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Process towards further signal analysis
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daquintero committed Sep 19, 2024
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57 changes: 54 additions & 3 deletions docs/examples/08a_pcb_interposer_characterisation/08a_pcb_interposer_characterisation.py
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import piel
import piel.experimental as pe
import piel.analysis.signals.time_data as tda
from piel.models.physical.electrical import E8364A, cables
from datetime import datetime

Expand Down Expand Up @@ -642,8 +643,58 @@ def calibration_propagation_delay_experiment(

# `piel.analysis` also provides some functionality to analyse the corresponding time-data accordingly.
#
# For example, we might want to extract only the rising edge section of the signals:
# For example, we might want to extract only the rising edge section of one of the measured signals:

piel.analysis.signals.time_data
example_signal = calibration_propagation_delay_experiment_data.data.collection[
3
].dut_waveform
example_signal_measurements = (
calibration_propagation_delay_experiment_data.data.collection[3].measurements
)

help(tda.extract_rising_edges)

example_signal_rising_edge_list = tda.extract_rising_edges(
signal=example_signal, lower_threshold_ratio=0.1, upper_threshold_ratio=0.9
)

# We can, for example, plot all these signals overlaid on top of each other - easily by just offsetting to a base reference time:

dir(piel)
offset_example_signal_rising_edge_list = tda.offset_time_signals(
example_signal_rising_edge_list
)

piel.visual.plot.signals.plot_multi_data_time_signal(
offset_example_signal_rising_edge_list
)

# We can technically also extract some statistical metrics from this data which we could use to compare with the measured statistics:

offset_example_signal_rising_edge_metrics = tda.extract_rising_edge_statistical_metrics(
offset_example_signal_rising_edge_list
)
offset_example_signal_rising_edge_metrics.table

# | | Metric | Value |
# |---:|:-------------------|------------:|
# | 0 | Value | 0.020914 |
# | 1 | Mean | 0.020914 |
# | 2 | Min | -0.108703 |
# | 3 | Max | 0.150609 |
# | 4 | Standard Deviation | 0.00727889 |
# | 5 | Count | 17 |

# We could now compare this to the metrics the oscilloscope calculated for us previously:

example_signal_measurements["pk-pk_ch2__v"].table

# | | Metric | Value |
# |---:|:-------------------|:-------------------|
# | 0 | Value | 0.274796879094793 |
# | 1 | Mean | 0.276517693380144 |
# | 2 | Min | 0.02445312536438 |
# | 3 | Max | 0.345625005150214 |
# | 4 | Standard Deviation | 0.0039634275277739 |
# | 5 | Count | 9.91k |

# We can see the measurements are approximately close enough which is pretty cool! I would still trust the device measurements more, but with this functionality it is possible to compare a given waveform to a stastistical output from a machine.
2 changes: 2 additions & 0 deletions ...risation/data/calibration_multi_frequency_through_propagation_measurement/0/instance.json
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2 changes: 2 additions & 0 deletions ...risation/data/calibration_multi_frequency_through_propagation_measurement/1/instance.json
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2 changes: 2 additions & 0 deletions ...risation/data/calibration_multi_frequency_through_propagation_measurement/2/instance.json
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2 changes: 2 additions & 0 deletions ...risation/data/calibration_multi_frequency_through_propagation_measurement/3/instance.json
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8 changes: 8 additions & 0 deletions ...risation/data/calibration_multi_frequency_through_propagation_measurement/experiment.json
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2 changes: 2 additions & 0 deletions ...characterisation/data/pcb_multi_frequency_through_propagation_measurement/0/instance.json
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2 changes: 2 additions & 0 deletions ...characterisation/data/pcb_multi_frequency_through_propagation_measurement/1/instance.json
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2 changes: 2 additions & 0 deletions ...characterisation/data/pcb_multi_frequency_through_propagation_measurement/3/instance.json
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5 changes: 4 additions & 1 deletion .../examples/08a_pcb_interposer_characterisation/data/pcb_rf_vna_measurement/0/instance.json
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