diff --git a/.github/workflows/Extinction.yml b/.github/workflows/Extinction.yml index 6c22e683..af597642 100644 --- a/.github/workflows/Extinction.yml +++ b/.github/workflows/Extinction.yml @@ -122,4 +122,23 @@ jobs: env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + # Step 1: generate the plots + - name: UWuppertal Results + run: | + echo $GITHUB_WORKSPACE + cd $GITHUB_WORKSPACE/Extinction/FM_Burner/Computational_Results/2023/UWuppertal + python FM_Burner_plot_cmp.py + + # Step 2: push the plots to the releases page + - name: Push UWuppertal Results to release + if: ( github.event_name == 'push' || github.event_name == 'workflow_dispatch' ) && github.repository_owner == 'MaCFP' + run: | + cd $GITHUB_WORKSPACE/Extinction/FM_Burner/Computational_Results/2023/UWuppertal + cp README.md ./Plots/. + zip Plots.zip -r Plots + mv Plots.zip FM_Burner_UWuppertal_Plots.zip + gh release upload SCRIPT_FIGURES FM_Burner_UWuppertal_Plots.zip --clobber + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + diff --git a/Extinction/FM_Burner/Computational_Results/2023/UWuppertal/FM_Burner_cmp_config.csv b/Extinction/FM_Burner/Computational_Results/2023/UWuppertal/FM_Burner_cmp_config.csv new file mode 100644 index 00000000..dd45ffcc --- /dev/null +++ b/Extinction/FM_Burner/Computational_Results/2023/UWuppertal/FM_Burner_cmp_config.csv @@ -0,0 +1,150 @@ +Exp_Filename ,Exp_Header_Row,Exp_Data_Row,Exp_x_Col_Name ,Exp_y_Col_Name ,Exp_Data_Label,Exp_Marker_Style,Exp_Line_Style,Exp_Line_Width,Cmp_Filename ,Cmp_Header_Row,Cmp_Data_Row,Cmp_x_Col_Name ,Cmp_y_Col_Name ,Cmp_Data_Label,Cmp_Line_Style,Plot_Title ,Plot_x_Label ,Plot_y_Label ,Plot_x_Min,Plot_x_Max,Plot_y_Min,Plot_y_Max,Plot_Flip_Axis,Plot_Legend_Location,Plot_Filename +Radiation_global/Radiant_power_distribution.csv,1,3,height ,Air-mean ,Exp ,o , , ,v1_FM_15cm_Burner_C2H4_20p9_5mm_line.csv,2,3,z ,RHRRPUL ,v1 (dt10 104) ,- ,Mean Radiation Emission; 21 % ,Height (cm) ,Heat Flux (kW/m) ,0,90,0,15,True ,upper right ,Mean_Vertical_Radiation_Emission_20p9_dt10.pdf +Radiation_global/Radiant_power_distribution.csv,1,3,height ,Air-mean ,Exp ,o , , ,v2_FM_15cm_Burner_C2H4_20p9_5mm_line.csv,2,3,z ,RHRRPUL ,v2 (dt10 512) ,-. ,Mean Radiation Emission; 21 % ,Height (cm) ,Heat Flux (kW/m) ,0,90,0,15,True ,upper right ,Mean_Vertical_Radiation_Emission_20p9_dt10.pdf +Radiation_global/Radiant_power_distribution.csv,1,3,height ,Air-RMS ,Exp ,o , , ,v1_FM_15cm_Burner_C2H4_20p9_5mm_line.csv,2,3,z ,RHRRPUL_rms ,v1 (dt10 104) ,- ,Mean Radiation Emission; 21 % ,Height (cm) ,Heat Flux (kW/m) ,0,90,0,15,True ,upper right ,RMS_Vertical_Radiation_Emission_20p9_dt10.pdf +Radiation_global/Radiant_power_distribution.csv,1,3,height ,Air-RMS ,Exp ,o , , ,v2_FM_15cm_Burner_C2H4_20p9_5mm_line.csv,2,3,z ,RHRRPUL_rms ,v2 (dt10 512) ,-. ,Mean Radiation Emission; 21 % ,Height (cm) ,Heat Flux (kW/m) ,0,90,0,15,True ,upper right ,RMS_Vertical_Radiation_Emission_20p9_dt10.pdf + 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,2,v3_FM_15cm_Burner_C2H4_20p9_5mm_chir.csv,1,2,Time ,Chi_r ,v3 ,-. ,Radiant Fraction; 21 % O$_2$ ,Time (s) ,Radiant Fraction ,10,20,0.2,0.4, ,lower right ,Chi_v1_v2_v3_v4_t10_20.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v4_FM_15cm_Burner_C2H4_20p9_5mm_chir.csv,1,2,Time ,Chi_r ,v4 ,: ,Radiant Fraction; 21 % O$_2$ ,Time (s) ,Radiant Fraction ,10,20,0.2,0.4, ,lower right ,Chi_v1_v2_v3_v4_t10_20.pdf + + +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v4_FM_15cm_Burner_C2H4_20p9_5mm_chir.csv,1,2,Time ,Chi_r ,v4 ,- ,Radiant Fraction; 21 % O$_2$ ,Time (s) ,Radiant Fraction ,0,200,0.2,0.4, ,lower right ,Chi_v4_v7_v10_t0_200.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v10_FM_15cm_Burner_C2H4_20p9_5mm_chir.csv,1,2,Time ,Chi_r ,v10 ,-- ,Radiant Fraction; 21 % O$_2$ ,Time (s) ,Radiant Fraction ,0,200,0.2,0.4, ,lower right ,Chi_v4_v7_v10_t0_200.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v7_FM_15cm_Burner_C2H4_20p9_5mm_chir.csv,1,2,Time ,Chi_r ,v7 ,-. ,Radiant Fraction; 21 % O$_2$ ,Time (s) ,Radiant Fraction ,0,200,0.2,0.4, ,lower right ,Chi_v4_v7_v10_t0_200.pdf + +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v4_FM_15cm_Burner_C2H4_20p9_5mm_chir.csv,1,2,Time ,Chi_r ,v4 ,- ,Radiant Fraction; 21 % O$_2$ ,Time (s) ,Radiant Fraction ,10,20,0.2,0.4, ,lower right ,Chi_v4_v7_v10_t10_20.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v10_FM_15cm_Burner_C2H4_20p9_5mm_chir.csv,1,2,Time ,Chi_r ,v10 ,-- ,Radiant Fraction; 21 % O$_2$ ,Time (s) ,Radiant Fraction ,10,20,0.2,0.4, ,lower right ,Chi_v4_v7_v10_t10_20.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v7_FM_15cm_Burner_C2H4_20p9_5mm_chir.csv,1,2,Time ,Chi_r ,v7 ,-. ,Radiant Fraction; 21 % O$_2$ ,Time (s) ,Radiant Fraction ,10,20,0.2,0.4, ,lower right ,Chi_v4_v7_v10_t10_20.pdf + + 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,2,v7__MEAN_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_2.4 ,v7 (dt1000 - 104) ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,300, ,lower left ,MEAN_Radiative_Wall_Flux_2s_104.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v9__MEAN_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_2.4 ,v9 (dt200 - 104) ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,300, ,lower left ,MEAN_Radiative_Wall_Flux_2s_104.pdf + +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v2__MEAN_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_2.4 ,v2 (dt10 - 512) ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,300,1000, ,lower left ,MEAN_Radiative_Wall_Flux_2s_512.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v4__MEAN_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_2.4 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,0,0.83,300,1000, ,lower left ,MEAN_Radiative_Wall_Flux_2s_512.pdf + + +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v2__MEAN_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_2.4 ,v2 (dt10 - 512) 2.4 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,200,1000, ,lower left ,MEAN_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v2_512.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v2__MEAN_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_7.2 ,v2 (dt10 - 512) 7.2 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,200,1000, ,lower left ,MEAN_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v2_512.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v2__MEAN_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_12.0 ,v2 (dt10 - 512) 12.0 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,200,1000, ,lower left 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,MEAN_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v7_104.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v7__MEAN_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_7.2 ,v7 (dt1000 - 104) 7.1 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,300, ,lower left ,MEAN_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v7_104.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v7__MEAN_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_12.0 ,v7 (dt1000 - 104) 12.0 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,300, ,lower left ,MEAN_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v7_104.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v7__MEAN_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_16.8 ,v7 (dt1000 - 104) 16.8 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,300, ,lower left ,MEAN_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v7_104.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v7__MEAN_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_197.0 ,v7 (dt1000 - 104) 197.0 ,-. ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,300, ,lower left ,MEAN_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v7_104.pdf + +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v8__MEAN_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_2.4 ,v8 (dt1000 - 512) 2.4 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,200,1000, ,lower left ,MEAN_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v8_512.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v8__MEAN_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_7.1 ,v8 (dt1000 - 512) 7.1 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,200,1000, ,lower left ,MEAN_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v8_512.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v8__MEAN_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_12.0 ,v8 (dt1000 - 512) 12.0 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,200,1000, ,lower left ,MEAN_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v8_512.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v8__MEAN_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_16.7 ,v8 (dt1000 - 512) 16.7 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,200,1000, ,lower left ,MEAN_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v8_512.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v8__MEAN_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_196.9 ,v8 (dt1000 - 512) 196.8,-. ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,200,1000, ,lower left 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512) ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,200, ,lower left ,STD_Radiative_Wall_Flux_2s_512.pdf + + +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v2__STD_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_2.4 ,v2 (dt10 - 512) 2.4 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,200, ,lower right ,STD_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v2_512.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v2__STD_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_7.2 ,v2 (dt10 - 512) 7.2 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,200, ,lower right ,STD_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v2_512.