C Scilab Technical Insights 20171116scilab.io/wp-content/uploads/2017/12/C_Scilab_Technical_Insights... · SIGNAL PROCESSING 70% 19% STATISTICS 60% 10 % Functionnalcoveragewithadditionaltoolboxes

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Copyright © ESI Group, 2017. All rights reserved.Copyright © ESI Group, 2017. All rights reserved.

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Scilab Technical InsightsTechnical insights into latest version Scilab 6 and new developments

G/EO/17,014

Raphael Auphan & Yann Debray | Scilab - ESI Group2017/11/16

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Copyright © ESI Group, 2017. All rights reserved.

Agenda

•10:00 – 10:30 Keynote by Franck Delplace

•10:30 – 11:00 Scilab & Scilab Cloud

•11:00 – 11:30 Use cases

•11:30 – 12:00 Technical Insights

•12:00 – 12:15 Q & A - Closing

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Copyright © ESI Group, 2017. All rights reserved.©ScilabEnterprises

Application Development&Deployment Algorithms

Data

UserInterface

PackagedEngineeringApp

High-level view of ScilabThreekeyareasofvalue

Maths-basedModel

Visualization Optimization

Codegeneration

Modeling&Simulation ScientificDataAnalytics

Post-processingStatistics

Imageprocessing

Data-drivenModel

MachineLearningModelReduction

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Current evolution of Scilab

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Matlab vs Scilab: with & without toolboxes

Functional coverage with bare softwareSCILAB MATLAB

MATHEMATICS 100% 100%CONTROL SYSTEM 77% 0%

OPTIMIZATION 74%14%

SIGNAL PROCESSING 70% 19%

STATISTICS 60%10%

Functionnal coverage with additional toolboxesSCILAB + TOOLBOXES MATLAB + TOOLBOXES (*)

MATHEMATICS 100% 100%CONTROL SYSTEM 87% 100%OPTIMIZATION 82% 100%SIGNAL PROCESSING 70% 100%STATISTICS 70% 100%

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(*)ForreferencethetoolboxesrequiredforMatlab,eachtoolbox+1,000€/seat

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Scilab platform to support new codes and leverage legacy codes

ScilabDesktop/ScilabCloud

Numericalcomputing

Scilabinterpreter Matlabinterpreter(powered byOctave)

Datavisualization Algorithmdevelopment

Applicationdeployment

Pythoninterpreter(powered byPython,Numpy &Scipy)

Xcos systemmodeling

Imageprocessing

Signalprocessing

Machinelearning

Controlsystems

Codegeneration

Proprietarytoolboxes…

Scilabcode

Matlabcode

Pythoncode

Systemdiagrams

Domainspecifictoolboxes

Platform

Apps

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Integrate Scilab, Matlab & Python codes in a Scilab application

In a Scilab app:

• Scilab code

function y=regr(A)//Linear regression

reglin(A);endfunction

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In a Scilab app:

• Scilab code• Matlab code

function y=regr(A)//Linearregressionreglin(A);

endfunction

function y=regr(A)%Linearregressiony =fitlm(A);

end

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In a Scilab app:

• Scilab code• Matlab code• Python code

function y=regr(A)//Linearregressionreglin(A);

endfunction

functiony=regr(A)%Linearregressiony=fitlm(A);

end

from scipy imports

def regr(A):y=s.linregress(A)returny

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Engineers working with different environments can collaborate with Scilab

development deployment

Scilabcode

Matlabcode

ScilabPlatform

Matlabinterpreter(powered byOctave)

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Last version Scilab 6

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Better memory managementNo more stack

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Static code analyzerLint

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show (or s) command shows information (number, enable, functionname, line and condition) about your existing breakpoints

Debugger

Adding a breakpoint to stop the for loop of the compute functionat line 4 when the variable i reach the value of 4Disable breakpoints:disable 1Execute the codeIterate and test intermediate values

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Coverage and Profiling

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What’s NEW in Scilab

• Scilab Computer Vision Module

• Scilab Model Reduction Toolbox

• Link between Google Spreadsheet & Scilab Cloud

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Scilab Computer Vision ModuleExamples of applications

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Find Objects in PictureConverting from Color to Gray and Binary image

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Find Objects in PictureDilation or erosion

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Affine transformationsTranslation, rotation, scaling, shearing

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Scilab Model Reduction ToolboxExamples of applications in

Computational Fluid Dynamics & Multiphysics

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First 4 dominant POD modes for pressure

4 modes

99.96% of the global energy

Mod

es e

nerg

y

APPLICATION : SHAPE OPTIMIZATIONSurrogate Model of an Airfoil

Minimize Drag over lift: 𝑓 𝑥 = 𝐶&𝐶'

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APPLICATION : NON-INTRUSIVE PREDICTION

Interpolation tool for parametric study

Multi-parametric prediction for steady cases - CVD reactor ACE+

Mono-parametric prediction for unsteady cases - Wind Turbine

Velocity field prediction for new rotation speed

Im(Az) field prediction for Frequency / Current / Flow

varying parameters

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APPLICATION : DATA COMPRESSION

Snapshots projection on reduced basis for later reconstruction

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APPLICATION : INTRUSIVE PREDICTION

Mathematical based model reduction

Solving ODE on each mesh node→

Solving ODE on each simulation mode

Navier-Stokes Momentum Equation projected on reduced basis

Mathematical model for wind turbine

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Link between Google Spreadsheet & Scilab CloudWeather data example

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Link between Google Spreadsheet & Scilab Cloud

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Conclusion

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Scilab in ESI Group – high-level roadmap

Strengthen connectivity with CAE software and offer Reduced Model capabilities

Integrate Python & Matlab execution in Scilab software so that multiple codes can run alongside Scilab code (team flexibility, broadening the ecosystem)

Develop further offerings with partners for model-based code generation for embedded systems (ready for cyber-physical systems)

Leverage Scilab and Scilab Cloud to augment scientific and engineering collaboration between teams in academia/research and in the industry

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We are avai lable for meet ing wi th you this af ternoon.Come and meet us!

Contact:[email protected]:+33688206701

Documents

C Scilab Technical Insights 20171116scilab.io/wp-content/uploads/2017/12/C_Scilab_Technical_Insights... · SIGNAL PROCESSING 70% 19% STATISTICS 60% 10 % Functionnalcoveragewithadditionaltoolboxes