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-rwxr-xr-xbin/plot-conversion-efficiency216
1 files changed, 216 insertions, 0 deletions
diff --git a/bin/plot-conversion-efficiency b/bin/plot-conversion-efficiency
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+++ b/bin/plot-conversion-efficiency
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+#!/usr/bin/env python3
+# vim:tabstop=4 softtabstop=4 shiftwidth=4 textwidth=160 smarttab expandtab colorcolumn=160
+#
+# Copyright (C) 2023 Daniel Friesel
+#
+# SPDX-License-Identifier: GPL-2.0-or-later
+
+"""plot-conversion-efficiency - Show several conversion efficiency plots at once
+
+DESCRIPTION
+
+fixme
+
+OPTIONS
+"""
+
+import argparse
+import bisect
+import numpy as np
+from matplotlib import cm
+import matplotlib.pyplot as plt
+
+matplotlib_theme = "fast"
+
+
+def neighbouring_avg(data, timestamp, eps=0.1):
+ samples = list()
+ range_left = bisect.bisect_left(data[:, 0], timestamp - eps)
+ range_right = bisect.bisect_right(data[:, 0], timestamp + eps)
+ samples = data[range_left:range_right, 1]
+ if not len(samples):
+ return None
+ return np.mean(samples)
+
+
+def load_korad(filename, skip=None, limit=None):
+ if filename.endswith(".xz"):
+ import lzma
+
+ with lzma.open(filename, "rt") as f:
+ log_data = f.read()
+ else:
+ with open(filename, "r") as f:
+ log_data = f.read()
+ lines = log_data.split("\n")
+ data_count = sum(map(lambda x: len(x) > 0 and x[0] != "#", lines))
+ data_lines = filter(lambda x: len(x) > 0 and x[0] != "#", lines)
+
+ data = np.empty((data_count, 3))
+ skip_index = 0
+ limit_index = data_count
+
+ for i, line in enumerate(data_lines):
+ fields = line.split()
+ if len(fields) == 3:
+ timestamp, voltage, current = map(float, fields)
+ elif len(fields) == 5:
+ timestamp, voltage, current, max_voltage, max_current = map(float, fields)
+ else:
+ raise RuntimeError('cannot parse line "{}"'.format(line))
+
+ if i == 0:
+ first_timestamp = timestamp
+
+ timestamp = timestamp - first_timestamp
+
+ if skip is not None and timestamp < skip:
+ skip_index = i + 1
+ continue
+
+ if limit is not None and timestamp > limit:
+ limit_index = i - 1
+ break
+
+ data[i] = [timestamp, current, voltage]
+
+ data = data[skip_index:limit_index]
+
+ return data
+
+
+def load_ads1115(filename, in_channel, out_channel):
+ readings = list()
+ first_timestamp = None
+ with open(filename, "r") as f:
+ for line in f:
+ if line.startswith("#"):
+ continue
+ timestamp, channel, voltage = line.split()
+ timestamp = float(timestamp)
+ channel = int(channel)
+ voltage = float(voltage)
+ if abs(voltage) > 1:
+ if first_timestamp is None:
+ first_timestamp = timestamp
+ timestamp -= first_timestamp
+ readings.append((timestamp, channel, voltage))
+ vin_t = list()
+ vout_t = list()
+ vout_vin = list()
+
+ last_vin = None
+ last_vout = None
+ for timestamp, channel, voltage in readings:
+ if channel == in_channel:
+ vin_t.append((timestamp, voltage))
+ if last_vin is not None and last_vout is not None:
+ vout_vin.append((np.mean((last_vin, voltage)), last_vout))
+ last_vin = voltage
+ elif channel == out_channel:
+ vout_t.append((timestamp, voltage))
+ if last_vin is not None and last_vout is not None:
+ vout_vin.append((last_vin, np.mean((last_vout, voltage))))
+ last_vout = voltage
+
+ return np.array(vin_t), np.array(vout_t), np.array(vout_vin)
+
+
+def main():
+ parser = argparse.ArgumentParser(
+ formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__
+ )
+ parser.add_argument(
+ "--skip",
+ metavar="N",
+ type=float,
+ default=0,
+ help="Skip the first N seconds of data. This is useful to avoid startup code influencing the results of a long-running measurement",
+ )
+ parser.add_argument(
+ "--limit",
+ type=float,
+ metavar="N",
+ help="Limit analysis to the first N seconds of data",
+ )
+ parser.add_argument(
+ "--in-channel",
+ metavar="CHANNEL",
+ type=int,
+ default=0,
+ help="ADS1115 input channel",
+ )
+ parser.add_argument(
+ "--out-channel",
+ metavar="CHANNEL",
+ type=int,
+ default=2,
+ help="ADS1115 output channel",
+ )
+ parser.add_argument(
+ "--dark-mode", action="store_true", help="Show plots on a dark background"
+ )
+ parser.add_argument("--title", type=str, help="Plot title")
+ parser.add_argument(
+ "files",
+ type=str,
+ nargs="+",
+ help="Pairs of <current[A]>:<korad filename>:<ads1115 filename>",
+ )
+
+ args = parser.parse_args()
+
+ if args.dark_mode:
+ global matplotlib_theme
+ matplotlib_theme = "dark_background"
+
+ cmap = cm.get_cmap("winter")
+ handles = list()
+ for i, pair in enumerate(args.files):
+ out_current, korad_file, ads1115_file = pair.split(":")
+ out_current = float(out_current)
+ iin_t = load_korad(korad_file, args.skip, args.limit)
+ vin_t, vout_t, vout_vin = load_ads1115(
+ ads1115_file, args.in_channel, args.out_channel
+ )
+
+ voltage_in = list()
+ power_in = list()
+ power_out = list()
+
+ for timestamp, in_current, in_voltage in iin_t:
+ v_in = neighbouring_avg(vin_t, timestamp)
+ v_out = neighbouring_avg(vout_t, timestamp)
+
+ if v_in is None or v_out is None:
+ continue
+
+ voltage_in.append(v_in)
+ power_in.append(v_in * in_current)
+ power_out.append(v_out * out_current)
+
+ voltage_in = np.array(voltage_in)
+ power_in = np.array(power_in)
+ power_out = np.array(power_out)
+
+ (handle,) = plt.plot(
+ voltage_in,
+ power_out / power_in,
+ linestyle="none",
+ marker="s",
+ color=cmap(i / len(args.files)),
+ markersize=2,
+ label=f"{out_current:0.2f} A",
+ )
+ handles.append(handle)
+
+ plt.legend(handles=handles, title="Output Current")
+ if args.title:
+ plt.title(args.title)
+ plt.xlabel("Input Voltage [V]")
+ plt.ylabel("Conversion Efficiency [%]")
+ plt.show()
+
+
+if __name__ == "__main__":
+ main()