From ea2707c1467521cdc83eb3c8990dd7dfcec00e14 Mon Sep 17 00:00:00 2001 From: Jean-Philippe Bernard Date: Mon, 18 Mar 2024 12:41:07 +0100 Subject: [PATCH] improved --- Docs/developers/Code_Structure_Flowcharts/dustem-wrapper_flow_chart1.drawio | 2 +- LabTools/IRAP/JPB/make_sed_phangs_tables.pro | 15 +++++---------- LabTools/IRAP/JPB/srun/make_phangs_grids.pro | 4 +++- 3 files changed, 9 insertions(+), 12 deletions(-) diff --git a/Docs/developers/Code_Structure_Flowcharts/dustem-wrapper_flow_chart1.drawio b/Docs/developers/Code_Structure_Flowcharts/dustem-wrapper_flow_chart1.drawio index f087d54..d470cc3 100644 --- a/Docs/developers/Code_Structure_Flowcharts/dustem-wrapper_flow_chart1.drawio +++ b/Docs/developers/Code_Structure_Flowcharts/dustem-wrapper_flow_chart1.drawio @@ -1 +1 @@ -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eqhyQCte8yZMJa9NLOqWZmk7SKZaYiI+kAmqn1Frfd00XE6WAw/XwpxvvZA1Wc+t/7lo3W0zJ6Xt0kY0ah7T9MGVijdlwkw7q2dVHt6tKKEbcuKpkrjfiUiX0eiOZU5dMrNyoRZngx1R1VibD7cCmrzIzeuCsofO12gg9uyMtjqUhPApEqbb5+z8b6uIfVbVq7mNen464JwCpz9JrKglrVCVx0ZUigkvluZXEtLIy9wpGhnp9jPa5egEJZoyg65/av2LwX/6bfdv+8GX36dMfuv2fb9mXPyVKiuC6mo4RsoVCqUxxo+EHXiqUcyEGK4Kn4jWYSSp1kTYCTouhCoJW8XOK9ltzwqpYixQnQhVvPcVvhGJ7OvZJre8WNuhtHOZNUQ+XIw5f/xa6ML/i/w==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 \ No newline at end of file diff --git a/LabTools/IRAP/JPB/make_sed_phangs_tables.pro b/LabTools/IRAP/JPB/make_sed_phangs_tables.pro index eb06cff..ee2f517 100644 --- a/LabTools/IRAP/JPB/make_sed_phangs_tables.pro +++ b/LabTools/IRAP/JPB/make_sed_phangs_tables.pro @@ -119,12 +119,6 @@ CASE use_grid_type OF iv_min = [0.1,1.e-10 ,1.e-4,1.e-4] iv_max = [100,0.02,1.e-1,1.e-1] plog=[1,0,1,1] - ;fpd=['(*!dustem_params).gas.g0', $ ;gas.G0=-1 (not used) - ; '(*!dustem_params).g0', $ ;g0=1 (not used) - ; 'dustem_plugin_dl07_isrf_model_2', $ ;alpha=1 - ; 'dustem_plugin_dl07_isrf_model_4' $ ;Umax=1.e7 - ; ] - ;fiv=[-1.,1.,2.,1.e7] ;This sets the fixed parameters fpd=['dustem_plugin_dl07_isrf_model_2', $ ;alpha=1 'dustem_plugin_dl07_isrf_model_4' $ ;Umax=1.e7 ] @@ -136,12 +130,13 @@ CASE use_grid_type OF !dustem_nocatch=1 ;!dustem_verbose=0 ;(*!dustem_params).KEYWORDS='quiet '+(*!dustem_params).KEYWORDS ;This makes Fortran be quiet too - ;=== select filters to be used for the grid - filters=[(*!dustem_filters).nircam.filter_names,(*!dustem_filters).miri.filter_names, $ - (*!dustem_filters).iras.filter_names,(*!dustem_filters).pacs.filter_names,(*!dustem_filters).spire.filter_names] + ;=== select filters to be used for the grid (no definition -> all known filters used) + ;filters=[(*!dustem_filters).nircam.filter_names,(*!dustem_filters).miri.filter_names, $ + ; (*!dustem_filters).iras.filter_names,(*!dustem_filters).pacs.filter_names,(*!dustem_filters).spire.filter_names] ;define the number of free parameters in the grid and the grid fits table name IF keyword_set(test) THEN BEGIN - iv_Nvalues=[2,2,3,3] + ;iv_Nvalues=[2,2,3,3] + iv_Nvalues=[2,2,2,2] table_name=dir+'TEST_'+model+'_DL07ISRF_YPAH_YVSG_Umin_gamma_4Phangs'+'.fits' ENDIF ELSE BEGIN iv_Nvalues=[6,5,20,20] diff --git a/LabTools/IRAP/JPB/srun/make_phangs_grids.pro b/LabTools/IRAP/JPB/srun/make_phangs_grids.pro index 1439dff..928481e 100644 --- a/LabTools/IRAP/JPB/srun/make_phangs_grids.pro +++ b/LabTools/IRAP/JPB/srun/make_phangs_grids.pro @@ -21,8 +21,10 @@ bidon=1 ;make grid ;=== This is for DBP90 a la DL07 -make_sed_phangs_tables,grid_type=3,/test,/show_seds +make_sed_phangs_tables,grid_type=3 +;make_sed_phangs_tables,grid_type=3,/test,/show_seds ;make_sed_phangs_tables,/test,grid_type=2,/show_seds,isrf_class=15 +;=== This is for ISRF classes grids (was only run with V1 of Grids saving (ie with no spectra saved) ;make_sed_phangs_tables,grid_type=2,isrf_class=30 ;titan Pg30. Canceled. Class does not exist ;make_sed_phangs_tables,grid_type=2,isrf_class=29 ;titan Pg29 ;make_sed_phangs_tables,grid_type=2,isrf_class=28 ;titan Pg28 -- libgit2 0.21.2