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v2__STD_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_12.0 ,v2 (dt10 - 512) 12.0 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,200, ,lower right ,STD_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v2_512.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v2__STD_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_16.8 ,v2 (dt10 - 512) 16.8 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,200, ,lower right ,STD_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v2_512.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v2__STD_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_59.7 ,v2 (dt10 - 512) 59.7 ,-- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,200, ,lower right ,STD_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v2_512.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v2__STD_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_198.4 ,v2 (dt10 - 512) 198.4 ,-. ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,200, ,lower right ,STD_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v2_512.pdf + +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v7__STD_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_2.4 ,v7 (dt1000 - 104) 2.4 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,200, ,upper right ,STD_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v7_104.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v7__STD_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_7.2 ,v7 (dt1000 - 104) 7.1 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,200, ,upper right ,STD_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v7_104.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v7__STD_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_12.0 ,v7 (dt1000 - 104) 12.0 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,200, ,upper right ,STD_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v7_104.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v7__STD_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_16.8 ,v7 (dt1000 - 104) 16.8 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,200, ,upper right ,STD_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v7_104.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v7__STD_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_59.6 ,v7 (dt1000 - 104) 59.6 ,-- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,200, ,upper right ,STD_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v7_104.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v7__STD_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_197.0 ,v7 (dt1000 - 104) 197.0 ,-. ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,200, ,upper right ,STD_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v7_104.pdf + +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v8__STD_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_2.4 ,v8 (dt1000 - 512) 2.4 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,200, ,lower left ,STD_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v8_512.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v8__STD_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_7.1 ,v8 (dt1000 - 512) 7.1 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,200, ,lower left ,STD_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v8_512.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v8__STD_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_12.0 ,v8 (dt1000 - 512) 12.0 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,200, ,lower left ,STD_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v8_512.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v8__STD_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_16.7 ,v8 (dt1000 - 512) 16.7 ,- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,200, ,lower left ,STD_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v8_512.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v8__STD_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_59.4 ,v8 (dt1000 - 512) 59.4 ,-- ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,200, ,lower left ,STD_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v8_512.pdf +Radiation_global/Radiant_fraction2.csv ,1,2,Time ,20p9 percent ,Exp , ,- ,2,v8__STD_radfl_FM_rad_distribution_x06_y0.h5_.csv,1,2,z-position ,avgTimeRange_196.9 ,v8 (dt1000 - 512) 196.8,-. ,Radiative Heat Flux ,Position (m) ,Radiative Flux (W/m2) ,0,0.83,0,200, ,lower left ,STD_Radiative_Wall_Flux_2s_7s_12s_17s_198s_v8_512.pdf diff --git a/Extinction/FM_Burner/Computational_Results/2023/UWuppertal/FM_Burner_plot_cmp.py b/Extinction/FM_Burner/Computational_Results/2023/UWuppertal/FM_Burner_plot_cmp.py new file mode 100644 index 00000000..b0f2cf60 --- /dev/null +++ b/Extinction/FM_Burner/Computational_Results/2023/UWuppertal/FM_Burner_plot_cmp.py @@ -0,0 +1,31 @@ +#!/usr/bin/env python3 +# McDermott +# Feb 2021 + +# first, make sure the macfp module directory is in your path +# if not, uncomment the lines below and replace +# with the path (absolute or relative) to your macfp-db repository + +import sys +# sys.path.append('/macfp-db/Utilities/') +sys.path.append('../../../../../../macfp-db/Utilities/') +# sys.path.append('/Users/fbraenns/09_TOOLs_Application/MaCFP/00_Repositories/macfp-db_FORK.github/Utilities/') + +import macfp +import importlib +importlib.reload(macfp) # use for development (while making changes to macfp.py) +import matplotlib.pyplot as plt + +macfp.dataplot(config_filename='FM_Burner_cmp_config.csv', + institute='UWuppertal', + revision='MaCFP-3, Tsukuba, 2023', + expdir='../../../Experimental_Data/', + # cmpdir='./Output/', + # cmpdir='../NIST/Output/', + cmpdir='./Output/', + pltdir='./Plots/', + close_figs=True, + verbose=True, + plot_list=['all']) + +# plt.show() diff --git a/Extinction/FM_Burner/Computational_Results/2023/UWuppertal/FM_Burner_post_process.py b/Extinction/FM_Burner/Computational_Results/2023/UWuppertal/FM_Burner_post_process.py new file mode 100644 index 00000000..00d3a3a4 --- /dev/null +++ b/Extinction/FM_Burner/Computational_Results/2023/UWuppertal/FM_Burner_post_process.py @@ -0,0 +1,62 @@ +#!/usr/bin/env python3 +# McDermott +# 16 Aug 2023 +# +# Read and process FDS output files for FM_Burner cases to compute +# Eta (combustion efficiency) and Chi_r (radiative fraction) + +import numpy as np +import pandas as pd + +outdir = './Output/' + +# create files with XO2 as independent column + +fuel_name = ['C2H4','C3H6','C3H8','CH4'] +fuel_name = ['C2H4'] +res_name = ['5mm'] + +# for fuel in fuel_name: +# for res in res_name: + +# # DEV = pd.read_csv(outdir+'FM_15cm_Burner_'+fuel+'_'+res+'_devc.csv', sep=',', header=1) +# HRR = pd.read_csv(outdir+'FM_15cm_Burner_'+fuel+'_'+res+'_hrr.csv', sep=',', header=1) + +# # XO2_FDS = DEV["XO2"].values[:].astype(float) +# Qdot_FDS = HRR["HRR"].values[:].astype(float) +# Qrad_FDS = HRR["Q_RADI"].values[:].astype(float) + +# ETA = Qdot_FDS/np.max(Qdot_FDS) +# CHI_R = np.minimum(1.,np.maximum(0.,-Qrad_FDS)/np.maximum(0.001,Qdot_FDS)) + +# df = pd.DataFrame({'XO2': XO2_FDS[1:], +# 'eta': ETA[1:], +# 'Chi_R': CHI_R[1:]}) + +# df.to_csv(outdir+'FM_15cm_Burner_'+fuel+'_'+res+'.csv',index=False,float_format='%5.3f') + +# create files with Time as independent column for C2H4 fuel + +# O2_name = ['20p9','19p0','16p8','15p2'] +O2_name = ['20p9'] +res_name = ['5mm'] + +variantL = ["v1_","v2_", "v3_","v4_","v5_","v6_","v7_","v8_","v9_","v10_"] +for variant in variantL: + for O2 in O2_name: + for res in res_name: + + HRR = pd.read_csv(outdir+ variant+'FM_15cm_Burner_C2H4_'+O2+'_'+res+'_hrr.csv', sep=',', header=1); + + Time_FDS = HRR["Time"].values[:].astype(float) + Qdot_FDS = HRR["HRR"].values[:].astype(float) + Qrad_FDS = HRR["Q_RADI"].values[:].astype(float) + + CHI_R = np.minimum(1.,np.maximum(0.,-Qrad_FDS)/np.maximum(0.001,Qdot_FDS)) + + df = pd.DataFrame({'Time': Time_FDS[1:], + 'Chi_r': CHI_R[1:]}) + + df.to_csv(outdir+variant+'FM_15cm_Burner_C2H4_'+O2+'_'+res+'_chir.csv',index=False,float_format='%5.3f') + + diff --git a/Extinction/FM_Burner/Computational_Results/2023/UWuppertal/Output/v10_FM_15cm_Burner_C2H4_20p9_5mm_chir.csv b/Extinction/FM_Burner/Computational_Results/2023/UWuppertal/Output/v10_FM_15cm_Burner_C2H4_20p9_5mm_chir.csv new file mode 100644 index 00000000..1dee8fd9 --- /dev/null +++ b/Extinction/FM_Burner/Computational_Results/2023/UWuppertal/Output/v10_FM_15cm_Burner_C2H4_20p9_5mm_chir.csv @@ -0,0 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5.4159432E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.5663636E+001, 2.1804610E+000, 5.2920747E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.6160606E+001, 2.0976775E+000, 5.1701315E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.6657576E+001, 2.0167851E+000, 5.0492988E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.7154545E+001, 1.9379240E+000, 4.9307967E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.7651515E+001, 1.8618535E+000, 4.8170195E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.8148485E+001, 1.7891078E+000, 4.7092234E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.8645455E+001, 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7.9186100E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.5724242E+001, 4.3154746E+000, 7.8313177E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.6221212E+001, 4.2081359E+000, 7.7484701E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.6718182E+001, 4.1009889E+000, 7.6639101E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.7215152E+001, 3.9956219E+000, 7.5805753E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.7712121E+001, 3.8928464E+000, 7.5040658E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.8209091E+001, 