diff options
Diffstat (limited to 'lib/keysightdlog.py')
| -rwxr-xr-x | lib/keysightdlog.py | 136 |
1 files changed, 85 insertions, 51 deletions
diff --git a/lib/keysightdlog.py b/lib/keysightdlog.py index 0cf8da1..89264b9 100755 --- a/lib/keysightdlog.py +++ b/lib/keysightdlog.py @@ -8,69 +8,74 @@ 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)) + +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.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) + 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) + print("{} {}".format(X[i], Y[i]), file=f) else: plt.show() + filename = sys.argv[1] -with open(filename, 'rb') as logfile: +with open(filename, "rb") as logfile: lines = [] - line = '' + line = "" - if '.xz' in filename: + if ".xz" in filename: f = lzma.open(logfile) else: f = logfile - while line != '</dlog>\n': + while line != "</dlog>\n": line = f.readline().decode() lines.append(line) - xml_header = ''.join(lines) + 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>') + 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')) + 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) + 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) + data = np.ndarray( + shape=(num_channels, int(len(raw_data) / (4 * num_channels))), dtype=np.float32 + ) - iterator = struct.iter_unpack('>f', raw_data) + iterator = struct.iter_unpack(">f", raw_data) channel_offset = 0 measurement_offset = 0 for value in iterator: @@ -82,34 +87,59 @@ with open(filename, 'rb') as logfile: 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)) + 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)) + 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)) + 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] + 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))) + 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) + 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: @@ -119,12 +149,16 @@ for i, channel in enumerate(channels): 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])) + 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])) |