From bccda426566ff24ef023928020dd04dbab649778 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 11 Mar 2024 20:24:43 +0000 Subject: [PATCH 01/24] Bump jinja2 from 3.0.3 to 3.1.3 in /docs/sphinx Bumps [jinja2](https://github.com/pallets/jinja) from 3.0.3 to 3.1.3. - [Release notes](https://github.com/pallets/jinja/releases) - [Changelog](https://github.com/pallets/jinja/blob/main/CHANGES.rst) - [Commits](https://github.com/pallets/jinja/compare/3.0.3...3.1.3) --- updated-dependencies: - dependency-name: jinja2 dependency-type: direct:production ... Signed-off-by: dependabot[bot] --- docs/sphinx/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/sphinx/requirements.txt b/docs/sphinx/requirements.txt index faa302d06..bc76724c3 100644 --- a/docs/sphinx/requirements.txt +++ b/docs/sphinx/requirements.txt @@ -9,5 +9,5 @@ numpy matplotlib scipy sqlalchemy -jinja2==3.0.3 +jinja2==3.1.3 sphinx_gallery From fcd026d2c1af6c7e7433f3563fc818753307ec88 Mon Sep 17 00:00:00 2001 From: "Edward J. Parkinson" Date: Fri, 22 Mar 2024 13:30:47 +0000 Subject: [PATCH 02/24] address segmentation fault due to domain not being allocated --- source/rad_hydro_files.c | 23 +++++++++++++++-------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/source/rad_hydro_files.c b/source/rad_hydro_files.c index 22008a3f5..feb108baf 100644 --- a/source/rad_hydro_files.c +++ b/source/rad_hydro_files.c @@ -42,6 +42,8 @@ #include #include #include +#include + #include "atomic.h" #include "python.h" @@ -143,7 +145,7 @@ main (argc, argv) struct photon ptest; //We need a test photon structure in order to compute t - FILE *fptr_hc, *fptr_drive, *fptr_ion, *fptr_spec, *fptr_pcon, *fptr_debug, *fptr_flux, *fptr_flux_theta, *fptr_flux_phi, *fptr_flux_r, *fopen (); /*This is the file to communicate with zeus */ + FILE *fptr_hc, *fptr_drive, *fptr_ion, *fptr_spec, *fptr_pcon, *fptr_debug, *fptr_flux, *fptr_flux_theta, *fptr_flux_phi, *fptr_flux_r, *fopen (); /*This is the file to communicate with zeus */ domain = geo.hydro_domain_number; /* Initialize MPI, which is needed because some of the routines are MPI enabled */ @@ -166,7 +168,8 @@ main (argc, argv) /* MPI intialiazation is complete */ - + /* Disable GSL error handling, so we can handle errors ourselves */ + gsl_set_error_handler_off (); strcpy (parameter_file, "NONE"); @@ -181,9 +184,13 @@ main (argc, argv) strcat (windsavefile, ".wind_save"); strcat (outputfile, ".txt"); - -/* Read in the wind file */ - + /* Allocate memory required for domain, and read in the wind file */ + zdom = calloc (MAX_DOM, sizeof (domain_dummy)); + if (zdom == NULL) + { + Error ("Failed to allocate memory for domain\n"); + return (EXIT_FAILURE); + } if (wind_read (windsavefile) < 0) { Error ("py_wind: Could not open %s", windsavefile); @@ -269,7 +276,7 @@ main (argc, argv) } else if (zdom[domain].coord_type == RTHETA) { - fprintf (fptr_drive, "i j rcen thetacen vol rho ne F_vis_x F_vis_y F_vis_z F_vis_mod F_UV_theta F_UV_phi F_UV_r F_UV_mod F_Xray_x F_Xray_y F_Xray_z F_Xray_mod es_f_x es_f_y es_f_z es_f_mod bf_f_x bf_f_y bf_f_z bf_f_mod\n"); //directional flux by band + fprintf (fptr_drive, "i j rcen thetacen vol rho ne F_vis_x F_vis_y F_vis_z F_vis_mod F_UV_theta F_UV_phi F_UV_r F_UV_mod F_Xray_x F_Xray_y F_Xray_z F_Xray_mod es_f_x es_f_y es_f_z es_f_mod bf_f_x bf_f_y bf_f_z bf_f_mod\n"); //directional flux by band fprintf (fptr_flux, "i j rcen thetacen F_vis_x F_vis_y F_vis_z F_vis_mod F_UV_x F_UV_y F_UV_z F_UV_mod F_Xray_x F_Xray_y F_Xray_z F_Xray_mod\n"); //directional flux by band } @@ -305,8 +312,8 @@ main (argc, argv) nplasma = wmain[nwind].nplasma; wind_n_to_ij (domain, plasmamain[nplasma].nwind, &i, &j); - fprintf (fptr_flux_theta, "%3d %3d %3d %10.3e %10.3e ", i, j, wmain[nwind].inwind, wmain[nwind].xcen[0], wmain[nwind].xcen[2]); //output geometric things - fprintf (fptr_flux_phi, "%3d %3d %3d %10.3e %10.3e ", i, j, wmain[nwind].inwind, wmain[nwind].xcen[0], wmain[nwind].xcen[2]); //output geometric things + fprintf (fptr_flux_theta, "%3d %3d %3d %10.3e %10.3e ", i, j, wmain[nwind].inwind, wmain[nwind].xcen[0], wmain[nwind].xcen[2]); //output geometric things + fprintf (fptr_flux_phi, "%3d %3d %3d %10.3e %10.3e ", i, j, wmain[nwind].inwind, wmain[nwind].xcen[0], wmain[nwind].xcen[2]); //output geometric things fprintf (fptr_flux_r, "%3d %3d %3d %10.3e %10.3e ", i, j, wmain[nwind].inwind, wmain[nwind].xcen[0], wmain[nwind].xcen[2]); //output geometric things for (ii = 0; ii < NFLUX_ANGLES; ii++) { From c6088d932787c2c894180ca8af887b8a3a660b04 Mon Sep 17 00:00:00 2001 From: Edward Parkinson Date: Sat, 4 May 2024 21:30:40 +0100 Subject: [PATCH 03/24] Updating tde_standard to match paper release --- examples/basic/tde_standard.pf | 55 +++++++++++++++++----------------- 1 file changed, 28 insertions(+), 27 deletions(-) diff --git a/examples/basic/tde_standard.pf b/examples/basic/tde_standard.pf index b8d7228e5..373052207 100644 --- a/examples/basic/tde_standard.pf +++ b/examples/basic/tde_standard.pf @@ -1,16 +1,16 @@ System_type(star,cv,bh,agn,previous) agn ### Parameters for the Central Object -Central_object.mass(msol) 3e7 -Central_object.radius(cm) 2.65e13 +Central_object.mass(msol) 3e6 +Central_object.radius(cm) 2.65e12 ### Parameters for the Disk (if there is one) -Disk.type(none,flat,vertically.extended) flat +Disk.type(none,flat,vertically.extended,rmin>central.obj.rad) flat Disk.radiation(yes,no) yes -Disk.rad_type_to_make_wind(bb,models) bb +Disk.rad_type_to_make_wind(bb,models,mod_bb) bb Disk.temperature.profile(standard,readin) standard -Disk.mdot(msol/yr) 0.01 -Disk.radmax(cm) 1e15 +Disk.mdot(msol/yr) 9.98e-03 +Disk.radmax(cm) 1.00885e+14 ### Parameters for Boundary Layer or the compact object in an X-ray Binary or AGN Central_object.radiation(yes,no) no @@ -24,47 +24,48 @@ Wind.dim.in.z_or_theta.direction 100 ### Parameters associated with photon number, cycles,ionization and radiative transfer options Photons_per_cycle 5e7 -Ionization_cycles 15 +Ionization_cycles 25 Spectrum_cycles 5 Wind.ionization(on.the.spot,ML93,LTE_tr,LTE_te,fixed,matrix_bb,matrix_pow,matrix_est) matrix_pow Line_transfer(pure_abs,pure_scat,sing_scat,escape_prob,thermal_trapping,macro_atoms_escape_prob,macro_atoms_thermal_trapping) macro_atoms_thermal_trapping -Atomic_data data/h20_hetop_standard80.dat +Matom_transition_mode(mc_jumps,matrix) matrix Surface.reflection.or.absorption(reflect,absorb,thermalized.rerad) reflect Wind_heating.extra_processes(none,adiabatic,nonthermal,both) adiabatic +Atomic_data data/h20_hetop_standard80.dat ### Parameters for Domain 0 -Wind.mdot(msol/yr) 0.01 +Wind.mdot(msol/yr) 9.98e-03 SV.diskmin(units_of_rstar) 1 -SV.diskmax(units_of_rstar) 30 +SV.diskmax(units_of_rstar) 3.80699e+01 SV.thetamin(deg) 20 SV.thetamax(deg) 65 -SV.mdot_r_exponent 0 -SV.v_infinity(in_units_of_vescape 1 -SV.acceleration_length(cm) 5e16 -SV.acceleration_exponent 1.5 +SV.mdot_r_exponent 2.0 +SV.v_infinity(in_units_of_vescape 0.3 +SV.acceleration_length(cm) 8.856670e+14 +SV.acceleration_exponent 4.000000 SV.gamma(streamline_skew;1=usually) 1.0 SV.v_zero_mode(fixed,sound_speed) sound_speed SV.v_zero(multiple_of_sound_speed) 1 Wind.radmax(cm) 5e17 -Wind.t.init 40000 +Wind.t.init 35000 Wind.filling_factor(1=smooth,<1=clumped) 0.1 ### Parameters defining the spectra seen by observers -Disk.rad_type_in_final_spectrum(bb,models,uniform) bb +Disk.rad_type_in_final_spectrum(bb,models,uniform,mono,mod_bb) bb -### The minimum and maximum wavelengths in the final spectra -Spectrum.nwave 10000 -Spectrum.wavemin(Angstroms) 100 -Spectrum.wavemax(Angstroms) 10000 +### The minimum and maximum wavelengths in the final spectra and the number of wavelength bins +Spectrum.nwave 10000 +Spectrum.wavemin(Angstroms) 100 +Spectrum.wavemax(Angstroms) 10000 ### The observers and their location relative to the system -Spectrum.no_observers 5 -Spectrum.angle(0=pole) 10 -Spectrum.angle(0=pole) 35 -Spectrum.angle(0=pole) 60 -Spectrum.angle(0=pole) 75 -Spectrum.angle(0=pole) 85 +Spectrum.no_observers 5 +Spectrum.angle(0=pole) 10 +Spectrum.angle(0=pole) 35 +Spectrum.angle(0=pole) 60 +Spectrum.angle(0=pole) 75 +Spectrum.angle(0=pole) 85 Spectrum.live_or_die(live.or.die,extract) extract Spectrum.type(flambda,fnu,basic) flambda @@ -72,4 +73,4 @@ Spectrum.type(flambda,fnu,basic) flambda Reverb.type(none,photon,wind,matom) none ### Other parameters -Photon_sampling.approach(T_star,cv,yso,AGN,tde_bb,min_max_freq,user_bands,cloudy_test,wide,logarithmic) agn +Photon_sampling.approach(T_star,cv,yso,AGN,tde_bb,min_max_freq,user_bands,cloudy_test,wide,logarithmic) tde_bb From e7615e1839c3b9f6b16e11309ac7dc4d7d5a1194 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 6 May 2024 21:22:47 +0000 Subject: [PATCH 04/24] Bump jinja2 from 3.1.3 to 3.1.4 in /docs/sphinx Bumps [jinja2](https://github.com/pallets/jinja) from 3.1.3 to 3.1.4. - [Release notes](https://github.com/pallets/jinja/releases) - [Changelog](https://github.com/pallets/jinja/blob/main/CHANGES.rst) - [Commits](https://github.com/pallets/jinja/compare/3.1.3...3.1.4) --- updated-dependencies: - dependency-name: jinja2 dependency-type: direct:production ... Signed-off-by: dependabot[bot] --- docs/sphinx/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/sphinx/requirements.txt b/docs/sphinx/requirements.txt index bc76724c3..7c772fd99 100644 --- a/docs/sphinx/requirements.txt +++ b/docs/sphinx/requirements.txt @@ -9,5 +9,5 @@ numpy matplotlib scipy sqlalchemy -jinja2==3.1.3 +jinja2==3.1.4 sphinx_gallery From 7dc2b19deed9f24de298eeb6ae08165034a1d812 Mon Sep 17 00:00:00 2001 From: James Matthews Date: Thu, 30 May 2024 09:38:23 +0100 Subject: [PATCH 05/24] Update README.md in pydocs --- docs/pydocs/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/pydocs/README.md b/docs/pydocs/README.md index 1eaebca46..fb1b28e0f 100644 --- a/docs/pydocs/README.md +++ b/docs/pydocs/README.md @@ -2,3 +2,5 @@ This directory contains help files created from doc strings from python scripts in the py_progs directory. To create the documentation yourself, simply run `write_docs.py`. + +This procedure should be largely superseded by https://agnwinds.readthedocs.io/en/dev/py_progs.html but that page is not currently complete. From 7d3b3d1839cf8efbb8637ed53e01a440eb5b78b2 Mon Sep 17 00:00:00 2001 From: "Edward J. Parkinson" Date: Fri, 16 Aug 2024 16:25:49 +0100 Subject: [PATCH 06/24] rename python to sirocco in sphinx documentation --- docs/sphinx/source/atomic.rst | 16 ++-- docs/sphinx/source/atomic/bound_bound.rst | 12 +-- ...und_bound_electron_collision_strengths.rst | 10 +-- docs/sphinx/source/atomic/bound_free.rst | 2 +- .../source/atomic/bound_free_topbase.rst | 10 +-- .../source/atomic/bound_free_verner.rst | 12 +-- .../source/atomic/direct_ionization.rst | 4 +- docs/sphinx/source/atomic/electron_yields.rst | 4 +- docs/sphinx/source/atomic/elem_ions.rst | 10 +-- docs/sphinx/source/atomic/free-free.rst | 4 +- docs/sphinx/source/atomic/levels.rst | 6 +- docs/sphinx/source/atomic/photon_yields.rst | 4 +- docs/sphinx/source/conf.py | 29 ++++---- docs/sphinx/source/coordinate.rst | 8 +- docs/sphinx/source/developer/cuda.rst | 51 ++++++------- .../source/developer/programmer_notes.rst | 70 +++++++++--------- docs/sphinx/source/developer/tests.rst | 56 +++++++------- docs/sphinx/source/examples.rst | 4 +- .../source/examples/demo-models/tde/tde.rst | 10 +-- docs/sphinx/source/index.rst | 29 ++++---- docs/sphinx/source/input/overview.rst | 14 ++-- .../parameters/Input_spectra.model_file.rst | 4 +- .../Spectrum/Spectrum.live_or_die.rst | 2 +- .../Spectrum.select_photons_by_position.rst | 2 +- ...ndary_layer.rad_type_in_final_spectrum.rst | 2 +- .../Boundary_layer.rad_type_to_make_wind.rst | 2 +- ...tral_object.rad_type_in_final_spectrum.rst | 2 +- .../Central_object.rad_type_to_make_wind.rst | 2 +- .../source/input/parameters/other/Diag.rst | 2 +- .../Diag.lowest_ion_density_for_photoabs.rst | 2 +- .../other/Diag/Diag.partial_cells.rst | 2 +- .../Photon_sampling.nbands.rst | 2 +- .../parameters/other/Reverb/Reverb.type.rst | 2 +- .../input/parameters/rt_ion/Atomic_data.rst | 2 +- .../parameters/rt_ion/Spectrum_cycles.rst | 2 +- .../parameters/wind/Wind/Wind.ionization.rst | 2 +- .../input/parameters/wind/Wind/Wind.type.rst | 2 +- .../source/input/system_description.rst | 2 +- .../sphinx/source/input/wind_model_params.rst | 6 +- docs/sphinx/source/installation.rst | 72 +++++++++--------- docs/sphinx/source/meta.rst | 12 +-- docs/sphinx/source/operation.rst | 2 +- .../source/operation/ionization_cycles.rst | 4 +- .../source/operation/spectral_cycles.rst | 2 +- docs/sphinx/source/output.rst | 2 +- docs/sphinx/source/output/diagnostics.rst | 4 +- docs/sphinx/source/output/evaluation.rst | 16 ++-- docs/sphinx/source/output/model.rst | 10 +-- docs/sphinx/source/output/spectrum_files.rst | 6 +- ...pectrum_generation_large_optical_depth.rst | 2 +- docs/sphinx/source/physics.rst | 2 +- docs/sphinx/source/physics/aniso.rst | 2 +- docs/sphinx/source/physics/limitations.rst | 38 +++++----- docs/sphinx/source/physics/macroatoms.rst | 36 ++++----- .../source/physics/radiative_transfer.rst | 12 +-- .../source/physics/relativity_comoving.rst | 2 +- docs/sphinx/source/plotting.rst | 2 +- .../source/plotting/plot_spectrum.ipynb | 24 +++--- docs/sphinx/source/plotting/plot_wind.ipynb | 26 +++---- docs/sphinx/source/py_progs.rst | 6 +- docs/sphinx/source/py_progs/MakeMacro.rst | 6 +- docs/sphinx/source/py_progs/development.rst | 2 +- docs/sphinx/source/py_progs/plotting.rst | 2 +- docs/sphinx/source/py_progs/py4py.rst | 4 +- docs/sphinx/source/py_progs/regression.rst | 20 ++--- docs/sphinx/source/py_progs/running.rst | 4 +- docs/sphinx/source/quick.rst | 18 ++--- docs/sphinx/source/radiation.rst | 52 +++++++------ docs/sphinx/source/radiation/banding.rst | 8 +- ...running_python.rst => running_sirocco.rst} | 74 +++++++++---------- docs/sphinx/source/wind_models.rst | 2 +- .../source/wind_models/importing_models.rst | 44 +++++------ docs/sphinx/source/wind_models/kwd.rst | 4 +- examples/reverb/reverb.ipynb | 32 ++++---- 74 files changed, 482 insertions(+), 479 deletions(-) rename docs/sphinx/source/{running_python.rst => running_sirocco.rst} (68%) diff --git a/docs/sphinx/source/atomic.rst b/docs/sphinx/source/atomic.rst index 662681d3d..2c4241f07 100644 --- a/docs/sphinx/source/atomic.rst +++ b/docs/sphinx/source/atomic.rst @@ -1,19 +1,19 @@ Atomic Data ########### -Any Python model is only as good as the atomic data which goes into making the model. -All of the atomic data that Python accepts is read in by the routine get_atomicdata, +Any Sirocco model is only as good as the atomic data which goes into making the model. +All of the atomic data that Sirocco accepts is read in by the routine get_atomicdata, and all of the data is read in from a series of ascii data files and stored in structures that are defined in atomic.h. The purpose of documentation is as follows: -* to explain the atomic data formats used by Python and the relationship of different sets +* to explain the atomic data formats used by Sirocco and the relationship of different sets of data to one another -* to explain where the data currently used in Python and to explain how the raw data - is translated in to a format the Python accepts +* to explain where the data currently used in Sirocco and to explain how the raw data + is translated in to a format the Sirocco accepts The routines used to translate raw data format for two-level atoms (as well as much of the raw data) are contained in a separate github `repository `_ @@ -25,7 +25,7 @@ The routines used to generate data for MacroAtoms are described in :doc:`Generat Choosing a dataset ----------------------- -The "masterfile" that determines what data will be read into Python is determined by the +The "masterfile" that determines what data will be read into Sirocco is determined by the line in the parameter file, which will read something like:: Atomic_data data/standard80.dat @@ -33,7 +33,7 @@ line in the parameter file, which will read something like:: where the file `data/standard80.dat` will contain names (one to a line) of files which will be read in sequentially. -All of the atomic data that comes as standard with Python is stored in the `xdata` directory (and its subdirectories) but users are not required to put their data +All of the atomic data that comes as standard with Sirocco is stored in the `xdata` directory (and its subdirectories) but users are not required to put their data there. Various experimental or testing dataset masterfiles are stored in the `zdata` directory. Symbolic links to these directories are setup by running `Setup_Py_Dir`. @@ -44,7 +44,7 @@ are setup by running `Setup_Py_Dir`. Data hierarchy and I/O ----------------------- As mentioned above, the masterfile will contain names (one to a line) of files which will -be read in sequentially. Every line in the atomic data files read by Python consists of a keyword that defines the type +be read in sequentially. Every line in the atomic data files read by Sirocco consists of a keyword that defines the type of data and various data values that are required for that particular data type. Lines that beging with # or are empty are ignored. diff --git a/docs/sphinx/source/atomic/bound_bound.rst b/docs/sphinx/source/atomic/bound_bound.rst index cd9b62b8c..237c9fbf5 100644 --- a/docs/sphinx/source/atomic/bound_bound.rst +++ b/docs/sphinx/source/atomic/bound_bound.rst @@ -11,7 +11,7 @@ The file gfall.dat was used, though a few extra lines known to have been missing -There are two main sources of data currently used in Python. +There are two main sources of data currently used in Sirocco. * Kurucz * Verner @@ -22,11 +22,11 @@ We have also used a line list from Verner in the past -Translation to Python format +Translation to Sirocco format ============================ -There are several steps to creating the data used in Python from that in gfall.dat, that are carried out by py_read_kurucz and py_link. The first routine reads the gfall.dat file and creates two output files, a file containing the lines and the associated such as the effective oscillatory strength and a file which contains information about the ion levels. py_read_kurucz chooses only a portion of the Kurucz lines, namely those associated with ions with ionization potentials in a certain range and lines with gf factors exceeding a certain value. The second program py_link attempts to create a model ion with links between the levels and the ions. Both of these routines are driven by .pf files, similar to what are used in python. Examples of the .pf files are in the directory py_kurucz +There are several steps to creating the data used in Sirocco from that in gfall.dat, that are carried out by py_read_kurucz and py_link. The first routine reads the gfall.dat file and creates two output files, a file containing the lines and the associated such as the effective oscillatory strength and a file which contains information about the ion levels. py_read_kurucz chooses only a portion of the Kurucz lines, namely those associated with ions with ionization potentials in a certain range and lines with gf factors exceeding a certain value. The second program py_link attempts to create a model ion with links between the levels and the ions. Both of these routines are driven by .pf files, similar to what are used in python. Examples of the .pf files are in the directory py_kurucz -In practice we have not used these data for any Python publications. At some point early in the AGN project, NSH increased the number of lines, and generated lines\_linked\_ver\_2.dat and levels\_ver\_2.dat. I think this was because there was a small bug which meant the oscillator strength cut that was stated was not that which was applied. +In practice we have not used these data for any Sirocco publications. At some point early in the AGN project, NSH increased the number of lines, and generated lines\_linked\_ver\_2.dat and levels\_ver\_2.dat. I think this was because there was a small bug which meant the oscillator strength cut that was stated was not that which was applied. Data format =========== @@ -68,13 +68,13 @@ one needs to index everything self-consistentl -Python structure +Sirocco structure ================ Line data is stored in the data structure **lines** Comments ======== -The version of gfall.dat that is used in Python is out of date, though whether this affects any of the lines actually used in python is unclear. The website listed above has a link to newer versions of gfall.dat. +The version of gfall.dat that is used in Sirocco is out of date, though whether this affects any of the lines actually used in python is unclear. The website listed above has a link to newer versions of gfall.dat. diff --git a/docs/sphinx/source/atomic/bound_bound_electron_collision_strengths.rst b/docs/sphinx/source/atomic/bound_bound_electron_collision_strengths.rst index be4f105be..f9465d19f 100644 --- a/docs/sphinx/source/atomic/bound_bound_electron_collision_strengths.rst +++ b/docs/sphinx/source/atomic/bound_bound_electron_collision_strengths.rst @@ -19,12 +19,12 @@ These values of :math:`\Upsilon` simply replace :math:`\Omega`. In the asbsence of data in this format, the Van Regemorter approximation is utilized. -Translation to Python format +Translation to Sirocco format ============================ It is necessary to link each line in our line list with the relevant electron collision strength. This is achieved using the python script "coll_stren_lookup.py" which first reads in the "lines_linked_ver_2.py" line list, then attempts to work out which lines are which by comparing the energy and the oscillator strength of the line. If these match to within a factor of 10% then the code logs this as a possible match. If better matches come along, then the code adopts those instead. -Each matched line get a line in the data file which is basically all of the line data for the matched line. This is to give Python the best chance of linking it up with the line internally. +Each matched line get a line in the data file which is basically all of the line data for the matched line. This is to give Sirocco the best chance of linking it up with the line internally. Data format =========== @@ -66,10 +66,10 @@ and So, to get :math:`\Upsilon` for a given T, one converts T to x via the correct equation, then linearly interpolate between values of :math:`y(x)`, then convert back to :math:`\Upsilon`. -Python structure +Sirocco structure ================ -The data is stored in Python in the Coll\_stren structure which has memebers +The data is stored in Sirocco in the Coll\_stren structure which has memebers - int n - internal index @@ -105,7 +105,7 @@ There are addtional transition types in the Chianti database - 6 - Proton transitions -The latter are not currently used in **Python** +The latter are not currently used in *Sirocco* Discussion of how Chianti handles transitions can be found in `The CHIANTI upsilon files (ups and scups) `_ diff --git a/docs/sphinx/source/atomic/bound_free.rst b/docs/sphinx/source/atomic/bound_free.rst index 8374554b4..29bc0693b 100644 --- a/docs/sphinx/source/atomic/bound_free.rst +++ b/docs/sphinx/source/atomic/bound_free.rst @@ -5,7 +5,7 @@ Bound Free (Overview) Source ====== -Photonization data in Python is gennerally obtained from two sources +Photonization data in Sirocco is gennerally obtained from two sources - The `Opacity project `_. See also `Cunto et at 1993, A&A, 275, L5 `_ diff --git a/docs/sphinx/source/atomic/bound_free_topbase.rst b/docs/sphinx/source/atomic/bound_free_topbase.rst index b06a88815..257988b94 100644 --- a/docs/sphinx/source/atomic/bound_free_topbase.rst +++ b/docs/sphinx/source/atomic/bound_free_topbase.rst @@ -8,10 +8,10 @@ Source Obtained from The `Opacity project `_. See also `Cunto et at 1993, A&A, 275, L5 `_ -Translation to Python format +Translation to Sirocco format ============================ -ksl - It's not clear that we are now making use of the topbase data in this way but my original attempt to incorporate topbase photoinization data into Python is contained in the directory topbase. Processing of these files was done by py_top_phot. My feeling is that we can replace these remarks with those that are more up to date, once Nick and James discuss this section, and delete any mention of my original attempt on this in the data-gen archive. +ksl - It's not clear that we are now making use of the topbase data in this way but my original attempt to incorporate topbase photoinization data into Sirocco is contained in the directory topbase. Processing of these files was done by py_top_phot. My feeling is that we can replace these remarks with those that are more up to date, once Nick and James discuss this section, and delete any mention of my original attempt on this in the data-gen archive. @@ -63,15 +63,15 @@ For the macro atom case, the indices relate to the energy levels, that is these The TopBase energies are inaccurate and so generally adjustments are made using Chianti or some other source to fix up the energy levels. -Python structure +Sirocco structure ================ The data are stored in the Topbase_phot stucture which can be found in atomic.h -Criteria for usage in Python run +Criteria for usage in Sirocco run ================================ -Data has to be read into Python in a logical order. For a set of phototionization x-sections to be accepted, the energy levels (or configuratios) must already have been defiend. See :doc:`levels` +Data has to be read into Sirocco in a logical order. For a set of phototionization x-sections to be accepted, the energy levels (or configuratios) must already have been defiend. See :doc:`levels` The items that must match are: diff --git a/docs/sphinx/source/atomic/bound_free_verner.rst b/docs/sphinx/source/atomic/bound_free_verner.rst index 84565898c..3c3e9b757 100644 --- a/docs/sphinx/source/atomic/bound_free_verner.rst +++ b/docs/sphinx/source/atomic/bound_free_verner.rst @@ -15,12 +15,12 @@ There are three sources for this data - `Kaastra \& Mewe 1993 `_ :Electron and photon yield data -Translation to Python format +Translation to Sirocco format ============================ **Tabulation Process** -The raw VFKY data comes in a series of fit parameters. We decided, circa Python 78, to tabulate this data instead. Partly, I think I because the on the fly method was time consuming (yes, computing all the pow() commands to commute the cross sections on the fly took a huge amount of time) and we decided that tabulating pre program was better than doing it in the program, so that everything was of the same format. +The raw VFKY data comes in a series of fit parameters. We decided, circa Sirocco 78, to tabulate this data instead. Partly, I think I because the on the fly method was time consuming (yes, computing all the pow() commands to commute the cross sections on the fly took a huge amount of time) and we decided that tabulating pre program was better than doing it in the program, so that everything was of the same format. The script which does this is progs/tabulate\_xs/photo\_xs.py -- it creates a file like photo\_vfky\_tabulated.data. @@ -35,7 +35,7 @@ It then writes out the inner shell cross sections into "vfky_innershell_tab.data Data format =========== -Explain the ascii format of the file which is read into Python +Explain the ascii format of the file which is read into Sirocco **VFKY_outershell_tab.data** @@ -61,13 +61,13 @@ This data is linked to the relevant ion via z and state, islp and level are not This data is linked to the relevant ion via z and state. the n_shell and l_subshell numbers are used to cross reference to the electron yield records. As above, the last record shows how many points are in the fit, and the data pairs making up the fit follow with the keyword InnerVY. -Python structure +Sirocco structure ================ -Where the data is stored internally in Python +Where the data is stored internally in Sirocco Comments ======== -The manner in which this data is read into Python is a bit labyrinthine at the moment. The intention is to use a combination of VFKY and VY for all ground states, an +The manner in which this data is read into Sirocco is a bit labyrinthine at the moment. The intention is to use a combination of VFKY and VY for all ground states, an diff --git a/docs/sphinx/source/atomic/direct_ionization.rst b/docs/sphinx/source/atomic/direct_ionization.rst index a5f51602f..93c5ff96f 100644 --- a/docs/sphinx/source/atomic/direct_ionization.rst +++ b/docs/sphinx/source/atomic/direct_ionization.rst @@ -10,7 +10,7 @@ Source The data comes directly from `Dere 2006, A&A, 466, 771 `_ . This paper gives direct ionization and excitation-autoionization rate coefficients for many ions as a function of temperature for Maxwellian electron distributions. -Translation to Python format +Translation to Sirocco format ============================ @@ -40,7 +40,7 @@ The rate coefficient R(T) is recovered from the scaled rate coefficient in the t where :math:`E_{1}` is the first exponential integral. In python we use the gsl_sf_expint_E1 routine in gsl. -Python structure +Sirocco structure ================ This data is stored in the dere_di_rate structure with members diff --git a/docs/sphinx/source/atomic/electron_yields.rst b/docs/sphinx/source/atomic/electron_yields.rst index a04e36e2b..51ef88bb0 100644 --- a/docs/sphinx/source/atomic/electron_yields.rst +++ b/docs/sphinx/source/atomic/electron_yields.rst @@ -9,7 +9,7 @@ Source This data comes from `Kaastra and Mewe 1993, A&A, 97, 443 `_ . The data is downloaded from the vizier site linked and put into a file called "electron_yield.data" -Translation to Python format +Translation to Sirocco format ============================ The translation takes place using the python script "kaastra_2_py.py" which takes the saved raw data file "electron_yield.data" and compares it line by line to the inner shell cross section data in "vy_innershell_tab.data"(see above). The n shell and l subshell to which each record applies is coded in the KM data and needs to be decoded. This is what the script does, and all the script then does is output the yield data into a new file "kaastra_electron_yield.data" which contains the n and l cross reference. @@ -35,7 +35,7 @@ This is the data format of the electron yield data The data is linked to the correct inner shell photoionization cross section (and hence rate) via z, state, n shell and l subshell. The IP is not used. is the mean electron energy ejected, used to calculate the PI heating rate in radiation.c. The last ten columns in the file (2 shown in the table above) show the chance of various numbers of electrons being ejected in units of 1/10000. -Python structure +Sirocco structure ================ The data is stored in python in the inner_elec_yield structure which contains diff --git a/docs/sphinx/source/atomic/elem_ions.rst b/docs/sphinx/source/atomic/elem_ions.rst index 3df524e85..034e0d65d 100644 --- a/docs/sphinx/source/atomic/elem_ions.rst +++ b/docs/sphinx/source/atomic/elem_ions.rst @@ -2,7 +2,7 @@ Elements and Ions ################# -The first file that must be read into **Python** is the file that defines the elements and ions. The +The first file that must be read into *Sirocco* is the file that defines the elements and ions. The Source: ======= @@ -13,7 +13,7 @@ This data comes from `Verner, Barthel & Tytler, 1994, ApJ 108, 287. `_ . The data is downloaded from the vizier site linked and put into a file called "fluorescent\_yield.data" -Translation to Python format +Translation to Sirocco format ============================ The translation takes place using the python script "kaastra_2_py.py". All identical to electron yield, but input file is "fluorescent_yield.data" and output is "kaastra_fluorescent_yield.data" @@ -34,7 +34,7 @@ This is the data format of the electron yield data The data is linked to the correct inner shell photoionization cross section (and hence rate) via z, state, n shell and l subshell. The photon energy field is thew energy of the fluorescent photon in eV, and yield is the number of said photons emitted per ionization multiplied by :math:`10^4`. -Python structure +Sirocco structure ================ The data is stored in python in the inner_fluor_yield structure which contains diff --git a/docs/sphinx/source/conf.py b/docs/sphinx/source/conf.py index 401e2d7fd..423791037 100755 --- a/docs/sphinx/source/conf.py +++ b/docs/sphinx/source/conf.py @@ -58,7 +58,7 @@ # -- Whether or not to include ToDo notes --------------------------------- todo_include_todos = True # -- GitHub repo for issues ----------------------------------------------- -issues_github_path = "agnwinds/python" +issues_github_path = "sirocco-rt/sirocco" # -- Suppress warnings about autosectionlabel duplicates ------------------ suppress_warnings = [ 'autosectionlabel.*', @@ -77,7 +77,7 @@ master_doc = 'index' # General information about the project. -project = 'python' +project = "Sirocco" copyright = '2018-2024, Knox Long, Christian Knigge, Stuart Sim, Nick Higginbottom, James Matthews, Sam Mangham, Edward Parkinson, Nicolas Scepi, Austen Wallis, Amin Mosallanezhad' author = 'Knox Long, Christian Knigge, Stuart Sim, Nick Higginbottom, James Matthews, Sam Mangham, Edward Parkinson, Nicolas Scepi, Austen Wallis, Amin Mosallanezhad' @@ -170,7 +170,7 @@ # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. -htmlhelp_basename = 'pythondoc' +htmlhelp_basename = "siroccodoc" # -- Options for LaTeX output --------------------------------------------- @@ -196,21 +196,14 @@ # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ - ( - master_doc, 'python.tex', 'python Documentation', - author, - 'manual' - ), + (master_doc, "sirocco.tex", "Sirocco Documentation", author, "manual"), ] # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). -man_pages = [ - (master_doc, 'python', 'python Documentation', - [author], 1) -] +man_pages = [(master_doc, "Sirocco", "Sirocco Documentation", [author], 1)] # -- Options for Texinfo output ------------------------------------------- @@ -218,7 +211,13 @@ # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ - (master_doc, 'python', 'python Documentation', - author, 'python', 'One line description of project.', - 'Miscellaneous'), + ( + master_doc, + "Sirocco", + "Sirocco Documentation", + author, + "Sirocco", + "One line description of project.", + "Miscellaneous", + ), ] diff --git a/docs/sphinx/source/coordinate.rst b/docs/sphinx/source/coordinate.rst index 453f32818..42a212a3c 100644 --- a/docs/sphinx/source/coordinate.rst +++ b/docs/sphinx/source/coordinate.rst @@ -1,7 +1,7 @@ Coordinate grids ---------------- -Python supports 3 main coordinate gridding schemes, as well as one that is +Sirocco supports 3 main coordinate gridding schemes, as well as one that is tailored to handle vertically extended disks. These schemes are * 1-d spherical @@ -10,12 +10,12 @@ tailored to handle vertically extended disks. These schemes are These options are controlled by the :doc:`/input/parameters/wind/Wind/Wind.coord_system` keyword. For vertically extended disks, a modified version of the a cylindrical schme is provided where the cells viewed along the x,z plane are parallelograms, so that the grid follows the disk surface. -Although Python incorporates several models, such as the SV model for disk winds, that are continuous, the velocities and other proporites are placed on a grid, as part of the setup that goes on at the beginining of program execution. +Although Sirocco incorporates several models, such as the SV model for disk winds, that are continuous, the velocities and other proporites are placed on a grid, as part of the setup that goes on at the beginining of program execution. It is up to the user to choose an appropriate coordinate system and the number of grid points to include in any particular run of the program. -As implemented within Python, the cells are created with a logarithmic spacing, that is the cells are larger the further they are from the central source (or disk plane). The one exception to this is that for polar coordinates, the angular separation of cells is fixed. For imported models, on +As implemented within Sirocco, the cells are created with a logarithmic spacing, that is the cells are larger the further they are from the central source (or disk plane). The one exception to this is that for polar coordinates, the angular separation of cells is fixed. For imported models, on the other hand, the user sets the exact coordinate gridding. Obviously, the larger the coordinate grid, 100 x 100, say, compared to 30 x 30, the better the @@ -34,7 +34,7 @@ Partial cells As note above, parameterized models often have region of space that are in the wind and regions whch are not. If one overlays, a coordinate grid on such a model, there will be cells that cross edges of the wind. These partial cells present particular problems. -In Python, velocities are interpolated on the corners of wind cells, but densities are are calculated based on +In Sirocco, velocities are interpolated on the corners of wind cells, but densities are are calculated based on the average radiation field in a cell, and hence ion densities are actually cell centered. As photons pass through a cell, they encounter resonances and the actuall opacities are based on an interpolated value of the densities. This presents no particular problem in regions inside the wind, but it is an issue for partial cells. diff --git a/docs/sphinx/source/developer/cuda.rst b/docs/sphinx/source/developer/cuda.rst index f32daa52f..3123be3ea 100644 --- a/docs/sphinx/source/developer/cuda.rst +++ b/docs/sphinx/source/developer/cuda.rst @@ -1,10 +1,10 @@ Matrix Acceleration using CUDA ############################### -Python can use CUDA/GPUs to accelerate solving linear systems using the cuSOLVER library, which is part of the NVIDIA +Sirocco can use CUDA/GPUs to accelerate solving linear systems using the cuSOLVER library, which is part of the NVIDIA CUDA Toolkit. -*This pilot study into using GPUs in Python was conducted as an HPC RSE project at the University of Southampton.* +*This pilot study into using GPUs in Sirocco was conducted as an HPC RSE project at the University of Southampton.* When should you use CUDA? ========================= @@ -32,7 +32,7 @@ algorithm. Requirements ============ -To use the CUDA matrix acceleration in Python, your machine needs to have the following installed, +To use the CUDA matrix acceleration in Sirocco, your machine needs to have the following installed, - A CUDA-capable NVIDIA GPU - NVIDIA CUDA Toolkit @@ -65,16 +65,16 @@ How to Enable and Run CUDA Compilation ----------- -CUDA is an additional acceleration method and is therefore not enabled by default. To enable CUDA, Python has to be +CUDA is an additional acceleration method and is therefore not enabled by default. To enable CUDA, Sirocco has to be compiled with the additional :code:`-DCUDA_ON` flag and linked with the appropriate libraries using the NVIDIDA CUDA -compiler (nvcc). There are several ways to enable the CUDA components of Python. The most simple is to run the configure -script in the root Python directory with the arguments :code:`--with-cuda`, +compiler (nvcc). There are several ways to enable the CUDA components of Sirocco. The most simple is to run the configure +script in the root Sirocco directory with the arguments :code:`--with-cuda`, .. code:: bash - [$PYTHON] $ ./configure --with-cuda + [$SIROCCO] $ ./configure --with-cuda - Configuring Makefile for Python radiative transfer code + Configuring Makefile for Sirocco radiative transfer code Checking for mpicc...yes Checking for gcc...yes Checking for nvcc...yes @@ -83,15 +83,15 @@ script in the root Python directory with the arguments :code:`--with-cuda`, If the NVIDIA CUDA Toolkit is found, you will see the output informing that the CUDA compiler :code:`nvcc` was found. What essentially happens when you run :code:`code` is that a value for the variable :code:`NVCC` is set in the Makefile -in Python's source directory. If you re-run :code:`configure` without :code:`--with-cuda`, then :code:`NVCC` will be -unset and CUDA will not be used. CUDA can be disbaled or enabled *"on the fly"* by modifying this variable without +in Sirocco's source directory. If you re-run :code:`configure` without :code:`--with-cuda`, then :code:`NVCC` will be +unset and CUDA will not be used. CUDA can be disabled or enabled *"on the fly"* by modifying this variable without running the configure script and by modifying the Makefile or passing the value of the variable when calling the Makefile, e.g., .. code:: bash - [$PYTHON/source] $ make clean - [$PYTHON/source] $ make python NVCC=nvcc + [$SIROCCO/source] $ make clean + [$SIROCCO/source] $ make sirocco NVCC=nvcc :code:`make clean` has to be run whenever CUDA is switched been enabled or disabled, due to code conditionally compiling depending on if CUDA is enabled or not. @@ -99,7 +99,7 @@ depending on if CUDA is enabled or not. Running ------- -To run Python with CUDA, you run it in the exact way even parallel models running with MPI. On a HPC system the +To run Sirocco with CUDA, you run it in the exact way even parallel models running with MPI. On a HPC system the appropriate GPU resources will need to be requested in a job submission script. For example, on Iridis at the University of Southampton, a functional job submission script may look like this, @@ -109,6 +109,7 @@ of Southampton, a functional job submission script may look like this, #SBATCH --nodes=1 #SBATCH --ntasks-per-node=40 + #SBATCH --gpus-per-node=1 #SBATCH --time=06:00:00 #SBATCH --partition=gpu @@ -116,13 +117,13 @@ of Southampton, a functional job submission script may look like this, mpirun -n $SLURM_NTASKS py model.pf -If CUDA is enabled and no GPU resources are found, Python will exit early in the program with an appropriate error +If CUDA is enabled and no GPU resources are found, Sirocco will exit early in the program with an appropriate error message. Note that a CUDA-aware MPI implementation is not required, as no data is communicated between GPUs. Implementation ============== -In this part of the documentation, we will cover the implementation details of cuSolver in Python. cuSolver is a matrix +In this part of the documentation, we will cover the implementation details of cuSolver in Sirocco. cuSolver is a matrix library within the NVIDIA CUDA ecosystem, designed to accelerate both dense and sparse linear algebra problems, including matrix factorisation, linear system solving and matrix inversion. To use cuSolver, very little GPU specific code needs to be written, other than code to allocate memory on the GPU. There are therefore a number of similarities @@ -149,7 +150,7 @@ A more detailed and thorough explanation of the CUDA programming model can be fo Basics ------ -Python uses the dense matrix functions in cuSolver, which are accessed through the :code:`cusolverDn.h` header file. To +Sirocco uses the dense matrix functions in cuSolver, which are accessed through the :code:`cusolverDn.h` header file. To use cuSolver, it must first be initialised. To do so, we use :code:`cusolverDnCreate` to create a :code:`cuSolverDnHandle_t` variable which is used by cuSolver internally for resource and context management. @@ -206,14 +207,14 @@ When writing CUDA C, it is convention to put the CUDA code into :code:`.cu` file Even when using a library like cuSolver, it is still convention to place that code into :code:`.cu` files as we still need to access some CUDA library functions, such as :code:`cudaMalloc` or :code:`cudaMemCpy`. -The CUDA code associated with matrix parallelisation has been written in the file :code:`$PYTHON/source/matrix_gpu.cu` -with the header file :code:`$PYTHON/source/matrix_gpu.h` which includes the function prototypes for the GPU matrix code. -The GSL matrix code is kept in :code:`$PYTHON/source/matrix_cpu.c` with function prototypes in -:code:`$PYTHON/source/templates.h`. +The CUDA code associated with matrix parallelisation has been written in the file :code:`$SIROCCO/source/matrix_gpu.cu` +with the header file :code:`$SIROCCO/source/matrix_gpu.h` which includes the function prototypes for the GPU matrix code. +The GSL matrix code is kept in :code:`$SIROCCO/source/matrix_cpu.c` with function prototypes in +:code:`$SIROCCO/source/templates.h`. To be able to switch between the CUDA and GSL matrix implementations with the minimal amount of code changes, a :code:`solve_matrix` wrapper function has been created. Either GSL or cuSolver is called within this wrapper, depending -on if Python was compiled with the flag :code:`-DCUDA_ON` as discussed earlier. This wrapper takes on the same name as +on if Sirocco was compiled with the flag :code:`-DCUDA_ON` as discussed earlier. This wrapper takes on the same name as the original GSL implementation, meaning no code changes have occurred in that regard. .. code:: c @@ -328,7 +329,7 @@ simplified) example of the cuSolver implementation to solve a linear system. )); /* We now have to copy d_b_vector back to the CPU, so we can use that value in - the rest of Python */ + the rest of Sirocco */ copy_data_to_cpu(); return EXIT_SUCCESS; @@ -366,7 +367,7 @@ The steps for compiling and link GPU and CPU code are outlined below in pseudo-M NVCC = nvcc # Define C and CUDA libraries. We still include GSL as other GSL numerical routines are - # used in Python + # used in Sirocco C_LIBS = -lgsl -lgslcblas -lm CUDA_LIBS = -lcudart -lcusolver @@ -384,7 +385,7 @@ The steps for compiling and link GPU and CPU code are outlined below in pseudo-M # Link the CUDA and C object code and libraries together using the C compiler $(CC) $(CUDA_OBJECTS) $(C_OBJECTS) -o python $(CUDA_LIBS) $(C_LIBS) -These steps are effectively replicated in the Makefile :code:`$PYTHON/source/Makefile`, where a deconstructed example is +These steps are effectively replicated in the Makefile :code:`$SIROCCO/source/Makefile`, where a deconstructed example is shown below. .. code:: Makefile @@ -407,7 +408,7 @@ shown below. $(NVCC) $(NVCC_FLAGS) -DCUDA_ON -I$(INCLUDE) -c $< -o $@ endif - # So to compile Python, we have something which looks vaguely like this. Note that + # So to compile Sirocco, we have something which looks vaguely like this. Note that # we use the CUDA_OBJECTS recipe as a requirement for the python recipe. This CUSOLVER_STATUS_SUCCESS # the CUDA source to be compiled to object code *if* NVCC is defined python: startup python.o $(python_objects) $(CUDA_OBJECTS) diff --git a/docs/sphinx/source/developer/programmer_notes.rst b/docs/sphinx/source/developer/programmer_notes.rst index dd325d10b..b426edf75 100644 --- a/docs/sphinx/source/developer/programmer_notes.rst +++ b/docs/sphinx/source/developer/programmer_notes.rst @@ -1,13 +1,13 @@ Programming Notes ################# -Python is written in C and is normally tested on linux machines and on Macs, where the compiler usually turns out to be clang. It is also regularly compiled with gcc as part of the travis-CI tests. Certain portions of the code are parallelized using the Message Parsing Interface (MPI). +Sirocco is written in C and is normally tested on linux machines and on Macs, where the compiler usually turns out to be clang. It is also regularly compiled with gcc as part of the travis-CI tests. Certain portions of the code are parallelized using the Message Parsing Interface (MPI). Version control is (obviously) managed through **git**. The stable version is on the `master` branch; the main development is carried out on the `dev` branch. This is generally the branch to start with in developing new code. If possible, a developer should use the so-called Fork and Pull model for their version control workflow. See e.g. `this gist post `_. -If one modifies the code, a developer needs to be sure to have ``$PYTHON/py_progs`` both in ``PYTHONPATH`` and ``PATH``. One should also have a version of indent installed, preferably but not necessarily gnu_indent installed. This is because, the Makefile will call a script run_indent.py on files that the developer has changed, which enforces a specific indent style on the code. +If one modifies the code, a developer needs to be sure to have ``$SIROCCO/py_progs`` both in ``PYTHONPATH`` and ``PATH``. One should also have a version of indent installed, preferably but not necessarily gnu_indent installed. This is because, the Makefile will call a script run_indent.py on files that the developer has changed, which enforces a specific indent style on the code. -In addition to indent, one should have cproto or something equivalent installed. cproto is used to prototypes for all of the subroutines (through the make command +In addition to indent, one should have cproto or something equivalent installed. cproto is used to prototypes for all of the subroutines (through the make command .. code:: bash @@ -17,20 +17,20 @@ In addition to indent, one should have cproto or something equivalent installed. All new routines should have Doxygen headers. -printf statements should be avoided, particular in the main code. Python has its own replacements for these commands, e.g Log and Error which standardize the outputsand allow for managing what is printed to the screen in multprocessor mode. There is alsoa command line switch that contorls the amount of information that is printed to the screen. Specific errors are only logged for a limited number of times, after which theyare merely counted. A log of the number of times each error has occurred is printed outat the end of each run and for each thread. (Additional detailes can be found in the Doxygenheader for xlog.c) +printf statements should be avoided, particular in the main code. Sirocco has its own replacements for these commands, e.g Log and Error which standardize the outputsand allow for managing what is printed to the screen in multprocessor mode. There is alsoa command line switch that contorls the amount of information that is printed to the screen. Specific errors are only logged for a limited number of times, after which theyare merely counted. A log of the number of times each error has occurred is printed outat the end of each run and for each thread. (Additional detailes can be found in the Doxygenheader for xlog.c) Structures ========== -In order to understand the code, one needs to understand the data structures. +In order to understand the code, one needs to understand the data structures. The main header files are: -* atomic.h - This contains all of the structures that hold atomic data, e.g oscillator - strengths, photoionization cross-sections, elemental abundances, etc. These data are - read in at the beginning of the program (see atomicdata.c and other similarly named +* atomic.h - This contains all of the structures that hold atomic data, e.g oscillator + strengths, photoionization cross-sections, elemental abundances, etc. These data are + read in at the beginning of the program (see atomicdata.c and other similarly named routines) -* python.h - This contains the structures and other data that comprise the wind as well +* python.h - This contains the structures and other data that comprise the wind as well as the parameters of the model. (This is fairly well-documented, or should be) @@ -40,63 +40,63 @@ Program Flow Basically, (as decribed from a more scientific perspective elsewhere), the program consists of a number stages -* Data gathering and initialization: This consiss of reading in all of the parameters - for the model to be calculated, reading in the associated atomic data, and setting up +* Data gathering and initialization: This consiss of reading in all of the parameters + for the model to be calculated, reading in the associated atomic data, and setting up program to run. This procuess involves allocating space for many of the data structures. -* Ionization cycles: During this portion of the program fleets of photons are generated +* Ionization cycles: During this portion of the program fleets of photons are generated and propogated through the wind, interacting with it in various ways. These photons are generated over a large range of frequencies, because their purpose is to allow the program - to determine the ionization state of the wind. During this + to determine the ionization state of the wind. During this process various estimators are accumulated that describe the interaction of the photons - with the wind. Once all of the photons have propagated through the wind the various + with the wind. Once all of the photons have propagated through the wind the various estimators are used to calculate a new estimate of the ionization state of the various - wind cells that constitute the wind. This process is repeated for a number of cycles, + wind cells that constitute the wind. This process is repeated for a number of cycles, by which time, hopefully, the wind will have reached a "steady state". -* Spectral cycles: Once the ionization cycles have been completed, the ionization state +* Spectral cycles: Once the ionization cycles have been completed, the ionization state of the wind is fixed, and more detailed spectra are calculated. For this, photons are generated in a limited spectral range, depending on the interests of the user. In contrast to the ionization state, where "cycles" are crucial concept, the only reason to have spectrl - cycles in the "Spectral cycle" phase is to allow one to gradually improve the statistics - of the spectrum. At the end of each spectral cycle, the detailed spectra are written out, + cycles in the "Spectral cycle" phase is to allow one to gradually improve the statistics + of the spectrum. At the end of each spectral cycle, the detailed spectra are written out, so one can see the spectra building up. Parallel Operation ================== -Python uses MPI to parallelize the most compute intensive portions of the routine. It has +Sirocco uses MPI to parallelize the most compute intensive portions of the routine. It has been run on large machines with 100s of cores without problem. The portions of the routine that are parallelize are: -* Photon generation and transfer: When run in multiprocesser mode, each thread creates only a +* Photon generation and transfer: When run in multiprocesser mode, each thread creates only a fraction of the total number of photons. The weight of the photons in each thread is such that the sum of the weights is the total energy expected to be produced in one observer frame second. These photons are propagated through the wind, and estimators based on these photons are accumulated. - At the end of photon transfer by all threads, the various quantities, including the spectra, that - have been accumulated in the separate threads are gathered together and averaged or summed as + At the end of photon transfer by all threads, the various quantities, including the spectra, that + have been accumulated in the separate threads are gathered together and averaged or summed as appropriate. For ionization cycles, this means that all of the data needed to calculate the ionization in any cell is available on each of the threads. * Ionization calculation: Although all of the threads have all of the data needed to calculate the ionization in any cell, in practice what happens is that the program assigns a different set of - cells to each thread to calculate the ionization. After the thread calculates the new ionization + cells to each thread to calculate the ionization. After the thread calculates the new ionization state for its assigned cells, the ionization states are then gathered back and broadcast to all - of the threads, in preparation for the next cycle. + of the threads, in preparation for the next cycle. * Preparation for detailed radiative transfer in the macro-atom mode. When photons go through the - grid in the simple-atom mode, photon frequencies do not change a great deal, however in macro-atom - mode the frequencies can shift by large amounts. This creates a problem during the detailed spectral - generation stage, because one does not know before hand how many photons that started out of the + grid in the simple-atom mode, photon frequencies do not change a great deal, however in macro-atom + mode the frequencies can shift by large amounts. This creates a problem during the detailed spectral + generation stage, because one does not know before hand how many photons that started out of the desired band end up in the desired band. (In macro-atom mode, a photon bundle that starts out at - 8 keV photoionizes an Fe ion can turn (for example) into an Hbeta photon). To handle this, one - needs to estimate how often this happens and include this (effectively as a source function) in + 8 keV photoionizes an Fe ion can turn (for example) into an Hbeta photon). To handle this, one + needs to estimate how often this happens and include this (effectively as a source function) in radiative transfer involving macro-atoms. This is parallelized, in the same manner as the ionization - calculation by assigning various cells to various threads and gathering the results back before + calculation by assigning various cells to various threads and gathering the results back before the radiative transfer step in the detailed spectrum phase. -MPI requires intialization. For python this is carried out in python.c. Various subroutines make -use of MPI, and as a result, programmers need to be aware of this fact when they write auxiliary -routines that use the various subroutines called by Python. +MPI requires intialization. For python this is carried out in python.c. Various subroutines make +use of MPI, and as a result, programmers need to be aware of this fact when they write auxiliary +routines that use the various subroutines called by Sirocco. Input naming conventions ======================== @@ -119,7 +119,7 @@ that is, they all begin with Central_object. This convention should be followed External variables ================== -Python uses lots (and likely too many), what are properly know as external variables. (In C, a global +Sirocco uses lots (and likely too many), what are properly know as external variables. (In C, a global variable is a variable whose scope is all of the routines in a speciric file. An external varriable is one that is shared across multiple files.) @@ -128,7 +128,7 @@ In the latest generations of gcc, the standards for extenral variiables have be If one wishes to define an external variable, one must first declare it as eternal, and then one must initialize it outside a specific routine exactly in one place. -The standard convention is that the variables are declared as external in a header file, e.g python.h, +The standard convention is that the variables are declared as external in a header file, e.g python.h, and then intialized in a separate .c file, e.g python_extern_init.c. Unless, a variable is actually initialized, no space will be allocated for the variable. diff --git a/docs/sphinx/source/developer/tests.rst b/docs/sphinx/source/developer/tests.rst index 1230bdc7e..8f9115416 100644 --- a/docs/sphinx/source/developer/tests.rst +++ b/docs/sphinx/source/developer/tests.rst @@ -1,31 +1,31 @@ Unit Test Framework ################### -Unit tests are an excellent way to ensure that any code you write is robust and correct. To manage unit testing, Python +Unit tests are an excellent way to ensure that any code you write is robust and correct. To manage unit testing, Sirocco uses the `CUnit `_ test framework. Unit tests are run by using :code:`make check` in -either the root directory of Python, or in the source/test directory. +either the root directory of Sirocco, or in the source/test directory. Installing CUnit ================ -Python has been tested to work with CUnit (and CUnity) versions newer than 2.1-3. A recent version of CUnit is provided -in the :code:`$PYTHON/software` directory and can be installed as a static library by using the Makefile in Python's +Sirocco has been tested to work with CUnit (and CUnity) versions newer than 2.1-3. A recent version of CUnit is provided +in the :code:`$SIROCCO/software` directory and can be installed as a static library by using the Makefile in Sirocco's root directory. To build CUnit from source, you will need `CMake `_ installed, which is a modern build system for C and C++ projects. -CUnit will be installed (as a static library) at the same time as GSL and Python during the first-time install, e.g., +CUnit will be installed (as a static library) at the same time as GSL and Sirocco during the first-time install, e.g., .. code:: bash - $ [$PYTHON] ./configure - $ [$PYTHON] make install + $ [$SIROCCO] ./configure + $ [$SIROCCO] make install -It is also possible to install only CUnit, using the same Makefile, if Python and GSL are already installed on your +It is also possible to install only CUnit, using the same Makefile, if Sirocco and GSL are already installed on your system, .. code:: bash - $ [$PYTHON] make cunit + $ [$SIROCCO] make cunit If compilation of CUnit fails, it's more than likely that you could install a dynamic version of an older version of the library from your system's package manager, e.g. @@ -43,15 +43,15 @@ Running Tests To run the tests, navigate into one of three directories, -- :code:`$PYTHON` -- :code:`$PYTHON/source` -- :code:`$PYTHON/tests` +- :code:`$SIROCCO` +- :code:`$SIROCCO/source` +- :code:`$SIROCCO/tests` Then run the command :code:`make check` which will compile and run the unit tests, .. code:: bash - $ [$PYTHON/source] make check + $ [$SIROCCO/source] make check CUnit - A unit testing framework for C - Version 3.2.7-cunity http://cunit.sourceforge.net/ @@ -103,10 +103,10 @@ using an assert which compares two floating point arrays to within a tolerance. It should be noted that this assertion is not part of the standard CUnit assertions. It is possible to make a new assertion by writing a macro (or function) which implements the base :code:`CU_assertImplementation` assert -implementation. If you need to create your own assertion, these should be kept in :code:`$PYTHON/source/tests/assert.h`. +implementation. If you need to create your own assertion, these should be kept in :code:`$SIROCCO/source/tests/assert.h`. .. code:: c - :caption: :code:`$PYTHON/source/tests/tests/test_matrix.c` + :caption: :code:`$SIROCCO/source/tests/tests/test_matrix.c` #include "assert.h" @@ -185,7 +185,7 @@ test. :code:`CU_add_test` returns a pointer to the test in the suite. If for wha returned instead. .. code:: c - :caption: :code:`$PYTHON/source/tests/tests/test_matrix.c` + :caption: :code:`$SIROCCO/source/tests/tests/test_matrix.c` void create_matrix_test_suite(void) { /* Create a test suite - if suite can't be made, return error code */ @@ -209,7 +209,7 @@ unit tests, is shown in the code exert below. These functions should not take an indicate if everything went OK or not. .. code:: c - :caption: :code:`$PYTHON/source/tests/tests/test_matrix.c` + :caption: :code:`$SIROCCO/source/tests/tests/test_matrix.c` @@ -231,7 +231,7 @@ function in the main function of the unit test framework, ensuring we do so afte initialized; this is done by the function :code:`CU_initialize_registry`. .. code:: c - :caption: :code:`$PYTHON/source/tests/unit_test_main.c` + :caption: :code:`$SIROCCO/source/tests/unit_test_main.c` int main(int argc, char **argv) { /* Create the test registry */ @@ -269,18 +269,18 @@ Directory and structure ----------------------- Unit tests should be kept in logically named files within the unit test directory located at -:code:`$PYTHON/source/tests/tests`. Any file in this directory should be added to the unit test Makefile, which is -located at :code:`$PYTHON/source/tests/Makefile`, specifically to the :code:`TEST_SOURCES` variable which is a list of +:code:`$SIROCCO/source/tests/tests`. Any file in this directory should be added to the unit test Makefile, which is +located at :code:`$SIROCCO/source/tests/Makefile`, specifically to the :code:`TEST_SOURCES` variable which is a list of all the source code required specifically for the unit test framework; this includes both the unit tests themselves and any other code required to, e.g., build and control the test registry. Prototypes for wrapper functions for creating -test suites (which are called in the main function) should be placed in :code:`$PYTHON/source/tests/tests/tests.h` -header file. Any data required for the tests should be kept in the data directory, :code:`$PYTHON/source/tests/data`, in +test suites (which are called in the main function) should be placed in :code:`$SIROCCO/source/tests/tests/tests.h` +header file. Any data required for the tests should be kept in the data directory, :code:`$SIROCCO/source/tests/data`, in appropriately organised directories as shown below. .. code:: bash - :caption: :code:`$PYTHON/source/tests` + :caption: :code:`$SIROCCO/source/tests` - $ tree $PYTHON/source/tests + $ tree $SIROCCO/source/tests ├── Makefile ├── assert.h @@ -298,7 +298,7 @@ appropriately organised directories as shown below. │   └── tests.h └── unit_test_main.c -We also need to include the Python source code we are testing in the :code:`PYTHON_SOURCES` variable of the Makefile. If -there are any CUDA files required, these should be added to the :code:`CUDA_SOURCES` variable. In theory, we should only -need to include the files containing the code we are testing. But in practise, we choose to instead include all of -Python's source files (as it makes our lives easier) which increases compile time and the size of the final binary. +We also need to include the Sirocco source code we are testing in the :code:`SIROCCO_SOURCES` variable of the Makefile. +If there are any CUDA files required, these should be added to the :code:`CUDA_SOURCES` variable. In theory, we should +only need to include the files containing the code we are testing. But in practise, we choose to instead include all of +Sirocco's source files (as it makes our lives easier) which increases compile time and the size of the final binary. diff --git a/docs/sphinx/source/examples.rst b/docs/sphinx/source/examples.rst index fc9de546f..9020307e9 100644 --- a/docs/sphinx/source/examples.rst +++ b/docs/sphinx/source/examples.rst @@ -1,9 +1,9 @@ Examples ######## -*python* is a large and complicated code with a very wide range of capabilities. Some are very non-intuitive. This section of the documentation provides worked examples on how to set up, run, and analyse the outputs of the code. +Sirocco is a large and complicated code with a very wide range of capabilities. Some are very non-intuitive. This section of the documentation provides worked examples on how to set up, run, and analyse the outputs of the code. -The files relating to these examples can be found in the `examples/` section of the *python* repository. +The files relating to these examples can be found in the `examples/` section of the Sirocco repository. .. toctree:: diff --git a/docs/sphinx/source/examples/demo-models/tde/tde.rst b/docs/sphinx/source/examples/demo-models/tde/tde.rst index af0567239..b427eb5dd 100644 --- a/docs/sphinx/source/examples/demo-models/tde/tde.rst +++ b/docs/sphinx/source/examples/demo-models/tde/tde.rst @@ -3,7 +3,7 @@ Demo: Tidal Disruption Event ############################ -One of the recent applications of Python is modelling outflows in Tidal +One of the recent applications of Sirocco is modelling outflows in Tidal Disruption Events (TDEs). We have explored how line formation in an accretion disc wind could explain the BAL vs. BEL dichotomy observed in the UV spectra of TDEs. We have also explored how reprocessing in an accretion disc wring could @@ -21,7 +21,7 @@ Key Model Parameters We model a disc wind outflow using the kinematic `Shlosman & Vitello (1993) `_ (SV93) biconical disc wind model. This model has seen extensive use throughout the -history of Python to model across all length scales of accretion, from CVs to +history of Sirocco to model across all length scales of accretion, from CVs to QSO winds. Further information about the SV93 model can be found in the documentation `here <../../sv.rst>`_. @@ -59,7 +59,7 @@ Wind geometry parameters: \lambda &=~0 \\ f_{v} &=~0.1 \\ -For parameters controlling the radiative transfer and flow of Python, the +For parameters controlling the radiative transfer and flow of Sirocco, the parameter file for this model can be found :download:`here `. Radiation Sources @@ -92,7 +92,7 @@ total of 48 cores. Each processor core runs at a clock frequency of 2.1 GHz, wit a maximum boost clock of 3.7 GHz. The model uses roughly 70 GB of the available DDR4 2666 MHz memory available in this system. -With this configuration using :math:`10^{8}` photons and Python's "-p 2" option +With this configuration using :math:`10^{8}` photons and Sirocco's "-p 2" option for logarithmic photon number stepping, the model takes roughly 10 ionization cycles to converge in roughly 7.5 hours, or 360 total CPU hours. The spectral cycles take a significantly longer time to complete. For six inclination angles and @@ -154,7 +154,7 @@ formation processes which scale with electron density, such as collisional excitation, decreasing with radius also. The outer top edge of the wind is cool, reaching temperature as low as -:math:`T_{e} \sim 10^{3}` K. Python does not implement any dust or molecular +:math:`T_{e} \sim 10^{3}` K. Sirocco does not implement any dust or molecular physics, hence the treatment of this region of the wind is highly approximate. However, since the line formation we are interested in does not occur in this region, our neglect of this physics should not effect the emergency spectrum. diff --git a/docs/sphinx/source/index.rst b/docs/sphinx/source/index.rst index df9b1cede..018747394 100644 --- a/docs/sphinx/source/index.rst +++ b/docs/sphinx/source/index.rst @@ -1,29 +1,26 @@ -.. python documentation master file, created by +.. sirocco documentation master file, created by sphinx-quickstart on Sun Jan 14 18:04:35 2018. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. ######## -*python* +Sirocco ######## -------------------------------------- Radiative transfer and ionisation code -------------------------------------- -Python is a Monte-Carlo radiative transfer code designed to simulate the spectrum of biconical (or spherical) +Sirocco is a Monte-Carlo radiative transfer code designed to simulate the spectrum of biconical (or spherical) winds in disk systems. It was origianally written by `Long and Knigge (2002) `_ and was intended for simulating the spectra of winds in cataclysmic variables. Since then, it has also been used to simulate the spectra of systems ranging from young stellar objects to AGN. -The name Python is today unfortunate, and changing the name is an ongoing debate within the development team. -The program is written in C and can be compiled on systems runining various flavors of linux, including OSX on Macs. - - -The code is is available on `github `_ Issues regarding the code and suggestions for improvement the code regarding the should be reported there. We actively -encourage other to make use of the code for their own science. If anyone has questions about -whether the code might be useful for a project, we encourage you to contact one of the authors of -the code. +The program is written in C and can be compiled on systems runining various flavors of linux, including macOS and the +Windows Subsystem for Linux (WSL). The code is is available on `GitHub `_. Issues +regarding the code and suggestions for improvement should be reported there. We actively encourage other to make use of +the code for their own science. If anyone has questions about whether the code might be useful for a project, we +encourage you to contact one of the authors of the code. ------------- @@ -32,15 +29,15 @@ Documentation Various documentation exists: -* A :doc:`Quick Guide ` describing how to install and run Python (in a fairly mechanistic fashion). +* A :doc:`Quick Guide ` describing how to install and run Sirocco (in a fairly mechanistic fashion). -For more information on how this page was generated and how to create documentation for *python*, +For more information on how this page was generated and how to create documentation for Sirocco, look at the page for :doc:`documentation on the documentation `. ------- Authors ------- -The authors of the *python* code and their institutions are: +The authors of the Sirocco code and their institutions are: Knox Long Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA @@ -62,7 +59,7 @@ Sam Mangham Department of Physics and Astronomy, University of Southampton, Southampton, SO17 1BJ, UK Edward Parkinson - Department of Physics and Astronomy, University of Southampton, Southampton, SO17 1BJ, UK + Department of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK Mandy Hewitt School of Mathematics and Physics, Queen's University Belfast, University Road, Belfast, BT7 1NN, UK @@ -86,7 +83,7 @@ Amin Mosallanezhad quick installation - running_python + running_sirocco input output plotting diff --git a/docs/sphinx/source/input/overview.rst b/docs/sphinx/source/input/overview.rst index 0402c988a..b0b252a13 100644 --- a/docs/sphinx/source/input/overview.rst +++ b/docs/sphinx/source/input/overview.rst @@ -1,7 +1,7 @@ Overview ######## -Python uses a keyword-based parameter file to specify a model. Below is an example portion of a parameter file for a star system type (which must have the extension .pf). The parameters look as follows: +Sirocco uses a keyword-based parameter file to specify a model. Below is an example portion of a parameter file for a star system type (which must have the extension .pf). The parameters look as follows: .. code:: @@ -26,15 +26,15 @@ The comment generally specifies a set of valid choices or the units in which inf When a series of choices is presented, one does not need to enter the complete word, just enough to provide unique match to the choice. -The user does not need to create a parameter .pf file before running Python. Invoking Python without a parameter file will cause Python to prompt the user for the necessary information to create a parameter file. The user can specfiy any name for the parameter file. The example below calls the filename 'my_new_model' +The user does not need to create a parameter .pf file before running Sirocco. Invoking Sirocco without a parameter file will cause Sirocco to prompt the user for the necessary information to create a parameter file. The user can specfiy any name for the parameter file. The example below calls the filename 'my_new_model' -.. Instead, assuming one is not working from a template parameter file, one simply invokes Python. +.. Instead, assuming one is not working from a template parameter file, one simply invokes Sirocco. .. code:: console py my_new_model -Python then queries the user for answers to a series of questions, creating in the process a pf file, my_new_model.pf, +Sirocco then queries the user for answers to a series of questions, creating in the process a pf file, my_new_model.pf, that can be edited and used in future runs. An example of a line presented to the user in interactive mode is: @@ -47,7 +47,7 @@ There the number in the second set of parenthesis is a suggested value of the pa The user types in a new value and a carriage return, or, if the the suggested value seems appropriate, responds with a carriage return, in which case the suggested value will be used. -The user can use the :code:`-i` switch when invoking Python. This indicates that Python should accumulate all of the necessary inputs, write out the parameter file, and exit, which is useful if one is not completely sure what one wants. +The user can use the :code:`-i` switch when invoking Sirocco. This indicates that Sirocco should accumulate all of the necessary inputs, write out the parameter file, and exit, which is useful if one is not completely sure what one wants. .. code:: console @@ -57,8 +57,8 @@ The user can use the :code:`-i` switch when invoking Python. This indicates that Changes in the input files as the code evolves ---------------------------------------------- -Occassionally, new input variables will be introduced into Python. In this case, when one tries to run a parameter file -created with a previous version of Python in single processor mode, the program will query the user for the parameters that are missing, and then +Occassionally, new input variables will be introduced into Sirocco. In this case, when one tries to run a parameter file +created with a previous version of Sirocco in single processor mode, the program will query the user for the parameters that are missing, and then attempt to run the program as normal. If the original name of the parameter file was :code:`test.pf`, the modified version of the parameter file will be written to :code:`test.out.pf`, so diff --git a/docs/sphinx/source/input/parameters/Input_spectra.model_file.rst b/docs/sphinx/source/input/parameters/Input_spectra.model_file.rst index 538780634..5d94ef1ca 100644 --- a/docs/sphinx/source/input/parameters/Input_spectra.model_file.rst +++ b/docs/sphinx/source/input/parameters/Input_spectra.model_file.rst @@ -1,12 +1,12 @@ Input_spectra.model_file ======================== In addition to being able to generate several types of spectra, such -as blackbodies and power laws, Python can read in a series of spectra +as blackbodies and power laws, Sirocco can read in a series of spectra which are tabulated and are describable in terms of (usually) temperature and gravity). This parameter defines the name of the file which gives the location of the individual spectra and the temperate and gravity associated with each spectrum. (One may wish to use the same files for several radiation sources, viz the disk and the star). -Python actually only reads in the data the first time. +Sirocco actually only reads in the data the first time. Type String diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.live_or_die.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.live_or_die.rst index 3a6c18964..fa62948ba 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.live_or_die.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.live_or_die.rst @@ -1,6 +1,6 @@ Spectrum.live_or_die ==================== -Normally in creating detailed spectrum Python "extracts" photons in a certain +Normally in creating detailed spectrum Sirocco "extracts" photons in a certain direction reweighting them to account for the fact that they have been extracted in a certain direction. It is possible to just count the photons that are emitted in a single angle range. The two methods should yield the same or very similar results diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_photons_by_position.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_photons_by_position.rst index a7f0bdb47..acf4659f1 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_photons_by_position.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_photons_by_position.rst @@ -13,7 +13,7 @@ between photons that read the observer from the near or far side of the disk. *Note: This option is only available in extract mode. If one attempts to select -photons by position in live or die mode. The Python will warn the user and exit.* +photons by position in live or die mode. The Sirocco will warn the user and exit.* Type Boolean (yes/no) diff --git a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_in_final_spectrum.rst b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_in_final_spectrum.rst index 2b83f64b9..bd6fe2498 100644 --- a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_in_final_spectrum.rst +++ b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_in_final_spectrum.rst @@ -15,7 +15,7 @@ Values proportional to its surface area. models - Radiate according to a model. Python can support tabulated models that output with a binned luminosity distribution + Radiate according to a model. Sirocco can support tabulated models that output with a binned luminosity distribution depending on system properties like temperature and gravity. See :ref:`Input_spectra.model_file`. The total luminosity will be set by :ref:`Boundary_layer.luminosity`. diff --git a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_to_make_wind.rst b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_to_make_wind.rst index bc57ea51e..29ba4df1d 100644 --- a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_to_make_wind.rst +++ b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_to_make_wind.rst @@ -15,7 +15,7 @@ Values proportional to its surface area. models - Radiate according to a model. Python can support tabulated models that output with a binned luminosity distribution + Radiate according to a model. Sirocco can support tabulated models that output with a binned luminosity distribution depending on system properties like temperature and gravity. See :ref:`Input_spectra.model_file`. The total luminosity will be set by :ref:`Boundary_layer.luminosity`. diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_in_final_spectrum.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_in_final_spectrum.rst index 49e7ff906..732296341 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_in_final_spectrum.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_in_final_spectrum.rst @@ -16,7 +16,7 @@ Values models Available for :ref:`System_type` of ``star`` or ``cv``. - Radiate according to a model. Python can support tabulated models that output with a binned luminosity distribution + Radiate according to a model. Sirocco can support tabulated models that output with a binned luminosity distribution depending on system properties like temperature and gravity. See :ref:`Input_spectra.model_file`. The total luminosity will be set by :ref:`Central_object.luminosity`. diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_to_make_wind.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_to_make_wind.rst index df76eba46..dabc4819a 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_to_make_wind.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_to_make_wind.rst @@ -13,7 +13,7 @@ Values proportional to its surface area. models - Radiate according to a model. Python can support tabulated models that output with a binned luminosity distribution + Radiate according to a model. Sirocco can support tabulated models that output with a binned luminosity distribution depending on system properties like temperature and gravity. See :ref:`Input_spectra.model_file`. The total luminosity will be set by :ref:`Central_object.luminosity`. diff --git a/docs/sphinx/source/input/parameters/other/Diag.rst b/docs/sphinx/source/input/parameters/other/Diag.rst index 944d7e36b..81efde450 100644 --- a/docs/sphinx/source/input/parameters/other/Diag.rst +++ b/docs/sphinx/source/input/parameters/other/Diag.rst @@ -1,6 +1,6 @@ Diag ==== -A series of advanced/diagnostic commands (Accessed using the ``-d`` flag, see :ref:`Running Python`). +A series of advanced/diagnostic commands (Accessed using the ``-d`` flag, see :ref:`Running Sirocco`). The commands generally allow access to additional information from the simulation, or allow more precise control. Advanced commands have an @ symbol in front of them in the parameter file. diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.lowest_ion_density_for_photoabs.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.lowest_ion_density_for_photoabs.rst index 527af02ec..f163fc045 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.lowest_ion_density_for_photoabs.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.lowest_ion_density_for_photoabs.rst @@ -1,6 +1,6 @@ Diag.lowest_ion_density_for_photoabs ==================================== -For efficiency reasons, Python does not try to calculate photoabsorption +For efficiency reasons, Sirocco does not try to calculate photoabsorption for an ion with an extremly low density. This advance parameter changes this density limit diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.partial_cells.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.partial_cells.rst index 57c67da53..b79fd5aec 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.partial_cells.