3.7929882E+000, 7.4341622E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.8706061E+001, 3.6955467E+000, 7.3656786E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.9203030E+001, 3.6005480E+000, 7.2973277E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.9700000E+001, 3.5074704E+000, 7.2259991E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.0196970E+001, 3.4149144E+000, 7.1461471E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.0693939E+001, 3.3217836E+000, 7.0549981E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.1190909E+001, 3.2274427E+000, 6.9561346E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.1687879E+001, 3.1330197E+000, 6.8531759E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.2184848E+001, 3.0377178E+000, 6.7426781E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.2681818E+001, 2.9482310E+000, 6.6460092E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.3178788E+001, 2.8547336E+000, 6.5273403E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.3675758E+001, 2.7623529E+000, 6.4045709E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.4172727E+001, 2.6710896E+000, 6.2775872E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.4669697E+001, 2.5800369E+000, 6.1447255E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.5166667E+001, 2.4878351E+000, 6.0043811E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.5663636E+001, 2.3949561E+000, 5.8602379E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.6160606E+001, 2.3025832E+000, 5.7167141E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.6657576E+001, 2.2118704E+000, 5.5749503E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.7154545E+001, 2.1228759E+000, 5.4333995E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.7651515E+001, 2.0367494E+000, 5.2945397E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.8148485E+001, 1.9541386E+000, 5.1612880E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.8645455E+001, 1.8752288E+000, 5.0337692E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.9142424E+001, 1.7991965E+000, 4.9084384E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.9639394E+001, 1.7262840E+000, 4.7856556E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.0136364E+001, 1.6559696E+000, 4.6641823E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.0633333E+001, 1.5881312E+000, 4.5426113E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.1130303E+001, 1.5216318E+000, 4.4190791E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.1627273E+001, 1.4576626E+000, 4.2996375E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.2124242E+001, 1.3962997E+000, 4.1841565E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.2621212E+001, 1.3372712E+000, 4.0692872E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.3118182E+001, 1.2801036E+000, 3.9524517E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.3615152E+001, 1.2257684E+000, 3.8355394E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.4112121E+001, 1.1739774E+000, 3.7203925E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.4609091E+001, 1.1248286E+000, 3.6116598E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.5106061E+001, 1.0778761E+000, 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8.8987738E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN, 1.9018072E+001, 1.5858365E-018, 8.8657037E-012, 3.4287895E-008, 3.2718787E-006, 3.2715306E-004, 1.1842291E-017, 1.2174206E-010, 3.5844213E-007, 4.9362035E-005, 5.7469651E-003 + 4.0754545E+001, 5.5470457E+000, 8.8241497E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN, 1.9512048E+001, 1.5858365E-018, 8.8657037E-012, 3.4287895E-008, 3.2718787E-006, 3.2715306E-004, 1.1842291E-017, 1.2174206E-010, 3.5844213E-007, 4.9362035E-005, 5.7469651E-003 + 4.1251515E+001, 5.4204428E+000, 8.7452618E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN, 2.0006024E+001, 2.7792499E-018, 2.7323083E-011, 5.2983178E-008, 2.2617802E-006, 1.7963514E-004, 2.0199398E-017, 3.7531689E-010, 5.1718100E-007, 1.0988444E-005, 4.0728694E-003 + 4.1748485E+001, 5.2947370E+000, 8.6627012E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN, 2.0500000E+001, 2.7792499E-018, 2.7323083E-011, 5.2983178E-008, 2.2617802E-006, 1.7963514E-004, 2.0199398E-017, 3.7531689E-010, 5.1718100E-007, 1.0988444E-005, 4.0728694E-003 + 4.2245455E+001, 5.1693178E+000, 8.5747693E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.2742424E+001, 5.0446692E+000, 8.4836107E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.3239394E+001, 4.9220959E+000, 8.3922103E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.3736364E+001, 4.7993092E+000, 8.2966139E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.4233333E+001, 4.6764585E+000, 8.1965050E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.4730303E+001, 4.5538886E+000, 8.0953415E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.5227273E+001, 4.4332735E+000, 7.9962834E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.5724242E+001, 4.3143186E+000, 7.8986976E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.6221212E+001, 4.1959788E+000, 7.7992917E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.6718182E+001, 4.0777371E+000, 7.6945234E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.7215152E+001, 3.9596424E+000, 7.5847276E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.7712121E+001, 3.8430646E+000, 7.4732810E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.8209091E+001, 3.7287044E+000, 7.3629767E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.8706061E+001, 3.6178379E+000, 7.2559685E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.9203030E+001, 3.5094373E+000, 7.1506379E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.9700000E+001, 3.4043451E+000, 7.0512355E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.0196970E+001, 3.3028832E+000, 6.9607642E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.0693939E+001, 3.2046904E+000, 6.8761138E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.1190909E+001, 3.1086918E+000, 6.7937788E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.1687879E+001, 3.0136968E+000, 6.7089699E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.2184848E+001, 2.9182600E+000, 6.6155521E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.2681818E+001, 2.8295607E+000, 6.5370261E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.3178788E+001, 2.7367524E+000, 6.4323613E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.3675758E+001, 2.6446483E+000, 6.3201369E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.4172727E+001, 2.5530331E+000, 6.1980230E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.4669697E+001, 2.4626008E+000, 6.0701604E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.5166667E+001, 2.3734974E+000, 5.9385284E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.5663636E+001, 2.2866787E+000, 5.8070188E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.6160606E+001, 2.2017032E+000, 5.6724203E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.6657576E+001, 2.1185140E+000, 5.5356077E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.7154545E+001, 2.0372370E+000, 5.3964341E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.7651515E+001, 1.9585571E+000, 5.2587246E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.8148485E+001, 1.8818209E+000, 5.1214198E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.8645455E+001, 1.8074488E+000, 4.9855791E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.9142424E+001, 1.7351452E+000, 4.8514990E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.9639394E+001, 1.6649974E+000, 4.7209069E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.0136364E+001, 1.5963821E+000, 4.5920174E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.0633333E+001, 1.5295745E+000, 4.4640784E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.1130303E+001, 1.4648925E+000, 4.3353759E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.1627273E+001, 1.4028217E+000, 4.2088824E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.2124242E+001, 1.3415398E+000, 4.0765140E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.2621212E+001, 1.2809815E+000, 3.9348967E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.3118182E+001, 1.2217386E+000, 3.7893853E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.3615152E+001, 1.1647586E+000, 3.6477220E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.4112121E+001, 1.1108985E+000, 3.5147032E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.4609091E+001, 1.0598523E+000, 3.3873588E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.5106061E+001, 1.0112890E+000, 3.2644301E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.5603030E+001, 9.6465038E-001, 3.1451440E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.6100000E+001, 9.1966452E-001, 3.0287663E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.6596970E+001, 8.7671316E-001, 2.9191928E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.7093939E+001, 8.3596372E-001, 2.8169910E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.7590909E+001, 7.9717898E-001, 2.7205136E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.8087879E+001, 7.6023645E-001, 2.6270969E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.8584848E+001, 7.2507922E-001, 2.5340348E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.9081818E+001, 6.9147565E-001, 2.4405868E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.9578788E+001, 6.5921265E-001, 2.3457011E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 7.0075758E+001, 6.2812730E-001, 2.2514022E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 7.0572727E+001, 5.9842880E-001, 2.1611914E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 7.1069697E+001, 