Sensitivity Analysis ===================== Sensitivity Base class ------------------------- .. autoclass:: pystran.SensitivityAnalysis :members: print_methods Interaction with models ^^^^^^^^^^^^^^^^^^^^^^^^^ .. automethod:: pystran.SensitivityAnalysis.WritePre .. automethod:: pystran.SensitivityAnalysis.ReadRuns .. automethod:: pystran.SensitivityAnalysis.run_pyFUSE Morris Screening method ------------------------- Main Morris screening methods ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: pystran.MorrisScreening :members: Sampling_Function_2, Optimized_Groups, Morris_Measure_Groups, runTestModel Testing the selected traject optimization ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. automethod:: pystran.MorrisScreening.Optimized_diagnostic .. image:: ex_Morris_oldsampl.png :scale: 50 % .. image:: ex_Morris_optsampl.png :scale: 50 % Output plots and exports ^^^^^^^^^^^^^^^^^^^^^^^^^ .. automethod:: pystran.MorrisScreening.plotmu .. image:: ex_morris_mu.png :scale: 75 % .. automethod:: pystran.MorrisScreening.plotmustar .. image:: ex_morris_mustar.png :scale: 75 % .. automethod:: pystran.MorrisScreening.plotsigma .. image:: ex_morris_sigma.png :scale: 75 % .. automethod:: pystran.MorrisScreening.plotmustarsigma .. image:: ex_plotmustarsigma.png :scale: 75 % .. automethod:: pystran.MorrisScreening.latexresults :: \begin{table}{lccc} \tablewidth{0pc} \tablecolumns{4} \caption{Morris evaluation criteria\label{tab:morris1tot}} \tablehead{ & $\mu$ & $\mu^*$ & $\sigma$ } \startdata $X_1$ & 0.019 & 0.053 & 0.062\\ $X_2$ & -0.014 & 0.28 & 0.37\\ $X_3$ & 0.35 & 1.8 & 2.2\\ $X_4$ & 0.58 & 0.77 & 0.66\\ $X_5$ & -0.0091 & 0.029 & 0.035\\ $X_6$ & 0.023 & 0.088 & 0.11\\ \enddata \end{table} .. automethod:: pystran.MorrisScreening.txtresults :: Par mu mustar sigma $X_1$ 0.01923737 0.05295802 0.06235872 $X_2$ -0.01423402 0.27864841 0.36502709 $X_3$ 0.35127671 1.79863549 2.19419533 $X_4$ 0.57758340 0.76929478 0.66397173 $X_5$ -0.00912314 0.02933937 0.03503700 $X_6$ 0.02319802 0.08834758 0.10503778 Standardized Regression Coefficients (SRC) method -------------------------------------------------- Main SRC methods ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: pystran.SRCSensitivity :members: PrepareSample, Calc_SRC Quick analysis of the scatter plots of the ouput versus the parameter values ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. automethod:: pystran.SRCSensitivity.quickscatter Output image: .. image:: ex_SRC_scatter.png :scale: 50 % Output plots and exports ^^^^^^^^^^^^^^^^^^^^^^^^^ .. automethod:: pystran.SRCSensitivity.plot_tornado Output image: .. image:: ex_SRC_tornado.png :scale: 50 % .. automethod:: pystran.SRCSensitivity.plot_SRC Output image if a single output is selected: .. image:: ex_SRC_single.png :scale: 50 % Output image if a all outputs are selected: .. image:: ex_SRC_all.png :scale: 50 % .. automethod:: pystran.SRCSensitivity.latexresults :: \begin{table}{lccccc} \tablewidth{0pc} \tablecolumns{6} \caption{Global SRC parameter ranking\label{tab:SRCresult}} \tablehead{ & o1 & o2 & o3 & o4 & o5 } \startdata $X_1$ & 5 & 5 & 5 & 5 & 5\\ $X_2$ & 4 & 4 & 4 & 4 & 4\\ $X_3$ & 3 & 3 & 3 & 3 & 3\\ $X_4$ & 2 & 2 & 2 & 2 & 2\\ $X_5$ & 1 & 1 & 1 & 1 & 1\\ $X_6$ & 6 & 6 & 6 & 6 & 6\\ \enddata \end{table} .. automethod:: pystran.SRCSensitivity.txtresults :: Par o1 o2 o3 o4 o5 $X_1$ 0.0778505546741 0.0778505546741 0.0778505546741 0.0778505546741 0.0778505546741 $X_2$ 0.233670804823 0.233670804823 0.233670804823 0.233670804823 0.233670804823 $X_3$ -0.6 0.389115599046 0.389115599046 0.389115599046 0.389115599046 $X_4$ 0.544743494911 0.544743494911 0.544743494911 