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.partial_cells.rst @@ -2,7 +2,7 @@ Diag.partial_cells ================================ Additional options for how to deal with cells that are partially filled by wind. -Somewhat degenerate with the ``-include_partial_cells`` flag under :ref:`Running Python`. +Somewhat degenerate with the ``-include_partial_cells`` flag under :ref:`Running Sirocco`. Type diff --git a/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.nbands.rst b/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.nbands.rst index a1ed90599..5a4443350 100644 --- a/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.nbands.rst +++ b/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.nbands.rst @@ -1,6 +1,6 @@ Photon_sampling.nbands ====================== -Python uses stratified samplign to generate photons during the ionization phase. This +Sirocco uses stratified samplign to generate photons during the ionization phase. This parameter allows the user to define the number of bands for stratified sampling, if s/he wants to customize the bands used for the generation of photons diff --git a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.type.rst b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.type.rst index 24af14f59..9159369a3 100644 --- a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.type.rst +++ b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.type.rst @@ -5,7 +5,7 @@ path of photons emitted in the simulation as they travel through the geometry, assuming that any delays from recombination etc. are negligible and all delays are due to light travel time. For each final spectrum, all contributing photons are output to a '.delay_dump' file that can then be processed using -our 'tfpy' Python (no relation) library. +our 'tfpy' Sirocco (no relation) library. Type Enumerator diff --git a/docs/sphinx/source/input/parameters/rt_ion/Atomic_data.rst b/docs/sphinx/source/input/parameters/rt_ion/Atomic_data.rst index 95754072c..34f52b9c4 100644 --- a/docs/sphinx/source/input/parameters/rt_ion/Atomic_data.rst +++ b/docs/sphinx/source/input/parameters/rt_ion/Atomic_data.rst @@ -1,6 +1,6 @@ Atomic_data =========== -Python uses an atomic data file, as found in the `agnwinds/data` repository. +Sirocco uses an atomic data file, as found in the `agnwinds/data` repository. This is the relative path to the Atomic Data header file on disk. See :ref:`Atomic data` Type diff --git a/docs/sphinx/source/input/parameters/rt_ion/Spectrum_cycles.rst b/docs/sphinx/source/input/parameters/rt_ion/Spectrum_cycles.rst index 7bb24a30f..82e5add7d 100644 --- a/docs/sphinx/source/input/parameters/rt_ion/Spectrum_cycles.rst +++ b/docs/sphinx/source/input/parameters/rt_ion/Spectrum_cycles.rst @@ -1,7 +1,7 @@ Spectrum_cycles =============== -In Python, the detailed spectra are calculate with flights of photons that have +In Sirocco, the detailed spectra are calculate with flights of photons that have the same number of photon bundles as in ionization cycles. The detailed spectra are calculated between a minimum and maximum wavelenth, and at specific inclination angles. Various other conditions can be placed on each spectrum that is accumulated. diff --git a/docs/sphinx/source/input/parameters/wind/Wind/Wind.ionization.rst b/docs/sphinx/source/input/parameters/wind/Wind/Wind.ionization.rst index 2940a1413..234bf9b62 100644 --- a/docs/sphinx/source/input/parameters/wind/Wind/Wind.ionization.rst +++ b/docs/sphinx/source/input/parameters/wind/Wind/Wind.ionization.rst @@ -1,6 +1,6 @@ Wind.ionization =============== -The approach used by Python to calculate the ionization +The approach used by Sirocco to calculate the ionization of the wind during ionization cycles. A number of these modes are historical or included for diagnostic purposes. diff --git a/docs/sphinx/source/input/parameters/wind/Wind/Wind.type.rst b/docs/sphinx/source/input/parameters/wind/Wind/Wind.type.rst index 246fd67f7..3d5f4e567 100644 --- a/docs/sphinx/source/input/parameters/wind/Wind/Wind.type.rst +++ b/docs/sphinx/source/input/parameters/wind/Wind/Wind.type.rst @@ -20,7 +20,7 @@ Values A special purpose mode used by the python collaboration for importing models from Zeus and Pluto. (Depreciated) (:ref:`Hydro`) imported - A general purpose mode for importing a wind from an ascii file (see also :doc:`Python Script documentation <../wind_models/importing_models>`). + A general purpose mode for importing a wind from an ascii file (see also :doc:`Sirocco Script documentation <../wind_models/importing_models>`). kwd The Knigge-Woods-Drew parameterization of a bi-conical flow. (:ref:`KWD`) diff --git a/docs/sphinx/source/input/system_description.rst b/docs/sphinx/source/input/system_description.rst index fbe241933..0e04cbd80 100644 --- a/docs/sphinx/source/input/system_description.rst +++ b/docs/sphinx/source/input/system_description.rst @@ -1,7 +1,7 @@ System Description ################## -The first set of parameters which Python needs are information about the overall system. +The first set of parameters which Sirocco needs are information about the overall system. .. code:: diff --git a/docs/sphinx/source/input/wind_model_params.rst b/docs/sphinx/source/input/wind_model_params.rst index f350341e7..dc50d8a82 100644 --- a/docs/sphinx/source/input/wind_model_params.rst +++ b/docs/sphinx/source/input/wind_model_params.rst @@ -1,7 +1,7 @@ Wind Model Parameters ###################### -Python allows for various types of wind models, which are defined by the following parameters. This page focuses on the actual parameters in the file, but further description of the wind models and instructions on how to import models can be found under :doc:`Wind Models <../wind_models>`. +Sirocco allows for various types of wind models, which are defined by the following parameters. This page focuses on the actual parameters in the file, but further description of the wind models and instructions on how to import models can be found under :doc:`Wind Models <../wind_models>`. .. code:: @@ -16,7 +16,7 @@ Python allows for various types of wind models, which are defined by the followi for example a fast flow near the poles of a system, and a slow for near the disk. If the number of components exceeds 1, then the remaining questions relating to the wind will be posed multiple times. -:ref:`Wind.type`: The wind models incorporated into Python currently are: +:ref:`Wind.type`: The wind models incorporated into Sirocco currently are: :ref:`SV` The Shlosman and Vitello parameterization of a bi-conical flow. @@ -40,7 +40,7 @@ If the number of components exceeds 1, then the remaining questions relating to A model of a thin shell useful for diagnostic studies. :ref:`Imported ` - A general purpose mode for importing a wind from an ascii file (see also :doc:`Python Script documentation <../wind_models/importing_models>`). + A general purpose mode for importing a wind from an ascii file (see also :doc:`Sirocco Script documentation <../wind_models/importing_models>`). :ref:`Wind.coord_system` is the coordinate system in which the wind is defined. diff --git a/docs/sphinx/source/installation.rst b/docs/sphinx/source/installation.rst index d61e1aeb3..22eb8c9cb 100644 --- a/docs/sphinx/source/installation.rst +++ b/docs/sphinx/source/installation.rst @@ -1,8 +1,8 @@ Getting Started ############### -What machines will python run on? *Python* is written C. We have regularly run *Python* on various linux systems as well as a variity of Mac machines. -It is compiled using mpicc/gcc, but it can be compiled simply with gcc. +What machines will python run on? Sirocco is written C. We have regularly run Sirocco on various linux systems as well as a variity of Mac machines. +It is compiled using mpicc/gcc, but it can be compiled simply with gcc. It uses the Gnu Scientific Libraries (gsl) @@ -11,17 +11,17 @@ It uses the Gnu Scientific Libraries (gsl) Installation ============ -Python and the various routines associated are set up in a self-contained directory structure. -The basic directory structure and the data files that one needs to run Python need to be retrieved and compiled. +Sirocco and the various routines associated are set up in a self-contained directory structure. +The basic directory structure and the data files that one needs to run Sirocco need to be retrieved and compiled. -If you want to obtain a stable (!) release, go to the `Releases `_ page. +If you want to obtain a stable (!) release, go to the `Releases `_ page. If you want to download the latest dev version, you can zip up the git repository by clicking on the zip icon to the right of the GitHub page. Alternatively, clone it directly as .. code:: bash - $ git clone https://github.com/agnwinds/python.git + $ git clone git@github.com:sirocco-rt/sirocco.git If you anticipate contributing to development we suggest Forking the repository and submitting pull requests with any proposed changes. @@ -29,8 +29,8 @@ Once you have the files, you need to cd to the new directory and set your enviro .. code:: bash - $ export PYTHON = /path/to/python/ - $ cd $PYTHON + $ export SIROCCO=/path/to/sirocco/ + $ cd $SIROCCO $ ./configure $ make install $ make clean @@ -41,19 +41,19 @@ note that export syntax is for bash- for csh use .. code:: console - $ setenv PYTHON /path/to/python/ + $ setenv SIROCCO /path/to/sirocco/ -The atomic data needed to run Python is included in the distribution. +The atomic data needed to run Sirocco is included in the distribution. -The source code for Python is under actively development and is updated fairly often. Normally, one does not need to redo the entire installation process, since this includes GSL setup. +The source code for Sirocco is under actively development and is updated fairly often. Normally, one does not need to redo the entire installation process, since this includes GSL setup. Instead, one can pull in changes and recompile the source code by running .. code:: bash - $ cd $PYTHON/source - $ make python + $ cd $SIROCCO/source + $ make sirocco which will compile the main program. The program plus full set of auxiliary programs (such as windsave2table and py_wind, see below) can be compiled using :code:`make all`. @@ -64,35 +64,35 @@ To run python you need to add the following to your $PATH variable: .. code:: bash - $PYTHON/bin + $SIROCCO/bin You can then setup your symbolic links by running .. code:: bash - $ Setup_Py_Dir + $ Setup_Sirocco_Dir and run the code by typing, e.g. .. code:: bash - $ py root.pf + $ sirocco root.pf Running in parallel mode ------------------------ -While Python can be run in single processor mode, it is generally more efficient to run on multiple processors. in multiprocessor mode, -When multiprocessing is invoked, Python uses mulitple threads for photon transfer and in calcuation ionization equilibrium. As these -comprise the bulk of the computational load the total time to run is roughly an inverse of the number of threads. Python uses `MPI `_ for parallel processing and so software libraries that implement this must be on the machine that is -being used. For Macs, mpi can installed with HomeBrew or Fink. For linux machines, two common libraries are `Open-MPI `_ and `MPICH `_ If not already installed, one should -install them. +While Sirocco can be run in single processor mode, it is generally more efficient to run on multiple processors. in multiprocessor mode, +When multiprocessing is invoked, Sirocco uses mulitple threads for photon transfer and in calcuation ionization equilibrium. As these +comprise the bulk of the computational load the total time to run is roughly an inverse of the number of threads. Sirocco uses `MPI `_ for parallel processing and so software libraries that implement this must be on the machine that is +being used. For Macs, mpi can installed with HomeBrew or Fink. For linux machines, two common libraries are `Open-MPI `_ and `MPICH `_ If not already installed, one should +install them. -With mpi installed (and after recompiling with mpicc, which is the default) one would simply run the above program with +With mpi installed (and after recompiling with mpicc, which is the default) one would simply run the above program with .. code:: bash - $ mpirun -np 8 py root.pf + $ mpirun -np 8 sirocco root.pf where -np followed by a number designates the number of threads assigned. @@ -103,38 +103,38 @@ Auxiliary programs There are two programs that are useful for extracting information about models * windsave2table generates a series of astropy tables that can be used to inspect elements of the various models, including densities of specific ions -* py_wind is a mainly interactive routine that prints similar infomation to the screen. +* sirocco_wind is a mainly interactive routine that prints similar infomation to the screen. The two files are run as follows .. code:: bash $ windsave2table root - $ py_wind root + $ sirocco_wind root Brief descriptions of command line options for running these routines can obtained using a -h switch -Python scripts +Sirocco scripts -------------- -There are a number of python, the progamming language scripts, that can be used to plot results -from a Python run. These are not particularly well documented and many have been developed -for looking at various aspects of the code. A few may require python packages to be installed. +There are a number of Python, the programming language scripts, that can be used to plot results +from a Sirocco run. These are not particularly well documented and many have been developed +for looking at various aspects of the code. A few may require Python packages to be installed. However, a number are likely to be useful. -To make use of these scipts one should add +To make use of these scripts one should add -$PYTHON/py_progs both to the PATH and PYTHONPATH variables +$SIROCCO/py_progs both to the PATH and PYTHONPATH variables One script that is particularly useful is run_check.py, which is run as follows .. code:: bash - $run_check.py root + $ run_check.py root -This should create an html file that contains a summary set of information about a run, with plots that -indicate how much of the wind has converged as a function of cycle, which cells have converged at the end, what +This should create an html file that contains a summary set of information about a run, with plots that +indicate how much of the wind has converged as a function of cycle, which cells have converged at the end, what the electron and temperature structrue of the wind is, as well as quick plots of the spectra that were produced. Directory structure @@ -148,7 +148,7 @@ source bin Location of executables -docs +docs Location of documentation, including sphinx docs, doxygen, parameters and documentation for the python programs in py_progs. data @@ -169,4 +169,4 @@ examples Please help by reporting bugs in installation --------------------------------------------- -This can be done by submitting a bug under the `Issues `_ page +This can be done by submitting a bug under the `Issues `_ page diff --git a/docs/sphinx/source/meta.rst b/docs/sphinx/source/meta.rst index 1592fc22f..ef52b176c 100644 --- a/docs/sphinx/source/meta.rst +++ b/docs/sphinx/source/meta.rst @@ -1,7 +1,7 @@ Meta-documentation ################## -How to document Python +How to document Sirocco ====================== This documentation is written in **ReStructured Text**, and parsed by **Sphinx**. @@ -12,8 +12,8 @@ Installing the documentation tools ---------------------------------- This guide is produced using **Sphinx**. -**Sphinx** is written in **python** and available from the **pip** package manager. -We require the **Python 3** version of **Sphinx**. Install it, and the other modules required, as: +**Sphinx** is written in Sirocco and available from the **pip** package manager. +We require the **Sirocco 3** version of **Sphinx**. Install it, and the other modules required, as: .. code :: bash @@ -91,7 +91,7 @@ When referring to a library or program name, render it in **bold** as: .. code :: rst - Though this program is called **Python**, it is written in **C**, using the **GSL** library. + Though this program is called *Sirocco*, it is written in **C**, using the **GSL** library. Content of interest to developers but not users should be broken out into a callout as: @@ -149,7 +149,7 @@ A definition list consists of titles followed by a definition block indented by The headings, in the order we expect, are: Name - The parameter name, as used by Python input files. + The parameter name, as used by Sirocco input files. Description A description of the parameter and its function. @@ -286,7 +286,7 @@ Documenting Python Scripts The :doc:`py_progs` page is intended to document various python scripts contained within the py_progs folder. The aim is to do this using Sphinx's `autodoc extension `_, invoked by adding ``sphinx.ext.autodoc`` to extensions list in the conf.py file. py_progs is also added to the path using ``sys.path.insert(0, '../../py_progs/')``. -The above link contains full documentation of the commands. A module in py_progs can be documented by adding the following text to the rst file, where module.py is the name of the module you wish to document. +The above link contains full documentation of the commands. A module in py_progs can be documented by adding the following text to the rst file, where module.py is the name of the module you wish to document. .. code :: rst diff --git a/docs/sphinx/source/operation.rst b/docs/sphinx/source/operation.rst index b776393f3..220478026 100644 --- a/docs/sphinx/source/operation.rst +++ b/docs/sphinx/source/operation.rst @@ -1,7 +1,7 @@ Code Operation ##################### -The basic code operation of Python is split into different cycles; First, the ionization state is calculated (:doc:`Ionization Cycles `). As these photons pass through the simulation grid, their heating and ionizing effect on the plasma is recorded through the use of Monte Carlo estimators. This process continues until the code converges on a solution in which the heating and cooling processes are balanced and the temperature stops changing significantly (see :doc:`Convergence & Errors `). Once the ionization and temperature structure of the outflow has been calculated, the spectrum is synthesized by tracking photons through the plasma until sufficient signal-to-noise is achieved in the output spectrum for lines to be easily identified (:doc:`Spectral Cycles `). +The basic code operation of Sirocco is split into different cycles; First, the ionization state is calculated (:doc:`Ionization Cycles `). As these photons pass through the simulation grid, their heating and ionizing effect on the plasma is recorded through the use of Monte Carlo estimators. This process continues until the code converges on a solution in which the heating and cooling processes are balanced and the temperature stops changing significantly (see :doc:`Convergence & Errors `). Once the ionization and temperature structure of the outflow has been calculated, the spectrum is synthesized by tracking photons through the plasma until sufficient signal-to-noise is achieved in the output spectrum for lines to be easily identified (:doc:`Spectral Cycles `). .. toctree:: :glob: diff --git a/docs/sphinx/source/operation/ionization_cycles.rst b/docs/sphinx/source/operation/ionization_cycles.rst index 60cc2ba7c..92bb05c7b 100644 --- a/docs/sphinx/source/operation/ionization_cycles.rst +++ b/docs/sphinx/source/operation/ionization_cycles.rst @@ -7,7 +7,7 @@ with a guess at the ionization structure, usually by setting the temperature of at a specific value and assuming that the ionization equilibrium is simple given by the Saha equation for that particular temperature. -In Python, one then generates a set of photon bundles over a wide frequency range, and then +In Sirocco, one then generates a set of photon bundles over a wide frequency range, and then causes these photons to pass through and interact via various processes with the wind. As the photons transit the wind, estimatores for various processes are accumulated, which characterize the intensity and spectrum of the radiation field in various parts of @@ -18,6 +18,6 @@ estimators to modify the ionization state and electron temperature in each cell, assumed density and velocity field of the wind. There are a variety of approaches to carrying out this calculation and various limitations placed on the rate at which the plasma is is allowed to change between cycles. As the accuracy of any Monte Carlo simulation depends on numbers of photons bundles one uses to approximate the spectrum there are various -options within Python to choose the number of photons with various energy/wavelength bins, +options within Sirocco to choose the number of photons with various energy/wavelength bins, and other options to begin with a smaller number of photons and increase this number in later cycles. diff --git a/docs/sphinx/source/operation/spectral_cycles.rst b/docs/sphinx/source/operation/spectral_cycles.rst index c67d3eccc..eb261ef2b 100644 --- a/docs/sphinx/source/operation/spectral_cycles.rst +++ b/docs/sphinx/source/operation/spectral_cycles.rst @@ -19,7 +19,7 @@ by the packet diminishes as it moves through the wind. Then one could simply create a spectrum from photon packets that exit the simulation volume at a particular incination angle (plus or minus some delta) to construct the spectrum. This so-called live-or-die option -is implemented in Python, but only as a diagnostic option, because it is +is implemented in Sirocco, but only as a diagnostic option, because it is inefficient since most photon packets exit the system at inclination angles that are not of interest. diff --git a/docs/sphinx/source/output.rst b/docs/sphinx/source/output.rst index 5ecd1211f..40431a039 100644 --- a/docs/sphinx/source/output.rst +++ b/docs/sphinx/source/output.rst @@ -1,7 +1,7 @@ Outputs & Evaluation ##################### -Python produces a large number of files in both binary and ascii format. Tools exist to examine the binary files. +Sirocco produces a large number of files in both binary and ascii format. Tools exist to examine the binary files. .. toctree:: diff --git a/docs/sphinx/source/output/diagnostics.rst b/docs/sphinx/source/output/diagnostics.rst index 48874b679..e7f29e1a8 100644 --- a/docs/sphinx/source/output/diagnostics.rst +++ b/docs/sphinx/source/output/diagnostics.rst @@ -1,12 +1,12 @@ Diagnostic files ################ -Python logs a considerable amount of information as it runs. +Sirocco logs a considerable amount of information as it runs. Some of this information is printed to the screen but a much more voluminous version of progress of the program is placed in a sub-directory, named diag_whatever, where whatever is the root name of the model being run. In this directory one will find log files, e.g. **whatever_0.diag**, **whatever_1.diag**, and so on, where the in a multiprocessor run, the number refers to the log of a specific thread. -Inspecting these logs is important for understanding whether a Python run is successful, +Inspecting these logs is important for understanding whether a Sirocco run is successful, and for understanding why if failed if that is the case. diff --git a/docs/sphinx/source/output/evaluation.rst b/docs/sphinx/source/output/evaluation.rst index b6da4fb9e..d2cecf048 100644 --- a/docs/sphinx/source/output/evaluation.rst +++ b/docs/sphinx/source/output/evaluation.rst @@ -1,16 +1,16 @@ Evaluation ########### -Determining whether Python has run successfully from a a scientific point of view depends very specifically on one's goals. Did the spectra turn out to be what one expected? Here by evaluation we mean, did my run complete without significant errors and did the ionization structure converge to a "steady state" given the number of ionization cycles, the number of photons, and the frequency distributions of the photons we chose. +Determining whether Sirocco has run successfully from a a scientific point of view depends very specifically on one's goals. Did the spectra turn out to be what one expected? Here by evaluation we mean, did my run complete without significant errors and did the ionization structure converge to a "steady state" given the number of ionization cycles, the number of photons, and the frequency distributions of the photons we chose. Convergence ============ -:doc:`Ionization cycles <../operation/ionization_cycles>` in Python are intended to establish the correct ion densities and temperature of the various cells in the wind. The degree to which this happens for a given number of ionization cycles depends on how far the initial guess of electrons temperatures in various portions of the wind and the number of photons generated during each photoionization cycles. Furthermore, the accuracy of the final model depends on the number of photons that pass through each cell. As a result, the accuracy with which ion abundances and temperature are determined will differ on a cell by cell basis. In a typical model with a biconical outflow, a small cells at the outer edge of the accretion disk will record fewer photon passages than one in the middle of the grid that is exposed to large numbers of photons from the disk. +:doc:`Ionization cycles <../operation/ionization_cycles>` in Sirocco are intended to establish the correct ion densities and temperature of the various cells in the wind. The degree to which this happens for a given number of ionization cycles depends on how far the initial guess of electrons temperatures in various portions of the wind and the number of photons generated during each photoionization cycles. Furthermore, the accuracy of the final model depends on the number of photons that pass through each cell. As a result, the accuracy with which ion abundances and temperature are determined will differ on a cell by cell basis. In a typical model with a biconical outflow, a small cells at the outer edge of the accretion disk will record fewer photon passages than one in the middle of the grid that is exposed to large numbers of photons from the disk. -A very basic question about a particular run is, has it reached a "steady state" and if it is in a steady state are cells stable in the sense that fluctuations are small. Hopefully, each ionization cycle brings one closer and closer to to a solution after which the ionization structure no longer evolves. Of course, since Python is a Monte Carlo code, the degree to which the solution stays constant from cycle to cycle is limited by counting statistics. To check convergence in Python, we monitor the the radiation temperature :math:`T_r`, the electron temperature :math:`T_e`, and the total heating :math:`\mathrm{Heat_{tot}}` and cooling :math:`\mathrm{Cooling_{tot}}` in each cell as a function of the ionization cycle :math:`n`. +A very basic question about a particular run is, has it reached a "steady state" and if it is in a steady state are cells stable in the sense that fluctuations are small. Hopefully, each ionization cycle brings one closer and closer to to a solution after which the ionization structure no longer evolves. Of course, since Sirocco is a Monte Carlo code, the degree to which the solution stays constant from cycle to cycle is limited by counting statistics. To check convergence in Sirocco, we monitor the the radiation temperature :math:`T_r`, the electron temperature :math:`T_e`, and the total heating :math:`\mathrm{Heat_{tot}}` and cooling :math:`\mathrm{Cooling_{tot}}` in each cell as a function of the ionization cycle :math:`n`. -To estimate whether a a model calculation has reached a steady state, Python carries out three tests, one comparing the difference in the radiation temperature between the current and the preceding ionization cycle, one comparing the electron temperature in the same manner and once comparing heating and cooling rates in the current cycle. If a cell satisfies the following 3 tests, +To estimate whether a a model calculation has reached a steady state, Sirocco carries out three tests, one comparing the difference in the radiation temperature between the current and the preceding ionization cycle, one comparing the electron temperature in the same manner and once comparing heating and cooling rates in the current cycle. If a cell satisfies the following 3 tests, .. math:: \left | \frac{T_e^n-T_e^{n-1}}{T_e^n+T_e^{n-1}} \right | < \epsilon @@ -28,14 +28,14 @@ where :math:`\epsilon = 0.05`, it is flagged as converged. It is rare that all of the cells in a model will satisfy all of these criteria. That is is because the number photons passing that pass through a cell vary considerably and the statistical noise depends on the the number of photons. It is important to note that the photons that contribute most to the spectra of an object will be those which have the most photons passing through them. Typically, we consider a model to be well converged if 90% of the cells are converged by this criterion. -The routine ``run_check.py`` (see :doc:`Python Script documentation <../py_progs>`) produces two plots related to convergence, one showing the fraction of cells that have passed each of the tests above as a function of cycle, and the other showing the number of failed checks for each cell in the wind. +The routine ``run_check.py`` (see :doc:`Sirocco Script documentation <../py_progs>`) produces two plots related to convergence, one showing the fraction of cells that have passed each of the tests above as a function of cycle, and the other showing the number of failed checks for each cell in the wind. -Note that it is not always important that all cells be converged. The Monte Carlo process preferentially picks out the cells which affect the emergent radiation field. Portions of the grid which do not get many photons are typically the ones that are "not converged", but since they don't contribute much to the emergent radiation, one does not need them to be converged (except if one wants to make nice plots of the temperature as a function of position in the wind or of the density of a particular ion species). On the other hand, if one is using Python in conjunction with a hydrodynamical code one wants all the cells to be converged. +Note that it is not always important that all cells be converged. The Monte Carlo process preferentially picks out the cells which affect the emergent radiation field. Portions of the grid which do not get many photons are typically the ones that are "not converged", but since they don't contribute much to the emergent radiation, one does not need them to be converged (except if one wants to make nice plots of the temperature as a function of position in the wind or of the density of a particular ion species). On the other hand, if one is using Sirocco in conjunction with a hydrodynamical code one wants all the cells to be converged. Errors ============ -Python is designed to continue to run unless something catastrophic happens. +Sirocco is designed to continue to run unless something catastrophic happens. As it runs, it logs error messages that can be found in the .diag files. These messages are a combinations of warnings, and/or unusual occurrences, that if they start occurring often suggest a real problem. @@ -49,7 +49,7 @@ These error messages are all of the form: that is they begin with the word :code:`Error`. followed by the subroutine in the code where the error occurred followed by what is hopefully a helpful. If one is concerned about a particular message, one can hopefully determine what is happening by looking for that message in the log files. -Python keeps a count of the number of times a particular message has occurred and at the end of the program, and the very end of the +Sirocco keeps a count of the number of times a particular message has occurred and at the end of the program, and the very end of the diag files contain a listing of how many times a particular error has occurred. .. code:: diff --git a/docs/sphinx/source/output/model.rst b/docs/sphinx/source/output/model.rst index 5d2fcaf65..2068af469 100644 --- a/docs/sphinx/source/output/model.rst +++ b/docs/sphinx/source/output/model.rst @@ -1,7 +1,7 @@ Model ##### -As Python is run, it repeatedly writes out two binary files that contain essentially all information about the wind as calculated in the ionization phase of the program, +As Sirocco is run, it repeatedly writes out two binary files that contain essentially all information about the wind as calculated in the ionization phase of the program, along with status of the program at the last point where the file was written. These files along with the parameter file are sufficient to restart the program, if for example, one wants to check point the program after a certain time, and restart where one left off, @@ -14,7 +14,7 @@ or to add spectral cycles to get better spectra. .spec_save A binary file that contains all of the information about the spectra that have created. This file is not of interest to users directly. It is used when restarting -Two routines exist as part of the Python distribution allow the user to gain insight into the actual model +Two routines exist as part of the Sirocco distribution allow the user to gain insight into the actual model windsave2table Executed from the command line with :code:`windsave2table rootname`. @@ -25,9 +25,9 @@ windsave2table There are varrious options for how much data is to be printed out. A summary of these can be obtained with code:`windsave2table -h` -py_wind - Executed from the command line with :code:`py_wind rootname` +sirocco_wind + Executed from the command line with :code:`sirocco_wind rootname` Allows the user to query for information about the model interactively. The results can be written to ascii files for future reference - Again, there are various options, and a summary can be obtained with :code:`py_wind -h` + Again, there are various options, and a summary can be obtained with :code:`sirocco_wind -h` diff --git a/docs/sphinx/source/output/spectrum_files.rst b/docs/sphinx/source/output/spectrum_files.rst index 0bc170ab0..b689277e9 100644 --- a/docs/sphinx/source/output/spectrum_files.rst +++ b/docs/sphinx/source/output/spectrum_files.rst @@ -1,10 +1,10 @@ Spectra Files ############# -Python is intended to produce simulated spectra. These spectra are all ascii tables intended to be accessible with software packages such as astropy. +Sirocco is intended to produce simulated spectra. These spectra are all ascii tables intended to be accessible with software packages such as astropy. All of the ascii begin with commented headers that contain all of the parameters of associated with a run, -along with the date of the run and the specific version of Python used to make the run. +along with the date of the run and the specific version of Sirocco used to make the run. In principle, if one still has access to any of the spectra, one can reproduce the entire run again. Broad band spectra are created from the last ionization cycle. (More accurately the broad band spectra are written out at the end of each ionization cycle, so one the program is finished one has the @@ -67,7 +67,7 @@ The remaining columns are: an ascii file that contains the final detailed spectra for the wavelengths of interest at a distance of **100 pc** on a linear frequency scale. The units for the detailed spectra are determined by the input parameter Spectrum.type. - Photons bundles are generated in cycles in Python and the *.spec* file is actually written out at the end of each cycle + Photons bundles are generated in cycles in Sirocco and the *.spec* file is actually written out at the end of each cycle as the program is running in the spectrum-generation phase of the program. So one can inspect the spectrum as it is building up. The beginning of the file (omitting the header) is as follows: diff --git a/docs/sphinx/source/output/spectrum_generation_large_optical_depth.rst b/docs/sphinx/source/output/spectrum_generation_large_optical_depth.rst index 345ba455a..580696544 100644 --- a/docs/sphinx/source/output/spectrum_generation_large_optical_depth.rst +++ b/docs/sphinx/source/output/spectrum_generation_large_optical_depth.rst @@ -53,7 +53,7 @@ Removing Photons due to Too Many Scatters ========================================= As well as edge effects, flux can be lost due to photons being removed from the -photon sample due to scattering too many times. In Python, when a photon has undergone +photon sample due to scattering too many times. In Sirocco, when a photon has undergone `MAXSCAT = 500` scatters, a photon is assumed to have become stuck in the wind and hence it is terminated and no longer tracked. diff --git a/docs/sphinx/source/physics.rst b/docs/sphinx/source/physics.rst index be1ad0d44..84b4544ce 100644 --- a/docs/sphinx/source/physics.rst +++ b/docs/sphinx/source/physics.rst @@ -1,7 +1,7 @@ Physics & Radiative Transfer ------------------------------ -Various physical concepts are incorporated into Python. +Various physical concepts are incorporated into Sirocco. Some of these are descibed below: .. toctree:: diff --git a/docs/sphinx/source/physics/aniso.rst b/docs/sphinx/source/physics/aniso.rst index a489b0d7a..585536df6 100644 --- a/docs/sphinx/source/physics/aniso.rst +++ b/docs/sphinx/source/physics/aniso.rst @@ -1,7 +1,7 @@ Anisotropic Scattering -------------------------- -Python has a number of radiative transfer modes, controlled via the :doc:`/input/parameters/rt_ion/Line_transfer` keyword. Included in this mode is the treatment of line anisotropy; whether re-emission of a line photon is isotropic or not. When the scattering is isotropic, a new direction is simply chosen by choosing a random direction in the co-moving frame of the plasma. +Sirocco has a number of radiative transfer modes, controlled via the :doc:`/input/parameters/rt_ion/Line_transfer` keyword. Included in this mode is the treatment of line anisotropy; whether re-emission of a line photon is isotropic or not. When the scattering is isotropic, a new direction is simply chosen by choosing a random direction in the co-moving frame of the plasma. If anisotropic scattering is on, via one of the thermal trapping modes, the new direction is chosen according to a rejection method. The aim is to account for the fact the photon undergoes many interactions in the resonant zone due to the thermal width of the line, and finds it easier to escape along the direction in which the optical depth is lowest (where the velocity gradient is highest). Specifically, the code undergoes the following steps: diff --git a/docs/sphinx/source/physics/limitations.rst b/docs/sphinx/source/physics/limitations.rst index cb44b101d..463c36f5f 100644 --- a/docs/sphinx/source/physics/limitations.rst +++ b/docs/sphinx/source/physics/limitations.rst @@ -1,67 +1,67 @@ Limitations and Caveats ------------------------- -There are a number of limitations of Python as a code. This page is a non-exhaustive list, partly taken from the release paper. +There are a number of limitations of Sirocco as a code. This page is a non-exhaustive list, partly taken from the release paper. Specific issues and code problems ====================================== -**Pecular emission features in high optical depth models:** As described in `issue 659 `_ +**Pecular emission features in high optical depth models:** As described in `issue 659 `_ -**Free-free Gaunt factors:** As described in `issue 33 `_, we use a free-free integrated Gaunt factor for both free-free opacity and cooling calculations, but one should really use a frequency-specific (photon by photon) Gaunt factor for the opacity calculation. +**Free-free Gaunt factors:** As described in `issue 33 `_, we use a free-free integrated Gaunt factor for both free-free opacity and cooling calculations, but one should really use a frequency-specific (photon by photon) Gaunt factor for the opacity calculation. -**Compton heating energy leak:** As described in `issue 295 `_, +**Compton heating energy leak:** As described in `issue 295 `_, there is the potential for a slight energy leak (or poor statistics) for Compton heating in non-macro atom mode. -**Iron Fluorescence:** As described in `issue 499 `_, +**Iron Fluorescence:** As described in `issue 499 `_, Fluorescence data is not dealt with properly in non-macro-atom mode. The user should use a Fe macro-atom data set for -situations where this is important. +situations where this is important. Conceptual and Practical difficulties ====================================== -Python is a complex code that includes a wide range of physical processes. As a result, it is capable of synthesizing the electromagnetic signatures of a diverse set of astrophysical systems on all scales. However, there are, of course, important limitations that the user should be aware of. Perhaps most importantly, Python is *not* a stellar atmosphere code: it is not designed for hugely optically thick and/or static media. Below, we explore these and some other constraints in more detail. +Sirocco is a complex code that includes a wide range of physical processes. As a result, it is capable of synthesizing the electromagnetic signatures of a diverse set of astrophysical systems on all scales. However, there are, of course, important limitations that the user should be aware of. Perhaps most importantly, Sirocco is *not* a stellar atmosphere code: it is not designed for hugely optically thick and/or static media. Below, we explore these and some other constraints in more detail. - **Extreme optical depths** - Since Python is a Monte Carlo code that tracks the discrete interactions of simulated photon packets, the computational cost of a wind model scales with the characteristic optical depth, :math:`\tau_{ch}`, it presents to these packets. In most environments of interest, at least hydrogen is nearly fully ionized, so it is convenient to define :math:`\tau_{ch}` as the characteristic *electron scattering* optical depth. More specifically, the computational cost depends on the mean number of scatterings, :math:`\overline{N}_{scat}`, packets have to undergo before escaping. As a practical guide to what is feasible, we note that in some of our TDE model models :math:`\overline{N}_{scat}` is as high as `100 - 1000`, corresponding to :math:`\tau_{ch} \sim 10 - 100`. Such models take :math:`O(1000)` core-hours to converge and produce synthetic spectra. + Since Sirocco is a Monte Carlo code that tracks the discrete interactions of simulated photon packets, the computational cost of a wind model scales with the characteristic optical depth, :math:`\tau_{ch}`, it presents to these packets. In most environments of interest, at least hydrogen is nearly fully ionized, so it is convenient to define :math:`\tau_{ch}` as the characteristic *electron scattering* optical depth. More specifically, the computational cost depends on the mean number of scatterings, :math:`\overline{N}_{scat}`, packets have to undergo before escaping. As a practical guide to what is feasible, we note that in some of our TDE model models :math:`\overline{N}_{scat}` is as high as `100 - 1000`, corresponding to :math:`\tau_{ch} \sim 10 - 100`. Such models take :math:`O(1000)` core-hours to converge and produce synthetic spectra. It is important to understand that optically thick media can give rise to a variety of symptoms. Moreover, geometry matters. In 2-D models, optical depths can be highly direction dependent, with :math:`\tau_{min} << \tau_{max}`. In this case, photon packets can and will escape preferentially along the most transparent direction(s), i.e. :math:`\tau_{ch} \sim \tau_{min}`. This is both good and bad. Good, because :math:`\tau_{ch} << \tau_{max}` implies that even models with very high :math:`\tau_{max}` are, in principle, computationally feasible. Bad, because such models often encounter serious convergence problems, as there are not enough photon packets crossing cells embedded deep within (or located behind) high-:math:`\tau` structures. In practice, unconverged cells in such regions can often be safely ignored, as they are bound to contribute little to the synthesized spectra. However, this decision has to be made by the user. - In 1-D (spherical) models, photon packets can more easily get trapped, as there are no preferred directions along which they can escape. In practice, computational resources and/or convergence issues tend to be the limiting factor for optically thick 1-D models. However, in "simple atom" mode, there is an additional failure mode for such models. In the limit where most photons packets are (re)absorbed and (re)emitted many times in a single cell, it becomes extremely difficult for the code to maintain energy conservation. The issue is that the radiation field locally becomes nearly isotropic. The *net (outward) flux* can then be a tiny fraction of the *mean (angle-averaged) intensity*. In Monte Carlo terms, the luminosity (number of photon packets) that has to be created within a cell is far greater than the net number of packets that actually leaves the cell. The luminosity carried by the escaping population can then become highly uncertain simply due to Poisson scatter. Yet global energy conservation across a simulation relies on the correct flow rates of photon packets across cells. This is not a concern when Python is run in macro-atom mode, as this automatically enforces radiative equilibrium and hence guarantees global energy conservation. + In 1-D (spherical) models, photon packets can more easily get trapped, as there are no preferred directions along which they can escape. In practice, computational resources and/or convergence issues tend to be the limiting factor for optically thick 1-D models. However, in "simple atom" mode, there is an additional failure mode for such models. In the limit where most photons packets are (re)absorbed and (re)emitted many times in a single cell, it becomes extremely difficult for the code to maintain energy conservation. The issue is that the radiation field locally becomes nearly isotropic. The *net (outward) flux* can then be a tiny fraction of the *mean (angle-averaged) intensity*. In Monte Carlo terms, the luminosity (number of photon packets) that has to be created within a cell is far greater than the net number of packets that actually leaves the cell. The luminosity carried by the escaping population can then become highly uncertain simply due to Poisson scatter. Yet global energy conservation across a simulation relies on the correct flow rates of photon packets across cells. This is not a concern when Sirocco is run in macro-atom mode, as this automatically enforces radiative equilibrium and hence guarantees global energy conservation. - **The Sobolev and related approximations** - Python is designed to synthesize spectra formed in *moving* media and treats line transfer in the Sobolev approximation. This means that a photon packet is only allowed to interact with a bound-bound transition at the exact location where its co-moving frequency coincides with the rest frequency of the transition. In reality, interactions take place across a spatial scale defined by the *Sobolev length*, :math:`\Delta s \simeq v_{th} \left(dv_{s}/ds\right)`, where :math:`v_{th}` is the local thermal speed, :math:`s` is distance along the photon packet's line of flight, and :math:`v_{s}` is the projected velocity along this direction. The Sobolev approximation therefore amounts to the assumption that the physical properties of the flow do not change significantly on scales as small as :math:`\Delta s`. + Sirocco is designed to synthesize spectra formed in *moving* media and treats line transfer in the Sobolev approximation. This means that a photon packet is only allowed to interact with a bound-bound transition at the exact location where its co-moving frequency coincides with the rest frequency of the transition. In reality, interactions take place across a spatial scale defined by the *Sobolev length*, :math:`\Delta s \simeq v_{th} \left(dv_{s}/ds\right)`, where :math:`v_{th}` is the local thermal speed, :math:`s` is distance along the photon packet's line of flight, and :math:`v_{s}` is the projected velocity along this direction. The Sobolev approximation therefore amounts to the assumption that the physical properties of the flow do not change significantly on scales as small as :math:`\Delta s`. The optical depth associated with the interaction is proportional to the *Sobolev length*, :math:`\Delta s \simeq v_{th} \left(dv_{s}/ds\right)`, where :math:`v_{th}` is the local thermal speed, :math:`s` is distance along the photon packet's line of flight, and :math:`v_{s}` is projected velocity along this direction. Since :math:`v_{th}` is usually close to the sound speed, the Sobolev approximation is sometimes also called the supersonic approximation. - In line with this treatment, Python neglects any thermal or microturbulent broadening associated with bound-bound transitions. It is the responsibility of the user to ensure that the Sobolev approximation is valid for the models they run. Static media and flows in which thermal speeds are comparable to bulk velocities should not be simulated with Python. + In line with this treatment, Sirocco neglects any thermal or microturbulent broadening associated with bound-bound transitions. It is the responsibility of the user to ensure that the Sobolev approximation is valid for the models they run. Static media and flows in which thermal speeds are comparable to bulk velocities should not be simulated with Sirocco. - **The size of the line list** - An important challenge for Python is to identify spectral lines that might be in resonance with a particular photon packet as it passes through a given cell. The computational cost of this task scales directly with the size of the line list. It is therefore not feasible to simply import the latest Kurucz line list (http://kurucz.harvard.edu/linelists/gfnew/), for example, which contains :math:`\simeq 10^9` transitions. Again, Python is *not* a stellar atmosphere code. The default line list we use contains :math:`\simeq 10^4` transitions, which is sufficient for many applications. + An important challenge for Sirocco is to identify spectral lines that might be in resonance with a particular photon packet as it passes through a given cell. The computational cost of this task scales directly with the size of the line list. It is therefore not feasible to simply import the latest Kurucz line list (http://kurucz.harvard.edu/linelists/gfnew/), for example, which contains :math:`\simeq 10^9` transitions. Again, Sirocco is *not* a stellar atmosphere code. The default line list we use contains :math:`\simeq 10^4` transitions, which is sufficient for many applications. - One use case for Python where it is important to have as complete a line list as possible is its deployment as the radiation module in radiation-hydrodynamics simulations where line-driving forces are important (Higginbottom et al., 2020; Higginbottom et al., 2014). Our approach here is to use Python to estimate the ionization state and SED in each cell. This is then be passed to a stand-alone code that estimates the total line forces by summing over the complete Kurucz line list. + One use case for Sirocco where it is important to have as complete a line list as possible is its deployment as the radiation module in radiation-hydrodynamics simulations where line-driving forces are important (Higginbottom et al., 2020; Higginbottom et al., 2014). Our approach here is to use Sirocco to estimate the ionization state and SED in each cell. This is then be passed to a stand-alone code that estimates the total line forces by summing over the complete Kurucz line list. - **General Relativity** -Python self-consistently carries out the special relativistic frame transformations required as photon packets travel through the grid and interact with the moving material in each cell. However, it does not account for any purely *general* relativistic effects. In particular, photon packets in Python always travel in straight lines, rather than along geodesics. This will primarily affect the angular distributions of photon packets that are emitted or travel within, say, :math:`\simeq 10` gravitational radii of a compact object. This caveat should be kept in mind when modelling AGN and XRBs with Python. However, we console ourselves with the thought that the physical and radiative properties of accretion flows in this regime remain quite uncertain. +Sirocco self-consistently carries out the special relativistic frame transformations required as photon packets travel through the grid and interact with the moving material in each cell. However, it does not account for any purely *general* relativistic effects. In particular, photon packets in Sirocco always travel in straight lines, rather than along geodesics. This will primarily affect the angular distributions of photon packets that are emitted or travel within, say, :math:`\simeq 10` gravitational radii of a compact object. This caveat should be kept in mind when modelling AGN and XRBs with Sirocco. However, we console ourselves with the thought that the physical and radiative properties of accretion flows in this regime remain quite uncertain. - **Polarization** -In principle, polarization can be included quite naturally in Monte-Carlo radiative transfer, but the current version of Python does not include a treatment of polarization. +In principle, polarization can be included quite naturally in Monte-Carlo radiative transfer, but the current version of Sirocco does not include a treatment of polarization. - **Thermal and statistical equilibrium** -Python assumes that the flow is always and everywhere in thermal and statistical equilibrium. That is, the code iterates towards a temperature and ionization state for each cell in which the heating and cooling rates in each cell balance and the net transition rate *into* any given atomic/ionic level matches the net transition rate *out of* that level. This implies that there is no concept of time in Python -- the code is not designed to deal with non-equilibrium and/or time-dependent conditions. +Sirocco assumes that the flow is always and everywhere in thermal and statistical equilibrium. That is, the code iterates towards a temperature and ionization state for each cell in which the heating and cooling rates in each cell balance and the net transition rate *into* any given atomic/ionic level matches the net transition rate *out of* that level. This implies that there is no concept of time in Sirocco -- the code is not designed to deal with non-equilibrium and/or time-dependent conditions. -This limitation can be important even if the input radiation field is steady. For example, if the flow velocity in a grid cell with characteristic size :math:`\Delta x` is given by :math:`v`, matter will flow through the cell on a time-scale :math:`t_{flow} \sim \Delta x / v`. However, ionization equilibrium can only be established on a time-scale of :math:`t_{rec} \sim \alpha N_e`, where :math:`\alpha` is the relevant recombination coefficient, and :math:`N_e` is the local electron density. Thus if :math:`t_{flow} < t_{rec}`, the cell cannot be in ionization equilibrium. In sufficiently fast-moving flows, the ionization state can then become "frozen-in", i.e. fixed to approximately the state at the last point where equilibrium could be established. Since Python currently has no concept of these time scales, it does not check for such non-equilibrium conditions. It is up to the user to carry out the relevant sanity checks on their models. +This limitation can be important even if the input radiation field is steady. For example, if the flow velocity in a grid cell with characteristic size :math:`\Delta x` is given by :math:`v`, matter will flow through the cell on a time-scale :math:`t_{flow} \sim \Delta x / v`. However, ionization equilibrium can only be established on a time-scale of :math:`t_{rec} \sim \alpha N_e`, where :math:`\alpha` is the relevant recombination coefficient, and :math:`N_e` is the local electron density. Thus if :math:`t_{flow} < t_{rec}`, the cell cannot be in ionization equilibrium. In sufficiently fast-moving flows, the ionization state can then become "frozen-in", i.e. fixed to approximately the state at the last point where equilibrium could be established. Since Sirocco currently has no concept of these time scales, it does not check for such non-equilibrium conditions. It is up to the user to carry out the relevant sanity checks on their models. - **Dimensionality and resolution limits** -At present, Python is (at most) a 2.5-dimensional code. That is, the coordinate grid is restricted to 2D and assumed to be symmetric about the y-axis. However, photon packet transport takes place in 2D and allows for a rotational component of motion around the y-axis. In principle, upgrading Python to "full" 3D is fairly straightforward, but running such models would require significantly more computing resources. Memory requirements, in particular, scale directly with the number of grid cells used in a simulation. This is actually already a limitation for high-resolution models in 2D (or 2.5-D) and the reason we have set an upper limit of 500x500 cells as a default. +At present, Sirocco is (at most) a 2.5-dimensional code. That is, the coordinate grid is restricted to 2D and assumed to be symmetric about the y-axis. However, photon packet transport takes place in 2D and allows for a rotational component of motion around the y-axis. In principle, upgrading Sirocco to "full" 3D is fairly straightforward, but running such models would require significantly more computing resources. Memory requirements, in particular, scale directly with the number of grid cells used in a simulation. This is actually already a limitation for high-resolution models in 2D (or 2.5-D) and the reason we have set an upper limit of 500x500 cells as a default. -It is worth noting that Python's memory requirements for computationally demanding simulations are driven by the approach to parallelization that has been adopted. Currently, parallelization relies exclusively on ``MPI`` and requires the computational grid to be copied in full to each ``MPI`` process (i.e. each core). Memory requirements therefore increase rapidly as the number of processors is increased. This situation could be improved by adopting a hybrid ``OpenMP`` and ``MPI`` approach. +It is worth noting that Sirocco's memory requirements for computationally demanding simulations are driven by the approach to parallelization that has been adopted. Currently, parallelization relies exclusively on ``MPI`` and requires the computational grid to be copied in full to each ``MPI`` process (i.e. each core). Memory requirements therefore increase rapidly as the number of processors is increased. This situation could be improved by adopting a hybrid ``OpenMP`` and ``MPI`` approach. diff --git a/docs/sphinx/source/physics/macroatoms.rst b/docs/sphinx/source/physics/macroatoms.rst index 6b4be7667..c01660cdb 100644 --- a/docs/sphinx/source/physics/macroatoms.rst +++ b/docs/sphinx/source/physics/macroatoms.rst @@ -5,25 +5,25 @@ Macro Atoms .. todo:: Add description of accelerated macro-atom scheme -The macro-atom scheme was created by Leon Lucy and is outlined in his 2002/03 papers. It was implemented in Python by Stuart Sim, initially for the study of recombination lines in YSOs (Sim et al. 2005). +The macro-atom scheme was created by Leon Lucy and is outlined in his 2002/03 papers. It was implemented in Sirocco by Stuart Sim, initially for the study of recombination lines in YSOs (Sim et al. 2005). -Lucy (2002,2004) hereafter L02, L03 has shown that it is possible to calculate the emissivity of a gas in statistical equilibrium without approximation for problems with large departures from LTE. His `macro-atom` scheme allows for all possible transition paths from a given level, dispensing with the two-level approximation, and provides a full non-LTE solution for the level populations based on Monte Carlo estimators. The macro-atom technique has already been used to model Wolf-Rayet star winds (Sim 2004), AGN disc winds (Sim et al. 2008), supernovae (Kromer and Sim 2009, Kerzendorf and Sim 2014) and YSOs (Sim et al. 2005). A full description of the approach can be found in L02 and L03. +Lucy (2002,2004) hereafter L02, L03 has shown that it is possible to calculate the emissivity of a gas in statistical equilibrium without approximation for problems with large departures from LTE. His `macro-atom` scheme allows for all possible transition paths from a given level, dispensing with the two-level approximation, and provides a full non-LTE solution for the level populations based on Monte Carlo estimators. The macro-atom technique has already been used to model Wolf-Rayet star winds (Sim 2004), AGN disc winds (Sim et al. 2008), supernovae (Kromer and Sim 2009, Kerzendorf and Sim 2014) and YSOs (Sim et al. 2005). A full description of the approach can be found in L02 and L03. Following L02, let us consider an atomic species interacting with a radiation field. If the quantity :math:`\epsilon_j` represents the ionization plus excitation energy of a level :math:`j` then the rates at which the level absorbs and emits radiant energy are given by .. math:: - \dot{A}_{j}^{R} = R_{l j} \epsilon_{j l} + \dot{A}_{j}^{R} = R_{l j} \epsilon_{j l} and .. math:: - \dot{E}_{j}^{R} = R_{j l} \epsilon_{j l} + \dot{E}_{j}^{R} = R_{j l} \epsilon_{j l} where :math:`\epsilon_{j l} = \epsilon_j - \epsilon_l`. -Here, we have adopted Lucy's convention, in which the subscript +Here, we have adopted Lucy's convention, in which the subscript :math:`l` denotes a summation over all lower states (:math:`j`). Similarly, the rates corresponding to _kinetic_ (collisional) @@ -48,22 +48,22 @@ If we now impose statistical equilibrium ({\cal R}_{l j}-{\cal R}_{j l})+({\cal R}_{u j}-{\cal R}_{ju})=0 \;\;\;, -we obtain +we obtain .. math:: - \dot{E}_{j}^{R}+\dot{E}_{j}^{C}+{\cal R}_{ju}\epsilon_{j}+ {\cal R}_{j \ell}\epsilon_{l} \nonumber \\ = \dot{A}_{j}^{R}+\dot{A}_{j}^{C}+{\cal R}_{u j} \epsilon_{j} +{\cal R}_{l j} \epsilon_{l}. + \dot{E}_{j}^{R}+\dot{E}_{j}^{C}+{\cal R}_{ju}\epsilon_{j}+ {\cal R}_{j \ell}\epsilon_{l} \nonumber \\ = \dot{A}_{j}^{R}+\dot{A}_{j}^{C}+{\cal R}_{u j} \epsilon_{j} +{\cal R}_{l j} \epsilon_{l}. This equation is the starting point for the macro-atom scheme. It shows that, when assuming radiative equilibrium, the energy flows through a system depend only on the transition probabilities and atomic physics associated with the levels the energy flow interacts with. By quantising this energy flow into radiant (:math:`r-`) and kinetic (:math:`k-`) packets, we can simulate the energy transport through a plasma discretised into volume elements (macro-atoms),whose associated transition probabilities govern the interaction of radiant and kinetic energy with the ionization and excitation energy associated with the ions of the plasma. -Although the equation above assumes strict radiative equilbrium,it is trivial to adjust it to include non-radiative source and sink terms. For example, in an expanding parcel of plasma, adiabatic cooling may be included with a simple modification to the RHS. Currently, we include adiabatic cooling by destroying packets with a probability +Although the equation above assumes strict radiative equilbrium,it is trivial to adjust it to include non-radiative source and sink terms. For example, in an expanding parcel of plasma, adiabatic cooling may be included with a simple modification to the RHS. Currently, we include adiabatic cooling by destroying packets with a probability :math:`p_{i,\mathrm{destruct}} = {\cal C}_{\rm adiabatic} / {\cal C}_{\rm tot}`. A Hybrid Scheme ============================= -A pure macro-atom approach with only H and He can be easily used for some situations -- for example, in the YSO application described by, which uses a H-only model. However, in accretion disc winds, the densities can be very high, and higher :math:`Z` elements must be included. Including all these elements as macro-atoms is not currently computationally feasible in the code for anything but the simplest models. We thus often use a `hybrid scheme`, which treats H and He with the macro-atom approach, but models all other atoms as `simple-atoms`. +A pure macro-atom approach with only H and He can be easily used for some situations -- for example, in the YSO application described by, which uses a H-only model. However, in accretion disc winds, the densities can be very high, and higher :math:`Z` elements must be included. Including all these elements as macro-atoms is not currently computationally feasible in the code for anything but the simplest models. We thus often use a `hybrid scheme`, which treats H and He with the macro-atom approach, but models all other atoms as `simple-atoms`. Simple-atoms still interact with :math:`r`- and :math:`k`-packets but do not possess internal transition probabilities. As a result, they are analogous to the two-level atom treatment, as any excitation is immediately followed by a deactivation into an :math:`r`- or :math:`k`-packet. The choice of radiative or kinetic deactivation is made according to the relative rates in the two-level atom formalism. For a bound-bound transition :math:`u\to j`, these two probabilities @@ -85,7 +85,7 @@ This hybrid approach preserves the fast treatment of, for example, UV resonance Macro-atom Emissivity Calculation ======================================== -In order to preserve the philosophy that a detailed spectrum is calculated in a limited wavelength regime, Python carries out a macro-atom emissivity calculation before the spectral cycles. The aim of this step is to calculate the luminosity contributed by macro-atoms -- equivalent to the total amount of reprocessed emission -- in the wavelength range being considered. +In order to preserve the philosophy that a detailed spectrum is calculated in a limited wavelength regime, Sirocco carries out a macro-atom emissivity calculation before the spectral cycles. The aim of this step is to calculate the luminosity contributed by macro-atoms -- equivalent to the total amount of reprocessed emission -- in the wavelength range being considered. This process can be very computationally intensive, especially if the wavelength regime being simulated has very little emission from bound-free and line processes in the wind, but the overall broad-band emissivity is high. During the ionization cycles, the amount of energy absorbed into :math:`k`-packets and every macro-atom level is recorded using MC estimators. Once the ionization cycles are finished, and the model has converged, these absorption energies are split into a certain number of packets and tracked through the macro-atom machinery until a deactivation occurs. When this happens, the emissivity of the level the macro-atom de-activated from is incremented if the packet lies in the requested wavelength range. If it does not, then the packet is thrown away. It is easy to see how what is essentially a MC rejection method can be an inefficient way of sampling this parameter space. Fortunately, this problem is parallelised in the code. @@ -99,20 +99,20 @@ Bound-free Continua of Simple Atoms ============================================= .. todo:: this section is not yet completely accurate. -Historically, when using the indivisible packet form of radiative transfer (`macro_atoms_thermal_trapping`, for example), the bound-free continua of simple atoms were treated in a simplified two-level framework. In this case, simple atoms are those `without` a full macro-atom model atom, usually the metals. In this two-level scheme, whenever a simple atom undergoes a bound-free interaction, it is excited into the continuum state, and this is immediately followed by recombination, and an :math:`r`-packet or :math:`k`-packet is created immediately. As a result, the scheme does not capture the physical situation whereby a recombination cascade can occur from an initial recombination to excited levels, leading to a gradual reddening of the photon if there are many interactions. This situation **is** modelled well by a full macro-atom treatment. As of 2024, this is once again the default behaviour. +Historically, when using the indivisible packet form of radiative transfer (`macro_atoms_thermal_trapping`, for example), the bound-free continua of simple atoms were treated in a simplified two-level framework. In this case, simple atoms are those `without` a full macro-atom model atom, usually the metals. In this two-level scheme, whenever a simple atom undergoes a bound-free interaction, it is excited into the continuum state, and this is immediately followed by recombination, and an :math:`r`-packet or :math:`k`-packet is created immediately. As a result, the scheme does not capture the physical situation whereby a recombination cascade can occur from an initial recombination to excited levels, leading to a gradual reddening of the photon if there are many interactions. This situation **is** modelled well by a full macro-atom treatment. As of 2024, this is once again the default behaviour. -To try and slightly improve this scheme, we implemented a "total emissivity" upweighting scheme around 2018. The basic idea is that we pay attention to only the heating and cooling. In particular, the rates of all simple atom bound-free emission are governed by the `emissivity` of the bound-free process. -**Currently, this mode is turned off by default**, due to `various issues `_ associated with energy conservation, as also described below. +To try and slightly improve this scheme, we implemented a "total emissivity" upweighting scheme around 2018. The basic idea is that we pay attention to only the heating and cooling. In particular, the rates of all simple atom bound-free emission are governed by the `emissivity` of the bound-free process. +**Currently, this mode is turned off by default**, due to `various issues `_ associated with energy conservation, as also described below. -This result in two changes to the code for ionization cycles: - * whenever a k-packet is eliminated via a bound-free channel of a simple macro atom (simulating energy flow from the :math:`k`-packet pool to the radiation pool, :math:`k \to r`), we have that packet carry additional energy corresponding to the required ionization energy for that particular bf process. This means we upweight the energy of the packet by a factor :math:`f_{\rm up} = \nu / (\nu - \nu_0)`, where :math:`\nu` is the frequency of the new bound-free photon and :math:`\nu_0` is the threshold frequency. This quantity is the ratio of the total energy carried by photons in the packet to the energy supplied to photons in the packet from the thermal pool. +This result in two changes to the code for ionization cycles: + * whenever a k-packet is eliminated via a bound-free channel of a simple macro atom (simulating energy flow from the :math:`k`-packet pool to the radiation pool, :math:`k \to r`), we have that packet carry additional energy corresponding to the required ionization energy for that particular bf process. This means we upweight the energy of the packet by a factor :math:`f_{\rm up} = \nu / (\nu - \nu_0)`, where :math:`\nu` is the frequency of the new bound-free photon and :math:`\nu_0` is the threshold frequency. This quantity is the ratio of the total energy carried by photons in the packet to the energy supplied to photons in the packet from the thermal pool. * whenever an r-packet is “absorbed” by a simple macro atom bound-free process we track explicitly only the flow of energy to the thermal pool. This means we force the creation of a :math:`k`-packet, whereas before there woud be a choice, but we only take the contribution of the absorption to heating only: i.e. we downweight the packet energy by a factor :math:`f_{\rm down} = (\nu - \nu_0) / \nu`. -In the spectral cycles, interactions with simple bound-free continua now kill the photon, and :math:`k \to r` follow the same behaviour as above, because in these cycles we introduce a precalculated band-limited :math:`k`-packet emissivity. +In the spectral cycles, interactions with simple bound-free continua now kill the photon, and :math:`k \to r` follow the same behaviour as above, because in these cycles we introduce a precalculated band-limited :math:`k`-packet emissivity. -**It is possible for some numerical problems to occur.** For example, there is nothing to stop the value of :math:`f_{\rm up}` being quite large, if the photon is being emitted close to the edge. This is most likely to happen when the electron temperature :math:`T_e` is quite low, but there is nothing to stop it happening anywhere. This is most likely to lead to problems when the factor :math:`f_{\rm up}` is comparable to the typical number of photon passages per cell, since then a single photon can dominate the heating or ionization estimators in a given cell and lead to convergence problems by dramatically exacerbating shot noise. +**It is possible for some numerical problems to occur.** For example, there is nothing to stop the value of :math:`f_{\rm up}` being quite large, if the photon is being emitted close to the edge. This is most likely to happen when the electron temperature :math:`T_e` is quite low, but there is nothing to stop it happening anywhere. This is most likely to lead to problems when the factor :math:`f_{\rm up}` is comparable to the typical number of photon passages per cell, since then a single photon can dominate the heating or ionization estimators in a given cell and lead to convergence problems by dramatically exacerbating shot noise. .. admonition :: Activating the scheme - This mode can be turned on using the :ref:`Diag.use_upweighting_of_simple_macro_atoms`. + This mode can be turned on using the :ref:`Diag.use_upweighting_of_simple_macro_atoms`. In this case the code will go back to using the two-level framework for simple atom bound free continua. diff --git a/docs/sphinx/source/physics/radiative_transfer.rst b/docs/sphinx/source/physics/radiative_transfer.rst index 6a8aab066..e608a05ad 100644 --- a/docs/sphinx/source/physics/radiative_transfer.rst +++ b/docs/sphinx/source/physics/radiative_transfer.rst @@ -1,17 +1,17 @@ Radiative Transfer Modes ######################################################## -Python has a number of different radiative transfer modes, which affect the treatment of lines and scattering, and also whether we use indivisible packet constraints or allow photon weights to be attenuated by continuum absorption. These modes are selected with the parameter :ref:`Line_transfer`. The different modes are briefly described on that parameter page. This page is designed to give an overview of the assumptions and concepts behind, as well as the basic operation of, the different techniques. The aim is that, in partnership with the parameter page and the atomic data documentation, all the information regarding the different radiative transfer modes should be present. +Sirocco has a number of different radiative transfer modes, which affect the treatment of lines and scattering, and also whether we use indivisible packet constraints or allow photon weights to be attenuated by continuum absorption. These modes are selected with the parameter :ref:`Line_transfer`. The different modes are briefly described on that parameter page. This page is designed to give an overview of the assumptions and concepts behind, as well as the basic operation of, the different techniques. The aim is that, in partnership with the parameter page and the atomic data documentation, all the information regarding the different radiative transfer modes should be present. -For introductions and references regarding Monte Carlo radiative transfer techniques generally, we recommend reading `Noebauer & Sim 2019 `_. For specifics regarding Python, we recommend reading `Long & Knigge 2002 `_ as well as PhD theses by `Higginbottom `_ and `Matthews `_. +For introductions and references regarding Monte Carlo radiative transfer techniques generally, we recommend reading `Noebauer & Sim 2019 `_. For specifics regarding Sirocco, we recommend reading `Long & Knigge 2002 `_ as well as PhD theses by `Higginbottom `_ and `Matthews `_. Sobolev Approximation ====================== -Python always uses the Sobolev approximation to treat line transfer. In this approximation, it is assumed that the thermal line width is small compared to the velocity gradient. The Sobolev approximation is described extensively in astrophysics literature, and so we do not describe it in detail here. We refer the users to section 8.2 of `Noebauer & Sim 2019 `_ and references there in for a discussion of the Sobolev escape probabilities approach. +Sirocco always uses the Sobolev approximation to treat line transfer. In this approximation, it is assumed that the thermal line width is small compared to the velocity gradient. The Sobolev approximation is described extensively in astrophysics literature, and so we do not describe it in detail here. We refer the users to section 8.2 of `Noebauer & Sim 2019 `_ and references there in for a discussion of the Sobolev escape probabilities approach. Weight Reduction v Indivisible Packets ======================================= -Python was originally written in such a way that photon packet weights were not indivisible and allowed to be attenuated. This is the way the code is described in the original `Long & Knigge 2002 `_ paper. In the standard, weight reduction mode, photon weights are attenuated by continuum opacities (free-free, bound-free). Conservation of energy is then hopefully achieved by calculating the emission from the wind . +Sirocco was originally written in such a way that photon packet weights were not indivisible and allowed to be attenuated. This is the way the code is described in the original `Long & Knigge 2002 `_ paper. In the standard, weight reduction mode, photon weights are attenuated by continuum opacities (free-free, bound-free). Conservation of energy is then hopefully achieved by calculating the emission from the wind . In indivisible packet mode, there is a fundamental shift in philosophy. All energy packets are strictly indivisible and conserve energy whenever they undergo radiative processes (the only exception is adiabatic cooling). Thus, even bound-free absorption is dealt with at a single interaction point. @@ -37,7 +37,7 @@ Simple-atoms still interact with r- and k-packets, but do not possess internal t Isotropic v Anisotropic Line Scattering ============================================ -Python always treats electron scattering as an isotropic process, and continuum emission processes are also treated as isotropic, except for Compton scattering. For Compton scattering, the direction and energy change is calculated self-consistently according to the energy change formula :math:`E/E'=1+(h \nu/mc^2)(1+\cos\theta)`. We first draw a random cross section that our photon packet will see. This cross section represents an energy change and hence a direction. The distribution of angles is taken care of by using a differential cross section vs energy change function. +Sirocco always treats electron scattering as an isotropic process, and continuum emission processes are also treated as isotropic, except for Compton scattering. For Compton scattering, the direction and energy change is calculated self-consistently according to the energy change formula :math:`E/E'=1+(h \nu/mc^2)(1+\cos\theta)`. We first draw a random cross section that our photon packet will see. This cross section represents an energy change and hence a direction. The distribution of angles is taken care of by using a differential cross section vs energy change function. .. admonition:: Caution @@ -79,6 +79,6 @@ In the case of a resonance scatter with line transition u to j, the new frequenc The above formulae are the non-relativistic case, which is currently used in the code. However, this should in general be improved to use the special relativistic formula. This would produce more accurate Doppler shifts for the fastest regions of an outflow, as the current treatment introduces errors of order 5 Angstroms at the blue edges of the highest velocity absorption lines in quasar and CV wind models. -When real photons resonantly (or electron) scatter off real plasma in a flow, they conserve energy and frequency in the co-moving frame of the plasma. In the case of an outflow, doing the frame transformation from system->flow->system over the course of an interaction results in a redshifting of a photon, and as a result an energy loss - in other words, the photon does work on the flow even though energy is conserved in the co-moving frame. Indivisible packet schemes (such as macro-atoms) often enforce strict energy conservation in the frame of a given cell (physically, but see also `Lucy 2002 `_). This means that, when keeping track of packets in the observer frame, one needs to correct the energies (not just the frequencies) using a Doppler shift. Python does **not** currently conserve energy in the co-moving frame. +When real photons resonantly (or electron) scatter off real plasma in a flow, they conserve energy and frequency in the co-moving frame of the plasma. In the case of an outflow, doing the frame transformation from system->flow->system over the course of an interaction results in a redshifting of a photon, and as a result an energy loss - in other words, the photon does work on the flow even though energy is conserved in the co-moving frame. Indivisible packet schemes (such as macro-atoms) often enforce strict energy conservation in the frame of a given cell (physically, but see also `Lucy 2002 `_). This means that, when keeping track of packets in the observer frame, one needs to correct the energies (not just the frequencies) using a Doppler shift. Sirocco does **not** currently conserve energy in the co-moving frame. .. todo:: test whether this is an issue. diff --git a/docs/sphinx/source/physics/relativity_comoving.rst b/docs/sphinx/source/physics/relativity_comoving.rst index 25c0bf949..ee0ec1e3e 100644 --- a/docs/sphinx/source/physics/relativity_comoving.rst +++ b/docs/sphinx/source/physics/relativity_comoving.rst @@ -1,7 +1,7 @@ Special Relativity and Co-Moving Frames --------------------------------------- -The current version of Python incorporates special relativity and takes co-moving frame +The current version of Sirocco incorporates special relativity and takes co-moving frame effects into account by default. Global properties of the wind, such as a densities are defined in the observer diff --git a/docs/sphinx/source/plotting.rst b/docs/sphinx/source/plotting.rst index ca9ebcc50..876a1a93a 100644 --- a/docs/sphinx/source/plotting.rst +++ b/docs/sphinx/source/plotting.rst @@ -1,7 +1,7 @@ Plotting \& Processing Outputs ##################################### -Python produces a large number of files in both binary and ascii format. Tools exist to examine the binary files. +Sirocco produces a large number of files in both binary and ascii format. Tools exist to examine the binary files. .. toctree:: diff --git a/docs/sphinx/source/plotting/plot_spectrum.ipynb b/docs/sphinx/source/plotting/plot_spectrum.ipynb index f79cec7ee..8948163f6 100644 --- a/docs/sphinx/source/plotting/plot_spectrum.ipynb +++ b/docs/sphinx/source/plotting/plot_spectrum.ipynb @@ -9,12 +9,12 @@ "\n", " mkdir cv_test\n", " cd cv_test\n", - " cp $PYTHON/examples/basic/cv_standard.pf .\n", - " py cv_standard \n", - "\n", + " cp $SIROCCO/examples/basic/cv_standard.pf .\n", + " sirocco cv_standard \n", + " \n", "The model will take about 5 minutes to run on a single core. It will not converge and the spectrum will be a bit noisy, but will give us a model to use as an example. \n", "\n", - "The simplest way to make a quick look spectrum plot is using the ```plot_spec.py``` routine in ```$PYTHON/py_progs```. In this example, I will assume py_progs has been added to ```$PATH``` and to ```$PYTHONPATH```. ```plot_spec.py``` can be run from the command line using\n", + "The simplest way to make a quick look spectrum plot is using the ```plot_spec.py``` routine in ```$SIROCCO/py_progs```. In this example, I will assume py_progs has been added to ```$PATH``` and to ```$PYTHONPATH```. ```plot_spec.py``` can be run from the command line using\n", "\n", " plot_spec.py [-wmin 850 -wmax 1850 -smooth 21] cv_standard\n", " \n", @@ -48,7 +48,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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" ] @@ -69,7 +69,7 @@ ], "source": [ "%matplotlib inline\n", - "import plot_spec \n", + "import plot_spec\n", "wmin, wmax = 850,1800\n", "smooth = 21\n", "fname = \"cv_test/cv_standard\"\n", @@ -99,7 +99,7 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "import astropy.io.ascii as io \n", + "import astropy.io.ascii as io\n", "\n", "s = io.read(\"{}.spec\".format(fname))\n", "\n", @@ -157,7 +157,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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" ] diff --git a/docs/sphinx/source/plotting/plot_wind.ipynb b/docs/sphinx/source/plotting/plot_wind.ipynb index 1b373896b..0f128de7e 100644 --- a/docs/sphinx/source/plotting/plot_wind.ipynb +++ b/docs/sphinx/source/plotting/plot_wind.ipynb @@ -5,12 +5,12 @@ "metadata": {}, "source": [ "# Plotting Wind Properties\n", - "As described under [Models](../output/model.rst), Python saves wind properties in binary wind_save files. This notebook explains how to read and plot wind variables for the ```cv_standard``` file found in the examples. Before running the python commands, you need to run the model from the command line. I suggest running the following commands, after you have compiled python:\n", + "As described under [Models](../output/model.rst), Sirocco saves wind properties in binary wind_save files. This notebook explains how to read and plot wind variables for the ```cv_standard``` file found in the examples. Before running the python commands, you need to run the model from the command line. I suggest running the following commands, after you have compiled python:\n", "\n", " mkdir cv_test\n", " cd cv_test\n", - " cp $PYTHON/examples/basic/cv_standard.pf .\n", - " py cv_standard \n", + " cp $SIROCCO/examples/basic/cv_standard.pf .