5.7009812E-001, 2.0753822E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 7.1566667E+001, 5.4335069E-001, 1.9954441E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 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3.4719115E-019, 9.4295976E-012, 5.6695323E-007, 8.7437594E-004, 1.0625520E-002 + 3.8766667E+001, 6.7207359E+000, 1.0084463E+001,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN, 1.7536145E+001, 6.2396598E-020, 5.3387522E-013, 6.3181133E-008, 3.9912784E-005, 1.5010685E-003, 3.4719115E-019, 9.4295976E-012, 5.6695323E-007, 8.7437594E-004, 1.0625520E-002 + 3.9263636E+001, 6.5788086E+000, 1.0017775E+001,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN, 1.8030120E+001, 1.1520572E-019, 3.3819290E-013, 6.4520912E-008, 4.4068697E-006, 8.2045349E-004, 7.5634131E-019, 4.1320495E-012, 6.1435298E-007, 8.4045978E-005, 7.7599250E-003 + 3.9760606E+001, 6.4373753E+000, 9.9499972E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN, 1.8524096E+001, 1.1520572E-019, 3.3819290E-013, 6.4520912E-008, 4.4068697E-006, 8.2045349E-004, 7.5634131E-019, 4.1320495E-012, 6.1435298E-007, 8.4045978E-005, 7.7599250E-003 + 4.0257576E+001, 6.2949835E+000, 9.8769139E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN, 1.9018072E+001, 2.4333594E-019, 1.2578288E-012, 7.7750902E-008, 1.5052392E-006, 3.8693480E-004, 1.7776552E-018, 1.5614713E-011, 7.1275375E-007, 1.0674404E-005, 4.8453503E-003 + 4.0754545E+001, 6.1523958E+000, 9.7966609E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN, 1.9512048E+001, 2.4333594E-019, 1.2578288E-012, 7.7750902E-008, 1.5052392E-006, 3.8693480E-004, 1.7776552E-018, 1.5614713E-011, 7.1275375E-007, 1.0674404E-005, 4.8453503E-003 + 4.1251515E+001, 6.0105272E+000, 9.7083827E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN, 2.0006024E+001, 5.4517397E-019, 4.2218947E-012, 8.4203912E-008, 1.3970400E-006, 1.3136826E-004, 4.2217453E-018, 5.2297651E-011, 7.5802073E-007, 9.8802772E-006, 2.4404524E-003 + 4.1748485E+001, 5.8708262E+000, 9.6180232E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN, 2.0500000E+001, 5.4517397E-019, 4.2218947E-012, 8.4203912E-008, 1.3970400E-006, 1.3136826E-004, 4.2217453E-018, 5.2297651E-011, 7.5802073E-007, 9.8802772E-006, 2.4404524E-003 + 4.2245455E+001, 5.7324538E+000, 9.5242679E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.2742424E+001, 5.5962342E+000, 9.4308922E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.3239394E+001, 5.4614450E+000, 9.3336025E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.3736364E+001, 5.3291215E+000, 9.2341520E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.4233333E+001, 5.1988572E+000, 9.1288875E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.4730303E+001, 5.0710109E+000, 9.0257885E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.5227273E+001, 4.9434236E+000, 8.9193399E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.5724242E+001, 4.8167907E+000, 8.8111723E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.6221212E+001, 4.6903111E+000, 8.7026781E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.6718182E+001, 4.5640616E+000, 8.5956318E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.7215152E+001, 4.4383954E+000, 8.4913160E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.7712121E+001, 4.3137370E+000, 8.3870857E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.8209091E+001, 4.1893171E+000, 8.2788855E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.8706061E+001, 4.0644828E+000, 8.1617338E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.9203030E+001, 3.9408246E+000, 8.0389726E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 4.9700000E+001, 3.8196613E+000, 7.9119747E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.0196970E+001, 3.7007543E+000, 7.7791654E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.0693939E+001, 3.5837089E+000, 7.6402706E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.1190909E+001, 3.4694211E+000, 7.4987890E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.1687879E+001, 3.3583686E+000, 7.3578855E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.2184848E+001, 3.2493630E+000, 7.2179486E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.2681818E+001, 3.1506045E+000, 7.1059459E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.3178788E+001, 3.0534136E+000, 7.0040122E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.3675758E+001, 2.9488656E+000, 6.8606998E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.4172727E+001, 2.8472062E+000, 6.7153483E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.4669697E+001, 2.7479169E+000, 6.5691217E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.5166667E+001, 2.6515432E+000, 6.4260674E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.5663636E+001, 2.5570655E+000, 6.2863250E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.6160606E+001, 2.4642779E+000, 6.1506421E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.6657576E+001, 2.3751160E+000, 6.0214158E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.7154545E+001, 2.2892203E+000, 5.8964205E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.7651515E+001, 2.2067379E+000, 5.7789304E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.8148485E+001, 2.1272266E+000, 5.6676632E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.8645455E+001, 2.0500372E+000, 5.5571108E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.9142424E+001, 1.9744058E+000, 5.4453801E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 5.9639394E+001, 1.9005601E+000, 5.3321360E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.0136364E+001, 1.8275067E+000, 5.2150657E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.0633333E+001, 1.7557746E+000, 5.0934549E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.1130303E+001, 1.6855535E+000, 4.9673092E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.1627273E+001, 1.6174702E+000, 4.8386710E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.2124242E+001, 1.5508272E+000, 4.7070008E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.2621212E+001, 1.4858581E+000, 4.5752869E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.3118182E+001, 1.4230647E+000, 4.4496794E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.3615152E+001, 1.3636889E+000, 4.3366827E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.4112121E+001, 1.3065270E+000, 4.2277442E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.4609091E+001, 1.2516093E+000, 4.1183031E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.5106061E+001, 1.1985991E+000, 4.0061075E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.5603030E+001, 1.1476457E+000, 3.8926972E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.6100000E+001, 1.0985477E+000, 3.7782604E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.6596970E+001, 1.0515066E+000, 3.6659805E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.7093939E+001, 1.0060401E+000, 3.5561592E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.7590909E+001, 9.6189956E-001, 3.4476184E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.8087879E+001, 9.1991597E-001, 3.3456343E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.8584848E+001, 8.7987674E-001, 3.2478735E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.9081818E+001, 8.4092193E-001, 3.1489494E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 6.9578788E+001, 8.0298702E-001, 3.0457506E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 7.0075758E+001, 7.6600509E-001, 2.9351094E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 7.0572727E+001, 7.3058466E-001, 2.8231200E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 7.1069697E+001, 6.9554079E-001, 2.7034693E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 7.1566667E+001, 6.6090130E-001, 2.5789631E+000,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN + 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b/Extinction/FM_Burner/Computational_Results/2023/UWuppertal/README.md @@ -0,0 +1,145 @@ +## General Information and Simulation Setup + +### Contributor + +Name: Shivam Mittal, Fabian Brännström + +Institution: University of Wuppertal (UWuppertal) + +Department: Fire Dynamics + +Country: Germany + +### Test case + +Case 5 - FM Burner + +------------------------------------------------------------------------ + +### CFD package + +Code: FDS + +Version: FDS6.7.8-88-g922085eba-nightly + +------------------------------------------------------------------------ + +### Resolution + + +#### Computational domain discretization (flow solver) + - closed domain with part of hood + - Domain: 1.2 m x 1.2 m x 1.2 m (closed domain with no hood ) + - Cell size: 0.5 cm (in fine flame region) + - Cell type: cubes + - Total cells: XXX + + +#### Angular space discretization (radiation solver) + - Number of solid angles: 104, 512 + - Time steps between full solution of all angles: 1, 10, 50, 200, 1000 + - This corresponds roughly to: 5e-4s, 5e-3s, 2.5e-2s, 5e-1s, 1e-2s + +Comments: Otherwise default FDS radiation solver + +### Initial conditions + +Comments: +- Temperature: 300 K +- Pressure: 101325 Pa + +------------------------------------------------------------------------ + +### Boundary conditions + +Comments: + +- Fuel temperature: 298 K +- Mass flow rate: 0.318 g/s (15 kW) - slightly larger inlet velocity than in experiment +- Co-flow velocity: 0.041 m/s +- Co-flow 02 mass fraction: 0.231 + +------------------------------------------------------------------------ + +### Models (include parameters) + +Turbulence model: Deardoff (algebraic k_sgs); Sc_t=0.5; Pr_t=0.5 + +Combustion model: Two-step fast-fast serial reactions + +Radiation model: Finite-volume, upwind, first-order accurate + +Radiative fraction: Predicted based on RadCal tabulated absorption