Plot voltage thresholds from several tests

Set up

Here we show how we can compute the thresholds of several configuratations as voltage values. This is particularly useful when we are in a mixed wave of standing wave propagation modes.

[1]:

from functools import partial

from multipac_testbench import TestCampaign
from multipac_testbench.data import config_path
from multipac_testbench.data.multipactor_tests import (
    test_140MHz_SWR4_11,
    test_140MHz_SWR3_12,
    test_140MHz_SWR2_13,
    test_140MHz_SWR1_14
)
from multipac_testbench.instruments import CurrentProbe, SWR
from multipac_testbench.threshold import AveragedThresholdSet
from multipac_testbench.util.multipactor_detectors import quantity_is_above_threshold

tests = (test_140MHz_SWR4_11, test_140MHz_SWR3_12, test_140MHz_SWR2_13, test_140MHz_SWR1_14)
freqs = (140.0, 140.0, 140.0, 140.0)
swrs = (4.0, 3.0, 2.0, 1.0)

test_campaign =  TestCampaign.from_filepaths(
    tests,
    freqs,
    swrs,
    config_path,
    is_raw=True
)

[INFO    ] [loader.py           ] Applied trigger_policy = 'keep_all' on /home/placais/Documents/simulation/python/multipac_testbench/src/multipac_testbench/data/multipactor_tests/2025.06.20_140MHz-SWR4-11.csv
[WARNING ] [power.py            ] ReflectedPower typically measured on channel B, so you should provide the arguments for the channel B fix.
[WARNING ] [factory.py          ] column_header = 'NI9205_E1' not present in provided file. Skipping associated instrument.
[WARNING ] [test_conditions.py  ] freq_mhz was supplied explicitly but FrequencySetpoint is also defined in the TOML. The explicit value takes precedence. Consider removing the freq_mhz argument and relying on the TOML.
[INFO    ] [loader.py           ] Applied trigger_policy = 'keep_all' on /home/placais/Documents/simulation/python/multipac_testbench/src/multipac_testbench/data/multipactor_tests/2025.06.20_140MHz-SWR3-12.csv
[WARNING ] [power.py            ] ReflectedPower typically measured on channel B, so you should provide the arguments for the channel B fix.
[WARNING ] [factory.py          ] column_header = 'NI9205_E1' not present in provided file. Skipping associated instrument.
[INFO    ] [loader.py           ] Applied trigger_policy = 'keep_all' on /home/placais/Documents/simulation/python/multipac_testbench/src/multipac_testbench/data/multipactor_tests/2025.06.20_140MHz-SWR2-13.csv
[WARNING ] [power.py            ] ReflectedPower typically measured on channel B, so you should provide the arguments for the channel B fix.
[WARNING ] [factory.py          ] column_header = 'NI9205_E1' not present in provided file. Skipping associated instrument.
[INFO    ] [loader.py           ] Applied trigger_policy = 'keep_all' on /home/placais/Documents/simulation/python/multipac_testbench/src/multipac_testbench/data/multipactor_tests/2025.06.20_140MHz-SWR1-14.csv
[WARNING ] [power.py            ] ReflectedPower typically measured on channel B, so you should provide the arguments for the channel B fix.
[WARNING ] [factory.py          ] column_header = 'NI9205_E1' not present in provided file. Skipping associated instrument.

/home/placais/Documents/simulation/python/multipac_testbench/src/multipac_testbench/util/physics.py:28: RuntimeWarning: invalid value encountered in sqrt
  reflection_coefficient = np.abs(np.sqrt(reflected_power / forward_power))
/home/placais/Documents/simulation/python/multipac_testbench/src/multipac_testbench/util/physics.py:28: RuntimeWarning: invalid value encountered in sqrt
  reflection_coefficient = np.abs(np.sqrt(reflected_power / forward_power))

Calculate multipactor thresholds

Set a multipactor detection criterion:

[2]:

current_multipactor_criterions = {'threshold': 12., 'minimum_number_of_points': 1}
current_multipac_detector = partial(quantity_is_above_threshold, **current_multipactor_criterions)

Compute all thresholds, for every test at every pick-up:

[3]:

raw_thresholds = test_campaign.determine_thresholds(current_multipac_detector, CurrentProbe)
for t in raw_thresholds.values():
    t.remove_singularities()             # Avoid multipactor measured on a single point
    t.remove_detected_by("NI9205_MP8l")  # Avoid accounting for same MP twice (E8 is in front of E4)