\n", + " sirocco cv_standard \n", "\n", "The model will take about 5 minutes to run on a single core. It will not converge, but will give us a model to use as an example. You should then run ```windsave2table``` on the output\n", "\n", @@ -20,7 +20,7 @@ "\n", "## Make A Basic Quick Look Wind Plot\n", "\n", - "The simplest way to make a quick look plot of the electron temperature is using the ```plot_wind.py``` routine in ```$PYTHON/py_progs```. In this example, I will assume py_progs has been added to ```$PATH``` and to ```$PYTHONPATH```. ```plot_wind.py``` can be run from the command line using\n", + "The simplest way to make a quick look plot of the electron temperature is using the ```plot_wind.py``` routine in ```$SIROCCO/py_progs```. In this example, I will assume py_progs has been added to ```$PATH``` and to ```$PYTHONPATH```. ```plot_wind.py``` can be run from the command line using\n", "\n", " plot_wind.py cv_standard t_e\n", " \n", @@ -44,7 +44,7 @@ }, { "data": { - "image/png": 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l5Wn58uW69dZbtXLlyoB5TGZUVFTo6NGjGjRoUCuNEgCAOOGg6pCdor7S1FBhWr58uW655RatWrUqaMJUU1Oj9957r0n70aNHNXXqVEnSuHHjWnW8AAAgNkV9nvjQQw+pqKhIaWlp6tevn+bPn99kn9GjRysrK0tHjhzRoEGDlJ2drQEDBqhr166qqqrS+vXrdeTIEeXm5mrmzJkR+BQAAMSQOK00Rf1H3rt3ryTp+PHjeuSRR5rdJzMzU1lZWerUqZNmzJihrVu36pVXXtGxY8eUmpqqAQMG6Pbbb1deXp7l23oA4BSuMSEGZoXR6TufhxH8ShixkWEYBZEeAiIo6pOmoqIiFRUVmdrX4/HoySefbN0BAQAQ7xJk79pK9TYeqxVF/ZwmAACAaBD1lSYAABBl7J7TZNh4rFZEpQkAAMAEKk0AAMAaKk0AAAAIhkoTAACwJlE8PQcAAIDmUWkCAADWMKcJAAAAwVBpAgAA1iTK3gzCIXOaSJoAAIA1dk8Ed8jPwnJ7DgAAwASSJgAAYE1SK2xhKCwslMvl0r333ht0n+eff165ubn6yle+Io/HoyFDhugvf/mLpX5ImgAAgGNt27ZNTz/9tAYOHNjifiUlJcrNzdWrr76q7du366qrrtINN9ygHTt2mO6LOU0AAMAau5cc+NdEcJ/PF9DsdrvldruDhh0/flwTJkzQb3/7W82fP7/FLh577LGA1//5n/+pl156Sa+88oq+/vWvmxomSRMARBHXgjCCe4YY90oYfeqJcIIjwjAKIj0EBJGRkRHwuqCgQHPnzg26/4wZMzRq1Cjl5OScM2n6d/X19aqpqVGnTp1Mx5A0AQAAa1qp0lRZWSmPx9PY3FKV6bnnntO7776rbdu2hdTlL3/5S504cUJjx441HUPSBAAAooLH4wlImoKprKzUPffco40bNyolJcVyP2vWrNHcuXP10ksvqWvXrqbjSJoAAIA1CbJ3bSWLj6Vt375dn3zyiS6//PLGtrq6OpWUlOjJJ5+U3+9XYmLzA/z973+vqVOnau3atcrJybHUL0kTAABwlBEjRqi8vDygbfLkyerfv79+/vOfB02Y1qxZoylTpmjNmjUaNWqU5X5JmgAAgDV2z2mqs7Z7hw4ddNlllwW0paamqnPnzo3t+fn5qqqq0jPPPCPpbMJ0xx136PHHH9e3vvUtHTp0SJLUvn17eb1eU/2yThMAAIg5Bw8e1P79+xtfP/XUU6qtrdWMGTPUvXv3xu2ee+4xfUwqTQAAwJoIV5qaU1xcHPC6qKioxfdDQaUJAADABCpNAADAmkTZ+/ScncdqRVSaAAAATKDSBAAArInCOU1tgUoTAACACVSaAACANYmyN4OotfFYrYikCQAAWGP37TmHZCPcngMAADDBIbkdADiH670wgvuGEbsuxLja9WF0OjSM2MMhRxrGmDD6RdhYcgAAAADBUGkCAADWMKcJAAAAwTgktwMAAFGDShMAAACCcUhuBwAAokaC7H3izSElHIcMEwAAILKoNAEAAGuY0wQAAIBgHJLbAQCAqEGlCQAAAME4JLcDAABRg9+eAwAAQDBUmgAAgDXMaQIAAEAwDsntAKBtZass9ODzvxl67Oth/LX8TqiB3wm5S8NoH3IsHCxR9mYQDpnTRNIEAACs4fYcAAAAgnFIbgcAAKIGSw4AAAAgGCpNAADAGuY0AQAAIBiH5HYAACBqUGkCAABAMA7J7QAAQNRIkL1PvDmkhOOQYQIAAEQWlSYAAGANc5oAAAAQjENyOwAAEDWoNAEAACAYh+R2AAAgasTpb8+RNAGIaj/Rf0Wk35PKCT14Qxh/te4MPVQDQgsz3msfRqdA/CBpAgAA1jCnCQAAAMFEfdJUVVWlxx57TCNHjtQFF1ygdu3aKT09XTfffLPeeuutZmN8Pp9mzZqlXr16ye12q1evXpo1a5Z8Pl8bjx4AgBiUqC+qTXZszGmyxxNPPKEFCxaod+/eys3NVdeuXVVRUaEXX3xRL774otasWaOxY8c27n/ixAkNHz5cO3fuVG5ursaNG6ddu3bpV7/6lTZt2qTS0lKlpqZG8BMBAOBwcXp7LuqHOXjwYJWUlGjYsGEB7Vu2bNGIESN0991366abbpLb7ZYkLVy4UDt37tTs2bO1YMGCxv0LCgr00EMPaeHChZo3b16bfgYAAOB8UX97bsyYMU0SJkkaNmyYrrrqKn322WcqLy+XJBmGoaVLlyotLU1z5swJ2D8/P18dO3bUsmXLZBhGm4wdAICYlNgKmwNEfdLUkuTkZElSUtLZgllFRYUOHDigoUOHNrkFl5KSoiuvvFJVVVX66KOPgh7T7/fL5/MFbAAAAI5Nmvbv36/XX39d6enpGjDg7OIkFRUVkqS+ffs2G9PQ3rBfcwoLC+X1ehu3jIwMm0cOAIDD2TkJ3O75Ua3IkUnTmTNnNHHiRPn9fi1cuFCJiWfretXV1ZIkr9fbbJzH4wnYrzn5+fmqrq5u3CorK20ePQAAcCKH5HZfqK+v15QpU1RSUqJp06Zp4sSJth7f7XY3TioHAADNaFhywM7jOYCjKk2GYWjatGlatWqVbr/9di1ZsiTg/YYKU7BKUsP8pGCVKAAAgGAcU2mqr69XXl6eli9frnHjxqmoqEgJCYE537nmLJ1rzhMAADAhTn+w1xGVpi8nTLfeeqtWrlzZOI/py/r27asePXqorKxMJ06cCHjv1KlTKikpUY8ePdSnT5+2GjoAAIgRUZ801dfXa+rUqVq+fLluueUWrVq1qtmESZJcLpfy8vJ0/PhxPfTQQwHvFRYW6ujRo8rLy5PL5WqLoQMAEJvi9Om5qB/mQw89pKKiIqWlpalfv36aP39+k31Gjx6trKwsSdLs2bP18ssva+HChdqxY4cuv/xy7dq1S+vXr1dWVpZmz57dxp8AAADEgqhPmvbu3StJOn78uB555JFm98nMzGxMmlJTU1VcXKx58+Zp3bp1Ki4uVnp6umbOnKmCggJ+dw6IgBJ9M+TYbyr0tdKe1fiQYw+c7h5yrNJCD1Vm6KHG6jD6Bazgt+eiU1FRkYqKiizFeL1eLVq0SIsWLWqdQQEAgLgT9UkTAACIMnFaaYr6ieAAAADRwCG5HQAAiBZGgmTYuLaS4ZASjkOGCQAAEFlUmgAAgCV1SWc3O4/nBA4ZJgAAiBbxmjRxew4AAMAEh+R2AAAgWtQmulSbaN9PktUmGpIM247XWqg0AQAAmEClCQAAWFKXlKS6JPsqTXVJhqQzth2vtVBpAgAAMIFKEwAAsKQuMVF1Ns5pqkuk0gQAANDqCgsL5XK5dO+997a43+bNm3X55ZcrJSVFF110kZYsWWKpHypNAADAknolqk72VZrqw3hybtu2bXr66ac1cODAFvfbs2ePrr/+ek2bNk2rVq1SWVmZfvjDH+orX/mKbr75ZlN9UWkCAABRwefzBWx+v7/F/Y8fP64JEybot7/9rTp27NjivkuWLNEFF1ygxx57TBdffLHy8vI0ZcoU/fd//7fp8VFpAmDO+6H/q/KSy9JCjt2hrJBjtys75Njq0vSQY8NhrI5It4AltUpUrY2Vptp/VZoyMjIC2gsKCjR37tygcTNmzNCoUaOUk5Oj+fPnt9jHm2++qZEjRwa0XXPNNVq2bJnOnDmj5OTkc46TpAkAAESFyspKeTyextdutzvovs8995zeffddbdu2zdSxDx06pG7dugW0devWTbW1tfr000/VvXv3cx6DpAkAAFhSp0TV2TjDp071kiSPxxOQNAVTWVmpe+65Rxs3blRKSorpflyuwOqYYRjNtgdD0gQAABxl+/bt+uSTT3T55Zc3ttXV1amkpERPPvmk/H6/EhMTA2LS09N16NChgLZPPvlESUlJ6ty5s6l+SZoAAIAl9learM2PGjFihMrLywPaJk+erP79++vnP/95k4RJkoYMGaJXXnkloG3jxo3Kzs42NZ9JImkCAAAO06FDB1122WUBbampqercuXNje35+vqqqqvTMM89IkqZPn64nn3xSs2bN0rRp0/Tmm29q2bJlWrNmjel+SZoAAIAlka40mXHw4EHt37+/8fWFF16oV199VTNnztSvf/1r9ejRQ//zP/9jeo0miaQJAADEgOLi4oDXRUVFTfYZPny43n333ZD7IGkCAACWOKHS1BpImgAAgCV1SlRtHCZN/IwKAACACVSaAACAJXVKapXFLaMdlSYAAAATqDQBAABL6pSgOjVdQDL04zkDlSYAAAATqDQBAABLzi45EH+VJpImIJ7cF8ZjvcNDD/3osj4hx76tb4Yce4X+GnJs5dWVIce+o6EhxwKIXiRNAADAklolqtbGSlOtbUdqXcxpAgAAMIFKEwAAsKReSbbOaapnRXAAAIDYQaUJAABYEq9Pz1FpAgAAMIFKEwAAsIRKEwAAAIKi0gQAACyx/7fnDNuO1ZqoNAEAAJhApQkAAFhi/4rgzqg0kTQBAABL6pSkOhtTCCaCAwAAxBAqTQAAwJJ6m5ccqHfI7TkqTQAAACZQaQKcplsYP2w5Iox+Lws99Ig6hxw7Un8JOfZtfTPk2Bd0W8ixQKyzf3FLKk0AAAAxg0oTAACwpFYJNi85UG/bsVoTlSYAAAATqDQBAABL7F+niTlNAAAAMYNKEwAAsMT+p+eY0wQAABAzqDQBAABLqDQBAAAgKCpNAADAkjol2rpOE5UmAACAGEKlCQAAWMI6TQAAAAiKShMAALCkTgk2Pz1XZ9uxWhNJEwAAsMT+JQfsO1ZrImkCImC9yxVy7HUXh9HxiNBDjdTQYzO1N+TYUXo95NhJIUcCQFMkTQAAwJJ4rTQxERwAAMAERyRNq1at0l133aXs7Gy53W65XC4VFRU1u+/cuXPlcrma3VJSUtp24AAAxKCGxS3t2pxSaXLE7bkHH3xQ+/btU5cuXdS9e3ft27fvnDGTJk1SZmZmQFtSkiM+LgAAiEKOyCKWLl2qvn37qlevXnr00UeVn59/zpg777xT3/nOd1p/cAAAxBn7F7d0xs+oOCJpysnJifQQAABAnHNE0hSKLVu26O2331ZiYqL69++vnJwcud3uc8b5/X75/f7G1z6frzWHCQCA48Tr03MxmzTNmTMn4HX37t21YsUK5ebmthhXWFioefPmtebQAACAAzni6TkrsrKytGLFCu3du1eff/65Kioq9PDDD+vYsWO68cYbtWvXrhbj8/PzVV1d3bhVVla20cgBAHCGhp9RsW9zRjoSc5Wm0aNHB7zu06ePHnzwQXXr1k0/+MEPNH/+fK1duzZovNvtNnUbDwAAxBdnpHY2mDRpkpKSklRWVhbpoQAA4Gh2rtHUsDlB3CRN7dq1U4cOHXTy5MlIDwUAADhQzN2eC6aiokJHjx7VoEGDIj0UAAAczf51mupsO1ZriqlKU01Njd57770m7UePHtXUqVMlSePGjWvrYQEAgBjgiErT0qVLVVpaKkkqLy9vbCsuLpZ0dvL36NGjdeTIEQ0aNEjZ2dkaMGCAunbtqqqqKq1fv15HjhxRbm6uZs6cGamPAQBATKi3eZ2meofMaTKVNO3fv1+SlJycrO7duwe0WeosKUnnn3++zjvvPEtxpaWlWrFiRUBbWVlZ46TuzMxMjR49Wp06ddKMGTO0detWvfLKKzp27JhSU1M1YMAA3X777crLy1NiojP+YAAAQHRxGYZhnGunhIQEuVwu9e/fXx988EFAWyh69+6t6dOna9asWSHFtyWfzyev16vq6mp5PJ5IDwdRZGmI178kjU0JvV/P7aHHakAYsT3CiP3+Of+aARBlmvv+a2j7afVsuT32Lc/j9/n1S+/CqP+uNVVpuuCCC+RyuRqrTF9us6K+vl7Hjh3TRx99pJ/97Gc6ffq07rvvPmsjBgAAEdWwuKWdx3MCU0nT3r17TbWZ9frrr+v666/X0qVLSZoAAIAjRGQieE5Oji6++GL97W9/i0T3AAAgDLVKVKKNlSanLG4ZsafnFixYoMOHD0eqewAAAEsiljRde+21keoaAACEwf7FLR2xApJDZl4BAABEmDNSOwAAEDXidXFLKk0AAAAmUGkCAACW1NlcabLzWK2JShMAAIAJVJoAAIAltUpUQhyu00SlCQAAwASSJgAAYMnZOU1JNm7WKk2LFy/WwIED5fF45PF4NGTIEK1fv77FmGeffVaDBg3Seeedp+7du2vy5Mk6cuSIpX5JmgAAgKP07NlTjz76qN555x298847uvrqq3XTTTfpgw8+aHb/0tJS3XHHHZo6dao++OADrV27Vtu2bVNeXp6lfpnTBAAALIn003M33HBDwOtHHnlEixcv1tatW3XppZc22X/r1q3KzMzUT37yE0nShRdeqLvuuksLFy601C9JE+LaYpcr5Ng8b+j9Jo8LPVZdw4gNx/eNCHUMIF74fL6A1263W263u8WYuro6rV27VidOnNCQIUOa3eeKK67QAw88oFdffVXXXXedPvnkE61bt06jRo2yND5uzwEAAEsaKk12bpKUkZEhr9fbuBUWFgYdQ3l5udLS0uR2uzV9+nS98MILuuSSS5rd94orrtCzzz6rW2+9Ve3atVN6errOP/98PfHEE5Y+N0kTAACICpWVlaqurm7c8vPzg+77ta99TTt37tTWrVt19913a9KkSdq9e3ez++7evVs/+clPNGfOHG3fvl0bNmzQnj17NH36dEvj4/YcAACwpLV+e67haTgz2rVrpz59+kiSsrOztW3bNj3++ON66qmnmuxbWFiooUOH6mc/+5kkaeDAgUpNTdWwYcM0f/58de/e3VSfJE0AAMCSWiXKFWWLWxqGIb/f3+x7J0+eVFJSYMqTmJjYGGcWSRMAAHCU+++/X9ddd50yMjJUU1Oj5557TsXFxdqwYYMkKT8/X1VVVXrmmWcknX3abtq0aVq8eLGuueYaHTx4UPfee68GDx6sHj16mO6XpAkAAFhSp0Ql2JhCWL3Vd/jwYU2cOFEHDx6U1+vVwIEDtWHDBuXm5kqSDh48qP379zfuf+edd6qmpkZPPvmkfvrTn+r888/X1VdfrQULFljql6QJAAA4yrJly1p8v6ioqEnbj3/8Y/34xz8Oq1+SJgAAYEmdzT/Ya+ek8tbEkgMAAAAmUGkCAACWUGkCAABAUFSaAACAJdG4TlNboNIEAABgApUmAABgSb2SVGdjClHvkHSEShMAAIAJzkjtgBYUuVwhx97dOYyOrw4j1huh2J+Y/40lAAimzuY5TTw9BwAAEEOoNAEAAEvqlGBzpckZNRxnjBIAACDCqDQBAABLzq6rxDpNAAAAaAaVJgAAYEmdkuSyMYWwc82n1uSMUQIAgKhRr0Rblwmo5/YcAABA7KDSBAAALKmzeSI4i1sCAADEECpNAADAEipNAAAACIpKEwAAsKRWCTL4GRUAAAA0h0oTAACw5OxilCxuCURMkcsVUtwEbxidXh1GbLcwYsMxy4hQxwAQ30iaAACAJTw9BwAAgKCoNAEAAEvqba408dtzAAAAMYRKEwAAsKRWiUqg0gQAAIDmUGkCAACW1ClRho0pBJUmAACAGEKlCQAAWHK20hR/c5pImgAAgCXxmjRxew4AAMAEKk0AAMCSuvpEGfU2VppsPFZrotIEAABgApUmAABgSV1toupr7asOGTYeqzVRaQIAADCBShNstdjlCjl2TIhxySND7lJKDSO2bxixFWHEAkCE1dUmyVVrXwph2His1kSlCQAAwARnpHYAACBq1NUmyGXrnCZn1HAcMcpVq1bprrvuUnZ2ttxut1wul4qKioLu7/P5NGvWLPXq1Utut1u9evXSrFmz5PP52m7QAAAgpjii0vTggw9q37596tKli7p37659+/YF3ffEiRMaPny4du7cqdzcXI0bN067du3Sr371K23atEmlpaVKTQ1nIgsAAPGtrjbR5koTT8/ZZunSpdq7d6/++c9/avr06S3uu3DhQu3cuVOzZ8/Wxo0b9eijj2r9+vWaM2eOdu7cqYULF7bRqAEAQCxxRNKUk5OjXr16nXM/wzC0dOlSpaWlac6cOQHv5efnq2PHjlq2bJkMw2itoQIAEPNqaxNVe8bGjUpT26uoqNCBAwc0dOjQJrfgUlJSdOWVV6qqqkofffRR0GP4/X75fL6ADQAAIOaSJknq27f5BXQa2hv2a05hYaG8Xm/jlpGRYf9AAQBwMKMuSfU2bkadI6ZYx1bSVF1dLUnyer3Nvu/xeAL2a05+fr6qq6sbt8rKSvsHCgAAHMcZqV0bcrvdcrvdkR4GAADRqzbx7Gbn8RwgpipNDRWmYJWkhvlJwSpRAAAAwcRUpelcc5bONecJAACYEKeVpphLmnr06KGysjKdOHEi4Am6U6dOqaSkRD169FCfPn0iOEoAAByuziXVhv4D7c0ezwFi6vacy+VSXl6ejh8/roceeijgvcLCQh09elR5eXlyuZzxhwMAAKKHIypNS5cuVWlpqSSpvLy8sa24uFiSNHr0aI0ePVqSNHv2bL388stauHChduzYocsvv1y7du3S+vXrlZWVpdmzZ0fiIwAAEDtq/7XZeTwHcETSVFpaqhUrVgS0lZWVqaysTJKUmZnZmDSlpqaquLhY8+bN07p161RcXKz09HTNnDlTBQUF/O4cAAAIicvgN0Va5PP55PV6VV1d3bjOE4KrCOPWZ98RIQZeHHKX4f3r5kQYsc/wvx2A6Nbc919Dm8qqpTQbvxOP+6Sh0f9dG1NzmgAAAFqLI27PAQCAKBKnc5qoNAEAAJhApQkAAFhTK+mMzcdzACpNAAAAJlBpAgAA1tT9a7PzeA5ApQkAAMAEKk0AAMAanp4DAABAMFSaAACANVSaAAAAEAyVJgAAYA2VJgAAgOi3ePFiDRw4UB6PRx6PR0OGDNH69etbjPH7/XrggQfUq1cvud1u9e7dW7/73e8s9UulCQAAWFMne6tDFtdp6tmzpx599FH16dNHkrRixQrddNNN2rFjhy699NJmY8aOHavDhw9r2bJl6tOnjz755BPV1lr7ECRNAADAmgjfnrvhhhsCXj/yyCNavHixtm7d2mzStGHDBm3evFl///vf1alTJ0lSZmam5WGSNKGJMpcr5NjB3jA6rggxrmsYfX4SRuzrRhjBAIB/5/P5Al673W653e4WY+rq6rR27VqdOHFCQ4YMaXafl19+WdnZ2Vq4cKFWrlyp1NRU3XjjjXr44YfVvn170+MjaQIAANa0UqUpIyMjoLmgoEBz585tNqS8vFxDhgzRqVOnlJaWphdeeEGXXHJJs/v+/e9/V2lpqVJSUvTCCy/o008/1Q9/+EN99tlnluY1kTQBAICoUFlZKY/H0/i6pSrT1772Ne3cuVPHjh3TH//4R02aNEmbN29uNnGqr6+Xy+XSs88+K6/37C2RRYsW6fvf/75+/etfm642kTQBAABrzvxrs/N4UuPTcGa0a9eucSJ4dna2tm3bpscff1xPPfVUk327d++ur371q40JkyRdfPHFMgxD//jHP9S3b19TfbLkAAAAcDzDMOT3+5t9b+jQoTpw4ICOHz/e2Pbhhx8qISFBPXv2NN0HSRMAALCmrhU2C+6//35t2bJFe/fuVXl5uR544AEVFxdrwoQJkqT8/HzdcccdjfuPHz9enTt31uTJk7V7926VlJToZz/7maZMmcJEcAAAELsOHz6siRMn6uDBg/J6vRo4cKA2bNig3NxcSdLBgwe1f//+xv3T0tL02muv6cc//rGys7PVuXNnjR07VvPnz7fUL0kTAACwJsKLWy5btqzF94uKipq09e/fX6+99pq1jv4Nt+cAAABMoNIEAACs4Qd7AQAAEAyVJgAAYA2VJgAAAARDpQkAAFhDpQkAAADBUGkCAADWRHidpkghaUIT4fwGY82J0GM7XRZi4Duh96kPjTCCAQDxhKQJAABYE6dzmkiaAACANWckJdp8PAdgIjgAAIAJVJoAAIA1dbJ38rZDJoJTaQIAADCBShMAALAmTieCU2kCAAAwgUoTAACwJk4Xt6TSBAAAYAKVJgAAYE2t7F2niTlNAAAAsYNKEwAAsOaM7C27sCI4AABA7KDSBAAArGFFcAAAAARDpSlGLXK5Qo69Lox+O3UKI/iT0MJ8laF36Qk9FADiF+s0AQAAIBgqTQAAwJpa2Vt2YZ0mAACA2EGlCQAAWHNGUuhTZ5s/ngOQNAEAAGtYcgAAAADBUGkCAADWMBEcAAAAwVBpAgAA1rC4JQAAAIKh0gQAAKyxe4kAhyw5QKUJAADABCpNAADAmjrZW3ZhThMAAEDsoNIEAACsqZW9P6PikHWaSJpiVLcwYtuHEXv4k9Bju4VYnvV8boTeKQAAJpE0AQAAa+K00sScJgAAABNiMmnKzMyUy+Vqdps+fXqkhwcAgLPV6uzaSnZtDqk0xeztOa/Xq3vvvbdJe3Z2dtsPBgAAOF7MJk3nn3++5s6dG+lhAAAQe+pk75wm1mkCAACIHTFbafL7/VqxYoWqqqrUsWNHXXHFFRo0aFCkhwUAgPPZPQeJOU2RdejQId15550Bbddee61WrlypLl26BI3z+/3y+/2Nr30+X2sNEQAAZ4rTpCkmb89NmTJFxcXF+uc//ymfz6etW7fquuuu04YNG3TjjTfKMIIvhlhYWCiv19u4ZWRktOHIAQBAtHIZLWUQMaS+vl7Dhw9XaWmp/vSnP2nUqFHN7tdcpSkjI0PV1dXyeDxtNdywPesKfYbe0DD6DWc18W6dQwz8NC4uYQBoUz6fT16vN+D7r6FNX6+WEm38TqzzSTu8Uf9dG5OVpuYkJCRo8uTJkqSysrKg+7ndbnk8noANAAAgZuc0NadhLtPJkycjPBIAABzM7iUCWHIg+rz11luSzq4YDgAAYEXMJU27d+/WsWPHmrSXlpZq0aJFcrvdGjNmTNsPDACAWFHbCpsDxNztuT/84Q9auHChRowYoczMTLndbr3//vvauHGjEhIStGTJEl1wwQWRHiYAAHCYmEuarrrqKv3tb3/Tu+++q82bN+vUqVPq1q2bbr31Vs2cOVODBw+O9BABAHC2Wkl2PrjskDlNMZc0DR8+XMOHD4/0MCIunGf+wlnOs1NKGMGXhRELAEAri7mkCQAAtLJaSfU2Hs/OY7WimJsIDgAA0BqoNAEAAGvqZO+cJipNAAAAsYNKEwAAsKZW9pZdqDQBAADEDipNAADAGipNAAAACIZKEwAAsOaM4rLSRNIEAACsqZe9Sw7YeaxWxO05AAAAE6g0AQAAa2oluWw8HpUmAAAA+y1evFgDBw6Ux+ORx+PRkCFDtH79elOxZWVlSkpKUlZWluV+SZoAAIA1ta2wWdCzZ089+uijeuedd/TOO+/o6quv1k033aQPPvigxbjq6mrdcccdGjFihLUO/4WkCQAAOMoNN9yg66+/Xv369VO/fv30yCOPKC0tTVu3bm0x7q677tL48eM1ZMiQkPplTlMUm+cK/YbxgDD69YQT+80wgsMZNACg7ZxRq8xp8vl8Ac1ut1tut7vF0Lq6Oq1du1YnTpxoMRlavny5Pv74Y61atUrz588PaZhUmgAAQFTIyMiQ1+tt3AoLC4PuW15errS0NLndbk2fPl0vvPCCLrnkkmb3raio0H333adnn31WSUmh14uoNAEAAGvq1CqVpsrKSnk8X9zvaKnK9LWvfU07d+7UsWPH9Mc//lGTJk3S5s2bmyROdXV1Gj9+vObNm6d+/fqFNUyXYRgOedAvMnw+n7xer6qrqwP+INtCpG7P/UcYsZnDwwgOddBPcAkDgN2a+/5raJOqJZeN34mGT1J437U5OTnq3bu3nnrqqYD2Y8eOqWPHjkpMTGxsq6+vl2EYSkxM1MaNG3X11Veb6oNKEwAAsC7K/r1qGIb8fn+Tdo/Ho/Ly8oC23/zmN3rjjTe0bt06XXjhhab7IGkCAACOcv/99+u6665TRkaGampq9Nxzz6m4uFgbNmyQJOXn56uqqkrPPPOMEhISdNlllwXEd+3aVSkpKU3az4WkCQAAOMrhw4c1ceJEHTx4UF6vVwMHDtSGDRuUm5srSTp48KD2799ve7/MaToH5jRZw5wmAIgN55zTFNYCNU16U7hzmtoCSw4AAACYQNIEAABgAkkTAACACSRNAAAAJvD0HAAAsOjMvzY7jxf9SJoAAIBFtf/a7Dxe9OP2HAAAgAlUmgAAgEXcnkOUaR+hfjPDWRnzp2HEVocRCwBAKyNpAgAAFjGnCQAAAEFQaQIAABbVyt55SFSaAAAAYgaVJgAAYFF8Pj1HpQkAAMAEKk0AAMAinp4DAABAEFSaAACARTw9BwAAgCCoNAEAAIuY0wQAAIAgqDQBAACLWKcJAAAAQVBpAgAAFsXnnCaSpijWPozYPuF0PDv00OdvuC7k2DF6NfSOAQBtiCUHAAAAEASVJgAAYFF83p6j0gQAAGAClSYAAGARSw4AAAAgCCpNAADAIuY0AQAAIAgqTQAAwCLWaQIAAEAQVJoAAIBFzGkCAABAEFSaAACARazTBAAAgCCoNAEAAIuoNAEAACAIKk0AAMCi+Hx6jqQpioXzhzNwQOixn96eFnLsX3VFyLFjQo4EAKD1kTQBAACLWBE8pmzbtk3XX3+9OnbsqNTUVA0ePFirV6+O9LAAAIgBta2wRb+YrDQVFxfrmmuuUbt27XTbbbfJ6/Xq+eef14QJE7R3717df//9kR4iAABwmJhLmmpra5WXlyeXy6WSkhJ9/etflyQVFBRoyJAhKigo0C233KK+fftGeKQAADjVGdmbQrDkQES88cYb+vjjjzV+/PjGhEmSOnTooF/84heqra3V8uXLIzhCAADgRDFXaSouLpYkjRw5ssl7DW2bN29uyyEBABBjWHIgJlRUVEhSs7ffOnbsqC5dujTu0xy/3y+/39/42ufz2T9IAADgODF3e666ulqS5PV6m33f4/E07tOcwsJCeb3exi0jI6NVxgkAgHM1LDlg1+aMSlPMJU3hys/PV3V1deNWWVkZ6SEBAIAoEHO35xoqTMGqST6fL2gVSpLcbrfcbnerjA0AgNgQn3OaYq7S1DCXqbl5S0ePHtWnn37KcgMAAMCymEuahg8fLknauHFjk/ca2hr2AQAAobBzPlPDFv1iLmkaMWKELrroIq1evVo7d+5sbK+pqdHDDz+spKQk3XnnnREbHwAAcKaYm9OUlJSkpUuX6pprrtGwYcM0btw4eTwePf/889qzZ4/mz5+vfv36RXqYAAA4WHzOaYq5pEmSrrrqKpWWlqqgoEB/+MMfdPr0aV166aV6+OGHNWHCBEvHMgxDUmTWa/o8jFhfXeixNT4j5Fi/ToUc6xNrYgFAtGj43mv4Hgzkb6YtHHYfr3W4jObPBv7lH//4B2s1AQDiVmVlpXr27ClJOnXqlC688EIdOnTI9n7S09O1Z88epaSk2H5su5A0nUN9fb0OHDigDh06yOVyBbzn8/mUkZGhyspKeTyeCI3QGThX5nGuzOE8mce5Mo9z9QXDMFRTU6MePXooIeGLKdCnTp3S6dOnbe+vXbt2UZ0wSTF6e85OCQkJjRl2MB6PJ+7/5zKLc2Ue58oczpN5nCvzOFdnNbeuYUpKStQnN60l5p6eAwAAaA0kTQAAACaQNIXB7XaroKCAn10xgXNlHufKHM6TeZwr8zhXaAkTwQEAAEyg0gQAAGACSRMAAIAJJE0AAAAmkDQBAACYQNIUom3btun6669Xx44dlZqaqsGDB2v16tWRHlZUyczMlMvlanabPn16pIfX5latWqW77rpL2dnZcrvdcrlcKioqCrq/z+fTrFmz1KtXL7ndbvXq1UuzZs2KyO8gtjUr52ru3LlBr7NYX4CvqqpKjz32mEaOHKkLLrhA7dq1U3p6um6++Wa99dZbzcbE63Vl9VzF83WF4FgRPATFxcW65ppr1K5dO912223yer16/vnnNWHCBO3du1f3339/pIcYNbxer+69994m7dnZ2W0/mAh78MEHtW/fPnXp0kXdu3fXvn37gu574sQJDR8+XDt37lRubq7GjRunXbt26Ve/+pU2bdqk0tJSpaamtuHo25aVc9Vg0qRJyszMDGhLSortv+KeeOIJLViwQL1791Zubq66du2qiooKvfjii3rxxRe1Zs0ajR07tnH/eL6urJ6rBvF4XaEFBiw5c+aM0bt3b8PtdhvvvvtuY7vP5zMuvfRSIykpyfjwww8jOMLo0atXL6NXr16RHkbUeO2114y9e/cahmEYhYWFhiRj+fLlze47Z84cQ5Ixe/bsZtvnzJnT2sONKCvnqqCgwJBkbNq0qe0GGCX++Mc/GiUlJU3aS0pKjOTkZKNTp07GqVOnGtvj+bqyeq7i+bpCcNyes+iNN97Qxx9/rPHjx+vrX/96Y3uHDh30i1/8QrW1tVq+fHkER4holZOTo169ep1zP8MwtHTpUqWlpWnOnDkB7+Xn56tjx45atmyZjBheYs3suYp3Y8aM0bBhw5q0Dxs2TFdddZU+++wzlZeXS+K6snKugGCoMVpUXFwsSRo5cmST9xraNm/e3JZDimp+v18rVqxQVVWVOnbsqCuuuEKDBg2K9LCiWkVFhQ4cOKBrrrmmya2SlJQUXXnllXrppZf00UcfqW/fvhEaZfTZsmWL3n77bSUmJqp///7KycmJ61Wdk5OTJX1xK4nrKrh/P1dfxnWFLyNpsqiiokKSmv1LpWPHjurSpUvjPpAOHTqkO++8M6Dt2muv1cqVK9WlS5fIDCrKtXSNfbm9oqIi7r7cWvLv1ZPu3btrxYoVys3NjdCIImf//v16/fXXlZ6ergEDBkjiugqmuXP1ZVxX+DJuz1lUXV0t6ewE5+Z4PJ7GfeLdlClTVFxcrH/+85/y+XzaunWrrrvuOm3YsEE33nhjzN4GCJeZa+zL+8W7rKwsrVixQnv37tXnn3+uiooKPfzwwzp27JhuvPFG7dq1K9JDbFNnzpzRxIkT5ff7tXDhQiUmJkriumpOsHMlcV2heVSa0Gr+/V9o3/zmN/WnP/1Jw4cPV2lpqV599VWNGjUqQqNDrBg9enTA6z59+ujBBx9Ut27d9IMf/EDz58/X2rVrIzO4NlZfX68pU6aopKRE06ZN08SJEyM9pKh1rnPFdYXmUGmyqOFfacH+Nebz+YL+Sw5SQkKCJk+eLEkqKyuL8Giik5lr7Mv7oXmTJk1SUlJS3FxnhmFo2rRpWrVqlW6//XYtWbIk4H2uqy+c61y1JN6uKwQiabLoy/f9/93Ro0f16aefxtV8gFA0zGU6efJkhEcSnVq6xr7cznXWsnbt2qlDhw5xcZ3V19dr6tSp+t3vfqdx48apqKhICQmBf71zXZ1l5ly1JJ6uKzRF0mTR8OHDJUkbN25s8l5DW8M+aF7D6rv/vmAczurbt6969OihsrIynThxIuC9U6dOqaSkRD169FCfPn0iNEJnqKio0NGjR2P+Oquvr1deXp6WL1+uW2+9VStXrgyYm9OA68r8uWpJvFxXaB5Jk0UjRozQRRddpNWrV2vnzp2N7TU1NXr44YeVlJTU5GmxeLR7924dO3asSXtpaakWLVokt9utMWPGtP3AHMDlcikvL0/Hjx/XQw89FPBeYWGhjh49qry8PLlcrgiNMHrU1NTovffea9J+9OhRTZ06VZI0bty4th5Wm2momixfvly33HKLVq1aFTQJiPfrysq5ivfrCsG5DB5hsmzTpk265ppr5Ha7NW7cOHk8Hj3//PPas2eP5s+frwceeCDSQ4y4uXPnauHChRoxYoQyMzPldrv1/vvva+PGjUpISNCSJUuUl5cX6WG2qaVLl6q0tFSSVF5ernfffVdDhw5t/Jf96NGjGyefnjhxQt/+9rcbf+7i8ssv165du7R+/XplZWXF9M9dSObP1d69e3XhhRcqOztbAwYMUNeuXVVVVaX169fryJEjys3N1Z/+9Ce1a9cukh+n1cydO1fz5s1TWlqa7rnnnmbXGRo9erSysrIkxfd1ZeVcxft1hRZEbC1yh3vrrbeMa6+91vB6vUb79u2N7OxsY9WqVZEeVtQoLi42xo4da/Tp08fo0KGDkZycbPTs2dO47bbbjLfeeivSw4uISZMmGZKCbgUFBQH7Hzt2zJg5c6aRkZFhJCcnGxkZGcbMmTONY8eOReYDtCGz56q6utqYMWOGcfnllxtdunQxkpKSDK/Xa3z72982lixZYtTW1kb2g7Syc50nNfPzM/F6XVk5V/F+XSE4Kk0AAAAmMKcJAADABJImAAAAE0iaAAAATCBpAgAAMIGkCQAAwASSJgAAABNImgAAAEwgaQIAADCBpAkAAMAEkiYAUSszM1Mul0t79+6N9FAAgKQJAADAjKY/8wwAUaJ3795KSUlRcnJypIcCAOIHewEAAEzg9hwAAIAJJE0AohYTwQFEE5ImAAAAE0iaAAAATCBpAgAAMIGkCQAAwASSJgAAABNImgAAAEwgaQIAADCBpAkAAMAEkiYAAAATSJoAAABMIGkCAAAwgaQJAADABJdhGEakBwEAABDtqDQBAACYQNIEAABgAkkTAACACSRNAAAAJpA0AQAAmEDSBAAAYAJJEwAAgAkkTQAAACaQNAEAAJhA0gQAAGACSRMAAIAJJE0AAAAmkDQBAACY8P8BY8sNhciP3BgAAAAASUVORK5CYII=\n", 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l5Wn58uW69dZbtXLlyoB5TGZUVFTo6NGjGjRoUCuNEgCAOOGg6pCdor7S1FBhWr58uW655RatWrUqaMJUU1Oj9957r0n70aNHNXXqVEnSuHHjWnW8AAAgNkV9nvjQQw+pqKhIaWlp6tevn+bPn99kn9GjRysrK0tHjhzRoEGDlJ2drQEDBqhr166qqqrS+vXrdeTIEeXm5mrmzJkR+BQAAMSQOK00Rf1H3rt3ryTp+PHjeuSRR5rdJzMzU1lZWerUqZNmzJihrVu36pVXXtGxY8eUmpqqAQMG6Pbbb1deXp7l23oA4BSuMSEGZoXR6TufhxH8ShixkWEYBZEeAiIo6pOmoqIiFRUVmdrX4/HoySefbN0BAQAQ7xJk79pK9TYeqxVF/ZwmAACAaBD1lSYAABBl7J7TZNh4rFZEpQkAAMAEKk0AAMAaKk0AAAAIhkoTAACwJlE8PQcAAIDmUWkCAADWMKcJAAAAwVBpAgAA1iTK3gzCIXOaSJoAAIA1dk8Ed8jPwnJ7DgAAwASSJgAAYE1SK2xhKCwslMvl0r333ht0n+eff165ubn6yle+Io/HoyFDhugvf/mLpX5ImgAAgGNt27ZNTz/9tAYOHNjifiUlJcrNzdWrr76q7du366qrrtINN9ygHTt2mO6LOU0AAMAau5cc+NdEcJ/PF9DsdrvldruDhh0/flwTJkzQb3/7W82fP7/FLh577LGA1//5n/+pl156Sa+88oq+/vWvmxomSRMARBHXgjCCe4YY90oYfeqJcIIjwjAKIj0EBJGRkRHwuqCgQHPnzg26/4wZMzRq1Cjl5OScM2n6d/X19aqpqVGnTp1Mx5A0AQAAa1qp0lRZWSmPx9PY3FKV6bnnntO7776rbdu2hdTlL3/5S504cUJjx441HUPSBAAAooLH4wlImoKprKzUPffco40bNyolJcVyP2vWrNHcuXP10ksvqWvXrqbjSJoAAIA1CbJ3bSWLj6Vt375dn3zyiS6//PLGtrq6OpWUlOjJJ5+U3+9XYmLzA/z973+vqVOnau3atcrJybHUL0kTAABwlBEjRqi8vDygbfLkyerfv79+/vOfB02Y1qxZoylTpmjNmjUaNWqU5X5JmgAAgDV2z2mqs7Z7hw4ddNlllwW0paamqnPnzo3t+fn5qqqq0jPPPCPpbMJ0xx136PHHH9e3vvUtHTp0SJLUvn17eb1eU/2yThMAAIg5Bw8e1P79+xtfP/XUU6qtrdWMGTPUvXv3xu2ee+4xfUwqTQAAwJoIV5qaU1xcHPC6qKioxfdDQaUJAADABCpNAADAmkTZ+/ScncdqRVSaAAAATKDSBAAArInCOU1tgUoTAACACVSaAACANYmyN4OotfFYrYikCQAAWGP37TmHZCPcngMAADDBIbkdADiH670wgvuGEbsuxLja9WF0OjSM2MMhRxrGmDD6RdhYcgAAAADBUGkCAADWMKcJAAAAwTgktwMAAFGDShMAAACCcUhuBwAAokaC7H3izSElHIcMEwAAILKoNAEAAGuY0wQAAIBgHJLbAQCAqEGlCQAAAME4JLcDAABRg9+eAwAAQDBUmgAAgDXMaQIAAEAwDsntAKBtZass9ODzvxl67Oth/LX8TqiB3wm5S8NoH3IsHCxR9mYQDpnTRNIEAACs4fYcAAAAgnFIbgcAAKIGSw4AAAAgGCpNAADAGuY0AQAAIBiH5HYAACBqUGkCAABAMA7J7QAAQNRIkL1PvDmkhOOQYQIAAEQWlSYAAGANc5oAAAAQjENyOwAAEDWoNAEAACAYh+R2AAAgasTpb8+RNAGIaj/Rf0Wk35PKCT14Qxh/te4MPVQDQgsz3msfRqdA/CBpAgAA1jCnCQAAAMFEfdJUVVWlxx57TCNHjtQFF1ygdu3aKT09XTfffLPeeuutZmN8Pp9mzZqlXr16ye12q1evXpo1a5Z8Pl8bjx4AgBiUqC+qTXZszGmyxxNPPKEFCxaod+/eys3NVdeuXVVRUaEXX3xRL774otasWaOxY8c27n/ixAkNHz5cO3fuVG5ursaNG6ddu3bpV7/6lTZt2qTS0lKlpqZG8BMBAOBwcXp7LuqHOXjwYJWUlGjYsGEB7Vu2bNGIESN0991366abbpLb7ZYkLVy4UDt37tTs2bO1YMGCxv0LCgr00EMPaeHChZo3b16bfgYAAOB8UX97bsyYMU0SJkkaNmyYrrrqKn322WcqLy+XJBmGoaVLlyotLU1z5swJ2D8/P18dO3bUsmXLZBhGm4wdAICYlNgKmwNEfdLUkuTkZElSUtLZgllFRYUOHDigoUOHNrkFl5KSoiuvvFJVVVX66KOPgh7T7/fL5/MFbAAAAI5Nmvbv36/XX39d6enpGjDg7OIkFRUVkqS+ffs2G9PQ3rBfcwoLC+X1ehu3jIwMm0cOAIDD2TkJ3O75Ua3IkUnTmTNnNHHiRPn9fi1cuFCJiWfretXV1ZIkr9fbbJzH4wnYrzn5+fmqrq5u3CorK20ePQAAcCKH5HZfqK+v15QpU1RSUqJp06Zp4sSJth7f7XY3TioHAADNaFhywM7jOYCjKk2GYWjatGlatWqVbr/9di1ZsiTg/YYKU7BKUsP8pGCVKAAAgGAcU2mqr69XXl6eli9frnHjxqmoqEgJCYE537nmLJ1rzhMAADAhTn+w1xGVpi8nTLfeeqtWrlzZOI/py/r27asePXqorKxMJ06cCHjv1KlTKikpUY8ePdSnT5+2GjoAAIgRUZ801dfXa+rUqVq+fLluueUWrVq1qtmESZJcLpfy8vJ0/PhxPfTQQwHvFRYW6ujRo8rLy5PL5WqLoQMAEJvi9Om5qB/mQw89pKKiIqWlpalfv36aP39+k31Gjx6trKwsSdLs2bP18ssva+HChdqxY4cuv/xy7dq1S+vXr1dWVpZmz57dxp8AAADEgqhPmvbu3StJOn78uB555JFm98nMzGxMmlJTU1VcXKx58+Zp3bp1Ki4uVnp6umbOnKmCggJ+dw6IgBJ9M+TYbyr0tdKe1fiQYw+c7h5yrNJCD1Vm6KHG6jD6Bazgt+eiU1FRkYqKiizFeL1eLVq0SIsWLWqdQQEAgLgT9UkTAACIMnFaaYr6ieAAAADRwCG5HQAAiBZGgmTYuLaS4ZASjkOGCQAAEFlUmgAAgCV1SWc3O4/nBA4ZJgAAiBbxmjRxew4AAMAEh+R2AAAgWtQmulSbaN9PktUmGpIM247XWqg0AQAAmEClCQAAWFKXlKS6JPsqTXVJhqQzth2vtVBpAgAAMIFKEwAAsKQuMVF1Ns5pqkuk0gQAANDqCgsL5XK5dO+997a43+bNm3X55ZcrJSVFF110kZYsWWKpHypNAADAknolqk72VZrqw3hybtu2bXr66ac1cODAFvfbs2ePrr/+ek2bNk2rVq1SWVmZfvjDH+orX/mKbr75ZlN9UWkCAABRwefzBWx+v7/F/Y8fP64JEybot7/9rTp27NjivkuWLNEFF1ygxx57TBdffLHy8vI0ZcoU/fd//7fp8VFpAmDO+6H/q/KSy9JCjt2hrJBjtys75Njq0vSQY8NhrI5It4AltUpUrY2Vptp/VZoyMjIC2gsKCjR37tygcTNmzNCoUaOUk5Oj+fPnt9jHm2++qZEjRwa0XXPNNVq2bJnOnDmj5OTkc46TpAkAAESFyspKeTyextdutzvovs8995zeffddbdu2zdSxDx06pG7dugW0devWTbW1tfr000/VvXv3cx6DpAkAAFhSp0TV2TjDp071kiSPxxOQNAVTWVmpe+65Rxs3blRKSorpflyuwOqYYRjNtgdD0gQAABxl+/bt+uSTT3T55Zc3ttXV1amkpERPPvmk/H6/EhMTA2LS09N16NChgLZPPvlESUlJ6ty5s6l+SZoAAIAl9learM2PGjFihMrLywPaJk+erP79++vnP/95k4RJkoYMGaJXXnkloG3jxo3Kzs42NZ9JImkCAAAO06FDB1122WUBbampqercuXNje35+vqqqqvTMM89IkqZPn64nn3xSs2bN0rRp0/Tmm29q2bJlWrNmjel+SZoAAIAlka40mXHw4EHt37+/8fWFF16oV199VTNnztSvf/1r9ejRQ//zP/9jeo0miaQJAADEgOLi4oDXRUVFTfYZPny43n333ZD7IGkCAACWOKHS1BpImgAAgCV1SlRtHCZN/IwKAACACVSaAACAJXVKapXFLaMdlSYAAAATqDQBAABL6pSgOjVdQDL04zkDlSYAAAATqDQBAABLzi45EH+VJpImIJ7cF8ZjvcNDD/3osj4hx76tb4Yce4X+GnJs5dWVIce+o6EhxwKIXiRNAADAklolqtbGSlOtbUdqXcxpAgAAMIFKEwAAsKReSbbOaapnRXAAAIDYQaUJAABYEq9Pz1FpAgAAMIFKEwAAsIRKEwAAAIKi0gQAACyx/7fnDNuO1ZqoNAEAAJhApQkAAFhi/4rgzqg0kTQBAABL6pSkOhtTCCaCAwAAxBAqTQAAwJJ6m5ccqHfI7TkqTQAAACZQaQKcplsYP2w5Iox+Lws99Ig6hxw7Un8JOfZtfTPk2Bd0W8ixQKyzf3FLKk0AAAAxg0oTAACwpFYJNi85UG/bsVoTlSYAAAATqDQBAABL7F+niTlNAAAAMYNKEwAAsMT+p+eY0wQAABAzqDQBAABLqDQBAAAgKCpNAADAkjol2rpOE5UmAACAGEKlCQAAWMI6TQAAAAiKShMAALCkTgk2Pz1XZ9uxWhNJEwAAsMT+JQfsO1ZrImkCImC9yxVy7HUXh9HxiNBDjdTQYzO1N+TYUXo95NhJIUcCQFMkTQAAwJJ4rTQxERwAAMAERyRNq1at0l133aXs7Gy53W65XC4VFRU1u+/cuXPlcrma3VJSUtp24AAAxKCGxS3t2pxSaXLE7bkHH3xQ+/btU5cuXdS9e3ft27fvnDGTJk1SZmZmQFtSkiM+LgAAiEKOyCKWLl2qvn37qlevXnr00UeVn59/zpg777xT3/nOd1p/cAAAxBn7F7d0xs+oOCJpysnJifQQAABAnHNE0hSKLVu26O2331ZiYqL69++vnJwcud3uc8b5/X75/f7G1z6frzWHCQCA48Tr03MxmzTNmTMn4HX37t21YsUK5ebmthhXWFioefPmtebQAACAAzni6TkrsrKytGLFCu3du1eff/65Kioq9PDDD+vYsWO68cYbtWvXrhbj8/PzVV1d3bhVVla20cgBAHCGhp9RsW9zRjoSc5Wm0aNHB7zu06ePHnzwQXXr1k0/+MEPNH/+fK1duzZovNvtNnUbDwAAxBdnpHY2mDRpkpKSklRWVhbpoQAA4Gh2rtHUsDlB3CRN7dq1U4cOHXTy5MlIDwUAADhQzN2eC6aiokJHjx7VoEGDIj0UAAAczf51mupsO1ZriqlKU01Njd57770m7UePHtXUqVMlSePGjWvrYQEAgBjgiErT0qVLVVpaKkkqLy9vbCsuLpZ0dvL36NGjdeTIEQ0aNEjZ2dkaMGCAunbtqqqqKq1fv15HjhxRbm6uZs6cGamPAQBATKi3eZ2meofMaTKVNO3fv1+SlJycrO7duwe0WeosKUnnn3++zjvvPEtxpaWlWrFiRUBbWVlZ46TuzMxMjR49Wp06ddKMGTO0detWvfLKKzp27JhSU1M1YMAA3X777crLy1NiojP+YAAAQHRxGYZhnGunhIQEuVwu9e/fXx988EFAWyh69+6t6dOna9asWSHFtyWfzyev16vq6mp5PJ5IDwdRZGmI178kjU0JvV/P7aHHakAYsT3CiP3+Of+aARBlmvv+a2j7afVsuT32Lc/j9/n1S+/CqP+uNVVpuuCCC+RyuRqrTF9us6K+vl7Hjh3TRx99pJ/97Gc6ffq07rvvPmsjBgAAEdWwuKWdx3MCU0nT3r17TbWZ9frrr+v666/X0qVLSZoAAIAjRGQieE5Oji6++GL97W9/i0T3AAAgDLVKVKKNlSanLG4ZsafnFixYoMOHD0eqewAAAEsiljRde+21keoaAACEwf7FLR2xApJDZl4BAABEmDNSOwAAEDXidXFLKk0AAAAmUGkCAACW1NlcabLzWK2JShMAAIAJVJoAAIAltUpUQhyu00SlCQAAwASSJgAAYMnZOU1JNm7WKk2LFy/WwIED5fF45PF4NGTIEK1fv77FmGeffVaDBg3Seeedp+7du2vy5Mk6cuSIpX5JmgAAgKP07NlTjz76qN555x298847uvrqq3XTTTfpgw8+aHb/0tJS3XHHHZo6dao++OADrV27Vtu2bVNeXp6lfpnTBAAALIn003M33HBDwOtHHnlEixcv1tatW3XppZc22X/r1q3KzMzUT37yE0nShRdeqLvuuksLFy601C9JE+LaYpcr5Ng8b+j9Jo8LPVZdw4gNx/eNCHUMIF74fL6A1263W263u8WYuro6rV27VidOnNCQIUOa3eeKK67QAw88oFdffVXXXXedPvnkE61bt06jRo2yND5uzwEAAEsaKk12bpKUkZEhr9fbuBUWFgYdQ3l5udLS0uR2uzV9+nS98MILuuSSS5rd94orrtCzzz6rW2+9Ve3atVN6errOP/98PfHEE5Y+N0kTAACICpWVlaqurm7c8vPzg+77ta99TTt37tTWrVt19913a9KkSdq9e3ez++7evVs/+clPNGfOHG3fvl0bNmzQnj17NH36dEvj4/YcAACwpLV+e67haTgz2rVrpz59+kiSsrOztW3bNj3++ON66qmnmuxbWFiooUOH6mc/+5kkaeDAgUpNTdWwYcM0f/58de/e3VSfJE0AAMCSWiXKFWWLWxqGIb/f3+x7J0+eVFJSYMqTmJjYGGcWSRMAAHCU+++/X9ddd50yMjJUU1Oj5557TsXFxdqwYYMkKT8/X1VVVXrmmWcknX3abtq0aVq8eLGuueYaHTx4UPfee68GDx6sHj16mO6XpAkAAFhSp0Ql2JhCWL3Vd/jwYU2cOFEHDx6U1+vVwIEDtWHDBuXm5kqSDh48qP379zfuf+edd6qmpkZPPvmkfvrTn+r888/X1VdfrQULFljql6QJAAA4yrJly1p8v6ioqEnbj3/8Y/34xz8Oq1+SJgAAYEmdzT/Ya+ek8tbEkgMAAAAmUGkCAACWUGkCAABAUFSaAACAJdG4TlNboNIEAABgApUmAABgSb2SVGdjClHvkHSEShMAAIAJzkjtgBYUuVwhx97dOYyOrw4j1huh2J+Y/40lAAimzuY5TTw9BwAAEEOoNAEAAEvqlGBzpckZNRxnjBIAACDCqDQBAABLzq6rxDpNAAAAaAaVJgAAYEmdkuSyMYWwc82n1uSMUQIAgKhRr0Rblwmo5/YcAABA7KDSBAAALKmzeSI4i1sCAADEECpNAADAEipNAAAACIpKEwAAsKRWCTL4GRUAAAA0h0oTAACw5OxilCxuCURMkcsVUtwEbxidXh1GbLcwYsMxy4hQxwAQ30iaAACAJTw9BwAAgKCoNAEAAEvqba408dtzAAAAMYRKEwAAsKRWiUqg0gQAAIDmUGkCAACW1ClRho0pBJUmAACAGEKlCQAAWHK20hR/c5pImgAAgCXxmjRxew4AAMAEKk0AAMCSuvpEGfU2VppsPFZrotIEAABgApUmAABgSV1toupr7asOGTYeqzVRaQIAADCBShNstdjlCjl2TIhxySND7lJKDSO2bxixFWHEAkCE1dUmyVVrXwph2His1kSlCQAAwARnpHYAACBq1NUmyGXrnCZn1HAcMcpVq1bprrvuUnZ2ttxut1wul4qKioLu7/P5NGvWLPXq1Utut1u9evXSrFmz5PP52m7QAAAgpjii0vTggw9q37596tKli7p37659+/YF3ffEiRMaPny4du7cqdzcXI0bN067du3Sr371K23atEmlpaVKTQ1nIgsAAPGtrjbR5koTT8/ZZunSpdq7d6/++c9/avr06S3uu3DhQu3cuVOzZ8/Wxo0b9eijj2r9+vWaM2eOdu7cqYULF7bRqAEAQCxxRNKUk5OjXr16nXM/wzC0dOlSpaWlac6cOQHv5efnq2PHjlq2bJkMw2itoQIAEPNqaxNVe8bGjUpT26uoqNCBAwc0dOjQJrfgUlJSdOWVV6qqqkofffRR0GP4/X75fL6ADQAAIOaSJknq27f5BXQa2hv2a05hYaG8Xm/jlpGRYf9AAQBwMKMuSfU2bkadI6ZYx1bSVF1dLUnyer3Nvu/xeAL2a05+fr6qq6sbt8rKSvsHCgAAHMcZqV0bcrvdcrvdkR4GAADRqzbx7Gbn8RwgpipNDRWmYJWkhvlJwSpRAAAAwcRUpelcc5bONecJAACYEKeVpphLmnr06KGysjKdOHEi4Am6U6dOqaSkRD169FCfPn0iOEoAAByuziXVhv4D7c0ezwFi6vacy+VSXl6ejh8/roceeijgvcLCQh09elR5eXlyuZzxhwMAAKKHIypNS5cuVWlpqSSpvLy8sa24uFiSNHr0aI0ePVqSNHv2bL388stauHChduzYocsvv1y7du3S+vXrlZWVpdmzZ0fiIwAAEDtq/7XZeTwHcETSVFpaqhUrVgS0lZWVqaysTJKUmZnZmDSlpqaquLhY8+bN07p161RcXKz09HTNnDlTBQUF/O4cAAAIicvgN0Va5PP55PV6VV1d3bjOE4KrCOPWZ98RIQZeHHKX4f3r5kQYsc/wvx2A6Nbc919Dm8qqpTQbvxOP+6Sh0f9dG1NzmgAAAFqLI27PAQCAKBKnc5qoNAEAAJhApQkAAFhTK+mMzcdzACpNAAAAJlBpAgAA1tT9a7PzeA5ApQkAAMAEKk0AAMAanp4DAABAMFSaAACANVSaAAAAEAyVJgAAYA2VJgAAgOi3ePFiDRw4UB6PRx6PR0OGDNH69etbjPH7/XrggQfUq1cvud1u9e7dW7/73e8s9UulCQAAWFMne6tDFtdp6tmzpx599FH16dNHkrRixQrddNNN2rFjhy699NJmY8aOHavDhw9r2bJl6tOnjz755BPV1lr7ECRNAADAmgjfnrvhhhsCXj/yyCNavHixtm7d2mzStGHDBm3evFl///vf1alTJ0lSZmam5WGSNKGJMpcr5NjB3jA6rggxrmsYfX4SRuzrRhjBAIB/5/P5Al673W653e4WY+rq6rR27VqdOHFCQ4YMaXafl19+WdnZ2Vq4cKFWrlyp1NRU3XjjjXr44YfVvn170+MjaQIAANa0UqUpIyMjoLmgoEBz585tNqS8vFxDhgzRqVOnlJaWphdeeEGXXHJJs/v+/e9/V2lpqVJSUvTCCy/o008/1Q9/+EN99tlnluY1kTQBAICoUFlZKY/H0/i6pSrT1772Ne3cuVPHjh3TH//4R02aNEmbN29uNnGqr6+Xy+XSs88+K6/37C2RRYsW6fvf/75+/etfm642kTQBAABrzvxrs/N4UuPTcGa0a9eucSJ4dna2tm3bpscff1xPPfVUk327d++ur371q40JkyRdfPHFMgxD//jHP9S3b19TfbLkAAAAcDzDMOT3+5t9b+jQoTpw4ICOHz/e2Pbhhx8qISFBPXv2NN0HSRMAALCmrhU2C+6//35t2bJFe/fuVXl5uR544AEVFxdrwoQJkqT8/HzdcccdjfuPHz9enTt31uTJk7V7926VlJToZz/7maZMmcJEcAAAELsOHz6siRMn6uDBg/J6vRo4cKA2bNig3NxcSdLBgwe1f//+xv3T0tL02muv6cc//rGys7PVuXNnjR07VvPnz7fUL0kTAACwJsKLWy5btqzF94uKipq09e/fX6+99pq1jv4Nt+cAAABMoNIEAACs4Qd7AQAAEAyVJgAAYA2VJgAAAARDpQkAAFhDpQkAAADBUGkCAADWRHidpkghaUIT4fwGY82J0GM7XRZi4Duh96kPjTCCAQDxhKQJAABYE6dzmkiaAACANWckJdp8PAdgIjgAAIAJVJoAAIA1dbJ38rZDJoJTaQIAADCBShMAALAmTieCU2kCAAAwgUoTAACwJk4Xt6TSBAAAYAKVJgAAYE2t7F2niTlNAAAAsYNKEwAAsOaM7C27sCI4AABA7KDSBAAArGFFcAAAAARDpSlGLXK5Qo69Lox+O3UKI/iT0MJ8laF36Qk9FADiF+s0AQAAIBgqTQAAwJpa2Vt2YZ0mAACA2EGlCQAAWHNGUuhTZ5s/ngOQNAEAAGtYcgAAAADBUGkCAADWMBEcAAAAwVBpAgAA1rC4JQAAAIKh0gQAAKyxe4kAhyw5QKUJAADABCpNAADAmjrZW3ZhThMAAEDsoNIEAACsqZW9P6PikHWaSJpiVLcwYtuHEXv4k9Bju4VYnvV8boTeKQAAJpE0AQAAa+K00sScJgAAABNiMmnKzMyUy+Vqdps+fXqkhwcAgLPV6uzaSnZtDqk0xeztOa/Xq3vvvbdJe3Z2dtsPBgAAOF7MJk3nn3++5s6dG+lhAAAQe+pk75wm1mkCAACIHTFbafL7/VqxYoWqqqrUsWNHXXHFFRo0aFCkhwUAgPPZPQeJOU2RdejQId15550Bbddee61WrlypLl26BI3z+/3y+/2Nr30+X2sNEQAAZ4rTpCkmb89NmTJFxcXF+uc//ymfz6etW7fquuuu04YNG3TjjTfKMIIvhlhYWCiv19u4ZWRktOHIAQBAtHIZLWUQMaS+vl7Dhw9XaWmp/vSnP2nUqFHN7tdcpSkjI0PV1dXyeDxtNdywPesKfYbe0DD6DWc18W6dQwz8NC4uYQBoUz6fT16vN+D7r6FNX6+WEm38TqzzSTu8Uf9dG5OVpuYkJCRo8uTJkqSysrKg+7ndbnk8noANAAAgZuc0NadhLtPJkycjPBIAABzM7iUCWHIg+rz11luSzq4YDgAAYEXMJU27d+/WsWPHmrSXlpZq0aJFcrvdGjNmTNsPDACAWFHbCpsDxNztuT/84Q9auHChRowYoczMTLndbr3//vvauHGjEhIStGTJEl1wwQWRHiYAAHCYmEuarrrqKv3tb3/Tu+++q82bN+vUqVPq1q2bbr31Vs2cOVODBw+O9BABAHC2Wkl2PrjskDlNMZc0DR8+XMOHD4/0MCIunGf+wlnOs1NKGMGXhRELAEAri7mkCQAAtLJaSfU2Hs/OY7WimJsIDgAA0BqoNAEAAGvqZO+cJipNAAAAsYNKEwAAsKZW9pZdqDQBAADEDipNAADAGipNAAAACIZKEwAAsOaM4rLSRNIEAACsqZe9Sw7YeaxWxO05AAAAE6g0AQAAa2oluWw8HpUmAAAA+y1evFgDBw6Ux+ORx+PRkCFDtH79elOxZWVlSkpKUlZWluV+SZoAAIA1ta2wWdCzZ089+uijeuedd/TOO+/o6quv1k033aQPPvigxbjq6mrdcccdGjFihLUO/4WkCQAAOMoNN9yg66+/Xv369VO/fv30yCOPKC0tTVu3bm0x7q677tL48eM1ZMiQkPplTlMUm+cK/YbxgDD69YQT+80wgsMZNACg7ZxRq8xp8vl8Ac1ut1tut7vF0Lq6Oq1du1YnTpxoMRlavny5Pv74Y61atUrz588PaZhUmgAAQFTIyMiQ1+tt3AoLC4PuW15errS0NLndbk2fPl0vvPCCLrnkkmb3raio0H333adnn31WSUmh14uoNAEAAGvq1CqVpsrKSnk8X9zvaKnK9LWvfU07d+7UsWPH9Mc//lGTJk3S5s2bmyROdXV1Gj9+vObNm6d+/fqFNUyXYRgOedAvMnw+n7xer6qrqwP+INtCpG7P/UcYsZnDwwgOddBPcAkDgN2a+/5raJOqJZeN34mGT1J437U5OTnq3bu3nnrqqYD2Y8eOqWPHjkpMTGxsq6+vl2EYSkxM1MaNG3X11Veb6oNKEwAAsC7K/r1qGIb8fn+Tdo/Ho/Ly8oC23/zmN3rjjTe0bt06XXjhhab7IGkCAACOcv/99+u6665TRkaGampq9Nxzz6m4uFgbNmyQJOXn56uqqkrPPPOMEhISdNlllwXEd+3aVSkpKU3az4WkCQAAOMrhw4c1ceJEHTx4UF6vVwMHDtSGDRuUm5srSTp48KD2799ve7/MaToH5jRZw5wmAIgN55zTFNYCNU16U7hzmtoCSw4AAACYQNIEAABgAkkTAACACSRNAAAAJvD0HAAAsOjMvzY7jxf9SJoAAIBFtf/a7Dxe9OP2HAAAgAlUmgAAgEXcnkOUaR+hfjPDWRnzp2HEVocRCwBAKyNpAgAAFjGnCQAAAEFQaQIAABbVyt55SFSaAAAAYgaVJgAAYFF8Pj1HpQkAAMAEKk0AAMAinp4DAABAEFSaAACARTw9BwAAgCCoNAEAAIuY0wQAAIAgqDQBAACLWKcJAAAAQVBpAgAAFsXnnCaSpijWPozYPuF0PDv00OdvuC7k2DF6NfSOAQBtiCUHAAAAEASVJgAAYFF83p6j0gQAAGAClSYAAGARSw4AAAAgCCpNAADAIuY0AQAAIAgqTQAAwCLWaQIAAEAQVJoAAIBFzGkCAABAEFSaAACARazTBAAAgCCoNAEAAIuoNAEAACAIKk0AAMCi+Hx6jqQpioXzhzNwQOixn96eFnLsX3VFyLFjQo4EAKD1kTQBAACLWBE8pmzbtk3XX3+9OnbsqNTUVA0ePFirV6+O9LAAAIgBta2wRb+YrDQVFxfrmmuuUbt27XTbbbfJ6/Xq+eef14QJE7R3717df//9kR4iAABwmJhLmmpra5WXlyeXy6WSkhJ9/etflyQVFBRoyJAhKigo0C233KK+fftGeKQAADjVGdmbQrDkQES88cYb+vjjjzV+/PjGhEmSOnTooF/84heqra3V8uXLIzhCAADgRDFXaSouLpYkjRw5ssl7DW2bN29uyyEBABBjWHIgJlRUVEhSs7ffOnbsqC5dujTu0xy/3y+/39/42ufz2T9IAADgODF3e666ulqS5PV6m33f4/E07tOcwsJCeb3exi0jI6NVxgkAgHM1LDlg1+aMSlPMJU3hys/PV3V1deNWWVkZ6SEBAIAoEHO35xoqTMGqST6fL2gVSpLcbrfcbnerjA0AgNgQn3OaYq7S1DCXqbl5S0ePHtWnn37KcgMAAMCymEuahg8fLknauHFjk/ca2hr2AQAAobBzPlPDFv1iLmkaMWKELrroIq1evVo7d+5sbK+pqdHDDz+spKQk3XnnnREbHwAAcKaYm9OUlJSkpUuX6pprrtGwYcM0btw4eTwePf/889qzZ4/mz5+vfv36RXqYAAA4WHzOaYq5pEmSrrrqKpWWlqqgoEB/+MMfdPr0aV166aV6+OGHNWHCBEvHMgxDUmTWa/o8jFhfXeixNT4j5Fi/ToUc6xNrYgFAtGj43mv4Hgzkb6YtHHYfr3W4jObPBv7lH//4B2s1AQDiVmVlpXr27ClJOnXqlC688EIdOnTI9n7S09O1Z88epaSk2H5su5A0nUN9fb0OHDigDh06yOVyBbzn8/mUkZGhyspKeTyeCI3QGThX5nGuzOE8mce5Mo9z9QXDMFRTU6MePXooIeGLKdCnTp3S6dOnbe+vXbt2UZ0wSTF6e85OCQkJjRl2MB6PJ+7/5zKLc2Ue58oczpN5nCvzOFdnNbeuYUpKStQnN60l5p6eAwAAaA0kTQAAACaQNIXB7XaroKCAn10xgXNlHufKHM6TeZwr8zhXaAkTwQEAAEyg0gQAAGACSRMAAIAJJE0AAAAmkDQBAACYQNIUom3btun6669Xx44dlZqaqsGDB2v16tWRHlZUyczMlMvlanabPn16pIfX5latWqW77rpL2dnZcrvdcrlcKioqCrq/z+fTrFmz1KtXL7ndbvXq1UuzZs2KyO8gtjUr52ru3LlBr7NYX4CvqqpKjz32mEaOHKkLLrhA7dq1U3p6um6++Wa99dZbzcbE63Vl9VzF83WF4FgRPATFxcW65ppr1K5dO912223yer16/vnnNWHCBO3du1f3339/pIcYNbxer+69994m7dnZ2W0/mAh78MEHtW/fPnXp0kXdu3fXvn37gu574sQJDR8+XDt37lRubq7GjRunXbt26Ve/+pU2bdqk0tJSpaamtuHo25aVc9Vg0qRJyszMDGhLSortv+KeeOIJLViwQL1791Zubq66du2qiooKvfjii3rxxRe1Zs0ajR07tnH/eL6urJ6rBvF4XaEFBiw5c+aM0bt3b8PtdhvvvvtuY7vP5zMuvfRSIykpyfjwww8jOMLo0atXL6NXr16RHkbUeO2114y9e/cahmEYhYWFhiRj+fLlze47Z84cQ5Ixe/bsZtvnzJnT2sONKCvnqqCgwJBkbNq0qe0GGCX++Mc/GiUlJU3aS0pKjOTkZKNTp07GqVOnGtvj+bqyeq7i+bpCcNyes+iNN97Qxx9/rPHjx+vrX/96Y3uHDh30i1/8QrW1tVq+fHkER4holZOTo169ep1zP8MwtHTpUqWlpWnOnDkB7+Xn56tjx45atmyZjBheYs3suYp3Y8aM0bBhw5q0Dxs2TFdddZU+++wzlZeXS+K6snKugGCoMVpUXFwsSRo5cmST9xraNm/e3JZDimp+v18rVqxQVVWVOnbsqCuuuEKDBg2K9LCiWkVFhQ4cOKBrrrmmya2SlJQUXXnllXrppZf00UcfqW/fvhEaZfTZsmWL3n77bSUmJqp///7KycmJ61Wdk5OTJX1xK4nrKrh/P1dfxnWFLyNpsqiiokKSmv1LpWPHjurSpUvjPpAOHTqkO++8M6Dt2muv1cqVK9WlS5fIDCrKtXSNfbm9oqIi7r7cWvLv1ZPu3btrxYoVys3NjdCIImf//v16/fXXlZ6ergEDBkjiugqmuXP1ZVxX+DJuz1lUXV0t6ewE5+Z4PJ7GfeLdlClTVFxcrH/+85/y+XzaunWrrrvuOm3YsEE33nhjzN4GCJeZa+zL+8W7rKwsrVixQnv37tXnn3+uiooKPfzwwzp27JhuvPFG7dq1K9JDbFNnzpzRxIkT5ff7tXDhQiUmJkriumpOsHMlcV2heVSa0Gr+/V9o3/zmN/WnP/1Jw4cPV2lpqV599VWNGjUqQqNDrBg9enTA6z59+ujBBx9Ut27d9IMf/EDz58/X2rVrIzO4NlZfX68pU6aopKRE06ZN08SJEyM9pKh1rnPFdYXmUGmyqOFfacH+Nebz+YL+Sw5SQkKCJk+eLEkqKyuL8Giik5lr7Mv7oXmTJk1SUlJS3FxnhmFo2rRpWrVqlW6//XYtWbIk4H2uqy+c61y1JN6uKwQiabLoy/f9/93Ro0f16aefxtV8gFA0zGU6efJkhEcSnVq6xr7cznXWsnbt2qlDhw5xcZ3V19dr6tSp+t3vfqdx48apqKhICQmBf71zXZ1l5ly1JJ6uKzRF0mTR8OHDJUkbN25s8l5DW8M+aF7D6rv/vmAczurbt6969OihsrIynThxIuC9U6dOqaSkRD169FCfPn0iNEJnqKio0NGjR2P+Oquvr1deXp6WL1+uW2+9VStXrgyYm9OA68r8uWpJvFxXaB5Jk0UjRozQRRddpNWrV2vnzp2N7TU1NXr44YeVlJTU5GmxeLR7924dO3asSXtpaakWLVokt9utMWPGtP3AHMDlcikvL0/Hjx/XQw89FPBeYWGhjh49qry8PLlcrgiNMHrU1NTovffea9J+9OhRTZ06VZI0bty4th5Wm2momixfvly33HKLVq1aFTQJiPfrysq5ivfrCsG5DB5hsmzTpk265ppr5Ha7NW7cOHk8Hj3//PPas2eP5s+frwceeCDSQ4y4uXPnauHChRoxYoQyMzPldrv1/vvva+PGjUpISNCSJUuUl5cX6WG2qaVLl6q0tFSSVF5ernfffVdDhw5t/Jf96NGjGyefnjhxQt/+9rcbf+7i8ssv165du7R+/XplZWXF9M9dSObP1d69e3XhhRcqOztbAwYMUNeuXVVVVaX169fryJEjys3N1Z/+9Ce1a9cukh+n1cydO1fz5s1TWlqa7rnnnmbXGRo9erSysrIkxfd1ZeVcxft1hRZEbC1yh3vrrbeMa6+91vB6vUb79u2N7OxsY9WqVZEeVtQoLi42xo4da/Tp08fo0KGDkZycbPTs2dO47bbbjLfeeivSw4uISZMmGZKCbgUFBQH7Hzt2zJg5c6aRkZFhJCcnGxkZGcbMmTONY8eOReYDtCGz56q6utqYMWOGcfnllxtdunQxkpKSDK/Xa3z72982lixZYtTW1kb2g7Syc50nNfPzM/F6XVk5V/F+XSE4Kk0AAAAmMKcJAADABJImAAAAE0iaAAAATCBpAgAAMIGkCQAAwASSJgAAABNImgAAAEwgaQIAADCBpAkAAMAEkiYAUSszM1Mul0t79+6N9FAAgKQJAADAjKY/8wwAUaJ3795KSUlRcnJypIcCAOIHewEAAEzg9hwAAIAJJE0AohYTwQFEE5ImAAAAE0iaAAAATCBpAgAAMIGkCQAAwASSJgAAABNImgAAAEwgaQIAADCBpAkAAMAEkiYAAAATSJoAAABMIGkCAAAwgaQJAADABJdhGEakBwEAABDtqDQBAACYQNIEAABgAkkTAACACSRNAAAAJpA0AQAAmEDSBAAAYAJJEwAAgAkkTQAAACaQNAEAAJhA0gQAAGACSRMAAIAJJE0AAAAmkDQBAACY8P8BY8sNhciP3BgAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -55,8 +55,8 @@ ], "source": [ "%matplotlib inline\n", - "import matplotlib.pyplot as plt \n", - "import numpy as np \n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "import plot_wind\n", "fname = \"cv_test/cv_standard.master.txt\"\n", "plot_wind.doit(fname, var=\"t_e\")" @@ -86,7 +86,7 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "import astropy.io.ascii as io \n", + "import astropy.io.ascii as io\n", "\n", "data = io.read(fname)\n", "\n", @@ -142,7 +142,7 @@ } ], "source": [ - "import py_plot_util as util \n", + "import py_plot_util as util\n", "descr = util.get_windsave_descriptions(data)" ] }, @@ -168,7 +168,7 @@ }, { "data": { - "image/png": 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iHsO4PuW0cRPSkNnlpjtLvvLL1WYNABeJhA8gAABws2TNQLhzbQgAAEnibABh5zB1vjdnnzp1Sl/96ld1zTXXaMKECcrLy9N9992nd955x6gNAggAABwUjwDifG/Ofv/993XgwAF9/etf14EDB/Tiiy/qzTff1JIlS4zaYAgDAIAkc743Z2dnZw/b8PGHP/yhrr/+enV2dqqgoGBMbSR8AMEqDACAm1mytxTz7MYJfX19YeXRfKlkb2+vPB6PLr300jHXSfghDJ/Pp7a2NjU1NcW7KwAADBOtIYz8/HxlZ2eHjpqamqj07+TJk1q9erXuvfdeZWVljblewmcgAAC4GHR1dYX9go9G9uHUqVO65557FAwGtXnzZqO6BBAAADgoWss4s7KyjDIEF3Lq1Cnddddd6ujo0CuvvGJ8bwIIAAAc5MZ9IM4GD//1X/+lPXv2aNKkScb3IIAAACDJnO/N2Xl5ebrzzjt14MAB7dq1S4FAQEeOHJEk5eTkKC0tbUxtJHwAwSoMQCpetdG4junW1CkR/C/mMd2aOoI/tDzB87/c76N+/cLD5o0ANsQjA3G+N2evW7dOv/jFLyRJs2fPDqu3Z88eLVy4cExtJHwAwbswAABuZlkeWTYCiEjqXujN2Rd6q/ZYJPwyTgAAEHsJn4EAAMDNgvLY2kjKTl0nEUAAAOAgN67CiAYCCAAAHBSPORCxwBwIAABgLOEzECzjBAC4WbIOYSR8BoKXaQEA3OzsEIadw40SPoAAAACxl/BDGAAAuJllcwjDrRkIAgjAhYpWm21N7ckwb8NjOG3IMt2WWlKK4c+9SLbL/s1Pv2xeCYghS5KdjR/t7xnpDIYwAACAMTIQAAA4KCiPPOxECQAATLCRFAAAwIcSPgPBRlIAADcLWh552EjKfdhICgDgZpZl/3CjhM9AAADgZsyBAAAA+BAZCAAAHJSsGQgCCAAAHJSskygJIACHzfym2bbUkuQZb3h9BIuQUk6Z1zH1ux/8H+cbARAXBBAAADjI7koKVmEAAHAROhNA2JkDEcXORBGrMAAAgDEyEAAAOIhVGAAAwJj14WGnvhsl/BCG3+9XYWGhSktL490VAAAuGgkfQPAuDACAm50dwrBzuBFDGAAAOClJxzAIIAAAcJLdLAIZCCA5fGrDBrMKhrtKSpInYPYDIxa7Sv6/GnaVBPAXBBAAADiInSgBAICxZN0HIuFXYQAAgNgjAwEAgJMsj72JkC7NQBBAAADgoGSdA8EQBgAAMEYGAgAAJ7GRFAAAMMUqDAAAgA+RgQAAwGkuHYawgwACF7UrfvQ980qGW1ObbkstSZ7TxjWM23jza2xNDcQCQxgu5ff7VVhYqNLS0nh3BQCA4awoHIYaGxu1ePFi5eXlyePxaOfOnWGfv/jii1q0aJEmT54sj8ej1tZW4zYSPoDw+Xxqa2tTU1NTvLsCAIArDAwMqLi4WLW1taN+/pnPfEbf+c53Im6DIQwAABzlUSTDjOH1zXi9Xnm93lE/r6iokCS99dZbkXaKAAIAAEdFaR+Ivr6+sOL09HSlp6fbuLE9CT+EAQDAxSA/P1/Z2dmho6amJq79IQMBAICTopSB6OrqUlZWVqg4ntkHiQACAABnReltnFlZWWEBRLwxhAEAAIyRgQAAwEHxeJ13f3+/2tvbQ+cdHR1qbW1VTk6OCgoK9O6776qzs1PvvPOOJOnQoUOSpClTpmjKlCljaoMMBAAATorDRlLNzc2aM2eO5syZI0mqrq7WnDlz9I1vfEOS9Itf/EJz5szRbbfdJkm65557NGfOHD355JNjboMMBJLGjOcimJE8PoJxyaBZHetUJHG6WZ32quoI2gCQrBYuXCjrPKmL+++/X/fff7+tNgggAABwUpQmUboNAQQAAA7yWGcOO/XdiAACAAAnRWkfCLdhEiUAADBGBgIAACcxBwIAABhjCAMAAOAMMhAAADgpSTMQBBAAADgpSQMIhjAAAIAxMhBwrU/XPWp0fdrHzGcqBwPmMXTgtFkdw52vJUkd/+th80oA3ClJV2HEJQOxbNkyTZw4UXfeeWdY+a5duzRz5kxdddVVeuqpp+LRNQAAoursTpR2DjeKSwCxatUqPfPMM2Flp0+fVnV1tV555RUdOHBAjz/+uN599914dA8AAFxAXAKIsrIyZWZmhpXt379fRUVFmjp1qjIzM3Xrrbeqvr4+Ht0DACB64vA671gwDiAaGxu1ePFi5eXlyePxaOfOncOu2bx5s2bMmKGMjAyVlJRo7969F7zvO++8o6lTp4bOp02bpsOHD5t2DwAAxIBxADEwMKDi4mLV1taO+PmOHTtUVVWltWvX6uDBg1qwYIG8Xq86OzvPe9+R3lvu8bhz4ggAAGPlkc05EPH+AqMwXoXh9Xrl9XpH/XzDhg1avny5HnzwQUnSpk2bVF9fry1btqimpmbUelOnTg3LOPzxj3/UDTfcMOr1g4ODGhwcDJ339fWZfA0AAGBDVOdADA0NqaWlReXl5WHl5eXl2rdv33nrXn/99fr973+vw4cP68SJE9q9e7cWLVo06vU1NTXKzs4OHfn5+VH5DgAARNXZZZx2DheKagBx9OhRBQIB5ebmhpXn5ubqyJEjofNFixbpC1/4gnbv3q1p06apqalJ48aN0/e//32VlZVpzpw5evjhhzVp0qRR21qzZo16e3tDR1dXVzS/CgAA0ZGkkygd2Ujqo3MXLMsKKxttdcWSJUu0ZMmSMbWRnp6u9PT0yDsJAAAiFtUAYvLkyUpNTQ3LNkhST0/PsKwEAAAXhSR9F0ZUA4i0tDSVlJSooaFBy5YtC5U3NDTo9ttvj2ZTIX6/X36/X4FAwJH7IzpurF9jXCdzvNm4XyCCccKhU+b/CwymmNU5FXDn+CWA2LC7m6Rbd6I0/unZ39+v9vb20HlHR4daW1uVk5OjgoICVVdXq6KiQnPnztW8efO0detWdXZ2asWKFVHt+Fk+n08+n099fX3Kzs52pA0AABDOOIBobm5WWVlZ6Ly6ulqSVFlZqW3btunuu+/WsWPHtH79enV3d2vWrFnavXu3pk+fHr1eAwCQKBjCOGPhwoUjbvp0rpUrV2rlypURdwoAgKSRpAFEXN6FAQAAElvCBxB+v1+FhYUqLS2Nd1cAABiG13m7lM/nU1tbm5qamuLdFQAAhkvSnSgd2UgKAAB8iDkQAAAAZ5CBAADAQWwk5VLsRJkYJo5/37jO6aBZguxUINW4jfcj+D8zaDge+eb//LpxGwCSCEMY7sQkSgAAYi/hMxAAALia3aWYLs1AEEAAAOAkhjAAAADOIAMBAICTyEC4E1tZAwDcjK2sXYpVGAAAxF7CBxAAACD2mAMBAICTknQOBAEEAAAOYitr4ByV+5cbXT853fx1tENBs8fz/dOXGLcRif1/++2YtAMAkWpsbNQTTzyhlpYWdXd3q66uTkuXLg19blmWHn30UW3dulXvvfeebrjhBvn9fhUVFY25DeZAAADgNMvGEYGBgQEVFxertrZ2xM+/+93vasOGDaqtrVVTU5OmTJmiv/mbv9GJEyfG3AYZCAAAnBSlORB9fX1hxenp6UpPTx+xitfrldfrHfl2lqVNmzZp7dq1+vznPy9J2r59u3Jzc/X888/roYceGlO3Ej4DwT4QAICLQX5+vrKzs0NHTU1NRPfp6OjQkSNHVF5eHipLT0/XX//1X2vfvn1jvk/CZyB8Pp98Pp/6+vqUnZ0d7+4AABAmWpMou7q6lJWVFSofLftwIUeOHJEk5ebmhpXn5ubq7bffHvN9Ej6AAADA1aI0hJGVlRUWQNjl8YRPbrcsa1jZ+ST8EAYAABi7KVOmSPpLJuKsnp6eYVmJ8yGAAADAQW57F8aMGTM0ZcoUNTQ0hMqGhob06quvav78+WO+D0MYAAA4KQ47Ufb396u9vT103tHRodbWVuXk5KigoEBVVVX69re/rauuukpXXXWVvv3tb+tjH/uY7r333jG3QQABAECSaW5uVllZWei8urpaklRZWalt27bpK1/5ij744AOtXLkytJHUyy+/rMzMzDG3QQABAICT4pCBWLhwoSxr9Ioej0fr1q3TunXrIu4WAQQi8on0fqPrTwXNp9sMGm5lPS4lw7iNf13wQ+M6AGAiWd+FkfCTKNlICgDgana2sbabvXBQwgcQPp9PbW1tampqindXAAC4aDCEAQCAk+IwByIWCCAAAHAQcyAAAAA+RAYCAAAnMYQBAABMMYQBAADwITIQAAA4iSEMAABgjAACyeq7bV7jOpddYnb9yaBhBUnvB9OM6wAAYiPh50CwlTUAwM08UTjcKOEDCLayBgC4WpK+C4MhDAAAHMQyTgAAgA+RgQAAwEmswgAAABFxaRBgB0MYAADAGBkIAAAclKyTKAkgAABwUpLOgWAIAwAAGCMDAeVe0mtc55SVanR9JFtZXxLMMLr+O9f+zLgNAHAaQxgAAMAcQxgAAABnkIEAAMBBDGEAAABzSTqEQQABAICTkjSASPg5EH6/X4WFhSotLY13VwAAuGgkfADh8/nU1tampqameHcFAIBhzs6BsHO4EUMYAAA4iSEMAACAM8hAQDmp/cZ1jHeiTEkzbuOSQMC4DgC4jcey5LEiTyPYqeskAggAAJzEEAYAAMAZZCAAAHAQO1ECAABzDGEAAACcQQYCAAAHMYQBAADMJekQBgEEAAAOStYMBHMgAABIMidOnFBVVZWmT5+u8ePHa/78+VF/ZxQBBAAATrKicBh68MEH1dDQoGeffVZvvPGGysvLdcstt+jw4cP2v8+HGMJIQvve/qTR9ZemmMeRQcPY86Q1aNxGqoLGdQDAjWI5DPHBBx/o5z//uf7lX/5Fn/vc5yRJ69at086dO7VlyxZ961vfiko7BBAAACSAvr6+sPP09HSlp6cPu+706dMKBALKyMgIKx8/frxee+21qPWHIQwAAJxkWfYPSfn5+crOzg4dNTU1IzaXmZmpefPm6Zvf/KbeeecdBQIBPffcc3r99dfV3d0dta9FBgIAAAdFaxVGV1eXsrKyQuUjZR/OevbZZ/WlL31JU6dOVWpqqq677jrde++9OnDgQOQd+QgyEAAAJICsrKyw43wBxKc+9Sm9+uqr6u/vV1dXl/bv369Tp05pxowZUesPAQQAAE6KwyqMsyZMmKDLL79c7733nurr63X77bdHfrOPYAgDAAAHeYJnDjv1TdXX18uyLM2cOVPt7e16+OGHNXPmTD3wwAORd+QjyEAAAJBkent75fP5dPXVV+u+++7TZz/7Wb388su65JJLotaGqwKI733veyoqKtKsWbP03HPPxbs7AADYF4chjLvuukv//d//rcHBQXV3d6u2tlbZ2dn2v8s5XDOE8cYbb+j5559XS0uLJOnmm2/W3/3d3+nSSy+Nb8cAALCBd2E47A9/+IPmz5+vjIwMZWRkaPbs2XrppZfi3S0AAOyJ0j4QbhO1DERjY6OeeOIJtbS0qLu7W3V1dVq6dGnYNZs3b9YTTzyh7u5uFRUVadOmTVqwYIEkadasWXr00Ud1/PhxSdIrr7yiT37SbEtmnHFpitm20QF5jNsIWmZ1TlquSXYBAKIgahmIgYEBFRcXq7a2dsTPd+zYoaqqKq1du1YHDx7UggUL5PV61dnZKUkqLCzUqlWrdNNNN2nZsmUqLS3VuHGj/9IZHBxUX19f2AEAgNucHcKwc7hR1AIIr9erb33rW/r85z8/4ucbNmzQ8uXL9eCDD+rTn/60Nm3apPz8fG3ZsiV0zUMPPaQDBw5oz549SktL05VXXjlqezU1NWFbeubn50frqwAAED1x3AfCSTGZAzE0NKSWlhaVl5eHlZeXl2vfvn2h856eHknSoUOHtH//fi1atGjUe65Zs0a9vb2ho6ury5nOAwCAYWIyMH306FEFAgHl5uaGlefm5urIkSOh86VLl+r48eOaMGGCfvzjH593CGO0t5ABAOAmyboKI6Yz2zye8Il3lmWFlZ2bjQAAICnYXUnh0lUYMRnCmDx5slJTU8OyDdKZIYuPZiUAAID7xSSASEtLU0lJiRoaGsLKGxoaNH/+fFv39vv9KiwsVGlpqa37AADghGRdhRG1IYz+/n61t7eHzjs6OtTa2qqcnBwVFBSourpaFRUVmjt3rubNm6etW7eqs7NTK1assNWuz+eTz+dTX19f1LfpBADANrsrKZI9gGhublZZWVnovLq6WpJUWVmpbdu26e6779axY8e0fv16dXd3a9asWdq9e7emT58erS4AAOA6TKK8gIULF8q6wESPlStXauXKldFqEgAAxEnC7y/s9/vl9/sVCATi3RXXyEwxe3l8IILoNmi4+/UllvkL7W+Y3mFcBwBcJ2idOezUdyHXvEwrUj6fT21tbWpqaop3VwAAGI6dKAEAAM5I+CEMAADczCObkyij1pPoIoAAAMBJ7EQJAABwRsIHEOxECQBws2TdiTLhAwhWYQAAXI1VGAAAAGcwiRIAAAd5LEseGxMh7dR1EgEEAABOCn542KnvQgQQAAA4KFkzEAk/B4JVGAAAxF7CBxCswgAAuFqSrsJgCAMAACexEyUAAMAZZCAAAHCQ3d0k3boTJQEEAABOYggDAADgjIQPIFjGCQBwM0/Q/uFGCR9AsIwTAOBqZ4cw7BwulPABBAAAiD0mUQIA4CS7m0G5MwFBAAEAgJOS9V0YBBAAADiJZZwAAABnkIEAAMBJliQ7SzHdmYAggAAAwEnJOgci4Ycw2EgKAIDYS/gAgo2kAACuZsnmRlJmzZ0+fVpf+9rXNGPGDI0fP16f/OQntX79egWD0d3SkiEMAACcFONVGI8//riefPJJbd++XUVFRWpubtYDDzyg7Oxs/eM//mPk/fgIAggAAJLIb37zG91+++267bbbJElXXHGFfvrTn6q5uTmq7ST8EAYAAK4WjMIhqa+vL+wYHBwcsbnPfvaz+vd//3e9+eabkqTf/e53eu2113TrrbdG9WuRgUhCU1MzHW8jaLgm6ZQVcKgnAOBu0VqFkZ+fH1b+yCOPaN26dcOu/+pXv6re3l5dffXVSk1NVSAQ0GOPPaYvfvGLEfdhJAQQAAAkgK6uLmVlZYXO09PTR7xux44deu655/T888+rqKhIra2tqqqqUl5eniorK6PWHwIIAACcFKVJlFlZWWEBxGgefvhhrV69Wvfcc48k6ZprrtHbb7+tmpoaAggAABJGjFdhvP/++0pJCZ/imJqayjJOAAASSowDiMWLF+uxxx5TQUGBioqKdPDgQW3YsEFf+tKXIu/DCAggAABIIj/84Q/19a9/XStXrlRPT4/y8vL00EMP6Rvf+EZU2yGAAADASUFJHpv1DWRmZmrTpk3atGmTjUYvLOH3geBdGAAANzu7jNPO4UYJH0DwLgwAAGKPIQwAAJwU40mUsUIAAQCAk4KW5LERBATdGUAk/BAGAACIPTIQAAA4iSEMAABgzmYAIXcGEAxhAAAAY2QgAABwEkMYAADAWNCSrWEIl67CIIAAAMBJVvDMYae+CzEHAgAAGCMDAQCAk5gDAQAAjCXpHAiGMAAAgDEyEAAAOClJhzASPgPh9/tVWFio0tLSeHcFAIDhLP0liIjoiPcXGFnCBxA+n09tbW1qamqKd1cAALhoMIQBAICTknQIgwACAAAnBYOSbGwGFWQjKQAAkCTIQAAA4CSGMAAAgDECCAAAYIydKAEAAM4gAwEAgIMsKyjLxiu57dR1EgEEAABOsix7wxAunQPBEAYAADBGBgIAACdZNidRujQDQQABAICTgkHJY2Meg0vnQDCEAQAAjJGBAADASQxhAAAAU1YwKMvGEIZbl3EyhAEAAIyRgQAAwEkMYQAAAGNBS/IQQAAAABOWJcnOMk53BhDMgQAAAMZcFUBs3LhRRUVFKiws1KpVq2S5NOoCAGCsrKBl+3Aj1wQQf/7zn1VbW6uWlha98cYbamlp0W9/+9t4dwsAAHusoP3DhVw1B+L06dM6efKkJOnUqVO67LLL4twjAAAwkqhlIBobG7V48WLl5eXJ4/Fo586dw67ZvHmzZsyYoYyMDJWUlGjv3r2hzz7xiU/on/7pn1RQUKC8vDzdcsst+tSnPhWt7gEAEBcMYVzAwMCAiouLVVtbO+LnO3bsUFVVldauXauDBw9qwYIF8nq96uzslCS999572rVrl9566y0dPnxY+/btU2NjY7S6BwBAfDCEcX5er1der3fUzzds2KDly5frwQcflCRt2rRJ9fX12rJli2pqavRv//ZvuvLKK5WTkyNJuu222/Tb3/5Wn/vc50a83+DgoAYHB0Pnvb29kqS+vr5ofaWEFTwRcL4Nw01RTkXwP8CpCfxbAnDG2d8VsZisf1qnbO0jdVqnoteZKIrJHIihoSG1tLRo9erVYeXl5eXat2+fJCk/P1/79u3TyZMndckll+hXv/qV/v7v/37Ue9bU1OjRRx8dVp6fnx/dziOOsuPdAQBJ7tixY8rOduZnTVpamqZMmaLXjuy2fa8pU6YoLS0tCr2KnpgEEEePHlUgEFBubm5YeW5uro4cOSJJuvHGG3Xrrbdqzpw5SklJ0c0336wlS5aMes81a9aouro6dH78+HFNnz5dnZ2djj0MdpWWlqqpqcm1947kHmOtM5brLnTNaJ+PVN7X16f8/Hx1dXUpKyvrgv2LByefh2jcP1Gfh9E+u9ifCTc/D2O91vTf/Hyf9fb2qqCgIJT1dkJGRoY6Ojo0NDRk+15paWnKyMiIQq+iJ6arMDweT9i5ZVlhZY899pgee+yxMd0rPT1d6enpw8qzs7Nd+8MhNTXVsb5F496R3GOsdcZy3YWuGe3z89XLysq6KJ+HaNw/UZ+HC312sT4Tbn4exnptpP/m5/ssJcXZ3QwyMjJc94s/WmKyD8TkyZOVmpoayjac1dPTMywrkcx8Pp+r7x3JPcZaZyzXXeia0T538r+rk5zut937J+rzYNIPt3Hzzwgnn4exXhvpv3miPg9u57EcmEHi8XhUV1enpUuXhspuuOEGlZSUaPPmzaGywsJC3X777aqpqbHdZl9fn7Kzs9Xb2+vavy4QOzwP+CieCZyL58G+qA1h9Pf3q729PXTe0dGh1tZW5eTkqKCgQNXV1aqoqNDcuXM1b948bd26VZ2dnVqxYkVU2k9PT9cjjzwy4rAGLj48D/gongmci+fBvqhlIH71q1+prKxsWHllZaW2bdsm6cxGUt/97nfV3d2tWbNmaePGjaMu0wQAAO7lyBAGAABIbq55mRYAAEgcBBAAAMAYAQQAADBGAAEAAIxdNAHE9773PRUVFWnWrFl67rnn4t0dxMGyZcs0ceJE3XnnnWHlu3bt0syZM3XVVVfpqaeeilPvEGujPQ+jlSO5jfTv3tXVpYULF6qwsFDXXnutXnjhhTj20H0uilUYb7zxhiorK0Mv7rr55pv1y1/+Updeeml8O4aY2rNnj/r7+7V9+3b97Gc/kySdPn1ahYWF2rNnj7KysnTdddfp9ddfd3R/fLjDSM/D+cqR3Eb6d+/u7taf/vQnzZ49Wz09Pbruuut06NAhTZgwIc69dYeLIgPxhz/8QfPnzw/tST579my99NJL8e4WYqysrEyZmZlhZfv371dRUZGmTp2qzMxM3Xrrraqvr49TDxFLIz0P5ytHchvp3/3yyy/X7NmzJUmXXXaZcnJy9O6778ahd+6UEAFEY2OjFi9erLy8PHk8Hu3cuXPYNZs3b9aMGTOUkZGhkpIS7d27N/TZrFmztGfPHh0/flzHjx/XK6+8osOHD8fwG8Auu8/AaN555x1NnTo1dD5t2jSejQTg1POAxBSL56G5uVnBYFD5+flR6nXiS4gAYmBgQMXFxaqtrR3x8x07dqiqqkpr167VwYMHtWDBAnm9XnV2dko6886NVatW6aabbtKyZctUWlqqceNi+iJS2GT3GRjNSCN4H31rLNzHqecBicnp5+HYsWO67777tHXr1mh2O/FZCUaSVVdXF1Z2/fXXWytWrAgru/rqq63Vq1ePeI/ly5dbu3btcqqLcJidZ2DPnj3WHXfcETr/9a9/bS1dujR0vmrVKusnP/lJ9DsNx0TzebhQOdwv2s/DyZMnrQULFljPPPOMI/1NZAmRgTifoaEhtbS0qLy8PKy8vLw8NGlSOvPqcEk6dOiQ9u/fr0WLFsW0n3DOWJ+BkVx//fX6/e9/r8OHD+vEiRPavXs3z0aCs/M8IPnYeR4sy9L999+vm266SRUVFU52MyElfB7/6NGjCgQCys3NDSvPzc3VkSNHQudLly7V8ePHNWHCBP34xz9mCCOJjPUZWLRokQ4cOKCBgQFNmzZNdXV1Ki0t1fe//32VlZUpGAzqK1/5iiZNmhTrr4Aosvs8jFaOxGTneRgcHNSOHTt07bXXhuZVPPvss7rmmmti+RVcK2l+i3503NqyrLAy/vJIfhd6BkZbXbFkyRItWbLE0b4h9iJ9HliFk5wifR6CwaCj/UpkCT+EMXnyZKWmpoZFktKZIYuPRpxITjwDOBfPA87F8+CchA8g0tLSVFJSooaGhrDyhoYGzZ8/P069QizxDOBcPA84F8+DcxJiCKO/v1/t7e2h846ODrW2tionJ0cFBQWqrq5WRUWF5s6dq3nz5mnr1q3q7OzUihUr4thrRBPPAM7F84Bz8TzESVzXgIzRnj17LEnDjsrKytA1fr/fmj59upWWlmZdd9111quvvhq/DiPqeAZwLp4HnIvnIT4uindhAACA6Er4ORAAACD2CCAAAIAxAggAAGCMAAIAABgjgAAAAMYIIAAAgDECCAAAYIwAAgAAGCOAAAAAxgggAACAMQIIAABgjAACAAAY+/893BZ3NAusfgAAAABJRU5ErkJggg==\n", + "image/png": 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", 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\n", 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", 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" ] @@ -246,7 +246,7 @@ } ], "source": [ - "import py_plot_output as plot \n", + "import py_plot_output as plot\n", "plot.make_wind_plot(data, \"cv_standard_wind\", var = [\"ne\", \"t_e\", \"t_r\", \"xi\", \"ntot\", \"v_x\", \"h1\", \"c4\"], shape=(4,2) )" ] }, @@ -274,7 +274,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] diff --git a/docs/sphinx/source/py_progs.rst b/docs/sphinx/source/py_progs.rst index 5293aa522..1da99f562 100644 --- a/docs/sphinx/source/py_progs.rst +++ b/docs/sphinx/source/py_progs.rst @@ -1,8 +1,8 @@ -Python Scripts +Sirocco Scripts ################################ -There are several `Python` scripts written to prepare input for -and analyse the output of *python* (the C code). +There are several Sirocco scripts written to prepare input for +and analyse the output of Sirocco. Some of the more useful scripts/modules are documented below. Alternatively, you can generate documentation for all the scripts by navigating to :code:`docs/pydocs` and running :code:`write_docs.py`. diff --git a/docs/sphinx/source/py_progs/MakeMacro.rst b/docs/sphinx/source/py_progs/MakeMacro.rst index b47a56195..ecb0e3666 100644 --- a/docs/sphinx/source/py_progs/MakeMacro.rst +++ b/docs/sphinx/source/py_progs/MakeMacro.rst @@ -8,9 +8,9 @@ To facillitate generation of data for MacroAtoms several routines exist, includi * MacroCombine.py These routines use data contained in a (local) Chianti repository and from TopBase to construct atomic data files that can be used to generate the atomic data -needed for MacroAtoms. Chiani is the primary source of data, but unfortunately for Python, it does not include photionization x-sections, and so these are obtained from TopBase. +needed for MacroAtoms. Chiani is the primary source of data, but unfortunately for Sirocco, it does not include photionization x-sections, and so these are obtained from TopBase. -**It should be emphacised at the outset that these routines, while they generate that can be read and used within in Python, they do not guaranteed that the Macro atom models are physically sensible. In particular, it is easy to generate models that are overly complex or ones that do not include all of the sublevels of a particular ion** +**It should be emphacised at the outset that these routines, while they generate that can be read and used within in Sirocco, they do not guaranteed that the Macro atom models are physically sensible. In particular, it is easy to generate models that are overly complex or ones that do not include all of the sublevels of a particular ion** Mechanics @@ -34,7 +34,7 @@ If for example, one wishes to make C a macro atom, one needs to run MakeMacro as MakeMacro.py c_5 20 MakeMacro.py c_6 20 True -These commands create all of the files needed to make carbon a macro atom. The number 20 implies that one wants to create 20 levels for each of the ions, and need not be the same for each of the ions. This number should be larger than the number of levels one wants to ulitmately use with Python, and can be smaller. The extra command line option True for c_6 imples causes a bare C ion to be created for C +These commands create all of the files needed to make carbon a macro atom. The number 20 implies that one wants to create 20 levels for each of the ions, and need not be the same for each of the ions. This number should be larger than the number of levels one wants to ulitmately use with Sirocco, and can be smaller. The extra command line option True for c_6 imples causes a bare C ion to be created for C The files that will be created include: diff --git a/docs/sphinx/source/py_progs/development.rst b/docs/sphinx/source/py_progs/development.rst index a957dcd16..aa2756afc 100644 --- a/docs/sphinx/source/py_progs/development.rst +++ b/docs/sphinx/source/py_progs/development.rst @@ -1,7 +1,7 @@ Developer Tools --------------- -Several scripts have been written to assist in developing and documenting **Python**. +Several scripts have been written to assist in developing and documenting *Sirocco*. Documentation diff --git a/docs/sphinx/source/py_progs/plotting.rst b/docs/sphinx/source/py_progs/plotting.rst index 9b299913c..ffd47d0d7 100644 --- a/docs/sphinx/source/py_progs/plotting.rst +++ b/docs/sphinx/source/py_progs/plotting.rst @@ -1,6 +1,6 @@ Plotting ---------- -Several scripts have been developed to plot **Python** output files. +Several scripts have been developed to plot *Sirocco* output files. .. toctree:: diff --git a/docs/sphinx/source/py_progs/py4py.rst b/docs/sphinx/source/py_progs/py4py.rst index 6b6ac41b9..06db4a4a0 100644 --- a/docs/sphinx/source/py_progs/py4py.rst +++ b/docs/sphinx/source/py_progs/py4py.rst @@ -1,7 +1,7 @@ py4py -------- -`py4py` is a module written in *Python* for reading, processing and visualising the input and output -files of the *c* code **Python**. +`py4py` is a module written in Sirocco for reading, processing and visualising the input and output +files of the *c* code *Sirocco*. Installation instructions can be found in the associated README.md diff --git a/docs/sphinx/source/py_progs/regression.rst b/docs/sphinx/source/py_progs/regression.rst index 56f15a369..dfe0ba410 100644 --- a/docs/sphinx/source/py_progs/regression.rst +++ b/docs/sphinx/source/py_progs/regression.rst @@ -1,14 +1,14 @@ Regression ---------- -Primarily to verify that changes made to Python do not inadvertently cause unexpected changes +Primarily to verify that changes made to Sirocco do not inadvertently cause unexpected changes if models, several routines exist to run a fixed set of (relatively fast) models that are **nominally** contained in Examples/regress. Developers are encouraged to use this routines, before they merge anything into one of the -major branches of Python. +major branches of Sirocco. -The routines involved are +The routines involved are * `regression.py` * `regression_check.py` @@ -21,18 +21,18 @@ Setup ===== Typically one should set up a directory, e.g Regression to run the routines, and, if for example, -py87f, is the version of Python being used when you set up the directory, being run. +sirocco87f, is the version of Sirocco being used when you set up the directory, being run. -Python should be compiled with mpicc before running the regression program +Sirocco should be compiled with mpicc before running the regression program Basic Procedure =============== Run:: - regression.py py87f + regression.py sirocco87f -This will create a directory py87f_231108 where 231108 is the current date. The pf files from +This will create a directory sirocco87f_231108 where 231108 is the current date. The pf files from the regression directory as well as various ancillary files will be copied into this directory, and all of the models contained therein will run sequentially. In the absence of command line @@ -45,9 +45,9 @@ to change occassionaly. Once changes have been made to python, one reruns the program, e.g:: - regression.py py + regression.py sirocco -This will create a directory py_2311108 (assuming it this is the same day) and repead the previous +This will create a directory sirocco_2311108 (assuming it this is the same day) and repead the previous precedured. **If the program is run on the same day with the same version of python, the older models @@ -83,7 +83,7 @@ regression_check.py to compare any two sets of runs:: where one gives the names of the two directories to be compared. While the regression procedure described here is generally set up to run on the models that are contained in the Examples/regress directory, -regression.py has switches that allow one to do tests on models that are in any input directory. This can be useful, if one wishes to test +regression.py has switches that allow one to do tests on models that are in any input directory. This can be useful, if one wishes to test different models in order to solve specific problems, or to run a set of models sequentially. API Documentation diff --git a/docs/sphinx/source/py_progs/running.rst b/docs/sphinx/source/py_progs/running.rst index c4e696d8a..03779aa42 100644 --- a/docs/sphinx/source/py_progs/running.rst +++ b/docs/sphinx/source/py_progs/running.rst @@ -1,4 +1,4 @@ -Running Python +Running Sirocco -------------- .. autosummary:: @@ -8,7 +8,7 @@ Running Python pf_grid run_many -Several scripts have been developed to run **Python** and **Cloudy** simulations for comparison. +Several scripts have been developed to run *Sirocco* and **Cloudy** simulations for comparison. .. autosummary:: :toctree: running diff --git a/docs/sphinx/source/quick.rst b/docs/sphinx/source/quick.rst index 62f5faf41..6c200fd96 100644 --- a/docs/sphinx/source/quick.rst +++ b/docs/sphinx/source/quick.rst @@ -1,14 +1,14 @@ -*Quick Guide to Python* +*Quick Guide to Sirocco* ######################################## -This quide is intended to allow users to install Python, to run Python as a -computer program and then to check whether the run has completed as expected. +This guide is intended to allow users to install Sirocco, to run Sirocco as a +computer program and then to check whether the run has completed as expected. -It does not describe (except in passing) any information about the physics of -Python, the details of a particular wind model, or criteria for evaluating whether -the inputs correspond to a plausible model of an astrophysical system. +It does not describe (except in passing) any information about the physics of +Sirocco, the details of a particular wind model, or criteria for evaluating whether +the inputs correspond to a plausible model of an astrophysical system. -* :ref:`Installation ` -- how to install Python from github and to run a model -* :ref:`Creating the input file for Python ` -- Simple instructions how to set up a model interactively -* :ref:`The files produced by Python ` -- A quick look at the output files +* :ref:`Installation ` -- how to install Sirocco from github and to run a model +* :ref:`Creating the input file for Sirocco ` -- Simple instructions how to set up a model interactively +* :ref:`The files produced by Sirocco ` -- A quick look at the output files * :ref:`Evaluation of the results ` -- A discussion of whether a model has run as required, or not diff --git a/docs/sphinx/source/radiation.rst b/docs/sphinx/source/radiation.rst index 06427089b..a5e2acb72 100644 --- a/docs/sphinx/source/radiation.rst +++ b/docs/sphinx/source/radiation.rst @@ -2,15 +2,16 @@ Radiation Sources ################# .. todo:: - Fill in. Add description of how to use your own input spectrum. Finish links to keywords. + Fill in. Add description of how to use your own input spectrum. Finish links to keywords. External Radiation Sources ========================== -In generic terms, there are two main external radiation sources for any Python calculation: a **Central Source** which can be a normal star, a WD, or a BH, and a **disk**. Even though Python supports the existence of a secondary star for the purposes of calculating when light from a disk system is occulted, the secondary star does not radiate. +In generic terms, there are two main external radiation sources for any Sirocco calculation: a **Central Source** which can be a normal star, a WD, or a BH, and a **disk**. Even though Sirocco supports the existence of a secondary star for the purposes of calculating when light from a disk system is occulted, the secondary star does not radiate. - -Photons for radiation from the central object emerge uniformly over its surface, except when a lamp-post geometry is specified for the ``bh`` or ``agn`` system types. In this lamp-post case, radiation originates from a point source above and below the central object, with a specified height. +Photons for radiation from the central object emerge uniformly over its surface, except when a lamp-post geometry is +specified for the ``bh`` or ``agn`` system types. In this lamp-post case, radiation originates from a point source above +and below the central object, with a specified height. Emission from a boundary layer can also be defined when this is relevant, from which radiation also emerges uniformly over the surface of the central object. @@ -18,15 +19,16 @@ The Wind as a radiation source ============================== In macro-atom calculations the wind is NOT a radition source. All of the photons in a macro-atom -calculation are generated externally, and with minor exceptions photons preserve their weight +calculation are generated externally, and with minor exceptions photons preserve their weight throughout their passage through the wind. (The minor exceptions have to do with processes like adiabiatic cooling, which result in the loss of photons). In the simple-atom approach, various processes cause photons passing through the wind to lose energy as they pass through the wind. This energy heats the plasma. To account for this, photons are -generated from the wind at the beginning of each cycle. Processes include, free-free emission, free-bound emission and line emission. +generated from the wind at the beginning of each cycle. Processes include, free-free emission, free-bound emission and line emission. -In non-macro-atom calculations wind radiation can be turned on and off using the :doc:`/input/parameters/wind/Wind/Wind.radiation` keyword. +In non-macro-atom calculations wind radiation can be turned on and off using the +:doc:`/input/parameters/wind/Wind/Wind.radiation` keyword. (In various files that contain the spectra there is a column WCreated that in the simple atom mode gives the spectrum of photons that were created in the wind. This column, also exists in the macro-atom case, where it records the spectrum of pbotons that have interacted with the wind and been re-emitted.) @@ -43,10 +45,10 @@ For the most part, the various radiation sources can radiate by any of the follo 3. Power law radiation, specified in terms of a spectral index, and a luminosity between 2 and 10 keV -4. One or more spectral models read from a series of files. The models must specified in terms of two - parameters, usually T and log g, each model consists of an ascii file containing the spectra. - An example of the ascii files - that can be read in is contained in the xdata folder that is part of the distribution (See below). +4. One or more spectral models read from a series of files. The models must specified in terms of two + parameters, usually T and log g, each model consists of an ascii file containing the spectra. + An example of the ascii files + that can be read in is contained in the xdata folder that is part of the distribution (See below). In the ionization cycles, the spectra of the central source, boundary layer (if present) and disk are determined by these three keywords: @@ -55,18 +57,20 @@ In the ionization cycles, the spectra of the central source, boundary layer (if * :doc:`/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_to_make_wind` * :doc:`/input/parameters/Disk/Disk.rad_type_to_make_wind` -It is possible to choose different input spectra for the ionization and spectral cycles, so a corresponding keyword of the form :doc:`/input/parameters/Disk/Disk.rad_type_in_final_spectrum` is also needed. +It is possible to choose different input spectra for the ionization and spectral cycles, so a corresponding keyword of +the form :doc:`/input/parameters/Disk/Disk.rad_type_in_final_spectrum` is also needed. Spectra from a model grid (details) =================================== -Python was initially written to model the winds of cataclysmic variables (CVs). Although the spectra of the disks of cataclymic +Sirocco was initially written to model the winds of cataclysmic variables (CVs). Although the spectra of the disks of cataclymic variables are often modelled in terms of blackbodies, the spectra of CVs show clear evidence of features that arise from the i -disk (as well as the wind). The features that arise from the disk resemble in many respects those that arise from -an appropriately weighted set of stellar atmospheres. To allow for this possibility, Python can be configured to read a set of +disk (as well as the wind). The features that arise from the disk resemble in many respects those that arise from +an appropriately weighted set of stellar atmospheres. To allow for this possibility, Sirocco can be configured to read +a set of models characterized by a temperature and log g, and produce spectra of either the central object or the disk by interpolating on t -and log g. The data that must read in consists of a file that associates a temperature and log g with the indvidual spectra. +and log g. The data that must read in consists of a file that associates a temperature and log g with the indvidual spectra. For example, as part of the standard distruction there is a file kurucz91.ls, which starts as follows @@ -88,14 +92,16 @@ For example, as part of the standard distruction there is a file kurucz91.ls, wh data/kurucz91/fp00t3750g00k2c125.txt 3750 0.0 ... -In this case we have spectra at a temperature of 3500, for 11 different values of log g, +In this case we have spectra at a temperature of 3500, for 11 different values of log g, before going on to temperature of 3750 K. Each spectrum is one of the Kurucz models, and these contain entries which contain a -set of wavelengths and a quantity that is understood to be proportional to :math:`F_{\lambda}`. - -The 3 column format above is required. If one wants to use a set of models that have only a T parameter one should simply -choose a value for the second column. The use case here is fairly specific, especially with regard to the first parameter T. -If the disk or central object temperature outside the -temperatures in the grid, then Python will "adjust" the spectrum assuming that the overall spectrum changes as a BB would, but +set of wavelengths and a quantity that is understood to be proportional to :math:`F_{\lambda}`. + +The 3 column format above is required. If one wants to use a set of models that have only a T parameter one should +simply +choose a value for the second column. The use case here is fairly specific, especially with regard to the first parameter T. +If the disk or central object temperature outside the +temperatures in the grid, then Sirocco will "adjust" the spectrum assuming that the overall spectrum changes as a BB +would, but the features in the spectrum are unchanged. If the gravity goes outside the range of the grid, the closest value is chosen. One need not use Kurucz models, of course. Any set of models can be used, as long as the files contain two diff --git a/docs/sphinx/source/radiation/banding.rst b/docs/sphinx/source/radiation/banding.rst index 5718bca75..802c9558f 100644 --- a/docs/sphinx/source/radiation/banding.rst +++ b/docs/sphinx/source/radiation/banding.rst @@ -36,7 +36,7 @@ be generated as most of the luminosity is generated at lower frequencies. It is possible to get around this problem by generating an increasingly large number of photons. But, this is computationally expensive and inefficient. -In order to cope with cases this, Python implements *importance sampling* which +In order to cope with cases this, Sirocco implements *importance sampling* which effectively increases the number of photons which are sampled from specific frequency bands considered important. Photons are now generated with the weight, @@ -61,7 +61,7 @@ preferred sampling method. Available Sampling Schemes ========================== -Python currently implements seven pre-defined frequency bands and and two +Sirocco currently implements seven pre-defined frequency bands and and two flexible *run time* banding schemes. The parameter used to define the photon sampling scheme is, @@ -120,7 +120,7 @@ where the sum of the fractions for each band must be equal to or less than one. .. admonition :: Maximum Number of Bands Currently, a maximum of 20 frequency bands can be defined. If a user - attemps to specify more than than 20 bands, Python will create an error + attemps to specify more than than 20 bands, Sirocco will create an error message and fallback to using 20 bands. cloudy_test @@ -147,7 +147,7 @@ many orders of magnitude. .. admonition :: Maximum Number of Bands Currently, a maximum of 20 frequency bands can be defined. If a user - attemps to specify more than than 20 bands, Python will create an error + attemps to specify more than than 20 bands, Sirocco will create an error message and fallback to using 20 bands. .. admonition :: Minimum Fraction diff --git a/docs/sphinx/source/running_python.rst b/docs/sphinx/source/running_sirocco.rst similarity index 68% rename from docs/sphinx/source/running_python.rst rename to docs/sphinx/source/running_sirocco.rst index 5eee27b86..c0e9cdbff 100644 --- a/docs/sphinx/source/running_python.rst +++ b/docs/sphinx/source/running_sirocco.rst @@ -1,30 +1,30 @@ -Running Python +Running Sirocco ############## -The normal way to run Python is simply to enter +The normal way to run Sirocco is simply to enter .. code :: bash - py xxx + sirocco xxx where ``xxx`` is the root name of a parameter file. (The full name ``xxx.pf`` can also be entered). -However Python features a number of command line options which can be used +However Sirocco features a number of command line options which can be used to modify it's operation. These include the following: -h - Causes Python to print out a brief help message and quit. The help message + Causes Sirocco to print out a brief help message and quit. The help message principally describes the command line options. -i (or --dry-run) - Causes Python to read and verify the inputs, writing a clean version of the input + Causes Sirocco to read and verify the inputs, writing a clean version of the input file ``xxx.pf`` to the output file ``xxx.out.pf``, and then stop. This option is useful for setting up a proper ``.pf`` file. (Often one will want to copy ``xxx.out.pf`` back to ``xxx.pf`` before proceeding. -t time_max - Limits a run of python to approximately time_max in sec. This switch is + Limits a run of Sirocco to approximately time_max in sec. This switch is used in situations where one would like to check whether the routine is operating properly be continuing, or where one needs to checkpoint the program after a certain period of time (due for example to time limits placed on jobs in a Beowulf cluster). @@ -33,8 +33,8 @@ to modify it's operation. These include the following: between time_max and the maximum time one wants the program to run -r - Restarts a run that has been interrupted or halted, by reading a the ``xxx.windsave`` - and ``xxx.specsave`` file (if it exists). Note that very few values in the .pf + Restarts a run that has been interrupted or halted, by reading a the ``xxx.wind_save`` + and ``xxx.spec_save`` file (if it exists). Note that very few values in the .pf file are read when this options is used, as most of the information there has already been utilized in setting up and executing the run. The main ones that can be changed are the numbers of cycles for either ionizaion or detailed spectral @@ -44,25 +44,25 @@ to modify it's operation. These include the following: which will allow one to set all of the detailed spectral parameters anew. -v n - Changes the amount of information printed to the screen by Python during a run. + Changes the amount of information printed to the screen by Sirocco during a run. The default is 4. Larger numbers increase this. Smaller numbers decrease it. The log files are not affected. --rseed - Causes Python to use a random number seed that is time-based, rather than fixed. + Causes Sirocco to use a random number seed that is time-based, rather than fixed. In most cases, a fixed seed is preferred so that problems can be replicated, but if is repeating the same calculation multiple times, then one may want a random seed. ---rng +--rng Save or load the RNG state to file, to allow persistent RNG states between restarts --version - Causes Python to print out the version number and commit hash (and whether + Causes Sirocco to print out the version number and commit hash (and whether uncommitted files exist, and then stop. -p n_steps Changes the number of photons generated during ionization cycles so that the - number increases logarithmically to the maximum value. This can help speed up python + number increases logarithmically to the maximum value. This can help speed up Sirocco simulations but check the covergence of the wind. The number ``n_steps`` is optional, and specifies the number of decades over which the increase takes place. @@ -71,7 +71,7 @@ to modify it's operation. These include the following: Special switches ================ -Python has a number of other switches that are not intended for the general user, but +Sirocco has a number of other switches that are not intended for the general user, but which may be useful in certain special cases. These include: -d @@ -85,49 +85,49 @@ which may be useful in certain special cases. These include: -e n Where ``n`` is a number, changes the number of errors of a specific type that are allowed to occur before the program gives up. For a variety of reasons, - errors are expected during Python runs. + errors are expected during Sirocco runs. Most of these errors are harmless in the sense that they occur rarely. - But if an error occurs too often, something is seriously and so Python halts at that point. - The default is :math:`10^{5}` (per thread). + But if an error occurs too often, something is seriously and so Sirocco halts at that point. + The default is :math:`10^{5}` (per process). -e_write n Changes the number of times an error message of a specific type is written to a diagnostic file. When errors occur, a line describing the error is written to the diagnostic file the first ``n`` times the error occurs. After that statistics are maintained as to the number of times the error occurred, but it is not printed - to the diagnostic file. The default is 100 (per thread) + to the diagnostic file. The default is 100 (per process) --classic +-classic Reverts to using v/c corrections for special relativity and eliminates work done to treat co-moving frames properly. This is for testing, and is likely to be removed in the not too distant future. --srclassic - Use Python with full special relativity for Doppler shits, etc., but do not include any co-moving frame effects. +-srclassic + Use Sirocco with full special relativity for Doppler shits, etc., but do not include any co-moving frame effects. --no-matrix-storage - Do not store macro-atom transition matrices if using the macro-atom line transfer and the matrix matom_transition_mode.\n\ +-no-matrix-storage + Do not store macro-atom transition matrices if using the macro-atom line transfer and the matrix matom_transition_mode. -ignore_partial_cells - Ignore wind cells that are only partially filled by the wind (This is now the default) + Ignore wind cells that are only partially filled by the wind (This is now the default) -include_partial_cells - Include wind cells that are only partially filled by the wind + Include wind cells that are only partially filled by the wind -Running Different Versions of Python +Running Different Versions of Sirocco ================================= -Once you have Python up and running, you can also install and run different versions of Python. This is particularly useful if you want to run and compare an older model from a previous paper or how the outputs have evolved. +Once you have Sirocco up and running, you can also install and run different versions of Sirocco. This is particularly useful if you want to run and compare an older model from a previous paper or how the outputs have evolved. -You can store multiple older versions of Python by recompiling a newer version. +You can store multiple older versions of Sirocco by recompiling a newer version. -* Pull in the version of the program you want using git. -* Then navigate with the terminal into Python's :code:`source` folder. -* Run "make all" to recompile all programs with the new updates. +* Pull in the version of the program you want using git. +* Then navigate with the terminal into Sirocco's :code:`source` folder. +* Run "make all" to recompile all programs with the new updates. * The process will put the new binaries into the :code:`bin/` directory and not delete what was already there. - -You can then run a specific installed version by replacing the py command, eg :code:`py root.pf`, with the version you desire, eg. - + +You can then run a specific installed version by replacing the Sirocco executable, eg :code:`sirocco root.pf`, with the version you desire, eg. + .. code :: bash - - py87a root.pf \ No newline at end of file + + sirocco87a root.pf \ No newline at end of file diff --git a/docs/sphinx/source/wind_models.rst b/docs/sphinx/source/wind_models.rst index ab184a4b9..8d82ad7af 100644 --- a/docs/sphinx/source/wind_models.rst +++ b/docs/sphinx/source/wind_models.rst @@ -1,7 +1,7 @@ Wind Models ########### -*python* has various wind models available, including parameterised wind models and the ability to import models. The links below describe comprehensive details of some of the models available. +Sirocco has various wind models available, including parameterised wind models and the ability to import models. The links below describe comprehensive details of some of the models available. A complete list of the types of wind models can be found in the :ref:`Wind Model Parameters` section. Subsequently, a complete list of each wind model's possible parameters can be found in the :ref:`Winds` section. diff --git a/docs/sphinx/source/wind_models/importing_models.rst b/docs/sphinx/source/wind_models/importing_models.rst index c7f411884..29df5393c 100644 --- a/docs/sphinx/source/wind_models/importing_models.rst +++ b/docs/sphinx/source/wind_models/importing_models.rst @@ -3,7 +3,7 @@ Importing Models ################ -Python can read 1D or 2.5D grids of density and velocity, instead of setting up +Sirocco can read 1D or 2.5D grids of density and velocity, instead of setting up the model from an analytic prescription. Caution should be exercised with this mode, as it is still in a development phase, and the mode requires the user to ensure that things like mass and angular momentum conservation are enforced. @@ -52,7 +52,7 @@ In order to create your own model, there are a few important things to consider: * Ghost cells **must** be included. This means that additional rows and columns of cells must be included at the edges of the grid, and they must be excluded from the wind so that their temperatures and densities are set to zero, but - have a velocity that python can interpolate with. + have a velocity that Sirocco can interpolate with. * i and j correspond to rows and columns respectively, so that the first row of cells at the disk plane has i = 0. * rho the density of the cell in cgs units @@ -70,7 +70,7 @@ Spherical Grids --------------- Using a spherical coordinate system, a 1D spherically symmetric model can be -read into Python. +read into Sirocco. To read in a grid of this type, the following columns are required for each cell: @@ -88,7 +88,7 @@ To read in a grid of this type, the following columns are required for each cell Cylindrical Grids ----------------- -Using cylindrical coordinates, a 2.5D model can be read into Python. +Using cylindrical coordinates, a 2.5D model can be read into Sirocco. .. admonition :: Grid Coordinates @@ -113,18 +113,18 @@ To read in a grid of this type, the following columns are required for each cell In principle, it is possible to read in an unstructured or non-linear cylindrical grid, i.e. where the cells are not regularly spaced, however, - Python has been designed for structured grids with regular grid spacing, and + Sirocco has been designed for structured grids with regular grid spacing, and as such there may be undefined behaviour for unstructured grids. Polar Grids ----------- -Using polar coordinates, a 2.5D model can be read into Python. +Using polar coordinates, a 2.5D model can be read into Sirocco. .. admonition :: Cartesian Velocity The velocity in for the polar grid is required to be in Cartesian - coordinates due to conventions within the Python programming style. As such, + coordinates due to conventions within the Sirocco programming style. As such, any polar velocity components must first be projected into their Cartesian equivalent. @@ -151,7 +151,7 @@ Setting Wind Temperatures ------------------------- Reading in a temperature is optional when importing a model. However, if one -temperature value for a cell is provided, then Python assumes that this is +temperature value for a cell is provided, then Sirocco assumes that this is the electron temperature and the radiation temperature will be initialised as, .. math :: @@ -184,7 +184,7 @@ in the wind. The following enumerator flags are used, W_ALL_INWIND = 0 // this cell is in the wind Whilst it is possible to set in `inwind = 1` for a grid cell, that is that the -cell is partially in the wind, Python will instead set these cells with +cell is partially in the wind, Sirocco will instead set these cells with `inwind = -2` and ignore these grid cells. Spherical @@ -217,7 +217,7 @@ guard cells in the same way as the a spherical grid, as above. For these cells, and all cells which do not make up the wind, an inwind value of -1 or -2 should be set. In this example, the theta cells extend beyond 90°. But, as they are not inwind, -Python is happy to include these cells. For a stellar wind in polar coordinates, +Sirocco is happy to include these cells. For a stellar wind in polar coordinates, these extra :math:`\theta` cells extending beyond 90° are required. .. figure:: ../images/import_polar_inwind.png @@ -234,26 +234,26 @@ these extra :math:`\theta` cells extending beyond 90° are required. A colour plot of the inwind variable for a stellar wind imposed on a polar coordinate grid. Important to note is the "halo" of inwind = -1 cells surrounding the inwind cells. The cells with inwind = 1 will be set to - inwind = -2 when imported into Python and ignored. + inwind = -2 when imported into Sirocco and ignored. Maximum and Minimum Wind Radius -------------------------------- The maximum and minimum spherical extent of the wind is calculated automatically -by Python, and does not take into account guard cells when it is doing this. +by Sirocco, and does not take into account guard cells when it is doing this. -Generating example inputs for testing and familiarizing oneself with Python's import capability +Generating example inputs for testing and familiarizing oneself with Sirocco's import capability =============================================================================================== -If one is trying to use the import capability of Python for the first time, +If one is trying to use the import capability of Sirocco for the first time, it will be useful to familiarize oneself with the process, and the file format -for a particular coordinate system, by running first running Python on a model +for a particular coordinate system, by running first running Sirocco on a model that is something similar to model to be imported, but which takes advantage of one of the kinematic models available with the code. For example, suppose you have a hydrodynamical simulation of an AGN wind which -is in polar coordinates and you want to use Python to calculate the spectrum. +is in polar coordinates and you want to use Sirocco to calculate the spectrum. Then you might create a model of an AGN with a similar coordinate system using, say, a Knigge Wood & Drew wind (and similar atomic data). For specificity, suppose this model has the root name "test" @@ -270,7 +270,7 @@ This produces a large number of ascii tables, which are described elsewhere In the py_progs directory, you will find 3 scripts, :code:`import_1d.py`, :code:`import_cyl.py` and :code:`import_rtheta.py`, which will convert one of the output files :code:`test.0.master.txt` to an import file, :code:`test.import.txt`, -that can be used with the import mode of Python. The 3 different routines are +that can be used with the import mode of Sirocco. The 3 different routines are for 1d spherical coordinates, and polar (r-theta) coordinates respectively. Assuming the py_progs directory is in your PATH, and given that our example is @@ -281,21 +281,21 @@ for cylindrical coordinates, one would run: import_cyl.py test At that point, you can test this import file, by modifying the first .pf file to -import mode (imported). Running Python on this file, will result in your being +import mode (imported). Running Sirocco on this file, will result in your being asked the name of the import file, and give you a "baseline" to import the hydrodynamical simulation to work. Note that one should not assume that spectra produced by the original run of -Python and the run of the imported model will be identical. There are several +Sirocco and the run of the imported model will be identical. There are several reasons for this: -First, in creating the original model, Python accounts for the possibility that +First, in creating the original model, Sirocco accounts for the possibility that some cells are partially in the wind. This is not possible in the imported models. Only cells that are complete in the wind are counted. -Second, within Python, positions and velocities are assumed defined at the +Second, within Sirocco, positions and velocities are assumed defined at the corners of cells, whereas densities are assumed to be cell centered. If one provides a table where all of the quantities are at the same exact position (namely density is at the same position as x), there will be a slight discrepancy between the way in model as calculated internally and as represented -within Python. +within Sirocco. diff --git a/docs/sphinx/source/wind_models/kwd.rst b/docs/sphinx/source/wind_models/kwd.rst index f8fb85693..974ba354f 100644 --- a/docs/sphinx/source/wind_models/kwd.rst +++ b/docs/sphinx/source/wind_models/kwd.rst @@ -1,7 +1,7 @@ KWD biconical wind prescription ############################################################ -`Knigge, Woods & Drew (1995) `_ developed a parameterization for a bi-conical flow, which in slightly modified form is built into Python. +`Knigge, Woods & Drew (1995) `_ developed a parameterization for a bi-conical flow, which in slightly modified form is built into Sirocco. In this parameterization, the wind is envisioned to have poloidal streamlines that all point to a position a distance d below the disk, as is shown below: @@ -12,7 +12,7 @@ shown below: As described by KWD95, streamlines emerge throughout the entire disk, with the innermost streamline just grazing the surface of the central object and the outermost streamline -emerging from the outer radius of the disk. In the current version of Python, while this +emerging from the outer radius of the disk. In the current version of Sirocco, while this is the default choice, the wind region can be restricted to streamlines that arise from between :math:`r_{min}` and :math:`r_{rmax}` as depicted by the above diagram. For fixed values of :math:`r_{min}` and :math:`r_{rmax}`, the wind will tend to be more collimated the larger the value of d. diff --git a/examples/reverb/reverb.ipynb b/examples/reverb/reverb.ipynb index cfa8dadeb..b210afc63 100644 --- a/examples/reverb/reverb.ipynb +++ b/examples/reverb/reverb.ipynb @@ -5,9 +5,9 @@ "metadata": {}, "source": [ "# Reverberation Mapping\n", - "`Python` has the capability to generate transfer functions/reverberation signatures for the systems it models. These describe how a change in the ionising continuum is reprocessed into a change in line emission. These signatures can (approximately) be recovered from observation, if there's a sufficiently series of line spectra with sufficiently high time- and wavelength-resolution. \n", + "`Sirocco` has the capability to generate transfer functions/reverberation signatures for the systems it models. These describe how a change in the ionising continuum is reprocessed into a change in line emission. These signatures can (approximately) be recovered from observation, if there's a sufficiently series of line spectra with sufficiently high time- and wavelength-resolution. \n", "\n", - "The transfer function is the term $\\Psi$ in the equation $L(v, t) = \\int_0^\\infty \\Psi(v, \\tau) C(t - \\tau) d\\tau$. `Python` can also generate response functions, $\\Psi_r$, used in $\\delta L(v, t) = \\int_0^\\infty \\Psi_r(v, \\tau) \\delta C(t - \\tau) d\\tau$ (a more observationally-accessible property).\n", + "The transfer function is the term $\\Psi$ in the equation $L(v, t) = \\int_0^\\infty \\Psi(v, \\tau) C(t - \\tau) d\\tau$. `Sirocco` can also generate response functions, $\\Psi_r$, used in $\\delta L(v, t) = \\int_0^\\infty \\Psi_r(v, \\tau) \\delta C(t - \\tau) d\\tau$ (a more observationally-accessible property).\n", "\n", "\n", "The paper discussing our implementation, and the differences between $\\Psi$ and $\\Psi_r$ are:\n", @@ -22,7 +22,7 @@ "\n", "### Building the Model\n", "\n", - "To generate the data for reverberation mapping, `Python` tracks the paths of photons as they travel throughout the wind, using them to build up a map of the response delay for each region of the wind. The settings to govern that can be found in the `### Parameters for Reverberation Modeling` section of the `.pf` file. \n", + "To generate the data for reverberation mapping, `Sirocco` tracks the paths of photons as they travel throughout the wind, using them to build up a map of the response delay for each region of the wind. The settings to govern that can be found in the `### Parameters for Reverberation Modeling` section of the `.pf` file. \n", "\n", "#### reverb.pf\n", "```\n", @@ -42,12 +42,12 @@ "\n", "An important setting is `Reverb.filter_lines`. If this is set to `0`, *every* single photon that contributes to every single spectrum (including the pseudo-photons generated after each scattering event in `extract` mode). This produces unwieldy and incredibly vast output files! The `-1` filter mode instead includes only those photons whose last interaction was a line scatter or line emission. This produces large, but less overwhelming output files.\n", "\n", - "`Reverb.filter_lines N` allows the user to include `N` different `Reverb.filter_line X` lines, where `X` is the internal `Python` data file line number for the line. This is a bit clunky and is easiest done by exploring the output files. This exploratory process is covered later on. \n", + "`Reverb.filter_lines N` allows the user to include `N` different `Reverb.filter_line X` lines, where `X` is the internal `Sirocco` data file line number for the line. This is a bit clunky and is easiest done by exploring the output files. This exploratory process is covered later on. \n", "\n", "Ideally, this could be done in the input file by specifying the transition by species, upper and lower lines.\n", "\n", "### Running the Model\n", - "If you'd like to run `Python` directly through this notebook, you'll have to make sure whatever virtual environment you're running it in has access to the `Python` executables, and set the notebook itself to be trusted using `jupyter trust reverb.ipynb`. Then:" + "If you'd like to run `Sirocco` directly through this notebook, you'll have to make sure whatever virtual environment you're running it in has access to the `Sirocco` executables, and set the notebook itself to be trusted using `jupyter trust reverb.ipynb`. Then:" ] }, { @@ -58,7 +58,7 @@ "source": [ "import subprocess\n", "with open(\"reverb.txt\", \"w\") as output_file:\n", - " subprocess.run(['mpirun', '-np', '4', 'py', 'reverb'], stdout=output_file, stderr=output_file)" + " subprocess.run(['mpirun', '-np', '4', 'sirocco', 'reverb'], stdout=output_file, stderr=output_file)" ] }, { @@ -68,7 +68,7 @@ "Or alternatively, run it manually on the command line as:\n", "\n", "```bash\n", - "mpirun -np 4 py reverb &> reverb.txt\n", + "mpirun -np 4 sirocco reverb &> reverb.txt\n", "```\n", "\n", "\n", @@ -101,7 +101,7 @@ } ], "source": [ - "%matplotlib inline \n", + "%matplotlib inline\n", "from py4py.reverb import open_database\n", "\n", "db = open_database('reverb')" @@ -121,7 +121,7 @@ "\n", "If there are errors during reading, this will leave a malformed `reverb.db` database- to restart generation with a fixed DB file, you'll have to delete the `reverb.db` file.