coefficients + +- Comments: - + +------------------------------------------------------------------------ + +### Discretization methods + +Time: Predictor-Corrector; second-order accurate + +CFL: 0.8 to 1.0 + +Advection: CHARM Flux Limiter + +Diffusion: Second-order accurate central difference + +Pressure-velocity coupling: Low Mach number approximation; solution of Poisson equation for pressure + +------------------------------------------------------------------------ + +### Computational Cost (hh:mm:ss) + + +Simulation time: 70-200 sec + +Number of CPUs (MPI Processes): 64 cores + +Wall clock time: - + +Wall clock time per time step: - + +CPU cost (Number of CPUs \* Wall clock time / Simulation time / Total +cells): - + +------------------------------------------------------------------------ + +### Radiation Setups + + | Name | Time Step | Radiation Angles | + |------|-----------|------------------| + | V1 | 10 | 104 | + | V2 | 10 | 512 | + | V3 | 1 | 104 | + | V4 | 1 | 512 | + | V5 | 50 | 104 | + | V6 | 50 | 512 | + | V7 | 1000 | 104 | + | V8 | 1000 | 512 | + | V9 | 200 | 104 | + | V10 | 200 | 512 | + +- The `Time Step` referes to the number of time steps when complete radiation solving is accomplished. +- Average time step size is about `5e-4` between each time step (though this depends especially during the initial development phase) + +------------------------------------------------------------------------ + +### Averaging period +- Different averaging times is evaluated for radiative wall heat flux. + - Averaging is started from the end of the time data and varied in different steps. + +- If not otherwise stated the complete time span is used for averaging. + + +------------------------------------------------------------------------ + +### Special issues/problems +- + +------------------------------------------------------------------------ + +### Relevant publications + +none + diff --git a/Extinction/FM_Burner/Computational_Results/2023/UWuppertal/macfp.py b/Extinction/FM_Burner/Computational_Results/2023/UWuppertal/macfp.py new file mode 100644 index 00000000..bb0c6f7e --- /dev/null +++ b/Extinction/FM_Burner/Computational_Results/2023/UWuppertal/macfp.py @@ -0,0 +1,1142 @@ +#!/usr/bin/env python3 +""" +macfp.py +by Randy McDermott +Feb 2020 + +Measurement and Computation of Fire Phenomena (MaCFP) + +Collection of functions for plotting and analysis +""" + +import sys +import math +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from scipy import signal #Savitzy-Golay filter +import seaborn as sns # colorblind pallete + +def plot_to_fig(x_data,y_data,**kwargs): + """ + Create a simple x,y plot and return the fig handle + """ + # # useful debug statements + # print(x_data) + # print(y_data) + # for key, value in kwargs.items(): + # print ("%s == %s" %(key, value)) + + ##### default parameters ###### + default_figure_size = (8,6) + default_ticklabel_fontsize = 16 + default_axeslabel_fontsize = 18 + default_legend_fontsize = 16 + default_title_fontsize = 18 + default_subtitle_fontsize = 16 + default_stamp_fontsize = 12 + ############################### + + if kwargs.get('figure_size'): + figure_size=kwargs.get('figure_size') + else: + figure_size=default_figure_size + + # if figure handle is passed, append to current figure, else generate a new figure + if kwargs.get('figure_handle'): + fig = kwargs.get('figure_handle') + ax = fig.axes[0] + plt.figure(fig.number) + else: + fig, ax = plt.subplots(nrows=1, ncols=1, sharex=True, sharey=True, gridspec_kw={'hspace': 0, 'wspace': 0}, figsize=figure_size) + + if kwargs.get('plot_type'): + plot_type=kwargs.get('plot_type') + else: + plot_type='linear' + + # generate the main x,y plot + if plot_type=='linear': + ax.plot(x_data,y_data, + markevery=kwargs.get('data_markevery'), + label=kwargs.get('data_label'), + markerfacecolor=kwargs.get('marker_fill_color'), + markeredgecolor=kwargs.get('marker_edge_color'), + markeredgewidth=kwargs.get('line_width'), + marker=kwargs.get('marker_style'), + markersize=kwargs.get('marker_size'), + linestyle=kwargs.get('line_style'), + linewidth=kwargs.get('line_width'), + color=kwargs.get('line_color')) + + if plot_type=='loglog': + ax.loglog(x_data,y_data, + markevery=kwargs.get('data_markevery'), + label=kwargs.get('data_label'), + markerfacecolor=kwargs.get('marker_fill_color'), + markeredgecolor=kwargs.get('marker_edge_color'), + markeredgewidth=kwargs.get('line_width'), + marker=kwargs.get('marker_style'), + markersize=kwargs.get('marker_size'), + linestyle=kwargs.get('line_style'), + linewidth=kwargs.get('line_width'), + color=kwargs.get('line_color')) + + if plot_type=='semilogx': + ax.semilogx(x_data,y_data, + markevery=kwargs.get('data_markevery'), + label=kwargs.get('data_label'), + markerfacecolor=kwargs.get('marker_fill_color'), + markeredgecolor=kwargs.get('marker_edge_color'), + markeredgewidth=kwargs.get('line_width'), + marker=kwargs.get('marker_style'), + markersize=kwargs.get('marker_size'), + linestyle=kwargs.get('line_style'), + linewidth=kwargs.get('line_width'), + color=kwargs.get('line_color')) + + if plot_type=='semilogy': + ax.semilogy(x_data,y_data, + markevery=kwargs.get('data_markevery'), + label=kwargs.get('data_label'), + markerfacecolor=kwargs.get('marker_fill_color'), + markeredgecolor=kwargs.get('marker_edge_color'), + markeredgewidth=kwargs.get('line_width'), + marker=kwargs.get('marker_style'), + markersize=kwargs.get('marker_size'), + linestyle=kwargs.get('line_style'), + linewidth=kwargs.get('line_width'), + color=kwargs.get('line_color')) + + # if error range is passed, add it to the plot + if kwargs.get('y_error_absolute') and not kwargs.get('y_error_relative'): + if kwargs.get('y_error_absolute')>0.: + ax.fill_between(x_data,y_data-kwargs.get('y_error_absolute'),y_data+kwargs.get('y_error_absolute'), + alpha=0.1,color=kwargs.get('marker_edge_color')) + + if kwargs.get('y_error_relative') and not kwargs.get('y_error_absolute'): + if kwargs.get('y_error_relative')>0.: + ax.fill_between(x_data,y_data*(1.-kwargs.get('y_error_relative')),y_data*(1.+kwargs.get('y_error_relative')), + alpha=0.1,color=kwargs.get('marker_edge_color')) + + if kwargs.get('y_error_relative') and kwargs.get('y_error_absolute'): + if kwargs.get('y_error_relative')>0.: + ax.fill_between(x_data,y_data*(1.-kwargs.get('y_error_relative'))-kwargs.get('y_error_absolute'),y_data*(1.+kwargs.get('y_error_relative'))+kwargs.get('y_error_absolute'), + alpha=0.1,color=kwargs.get('marker_edge_color')) + + if kwargs.get('ticklabel_fontsize'): + ticklabel_fontsize=kwargs.get('ticklabel_fontsize') + else: + ticklabel_fontsize=default_ticklabel_fontsize + + plt.setp( ax.xaxis.get_majorticklabels(), rotation=0, fontsize=ticklabel_fontsize ) + plt.setp( ax.yaxis.get_majorticklabels(), rotation=0, fontsize=ticklabel_fontsize ) + + if kwargs.get('axeslabel_fontsize'): + axeslabel_fontsize=kwargs.get('axeslabel_fontsize') + else: + axeslabel_fontsize=default_axeslabel_fontsize + + plt.xlabel(kwargs.get('x_label'), fontsize=axeslabel_fontsize) + plt.ylabel(kwargs.get('y_label'), fontsize=axeslabel_fontsize) + + if kwargs.get('legend_fontsize'): + legend_fontsize=kwargs.get('legend_fontsize') + else: + legend_fontsize=default_legend_fontsize + + if kwargs.get('legend_location')=='outside': + plt.legend(fontsize=legend_fontsize,bbox_to_anchor=(1,1),loc='upper left',framealpha=kwargs.get('legend_framealpha')) + else: + if kwargs.get('show_legend'): + plt.legend(fontsize=legend_fontsize,loc=kwargs.get('legend_location'),framealpha=kwargs.get('legend_framealpha')) + + # plot titles + if kwargs.get('title_fontsize'): + title_fontsize=kwargs.get('title_fontsize') + else: + title_fontsize=default_title_fontsize + + if kwargs.get('subtitle_fontsize'): + subtitle_fontsize=kwargs.get('subtitle_fontsize') + else: + subtitle_fontsize=default_subtitle_fontsize + + # set axes and tick properties + if kwargs.get('x_min')==None or kwargs.get('x_max')==None or math.isnan(kwargs.get('x_min')) or math.isnan(kwargs.get('x_max')): + xmin=min(x_data) - 0.05*(max(x_data)-min(x_data)) + xmax=max(x_data) + 0.05*(max(x_data)-min(x_data)) + else: + xmin=kwargs.get('x_min') + xmax=kwargs.get('x_max') + + if kwargs.get('y_min')==None or kwargs.get('y_max')==None or math.isnan(kwargs.get('y_min')) or math.isnan(kwargs.get('y_max')): + ymin=min(y_data) - 0.05*(max(y_data)-min(y_data)) + ymax=max(y_data) + 0.05*(max(y_data)-min(y_data)) + else: + ymin=kwargs.get('y_min') + ymax=kwargs.get('y_max') + + ax.set_xlim(xmin,xmax) + ax.set_ylim(ymin,ymax) + + # if axis labels are too large or small, use sci notation + axis_exponent_min = -3 + axis_exponent_max = 3 + ax.ticklabel_format(axis='both',scilimits=(axis_exponent_min,axis_exponent_max)) + + if kwargs.get('x_nticks'): + ax.set_xticks(np.linspace(start = kwargs.get('x_min'), stop = kwargs.get('x_max'), num = kwargs.get('x_nticks'), endpoint=True)) + + if kwargs.get('y_nticks'): + ax.set_yticks(np.linspace(start = kwargs.get('y_min'), stop = kwargs.get('y_max'), num = kwargs.get('y_nticks'), endpoint=True)) + + # if plot_type=='linear': + # xpos=xmin+0.05*(xmax-xmin) + # ypos1=ymin+0.900*(ymax-ymin) + # ypos2=ymin+0.825*(ymax-ymin) + # ax.text(xpos,ypos1, kwargs.get('plot_title'), fontsize=title_fontsize) + # ax.text(xpos,ypos2, kwargs.get('plot_subtitle'), fontsize=subtitle_fontsize) + + # # plot Institute + MaCFP stamp + # ax.text(xmin+0.025*(xmax-xmin),ymax+0.01*(ymax-ymin), kwargs.get('institute_label'), fontsize=default_stamp_fontsize) + # ax.text(xmax-0.025*(xmax-xmin),ymax+0.01*(ymax-ymin), 'MaCFP 2021', fontsize=default_stamp_fontsize, ha='right') + + # this relative method seems to work for all plot types (linear,loglog,etc.) + xpos=0.05 + ypos1=0.900 + ypos2=0.825 + ax.text(xpos,ypos1, kwargs.get('plot_title'), fontsize=title_fontsize, transform=ax.transAxes) + ax.text(xpos,ypos2, kwargs.get('plot_subtitle'), fontsize=subtitle_fontsize, transform=ax.transAxes) + + # plot Institute + MaCFP (or revision) stamp + inst_label_x = 0.025 + if len(str(ymax))>abs(axis_exponent_max) or len(str(ymin))>abs(axis_exponent_min): + inst_label_x = 0.06 # else the institute label overlays the exponential notation multiplier + ax.text(inst_label_x,1.01, kwargs.get('institute_label'), fontsize=default_stamp_fontsize, transform=ax.transAxes) + ax.text(0.975, 1.01, kwargs.get('revision_label'), fontsize=default_stamp_fontsize, ha='right', transform=ax.transAxes) + + # note: this absolute method works better than fig.tight_layout(), which may change for each call of the figure + left_adjust, bottom_adjust = get_subplots_adjust_parameters(ticklabel_fontsize,axeslabel_fontsize) + if ymin<0: + left_adjust = left_adjust + 0.025 # account for negative sign + + if kwargs.get('figure_left_adjust'): + left_adjust=kwargs.get('figure_left_adjust') + + if kwargs.get('figure_bottom_adjust'): + bottom_adjust=kwargs.get('figure_bottom_adjust') + + if kwargs.get('figure_right_adjust'): + right_adjust=kwargs.get('figure_right_adjust') + else: + right_adjust = 0.95 + + if kwargs.get('figure_top_adjust'): + top_adjust=kwargs.get('figure_top_adjust') + else: + top_adjust = 0.95 + + fig.subplots_adjust(left=left_adjust, bottom=bottom_adjust, right=right_adjust, top=top_adjust, wspace=0.2, hspace=0.2) + + return fig + + +def get_subplots_adjust_parameters(ticklabel_fontsize,axeslabel_fontsize): + """ + automatically select axes adjustments based on fontsize, + this is a brute force method, but I could not find a more + automatic way to do it since tight_layout() is not working + """ + left_adjust = 0.125 + bottom_adjust = 0.125 + + if ticklabel_fontsize + axeslabel_fontsize > 34: + # determined this adjustment by trial and error + left_adjust = 0.125 + 0.006875*(ticklabel_fontsize + axeslabel_fontsize - 34) + bottom_adjust = 0.125 + 0.001875*(ticklabel_fontsize + axeslabel_fontsize - 34) + + return left_adjust, bottom_adjust + + + +def get_nticks(x_min,x_max,x_tick): + """ + converts float x_tick, if it exists, to integer nticks + """ + nticks = int((float(x_max)-float(x_min))/float(x_tick)) + 1 + + return nticks + + +def define_plot_parameters(C,irow): + """ + gathers parameters from config file + """ + class plot_parameters: + + def __repr__(self): + return str(self) + + try: + Exp_Filename = C.values[irow,C.columns.get_loc('Exp_Filename')] + except: + sys.exit('Required column header missing: Exp_Filename') + + try: + Exp_Header_Row = int(C.values[irow,C.columns.get_loc('Exp_Header_Row')]) + except: + Exp_Header_Row = 1 + + try: + Exp_Data_Row = int(C.values[irow,C.columns.get_loc('Exp_Data_Row')]) + except: + Exp_Data_Row = Exp_Header_Row+1 + + try: + Exp_x_Col_Name = C.values[irow,C.columns.get_loc('Exp_x_Col_Name')] + except: + sys.exit('Required column header missing: Exp_x_Col_Name') + + try: + Exp_y_Col_Name = C.values[irow,C.columns.get_loc('Exp_y_Col_Name')] + except: + sys.exit('Required column header missing: Exp_y_Col_Name') + + try: + Exp_Data_Markevery = C.values[irow,C.columns.get_loc('Exp_Data_Markevery')] + except: + Exp_Data_Markevery = 1 + + try: + Exp_Data_Label = C.values[irow,C.columns.get_loc('Exp_Data_Label')] + except: + Exp_Data_Label = 'Exp' + + # what's the difference between 'None' and None? + # 'None' is a string that will not print a line in the call to axes.plot() + # None is a type that let's axes.plot use its default + # None gets filled in by df.fillna() for None values in existing columns in the DataFrame + # Therefore, in that case, the user probably means they do not want a value + # The except block is the case where the column does not exist + # Here we assume the user wants axes.plot to use its default + + try: + Exp_Marker_Style = C.values[irow,C.columns.get_loc('Exp_Marker_Style')] + if Exp_Marker_Style==None or pd.isnull(Exp_Marker_Style): + Exp_Marker_Style = 'None' + except: + Exp_Marker_Style = 'o' + + try: + Exp_Marker_Edge_Color = C.values[irow,C.columns.get_loc('Exp_Marker_Edge_Color')] + if pd.isnull(Exp_Marker_Edge_Color): + Exp_Marker_Edge_Color = 'black' + except: + Exp_Marker_Edge_Color = 'black' + + try: + Exp_Marker_Fill_Color = C.values[irow,C.columns.get_loc('Exp_Marker_Fill_Color')] + if pd.isnull(Exp_Marker_Fill_Color): + Exp_Marker_Fill_Color = 'None' + except: + Exp_Marker_Fill_Color = 'None' + + try: + Exp_Marker_Size = C.values[irow,C.columns.get_loc('Exp_Marker_Size')] + if pd.isnull(Exp_Marker_Size): + Exp_Marker_Size = 6 + except: + Exp_Marker_Size = 6 + + try: + Exp_Line_Style = C.values[irow,C.columns.get_loc('Exp_Line_Style')] + if Exp_Line_Style==None or pd.isnull(Exp_Line_Style): + Exp_Line_Style = 'None' + except: + # if no column exists, we assume just markers for experimental data + Exp_Line_Style = 'None' + + try: + Exp_Line_Color = C.values[irow,C.columns.get_loc('Exp_Line_Color')] + if Exp_Line_Color==None or pd.isnull(Exp_Line_Color): + Exp_Line_Color = 'None' + except: + if Exp_Line_Style: + Exp_Line_Color = 'black' + else: + Exp_Line_Color = 'None' + + try: + Exp_Line_Width = C.values[irow,C.columns.get_loc('Exp_Line_Width')] + if Exp_Line_Width==None or pd.isnull(Exp_Line_Width): + Exp_Line_Width = 1. + except: + Exp_Line_Width = 1. + + try: + Exp_Error_Absolute = C.values[irow,C.columns.get_loc('Exp_Error_Absolute')] + if pd.isnull(Exp_Error_Absolute): + Exp_Error_Absolute = 0. + except: + Exp_Error_Absolute = 0. + + try: + Exp_Error_Relative = C.values[irow,C.columns.get_loc('Exp_Error_Relative')] + if pd.isnull(Exp_Error_Relative): + Exp_Error_Relative = 0. + except: + Exp_Error_Relative = 0. + + try: + Cmp_Filename = C.values[irow,C.columns.get_loc('Cmp_Filename')] + except: + sys.exit('Required column header missing: Cmp_Filename') + + try: + Cmp_Header_Row = C.values[irow,C.columns.get_loc('Cmp_Header_Row')] + if pd.isnull(Cmp_Header_Row): + Cmp_Header_Row = 1 + except: + Cmp_Header_Row = 1 + + try: + Cmp_Data_Row = C.values[irow,C.columns.get_loc('Cmp_Data_Row')] + if pd.isnull(Cmp_Data_Row): + Cmp_Data_Row = Cmp_Header_Row+1 + except: + Cmp_Data_Row = Cmp_Header_Row+1 + + try: + Cmp_x_Col_Name = C.values[irow,C.columns.get_loc('Cmp_x_Col_Name')] + except: + sys.exit('Required column header missing: Cmp_x_Col_Name') + + try: + Cmp_y_Col_Name = C.values[irow,C.columns.get_loc('Cmp_y_Col_Name')] + except: + sys.exit('Required column header missing: Cmp_y_Col_Name') + + try: + Cmp_Data_Markevery = C.values[irow,C.columns.get_loc('Cmp_Data_Markevery')] + if pd.isnull(Cmp_Data_Markevery): + Cmp_Data_Markevery = 1 + except: + Cmp_Data_Markevery = 1 + + try: + Cmp_Data_Label = C.values[irow,C.columns.get_loc('Cmp_Data_Label')] + except: + Cmp_Data_Label = 'Cmp' + + try: + Cmp_Marker_Style = C.values[irow,C.columns.get_loc('Cmp_Marker_Style')] + if pd.isnull(Cmp_Marker_Style): + Cmp_Marker_Style = 'None' + except: + Cmp_Marker_Style = 'None' + + try: + Cmp_Marker_Edge_Color = C.values[irow,C.columns.get_loc('Cmp_Marker_Edge_Color')] + if pd.isnull(Cmp_Marker_Edge_Color): + Cmp_Marker_Edge_Color = 'black' + except: + Cmp_Marker_Edge_Color = 'black' + + try: + Cmp_Marker_Fill_Color = C.values[irow,C.columns.get_loc('Cmp_Marker_Fill_Color')] + if pd.isnull(Cmp_Marker_Fill_Color): + Cmp_Marker_Fill_Color = 'None' + except: + Cmp_Marker_Fill_Color = 'None' + + try: + Cmp_Marker_Size = C.values[irow,C.columns.get_loc('Cmp_Marker_Size')] + if pd.isnull(Cmp_Marker_Size): + Cmp_Marker_Size = 6 + except: + Cmp_Marker_Size = 6 + + try: + Cmp_Line_Style = C.values[irow,C.columns.get_loc('Cmp_Line_Style')] + if Cmp_Line_Style==None or pd.isnull(Cmp_Line_Style): + Cmp_Line_Style = 'None' + except: + Cmp_Line_Style = 'None' + + try: + Cmp_Line_Color = C.values[irow,C.columns.get_loc('Cmp_Line_Color')] + if pd.isnull(Cmp_Line_Color): + Cmp_Line_Color = 'black' + except: + Cmp_Line_Color = 'black' + + try: + Cmp_Line_Width = C.values[irow,C.columns.get_loc('Cmp_Line_Width')] + if Cmp_Line_Width==None or pd.isnull(Cmp_Line_Width): + Cmp_Line_Width = 0. + except: + Cmp_Line_Width = 1. + + try: + Plot_Title = C.values[irow,C.columns.get_loc('Plot_Title')] + if pd.isnull(Plot_Title): + Plot_Title = '' + except: + Plot_Title = '' + + try: + Plot_Subtitle = C.values[irow,C.columns.get_loc('Plot_Subtitle')] + if pd.isnull(Plot_Subtitle): + Plot_Subtitle = '' + except: + Plot_Subtitle = '' + + try: + Plot_x_Label = C.values[irow,C.columns.get_loc('Plot_x_Label')] + if pd.isnull(Plot_x_Label): + Plot_x_Label = 'x' + except: + Plot_x_Label = 'x' + try: + Plot_y_Label = C.values[irow,C.columns.get_loc('Plot_y_Label')] + if pd.isnull(Plot_y_Label): + Plot_y_Label = 'y' + except: + Plot_y_Label = 'y' + + try: + Plot_x_Min = C.values[irow,C.columns.get_loc('Plot_x_Min')] + if pd.isnull(Plot_x_Min): + Plot_x_Min = None + except: + Plot_x_Min = None + + try: + Plot_x_Max = C.values[irow,C.columns.get_loc('Plot_x_Max')] + if pd.isnull(Plot_x_Max): + Plot_x_Max = None + except: + Plot_x_Max = None + + try: + Plot_x_Tick = C.values[irow,C.columns.get_loc('Plot_x_Tick')] + if pd.isnull(Plot_x_Tick): + Plot_x_Tick = None + except: + Plot_x_Tick = None + + try: + Plot_y_Min = C.values[irow,C.columns.get_loc('Plot_y_Min')] + if pd.isnull(Plot_y_Min): + Plot_y_Min = None + except: + Plot_y_Min = None + + try: + Plot_y_Max = C.values[irow,C.columns.get_loc('Plot_y_Max')] + if pd.isnull(Plot_y_Max): + Plot_y_Max = None + except: + Plot_y_Max = None + + try: + Plot_y_Tick = C.values[irow,C.columns.get_loc('Plot_y_Tick')] + if pd.isnull(Plot_y_Tick): + Plot_y_Tick = None + except: + Plot_y_Tick = None + + try: + Plot_Flip_Axis = C.values[irow,C.columns.get_loc('Plot_Flip_Axis')] + if pd.isnull(Plot_Flip_Axis): + Plot_Flip_Axis = False + except: + Plot_Flip_Axis = False + + try: + Plot_Show_Legend = C.values[irow,C.columns.get_loc('Plot_Show_Legend')] + if pd.isnull(Plot_Show_Legend): + Plot_Show_Legend = False + except: + Plot_Show_Legend = True + + try: + Plot_Legend_Location = C.values[irow,C.columns.get_loc('Plot_Legend_Location')] + if Plot_Legend_Location==None or pd.isnull(Plot_Legend_Location): + Plot_Legend_Location = 'best' + except: + Plot_Legend_Location = 'best' + + if Plot_Legend_Location.isdigit(): + Plot_Legend_Location=int(Plot_Legend_Location) + + # Plot_Filename = C.values[irow,C.columns.get_loc('Plot_Filename')] + # print(Plot_Filename) + # exit() + try: + Plot_Filename = C.values[irow,C.columns.get_loc('Plot_Filename')] + except: + sys.exit('Required column header missing: Plot_Filename') + + try: + Plot_x_Nticks = C.values[irow,C.columns.get_loc('Plot_x_Nticks')] + if pd.isnull(Plot_x_Nticks): + Plot_x_Nticks = None + except: + Plot_x_Nticks = None + + try: + if float(Plot_x_Tick)>0. and float(Plot_x_Tick)<1.e10 and float(Plot_x_Min)>-1.e10 and float(Plot_x_Max)<1.e10: + Plot_x_Nticks = get_nticks(Plot_x_Min,Plot_x_Max,Plot_x_Tick) + if pd.isnull(Plot_x_Nticks): + Plot_x_Nticks = None + except: + Plot_x_Nticks = None + + try: + Plot_y_Nticks = C.values[irow,C.columns.get_loc('Plot_y_Nticks')] + if pd.isnull(Plot_y_Nticks): + Plot_y_Nticks = None + except: + Plot_y_Nticks = None + + try: + if float(Plot_y_Tick)>0. and float(Plot_y_Tick)<1.e10 and float(Plot_y_Min)>-1.e10 and float(Plot_y_Max)<1.e10: + Plot_y_Nticks = get_nticks(Plot_y_Min,Plot_y_Max,Plot_y_Tick) + if pd.isnull(Plot_y_Nticks): + Plot_y_Nticks = None + except: + Plot_y_Nticks = None + + try: + Plot_Left_Adjust = C.values[irow,C.columns.get_loc('Plot_Left_Adjust')] + if pd.isnull(Plot_Left_Adjust): + Plot_Left_Adjust = None + except: + Plot_Left_Adjust = None + + try: + Plot_Right_Adjust = C.values[irow,C.columns.get_loc('Plot_Right_Adjust')] + if pd.isnull(Plot_Right_Adjust): + Plot_Right_Adjust = None + except: + Plot_Right_Adjust = None + + try: + Plot_Bottom_Adjust = C.values[irow,C.columns.get_loc('Plot_Bottom_Adjust')] + if pd.isnull(Plot_Bottom_Adjust): + Plot_Bottom_Adjust = None + except: + Plot_Bottom_Adjust = None + + try: + Plot_Top_Adjust = C.values[irow,C.columns.get_loc('Plot_Top_Adjust')] + if pd.isnull(Plot_Top_Adjust): + Plot_Top_Adjust = None + except: + Plot_Top_Adjust = None + + try: + Plot_Figure_Width = C.values[irow,C.columns.get_loc('Plot_Figure_Width')] + if Plot_Figure_Width==None or pd.isnull(Plot_Figure_Width): + Plot_Figure_Width=8 + except: + Plot_Figure_Width = 8 + + try: + Plot_Figure_Height = C.values[irow,C.columns.get_loc('Plot_Figure_Height')] + if Plot_Figure_Height==None or pd.isnull(Plot_Figure_Height): + Plot_Figure_Height=6 + except: + Plot_Figure_Height = 6 + + try: + Seaborn_Color = C.values[irow,C.columns.get_loc('Seaborn_Color')] + if pd.isnull(Seaborn_Color): + Seaborn_Color = False + except: + Seaborn_Color = False + + try: + Savgol_Filter = C.values[irow,C.columns.get_loc('Savgol_Filter')] + if pd.isnull(Savgol_Filter): + Savgol_Filter = False + except: + Savgol_Filter = False + + try: + Savgol_Window = int(C.values[irow,C.columns.get_loc('Savgol_Window')]) + if pd.isnull(Savgol_Window): + Savgol_Window = 11 + except: + Savgol_Window = 11 + + try: + Savgol_Polyorder = int(C.values[irow,C.columns.get_loc('Savgol_Polyorder')]) + if pd.isnull(Savgol_Polyorder): + Savgol_Polyorder = 3 + except: + Savgol_Polyorder = 3 + + return plot_parameters + + +def dataplot(config_filename,**kwargs): + + import os + # import sys + # sys.path.append('../../../Utilities/') # relative path of macfp module + + # import macfp + # # need this while macfp is in development + # import importlib + # importlib.reload(macfp) + # ======================================= + + # import numpy as np + import matplotlib.pyplot as plt + import pandas as pd + # from matplotlib import rc + # rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']}) + # rc('font',**{'family':'serif','serif':['Palatino']}) + + plt.rcParams['mathtext.fontset'] = 'dejavusans' # valid strings are ['dejavusans', 'dejavuserif', 'cm', 'stix', 'stixsans', 'custom'] + plt.rcParams['pdf.compression'] = 6 # 0 to 9 (file size was no smaller with 9) + plt.rcParams['pdf.fonttype'] = 3 # 42 embeds true type fonts into pdf (large file size) + # plt.rcParams['text.usetex'] = True # supports latex math + + # defaults + institute = '' + revision = 'MaCFP-3, Tsukuba, 2023' + configdir = '' + expdir = '' + cmpdir = '' + pltdir = '' + close_figs = False + verbose = False + plot_list = ['all'] + plot_range = range(10000) + + if kwargs.get('institute'): + institute = kwargs.get('institute') + + if kwargs.get('revision'): + revision = kwargs.get('revision') + + if kwargs.get('expdir'): + expdir = kwargs.get('expdir') + + if kwargs.get('cmpdir'): + cmpdir = kwargs.get('cmpdir') + + if kwargs.get('pltdir'): + pltdir = kwargs.get('pltdir') + + if kwargs.get('close_figs'): + close_figs = kwargs.get('close_figs') + + if kwargs.get('verbose'): + verbose = kwargs.get('verbose') + + if kwargs.get('plot_list'): + plot_list = kwargs.get('plot_list') + + if kwargs.get('plot_range'): + plot_range_in = kwargs.get('plot_range') + plot_range = range(plot_range_in[0]-2,plot_range_in[-1]) + + # read the config file + df = pd.read_csv(configdir+config_filename, sep=' *, *', engine='python', comment='#') + C = df.where(pd.notnull(df), None) + + Plot_Filename_Last = None + Exp_Data_Label_Last = None + f_Last = plt.figure() + + # loop over the rows of the config file + for irow in C.index: + + if irow not in plot_range: + continue + + # define plot parameters and return them in an object called pp + pp = define_plot_parameters(C,irow) + + # print(pp.__dict__) # helpful for debug + + if 'all' not in plot_list: + if pp.Plot_Filename not in plot_list: + continue + + if pp.Plot_Filename!=Plot_Filename_Last: + + if verbose: + print(pp.Plot_Filename) + print('Generating plot ' + pltdir + pp.Plot_Filename + '...') + + if close_figs: + plt.close('all') + + # read data from exp file + # set header to the row where column names are stored (Python is 0 based) + E = pd.read_csv(expdir+pp.Exp_Filename, header=pp.Exp_Header_Row-1, sep=' *, *', engine='python') + if (pp.Seaborn_Color): + sns.color_palette('colorblind') + if (pp.Exp_Data_Row-pp.Exp_Header_Row==1): + x = E[pp.Exp_x_Col_Name].values[:].astype(float) + y = E[pp.Exp_y_Col_Name].values[:].astype(float) + else: + # don't exactly understand this, but df.values behave differently if they are object type + # when the header and data rows are separated, then there are usually strings in the df.values + x = E[pp.Exp_x_Col_Name].values[pp.Exp_Data_Row-2:].astype(float) + y = E[pp.Exp_y_Col_Name].values[pp.Exp_Data_Row-2:].astype(float) + + if (pp.Plot_Flip_Axis): + if (pp.Seaborn_Color): + f = plot_to_fig(x_data=y, y_data=x, + data_markevery=pp.Exp_Data_Markevery, + data_label=pp.Exp_Data_Label, + y_error_absolute=pp.Exp_Error_Absolute, + y_error_relative=pp.Exp_Error_Relative, + x_label=pp.Plot_y_Label, + y_label=pp.Plot_x_Label, + marker_style=pp.Exp_Marker_Style, + marker_size=pp.Exp_Marker_Size, + line_style=pp.Exp_Line_Style, + line_width=pp.Exp_Line_Width, + x_min=pp.Plot_y_Min,x_max=pp.Plot_y_Max,x_nticks=pp.Plot_y_Nticks, + y_min=pp.Plot_x_Min,y_max=pp.Plot_x_Max,y_nticks=pp.Plot_x_Nticks, + show_legend=pp.Plot_Show_Legend,legend_location=pp.Plot_Legend_Location, + figure_size=(pp.Plot_Figure_Width,pp.Plot_Figure_Height), + figure_left_adjust=pp.Plot_Left_Adjust, + figure_right_adjust=pp.Plot_Right_Adjust, + figure_bottom_adjust=pp.Plot_Bottom_Adjust, + figure_top_adjust=pp.Plot_Top_Adjust + ) + else: + f = plot_to_fig(x_data=y, y_data=x, + data_markevery=pp.Exp_Data_Markevery, + data_label=pp.Exp_Data_Label, + y_error_absolute=pp.Exp_Error_Absolute, + y_error_relative=pp.Exp_Error_Relative, + x_label=pp.Plot_y_Label, + y_label=pp.Plot_x_Label, + marker_style=pp.Exp_Marker_Style, + marker_fill_color=pp.Exp_Marker_Fill_Color, + marker_edge_color=pp.Exp_Marker_Edge_Color, + marker_size=pp.Exp_Marker_Size, + line_style=pp.Exp_Line_Style, + line_color=pp.Exp_Line_Color, + line_width=pp.Exp_Line_Width, + x_min=pp.Plot_y_Min,x_max=pp.Plot_y_Max,x_nticks=pp.Plot_y_Nticks, + y_min=pp.Plot_x_Min,y_max=pp.Plot_x_Max,y_nticks=pp.Plot_x_Nticks, + show_legend=pp.Plot_Show_Legend,legend_location=pp.Plot_Legend_Location, + figure_size=(pp.Plot_Figure_Width,pp.Plot_Figure_Height), + figure_left_adjust=pp.Plot_Left_Adjust, + figure_right_adjust=pp.Plot_Right_Adjust, + figure_bottom_adjust=pp.Plot_Bottom_Adjust, + figure_top_adjust=pp.Plot_Top_Adjust + ) + + else: + if (pp.Seaborn_Color): + # plot the exp data + f = plot_to_fig(x_data=x, y_data=y, + data_markevery=pp.Exp_Data_Markevery, + data_label=pp.Exp_Data_Label, + y_error_absolute=pp.Exp_Error_Absolute, + y_error_relative=pp.Exp_Error_Relative, + x_label=pp.Plot_x_Label, + y_label=pp.Plot_y_Label, + marker_style=pp.Exp_Marker_Style, + marker_size=pp.Exp_Marker_Size, + line_style=pp.Exp_Line_Style, + line_width=pp.Exp_Line_Width, + x_min=pp.Plot_x_Min,x_max=pp.Plot_x_Max,x_nticks=pp.Plot_x_Nticks, + y_min=pp.Plot_y_Min,y_max=pp.Plot_y_Max,y_nticks=pp.Plot_y_Nticks, + show_legend=pp.Plot_Show_Legend,legend_location=pp.Plot_Legend_Location, + figure_size=(pp.Plot_Figure_Width,pp.Plot_Figure_Height), + figure_left_adjust=pp.Plot_Left_Adjust, + figure_right_adjust=pp.Plot_Right_Adjust, + figure_bottom_adjust=pp.Plot_Bottom_Adjust, + figure_top_adjust=pp.Plot_Top_Adjust + ) + else: + # plot the exp data + f = plot_to_fig(x_data=x, y_data=y, + data_markevery=pp.Exp_Data_Markevery, + data_label=pp.Exp_Data_Label, + y_error_absolute=pp.Exp_Error_Absolute, + y_error_relative=pp.Exp_Error_Relative, + x_label=pp.Plot_x_Label, + y_label=pp.Plot_y_Label, + marker_style=pp.Exp_Marker_Style, + marker_fill_color=pp.Exp_Marker_Fill_Color, + marker_edge_color=pp.Exp_Marker_Edge_Color, + marker_size=pp.Exp_Marker_Size, + line_style=pp.Exp_Line_Style, + line_color=pp.Exp_Line_Color, + line_width=pp.Exp_Line_Width, + x_min=pp.Plot_x_Min,x_max=pp.Plot_x_Max,x_nticks=pp.Plot_x_Nticks, + y_min=pp.Plot_y_Min,y_max=pp.Plot_y_Max,y_nticks=pp.Plot_y_Nticks, + show_legend=pp.Plot_Show_Legend,legend_location=pp.Plot_Legend_Location, + figure_size=(pp.Plot_Figure_Width,pp.Plot_Figure_Height), + figure_left_adjust=pp.Plot_Left_Adjust, + figure_right_adjust=pp.Plot_Right_Adjust, + figure_bottom_adjust=pp.Plot_Bottom_Adjust, + figure_top_adjust=pp.Plot_Top_Adjust + ) + + + # plt.figure(f.number) # make figure current + # plt.show() + else: + f = f_Last + + if pp.Exp_Data_Label!