Note
Click here to download the full example code
Overview of the MockModel for load case LC2¶
A toy model implementing an unphysical analytical law; used for testing purpose
from __future__ import annotations
from vimseo import EXAMPLE_RUNS_DIR
from vimseo.api import create_model
from vimseo.core.model_settings import IntegratedModelSettings
First, let's instantiate the model for a given load case:
model_name = "MockModel"
load_case = "LC2"
model = create_model(
model_name,
load_case,
check_subprocess=True,
model_options=IntegratedModelSettings(
directory_archive_root=EXAMPLE_RUNS_DIR / "archive/model_gallery",
directory_scratch_root=EXAMPLE_RUNS_DIR / "scratch/model_gallery",
cache_file_path=EXAMPLE_RUNS_DIR
/ f"caches/model_gallery/{model_name}_{load_case}_cache.hdf",
),
)
The model description can be accessed like this:
print(model.description)
Out:
Model MockModel: A toy model implementing an unphysical analytical law; used for testing purpose
Load case:
Load case LC2: A second mock load case.
Boundary condition variables:
[]
Plot parameters:
{
"curves": [
[
"y1",
"y1_2"
]
]
}
Load:
Load(direction='', sign='', type='')
Default values:
Default geometrical variables:
{"x1": [0.1], "x1_2": [0.0]}
Default numerical variables:
{}
Default boundary conditions variables:
{}
Default material variables:
{}
model_inputs:
[
"x1",
"x1_2"
]
model_outputs:
[
"y1",
"y1_2",
"model",
"load_case",
"error_code",
"description",
"job_name",
"persistent_result_files",
"n_cpus",
"date",
"cpu_time",
"user",
"machine",
"vims_git_version",
"directory_archive_root",
"directory_archive_job",
"directory_scratch_root",
"directory_scratch_job"
]
An illustration of the load case:
model.show_image()
The model is executed with its default input values:
model.execute()
Out:
{'x1': array([0.1]), 'x1_2': array([0.]), 'cpu_time': array([0.00527096]), 'date': array(['2025-11-05 22:19:45.948115'], dtype='<U26'), 'description': array([''], dtype='<U1'), 'directory_archive_job': array(['..\\model_runs\\archive\\model_gallery\\MockModel\\LC2\\1'],
dtype='<U51'), 'directory_archive_root': array(['C:\\Users\\sebastien.bocquet\\PycharmProjects\\vims\\docs\\runnable_examples\\model_runs\\archive\\model_gallery'],
dtype='<U103'), 'directory_scratch_job': array([''], dtype='<U1'), 'directory_scratch_root': array(['C:\\Users\\sebastien.bocquet\\PycharmProjects\\vims\\docs\\runnable_examples\\model_runs\\scratch\\model_gallery'],
dtype='<U103'), 'error_code': array([0]), 'job_name': array([''], dtype='<U1'), 'load_case': array(['LC2'], dtype='<U3'), 'machine': array(['B20191729'], dtype='<U9'), 'model': array(['MockModel'], dtype='<U9'), 'n_cpus': array([1]), 'persistent_result_files': array([''], dtype='<U1'), 'user': array(['sebastien.bocquet'], dtype='<U17'), 'vims_git_version': array(['95990fa211debd91a91ffe1d89b2b0b4b855f387'], dtype='<U40'), 'y1': array([5.2]), 'y1_2': array([6.2])}
And the results are visualised with the pre-defined plots:
figures = model.plot_results(show=True)
Out:
{'text/html': '<div> <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG"></script><script type="text/javascript">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: "STIX-Web"}});}</script> <script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: \'local\'};</script>\n <script charset="utf-8" src="https://cdn.plot.ly/plotly-2.35.0.min.js"></script> <div id="5b23944e-410c-4110-9d06-4dd6c443920f" class="plotly-graph-div" style="height:525px; width:100%;"></div> <script type="text/javascript"> window.PLOTLYENV=window.PLOTLYENV || {}; if (document.getElementById("5b23944e-410c-4110-9d06-4dd6c443920f")) { Plotly.newPlot( "5b23944e-410c-4110-9d06-4dd6c443920f", [{"line":{"color":"blue","dash":"solid","width":2},"mode":"lines","name":"y1_2","showlegend":true,"x":[5.2],"y":[6.2],"type":"scatter"}], {"template":{"data":{"candlestick":[{"decreasing":{"line":{"color":"#000033"}},"increasing":{"line":{"color":"#000032"}},"type":"candlestick"}],"contourcarpet":[{"colorscale":[[0.0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1.0,"#000020"]],"type":"contourcarpet"}],"contour":[{"colorscale":[[0.0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1.0,"#000020"]],"type":"contour"}],"heatmap":[{"colorscale":[[0.0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1.0,"#000020"]],"type":"heatmap"}],"histogram2d":[{"colorscale":[[0.0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1.0,"#000020"]],"type":"histogram2d"}],"icicle":[{"textfont":{"color":"white"},"type":"icicle"}],"sankey":[{"textfont":{"color":"#000036"},"type":"sankey"}],"scatter":[{"marker":{"line":{"width":0}},"type":"scatter"}],"table":[{"cells":{"fill":{"color":"#000038"},"font":{"color":"#000037"},"line":{"color":"#000039"}},"header":{"fill":{"color":"#000040"},"font":{"color":"#000036"},"line":{"color":"#000039"}},"type":"table"}],"waterfall":[{"connector":{"line":{"color":"#000036","width":2}},"decreasing":{"marker":{"color":"#000033"}},"increasing":{"marker":{"color":"#000032"}},"totals":{"marker":{"color":"#000034"}},"type":"waterfall"}]},"layout":{"coloraxis":{"colorscale":[[0.0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1.0,"#000020"]]},"colorscale":{"diverging":[[0.0,"#000021"],[0.1,"#000022"],[0.2,"#000023"],[0.3,"#000024"],[0.4,"#000025"],[0.5,"#000026"],[0.6,"#000027"],[0.7,"#000028"],[0.8,"#000029"],[0.9,"#000030"],[1.0,"#000031"]],"sequential":[[0.0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1.0,"#000020"]],"sequentialminus":[[0.0,"#000011"],[0.1111111111111111,"#000012"],[0.2222222222222222,"#000013"],[0.3333333333333333,"#000014"],[0.4444444444444444,"#000015"],[0.5555555555555556,"#000016"],[0.6666666666666666,"#000017"],[0.7777777777777778,"#000018"],[0.8888888888888888,"#000019"],[1.0,"#000020"]]},"colorway":["#000001","#000002","#000003","#000004","#000005","#000006","#000007","#000008","#000009","#000010"]}},"title":{"text":""},"xaxis":{"title":{"text":"y1"},"showgrid":true},"yaxis":{"title":{"text":""},"showgrid":true}}, {"responsive": true} ) }; </script> </div>'}
Plot of y1_2_vs_y1
figures["y1_2_vs_y1"]
Total running time of the script: ( 0 minutes 0.117 seconds)
Download Python source code: plot_MockModel_LC2.py