\n", "\n", - "Once the database has been built, you can begin producing transfer functions. Each is only meaningful for a single emission line, so you need the `python` line number. This is an internal number that depends on your choice of data files. In practical terms, the easiest way to determine it is to filter the output data file by its second (`Lambda`, wavelength in $\\mathring{A}$) column to get the last (`Res.` for resonance) column, e.g. to find the $C_{IV}$ line you would do something like: \n", + "Once the database has been built, you can begin producing transfer functions. Each is only meaningful for a single emission line, so you need the `Sirocco` line number. This is an internal number that depends on your choice of data files. In practical terms, the easiest way to determine it is to filter the output data file by its second (`Lambda`, wavelength in $\\mathring{A}$) column to get the last (`Res.` for resonance) column, e.g. to find the $C_{IV}$ line you would do something like: \n", "\n", "```bash\n", " awk '{if($2<1551 && $2>1549)print}' < reverb.delay_dump \n", @@ -174,7 +174,7 @@ "\n", "The photons used to generate this transfer function are then limited by a set of functions:\n", "\n", - "- The `line()` function limits the produced transfer function to only include photons in the specific line. The wavelength of the line is also needed- ideally, it would be possible to get this straight from the `Python` atomic data files eventually. \n", + "- The `line()` function limits the produced transfer function to only include photons in the specific line. The wavelength of the line is also needed- ideally, it would be possible to get this straight from the `Sirocco` atomic data files eventually. \n", "\n", "- As the data file contains multiple spectra, we select one with `spectrum()`, providing the spectrum number (1-indexed).\n", "\n", @@ -201,7 +201,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] @@ -219,7 +219,7 @@ " }\n", ")\n", "\n", - "from IPython.display import Image\n", + "from ISirocco.display import Image\n", "Image(filename='C-IV.png')" ] }, @@ -288,7 +288,7 @@ }, { "data": { - "image/png": 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KyPkshyvfXEGulvEf70+P5T2cvjajOlxN3/19OT/rPJmLMik5UEKvDb1wC7FPpSFB+D2zkpr4AmpY5cVKzkw9g/cQb7q84vgyWPYmKaSaoUVP4PqXCNZYANVXqin6qYii7TW3y5suA+DWxY32I2t6cb4jfB1W4NmZaM9oyfksh7zVeehydLj4u9Dx6Y4ETQpqtpmNtqB0VxK5LBKfm3w4O/UsiX0T6bW+F763tdyDPaFlECc8bEA2yCQ/kgxGiPo8Sgy1/IFLBxcC7g8g4P4AZFmmIrWiJsFtKyL/3/k1xXQl8OrvVZPgRvriM8QHhVvbeB/1pXoKviggZ0UOJftLQAkdRnUgaHIQHUZ1QOHact+HoL8E4dXPi9PjT3M8/jhhr/56LlAhDpAF+xDDjzaQ8WYGxXuL6bm6J+pwx834agkkScIjwgOPCA86TuuIUW+k9FBpXS8uY1EGGQszUHgoaDe8Hd6DvdFEa/CI9kAdoW41BwyyLFO8t5icFTkUfFmAUWvEI8qD8EXhBD4SiFtQ6xmq0/TS0O9gP85OPUv6S+mUHy+n1797icQm2IVZsx/z8vIaLEfVEthjRlTJwRKODj2K//3+RK2NEkO0VtKX6Lm662pNkvuxCO0Zbd36X5KrhEekB5poDZqYmkSnidagDlc73SzAhlRmVpK3Oo+cz3KoTK1E6aUk4KEAgiYF4T3Iu1X//ciyTOaiTC48f4HOf+9M+OvhdtuWmP3YdpnVU2vJCc0e9GV6kick4xriSvcPu7fqL6TmovJW4TfGD78xNVPBDdqaCSflp8trbqfKKT5QTP6G/LrXKNwVePT0qEtytTf3ru4O7w0YKg0Yig1c3X2VnBU5FG0rAhna3dqOsHlh+N/n79TVZmxJkiRC54ZScaGCjIUZaKI1dYWSBcFWzEpqycnJrFmzhtOnT1NaWoqXlxfR0dE88sgjREW1vYUmz888T8WFCvrs6iPWl7ITpYcSr/5eePWvv8yJvlRfL9lpT2sp3l1M/trfJTt1TbJTtVOhUCtQqBUoPZS//axWovD43c9qBQqP3/386/NRgqHUgKGk5vIGfYkeQ7EBfYkefbG+7v7rPS7rfhsIcevsRpeXuhA0McihFyY7kiRJdH+/O9ozWlKmpKCOUOM9yLvpFwqCiUxOauvXr2fatGmMGTOGYcOG4ePjQ0lJCcePH+fGG29k2bJlPPjgg/aM1ank/6dm+n7nFzrT7mbHrWLdVqm8VHgP9MZ7YP0vRH2xvmZl518TnfaMFkOpAV2JDmOFEWOFseZShF9/rl0E0yJKUPmoUHmrUPooUXmrcOvohkcvD1TeKlQ+KpTeSlQ+Kjx6etDulnYO7zk6A4Wrguj/RHNk4BFOjT1Fv0P9cO/k+KooQutg8jm1rl278vnnnzN06NBrHtu3bx8TJkwgPT3d1vHZlK3G2SsvVZIYm4g6Qk3ffX3FMvctmFFvrEtwf0x4hoqai8xlvVyTnP6QqBRqhRhytkL56XKODD6Cuoeavnv72nQYVpxTa7tM7qkVFBTQr1+/6z7Wt29fLl++bLOgnJlslEl5NAWjzkjU2iiR0Fo4hUqBwksBYhHnZqeJ1hC1PopTY06RMjGlZkakOEgQrGTyN3J8fDyTJ0+uVzUfIDU1lSeeeIL4+HibB+eMMt/O5OrOq3Rf0h2P7h6ODkcQWjS/u/0IfzOcgi8LuPjPi44OR2gFTE5qK1asAKBXr15oNBpCQkLw9PQkOjoaWZbrHm/NSg+XkvZiGv7j/QmaFOTocAShVQidE0rgo4Gkv5JO/n/ym36BIDTC5KTm6+vL+vXrKSoqYt++fXzxxRf8/PPPFBYWsm7dOnx9LS9/8+GHH9K1a1fc3d3p378/e/fubfC5u3btQpKka24pKSkWb98UhnIDSROScAlwocdHPcQwiSDYiCRJ9PioB95DvEl5NIXSo2L1dcFyZp8Q8vDwoE+fPtx000306dMHDw8PDAYDr732mkUB/Pvf/2bmzJn84x//4OjRo9x4443ceeeddatfN+T06dPk5OTU3bp3727R9k11fvZ5Ks5WELUmStQoFAQbU7orif4qGhc/F06NOUVVbpWjQxJaKJvMctDr9bz66qsWvfbdd99l4sSJPPHEE0RFRfH+++8THBzM0qVLG31dQEAAQUFBdTel0n4XsF7efJmcj3IIfS4U31tFQVZBsAe3IDdiNsdQXVjN6XtPY6g0ODokoQUyefbj5MmTG3xMr9dbtHGdTsfhw4eZM2dOvftHjhzJ/v37G31tXFwcVVVV9OrVixdffJFbb73VohiaUpVdRcqUFDz7edL1n13tsg1BEGp49fUianUUp8ef5mzCWXqu6imG+gWzmNxTW7duHWq1mo4dO15z69Spk0Ubv3z5MgaD4ZryW4GBgeTm5l73NbW9uI0bN/LVV18RGRnJiBEjGly8dPny5cTFxREXF0dBQYFZ8clGmZSJKRi1v07fb8HV0gWhpfAf50/Ya2Hkrckj861MR4cjtDAm99R69+7N7bffzpgxY655rLKykjfeeMPiIP54JCbLcoNHZ5GRkURGRtb9PmTIENLT03n77bcZNmzYNc9PSEggISEBqOndmePSvy5RtL2IHh/1QNNTY9ZrBUGwXJcXu6BN0nLh+Qt49PSoqwUqCE0xuesxceJEjEbjdR9zcXHhlVdeMXvjfn5+KJXKa3pl+fn5ZhVPHjRoEOfOnTN7+40pO17Ghecv0OGeDgQ/EWzTtgVBaJwkSUSuiMSrvxfJE5IpO1nm6JCEFsLkpDZjxgzGjh173ceUSqVFSc3V1ZX+/fuzffv2evdv376dG2+80eR2jh07RnCw7RKPocJA0sNJuLR3IfKTSDGmLwgOoFQridkUg9JLyakxp9AV6BwdktACOHzl62effZZHHnmEgQMHMnToUJYtW0Z2djZPPvkkAI8++igAq1evBuC9994jLCyM6OhodDodn3/+OZs2bWLjxo02iyn1uVS0SVpif4jF1c/VZu0KgmAet45uxGyK4djwY5wed5obfrxBnNsWGuXwpPbggw9y5coV5s+fT05ODjExMWzdupUuXboAXHO9mk6nY86cOWRlZaFWq4mOjmbLli2MGjXKJvFc/vYy2R9k0+mZTrQf2d4mbQqCYDnvgd5Erogk+eFkzk4/S+THYvREaJhZK1+3dE1V7tbl6TjU+xCuwa70+18/lO5tY/FGQWgJLrx4gYwFGUS8F0GnmY3PuBZV+tsuh/fUnIUsy6RMSsFQaqDXzl4ioQmCk+n6Wle0p7Wcf/Y8Hj09aH+7GEkRriUGp3+V82kOhd8V0u3tbmiixfR9QXA2kkKi55qeePbxpDKj0tHhCE7KrJ5aXl4e27Zt4/jx41y9epV27dpxww03EB8fT1BQy65a7z/eH0OxgZDpIY4ORRCEBqg8VfT7Xz8UKnE8LlyfSX8ZycnJjB8/nl69erFmzRqqq6sJCgqiurqaNWvWEB0dzfjx40lKSrJ3vHbj0s6F0Nmh4gS0IDg5kdCExpg0UWTQoEE899xzjB49Gjc3t2se1+l0bN68mXfffZcDBw7YJVBb8PPzIywszNFhCGbQJmuRVBLq7mpHh2JnNR/DinMVyHrwiLJkf8UBWa309HTxWf9Veno6ly9frvu9qQP322+/ne+//97eYdmNmP0oOND1K9T83oHQX2g3wpeolZFNPrdlqykKnvzoOa7uLmbIRfNKutUQ11TWEp/13/zxvZAkqdEhur79+7fo987sfvysWbM4dOiQPWIRhHpkWUaXX41rYNtZv8410IXqfD1t6FhTcABFI7eWzux9kGWZe+65h+7du/PKK69w5swZe8QlCOiLDcg6GdfAttMDcQl0wVhpxFAq1hIT7EOiZoZgQ7eWzux9+Ne//sXixYvZsWMH69evZ/DgwYSHhzNhwgSeffZZe8QotFHVeTW1/tpaTw1Al1uNyrs1fMUIzU17RgtK8IjwuO7jtUnNERpbl/OPVqxYYdE2LOptKhQK4uPjWbFiBadOnaJDhw4899xzFgUgCA2pyqoCwLXjtZOTWiu3X/e1KlsU7xUsc/6Z8yQ92PhMdEcNPxYUFNS7bdy4ka+//prz589z/vx5Nm3axFdffVVvYou5LErYZWVlbNq0ifXr17Nr1y6GDx/OqlWrLA5CEK6n9ovdLaTtDD+6hvzaUxNJTbBQdUE1Lv4Nj244sqf23//+t+7nhQsXolar+eyzz9BoagpelJeXM2XKFHr37m3xNszet/vvv5/vvvuOfv368dBDD7Fq1Sr8/MQCfoLt1X6xu4a0oZ7arwlc9NQES+kKdHj0vP7QI9QkNWeYELJkyRJ27NhRl9AANBoNL730EiNGjOCFF16wqF2zk1pcXBzvvPMOnTt3tmiDgmCqqqwqlN5KVJ5tpw6nyluF0lOBLkskNcEyTfXUwDkmhJSVlZGdnU2vXr3q3Z+Tk4NWq7W4XZMS9pIlS6iqqjm/8be//e26Ca2qqoolS5ZYHIgg/JEuW9emhh5ruYa4ip6aYBGD1oBRa2xy+NEZpvSPGzeOSZMmsWHDBtLT00lPT2fDhg1MmTKF++67z+J2TUrYubm5REREMGrUKIYPH05kZCReXl6UlpZy9uxZdu3axXfffVe3oKcg2EJVtq5NDT3WcgtxFefUBItUF1QDOO05td9bunQps2fPZuLEiVRX18StUqmYMmUKb7/9tsXtmrRvr7/+Os8++ywrV67k008/5eTJk1y9ehVfX19iY2MZNWoUr7/+Oh06dLA4EKE5NV3Jo3na0Df6qC67Cp+bvIC28AX/2/vpGuJCyS9lNPX+XMvarypnONMiWENX8Ot5aP/GRzicIamp1Wo+/PBD3nrrLVJTU5FlmYiIiHrn2Cxh8r75+fkxZ84c5syZY9UGBcEUsizX9NQ6tr3hR7eOLlRl6ZBlWRTYFsxS11MLaHr40VloNBpiY2Nt1p7F+/buu+/aLAhB+KPqK3pknYxbSNu58LqWa4grcpWMvkhUFRHMU33Z9OHH1lpRxOKk9u2339oyDkGo57fp/G2wp/ZrIheTRQRz1fbUmhp+dIaJIvZi8T7k5+fzyy+/UFxcbMt4BAGAquyaD6dbmxx+rNln3a/vgSCYqrqgGslFQund8GUwoqfWAK1Wy6pVq7j77ruJjY3ljjvuYPbs2Xz66ae2jE9oo2qv02qrU/oBqsS1aoKZqgurUbVXNXku1pY9tddffx1JknjqqacseLXtWZyYu3TpwtKlS+t+z87O5tSpU5w+fdomgQltW+3Qm2twGzynFlxbKkv01ATzGCuMKNWNFytoakq/OYse/fLLL3z88cdWTfT43//+x6BBgyx+/R9Z3FMbN25cvd9DQkIYOXIkzzzzjNVBCYIuW4eLvwqFa2sY5TeP0l2BqoNK9NQEsxmrjEhujffSbDX8WFxczIQJE/j000/x9fW1MOKa0ou2ZPI+3HrrrUiShFKpxN/fn+HDh1NRUYFabcmy84LQuMpMXZs8n1bLrZMrVZdEUhPMI+tkFG5NHwja4lAxISGB8ePHc9ttt/Haa681+twHHnjguvfLskxhYaENovmNyUntlltuAUCv15OZmcm8efN49913+fHHHwkNDbVpUIJQdbEK927ujg7DYdw7u1J5scrRYQgtjLHK2GRSa2r48VJBAXFxcXW/JyQkkJCQUO85H3/8MefPn2fNmjUmxfXjjz+yZs0aPD09690vyzJ79uwxqQ1TmZzUXnnllXq/V1ZWMnXqVCZMmGDzoAShMkNHu9t8HB2Gw7h3caV4b6mjwxBaGGOVEcm16Qv2G0t7/v7+JCYmNvj4mTNn+Mc//sHevXtxdTVtNOXmm2/G09OT4cOHX/OYLS+8BjOS2vUS1wMPPMDYsWPZsmULXl5eDBs2zKbBCa3d9T9a+mI9hhID7p3dGnzObyrtEkPzqx+HW2c39FcN6EsMqBqZnl2ftaXLnOW9ECxli55aUw4cOMDly5eJiYmpu89gMLBnzx6WLVtGeXk5bm71a7Zu3ry5wfa2b99uRTTXMnnfJkyYcN37ZVnmiSeeQKVSkZGRYbPAhLardtjNrXPbK2Zcy71zzRFwZUYVnjENr40lCL8nV8kovJtOaoseH88AACAASURBVNYcvowdO7be8CTApEmT6N69O//4xz9M6r1lZWXRsWNHK6JomMlJLTMz85r7Tp8+TVxcHKmpqWLCiGAzlRk1Sc29S9tNam6/7ntVhk4kNcFkpvTUwLqeWrt27WjXrl29+zQaDe3bt6/Xe2vMnXfeyYkTJ6yIomEWJWyj0cjmzZsZPXo0M2fOFAlNsKkq0VP7racmJosIZmjOKf3WkGVzroYzj8n70LVrVyRJQq/Xk5ubi0qlYtasWSxYsMBuwQltU2VGFZKrhGsjlcZbO9cgFyQXiaoMMa1fMJ2xytjktZ32qNK/a9cus55vz9UnTE5q8+bNA0CpVOLn58fgwYOv6YIKgi1UXqzCvbMbkqLtLrsiKSTcQsW0fsE8hhIDSi/rKoq0dCbv22OPPWbPOAShTlWGrk0PPdZy7+xKpeipCSaqvlqNvkiPe1jT13e25nmurXnfhBaqMqOq7pxSW+bW2VUMPwomq0yrubxFHd74HIfWfk5NJDXBqRh1RnTZurrZf22Zexc3qrJ0GKutvf5MaAtqk5p7uGk9NUeup3by5Em7tS2SmuBUqrJ0IPPrhddtm1tnVzBCVZao1i80reJCBQDuXRtPas7QU7MnkdQEp1KZ9us1aiacF2jt3MNqEntlupgsIjSt8kIlKl8VLu0anzXsLEktKSmJM2fO1P2+fft2/vKXv7Bw4UIMBoPF7dpkH6qqqsjOzqaiogJ/f3/8/f1t0azQIGuHo/Q2iMEWbVx7vqgitQQAdTcZ0FrURvOzxfm/a9/PmvcAKlNL4BZTPqrWfpxtMcwpjpMdpTKt0qShR3CO/6UpU6Ywc+ZMIiMjuXTpEvfccw+33HILH3zwASUlJSxcuNCidi3et9LSUpYuXcqwYcPw8fEhIiKCmJgYgoKCCA0N5YknnuDQoUOWNi+0URWpVUguEm6d2u41arXcQl2QVFCR6gyJW3B2FRcqUHdtuhCGs/TUkpOT6devHwBffvklgwYNYuvWraxZs4b169db3K5FSW3x4sWEhYWxYsUK4uPj2bx5M8eOHePs2bMcOHCAV199Fb1eT3x8PHfccQfnzp2zOEChbalMrcK9qyuSsu1eo1ZLoZJwD3MVSU1okmyUqUw3r6fmyIkiUFMEubZO5I4dOxg1ahQA3bp1Iy8vz+J2LUrM+/fvZ/fu3Q3W+Ro4cCCTJ09m2bJlfPrpp+zevZvu3btbHKTQdlSk6lB3E9P5a7l3E0lNaFpVdhWyTm5ykgg4z8XXMTExLF26lLvvvpsdO3bUDTdmZWXh5+dncbsWJeYvv/zSpMKVbm5uTJ8+nccff9ykdj/44ANiY2Px9vbG29ubIUOGsGXLlrrHZVlm3rx5hISEoFarueWWWzh9+rQluyA4IVmWqUitQt1NzHyspe7mSqVIakITKi+Ydo0a/FYmy9E9tTfffJOPP/6YW265hYceeojevXsD8M033zBw4ECL27U6YSclJaFUKomMjARqZrCsWrWK6Oho5s6di1Jp6lpQ0KlTJ9588026d++O0Whk1apVjB07lsOHDxMbG8uiRYt45513WLlyJZGRkbz22mvEx8dz5swZvLy8rN0VwcGqrxgwlBhxFz21OupuruivGqku1OPS3hmOrwVnVHGuZjq/uptpxeWd4S9p2LBhFBQUUFJSgq+vb939U6dOxcPD8pUprE7MU6ZM4ejRowB1M1gKCwv54IMPePHFF81q65577uHOO+8kIiKCHj16sGDBAry8vDhw4ACyLPPee+/x/PPPM27cOGJiYli1ahWlpaWsW7fO2t0QnEBlas3UddFT+03tUKwYghQaU55UjkKtMOlSGGeZKAI1tYR/n9AAwsLCCAgIsLhNq5OavWawGAwGNmzYQFlZGTfeeCNpaWnk5uYycuTIuueo1WqGDRvG/v37rd0NwQnUfnGrw0VPrZZ7uEhqQtO0SVo8enqYNMHKWYYf7cXqxGzrGSwnT55kyJAhVFZW4unpyddff03v3r3rEldgYGC95wcGBpKVldVge8uXL2f58uUAFBQUmB2P0Hwqfu2puYeLnlqt2gQvzqs1rS1/1suTyvG52cfk5zvD8KO9WJ2Ya2ew7N27lx07dnDHHXcAls9giYyM5NixY/zyyy9MmzaNxx57jFOnTtU9/sd1eGRZbnRtnoSEBBITE0lMTBQXhTu5itQqXENcUKpbw/GibSg1ClyDVKKnZoK2+lnXl+qpyqhC00tj0vMlQKFo+NbSWb0Ltp7B4urqSkREBHFxcSxcuJA+ffqwePFigoKCAMjNza33/Pz8/Gt6b0LLVHFeTOe/HnWEKxXnRVITrk+bXFN5RxNtYlKTQKVq+NbSWb0L9prBUstoNFJVVUXXrl0JCgpi+/btDBgwAIDKykr27t3LW2+9ZfV2mo8tShFZW6LKWcpkXa33W8WZCvzu9bjm/sZZ+37aIona4v+04aSljlRy5b9ami4bZu3H2dvK1wuOUJ5UDoBHL9O/bxWmT0q3q9zcXPbv309+fj5GY/3P0fTp0y1q0+qkNnbsWB5//PG6c2m1wsLCzG7r+eef56677iI0NLRuVuOuXbvYsmULkiQxa9YsFixYQM+ePenRowfz58/H09OThx9+2NrdEBxMd9lA9WUjHlGiPNYfaaJcyP3UQHWhAZf2TvJtJDgNbZIWyU0y6cJr+K2n5miff/45jz/+OLIs4+vrW+80kiRJjktqGo2GBx98EB8fHyZOnMikSZMsrh6Sm5vLX/7yF3Jzc/Hx8SE2NpbvvvuO22+/HYC5c+dSUVHBjBkzKCoqYtCgQWzbtk1co9YKVJypWV7Fo6dIan/k0bOmN6k9U43PEJHUhPrKk8rxiPRAoTLtbJIkgcoJ/oxeeOEF5s6dy8svv4zKhlnW6pbWrl1LSUkJa9eu5bPPPuONN97gpptu4vHHH+f+++9HrTbtYkCAlStXNvq4JEnMmzePefPmWRe04HS0KSKpNaT2PdGmVOMzRCzJI9SnTdLiPci8oWNnmBBSUlLCxIkTbZrQwEaXJXh7ezNt2jQOHjzIyZMn6d+/P1OnTiUoKIipU6eSnJxsi80IrVh5cjUKdwn3zk4wLuJk3MNUSK6gTRaTRYT6DOUGKtMrzTqfJkmgcmn41lwmTJhQrwyirdj0GyQ7O5vNmzfz7bffolKpGD9+PJmZmcTGxrJw4ULmzJljy80JrYg2pRp1D5Wozn8dklLCo4dLXW9WEGoVHygGGbz6m3EKRnKOntq7777L2LFj2bFjB71798bFpX5Gffnlly1q1+qkVl1dzebNm1mxYgXbt2+nb9++zJ07l4ceeghPT08AvvjiCxISEkRSExqkTanGq7+Yzt8Qj56ulB0XPTWhvqJtRUguEu2GtzP5NRLOMVHko48+4vvvv8fPz4/z589fM1HEYUktODgYWZZ5+OGHeeONN4iNjb3mOfHx8dfU9xKEWoZKI5VpegInmHadTVvk0dOFgq/LMVbJKNxEb1aoUfhDIT43+aDUmDHzw0l6av/85z955513eOaZZ2zartW7tnjxYrKysnj//fevm9AAfH19SUtLs3ZTQitVcV4PRjFJpDEePV3AABWpYghSqFGVU0X5iXJ8R5rXYZComf3Y0K0pTS0RZiqDwcCYMWPMfl1TrE5qjzzyCO7uYkaWYDkx87FpddP6xWQR4VdFPxYB0H5ke7NeZ21Fkdolwo4cOUJiYiK33XYbY8eO5cSJE2bFMWnSJNauXWvWa0xhk5FVvV7PwYMHycjIQKer/6F79NFHbbEJoRXTnq4GCTwiRVJriEdPF5CgPKka/3GOjkZwBkXbinDxd8Gzj6dZr5Mk6yqK3HPPPfV+X7BgAUuXLuXAgQMNjtZdj1ar5ZNPPuGHH34gNjb2mokiS5YssSg+q5NaSkoKo0ePJi0tDVmWUSqV6PV6XFxccHNzE0nNKdniaD/bBm3UlMMqO1GNujsoPXKbeP71dLYyhkorXw+2KRnWeAkspQbU3RSUnygFDHaKQ5TJailko0zhtkJ8432RFGaeY7VhRRGDwcCXX35Zt0SYOZKTk+nbty9Qk0dsxepdmzVrFv379+fYsWMEBQVx7NgxiouLmTZtGvPnz7dFjEIrV35CxrOPmPzQFE2sgrITtqgzKbR0ZSfKqM6vNnvoEX6r0t+QgoIC4uLi6n5PSEggISGh3nMaWiLMHDt37jTr+aay+pzaoUOHePHFF9FoNCgUCvR6Pf369WPRokXMnj3bFjEKrZi+TKYiFTS9nWA6lpPT9FZQcc6IQSs7OhTBwYq21ZxP8403f1Z5bZmshm7+/v51S/gkJiZek9Cg6SXCTPHxxx83+NiTTz5p9n7VsvqbRJblumr8/v7+dQt2durUifPnz1vbvNDKaU/LIIMmVvTUmuIZqwQZyk+L3lpbV7itEE1vDW4hli2oa+16ag0tEWaOuXPnsnHjxmvuT0hI4LvvvjOrrd+zevgxJiaG48ePEx4ezsCBA3nzzTdRKpV8/PHHREREWNu80MqVnazpdXiKpNYkTWzN2f3ykwa8BzhBRVrBIQxaA8V7i+n4dEeLXl9bJqtBFpxyr10izBz/+c9/uO+++2jXrh0jRowAahLa999/z65du8wP4ldWJ7UXXniB8vKa9Xzmz5/P3Xffza233oqfnx9ffPGFtc0LrVz5CRmlJ7iHOToS56cOl1B4IM6rtXHF+4qRdTK+IywraNHk0jNNJLXGlggzx4gRI1ixYgXjx4/n+++/55NPPmHbtm3s2rWL8PBws9r6PauTWu2yMADh4eEkJSVRWFh4zfo4gnA9ZSdkNL0l82dwtUGSQkLTW0n5iYZmPwptwdVdV0EJPjf5WNyGNRVFmloizBzjxo2jsLCQYcOGERwczO7duy1ai/P37FIBrH1782fkCG2PLMuUn5Dxf0BMEjGVZ6yCgo16ZFkWB41t1NWdV/Ee4I3Ky7Kvb2vXU2tqibDG/PWvf73u/YGBgfTu3Zt333237r5mvU5t8uTJJj93xYoVlmxCaAOqMkBfBJ43iC9nU3n2UZLzcTVVl2TcQ8X71tboy/SUHiol9LlQq9pxVO3HkydPXvf+bt26UVZWVve4NQdsFiW1goKCer/v2bMHhUJRd53CqVOnMBqNDBs2zOLAhNavNLFmkojXAPHlbCqvuJpD7NJEA+6hoofb1pTsK0HWy7S7xfSq/H/U5EQRO7LXtWm/Z1FS++9//1v388KFC1Gr1Xz22WdoNDVV1svLy5kyZYrZF+MJbUtpohHJBTx7i6RmKk2sAklVk9T87xVlxdqaop01S834DLX8fJqzVOm3F6vPqS1ZsoQdO3bUJTQAjUbDSy+9xIgRI3jhhRes3UQrY4uZa9aWubJFaaj9VrdQcqgXmlgZhZu/Fa2YvuLv9ZVZ+XpbtWHa/4nSHTS9ofRQNdf+LVlbuqyTla8HEGvi2dPVnVfxGuhl3lIzf+As66nZi9X5uqysjOzsaz9MOTk5aLWN17MT2i5ZhtLEarziRG/DXF5xEqWJMrIsKou0JfoSPaWHS/G91bq1KSXJ+ouvnZnVuzBu3DgmTZrEhg0bSE9PJz09nQ0bNjBlyhTuu+8+W8QotEIVqS4YimWR1CzgFSehL4JKsURhm1L8czEYsOp8GlBT0NiK9dScndWd0KVLlzJ79mwmTpxIdXXNulgqlYopU6bw9ttvWx2g0DqVJtaU9/GKE8NV5vKKqzkHWZooow4X5yPbiqs7ryK5SnjfaN1qCo6cKAIwe/Zsxo4dy9ChQ1HYoWtodYtqtZoPP/yQK1eucPToUY4cOUJhYSEffvhhXU1IQfij0kQ3JDfQRLfiwX070cSA5Prb7FGhbSjaUYT3IG+Uauu6U7VV+h01/KjVannooYcIDAxk4sSJbNq0iYqKCpu1b9EupKVdO+6h0WiIjY3lhhtuqDdpRJZlMjMzLY9QaJVK/+eOV18XFC6ip2EuhauEZx8o+Z9Iam2F9ryWsqNldBjdwfrGrFz52lpLly7l0qVLbNmyhY4dO/Liiy/i5+fHmDFjWLFixTWXjJnLoqQ2ZMgQpkyZwoEDBxp8TlFREUuXLqVXr15s3rzZ4gCF1sdYBSWH3PAeKoYeLeUzVKL0IBh1IrG1Bfnr80GCgIcCrG7L0T21WgMHDmTBggWcOnWK48ePM3z4cFauXEmnTp246aabePvtt+tWfTGHRXk5JSWFBQsWcNddd6FUKunfvz/BwcG4u7tTVFREUlISycnJDBw4kPfee8+immBC61V62B25SoGPSGoW8xkqcWmxTOkR8Bns6GgEe5Jlmfx1+fgM88G9k7vV7VlbJsseIiIimD17NrNnz+by5ct88803fPPNNwDMmTPHrLYsSmrt2rXjrbfe4rXXXmPLli38/PPPXLx4kYqKCvz8/Hjssce4/fbbiYmJsaR5oZUr3lfzwfS5USQ1S3kPrRm2Ldkn4zNYDOG2ZmXHy9CmaOkxq4fN2nTmqft+fn5MnjzZrHKMv2fVCKparWb8+PGMHz/emmaENqZ4nzvqCB2ugU52uNiCuAVJuIdD8T6ZULHAfKuWvy4fSSXhP96aIgW/cfTsx1pPPPFEo6tfW0pMPROalSxDyX532o8SF+Zby2eoROEPsqjY34rJRpn89fm0v6M9Lh1sk4maXE/NDh544IF6v8uyzPfff09xcTGATdfedKqktnDhQr766ivOnDmDm5sbgwcPZuHChfWGMWVZ5tVXX2X58uUUFRUxaNAgPvjgA6Kjo5shQluUuLJFG9YmhK9sEEOYRa+qOCdRXaDCZ6gbYIslikqsfH2+DWKwxf/pVbNf4TPUlbw1HlSkFuERYQQuWRmD3srXg22+Upx4bKyZFf9cTNWlKsIXWb5o5vU09/Bjeno6ERERJCQkIEkSsizz888/M2PGDJtvy6n+enbt2sX06dPZv38/P/30EyqVij/96U8UFhbWPWfRokW88847vP/++xw6dIiAgADi4+MpLS11YOSCqYr31vzJ+QwVqzdby+emmiRUvNepjk0FG8pfn4/CQ4HfGD+btSk5oKLI//73P2666SYWLFiAu7s7t9xyC2q1muHDhzN8+HCbbsupPg0//PBDvd/XrFmDj48P+/btY/To0ciyzHvvvcfzzz/PuHHjAFi1ahUBAQGsW7eOqVOnOiJswQxFPylxCZTxiBJT0a3lEWXExd/I1Z9UBE+ytsi14GyMOiP5X+Tjd4+fVQWMryGBoplPZ0uSxPTp07n//vt57rnnWLZsWV0FKluzuqe2evVq9HpbDFtcq7S0FKPRiK9vTQHPtLQ0cnNzGTlyZN1z1Go1w4YNY/9+66vGC/Yly3D1JyW+txkQp4CsJymg3W16inaoELWNW5+i7UXoC/UEPGz9tWm/V1ul3xEXX/v7+7Ny5UomT57MU089ZZdtWJ3UJk2aVG940JZmzpxJnz59GDJkCAC5ublAzdLfvxcYGFj3mOC8tEkSulwJ3xEGR4fSaviO0KPLUaBNcaozCYIN5K7OReWrov1IW5x7/o0zVOkPDw/nb3/7m13atnoXGlr+QpZlq7qXzz77LD///DMbN25EqazfV/7jTK/GZn8tX76cuLg44uLirC6/IlinaEfN/2O7EeJ8mq34jqgZJSna4VRnEhyiNX3WKy9VUrCxgKBJQShcbZxpJFCqpAZvzeHOO++0W9s2ebf27dt3zR9RVlYWnp6eFrX3zDPPsH79en766SfCw3+b9RMUFARwTa8sPz//mt5brYSEBBITE0lMTMTf3zbXeQiWKfpJgXu4EXWYGCuzFXW4EfcwA1d/EkmtNX3Ws5dmgwwdn+poh9YlUKoavjUDe64FaJM9uP/++5FlGX9/f2JjY4mMjCQzM5Pg4GCz25o5cyYbNmxg165d9OzZs95jXbt2JSgoiO3btzNgwAAAKisr2bt3L2+99ZYtdkWwE9kAV3cpCXhADD3aWrsRei5vdEE2gCSuZ2/xDBUGsj/Kxm+MH+quattvoLb4owPZ87pKmyS1CxcucOXKFU6ePMmJEydIT09HoVCwYsUKs9qZMWMGa9asYdOmTfj6+tb1yDw9PfH09ESSJGbNmsWCBQvo2bMnPXr0YP78+Xh6evLwww/bYlcEOyn5RYGhWML3TyKp2Vr7eD25n7pRclCNzxDbLeEhOEb+unz0V/R0nGmPXho1J9VcWm/P3iZ75u7uTt++fenbt69V7Xz44YcAjBgxot79r7zyCvPmzQNg7ty5VFRUMGPGjLqLr7dt24aXl5dV2xbs68q3SiSVjO9IkdRszXekHpQyV771EkmthZNlmUv/uoQmVkO74VaucN0YZy7+aCWr9+zee+/FxcU25VtkWb7urTahQU23dd68eeTk5FBZWcnu3btF4eQW4Mq3SnxuNuJix89pW+XiK+Nzk4Er34oDu5bu6q6rlJ8sp9PMTvYbopOsW1Bt4cKFDBgwAG9vb/z9/Rk9ejSnTp0yKwSnPqe2ceNGW8TRQjhDiSuAC1a+3vySTNfqZ/IzK9J1lJ+6QLd3AqhfGqvSBnFYWxrKFv8fZTZow7pLUjrc3YELzwVTues47iHlljXS409WxVAjwgZttN5eRFMu/esSqg4qm6yb1igremq1lZ8GDBiALMu8/PLL/OlPfyIpKYn27U27/ODkyZMWb78prXdgVXAahVtqvmQ73KVp4pmCpTrcVcaF5+DKrs50fDjZ0eEIFqi4UMGVb67Q+e+dUartOOPHyorGTVV+cjSR1AS7u/JtGeoIF9Q9xPpp9uLRswr30BKu7OoikloLlfV/WUhKiY7T7TRBpJaNy/T/sfKTKS5dusTSpUvZv38/ubm5SJJEYGAgQ4cOZerUqYSGhlocT9vt5wvNQl9qoGinlvZ3eYrlUexIkqDDLRe5+ksI+nJxrNrS6Ev15Hyag/94f9w6utl/gzYsKfLHyk9N+fnnn4mKiuLLL78kOjqahx9+mD//+c9ER0fX3bdv3z6z46gl/voFu7rybTlylYz/ODGJwd78R6aTtaY3hbs7EzDK2vOuQnORZZkLcy9gKDHYbxr/7zXRUysoKCAuLq7u94SEBBISEq773NrKTz///PM1lZ8aMmvWLCZNmsSSJUuu+/jMmTOZNWsWhw4dMqm9PxJJTbCrgi9KcA1R4TPUDheRCvX49M/F1V9L/nfdRFJrQTIXZZK9LJvQuaH4DPax/wZriz82wN+/PYmJiU0288wzz7BhwwZ27txZr/JTU06fPs3atWsbfHzatGksX77c5Pb+yOrhx7Fjx/Ltt99iNIp6fkJ9+hIDV74rx/9+LySFGHq0N0kp43/HBQp3h6Ivs81lNoJ95a3L48LzFwj4cwDhC227EGijrCyTNXPmTNatW8dPP/10TeWnpgQHBzc6vLhv3z6LqlHVsrqnptFoePDBB/Hx8WHixIlMmjSJ7t27W9us0Apc/qYMuUom4AEx9Nhc/O9IJWtNDFd2diZwdKqjwxEaUbSriJSJKfgM96Hnyp7Nd+DXRE+tKU1VfmrKnDlzePLJJzl48CDx8fEEBgYiSRK5ubls376dlStX8t5771kcn9U9tbVr15KTk8NLL73Ejz/+SGRkJMOGDWP16tVUVIjqBm1ZwReluHVS4T1YDD02F59+ebgGlJP/XTdHhyI0ovx0OafGnkLdXU3M1zEo3Jpxzl5tmayGbk348MMPKS0tZcSIEQQHB9fd3n77bZM2P336dNasWcOxY8f485//zLBhw7j55pv585//zLFjx1i9ejVPPvmkxbtnk3fS29ubadOmcfDgQU6ePEn//v2ZOnUqQUFBTJ06leRkMcW4rdFd1lP4fZkYemxmkgIC7rxA4Z5Qqq82wyw6wWxV2VWcuPMESrWS2K2xuPg6YKjYiooiplR+asqDDz7IL7/8glarJSsri6ysLLRaLb/88gsPPPCAFTtm44ki2dnZbN68mW+//RaVSsX48ePJzMwkNjaWhQsXMmfOHFturoWyQTWPI69b9/p+j1ofA9mNPpq/rgq5GoIm6hp5broN4si38vW2qNtli/PJOuubSEoCIPCGYi6t6k3eJxo6jTpq+utDPrE+Bs83rG+jFdOX6jl510mqC6vpu6cv7l3cmz8IK4cfbcnFxcWq82fXY/WeVVdX85///IdRo0bRpUsXNm3axNy5c8nJyeHTTz9l69atrF27lvnz59siXqGFyP2sGs9+CjxjxVoozc2raxaeXS+Ru2OQo0MRfsdYbeT0/acpO1lG9H+i8ernoHPNVtZ+tLfMzEwmT55s8eutTmrBwcFMnTqVbt26cfjwYQ4ePMgTTzxR74RhfHy8WVebCy1b6VEDZceMBE0SFUQcJei2g5RdCKUs3bZHwYJlZFnm7JNnKfqhiMiPIulwRwfHBmTDi69trbCwkFWrVln8eqvT8uLFi7n//vtxd2+4G+3r60taWpq1mxJaiNzPqpFcIfBhMa3cUQKHJZK6agy5Pw0iYvImR4fT5l3850VyV+TS5aUuBE9x8IFGk2WybDAU3ojVq1c3+nhGRoZV7Vud1B555BH0ej379+8nIyMDna7+G/Loo7Y4fyO0FIZymbw1OvzuVeHSXkwQcRQXby1+A0+Su3MAXSdsQelW7eiQ2qzsT7JJfyWdwMcCCXs1zNHh1HBgj2zixIl4eHg0WDbP2muerU5qKSkpjB49mrS0NGRZRqlUotfrcXFxwc3NTSS1Nibv82r0V6HjU2Lo0dE6jtpLwf6+5O3pT0j8L44Op80pTy4ndU4qhVsL8f2TL5HLI52j/qmNCxqbKyQkhCVLlnDfffdd9/Fjx47Rv39/i9u3Ol3PmjWL/v37U1xcjIeHB8nJySQmJtKnT582ttaaIMsyl97X4dlXgc9QMUHE0Xx6XUATlkXWlmHYcU1G4Q90l3Wcfeosh3ofovjnYsLfCqf3t71RuDr+fBVQk9SsrChijf79+3PkzkefLAAAIABJREFUyJFGwpOsWkTU6j04dOgQu3fvRqPRoFAo0Ov19OvXj0WLFvH0009z4sQJazchtBBXdxrQnjYS+Zm7cxyRtnGSBJ3u2sOZDx7i6ukIfGPOOzqkVs1YZeTS+5e4OP8ihjIDIVNDCJsXhqu/E45aOHD4cc6cOZSVNbywbkREBDt37rS4fauTmizLeHh4AODv709WVhaRkZF06tSJ8+fFh6gtubREh4ufRMCfxQQRZxFw8xFSV48m69thIqnZiSzLXP7qMqlzU6m8UEn7Ue3p9lY3NL2cdFHc2ooiDnLzzTc3+rhGo2H48OEWt2/1nsXExHD8+HHCw8MZOHAgb775Jkqlko8//piICFss7S60BOVJBq58o6fLC64o3UUvzVko3aoJGbmfjK/+RPmlADSdrL1YXfi9kkMlpD6bSvHPxXhEexD7QyztR7Z3dFhNc4Kp+/Zi9Z698MILdeOf8+fPJzMzk1tvvZVt27Y1uF6O0PpkLNShUEPHmU441NLGdRq9G4WLnoyNf3J0KK1G5aVKkh9J5sjAI2jPaunxUQ/ijsW1jITm5BdfW8vqPbj99tvrfg4PDycpKYnCwkJ8fX1b4XkVrfVNfDve6iaKfjxs1et9zVj7qEEVP/72Y7oneetH0enxs7hWH4McE9uwxRCItUec7TtbHwN665sou2x9G8Eh173bNRhC7jvBpS/jCPvrUdQhxdd93tG/vGN1CH03PW51G2DeUibNSV+mJ3NRJplvZyIbZTo/35nOf++MyrsFJQMnKpNlD3bZs/bt27fChCY0JOODKCSVTOjUFEeHIjQg9OGDSAojGatF6SxLyAaZnBU5HOx+kIv/vIjfPX4MTBlI+MLwlpXQABA9tWuMGTPG5Od+8803lmxCaCG0FzzJ/XdXgiek4hZU6ehwhAa4BZQRPPokOd/E0vkvB1F3skFh7TaiaGcRqc+mUnasDK9BXkR/FY3PkGZYodpeJFpF8mqIRXvWoYOD65YJTiPtzVgkVwNdnjnt6FCEJnSZvJ/crdFcWHYz0fP/6+hwnJ72rJbU51K58s0V3Dq7EbU+ioAHA1r+KJQTDT9WVVWRnZ1NRUUF/v7++Pv7W92mRUnts88+s3rDQstXcrQ9Bf/tTJdnTuEWIHppzs7Nr5zQhxK5+NmNlDx8CO9euY4OySkZtAbSXkgj6/+yULgr6Pp6VzrN6oRS3VoKCji2okhpaSmff/4569ev5+DBg1RXVyPLMpIkERISwh133EFCQgIDBgywqH3nSNdCiyPLcGHBDbj4VRI6TZxLaylC/3IQl3ZaLnwwXFQZuY7KjEqO3nSUS/+6RNCkIAadH0SXv3dpRQmNmuFHSdHwzY4WL15MWFgYK1asID4+ns2bN3Ps2DHOnj3LgQMHePXVV9Hr9cTHx3PHHXdw7tw5s7dhk3T93Xff8X//93+kpaXxww8/EBoayieffELXrl0ZMWKELTYhOJmCLZ24uj+Q7gsSUXnaYPaf0CxUGh1dpuzj/DvxXN7VHf9bzf/SaK2u7rnK6fGnMVYZ6f3f3nS4q7WeZnFcT23//v3s3r2bmJiY6z4+cOBAJk+ezLJly/j000/ZvXs33bt3N2sbVqfltWvX8sADD9CjRw/S0tKorq6pBm4wGFi0aJG1zQtOSF+q4vwr/fCMLiL4kVRHhyOYKeTeY2gi8jm/eAR6raj+ApC1LIvjI46j8lXR73/9WnFCo6anplQ0fLOjL7/8si6hrV69Gr3++gfEbm5uTJ8+nccfN/8SEav3YNGiRXz88ccsXrwY1e+y/+DBgzl27Ji1zQtOKH1RJLocD7q/kYhCJcawWhqFSqbH3G1U5Xtz8dMbHR2OQxl1Rs48eYZz087hG+9Lv//1Q9PTSctb2YqTXHw9adIkCgsLbd6u1Unt3LlzDBky5Jr7PT09KSkpMbu9PXv2MGbMGDp27IgkSaxcubLe47IsM2/ePEJCQlCr1dxyyy2cPi1m3jWX0mM+XFrajeCHU/Hpf8XR4QgW8onNJmj0CS5tiKM0JdDR4TiELl/H8RHHyfkoh9C/hdL7v71xadcGeq5OktQaqsQvy3LdiJ8lrE5qISEhnD179pr79+zZQ7du3cxur6ysjJiYGP71r3+hVquveXzRokW88847vP/++xw6dIiAgADi4+MpLS21KH7BdIZKBcnT+uMaUEX4i6IX3tJ1e2oXLr4VJL82CqOuFU2EMEHpkVIOxx2mNLGUqHVRdHujG5KyhU/VN4dC0fCtGe3bt4+CgoJ692VlZeHp6Wlxm1an5YSEBP7617/yySefAJCZmcnevXuZO3cu8+bNM7u9UaNGMWrUKKBmhdTfk2WZ9957j+eff55x48YBsGrVKgICAli3bh1Tp05tonUZa0oa7b7N+gsub7F8RYU68nwrjyZdLavPmP5qJNoUb3p/fRCXqCjrYgAgwAZtWP7Hbzs2KJ/maYMjZPUFs57uoobIeTs4+dQ9/H97dx4fRZE/fPzTc0/uewhHSMINAeS+XIQFOURUHnF/mlWDB4giTxRROVwRFXXVn4guVxSUXV1h0V1Q10cOCQZF0CBXDIiBhCvkIPfknKOePwKRwCQkmYGZJPV+vfo1M93TNd/uVLq6uqur0j+4iYCAL50OYe8tzueLIV9d2xPU7PXZ/Prgr2iDtfT7rh++A3yv6e95HDcPEnqpu+66CyEEoaGh9OnTh27dunH69GnCw8ObnKbTW/bMM89QVFTEzTffTEVFBaNHj0av1zN37lxmzZrlbPK1pKenk5WVxbhx42rmGY1GRo4cye7duxtQqElNVbgriNPLogl/6BTB487jGYWJ5KzgP5wk/M4UTq/rj+nmPhjatNzxD4VNkP5cOqdeO4X/jf70+rQXOlMr7YDbQx6+PnHiBHl5eRw+fJhDhw6RkZGBSqVi7dq1TU7TJcX1kiVLWLhwIampqdjtdnr27OlU9bEuWVnVD4uaTLXvAZhMJs6ePetwnYSEBBISEgDIzXVBp7GtUGWWjtS4GzB2LqXTkiPuDkdysU5P7aLwp3acT/oL4ZNnoDYWuDukJklIWEtCwocA5ObWvt9rLbKSGptK/lf5hM8Ip8u7XTxnJOrrzYNqagaDgX79+tGvXz+XpenUX7W8vJzFixfTp08fwsLCGD16NNOnT+ftt9+mvLzcVTFe4fJuai4+je7IjBkzSE5OJjk5mdDQkGsWU0tltyoceaAf1mItvT7aj8bX5u6QJBfTeFvo9b9fYa/yJTfpOYS9eR7sZ8x4kOTkJJKTk2p1t1T2axn7huyjYGsBXVZ2odvqbq23QAOq2/Sr6pmu7moN+hpiypQpaLWub5jT5L+s1Wrlj3/8I6+88gpRUVHMnj2bWbNm0bFjR1588UXGjh1b5zMITdWmTRvg9xrbRTk5OVfU3iTXSF/clcKkYLouS8EnRjbGaal8uuYRNHQplVn9Kdz/oLvDcZnin4rZN3gfVZlV9N3el3Yz27k7JA+hqWe6uqs16GuIzz77jMDAwCatW58m10ETEhJIS0vj559/plevXrWWpaSkMHr0aBISEnjsscecDvKiqKgo2rRpw7Zt22r6BauoqGDXrl288cYbLvsdqdq5de05/VYnwh86RZs/O768K7UcPp23UJkTQ3HKn9H6ncGny9fuDslpFekV2EptYINjs45hijURdk8YxqimHYhbBgVn7zzV16DP3ZpcU/v0009ZuHDhFQUaQExMDPPnz+fTTz9tdLpms5kDBw5w4MAB7HY7p06d4sCBA5w6dQpFUXjiiSd47bXX+Pe//01KSgrTpk3Dx8eH2NjYpm6K5ED+9hB+nR1D4JhcuvyvfA6wtQga+jaG8GTyfniK8sz+7g7HaWF/CmP4ueF0WdEFbaCW9IXp7I3ey8/Df+bM385QlVPl7hDdwPnLj56syVvwyy+/8Mc//rHO5WPHjiUlJaXR6SYnJ9fcOCwvL2fRokX069eP559/HqhubTlnzhxmzZrFwIEDOXfuHFu3bsXXt5U1y72GzId9+eXefnj3MNPro/2otLLXkNZCUdkIHfUC2oCT5O5cTFVBlLtDcpouVEe7R9vRb1c/hmYMJfq1aGylNtJmp7G77W4OTjhI1t+zsBa3pj5M6778mJuby8CBA2umiw3tmosm10ELCgrqHfsmNDSUwsLGD0Q4atSoOp80h+pGIi+88EKTnoGTrq70qDcHJw9G7Wul97+T0fi59x9dCLBXgLVIwVakwlqk1Ey2YgVbmRp7OdjLwVamOHxvLwe7RUFYqWP6fRl2LvRiXt1IjEsmRXXpe4GiBZUOFL0GlU6g0oOiA5VOoOipmacyCtQ+ArW3QO1D9fvLJpW3AY2fHU2gFbWPHXcO2aXSlRI2Zj5Z/11O9tY3aTPhCbT+p90XkAsZOhqIeDaCiGcjMKeYyfkkh5x/5nA07igqg4rgycGExYYRPDEYlb7511ocu1hTcyw0NJTk5OTrF44Dp06don379qia8OhBkws1m81Wq6/Hy6lUKmw22VKuOSk75s3BW4aAAn2//BFDO9eOkSYE2IpVVOVosOSqseTqqMpVsOSqLkwKVbkqLOcVrIUqbBcKL2Fp2BFe0QlURlAbQeV1yXsjqAwCRUMdk7gwVRdkQlD9nP6F6dLPwq5Uv7dXF4L2yur+A0Wlgr0KbEUKokrBXknNq71cwVaqYC+vbzt+f7Bf0djRBNrQBFnRXnjVBNrQBlnRBNnQtbGgM1kuebWi9rI3+e/iiMY7F9O4uWRtWUr2lrcwTXgCrV/Luq/qE+ODzxIfol6OonhvMTkf55CzIYfcjbmo/dWETg3FFGsi4KaAFtbbiPP31K61yMhIevXqxfLlyxk5cmSj1m3ylgkhuPfee9Hr9Q6XV1ZWNjVpyQ1KU304eNtghE3hhq/34t2ttFHrCwGWPDWVp7RUntFQeUZ7YdLUerWXOz7zUnkLdKF2tKF2dGECr24WNP4Cjb9AfeFV4ydQ+9t/n+8nUHt5ofKqLrwUt/X01LAeRYQNbKVgMyvVU6ny+3tzDrZiNZYCNdZ8DZZ8NdYCDdZ8DVXZWsqOGLEWqLEWOv6XVfva0AV2Rhdcii60FEPbEvRtSjCEV0/68GK0fo27f6QNOIVp3FNkb1lK9palhN38NLqAk41KozlQFAX/of74D/Wn09JOFH5TSPY/s8ndkEvWmix04TrC7g4j7J4wfAf6Nv+RrwFPv3e2du1a0tPTefrpp9m7d2+j1lVEfdf66vHAAw806HueNEr2wGgDyS+2b3oCZrPzQdx/v/NpePV0MoGptT4VfV/C4VvTUBlV9NnaFZ8Yxy3DhBBUZVko/62S8rRKytMqKD9+8X0ltuLaNXNFo6Brp0XfXoe+/YXXcC3aMC3aUA26UD3aUC3aUC1qY1P/yZytobiioYAL8kUDu2+zWwSWXBtV2Taqsi5MF95bsi1UZdmpPGOj4pQNcdmmqf0UDBFqjF3UGLtq8OqmxqurBmNXDdoQBUXZ5/A3zYc1HBoXir1Sofd/z+M/rJ59dtT5rrYIcf550oET9jh9Cc1WbiPvyzxyPskh7795iCqBsbORsNgwTLEmvLp5OR3n9TBw4MBa+2LgwEiSk5+r5/urrrrvzGYzaWlpAAwfPpx58+Zx2223ERQURERERIPiOnToEH369GnQdxujyTU1TyqspKY7/3khqf9zHH2Ejj5bumKMrK55WwqtlKaUU3q4vPr1wntrwe8Fl6JRMETpMHbS4z/cB0MnPYZI3YVCTIcuTIOiqu+s1rMvgXgilVZB31aDvq2jfff75WJhF1Tl2Kk8ZaPipJ2Kk7bq9xk2yo5YyfuyEnFJR+iaAAWvHqF497Hg07d68u5jQeMj8Oltpd/uHA6ND+HgmBB6bsgnZLJrL017IrVRTdhdYYTdFYal0ML5f58n+5/ZnHzpJCdfPIlPf5/qRwTuDkPfzvEVK8/k/OXH5ORkRo8eXfN50aJFLFq0iLi4uAY/iD1o0CBiY2N56aWXaN/eicrGZeRRpZUSdsHJV86R8XwmvgO8iFzSjrwviyjZa6b4x1LKj/1++Vjtp8Y7xkDon4LwjjHi1U2PsZMBfYQOlaYlXIppeRSVgr6NGn0bNX6Dr1xutwoqT9ooO2aj/JiVsmM2Sn+pIHeDF+dW/15rNna24DvIgt/wSrqsKODEvABSbg8m6uViIuaXuLVBy/WkDdAS/mA44Q+GU5lZSc6/qhuYHJ97nONPHyfgpgDCYsMIvTMUbVBzGL7GucuPV2vQ1xAHDx5k3rx5dO3aldmzZzN//nwCAgKcShNkodYqWQqtpEw5TtHOEnRtNJgPl3F4/G8A6Npo8R3iTZu4EHxuMOLd2wt9e20LuY8gXaTSKBg7aTB20sDEi7WMYwgBlafVmA9qq6f9Wgq/1ZPzSfWlNsVQfd8zfaE/Bdv19PpPHlr/1vXIh76tng5PdKDDEx0o+62MnE9yyP44m2MzjvHbrN8ImhiEKdZE8ORg1F6eOKSPZzQU6d69O5s2bWL37t3MmzePTp06sWDBAmbPno2uiSOJgCdsmXRd2Cshf5uRzNW+5H91oOZWlCZEQ9jdQfgN98FviDf6DjpZgLViigKGCBuGCFvNJcaLBV3xDzqKduso2KnHkqOmMNHA98FtCRpfQdtHSwm6ucLDmx+4nlcXLyKfj6TjXzpi3m8m+5/Z1ffgPs9D5a2i43Md6Tivo7vDdMBz/lLDhw8nKSmJzZs3s3DhQt555x1eeukl7m9i+wNZqLVw5oNazq7wI/sTb+wlKkCg8lLR/skw2s82oTM1h0slkjv9XtCVE/Y/1R2VV2WrOLPMmzNv+5L/lYH8r4yo/OyYxo+kXewv+HRvXaOiK4qCb39ffPv70umvnTj95mlOzDtB+W/XrmP3pvOMmtrlRo4cyfLly1mwYAEPPPCALNSk2gp36Ul/LpCiJAOKzo4mQGAvgdC7yui6agTaIPmnl5pOZ7IT/UoJHeaaOfZIILmfeqE2CLI2d+Hcxp74D8wkKv5HAgZmXT2xFsaSb+HMu2cwdDLQ+a3O7g7Hgfofvr5eVqxYQWpqas2Um5uLSqUiMjKS22+/vcnpyiNbC2PJU/HrI8Gc/8wbbRsrAWPKKUoyYK+EHh/lEhZbiqLIP7vkGtogQc9/5ZP9cQW/PR4AQkXAsNOUpgVx4L47CB1/nC6LdqELbPmtJaF6INIj9x7Bct5C/z390fh76v+a++NavHgxvXv3pm/fvtx777307t2bXr164eXl3KMS7t8yyWXK0jQcHN2Gyiw1YX82U7TLQOE3Rkz3m+n0ej46k2t7nZAkqL482ebeMoJuruD4jBKyP++Gvm0JYZOPkft1J4oPhdF37Zd4RRa5O9Rr7uSSkxRsLaBrQld8b/DU/mg94/Jjdnb2NUnX/XVQySXslXB4kglriYLvDVXkfOyDxs/ODUnn6LHuvCzQpGtOZ7LT46+J3PD3zai9LOR80RWfbnlYzVpSHh+PrdITWwK6Tv72fDJeyMB0n4nwh8PdHU49ZC/9UjNwZoUv5ce02IrUVJzS0PmdPAb8nEnAH2R3ZdL1FTDoHAP//SmdF35HRaYvthIDZceDyNzQw92hXTOVZys5EnsEr55edF3ZtRm0IHZukNCmSk9Pb/B3hRCcPt34jrTdXwe9ngID4e67m77++vVOh5A3z/nBTIM7XzlabNEHzwDBtP/jJiIn/AuNqICVdSTwf4c7HQMYXJCGK7oZcva8zBXdZLmiFuzjgjScvW/lgu04dgyo/qu0H3yUNjH/JWP9OM5sHk3RTl86DD129TSaMGTVlVyRPxvGbrGTencqtjIbN2y8AbW3p9dI3ddQZNiwYUyaNImHH36YYcOGOfxOQUEB69ev55133mHWrFk8/vjjjfqN1lWotWAdx31K3uGh2CsNqHWt46a85PnUxkpsFQbATse7trk7nGsifWE6Rd8V0ePjHnj38HZ3OA3gvntqR48eZcmSJUyaNAm1Ws2AAQMIDw/HYDBQUFBAamoqR44cYfDgwbz99tuMHz++0b8hLz+2EH4RJ+jwx01kfj+BXz95DGvl9TtTlSRHrGV6fn3nbs5tGUaH/7MT306Z7g7J5c5/fp7Tb5ym7cy2mGJN7g6nEdxzTy0gIIA33niDs2fPsnLlSrp3705hYSHp6eloNBri4uLYv38/33//fZMKNJA1tRYl+ra/o9JYOLntTgqO9SVywgZMg3aiUstGItL1Y7epyN4xiIz146jM86fjn7YSGfu1u8NyufL0co7GHcWnvw+dlnZydziN4P7Wj0ajkalTpzJ1avWIIcuWLSM+Pt4lactCrQVRFIia9AmB3fdz/D8P8usnj5Ox5U+0HbaNNkO/Qe/X+JHIJamhKs+pyPrXWDK3DqUyNwjfLifpOfcf+PfIcHdoLle0p4hfH/oVIQS9NvZCbfD0+2iXcn+hdrl169bJQk2qW0Cno/R/6hnyUgZx5ttJpP/3z6T/v7sJ7JxC6A0/EPI/NnSm5vRPKHmqykw15zcZOf8fIwWJBrC1J6DPMbrM+DfBg1JbXC/+lZmVnJh3gux/ZKML19Hr014Yox2PP+jZWu6dJ1motVCKAiG9fyKk90+UZbcl68fR5B4cxrF/zeTYxmx8+mkJvFlP4Fg9/jfqUBta2NFHuiasZoWi7/QUJhooTNRT8lN1D//GbhYinimmTcRyvNqed3OUrmersHHmrTOcfOUkwiKImB9BxPwINL7N8RDqeTW1lJQUBg0aRExMTK2pXbt2jU7Ls7ZMuia8TJlET/6YqFs/pvRcBOd91lCwrZIz/2vm9F/NKFrw6afFb5gOv6E6/IZqMXRUN4NnbaRrSdih/LiGkp90lCTrKN6jp+QnHcKqoGgFfkMqiXypkNA7y/DucWHk7s9bVoEmhOD8pvMcf+o4FekVhEwJodObnZpp7exSnlVT6969O+vWrSMlJYWUlBRWr15NSkoK+fn59OjRg127djU4LVmotSKKAj5tT+Hzf32JfM4Xq9lO0bdVFCZVUbyninMJZZxdVgqAJkjBp48W7z5afPpo8O6rxbunBrWXZ/0zSK5hLbJTekRHWaqW0lQt5v06SvbpsBVV/71VBjs+/S10eLqYgNGV+A+vRO3dssdRMx82k/ZEGoU7CvGO8abv9r4EjrnyGdHmx/Nqamq1mp49e9KzZ0/+9Kc/1cyvqKjgyJEjjUrLs7ZMuq40PiqCJxkInlTd/N9uEZSmWCn+oQrzQQulhyycW1OGvfTCwUsBfQc1Xl3VGLsYMHbV4tVFi7GrFkOkBpVW1uw8ma1MUJFho/yElYp0G+VpNspSrZSmWqnKtANtAFD0Ap/eVZjuKcV3YBW+g6rw6mlB1UqOFpY8C+nPp5O5KhNNgIYuy7sQPiMclaYlndB51rbMnDnT4XyDwUC/fv0alVYryaZSQ6i0Cr79tPj2+32MNWEXlJ+wUXrIQulhK2W/WSn/zUrOJ2ashZc8KqCALlyNIUKDPkJT86rvUP1eF65GG6pGpZEF37Vgr7JTlWWjMtNGVaa95rXipI2KEzbK021Ysms/2qHyVvDuqSFwrA7vnhq8eqTj1dOCMcqK0grbEdktdjJXZZKxKANrsZV2j7UjcnEk2qCWNuage2tqp06dqo5CUfD19SUgIIBHHnnEZem3rkJNaQeal5u+/r3Oj40UXLbA6TQ2vH3OqfX/Rzeywd9VqO7MygsINQEm4EY4tDUbUeWHzdwem7kD9jIT9jIT5SfCMKeEYS8zgV1/WWp2FF0xKkMBir4Av+ACtN75aL0K0HgVoDGUoDGUoL74qjej0lbU24LO+46bG739tfSOcW59AL8I59O4jL1SwZKnxpKnwXJegyVPjfWS95Y8DZZcDZWZWqoyNVhyHRx41XYMHSoxRFUQPKkcY1Q5hsgKDNHV77Vhltr7tupCl2GWC1NT3DaniSte6l7nk3htYKO+nr89n7T4NMpSywgYE0DntzvjE+OKrss8kXvHU4uMjKx1vz48PJxp06axcOFCjEbn71W2rkJNchlFAUVfjEqfijY49YrlQoCo8sdeZsJWFoaoDMJeEYi9MhB7RRCiMpDSc92xlAVht9SdkRWV5UIhZ0atL0GtK0OlrUCtK0elrUB/2oTaqwq1wVI9eVWhMlhQ6ayotDYUja36VWtDpbGhaO0XXm2oNHaUfC2oRPXBXSVQVIBS3UgCAYqq+r2wKWBXEHblwvtL5hl12CsV7JUq7BUK9orLXi+ZbzOrsBWrsJWosZaosBWrsZWosF54tZWosRarsJfVXVVS+9jQBNvQhlgxRFThN7QUfVsLujaF6MOr0LWtRN+2Em2opXp7pDqVHy8n7ak08jbnYYg2ELMphuDbgltBIyn3HfoTExOB6kY4mZmZ7Nu3j2XLlrF161Z27NiBj49zJxOyUJOuiepCrwiVvghNoONObKOjq19tVQasZQFYK32xVfhirfDBVumLtdz3knm+2Cp9sFmMWEqDsFsM2CxG7Md8sJfrruOWOU9lsKP2taH2s6O58KoLt6DpakftZ0Pta0cbaEMTbEUbYkV7oQDTBle/V+nraKBhz7++G9KMWUusnFxykjNLz6BoFaJejaLDkx1Q6VvDWYB7Lz/edNNNtT7Hxsby5JNP8oc//IEXXniBN99806n0ZaEmuZ1aV4Fal4WerEav633HzQg72Cu12Cq02Mp02Cq02C1qhEVd/Wq97NWiwm6tXi7C2iFEdc0LoVTX0OwKWevbgwJt7j6DohbVtbiLrypQVALU1fMU7yBUBoHKYK/3VdEL1N42VC3tFk0zIuyCrL9nkT4/naqsKkxxJqJfiUbf9vJL5S2Zey8/XryndrmZM2eycuVK4uPjEUIQEdG0y/qyUJOaPUUFaqMFtdECgWWNW7m346FnIp5Ma3gafi39UlXLcXjyYfK/ysd3iC8xm2PwG+zn7pDcxH0SxfasAAATZklEQVSH/svvqV2uY8eOKIqCzWZrUvqyUJMkqdUwxZoIuycMU6wJRdVaT0bcW1P76aefHM7//PPPWbNmDZs3b3Yq/WZ7AXnFihVERUVhMBgYMGBAo544lySpdTL92USbe9u04gINfr+n5tzI1009Bg8YMOCKKSIigg0bNnDrrbfWzGuqZllT27BhA/Hx8axYsYIbb7yRFStWMHHiRFJTU5t8HVaSJKn1cK4+48wxOCkpCahu/Zidnc3PP//M6tWrCQkJYcmSJU7FBc20UHvrrbeYNm0a06dPB+Ddd9/l66+/ZuXKlbz66qtujk6SJMmTOd/60Zlj8KhRo2ruqQkh0Gq1TJ06laVLlxIcHOxUXNAMC7Wqqir27dvH3Llza80fN24cu3fvdlNUkiRJzYVzhZqzx+CLz6ld7FGkW7dueHl5NTmeyzW7Qu38+fPYbDZMptpDp5tMJrZv337F9xMSEkhISADg6NGjDBzYuJ4GXK+t80l41Z9Gbm4uoaGhdS5/433nQ4D2zidx0vkkSHT22aykOpdcbT9Kjlz5P9j4/fh2k345NzeX8+erRwkoKytr8P96S/07X9yujIyMWvNDQkIYOHBwneuVl5fX2nczZsxgxowZNZ8bewy+3OXPqblasyvULrq8SagQwmEz0cv/IK3BwIEDSU5OdncYzZ7cj67h6fvR0+Nrqrq26+uvv3ZJ+g09Bl9vza71Y0hICGq1mqys2g/q5uTkXHHmIEmSJLmWpx+Dm12hptPpGDBgANu2bas1f9u2bQwfPtxNUUmSJLUOnn4MVr/wwgsvuDuIxvLz82PRokWEh4djNBp5+eWXSUpK4oMPPiAgIMDd4XkEZ57zkH4n96NrePp+9PT4mupabZcnH4MVIUSzHL52xYoVvP7665w7d46YmBiWLl3KyJENH1JFkiRJajpPPQY320JNkiRJki7X7O6pSZIkSVJdZKHWQuXn5zN79my6d++O0WikQ4cOPProo+Tl5bk7NI8n+xV13quvvsqgQYPw8/MjNDSUyZMnk5KS4u6wAM+OzRVae/6VhVoLlZmZydmzZ3n99dc5fPgwH330EUlJSdxzzz3uDs2jXezTbsGCBezfv5/hw4czceLEOseAkhzbuXMnjz32GLt372bHjh1oNBrGjh1Lfr77BzL15NicJfOvvKfWqnz11VfceuutFBYW4ufXWseRqt+QIUPo06cP7733Xs28Ll26MHXqVNmvqBPMZjP+/v5s2rSJyZMnuzucWjw5tsaS+VfW1FqV4uJi9Hq9S/tZa0ku9mk3bty4WvNlv6LOKykpwW63ExgY6O5QruDJsTWGzL/VZKHWShQWFvKXv/yF6dOno9E0297Rrqn6+rS7vPcEqXHi4+O54YYbGDZsmLtDuYInx9YYMv9Wk4VaM/Pcc8+hKEq9086dO2utU1payuTJk2nXrh2vv/66ewJvRjy1T7vmas6cOXz33Xd89tlnqNVqd4dTiyfH1lStPf/KU/Zm5oknnuDee++t9zuXDtJnNpu55ZZbAPjyyy8xGAzXNL7mzNP7tGuOnnzySdavX09iYiLR0dHuDqcWT46tKWT+rSYLtWYmJCSEkJCQBn23pKSEiRMnIoTg66+/xsfH5xpH17xd2qfdXXfdVTN/27Zt3HnnnW6MrHmKj49n/fr17Ny5k+7du7s7nFo8Obamkvm3mizUWqiSkhLGjRtHcXExmzZtorS0lNLSUgCCgoLQ6XRujtAzzZkzh/vuu4/BgwczYsQIVq1aRWZmJjNnznR3aM3KrFmz+Mc//sGmTZsIDAysqT34+Pi4/eTKk2Nzlsy/gJBapMTERAE4nBITE90dnkdbvny56Nixo9DpdKJ///7i22+/dXdIzU5deW/RokXuDs2jY3OF1p5/5XNqkiRJUoshWz9KkiRJLYYs1CRJkqQWQxZqkiRJUoshCzVJkiSpxZCFmiRJktRiyEJNkiRJajFkoSZJkiS1GLJQkyRJkloMWahJ0gXDhw+nb9++xMTE8OKLL7o7HJe47bbbCAwMZOrUqe4ORXKBlphHXU32KCJJFxQXF+Pn54fNZuPGG29k5cqV3HDDDe4OyymJiYmYzWbWrVvHp59+6u5wJCe1xDzqarKmJgEwbdo0br31Vo9N71IFBQWYTCaOHz/u0nT9/PyA6hGEq6qq6v3uqFGjePzxx136+9fC6NGj8fX1dbhs6tSpvPXWW9c5IvdpSr7xtH3UmDzaVLfeeivTpk27JmlfD7JQa8YmT57M2LFjHS47cuQIiqKwbdu26xxVtWXLlvHRRx/VfHZlIfDKK69wyy230KlTJwCSkpK47bbbaNeuHYqi8OGHHzpcb8WKFURFRWEwGBgwYAC7du264jtDhgwhLCyMsWPHtvgz4EWLFvHyyy9TVFTk7lDqtHPnzloD3yYnJ6MoChkZGbWWX/xcn8vzTUM0dB+5Mg9eTWPz6MyZM3nyyScb/TvNlSzUmrGHH36YHTt2OPyHXrNmDR07dmTMmDHXPzDA39+fgIAAl6dbVlbG+++/z0MPPVQzz2w2ExMTw7JlyzAajQ7X27BhA/Hx8SxYsID9+/czfPhwJk6cyKlTp2p9b+/evZw9e5YDBw6QkpLi8vhdLSYmxuF0+vTpq67bu3dvoqOja518tFSO8k1DNHQfuSoPNuTv2Zg8KoTgiy++4Pbbb2/EVjdzbh0jQHKKxWIRbdq0Ec8//3yt+VVVVSIsLEwsXrxYCCGE3W4Xf/3rX0V0dLQwGAwiJiZG/OMf/6i1TlxcnJg0aVLN54qKChEfHy/CwsKEXq8XQ4YMEbt27apZbrfbxZtvvik6d+4sdDqdaNeunZg3b57D9OLi4q4Y5mPx4sUiKChIVFRU1IojNjZWTJ48uc5t3rhxowgKChJ2u93hcm9vb/HBBx9cMX/w4MHi4YcfrjWvc+fOtWK+1GuvvSbeeOONOuO46aabxKxZs2o+b9++Xfj7+4tVq1bV+s7MmTPFnDlzRGBgoAgJCRFvv/22qKioEI899pjw9/cXHTp0EH//+9/r/B0hhPj222/FkCFDhLe3t/Dz8xODBw8Whw8frnedSyUmJoo777zT4bLFixeLESNGNDit623nzp0CEDt27BBCCPHTTz8JQKSnpwshhNixY4cAREZGRr3p1JVvpkyZUudQNBfzUWP3kavy4NVcLY8KIcTevXtFUFCQsFgsDpeXlpaKuLg44e3tLcLCwsSSJUvEpEmTRFxcXJNi8gSyptaMaTQa4uLi+PDDD7Hb7TXzv/jiC86fP88DDzwAwHPPPceaNWtYvnw5qampzJ8/n0ceeYT//ve/dab9zDPPsGHDBtauXcv+/fvp3bs3EyZM4Ny5cwAsWLCAl156ifnz5/PLL7+wceNGOnTo4DCtZcuWMWzYMB544AHOnTvHuXPneOqpp7Db7WzevLnme0VFRfznP/+p92x6165dDBgwAEVRGryfqqqq2LdvH+PGjas1f9y4cezevRuAwsJCzp8/D0BFRQVbt25t8IjIn332GVOmTCEhIYFHHnmk1rKPP/4YX19f9u7dy7x583jiiSe444476Nq1K8nJycTFxfHwww+TmZnpMG2r1crtt9/OjTfeyMGDB9m7dy/x8fGo1eoGb399Bg8ezI8//kh5eblL0nO1i/cDs7OzHS6/uN/qum94UV355r333uPcuXNkZGSgUqnYuHFjTR6NjY0FXLOPGpIHr6YpeXTTpk1MmjQJjcbxeNBz585l27ZtfPbZZ3zzzTfs37+fpKSkBsXjsdxdqkrOOXbsmADEli1baubdcsstYsKECUIIIcxmszAYDCIpKanWevHx8WLixIk1ny+tWZnNZqHVasW6detqllutVhEdHS0WLlwoSkpKhF6vFytXrqwzrstrfpfXbIQQYtasWWL8+PE1n1esWCFMJlOdZ5VCCHH77beL+++/v87ljs6Sz549K4ArBktcvHix6Nq1qxBCiOPHj4v+/fuL3r17i169etXUcutycXtWr14t/Pz8au3/S78zdOjQms92u12EhITUqolWVVUJrVYrNm7c6PB38vLyBCB27txZbzx1GTNmjAgJCRFGo1G0a9dO7N69u9bygwcPCkCkpaU1Kf1r7WJefPrpp4UQV9bU5syZI8LDw6+aztXyzc8//ywAkZWVdcWyxu6jpubBq2lsHhVCiJ49e4rPPvvM4bKSkhKh0+nERx99VGuev79/s66pOS6+pWajS5cujBw5krVr1zJu3DgyMzPZsmULGzZsACA1NZWKigomTJhQ6yzVYrEQGRnpMM3jx49jsVgYMWJEzTy1Ws2wYcNITU0lNTWVyspKp+/XTZ8+nf79+3PmzBnat2/P2rVriYuLq/OsEqC8vByTydSk37v8LF0IUTMvOjqaffv2NSq9zZs3s3r1apKSkhg2bJjD7/Tp06fW74eFhdG7d++aeVqtlsDAQHJychyuHxQUxLRp0xg/fjxjxoxhzJgx3HXXXXXWii+3ffv2epdfvP/jqTU1b29vpk6dyurVq3nqqadqLTtz5gyrV69m9uzZAKxatYpVq1Zhs9k4evQovXr1AiAuLu6q+ebQoUOEhoY6/I4r91F9efBqGptH09LSOHHiBOPHj3e4/Pjx41RVVdXKuz4+PrXyZ3MkLz+2AA8//DCbNm0iPz+fDz/8kKCgIG677TaAmsuSX3zxBQcOHKiZfvnlF7Zu3eowPXHh0UVH/2yKotQsd1bfvn3p378/H374ISkpKSQnJ/Pggw/Wu05ISAgFBQWN+p2QkBDUajVZWVm15ufk5DS5gITqAis8PJw1a9bUuU+0Wm2tz4qiOJx36eXjy33wwQfs3buXkSNH8vnnn9O1a1e2bNnS5LgvlZ+fD0BoaKhL0rsWli5dSkhICH/4wx/4/PPPAVi/fj033ngj3bp147nnngOqW/kdOHCA9evX06dPn5q8/uSTT1413xw6dKjWCcilXLGPrlUerM+mTZsYM2YM3t7eDpe76v/Y08hCrQWYOnUqBoOBjz76iLVr13L//ffXHDh79uyJXq/n5MmTdO7cudbUsWNHh+l17twZnU7Hd999VzPPZrPxww8/0LNnz5o0v/nmmwbHqNPpsNlsV8yfPn06H374Ie+//z4jRoygW7du9abTr18/UlNTG/y7F397wIABVzzesG3bNoYPH96otC4VFRXFzp072bp1KzNmzLimB4m+ffvy7LPPsnPnTkaNGsW6detckm5KSgpt27a9ZgdWVzCZTOzZs4dRo0bx/vvvA9VN46dMmUJiYuIVB+2UlBRiYmJqzbtavsnIyKgz77liH12rPFifzZs3c8cdd9S5vHPnzmi1Wvbs2VMzr7S0tFm0+q2PvPzYAhiNRmJjY3nhhRcoKCio1dDC19eXuXPnMnfuXIQQjBw5ErPZzJ49e1CpVMyYMeOK9Ly9vXn00UeZN28eISEhREVFsXTpUrKzs3nsscfw9fUlPj6e+fPno9frGTlyJHl5eezbt49HH33UYYyRkZH8+OOPZGRk4OPjQ1BQECqVinvuuYc5c+awcuVKVq1addVtHT9+PM8++yx5eXkEBwcD1c2p09LSgOqa6alTpzhw4ABBQUFEREQAMGfOHO677z4GDx7MiBEjWLVqFZmZmcycObPR+/tS0dHRJCYmMmrUKGbMmEFCQkKjGrFcTXp6OqtXr655BurEiRMcOnSozv3cWLt27WLChAkuSetaCg0NJSEhgeTkZAYNGkRSUlKdl88dFWqO8s2l7HY7p0+f5vTp07Rr1w6V6vfz/YbsI3fmQUdyc3PZs2dPvb3I+Pj48NBDD/Hss88SGhpK27ZtefHFFx2efDYr7rudJ7nSvn37BCCGDx9+xTK73S7eeecd0aNHD6HT6URISIgYO3as2Lp1a8136mvSr9PprmjSb7PZxKuvviqioqKEVqsV7du3FwsWLKgzvV9//VUMHTpUGI3GWjf6hRDigQceED4+PsJsNjdoW4cOHSr+9re/1XxOTEx02CT78pvdy5cvFx07dhQ6nU7079//ipv2jXF5w5e0tDTRvn17MX369Jpm444ax/Tq1UssWrSo1jyTySTeffddh7+TlZUlpkyZItq2bSt0Op3o0KGDePrpp0VVVVWTY7+ovLxc+Pn5iR9++MHptK6XyxuKOHLHHXeIr7766or5l+ebS23fvl1EREQIjUZT6zGThu4jd+TB+qxZs0YMGzbsqt8zm83ivvvuE97e3iI0NFS8+OKLzb5Jv+z7UXK7iRMn0r59e957770Gff/rr78mPj6e1NRUlzVtb42WL1/O5s2b67y32lx16dKFHTt2XNGYpin5prnuo9tvv50RI0bwzDPPuDuU605efpTcJj8/n+3bt7N161YOHjzY4PUmTJjArFmzOHPmTJ33BaWr02q1vPvuu+4Ow6XKy8vJy8tz2Dq0Kfmmue6jESNGcM8997g7DLeQNTXJbSIjI8nPz2fhwoU8++yz7g5HagF+/vlnZs+ezffff+/uUCQ3kYWaJEmS1GLIJv2SJElSiyELNUmSJKnFkIWaJEmS1GLIQk2SJElqMWShJkmSJLUYslCTJEmSWgxZqEmSJEkthizUJEmSpBZDFmqSJElSiyELNUmSJKnF+P91GRr36C7hZwAAAABJRU5ErkJggg==\n", 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", "text/plain": [ "" ] @@ -307,7 +307,7 @@ " velocity=True,\n", ")\n", "\n", - "from IPython.display import Image\n", + "from ISirocco.display import Image\n", "Image(filename='C-IV.png')" ] }, @@ -359,7 +359,7 @@ "\n", "The basic transfer function generated is more properly the **'emissivity-weighted'** transfer function; it assumes that there are no changes in ionisation state in the wind, and that a 10% increase in central source luminosity results in a 10% increase in reprocessed luminosity in the wind. Given observations of the breathing BLR in AGN or of periods of anticorrelation between continuum and Hα luminosity in the AGN NGC5548, this is unlikely to be true!\n", "\n", - "`Python` can produce **responsivity-weighted** transfer functions, A.K.A 'response functions'. It does this by performing two separate runs of models that bracket the main model in luminosity, and approximates the response function at the central luminosity by using the gradient between the two transfer functions. We produce two copies of the main model, each with all sources of luminosity adjusted up and down by the same value. Typically we choose 10%, assuming that the responsivity is constant across this region. As the regions of peak response typically move through the wind with changing luminosity (e.g. ionisation fronts being pushed back when continuum luminosity increases), selecting too large a bracket can result in issues with the emitting wind regions failing to overlap.\n", + "`Sirocco` can produce **responsivity-weighted** transfer functions, A.K.A 'response functions'. It does this by performing two separate runs of models that bracket the main model in luminosity, and approximates the response function at the central luminosity by using the gradient between the two transfer functions. We produce two copies of the main model, each with all sources of luminosity adjusted up and down by the same value. Typically we choose 10%, assuming that the responsivity is constant across this region. As the regions of peak response typically move through the wind with changing luminosity (e.g. ionisation fronts being pushed back when continuum luminosity increases), selecting too large a bracket can result in issues with the emitting wind regions failing to overlap.\n", "\n", "\n", "### Building the Models\n", @@ -479,7 +479,7 @@ }, { "data": { - "image/png": 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\n", 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", "text/plain": [ "" ] From ef612f270de14593ab6de8a3b98b7d0c31c63ba0 Mon Sep 17 00:00:00 2001 From: "Edward J. Parkinson" Date: Fri, 16 Aug 2024 16:42:29 +0100 Subject: [PATCH 07/24] fix an incorrect change to Sirocco --- docs/sphinx/source/meta.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/sphinx/source/meta.rst b/docs/sphinx/source/meta.rst index ef52b176c..323e968cb 100644 --- a/docs/sphinx/source/meta.rst +++ b/docs/sphinx/source/meta.rst @@ -13,7 +13,7 @@ Installing the documentation tools This guide is produced using **Sphinx**. **Sphinx** is written in Sirocco and available from the **pip** package manager. -We require the **Sirocco 3** version of **Sphinx**. Install it, and the other modules required, as: +We require the **Python 3** version of **Sphinx**. Install it, and the other modules required, as: .. code :: bash From 2bf5d9be6069602e124e4193faa927fe89d130bb Mon Sep 17 00:00:00 2001 From: "Edward J. Parkinson" Date: Fri, 16 Aug 2024 17:03:11 +0100 Subject: [PATCH 08/24] change Sirocco -> SIROCCO now that I know it's an acronym --- docs/sphinx/source/atomic.rst | 16 +++---- docs/sphinx/source/atomic/bound_bound.rst | 12 ++--- ...und_bound_electron_collision_strengths.rst | 10 ++--- docs/sphinx/source/atomic/bound_free.rst | 2 +- .../source/atomic/bound_free_topbase.rst | 10 ++--- .../source/atomic/bound_free_verner.rst | 12 ++--- .../source/atomic/direct_ionization.rst | 4 +- docs/sphinx/source/atomic/electron_yields.rst | 4 +- docs/sphinx/source/atomic/elem_ions.rst | 10 ++--- docs/sphinx/source/atomic/free-free.rst | 4 +- docs/sphinx/source/atomic/levels.rst | 6 +-- docs/sphinx/source/atomic/photon_yields.rst | 4 +- docs/sphinx/source/conf.py | 12 ++--- docs/sphinx/source/coordinate.rst | 8 ++-- docs/sphinx/source/developer/cuda.rst | 32 +++++++------- .../source/developer/programmer_notes.rst | 10 ++--- docs/sphinx/source/developer/tests.rst | 16 +++---- docs/sphinx/source/examples.rst | 4 +- .../source/examples/demo-models/tde/tde.rst | 10 ++--- docs/sphinx/source/index.rst | 15 ++++--- docs/sphinx/source/input/overview.rst | 14 +++--- .../parameters/Input_spectra.model_file.rst | 4 +- .../Spectrum/Spectrum.live_or_die.rst | 2 +- .../Spectrum.select_photons_by_position.rst | 2 +- ...ndary_layer.rad_type_in_final_spectrum.rst | 2 +- .../Boundary_layer.rad_type_to_make_wind.rst | 2 +- ...tral_object.rad_type_in_final_spectrum.rst | 2 +- .../Central_object.rad_type_to_make_wind.rst | 2 +- .../source/input/parameters/other/Diag.rst | 2 +- .../Diag.lowest_ion_density_for_photoabs.rst | 2 +- .../other/Diag/Diag.partial_cells.rst | 2 +- .../Photon_sampling.nbands.rst | 2 +- .../parameters/other/Reverb/Reverb.type.rst | 2 +- .../input/parameters/rt_ion/Atomic_data.rst | 2 +- .../parameters/rt_ion/Spectrum_cycles.rst | 2 +- .../parameters/wind/Wind/Wind.ionization.rst | 2 +- .../input/parameters/wind/Wind/Wind.type.rst | 2 +- .../source/input/system_description.rst | 2 +- .../sphinx/source/input/wind_model_params.rst | 6 +-- docs/sphinx/source/installation.rst | 22 +++++----- docs/sphinx/source/meta.rst | 8 ++-- docs/sphinx/source/operation.rst | 2 +- .../source/operation/ionization_cycles.rst | 4 +- .../source/operation/spectral_cycles.rst | 2 +- docs/sphinx/source/output.rst | 2 +- docs/sphinx/source/output/diagnostics.rst | 4 +- docs/sphinx/source/output/evaluation.rst | 16 +++---- docs/sphinx/source/output/model.rst | 4 +- docs/sphinx/source/output/spectrum_files.rst | 6 +-- ...pectrum_generation_large_optical_depth.rst | 2 +- docs/sphinx/source/physics.rst | 2 +- docs/sphinx/source/physics/aniso.rst | 2 +- docs/sphinx/source/physics/limitations.rst | 28 ++++++------ docs/sphinx/source/physics/macroatoms.rst | 4 +- .../source/physics/radiative_transfer.rst | 12 ++--- .../source/physics/relativity_comoving.rst | 2 +- docs/sphinx/source/plotting.rst | 2 +- docs/sphinx/source/plotting/plot_wind.ipynb | 2 +- docs/sphinx/source/py_progs.rst | 6 +-- docs/sphinx/source/py_progs/MakeMacro.rst | 6 +-- docs/sphinx/source/py_progs/development.rst | 2 +- docs/sphinx/source/py_progs/plotting.rst | 2 +- docs/sphinx/source/py_progs/py4py.rst | 4 +- docs/sphinx/source/py_progs/regression.rst | 8 ++-- docs/sphinx/source/py_progs/running.rst | 4 +- docs/sphinx/source/quick.rst | 12 ++--- docs/sphinx/source/radiation.rst | 8 ++-- docs/sphinx/source/radiation/banding.rst | 8 ++-- docs/sphinx/source/running_sirocco.rst | 38 ++++++++-------- docs/sphinx/source/wind_models.rst | 2 +- .../source/wind_models/importing_models.rst | 44 +++++++++---------- docs/sphinx/source/wind_models/kwd.rst | 4 +- examples/reverb/reverb.ipynb | 20 ++++----- 73 files changed, 276 insertions(+), 275 deletions(-) diff --git a/docs/sphinx/source/atomic.rst b/docs/sphinx/source/atomic.rst index 2c4241f07..2e96a2edc 100644 --- a/docs/sphinx/source/atomic.rst +++ b/docs/sphinx/source/atomic.rst @@ -1,19 +1,19 @@ Atomic Data ########### -Any Sirocco model is only as good as the atomic data which goes into making the model. -All of the atomic data that Sirocco accepts is read in by the routine get_atomicdata, +Any SIROCCO model is only as good as the atomic data which goes into making the model. +All of the atomic data that SIROCCO accepts is read in by the routine get_atomicdata, and all of the data is read in from a series of ascii data files and stored in structures that are defined in atomic.h. The purpose of documentation is as follows: -* to explain the atomic data formats used by Sirocco and the relationship of different sets +* to explain the atomic data formats used by SIROCCO and the relationship of different sets of data to one another -* to explain where the data currently used in Sirocco and to explain how the raw data - is translated in to a format the Sirocco accepts +* to explain where the data currently used in SIROCCO and to explain how the raw data + is translated in to a format the SIROCCO accepts The routines used to translate raw data format for two-level atoms (as well as much of the raw data) are contained in a separate github `repository `_ @@ -25,7 +25,7 @@ The routines used to generate data for MacroAtoms are described in :doc:`Generat Choosing a dataset ----------------------- -The "masterfile" that determines what data will be read into Sirocco is determined by the +The "masterfile" that determines what data will be read into SIROCCO is determined by the line in the parameter file, which will read something like:: Atomic_data data/standard80.dat @@ -33,7 +33,7 @@ line in the parameter file, which will read something like:: where the file `data/standard80.dat` will contain names (one to a line) of files which will be read in sequentially. -All of the atomic data that comes as standard with Sirocco is stored in the `xdata` directory (and its subdirectories) but users are not required to put their data +All of the atomic data that comes as standard with SIROCCO is stored in the `xdata` directory (and its subdirectories) but users are not required to put their data there. Various experimental or testing dataset masterfiles are stored in the `zdata` directory. Symbolic links to these directories are setup by running `Setup_Py_Dir`. @@ -44,7 +44,7 @@ are setup by running `Setup_Py_Dir`. Data hierarchy and I/O ----------------------- As mentioned above, the masterfile will contain names (one to a line) of files which will -be read in sequentially. Every line in the atomic data files read by Sirocco consists of a keyword that defines the type +be read in sequentially. Every line in the atomic data files read by SIROCCO consists of a keyword that defines the type of data and various data values that are required for that particular data type. Lines that beging with # or are empty are ignored. diff --git a/docs/sphinx/source/atomic/bound_bound.rst b/docs/sphinx/source/atomic/bound_bound.rst index 237c9fbf5..b823b3ef6 100644 --- a/docs/sphinx/source/atomic/bound_bound.rst +++ b/docs/sphinx/source/atomic/bound_bound.rst @@ -11,7 +11,7 @@ The file gfall.dat was used, though a few extra lines known to have been missing -There are two main sources of data currently used in Sirocco. +There are two main sources of data currently used in SIROCCO. * Kurucz * Verner @@ -22,11 +22,11 @@ We have also used a line list from Verner in the past -Translation to Sirocco format +Translation to SIROCCO format ============================ -There are several steps to creating the data used in Sirocco from that in gfall.dat, that are carried out by py_read_kurucz and py_link. The first routine reads the gfall.dat file and creates two output files, a file containing the lines and the associated such as the effective oscillatory strength and a file which contains information about the ion levels. py_read_kurucz chooses only a portion of the Kurucz lines, namely those associated with ions with ionization potentials in a certain range and lines with gf factors exceeding a certain value. The second program py_link attempts to create a model ion with links between the levels and the ions. Both of these routines are driven by .pf files, similar to what are used in python. Examples of the .pf files are in the directory py_kurucz +There are several steps to creating the data used in SIROCCO from that in gfall.dat, that are carried out by py_read_kurucz and py_link. The first routine reads the gfall.dat file and creates two output files, a file containing the lines and the associated such as the effective oscillatory strength and a file which contains information about the ion levels. py_read_kurucz chooses only a portion of the Kurucz lines, namely those associated with ions with ionization potentials in a certain range and lines with gf factors exceeding a certain value. The second program py_link attempts to create a model ion with links between the levels and the ions. Both of these routines are driven by .pf files, similar to what are used in python. Examples of the .pf files are in the directory py_kurucz -In practice we have not used these data for any Sirocco publications. At some point early in the AGN project, NSH increased the number of lines, and generated lines\_linked\_ver\_2.dat and levels\_ver\_2.dat. I think this was because there was a small bug which meant the oscillator strength cut that was stated was not that which was applied. +In practice we have not used these data for any SIROCCO publications. At some point early in the AGN project, NSH increased the number of lines, and generated lines\_linked\_ver\_2.dat and levels\_ver\_2.dat. I think this was because there was a small bug which meant the oscillator strength cut that was stated was not that which was applied. Data format =========== @@ -68,13 +68,13 @@ one needs to index everything self-consistentl -Sirocco structure +SIROCCO structure ================ Line data is stored in the data structure **lines** Comments ======== -The version of gfall.dat that is used in Sirocco is out of date, though whether this affects any of the lines actually used in python is unclear. The website listed above has a link to newer versions of gfall.dat. +The version of gfall.dat that is used in SIROCCO is out of date, though whether this affects any of the lines actually used in python is unclear. The website listed above has a link to newer versions of gfall.dat. diff --git a/docs/sphinx/source/atomic/bound_bound_electron_collision_strengths.rst b/docs/sphinx/source/atomic/bound_bound_electron_collision_strengths.rst index f9465d19f..88c9da405 100644 --- a/docs/sphinx/source/atomic/bound_bound_electron_collision_strengths.rst +++ b/docs/sphinx/source/atomic/bound_bound_electron_collision_strengths.rst @@ -19,12 +19,12 @@ These values of :math:`\Upsilon` simply replace :math:`\Omega`. In the asbsence of data in this format, the Van Regemorter approximation is utilized. -Translation to Sirocco format +Translation to SIROCCO format ============================ It is necessary to link each line in our line list with the relevant electron collision strength. This is achieved using the python script "coll_stren_lookup.py" which first reads in the "lines_linked_ver_2.py" line list, then attempts to work out which lines are which by comparing the energy and the oscillator strength of the line. If these match to within a factor of 10% then the code logs this as a possible match. If better matches come along, then the code adopts those instead. -Each matched line get a line in the data file which is basically all of the line data for the matched line. This is to give Sirocco the best chance of linking it up with the line internally. +Each matched line get a line in the data file which is basically all of the line data for the matched line. This is to give SIROCCO the best chance of linking it up with the line internally. Data format =========== @@ -66,10 +66,10 @@ and So, to get :math:`\Upsilon` for a given T, one converts T to x via the correct equation, then linearly interpolate between values of :math:`y(x)`, then convert back to :math:`\Upsilon`. -Sirocco structure +SIROCCO structure ================ -The data is stored in Sirocco in the Coll\_stren structure which has memebers +The data is stored in SIROCCO in the Coll\_stren structure which has memebers - int n - internal index @@ -105,7 +105,7 @@ There are addtional transition types in the Chianti database - 6 - Proton transitions -The latter are not currently used in *Sirocco* +The latter are not currently used in *SIROCCO* Discussion of how Chianti handles transitions can be found in `The CHIANTI upsilon files (ups and scups) `_ diff --git a/docs/sphinx/source/atomic/bound_free.rst b/docs/sphinx/source/atomic/bound_free.rst index 29bc0693b..b1b2cbb5e 100644 --- a/docs/sphinx/source/atomic/bound_free.rst +++ b/docs/sphinx/source/atomic/bound_free.rst @@ -5,7 +5,7 @@ Bound Free (Overview) Source ====== -Photonization data in Sirocco is gennerally obtained from two sources +Photonization data in SIROCCO is gennerally obtained from two sources - The `Opacity project `_. See also `Cunto et at 1993, A&A, 275, L5 `_ diff --git a/docs/sphinx/source/atomic/bound_free_topbase.rst b/docs/sphinx/source/atomic/bound_free_topbase.rst index 257988b94..132f510cb 100644 --- a/docs/sphinx/source/atomic/bound_free_topbase.rst +++ b/docs/sphinx/source/atomic/bound_free_topbase.rst @@ -8,10 +8,10 @@ Source Obtained from The `Opacity project `_. See also `Cunto et at 1993, A&A, 275, L5 `_ -Translation to Sirocco format +Translation to SIROCCO format ============================ -ksl - It's not clear that we are now making use of the topbase data in this way but my original attempt to incorporate topbase photoinization data into Sirocco is contained in the directory topbase. Processing of these files was done by py_top_phot. My feeling is that we can replace these remarks with those that are more up to date, once Nick and James discuss this section, and delete any mention of my original attempt on this in the data-gen archive. +ksl - It's not clear that we are now making use of the topbase data in this way but my original attempt to incorporate topbase photoinization data into SIROCCO is contained in the directory topbase. Processing of these files was done by py_top_phot. My feeling is that we can replace these remarks with those that are more up to date, once Nick and James discuss this section, and delete any mention of my original attempt on this in the data-gen archive. @@ -63,15 +63,15 @@ For the macro atom case, the indices relate to the energy levels, that is these The TopBase energies are inaccurate and so generally adjustments are made using Chianti or some other source to fix up the energy levels. -Sirocco structure +SIROCCO structure ================ The data are stored in the Topbase_phot stucture which can be found in atomic.h -Criteria for usage in Sirocco run +Criteria for usage in SIROCCO run ================================ -Data has to be read into Sirocco in a logical order. For a set of phototionization x-sections to be accepted, the energy levels (or configuratios) must already have been defiend. See :doc:`levels` +Data has to be read into SIROCCO in a logical order. For a set of phototionization x-sections to be accepted, the energy levels (or configuratios) must already have been defiend. See :doc:`levels` The items that must match are: diff --git a/docs/sphinx/source/atomic/bound_free_verner.rst b/docs/sphinx/source/atomic/bound_free_verner.rst index 3c3e9b757..1e27207fd 100644 --- a/docs/sphinx/source/atomic/bound_free_verner.rst +++ b/docs/sphinx/source/atomic/bound_free_verner.rst @@ -15,12 +15,12 @@ There are three sources for this data - `Kaastra \& Mewe 1993 `_ :Electron and photon yield data -Translation to Sirocco format +Translation to SIROCCO format ============================ **Tabulation Process** -The raw VFKY data comes in a series of fit parameters. We decided, circa Sirocco 78, to tabulate this data instead. Partly, I think I because the on the fly method was time consuming (yes, computing all the pow() commands to commute the cross sections on the fly took a huge amount of time) and we decided that tabulating pre program was better than doing it in the program, so that everything was of the same format. +The raw VFKY data comes in a series of fit parameters. We decided, circa SIROCCO 78, to tabulate this data instead. Partly, I think I because the on the fly method was time consuming (yes, computing all the pow() commands to commute the cross sections on the fly took a huge amount of time) and we decided that tabulating pre program was better than doing it in the program, so that everything was of the same format. The script which does this is progs/tabulate\_xs/photo\_xs.py -- it creates a file like photo\_vfky\_tabulated.data. @@ -35,7 +35,7 @@ It then writes out the inner shell cross sections into "vfky_innershell_tab.data Data format =========== -Explain the ascii format of the file which is read into Sirocco +Explain the ascii format of the file which is read into SIROCCO **VFKY_outershell_tab.data** @@ -61,13 +61,13 @@ This data is linked to the relevant ion via z and state, islp and level are not This data is linked to the relevant ion via z and state. the n_shell and l_subshell numbers are used to cross reference to the electron yield records. As above, the last record shows how many points are in the fit, and the data pairs making up the fit follow with the keyword InnerVY. -Sirocco structure +SIROCCO structure ================ -Where the data is stored internally in Sirocco +Where the data is stored internally in SIROCCO Comments ======== -The manner in which this data is read into Sirocco is a bit labyrinthine at the moment. The intention is to use a combination of VFKY and VY for all ground states, an +The manner in which this data is read into SIROCCO is a bit labyrinthine at the moment. The intention is to use a combination of VFKY and VY for all ground states, an diff --git a/docs/sphinx/source/atomic/direct_ionization.rst b/docs/sphinx/source/atomic/direct_ionization.rst index 93c5ff96f..950801fe8 100644 --- a/docs/sphinx/source/atomic/direct_ionization.rst +++ b/docs/sphinx/source/atomic/direct_ionization.rst @@ -10,7 +10,7 @@ Source The data comes directly from `Dere 2006, A&A, 466, 771 `_ . This paper gives direct ionization and excitation-autoionization rate coefficients for many ions as a function of temperature for Maxwellian electron distributions. -Translation to Sirocco format +Translation to SIROCCO format ============================ @@ -40,7 +40,7 @@ The rate coefficient R(T) is recovered from the scaled rate coefficient in the t where :math:`E_{1}` is the first exponential integral. In python we use the gsl_sf_expint_E1 routine in gsl. -Sirocco structure +SIROCCO structure ================ This data is stored in the dere_di_rate structure with members diff --git a/docs/sphinx/source/atomic/electron_yields.rst b/docs/sphinx/source/atomic/electron_yields.rst index 51ef88bb0..4bc1b901b 100644 --- a/docs/sphinx/source/atomic/electron_yields.rst +++ b/docs/sphinx/source/atomic/electron_yields.rst @@ -9,7 +9,7 @@ Source This data comes from `Kaastra and Mewe 1993, A&A, 97, 443 `_ . The data is downloaded from the vizier site linked and put into a file called "electron_yield.data" -Translation to Sirocco format +Translation to SIROCCO format ============================ The translation takes place using the python script "kaastra_2_py.py" which takes the saved raw data file "electron_yield.data" and compares it line by line to the inner shell cross section data in "vy_innershell_tab.data"(see above). The n shell and l subshell to which each record applies is coded in the KM data and needs to be decoded. This is what the script does, and all the script then does is output the yield data into a new file "kaastra_electron_yield.data" which contains the n and l cross reference. @@ -35,7 +35,7 @@ This is the data format of the electron yield data The data is linked to the correct inner shell photoionization cross section (and hence rate) via z, state, n shell and l subshell. The IP is not used. is the mean electron energy ejected, used to calculate the PI heating rate in radiation.c. The last ten columns in the file (2 shown in the table above) show the chance of various numbers of electrons being ejected in units of 1/10000. -Sirocco structure +SIROCCO structure ================ The data is stored in python in the inner_elec_yield structure which contains diff --git a/docs/sphinx/source/atomic/elem_ions.rst b/docs/sphinx/source/atomic/elem_ions.rst index 034e0d65d..f70d49268 100644 --- a/docs/sphinx/source/atomic/elem_ions.rst +++ b/docs/sphinx/source/atomic/elem_ions.rst @@ -2,7 +2,7 @@ Elements and Ions ################# -The first file that must be read into *Sirocco* is the file that defines the elements and ions. The +The first file that must be read into *SIROCCO* is the file that defines the elements and ions. The Source: ======= @@ -13,7 +13,7 @@ This data comes from `Verner, Barthel & Tytler, 1994, ApJ 108, 287. `_ . The data is downloaded from the vizier site linked and put into a file called "fluorescent\_yield.data" -Translation to Sirocco format +Translation to SIROCCO format ============================ The translation takes place using the python script "kaastra_2_py.py". All identical to electron yield, but input file is "fluorescent_yield.data" and output is "kaastra_fluorescent_yield.data" @@ -34,7 +34,7 @@ This is the data format of the electron yield data The data is linked to the correct inner shell photoionization cross section (and hence rate) via z, state, n shell and l subshell. The photon energy field is thew energy of the fluorescent photon in eV, and yield is the number of said photons emitted per ionization multiplied by :math:`10^4`. -Sirocco structure +SIROCCO structure ================ The data is stored in python in the inner_fluor_yield structure which contains diff --git a/docs/sphinx/source/conf.py b/docs/sphinx/source/conf.py index 423791037..99081aedb 100755 --- a/docs/sphinx/source/conf.py +++ b/docs/sphinx/source/conf.py @@ -77,7 +77,7 @@ master_doc = 'index' # General information about the project. -project = "Sirocco" +project = "SIROCCO" copyright = '2018-2024, Knox Long, Christian Knigge, Stuart Sim, Nick Higginbottom, James Matthews, Sam Mangham, Edward Parkinson, Nicolas Scepi, Austen Wallis, Amin Mosallanezhad' author = 'Knox Long, Christian Knigge, Stuart Sim, Nick Higginbottom, James Matthews, Sam Mangham, Edward Parkinson, Nicolas Scepi, Austen Wallis, Amin Mosallanezhad' @@ -196,14 +196,14 @@ # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ - (master_doc, "sirocco.tex", "Sirocco Documentation", author, "manual"), + (master_doc, "sirocco.tex", "SIROCCO Documentation", author, "manual"), ] # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). -man_pages = [(master_doc, "Sirocco", "Sirocco Documentation", [author], 1)] +man_pages = [(master_doc, "SIROCCO", "SIROCCO Documentation", [author], 1)] # -- Options for Texinfo output ------------------------------------------- @@ -213,10 +213,10 @@ texinfo_documents = [ ( master_doc, - "Sirocco", - "Sirocco Documentation", + "SIROCCO", + "SIROCCO Documentation", author, - "Sirocco", + "SIROCCO", "One line description of project.", "Miscellaneous", ), diff --git a/docs/sphinx/source/coordinate.rst b/docs/sphinx/source/coordinate.rst index 42a212a3c..08894f5f2 100644 --- a/docs/sphinx/source/coordinate.rst +++ b/docs/sphinx/source/coordinate.rst @@ -1,7 +1,7 @@ Coordinate grids ---------------- -Sirocco supports 3 main coordinate gridding schemes, as well as one that is +SIROCCO supports 3 main coordinate gridding schemes, as well as one that is tailored to handle vertically extended disks. These schemes are * 1-d spherical @@ -10,12 +10,12 @@ tailored to handle vertically extended disks. These schemes are These options are controlled by the :doc:`/input/parameters/wind/Wind/Wind.coord_system` keyword. For vertically extended disks, a modified version of the a cylindrical schme is provided where the cells viewed along the x,z plane are parallelograms, so that the grid follows the disk surface. -Although Sirocco incorporates several models, such as the SV model for disk winds, that are continuous, the velocities and other proporites are placed on a grid, as part of the setup that goes on at the beginining of program execution. +Although SIROCCO incorporates several models, such as the SV model for disk winds, that are continuous, the velocities and other proporites are placed on a grid, as part of the setup that goes on at the beginining of program execution. It is up to the user to choose an appropriate coordinate system and the number of grid points to include in any particular run of the program. -As implemented within Sirocco, the cells are created with a logarithmic spacing, that is the cells are larger the further they are from the central source (or disk plane). The one exception to this is that for polar coordinates, the angular separation of cells is fixed. For imported models, on +As implemented within SIROCCO, the cells are created with a logarithmic spacing, that is the cells are larger the further they are from the central source (or disk plane). The one exception to this is that for polar coordinates, the angular separation of cells is fixed. For imported models, on the other hand, the user sets the exact coordinate gridding. Obviously, the larger the coordinate grid, 100 x 100, say, compared to 30 x 30, the better the @@ -34,7 +34,7 @@ Partial cells As note above, parameterized models often have region of space that are in the wind and regions whch are not. If one overlays, a coordinate grid on such a model, there will be cells that cross edges of the wind. These partial cells present particular problems. -In Sirocco, velocities are interpolated on the corners of wind cells, but densities are are calculated based on +In SIROCCO, velocities are interpolated on the corners of wind cells, but densities are are calculated based on the average radiation field in a cell, and hence ion densities are actually cell centered. As photons pass through a cell, they encounter resonances and the actuall opacities are based on an interpolated value of the densities. This presents no particular problem in regions inside the wind, but it is an issue for partial cells. diff --git a/docs/sphinx/source/developer/cuda.rst b/docs/sphinx/source/developer/cuda.rst index 3123be3ea..54681b5e8 100644 --- a/docs/sphinx/source/developer/cuda.rst +++ b/docs/sphinx/source/developer/cuda.rst @@ -1,10 +1,10 @@ Matrix Acceleration using CUDA ############################### -Sirocco can use CUDA/GPUs to accelerate solving linear systems using the cuSOLVER library, which is part of the NVIDIA +SIROCCO can use CUDA/GPUs to accelerate solving linear systems using the cuSOLVER library, which is part of the NVIDIA CUDA Toolkit. -*This pilot study into using GPUs in Sirocco was conducted as an HPC RSE project at the University of Southampton.* +*This pilot study into using GPUs in SIROCCO was conducted as an HPC RSE project at the University of Southampton.* When should you use CUDA? ========================= @@ -32,7 +32,7 @@ algorithm. Requirements ============ -To use the CUDA matrix acceleration in Sirocco, your machine needs to have the following installed, +To use the CUDA matrix acceleration in SIROCCO, your machine needs to have the following installed, - A CUDA-capable NVIDIA GPU - NVIDIA CUDA Toolkit @@ -65,16 +65,16 @@ How to Enable and Run CUDA Compilation ----------- -CUDA is an additional acceleration method and is therefore not enabled by default. To enable CUDA, Sirocco has to be +CUDA is an additional acceleration method and is therefore not enabled by default. To enable CUDA, SIROCCO has to be compiled with the additional :code:`-DCUDA_ON` flag and linked with the appropriate libraries using the NVIDIDA CUDA -compiler (nvcc). There are several ways to enable the CUDA components of Sirocco. The most simple is to run the configure -script in the root Sirocco directory with the arguments :code:`--with-cuda`, +compiler (nvcc). There are several ways to enable the CUDA components of SIROCCO. The most simple is to run the configure +script in the root SIROCCO directory with the arguments :code:`--with-cuda`, .. code:: bash [$SIROCCO] $ ./configure --with-cuda - Configuring Makefile for Sirocco radiative transfer code + Configuring Makefile for SIROCCO radiative transfer code Checking for mpicc...yes Checking for gcc...yes Checking for nvcc...yes @@ -83,7 +83,7 @@ script in the root Sirocco directory with the arguments :code:`--with-cuda`, If the NVIDIA CUDA Toolkit is found, you will see the output informing that the CUDA compiler :code:`nvcc` was found. What essentially happens when you run :code:`code` is that a value for the variable :code:`NVCC` is set in the Makefile -in Sirocco's source directory. If you re-run :code:`configure` without :code:`--with-cuda`, then :code:`NVCC` will be +in SIROCCO's source directory. If you re-run :code:`configure` without :code:`--with-cuda`, then :code:`NVCC` will be unset and CUDA will not be used. CUDA can be disabled or enabled *"on the fly"* by modifying this variable without running the configure script and by modifying the Makefile or passing the value of the variable when calling the Makefile, e.g., @@ -99,7 +99,7 @@ depending on if CUDA is enabled or not. Running ------- -To run Sirocco with CUDA, you run it in the exact way even parallel models running with MPI. On a HPC system the +To run SIROCCO with CUDA, you run it in the exact way even parallel models running with MPI. On a HPC system the appropriate GPU resources will need to be requested in a job submission script. For example, on Iridis at the University of Southampton, a functional job submission script may look like this, @@ -117,13 +117,13 @@ of Southampton, a functional job submission script may look like this, mpirun -n $SLURM_NTASKS py model.pf -If CUDA is enabled and no GPU resources are found, Sirocco will exit early in the program with an appropriate error +If CUDA is enabled and no GPU resources are found, SIROCCO will exit early in the program with an appropriate error message. Note that a CUDA-aware MPI implementation is not required, as no data is communicated between GPUs. Implementation ============== -In this part of the documentation, we will cover the implementation details of cuSolver in Sirocco. cuSolver is a matrix +In this part of the documentation, we will cover the implementation details of cuSolver in SIROCCO. cuSolver is a matrix library within the NVIDIA CUDA ecosystem, designed to accelerate both dense and sparse linear algebra problems, including matrix factorisation, linear system solving and matrix inversion. To use cuSolver, very little GPU specific code needs to be written, other than code to allocate memory on the GPU. There are therefore a number of similarities @@ -150,7 +150,7 @@ A more detailed and thorough explanation of the CUDA programming model can be fo Basics ------ -Sirocco uses the dense matrix functions in cuSolver, which are accessed through the :code:`cusolverDn.h` header file. To +SIROCCO uses the dense matrix functions in cuSolver, which are accessed through the :code:`cusolverDn.h` header file. To use cuSolver, it must first be initialised. To do so, we use :code:`cusolverDnCreate` to create a :code:`cuSolverDnHandle_t` variable which is used by cuSolver internally for resource and context management. @@ -214,7 +214,7 @@ The GSL matrix code is kept in :code:`$SIROCCO/source/matrix_cpu.c` with functio To be able to switch between the CUDA and GSL matrix implementations with the minimal amount of code changes, a :code:`solve_matrix` wrapper function has been created. Either GSL or cuSolver is called within this wrapper, depending -on if Sirocco was compiled with the flag :code:`-DCUDA_ON` as discussed earlier. This wrapper takes on the same name as +on if SIROCCO was compiled with the flag :code:`-DCUDA_ON` as discussed earlier. This wrapper takes on the same name as the original GSL implementation, meaning no code changes have occurred in that regard. .. code:: c @@ -329,7 +329,7 @@ simplified) example of the cuSolver implementation to solve a linear system. )); /* We now have to copy d_b_vector back to the CPU, so we can use that value in - the rest of Sirocco */ + the rest of SIROCCO */ copy_data_to_cpu(); return EXIT_SUCCESS; @@ -367,7 +367,7 @@ The steps for compiling and link GPU and CPU code are outlined below in pseudo-M NVCC = nvcc # Define C and CUDA libraries. We still include GSL as other GSL numerical routines are - # used in Sirocco + # used in SIROCCO C_LIBS = -lgsl -lgslcblas -lm CUDA_LIBS = -lcudart -lcusolver @@ -408,7 +408,7 @@ shown below. $(NVCC) $(NVCC_FLAGS) -DCUDA_ON -I$(INCLUDE) -c $< -o $@ endif - # So to compile Sirocco, we have something which looks vaguely like this. Note that + # So to compile SIROCCO, we have something which looks vaguely like this. Note that # we use the CUDA_OBJECTS recipe as a requirement for the python recipe. This CUSOLVER_STATUS_SUCCESS # the CUDA source to be compiled to object code *if* NVCC is defined python: startup python.o $(python_objects) $(CUDA_OBJECTS) diff --git a/docs/sphinx/source/developer/programmer_notes.rst b/docs/sphinx/source/developer/programmer_notes.rst index b426edf75..22fefbc8b 100644 --- a/docs/sphinx/source/developer/programmer_notes.rst +++ b/docs/sphinx/source/developer/programmer_notes.rst @@ -1,7 +1,7 @@ Programming Notes ################# -Sirocco is written in C and is normally tested on linux machines and on Macs, where the compiler usually turns out to be clang. It is also regularly compiled with gcc as part of the travis-CI tests. Certain portions of the code are parallelized using the Message Parsing Interface (MPI). +SIROCCO is written in C and is normally tested on linux machines and on Macs, where the compiler usually turns out to be clang. It is also regularly compiled with gcc as part of the travis-CI tests. Certain portions of the code are parallelized using the Message Parsing Interface (MPI). Version control is (obviously) managed through **git**. The stable version is on the `master` branch; the main development is carried out on the `dev` branch. This is generally the branch to start with in developing new code. If possible, a developer should use the so-called Fork and Pull model for their version control workflow. See e.g. `this gist post `_. @@ -17,7 +17,7 @@ In addition to indent, one should have cproto or something equivalent installed. All new routines should have Doxygen headers. -printf statements should be avoided, particular in the main code. Sirocco has its own replacements for these commands, e.g Log and Error which standardize the outputsand allow for managing what is printed to the screen in multprocessor mode. There is alsoa command line switch that contorls the amount of information that is printed to the screen. Specific errors are only logged for a limited number of times, after which theyare merely counted. A log of the number of times each error has occurred is printed outat the end of each run and for each thread. (Additional detailes can be found in the Doxygenheader for xlog.c) +printf statements should be avoided, particular in the main code. SIROCCO has its own replacements for these commands, e.g Log and Error which standardize the outputsand allow for managing what is printed to the screen in multprocessor mode. There is alsoa command line switch that contorls the amount of information that is printed to the screen. Specific errors are only logged for a limited number of times, after which theyare merely counted. A log of the number of times each error has occurred is printed outat the end of each run and for each thread. (Additional detailes can be found in the Doxygenheader for xlog.c) Structures ========== @@ -64,7 +64,7 @@ of a number stages Parallel Operation ================== -Sirocco uses MPI to parallelize the most compute intensive portions of the routine. It has +SIROCCO uses MPI to parallelize the most compute intensive portions of the routine. It has been run on large machines with 100s of cores without problem. The portions of the routine that are parallelize are: @@ -96,7 +96,7 @@ The portions of the routine that are parallelize are: MPI requires intialization. For python this is carried out in python.c. Various subroutines make use of MPI, and as a result, programmers need to be aware of this fact when they write auxiliary -routines that use the various subroutines called by Sirocco. +routines that use the various subroutines called by SIROCCO. Input naming conventions ======================== @@ -119,7 +119,7 @@ that is, they all begin with Central_object. This convention should be followed External variables ================== -Sirocco uses lots (and likely too many), what are properly know as external variables. (In C, a global +SIROCCO uses lots (and likely too many), what are properly know as external variables. (In C, a global variable is a variable whose scope is all of the routines in a speciric file. An external varriable is one that is shared across multiple files.) diff --git a/docs/sphinx/source/developer/tests.rst b/docs/sphinx/source/developer/tests.rst index 8f9115416..aacd80168 100644 --- a/docs/sphinx/source/developer/tests.rst +++ b/docs/sphinx/source/developer/tests.rst @@ -1,26 +1,26 @@ Unit Test Framework ################### -Unit tests are an excellent way to ensure that any code you write is robust and correct. To manage unit testing, Sirocco +Unit tests are an excellent way to ensure that any code you write is robust and correct. To manage unit testing, SIROCCO uses the `CUnit `_ test framework. Unit tests are run by using :code:`make check` in -either the root directory of Sirocco, or in the source/test directory. +either the root directory of SIROCCO, or in the source/test directory. Installing CUnit ================ -Sirocco has been tested to work with CUnit (and CUnity) versions newer than 2.1-3. A recent version of CUnit is provided -in the :code:`$SIROCCO/software` directory and can be installed as a static library by using the Makefile in Sirocco's +SIROCCO has been tested to work with CUnit (and CUnity) versions newer than 2.1-3. A recent version of CUnit is provided +in the :code:`$SIROCCO/software` directory and can be installed as a static library by using the Makefile in SIROCCO's root directory. To build CUnit from source, you will need `CMake `_ installed, which is a modern build system for C and C++ projects. -CUnit will be installed (as a static library) at the same time as GSL and Sirocco during the first-time install, e.g., +CUnit will be installed (as a static library) at the same time as GSL and SIROCCO during the first-time install, e.g., .. code:: bash $ [$SIROCCO] ./configure $ [$SIROCCO] make install -It is also possible to install only CUnit, using the same Makefile, if Sirocco and GSL are already installed on your +It is also possible to install only CUnit, using the same Makefile, if SIROCCO and GSL are already installed on your system, .. code:: bash @@ -298,7 +298,7 @@ appropriately organised directories as shown below. │   └── tests.h └── unit_test_main.c -We also need to include the Sirocco source code we are testing in the :code:`SIROCCO_SOURCES` variable of the Makefile. +We also need to include the SIROCCO source code we are testing in the :code:`SIROCCO_SOURCES` variable of the Makefile. If there are any CUDA files required, these should be added to the :code:`CUDA_SOURCES` variable. In theory, we should only need to include the files containing the code we are testing. But in practise, we choose to instead include all of -Sirocco's source files (as it makes our lives easier) which increases compile time and the size of the final binary. +SIROCCO's source files (as it makes our lives easier) which increases compile time and the size of the final binary. diff --git a/docs/sphinx/source/examples.rst b/docs/sphinx/source/examples.rst index 9020307e9..d3b91e6b6 100644 --- a/docs/sphinx/source/examples.rst +++ b/docs/sphinx/source/examples.rst @@ -1,9 +1,9 @@ Examples ######## -Sirocco is a large and complicated code with a very wide range of capabilities. Some are very non-intuitive. This section of the documentation provides worked examples on how to set up, run, and analyse the outputs of the code. +SIROCCO is a large and complicated code with a very wide range of capabilities. Some are very non-intuitive. This section of the documentation provides worked examples on how to set up, run, and analyse the outputs of the code. -The files relating to these examples can be found in the `examples/` section of the Sirocco repository. +The files relating to these examples can be found in the `examples/` section of the SIROCCO repository. .. toctree:: diff --git a/docs/sphinx/source/examples/demo-models/tde/tde.rst b/docs/sphinx/source/examples/demo-models/tde/tde.rst index b427eb5dd..f3f0db6d2 100644 --- a/docs/sphinx/source/examples/demo-models/tde/tde.rst +++ b/docs/sphinx/source/examples/demo-models/tde/tde.rst @@ -3,7 +3,7 @@ Demo: Tidal Disruption Event ############################ -One of the recent applications of Sirocco is modelling outflows in Tidal +One of the recent applications of SIROCCO is modelling outflows in Tidal Disruption Events (TDEs). We have explored how line formation in an accretion disc wind could explain the BAL vs. BEL dichotomy observed in the UV spectra of TDEs. We have also explored how reprocessing in an accretion disc wring could @@ -21,7 +21,7 @@ Key Model Parameters We model a disc wind outflow using the kinematic `Shlosman & Vitello (1993) `_ (SV93) biconical disc wind model. This model has seen extensive use throughout the -history of Sirocco to model across all length scales of accretion, from CVs to +history of SIROCCO to model across all length scales of accretion, from CVs to QSO winds. Further information about the SV93 model can be found in the documentation `here <../../sv.rst>`_. @@ -59,7 +59,7 @@ Wind geometry parameters: \lambda &=~0 \\ f_{v} &=~0.1 \\ -For parameters controlling the radiative transfer and flow of Sirocco, the +For parameters controlling the radiative transfer and flow of SIROCCO, the parameter file for this model can be found :download:`here `. Radiation Sources @@ -92,7 +92,7 @@ total of 48 cores. Each processor core runs at a clock frequency of 2.1 GHz, wit a maximum boost clock of 3.7 GHz. The model uses roughly 70 GB of the available DDR4 2666 MHz memory available in this system. -With this configuration using :math:`10^{8}` photons and Sirocco's "-p 2" option +With this configuration using :math:`10^{8}` photons and SIROCCO's "-p 2" option for logarithmic photon number stepping, the model takes roughly 10 ionization cycles to converge in roughly 7.5 hours, or 360 total CPU hours. The spectral cycles take a significantly longer time to complete. For six inclination angles and @@ -154,7 +154,7 @@ formation processes which scale with electron density, such as collisional excitation, decreasing with radius also. The outer top edge of the wind is cool, reaching temperature as low as -:math:`T_{e} \sim 10^{3}` K. Sirocco does not implement any dust or molecular +:math:`T_{e} \sim 10^{3}` K. SIROCCO does not implement any dust or molecular physics, hence the treatment of this region of the wind is highly approximate. However, since the line formation we are interested in does not occur in this region, our neglect of this physics should not effect the emergency spectrum. diff --git a/docs/sphinx/source/index.rst b/docs/sphinx/source/index.rst index 018747394..ae60aca32 100644 --- a/docs/sphinx/source/index.rst +++ b/docs/sphinx/source/index.rst @@ -3,14 +3,15 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -######## -Sirocco -######## +#################################################################################### +SIROCCO - Simulating Ionization and Radiation in Outflows Created by Compact Objects +#################################################################################### + -------------------------------------- Radiative transfer and ionisation code -------------------------------------- -Sirocco is a Monte-Carlo radiative transfer code designed to simulate the spectrum of biconical (or spherical) +SIROCCO is a Monte-Carlo radiative transfer code designed to simulate the spectrum of biconical (or spherical) winds in disk systems. It was origianally written by `Long and Knigge (2002) `_ and was intended for simulating the spectra of winds in cataclysmic variables. Since then, it has @@ -29,15 +30,15 @@ Documentation Various documentation exists: -* A :doc:`Quick Guide ` describing how to install and run Sirocco (in a fairly mechanistic fashion). +* A :doc:`Quick Guide ` describing how to install and run SIROCCO (in a fairly mechanistic fashion). -For more information on how this page was generated and how to create documentation for Sirocco, +For more information on how this page was generated and how to create documentation for SIROCCO, look at the page for :doc:`documentation on the documentation `. ------- Authors ------- -The authors of the Sirocco code and their institutions are: +The authors of the SIROCCO code and their institutions are: Knox Long Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA diff --git a/docs/sphinx/source/input/overview.rst b/docs/sphinx/source/input/overview.rst index b0b252a13..344f1681b 100644 --- a/docs/sphinx/source/input/overview.rst +++ b/docs/sphinx/source/input/overview.rst @@ -1,7 +1,7 @@ Overview ######## -Sirocco uses a keyword-based parameter file to specify a model. Below is an example portion of a parameter file for a star system type (which must have the extension .pf). The parameters look as follows: +SIROCCO uses a keyword-based parameter file to specify a model. Below is an example portion of a parameter file for a star system type (which must have the extension .pf). The parameters look as follows: .. code:: @@ -26,15 +26,15 @@ The comment generally specifies a set of valid choices or the units in which inf When a series of choices is presented, one does not need to enter the complete word, just enough to provide unique match to the choice. -The user does not need to create a parameter .pf file before running Sirocco. Invoking Sirocco without a parameter file will cause Sirocco to prompt the user for the necessary information to create a parameter file. The user can specfiy any name for the parameter file. The example below calls the filename 'my_new_model' +The user does not need to create a parameter .pf file before running SIROCCO. Invoking SIROCCO without a parameter file will cause SIROCCO to prompt the user for the necessary information to create a parameter file. The user can specfiy any name for the parameter file. The example below calls the filename 'my_new_model' -.. Instead, assuming one is not working from a template parameter file, one simply invokes Sirocco. +.. Instead, assuming one is not working from a template parameter file, one simply invokes SIROCCO. .. code:: console py my_new_model -Sirocco then queries the user for answers to a series of questions, creating in the process a pf file, my_new_model.pf, +SIROCCO then queries the user for answers to a series of questions, creating in the process a pf file, my_new_model.pf, that can be edited and used in future runs. An example of a line presented to the user in interactive mode is: @@ -47,7 +47,7 @@ There the number in the second set of parenthesis is a suggested value of the pa The user types in a new value and a carriage return, or, if the the suggested value seems appropriate, responds with a carriage return, in which case the suggested value will be used. -The user can use the :code:`-i` switch when invoking Sirocco. This indicates that Sirocco should accumulate all of the necessary inputs, write out the parameter file, and exit, which is useful if one is not completely sure what one wants. +The user can use the :code:`-i` switch when invoking SIROCCO. This indicates that SIROCCO should accumulate all of the necessary inputs, write out the parameter file, and exit, which is useful if one is not completely sure what one wants. .. code:: console @@ -57,8 +57,8 @@ The user can use the :code:`-i` switch when invoking Sirocco. This indicates tha Changes in the input files as the code evolves ---------------------------------------------- -Occassionally, new input variables will be introduced into Sirocco. In this case, when one tries to run a parameter file -created with a previous version of Sirocco in single processor mode, the program will query the user for the parameters that are missing, and then +Occassionally, new input variables will be introduced into SIROCCO. In this case, when one tries to run a parameter file +created with a previous version of SIROCCO in single processor mode, the program will query the user for the parameters that are missing, and then attempt to run the program as normal. If the original name of the parameter file was :code:`test.pf`, the modified version of the parameter file will be written to :code:`test.out.pf`, so diff --git a/docs/sphinx/source/input/parameters/Input_spectra.model_file.rst b/docs/sphinx/source/input/parameters/Input_spectra.model_file.rst index 5d94ef1ca..0ad9dbe77 100644 --- a/docs/sphinx/source/input/parameters/Input_spectra.model_file.rst +++ b/docs/sphinx/source/input/parameters/Input_spectra.model_file.rst @@ -1,12 +1,12 @@ Input_spectra.model_file ======================== In addition to being able to generate several types of spectra, such -as blackbodies and power laws, Sirocco can read in a series of spectra +as blackbodies and power laws, SIROCCO can read in a series of spectra which are tabulated and are describable in terms of (usually) temperature and gravity). This parameter defines the name of the file which gives the location of the individual spectra and the temperate and gravity associated with each spectrum. (One may wish to use the same files for several radiation sources, viz the disk and the star). -Sirocco actually only reads in the data the first time. +SIROCCO actually only reads in the data the first time. Type String diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.live_or_die.