=Exp_Data_Label_Last: + + # read data from exp file + # set header to the row where column names are stored (Python is 0 based) + E = pd.read_csv(expdir+pp.Exp_Filename, header=pp.Exp_Header_Row-1, sep=' *, *', engine='python') + + if (pp.Exp_Data_Row-pp.Exp_Header_Row==1): + x = E[pp.Exp_x_Col_Name].values[:].astype(float) + y = E[pp.Exp_y_Col_Name].values[:].astype(float) + else: + # don't exactly understand this, but df.values behave differently if they are object type + # when the header and data rows are separated, then there are usually strings in the df.values + x = E[pp.Exp_x_Col_Name].values[pp.Exp_Data_Row-2:].astype(float) + y = E[pp.Exp_y_Col_Name].values[pp.Exp_Data_Row-2:].astype(float) + + if (pp.Plot_Flip_Axis): + if (pp.Seaborn_Color): + f = plot_to_fig(x_data=y, y_data=x, + figure_handle=f, + data_markevery=pp.Exp_Data_Markevery, + data_label=pp.Exp_Data_Label, + y_error_absolute=pp.Exp_Error_Absolute, + y_error_relative=pp.Exp_Error_Relative, + x_label=pp.Plot_y_Label, + y_label=pp.Plot_x_Label, + marker_style=pp.Exp_Marker_Style, + marker_size=pp.Exp_Marker_Size, + line_style=pp.Exp_Line_Style, + line_width=pp.Exp_Line_Width, + x_min=pp.Plot_y_Min,x_max=pp.Plot_y_Max,x_nticks=pp.Plot_y_Nticks, + y_min=pp.Plot_x_Min,y_max=pp.Plot_x_Max,y_nticks=pp.Plot_x_Nticks, + show_legend=pp.Plot_Show_Legend,legend_location=pp.Plot_Legend_Location, + figure_size=(pp.Plot_Figure_Width,pp.Plot_Figure_Height), + figure_left_adjust=pp.Plot_Left_Adjust, + figure_right_adjust=pp.Plot_Right_Adjust, + figure_bottom_adjust=pp.Plot_Bottom_Adjust, + figure_top_adjust=pp.Plot_Top_Adjust + ) + else: + f = plot_to_fig(x_data=y, y_data=x, + figure_handle=f, + data_markevery=pp.Exp_Data_Markevery, + data_label=pp.Exp_Data_Label, + y_error_absolute=pp.Exp_Error_Absolute, + y_error_relative=pp.Exp_Error_Relative, + x_label=pp.Plot_y_Label, + y_label=pp.Plot_x_Label, + marker_style=pp.Exp_Marker_Style, + marker_fill_color=pp.Exp_Marker_Fill_Color, + marker_edge_color=pp.Exp_Marker_Edge_Color, + marker_size=pp.Exp_Marker_Size, + line_style=pp.Exp_Line_Style, + line_color=pp.Exp_Line_Color, + line_width=pp.Exp_Line_Width, + x_min=pp.Plot_y_Min,x_max=pp.Plot_y_Max,x_nticks=pp.Plot_y_Nticks, + y_min=pp.Plot_x_Min,y_max=pp.Plot_x_Max,y_nticks=pp.Plot_x_Nticks, + show_legend=pp.Plot_Show_Legend,legend_location=pp.Plot_Legend_Location, + figure_size=(pp.Plot_Figure_Width,pp.Plot_Figure_Height), + figure_left_adjust=pp.Plot_Left_Adjust, + figure_right_adjust=pp.Plot_Right_Adjust, + figure_bottom_adjust=pp.Plot_Bottom_Adjust, + figure_top_adjust=pp.Plot_Top_Adjust + ) + else: + if (pp.Seaborn_Color): + # plot the exp data + f = plot_to_fig(x_data=x, y_data=y, + figure_handle=f, + data_markevery=pp.Exp_Data_Markevery, + data_label=pp.Exp_Data_Label, + y_error_absolute=pp.Exp_Error_Absolute, + y_error_relative=pp.Exp_Error_Relative, + x_label=pp.Plot_x_Label, + y_label=pp.Plot_y_Label, + marker_style=pp.Exp_Marker_Style, + marker_size=pp.Exp_Marker_Size, + line_style=pp.Exp_Line_Style, + line_width=pp.Exp_Line_Width, + x_min=pp.Plot_x_Min,x_max=pp.Plot_x_Max,x_nticks=pp.Plot_x_Nticks, + y_min=pp.Plot_y_Min,y_max=pp.Plot_y_Max,y_nticks=pp.Plot_y_Nticks, + show_legend=pp.Plot_Show_Legend,legend_location=pp.Plot_Legend_Location, + figure_size=(pp.Plot_Figure_Width,pp.Plot_Figure_Height), + figure_left_adjust=pp.Plot_Left_Adjust, + figure_right_adjust=pp.Plot_Right_Adjust, + figure_bottom_adjust=pp.Plot_Bottom_Adjust, + figure_top_adjust=pp.Plot_Top_Adjust + ) + else: + # plot the exp data + f = plot_to_fig(x_data=x, y_data=y, + figure_handle=f, + data_markevery=pp.Exp_Data_Markevery, + data_label=pp.Exp_Data_Label, + y_error_absolute=pp.Exp_Error_Absolute, + y_error_relative=pp.Exp_Error_Relative, + x_label=pp.Plot_x_Label, + y_label=pp.Plot_y_Label, + marker_style=pp.Exp_Marker_Style, + marker_fill_color=pp.Exp_Marker_Fill_Color, + marker_edge_color=pp.Exp_Marker_Edge_Color, + marker_size=pp.Exp_Marker_Size, + line_style=pp.Exp_Line_Style, + line_color=pp.Exp_Line_Color, + line_width=pp.Exp_Line_Width, + x_min=pp.Plot_x_Min,x_max=pp.Plot_x_Max,x_nticks=pp.Plot_x_Nticks, + y_min=pp.Plot_y_Min,y_max=pp.Plot_y_Max,y_nticks=pp.Plot_y_Nticks, + show_legend=pp.Plot_Show_Legend,legend_location=pp.Plot_Legend_Location, + figure_size=(pp.Plot_Figure_Width,pp.Plot_Figure_Height), + figure_left_adjust=pp.Plot_Left_Adjust, + figure_right_adjust=pp.Plot_Right_Adjust, + figure_bottom_adjust=pp.Plot_Bottom_Adjust, + figure_top_adjust=pp.Plot_Top_Adjust + ) + + # get the model results + # print(pp.Cmp_Header_Row) + M = pd.read_csv(cmpdir+pp.Cmp_Filename, header=pp.Cmp_Header_Row-1, sep=' *, *', engine='python') + # print(100*".") + # print(M) + # print(M.columns) + + if (pp.Cmp_Data_Row-pp.Cmp_Header_Row==1): + x = M[pp.Cmp_x_Col_Name].values[:].astype(float) + y = M[pp.Cmp_y_Col_Name].values[:].astype(float) + else: + x = M[pp.Cmp_x_Col_Name].values[pp.Cmp_Data_Row-2:].astype(float) + y = M[pp.Cmp_y_Col_Name].values[pp.Cmp_Data_Row-2:].astype(float) + + if (pp.Savgol_Filter): + ys = signal.savgol_filter(y, window_length=pp.Savgol_Window,polyorder=pp.Savgol_Polyorder,mode="nearest") + y =ys + + if (pp.Plot_Flip_Axis): + if (pp.Seaborn_Color): + f = plot_to_fig(x_data=y, y_data=x, + institute_label=institute, + revision_label=revision, + figure_handle=f, + data_markevery=pp.Cmp_Data_Markevery, + x_label=pp.Plot_y_Label, + y_label=pp.Plot_x_Label, + data_label=pp.Cmp_Data_Label, + marker_style=pp.Cmp_Marker_Style, + marker_size=pp.Cmp_Marker_Size, + line_style=pp.Cmp_Line_Style, + line_width=pp.Cmp_Line_Width, + x_min=pp.Plot_y_Min,x_max=pp.Plot_y_Max,x_nticks=pp.Plot_y_Nticks, + y_min=pp.Plot_x_Min,y_max=pp.Plot_x_Max,y_nticks=pp.Plot_x_Nticks, + show_legend=pp.Plot_Show_Legend,legend_location=pp.Plot_Legend_Location, + plot_title=pp.Plot_Title, + plot_subtitle=pp.Plot_Subtitle, + figure_left_adjust=pp.Plot_Left_Adjust, + figure_right_adjust=pp.Plot_Right_Adjust, + figure_bottom_adjust=pp.Plot_Bottom_Adjust, + figure_top_adjust=pp.Plot_Top_Adjust + ) + else: + f = plot_to_fig(x_data=y, y_data=x, + institute_label=institute, + revision_label=revision, + figure_handle=f, + data_markevery=pp.Cmp_Data_Markevery, + x_label=pp.Plot_y_Label, + y_label=pp.Plot_x_Label, + data_label=pp.Cmp_Data_Label, + marker_style=pp.Cmp_Marker_Style, + marker_size=pp.Cmp_Marker_Size, + marker_fill_color=pp.Cmp_Marker_Fill_Color, + marker_edge_color=pp.Cmp_Marker_Edge_Color, + line_style=pp.Cmp_Line_Style, + line_width=pp.Cmp_Line_Width, + line_color=pp.Cmp_Line_Color, + x_min=pp.Plot_y_Min,x_max=pp.Plot_y_Max,x_nticks=pp.Plot_y_Nticks, + y_min=pp.Plot_x_Min,y_max=pp.Plot_x_Max,y_nticks=pp.Plot_x_Nticks, + show_legend=pp.Plot_Show_Legend,legend_location=pp.Plot_Legend_Location, + plot_title=pp.Plot_Title, + plot_subtitle=pp.Plot_Subtitle, + figure_left_adjust=pp.Plot_Left_Adjust, + figure_right_adjust=pp.Plot_Right_Adjust, + figure_bottom_adjust=pp.Plot_Bottom_Adjust, + figure_top_adjust=pp.Plot_Top_Adjust + ) + else: + if (pp.Seaborn_Color): + f = plot_to_fig(x_data=x, y_data=y, + institute_label=institute, + revision_label=revision, + figure_handle=f, + data_markevery=pp.Cmp_Data_Markevery, + x_label=pp.Plot_x_Label, + y_label=pp.Plot_y_Label, + data_label=pp.Cmp_Data_Label, + marker_style=pp.Cmp_Marker_Style, + marker_size=pp.Cmp_Marker_Size, + line_style=pp.Cmp_Line_Style, + line_width=pp.Cmp_Line_Width, + x_min=pp.Plot_x_Min,x_max=pp.Plot_x_Max,x_nticks=pp.Plot_x_Nticks, + y_min=pp.Plot_y_Min,y_max=pp.Plot_y_Max,y_nticks=pp.Plot_y_Nticks, + show_legend=pp.Plot_Show_Legend,legend_location=pp.Plot_Legend_Location, + plot_title=pp.Plot_Title, + plot_subtitle=pp.Plot_Subtitle, + figure_left_adjust=pp.Plot_Left_Adjust, + figure_right_adjust=pp.Plot_Right_Adjust, + figure_bottom_adjust=pp.Plot_Bottom_Adjust, + figure_top_adjust=pp.Plot_Top_Adjust + ) + else: + f = plot_to_fig(x_data=x, y_data=y, + institute_label=institute, + revision_label=revision, + figure_handle=f, + data_markevery=pp.Cmp_Data_Markevery, + x_label=pp.Plot_x_Label, + y_label=pp.Plot_y_Label, + data_label=pp.Cmp_Data_Label, + marker_style=pp.Cmp_Marker_Style, + marker_fill_color=pp.Cmp_Marker_Fill_Color, + marker_edge_color=pp.Cmp_Marker_Edge_Color, + marker_size=pp.Cmp_Marker_Size, + line_style=pp.Cmp_Line_Style, + line_color=pp.Cmp_Line_Color, + line_width=pp.Cmp_Line_Width, + x_min=pp.Plot_x_Min,x_max=pp.Plot_x_Max,x_nticks=pp.Plot_x_Nticks, + y_min=pp.Plot_y_Min,y_max=pp.Plot_y_Max,y_nticks=pp.Plot_y_Nticks, + show_legend=pp.Plot_Show_Legend,legend_location=pp.Plot_Legend_Location, + plot_title=pp.Plot_Title, + plot_subtitle=pp.Plot_Subtitle, + figure_left_adjust=pp.Plot_Left_Adjust, + figure_right_adjust=pp.Plot_Right_Adjust, + figure_bottom_adjust=pp.Plot_Bottom_Adjust, + figure_top_adjust=pp.Plot_Top_Adjust + ) + + plt.figure(f.number) # make figure current + # plt.show() + + # create plot directory if it does not exist + isDir = os.path.isdir(pltdir) + if not isDir: + os.mkdir(pltdir) + + plt.savefig(pltdir + pp.Plot_Filename) + + Plot_Filename_Last = pp.Plot_Filename + Exp_Data_Label_Last = pp.Exp_Data_Label + f_Last = f