#!/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()