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.live_or_die.rst index fa62948ba..0ebc3336b 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.live_or_die.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.live_or_die.rst @@ -1,6 +1,6 @@ Spectrum.live_or_die ==================== -Normally in creating detailed spectrum Sirocco "extracts" photons in a certain +Normally in creating detailed spectrum SIROCCO "extracts" photons in a certain direction reweighting them to account for the fact that they have been extracted in a certain direction. It is possible to just count the photons that are emitted in a single angle range. The two methods should yield the same or very similar results diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_photons_by_position.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_photons_by_position.rst index acf4659f1..ac8f9c979 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_photons_by_position.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_photons_by_position.rst @@ -13,7 +13,7 @@ between photons that read the observer from the near or far side of the disk. *Note: This option is only available in extract mode. If one attempts to select -photons by position in live or die mode. The Sirocco will warn the user and exit.* +photons by position in live or die mode. The SIROCCO will warn the user and exit.* Type Boolean (yes/no) diff --git a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_in_final_spectrum.rst b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_in_final_spectrum.rst index bd6fe2498..f3c94492b 100644 --- a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_in_final_spectrum.rst +++ b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_in_final_spectrum.rst @@ -15,7 +15,7 @@ Values proportional to its surface area. models - Radiate according to a model. Sirocco can support tabulated models that output with a binned luminosity distribution + Radiate according to a model. SIROCCO can support tabulated models that output with a binned luminosity distribution depending on system properties like temperature and gravity. See :ref:`Input_spectra.model_file`. The total luminosity will be set by :ref:`Boundary_layer.luminosity`. diff --git a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_to_make_wind.rst b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_to_make_wind.rst index 29ba4df1d..23d2068a9 100644 --- a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_to_make_wind.rst +++ b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_to_make_wind.rst @@ -15,7 +15,7 @@ Values proportional to its surface area. models - Radiate according to a model. Sirocco can support tabulated models that output with a binned luminosity distribution + Radiate according to a model. SIROCCO can support tabulated models that output with a binned luminosity distribution depending on system properties like temperature and gravity. See :ref:`Input_spectra.model_file`. The total luminosity will be set by :ref:`Boundary_layer.luminosity`. diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_in_final_spectrum.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_in_final_spectrum.rst index 732296341..5066717dc 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_in_final_spectrum.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_in_final_spectrum.rst @@ -16,7 +16,7 @@ Values models Available for :ref:`System_type` of ``star`` or ``cv``. - Radiate according to a model. Sirocco can support tabulated models that output with a binned luminosity distribution + Radiate according to a model. SIROCCO can support tabulated models that output with a binned luminosity distribution depending on system properties like temperature and gravity. See :ref:`Input_spectra.model_file`. The total luminosity will be set by :ref:`Central_object.luminosity`. diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_to_make_wind.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_to_make_wind.rst index dabc4819a..dc690bc39 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_to_make_wind.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_to_make_wind.rst @@ -13,7 +13,7 @@ Values proportional to its surface area. models - Radiate according to a model. Sirocco can support tabulated models that output with a binned luminosity distribution + Radiate according to a model. SIROCCO can support tabulated models that output with a binned luminosity distribution depending on system properties like temperature and gravity. See :ref:`Input_spectra.model_file`. The total luminosity will be set by :ref:`Central_object.luminosity`. diff --git a/docs/sphinx/source/input/parameters/other/Diag.rst b/docs/sphinx/source/input/parameters/other/Diag.rst index 81efde450..0f7dbbad5 100644 --- a/docs/sphinx/source/input/parameters/other/Diag.rst +++ b/docs/sphinx/source/input/parameters/other/Diag.rst @@ -1,6 +1,6 @@ Diag ==== -A series of advanced/diagnostic commands (Accessed using the ``-d`` flag, see :ref:`Running Sirocco`). +A series of advanced/diagnostic commands (Accessed using the ``-d`` flag, see :ref:`Running SIROCCO`). The commands generally allow access to additional information from the simulation, or allow more precise control. Advanced commands have an @ symbol in front of them in the parameter file. diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.lowest_ion_density_for_photoabs.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.lowest_ion_density_for_photoabs.rst index f163fc045..92a93afa6 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.lowest_ion_density_for_photoabs.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.lowest_ion_density_for_photoabs.rst @@ -1,6 +1,6 @@ Diag.lowest_ion_density_for_photoabs ==================================== -For efficiency reasons, Sirocco does not try to calculate photoabsorption +For efficiency reasons, SIROCCO does not try to calculate photoabsorption for an ion with an extremly low density. This advance parameter changes this density limit diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.partial_cells.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.partial_cells.rst index b79fd5aec..3671e0029 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.partial_cells.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.partial_cells.rst @@ -2,7 +2,7 @@ Diag.partial_cells ================================ Additional options for how to deal with cells that are partially filled by wind. -Somewhat degenerate with the ``-include_partial_cells`` flag under :ref:`Running Sirocco`. +Somewhat degenerate with the ``-include_partial_cells`` flag under :ref:`Running SIROCCO`. Type diff --git a/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.nbands.rst b/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.nbands.rst index 5a4443350..1af323989 100644 --- a/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.nbands.rst +++ b/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.nbands.rst @@ -1,6 +1,6 @@ Photon_sampling.nbands ====================== -Sirocco uses stratified samplign to generate photons during the ionization phase. This +SIROCCO uses stratified samplign to generate photons during the ionization phase. This parameter allows the user to define the number of bands for stratified sampling, if s/he wants to customize the bands used for the generation of photons diff --git a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.type.rst b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.type.rst index 9159369a3..1728e1da2 100644 --- a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.type.rst +++ b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.type.rst @@ -5,7 +5,7 @@ path of photons emitted in the simulation as they travel through the geometry, assuming that any delays from recombination etc. are negligible and all delays are due to light travel time. For each final spectrum, all contributing photons are output to a '.delay_dump' file that can then be processed using -our 'tfpy' Sirocco (no relation) library. +our 'tfpy' SIROCCO (no relation) library. Type Enumerator diff --git a/docs/sphinx/source/input/parameters/rt_ion/Atomic_data.rst b/docs/sphinx/source/input/parameters/rt_ion/Atomic_data.rst index 34f52b9c4..16c98c809 100644 --- a/docs/sphinx/source/input/parameters/rt_ion/Atomic_data.rst +++ b/docs/sphinx/source/input/parameters/rt_ion/Atomic_data.rst @@ -1,6 +1,6 @@ Atomic_data =========== -Sirocco uses an atomic data file, as found in the `agnwinds/data` repository. +SIROCCO uses an atomic data file, as found in the `agnwinds/data` repository. This is the relative path to the Atomic Data header file on disk. See :ref:`Atomic data` Type diff --git a/docs/sphinx/source/input/parameters/rt_ion/Spectrum_cycles.rst b/docs/sphinx/source/input/parameters/rt_ion/Spectrum_cycles.rst index 82e5add7d..a289f62c2 100644 --- a/docs/sphinx/source/input/parameters/rt_ion/Spectrum_cycles.rst +++ b/docs/sphinx/source/input/parameters/rt_ion/Spectrum_cycles.rst @@ -1,7 +1,7 @@ Spectrum_cycles =============== -In Sirocco, the detailed spectra are calculate with flights of photons that have +In SIROCCO, the detailed spectra are calculate with flights of photons that have the same number of photon bundles as in ionization cycles. The detailed spectra are calculated between a minimum and maximum wavelenth, and at specific inclination angles. Various other conditions can be placed on each spectrum that is accumulated. diff --git a/docs/sphinx/source/input/parameters/wind/Wind/Wind.ionization.rst b/docs/sphinx/source/input/parameters/wind/Wind/Wind.ionization.rst index 234bf9b62..4454b1a76 100644 --- a/docs/sphinx/source/input/parameters/wind/Wind/Wind.ionization.rst +++ b/docs/sphinx/source/input/parameters/wind/Wind/Wind.ionization.rst @@ -1,6 +1,6 @@ Wind.ionization =============== -The approach used by Sirocco to calculate the ionization +The approach used by SIROCCO to calculate the ionization of the wind during ionization cycles. A number of these modes are historical or included for diagnostic purposes. diff --git a/docs/sphinx/source/input/parameters/wind/Wind/Wind.type.rst b/docs/sphinx/source/input/parameters/wind/Wind/Wind.type.rst index 3d5f4e567..e1fc40403 100644 --- a/docs/sphinx/source/input/parameters/wind/Wind/Wind.type.rst +++ b/docs/sphinx/source/input/parameters/wind/Wind/Wind.type.rst @@ -20,7 +20,7 @@ Values A special purpose mode used by the python collaboration for importing models from Zeus and Pluto. (Depreciated) (:ref:`Hydro`) imported - A general purpose mode for importing a wind from an ascii file (see also :doc:`Sirocco Script documentation <../wind_models/importing_models>`). + A general purpose mode for importing a wind from an ascii file (see also :doc:`SIROCCO Script documentation <../wind_models/importing_models>`). kwd The Knigge-Woods-Drew parameterization of a bi-conical flow. (:ref:`KWD`) diff --git a/docs/sphinx/source/input/system_description.rst b/docs/sphinx/source/input/system_description.rst index 0e04cbd80..6ebeb8971 100644 --- a/docs/sphinx/source/input/system_description.rst +++ b/docs/sphinx/source/input/system_description.rst @@ -1,7 +1,7 @@ System Description ################## -The first set of parameters which Sirocco needs are information about the overall system. +The first set of parameters which SIROCCO needs are information about the overall system. .. code:: diff --git a/docs/sphinx/source/input/wind_model_params.rst b/docs/sphinx/source/input/wind_model_params.rst index dc50d8a82..6d484c8dd 100644 --- a/docs/sphinx/source/input/wind_model_params.rst +++ b/docs/sphinx/source/input/wind_model_params.rst @@ -1,7 +1,7 @@ Wind Model Parameters ###################### -Sirocco allows for various types of wind models, which are defined by the following parameters. This page focuses on the actual parameters in the file, but further description of the wind models and instructions on how to import models can be found under :doc:`Wind Models <../wind_models>`. +SIROCCO allows for various types of wind models, which are defined by the following parameters. This page focuses on the actual parameters in the file, but further description of the wind models and instructions on how to import models can be found under :doc:`Wind Models <../wind_models>`. .. code:: @@ -16,7 +16,7 @@ Sirocco allows for various types of wind models, which are defined by the follow for example a fast flow near the poles of a system, and a slow for near the disk. If the number of components exceeds 1, then the remaining questions relating to the wind will be posed multiple times. -:ref:`Wind.type`: The wind models incorporated into Sirocco currently are: +:ref:`Wind.type`: The wind models incorporated into SIROCCO currently are: :ref:`SV` The Shlosman and Vitello parameterization of a bi-conical flow. @@ -40,7 +40,7 @@ If the number of components exceeds 1, then the remaining questions relating to A model of a thin shell useful for diagnostic studies. :ref:`Imported ` - A general purpose mode for importing a wind from an ascii file (see also :doc:`Sirocco Script documentation <../wind_models/importing_models>`). + A general purpose mode for importing a wind from an ascii file (see also :doc:`SIROCCO Script documentation <../wind_models/importing_models>`). :ref:`Wind.coord_system` is the coordinate system in which the wind is defined. diff --git a/docs/sphinx/source/installation.rst b/docs/sphinx/source/installation.rst index 22eb8c9cb..d195f388d 100644 --- a/docs/sphinx/source/installation.rst +++ b/docs/sphinx/source/installation.rst @@ -1,7 +1,7 @@ Getting Started ############### -What machines will python run on? Sirocco is written C. We have regularly run Sirocco on various linux systems as well as a variity of Mac machines. +What machines will python run on? SIROCCO is written C. We have regularly run SIROCCO on various linux systems as well as a variity of Mac machines. It is compiled using mpicc/gcc, but it can be compiled simply with gcc. It uses the Gnu Scientific Libraries (gsl) @@ -11,8 +11,8 @@ It uses the Gnu Scientific Libraries (gsl) Installation ============ -Sirocco and the various routines associated are set up in a self-contained directory structure. -The basic directory structure and the data files that one needs to run Sirocco need to be retrieved and compiled. +SIROCCO and the various routines associated are set up in a self-contained directory structure. +The basic directory structure and the data files that one needs to run SIROCCO need to be retrieved and compiled. If you want to obtain a stable (!) release, go to the `Releases `_ page. @@ -44,10 +44,10 @@ note that export syntax is for bash- for csh use $ setenv SIROCCO /path/to/sirocco/ -The atomic data needed to run Sirocco is included in the distribution. +The atomic data needed to run SIROCCO is included in the distribution. -The source code for Sirocco is under actively development and is updated fairly often. Normally, one does not need to redo the entire installation process, since this includes GSL setup. +The source code for SIROCCO is under actively development and is updated fairly often. Normally, one does not need to redo the entire installation process, since this includes GSL setup. Instead, one can pull in changes and recompile the source code by running .. code:: bash @@ -70,7 +70,7 @@ You can then setup your symbolic links by running .. code:: bash - $ Setup_Sirocco_Dir + $ Setup_SIROCCO_Dir and run the code by typing, e.g. @@ -82,9 +82,9 @@ and run the code by typing, e.g. Running in parallel mode ------------------------ -While Sirocco can be run in single processor mode, it is generally more efficient to run on multiple processors. in multiprocessor mode, -When multiprocessing is invoked, Sirocco uses mulitple threads for photon transfer and in calcuation ionization equilibrium. As these -comprise the bulk of the computational load the total time to run is roughly an inverse of the number of threads. Sirocco uses `MPI `_ for parallel processing and so software libraries that implement this must be on the machine that is +While SIROCCO can be run in single processor mode, it is generally more efficient to run on multiple processors. in multiprocessor mode, +When multiprocessing is invoked, SIROCCO uses mulitple threads for photon transfer and in calcuation ionization equilibrium. As these +comprise the bulk of the computational load the total time to run is roughly an inverse of the number of threads. SIROCCO uses `MPI `_ for parallel processing and so software libraries that implement this must be on the machine that is being used. For Macs, mpi can installed with HomeBrew or Fink. For linux machines, two common libraries are `Open-MPI `_ and `MPICH `_ If not already installed, one should install them. @@ -114,11 +114,11 @@ The two files are run as follows Brief descriptions of command line options for running these routines can obtained using a -h switch -Sirocco scripts +SIROCCO scripts -------------- There are a number of Python, the programming language scripts, that can be used to plot results -from a Sirocco run. These are not particularly well documented and many have been developed +from a SIROCCO run. These are not particularly well documented and many have been developed for looking at various aspects of the code. A few may require Python packages to be installed. However, a number are likely to be useful. diff --git a/docs/sphinx/source/meta.rst b/docs/sphinx/source/meta.rst index 323e968cb..b24a3f292 100644 --- a/docs/sphinx/source/meta.rst +++ b/docs/sphinx/source/meta.rst @@ -1,7 +1,7 @@ Meta-documentation ################## -How to document Sirocco +How to document SIROCCO ====================== This documentation is written in **ReStructured Text**, and parsed by **Sphinx**. @@ -12,7 +12,7 @@ Installing the documentation tools ---------------------------------- This guide is produced using **Sphinx**. -**Sphinx** is written in Sirocco and available from the **pip** package manager. +**Sphinx** is written in SIROCCO and available from the **pip** package manager. We require the **Python 3** version of **Sphinx**. Install it, and the other modules required, as: .. code :: bash @@ -91,7 +91,7 @@ When referring to a library or program name, render it in **bold** as: .. code :: rst - Though this program is called *Sirocco*, it is written in **C**, using the **GSL** library. + Though this program is called *SIROCCO*, it is written in **C**, using the **GSL** library. Content of interest to developers but not users should be broken out into a callout as: @@ -149,7 +149,7 @@ A definition list consists of titles followed by a definition block indented by The headings, in the order we expect, are: Name - The parameter name, as used by Sirocco input files. + The parameter name, as used by SIROCCO input files. Description A description of the parameter and its function. diff --git a/docs/sphinx/source/operation.rst b/docs/sphinx/source/operation.rst index 220478026..416ec087d 100644 --- a/docs/sphinx/source/operation.rst +++ b/docs/sphinx/source/operation.rst @@ -1,7 +1,7 @@ Code Operation ##################### -The basic code operation of Sirocco is split into different cycles; First, the ionization state is calculated (:doc:`Ionization Cycles `). As these photons pass through the simulation grid, their heating and ionizing effect on the plasma is recorded through the use of Monte Carlo estimators. This process continues until the code converges on a solution in which the heating and cooling processes are balanced and the temperature stops changing significantly (see :doc:`Convergence & Errors `). Once the ionization and temperature structure of the outflow has been calculated, the spectrum is synthesized by tracking photons through the plasma until sufficient signal-to-noise is achieved in the output spectrum for lines to be easily identified (:doc:`Spectral Cycles `). +The basic code operation of SIROCCO is split into different cycles; First, the ionization state is calculated (:doc:`Ionization Cycles `). As these photons pass through the simulation grid, their heating and ionizing effect on the plasma is recorded through the use of Monte Carlo estimators. This process continues until the code converges on a solution in which the heating and cooling processes are balanced and the temperature stops changing significantly (see :doc:`Convergence & Errors `). Once the ionization and temperature structure of the outflow has been calculated, the spectrum is synthesized by tracking photons through the plasma until sufficient signal-to-noise is achieved in the output spectrum for lines to be easily identified (:doc:`Spectral Cycles `). .. toctree:: :glob: diff --git a/docs/sphinx/source/operation/ionization_cycles.rst b/docs/sphinx/source/operation/ionization_cycles.rst index 92bb05c7b..b98621cc8 100644 --- a/docs/sphinx/source/operation/ionization_cycles.rst +++ b/docs/sphinx/source/operation/ionization_cycles.rst @@ -7,7 +7,7 @@ with a guess at the ionization structure, usually by setting the temperature of at a specific value and assuming that the ionization equilibrium is simple given by the Saha equation for that particular temperature. -In Sirocco, one then generates a set of photon bundles over a wide frequency range, and then +In SIROCCO, one then generates a set of photon bundles over a wide frequency range, and then causes these photons to pass through and interact via various processes with the wind. As the photons transit the wind, estimatores for various processes are accumulated, which characterize the intensity and spectrum of the radiation field in various parts of @@ -18,6 +18,6 @@ estimators to modify the ionization state and electron temperature in each cell, assumed density and velocity field of the wind. There are a variety of approaches to carrying out this calculation and various limitations placed on the rate at which the plasma is is allowed to change between cycles. As the accuracy of any Monte Carlo simulation depends on numbers of photons bundles one uses to approximate the spectrum there are various -options within Sirocco to choose the number of photons with various energy/wavelength bins, +options within SIROCCO to choose the number of photons with various energy/wavelength bins, and other options to begin with a smaller number of photons and increase this number in later cycles. diff --git a/docs/sphinx/source/operation/spectral_cycles.rst b/docs/sphinx/source/operation/spectral_cycles.rst index eb261ef2b..beaf6b618 100644 --- a/docs/sphinx/source/operation/spectral_cycles.rst +++ b/docs/sphinx/source/operation/spectral_cycles.rst @@ -19,7 +19,7 @@ by the packet diminishes as it moves through the wind. Then one could simply create a spectrum from photon packets that exit the simulation volume at a particular incination angle (plus or minus some delta) to construct the spectrum. This so-called live-or-die option -is implemented in Sirocco, but only as a diagnostic option, because it is +is implemented in SIROCCO, but only as a diagnostic option, because it is inefficient since most photon packets exit the system at inclination angles that are not of interest. diff --git a/docs/sphinx/source/output.rst b/docs/sphinx/source/output.rst index 40431a039..4a26b492c 100644 --- a/docs/sphinx/source/output.rst +++ b/docs/sphinx/source/output.rst @@ -1,7 +1,7 @@ Outputs & Evaluation ##################### -Sirocco produces a large number of files in both binary and ascii format. Tools exist to examine the binary files. +SIROCCO produces a large number of files in both binary and ascii format. Tools exist to examine the binary files. .. toctree:: diff --git a/docs/sphinx/source/output/diagnostics.rst b/docs/sphinx/source/output/diagnostics.rst index e7f29e1a8..74e71b770 100644 --- a/docs/sphinx/source/output/diagnostics.rst +++ b/docs/sphinx/source/output/diagnostics.rst @@ -1,12 +1,12 @@ Diagnostic files ################ -Sirocco logs a considerable amount of information as it runs. +SIROCCO logs a considerable amount of information as it runs. Some of this information is printed to the screen but a much more voluminous version of progress of the program is placed in a sub-directory, named diag_whatever, where whatever is the root name of the model being run. In this directory one will find log files, e.g. **whatever_0.diag**, **whatever_1.diag**, and so on, where the in a multiprocessor run, the number refers to the log of a specific thread. -Inspecting these logs is important for understanding whether a Sirocco run is successful, +Inspecting these logs is important for understanding whether a SIROCCO run is successful, and for understanding why if failed if that is the case. diff --git a/docs/sphinx/source/output/evaluation.rst b/docs/sphinx/source/output/evaluation.rst index d2cecf048..e2b615ea3 100644 --- a/docs/sphinx/source/output/evaluation.rst +++ b/docs/sphinx/source/output/evaluation.rst @@ -1,16 +1,16 @@ Evaluation ########### -Determining whether Sirocco has run successfully from a a scientific point of view depends very specifically on one's goals. Did the spectra turn out to be what one expected? Here by evaluation we mean, did my run complete without significant errors and did the ionization structure converge to a "steady state" given the number of ionization cycles, the number of photons, and the frequency distributions of the photons we chose. +Determining whether SIROCCO has run successfully from a a scientific point of view depends very specifically on one's goals. Did the spectra turn out to be what one expected? Here by evaluation we mean, did my run complete without significant errors and did the ionization structure converge to a "steady state" given the number of ionization cycles, the number of photons, and the frequency distributions of the photons we chose. Convergence ============ -:doc:`Ionization cycles <../operation/ionization_cycles>` in Sirocco are intended to establish the correct ion densities and temperature of the various cells in the wind. The degree to which this happens for a given number of ionization cycles depends on how far the initial guess of electrons temperatures in various portions of the wind and the number of photons generated during each photoionization cycles. Furthermore, the accuracy of the final model depends on the number of photons that pass through each cell. As a result, the accuracy with which ion abundances and temperature are determined will differ on a cell by cell basis. In a typical model with a biconical outflow, a small cells at the outer edge of the accretion disk will record fewer photon passages than one in the middle of the grid that is exposed to large numbers of photons from the disk. +:doc:`Ionization cycles <../operation/ionization_cycles>` in SIROCCO are intended to establish the correct ion densities and temperature of the various cells in the wind. The degree to which this happens for a given number of ionization cycles depends on how far the initial guess of electrons temperatures in various portions of the wind and the number of photons generated during each photoionization cycles. Furthermore, the accuracy of the final model depends on the number of photons that pass through each cell. As a result, the accuracy with which ion abundances and temperature are determined will differ on a cell by cell basis. In a typical model with a biconical outflow, a small cells at the outer edge of the accretion disk will record fewer photon passages than one in the middle of the grid that is exposed to large numbers of photons from the disk. -A very basic question about a particular run is, has it reached a "steady state" and if it is in a steady state are cells stable in the sense that fluctuations are small. Hopefully, each ionization cycle brings one closer and closer to to a solution after which the ionization structure no longer evolves. Of course, since Sirocco is a Monte Carlo code, the degree to which the solution stays constant from cycle to cycle is limited by counting statistics. To check convergence in Sirocco, we monitor the the radiation temperature :math:`T_r`, the electron temperature :math:`T_e`, and the total heating :math:`\mathrm{Heat_{tot}}` and cooling :math:`\mathrm{Cooling_{tot}}` in each cell as a function of the ionization cycle :math:`n`. +A very basic question about a particular run is, has it reached a "steady state" and if it is in a steady state are cells stable in the sense that fluctuations are small. Hopefully, each ionization cycle brings one closer and closer to to a solution after which the ionization structure no longer evolves. Of course, since SIROCCO is a Monte Carlo code, the degree to which the solution stays constant from cycle to cycle is limited by counting statistics. To check convergence in SIROCCO, we monitor the the radiation temperature :math:`T_r`, the electron temperature :math:`T_e`, and the total heating :math:`\mathrm{Heat_{tot}}` and cooling :math:`\mathrm{Cooling_{tot}}` in each cell as a function of the ionization cycle :math:`n`. -To estimate whether a a model calculation has reached a steady state, Sirocco carries out three tests, one comparing the difference in the radiation temperature between the current and the preceding ionization cycle, one comparing the electron temperature in the same manner and once comparing heating and cooling rates in the current cycle. If a cell satisfies the following 3 tests, +To estimate whether a a model calculation has reached a steady state, SIROCCO carries out three tests, one comparing the difference in the radiation temperature between the current and the preceding ionization cycle, one comparing the electron temperature in the same manner and once comparing heating and cooling rates in the current cycle. If a cell satisfies the following 3 tests, .. math:: \left | \frac{T_e^n-T_e^{n-1}}{T_e^n+T_e^{n-1}} \right | < \epsilon @@ -28,14 +28,14 @@ where :math:`\epsilon = 0.05`, it is flagged as converged. It is rare that all of the cells in a model will satisfy all of these criteria. That is is because the number photons passing that pass through a cell vary considerably and the statistical noise depends on the the number of photons. It is important to note that the photons that contribute most to the spectra of an object will be those which have the most photons passing through them. Typically, we consider a model to be well converged if 90% of the cells are converged by this criterion. -The routine ``run_check.py`` (see :doc:`Sirocco Script documentation <../py_progs>`) produces two plots related to convergence, one showing the fraction of cells that have passed each of the tests above as a function of cycle, and the other showing the number of failed checks for each cell in the wind. +The routine ``run_check.py`` (see :doc:`SIROCCO Script documentation <../py_progs>`) produces two plots related to convergence, one showing the fraction of cells that have passed each of the tests above as a function of cycle, and the other showing the number of failed checks for each cell in the wind. -Note that it is not always important that all cells be converged. The Monte Carlo process preferentially picks out the cells which affect the emergent radiation field. Portions of the grid which do not get many photons are typically the ones that are "not converged", but since they don't contribute much to the emergent radiation, one does not need them to be converged (except if one wants to make nice plots of the temperature as a function of position in the wind or of the density of a particular ion species). On the other hand, if one is using Sirocco in conjunction with a hydrodynamical code one wants all the cells to be converged. +Note that it is not always important that all cells be converged. The Monte Carlo process preferentially picks out the cells which affect the emergent radiation field. Portions of the grid which do not get many photons are typically the ones that are "not converged", but since they don't contribute much to the emergent radiation, one does not need them to be converged (except if one wants to make nice plots of the temperature as a function of position in the wind or of the density of a particular ion species). On the other hand, if one is using SIROCCO in conjunction with a hydrodynamical code one wants all the cells to be converged. Errors ============ -Sirocco is designed to continue to run unless something catastrophic happens. +SIROCCO is designed to continue to run unless something catastrophic happens. As it runs, it logs error messages that can be found in the .diag files. These messages are a combinations of warnings, and/or unusual occurrences, that if they start occurring often suggest a real problem. @@ -49,7 +49,7 @@ These error messages are all of the form: that is they begin with the word :code:`Error`. followed by the subroutine in the code where the error occurred followed by what is hopefully a helpful. If one is concerned about a particular message, one can hopefully determine what is happening by looking for that message in the log files. -Sirocco keeps a count of the number of times a particular message has occurred and at the end of the program, and the very end of the +SIROCCO keeps a count of the number of times a particular message has occurred and at the end of the program, and the very end of the diag files contain a listing of how many times a particular error has occurred. .. code:: diff --git a/docs/sphinx/source/output/model.rst b/docs/sphinx/source/output/model.rst index 2068af469..813057808 100644 --- a/docs/sphinx/source/output/model.rst +++ b/docs/sphinx/source/output/model.rst @@ -1,7 +1,7 @@ Model ##### -As Sirocco is run, it repeatedly writes out two binary files that contain essentially all information about the wind as calculated in the ionization phase of the program, +As SIROCCO is run, it repeatedly writes out two binary files that contain essentially all information about the wind as calculated in the ionization phase of the program, along with status of the program at the last point where the file was written. These files along with the parameter file are sufficient to restart the program, if for example, one wants to check point the program after a certain time, and restart where one left off, @@ -14,7 +14,7 @@ or to add spectral cycles to get better spectra. .spec_save A binary file that contains all of the information about the spectra that have created. This file is not of interest to users directly. It is used when restarting -Two routines exist as part of the Sirocco distribution allow the user to gain insight into the actual model +Two routines exist as part of the SIROCCO distribution allow the user to gain insight into the actual model windsave2table Executed from the command line with :code:`windsave2table rootname`. diff --git a/docs/sphinx/source/output/spectrum_files.rst b/docs/sphinx/source/output/spectrum_files.rst index b689277e9..d38a1f62a 100644 --- a/docs/sphinx/source/output/spectrum_files.rst +++ b/docs/sphinx/source/output/spectrum_files.rst @@ -1,10 +1,10 @@ Spectra Files ############# -Sirocco is intended to produce simulated spectra. These spectra are all ascii tables intended to be accessible with software packages such as astropy. +SIROCCO is intended to produce simulated spectra. These spectra are all ascii tables intended to be accessible with software packages such as astropy. All of the ascii begin with commented headers that contain all of the parameters of associated with a run, -along with the date of the run and the specific version of Sirocco used to make the run. +along with the date of the run and the specific version of SIROCCO used to make the run. In principle, if one still has access to any of the spectra, one can reproduce the entire run again. Broad band spectra are created from the last ionization cycle. (More accurately the broad band spectra are written out at the end of each ionization cycle, so one the program is finished one has the @@ -67,7 +67,7 @@ The remaining columns are: an ascii file that contains the final detailed spectra for the wavelengths of interest at a distance of **100 pc** on a linear frequency scale. The units for the detailed spectra are determined by the input parameter Spectrum.type. - Photons bundles are generated in cycles in Sirocco and the *.spec* file is actually written out at the end of each cycle + Photons bundles are generated in cycles in SIROCCO and the *.spec* file is actually written out at the end of each cycle as the program is running in the spectrum-generation phase of the program. So one can inspect the spectrum as it is building up. The beginning of the file (omitting the header) is as follows: diff --git a/docs/sphinx/source/output/spectrum_generation_large_optical_depth.rst b/docs/sphinx/source/output/spectrum_generation_large_optical_depth.rst index 580696544..f66b2d8d8 100644 --- a/docs/sphinx/source/output/spectrum_generation_large_optical_depth.rst +++ b/docs/sphinx/source/output/spectrum_generation_large_optical_depth.rst @@ -53,7 +53,7 @@ Removing Photons due to Too Many Scatters ========================================= As well as edge effects, flux can be lost due to photons being removed from the -photon sample due to scattering too many times. In Sirocco, when a photon has undergone +photon sample due to scattering too many times. In SIROCCO, when a photon has undergone `MAXSCAT = 500` scatters, a photon is assumed to have become stuck in the wind and hence it is terminated and no longer tracked. diff --git a/docs/sphinx/source/physics.rst b/docs/sphinx/source/physics.rst index 84b4544ce..7c18dad25 100644 --- a/docs/sphinx/source/physics.rst +++ b/docs/sphinx/source/physics.rst @@ -1,7 +1,7 @@ Physics & Radiative Transfer ------------------------------ -Various physical concepts are incorporated into Sirocco. +Various physical concepts are incorporated into SIROCCO. Some of these are descibed below: .. toctree:: diff --git a/docs/sphinx/source/physics/aniso.rst b/docs/sphinx/source/physics/aniso.rst index 585536df6..c1b5bd89f 100644 --- a/docs/sphinx/source/physics/aniso.rst +++ b/docs/sphinx/source/physics/aniso.rst @@ -1,7 +1,7 @@ Anisotropic Scattering -------------------------- -Sirocco has a number of radiative transfer modes, controlled via the :doc:`/input/parameters/rt_ion/Line_transfer` keyword. Included in this mode is the treatment of line anisotropy; whether re-emission of a line photon is isotropic or not. When the scattering is isotropic, a new direction is simply chosen by choosing a random direction in the co-moving frame of the plasma. +SIROCCO has a number of radiative transfer modes, controlled via the :doc:`/input/parameters/rt_ion/Line_transfer` keyword. Included in this mode is the treatment of line anisotropy; whether re-emission of a line photon is isotropic or not. When the scattering is isotropic, a new direction is simply chosen by choosing a random direction in the co-moving frame of the plasma. If anisotropic scattering is on, via one of the thermal trapping modes, the new direction is chosen according to a rejection method. The aim is to account for the fact the photon undergoes many interactions in the resonant zone due to the thermal width of the line, and finds it easier to escape along the direction in which the optical depth is lowest (where the velocity gradient is highest). Specifically, the code undergoes the following steps: diff --git a/docs/sphinx/source/physics/limitations.rst b/docs/sphinx/source/physics/limitations.rst index 463c36f5f..4af8ff918 100644 --- a/docs/sphinx/source/physics/limitations.rst +++ b/docs/sphinx/source/physics/limitations.rst @@ -1,7 +1,7 @@ Limitations and Caveats ------------------------- -There are a number of limitations of Sirocco as a code. This page is a non-exhaustive list, partly taken from the release paper. +There are a number of limitations of SIROCCO as a code. This page is a non-exhaustive list, partly taken from the release paper. Specific issues and code problems ====================================== @@ -21,47 +21,47 @@ situations where this is important. Conceptual and Practical difficulties ====================================== -Sirocco is a complex code that includes a wide range of physical processes. As a result, it is capable of synthesizing the electromagnetic signatures of a diverse set of astrophysical systems on all scales. However, there are, of course, important limitations that the user should be aware of. Perhaps most importantly, Sirocco is *not* a stellar atmosphere code: it is not designed for hugely optically thick and/or static media. Below, we explore these and some other constraints in more detail. +SIROCCO is a complex code that includes a wide range of physical processes. As a result, it is capable of synthesizing the electromagnetic signatures of a diverse set of astrophysical systems on all scales. However, there are, of course, important limitations that the user should be aware of. Perhaps most importantly, SIROCCO is *not* a stellar atmosphere code: it is not designed for hugely optically thick and/or static media. Below, we explore these and some other constraints in more detail. - **Extreme optical depths** - Since Sirocco is a Monte Carlo code that tracks the discrete interactions of simulated photon packets, the computational cost of a wind model scales with the characteristic optical depth, :math:`\tau_{ch}`, it presents to these packets. In most environments of interest, at least hydrogen is nearly fully ionized, so it is convenient to define :math:`\tau_{ch}` as the characteristic *electron scattering* optical depth. More specifically, the computational cost depends on the mean number of scatterings, :math:`\overline{N}_{scat}`, packets have to undergo before escaping. As a practical guide to what is feasible, we note that in some of our TDE model models :math:`\overline{N}_{scat}` is as high as `100 - 1000`, corresponding to :math:`\tau_{ch} \sim 10 - 100`. Such models take :math:`O(1000)` core-hours to converge and produce synthetic spectra. + Since SIROCCO is a Monte Carlo code that tracks the discrete interactions of simulated photon packets, the computational cost of a wind model scales with the characteristic optical depth, :math:`\tau_{ch}`, it presents to these packets. In most environments of interest, at least hydrogen is nearly fully ionized, so it is convenient to define :math:`\tau_{ch}` as the characteristic *electron scattering* optical depth. More specifically, the computational cost depends on the mean number of scatterings, :math:`\overline{N}_{scat}`, packets have to undergo before escaping. As a practical guide to what is feasible, we note that in some of our TDE model models :math:`\overline{N}_{scat}` is as high as `100 - 1000`, corresponding to :math:`\tau_{ch} \sim 10 - 100`. Such models take :math:`O(1000)` core-hours to converge and produce synthetic spectra. It is important to understand that optically thick media can give rise to a variety of symptoms. Moreover, geometry matters. In 2-D models, optical depths can be highly direction dependent, with :math:`\tau_{min} << \tau_{max}`. In this case, photon packets can and will escape preferentially along the most transparent direction(s), i.e. :math:`\tau_{ch} \sim \tau_{min}`. This is both good and bad. Good, because :math:`\tau_{ch} << \tau_{max}` implies that even models with very high :math:`\tau_{max}` are, in principle, computationally feasible. Bad, because such models often encounter serious convergence problems, as there are not enough photon packets crossing cells embedded deep within (or located behind) high-:math:`\tau` structures. In practice, unconverged cells in such regions can often be safely ignored, as they are bound to contribute little to the synthesized spectra. However, this decision has to be made by the user. - In 1-D (spherical) models, photon packets can more easily get trapped, as there are no preferred directions along which they can escape. In practice, computational resources and/or convergence issues tend to be the limiting factor for optically thick 1-D models. However, in "simple atom" mode, there is an additional failure mode for such models. In the limit where most photons packets are (re)absorbed and (re)emitted many times in a single cell, it becomes extremely difficult for the code to maintain energy conservation. The issue is that the radiation field locally becomes nearly isotropic. The *net (outward) flux* can then be a tiny fraction of the *mean (angle-averaged) intensity*. In Monte Carlo terms, the luminosity (number of photon packets) that has to be created within a cell is far greater than the net number of packets that actually leaves the cell. The luminosity carried by the escaping population can then become highly uncertain simply due to Poisson scatter. Yet global energy conservation across a simulation relies on the correct flow rates of photon packets across cells. This is not a concern when Sirocco is run in macro-atom mode, as this automatically enforces radiative equilibrium and hence guarantees global energy conservation. + In 1-D (spherical) models, photon packets can more easily get trapped, as there are no preferred directions along which they can escape. In practice, computational resources and/or convergence issues tend to be the limiting factor for optically thick 1-D models. However, in "simple atom" mode, there is an additional failure mode for such models. In the limit where most photons packets are (re)absorbed and (re)emitted many times in a single cell, it becomes extremely difficult for the code to maintain energy conservation. The issue is that the radiation field locally becomes nearly isotropic. The *net (outward) flux* can then be a tiny fraction of the *mean (angle-averaged) intensity*. In Monte Carlo terms, the luminosity (number of photon packets) that has to be created within a cell is far greater than the net number of packets that actually leaves the cell. The luminosity carried by the escaping population can then become highly uncertain simply due to Poisson scatter. Yet global energy conservation across a simulation relies on the correct flow rates of photon packets across cells. This is not a concern when SIROCCO is run in macro-atom mode, as this automatically enforces radiative equilibrium and hence guarantees global energy conservation. - **The Sobolev and related approximations** - Sirocco is designed to synthesize spectra formed in *moving* media and treats line transfer in the Sobolev approximation. This means that a photon packet is only allowed to interact with a bound-bound transition at the exact location where its co-moving frequency coincides with the rest frequency of the transition. In reality, interactions take place across a spatial scale defined by the *Sobolev length*, :math:`\Delta s \simeq v_{th} \left(dv_{s}/ds\right)`, where :math:`v_{th}` is the local thermal speed, :math:`s` is distance along the photon packet's line of flight, and :math:`v_{s}` is the projected velocity along this direction. The Sobolev approximation therefore amounts to the assumption that the physical properties of the flow do not change significantly on scales as small as :math:`\Delta s`. + SIROCCO is designed to synthesize spectra formed in *moving* media and treats line transfer in the Sobolev approximation. This means that a photon packet is only allowed to interact with a bound-bound transition at the exact location where its co-moving frequency coincides with the rest frequency of the transition. In reality, interactions take place across a spatial scale defined by the *Sobolev length*, :math:`\Delta s \simeq v_{th} \left(dv_{s}/ds\right)`, where :math:`v_{th}` is the local thermal speed, :math:`s` is distance along the photon packet's line of flight, and :math:`v_{s}` is the projected velocity along this direction. The Sobolev approximation therefore amounts to the assumption that the physical properties of the flow do not change significantly on scales as small as :math:`\Delta s`. The optical depth associated with the interaction is proportional to the *Sobolev length*, :math:`\Delta s \simeq v_{th} \left(dv_{s}/ds\right)`, where :math:`v_{th}` is the local thermal speed, :math:`s` is distance along the photon packet's line of flight, and :math:`v_{s}` is projected velocity along this direction. Since :math:`v_{th}` is usually close to the sound speed, the Sobolev approximation is sometimes also called the supersonic approximation. - In line with this treatment, Sirocco neglects any thermal or microturbulent broadening associated with bound-bound transitions. It is the responsibility of the user to ensure that the Sobolev approximation is valid for the models they run. Static media and flows in which thermal speeds are comparable to bulk velocities should not be simulated with Sirocco. + In line with this treatment, SIROCCO neglects any thermal or microturbulent broadening associated with bound-bound transitions. It is the responsibility of the user to ensure that the Sobolev approximation is valid for the models they run. Static media and flows in which thermal speeds are comparable to bulk velocities should not be simulated with SIROCCO. - **The size of the line list** - An important challenge for Sirocco is to identify spectral lines that might be in resonance with a particular photon packet as it passes through a given cell. The computational cost of this task scales directly with the size of the line list. It is therefore not feasible to simply import the latest Kurucz line list (http://kurucz.harvard.edu/linelists/gfnew/), for example, which contains :math:`\simeq 10^9` transitions. Again, Sirocco is *not* a stellar atmosphere code. The default line list we use contains :math:`\simeq 10^4` transitions, which is sufficient for many applications. + An important challenge for SIROCCO is to identify spectral lines that might be in resonance with a particular photon packet as it passes through a given cell. The computational cost of this task scales directly with the size of the line list. It is therefore not feasible to simply import the latest Kurucz line list (http://kurucz.harvard.edu/linelists/gfnew/), for example, which contains :math:`\simeq 10^9` transitions. Again, SIROCCO is *not* a stellar atmosphere code. The default line list we use contains :math:`\simeq 10^4` transitions, which is sufficient for many applications. - One use case for Sirocco where it is important to have as complete a line list as possible is its deployment as the radiation module in radiation-hydrodynamics simulations where line-driving forces are important (Higginbottom et al., 2020; Higginbottom et al., 2014). Our approach here is to use Sirocco to estimate the ionization state and SED in each cell. This is then be passed to a stand-alone code that estimates the total line forces by summing over the complete Kurucz line list. + One use case for SIROCCO where it is important to have as complete a line list as possible is its deployment as the radiation module in radiation-hydrodynamics simulations where line-driving forces are important (Higginbottom et al., 2020; Higginbottom et al., 2014). Our approach here is to use SIROCCO to estimate the ionization state and SED in each cell. This is then be passed to a stand-alone code that estimates the total line forces by summing over the complete Kurucz line list. - **General Relativity** -Sirocco self-consistently carries out the special relativistic frame transformations required as photon packets travel through the grid and interact with the moving material in each cell. However, it does not account for any purely *general* relativistic effects. In particular, photon packets in Sirocco always travel in straight lines, rather than along geodesics. This will primarily affect the angular distributions of photon packets that are emitted or travel within, say, :math:`\simeq 10` gravitational radii of a compact object. This caveat should be kept in mind when modelling AGN and XRBs with Sirocco. However, we console ourselves with the thought that the physical and radiative properties of accretion flows in this regime remain quite uncertain. +SIROCCO self-consistently carries out the special relativistic frame transformations required as photon packets travel through the grid and interact with the moving material in each cell. However, it does not account for any purely *general* relativistic effects. In particular, photon packets in SIROCCO always travel in straight lines, rather than along geodesics. This will primarily affect the angular distributions of photon packets that are emitted or travel within, say, :math:`\simeq 10` gravitational radii of a compact object. This caveat should be kept in mind when modelling AGN and XRBs with SIROCCO. However, we console ourselves with the thought that the physical and radiative properties of accretion flows in this regime remain quite uncertain. - **Polarization** -In principle, polarization can be included quite naturally in Monte-Carlo radiative transfer, but the current version of Sirocco does not include a treatment of polarization. +In principle, polarization can be included quite naturally in Monte-Carlo radiative transfer, but the current version of SIROCCO does not include a treatment of polarization. - **Thermal and statistical equilibrium** -Sirocco assumes that the flow is always and everywhere in thermal and statistical equilibrium. That is, the code iterates towards a temperature and ionization state for each cell in which the heating and cooling rates in each cell balance and the net transition rate *into* any given atomic/ionic level matches the net transition rate *out of* that level. This implies that there is no concept of time in Sirocco -- the code is not designed to deal with non-equilibrium and/or time-dependent conditions. +SIROCCO assumes that the flow is always and everywhere in thermal and statistical equilibrium. That is, the code iterates towards a temperature and ionization state for each cell in which the heating and cooling rates in each cell balance and the net transition rate *into* any given atomic/ionic level matches the net transition rate *out of* that level. This implies that there is no concept of time in SIROCCO -- the code is not designed to deal with non-equilibrium and/or time-dependent conditions. -This limitation can be important even if the input radiation field is steady. For example, if the flow velocity in a grid cell with characteristic size :math:`\Delta x` is given by :math:`v`, matter will flow through the cell on a time-scale :math:`t_{flow} \sim \Delta x / v`. However, ionization equilibrium can only be established on a time-scale of :math:`t_{rec} \sim \alpha N_e`, where :math:`\alpha` is the relevant recombination coefficient, and :math:`N_e` is the local electron density. Thus if :math:`t_{flow} < t_{rec}`, the cell cannot be in ionization equilibrium. In sufficiently fast-moving flows, the ionization state can then become "frozen-in", i.e. fixed to approximately the state at the last point where equilibrium could be established. Since Sirocco currently has no concept of these time scales, it does not check for such non-equilibrium conditions. It is up to the user to carry out the relevant sanity checks on their models. +This limitation can be important even if the input radiation field is steady. For example, if the flow velocity in a grid cell with characteristic size :math:`\Delta x` is given by :math:`v`, matter will flow through the cell on a time-scale :math:`t_{flow} \sim \Delta x / v`. However, ionization equilibrium can only be established on a time-scale of :math:`t_{rec} \sim \alpha N_e`, where :math:`\alpha` is the relevant recombination coefficient, and :math:`N_e` is the local electron density. Thus if :math:`t_{flow} < t_{rec}`, the cell cannot be in ionization equilibrium. In sufficiently fast-moving flows, the ionization state can then become "frozen-in", i.e. fixed to approximately the state at the last point where equilibrium could be established. Since SIROCCO currently has no concept of these time scales, it does not check for such non-equilibrium conditions. It is up to the user to carry out the relevant sanity checks on their models. - **Dimensionality and resolution limits** -At present, Sirocco is (at most) a 2.5-dimensional code. That is, the coordinate grid is restricted to 2D and assumed to be symmetric about the y-axis. However, photon packet transport takes place in 2D and allows for a rotational component of motion around the y-axis. In principle, upgrading Sirocco to "full" 3D is fairly straightforward, but running such models would require significantly more computing resources. Memory requirements, in particular, scale directly with the number of grid cells used in a simulation. This is actually already a limitation for high-resolution models in 2D (or 2.5-D) and the reason we have set an upper limit of 500x500 cells as a default. +At present, SIROCCO is (at most) a 2.5-dimensional code. That is, the coordinate grid is restricted to 2D and assumed to be symmetric about the y-axis. However, photon packet transport takes place in 2D and allows for a rotational component of motion around the y-axis. In principle, upgrading SIROCCO to "full" 3D is fairly straightforward, but running such models would require significantly more computing resources. Memory requirements, in particular, scale directly with the number of grid cells used in a simulation. This is actually already a limitation for high-resolution models in 2D (or 2.5-D) and the reason we have set an upper limit of 500x500 cells as a default. -It is worth noting that Sirocco's memory requirements for computationally demanding simulations are driven by the approach to parallelization that has been adopted. Currently, parallelization relies exclusively on ``MPI`` and requires the computational grid to be copied in full to each ``MPI`` process (i.e. each core). Memory requirements therefore increase rapidly as the number of processors is increased. This situation could be improved by adopting a hybrid ``OpenMP`` and ``MPI`` approach. +It is worth noting that SIROCCO's memory requirements for computationally demanding simulations are driven by the approach to parallelization that has been adopted. Currently, parallelization relies exclusively on ``MPI`` and requires the computational grid to be copied in full to each ``MPI`` process (i.e. each core). Memory requirements therefore increase rapidly as the number of processors is increased. This situation could be improved by adopting a hybrid ``OpenMP`` and ``MPI`` approach. diff --git a/docs/sphinx/source/physics/macroatoms.rst b/docs/sphinx/source/physics/macroatoms.rst index c01660cdb..24713b5e8 100644 --- a/docs/sphinx/source/physics/macroatoms.rst +++ b/docs/sphinx/source/physics/macroatoms.rst @@ -5,7 +5,7 @@ Macro Atoms .. todo:: Add description of accelerated macro-atom scheme -The macro-atom scheme was created by Leon Lucy and is outlined in his 2002/03 papers. It was implemented in Sirocco by Stuart Sim, initially for the study of recombination lines in YSOs (Sim et al. 2005). +The macro-atom scheme was created by Leon Lucy and is outlined in his 2002/03 papers. It was implemented in SIROCCO by Stuart Sim, initially for the study of recombination lines in YSOs (Sim et al. 2005). Lucy (2002,2004) hereafter L02, L03 has shown that it is possible to calculate the emissivity of a gas in statistical equilibrium without approximation for problems with large departures from LTE. His `macro-atom` scheme allows for all possible transition paths from a given level, dispensing with the two-level approximation, and provides a full non-LTE solution for the level populations based on Monte Carlo estimators. The macro-atom technique has already been used to model Wolf-Rayet star winds (Sim 2004), AGN disc winds (Sim et al. 2008), supernovae (Kromer and Sim 2009, Kerzendorf and Sim 2014) and YSOs (Sim et al. 2005). A full description of the approach can be found in L02 and L03. @@ -85,7 +85,7 @@ This hybrid approach preserves the fast treatment of, for example, UV resonance Macro-atom Emissivity Calculation ======================================== -In order to preserve the philosophy that a detailed spectrum is calculated in a limited wavelength regime, Sirocco carries out a macro-atom emissivity calculation before the spectral cycles. The aim of this step is to calculate the luminosity contributed by macro-atoms -- equivalent to the total amount of reprocessed emission -- in the wavelength range being considered. +In order to preserve the philosophy that a detailed spectrum is calculated in a limited wavelength regime, SIROCCO carries out a macro-atom emissivity calculation before the spectral cycles. The aim of this step is to calculate the luminosity contributed by macro-atoms -- equivalent to the total amount of reprocessed emission -- in the wavelength range being considered. This process can be very computationally intensive, especially if the wavelength regime being simulated has very little emission from bound-free and line processes in the wind, but the overall broad-band emissivity is high. During the ionization cycles, the amount of energy absorbed into :math:`k`-packets and every macro-atom level is recorded using MC estimators. Once the ionization cycles are finished, and the model has converged, these absorption energies are split into a certain number of packets and tracked through the macro-atom machinery until a deactivation occurs. When this happens, the emissivity of the level the macro-atom de-activated from is incremented if the packet lies in the requested wavelength range. If it does not, then the packet is thrown away. It is easy to see how what is essentially a MC rejection method can be an inefficient way of sampling this parameter space. Fortunately, this problem is parallelised in the code. diff --git a/docs/sphinx/source/physics/radiative_transfer.rst b/docs/sphinx/source/physics/radiative_transfer.rst index e608a05ad..6abe14ad6 100644 --- a/docs/sphinx/source/physics/radiative_transfer.rst +++ b/docs/sphinx/source/physics/radiative_transfer.rst @@ -1,17 +1,17 @@ Radiative Transfer Modes ######################################################## -Sirocco has a number of different radiative transfer modes, which affect the treatment of lines and scattering, and also whether we use indivisible packet constraints or allow photon weights to be attenuated by continuum absorption. These modes are selected with the parameter :ref:`Line_transfer`. The different modes are briefly described on that parameter page. This page is designed to give an overview of the assumptions and concepts behind, as well as the basic operation of, the different techniques. The aim is that, in partnership with the parameter page and the atomic data documentation, all the information regarding the different radiative transfer modes should be present. +SIROCCO has a number of different radiative transfer modes, which affect the treatment of lines and scattering, and also whether we use indivisible packet constraints or allow photon weights to be attenuated by continuum absorption. These modes are selected with the parameter :ref:`Line_transfer`. The different modes are briefly described on that parameter page. This page is designed to give an overview of the assumptions and concepts behind, as well as the basic operation of, the different techniques. The aim is that, in partnership with the parameter page and the atomic data documentation, all the information regarding the different radiative transfer modes should be present. -For introductions and references regarding Monte Carlo radiative transfer techniques generally, we recommend reading `Noebauer & Sim 2019 `_. For specifics regarding Sirocco, we recommend reading `Long & Knigge 2002 `_ as well as PhD theses by `Higginbottom `_ and `Matthews `_. +For introductions and references regarding Monte Carlo radiative transfer techniques generally, we recommend reading `Noebauer & Sim 2019 `_. For specifics regarding SIROCCO, we recommend reading `Long & Knigge 2002 `_ as well as PhD theses by `Higginbottom `_ and `Matthews `_. Sobolev Approximation ====================== -Sirocco always uses the Sobolev approximation to treat line transfer. In this approximation, it is assumed that the thermal line width is small compared to the velocity gradient. The Sobolev approximation is described extensively in astrophysics literature, and so we do not describe it in detail here. We refer the users to section 8.2 of `Noebauer & Sim 2019 `_ and references there in for a discussion of the Sobolev escape probabilities approach. +SIROCCO always uses the Sobolev approximation to treat line transfer. In this approximation, it is assumed that the thermal line width is small compared to the velocity gradient. The Sobolev approximation is described extensively in astrophysics literature, and so we do not describe it in detail here. We refer the users to section 8.2 of `Noebauer & Sim 2019 `_ and references there in for a discussion of the Sobolev escape probabilities approach. Weight Reduction v Indivisible Packets ======================================= -Sirocco was originally written in such a way that photon packet weights were not indivisible and allowed to be attenuated. This is the way the code is described in the original `Long & Knigge 2002 `_ paper. In the standard, weight reduction mode, photon weights are attenuated by continuum opacities (free-free, bound-free). Conservation of energy is then hopefully achieved by calculating the emission from the wind . +SIROCCO was originally written in such a way that photon packet weights were not indivisible and allowed to be attenuated. This is the way the code is described in the original `Long & Knigge 2002 `_ paper. In the standard, weight reduction mode, photon weights are attenuated by continuum opacities (free-free, bound-free). Conservation of energy is then hopefully achieved by calculating the emission from the wind . In indivisible packet mode, there is a fundamental shift in philosophy. All energy packets are strictly indivisible and conserve energy whenever they undergo radiative processes (the only exception is adiabatic cooling). Thus, even bound-free absorption is dealt with at a single interaction point. @@ -37,7 +37,7 @@ Simple-atoms still interact with r- and k-packets, but do not possess internal t Isotropic v Anisotropic Line Scattering ============================================ -Sirocco always treats electron scattering as an isotropic process, and continuum emission processes are also treated as isotropic, except for Compton scattering. For Compton scattering, the direction and energy change is calculated self-consistently according to the energy change formula :math:`E/E'=1+(h \nu/mc^2)(1+\cos\theta)`. We first draw a random cross section that our photon packet will see. This cross section represents an energy change and hence a direction. The distribution of angles is taken care of by using a differential cross section vs energy change function. +SIROCCO always treats electron scattering as an isotropic process, and continuum emission processes are also treated as isotropic, except for Compton scattering. For Compton scattering, the direction and energy change is calculated self-consistently according to the energy change formula :math:`E/E'=1+(h \nu/mc^2)(1+\cos\theta)`. We first draw a random cross section that our photon packet will see. This cross section represents an energy change and hence a direction. The distribution of angles is taken care of by using a differential cross section vs energy change function. .. admonition:: Caution @@ -79,6 +79,6 @@ In the case of a resonance scatter with line transition u to j, the new frequenc The above formulae are the non-relativistic case, which is currently used in the code. However, this should in general be improved to use the special relativistic formula. This would produce more accurate Doppler shifts for the fastest regions of an outflow, as the current treatment introduces errors of order 5 Angstroms at the blue edges of the highest velocity absorption lines in quasar and CV wind models. -When real photons resonantly (or electron) scatter off real plasma in a flow, they conserve energy and frequency in the co-moving frame of the plasma. In the case of an outflow, doing the frame transformation from system->flow->system over the course of an interaction results in a redshifting of a photon, and as a result an energy loss - in other words, the photon does work on the flow even though energy is conserved in the co-moving frame. Indivisible packet schemes (such as macro-atoms) often enforce strict energy conservation in the frame of a given cell (physically, but see also `Lucy 2002 `_). This means that, when keeping track of packets in the observer frame, one needs to correct the energies (not just the frequencies) using a Doppler shift. Sirocco does **not** currently conserve energy in the co-moving frame. +When real photons resonantly (or electron) scatter off real plasma in a flow, they conserve energy and frequency in the co-moving frame of the plasma. In the case of an outflow, doing the frame transformation from system->flow->system over the course of an interaction results in a redshifting of a photon, and as a result an energy loss - in other words, the photon does work on the flow even though energy is conserved in the co-moving frame. Indivisible packet schemes (such as macro-atoms) often enforce strict energy conservation in the frame of a given cell (physically, but see also `Lucy 2002 `_). This means that, when keeping track of packets in the observer frame, one needs to correct the energies (not just the frequencies) using a Doppler shift. SIROCCO does **not** currently conserve energy in the co-moving frame. .. todo:: test whether this is an issue. diff --git a/docs/sphinx/source/physics/relativity_comoving.rst b/docs/sphinx/source/physics/relativity_comoving.rst index ee0ec1e3e..e95c907c1 100644 --- a/docs/sphinx/source/physics/relativity_comoving.rst +++ b/docs/sphinx/source/physics/relativity_comoving.rst @@ -1,7 +1,7 @@ Special Relativity and Co-Moving Frames --------------------------------------- -The current version of Sirocco incorporates special relativity and takes co-moving frame +The current version of SIROCCO incorporates special relativity and takes co-moving frame effects into account by default. Global properties of the wind, such as a densities are defined in the observer diff --git a/docs/sphinx/source/plotting.rst b/docs/sphinx/source/plotting.rst index 876a1a93a..517d09983 100644 --- a/docs/sphinx/source/plotting.rst +++ b/docs/sphinx/source/plotting.rst @@ -1,7 +1,7 @@ Plotting \& Processing Outputs ##################################### -Sirocco produces a large number of files in both binary and ascii format. Tools exist to examine the binary files. +SIROCCO produces a large number of files in both binary and ascii format. Tools exist to examine the binary files. .. toctree:: diff --git a/docs/sphinx/source/plotting/plot_wind.ipynb b/docs/sphinx/source/plotting/plot_wind.ipynb index 0f128de7e..7562aeba8 100644 --- a/docs/sphinx/source/plotting/plot_wind.ipynb +++ b/docs/sphinx/source/plotting/plot_wind.