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143 refactor notch approximation #150

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143 refactor notch approximation #150

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be2c83d
Introduce draft of NotchApproxBinner
johannes-mueller Dec 11, 2024
824bc11
Adjust Rainflow HCM to NotchApproxBinner
johannes-mueller Dec 11, 2024
b116ad2
Apply NotchApproxBinner to Seeger Beste
johannes-mueller Dec 12, 2024
88693bb
Adjust other modules to NotchApproxBinner
johannes-mueller Dec 12, 2024
339dcf1
Adjust HCM rainflow counting unit tests to NotchApproxBinner
johannes-mueller Dec 12, 2024
7efe416
Add docstrings to NotchApproxBinner
johannes-mueller Dec 13, 2024
7a93c98
Drop Binned class for notch approximation and adjust tests accordingly
johannes-mueller Dec 13, 2024
d922aeb
Drop assertions that checked for the LUTs of former Binned
johannes-mueller Dec 13, 2024
6735016
Add tests for secondary branch notch approximation
johannes-mueller Dec 13, 2024
fc48415
Drop `load` argument of notch approximation strain methods
johannes-mueller Dec 13, 2024
190e491
Let FKMNonLinearDetector initialize NotchApproxBinner
johannes-mueller Dec 13, 2024
603a28f
Use automatic binning of FKMNonLinearDetector
johannes-mueller Dec 13, 2024
980b022
Adjust jupyter notebook
johannes-mueller Dec 16, 2024
06e565c
Don't regenerate the test signal while collecting benchmark tests
johannes-mueller Dec 16, 2024
7de23fe
Add docstrings for .primary() and .secondary()
johannes-mueller Dec 16, 2024
3d8b326
Add abstract base class methods to NotchApproximationLawBase
johannes-mueller Dec 17, 2024
c2dd7b9
Drop obsolete file
johannes-mueller Dec 17, 2024
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34 changes: 18 additions & 16 deletions benchmarks/generate_time_signal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,21 +2,23 @@
import numpy as np
import pylife.stress.timesignal as TS

np.random.seed(23424711)

load_signal = TS.TimeSignalGenerator(
10,
{
'number': 50_000,
'amplitude_median': 50.0,
'amplitude_std_dev': 0.5,
'frequency_median': 4,
'frequency_std_dev': 3,
'offset_median': 0,
'offset_std_dev': 0.4,
},
None,
None,
).query(1_000_000)
if __name__ == "__main__":
np.random.seed(23424711)

np.savetxt('load_signal.csv', load_signal)
load_signal = TS.TimeSignalGenerator(
10,
{
'number': 50_000,
'amplitude_median': 50.0,
'amplitude_std_dev': 0.5,
'frequency_median': 4,
'frequency_std_dev': 3,
'offset_median': 0,
'offset_std_dev': 0.4,
},
None,
None,
).query(1_000_000)

np.savetxt('load_signal.csv', load_signal)
20 changes: 5 additions & 15 deletions demos/fkm_nonlinear/fkm_nonlinear_full.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -215,7 +215,7 @@
"plt.rcParams.update({'font.size': 22})\n",
"plt.plot(range(len(scaled_load_sequence)), load_sequence, \"o-\", c=\"g\", lw=3)\n",
"plt.grid()\n",
"plt.ylabel(\"$\\sigma_V$ [MPa]\")"
"plt.ylabel(r\"$\\sigma_V$ [MPa]\")"
]
},
{
Expand Down Expand Up @@ -559,18 +559,13 @@
"load_sequence_list = scaled_load_sequence\n",
"print(load_sequence_list)\n",
"\n",
"# wrap the notch approximation law by a binning class, which precomputes the values\n",
"maximum_absolute_load = max(np.abs(load_sequence_list))\n",
"print(f\"maximum_absolute_load: {maximum_absolute_load}\")\n",
"extended_neuber_binned = pylife.materiallaws.notch_approximation_law.Binned(\n",
" extended_neuber, maximum_absolute_load, 100)\n",
"\n",
"# create recorder object\n",
"recorder = pylife.stress.rainflow.recorders.FKMNonlinearRecorder()\n",
"\n",
"# create detector object\n",
"detector = pylife.stress.rainflow.fkm_nonlinear.FKMNonlinearDetector(\n",
" recorder=recorder, notch_approximation_law=extended_neuber_binned)\n",
" recorder=recorder, notch_approximation_law=extended_neuber\n",
")\n",
"\n",
"# perform HCM algorithm, first run\n",
"detector.process(load_sequence_list, flush=False)\n",
Expand Down Expand Up @@ -759,18 +754,13 @@
"load_sequence_list = scaled_load_sequence\n",
"print(load_sequence_list)\n",
"\n",
"# wrap the notch approximation law by a binning class, which precomputes the values\n",
"maximum_absolute_load = max(np.abs(load_sequence_list))\n",
"print(f\"maximum_absolute_load: {maximum_absolute_load}\")\n",
"seeger_beste_binned = pylife.materiallaws.notch_approximation_law.Binned(\n",
" seeger_beste, maximum_absolute_load, 100)\n",
"\n",
"# create recorder object\n",
"recorder = pylife.stress.rainflow.recorders.FKMNonlinearRecorder()\n",
"\n",
"# create detector object\n",
"detector = pylife.stress.rainflow.fkm_nonlinear.FKMNonlinearDetector(\n",
" recorder=recorder, notch_approximation_law=seeger_beste_binned)\n",
" recorder=recorder, notch_approximation_law=seeger_beste\n",
")\n",
"\n",
"# perform HCM algorithm, first run\n",
"detector.process(load_sequence_list, flush=False)\n",
Expand Down
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