Move keysightdlog to bin

1 files changed, 0 insertions, 164 deletions

diff --git a/lib/keysightdlog.py b/lib/keysightdlog.py
deleted file mode 100755
index 89264b9..0000000
--- a/lib/keysightdlog.py
+++ /dev/null
@@ -1,164 +0,0 @@
-#!/usr/bin/env python3
-
-import lzma
-import matplotlib.pyplot as plt
-import numpy as np
-import os
-import struct
-import sys
-import xml.etree.ElementTree as ET
-
-
-def plot_y(Y, **kwargs):
-    plot_xy(np.arange(len(Y)), Y, **kwargs)
-
-
-def plot_xy(X, Y, xlabel=None, ylabel=None, title=None, output=None):
-    fig, ax1 = plt.subplots(figsize=(10, 6))
-    if title != None:
-        fig.canvas.set_window_title(title)
-    if xlabel != None:
-        ax1.set_xlabel(xlabel)
-    if ylabel != None:
-        ax1.set_ylabel(ylabel)
-    plt.subplots_adjust(left=0.1, bottom=0.1, right=0.99, top=0.99)
-    plt.plot(X, Y, "bo", markersize=2)
-    if output:
-        plt.savefig(output)
-        with open("{}.txt".format(output), "w") as f:
-            print("X Y", file=f)
-            for i in range(len(X)):
-                print("{} {}".format(X[i], Y[i]), file=f)
-    else:
-        plt.show()
-
-
-filename = sys.argv[1]
-
-with open(filename, "rb") as logfile:
-    lines = []
-    line = ""
-
-    if ".xz" in filename:
-        f = lzma.open(logfile)
-    else:
-        f = logfile
-
-    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)
-    duration = int(dlog.find("frame").find("time").text)
-    interval = float(dlog.find("frame").find("tint").text)
-    real_duration = interval * int(len(raw_data) / (4 * num_channels))
-
-    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:
-        data[channel_offset, measurement_offset] = value[0]
-        if channel_offset + 1 == num_channels:
-            channel_offset = 0
-            measurement_offset += 1
-        else:
-            channel_offset += 1
-
-if int(real_duration) != duration:
-    print(
-        "Measurement duration: {:f} of {:d} seconds at {:f} µs per sample".format(
-            real_duration, duration, interval * 1000000
-        )
-    )
-else:
-    print(
-        "Measurement duration: {:d} seconds at {:f} µs per sample".format(
-            duration, interval * 1000000
-        )
-    )
-
-for i, channel in enumerate(channels):
-    channel_id, channel_model, channel_type = channel
-    print(
-        "channel {:d} ({:s}): min {:f}, max {:f}, mean {:f} {:s}".format(
-            channel_id,
-            channel_model,
-            np.min(data[i]),
-            np.max(data[i]),
-            np.mean(data[i]),
-            channel_type,
-        )
-    )
-
-    if (
-        i > 0
-        and channel_type == "A"
-        and channels[i - 1][2] == "V"
-        and channel_id == channels[i - 1][0]
-    ):
-        power = data[i - 1] * data[i]
-        power = 3.6 * data[i]
-        print(
-            "channel {:d} ({:s}): min {:f}, max {:f}, mean {:f} W".format(
-                channel_id, channel_model, np.min(power), np.max(power), np.mean(power)
-            )
-        )
-        min_power = np.min(power)
-        max_power = np.max(power)
-        power_border = np.mean([min_power, max_power])
-        low_power = power[power < power_border]
-        high_power = power[power >= power_border]
-        plot_y(power)
-        print(
-            "    avg low / high power (delta): {:f} / {:f} ({:f}) W".format(
-                np.mean(low_power),
-                np.mean(high_power),
-                np.mean(high_power) - np.mean(low_power),
-            )
-        )
-        # plot_y(low_power)
-        # plot_y(high_power)
-        high_power_durations = []
-        current_high_power_duration = 0
-        for is_hpe in power >= power_border:
-            if is_hpe:
-                current_high_power_duration += interval
-            else:
-                if current_high_power_duration > 0:
-                    high_power_durations.append(current_high_power_duration)
-                current_high_power_duration = 0
-        print(
-            "    avg high-power duration: {:f} µs".format(
-                np.mean(high_power_durations) * 1000000
-            )
-        )
-
-# print(xml_header)
-# print(raw_header)
-# print(channels)
-# print(data)
-# print(np.mean(data[0]))
-# print(np.mean(data[1]))
-# print(np.mean(data[0] * data[1]))