[INFO    ] [threshold.py        ] Signal does not end falling. Consider trimming trailing points.
[WARNING ] [threshold_set.py    ] Multiple instruments detected at the same position 0.611:
- NI9205_MP4l
- NI9205_MP8l
[INFO    ] [threshold.py        ] Signal does not end falling. Consider trimming trailing points.
[WARNING ] [threshold_set.py    ] Multiple instruments detected at the same position 0.611:
- NI9205_MP4l
- NI9205_MP8l
[INFO    ] [threshold.py        ] Signal does not end falling. Consider trimming trailing points.
[WARNING ] [threshold_set.py    ] Multiple instruments detected at the same position 0.611:
- NI9205_MP4l
- NI9205_MP8l
[INFO    ] [threshold.py        ] Signal does not end falling. Consider trimming trailing points.
[WARNING ] [threshold_set.py    ] Multiple instruments detected at the same position 0.611:
- NI9205_MP4l
- NI9205_MP8l
[INFO    ] [threshold_set.py    ] Removed the 34 thresholds detected by NI9205_MP8l
[INFO    ] [threshold_set.py    ] Removed the 0 thresholds detected by NI9205_MP8l
[INFO    ] [threshold_set.py    ] Removed the 8 thresholds detected by NI9205_MP8l
[INFO    ] [threshold_set.py    ] Removed the 38 thresholds detected by NI9205_MP8l

At every pick-up and for every test, average the thresholds measured after two power cycles:

[4]:

averaged_thresholds = {
    test: AveragedThresholdSet.from_threshold_set(
        threshold_set,
        threshold_predicate=lambda threshold: threshold.sample_index > 80,
    )
    for test, threshold_set in raw_thresholds.items()
}

Plot multipactor thresholds as voltages

We plot thresholds for every test, at every pick-up (local detection).

[5]:

axes_by_freq, dfs_by_freq = test_campaign.check_perez_scaling_law(
    averaged_thresholds,
    xdata=SWR,
    add_upper_thresholds=True,
    figsize=(8, 8),
)
_ = axes_by_freq[140.0].legend(
    loc=(1.01, 0.01),
    ncol=1,
)

../../_images/manual_notebooks_plot_voltage_thresholds_13_0.png

[6]:

df = dfs_by_freq[140.0]
df = df.reindex(sorted(df.columns), axis=1)
df.describe().style.format(precision=1)

[6]:

	NI9205_E2 @ lower threshold (according to NI9205_MP2l)	NI9205_E2 @ upper threshold (according to NI9205_MP2l)	NI9205_E3 @ lower threshold (according to NI9205_MP3l)	NI9205_E3 @ upper threshold (according to NI9205_MP3l)	NI9205_E4 @ lower threshold (according to NI9205_MP4l)	NI9205_E4 @ upper threshold (according to NI9205_MP4l)	NI9205_E5 @ lower threshold (according to NI9205_MP5l)	NI9205_E5 @ upper threshold (according to NI9205_MP5l)	NI9205_E6 @ lower threshold (according to NI9205_MP6l)	NI9205_E6 @ upper threshold (according to NI9205_MP6l)	NI9205_E7 @ lower threshold (according to NI9205_MP7l)	NI9205_E7 @ upper threshold (according to NI9205_MP7l)
count	3.0	3.0	2.0	2.0	4.0	4.0	3.0	3.0	2.0	2.0	4.0	3.0
mean	197.2	220.7	227.7	265.2	183.8	217.8	215.4	244.9	228.3	263.5	204.7	228.0
std	2.9	17.7	1.7	25.8	13.4	23.0	5.4	5.5	9.1	3.3	9.5	15.6
min	194.6	203.3	226.5	246.9	174.8	195.4	209.3	239.7	221.8	261.1	190.5	212.2
25%	195.7	211.6	227.1	256.0	175.7	199.2	213.4	242.0	225.1	262.3	203.5	220.4
50%	196.8	219.9	227.7	265.2	178.5	218.0	217.5	244.3	228.3	263.5	209.0	228.6
75%	198.6	229.3	228.3	274.3	186.7	236.6	218.5	247.5	231.5	264.6	210.2	236.0
max	200.4	238.8	228.9	283.4	203.5	239.6	219.4	250.7	234.8	265.8	210.3	243.4