use black(1) for uniform code formatting

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]))