diff options
Diffstat (limited to 'lib')
| -rwxr-xr-x | lib/keysightdlog.py | 164 |
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])) |