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "# Plotting Wind Properties\n", - "As described under [Models](../output/model.rst), Sirocco saves wind properties in binary wind_save files. This notebook explains how to read and plot wind variables for the ```cv_standard``` file found in the examples. Before running the python commands, you need to run the model from the command line. I suggest running the following commands, after you have compiled python:\n", + "As described under [Models](../output/model.rst), SIROCCO saves wind properties in binary wind_save files. This notebook explains how to read and plot wind variables for the ```cv_standard``` file found in the examples. Before running the python commands, you need to run the model from the command line. I suggest running the following commands, after you have compiled python:\n", "\n", " mkdir cv_test\n", " cd cv_test\n", diff --git a/docs/sphinx/source/py_progs.rst b/docs/sphinx/source/py_progs.rst index 1da99f562..3523167e5 100644 --- a/docs/sphinx/source/py_progs.rst +++ b/docs/sphinx/source/py_progs.rst @@ -1,8 +1,8 @@ -Sirocco Scripts +SIROCCO Scripts ################################ -There are several Sirocco scripts written to prepare input for -and analyse the output of Sirocco. +There are several SIROCCO scripts written to prepare input for +and analyse the output of SIROCCO. Some of the more useful scripts/modules are documented below. Alternatively, you can generate documentation for all the scripts by navigating to :code:`docs/pydocs` and running :code:`write_docs.py`. diff --git a/docs/sphinx/source/py_progs/MakeMacro.rst b/docs/sphinx/source/py_progs/MakeMacro.rst index ecb0e3666..752b04d44 100644 --- a/docs/sphinx/source/py_progs/MakeMacro.rst +++ b/docs/sphinx/source/py_progs/MakeMacro.rst @@ -8,9 +8,9 @@ To facillitate generation of data for MacroAtoms several routines exist, includi * MacroCombine.py These routines use data contained in a (local) Chianti repository and from TopBase to construct atomic data files that can be used to generate the atomic data -needed for MacroAtoms. Chiani is the primary source of data, but unfortunately for Sirocco, it does not include photionization x-sections, and so these are obtained from TopBase. +needed for MacroAtoms. Chiani is the primary source of data, but unfortunately for SIROCCO, it does not include photionization x-sections, and so these are obtained from TopBase. -**It should be emphacised at the outset that these routines, while they generate that can be read and used within in Sirocco, they do not guaranteed that the Macro atom models are physically sensible. In particular, it is easy to generate models that are overly complex or ones that do not include all of the sublevels of a particular ion** +**It should be emphacised at the outset that these routines, while they generate that can be read and used within in SIROCCO, they do not guaranteed that the Macro atom models are physically sensible. In particular, it is easy to generate models that are overly complex or ones that do not include all of the sublevels of a particular ion** Mechanics @@ -34,7 +34,7 @@ If for example, one wishes to make C a macro atom, one needs to run MakeMacro as MakeMacro.py c_5 20 MakeMacro.py c_6 20 True -These commands create all of the files needed to make carbon a macro atom. The number 20 implies that one wants to create 20 levels for each of the ions, and need not be the same for each of the ions. This number should be larger than the number of levels one wants to ulitmately use with Sirocco, and can be smaller. The extra command line option True for c_6 imples causes a bare C ion to be created for C +These commands create all of the files needed to make carbon a macro atom. The number 20 implies that one wants to create 20 levels for each of the ions, and need not be the same for each of the ions. This number should be larger than the number of levels one wants to ulitmately use with SIROCCO, and can be smaller. The extra command line option True for c_6 imples causes a bare C ion to be created for C The files that will be created include: diff --git a/docs/sphinx/source/py_progs/development.rst b/docs/sphinx/source/py_progs/development.rst index aa2756afc..e34baeb70 100644 --- a/docs/sphinx/source/py_progs/development.rst +++ b/docs/sphinx/source/py_progs/development.rst @@ -1,7 +1,7 @@ Developer Tools --------------- -Several scripts have been written to assist in developing and documenting *Sirocco*. +Several scripts have been written to assist in developing and documenting *SIROCCO*. Documentation diff --git a/docs/sphinx/source/py_progs/plotting.rst b/docs/sphinx/source/py_progs/plotting.rst index ffd47d0d7..c27486de1 100644 --- a/docs/sphinx/source/py_progs/plotting.rst +++ b/docs/sphinx/source/py_progs/plotting.rst @@ -1,6 +1,6 @@ Plotting ---------- -Several scripts have been developed to plot *Sirocco* output files. +Several scripts have been developed to plot *SIROCCO* output files. .. toctree:: diff --git a/docs/sphinx/source/py_progs/py4py.rst b/docs/sphinx/source/py_progs/py4py.rst index 06db4a4a0..1d3935795 100644 --- a/docs/sphinx/source/py_progs/py4py.rst +++ b/docs/sphinx/source/py_progs/py4py.rst @@ -1,7 +1,7 @@ py4py -------- -`py4py` is a module written in Sirocco for reading, processing and visualising the input and output -files of the *c* code *Sirocco*. +`py4py` is a module written in SIROCCO for reading, processing and visualising the input and output +files of the *c* code *SIROCCO*. Installation instructions can be found in the associated README.md diff --git a/docs/sphinx/source/py_progs/regression.rst b/docs/sphinx/source/py_progs/regression.rst index dfe0ba410..a80111963 100644 --- a/docs/sphinx/source/py_progs/regression.rst +++ b/docs/sphinx/source/py_progs/regression.rst @@ -1,12 +1,12 @@ Regression ---------- -Primarily to verify that changes made to Sirocco do not inadvertently cause unexpected changes +Primarily to verify that changes made to SIROCCO do not inadvertently cause unexpected changes if models, several routines exist to run a fixed set of (relatively fast) models that are **nominally** contained in Examples/regress. Developers are encouraged to use this routines, before they merge anything into one of the -major branches of Sirocco. +major branches of SIROCCO. The routines involved are @@ -21,9 +21,9 @@ Setup ===== Typically one should set up a directory, e.g Regression to run the routines, and, if for example, -sirocco87f, is the version of Sirocco being used when you set up the directory, being run. +sirocco87f, is the version of SIROCCO being used when you set up the directory, being run. -Sirocco should be compiled with mpicc before running the regression program +SIROCCO should be compiled with mpicc before running the regression program Basic Procedure =============== diff --git a/docs/sphinx/source/py_progs/running.rst b/docs/sphinx/source/py_progs/running.rst index 03779aa42..638202b4b 100644 --- a/docs/sphinx/source/py_progs/running.rst +++ b/docs/sphinx/source/py_progs/running.rst @@ -1,4 +1,4 @@ -Running Sirocco +Running SIROCCO -------------- .. autosummary:: @@ -8,7 +8,7 @@ Running Sirocco pf_grid run_many -Several scripts have been developed to run *Sirocco* and **Cloudy** simulations for comparison. +Several scripts have been developed to run *SIROCCO* and **Cloudy** simulations for comparison. .. autosummary:: :toctree: running diff --git a/docs/sphinx/source/quick.rst b/docs/sphinx/source/quick.rst index 6c200fd96..1e9fb6576 100644 --- a/docs/sphinx/source/quick.rst +++ b/docs/sphinx/source/quick.rst @@ -1,14 +1,14 @@ -*Quick Guide to Sirocco* +*Quick Guide to SIROCCO* ######################################## -This guide is intended to allow users to install Sirocco, to run Sirocco as a +This guide is intended to allow users to install SIROCCO, to run SIROCCO as a computer program and then to check whether the run has completed as expected. It does not describe (except in passing) any information about the physics of -Sirocco, the details of a particular wind model, or criteria for evaluating whether +SIROCCO, the details of a particular wind model, or criteria for evaluating whether the inputs correspond to a plausible model of an astrophysical system. -* :ref:`Installation ` -- how to install Sirocco from github and to run a model -* :ref:`Creating the input file for Sirocco ` -- Simple instructions how to set up a model interactively -* :ref:`The files produced by Sirocco ` -- A quick look at the output files +* :ref:`Installation ` -- how to install SIROCCO from github and to run a model +* :ref:`Creating the input file for SIROCCO ` -- Simple instructions how to set up a model interactively +* :ref:`The files produced by SIROCCO ` -- A quick look at the output files * :ref:`Evaluation of the results ` -- A discussion of whether a model has run as required, or not diff --git a/docs/sphinx/source/radiation.rst b/docs/sphinx/source/radiation.rst index a5e2acb72..488f38e82 100644 --- a/docs/sphinx/source/radiation.rst +++ b/docs/sphinx/source/radiation.rst @@ -7,7 +7,7 @@ Radiation Sources External Radiation Sources ========================== -In generic terms, there are two main external radiation sources for any Sirocco calculation: a **Central Source** which can be a normal star, a WD, or a BH, and a **disk**. Even though Sirocco supports the existence of a secondary star for the purposes of calculating when light from a disk system is occulted, the secondary star does not radiate. +In generic terms, there are two main external radiation sources for any SIROCCO calculation: a **Central Source** which can be a normal star, a WD, or a BH, and a **disk**. Even though SIROCCO supports the existence of a secondary star for the purposes of calculating when light from a disk system is occulted, the secondary star does not radiate. Photons for radiation from the central object emerge uniformly over its surface, except when a lamp-post geometry is specified for the ``bh`` or ``agn`` system types. In this lamp-post case, radiation originates from a point source above @@ -64,10 +64,10 @@ the form :doc:`/input/parameters/Disk/Disk.rad_type_in_final_spectrum` is also n Spectra from a model grid (details) =================================== -Sirocco was initially written to model the winds of cataclysmic variables (CVs). Although the spectra of the disks of cataclymic +SIROCCO was initially written to model the winds of cataclysmic variables (CVs). Although the spectra of the disks of cataclymic variables are often modelled in terms of blackbodies, the spectra of CVs show clear evidence of features that arise from the i disk (as well as the wind). The features that arise from the disk resemble in many respects those that arise from -an appropriately weighted set of stellar atmospheres. To allow for this possibility, Sirocco can be configured to read +an appropriately weighted set of stellar atmospheres. To allow for this possibility, SIROCCO can be configured to read a set of models characterized by a temperature and log g, and produce spectra of either the central object or the disk by interpolating on t and log g. The data that must read in consists of a file that associates a temperature and log g with the indvidual spectra. @@ -100,7 +100,7 @@ The 3 column format above is required. If one wants to use a set of models that simply choose a value for the second column. The use case here is fairly specific, especially with regard to the first parameter T. If the disk or central object temperature outside the -temperatures in the grid, then Sirocco will "adjust" the spectrum assuming that the overall spectrum changes as a BB +temperatures in the grid, then SIROCCO will "adjust" the spectrum assuming that the overall spectrum changes as a BB would, but the features in the spectrum are unchanged. If the gravity goes outside the range of the grid, the closest value is chosen. diff --git a/docs/sphinx/source/radiation/banding.rst b/docs/sphinx/source/radiation/banding.rst index 802c9558f..8e62aa614 100644 --- a/docs/sphinx/source/radiation/banding.rst +++ b/docs/sphinx/source/radiation/banding.rst @@ -36,7 +36,7 @@ be generated as most of the luminosity is generated at lower frequencies. It is possible to get around this problem by generating an increasingly large number of photons. But, this is computationally expensive and inefficient. -In order to cope with cases this, Sirocco implements *importance sampling* which +In order to cope with cases this, SIROCCO implements *importance sampling* which effectively increases the number of photons which are sampled from specific frequency bands considered important. Photons are now generated with the weight, @@ -61,7 +61,7 @@ preferred sampling method. Available Sampling Schemes ========================== -Sirocco currently implements seven pre-defined frequency bands and and two +SIROCCO currently implements seven pre-defined frequency bands and and two flexible *run time* banding schemes. The parameter used to define the photon sampling scheme is, @@ -120,7 +120,7 @@ where the sum of the fractions for each band must be equal to or less than one. .. admonition :: Maximum Number of Bands Currently, a maximum of 20 frequency bands can be defined. If a user - attemps to specify more than than 20 bands, Sirocco will create an error + attemps to specify more than than 20 bands, SIROCCO will create an error message and fallback to using 20 bands. cloudy_test @@ -147,7 +147,7 @@ many orders of magnitude. .. admonition :: Maximum Number of Bands Currently, a maximum of 20 frequency bands can be defined. If a user - attemps to specify more than than 20 bands, Sirocco will create an error + attemps to specify more than than 20 bands, SIROCCO will create an error message and fallback to using 20 bands. .. admonition :: Minimum Fraction diff --git a/docs/sphinx/source/running_sirocco.rst b/docs/sphinx/source/running_sirocco.rst index c0e9cdbff..37b18e528 100644 --- a/docs/sphinx/source/running_sirocco.rst +++ b/docs/sphinx/source/running_sirocco.rst @@ -1,7 +1,7 @@ -Running Sirocco +Running SIROCCO ############## -The normal way to run Sirocco is simply to enter +The normal way to run SIROCCO is simply to enter .. code :: bash @@ -10,21 +10,21 @@ The normal way to run Sirocco is simply to enter where ``xxx`` is the root name of a parameter file. (The full name ``xxx.pf`` can also be entered). -However Sirocco features a number of command line options which can be used +However SIROCCO features a number of command line options which can be used to modify it's operation. These include the following: -h - Causes Sirocco to print out a brief help message and quit. The help message + Causes SIROCCO to print out a brief help message and quit. The help message principally describes the command line options. -i (or --dry-run) - Causes Sirocco to read and verify the inputs, writing a clean version of the input + Causes SIROCCO to read and verify the inputs, writing a clean version of the input file ``xxx.pf`` to the output file ``xxx.out.pf``, and then stop. This option is useful for setting up a proper ``.pf`` file. (Often one will want to copy ``xxx.out.pf`` back to ``xxx.pf`` before proceeding. -t time_max - Limits a run of Sirocco to approximately time_max in sec. This switch is + Limits a run of SIROCCO to approximately time_max in sec. This switch is used in situations where one would like to check whether the routine is operating properly be continuing, or where one needs to checkpoint the program after a certain period of time (due for example to time limits placed on jobs in a Beowulf cluster). @@ -44,12 +44,12 @@ to modify it's operation. These include the following: which will allow one to set all of the detailed spectral parameters anew. -v n - Changes the amount of information printed to the screen by Sirocco during a run. + Changes the amount of information printed to the screen by SIROCCO during a run. The default is 4. Larger numbers increase this. Smaller numbers decrease it. The log files are not affected. --rseed - Causes Sirocco to use a random number seed that is time-based, rather than fixed. + Causes SIROCCO to use a random number seed that is time-based, rather than fixed. In most cases, a fixed seed is preferred so that problems can be replicated, but if is repeating the same calculation multiple times, then one may want a random seed. @@ -57,12 +57,12 @@ to modify it's operation. These include the following: Save or load the RNG state to file, to allow persistent RNG states between restarts --version - Causes Sirocco to print out the version number and commit hash (and whether + Causes SIROCCO to print out the version number and commit hash (and whether uncommitted files exist, and then stop. -p n_steps Changes the number of photons generated during ionization cycles so that the - number increases logarithmically to the maximum value. This can help speed up Sirocco + number increases logarithmically to the maximum value. This can help speed up SIROCCO simulations but check the covergence of the wind. The number ``n_steps`` is optional, and specifies the number of decades over which the increase takes place. @@ -71,7 +71,7 @@ to modify it's operation. These include the following: Special switches ================ -Sirocco has a number of other switches that are not intended for the general user, but +SIROCCO has a number of other switches that are not intended for the general user, but which may be useful in certain special cases. These include: -d @@ -85,9 +85,9 @@ which may be useful in certain special cases. These include: -e n Where ``n`` is a number, changes the number of errors of a specific type that are allowed to occur before the program gives up. For a variety of reasons, - errors are expected during Sirocco runs. + errors are expected during SIROCCO runs. Most of these errors are harmless in the sense that they occur rarely. - But if an error occurs too often, something is seriously and so Sirocco halts at that point. + But if an error occurs too often, something is seriously and so SIROCCO halts at that point. The default is :math:`10^{5}` (per process). -e_write n @@ -103,7 +103,7 @@ which may be useful in certain special cases. These include: too distant future. -srclassic - Use Sirocco with full special relativity for Doppler shits, etc., but do not include any co-moving frame effects. + Use SIROCCO with full special relativity for Doppler shits, etc., but do not include any co-moving frame effects. -no-matrix-storage Do not store macro-atom transition matrices if using the macro-atom line transfer and the matrix matom_transition_mode. @@ -114,19 +114,19 @@ which may be useful in certain special cases. These include: -include_partial_cells Include wind cells that are only partially filled by the wind -Running Different Versions of Sirocco +Running Different Versions of SIROCCO ================================= -Once you have Sirocco up and running, you can also install and run different versions of Sirocco. This is particularly useful if you want to run and compare an older model from a previous paper or how the outputs have evolved. +Once you have SIROCCO up and running, you can also install and run different versions of SIROCCO. This is particularly useful if you want to run and compare an older model from a previous paper or how the outputs have evolved. -You can store multiple older versions of Sirocco by recompiling a newer version. +You can store multiple older versions of SIROCCO by recompiling a newer version. * Pull in the version of the program you want using git. -* Then navigate with the terminal into Sirocco's :code:`source` folder. +* Then navigate with the terminal into SIROCCO's :code:`source` folder. * Run "make all" to recompile all programs with the new updates. * The process will put the new binaries into the :code:`bin/` directory and not delete what was already there. -You can then run a specific installed version by replacing the Sirocco executable, eg :code:`sirocco root.pf`, with the version you desire, eg. +You can then run a specific installed version by replacing the SIROCCO executable, eg :code:`sirocco root.pf`, with the version you desire, eg. .. code :: bash diff --git a/docs/sphinx/source/wind_models.rst b/docs/sphinx/source/wind_models.rst index 8d82ad7af..cd5d75707 100644 --- a/docs/sphinx/source/wind_models.rst +++ b/docs/sphinx/source/wind_models.rst @@ -1,7 +1,7 @@ Wind Models ########### -Sirocco has various wind models available, including parameterised wind models and the ability to import models. The links below describe comprehensive details of some of the models available. +SIROCCO has various wind models available, including parameterised wind models and the ability to import models. The links below describe comprehensive details of some of the models available. A complete list of the types of wind models can be found in the :ref:`Wind Model Parameters` section. Subsequently, a complete list of each wind model's possible parameters can be found in the :ref:`Winds` section. diff --git a/docs/sphinx/source/wind_models/importing_models.rst b/docs/sphinx/source/wind_models/importing_models.rst index 29df5393c..29e71581c 100644 --- a/docs/sphinx/source/wind_models/importing_models.rst +++ b/docs/sphinx/source/wind_models/importing_models.rst @@ -3,7 +3,7 @@ Importing Models ################ -Sirocco can read 1D or 2.5D grids of density and velocity, instead of setting up +SIROCCO can read 1D or 2.5D grids of density and velocity, instead of setting up the model from an analytic prescription. Caution should be exercised with this mode, as it is still in a development phase, and the mode requires the user to ensure that things like mass and angular momentum conservation are enforced. @@ -52,7 +52,7 @@ In order to create your own model, there are a few important things to consider: * Ghost cells **must** be included. This means that additional rows and columns of cells must be included at the edges of the grid, and they must be excluded from the wind so that their temperatures and densities are set to zero, but - have a velocity that Sirocco can interpolate with. + have a velocity that SIROCCO can interpolate with. * i and j correspond to rows and columns respectively, so that the first row of cells at the disk plane has i = 0. * rho the density of the cell in cgs units @@ -70,7 +70,7 @@ Spherical Grids --------------- Using a spherical coordinate system, a 1D spherically symmetric model can be -read into Sirocco. +read into SIROCCO. To read in a grid of this type, the following columns are required for each cell: @@ -88,7 +88,7 @@ To read in a grid of this type, the following columns are required for each cell Cylindrical Grids ----------------- -Using cylindrical coordinates, a 2.5D model can be read into Sirocco. +Using cylindrical coordinates, a 2.5D model can be read into SIROCCO. .. admonition :: Grid Coordinates @@ -113,18 +113,18 @@ To read in a grid of this type, the following columns are required for each cell In principle, it is possible to read in an unstructured or non-linear cylindrical grid, i.e. where the cells are not regularly spaced, however, - Sirocco has been designed for structured grids with regular grid spacing, and + SIROCCO has been designed for structured grids with regular grid spacing, and as such there may be undefined behaviour for unstructured grids. Polar Grids ----------- -Using polar coordinates, a 2.5D model can be read into Sirocco. +Using polar coordinates, a 2.5D model can be read into SIROCCO. .. admonition :: Cartesian Velocity The velocity in for the polar grid is required to be in Cartesian - coordinates due to conventions within the Sirocco programming style. As such, + coordinates due to conventions within the SIROCCO programming style. As such, any polar velocity components must first be projected into their Cartesian equivalent. @@ -151,7 +151,7 @@ Setting Wind Temperatures ------------------------- Reading in a temperature is optional when importing a model. However, if one -temperature value for a cell is provided, then Sirocco assumes that this is +temperature value for a cell is provided, then SIROCCO assumes that this is the electron temperature and the radiation temperature will be initialised as, .. math :: @@ -184,7 +184,7 @@ in the wind. The following enumerator flags are used, W_ALL_INWIND = 0 // this cell is in the wind Whilst it is possible to set in `inwind = 1` for a grid cell, that is that the -cell is partially in the wind, Sirocco will instead set these cells with +cell is partially in the wind, SIROCCO will instead set these cells with `inwind = -2` and ignore these grid cells. Spherical @@ -217,7 +217,7 @@ guard cells in the same way as the a spherical grid, as above. For these cells, and all cells which do not make up the wind, an inwind value of -1 or -2 should be set. In this example, the theta cells extend beyond 90°. But, as they are not inwind, -Sirocco is happy to include these cells. For a stellar wind in polar coordinates, +SIROCCO is happy to include these cells. For a stellar wind in polar coordinates, these extra :math:`\theta` cells extending beyond 90° are required. .. figure:: ../images/import_polar_inwind.png @@ -234,26 +234,26 @@ these extra :math:`\theta` cells extending beyond 90° are required. A colour plot of the inwind variable for a stellar wind imposed on a polar coordinate grid. Important to note is the "halo" of inwind = -1 cells surrounding the inwind cells. The cells with inwind = 1 will be set to - inwind = -2 when imported into Sirocco and ignored. + inwind = -2 when imported into SIROCCO and ignored. Maximum and Minimum Wind Radius -------------------------------- The maximum and minimum spherical extent of the wind is calculated automatically -by Sirocco, and does not take into account guard cells when it is doing this. +by SIROCCO, and does not take into account guard cells when it is doing this. -Generating example inputs for testing and familiarizing oneself with Sirocco's import capability +Generating example inputs for testing and familiarizing oneself with SIROCCO's import capability =============================================================================================== -If one is trying to use the import capability of Sirocco for the first time, +If one is trying to use the import capability of SIROCCO for the first time, it will be useful to familiarize oneself with the process, and the file format -for a particular coordinate system, by running first running Sirocco on a model +for a particular coordinate system, by running first running SIROCCO on a model that is something similar to model to be imported, but which takes advantage of one of the kinematic models available with the code. For example, suppose you have a hydrodynamical simulation of an AGN wind which -is in polar coordinates and you want to use Sirocco to calculate the spectrum. +is in polar coordinates and you want to use SIROCCO to calculate the spectrum. Then you might create a model of an AGN with a similar coordinate system using, say, a Knigge Wood & Drew wind (and similar atomic data). For specificity, suppose this model has the root name "test" @@ -270,7 +270,7 @@ This produces a large number of ascii tables, which are described elsewhere In the py_progs directory, you will find 3 scripts, :code:`import_1d.py`, :code:`import_cyl.py` and :code:`import_rtheta.py`, which will convert one of the output files :code:`test.0.master.txt` to an import file, :code:`test.import.txt`, -that can be used with the import mode of Sirocco. The 3 different routines are +that can be used with the import mode of SIROCCO. The 3 different routines are for 1d spherical coordinates, and polar (r-theta) coordinates respectively. Assuming the py_progs directory is in your PATH, and given that our example is @@ -281,21 +281,21 @@ for cylindrical coordinates, one would run: import_cyl.py test At that point, you can test this import file, by modifying the first .pf file to -import mode (imported). Running Sirocco on this file, will result in your being +import mode (imported). Running SIROCCO on this file, will result in your being asked the name of the import file, and give you a "baseline" to import the hydrodynamical simulation to work. Note that one should not assume that spectra produced by the original run of -Sirocco and the run of the imported model will be identical. There are several +SIROCCO and the run of the imported model will be identical. There are several reasons for this: -First, in creating the original model, Sirocco accounts for the possibility that +First, in creating the original model, SIROCCO accounts for the possibility that some cells are partially in the wind. This is not possible in the imported models. Only cells that are complete in the wind are counted. -Second, within Sirocco, positions and velocities are assumed defined at the +Second, within SIROCCO, positions and velocities are assumed defined at the corners of cells, whereas densities are assumed to be cell centered. If one provides a table where all of the quantities are at the same exact position (namely density is at the same position as x), there will be a slight discrepancy between the way in model as calculated internally and as represented -within Sirocco. +within SIROCCO. diff --git a/docs/sphinx/source/wind_models/kwd.rst b/docs/sphinx/source/wind_models/kwd.rst index 974ba354f..1b5311041 100644 --- a/docs/sphinx/source/wind_models/kwd.rst +++ b/docs/sphinx/source/wind_models/kwd.rst @@ -1,7 +1,7 @@ KWD biconical wind prescription ############################################################ -`Knigge, Woods & Drew (1995) `_ developed a parameterization for a bi-conical flow, which in slightly modified form is built into Sirocco. +`Knigge, Woods & Drew (1995) `_ developed a parameterization for a bi-conical flow, which in slightly modified form is built into SIROCCO. In this parameterization, the wind is envisioned to have poloidal streamlines that all point to a position a distance d below the disk, as is shown below: @@ -12,7 +12,7 @@ shown below: As described by KWD95, streamlines emerge throughout the entire disk, with the innermost streamline just grazing the surface of the central object and the outermost streamline -emerging from the outer radius of the disk. In the current version of Sirocco, while this +emerging from the outer radius of the disk. In the current version of SIROCCO, while this is the default choice, the wind region can be restricted to streamlines that arise from between :math:`r_{min}` and :math:`r_{rmax}` as depicted by the above diagram. For fixed values of :math:`r_{min}` and :math:`r_{rmax}`, the wind will tend to be more collimated the larger the value of d. diff --git a/examples/reverb/reverb.ipynb b/examples/reverb/reverb.ipynb index b210afc63..62174b33b 100644 --- a/examples/reverb/reverb.ipynb +++ b/examples/reverb/reverb.ipynb @@ -5,9 +5,9 @@ "metadata": {}, "source": [ "# Reverberation Mapping\n", - "`Sirocco` has the capability to generate transfer functions/reverberation signatures for the systems it models. These describe how a change in the ionising continuum is reprocessed into a change in line emission. These signatures can (approximately) be recovered from observation, if there's a sufficiently series of line spectra with sufficiently high time- and wavelength-resolution. \n", + "`SIROCCO` has the capability to generate transfer functions/reverberation signatures for the systems it models. These describe how a change in the ionising continuum is reprocessed into a change in line emission. These signatures can (approximately) be recovered from observation, if there's a sufficiently series of line spectra with sufficiently high time- and wavelength-resolution. \n", "\n", - "The transfer function is the term $\\Psi$ in the equation $L(v, t) = \\int_0^\\infty \\Psi(v, \\tau) C(t - \\tau) d\\tau$. `Sirocco` can also generate response functions, $\\Psi_r$, used in $\\delta L(v, t) = \\int_0^\\infty \\Psi_r(v, \\tau) \\delta C(t - \\tau) d\\tau$ (a more observationally-accessible property).\n", + "The transfer function is the term $\\Psi$ in the equation $L(v, t) = \\int_0^\\infty \\Psi(v, \\tau) C(t - \\tau) d\\tau$. `SIROCCO` can also generate response functions, $\\Psi_r$, used in $\\delta L(v, t) = \\int_0^\\infty \\Psi_r(v, \\tau) \\delta C(t - \\tau) d\\tau$ (a more observationally-accessible property).\n", "\n", "\n", "The paper discussing our implementation, and the differences between $\\Psi$ and $\\Psi_r$ are:\n", @@ -22,7 +22,7 @@ "\n", "### Building the Model\n", "\n", - "To generate the data for reverberation mapping, `Sirocco` tracks the paths of photons as they travel throughout the wind, using them to build up a map of the response delay for each region of the wind. The settings to govern that can be found in the `### Parameters for Reverberation Modeling` section of the `.pf` file. \n", + "To generate the data for reverberation mapping, `SIROCCO` tracks the paths of photons as they travel throughout the wind, using them to build up a map of the response delay for each region of the wind. The settings to govern that can be found in the `### Parameters for Reverberation Modeling` section of the `.pf` file. \n", "\n", "#### reverb.pf\n", "```\n", @@ -42,12 +42,12 @@ "\n", "An important setting is `Reverb.filter_lines`. If this is set to `0`, *every* single photon that contributes to every single spectrum (including the pseudo-photons generated after each scattering event in `extract` mode). This produces unwieldy and incredibly vast output files! The `-1` filter mode instead includes only those photons whose last interaction was a line scatter or line emission. This produces large, but less overwhelming output files.\n", "\n", - "`Reverb.filter_lines N` allows the user to include `N` different `Reverb.filter_line X` lines, where `X` is the internal `Sirocco` data file line number for the line. This is a bit clunky and is easiest done by exploring the output files. This exploratory process is covered later on. \n", + "`Reverb.filter_lines N` allows the user to include `N` different `Reverb.filter_line X` lines, where `X` is the internal `SIROCCO` data file line number for the line. This is a bit clunky and is easiest done by exploring the output files. This exploratory process is covered later on. \n", "\n", "Ideally, this could be done in the input file by specifying the transition by species, upper and lower lines.\n", "\n", "### Running the Model\n", - "If you'd like to run `Sirocco` directly through this notebook, you'll have to make sure whatever virtual environment you're running it in has access to the `Sirocco` executables, and set the notebook itself to be trusted using `jupyter trust reverb.ipynb`. Then:" + "If you'd like to run `SIROCCO` directly through this notebook, you'll have to make sure whatever virtual environment you're running it in has access to the `SIROCCO` executables, and set the notebook itself to be trusted using `jupyter trust reverb.ipynb`. Then:" ] }, { @@ -121,7 +121,7 @@ "\n", "If there are errors during reading, this will leave a malformed `reverb.db` database- to restart generation with a fixed DB file, you'll have to delete the `reverb.db` file.\n", "\n", - "Once the database has been built, you can begin producing transfer functions. Each is only meaningful for a single emission line, so you need the `Sirocco` line number. This is an internal number that depends on your choice of data files. In practical terms, the easiest way to determine it is to filter the output data file by its second (`Lambda`, wavelength in $\\mathring{A}$) column to get the last (`Res.` for resonance) column, e.g. to find the $C_{IV}$ line you would do something like: \n", + "Once the database has been built, you can begin producing transfer functions. Each is only meaningful for a single emission line, so you need the `SIROCCO` line number. This is an internal number that depends on your choice of data files. In practical terms, the easiest way to determine it is to filter the output data file by its second (`Lambda`, wavelength in $\\mathring{A}$) column to get the last (`Res.` for resonance) column, e.g. to find the $C_{IV}$ line you would do something like: \n", "\n", "```bash\n", " awk '{if($2<1551 && $2>1549)print}' < reverb.delay_dump \n", @@ -174,7 +174,7 @@ "\n", "The photons used to generate this transfer function are then limited by a set of functions:\n", "\n", - "- The `line()` function limits the produced transfer function to only include photons in the specific line. The wavelength of the line is also needed- ideally, it would be possible to get this straight from the `Sirocco` atomic data files eventually. \n", + "- The `line()` function limits the produced transfer function to only include photons in the specific line. The wavelength of the line is also needed- ideally, it would be possible to get this straight from the `SIROCCO` atomic data files eventually. \n", "\n", "- As the data file contains multiple spectra, we select one with `spectrum()`, providing the spectrum number (1-indexed).\n", "\n", @@ -219,7 +219,7 @@ " }\n", ")\n", "\n", - "from ISirocco.display import Image\n", + "from ISIROCCO.display import Image\n", "Image(filename='C-IV.png')" ] }, @@ -307,7 +307,7 @@ " velocity=True,\n", ")\n", "\n", - "from ISirocco.display import Image\n", + "from ISIROCCO.display import Image\n", "Image(filename='C-IV.png')" ] }, @@ -359,7 +359,7 @@ "\n", "The basic transfer function generated is more properly the **'emissivity-weighted'** transfer function; it assumes that there are no changes in ionisation state in the wind, and that a 10% increase in central source luminosity results in a 10% increase in reprocessed luminosity in the wind. Given observations of the breathing BLR in AGN or of periods of anticorrelation between continuum and Hα luminosity in the AGN NGC5548, this is unlikely to be true!\n", "\n", - "`Sirocco` can produce **responsivity-weighted** transfer functions, A.K.A 'response functions'. It does this by performing two separate runs of models that bracket the main model in luminosity, and approximates the response function at the central luminosity by using the gradient between the two transfer functions. We produce two copies of the main model, each with all sources of luminosity adjusted up and down by the same value. Typically we choose 10%, assuming that the responsivity is constant across this region. As the regions of peak response typically move through the wind with changing luminosity (e.g. ionisation fronts being pushed back when continuum luminosity increases), selecting too large a bracket can result in issues with the emitting wind regions failing to overlap.\n", + "`SIROCCO` can produce **responsivity-weighted** transfer functions, A.K.A 'response functions'. It does this by performing two separate runs of models that bracket the main model in luminosity, and approximates the response function at the central luminosity by using the gradient between the two transfer functions. We produce two copies of the main model, each with all sources of luminosity adjusted up and down by the same value. Typically we choose 10%, assuming that the responsivity is constant across this region. As the regions of peak response typically move through the wind with changing luminosity (e.g. ionisation fronts being pushed back when continuum luminosity increases), selecting too large a bracket can result in issues with the emitting wind regions failing to overlap.\n", "\n", "\n", "### Building the Models\n", From 48932d77b1a3ae75e595c7608393766170d9e77b Mon Sep 17 00:00:00 2001 From: nscepi Date: Fri, 30 Aug 2024 15:33:12 +0100 Subject: [PATCH 09/24] Use appropriate coordinates in flux estimators output files --- source/rad_hydro_files.c | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/source/rad_hydro_files.c b/source/rad_hydro_files.c index feb108baf..319e1aa67 100644 --- a/source/rad_hydro_files.c +++ b/source/rad_hydro_files.c @@ -312,9 +312,18 @@ main (argc, argv) nplasma = wmain[nwind].nplasma; wind_n_to_ij (domain, plasmamain[nplasma].nwind, &i, &j); - fprintf (fptr_flux_theta, "%3d %3d %3d %10.3e %10.3e ", i, j, wmain[nwind].inwind, wmain[nwind].xcen[0], wmain[nwind].xcen[2]); //output geometric things - fprintf (fptr_flux_phi, "%3d %3d %3d %10.3e %10.3e ", i, j, wmain[nwind].inwind, wmain[nwind].xcen[0], wmain[nwind].xcen[2]); //output geometric things - fprintf (fptr_flux_r, "%3d %3d %3d %10.3e %10.3e ", i, j, wmain[nwind].inwind, wmain[nwind].xcen[0], wmain[nwind].xcen[2]); //output geometric things + if (zdom[domain].coord_type == SPHERICAL || zdom[domain].coord_type == RTHETA) + { + fprintf (fptr_flux_theta, "%3d %3d %3d %10.8e %10.8e ", i, j, wmain[nwind].inwind, wmain[nwind].rcen, wmain[nwind].thetacen / RADIAN); //output geometric things + fprintf (fptr_flux_phi, "%3d %3d %3d %10.8e %10.8e ", i, j, wmain[nwind].inwind, wmain[nwind].rcen, wmain[nwind].thetacen / RADIAN); //output geometric things + fprintf (fptr_flux_r, "%3d %3d %3d %10.8e %10.8e ", i, j, wmain[nwind].inwind, wmain[nwind].rcen, wmain[nwind].thetacen / RADIAN); //output geometric things + } + else if (zdom[domain].coord_type == CYLIND) + { + fprintf (fptr_flux_theta, "%3d %3d %3d %10.3e %10.3e ", i, j, wmain[nwind].inwind, wmain[nwind].xcen[0], wmain[nwind].xcen[2]); //output geometric things + fprintf (fptr_flux_phi, "%3d %3d %3d %10.3e %10.3e ", i, j, wmain[nwind].inwind, wmain[nwind].xcen[0], wmain[nwind].xcen[2]); //output geometric things + fprintf (fptr_flux_r, "%3d %3d %3d %10.3e %10.3e ", i, j, wmain[nwind].inwind, wmain[nwind].xcen[0], wmain[nwind].xcen[2]); //output geometric things + } for (ii = 0; ii < NFLUX_ANGLES; ii++) { fprintf (fptr_flux_theta, "%10.3e ", plasmamain[nplasma].F_UV_ang_theta_persist[ii]); From fa4f15cc07c1df54d117bd34c05302e3eb8bd3da Mon Sep 17 00:00:00 2001 From: jhmatthews Date: Fri, 18 Oct 2024 10:34:55 +0100 Subject: [PATCH 10/24] added sirocco files --- source/sirocco.c | 780 ++++++ source/sirocco.h | 1810 ++++++++++++++ source/sirocco_extern_init.c | 137 + source/sirocco_optd.c | 715 ++++++ source/sirocco_optd.h | 98 + source/sirocco_optd_extern_init.c | 19 + source/sirocco_optd_output.c | 247 ++ source/sirocco_optd_trans.c | 263 ++ source/sirocco_optd_util.c | 392 +++ source/swind.c | 716 ++++++ source/swind_ion.c | 842 +++++++ source/swind_macro.c | 984 ++++++++ source/swind_sub.c | 3889 +++++++++++++++++++++++++++++ source/swind_write.c | 297 +++ 14 files changed, 11189 insertions(+) create mode 100644 source/sirocco.c create mode 100644 source/sirocco.h create mode 100644 source/sirocco_extern_init.c create mode 100644 source/sirocco_optd.c create mode 100644 source/sirocco_optd.h create mode 100644 source/sirocco_optd_extern_init.c create mode 100644 source/sirocco_optd_output.c create mode 100644 source/sirocco_optd_trans.c create mode 100644 source/sirocco_optd_util.c create mode 100644 source/swind.c create mode 100644 source/swind_ion.c create mode 100644 source/swind_macro.c create mode 100644 source/swind_sub.c create mode 100644 source/swind_write.c diff --git a/source/sirocco.c b/source/sirocco.c new file mode 100644 index 000000000..94e414834 --- /dev/null +++ b/source/sirocco.c @@ -0,0 +1,780 @@ + +/***********************************************************/ +/** @file sirocco.c + * @author ksl + * @date January, 2018 + * + * @brief This file contains main and various related routines + * that are central to the operation of Python + * + ***********************************************************/ + +#include +#include +#include +#include +#include + +#include + +#include "atomic.h" +#include "sirocco.h" +#include "models.h" + +#define NSPEC 20 + + +/**********************************************************/ +/** + * @brief The main routine for Python, which supervises the ingest of data defining a model, the initializtion + * of the model, and actual run of the model, and writing the data to the disk + * + * + * @param [in] int argc The number of command line arguments + * @param [in] char * argv[] The command line arguments + * @return Always returns 0 + * + * @details + * + * ### Notes ### + * + * The main routine of Python is fairly simple conceptually. One gather the data, one allocates and intializes + * all of the data structures, one runs the ionization cycles and one run the detalied spectral cycles. + * The main ruoutin of Python supervises all of these things + * + * The main routine is complicated (mainly) due to the multiple ways a model can be run. + * + * * A model can be run from scratch + * * A model can be contined from an earlier run (increasing only the number of ionization or spectral cycles) + * * A new model can be run beginning with an old windsave file (allowing various parameters of the radiation souces to be set differently + * from the old model) + * + * Additionally, there is logic associated with the fact that different types of models require different data. An AGN for example does + * not have (the possibility of) a secondary star. Most of the inputs from Python come from a parameter file, but Python also has a robust + * set of command line switches (See parse_command_line). + * + * Once all the inputs are obtained, the main routine calles routines to allocate data structures needed to hold the model and to complete + * the intialization of both the data structues and various other variables. + * + * The it runs the ionization cycles (See calculate_ionization ); + * + * Finally it runs the cycles that calulate the detailed spectra (See make_spectra); + * + * + **********************************************************/ + +int +main (argc, argv) + int argc; + char *argv[]; +{ + WindPtr w; + + double freqmin, freqmax; + int n; + char values[LINELENGTH], answer[LINELENGTH]; + int get_models (); // Note: Needed because get_models cannot be included in templates.h + int dummy_spectype; + int opar_stat, restart_stat; + double time_max; + double lstar; + +/* Initialize MPI */ + + int my_rank; // these two variables are used regardless of parallel mode + int np_mpi; // rank and number of processes, 0 and 1 in non-parallel + +#ifdef MPI_ON + int mpi_err = MPI_Init (&argc, &argv); + if (mpi_err != EXIT_SUCCESS) + { + Error ("Failed to initialise MPI\n"); + Exit (mpi_err); + } + MPI_Comm_rank (MPI_COMM_WORLD, &my_rank); + MPI_Comm_size (MPI_COMM_WORLD, &np_mpi); +#else + my_rank = 0; + np_mpi = 1; +#endif + +#ifdef CUDA_ON + int cuda_err = cusolver_create (); + if (cuda_err != EXIT_SUCCESS) + { + Error ("Failed to initialise CUDA/cuSolver\n"); + Exit (cuda_err); + } +#endif + + /* Turn off gsl error handling so that the code does not abort on error */ + gsl_set_error_handler_off (); + + np_mpi_global = np_mpi; // Global variable which holds the number of MPI processes + rank_global = my_rank; // Global variable which holds the rank of the active MPI process + Log_set_mpi_rank (my_rank, np_mpi); // communicates my_rank to kpar + +/* This completes the initialisation of mpi */ + + opar_stat = 0; /* Initialize opar_stat to indicate that if we do not open a rdpar file, + the assumption is that we are reading from the command line */ + restart_stat = FALSE; /* Assume initially that these is a new run from scratch, and not + a restart */ + time_max = 13.8e9 * 3.2e7; /* The maximum time the program will run without stopping. This + is initially set to the lifetime of the universe */ + + set_max_time (files.root, time_max); + + rel_mode = REL_MODE_FULL; + run_xtest = FALSE; + NWAVE_MAX = (int) NWAVE_IONIZ; + + /* Set the verbosity level for logging. To get more info raise the verbosity level to a higher number. To + get less set the verbosity to a lower level. The verbosity can be reset from the comamnd line */ + + verbosity = 3; + Log_set_verbosity (verbosity); + + /* initialise the advanced mode flags (all set to 0) which is a structure in sirocco.h */ + + init_advanced_modes (); + + /* Parse the command line. Get the root. create files.diagfolder + diagfiles */ + + + restart_stat = parse_command_line (argc, argv); + + /* If the restart flag has been set, we check to see if a windsave file exists. If it doues we will + we will restart from that point. If the windsave file does not exist we will start from scratch */ + + init_log_and_windsave (restart_stat); + + Log ("Thread %d starting.\n", my_rank); + + /* Start logging of errors and comments */ + + Log ("!!Python Version %s \n", VERSION); //54f -- ksl -- Now read from version.h + Log ("!!Git commit hash %s\n", GIT_COMMIT_HASH); + + /* warn the user if there are uncommited changes */ + + int git_diff_status = GIT_DIFF_STATUS; + if (git_diff_status > 0) + Log ("!!Git: This version was compiled with %i files with uncommitted changes.\n", git_diff_status); + + Log ("!!Python is running with %d processors\n", np_mpi_global); + Log ("This is MPI task number %d (a total of %d tasks are running).\n", rank_global, np_mpi_global); + + Debug ("Debug statements are on. To turn off use lower verbosity (< 5).\n"); + + xsignal (files.root, "%-20s Initializing variables for %s\n", "NOK", files.root); + + opar_stat = setup_created_files (); + +/* Allocate the domain structure */ + + zdom = (DomainPtr) calloc (sizeof (domain_dummy), MAX_DOM); + + /* BEGIN GATHERING INPUT DATA */ + + /* Describe the basic calculation in terms of the number of iterations which will + be used to calculate the wind parameters and the number of iterations and wavelength + range which will be used for the final spectrom. Also describe the observer's views + of the system */ + + + if (restart_stat == TRUE) /* We want to continue a run. This is generally used + because we had to limit to runtime of sirocco or we decided + we needed more ionization or spectral cycles */ + { + Log ("Continuing a previous run of %s \n", files.root); + strcpy (files.old_windsave, files.root); + strcat (files.old_windsave, ".wind_save"); + + if (wind_read (files.old_windsave) < 0) + { + Error ("sirocco: Unable to open %s\n", files.old_windsave); + Exit (0); + } + w = wmain; + + geo.run_type = RUN_TYPE_RESTART; + + xsignal (files.root, "%-20s Read %s\n", "COMMENT", files.old_windsave); + + if (geo.model_count > 0) //We have previously used models - we need to read them in again + { + for (n = 0; n < geo.model_count; n++) + { + get_models (geo.model_list[n], 2, &dummy_spectype); + } + } + if (geo.disk_tprofile == DISK_TPROFILE_READIN) //We also need to re-read in any previously used disk temperature profile + { + rdstr ("Disk.T_profile_file", files.tprofile); + read_non_standard_disk_profile (files.tprofile); + } + if (geo.pcycle > 0) + { + spec_read (files.specsave); + xsignal (files.root, "%-20s Read %s\n", "COMMENT", files.specsave); + } + } + + else if (restart_stat == FALSE) /* We are starting a new run, which is the normal mode of operation */ + { + + /* First, establish the overall system type. System type should be a physical system, + * to make things easier for the user. So really want system types to be something like + * CV, YSO, AGN so that defaults can be set. This is now issue #420 + */ + + geo.system_type = SYSTEM_TYPE_STAR; + geo.run_type = RUN_TYPE_NEW; + geo.mono_freq = 0; + + + strcpy (answer, "star"); + sprintf (values, "%d,%d,%d,%d,%d", SYSTEM_TYPE_STAR, SYSTEM_TYPE_CV, SYSTEM_TYPE_BH, SYSTEM_TYPE_AGN, SYSTEM_TYPE_PREVIOUS); + geo.system_type = rdchoice ("System_type(star,cv,bh,agn,previous)", values, answer); + + + if (geo.system_type == SYSTEM_TYPE_PREVIOUS) + { + + /* This option is for the confusing case where we want to start with a previous wind + model,(presumably because that run produced a wind close to the one we are looking for, + but we are going to change some parameters that do not affect the wind geometry, + We will write use new filenames for the results, so all of the previous work is still saved, + Note that wind_read also reads the atomic data file that was used to create the previous run of the data. + */ + + strcpy (files.old_windsave, "earlier.run"); + rdstr ("Wind.old_windfile(root_only)", files.old_windsave); + strcat (files.old_windsave, ".wind_save"); + + + Log ("Starting a new run from scratch starting using a previous windfile\n"); + + + if (wind_read (files.old_windsave) < 0) + { + Error ("sirocco: Unable to open %s\n", files.old_windsave); + Exit (0); + } + + geo.run_type = RUN_TYPE_PREVIOUS; + + w = wmain; + geo.wcycle = 0; + geo.pcycle = 0; + geo.model_count = 0; + } + + + if (geo.run_type == RUN_TYPE_NEW || geo.run_type == RUN_TYPE_PREVIOUS) + { + /* This option is the most common one, where we are starting to define a completely new system. + */ + + if (geo.run_type == RUN_TYPE_NEW) + { + init_geo (); + } + + /* get_stellar_params gets information like mstar, rstar, tstar etc. + it returns the luminosity of the star */ + + lstar = get_stellar_params (); + + /* Describe the disk */ + + get_disk_params (); + + + /* describe the boundary layer / agn components to the spectrum if they exist. + So that initial condiditions for the bl and agn are initialized sensibly this has + to come after the disk is defined. + */ + + get_bl_and_agn_params (lstar); + + /* At this point we check whether we have any sources of radiation and exit if we do not */ + + if (!geo.star_radiation && !geo.disk_radiation && !geo.bl_radiation && !geo.wind_radiation && !geo.agn_radiation) + { + Error ("sirocco: No radiation sources so nothing to do but quit!\n"); + Exit (0); + } + + /* Describe the wind (or more correctly the various domains). + */ + + rdpar_comment ("Parameters describing the various winds or coronae in the system"); + + if (geo.run_type == RUN_TYPE_NEW) + { + geo.ndomain = 1; + rdint ("Wind.number_of_components", &geo.ndomain); + + if (geo.ndomain > MAX_DOM) + { + Error ("Maximum number of wind components allowed is %d\n", MAX_DOM); + Exit (EXIT_FAILURE); + } + + for (n = 0; n < geo.ndomain; n++) + { + get_domain_params (n); + } + } + + } + } + + /* zdom only temporarily needs to be MAX_DOM. Now that we know the number of domains, we'll reallocate + * the domain to make it smaller */ + zdom = realloc (zdom, sizeof (domain_dummy) * geo.ndomain); + if (zdom == NULL) + { + Error ("sirocco: unable to re-allocate space for domain structure from %d domains to %d domains\n", MAX_DOM, geo.ndomain); + Exit (EXIT_FAILURE); + } + +/* Get the remainder of the input data. Note that the next few lines are read from the input file whether or not the windsave file was read in, + because these are things one would like to be able to change even if we have read in an old windsave file. init_photons reads in + the numbers of ionization and spectral cycles and then instatiates PhotPtr. It is possilbe that this should be moved to else where in + the flow of reading in data. + */ + + rdpar_comment ("Parameters associated with photon number, cycles,ionization and radiative transfer options"); + init_photons (); + init_ionization (); + + /* Note: ksl - At this point, SYSTEM_TYPE_PREVIOUS refers both to a restart and to a situation where + * one is starting from an early wind file as implemented this is quite restrictive about what one + * can change in the previous case. */ + + if (geo.run_type == RUN_TYPE_NEW) + { + + /* Getting the atomic data has been moved here as when it was deeply nested in init_ionization, it made it very + * difficult to write unit tests or other tests. The key issue is that init_ionization is initialising too + * much all at once, which makes it very difficult to initialise or modify specific things for testing or debug + * purposes */ + rdstr ("Atomic_data", geo.atomic_filename); + setup_atomic_data (geo.atomic_filename); + + /* Describe the wind, by calling get_wind_params one or more times + and then gets params by calling e.g. get_sv_wind_params() */ + + for (n = 0; n < geo.ndomain; n++) + { + rdpar_comment ("Parameters for Domain %d", n); + get_wind_params (n); + } + + } // End of block to define a model for the first time + else if (modes.zeus_connect == 1) /* We are in rad-hydro mode, we want the new density and temperature */ + { + /* Hydro takes the wind domain number as an argument in the current domains setup */ + Log ("We are going to read in the density and temperature from a zeus file\n"); + get_hydro (geo.hydro_domain_number); //This line just populates the hydro structures + } + + + /* Calculate parameters associated with the binary star system */ + + if (geo.binary) + binary_basics (); + + /* Check that the parameters which have been supplied for the star, disk and boundary layer will + allow generation of photons where that is appropriate */ + + if (geo.tstar <= 0.0) + geo.star_radiation = 0; + if (geo.disk_mdot <= 0.0 && geo.disk_tprofile == DISK_TPROFILE_STANDARD) + geo.disk_radiation = 0; + if (geo.t_bl <= 0.0 || geo.lum_bl <= 0.0) + geo.bl_radiation = 0; + + /* If the disk radius is <0, assume no disk was intended. */ + + if (geo.disk_rad_max <= 0.0) + { + geo.disk_type = DISK_NONE; + geo.disk_radiation = 0; + } + + if (geo.star_radiation) + Log ("There is a star which radiates\n"); + else + Log ("The star in the system does not radiate\n"); + + if (!geo.disk_type) + Log ("There is no disk in the system \n"); + else if (!geo.disk_radiation) + Log ("The disk exists, but only absorbs photons\n"); + else + Log ("There is a disk which radiates and absorbs\n"); + + if (geo.bl_radiation || (geo.agn_radiation && geo.system_type != SYSTEM_TYPE_AGN)) + Log ("There is a boundary layer which radiates\n"); + else + Log ("There is no boundary layer\n"); + + if (geo.agn_radiation) + Log ("There is a BH which radiates\n"); + else + Log ("There is no BH \n"); + + if (geo.rt_mode == RT_MODE_MACRO) + { + if (nlevels_macro == 0) + { + Error ("THIS IS A MACROATOM CALCULATION WITH NO MACROLEVELS\n"); + } + else + { + Log ("This is a macro-atom calculation\n"); + } + } + else + { + Log ("This is a simple atom calculation\n"); + } + + /* Describe the spectra which will be extracted and the way it will be extracted */ + + + if (geo.pcycles > 0 && geo.pcycle == 0) + { + + rdpar_comment ("Parameters defining the spectra seen by observers\n"); + + if (geo.star_radiation) + { + geo.star_spectype = geo.star_ion_spectype; + get_spectype (geo.star_radiation, "Central_object.rad_type_in_final_spectrum(bb,models,uniform,mono)", &geo.star_spectype); + } + + if (geo.disk_radiation) + { + geo.disk_spectype = geo.disk_ion_spectype; + get_spectype (geo.disk_radiation, "Disk.rad_type_in_final_spectrum(bb,models,uniform,mono,mod_bb)", &geo.disk_spectype); + } + + if (geo.bl_radiation) + { + geo.bl_spectype = geo.bl_ion_spectype; + get_spectype (geo.bl_radiation, "Boundary_layer.rad_type_in_final_spectrum(bb,models,uniform)", &geo.bl_spectype); + } + + if (geo.agn_radiation) + { + // This block will run for both AGN, *and* some versions of a boundary layer. + // Even though we're setting the same params, we need to change the wording based on the system, unfortunately. + geo.agn_spectype = geo.agn_ion_spectype; + + // If there is 'AGN radiation' that genuinely *is* AGN radiation (and not a star boundary layer + if (geo.system_type == SYSTEM_TYPE_AGN || geo.system_type == SYSTEM_TYPE_BH) + { + get_spectype (geo.agn_radiation, "Central_object.rad_type_in_final_spectrum(bb,models,power,cloudy,brems,mono)", &geo.agn_spectype); + } + else + { + get_spectype (geo.agn_radiation, "Boundary_layer.rad_type_in_final_spectrum(power)", &geo.agn_spectype); + } + + + if (geo.agn_spectype >= 0 && comp[geo.agn_spectype].nmods != 1) + { + Error ("sirocco: When using models with an AGN, there should be exactly 1 model, we have %i for spectrum cycles\n", + comp[geo.agn_ion_spectype].nmods); + exit (0); + } + } + init_observers (); + } + + geo.matom_radiation = 0; //initialise for ionization cycles - don't use pre-computed emissivities for macro-atom levels/ k-packets. + get_standard_care_factors (); + get_meta_params (); + +/* Establish limits on the frequency intervals to be used by the ionization cycles and + * the fraquency bands for stratified sampling. These bands are alos used as the spectral + * intervals for creating crude spectra in each of the cells + * + * This section of inputs might logically go earlier in the code, but + * was put here so it would add on to existing .pf files. It would be reasonble to consider moving + * it to a more logical location + */ + + rdpar_comment ("Other parameters"); + + bands_init (-1, &xband); + freqmin = xband.f1[0]; + freqmax = xband.f2[xband.nbands - 1]; + + if (modes.iadvanced) + { + /* Do we require extra diagnostics or not */ + strcpy (answer, "no"); + modes.diag_on_off = rdchoice ("@Diag.extra(yes,no)", "1,0", answer); + if (modes.diag_on_off) + { + get_extra_diagnostics (); + } + } + + + /* Wrap up and save all the inputs */ + + + if (strncmp (files.root, "mod", 3) == 0) + { + cpar ("mod.pf"); + } + + else if (opar_stat == 1) + { + cpar (files.input); + } + else + { + cpar (files.new_pf); + } + + /* At this point, all inputs have been obtained at this point and the inputs have been copied to "mod.pf" or "sirocco.pf" + * If we have used, the -i flag, we quit; otherwise we continue on to run the model */ + if (modes.quit_after_inputs) + { + Log ("This was was run with the -i or --dry-run flag set, so quitting now inputs have been gathered.\n"); + error_summary ("dry run."); + exit (0); + } + + + if (rdpar_check ()) + { + Log ("Some of the input have not been updated for the current version of Python. Please correct and rerun\n"); + exit (0); + } + + + /* INPUTS ARE FINALLY COMPLETE */ + + + Log ("There are %d domains\n", geo.ndomain); + for (n = 0; n < geo.ndomain; n++) + { + Log ("%20s type: %3d ndim: %3d mdim: %3d ndim2: %4d\n", zdom[n].name, zdom[n].wind_type, zdom[n].ndim, zdom[n].mdim, zdom[n].ndim2); + } + + + /* Now define the wind cones generically. modifies the global windcone structure */ + setup_windcone (); + + /* initialize the random number generator */ + /* By default, the random number generator start with fixed seeds (differnt + * for each processor, but this can be changed using a command line + * switch. + * + * An exception is when we are in zeus mode, where it would be inappropriate + * to use the same phtons in ezch cycle. There we initiate the seeds unsing + * the clock + */ + if (modes.rand_seed_usetime) + { + n = (unsigned int) clock () * (rank_global + 1); + init_rand (n); + + } + else + { + init_rand (1084515760 + (13 * rank_global)); + } + + /* DFUDGE is a distance that assures we can "push through" boundaries. setup_dfudge + sets the push through distance depending on the size of the system. + */ + + DFUDGE = setup_dfudge (); + + /* Next line finally defines the wind if this is the initial time this model is being run */ + + if (geo.run_type == RUN_TYPE_NEW) + { + define_wind (); + } + + Log ("DFUDGE set to %e based on geo.rmax\n", DFUDGE); + + if (modes.zeus_connect == 1) //We have restarted, but are in zeus connect mode, so we want to update density, temp and velocities + { + + /* Hydro takes the wind domain number as an argument in the current domains setup */ + hydro_restart (geo.hydro_domain_number); + } + + /* this routine checks, somewhat crudely, if the grid is well enough resolved */ + check_grid (); + + w = wmain; + if (modes.extra_diagnostics) + { + init_extra_diagnostics (); + } + + +/* The next section sets up two represenations of the disk structure + * + * disk_init is called primarily to get + * a defined set of annular rings which are kept throughout the + * ionization calculation. + * disk_init calculates the flux from the disk in the energy range set by + * freqmin and freqmax, and uses is this to identify the position of the + * rings in the disk, so that each ring contributes the same amount to + * the flux + * + * A second structure qdisk is needed + * because in the process of generating photons in various bands + * the annular rings are changed + * + */ + + + disk_init (geo.disk_rad_min, geo.disk_rad_max, geo.mstar, geo.disk_mdot, freqmin, freqmax, 0, &geo.f_disk); + qdisk_init (geo.disk_rad_min, geo.disk_rad_max, geo.mstar, geo.disk_mdot); + xsignal (files.root, "%-20s Finished initialization for %s\n", "NOK", files.root); + check_time (files.root); + + /* allow the user to quit after the wind has been defined */ + if (modes.quit_after_wind_defined) + { + wind_save (files.windsave); + Log ("This was was run with the ---grid-only flag set, so quitting now wind has been defined.\n"); + error_summary ("wind definition only (--grid-only)."); +#ifdef MPI_ON + MPI_Barrier (MPI_COMM_WORLD); + MPI_Finalize (); +#endif + return EXIT_SUCCESS; + } + + + /* Allow for the possibility of running a special diagnostic mode in + a stand alone routine xtest. This will happen with the command line + option -xtest. */ + if (run_xtest) + { + xtest (); + } + + + if (geo.wcycles == 0 && geo.pcycles == 0) + { + wind_save (files.windsave); + Log ("Both ionization and spectral cycles are set to 0; Saving windfile but then exiting\n"); + exit (1); //There is really nothing to do! + } + +/* XXXX - THE CALCULATION OF THE IONIZATION OF THE WIND */ + + geo.ioniz_or_extract = CYCLE_IONIZ; + + calculate_ionization (restart_stat); + +/* XXXX - END OF CYCLE TO CALCULATE THE IONIZATION OF THE WIND */ + Log (" Completed wind creation. The elapsed TIME was %f\n", timer ()); + /* Evaluate wind paths for last iteration */ + if (geo.reverb == REV_WIND || geo.reverb == REV_MATOM) + { //If this is a mode in which we keep wind arrays, update them + wind_paths_evaluate (w, my_rank); + } + +/* XXXX - THE CALCULATION OF A DETAILED SPECTRUM IN A SPECIFIC REGION OF WAVELENGTH SPACE */ + + freqmax = VLIGHT / (geo.swavemin * 1.e-8); + freqmin = VLIGHT / (geo.swavemax * 1.e-8); + + /* Perform the initilizations required to handle macro-atoms during the detailed + calculation of the spectrum. + + Next lines turns off macro atom estimators and other portions of the code that are + unnecessary during spectrum cycles. */ + + geo.ioniz_or_extract = CYCLE_EXTRACT; + + +/* Next step speeds up extraction stage */ + + if (!modes.keep_photoabs) + { + DENSITY_PHOT_MIN = -1.0; // Do not calculated photoabsorption in detailed spectrum + } + + /*Switch on k-packet/macro atom emissivities SS June 04 */ + + if (geo.rt_mode == RT_MODE_MACRO) + { + geo.matom_radiation = 1; + } + + /* Finished initializations required for macro-atom approach */ + + /* Calculate and store which bf processess need to be considered in each cell + * Note that this is not macro-specific but is just to speed the program up. + */ + + kbf_need (freqmin, freqmax); + + /* XXXX - Execute CYCLES TO CREATE THE DETAILED SPECTRUM */ + + make_spectra (restart_stat); + + +/* Finally done */ + +#ifdef MPI_ON + char dummy[LINELENGTH]; + sprintf (dummy, "End of program, Thread %d only", rank_global); // added so we make clear these are just errors for thread ngit status + error_summary (dummy); // Summarize the errors that were recorded by the program + Log ("Run py_error.py for full error report.\n"); + MPI_Finalize (); +#else + error_summary ("End of program"); // Summarize the errors that were recorded by the program +#endif + +#ifdef CUDA_ON + cusolver_destroy (); +#endif + + xsignal (files.root, "%-20s %s\n", "COMPLETE", files.root); + Log ("\nBrief Run Summary\nAt program completion, the elapsed TIME was %f\n", timer ()); + Log ("There were %d of %d ionization cycles and %d of %d spectral cycles run\n", geo.wcycle, geo.wcycles, geo.pcycle, geo.pcycles); + if (geo.rt_mode == RT_MODE_MACRO) + { + if (nlevels_macro == 0) + { + Log ("THIS WAS A MACROATOM CALCULATION WITH NO MACROLEVELS. (Use for diagnostics only)\n"); + } + else + { + Log ("This was a macro-atom calculation\n"); + } + } + else + { + Log ("This was a simple atom calculation\n"); + } + + Log ("Convergence statistics for the wind after the ionization calculation:\n"); + check_convergence (); + Log ("Information about luminosities and apparent fluxes due to various portions of the system:\n"); + phot_status (); + + clean_on_exit (); + + return (0); +} diff --git a/source/sirocco.h b/source/sirocco.h new file mode 100644 index 000000000..e5220fbf5 --- /dev/null +++ b/source/sirocco.h @@ -0,0 +1,1810 @@ +#ifdef MPI_ON +#include "mpi.h" +#endif + + + +#define UV_low 7.4e14 /**< The lower frequency bound of the UV band as defined in IOS 21348 + */ +#define UV_hi 3e16 /**< The lower frequency bound of the UV band as defined in IOS 21348 + */ + +extern int np_mpi_global; /**< Global variable which holds the number of MPI processes + */ + +extern int rank_global; + +extern int verbosity; /**< verbosity level for printing out information. 0 low, 10 is high + */ + +/* Integer representations for the logging level in Python's logging functions, + * as defined in xlog.c */ +#define SHOW_PARALLEL 1 +#define SHOW_ERROR 2 +#define SHOW_LOG 3 +#define SHOW_DEBUG 4 +#define SHOW_LOG_SILENT 5 +#define SHOW_ERROR_SILENT 5 + +#define TRUE 1 +#define FALSE 0 + + +#define USE_GRADIENTS TRUE /**< IF true use interpolated velocity gradients to calculate dv_ds */ + + +#define REL_MODE_LINEAR 0 /**< Only make v/c corrections when doing frame transfers */ +#define REL_MODE_FULL 1 /**< Make full corrections for special relativity including co-moving frame effects */ +#define REL_MODE_SR_FREQ 2 /**< Make full corrects for special relativity frequency shifts, but ignore co-moving frame effects */ + +extern int rel_mode; /**< How doppler effects and co-moving frames are */ + +extern int run_xtest; /**< Variable if TRUE causes a special test mode to be run */ + +extern int NDIM2; /**< The total number of wind cells in wmain + */ +extern int NPLASMA; /**< /The number of cells with non-zero volume or the size of plasma structure + */ + +/* These are tunable parameters that control various aspects of sirocco + * and the assorted programs. In some cases they affect the "care" with + * which a calculation is made, and this can in principle affect the + * speed of the program. One day if may be desirable to change some + * of these parameters with time in the program. At present they are + * simply collected here + * + * */ + +extern double DFUDGE; +#define XFUDGE 1e-5 /**< The scale factor used in setting up cell x cell dfudge + */ + +#define VCHECK 1.e6 /**< The maximum allowable error in calculation of the velocity in calculate_ds + */ + + +extern double SMAX_FRAC; /**< In translate_in_wind, a limit is placed on the maximum distance a + photon can travel in one step. It is a fraction SMAX_FRAC of the + distance of the photon from the origin. This had been hardwired to + 0.1 for up to 57h. Changing it to 0.5 speeds up the current version + of the code by as much as a factor of 2 for small sized grids. This + had been introduced as part of the attempt to assure ourselves that + line shapes were calculated as accurately as possible. The underlhying + rational for having a maximum disstance is associated with the fact that + we use linear interpolation along the line of sight to establish velocities + and most of our grid cells in 2.5d are actually hoop shaped, which means + one can travel a long distance within a hoop if the direction of the photon + is not more or less radial, but if moving along the hoop. + */ +extern double DENSITY_PHOT_MIN; /**< This constant is a minimum density for the purpose of + * calculating photoionization heating and recombination cooling. + * It is important that heating and cooling + * be calculated self-consistently. Program speed is somewhat sensitive + * to this parameter, at the 10% level if raised from 1e-3 to 1. + * There is a + * trade-off since lower minima may give better results, especially for + * macro atoms. + */ + +#define LDEN_MIN 1e-3 /**< The minimum density required for a line to be conidered for scattering + or emission in calculate_ds and lum_lines */ + +#define DILUTION_FACTOR_MINIMUM 1e-10 /**< The next term globally defines a minimum + * value for the dilution faction */ + +/* End of "care factor" definition */ + + +#define EPSILON 1.e-6 /**< A general purpose fairly small number */ +#define NSTAT 10 /**< JM increased this to ten to allow for adiabatic + */ +#define TAU_MAX 20. /**< Sets an upper limit in extract on when + * a photon can be assumed to be completely absorbed + */ +#define TAU_MIN 1e-6 /**< minimum value for tau for p_escape_from_tau function- + * below this we set to p_escape_ to 1 + */ + +#define TMAX_FACTOR 1.5 /**< Factor by which t_e can exceed + * t_r in order for absorbed to + * match emitted flux */ + +#define TMAX 5e8 /**< This is the maximum temperature permitted. Tthis was + * introduced following problems with adiabatically heated cells increasing + * forever. The value was suggested by DP as a + * sensible compton teperature for the PK05/P05 Zeus models. */ +#define TMIN 100 /**< A minimum temperature below which various emission processes + * are set to 0 */ + +#define DELTA_V 1. /**< This is the accuracy in velocity space (cm/s) that we sample edges + * when producing freebound photons */ + +#define DANG_LIVE_OR_DIE 0.2 /**< If constructing photons from a live or die run of the code, the + * angle over which photons will be accepted must be defined */ + +extern double PHOT_RANGE; /**< When a variable number of photons are called in different ionization + * cycles this is the log of the difference between NPHOT_MAX + * and the value in the first cycle + */ +extern int NPHOT_MAX; /**< The maximum number of photon bundles created per cycle */ +extern int NPHOT; /**< The number of photon bundles created, defined in setup.c */ + +extern int NWAVE_MAX; +extern int NWAVE_EXTRACT; /**< The number of wavelength bins for spectra during the spectrum cycles + */ +extern int NWAVE_NOW; /**< Either NWAVE_IONIZ or NWAVE_EXTRACT depending on whether in + * ionizaiton of spectrum cycles + */ + + +#define NWAVE_IONIZ 10000 /**< The number of wavelength bins for spectra during the ionization cycles + */ +#define NWAVE_MIN 100 /**< The minimum number of wavelength bins in during spectral cycles + */ +#define MAXSCAT 2000 + +/* Define the structures */ +#include "math_struc.h" + + +/* Geometry is an actual structure which exists at the beginning of the program + It carries the variables which define the geometry. Reasonable values of each of + these should be defined before it is altered with inputs from the terminal. + The geometry structure is used to transfer all of the information about a wind + */ + +/* Definitions of spectral types, which are all negative because when + * one reads a spectrum from a list of models these are numbered beginning + * with zero, see the discussion in get_models.c 080518 + */ +#define SPECTYPE_BB (-1) +#define SPECTYPE_UNIFORM (-2) +#define SPECTYPE_NONE (-3) +#define SPECTYPE_POW (-4) +#define SPECTYPE_CL_TAB (-5) // This is to emulate cloudy +#define SPECTYPE_BREM (-6) +#define SPECTYPE_MONO (-7) +#define SPECTYPE_BB_FCOL (-8) +#define SPECTYPE_MODEL (-99) // This is just used briefly, before a model number is assigned + +/* definitions of types of colour correction */ +#define FCOL_OFF 0 +#define FCOL_DONE 1 + +/* Number of model_lists that one can have, should be the same as NCOMPS in models.h */ +#define NCOMPS 10 +#ifdef LINELENGTH +#undef LINELENGTH +#endif +#define LINELENGTH 400 + +/* This structure contains the information needed for each separate region of space, e.g the + * wind and the disk + */ + +enum coord_type_enum +{ SPHERICAL = 0, //!< Spherical coordinates + CYLIND = 1, //!< Standard cylindirical coordinates + RTHETA = 2, //!< Polar coordinates + CYLVAR = 3 //!< Cylindrical coordinates but the z axis varies with rho +}; + + +/* List of possible wind_types */ + +#define SV 0 +#define STAR 1 +#define HYDRO 3 +#define CORONA 4 +#define KNIGGE 5 +#define HOMOLOGOUS 6 +#define SHELL 9 +#define IMPORT 10 +#define DISK_ATMOS 11 + + + +/* Next define structures that pertain to possible region geometries + as well as some used for vector operations +*/ + +/** + * A structure defining a plane in 3 dimensions + */ + +typedef struct plane +{ + double x[3]; /**< A position included in the plane (usually the "center" */ + double lmn[3]; /**< A unit vector perpendicular to the plane (usually in the "positive" direction */ +} plane_dummy, *PlanePtr; + + +/* These planes define the ends of a cylinder/pillbox which encapsulate the + secondary with respect to the origin which also the location of the central source. + The values are defined in the routin binary_basics*/ + +extern plane_dummy plane_m2_near, plane_m2_far; + +/** + * A structure defining a cone + * + * As currently defined cones are only defined azimuthally arounds the z axis and + * not in 3d. Thus they can be specified in terms of an intercept along the + * z axis and an opening anble or slope + */ +typedef struct cone +{ + double z; /**< The place where the cone intersects the z axis */ + double dzdr; /**< the slope */ +} +cone_dummy, *ConePtr; + +extern double velocity_electron[3]; // velocity of the electron when thermal effects are included + +/* End of structures which are used to define boundaries to the emission regions */ +/*******************DOMAIN structure***********************************************/ + +#define MAX_DOM 10 +#define NDIM_MAX 3000 /**< maximum size of the grid in each dimension */ +#define NDIM_MAX2D NDIM_MAX * NDIM_MAX // Maximum dimensions for 2D importing + +/** + * The structure which defines a wind model + * + * In sirocco multiple domains can be created for different + * portion of the wind. + * + */ + +#define COORD_TYPE_LOG 0 +#define COORD_TYPE_LINEAR 1 + +typedef struct domain +{ + char name[LINELENGTH]; + int wind_type; + int ndim, mdim, ndim2; /**< ndim is the size in the x direction, while mdim is the + size in z or theta direction */ + int nstart, nstop; /**< the beginning and end (-1) location in wmain of this component */ + enum coord_type_enum coord_type; /**< The type of coordinate system used for this domain */ + int log_linear; /**< 0 -> the grid spacing will be logarithmic in x and z, 1-> linear */ + double xlog_scale, zlog_scale; /**< Scale factors for setting up a logarithmic grid, the [1,1] cell + will be located at xlog_scale,zlog_scale */ + + /* The next few structures define the boundaries of an emission region */ + struct cone windcone[2]; /**< The cones that define the boundary of winds like SV or kwd */ + struct plane windplane[2]; /**< Planes which define the top and bottom of the wind */ +// double rho_min, rho_max; /**< The values defining inner and outer cylinders that bound the wind */ + + double *wind_x, *wind_z; + double *wind_midx, *wind_midz; + + ConePtr cones_rtheta; /**< A ptr to the cones that define boundaries of cells in the theta direction + when rtheta coords are being used */ +/* Next two lines are for cyl_var coordinates. They are used primarily for locating where a position is + * in a grid with cyl_var coordinates. See cylvar_where in grid + */ + + double **wind_z_var, **wind_midz_var; + +// double wind_z_var[NDIM_MAX][NDIM_MAX]; +// double wind_midz_var[NDIM_MAX][NDIM_MAX]; + + + /* Generic parameters for the wind */ + double wind_mdot, stellar_wind_mdot; /**< Mass loss rate in disk and stellar wind */ + double rmin, rmax; /**< Spherical extent of the wind */ + double zmin, zmax; /**< Vertical extent of the wind, often the same as rmax */ + double wind_rhomin_at_disk, wind_rhomax_at_disk; /**< Min/Max rho for wind in disk plane */ + double wind_thetamin, wind_thetamax; /**< Angles defining inner and outer cones of wind, measured from disk plane */ + double mdot_norm; /**< A normalization factor used in SV wind, and Knigge wind */ + + double twind; /**< Initial temperature for a domain */ + + /* Parameters defining Shlossman & Vitello Wind */ + double sv_lambda; /**< power law exponent describing from what portion of disk wind is radiated */ + double sv_rmin, sv_rmax, sv_thetamin, sv_thetamax, sv_gamma; /**< parameters defining the goemetry of the wind */ + double sv_v_zero; /**< velocity at base of wind */ + int sv_v_zero_mode; /**< use fixed initial velocity or multiple of sound speed */ +#define FIXED 0 +#define SOUND_SPEED 1 + double sv_r_scale, sv_alpha; /**< the scale length and power law exponent for the velocity law */ + double sv_v_infinity; /**< the factor by which the velocity at infinity exceeds the excape velocity */ + + + /* Parameters defining Knigge Wind */ + double kn_dratio; /**< parameter describing collimation of wind */ + double kn_lambda; /**< power law exponent describing from what portion of disk wind is radiated */ + double kn_r_scale, kn_alpha; /**< the scale length and power law exponent for the velocity law */ + double kn_v_infinity; /**< the factor by which the velocity at infinity exceeds the excape velocity */ + double kn_v_zero; /**< NSH 19/04/11 - Added in as the multiple of the sound speed to use as the initial velocity */ + + /* Parameters describing Castor and Larmors spherical wind */ + double cl_v_zero, cl_v_infinity, cl_beta; /**< Power law exponent */ + double cl_rmin; + + /*Parameters defining a corona in a ring above a disk */ + double corona_rmin, corona_rmax; /**< the minimum and maximu radius of the corona */ + double corona_zmax; /**< The maximum vertical extent of the corona */ + double corona_base_density, corona_scale_height; /**< the density at the base of the corona and the scale height */ + double corona_vel_frac; /**< the radial velocity of the corona in units of the keplerian velocity */ + + double fill; /**< The filling factor for the wind or corona */ +} +domain_dummy, *DomainPtr; // One structure for each domain + +extern DomainPtr zdom; //This is the array pointer that contains the domains +extern int current_domain; // This integer is used by swind only + + +/*******************GEOMETRY structure*********************************************/ +#define SYSTEM_TYPE_STAR 0 +#define SYSTEM_TYPE_CV 1 +#define SYSTEM_TYPE_BH 4 +#define SYSTEM_TYPE_AGN 2 +#define SYSTEM_TYPE_PREVIOUS 3 + +/* RUN_TYPE differs from SYSTEM_TYPE in that + it has implications on how the program is run + wherease SYSTEM_TYPE refers (mainly) to the type + of system, with the exception +*/ + +#define RUN_TYPE_NEW 0 +#define RUN_TYPE_RESTART 1 +#define RUN_TYPE_PREVIOUS 3 + + +/** + * the geometry structure contains information that applies to all domains, including + * the basic system geometry, descriptions of the radition sources, and truly + * global information including how ionization calculations are caried out. + * + * Information that is domain specific should be placed directly in the domain + * structure. + */ + +struct geometry +{ + +#define OBS_FRAME 0 +#define CMF_FRAME 1 + + int frame; /**< Records frame parmeters like density and volumes are stroed */ + int system_type; /**< See allowed types above. system_type should only be used for setup */ + int binary; /**< Indicates whether or not the system is a binary. TRUE or FALSE */ + + int ndomain; /**< The number of domains in a model */ + int ndim2; /**< The total number of windcells in all domains */ + int nplasma, nmacro; /**< The total number of cells in the plasma and macro structures in all domains */ + + /* variables which store the domain numbers of the wind, disk atmosphere. + Other components should be added here. Right now we need a wind_domain + number because the inputs for the disk and a putativel disk atmosphere are + interrsed. The first step will be to put this information into alocal variale + in sirocco.c. We should not have to carry this forward */ + + int wind_domain_number; + /* Ultimately the next variable should not be needed but to avoid a bad + * interaction with Nick's effort meld zeus calculations with Python + * I have added a new variable. It is likely that the domain number for + * this will always be 0, but one could imagine cases where that might + * not be the case ksl -160927 + */ + + int hydro_domain_number; /**< Created for the special case of runs with Zeus + */ + + + /* This section allows for restarting the program, and adds parameters used + * in the calculation */ + + int wcycle, pcycle; /**< The number of completed ionization and spectrum cycles */ + int wcycles, pcycles, pcycles_renorm; /**< The number of ionization and spectrum cycles desired, pcycles_renorm + * is only used on restarts. See spectrum_restart_renormalize + */ +#define CYCLE_IONIZ 0 +#define CYCLE_EXTRACT 1 + int ioniz_or_extract; /**< Set to CYCLE_IONIZ during ionization cycles, set to CYCLE_EXTRACT during calculation of + detailed spectrum. + */ + + + /* This section stores information which specifies the spectra to be extracted. Some of the parameters + * are used only in advanced modes. + */ + +#define NSPEC 20 + int nangles; /**< The number of angles to create spectra for */ + double angle[NSPEC], phase[NSPEC]; /**< The angle and associated binary phase (if relevant) for the extracted spectra */ + int scat_select[NSPEC], top_bot_select[NSPEC]; /**< Variables to constrain the spectra by number of scatters + * and whether the photons "originate" from above or relow the disk + * plane + */ + double rho_select[NSPEC], z_select[NSPEC], az_select[NSPEC], r_select[NSPEC]; /**< Variables which can be used to + * constrain the spectra by position + */ + double swavemin, swavemax, sfmin, sfmax; // The minimum and maximum wavelengths/freqs for detailed spectra + int select_extract, select_spectype; /**< select_extract is TRUE if extract mode, FALSE if Live or Die + * select_spectype indicates what type of spectrum, e.g FLAMBA, will + * be created. + */ + +/* Begin description of the actual geometry */ + + double rmax, rmax_sq; /**< The maximum distance (and distance**2) to which a photon should be followed */ + +/* Basic paremeters of the system, as opposed to elements of the wind or winds */ + + double mstar, rstar, rstar_sq, tstar, gstar; /**< Basic parameters for the star (often a WD) in the system */ + double tstar_init; /**< The temperature of the star, before backscattering is taken into account */ + double lum_star_init, lum_star_back; /**< The luminosity of the star as determined by tstar_init */ + + double tmax; /**< the maximum temperature of any element of the model + - used to help estimate things for an exponential representation of the spectrum in a cell */ + +#define DISK_MISSED 0 +#define DISK_HIT_TOP 1 +#define DISK_HIT_BOT 2 +#define DISK_HIT_EDGE 3 + +#define DISK_NONE 0 +#define DISK_FLAT 1 +#define DISK_VERTICALLY_EXTENDED 2 +#define DISK_WITH_HOLE 3 + + int disk_type; + +#define BACK_RAD_ABSORB_AND_DESTROY 0 /**< Disk simply absorbs the radiation and it is lost */ +#define BACK_RAD_SCATTER 1 /**< Disk reradiates the radiation immediately via electron scattering */ +#define BACK_RAD_ABSORB_AND_HEAT 2 /**< Correct disk temperature for illumination by photons + which hit the dsik. Disk radiation is absorbed and changes + the temperature of the disk for future ionization cycles + */ + + int absorb_reflect; /**< Controls what happens when a photon hits the disk or star + */ + +#define DISK_TPROFILE_STANDARD 0 /**< This is a standard Shakura-Sunyaev disk. The profile depends on mstar and mdot_disk + */ +#define DISK_TPROFILE_READIN 1 /**< Here the temperature profile for the disk is simply read in as a function of radius + */ + + int disk_tprofile; /**< Variable used to specify a standard accretion disk (0) or + one that has been read in and stored. */ + double disk_mdot; /**< mdot of DISK */ + double disk_rad_min, disk_rad_max; + double disk_z0, disk_z1; /**< For vertically extended disk, z=disk_z0*(r/diskrad)**disk_z1 *diskrad */ + double lum_disk_init, lum_disk_back; /**< The intrinsic luminosity of the disk, the back scattered luminosity */ + int run_type; /**< Variable that describes whether this is a continuation of a previous run + Added in order to separate the question of whether we are continuing an old run fro + the type of wind model. Bascially if run_type is 0, this is a run from scratch, + if SYSTEM_TYPE_PREVIOUS it is an old run */ + int star_radiation, disk_radiation; /**< 1 means consider radiation from star, disk, bl, and/or wind */ + int bl_radiation, wind_radiation, agn_radiation; + + int matom_radiation; /**< for use in macro atom computations of detailed spectra + - 1 means use emissivities for BOTH macro atom levels and kpkts. 