0.544743494911 0.544743494911 $X_5$ 0.700482475678 0.700482475678 0.700482475678 0.700482475678 0.700482475678 $X_6$ 0.0778270555871 0.0778270555871 0.0778270555871 0.0778270555871 0.0778270555871 Sobol Variance based method ----------------------------- Main Sobol variance methods ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: pystran.SobolVariance :members: Sobolsampling, SobolVariancePre, SobolVariancePost, runTestModel Check the convergence of the analysis ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. automethod:: pystran.SobolVariance.sens_evolution .. image:: ex_sobol_evSi.png :scale: 50 % .. image:: ex_sobol_evSTi.png :scale: 50 % Output plots and exports ^^^^^^^^^^^^^^^^^^^^^^^^^ .. automethod:: pystran.SobolVariance.plotSTij .. image:: ex_sobol_STij.png :scale: 75 % .. automethod:: pystran.SobolVariance.plotSTi .. image:: ex_sobol_STi.png :scale: 75 % .. automethod:: pystran.SobolVariance.plotSi .. image:: ex_sobol_Si.png :scale: 75 % .. automethod:: pystran.SobolVariance.latexresults :: \begin{table}{lcc} \tablewidth{0pc} \tablecolumns{3} \caption{First order and Total sensitivity index\label{tab:sobol1tot}} \tablehead{ & $S_i$ & $S_{Ti}$ } \startdata par1 & 0.72 & 0.79\\ par2 & 0.18 & 0.24\\ par3 & 0.023 & 0.034\\ par4 & 0.0072 & 0.010\\ par5 & 0.000044 & 0.00011\\ par6 & 0.000068 & 0.00011\\ par7 & 0.000028 & 0.00010\\ par8 & 0.000049 & 0.00011\\ SUM & 0.93 & 1.1\\ \enddata \end{table} .. automethod:: pystran.SobolVariance.txtresults :: Par Si STi par1 0.71602494 0.78797717 par2 0.17916175 0.24331297 par3 0.02346677 0.03437614 par4 0.00720168 0.01044238 par5 0.00004373 0.00010526 par6 0.00006803 0.00010556 par7 0.00002847 0.00010479 par8 0.00004918 0.00010522 SUM 0.92604455 1.07652948 Latin-Hypercube OAT method ----------------------------- Main GLobal OAT methods ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: pystran.GlobalOATSensitivity :members: PrepareSample, Calc_sensitivity, Output plots and exports ^^^^^^^^^^^^^^^^^^^^^^^^^ .. automethod:: pystran.GlobalOATSensitivity.Get_ranking .. automethod:: pystran.GlobalOATSensitivity.plotsens .. image:: ex_globaloat_CTRS.png :scale: 75 % .. image:: ex_globaloat_PE.png :scale: 75 % .. automethod:: pystran.GlobalOATSensitivity.plot_rankmatrix .. image:: ex_globaloat_sensmatrix.png :scale: 75 % .. automethod:: pystran.GlobalOATSensitivity.latexresults :: \begin{table}{lccccccccccc} \tablewidth{0pc} \tablecolumns{12} \caption{Global OAT parameter ranking\label{tab:globaloatrank}} \tablehead{ & o1 & o2 & o3 & o4 & o5 & o6 & o7 & o8 & o9 & o10 & o11 } \startdata $X_1$ & 4 & 4 & 2 & 3 & 3 & 3 & 2 & 3 & 4 & 4 & 3\\ $X_2$ & 3 & 1 & 3 & 4 & 4 & 1 & 3 & 2 & 1 & 3 & 2\\ $X_3$ & 1 & 6 & 4 & 6 & 2 & 4 & 5 & 4 & 2 & 1 & 4\\ $X_4$ & 2 & 2 & 6 & 5 & 6 & 6 & 4 & 6 & 3 & 5 & 1\\ $X_5$ & 6 & 5 & 5 & 1 & 5 & 2 & 6 & 1 & 6 & 6 & 6\\ $X_6$ & 5 & 3 & 1 & 2 & 1 & 5 & 1 & 5 & 5 & 2 & 5\\ \enddata \end{table} .. automethod:: pystran.GlobalOATSensitivity.txtresults :: Par o1 o2 o3 o4 o5 o6 o7 o8 o9 o10 o11 $X_1$ 4 4 2 3 3 3 2 3 4 4 3 $X_2$ 3 1 3 4 4 1 3 2 1 3 2 $X_3$ 1 6 4 6 2 4 5 4 2 1 4 $X_4$ 2 2 6 5 6 6 4 6 3 5 1 $X_5$ 6 5 5 1 5 2 6 1 6 6 6 $X_6$ 5 3 1 2 1 5 1 5 5 2 5 Dynamic Identifiability Analysis (DYNIA) ------------------------------------------ added soon Regional Sensitivity Analysis (RSA) ------------------------------------------ Extra info added soon Main RSA methods ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: pystran.RegionalSensitivity :members: PrepareSample, checkprobs, select_behavioural Generalised Likelihood Uncertainty Estimation (GLUE) ----------------------------------------------------- added soon