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Workshop2024 merge (#2328)
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* new short forward sampling presentation

* updated some scgpc inputs and added adaptive

* completed example of forward sampling exercise 5 for the short workshop format added

* advanced sampling presentation and exercises

* add presentation slides from RAVEN 2022 workshop

* add raven installation slides

* added TSA, CodeInterfaces workshop docs

* add slides for Ensemble and Hybrid model

* add presentation templates

* add DSS slides

* add Bayesian Calibration slides

* edit slides

* added raven models slideset

* edits slides

* modified test names and revisions

* moved subfolders

* changed order to reflect order of appearance in the introduction of the Model sections

* plot entity

* model order

* added starting models

* Apply suggestions from code review

---------

Co-authored-by: Congjian Wang - INL <[email protected]>

* Arbitrary Custom Input in InputSpec (#2332)

* added custom arbitrary input extention to inputspecs and unit test

* removing internalParallel for adaptsobol cluster test

* switching ylabel to subplot titles, helps when a lot of subplots

* changed varname to ylabel, but split up long names by underscores:

* Update FARM user manual and citation (#2333)

* Update FARM user manual and citation

* Remove space.

* Revert previous.

---------

* adding sparse sensing hands on files

* changing marker color and adding the presentation

* adding latest presentations

* moving hands-on session mid presentation

* reduce ppt sizes

* reduce ppts sizes

---------

Co-authored-by: Congjian Wang <[email protected]>
Co-authored-by: Diego Mandelli <[email protected]>
Co-authored-by: Andrea Alfonsi - NuCube <[email protected]>
Co-authored-by: Gabriel J. Soto <[email protected]>
Co-authored-by: Haoyu Wang <[email protected]>
Co-authored-by: Joshua J. Cogliati <[email protected]>
Co-authored-by: Jimmy-INL <[email protected]>
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8 people authored Jul 11, 2024
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Showing 64 changed files with 17,296 additions and 16 deletions.
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5 changes: 5 additions & 0 deletions doc/workshop/SparseSensing/data/Perturbations.csv
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Power (W),BCTemperature (K),filename
350,240,350_240.csv
350,270,350_270.csv
350,300,350_300.csv
350,330,350_330.csv
108 changes: 108 additions & 0 deletions doc/workshop/SparseSensing/exercises/testSPSLOptiTwist.xml
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<?xml version="1.0" ?>
<Simulation verbosity="debug">
<TestInfo>
<name>tests/framework/Postprocessors/SparseSensing</name>
<author>Mohammad Abdo (@Jimmy-INL)</author>
<created>2022-07-18</created>
<classesTested>Models.Postprocessors.SparseSensing</classesTested>
<description>This test aims to check the ability of the Sparse Sensing Postprocessor
to locate and plot the selected sensors to reconstruct the temperature field of the OPTI-TWIST prototype
when perturbing the heater power.
</description>
</TestInfo>

<RunInfo>
<WorkingDir>reconstructionOptiTwist</WorkingDir>
<Sequence>LoadCSV, mySPpp, print</Sequence>
<batchSize>1</batchSize>
</RunInfo>

<Files>
<Input name="twistFile" type=""></Input><!-- Provide the csv file that contains the names of the samples files-->
<Input name="refDO" type=""></Input><!-- Provide the csv of the case onwhich you want to show the sensors i.e., maximum initial power (350 W) and maximum initial Temperature-->
</Files>

<Steps>
<IOStep name="LoadCSV">
<Input class="Files" type="">twistFile</Input>
<Input class="Files" type=""></Input><!--Fill in-->
<Output class="DataObjects" type="HistorySet">TwistDO</Output>
<Output class="DataObjects" type="PointSet">myDO</Output>
</IOStep>
<PostProcess name="mySPpp">
<Input class="DataObjects" type="HistorySet">TwistDO</Input>
<Model class="Models" type="PostProcessor"></Model><!--Fill in-->
<Output class="DataObjects" type="DataSet">outPP</Output>
</PostProcess>
<IOStep name="print">
<Input class="DataObjects" type="DataSet">outPP</Input>
<Input class="DataObjects" type="PointSet">myDO</Input>
<Input class="DataObjects" type="HostorySet">TwistDO</Input>
<Output class="OutStreams" type="Print">outPP</Output>
<Output class="OutStreams" type="Plot">mySensorPlot</Output>
</IOStep>
</Steps>

