measurements from .dlog files +produced by devices such as the Keysight N6705B DC Power Analyzer. +Measurements can be exported to CSV or plotted on-screen. + +This program is not affiliated with Keysight and has not been thoroughly +tested yet. Use at your own risk. + +OPTIONS + + --csv-export <file.csv> + Export measurements as CSV to <file.csv> + --plot + Draw plots of voltage/current/power over time + --stat + Print mean voltage, current, and power + +""" + +import argparse +import csv +import lzma +import matplotlib.pyplot as plt +import numpy as np +import os +import struct +import sys +import xml.etree.ElementTree as ET + + +def running_mean(x: np.ndarray, N: int) -> np.ndarray: + """ + Compute `N` elements wide running average over `x`. + + :param x: 1-Dimensional NumPy array + :param N: how many items to average. Should be even for optimal results. + """ + + # to ensure that output.shape == input.shape, we need to insert data + # at the boundaries + boundary_array = np.insert(x, 0, np.full((N // 2), x[0])) + boundary_array = np.append(boundary_array, np.full((N // 2 + N % 2 - 1), x[-1])) + + return np.convolve(boundary_array, np.ones((N,)) / N, mode="valid") + + +class DLogChannel: + def __init__(self, desc_tuple): + self.slot = desc_tuple[0] + self.smu = desc_tuple[1] + self.unit = desc_tuple[2] + self.data = None + + def __repr__(self): + return f"""<DLogChannel(slot={self.slot}, smu="{self.smu}", unit="{self.unit}", data={self.data})>""" + + +class DLog: + def __init__(self, filename): + self.load_dlog(filename) + + def load_dlog(self, filename): + lines = [] + line = "" + + with open(filename, "rb") as f: + if ".xz" in filename: + f = lzma.open(f) + + while line != "</dlog>\n": + line = f.readline().decode() + lines.append(line) + xml_header = "".join(lines) + raw_header = f.read(8) + data_offset = f.tell() + raw_data = f.read() + + xml_header = xml_header.replace("1ua>", "X1ua>") + xml_header = xml_header.replace("2ua>", "X2ua>") + dlog = ET.fromstring(xml_header) + channels = [] + for channel in dlog.findall("channel"): + channel_id = int(channel.get("id")) + sense_curr = channel.find("sense_curr").text + sense_volt = channel.find("sense_volt").text + model = channel.find("ident").find("model").text + if sense_volt == "1": + channels.append((channel_id, model, "V")) + if sense_curr == "1": + channels.append((channel_id, model, "A")) + + num_channels = len(channels) + + self.channels = list(map(DLogChannel, channels)) + self.interval = float(dlog.find("frame").find("tint").text) + self.planned_duration = int(dlog.find("frame").find("time").text) + self.observed_duration = self.interval * int(len(raw_data) / (4 * num_channels)) + + self.timestamps = np.linspace( + 0, self.observed_duration, num=int(len(raw_data) / (4 * num_channels)) + ) + + if int(self.observed_duration) != self.planned_duration: + self.duration_deviates = True + else: + self.duration_deviates = False + + self.data = np.ndarray( + shape=(num_channels, int(len(raw_data) / (4 * num_channels))), + dtype=np.float32, + ) + + iterator = struct.iter_unpack(">f", raw_data) + channel_offset = 0 + measurement_offset = 0 + for value in iterator: + self.data[channel_offset, measurement_offset] = value[0] + if channel_offset + 1 == num_channels: + channel_offset = 0 + measurement_offset += 1 + else: + channel_offset += 1 + + # An SMU has four slots + self.slots = [dict(), dict(), dict(), dict()] + + for i, channel in enumerate(self.channels): + channel.data = self.data[i] + self.slots[channel.slot - 1][channel.unit] = channel + + def slot_has_data(self, slot): + return len(self.slots[slot - 1]) > 0 + + def slot_has_power(self, slot): + slot_data = self.slots[slot - 1] + if "W" in