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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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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 |
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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> | ||
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<RunInfo> | ||
<WorkingDir>reconstructionOptiTwist</WorkingDir> | ||
<Sequence>LoadCSV, mySPpp, print</Sequence> | ||
<batchSize>1</batchSize> | ||
</RunInfo> | ||
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<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> | ||
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<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> | ||
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<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> | ||
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<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> | ||
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<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> | ||
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</Simulation> |
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...rseSensing/inputs/gold/reconstructionOptiTwist/mySensorPlot_scatter-scatter.png
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doc/workshop/SparseSensing/inputs/gold/reconstructionOptiTwist/outPP.csv
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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 |
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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> | ||
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<Files> | ||
<Input name="twistFile" type="">../../data//Perturbations.csv</Input> | ||
<Input name="refDO" type="">../../data/350_330.csv</Input> | ||
</Files> | ||
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<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> | ||
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<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> | ||
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<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> | ||
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<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> | ||
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</Simulation> |
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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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import numpy as np | ||
import matplotlib.pyplot as plt | ||
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fig, ax = plt.subplots(figsize=(12,10)) | ||
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xx = np.linspace(0,1,100) | ||
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coeffs = [ | ||
(1, 0, 0.5), | ||
(0, 1, 1) | ||
] | ||
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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 | ||
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ax.plot(xx, tot, 'k-', lw=10, label='total') | ||
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ax.legend() | ||
#plt.show() | ||
fig.savefig('fourier.png') |
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