<Models>
<PostProcessor name="mySPSL" subType="SparseSensing" verbosity="debug">
<Goal subType=""> <!--Fill in-->
<features></features><!--Fill in att variable names needed for training from the CSV-->
<target>Temperature (K)</target>
<basis>SVD</basis>
<nModes></nModes> <!--Try different modes-->
<nSensors></nSensors> <!--Try different number of sensors-->
<optimizer>QR</optimizer>
</Goal>
</PostProcessor>
</Models>

<DataObjects>
<PointSet name="myDO">
<Output>X (m),Temperature (K),Y (m)</Output>
</PointSet>
<HistorySet name="TwistDO">
<Input> Power (W), BCTemperature (K)</Input>
<Output>X (m),Temperature (K),Y (m)</Output>
<options>
<pivotParameter>index</pivotParameter>
</options>
</HistorySet>
<DataSet name="outPP">
<Input>InputPlaceHolder</Input>
<Output>Temperature (K), X (m), Y (m)</Output>
<Index var="sensor">X (m),Y (m),Temperature (K)</Index>
</DataSet>
</DataObjects>

<OutStreams>
<Print name="outPP">
<type>csv</type>
<source>outPP</source>
<what>output</what>
</Print>
<Plot name="mySensorPlot">
<plotSettings>
<plot>
<type>scatter</type>
<x>myDO|Output|X (m)</x>
<y>myDO|Output|Y (m)</y>
<cmap>coolwarm</cmap>
<colorMap>myDO|Output|Temperature (K)</colorMap>
</plot>
<plot>
<type>scatter</type>
<x>outPP|Output|X (m)</x>
<y>outPP|Output|Y (m)</y>
<c>magenta</c>
<marker>*</marker>
</plot>
<xlabel>x</xlabel>
<ylabel>y</ylabel>
</plotSettings>
<actions>
<how>png</how>
</actions>
</Plot>
</OutStreams>

</Simulation>
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sensor,Temperature (K),X (m),Y (m)
1,241.2203563,0.037571896,-0.354205816
2,243.0797253,0.002587661,-0.127088999
3,240.0000001,0.037667017,0.479859575
4,243.6755973,0.000149014,0.051719659
108 changes: 108 additions & 0 deletions doc/workshop/SparseSensing/inputs/testSPSLOptiTwist.xml
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<?xml version="1.0" ?>
<Simulation verbosity="debug">
<TestInfo>
<name>tests/framework/Postprocessors/SparseSensing</name>
<author>Mohammad Abdo (@Jimmy-INL)</author>
<created>2022-07-18</created>
<classesTested>Models.Postprocessors.SparseSensing</classesTested>
<description>This test aims to check the ability of the Sparse Sensing Postprocessor
to locate and plot the selected sensors to reconstruct the temperature field of the OPTI-TWIST prototype
when perturbing the heater power.
</description>
</TestInfo>

<RunInfo>
<WorkingDir>reconstructionOptiTwist</WorkingDir>
<Sequence>LoadCSV, mySPpp, print</Sequence>
<batchSize>1</batchSize>
</RunInfo>

<Files>
<Input name="twistFile" type="">../../data//Perturbations.csv</Input>
<Input name="refDO" type="">../../data/350_330.csv</Input>
</Files>