slot_data: + return True + if "V" in slot_data and "A" in slot_data: + return True + return False + + def all_data_slots_have_power(self): + for slot in range(4): + if self.slot_has_data(slot) and not self.slot_has_power(slot): + return False + return True + + +def print_stats(dlog): + for channel in dlog.channels: + min_data = np.min(channel.data) + max_data = np.max(channel.data) + mean_data = np.mean(channel.data) + if channel.unit == "V": + precision = 3 + else: + precision = 6 + print(f"Slot {channel.slot} ({channel.smu}):") + print(f" Min {min_data:.{precision}f} {channel.unit}") + print(f" Mean {mean_data:.{precision}f} {channel.unit}") + print(f" Max {max_data:.{precision}f} {channel.unit}") + print() + + +def show_power_plot(dlog): + + handles = list() + + for slot in dlog.slots: + if "W" in slot: + (handle,) = plt.plot( + dlog.timestamps, slot["W"].data, "b-", label="P", markersize=1 + ) + handles.append(handle) + (handle,) = plt.plot( + dlog.timestamps, + running_mean(slot["W"].data, 10), + "r-", + label="mean(P, 10)", + markersize=1, + ) + handles.append(handle) + elif "V" in slot and "A" in slot: + (handle,) = plt.plot( + dlog.timestamps, + slot["V"].data * slot["A"].data, + "b-", + label="P = U * I", + markersize=1, + ) + handles.append(handle) + (handle,) = plt.plot( + dlog.timestamps, + running_mean(slot["V"].data * slot["A"].data, 10), + "r-", + label="mean(P, 10)", + markersize=1, + ) + handles.append(handle) + + plt.legend(handles=handles) + plt.xlabel("Time [s]") + plt.ylabel("Power [W]") + plt.grid(True) + plt.show() + + +def show_raw_plot(dlog): + handles = list() + + for channel in dlog.channels: + label = f"{channel.slot} / {channel.smu} {channel.unit}" + (handle,) = plt.plot( + dlog.timestamps, channel.data, "b-", label=label, markersize=1 + ) + handles.append(handle) + (handle,) = plt.plot( + dlog.timestamps, + running_mean(channel.data, 10), + "r-", + label=f"mean({label}, 10)", + markersize=1, + ) + handles.append(handle) + + plt.legend(handles=handles) + plt.xlabel("Time [s]") + plt.ylabel("Voltage [V] / Current [A] / Power [W]") + plt.grid(True) + plt.show() + + +def export_csv(dlog, filename): + cols, rows = dlog.data.shape + with open(filename, "w", newline="") as f: + writer = csv.writer(f) + channel_header = list( + map(lambda x: f"Slot {x.slot} {x.unit} ({x.smu})", dlog.channels) + ) + writer.writerow(["Timestamp [s]"] + channel_header) + for row in range(rows): + writer.writerow([dlog.timestamps[row]] + list(dlog.data[:, row])) + + +def main(): + parser = argparse.ArgumentParser( + formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__ + ) + parser.add_argument( + "--csv-export", type=str, help="Export measurements to CSV file" + ) + parser.add_argument( + "--plot", + help="Draw plots of voltage/current/power overtime", + action="store_true", + ) + parser.add_argument( + "--stat", help="Print mean voltage, current, and power", action="store_true" + ) + parser.add_argument( + "dlog_file", type=str, help="Input filename in Keysight dlog format" + ) + + args = parser.parse_args() + + print(args) + + dlog = DLog(args.dlog_file) + + if dlog.duration_deviates: + print( + "Measurement duration: {:f} of {:d} seconds at {:f} µs per sample".format( + dlog.observed_duration, dlog.planned_duration, dlog.interval * 1000000 + ) + ) + else: + print( + "Measurement duration: {:d} seconds at {:f} µs per sample".format( + dlog.planned_duration, dlog.interval * 1000000 + ) + ) + + if args.stat: + print_stats(dlog) + + if args.csv_export: + export_csv(dlog, args.csv_export) + + if args.plot: + if dlog.all_data_slots_have_power() and False: + show_power_plot(dlog) + else: + show_raw_plot(dlog) + + +if __name__ == "__main__": + main() |