Initial Commit

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.

# 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.

#!/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()