<Steps>
<IOStep name="LoadCSV">
<Input class="Files" type="">twistFile</Input>
<Input class="Files" type="">refDO</Input>
<Output class="DataObjects" type="HistorySet">TwistDO</Output>
<Output class="DataObjects" type="PointSet">myDO</Output>
</IOStep>
<PostProcess name="mySPpp">
<Input class="DataObjects" type="HistorySet">TwistDO</Input>
<Model class="Models" type="PostProcessor">mySPSL</Model>
<Output class="DataObjects" type="DataSet">outPP</Output>
</PostProcess>
<IOStep name="print">
<Input class="DataObjects" type="DataSet">outPP</Input>
<Input class="DataObjects" type="PointSet">myDO</Input>
<Input class="DataObjects" type="HostorySet">TwistDO</Input>
<Output class="OutStreams" type="Print">outPP</Output>
<Output class="OutStreams" type="Plot">mySensorPlot</Output>
</IOStep>
</Steps>

<Models>
<PostProcessor name="mySPSL" subType="SparseSensing" verbosity="debug">
<Goal subType="reconstruction">
<features>X (m),Y (m),Temperature (K)</features>
<target>Temperature (K)</target>
<basis>SVD</basis>
<nModes>4</nModes>
<nSensors>4</nSensors>
<optimizer>QR</optimizer>
</Goal>
</PostProcessor>
</Models>

<DataObjects>
<PointSet name="myDO">
<Output>X (m),Temperature (K),Y (m)</Output>
</PointSet>
<HistorySet name="TwistDO">
<Input> Power (W), BCTemperature (K)</Input>
<Output>X (m),Temperature (K),Y (m)</Output>
<options>
<pivotParameter>index</pivotParameter>
</options>
</HistorySet>
<DataSet name="outPP">
<Input>InputPlaceHolder</Input>
<Output>Temperature (K), X (m), Y (m)</Output>
<Index var="sensor">X (m),Y (m),Temperature (K)</Index>
</DataSet>
</DataObjects>

<OutStreams>
<Print name="outPP">
<type>csv</type>
<source>outPP</source>
<what>output</what>
</Print>
<Plot name="mySensorPlot">
<plotSettings>
<plot>
<type>scatter</type>
<x>myDO|Output|X (m)</x>
<y>myDO|Output|Y (m)</y>
<cmap>coolwarm</cmap>
<colorMap>myDO|Output|Temperature (K)</colorMap>
</plot>
<plot>
<type>scatter</type>
<x>outPP|Output|X (m)</x>
<y>outPP|Output|Y (m)</y>
<c>magenta</c>
<marker>*</marker>
</plot>
<xlabel>x</xlabel>
<ylabel>y</ylabel>
</plotSettings>
<actions>
<how>png</how>
</actions>
</Plot>
</OutStreams>

</Simulation>
14 changes: 14 additions & 0 deletions doc/workshop/SparseSensing/inputs/tests
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[Tests]
[./SPSLOptiTwist]
type = 'RavenFramework'
input = 'testSPSLOptiTwist.xml'
image = 'reconstructionOptiTwist/mySensorPlot_scatter-scatter.png'
required_libraries = 'imageio'
rel_err = 0.1
[./csv]
type = OrderedCSV
output = 'reconstructionOptiTwist/outPP.csv'
rel_err = 0.001
[../]
[../]
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24 changes: 24 additions & 0 deletions doc/workshop/TSA/scripts/fourier.py
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import numpy as np
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(12,10))

xx = np.linspace(0,1,100)

coeffs = [
(1, 0, 0.5),
(0, 1, 1)
]

tot = np.zeros(len(xx))
for (A, B, k) in coeffs:
c = 2 * np.pi / k
yy = A*np.sin(c * xx) + B*np.cos(c * xx)
ax.plot(xx, yy, ':', lw=5, label=f'({A},{B},{k})')
tot += yy

ax.plot(xx, tot, 'k-', lw=10, label='total')

ax.legend()
#plt.show()
fig.savefig('fourier.png')
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