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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()
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