0 means don't + (which is correct for the ionization cycles. */ + int ioniz_mode; /**< describes the type of ionization calculation which will + be carried out. The various ioniz_modes are defined by #defines IONMODE_MATRIX_BB + etc. See the documentation in this file for what these mean. */ +#define MACRO_IONIZ_MODE_NO_ESTIMATORS 0 +#define MACRO_IONIZ_MODE_ESTIMATORS 1 + int macro_ioniz_mode; /**< Controls the use of macro atom populations and + ionization fractions. If it is set to MACRO_IONIZ_MODE_ESTIMATOR then macro atom populations + computed from estimators are used. If set to MACRO_IONIZ_MODE_NO_ESTIMATORS then the macro atom + populations are computed as for simple ions. By default it is set to + MACRO_IONIZ_MODE_NO_ESTIMATORS initially and then set to MACRO_IONIZ_MODE_ESTIMATORS the first time that + Monte Carlo estimators are normalised. */ + int macro_simple; /**< As default this is set to FALSE, in which case a full + Macro Atom calculation is performed. If it is set to TRUE it means + that although Macro Atom data has been read in, all lines/continua are treated + using the simplified two-level approximation. Such a calculation should reproduce + similar results to the simple atom case. */ + +#define LINE_MODE_ABSORB 0 +#define LINE_MODE_SCAT 1 +#define LINE_MODE_SINGLE_SCAT 2 +#define LINE_MODE_ESC_PROB 3 + + + + int line_mode; /**< 0, the atomosphere is a completely absorbing and no photons + will be scattered. In this mode, assuming the wind is a source + of emission, the emissivity will be the Einstein A coefficient + 1, the atmosphere is completely scattering, there will be no + interchange of energy between the photons and the electrons + as a result of radiation transfer + 2, then a simple single scattering approximation is applied in which + case the scattered flux is just A21/(C21+A21). + 3, then radiation trapping aka escape probabilities are included as well. + 6 - 9, If set to 6-9 initially, this switches on the macro atom case + and then behaves like LINE_MODE_ESC_PROB. + */ +/* Note that the scatter_mode is actually a subsidiary variable of the line_mode. Choosing a line_mode + * results in the selection of a scatter_mode */ + +#define SCATTER_MODE_ISOTROPIC 0 +#define SCATTER_MODE_THERMAL 2 + + int scatter_mode; /**< The way in which scattering for resonance lines is treated + ** 0 isotropic + ** 2 thermally broadened anisotropic + */ + +#define RT_MODE_2LEVEL 1 +#define RT_MODE_MACRO 2 + + int rt_mode; /**< radiative transfer mode. + ** 2 for Macro Atom method, + ** 1 for non-Macro Atom methods + */ + + /* define a global transition mode for macro-atoms */ + /* the global storage mode is set in the modes structure */ + int matom_transition_mode; + + /* Define the choices for calculating the FB, see, e.g. integ_fb */ + +#define FB_FULL 0 /**< Calculate fb emissivity including energy associated with the threshold */ +#define FB_REDUCED 1 /**< Calculqate the fb emissivity without the threshold energy */ +#define FB_RATE 2 /**< Calulate the fb recombinarion rate */ + + + /* Define the modes for free bound integrals */ +#define OUTER_SHELL 1 +#define INNER_SHELL 2 + + /* The next set of variables defineds frequency bands used a boundaries for accumulating coarse (and fine) spectral information + about the spectrum of photons in a cell. There are the coarse bands currently used for creating spectral models and there + are a finer set of frequency intervals used for the fine spectra. The spectra themselves can be found in the + Plasma structure for each cell */ + +#define NXBANDS 20 /**< the maximum number of bands (frequency intervals that can be defined for + storing coarse spectra for each plasma cell */ + + int nxfreq; /**< the number of frequency intervals actually used */ + double xfreq[NXBANDS + 1]; /**< the frequency boundaries for the coarse spectra */ + +#define NBINS_IN_CELL_SPEC 1000 /**< The number of bins in the cell spectra */ + + double cell_log_freq_min, cell_log_freq_max, cell_delta_lfreq; /**< Parameters defining freqency intervals for cell spectra. + These are defined as logarithmic frequency intervals */ + + + /* The next set pf variables assign a SPECTYPE (see above) for + each possible source of radiation in a model. The value assigned can be different for + the ionization and detailed spectrum generation part of the code */ + + int star_ion_spectype, star_spectype; /**< The type of spectrum used to create the continuum + for the star in the ionization and final spectrum calculation */ + int disk_ion_spectype, disk_spectype; /**< Same as above but for the disk */ + int bl_ion_spectype, bl_spectype; /**< Same as above but for the boundary layer */ + int agn_ion_spectype, agn_spectype; /**< Same as above but for the AGN */ + + int colour_correction; /** colour correction mode */ + + /* Searchlight mode is very experimental. See notes in diag.c */ + double searchlight_x[3], searchlight_lmn[3];/**< location and direction of all photons in the spectral + * cycles when searchlight mode (an advanced option) is + * invoked + */ + double mono_freq; /**< The frequency of all photons in the so-called monochromatic mode, which + * can be use for certain diagnositc experiments + */ + + char model_list[NCOMPS][LINELENGTH]; /**< The file which contains the model names and the associated values for the model */ + int model_count; /**< The number of distinct models that have been read in */ + + double mdot_norm; /**< A normalization factor used in SV wind, and Knigge wind */ + int adiabatic; /**< 0-> Do not include adiabatic heating in calculating the cooling of the wind + 1-> Use adiabatic heating in calculating the cooling of the wind + */ + int nonthermal; /**< 0 --> No extra heating due to shocks + 1 --> Extra heating due to shocks (etc) (Added for FU Ori) + */ + + double shock_factor; /**< A scaling factor used for including an extra heating term (for FU Ori stars + */ + double frac_extra_kpkts; /**< in the case that we have extra heating and macro-atoms, the fraction of + photons to reserve for those generated directly by k-packets */ + + int auger_ionization; /**< 0 -> Do not include innershell photoionization /Auger effects; 1-> include them */ + +/* Initial values for defining wind structure for a planar geometry. These are currently only used by balance and this + may not be the best approach generally and depending on where this ends up. Some consolidation is desirable */ + double pl_vol, pl_vmax; + double pl_t_r, pl_t_e, pl_w; + double pl_nh; + + /* Variables having to do with heating and cooling */ + + double lum_tot, lum_star, lum_disk, lum_bl, lum_wind; /**< The total luminosities of the disk, star, bl, + * & wind are actually not used in a fundamental + * way in the program */ + double lum_agn; /**< The total luminosity of the AGN or point source at the center */ + double lum_ff, lum_rr, lum_lines; /**< The luminosity of the wind as a result of ff, fb, and line radiation */ + double cool_rr; /**< the cooling rate due to radiative recombination - not the same as the luminosity */ + double cool_comp; /**< The luminosity of the wind as a result of compton cooling */ + double cool_di; /**< The direct ionization luminosity */ + double cool_dr; /**< The luminosity of the wind due to dielectronic recombination */ + double cool_adiabatic; /**< The cooling of the wind due to adiabatic expansion */ + double heat_adiabatic; /**< The heating of the wind due to adiabatic heating - split out + * from cool_adiabatic to get an accurate idea of whether it is important */ + double heat_shock; /**< The amount of extra heating going into the wind due to shock heating. Added for FU Ori project */ + + double f1, f2; /**< The freguency minimum and maximum for which the band limted luminosities have been calculated */ + double f_tot, f_star, f_disk, f_bl, f_agn, f_wind; /**< The integrated specific L between a freq min and max which are + used to establish the band limited luminosity of photons of various types. + For detailed spectra cycles, this will the band limed luminosity between + the minimum and maximum wavelength of the detailed spectrum */ + +/* These variables are copies of the lum variables above, and are only calculated during ionization cycles + This is a bugfix for JM130621, windsave bug */ + double lum_ff_ioniz, lum_rr_ioniz, lum_lines_ioniz; + double cool_rr_ioniz; + double cool_comp_ioniz; + double cool_di_ioniz; /**< The direct ionization luminosity */ + double cool_dr_ioniz; + double cool_adiabatic_ioniz; + double lum_wind_ioniz, lum_star_ioniz, lum_disk_ioniz, lum_bl_ioniz, lum_tot_ioniz; + + double f_matom, f_kpkt; /**< Used in computations of detailed spectra - the + energy emitted in the band via k-packets and macro atoms respectively. */ + + double n_ioniz, cool_tot_ioniz; + +/* The next set of parameters relate to the secondary + */ + + double m_sec, q; /**< Mass of the secondary, mass ratio of system */ + double period; /**< Period of the systems in seconds */ + double a, l1, l2, phi; /**< Separation of primary and secondary, distance of l1 from primary,phi at l1 */ + double l1_from_m2, r2_far; /**< Distance to l1 from m2, distance to far side of secondary from primary */ + double r2_width; /**< Maximum half width of Roche surface of secondary in the plane of orbit */ + + double t_bl; /**< temperature of the boundary layer */ + double weight; /**< weight factor for photons/defined in define_phot */ + +/* The next set of parameters relate to the central source of an AGN + */ + + double brem_temp; /**< The temperature of a bremsstrahlung source */ + double brem_alpha; /**< The exponent of the nu term for a bremstrahlung source */ + + double pl_low_cutoff; /**< accessible only in advanced mode- see #34. default to zero */ + + double alpha_agn; /**< The power law index of a BH at the center of an AGN. Note that the luminosity + of the agn is elsewhere in the structure */ + double const_agn; /**< The constant for the Power law, there are lots of ways of defining the PL which is best? */ + double d_agn; /**< the distance to the agn - only used in balance to calculate the ionization fraction */ + + + int pl_geometry; /**< geometry of X-ray point source */ +#define PL_GEOMETRY_SPHERE 0 +#define PL_GEOMETRY_LAMP_POST 1 +#define PL_GEOMETRY_BUBBLE 2 + double lamp_post_height; /**< height of X-ray point source if lamp post */ + double bubble_size; /**< size of a bubble if any */ + +/* The next four variables added by nsh Apr 2012 to allow broken power law to match the cloudy table command */ + double agn_cltab_low; /**< break at which the low frequency power law ends */ + double agn_cltab_hi; /**< break at which the high frequency power law cuts in */ + double agn_cltab_low_alpha; /**< photon index for the low frequency end */ + double agn_cltab_hi_alpha; /**< photon index for the high frequency end */ + +// The next set of parameters describe the input datafiles that are read + char atomic_filename[132]; /**< The masterfile for the atomic data */ + char fixed_con_file[132]; /**< For fixed concentrations, the file specifying concentrations */ + + //Added by SWM for tracking C-IV/H-A hotspots + int nres_halpha; + + /* Variables used for reverberation mapping */ + + double fraction_converged, reverb_fraction_converged; + int reverb_filter_lines, *reverb_filter_line; + enum reverb_disk_enum + { REV_DISK_CORRELATED = 0, REV_DISK_UNCORRELATED = 1, REV_DISK_IGNORE = 3 } reverb_disk; + enum reverb_enum + { REV_NONE = 0, REV_PHOTON = 1, REV_WIND = 2, REV_MATOM = 3 } reverb; + enum reverb_vis_enum + { REV_VIS_NONE = 0, REV_VIS_VTK = 1, REV_VIS_DUMP = 2, REV_VIS_BOTH = 3 } reverb_vis; + int reverb_wind_cycles; + int reverb_path_bins, reverb_angle_bins; //** Number of bins for path arrays, vtk output angular bins */ + int reverb_dump_cells; /**< Number of cells to dump */ + double *reverb_dump_cell_x, *reverb_dump_cell_z; + int *reverb_dump_cell; + int reverb_lines, *reverb_line; /**< Number of lines to track, and array of line 'nres' values */ + + int spec_mod; /**< A flag saying we do have spectral models. + * Used in Compton scattering and matrix_ion + */ +}; + + +extern struct geometry geo; + +/******************************END GEOMETRY STRUCTURE, BEGIN XDISK*********************************/ +enum band_definition_enum +{ + T_STAR_BAND = 0, + MIN_MAX_FREQ_BAND = 1, + CV_BAND = 2, + YSO_BAND = 3, + USER_DEF_BAND = 4, + CLOUDY_TEST_BAND = 5, + WIDE_BAND = 6, + AGN_BAND = 7, + LOG_USER_DEF_BAND = 8, + TDE_BB_BAND = 9 +}; + +#define NRINGS 3001 /**< The actual number of rings completely defined + is NRINGS-1 ... or from 0 to NRINGS-2. This is + because you need an outer radius...but the rest + of this element is not filled in. */ + +/** + xdisk is a structure that is used to store information about the disk in a system +*/ +struct xdisk +{ + double r[NRINGS]; /**< The inner radius of an annulus */ + double t[NRINGS]; /**< The temperature at the middle of the annulus */ + double g[NRINGS]; /**< The gravity at the middle of the annulus */ + double v[NRINGS]; /**< The velocity at the middle of the annulus */ + double emit[NRINGS]; /**< The radiative energy of the photons emitted from the disk */ + double heat[NRINGS]; /**< The total energy flux of photons hitting each annulus */ + double ave_freq[NRINGS]; /**< The flux weighted average of frequency of photons hitting each annulus */ + double w[NRINGS]; /**< The weight relative to a BB of the photons that hit the disk */ + double t_hit[NRINGS]; /**< The effective T of photons hitting the disk, based on the average frequency */ + int nphot[NRINGS]; /**< The number of disk photons created in each annulus */ + int nhit[NRINGS]; /**< The number of photons which hit each annulus */ +}; +extern struct xdisk disk, qdisk; /**< disk defines zones in the disk which in a specified frequency band emit equal amounts + of radiation. disk gets reinitialized whenever the frequency interval of interest + is changed. qdisk stores the amount of heating of the disk as a result of + illumination by the star or wind. It's boundaries are fixed throughout a cycle */ + +#define NBLMODEL 5000 + +/** the blmodel structure is intended to store a non standard temperature + and (optionally gravity) profile for the disk + + n_params should be 1 or 2, depending on whether t, or t and g + are read in + */ + +struct blmodel +{ + int n_params; + int n_blpts; + double r[NBLMODEL]; + double t[NBLMODEL]; + double g[NBLMODEL]; +}; +extern struct blmodel blmod; + + +/*************************WIND_PATHS for Reveberation Mapping *****************************************/ +/** Used for reverperation mapping, Wind paths is defined per cell and contains a binned array holding the spectrum of paths. Layers are + For each frequency: + For each path bin: + What's the total fluxback of all these photons entering the cell? +*/ +typedef struct wind_paths +{ + double *ad_path_flux; /**< Array[by frequency, then path] of total flux of photons with the given v&p + */ + double *ad_path_flux_disk; + double *ad_path_flux_wind; + double *ad_path_flux_cent; /**< As above, by source */ + int *ai_path_num; /**< Array [by frequency, then path] of the number of photons in this bin + */ + int *ai_path_num_disk; + int *ai_path_num_wind; + int *ai_path_num_cent; /**< As above, by source */ + double d_flux, d_path; /**< Total flux, average path */ + int i_num; /**< Number of photons hitting this cell */ +} wind_paths_dummy, *Wind_Paths_Ptr; + +/******************************WIND STRUCTURE*******************************/ +#define NIONIZ 5 /*The number of ions (normally H and He) for which one separately tracks ionization + and recombinations */ + +/** + * The structure that defines the wind for an individual domain after the + * wind model has been intepreted + + * Note that most of the macro atom information is in a separate structure. This was + * done to make it easier to control the size of the entire structure 06jul-ksl + */ + + +typedef struct wind +{ + int ndom; /**< The domain associated with this element of the wind */ + int nwind; /**< A self-reference to this cell in the wind structure */ + int nwind_dom; /**< The element number of the wind cell in its wind domain */ + int nplasma; /**< A cross refrence to the corresponding cell in the plasma structure */ + double x[3]; /**< position of inner vertex of cell */ + double xcen[3]; /**< position of the "center" of a cell */ + double r, rcen; /**< radial location of cell (Used for spherical, spherical polar + coordinates. */ + double theta, thetacen; /**< Angle of coordinate from z axis in degrees */ + double dtheta, dr; /**< widths of bins, used in hydro import mode */ + struct cone wcone; /**< cone structure that defines the bottom edge of the cell in + CYLVAR coordinates */ + double v[3]; /**< velocity at inner vertex of cell in the observer frame. For 2d coordinate systems this + is defined in the xz plane */ + double v_grad[3][3]; /**< velocity gradient tensor at the inner vertex of the cell in the co-moving frame */ + double div_v; /**< Divergence of v at center of cell in the co-moving frame */ + double dvds_ave; /**< Average value of dvds */ + double dvds_max; /**< The maximum value of dvds */ + double vol; /**< valid volume of this cell (that is the volume of the cell that is considered + to be in the wind. This differs from the volume in the Plasma structure + where the volume is the volume that is actually filled with material. + The vol that is stored here after the progam has initialized itself is the + co-moving frame volume. + */ + double xgamma, xgamma_cen; /**< 1./sqrt(1-beta**2) at x at edge and center of cell */ + double dfudge; /**< A number which defines a push through distance for this cell, which replaces the + global variable DFUDGE in many instances */ + enum inwind_enum + { W_IN_DISK = -5, W_IN_STAR = -4, W_IGNORE = -2, W_NOT_INWIND = -1, + W_ALL_INWIND = 0, W_PART_INWIND = 1, W_NOT_ASSIGNED = -999 + } inwind; /**< Basic information on the nature of a particular cell. */ + Wind_Paths_Ptr paths, *line_paths; /**< Path data struct for each cell */ +} +wind_dummy, *WindPtr; + +extern WindPtr wmain; + +/*****************************PLASMA STRUCTURE**************************/ +/** Plasma is a structure that contains information about the properties of the + * plasma in regions of the geometry that are actually included in the wind + * Note that a number of the arrays are dynamically allocated. + */ + +typedef struct plasma +{ + int nwind; /**< A cross reference to the corresponding cell in the wind structure */ + int nplasma; /**< A self reference to this in the plasma structure */ + double ne; /**< Electron density in the shell (CMF) */ + double rho; /**< Density at the center of the cell. (CMF) For clumped models, this is rho of the clump */ + double vol; /**< Volume of this cell in CMF frame (more specifically the volume that is filled with material + which can differs from the valid volume of the cell due to clumping.) */ + double xgamma; /**< 1./sqrt(1-beta**2) at center of cell */ + double *density; /**< The number density of a specific ion in the CMF. The order of the ions is + the same as read in by the atomic data routines. */ + double *partition; /**< The partition function for each ion. */ + double *levden; /* The number density (occupation number?) of a specific level */ + + double kappa_ff_factor; /**< Multiplicative factor for calculating the FF heating for a photon. */ + + + double *recomb_simple; /**< "alpha_e - alpha" (in Leon's notation) for b-f processes in simple atoms. */ + double *recomb_simple_upweight; /* multiplicative factor to account for ratio of total to "cooling" energy for b-f processes in simple atoms. */ + +/* Beginning of macro information */ + double kpkt_emiss; /**< This is the luminosity produced due to the conversion k-packet -> r-packet in the cell + in the frequency range that is required for the final spectral synthesis. (SS) */ + + double kpkt_abs; /**< k-packet equivalent of matom_abs. (SS) */ + + /* kbf_use and kbf_nuse are set by the routine kbf_need, and they provide indices into the photoinization processes + * that are "significant" in a plasma cell, based on the density of a particular ion in a cell and the x-section + * at the photoinization edge. This process was introduced as a means to speed the program up by ignoring those + * bf processes that would contribute negligibly to the bf opacity + */ + + int *kbf_use; /**< List of the indices of the photoionization processes to be used for kappa_bf. */ + int kbf_nuse; /**< Total number of photoionization processes to be used for kappa_bf. (SS) */ + +/* End of macro information */ + + + double t_r, t_r_old; /**< radiation temperature of cell */ + double t_e, t_e_old; /**< electron temperature of cell */ + double dt_e, dt_e_old; /**< How much t_e changed in the previous iteration */ + double heat_tot, heat_tot_old; /**< heating from all sources */ + double abs_tot; + double heat_lines, heat_ff; + double heat_comp; /**< The compton heating for the cell */ + double heat_ind_comp; /**< The induced compton heatingfor the cell */ + double heat_lines_macro, heat_photo_macro; /**< bb and bf heating due to macro atoms. Subset of heat_lines + and heat_photo. SS June 04. */ + double heat_photo, heat_z; /**< photoionization heating total and of metals */ + double heat_auger; /**< photoionization heating due to inner shell ionizations */ + double heat_ch_ex; + double abs_photo, abs_auger; /**< this is the energy absorbed from the photon due to these processes - different from + the heating rate because of the binding energy */ + double w; /**< The dilution factor of the wind */ + + int ntot; /**< Total number of photon passages */ + + /* counters of the number of photon passages by origin */ + + int ntot_star; + int ntot_bl; + int ntot_disk; + int ntot_wind; + int ntot_agn; + + + int nscat_es; /**< The number of electrons scatters in the cell */ + int nscat_res; /**< The number of resonant line scatters in the cell */ + + double mean_ds; /**< Mean photon path length in a cell. */ + int n_ds; /**< Number of times a path lengyh was added; needed to compute mean_ds */ + int nrad; /**< Total number of photons created within the cell */ + int nioniz; /**< Total number of photon passages by photons capable of ionizing H */ + double *ioniz, *recomb; /**< Number of ionizations and recombinations for each ion. + The sense is ionization from ion[n], and recombinations + to each ion[n]. */ + double *inner_ioniz, *inner_recomb; + int *scatters; /**< The number of scatters in this cell for each ion. */ + double *xscatters; /**< Diagnostic measure of energy scattered out of beam on extract. */ + double *heat_ion; /**< The amount of energy being transferred to the electron pool + by this ion via photoionization. */ + double *heat_inner_ion; /**< The amount of energy being transferred to the electron pool + by this ion via photoionization. */ + double *cool_rr_ion; /**< The amount of energy being released from the electron pool + by this ion via recombination. */ + double *lum_rr_ion; /**< The recombination luminosity + by this ion via recombination. */ + + +#define MEAN_INTENSITY_BB_MODEL 1 +#define MEAN_INTENSITY_ESTIMATOR_MODEL 2 + + double *cool_dr_ion; + double j, ave_freq; /**< Mean (angle-averaged) total intensity, intensity-averaged frequency */ + + /* Information related to spectral bands used for modelling */ + double xj[NXBANDS], xave_freq[NXBANDS]; /**< Frequency limited versions of j and ave_freq */ + double fmin[NXBANDS], fmax[NXBANDS]; /**< Minimum (Maximum) frequency photon observed in a band - + * this is incremented during photon flight */ + double fmin_mod[NXBANDS], fmax_mod[NXBANDS]; /**< Minimum (Maximum) frequency of the band-limited model + * after allowing possibility that the observed limit, + * is primarily due to photon statistics. See epectral_estimators.c */ + double xsd_freq[NXBANDS]; /**< The standard deviation of the frequency in the band */ + int nxtot[NXBANDS]; /**< The total number of photon passages in frequency bands */ + + enum spec_mod_type_enum + { + SPEC_MOD_PL = 1, + SPEC_MOD_EXP = 2, + SPEC_MOD_FAIL = -1 + } spec_mod_type[NXBANDS]; /**< A switch to say which type of representation we are using for this band in this cell. + Negative means we have no useful representation, 0 means power law, 1 means exponential */ + + double pl_alpha[NXBANDS]; /**< Computed spectral index for a power law spectrum representing this cell */ + double pl_log_w[NXBANDS]; /**< This is the log version of the power law weight. It is in an attempt to allow very large + values of alpha to work with the PL spectral model to avoide NAN problems. + The pl_w version can be deleted once testing is complete */ + + + double exp_temp[NXBANDS]; /**< The effective temperature of an exponential representation of the radiation field in a cell */ + double exp_w[NXBANDS]; /**< The prefactor of an exponential representation of the radiation field in a cell */ + + double cell_spec_flux[NBINS_IN_CELL_SPEC]; /**< The array where the cell spectra are accumulated. */ + +#define NFLUX_ANGLES 36 /**< The number of bins into which the directional flux is calculated */ + + + /*Binned fluxes */ + double F_UV_ang_theta[NFLUX_ANGLES]; + double F_UV_ang_phi[NFLUX_ANGLES]; + double F_UV_ang_r[NFLUX_ANGLES]; + + + /*A version of the binned flux that is averaged over cycles */ + double F_UV_ang_theta_persist[NFLUX_ANGLES]; + double F_UV_ang_phi_persist[NFLUX_ANGLES]; + double F_UV_ang_r_persist[NFLUX_ANGLES]; + + /* The term direct here means from photons which have not been scattered. These are photons which have been + created by the central object, or the disk, or in the simple case the wind, but which have not undergone + any kind of interaction which would change their direction + */ + double j_direct, j_scatt; /**< Mean intensity due to direct photons and scattered photons. + Direct photons include photons created in the wind in simple mode. */ + double ip_direct, ip_scatt; /**< Ionization parameter due to direct photons and scattered photons. See ip */ + double max_freq; /**< The maximum frequency photon seen in this cell */ + double cool_tot; /**< The total cooling in a cell */ + /* The total luminosity of all processes in the cell, basically the emissivity of the cell times it volume. Not the same + as what escapes the cell, since photons can interact within the cell and lose weight or even be destroyed */ + double lum_lines, lum_ff, cool_adiabatic; + double lum_rr, lum_rr_metals; /**< the radiative recombination luminosity - not the same as the cooling rate */ + double cool_comp; /**< The compton luminosity of the cell */ + double cool_di; /**< The direct ionization luminosity */ + double cool_dr; /**< The dielectronic recombination luminosity of the cell */ + double cool_rr, cool_rr_metals; /**< fb luminosity & fb of metals metals */ + double lum_tot, lum_tot_old; /**< The specific radiative luminosity in frequencies defined by freqmin + and freqmax. This will depend on the last call to total_emission */ + double cool_tot_ioniz; + double lum_lines_ioniz, lum_ff_ioniz, cool_adiabatic_ioniz; + double lum_rr_ioniz; + double cool_comp_ioniz; /**< The compton luminosity of the cell */ + double cool_di_ioniz; /**< The direct ionization luminosity */ + double cool_dr_ioniz; /**< The dielectronic recombination luminosity of the cell */ + double cool_rr_ioniz, cool_rr_metals_ioniz; /**< fb luminosity & fb of metals metals */ + double lum_tot_ioniz; /**< The specfic radiative luminosity in frequencies defined by freqmin + and freqmax. This will depend on the last call to total_emission */ + double heat_shock; /**< An extra heating term added to allow for shock heating of the plasma (Implementef for FU Ori Project */ + + double bf_simple_ionpool_in, bf_simple_ionpool_out; /**MAXSCAT -> select everything + * 0 < nscat < MAXScat select only those photons which have + * scattered nscat times, number of scattering photons in + * spectrum, if nscat is negative + * then all photons with more than |nscat| are included. */ + int top_bot; /**< 0 ->select photons regardless of location + >0 -> select only photons whose "last" position is above the disk + <0 -> select only photons whose last position is below the disk */ + double x[3], r; /**< The position and radius of a special region from which to extract spectra. + x is taken to be the center of the region and r is taken to be the radius of + the region. */ + double *f; /**< The spectrum in linear (wavelength or frequency) units */ + double *lf; /**< The specturm in log (wavelength or frequency) units */ + + double *f_wind; /**< The spectrum of photons created in the wind or scattered in the wind. Created for + reflection studies but possibly useful for other reasons as well. */ + double *lf_wind; /**< The logarithmic version of this */ +} +spectrum_dummy, *SpecPtr; + + +extern SpecPtr xxspec; + +/* Parameters used only by swind + * swind_projecti 0 -> simply print the various parameters without + * atempting toproject onto a yz plane + * 1 -> project onto a yz plane. + */ + +extern int swind_min, swind_max, swind_delta, swind_project; +extern double *aaa; // A pointer to an array used by swind + +/***************************CDF STRUCTURE*****************************/ +/* This is the structure for storing cumulative distribution functions. The CDFs are +generated from a function which is usually only proportional to the probability density +function or from an array. It is sometimes useful, e.g. in calculating the reweighting function to +have access to the proper normalization. +*/ + + +extern struct Cdf cdf_ff; +extern struct Cdf cdf_fb; +extern struct Cdf cdf_bb; +extern struct Cdf cdf_brem; + + + +/* Variable used to allow something to be printed out the first few times + an event occurs */ +extern int itest, jtest; + +extern char hubeny_list[132]; /**< /Location of listing of files representing hubeny atmospheres + */ + + +/* ***********************XBAND STRUCTURE *********************/ +#define NBANDS 20 + +/** The xbands structure is associated with frequency limits used for photon + * generation and for calculating heating and cooling + * + * xbands is used to control the number of photons generated in each + * band + */ + +struct xbands +{ + double f1[NBANDS], f2[NBANDS]; /**< The miniumum and maximum frequency of each band */ + double alpha[NBANDS]; + double pl_const[NBANDS]; + double min_fraction[NBANDS]; + double nat_fraction[NBANDS]; /**< The fraction of the accepted luminosity in this band */ + double used_fraction[NBANDS]; + double flux[NBANDS]; /**< The "luminosity" within a band */ + double weight[NBANDS]; /**< The weight/energy for photons created with each and */ + int nphot[NBANDS]; /**< The number of photons created in each band */ + int nbands; /**< Actual number of bands in use */ +}; + +extern struct xbands xband; + + +/***************************FBSTRUC *********************************** +* The next section contains the freebound structures that can be used for both the +* +* ksl - 211030 most of the FBSTRUC was moved into recomb.c, since that is the only place +* these structures were used +* However kap_bf remains becuase it is used in the macro atom routines +*/ + +//#define NTEMPS 60 // The number of temperatures which are stored in each fbstruct + /* NSH this was increased from 30 to 60 to take account of 3 extra OOM + intemperature we wanted to have in fb */ +//#define NFB 20 // The maximum number of frequency intervals for which the fb emission is calculated + +//struct fbstruc +//{ +// double f1, f2; +// double cool[NIONS][NTEMPS]; //cooling rate due to radiative recombination +// double lum[NIONS][NTEMPS]; //emissivity due to radiative recombinaion +// double cool_inner[NIONS][NTEMPS]; //cooling rate due to recombinations to inner shells +//} +//freebound[NFB]; + +//extern double xnrecomb[NIONS][NTEMPS]; // There is only one set of recombination coefficients +//extern double xninnerrecomb[NIONS][NTEMPS]; // There is only one set of recombination coefficients + +// extern double fb_t[NTEMPS]; +// extern int nfb; // Actual number of freqency intervals calculated + +/* kap_bf stores opacities for a single cell and as calculated by the routine kappa_bf. + * It was made an external array to avoid having to pass it between various calling routines + * but this means that one has to be careful that data is not stale. It is required for + * macro-atoms where bf is a scattering process, but not for the simple case. + */ + +extern double kap_bf[NLEVELS]; + + + +// 12jun nsh - some commands to enable photon logging in given cells. There is also a pointer in the geo + +extern FILE *pstatptr; /**< pointer to a diagnostic file that will contain photon data for given cells + */ +extern int cell_phot_stats; /**< 1=do it, 0=dont do it + */ +#define NCSTAT 10 /**< The maximum number of cells we are going to log */ +extern int ncstat; /**< the actual number we are going to log */ +extern int ncell_stats[NCSTAT]; /**< the numbers of the cells we are going to log */ + + +/* Added variables which count number of times two situations occur (See #91) */ +extern int nerr_no_Jmodel; +extern int nerr_Jmodel_wrong_freq; + + + +enum partial_cells_enum +{ PC_INCLUDE = 0, /**< Include partial cells, as has been done historically */ + PC_ZERO_DEN = 1, /**< Exclude partial cells, by setting their density to 0 */ + PC_EXTEND = 2 /**< Exclude partial cells, by extending the density of a full cell, + * into a partial cell + */ +}; + + +/***********************ADVANCED_MODES STRUCTURE **********************/ +/** + * Structure that contains the various switches that contoll which + * which of the various modes for running the code are exercized + * + * Most (though perhaps not all) of these modes are accessed + * via special inputs that are only read in when sirocco + * is started up with athe -d command line option. + * + * All of the various variables take TRUE or FALSE, effectively + */ + +struct advanced_modes +{ + int iadvanced; /**< Set to TRUE when sirocco is invoked with -d flag. + * By itself, simply causes Python ot acces the + * various advanced modes commands in the .pf file + */ + int extra_diagnostics; /**< when set to TRUE, requests various additional + information about what the user wishes to + * write out + */ + int save_cell_stats; /**< when TRUE,save photons statistics by cell */ + int keep_ioncycle_windsaves; /**< when TRUE, saves wind files for each ionization cycle */ + int keep_ioncycle_spectra; /**< when TRUE, saves the total spectra for each ionization cycle */ + int make_tables; /**< when TRUE, create tables showing various parameters for each cycle */ + int track_resonant_scatters; /**< when TRUE, tracks resonant scatters */ + int save_photons; /**< when TRUE, tracks photons (in photon2d) */ + int save_extract_photons; /**< when TRUE, saves details on extracted photons */ + int adjust_grid; /**< when TRUE, allows wants to adjust the grid scale */ + int diag_on_off; /**< when TRUE, prints extra diagnostics */ + int use_debug; /**< when TRUE, prints out debug statements */ + int print_dvds_info; /**< when TRUE, print out information on the velocity gradients */ + int keep_photoabs; /**< when TRUE, keep photoabsorption in final spectrum */ + int quit_after_inputs; /**< when TRUE, quit after inputs are read in. Note that + ** this flag is set from the command line with the -i option + */ + int quit_after_wind_defined; /**< when TRUE, quit after the wind grid has been defined and saved */ + int fixed_temp; /**< do not alter temperature from that set in the parameter file */ + int zeus_connect; /**< We are connecting to zeus, do not seek new temp and output + * a heating and cooling file + */ + int rand_seed_usetime; /**< When TRUE use a seed set by the time. The default case here + * is to seed the random number generated with a fixed seed, + * not based on time. The decision of which seed to use + * is set by the command line option --rseed*/ + int photon_speedup; /**< The default is for each ionization cycle to have the same + * number of photons bundles. However, one can by using + * the command lines swich -p cause the number of photons + * to increase logarithmically in each ionization cyCle + * The variable captures this option. + */ + int save_rng; /**< IF TRUE, save the GSL RNG stage. This flag is set by the + * command line option --rng. It causes the state of + * random number generator to be written to one or more files + * in a hidden directory .ring_ROOT. A file is created + * for each thread. The purpose of this is to handle + * restarts of very long runs. */ + int load_rng; /**< IF TRUE, load the GSL RNG state. This flag is also set + * by the command line option --rng. It causes + * the state of the random numbe generator to be + * read from a file.*/ + int store_matom_matrix; /**< If TRUE, write the macro-atom matrix ot a file*/ + int jumps_for_detailed_spectra; /**< If true, use the older deprecated jump method for + * calculating emissivities + * in detailed spectra. Note that this is not + * the same as using matrices or jumps for ionization + * cyles, which is currently controlled directly + * from the normal .pf file + */ + int use_upweighting_of_simple_macro_atoms; /**< If TURE, use the deprecated method for simple atoms + *in macro scheme + */ + int run_xtest_diagnostics; /**< If TRUE, then xtest is being run, which is + * a special routine to run xtest specific diagnostic + * tests. + */ + int partial_cells; /**< Switch to decribe treatment of partial cells. */ + int searchlight; /**< Switch to invoke search light option. This is + a very experimental diagnostic mode in which photons + originate at a specific place in the grid in the + detailed spectrum stage. It probably should be + used in conjunction with analysing individual photons + as they pass through the grid. There are lots of + issues with how the detailed spectra are constucted + that make it less useful than it might seem. */ + int no_macro_pops_for_ions; /* if true, then use the ion densities from the ionization mode + for macro-atoms, rather than from macro_pops */ +}; + +extern struct advanced_modes modes; + + +extern FILE *optr; /**< pointer to a diagnostic file that will contain dvds information */ + + + +/***********************FILENAMES STRUCTURE ****************************/ +/** + * Structure containing all of the file and directory names used + * in the process of running a model + */ +struct filenames +{ + char root[LINELENGTH]; /**< main rootname */ + char windsave[LINELENGTH]; /**< wind save filename */ + char old_windsave[LINELENGTH]; /**< old windsave name */ + char specsave[LINELENGTH]; /**< spec save filename */ + char diag[LINELENGTH]; /**< diag file */ + char diagfolder[LINELENGTH]; /**< diag folder */ + char input[LINELENGTH]; /**< input name if creating new pf file */ + char new_pf[LINELENGTH]; /**< name of generated pf file */ + char lwspec[LINELENGTH]; /**< .log_spec_tot file (spectra from last ionization cycle + * in log wavelength scale) + */ + char lwspec_wind[LINELENGTH]; /**< .log_spec_tot_wind (same as above but in log units) */ + char spec[LINELENGTH]; /**< .spec file (extracted spectra on linear scale) */ + char lspec[LINELENGTH]; /**<. spec file (extracted spectra on a log scale) */ + char spec_wind[LINELENGTH]; /**< .spec file (extracted spectra limited to wind photons on a linear scale) */ + char lspec_wind[LINELENGTH]; /**< .spec file (extracted spectra limited to wind photons on a log scale) */ + char disk[LINELENGTH]; /**< disk diag file name */ + char tprofile[LINELENGTH]; /**< non standard tprofile fname */ + char phot[LINELENGTH]; /**< photfile e.g. sirocco.phot */ + char windrad[LINELENGTH]; /**< wind rad file */ + char extra[LINELENGTH]; /**< extra diagnositcs file opened by init_extra_diagnostics */ +}; + +extern struct filenames files; + + + +#define NMAX_OPTIONS 20 + +/* these two variables are used by xdefine_phot() in photon_gen.c + to set the mode for get_matom_f()in matom.c and tell it + whether it has already calculated the matom emissivities or not. */ +#define CALCULATE_MATOM_EMISSIVITIES 0 +#define USE_STORED_MATOM_EMISSIVITIES 1 + +/* Used in macro_gov elsewhere to descibe choices between being or going + to a kpkt or macro atom state */ +#define KPKT 2 +#define MATOM 1 +/* modes for kpkt calculations */ +#define KPKT_MODE_CONTINUUM 0 /* only account for k->r processes */ +#define KPKT_MODE_ALL 1 /* account for all cooling processes */ +#define KPKT_MODE_CONT_PLUS_ADIABATIC 2 /* account for k->r and adiabatic destruction */ + + +/* whether or not to use the implicit/accelerated macro-atom scheme, in which + a matrix inversion is used in the emissivity calcualtion rather than + a MC sampling of the transition probabilities */ +#define MATOM_MATRIX_EMISSIVITIES TRUE +#define STORE_B_MATRIX TRUE +#define MATOM_TRANSITION_MODE MATOM_MATRIX + +/* Variable introducted to cut off macroatom / estimator integrals + when exponential function reaches extreme values. Effectivevly a max limit + imposed on x = hnu/kT terms */ + +#define ALPHA_MATOM_NUMAX_LIMIT 30 /**< maximum value for h nu / k T to be considered in integrals */ +#define ALPHA_FF 100. /**< maximum h nu / kT to create the free free CDF + */ + + +/* non-radiative heat flow mode */ +#define KPKT_NET_HEAT_MODE 0 + + +/* DIAGNOSTIC for understanding problems with imported models + */ + + +#define BOUND_NONE 0 +#define BOUND_INNER_CONE 1 +#define BOUND_OUTER_CONE 2 +#define BOUND_RMAX 3 +#define BOUND_RMIN 4 +#define BOUND_ZMIN 5 +#define BOUND_ZMAX 6 +#define BOUND_INNER_RHO 7 +#define BOUND_OUTER_RHO 8 + +extern int xxxbound; + + +/** Structure associated with rdchoice. This + * structure is required only in cases where one + * wants to use rdchoice multiple times with different + * options. But it can, or course be used for + * any such call. It allows one to associate + * an input word with an output value, using the routine + * get_choices. There needs to be one structure + * for each input variable. At present, this is only + * used for the selection of spec_types + */ +#define MAX_RDPAR_CHOICES 20 + +typedef struct rdpar_choices +{ + char choices[MAX_RDPAR_CHOICES][LINELENGTH]; + int vals[MAX_RDPAR_CHOICES]; + int n; +} dummy_choices, *ChoicePtr; + +extern struct rdpar_choices zz_spec; + + +/* ************************************************************************** */ +/** + * The constants and structures which require global scope throughout the wind + * import functions are defined here. This includes the structure which will + * temporarily hold the imported model data. + * + * ************************************************************************** */ + + +/* + * The following definitions are used to try and improve the readability for + * some of the code used to read in 1D and 2D grids from an input file. They + * refer to how many columns are in the data file and what will be read in. + */ + +#define READ_NO_TEMP_1D 4 +#define READ_ELECTRON_TEMP_1D (READ_NO_TEMP_1D + 1) +#define READ_BOTH_TEMP_1D (READ_NO_TEMP_1D + 2) + +#define READ_NO_TEMP_2D 9 +#define READ_ELECTRON_TEMP_2D (READ_NO_TEMP_2D + 1) +#define READ_BOTH_TEMP_2D (READ_NO_TEMP_2D + 2) + +/** + * The Import structure will contain all of the required information for + * creating a wind grid using an imported model. + */ + +struct Import +{ + int init_temperature; /**< initialise to t.wind.init if TRUE */ + int ncell; /**< the total number of cells read in */ + int ndim, mdim; /**< the number of coordinates in the n and m dimensions */ + int *i, *j; /**< the i (row) and j (column) elements */ + int *inwind; /**< flag for the cell being inwind or not inwind */ + double *x, *z, *r, *theta; /**< the x/r or z/theta coordinates of the grid in cgs units */ + double *v_x, *v_y, *v_z; /**< the velocity in Cartesian coordinates in cgs units */ + double *v_r; /**< the radial velocity in cgs units */ + double *mass_rho; /**< the mass density in cgs units */ + double *t_e, *t_r; /**< the electron and radiation temperature in Kelvin */ + double *wind_x, *wind_z; /**< the wind grid coordinates */ + double *wind_midx, *wind_midz; /**< the wind grid mid points */ +}; + +extern struct Import *imported_model; // MAX_DOM is defined in sirocco.h and as such import.h has to be included after + +/* the functions contained in log., rdpar.c and lineio.c are + declare separately from templates. This is because some functions + only use log.h and don't use sirocco.h due to repeated definitions */ +#include "log.h" +#include "version.h" +#include "templates.h" + +/* We're going to keep the matrix GPU functions seperate from the other templates */ +#ifdef CUDA_ON +#include "matrix_gpu.h" +#endif diff --git a/source/sirocco_extern_init.c b/source/sirocco_extern_init.c new file mode 100644 index 000000000..59b10d33e --- /dev/null +++ b/source/sirocco_extern_init.c @@ -0,0 +1,137 @@ + +#include +#include +#include +#include +#include +#include "atomic.h" + +#include "sirocco.h" + + +int np_mpi_global; ///< Global variable which holds the number of MPI processes + +int rank_global; ///< Rank of a particular thread + +int verbosity; ///< verbosity level. 0 low, 10 is high + +int rel_mode; ///< How doppler effects and co-moving frames are treated + +int run_xtest; ///< Variable if TRUE causes a special test mode to be run + +int NDIM2; ///< The total number of wind cells in wmain +int NPLASMA; ///< The number of cells with non-zero volume or the size of plasma structure + +double DFUDGE; + +double SMAX_FRAC; /**< In translate_in_wind, a limit is placed on the maximum distance a + photon can travel in one step. It is a fraction SMAX_FRAC of the + distance of the photon from the origin. This had been hardwired to + 0.1 for up to 57h. Changing it to 0.5 speeds up the current version + of the code by as much as a factor of 2 for small sized grids. This + had been introduced as part of the attempt to assure ourselves that + line shapes were calculated as accurately as possible. The underlhying + rational for having a maximum disstance is associated with the fact that + we use linear interpolation along the line of sight to establish velocities + and most of our grid cells in 2.5d are actually hoop shaped, which means + one can travel a long distance within a hoop if the direction of the photon + is not more or less radial, but if moving along the hoop. + */ +double DENSITY_PHOT_MIN; /**< This constant is a minimum density for the purpose of calculating + photoionization heating and recombination cooling. It is important that heating and cooling + be calculated self-consistently. Program speed is somewhat sensitive + to this parameter, at the 10% level if raised from 1e-3 to 1. There is a + trade-off since lower minima may give better results, especially for macro atoms. */ + +double PHOT_RANGE; /**< When a variable number of photons are called in different ionization + cycles this is the log of the difference between NPHOT_MAX + and the value in the first cycle + */ +int NPHOT_MAX; /**< The maximum number of photon bundles created per cycle */ +int NPHOT; /**< The number of photon bundles created, defined in setup.c */ + +int NWAVE_MAX; +int NWAVE_EXTRACT; ///< The number of wavelength bins for spectra during the spectrum cycles +int NWAVE_NOW; ///< Either NWAVE_IONIZ or NWAVE_EXTRACT depending on whether in ionizaiton of spectrum cycles + +plane_dummy plane_m2_near, plane_m2_far; + +DomainPtr zdom; ///< This is the array pointer that contains the domains +int current_domain; ///< This integer is used by swind only + +struct geometry geo; + +struct xdisk disk, qdisk; /**< disk defines zones in the disk which in a specified frequency band emit equal amounts + of radiation. disk gets reinitialized whenever the frequency interval of interest + is changed. qdisk stores the amount of heating of the disk as a result of + illumination by the star or wind. It's boundaries are fixed throughout a cycle */ + +struct blmodel blmod; + +WindPtr wmain; + +PlasmaPtr plasmamain; + +PhotStorePtr photstoremain; + +MatomPhotStorePtr matomphotstoremain; + +MacroPtr macromain; + +//int xxxpdfwind; ///< When 1, line luminosity calculates pdf + +int size_Jbar_est, size_gamma_est, size_alpha_est; + +PhotPtr photmain; /**< A pointer to all of the photons that have been created in a subcycle. Added to ease + breaking the main routine of sirocco into separate rooutines for inputs and + running the program */ + +int nspectra; /**< After create_spectrum, the number of elements allocated for s, or + alternatively the number of spectra one has to work with. Note that + general s[0],s[1] and s[2] are the escaping, scattered and absorbed photons, + while elements higher than this will contain spectra as seen by different observers */ + +int nscat[MAXSCAT + 1], nres[MAXSCAT + 1], nstat[NSTAT]; + +SpecPtr xxspec; + +int swind_min, swind_max, swind_delta, swind_project; +double *aaa; ///< A pointer to an array used by swind + +struct Cdf cdf_ff; +struct Cdf cdf_fb; +struct Cdf cdf_vcos; +struct Cdf cdf_vdipole; +struct Cdf cdf_bb; +struct Cdf cdf_brem; + +int itest, jtest; + +char hubeny_list[132]; ///< Location of listing of files representing hubeny atmospheres + +struct xbands xband; + +double kap_bf[NLEVELS]; + +FILE *pstatptr; ///< pointer to a diagnostic file that will contain photon data for given cells +int cell_phot_stats; ///< 1=do it, 0=dont do it +int ncstat; ///< the actual number we are going to log +int ncell_stats[NCSTAT]; ///< the numbers of the cells we are going to log + +int nerr_no_Jmodel; +int nerr_Jmodel_wrong_freq; + +struct advanced_modes modes; + +FILE *optr; ///< pointer to a diagnostic file that will contain dvds information + +struct filenames files; + +int xxxbound; + +struct rdpar_choices zz_spec; + +struct Import *imported_model; ///< MAX_DOM is defined in sirocco.h and as such import.h has to be included after + + +double velocity_electron[3]; // velocity of the electron when thermal effects are included diff --git a/source/sirocco_optd.c b/source/sirocco_optd.c new file mode 100644 index 000000000..4eaee5807 --- /dev/null +++ b/source/sirocco_optd.c @@ -0,0 +1,715 @@ +/** ************************************************************************* */ +/** + * @file py_optical_depth.c + * @author Edward Parkinson + * @date February 2021 + * + * @brief File containing the main functions defining the program. + * + * ************************************************************************** */ + +#include +#include +#include +#include +#include +#include + +#include "atomic.h" +#include "sirocco.h" +#include "sirocco_optd.h" + +struct CommandlineArguments +{ + double freq_min; + double freq_max; + double n_freq; + double inclinations[MAX_CUSTOM_ANGLES]; +}; + +/* ************************************************************************* */ +/** + * @brief Create spectra of tau vs lambda for each observer angle + * + * @details + * + * This is the main function which will generate the optical depth spectra for + * each observer angle in xxspec. The algorithm is similar to extract and the + * tau_diag algorithm which this function is called in. + * + * A photon is generated at the central source of the model and is extracted + * from this location towards the observer where it escapes, where integrate_tau_across_wind + * returns the integrated optical depth along its path to escape. This is done + * for a range of photon frequencies to find how optical depth changes with + * frequency. + * + * This processes can take some time compared to tau_evalulate_photo_edges. But, + * since NUM_FREQUENCY_BINS photons are being generated for each spectrum and the fact + * that these photons do not interact, the spectra does not actually take that + * long to complete. + * + * ************************************************************************** */ + +void +create_optical_depth_spectrum (double u_freq_min, double u_freq_max, double *input_inclinations) +{ + int i, j; + int err; + double *tau_spectrum; + double c_optical_depth, c_column_density; + double c_frequency, freq_min, freq_max, d_freq; + struct photon photon; + + int n_inclinations; + SightLines_t *inclinations = initialize_inclination_angles (&n_inclinations, input_inclinations); + + printf ("Creating optical depth spectra:\n"); + + tau_spectrum = calloc (n_inclinations * NUM_FREQUENCY_BINS, sizeof *tau_spectrum); + if (tau_spectrum == NULL) + { + errormsg ("cannot allocate %lu bytes for tau_spectrum\n", n_inclinations * NUM_FREQUENCY_BINS * sizeof *tau_spectrum); + exit (EXIT_FAILURE); + } + + /* + * We have a complicated if statement first, though. If a freq_min + * or a freq_max was provided, then we need to get this first and set + * the frequency limits appropriately. If neither are defined, then we will + * use some hardwired limits. The frequency range of the extracted will be + * used, however if xxpsec is NULL (no observer spectrum exists), then the + * frequency range will be over a default 100 - 10,000 Angstrom band. + */ + + if (u_freq_min > 0 || u_freq_max > 0) + { + if (u_freq_min > 0) + { + freq_min = u_freq_min; + } + else + { + freq_min = VLIGHT / (10000 * ANGSTROM); + } + + if (u_freq_max > 0) + { + freq_max = u_freq_max; + } + else + { + freq_max = VLIGHT / (100 * ANGSTROM); + } + + if (freq_max < freq_min) + { + errormsg ("frequency range given has set freq_max (%e) < freq_min (%e) \n", freq_max, freq_min); + exit (EXIT_FAILURE); + } + } + else + { + if ((geo.nangles == 0 && xxspec == NULL) || (geo.swavemax == 0 && geo.swavemin == 0)) + { + freq_min = VLIGHT / (10000 * ANGSTROM); + freq_max = VLIGHT / (100 * ANGSTROM); + } + else + { + freq_min = VLIGHT / (geo.swavemax * ANGSTROM); + freq_max = VLIGHT / (geo.swavemin * ANGSTROM); + if (sane_check (freq_min)) + { + freq_min = VLIGHT / (10000 * ANGSTROM); + errormsg ("freq_min has an invalid value setting to %e\n", freq_min); + } + if (sane_check (freq_max)) + { + freq_max = VLIGHT / (100 * ANGSTROM); + errormsg ("freq_min has an invalid value setting to %e\n", freq_max); + } + } + } + + d_freq = (log10 (freq_max) - log10 (freq_min)) / NUM_FREQUENCY_BINS; + kbf_need (freq_min, freq_max); + + /* + * Now create the optical depth spectra for each inclination + */ + + for (i = 0; i < n_inclinations; i++) + { + printf (" - Creating spectrum: %s\n", inclinations[i].name); + c_frequency = log10 (freq_min); + + for (j = 0; j < NUM_FREQUENCY_BINS; j++) + { + c_optical_depth = 0.0; + c_column_density = 0.0; + + err = create_photon (&photon, pow (10, c_frequency), inclinations[i].lmn); + if (err == EXIT_FAILURE) + { + errormsg ("skipping photon of frequency %e\n", pow (10, c_frequency)); + continue; + } + + err = integrate_tau_across_wind (&photon, &c_column_density, &c_optical_depth); + if (err == EXIT_FAILURE) + continue; + + tau_spectrum[i * NUM_FREQUENCY_BINS + j] = c_optical_depth; + c_frequency += d_freq; + } + } + + write_optical_depth_spectrum (inclinations, n_inclinations, tau_spectrum, freq_min, d_freq); + free (tau_spectrum); + free (inclinations); +} + +/* ************************************************************************* */ +/** + * @brief Calculate the optical depth for various optical depth edges and + * extract the column density. + * + * @details + * + * This is the main function which will control the procedure for calculating + * various diagnostic numbers for the optical depth's experienced in the current + * model. Namely, this function aims to show the total integrated optical depth + * to each observer angle using (originally) the following optical depths: + * + * - Lymann edge + * - Balmer edge + * - Helium II edge + * + * Once these integrated optical depths have been calculated for each angle, a + * spectrum of optical depth vs wavelength is created for each angle. + * + * The aim of these diagnostic numbers it to provide some sort of quick metric + * on the optical thickness of the current model. + * + * ************************************************************************** */ + +void +evaluate_photoionization_edges (double *input_inclinations) +{ + int i, j; + int err; + double c_frequency, c_optical_depth, c_column_density; + double *optical_depth_values = NULL, *column_density_values = NULL; + struct photon photon; + enum RunModeEnum original_run_mode = RUN_MODE; + RUN_MODE = RUN_MODE_NO_ES_OPACITY; + + Edges_t edges[] = { + {"HLymanEdge", 3.387485e+15}, + {"HBalmerEdge", 8.293014e+14}, + {"HeI24eVEdge", 5.9483e+15}, + {"HeII54eVEdge", 1.394384e+16} + }; + + const int n_edges = sizeof edges / sizeof edges[0]; + + int n_inclinations; + SightLines_t *inclinations = initialize_inclination_angles (&n_inclinations, input_inclinations); + + optical_depth_values = calloc (n_inclinations * n_edges, sizeof *optical_depth_values); + if (optical_depth_values == NULL) + { + errormsg ("cannot allocate %lu bytes for optical_depths\n", n_inclinations * n_edges * sizeof *optical_depth_values); + exit (EXIT_FAILURE); + } + + column_density_values = calloc (n_inclinations, sizeof *column_density_values); + if (column_density_values == NULL) + { + errormsg ("cannot allocate %lu bytes for column_densities\n", n_inclinations * sizeof *column_density_values); + exit (EXIT_FAILURE); + } + + /* + * Now extract the optical depths and mass column densities. We loop over + * each PI edge for each inclination angle. + */ + + for (i = 0; i < n_inclinations; i++) + { + for (j = 0; j < n_edges; j++) + { + c_optical_depth = 0.0; + c_column_density = 0.0; + c_frequency = edges[j].freq; + + err = create_photon (&photon, c_frequency, inclinations[i].lmn); + if (err == EXIT_FAILURE) + { + errormsg ("skipping photon of frequency %e\n", c_frequency); + continue; + } + + err = integrate_tau_across_wind (&photon, &c_column_density, &c_optical_depth); + if (err == EXIT_FAILURE) + continue; // do not throw extra warning when one is already thrown in integrate_tau_across_wind + + optical_depth_values[i * n_edges + j] = c_optical_depth; + column_density_values[i] = c_column_density; + } + } + + print_optical_depths (inclinations, n_inclinations, edges, n_edges, optical_depth_values, column_density_values); + free (inclinations); + free (optical_depth_values); + free (column_density_values); + RUN_MODE = original_run_mode; +} + +/* ************************************************************************* */ +/** + * @brief Find the electron scattering photosphere. + * + * @details + * + * This is the main controlling function for finding the photosphere. The + * electron scattering optical depth is controlled by the global variable + * TAU_DEPTH. + * + * ************************************************************************** */ + +void +find_photosphere (void) +{ + int i, err; + double optical_depth, column_density; + struct photon photon; + SightLines_t *inclinations; + double input_inclinations[MAX_CUSTOM_ANGLES]; + + for (i = 0; i < MAX_CUSTOM_ANGLES; ++i) // required for initialize_inclination_angles, even though unused + input_inclinations[i] = -1.0; + + int n_inclinations; + inclinations = initialize_inclination_angles (&n_inclinations, input_inclinations); + + Positions_t *positions = calloc (n_inclinations, sizeof (Positions_t)); + if (positions == NULL) + { + errormsg ("unable to allocate memory for the positions array\n"); + exit (EXIT_FAILURE); + } + + const double test_freq = 8e14; // todo: this probably need to be a possible input + + printf ("Locating electron scattering photosphere surface for tau_es = %f\n", TAU_DEPTH); + + for (i = 0; i < n_inclinations; i++) + { + err = create_photon (&photon, test_freq, inclinations[i].lmn); + if (err) + { + positions[i].x = positions[i].y = positions[i].z = -1.0; + continue; + } + + optical_depth = column_density = 0; + + err = integrate_tau_across_wind (&photon, &column_density, &optical_depth); + if (err) + { + positions[i].x = positions[i].y = positions[i].z = -1.0; + continue; + } + + positions[i].x = photon.x[0]; + positions[i].y = photon.x[1]; + positions[i].z = photon.x[2]; + } + + write_photosphere_location_to_file (positions, n_inclinations); + free (inclinations); + free (positions); +} + +/* ************************************************************************* */ +/** + * @brief Help text for the program. + * + * ************************************************************************** */ + +void +print_help (void) +{ + char *help = + "A utility program to analyse the optical depth in a Python model.\n\n" + "usage: py_optical_depth [-h] [-d ndom] [-p tau_stop] [-cion nion] \n" + " [-freq_min min] [-freq_max max] [-i i1 i2 ...]\n" + " [--nonrel] [--smax frac] [--no-es] [--version]\n" + " root\n\n" + "This program can be used in multiple ways. By default, the integrated continuum\n" + "optical depth along the defined observer lines of sight for the model are returned.\n" + "If none of these have been defined, then a set of default lines of sight are used\n" + "instead or can be specified using -i. This program can also find the surface of\n" + "constant electron scattering optical using the -p option.\n\n" + "Please see below for a list of all flags.\n\n" + "-h Print this help message and exit\n" + "-d ndom Set the domain to launch photons from\n" + "-p tau_stop Integrate from outwards to find the electron scattering photosphere\n" + "-cion nion Extract the column density for an ion of number nion\n" + "-freq_min min The lower frequency boundary for optical depth spectra\n" + "-freq_max max The upper frequency boundary for optical depth spectra\n" + "-i i1 i2 i3 ... Calculate the optical depth for the given space seperated list of sight lines\n" + "--nonrel Use linear frequency transforms, to be used when Python was run\n" + " in non-relativistic mode\n" + "--smax frac Set the maximum fraction a photon can move in terms of cell distances\n" + "--no-es Do not include opacity contributions from electron scattering\n" + "--version Print the version information and exit.\n"; + printf ("%s", help); +} + +/* ************************************************************************* */ +/** + * @brief Parse run time arguments provided at the command line. + * + * @param[in] argc The number of arguments provided + * @param[in] argv The command line arguments + * + * @details + * + * Reads the command line arguments. Assumes the final argument is the root name + * of the model. If no arguments are provided, then the program will run in a + * default mode and query the user for the root name of the simulation. + * + * ************************************************************************** */ + +struct CommandlineArguments +get_arguments (int argc, char *argv[]) +{ + int i; + int n_args_read; + char input[LINELENGTH]; + struct CommandlineArguments arguments; + + arguments.freq_min = arguments.freq_max = 0; + arguments.n_freq = NUM_FREQUENCY_BINS; + for (i = 0; i < MAX_CUSTOM_ANGLES; ++i) + arguments.inclinations[i] = -1.0; + + /* + * If no command line arguments are provided, then use fgets to query the + * root name from the user and return + */ + + if (argc == 1) + { + printf ("Please input the model root name: "); + if (fgets (input, LINELENGTH, stdin) == NULL) + { + printf ("Unable to parse root name\n"); + exit (EXIT_FAILURE); + } + get_root (files.root, input); + return arguments; + } + + /* + * Otherwise, loop over the command line arguments and initialize various + * variables + */ + + n_args_read = 0; + for (i = 1; i < argc; ++i) + { + if (!strcmp (argv[i], "-h")) //NOTE: print help text + { + print_help (); + exit (EXIT_SUCCESS); + } + else if (!strcmp (argv[i], "--nonrel")) //NOTE: use linear frequency transforms + { + printf ("Using linear approximations for Doppler shifts\n"); + rel_mode = REL_MODE_LINEAR; + n_args_read = i; + } + else if (!strcmp (argv[i], "--version")) //NOTE: print version number + { + printf ("Python version %s\n", VERSION); + printf ("Built from git commit %s\n", GIT_COMMIT_HASH); + if (GIT_DIFF_STATUS) + printf ("This version was compiled with %d files with uncommited changes\n", GIT_DIFF_STATUS); + exit (EXIT_SUCCESS); + } + else if (!strcmp (argv[i], "-cion")) //NOTE: extract column density for specific ion, rather than H + { + char *check; + COLUMN_MODE = COLUMN_MODE_ION; + COLUMN_MODE_ION_NUMBER = (int) strtol (argv[i + 1], &check, 10); + if (*check != '\0') + { + printf ("Unable to convert argument provided for -cion into an integer\n"); + exit (EXIT_FAILURE); + } + if (COLUMN_MODE_ION_NUMBER < 0) + { + printf ("Argument for -cion cannot be negative\n"); + exit (EXIT_FAILURE); + } + n_args_read = i++; + } + else if (!strcmp (argv[i], "-i")) + { + int k; + char *stdtod_check; + int n_angles_provided = 0; + + for (k = 1; k < MAX_CUSTOM_ANGLES + 2; k++) + { + double inclination = strtod (argv[i + k], &stdtod_check); + if (inclination < 0 || inclination > 90) + { + printf ("Invalid inclination angle of %f. Has to be 0 < inclination < 90\n", inclination); + exit (EXIT_FAILURE); + } + if (k > MAX_CUSTOM_ANGLES) + { + errormsg ("Maximum number of inclination angles provided: only %d are allowed\n", MAX_CUSTOM_ANGLES); + exit (EXIT_FAILURE); + } + if (strcmp (argv[i + k], stdtod_check) == 0) // conversion error indicates no more numbers + { + break; + } + arguments.inclinations[k - 1] = inclination; + n_angles_provided += 1; + } + if (n_angles_provided == 0) + { + errormsg ("Invalid input: no inclination were provided for -i option\n"); + exit (EXIT_FAILURE); + } + + n_args_read = i++; + i += n_angles_provided - 1; + } + else if (!strcmp (argv[i], "-p")) //NOTE: run in photosphere mode + { + RUN_MODE = RUN_MODE_ES_PHOTOSPHERE; + char *check; + TAU_DEPTH = strtod (argv[i + 1], &check); + if (*check != '\0') + { + printf ("Unable to convert argument provided for -p into a double\n"); + exit (EXIT_FAILURE); + } + n_args_read = i++; + } + else if (!strcmp (argv[i], "--smax")) + { + char *check; + SMAX_FRAC = strtod (argv[i + 1], &check); + if (*check != '\0') + { + printf ("Unable to convert argument for -smax into a double"); + exit (EXIT_FAILURE); + } + n_args_read = i++; + } + else if (!strcmp (argv[i], "-d")) //NOTE: change the lanching domain for photons + { + char *check; + N_DOMAIN = (int) strtol (argv[i + 1], &check, 10); + if (*check != '\0') + { + printf ("Unable to convert argument provided for -d into an integer\n"); + exit (EXIT_FAILURE); + } + n_args_read = i++; + } + else if (!strcmp (argv[i], "-freq-min")) //NOTE: lower frequency boundary for optical depth spectrum + { + char *check; + arguments.freq_min = strtod (argv[i + 1], &check); + if (*check != '\0') + { + printf ("Unable to convert argument provided for -freq_min to a double\n"); + exit (EXIT_FAILURE); + } + n_args_read = i++; + } + else if (!strcmp (argv[i], "-freq-max")) //NOTE: upper frequency boundary for optical depth spectrum + { + char *check; + arguments.freq_max = strtod (argv[i + 1], &check); + if (*check != '\0') + { + printf ("Unable to convert argument provided for -freq_max to a double\n"); + exit (EXIT_FAILURE); + } + n_args_read = i++; + } + else if (!strcmp (argv[i], "--no-es")) + { + RUN_MODE = RUN_MODE_NO_ES_OPACITY; + n_args_read = i++; + } + else if (!strncmp (argv[i], "-", 1)) + { + printf ("Unknown argument %s\n", argv[i]); + print_help (); + exit (EXIT_FAILURE); + } + } + + if (n_args_read + 1 == argc) + { + printf ("All command line arguments have been processed and did not find a root name\n"); + exit (EXIT_FAILURE); + } + + get_root (files.root, argv[argc - 1]); + + return arguments; +} + +/* ************************************************************************* */ +/** + * @brief The main function of the program. + * + * @param argc The number of command line arguments + * @param argv The command line arguments + * + * @return EXIT_SUCCESS + * + * @details + * + * ************************************************************************** */ + +int +main (int argc, char *argv[]) +{ + char windsave_filename[LINELENGTH + 24]; + char specsave_filename[LINELENGTH + 24]; + + timer (); + + /* + * Initialize global variables which are required for photon + * transport and general program operation + */ + + Log_set_verbosity (2); + Log_print_max (10); + Log_quit_after_n_errors ((int) 1e8); + init_rand ((int) time (NULL)); + + rel_mode = REL_MODE_FULL; // this is updated in get_arguments if required + SMAX_FRAC = 1.0; + DENSITY_PHOT_MIN = 1.e-10; + COLUMN_MODE = COLUMN_MODE_RHO; + RUN_MODE = RUN_MODE_TAU_INTEGRATE; + N_DOMAIN = 0; + + struct CommandlineArguments arguments; + arguments = get_arguments (argc, argv); + printf ("%-20s Optical depth diagnostics beginning\n", "TAU"); + strcpy (windsave_filename, files.root); + strcat (windsave_filename, ".wind_save"); + strcpy (specsave_filename, files.root); + strcat (specsave_filename, ".spec_save"); + + /* + * Read in the wind_save file and initialize the wind cones and DFUDGE which + * are important for photon transport. The atomic data is also read in at + * this point (which is also very important) + */ + + zdom = calloc (MAX_DOM, sizeof (domain_dummy)); + if (zdom == NULL) + { + printf ("Unable to allocate memory for domain\n"); + return EXIT_FAILURE; + } + + if (wind_read (windsave_filename) < 0) + { + errormsg ("unable to open %s\n", windsave_filename); + exit (EXIT_FAILURE); + } + + DFUDGE = setup_dfudge (); + setup_windcone (); + + /* + * If a spec_save exists, and there are spectral cycles (possibly a redundant + * check), then read in the spec_save file. + */ + + if (access (specsave_filename, F_OK) == 0) + { + if (geo.pcycle > 0) + { + if (spec_read (specsave_filename) < 0) + { + errormsg ("Unable to open %s when spectrum cycles have been run\n", specsave_filename); + exit (EXIT_FAILURE); + } + } + } + + /* + * Do some further error checking on the COLUMN_MODE_ION to ensure that + * it is a sensible number and print the ion to be extracted + */ + + printf ("\n"); + if (RUN_MODE != RUN_MODE_ES_PHOTOSPHERE) + { + if (COLUMN_MODE == COLUMN_MODE_ION) + { + if (COLUMN_MODE_ION_NUMBER < 0 || COLUMN_MODE_ION_NUMBER > nions - 1) + { + errormsg ("The ion number %i is an invalid ion number: there are %i ions which have been loaded\n", COLUMN_MODE_ION_NUMBER, nions); + exit (EXIT_FAILURE); + } + + printf ("Extracting column density for %s %i\n", ele[ion[COLUMN_MODE_ION_NUMBER].nelem].name, ion[COLUMN_MODE_ION_NUMBER].istate); + } + else + { + printf ("Extracting mass and Hydrogen column density\n"); + } + } + else + { + printf ("Finding the photosphere location for electron scattering optical depth %f\n", TAU_DEPTH); + } + + /* + * Now we can finally being the optical depth diagnostics... first we close + * the log and re-open it, with a verbosity of 5. This clears the errors due + * to atomic data, which we should not need to worry about for this program + */ + + if (RUN_MODE == RUN_MODE_TAU_INTEGRATE || RUN_MODE == RUN_MODE_NO_ES_OPACITY) + { + evaluate_photoionization_edges (arguments.inclinations); + create_optical_depth_spectrum (arguments.freq_min, arguments.freq_max, arguments.inclinations); + } + else if (RUN_MODE == RUN_MODE_ES_PHOTOSPHERE) + { + find_photosphere (); + } + else + { + errormsg ("Mode %d is an unknown run mode, not sure how you got here so exiting the program\n", RUN_MODE); + exit (EXIT_FAILURE); + } + + printf ("\n%-20s Optical depth diagnostics completed\n", "TAU"); + printf ("Completed optical depth diagnostics. The elapsed TIME was %f\n", timer ()); + + error_summary ("end of program"); + + return EXIT_SUCCESS; +} diff --git a/source/sirocco_optd.h b/source/sirocco_optd.h new file mode 100644 index 000000000..e2c911dc0 --- /dev/null +++ b/source/sirocco_optd.h @@ -0,0 +1,98 @@ +/** ************************************************************************* */ +/** + * @file py_optical_depth.h + * @author Edward Parkinson + * @date February 2021 + * + * @details + * + * This header file contains the main constants, macros and types used in + * py_optical_depth. + * + * ************************************************************************** */ + +#define MAX_CUSTOM_ANGLES 10 +#define NUM_FREQUENCY_BINS 10000 +#define NAMELEN 32 + +// Error message macro, adds the file name and line to the start of the error +// message + +#define errormsg(fmt, ...) \ +{ \ + fprintf(stderr, "(%s:%i): ", __FILE__, __LINE__); \ + fprintf(stderr, fmt, ##__VA_ARGS__); \ +} + +/** Structure to hold the angles to extract the optical depth/column density from + */ + +typedef struct SightLines_s +{ + char name[NAMELEN]; + double angle; + double lmn[3]; +} SightLines_t; + +/** Structure to hold the name and frequency of a photoionization edge to + *evaluate the optical depth at + */ + +typedef struct Edges_s +{ + char name[50]; + double freq; +} Edges_t; + +/** Structure to save the angle and position of the electron scattering + * photosphere surface + */ + +typedef struct Positions_s +{ + double angle; + double x, y, z; +} Positions_t; + +/** Enumerator used to control the column density which is extracted, i.e. by + * default mass density/N_H is extracted by the density of an ion can also + * be extracted + */ + +enum COLUMN_DENSITY +{ + COLUMN_MODE_RHO, + COLUMN_MODE_ION, +}; + +extern int COLUMN_MODE; +extern int COLUMN_MODE_ION_NUMBER; + +// Control which domain to initially send photons from + +extern int N_DOMAIN; + +// Control how the optical depth integration is done. This can be done in +// integrated tau mode, which gets the integrated tau along the path. The other +// mode aims to find the surface of the electron scattering photosphere + +typedef enum RunModeEnum +{ + RUN_MODE_TAU_INTEGRATE = 0, + RUN_MODE_ES_PHOTOSPHERE = 1, + RUN_MODE_NO_ES_OPACITY = 2, +} RunMode_t; + +extern RunMode_t RUN_MODE; + +extern double TAU_DEPTH; + +// External functions from other files + +int create_photon (PhotPtr p_out, double freq, double *lmn); +SightLines_t *initialize_inclination_angles (int *n_angles, double *input_inclinations); +int integrate_tau_across_wind (PhotPtr photon, double *c_column_density, double *c_optical_depth); +void print_optical_depths (SightLines_t * inclinations, int n_inclinations, Edges_t edges[], int n_edges, double *optical_depth, + double *column_density); +void write_optical_depth_spectrum (SightLines_t * inclinations, int n_inclinations, double *tau_spectrum, double freq_min, double d_freq); +void write_photosphere_location_to_file (Positions_t * positions, int n_angles); diff --git a/source/sirocco_optd_extern_init.c b/source/sirocco_optd_extern_init.c new file mode 100644 index 000000000..09a953cca --- /dev/null +++ b/source/sirocco_optd_extern_init.c @@ -0,0 +1,19 @@ +#include +#include +#include +#include +#include +#include "atomic.h" +#include "sirocco.h" + +#include "sirocco_optd.h" + + +int COLUMN_MODE; +int COLUMN_MODE_ION_NUMBER; + +int N_DOMAIN; + +RunMode_t RUN_MODE; + +double TAU_DEPTH; diff --git a/source/sirocco_optd_output.c b/source/sirocco_optd_output.c new file mode 100644 index 000000000..859399753 --- /dev/null +++ b/source/sirocco_optd_output.c @@ -0,0 +1,247 @@ +/** ************************************************************************* */ +/** + * @file py_optical_depth_output.c + * @author Edward Parkinson + * @date May 2021 + * + * @brief Functions for writing the output to stdout and to file. + * + * ************************************************************************** */ + +#include +#include +#include + +#include "atomic.h" +#include "sirocco.h" +#include "sirocco_optd.h" + +/* ************************************************************************* */ +/** + * @brief Write a generic header to the provided file pointer. + * + * @details + * + * The version sirocco, commit hash and date are written to the file pointer + * provided. This assumes that the file pointer is pointing to the top of the + * file, as this is supposed to be writing a header. + * + * ************************************************************************** */ + +void +write_generic_file_header (FILE *fp) +{ + char time_string[LINELENGTH]; + + get_time (time_string); + fprintf (fp, "# Python Version %s\n", VERSION); + fprintf (fp, "# Git commit hash %s\n", GIT_COMMIT_HASH); + fprintf (fp, "# Date %s\n", time_string); + fprintf (fp, "#\n"); +} + +/* ************************************************************************** */ +/** + * @brief Print the various optical depths for the photoionization edges + * + * @param[in] SightLines_t *inclinations The inclination angles used + * @param[in] int n_inclinations The number of inclinations + * @param[in] Edges_t edges[] The photoionization edges + * @param[in] int n_edges The number of edges evaluated + * @param[in] double *optical_depth The optical depth of the edges + * @param[in] double *column_density The column density of the inclinations + * + * @details + * + * Prints the different optical depths for each angle and optical depth. + * Historically, this used to also print to file. But I found that it was mostly + * useless to do this. + * + * ************************************************************************** */ + +void +print_optical_depths (SightLines_t *inclinations, int n_inclinations, Edges_t edges[], int n_edges, double *optical_depth, + double *column_density) +{ + int i, j; + int len, c_linelen; + char str[LINELENGTH]; + const int MAX_COL = 120; + + printf ("\nOptical depths along the defined line of sights for domain %i:\n\n", N_DOMAIN); + + for (i = 0; i < n_inclinations; i++) + { + if (COLUMN_MODE == COLUMN_MODE_RHO) + { + printf ("%-8s: Mass column density : %3.2e cm^-2\n", inclinations[i].name, column_density[i]); + printf ("%-8s: Hydrogen column density : %3.2e cm^-2\n", "", column_density[i] * rho2nh); + } + else + { + printf ("%-8s: %s %i column density : %3.2e cm^-2\n", inclinations[i].name, ele[ion[COLUMN_MODE_ION_NUMBER].nelem].name, + ion[COLUMN_MODE_ION_NUMBER].istate, column_density[i]); + } + + c_linelen = 0; + for (j = 0; j < n_edges; j++) + { + len = snprintf (str, LINELENGTH, "tau_%-9s: %3.2e ", edges[j].name, optical_depth[i * n_edges + j]); + if (len < 0) + { + errormsg ("error when trying to write to string for output\n"); + exit (EXIT_FAILURE); + } + + c_linelen += len; + if (c_linelen > MAX_COL) + { + c_linelen = 0; + printf ("\n"); + } + + printf ("%s", str); + } + printf ("\n\n"); + } +} + +/* ************************************************************************* */ +/** + * @brief Write the optical depth spectrum to file. + * + * @param[in] SightLines_t *inclination The inclinations angles the optical + * depth was extracted from. + * @param[in] int n_inclinations The number of inclination angles + * @param[in] double *tau_spectrum The optical depth spectrum values + * @param[in] double freq_min The starting frequency of the + * spectrum + * @param[in] double dfreq The frequency spacing of the spectrum + * + * @details + * + * Simply writes the optical depth spectra to the file named + * root.tau_spec. + * + * ************************************************************************** */ + +void +write_optical_depth_spectrum (SightLines_t *inclinations, int n_inclinations, double *tau_spectrum, double freq_min, double d_freq) +{ + int i, j; + double c_wavelength, c_frequency; + char filename[LINELENGTH]; + FILE *fp; + + int len = snprintf (filename, LINELENGTH, "%s.spec_tau", files.root); + if (len < 0) + { + errormsg ("error when creating filename string\n"); + exit (EXIT_FAILURE); + } + + fp = fopen (filename, "w"); + if (fp == NULL) + { + errormsg ("unable to open %s in write mode\n", filename); + exit (EXIT_FAILURE); + } + + write_generic_file_header (fp); + fprintf (fp, "%-15s %-15s ", "Freq.", "Lambda"); + for (i = 0; i < n_inclinations; i++) + { + fprintf (fp, "%-15s ", inclinations[i].name); + } + fprintf (fp, "\n"); + + c_frequency = log10 (freq_min); + for (i = 0; i < NUM_FREQUENCY_BINS; i++) + { + c_wavelength = VLIGHT / pow (10, c_frequency) / ANGSTROM; + fprintf (fp, "%-15e %-15e ", pow (10, c_frequency), c_wavelength); + + for (j = 0; j < n_inclinations; j++) + { + fprintf (fp, "%-15e ", tau_spectrum[j * NUM_FREQUENCY_BINS + i]); + } + + fprintf (fp, "\n"); + c_frequency += d_freq; + } + + if (fclose (fp)) + { + errormsg ("unable to close %s, output may be unfinished!\n", filename); + } +} + +/* ************************************************************************* */ +/** + * @brief Write the photosphere location points to file. + * + * @param[in] Positions_t *positions An array of positions + * @param[in] int n_angles The number of angles which were used + * in the photosphere calculation. + * + * @details + * + * This uses the Positions_t type which keeps track of the x, y and z locations + * of where a photon reached a cumulative optical depth of TAU_DEPTH. + * + * ************************************************************************** */ + +void +write_photosphere_location_to_file (Positions_t *positions, int n_angles) +{ + int i; + double pos1d[3]; + char filename[LINELENGTH]; + FILE *fp; + + int len = snprintf (filename, LINELENGTH, "%s.photosphere", files.root); + if (len < 0) + { + errormsg ("error when creating filename string\n"); + exit (EXIT_FAILURE); + } + + fp = fopen (filename, "w"); + if (fp == NULL) + { + errormsg ("unable to open %s in write mode\n", filename); + exit (EXIT_FAILURE); + } + + write_generic_file_header (fp); + fprintf (fp, "# Electron scatter photosphere locations for tau_es = %f\n#\n", TAU_DEPTH); + + if (zdom[N_DOMAIN].coord_type != SPHERICAL) + { + fprintf (fp, "%-15s %-15s %-15s\n", "x", "y", "z"); + } + else + { + fprintf (fp, "%-15s\n", "r"); + } + + for (i = 0; i < n_angles; i++) + { + if (zdom[N_DOMAIN].coord_type != SPHERICAL) + { + fprintf (fp, "%-15e %-15e %-15e\n", positions[i].x, positions[i].y, positions[i].z); + } + else + { + pos1d[0] = positions[0].x; + pos1d[1] = positions[0].y; + pos1d[2] = positions[0].z; + fprintf (fp, "%-15e\n", length (pos1d)); + } + } + + if (fclose (fp)) + { + errormsg ("unable to close %s, output may be unfinished!\n", filename); + } +} diff --git a/source/sirocco_optd_trans.c b/source/sirocco_optd_trans.c new file mode 100644 index 000000000..1be605842 --- /dev/null +++ b/source/sirocco_optd_trans.c @@ -0,0 +1,263 @@ +/** ************************************************************************* */ +/** + * @file py_optical_depth_transport.c + * @author Edward Parkinson + * @date April 2019 + * + * @brief Functions related to the transport of photons and the main + * algorithm functions. + * + * ************************************************************************** */ + +#include +#include +#include + +#include "atomic.h" +#include "sirocco.h" +#include "sirocco_optd.h" +static const double MAXDIFF = VCHECK / VLIGHT; // For linear velocity requirement for photon transport + +/* ************************************************************************* */ +/** + * @brief Calculate the total optical depth a photon experiences across the + * cell of distance SMAX_FRAC * smax. + * @param[in] photon The photon packet + * @param[in,out] *c_column_density The column density the photon has moved + * through + * @param[in,out] *c_optical_depth The optical depth experienced by the photon + * + * @return p_istat The current photon status or EXIT_FAILURE on failure. + * + * @details + * + * This function is concerned with finding the opacity of the photon's current + * cell, the distance the photon can move in the cell and hence it increments + * the optical depth tau a photon has experienced as it moves through the wind. + * + * ************************************************************************** */ + +int +integrate_tau_across_cell (PhotPtr photon, double *c_column_density, double *c_optical_depth) +{ + int p_istat; + int n_domain, n_plasma; + double kappa_total, density; + double smax, diff; + double freq_inner, freq_outer, mean_freq; + WindPtr c_wind_cell; + PlasmaPtr c_plasma_cell; + struct photon p_start, p_stop, p_now; + + photon->grid = where_in_grid (wmain[photon->grid].ndom, photon->x); + if (photon->grid < 0) + { + printf ("integrate_tau_across_cell: photon is not in a grid cell\n"); + return EXIT_FAILURE; + } + + /* + * Determine where in the plasma grid the cell is. This is required so a + * column density can be calculated. Also calculate the maximum distance the + * photon can traverse across the cell + */ + + c_wind_cell = &wmain[photon->grid]; + n_domain = c_wind_cell->ndom; + n_plasma = wmain[photon->grid].nplasma; + c_plasma_cell = &plasmamain[n_plasma]; + + if (COLUMN_MODE == COLUMN_MODE_RHO) + { + density = c_plasma_cell->rho; + } + else + { + density = c_plasma_cell->density[COLUMN_MODE_ION_NUMBER]; + } + + smax = smax_in_cell (photon) * SMAX_FRAC; + if (smax < 0) + { + errormsg ("smax %e < 0 in cell %d\n", smax, photon->grid); + return EXIT_FAILURE; + } + + /* + * Transform the photon into the CMF and create a photon in the CMF at the + * end of the path of length smax + */ + + observer_to_local_frame (photon, &p_start); + stuff_phot (photon, &p_stop); + move_phot (&p_stop, smax); + observer_to_local_frame (&p_stop, &p_stop); + + /* At this point p_start and p_stop are in the local frame + * at the and p_stop is at the maximum distance it can + * travel. We want to check that the frequency shift is + * not too great along the path that a linear approximation + * to the change in frequency is not reasonable + */ + + while (smax > DFUDGE) + { + stuff_phot (photon, &p_now); + move_phot (&p_now, smax * 0.5); + observer_to_local_frame (&p_now, &p_now); + diff = fabs (p_now.freq - 0.5 * (p_start.freq + p_stop.freq)) / p_start.freq; + if (diff < MAXDIFF) + break; + stuff_phot (&p_now, &p_stop); + smax *= 0.5; + } + + freq_inner = p_start.freq; + freq_outer = p_stop.freq; + mean_freq = 0.5 * (freq_inner + freq_outer); + + /* + * Now we can finally calculate the opacity due to all the continuum + * processes. In macro-atom mode, we need to calculate the continuum opacity + * using kappa_bf and kappa_ff using the macro treatment. For simple mode, we + * can simply use radiation which **SHOULD** return the continuum opacity + * as well, plus something from induced Compton heating. In either cases, + * we still then need to add the optical depth from electron scattering at + * the end. + */ + + kappa_total = 0; + + if (RUN_MODE != RUN_MODE_ES_PHOTOSPHERE) + { + if (geo.rt_mode == RT_MODE_2LEVEL) + { + kappa_total += radiation (photon, smax); + } + else // macro atom case + { + if (c_wind_cell->vol > 0) + { + kappa_total += kappa_bf (c_plasma_cell, freq_inner, 0); + kappa_total += kappa_ff (c_plasma_cell, freq_inner); + } + } + } + + if (RUN_MODE != RUN_MODE_NO_ES_OPACITY) + { + kappa_total += klein_nishina (mean_freq) * c_plasma_cell->ne * zdom[n_domain].fill; + } + + /* + * Increment the optical depth and column density variables and move the + * photon to the edge of the cell + */ + + photon->nscat++; + + *c_column_density += smax * density; + *c_optical_depth += smax * kappa_total; + move_phot (photon, smax); + p_istat = photon->istat; + + return p_istat; +} + +/* ************************************************************************* */ +/** + * @brief Extract the optical depth the photon packet porig must + * travel through to reach the observer. + * + * @param[in] photon The photon packet to extract + * @param[out] *c_column_density The column depth of the extracted photon angle + * @param[out] *c_optical_depth The optical depth from photon origin to + * the observer + * + * @return EXIT_SUCCESS or EXIT_FAILURE + * + * @details + * + * The direction of the observer is set in porig.lmn and should be set prior + * to passing a photon to this function. If any values of lmn are negative, the + * photon will translate in the negative direction. However, have no fear as this + * is normal and is fine due to the assumed symmetry of models in Python. + * + * ************************************************************************** */ + +int +integrate_tau_across_wind (PhotPtr photon, double *c_column_density, double *c_optical_depth) +{ + int err; + int n_dom, where; + enum istat_enum p_istat; + const int max_translate_in_space = 10; + int n_in_space; + double norm[3]; + struct photon p_extract; + + p_istat = P_INWIND; // assume photon is in wind for initialisation reasons + stuff_phot (photon, &p_extract); + + n_in_space = 0; + while (p_istat == P_INWIND) + { + where = where_in_wind (p_extract.x, &n_dom); + + if (where < 0) + { + translate_in_space (&p_extract); + if (++n_in_space > max_translate_in_space) + { + errormsg ("something has gone wrong as this photon has translated in space %d times\n", n_in_space); + return EXIT_FAILURE; + } + } + else if ((p_extract.grid = where_in_grid (n_dom, p_extract.x)) >= 0) + { + err = integrate_tau_across_cell (&p_extract, c_column_density, c_optical_depth); + if (err) + return EXIT_FAILURE; + } + else + { + errormsg ("photon in unknown location grid stat %i\n", p_extract.grid); + return EXIT_FAILURE; + } + + p_istat = walls (&p_extract, photon, norm); + + if (RUN_MODE == RUN_MODE_ES_PHOTOSPHERE) + { + if (*c_optical_depth >= TAU_DEPTH) + { + p_istat = P_ABSORB; + } + } + } + + /* + * If we are in RUN_MODE_ES_PHOTOSPHERE, then we shouldn't care about hitting the + * star or disc, since we are aiming for the origin of the system + */ + + if (RUN_MODE == RUN_MODE_TAU_INTEGRATE) + { + if (p_istat == P_HIT_STAR || p_istat == P_HIT_DISK) + { + errormsg ("photon hit central source or disk incorrectly istat = %i\n", p_istat); + return EXIT_FAILURE; + } + } + else + { + stuff_phot (&p_extract, photon); + if (p_istat == P_HIT_DISK) + { + errormsg ("the photon hit the disk whilst in RUN_MODE_ES_PHOTOSPHERE when it should hit the central source\n"); + return EXIT_FAILURE; + } + } + + return EXIT_SUCCESS; +} diff --git a/source/sirocco_optd_util.c b/source/sirocco_optd_util.c new file mode 100644 index 000000000..b2420e251 --- /dev/null +++ b/source/sirocco_optd_util.c @@ -0,0 +1,392 @@ +/** ************************************************************************* */ +/** + * @file py_optical_depth_util.c + * @author Edward Parkinson + * @date May 2021 + * + * @brief Functions which aren't related to the transport of photons, or + * creation of the results. + * + * ************************************************************************** */ + +#include +#include +#include +#include + +#include "atomic.h" +#include "sirocco.h" +#include "sirocco_optd.h" + +/* ************************************************************************* */ +/** + * @brief Initialize the inclination angles for a 2D outward run. + * + * @param[out] int n_angles The number of angles initialized + * + * @return SightLines_t *inclinations The initialize inclinations structure + * + * @details + * + * If xxpsec has been read in, i.e. there were spectral cycles run the model, + * then the same inclination angles in xxspec are used. Otherwise, a set of + * default angles will be used instead, with a default phase of 0.5. The phase + * should not matter, as it works for, i.e., 0.5 and 1 and gives the same + * results as of writing this. + * + * ************************************************************************** */ + +SightLines_t * +outward_initialize_2d_model_angles (int *n_angles, double *input_inclinations) +{ + int i; + int len; + const double default_phase = 0.5; + const double default_angles[] = { 0.0, 10.0, 30.0, 45.0, 60.0, 75.0, 85.0, 90.0 }; + const int n_default_angles = sizeof default_angles / sizeof default_angles[0]; + + /* + * Count the number of inclination angles provided by the -i command. Since + * the array is initialized as -1, this assumes any values > -1 is a valid + * inclination angle. + */ + + int n_input = 0; + for (i = 0; i < MAX_CUSTOM_ANGLES; ++i) + { + if (input_inclinations[i] > -1) + { + n_input++; + } + } + + /* + * Use the angles specified for by the user for spectrum generation, this + * requires for xxspec to be initialised. Otherwise use the pre-defined angles + * above. + */ + + SightLines_t *inclinations = NULL; + + if (n_input > 0) + { + *n_angles = n_input; + inclinations = calloc (n_input, sizeof *inclinations); + if (inclinations == NULL) + { + errormsg ("cannot allocate %lu bytes for observers array\n", n_input * sizeof *inclinations); + exit (EXIT_FAILURE); + } + + for (i = 0; i < n_input; i++) + { + len = snprintf (inclinations[i].name, NAMELEN, "A%02.0fP%04.2f", input_inclinations[i], default_phase); + if (len < 0) + { + errormsg ("there was an error writing the name to the sight lines array\n"); + exit (EXIT_FAILURE); + } + + inclinations[i].lmn[0] = sin (input_inclinations[i] / RADIAN) * cos (-default_phase * 360.0 / RADIAN); + inclinations[i].lmn[1] = sin (input_inclinations[i] / RADIAN) * sin (-default_phase * 360.0 / RADIAN); + inclinations[i].lmn[2] = cos (input_inclinations[i] / RADIAN); + inclinations[i].angle = input_inclinations[i]; + } + } + else if (xxspec != NULL && geo.nangles > 0) + { + *n_angles = geo.nangles; + inclinations = calloc (geo.nangles, sizeof *inclinations); + if (inclinations == NULL) + { + errormsg ("cannot allocate %lu bytes for observers array\n", geo.nangles * sizeof *inclinations); + exit (EXIT_FAILURE); + } + + for (i = MSPEC; i < MSPEC + geo.nangles; i++) + { + strcpy (inclinations[i - MSPEC].name, xxspec[i].name); + stuff_v (xxspec[i].lmn, inclinations[i - MSPEC].lmn); + inclinations[i].angle = -1; // todo: implement way to get angle xxspec + } + } + else + { + printf ("\nNo spec.save file has been found, using a default set of inclination angles\n\n"); + + *n_angles = n_default_angles; + inclinations = calloc (n_default_angles, sizeof *inclinations); + if (inclinations == NULL) + { + errormsg ("cannot allocate %lu bytes for observers array\n", n_default_angles * sizeof *inclinations); + exit (EXIT_FAILURE); + } + + for (i = 0; i < n_default_angles; i++) + { + len = snprintf (inclinations[i].name, NAMELEN, "A%02.0fP%04.2f", default_angles[i], default_phase); + if (len < 0) + { + errormsg ("there was an error writing the name to the sight lines array\n"); + exit (EXIT_FAILURE); + } + + inclinations[i].lmn[0] = sin (default_angles[i] / RADIAN) * cos (-default_phase * 360.0 / RADIAN); + inclinations[i].lmn[1] = sin (default_angles[i] / RADIAN) * sin (-default_phase * 360.0 / RADIAN); + inclinations[i].lmn[2] = cos (default_angles[i] / RADIAN); + inclinations[i].angle = default_angles[i]; + } + } + + return inclinations; +} + +/* ************************************************************************* */ +/** + * @brief Initialize the inclination angles for a 1D outward run. + * + * @param[out] int n_angles The number of angles initialized + * + * @return SightLines_t *inclinations The initialize inclinations structure + * + * @details + * + * Since this is for a 1D model, it does not matter which inclination angle + * we use. We have opted to used an angle of 45 degrees, as this is also the + * angle we use to define a 1D spherical grid. It should not matter either + * way. + * + * ************************************************************************** */ + +SightLines_t * +outward_initialize_1d_model_angles (int *n_angles) +{ + int len; + const int n_default_angles = 1; + const double default_angle = 45.0; + const double default_phase = 0.5; + + *n_angles = n_default_angles; + SightLines_t *inclinations = calloc (n_default_angles, sizeof (SightLines_t)); + + if (inclinations == NULL) + { + errormsg ("unable to allocate %ld bytes for observers array\n", sizeof *inclinations); + exit (EXIT_FAILURE); + } + + len = snprintf (inclinations[0].name, NAMELEN, "A%02.0fP%04.2f", default_angle, default_phase); + if (len < 0) + { + errormsg ("there was an error writing the name to the sight lines array\n"); + exit (EXIT_FAILURE); + } + + inclinations[0].lmn[1] = sin (default_angle / RADIAN) * sin (-default_phase * 360.0 / RADIAN); + inclinations[0].lmn[0] = sin (default_angle / RADIAN) * cos (-default_phase * 360.0 / RADIAN); + inclinations[0].lmn[2] = cos (default_angle / RADIAN); + inclinations[0].angle = default_angle; + + return inclinations; +} + +/* ************************************************************************* */ +/** + * @brief Initialize the inclination angles to find the photosphere for a + * 2d model. + * + * @param[out] int n_angles The number of angles initialized + * + * @return SightLines_t *inclinations The initialize inclinations structure + * + * @details + * + * The same function is called for both 1D and 2D models. This creates extra + * work for 1D model, but as the algorithm takes very little time to run, it + * does not matter. + * + * 500 inclination angles are defined, to very finely resolve the photosphere + * surface. This is fixed for now. I think 500 is probably far too many, but + * it takes absolutely no time to run. The results from 500 angles probably + * need smoothing if the grid is coarse. + * + * ************************************************************************** */ + +SightLines_t * +photosphere_2d_initialize_angles (int *n_angles) +{ + int i; + double default_angle; + const double default_phase = 1.0; + const int n_default_angles = 500; + const double d_theta = 90.0 / (double) n_default_angles; + + *n_angles = n_default_angles; + SightLines_t *inclinations = calloc (n_default_angles, sizeof (SightLines_t)); + + if (inclinations == NULL) + { + errormsg ("unable to allocate memory for sight lines array\n"); + exit (EXIT_FAILURE); + } + + for (i = 0; i < n_default_angles; i++) + { + default_angle = i * d_theta; + inclinations[i].lmn[0] = sin (default_angle / RADIAN) * cos (-default_phase * 360.0 / RADIAN); + inclinations[i].lmn[1] = sin (default_angle / RADIAN) * sin (-default_phase * 360.0 / RADIAN); + inclinations[i].lmn[2] = cos (default_angle / RADIAN); + inclinations[i].angle = default_angle; + } + + return inclinations; +} + +/* ************************************************************************* */ +/** + * @brief Initialize the inclination angles to find the photosphere. + * + * @param[out] int n_angles The number of angles initialized + * + * @return SightLines_t *inclinations The initialize inclinations structure + * + * @details + * + * The same function is called for both 1D and 2D models. This creates extra + * work for 1D model, but as the algorithm takes very little time to run, it + * does not matter. + * + * 500 inclination angles are defined, to very finely resolve the photosphere + * surface. This is fixed for now. I think 500 is probably far too many, but + * it takes absolutely no time to run. The results from 500 angles probably + * need smoothing if the grid is coarse. + * + * ************************************************************************** */ + +SightLines_t * +photosphere_1d_initialize_angles (int *n_angles) +{ + const double default_phase = 1.0; + const int n_default_angles = 1; + const double default_angle = 45; + + *n_angles = n_default_angles; + SightLines_t *inclinations = calloc (n_default_angles, sizeof (SightLines_t)); + + if (inclinations == NULL) + { + errormsg ("unable to allocate memory for sight lines array\n"); + exit (EXIT_FAILURE); + } + + inclinations[0].lmn[0] = sin (default_angle / RADIAN) * cos (-default_phase * 360.0 / RADIAN); + inclinations[0].lmn[1] = sin (default_angle / RADIAN) * sin (-default_phase * 360.0 / RADIAN); + inclinations[0].lmn[2] = cos (default_angle / RADIAN); + inclinations[0].angle = default_angle; + + return inclinations; +} + +/* ************************************************************************* */ +/** + * @brief Wrapper function for initializing the inclination angles depending + * on the run mode of the program. + * + * @param[out] int n_angles The number of angles initialized + * + * @return SightLines_t *inclinations The initialize inclinations structure + * + * ************************************************************************** */ + +SightLines_t * +initialize_inclination_angles (int *n_angles, double *input_inclinations) +{ + SightLines_t *inclinations; + + if (RUN_MODE == RUN_MODE_ES_PHOTOSPHERE) + { + if (zdom[N_DOMAIN].coord_type == SPHERICAL) + { + inclinations = photosphere_1d_initialize_angles (n_angles); + } + else + { + inclinations = photosphere_2d_initialize_angles (n_angles); + } + } + else + { + if (zdom[N_DOMAIN].coord_type == SPHERICAL) + { + inclinations = outward_initialize_1d_model_angles (n_angles); + } + else + { + inclinations = outward_initialize_2d_model_angles (n_angles, input_inclinations); + } + } + + return inclinations; +} + +/* ************************************************************************* */ +/** + * @brief Generate a photon packet with a given frequency nu for use with the + * optical depth diagnostic routines. + * + * @param[in,out] p_out The photon packet to initialise + * @param[in] freq The frequency of the photon packet + * + * @return EXIT_SUCCESS or EXIT_FAILURE + * + * @details + * + * This routine assumes that the user wishes for the photon to be generated from + * the surface of the central source, taking into account the current direction + * of the sight-line being extracted. It's currently not possible, without a + * tedious re-write, to place the photon at the very origin of the grid when + * there is a central source because of how we check boundaries. + * + * Note that photons are initialised with a weight of f_tot as photons are + * required to have weight, but since functions do not care about the weight of + * the photon, it is set to something large to make sure it does not get + * destroyed by accident somehow. + * + * ************************************************************************** */ + +int +create_photon (PhotPtr p_out, double freq, double *lmn) +{ + int i; + + if (freq < 0) + { + errormsg ("photon can't be created with negative frequency\n"); + return EXIT_FAILURE; + } + + p_out->freq = p_out->freq_orig = freq; + p_out->origin = p_out->origin_orig = PTYPE_DISK; + p_out->istat = P_INWIND; + p_out->w = p_out->w_orig = geo.f_tot; + p_out->tau = 0.0; + p_out->frame = F_OBSERVER; + p_out->x[0] = p_out->x[1] = p_out->x[2] = 0.0; + stuff_v (lmn, p_out->lmn); + + if (RUN_MODE == RUN_MODE_ES_PHOTOSPHERE) + { + move_phot (p_out, zdom[N_DOMAIN].rmax - DFUDGE); + for (i = 0; i < 3; ++i) + { + p_out->lmn[i] *= -1.0; // Make the photon point inwards + } + + } + else + { + move_phot (p_out, geo.rstar + DFUDGE); + } + + return EXIT_SUCCESS; +} diff --git a/source/swind.c b/source/swind.c new file mode 100644 index 000000000..920b07e57 --- /dev/null +++ b/source/swind.c @@ -0,0 +1,716 @@ + +/***********************************************************/ +/** @file swind.c + * @author ksl + * @date May, 2018 + * + * @brief swind is a program which can be used to display various parameters of a wind +as calculated by sirocco. This is the main routine. + * + * + * Arguments: + * + * swind [-h] [-s] [-p parameter_file] [root] + * + * where + * * -h prints out a short help file and exits (see help routine below) + * * -s causes certain parameters in the windsave file to be printed out + * as individual ascii files after which the program exits + * * -d In cases where a windsavefile was made for each ionization + * cycle, this prints out ascii files for "certain" parameters + * for each of the ionization cycles, as well as the ascii files + * for the final ionization cycle (if windsave files for each + * ionization cycle were not created then -s and -d are + * equivalent). + * * -p parameter file + * Instead of reading the choices from the command line read them + * from a parameter file + * root optional root name of wind_save file. If this is not given, + * the user is queried for this + * + * + * Description: + * + * Py_wind simply reads and then displays portions of the wind file created by sirocco. + * It can select various parameters from the wind and it can write them to files so that + * the variables can be plotted. + * + * The normal mode of running swind is to run it interactively. As you run it interactively + * the variables you select are displayed ont the screen. The variables that you display + * can also be written to files (depending on the answer to the question Make_files) + * + * The commands that were executed in the interactive will be stored in swind.pf (if you end + * with a "q", and not an EOF response to the choice question) EOF terminates the program + * at that point before the command file is written to swind.pf + * + * The swind.pf file is useful if you want to run the exact same set of commands on another + * windfile. You should rename swind.pf to something_else.pf and run swind on that + * data set using the -p something_else.pf option + * + * The command_line switches -d and -s are intended to produce a standard set of output files sufficient + * for many purposes. + * + * ###Notes#### + * + * The files that are produced can contain either the original gridding which was used by sirocco, in which + * case the file prefix will be "x.", or "z", in which case it will be regridded to a + * linear array. This option is intended so one can create a contour plot more easily. + * + * The structure of swind is intended to make it easy to add additional variables to print to the + * scen or a file. Basically to add a new option, one needs to update the list of possibilities, + * add a new choice statement in one_choice, and then write the routine that grabs a new variable (or + * calculates a value from several), and add this to either swind_subs or swind_macro_subs. + * + * Because this was intended as a diagnositic routine, some of the more esoteric choices may seem + * a bit odd today. + * + * IMPORTANT: swind has not been adapted to handle multiple domains. Use windsave2table instead. + ***********************************************************/ + +#include +#include +#include +#include + +#include "atomic.h" +#include "sirocco.h" + +//char *choice_options; + + +/**********************************************************/ +/** 111125 - ksl - Replaced print statements giving choices with a string. The point is to be able to include + * the choices in the help string Note carefullly the format if you revise this + * lines should end with \n\ to make the string continue. Do not leave any trailing spaces after the last + * \ to avoid warnings + * + **********************************************************/ +char *choice_options = "\n\ + 1=onefile summary 2=all ions in a given cell\n\ + n=ne, R=rho, v=vel, i=ion info, j=ave_tau, f=ave_freq, p=nphot, S=sim_alpha\n\ + r=t_r, t=t_e, w=rad_weight, s=vol, l=lum, C=cooling/heating, b=adiabatic cooling\n\ + a=abs, L=line lum, g=photo, h=recomb, k=tau H, m=F_rad, x=total, y=mod_te,\n\ + o=overview, e=everything, P=Partial emission meas, I=Ionisation parameter\n\ + W=wind_region, D=dvds_ave, X=position summary, M=macro atom info *=shock heating \n\ + d=convergence status E=convergence_all_info B=PlasmaPtr J=Radiation density\n\ + H=All Heating and Cooling mechanisms in one shot O=Spectral model parameters S=Spectral models\n\ + z=Zoom,u=unZoom,Z=switch to/from raw and yz projected modes, F=Create files, A=Change file write defaults\n\ + #=Wind grid N=new.windfile q=quit (preferred over EOF) &=Coll Strengths Q=switch domain\n"; + + +/**********************************************************/ +/** + * @brief swind is a program which can be used to display various parameters of a wind + * as calculated by sirocco. This is the main routine + * + * @param [in] int argc The number of command line arguments + * @param [in] char * argv[] The command line arguments + * @return Always returns 0 + * + * @details + * Py_wind simply reads and then displays portions of the wind file created by sirocco. + * It can select various parameters from the wind and it can write them to files so that + * the variables can be plotted. + * + * The normal mode of running swind is to run it interactively. As you run it interactively + * the variables you select are displayed ont the screen. The variables that you display + * can also be written to files (depending on the answer to the question Make_files) + * + * The commands that were executed in the interactive will be stored in swind.pf (if you end + * with a "q", and not an EOF response to the choice question) EOF terminates the program + * at that point before the command file is written to swind.pf + * + * The swind.pf file is useful if you want to run the exact same set of commands on another + * windfile. You should rename swind.pf to something_else.pf and run swind on that + * data set using the -p something_else.pf option + * + * The command_line switches -d and -s are intended to produce a standard set of output files sufficient + * for many purposes. + * + * The main routine gets the input data, and repeately presents the user with a set of choices (in interactive + * mode) until the users dicides she has had enough. The case statements that are used to select what to do based + * on user inputs are (now) contained in the subroutine one_choice + * + * ### Notes ### + * The files that are produced can contain either the original gridding which was used by sirocco, in which + * case the file prefix will be "x.", or "z", in which case it will be regridded to a + * linear array. This option is intended so one can create a contour plot more easily. + * + **********************************************************/ + +int +main (argc, argv) + int argc; + char *argv[]; +{ + + + int i; + int ochoice; + char c; + + char root[LINELENGTH], input[LINELENGTH], wspecfile[LINELENGTH], specfile[LINELENGTH]; + char windradfile[LINELENGTH], windsavefile[LINELENGTH]; + char parameter_file[LINELENGTH]; + char photfile[LINELENGTH]; + char *fgets_rc; + double freq; + int interactive; + + + // swind uses rdpar, but only in an interactive mode. As a result + // there is no associated .pf file + + interactive = 1; /* Default to the standard operating mofe for swind */ + strcpy (parameter_file, "NONE"); + + /* Next command stops Debug statements printing out in swind */ + Log_set_verbosity (3); + + /* Parse the command line. It is Important that swind_help + * below is updated if this changes + */ + + if (argc == 1) + { + printf ("Root for wind file :"); + fgets_rc = fgets (input, LINELENGTH, stdin); + if (!fgets_rc) + { + Error ("Input rootname is NULL or EOF\n"); + exit (1); // Exit if NULL returned + } + get_root (root, input); + } + else + { + for (i = 1; i < argc; i++) + { + if (strcmp (argv[i], "-h") == 0) + { + swind_help (); + } + else if (strcmp (argv[i], "-d") == 0) + { + interactive = -1; + } + + else if (strcmp (argv[i], "-s") == 0) + { + interactive = 0; + } + else if (strcmp (argv[i], "-p") == 0) + { + interactive = 0; + i = i + 1; + strcpy (parameter_file, argv[i]); + } + else if (strncmp (argv[i], "-", 1) == 0) + { + Error ("swind: unknown switch %s\n", argv[i]); + swind_help (); + } + } + + strcpy (input, argv[argc - 1]); + get_root (root, input); + } + + + printf ("Reading data from file %s\n", root); + + /* Now create the names of all the files which will be written */ + + strcpy (wspecfile, root); + strcpy (specfile, root); + strcpy (windradfile, root); + strcpy (windsavefile, root); + strcpy (photfile, root); + + strcat (wspecfile, ".spec_tot"); + strcat (specfile, ".spec"); + strcat (windradfile, ".wind_rad"); + strcat (windsavefile, ".wind_save"); + strcat (photfile, ".phot"); + + + /* Initialize other variables here */ + + swind_project = 1; // The default is to try to project onto a yz plane + +/* Read in the wind file */ + +/* Note that wind_read allocates the space for the WindPtr array. The +reason that this is done in wind_read is because wind_read also reads +the geo structure. The geo struc contains the dimensions of the wind +array and so until it is read the space for the WindPtr structure cannot +be allocated. Howver, one cannot assign w in a subroutine. Therefore +the old call, namely wind_read(w,windsavefile, will not work. The statement +w=wmain is a way around this since wmain is external and therefore can +be assigned. Then w can be set to this ptr and all is restored. The +use of w is endemic in the program. and it is always called through main. +I did not change this now. Though it could be done. 02apr ksl */ + + zdom = calloc (MAX_DOM, sizeof (domain_dummy)); + if (zdom == NULL) + { + printf ("Unable to allocate memory for domain\n"); + return EXIT_FAILURE; + } + + if (wind_read (windsavefile) < 0) + { + Error ("swind: Could not open %s", windsavefile); + exit (0); + } + +/* aaa is used to store variable for writing to files for the purpose of plotting*/ + aaa = calloc (sizeof (freq), NDIM2); + + printf ("Read wind_file %s\n", windsavefile); + + get_atomic_data (geo.atomic_filename); + + printf ("Read Atomic data from %s\n", geo.atomic_filename); + + + printf ("There were %d domains. Starting with domain 0\n", geo.ndomain); + current_domain = 0; + printf ("JM COORD TYPE %d\n\n", zdom[current_domain].coord_type); + /*Set the current domain to zero */ +/* Produce a standard set of output files and exit*/ + if (interactive == 0 && strcmp (parameter_file, "NONE") == 0) + { + zoom (1); /* This affects the logfile */ + ochoice = 1; + complete_file_summary (wmain, root, ochoice); + exit (0); + } + else if (interactive == -1) + { + /* In cases, where the windsave file was written out for + * eaach ionization cycle Write the sumary ascii files + * for each of the ionization cycles as + * as well as the final cycle */ + zoom (1); /* This affects the logfile */ + ochoice = 1; + complete_file_summary (wmain, root, ochoice); + i = 0; + strcpy (root, ""); + sprintf (root, "sirocco%02d", i); + strcpy (windsavefile, ""); + sprintf (windsavefile, "sirocco%02d.wind_save", i); + while (wind_read (windsavefile) > 0) + { + Log ("Trying %s %s\n", windsavefile, root); + complete_file_summary (wmain, root, ochoice); + strcpy (root, ""); + sprintf (root, "sirocco%02d", i); + strcpy (windsavefile, ""); + sprintf (windsavefile, "sirocco%02d.wind_save", i); + i++; + } + exit (0); + } + + + + + if (strcmp (parameter_file, "NONE") != 0) + { + zoom (1); /* This affects the logfile */ + ochoice = 1; + opar (parameter_file); + } + + +/* Choices */ + ochoice = 0; + rdint ("Make_files(0=no,1=original,2=regrid_to_linear)", &ochoice); + c = 'i'; + zoom (1); + + + +/* 111126 - Consolodated choice statements into a string that is an external variable + * so that it can be printed out whenever necessary. + */ + + printf ("%s\n", choice_options); + printf ("Model %s :\n", root); + rdchar ("Choice", &c); + + while (c != EOF) + { + if (c == 'Q') + { + printf ("There were %d domains.\n", geo.ndomain); + rdint ("Domain_to_examine", ¤t_domain); + if (current_domain < 0 || current_domain >= geo.ndomain) + { + printf ("Unkown Domain (forcing current_domain to 0\n"); + current_domain = 0; + } + else + { + printf ("Swithching to domain %d\n", current_domain); + } + zoom (1); /* Unzoom */ + rdchar ("Choice", &c); + } + + one_choice (c, root, ochoice); + printf ("%s\n", choice_options); + rdchar ("Choice", &c); + } + + + return (0); +} + + + + +/**********************************************************/ +/** + * @brief Process a request to display one variable + * + * @param [in] char choice A character indicating the + * variable to be displayed + * @param [in] char * root The rootname of the windsave file + * @param [in] int ochoice An integer indicating whether the data + * are only to be displayed, only to be written to a file, or both. + * @return Always returns 0 + * + * @details + * The routine simply contains a large case statement for + * interpreting what to do based on the variable choice + * + * ### Notes ### + * + * In some cases, additional imputs are required. + * + **********************************************************/ + +int +one_choice (choice, root, ochoice) + char choice; + char *root; + int ochoice; +{ + double lambda, freq; + int n, istate, iswitch; + char windsavefile[LINELENGTH]; + + iswitch = 0; + + /* JM 1312 --initialise variables to avoid compilation warnings */ + istate = 0; + n = 0; + + + switch (choice) + { + case 'a': /* Energy absorbed */ + abs_summary (wmain, root, ochoice); + break; + case 'A': // Change the file defaults + rdint ("Make_files(0=no,1=original,2=regrid_to_linear)", &ochoice); + break; + case 'b': /*Adiabatic cooling */ + adiabatic_cooling_summary (wmain, root, ochoice); + break; + case 'B': + plasma_cell (wmain, root, ochoice); + break; + case 'c': + flux_summary (wmain, root, ochoice); + break; + case 'C': /*the ratio cooling to heating */ + coolheat_summary (wmain, root, ochoice); + break; + case 'd': + convergence_summary (wmain, root, ochoice); + break; + case 'D': /* dvds summary */ + dvds_summary (wmain, root, ochoice); + break; + case 'E': + convergence_all (wmain, root, ochoice); + break; + case 'e': /* print out everything about an element */ + wind_element (wmain); + break; + case 'f': /* Electron summary */ + freq_summary (wmain, root, ochoice); + break; + case 'F': /* Complete file summary */ + complete_file_summary (wmain, root, ochoice); + break; + case 'g': /*n photo */ + photo_summary (wmain, root, ochoice); + break; +// case 'G': Removed relevant data from sirocco so option removed May 18 /* inner shell summary */ +// inner_shell_summary (wmain, root, ochoice); +// break; + case 'h': /*n photo */ + Log ("Don't get discouraged. This takes a little while!"); + recomb_summary (wmain, root, ochoice); + break; + case 'H': /* heating and cooling mechanisms breakdown */ + heatcool_summary (wmain, root, ochoice); + break; + case 'i': /* Allow user to display information about ions in the wind */ + + rdint ("Ion_info_type(0=fraction,1=density,2=scatters,3=abs", &iswitch); + + n = 6; + istate = 4; + + while (rdint ("element(0=return)", &n) != EOF) + { + if (n <= 0) + break; + rdint ("ion", &istate); + ion_summary (wmain, n, istate, iswitch, root, ochoice); // 0 implies ion fractions + } + break; + case 'I': + IP_summary (wmain, root, ochoice); + break; + + case 'j': /* Calculate the average tau at the center of a cell */ + n = 6; + istate = 4; + lambda = 1550; + + rddoub ("wavelength", &lambda); + freq = VLIGHT / (lambda * 1.e-8); + + while (rdint ("element(0=return)", &n) != EOF) + { + if (n <= 0) + break; + rdint ("ion", &istate); + tau_ave_summary (wmain, n, istate, freq, root, ochoice); + } + break; + case 'J': /* radiation density in cell */ + J_summary (wmain, root, ochoice); + break; + case 'k': /* tau at H edge */ + tau_h_summary (wmain, root, ochoice); + break; + case 'K': /* cell J split by direct photons and scattered photons */ + J_scat_summary (wmain, root, ochoice); + break; + case 'l': /* Lum of shell */ + lum_summary (wmain, root, ochoice); + break; + case 'L': /*Line emission */ + line_summary (wmain, root, ochoice); + break; + case 'm': /* Radiation force */ + mo_summary (wmain, root, ochoice); + break; + case 'M': + macro_summary (wmain, root, ochoice); + break; + case 'n': /* Electron summary */ + electron_summary (wmain, root, ochoice); + break; + case 'N': /* Read a different wind save file */ + rdstr ("New.rootname", root); + strcpy (windsavefile, root); + strcat (windsavefile, ".wind_save"); + if (wind_read (windsavefile) < 0) + { + Error ("one_choice: Could not read %s", windsavefile); + } + + +/* aaa is used to store variable for writing to files for the purpose of plotting*/ + if (aaa != NULL) + { + free (aaa); + } + aaa = calloc (sizeof (freq), NDIM2); + + printf ("Read wind_file %s\n", windsavefile); + + get_atomic_data (geo.atomic_filename); + + printf ("Read Atomic data from %s\n", geo.atomic_filename); + + break; + case 'o': /* overview */ + overview (wmain, root); + break; + case 'O': /* spectral model parameters */ + model_bands (wmain, root, ochoice); + break; + case 'p': /* nphot summary */ + nphot_summary (wmain, root, ochoice); + break; + case 'P': /* Allow user to display information about the wind */ + + n = 6; + istate = 4; + + while (rdint ("element(0=return)", &n) != EOF) + { + if (n <= 0) + break; + rdint ("ion", &istate); + partial_measure_summary (wmain, n, istate, root, ochoice); + } + break; + case 'r': /* Temp summary */ + temp_rad (wmain, root, ochoice); + break; + case 'R': /* Rho summary */ + rho_summary (wmain, root, ochoice); + break; + case 's': /* Volume summary */ + vol_summary (wmain, root, ochoice); + break; + case 'S': + alpha_summary (wmain, root, ochoice); + break; + case 't': /* Temp summary */ + temp_summary (wmain, root, ochoice); + break; + case 'T': + thompson (wmain, root, ochoice); + break; + case 'v': /* Velocity summary */ + velocity_summary (wmain, root, ochoice); + break; + case 'V': /* Split of scatters in the cell between electron and resonant */ + nscat_split (wmain, root, ochoice); + break; + case 'w': /* inten weight summary */ + weight_summary (wmain, root, ochoice); + break; + case 'W': /*Show regions in the wind */ + wind_reg_summary (wmain, root, ochoice); + break; + case 'x': /*Total emission */ + total_emission_summary (root, ochoice); + break; + case 'X': /* Position summary */ + position_summary (wmain); + break; + case 'y': /* Recalculate temperatures */ + modify_te (wmain, root, ochoice); + break; + case 'Y': /* Split of photons from different sources */ + phot_split (wmain, root, ochoice); + break; + case 'z': /* inspect a specific region */ + zoom (0); + break; + case 'Z': /* Switch between raw and projected mode */ + if (swind_project == 0) + { + swind_project = 1; + Log ("Switching to raw display, whatever the coordinate system"); + } + else if (swind_project == 1) + { + swind_project = 0; + Log ("Switching to projected y z display"); + } + break; + + /* JM -- typing 1 gives you a summary of everything in one file with + astropy.io.ascii compliant headers */ + + case '1': + complete_physical_summary (wmain, root, ochoice); // + break; + + + case 'u': /* Go back to full image */ + zoom (1); + break; + case '2': + complete_ion_summary (wmain, root, ochoice); // + break; + case '#': + grid_summary (wmain, root, ochoice); // + break; + case '&': + collision_summary (wmain, root, ochoice); // + break; + case '*': + shock_heating_summary (wmain, root, ochoice); + break; + + case 'q': /* quit */ + /* Write out a parameterfile that gives all of the commands used in this run */ + cpar ("swind.pf"); + exit (0); + break; + + } + + return (0); +} + + + + +/**********************************************************/ +/** + * @brief print out some helpful information to the screen + * and exits + * + * @return N/A since the program exits once the help is + * written + * + * @details + * This routine simply writes information to the screen. + * It should be updated whenever the program changes in + * a major way. + * + * It is called if swind is invoked with -h, or if there + * if it is given a switch that it does not understand. + * + * ### Notes ### + * Unfortunately unlike python + * c is not self documenting + * + **********************************************************/ + +void +swind_help () +{ + + char *some_help; + +/* Beginning of a string to provide some help for swind */ + some_help = "\ +\n\ +This program reads a wind save file created by sirocco and examine the wind structure\\ +\n\ + Usage: swind [-h] [-s] [-p parameter_file] [root] \n\ +\n\ + where\n\ + -h prints out a short help file and exits (see help routine below) \n\ + -s causes certain parameters to be printed out in individual files \n\ + after which the program exits \n\ + -d searches for the diagnostic wind save files and prints information \n\ + to ascii files \n\ + -p Gets information from a parameter file \n\ + root optional root name of wind_save file. If this is not given, \n\ + the user is queried for this \n\ +\n\ +"; + + printf ("%s\n", some_help); + + printf ("Choices are indicated below\n%s\n", choice_options); + + exit (0); +} diff --git a/source/swind_ion.c b/source/swind_ion.c new file mode 100644 index 000000000..477608722 --- /dev/null +++ b/source/swind_ion.c @@ -0,0 +1,842 @@ +/***********************************************************/ +/** @file swind_ion.c + * @author ksl + * @date May, 2018 + * + * @brief This file contains various subroutines of swind. + * It is not part of sirocco! + * + * + ***********************************************************/ + +#include +#include +#include +#include + +#include "atomic.h" +#include "sirocco.h" + + +/**********************************************************/ +/** + * @brief Print informaition about a specific ion + * + * @param [in] WindPtr W The entire wind + * @param [in] int element An elemeent + * @param [in] int istate A particular ion + * @param [in] int iswitch A intenger indicating what specifically + * to display about the ion + * @param [in] char rootname [] The rootname of the windsave file + * @param [in] int ochoice A parameter indcating whether results should + * be written to a file + * @return Always returns 0 + * + * @details + * calculates and + * displays information for a specific element and ionization state. The information + * displayed depends upon the value of iswitch + * + * The choices for iswitch are: + * * 0 ion fractions + * * 1 ion denisities + * * 2 number of scatters involving this ion + * * 3 the scatterd flux for this ion + * + * + * ### Notes ### + * + **********************************************************/ +int +ion_summary (w, element, istate, iswitch, rootname, ochoice) + WindPtr w; + int element, istate; + int iswitch; + char rootname[]; + int ochoice; +{ + int nion, nelem; + int n; + char choice[LINELENGTH], iname[LINELENGTH]; + char name[LINELENGTH]; + char filename[LINELENGTH]; + double x; + int nplasma; + double nh; + + x = 0; + + nion = 0; + while (nion < nions && !(ion[nion].z == element && ion[nion].istate == istate)) + nion++; + if (nion == nions) + { + Log ("Error--element %d ion %d not found in define_wind\n", element, istate); + return (-1); + } + nelem = 0; + while (nelem < nelements && ele[nelem].z != element) + nelem++; + + strcpy (name, ""); + + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + nplasma = w[n].nplasma; + if (w[n].inwind >= 0) + { + if (iswitch == 0) + { + sprintf (name, "Element %d (%s) ion %d fractions\n", element, ele[nelem].name, istate); + aaa[n] = plasmamain[nplasma].density[nion]; + nh = rho2nh * plasmamain[nplasma].rho; + aaa[n] /= (nh * ele[nelem].abun); + } + else if (iswitch == 1) + { + sprintf (name, "Element %d (%s) ion %d density\n", element, ele[nelem].name, istate); + aaa[n] = plasmamain[nplasma].density[nion]; + } + else if (iswitch == 2) + { + sprintf (name, "Element %d (%s) ion %d #scatters\n", element, ele[nelem].name, istate); + aaa[n] = plasmamain[nplasma].scatters[nion]; + } + else if (iswitch == 3) + { + sprintf (name, "Element %d (%s) ion %d scattered flux\n", element, ele[nelem].name, istate); + aaa[n] = plasmamain[nplasma].xscatters[nion]; + } + else + { + Error ("ion_summary : Unknown switch %d \n", iswitch); + exit (0); + } + } + } + + display (name); + + + + + /* Store the appropriate values in a place where it does not matter */ + if (ochoice) + { + for (n = 0; n < NDIM2; n++) + { + nplasma = w[n].nplasma; + if (w[n].inwind >= 0) + { + if (iswitch == 0) + x /= ((plasmamain[nplasma].density[0] + plasmamain[nplasma].density[1]) * ele[nelem].abun); + else if (iswitch == 1) + { + x = plasmamain[nplasma].density[nion]; + x = log10 (x); + } + else if (iswitch == 2) + { + x = plasmamain[nplasma].scatters[nion]; + } + else if (iswitch == 3) + { + x = plasmamain[nplasma].xscatters[nion]; + } + else + { + Error ("ion_summary : Unknown switch %d \n", iswitch); + exit (0); + } + + + } + else + x = 0.0; + w[n].x[1] = x; + + } + + strcpy (filename, rootname); + if (iswitch == 0) + strcpy (choice, ".ion"); + else if (iswitch == 1) + strcpy (choice, ".ionc"); + else if (iswitch == 2) + strcpy (choice, ".ions"); + else if (iswitch == 3) + strcpy (choice, ".iona"); + else + { + Error ("ion_summary : Unknown switch %d \n", iswitch); + exit (0); + } + + strcat (choice, ele[nelem].name); + sprintf (iname, "%d", istate); + strcat (choice, iname); + + strcat (filename, choice); + write_array (filename, ochoice); + } + + return (0); +} + + +/**********************************************************/ +/** + * @brief Calculates tau_ave for a particular transition + * of an element and ion assuming one is in resonance + * + * @param [in] WindPtr W The entire wind + * @param [in] int element An elemeent + * @param [in] double freq Frequency of the line of interest + * @param [in] int istate A particular ion + * @param [in] char rootname [] The rootname of the windsave file + * @param [in] int ochoice A parameter indcating whether results should + * be written to a file + * @return Always returns 0 + * + * @details + * + * + * ### Notes ### + * + * IMPORTANT - This routine does not search for the atomic data associated + * with line, It assumes a nominal value. + * + **********************************************************/ +int +tau_ave_summary (w, element, istate, freq, rootname, ochoice) + WindPtr w; + int element, istate; + double freq; + char rootname[]; + int ochoice; +{ + int nion, nelem; + int n; + char choice[LINELENGTH], iname[LINELENGTH]; + char name[LINELENGTH]; + char filename[LINELENGTH]; + int nplasma; + + + +/* Find the CIV ion */ + nion = 0; + while (nion < nions && !(ion[nion].z == element && ion[nion].istate == istate)) + nion++; + if (nion == nions) + { + Log ("Error--element %d ion %d not found in define_wind\n", element, istate); + return (-1); + } + nelem = 0; + while (nelem < nelements && ele[nelem].z != element) + nelem++; + + strcpy (name, ""); + sprintf (name, "Element %d (%s) ion %d fractions\n", element, ele[nelem].name, istate); + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = PI_E2_OVER_M * plasmamain[nplasma].density[nion] / freq / w[n].dvds_ave; + } + } + + display (name); + + /* Store the appropriate values in a place where it does not matter */ + if (ochoice) + { + for (n = 0; n < NDIM2; n++) + { + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + w[n].x[1] = plasmamain[nplasma].density[nion] / (0.87 * plasmamain[nplasma].ne * ele[nelem].abun); + } + else + w[n].x[1] = 0; + } + + strcpy (filename, rootname); + + strcpy (choice, ".ion"); + strcat (choice, ele[nelem].name); + sprintf (iname, "%d", istate); + strcat (choice, iname); + + strcat (filename, choice); + + write_array (filename, ochoice); + } + + return (0); +} + + + +/**********************************************************/ +/** + * @brief Calculate the luminoisty for a specific line + * + * @param [in] WindPtr W The entire wind + * @param [in] char rootname [] The rootname of the windsave file + * @param [in] int ochoice A parameter indcating whether results should + * be written to a file + * @return Always returns 0 + * + * @details + * For a given line from a preset selection, outputs luminosity. + * Can cope with matom/non-matom lines. + * + * + * File rootname.line[ELEM][ION].dat + * + * + * ### Notes ### + * + * + * History: + * 31062016 SWM modified to add H-alpha, matoms + * + **********************************************************/ +int +line_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int nion, nelem; + int element, istate, iline, levu, levl, i_matom_search; + int n; + double x, lambda; + char choice[LINELENGTH], iname[LINELENGTH]; + char name[LINELENGTH]; + char filename[LINELENGTH]; + int nline; + double freq_search; + + double d1, d2, z, q, energy, rb, tot, omega; + int nplasma; + + z = q = 0; + iline = 0; + lambda = 0; + i_matom_search = 0; + rdint ("line (0=C-IV, 1=Ha, 2=Hb, 3=Matom", &iline); + switch (iline) + { + case 0: //Carbon-IV + element = 6; + istate = 4; + lambda = 1548.1949e-8; + break; + case 1: //Hydrogen Alpha + element = 1; + istate = 1; + lambda = 6562.7097e-8; + levu = 3; + levl = 2; + break; + case 2: //Hydrogen Beta + element = 1; + istate = 1; + lambda = 4861.363e-8; + levu = 4; + levl = 2; + break; + case 3: //Generic matom + i_matom_search = 1; + element = 1; + istate = 1; + levu = 2; + levl = 1; + rdint ("Element", &element); + rdint ("Ion", &istate); + rdint ("Upper level", &levu); + rdint ("Lower level", &levl); + break; + default: + Error ("line_summary: Not a valid line."); + exit (0); + + } + +/* Convert wavelength to energy and frequency */ + freq_search = VLIGHT / lambda; + energy = HC / lambda; + +/* Find the ion */ + if (i_matom_search) + { + printf ("Searching for matom line...\n"); + nline = 0; + while (nline < nlines && !(lin_ptr[nline]->z == element && lin_ptr[nline]->istate == istate + && lin_ptr[nline]->levu == levu && lin_ptr[nline]->levl == levl)) + { + nline++; + } + if (nline == nlines) + { + Error ("line_summary: Could not find line in linelist\n"); + exit (0); + } + nelem = 0; + while (nelem < nelements && ele[nelem].z != element) + nelem++; + if (nelem == nelements) + { + Log ("line_summary: Could not find element %d", element); + return (-1); + } + nion = 0; + while (nion < nions && !(ion[nion].z == element && ion[nion].istate == istate)) + nion++; + if (nion == nions) + { + Log ("Error--element %d ion %d not found in define_wind\n", element, istate); + return (-1); + } + } + else + { + nion = 0; + while (nion < nions && !(ion[nion].z == element && ion[nion].istate == istate)) + nion++; + if (nion == nions) + { + Log ("Error--element %d ion %d not found in define_wind\n", element, istate); + return (-1); + } + nelem = 0; + while (nelem < nelements && ele[nelem].z != element) + nelem++; + if (nelem == nelements) + { + Log ("line_summary: Could not find element %d", element); + return (-1); + } + nline = 0; + freq_search = VLIGHT / lambda; + + while (fabs (1. - lin_ptr[nline]->freq / freq_search) > 0.0001 && nline < nlines) + nline++; + if (nline == nlines) + { + Error ("line_summary: Could not find line in linelist\n"); + exit (0); + } + } + + + rdint ("line_transfer(0=pure.abs,1=pure.scat,2=sing.scat,3=escape.prob, 4=off, diagnostic)", &geo.line_mode); + if (geo.line_mode == LINE_MODE_ABSORB) + Log ("Pure_abs in line heating/cooling\n"); + else if (geo.line_mode == LINE_MODE_SCAT) + Log ("Pure_scat_in line heating/cooling\n"); + else if (geo.line_mode == LINE_MODE_SINGLE_SCAT) + Log ("Single scat for line heating/cooling\n"); + else if (geo.line_mode == LINE_MODE_ESC_PROB) + Log ("Escape probabilities for line heating/cooling\n"); + else if (geo.line_mode == 4) + Log ("No line transfer; diagnostic mode only\n"); + else + { + Log ("Unknown line mode\n"); + return (0); + } + + strcpy (name, ""); + if (lin_ptr[nline]->macro_info == TRUE) + { + sprintf (name, "%d Luminosity %d (%s) ion %d fractions\n", nline, element, ele[nelem].name, istate); + } + else + { + sprintf (name, "%d Luminosity %d (%s) ion %d matom %d-%d fractions\n", nline, element, ele[nelem].name, istate, + lin_ptr[nline]->levu, lin_ptr[nline]->levl); + } + + tot = 0.0; + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0.0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + + if (lin_ptr[nline]->macro_info == TRUE) + { + /* If this is a matom line */ + d2 = den_config (&plasmamain[nplasma], lin_ptr[nline]->nconfigu); + d1 = den_config (&plasmamain[nplasma], lin_ptr[nline]->nconfigl); + } + else + { + /* If this is not a matom line */ + two_level_atom (lin_ptr[nline], &plasmamain[nplasma], &d1, &d2); + } + + + if (geo.line_mode == 4) + { + /* diagnostic mode that just returns n2 * A21 * vol * hnu */ + x = (d2) * a21 (lin_ptr[nline]) * PLANCK * lin_ptr[nline]->freq * w[n].vol; + } + else + { + /* the below code is essentially duplicated from lum_lines() in lines.c, see #643 */ + x = lin_ptr[nline]->gu / lin_ptr[nline]->gl * d1 - d2; + z = exp (-H_OVER_K * lin_ptr[nline]->freq / plasmamain[nplasma].t_e); + q = 1. - scattering_fraction (lin_ptr[nline], &plasmamain[nplasma]); + x *= q * a21 (lin_ptr[nline]) * z / (1. - z); + x *= PLANCK * lin_ptr[nline]->freq * plasmamain[nplasma].vol; + + /* Include effects of line trapping */ + if (geo.line_mode == LINE_MODE_ESC_PROB) + x *= p_escape (lin_ptr[nline], &plasmamain[nplasma]); + } + + tot += x; + aaa[n] = x; + } + } + + display (name); + + tot = 2. * tot; // Why is there a factor of 2 here??? ksl + + Log ("The total %s ion %d luminosity (flux) is %8.2g (%8.2g)\n", ele[nelem].name, istate, tot, tot / (4 * PI * 1e4 * PC * PC)); + + /* Store the appropriate values in a place where it does not matter */ + if (ochoice) + { + for (n = 0; n < NDIM2; n++) + { + // Here is the calculation of the effective collisions strength + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + omega = 5.13 * pow (plasmamain[nplasma].t_e / 1.e5, 0.18); + rb = 8.629e-6 * exp (-energy / (BOLTZMANN * plasmamain[nplasma].t_e)) / sqrt (plasmamain[nplasma].t_e) * omega; + w[n].x[1] = plasmamain[nplasma].density[nion] * plasmamain[nplasma].ne * rb * energy * w[n].vol; + } + else + w[n].x[1] = 0; + } + + + strcpy (filename, rootname); + strcpy (choice, ".line"); + strcat (choice, ele[nelem].name); + sprintf (iname, "%d", istate); + strcat (choice, iname); + if (lin_ptr[nline]->macro_info == TRUE) + { + sprintf (iname, ".%d-%d", lin_ptr[nline]->levu, lin_ptr[nline]->levl); + strcat (choice, iname); + } + strcat (filename, choice); + write_array (filename, ochoice); + } + + return (0); +} + + + +/**********************************************************/ +/** + * @brief Get the total emission of the cells + * + * @param [in] WindPtrYW The entire wind + * @param [in] char rootname [] The rootname of the windsave file + * @param [in] int ochoice A parameter indcating whether results should + * be written to a file + * @return Always returns 0 + * + * @details + * + * Calls total_emission + * + * + * ### Notes ### + * + **********************************************************/ +int +total_emission_summary (rootname, ochoice) + char rootname[]; + int ochoice; +{ + double tot; + int n; + char filename[LINELENGTH]; + + + tot = 0.0; + for (n = 0; n < NPLASMA; n++) + { + aaa[n] = 0; + tot += aaa[n] = total_emission (&plasmamain[n], 0.0, VERY_BIG); + } + + display ("Calculated thermal luminosities of cells"); + + Log ("The total luminosity due to thermal process is %8.2g\n", tot); + + + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".abs"); + write_array (filename, ochoice); + } + + + return (0); +} + + +/**********************************************************/ +/** + * @brief Find the electron temperature where heating matches + * the cooling + * + * @param [in] WindPtr The entire wind + * @param [in] char rootname [] The rootname of the windsave file + * @param [in] int ochoice A parameter indcating whether results should + * be written to a file + * @return Always returns 0 + * + * @details + * + * + * ### Notes ### + * + **********************************************************/ +int +modify_te (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + double x; + double t_e, calc_te (); + char filename[LINELENGTH]; + int nplasma; + + + for (n = 0; n < NDIM2; n++) + { + nplasma = w[n].nplasma; + aaa[n] = 0; + if (w[n].inwind >= 0 && (x = plasmamain[nplasma].heat_tot) > 1.0) + { + aaa[n] = t_e = calc_te (&plasmamain[nplasma], MIN_TEMP, 1.2 * plasmamain[nplasma].t_r); + } + } + + display ("Calculate t_e"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".te_mod"); + write_array (filename, ochoice); + } + + return (0); +} + + + +/**********************************************************/ +/** + * @brief Write out the emission measure for a specific + * ionization state of an element + * + * @param [in] WindPtr W The entire wind + * @param [in] int element The z for an element + * @param [in] int istate The ion state of an element + * @param [in] char rootname [] The rootname of the windsave file + * @param [in] int ochoice A parameter indcating whether results should + * be written to a file + * @return Always returns 0 + * + * @details + * + * + * ### Notes ### + * + **********************************************************/ + +int +partial_measure_summary (w, element, istate, rootname, ochoice) + WindPtr w; + int element, istate; + char rootname[]; + int ochoice; +{ + int nion, nelem; + int n; + double total; + char choice[LINELENGTH], iname[LINELENGTH]; + char name[LINELENGTH]; + char filename[LINELENGTH]; + int nplasma; + + + + total = 0; + nion = 0; + while (nion < nions && !(ion[nion].z == element && ion[nion].istate == istate)) + nion++; + if (nion == nions) + { + Log ("Error--element %d ion %d not found in define_wind\n", element, istate); + return (-1); + } + nelem = 0; + while (nelem < nelements && ele[nelem].z != element) + nelem++; + + strcpy (name, ""); + sprintf (name, "Element %d (%s) ion %d fractions", element, ele[nelem].name, istate); + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + nplasma = w[n].nplasma; + if (plasmamain[nplasma].ne > 1.0 && w[n].inwind >= 0) + { + total += aaa[n] = plasmamain[nplasma].density[nion] * plasmamain[nplasma].ne * w[n].vol; + } + } + + display ("Calculate t_e"); + + Log ("The partial emission measure ne*nion*V is %8.2g\n", total); + + /* Store the appropriate values in a place where it does not matter */ + if (ochoice) + { + for (n = 0; n < NDIM2; n++) + { + nplasma = w[n].nplasma; + if (plasmamain[nplasma].ne > 1.0 && w[n].inwind >= 0) + w[n].x[1] = plasmamain[nplasma].density[nion] / (0.87 * plasmamain[nplasma].ne * ele[nelem].abun); + else + w[n].x[1] = 0; + } + + strcpy (filename, rootname); + strcpy (choice, ".part"); + strcat (choice, ele[nelem].name); + sprintf (iname, "%d", istate); + strcat (choice, iname); + + strcat (filename, choice); + write_array (filename, ochoice); + } + + return (0); +} + + +/**********************************************************/ +/** + * @brief Write out the collision strengths (and related information) + * for all of the lines + * + * @param [in] WindPtr W The entire wind + * @param [in] char rootname [] The rootname of the windsave file + * @param [in] int ochoice A parameter indcating whether results should + * be written to a file + * @return Always returns 0 + * + * @details + * + * + * ### Notes ### + * + **********************************************************/ + +int +collision_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int nline, int_te; + double t_e, qup, qdown, A, wavelength; + char filename[LINELENGTH], suffix[LINELENGTH]; + FILE *fopen (), *fptr = NULL; + + t_e = 10000.0; + + /* Input from user to request temperature */ + rddoub ("electron temperature for calculation:", &t_e); + int_te = (int) t_e; // for file label. + + if (ochoice) + { + /* open filename root.coll.dat */ + strcpy (filename, rootname); + sprintf (suffix, ".t%d.coll.dat", int_te); + strcat (filename, suffix); + fptr = fopen (filename, "w"); + + Log ("\nWriting collision strengths to file %s...\n\n", filename); + + fprintf (fptr, "# Collision strengths at electron temperature %.1fK\n", t_e); + fprintf (fptr, "# For atomic data file %s\n", geo.atomic_filename); + fprintf (fptr, "line wavelength z istate levu levl q12 q21 a21 macro_info\n"); + } + else + { + Log ("Collision strengths at electron temperature %.1fK\n", t_e); + Log ("line wavelength z istate levu levl q12 q21 a21 macro_info\n"); + } + + nline = 0; + + while (nline < nlines) + { + wavelength = VLIGHT / lin_ptr[nline]->freq / ANGSTROM; + + qup = q12 (lin_ptr[nline], t_e); + qdown = q21 (lin_ptr[nline], t_e); + A = a21 (lin_ptr[nline]); + + if (ochoice) + { + fprintf (fptr, "%d %8.4e %d %d %d %d %8.4e %8.4e %8.4e %d\n", + nline, wavelength, lin_ptr[nline]->z, + lin_ptr[nline]->istate, lin_ptr[nline]->levu, lin_ptr[nline]->levl, qup, qdown, A, lin_ptr[nline]->macro_info); + } + else + { + Log ("%d %8.4e %d %d %d %d %8.4e %8.4e %8.4e %d\n", + nline, wavelength, lin_ptr[nline]->z, + lin_ptr[nline]->istate, lin_ptr[nline]->levu, lin_ptr[nline]->levl, qup, qdown, A, lin_ptr[nline]->macro_info); + } + nline++; + } + + if (ochoice) + fclose (fptr); + + return (0); +} diff --git a/source/swind_macro.c b/source/swind_macro.c new file mode 100644 index 000000000..c76e89f51 --- /dev/null +++ b/source/swind_macro.c @@ -0,0 +1,984 @@ + +/***********************************************************/ +/** @file swind_macro.c + * @author ksl + * @date January, 2018 + * + * @brief swind is a program which can be used to display various parameters of a wind + * as calculated by sirocco. This file contains subroutines intended to + * display information about macro atoms + * used + * + ***********************************************************/ + +#include +#include +#include +#include + +#include "atomic.h" +#include "sirocco.h" + + +/**********************************************************/ +/** + * @brief prints the adiabatic cooling in each cell, + * and the total adiabatic cooling. + * + * @param [in] WindPtr w + * @param [in] char rootname[] + * @param [in] int ochoice + * @return 0 + * + * @details + * + * ### Notes ### + * + **********************************************************/ + +int +xadiabatic_cooling_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + double tot; + double adiabatic_cooling (); + char filename[LINELENGTH]; + double t_e; + + + + tot = 0.0; + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + t_e = plasmamain[w[n].nplasma].t_e; + num_recomb (&plasmamain[w[n].nplasma], t_e, 1); + tot += aaa[n] = adiabatic_cooling (&w[n], t_e); + } + } + + + display ("Adiabatic cooling"); + Log ("The total adiabatic cooling is %8.2g\n", tot); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".adiabatic"); + write_array (filename, ochoice); + } + return (0); + +} + +/**********************************************************/ +/** + * @brief is a routine intended to allow one to + * display information about macro atoms. It is the top level routine + * for macro information and requires further input from the user. + * + * @param [in] WindPtr w + * @param [in] char rootname[] + * @param [in] int ochoice + * @return + * + **********************************************************/ + +int +macro_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ +//OLD int nmacro; + int nconfig; + int icell; + int choose; + int version; + int nlev, nplasma; + + choose = 0; + nlev = 0; + nplasma = 0; + + /* get input from the user */ + rdint ("Detailed cell info (0), levels (1) \ +emissivities (2) P_escapes (3) Detailed Pops (4) taus (5) estimators (6)", &choose); + + if (choose == 0) + { + icell = 301; + rdint ("Cell.no", &icell); + + + + ion_overview (icell); + + /* What configurations are macro atom configurations */ + nconfig = -1; + rdint ("Configuration.Number (-1 for all b-values; -2 to rollup)", &nconfig); + + while (nconfig >= -1) + { + if (nconfig >= 0) + { + config_overview (nconfig, icell); +//OLD nmacro = config_overview (nconfig, icell); + } + else + { + depcoef_overview (icell); + } + rdint ("Configuration.Number(-1 for all b-values; -2 to rollup)", &nconfig); + } + } + + /* specific level information for each cell */ + else if (choose == 1) + { + nconfig = -1; + version = 0; + rdint ("Number density (0), Fractional Pop (1) or Departure coeff (2)", &version); + rdint ("Level.Number(-1 to rollup)", &nconfig); + while (nconfig >= 0) + { + depcoef_overview_specific (version, nconfig, w, rootname, ochoice); + rdint ("Level.Number(-1 to rollup)", &nconfig); + } + } + + + /* JM 1311 -- new loop added to report level emissivities for each cell */ + else if (choose == 2) + { + rdint ("Level emissivity to view (0 - kpkts, -1 - back):", &nlev); + while (nlev >= 0) + { + level_emissoverview (nlev, w, rootname, ochoice); + rdint ("Level emissivity to view (0 - kpkts, -1 - back):", &nlev); + } + } + + /* JM 1311 -- new loop added to report escape probabilities + Note this currently only works for Balmer lines */ + else if (choose == 3) + { + rdint ("Upper level escape to view (-1 - back, Halpha = 3):", &nlev); + while (nlev >= 0) + { + level_escapeoverview (nlev, w, rootname, ochoice); + rdint ("Upper level escape to view (-1 - back, Halpha = 3):", &nlev); + } + } + + /* JM 1311 -- new loop added to report level populations in a cell */ + else if (choose == 4) + { + rdint ("Cell to view (-1 to rollup):", &nplasma); + while (nplasma >= 0) + { + level_popsoverview (nplasma, w, rootname, ochoice); + rdint ("Cell to view (-1 to rollup):", &nplasma); + } + } + + /* JM 1311 -- new loop added to report taus + Note this currently only works for Balmer lines */ + else if (choose == 5) + { + rdint ("Upper level tau to view (-1 - back, Halpha = 3):", &nlev); + while (nlev >= 0) + { + level_tauoverview (nlev, w, rootname, ochoice); + rdint ("Upper level tau to view (-1 - back, Halpha = 3):", &nlev); + } + } + + /* JM 1311 -- add stuff for estimators here */ + // else + // { + // rdint ("Estimator to view: jbar (0) back (-1)", &version); + // while (version >= 0) + // { + // estimator_overview (version, w, rootname, ochoice); + // rdint ("Upper level tau to view (-1 - back, Halpha = 3):", &nlev); + // } + // } + + + + return (0); +} + +/**********************************************************/ +/** + * @brief macro_summary is a routine intended to allow one to + * display information about macro atoms. It is the top level routine + * for macro information and requires further input from the user. + * + * @param [in] int icell + * @return 0 + * + **********************************************************/ + +int +ion_overview (icell) + int icell; +{ + int n; + IonPtr p; + + p = &ion[0]; + + + printf (" Z Ion I.Potent f.lte n.lte f.nlte n.nlte macro\n"); + for (n = 0; n < nions; n++) + { + p = &ion[n]; + printf ("%2d %2d %8.2e %5d %5d %6d %6d %5d\n", p->z, p->istate, p->ip, + p->firstlevel, p->nlevels, p->first_levden, p->nlte, p->macro_info); + } + + return (p->macro_info); +} + + +/**********************************************************/ +/** + * @brief macro_summary is a routine intended to allow one to + * display information about macro atoms. It is the top level routine + * for macro information and requires further input from the user. + * + * @param [in] int n + * @param [in] int icell + * @return 0 + * + **********************************************************/ + +int +config_overview (n, icell) + int n, icell; +{ + ConfigPtr p; + PlasmaPtr x; + MacroPtr m; + double xden; + int i, ii; + + p = &xconfig[n]; + /* initialize the density */ + xden = -1; + + if (icell >= 0 && icell < NDIM2) + { + x = &plasmamain[icell]; + xden = x->levden[p->nden]; + } + else + { + Error ("%i is not a cell in plasma structure!!\n", icell); + return 0; + } + + + printf (" Z Ion nden macro n_bbu_jump n_bbd_jump n_bfu_jump nbfd_jump lev_den\n"); + + printf ("%2d %2d %4d %5d %10d %10d %10d %10d %8.2e\n", p->z, p->istate, + p->nden, p->macro_info, p->n_bbu_jump, p->n_bbd_jump, p->n_bfu_jump, p->n_bfd_jump, xden); + + if (icell >= 0 && icell < NDIM2 && p->macro_info == 1) + { + /* Print information about the jumps */ + printf ("bbu_jump: "); + for (i = 0; i < p->n_bbu_jump; i++) + printf (" %3d", p->bbu_jump[i]); + printf ("\n"); + + printf ("bbd_jump: "); + for (i = 0; i < p->n_bbd_jump; i++) + printf (" %3d", p->bbd_jump[i]); + printf ("\n"); + + printf ("bfu_jump: "); + for (i = 0; i < p->n_bfu_jump; i++) + printf (" %3d", p->bfu_jump[i]); + printf ("\n"); + + printf ("bfd_jump: "); + for (i = 0; i < p->n_bfd_jump; i++) + printf (" %3d", p->bfd_jump[i]); + printf ("\n"); + + m = ¯omain[icell]; + printf ("matom_emis: %8.2e\n", m->matom_emiss[n]); + printf ("matom_abs : %8.2e\n", m->matom_abs[n]); + + /* Detailed information on the bb transitions */ + printf ("bbu_jump:\n"); + for (i = 0; i < p->n_bbu_jump; i++) + { + //ii=p->bbu_jump[i]; + ii = i; + printf (" %3d %8.2e %8.2e\n", ii, (m->jbar[xconfig[n].bbu_indx_first + ii]), (m->jbar_old[xconfig[n].bbu_indx_first + ii])); + } + + printf ("bbd_jump:\n"); + for (i = 0; i < p->n_bbd_jump; i++) + { + // ii=p->bbd_jump[i]; + ii = i; + printf (" %3d %8.2e %8.2e\n", ii, (m->jbar[xconfig[n].bbu_indx_first + ii]), (m->jbar_old[xconfig[n].bbu_indx_first + ii])); + } + + /* Detailed information on the fb transitions */ + + printf ("bfu_jump:\n"); + for (i = 0; i < p->n_bfu_jump; i++) + { + // ii=p->bfu_jump[i]; + ii = i; + printf (" %3d %g %g %g %g %g %g %g %g\n", ii, + (m->gamma[xconfig[n].bfu_indx_first + ii]), + (m->gamma_old[xconfig[n].bfu_indx_first + ii]), + (m->alpha_st[xconfig[n].bfu_indx_first + ii]), + (m->alpha_st_old[xconfig[n].bfu_indx_first + ii]), + (m->alpha_st_e[xconfig[n].bfu_indx_first + ii]), + (m->alpha_st_e_old[xconfig[n].bfu_indx_first + ii]), + (m->recomb_sp[xconfig[n].bfd_indx_first + ii]), (m->recomb_sp_e[xconfig[n].bfd_indx_first + ii])); + } + + + + printf ("bfd_jump:\n"); + for (i = 0; i < p->n_bfd_jump; i++) + { + // ii=p->bfd_jump[i]; + ii = i; + printf (" %3d %g %g %g %g %g %g %g %g\n", ii, + (m->gamma[xconfig[n].bfu_indx_first + ii]), + (m->gamma_old[xconfig[n].bfu_indx_first + ii]), + (m->alpha_st[xconfig[n].bfu_indx_first + ii]), + (m->alpha_st_old[xconfig[n].bfu_indx_first + ii]), + (m->alpha_st_e[xconfig[n].bfu_indx_first + ii]), + (m->alpha_st_e_old[xconfig[n].bfu_indx_first + ii]), + (m->recomb_sp[xconfig[n].bfd_indx_first + ii]), (m->recomb_sp_e[xconfig[n].bfd_indx_first + ii])); + } + + + + } + else + { + printf ("This is not a macro level\n"); + } + return (p->macro_info); +} + + +/**********************************************************/ +/** + * @brief is a routine which prints information about departure + * coefficients and level populations in a cell. There is probably a crossover + * between this and level_popsoverview + * + * @param [in] int icell + * @return 0 + * + * + **********************************************************/ + +int +depcoef_overview (icell) + int icell; +{ + ConfigPtr p; + PlasmaPtr x, xdummy; + double xden, lteden; + int n; + + int copy_plasma (); + plasma_dummy pdum; + + + /* initialize the density */ + xden = -1; + if (icell >= 0 && icell < NDIM2) + { + x = &plasmamain[icell]; + xdummy = &pdum; + + copy_plasma (x, xdummy); + geo.macro_ioniz_mode = MACRO_IONIZ_MODE_NO_ESTIMATORS; + + partition_functions (xdummy, NEBULARMODE_TE); + saha (xdummy, xdummy->ne, xdummy->t_e); + geo.macro_ioniz_mode = MACRO_IONIZ_MODE_ESTIMATORS; + } + else + { + Error ("%i is not a cell in plasma structure!!\n", icell); + return 0; + } + + printf + (" The cell is at (%g, %g, %g) and has volume %g.\n", + wmain[x->nwind].xcen[0], wmain[x->nwind].xcen[1], wmain[x->nwind].xcen[2], wmain[x->nwind].vol); + printf (" Z Ion nden macro b fpop lte_fpop t_e\n"); + + for (n = 0; n < NLEVELS; n++) + { + p = &xconfig[n]; + if (icell >= 0 && icell < NDIM2 && p->macro_info == 1) + { + xden = den_config (x, n); + lteden = den_config (xdummy, n); + // xden = x->levden[p->nden]; + //lteden = xdummy->levden[p->nden]; + printf ("%2d %2d %4d %5d %8.2e %8.2e %8.2e %8.2e\n", p->z, p->istate, p->nden, p->macro_info, (xden / lteden), xden, lteden, x->t_e); + } + } + + return (0); +} + +/**********************************************************/ +/** + * @brief Routine to copy the necessary parts of a plasma structure for computing + * a set of level populations. x1 points to the cell from which data is copied + * and x2 points to the cell to which data is copied. + * + * @param [in] PlasmaPtr x1 + * @param [in] PlasmaPtr x2 + * @return 0 + * + **********************************************************/ + +int +copy_plasma (x1, x2) + PlasmaPtr x1, x2; +{ + int i; + + x2->nwind = x1->nwind; + x2->nplasma = x1->nplasma; + x2->ne = x1->ne; + x2->rho = x1->rho; + x2->vol = x1->vol; + x2->t_r = x1->t_r; + x2->t_e = x1->t_e; + x2->w = x1->w; + + if ((x2->density = calloc (sizeof (double), nions)) == NULL) + { + Error ("calloc_dyn_plasma: Error in allocating memory for density\n"); + exit (0); + } + if ((x2->partition = calloc (sizeof (double), nions)) == NULL) + { + Error ("calloc_dyn_plasma: Error in allocating memory for partition\n"); + exit (0); + } + if ((x2->levden = calloc (sizeof (double), nlte_levels)) == NULL) + { + Error ("calloc_dyn_plasma: Error in allocating memory for levden\n"); + exit (0); + } + for (i = 0; i < nions; i++) + { + x2->density[i] = x1->density[i]; + x2->partition[i] = x1->partition[i]; + } + for (i = 0; i < nlte_levels; i++) + { + x2->levden[i] = x1->levden[i]; + } + + + /* Note this isn't everything in the cell! + Only the things needed for these routines */ + + return (0); +} + + +/**********************************************************/ +/** + * @brief deallocate copied plasma arrays, helper routine + * + * @param [in] PlasmaPtr xcopy + * @return 00 + * + * + **********************************************************/ + +int +dealloc_copied_plasma (xcopy) + PlasmaPtr xcopy; +{ + free (xcopy->density); + free (xcopy->partition); + return (0); +} + +/**********************************************************/ +/** + * @brief gives populations of nconfig in each cell + * + * @param [in] int version 0 gives number densities + * @param [in] int nconfig + * @param [in] WindPtr w + * @param [in] char rootname[] + * @param [in] int ochoice + * @return 0 + * + * @details + * ??? DESCRIPTION ??? + * + * ### Notes ### + * ??? NOTES ??? + * + **********************************************************/ + +int +depcoef_overview_specific (version, nconfig, w, rootname, ochoice) + int version; + int nconfig; + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + char filename[LINELENGTH], lname[LINELENGTH]; + ConfigPtr p; + PlasmaPtr xplasma, xdummy; + double xden, lteden, ion_density; + int copy_plasma (); + plasma_dummy pdum; + + + nconfig = nconfig - 1; + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + xden = -1; + + xplasma = &plasmamain[w[n].nplasma]; + xdummy = &pdum; + + copy_plasma (xplasma, xdummy); + geo.macro_ioniz_mode = MACRO_IONIZ_MODE_NO_ESTIMATORS; + + partition_functions (xdummy, NEBULARMODE_TE); + saha (xdummy, xdummy->ne, xdummy->t_e); + + geo.macro_ioniz_mode = MACRO_IONIZ_MODE_ESTIMATORS; + + p = &xconfig[nconfig]; + xden = den_config (xplasma, nconfig); + lteden = den_config (xdummy, nconfig); + + ion_density = xplasma->density[p->nion]; + + if (version == 0) + { + aaa[n] = xden; + } + else if (version == 1) + { + aaa[n] = xden / ion_density; + } + else + { + aaa[n] = xden / lteden; + } + + dealloc_copied_plasma (xdummy); + } + } + + if (version == 0) + display ("Level Population Number Densities"); + else if (version == 1) + display ("Fractional Level Population"); + else + display ("Departure Coefficient"); + + if (ochoice) + { + strcpy (filename, rootname); + + sprintf (lname, ".lev%d_", nconfig + 1); + strcat (filename, lname); + + if (version == 0) + strcat (filename, "den"); + else if (version == 1) + strcat (filename, "frac_pop"); + else + strcat (filename, "dep_coef"); + + write_array (filename, ochoice); + } + + return (0); +} + + +/**********************************************************/ +/** + * @brief is a routine which prints information about departure + * coefficients and level populations in a cell. There is probably a crossover + * between this and depcoef_overview + * + * @param [in] int nplasma + * @param [in] WindPtr w + * @param [in] char rootname[] + * @param [in] int ochoice + * @return 0 + + * + **********************************************************/ + +int +level_popsoverview (nplasma, w, rootname, ochoice) + int nplasma; + WindPtr w; + char rootname[]; + int ochoice; +{ + int i; + PlasmaPtr xplasma, xdummy; + plasma_dummy pdum; + char filename[LINELENGTH]; + char lname[LINELENGTH]; + double xden, lteden; + int copy_plasma (); + + strcpy (filename, rootname); + strcpy (filename, rootname); + strcat (filename, ".lev_cell"); + + sprintf (lname, "%d", nplasma); + strcat (filename, lname); + strcat (filename, ".dat"); + + FILE *f = fopen (filename, "w"); + + if (f == NULL) + { + printf ("Error opening file!\n"); + Exit (0); + } + + xplasma = &plasmamain[nplasma]; + xdummy = &pdum; + + copy_plasma (xplasma, xdummy); + geo.macro_ioniz_mode = MACRO_IONIZ_MODE_NO_ESTIMATORS; + + Log ("# Level, Pops, Dep coefs\n"); + if (ochoice) + fprintf (f, "# Level, Pops, Dep coefs\n"); + + Log ("# Cell %i Radiation field t_r %8.4e w %8.4e\n", nplasma, xplasma->t_r, xplasma->w); + Log ("# Cell %i Physical t_e %8.4e ne %8.4e\n", nplasma, xplasma->t_e, xplasma->ne); + + if (ochoice) + { + fprintf (f, "# Cell %i Radiation field t_r %8.4e w %8.4e\n", nplasma, xplasma->t_r, xplasma->w); + fprintf (f, "# Cell %i Physical t_e %8.4e ne %8.4e\n", nplasma, xplasma->t_e, xplasma->ne); + } + + for (i = 0; i < nlevels_macro; i++) + { + partition_functions (xdummy, NEBULARMODE_TE); + saha (xdummy, xdummy->ne, xdummy->t_e); + + geo.macro_ioniz_mode = MACRO_IONIZ_MODE_ESTIMATORS; + + //p = &config[i]; + xden = den_config (xplasma, i); + lteden = den_config (xdummy, i); + Log ("%i %8.4e %8.4e\n", i + 1, xplasma->levden[i], xden / lteden); + if (ochoice) + fprintf (f, "%i %8.4e %8.4e\n", i + 1, xplasma->levden[i], xden / lteden); + } + fclose (f); + dealloc_copied_plasma (xdummy); + return (0); +} + +/**********************************************************/ +/** + * @brief Routine to print level emissivities in each cell. + * The emissivity is the quantity + * A21 * n2 * beta_sobolev * nu_21 * H * volume + * And thus has units of ergs/s + * + * @param [in] int nlev + * @param [in] WindPtr w + * @param [in] char rootname[] + * @param [in] int ochoice + * @return 0 + * + **********************************************************/ + +int +level_emissoverview (nlev, w, rootname, ochoice) + int nlev; + WindPtr w; + char rootname[]; + int ochoice; +{ + int n, nplasma; + char name[LINELENGTH], lname[LINELENGTH]; + char filename[LINELENGTH]; + + strcpy (name, ""); + + if (nlev != 0) + { + sprintf (name, "Level emissivities for Level %i", nlev); + } + else + { + sprintf (name, "Kpkt emissivities"); + } + + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + nplasma = w[n].nplasma; + + if (w[n].inwind >= 0 && plasmamain[nplasma].ne > 1.0) + { + if (nlev != 0) + { + aaa[n] = macromain[nplasma].matom_emiss[nlev - 1]; + } + else + { + aaa[n] = plasmamain[nplasma].kpkt_emiss; + } + } + } + + display (name); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".lev"); + + if (nlev != 0) + { + sprintf (lname, "%d", nlev); + strcat (filename, lname); + } + else + { + strcat (filename, "k"); + } + + strcat (filename, "_emiss"); + write_array (filename, ochoice); + + } + + return (0); +} + + +/**********************************************************/ +/** + * @brief Routine to print escape probabilities for a given + * Balmer line in each cell. + * + * @param [in] int nlev + * @param [in] WindPtr w + * @param [in] char rootname[] + * @param [in] int ochoice + * @return 0 + * + **********************************************************/ + +int +level_escapeoverview (nlev, w, rootname, ochoice) + int nlev; + WindPtr w; + char rootname[]; + int ochoice; +{ + PlasmaPtr xplasma; + int n, nplasma, nline, found; + char name[LINELENGTH], lname[LINELENGTH]; + char filename[LINELENGTH]; + double lambda; + + + found = 0; + nline = 0; + + + + /* Find the line in line list */ + while (found != 1 && nline < nlines) + { + + if (lin_ptr[nline]->levl == 2 && lin_ptr[nline]->levu == nlev && lin_ptr[nline]->z == 1 && lin_ptr[nline]->istate == 1) + { + found = 1; + } + + nline++; + } + + + if (nline == nlines) + { + Error ("level_escapeoverview: Could not find line in linelist\n"); + exit (0); + } + + nline--; + + lambda = (VLIGHT / lin_ptr[nline]->freq) * 1e8; + + strcpy (name, ""); + sprintf (name, "Balmer series P_escapes for Level %i, Lambda %.1f", nlev, lambda); + + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + nplasma = w[n].nplasma; + + if (w[n].inwind >= 0 && plasmamain[nplasma].ne > 1.0) + { + xplasma = &plasmamain[nplasma]; + aaa[n] = p_escape (lin_ptr[nline], xplasma); + + + } + } + + display (name); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".lev"); + + sprintf (lname, "%d", nlev); + strcat (filename, lname); + + strcat (filename, "_esc"); + write_array (filename, ochoice); + + } + + return (0); +} + + +/**********************************************************/ +/** + * @brief Routine to print taus for a given + * Balmer line in each cell. + * + * @param [in] int nlev + * @param [in] WindPtr w + * @param [in] char rootname[] + * @param [in] int ochoice + * @return 0 + * + **********************************************************/ + +int +level_tauoverview (nlev, w, rootname, ochoice) + int nlev; + WindPtr w; + char rootname[]; + int ochoice; +{ + PlasmaPtr xplasma; + WindPtr one; + int n, nplasma, nline, found; + char name[LINELENGTH], lname[LINELENGTH]; + char filename[LINELENGTH]; + double lambda; + + + found = 0; + nline = 0; + + + + /* Find the line in line list */ + while (found != 1 && nline < nlines) + { + + if (lin_ptr[nline]->levl == 2 && lin_ptr[nline]->levu == nlev && lin_ptr[nline]->z == 1 && lin_ptr[nline]->istate == 1) + { + found = 1; + } + + nline++; + } + + + if (nline == nlines) + { + Error ("level_tauoverview: Could not find line in linelist\n"); + exit (0); + } + + nline--; + + lambda = (VLIGHT / lin_ptr[nline]->freq) * 1e8; + + strcpy (name, ""); + sprintf (name, "Balmer series taus for Level %i, Lambda %.1f", nlev, lambda); + + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + nplasma = w[n].nplasma; + + if (w[n].inwind >= 0 && plasmamain[nplasma].ne > 1.0) + { + xplasma = &plasmamain[nplasma]; + one = &wmain[xplasma->nwind]; + aaa[n] = sobolev (one, one->x, xplasma->density[lin_ptr[nline]->nion], lin_ptr[nline], one->dvds_ave); + + + } + } + + display (name); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".lev"); + + sprintf (lname, "%d", nlev); + strcat (filename, lname); + + strcat (filename, "_tau"); + write_array (filename, ochoice); + + } + + return (0); +} diff --git a/source/swind_sub.c b/source/swind_sub.c new file mode 100644 index 000000000..52025393b --- /dev/null +++ b/source/swind_sub.c @@ -0,0 +1,3889 @@ + +/*********************************************************** + Space Telescope Science Institute + + Synopsis: + swind is a program which can be used to display various parameters of a wind + as calculated by sirocco. This file contains many of the subroutiens + used + +Arguments: + + +Returns: + +Description: + + +Notes: + +History: + 97jun ksl Coding on swind began. + 05jul ksl 56d -- Moved all of the subroutines to swind_sub so that it was + easier to make templates. + +**************************************************************/ + +#include +#include +#include +#include + +#include "atomic.h" +#include "sirocco.h" + + +/************************************************************************** + + + Synopsis: + + This routine controls what is displayed on the screen. + + + Description: + + There are basically two options, determined by the variable "determine" + If determine==1, then zoom sets up the variables swind_min, swind_max and + swind_delta so that the the entire wind is displayed, but it is + subsampled in the x direction. + + If determine!=1, then a section of the wind is displayed at full resolution + + Arguments: + + Returns: + + Notes: + + History: + 07jul ksl Made modifications to allow for the possibility that + the wind has fewer than 10 elemetns in the x direction + + ************************************************************************/ + +int +zoom (direction) + int direction; +{ + int center; + int ndim; + + + ndim = zdom[current_domain].ndim; + + if (direction == 1) + { /* then unzoom */ + Log ("Showing selected positions throughout wind\n"); + swind_min = 0; + swind_max = ndim; + swind_delta = ndim / 10; + /* + * Allow for the possibility that the wind has an xdimension + * < 10 + */ + if (swind_delta < 1) + swind_delta = 1; + } + else + { + Log ("Select part of wind to display\n"); + center = 5; + rdint ("Center_x", ¢er); + swind_min = center - 5; + if (swind_min < 0) + { + Log ("zoom: this choice of center is needlessly close to origin, adjusting swind_min to 0\n"); + swind_min = 0; + } + swind_max = swind_min + 10; + if (swind_max > ndim) + { + swind_max = ndim; + Log ("zoom: this choice of swind_max is lager than NDIM, adusting swind_max to NDIM"); + } + swind_delta = 1; + } + return (0); +} + + + + + + +/************************************************************************** + + + Synopsis: + + overview + + Description: + + Arguments: + + Returns: + + Notes: + + History: + + ************************************************************************/ +int +overview (w, rootname) + WindPtr w; + char rootname[]; +{ + int n; + double heating, lines, ff, photo; + + heating = lines = ff = photo = 0.0; + + for (n = 0; n < NPLASMA; n++) + { + heating += plasmamain[n].heat_tot; + lines += plasmamain[n].heat_lines; + photo += plasmamain[n].heat_photo; + ff += plasmamain[n].heat_ff; + } + Log (" Total cooling %8.2e heating %8.2e\n", geo.cool_tot_ioniz, heating); + Log (" Total emission %8.2e heating %8.2e\n", geo.lum_tot_ioniz, heating); + Log (" ff emission %8.2e heating %8.2e\n", geo.lum_ff_ioniz, ff); + Log (" fb emission %8.2e heating %8.2e\n", geo.cool_rr_ioniz, photo); + Log (" line emission %8.2e heating %8.2e\n", geo.lum_lines_ioniz, lines); + return (0); +} + + + + + +/************************************************************************** + + + Synopsis: + Summary of everything at a given position + + Description: + + Arguments: + + Returns: + + Notes: + + This has been modified to work with domains, but it is not obvious that + this is what one wants, because the position is fixed + + History: + + ************************************************************************/ +int +position_summary (w) + WindPtr w; +{ + double x[3], v[3]; + struct photon p; + int n; + int nplasma; + int inwind, ndom; + + x[0] = geo.rmax / 5; + x[1] = 0.0; + x[2] = x[0]; + +a:Log ("Input x=0,y=0,z=0 to return to main routine\n"); + rddoub ("x", &x[0]); + rddoub ("y", &x[1]); + rddoub ("z", &x[2]); + + if (length (x) == 0.0) + return (0); + + inwind = where_in_wind (x, &ndom); + if (inwind != W_ALL_INWIND) + { + Log ("Position %8.2e %8.2e %8.2e is not in an active region of grid %d %d\n", x[0], x[1], x[2], inwind, ndom); + ndom = 0; + } + + n = where_in_grid (ndom, x); + nplasma = wmain[n].nplasma; + + Log ("Position %8.2e %8.2e %8.2e Cell %5d\n", x[0], x[1], x[2], n); + + Log ("Vertex position %8.2e %8.2e %8.2e rtheta %8.2e %8.2e \n", w[n].x[0], w[n].x[1], w[n].x[2], w[n].r, w[n].theta); + Log ("Center position %8.2e %8.2e %8.2e rtheta %8.2e %8.2e \n", w[n].xcen[0], w[n].xcen[1], w[n].xcen[2], w[n].rcen, w[n].thetacen); + + Log ("Electron density: %8.2e rho %8.2e\n", plasmamain[nplasma].ne, plasmamain[nplasma].rho); + Log ("Vel cell: %8.2e %8.2e %8.2e\n", w[n].v[0], w[n].v[1], w[n].v[2]); + + p.x[0] = x[0]; + p.x[1] = x[1]; + p.x[2] = x[2]; + + vwind_xyz (ndom, &p, v); + Log ("Velocity: %8.2e %8.2e %8.2e\n", v[0], v[1], v[2]); + + + goto a; + + return (0); +} + + + + +/************************************************************************** + + + Synopsis: + A summary of the energy absorbed in a cell + + Description: + + Arguments: + + Returns: + + Notes: + + History: + + 10nov ksl What was here previously was bizarre as the filename was + being added to all the time and this caused ultimately + a segmentation problem + + + ************************************************************************/ +int +abs_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + double x, xtot; + char c; + char name[LINELENGTH]; + char filename[LINELENGTH]; + int nplasma; + + c = 't'; + xtot = 0.0; + + printf ("Absorption tot=t, lines=l,f=ff,b=fb,h=hydrogen,i=he1,j=he2,z=heavy elements\n"); + rdchar ("Choice", &c); + + + strcpy (filename, rootname); + + switch (c) + { + case 't': /* Total Absorption */ + strcpy (name, "Total Absorbtion"); + strcat (filename, "heat_tot"); + break; + case 'f': /* ff */ + strcpy (name, "Free Free Absorbtion"); + strcat (filename, "heat_ff"); + break; + case 'b': /* Photoionization */ + strcpy (name, "Total photoionization Heating"); + strcat (filename, "heat_photo"); + break; + case 'l': /* Line heating */ + strcpy (name, "Resonance Line Heating"); + strcat (filename, "heat_lines"); + break; + case 'h': /* H photoionization */ + strcpy (name, "H Photoionization Heating"); + strcat (filename, "heat_h"); + break; + case 'i': /* He1 photo */ + strcpy (name, "He I Photoionization Heating"); + strcat (filename, "heat_he1"); + break; + case 'j': /* He2 photo */ + strcpy (name, "He 2 Photoionization Heating"); + strcat (filename, "heat_he2"); + break; + case 'z': /* Metal photo */ + strcpy (name, "Metal Photoionization Heating"); + strcat (filename, "heat_z"); + break; + default: + printf ("Not a valid choice\n"); + return (0); + } + + + + for (n = 0; n < NDIM2; n++) + { + x = 0.0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + switch (c) + { + case 't': + { /* Total heating */ + x = plasmamain[nplasma].heat_tot; + break; + case 'f': /* ff heating */ + x = plasmamain[nplasma].heat_ff; + break; + case 'b': /* photoionization heating */ + x = plasmamain[nplasma].heat_photo; + break; + case 'l': /* Line heating */ + x = plasmamain[nplasma].heat_lines; + break; + case 'h': /* H heating */ + x = plasmamain[nplasma].heat_ion[0]; + break; + case 'i': /* He1 heating */ + x = plasmamain[nplasma].heat_ion[2]; + break; + case 'j': /* He2 heating */ + x = plasmamain[nplasma].heat_ion[3]; + break; + case 'z': /* Line heating of high z elements */ + x = plasmamain[nplasma].heat_z; + break; + default: + printf ("Not a valid choice\n"); + return (0); + } + xtot += x; + + } + aaa[n] = x; + } + } + + display (name); + /* Log ("Component heating %8.3e\n", xtot * swind_delta); + JM130624 v76: Removed swind_delta term as gives too high heating */ + Log ("Component heating %8.3e\n", xtot); + + if (ochoice) + { + write_array (filename, ochoice); + } + return (0); + +} + +/************************************************************************** + + + Synopsis: + A summary of shock heating + + Description: + + Arguments: + + Returns: + + Notes: + + History: + + ************************************************************************/ + +int +shock_heating_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + double tot; + double shock_heating (); + char filename[LINELENGTH]; + + + + tot = 0.0; + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + tot += aaa[n] = shock_heating (&w[n]); + } + } + + if (geo.nonthermal == 1) + { + display ("Shock heating"); + Log ("The total shock heating is %8.2g\n", tot); + } + else + { + display ("This is only potential shock heating - it was switched off in the model"); + Log ("The total shock heating is %8.2g\n", tot); + } + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".shock_heating"); + write_array (filename, ochoice); + } + return (0); + +} + + + + + +/************************************************************************** + + + Synopsis: + A summary of adiabatic cooling + + Description: + + Arguments: + + Returns: + + Notes: + + History: + + ************************************************************************/ + +int +adiabatic_cooling_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + double tot; + double adiabatic_cooling (); + char filename[LINELENGTH]; + double t_e; + + + + tot = 0.0; + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + t_e = plasmamain[w[n].nplasma].t_e; + // ksl - I could not determine what the next line was supposed to do + // num_recomb (&plasmamain[w[n].nplasma], t_e); + tot += aaa[n] = adiabatic_cooling (&w[n], t_e); + } + } + + if (geo.adiabatic == 1) + { + display ("Adiabatic cooling"); + Log ("The total adiabatic cooling is %8.2g\n", tot); + } + else + { + display ("This is only potential adiabatic cooling - it was switched off in the model"); + Log ("The total adiabatic cooling is %8.2g\n", tot); + } + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".adiabatic"); + write_array (filename, ochoice); + } + return (0); + +} + + + + + +/************************************************************************** + + + Synopsis: + summary of the lum of a cell. The routine + simply reads variables that are already contained + in the plasma structure + + + Description: + + Arguments: + + Returns: + + Notes: + + History: + + ************************************************************************/ +int +lum_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + double x, xtot; + char c; + char name[LINELENGTH]; + char filename[LINELENGTH]; + int nplasma; + + c = 't'; + strcpy (filename, rootname); + + printf ("lum tot=t,r=rad, lines=l,f=ff,b=fb,h=hydrogen,i=he1,j=he2,z=heavy elements\n"); + rdchar ("Choice", &c); + switch (c) + { + case 't': /* Total luminosity */ + strcpy (name, "Total Luminosity"); + strcat (filename, ".tot_lum"); + break; + case 'r': /* Radiative energo loss total */ + strcpy (name, "Total Radiative Luminosity"); + strcat (filename, ".rad_lum"); + break; + case 'f': /* Radiative energo loss total */ + strcpy (name, "Free Free Luminosity"); + strcat (filename, ".ff_lum"); + break; + case 'b': /* Radiative energo loss total */ + strcpy (name, "Free Bound (Total Recombination) Luminosity"); + strcat (filename, ".fb_lum"); + break; + case 'l': /* Line lumosity */ + strcpy (name, "Line Luminosity"); + strcat (filename, ".line_lum"); + break; + case 'h': /* H lumosity */ + strcpy (name, "H Recombination Luminosity"); + strcat (filename, ".h1_recomb_lum"); + break; + case 'i': /* Line lumosity */ + strcpy (name, "He I Recombination Luminosity"); + strcat (filename, ".he1_recom_lum"); + break; + case 'j': /* Line lumosity */ + strcpy (name, "He 2 Recombination Luminosoity"); + strcat (filename, ".he2_recomb_lum"); + break; + case 'z': /* Line lumosity */ + strcpy (name, "Metal Recombination Luminosity"); + strcat (filename, ".z_recomb_lum"); + break; + default: + printf ("Not a valid choice\n"); + return (0); + } + + + + xtot = 0.0; + + + for (n = 0; n < NDIM2; n++) + { + x = 0.0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + switch (c) + { + case 't': /* Total luminosity */ + x = plasmamain[nplasma].lum_tot_ioniz; + break; + case 'r': /* Radiative energo loss total */ + x = plasmamain[nplasma].lum_tot_ioniz; + break; + case 'f': /* Radiative energo loss total */ + x = plasmamain[nplasma].lum_ff_ioniz; + break; + case 'b': /* Radiative energo loss total */ + x = plasmamain[nplasma].cool_rr_ioniz; + break; + case 'l': /* Line luminosity */ + x = plasmamain[nplasma].lum_lines_ioniz; + break; + case 'h': /* H luminosity */ + x = plasmamain[nplasma].cool_rr_ion[0]; + break; + case 'i': /* Line luminosity */ + x = plasmamain[nplasma].cool_rr_ion[2]; + break; + case 'j': /* Line luminosity */ + x = plasmamain[nplasma].cool_rr_ion[3]; + break; + case 'z': /* Line luminosity */ + x = plasmamain[nplasma].cool_rr_metals_ioniz; + break; + default: + printf ("Not a valid choice\n"); + return (0); + } + xtot += x; + + } + aaa[n] = x; + } + + display (name); + + /* Log ("Luminosity total %8.3e\n", xtot * swind_delta); + JM130624 v76: Removed swind_delta term as gives too high heating */ + Log ("Luminosity total %8.3e\n", xtot); + + if (ochoice) + { + write_array (filename, ochoice); + } + return (0); + +} + + + + + +/************************************************************************** + + + Synopsis: + A summary of the number of photoionizations in a cell + + Description: + + Arguments: + + Returns: + + Notes: + + History: + + ************************************************************************/ +int +photo_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n, ion; + char filename[LINELENGTH]; + int nplasma; + + ion = 0; + + rdint ("ion(<5)", &ion); + if (!(0 <= ion && ion < NIONIZ)) + { + Log ("ion out of range\n"); + return (0); + } + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].ioniz[ion] * plasmamain[nplasma].density[ion]; + } + } + display ("No of ionizations per second in cell"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".photo_sum"); + } + return (0); + +} + + + + + +/************************************************************************** + + + Synopsis: + A summary of the number of recombinations in a cell + + Description: + + Arguments: + + Returns: + + Notes: + + History: + + ************************************************************************/ +int +recomb_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + int ion; + char filename[LINELENGTH]; + int nplasma; + + ion = 0; + rdint ("ion(<5)", &ion); + if (!(0 <= ion && ion < NIONIZ)) + { + Log ("recomb_summary: ioniz out of range\n"); + return (0); + } + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + num_recomb (&plasmamain[nplasma], plasmamain[nplasma].t_e, 1); + aaa[n] = plasmamain[nplasma].recomb[ion]; + } + } + + display (" Number of recombinations per second in cell"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".recomb"); + write_array (filename, ochoice); + } + return (0); + +} + + + + + + +/************************************************************************** + + + Synopsis: + A summary of electron densities + + Description: + + Arguments: + + Returns: + + Notes: + + History: + + ************************************************************************/ + +int +electron_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + char filename[LINELENGTH]; + int nplasma; + + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0.0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].ne; + } + } + display ("Electron densities"); + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".ne"); + write_array (filename, ochoice); + } + return (0); + +} + + + + + + +/************************************************************************** + + + Synopsis: +A summary of rho + + Description: + + Arguments: + + Returns: + + Notes: +111002 ksl Added to try to diagnose what was going + on with the torus + + History: + + ************************************************************************/ +int +rho_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + char filename[LINELENGTH]; + int nplasma; + + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0.0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].rho; + } + } + display ("Rho (gm/cm**3)"); + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".rho"); + write_array (filename, ochoice); + } + return (0); + +} + + + + + + +/************************************************************************** + + + Synopsis: + A summary of rho + + Description: + + Arguments: + + Returns: + + Notes: + Note that because of limitations in the way that display + works cell numbers greater than 99 are not displayed as + integers unfortunately + + + History: + 111002 ksl Added to try to diagnose what was going + on with the torus + + ************************************************************************/ +int +plasma_cell (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + char filename[LINELENGTH]; + int nplasma; + + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0.0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = nplasma; + } + } + display ("Plasma cell number"); + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".pnum"); + write_array (filename, ochoice); + } + return (0); + +} + + + + + + + + +/************************************************************************** + + + Synopsis: + A summary of the average frequency + + Description: + + Arguments: + + Returns: + + Notes: + + History: + + ************************************************************************/ +int +freq_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + char filename[LINELENGTH]; + int nplasma; + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].ave_freq; + } + } + display ("Average freqency"); + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".ave_freq"); + write_array (filename, ochoice); + + } + + return (0); + +} + + + + + + +/************************************************************************** + + + Synopsis: + A summary of the number of photons which passed through a cell. + + Description: + + Arguments: + + Returns: + + Notes: + + History: + 111002 ksl Modified to be able to display photons of + various types + + ************************************************************************/ +int +nphot_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + char filename[LINELENGTH]; + int nplasma; + int ichoice; + char string[LINELENGTH]; + + + + ichoice = 0; + while (rdint ("nphot(all=0,star=1,bl=2,disk=3,wind=4,agn=5,other=return)", &ichoice) != EOF) + { + if (ichoice < 0 || ichoice > 5) + { + return (0); + } + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + if (ichoice == 0) + { + aaa[n] = plasmamain[nplasma].ntot; + strcpy (string, "Nphot tot per cell"); + } + else if (ichoice == 1) + { + aaa[n] = plasmamain[nplasma].ntot_star; + strcpy (string, "Nphot star per cell"); + } + else if (ichoice == 2) + { + aaa[n] = plasmamain[nplasma].ntot_bl; + strcpy (string, "Nphot bl per cell"); + } + else if (ichoice == 3) + { + aaa[n] = plasmamain[nplasma].ntot_disk; + strcpy (string, "Nphot disk per cell"); + } + else if (ichoice == 4) + { + aaa[n] = plasmamain[nplasma].ntot_wind; + strcpy (string, "Nphot wind per cell"); + } + else if (ichoice == 5) + { + aaa[n] = plasmamain[nplasma].ntot_agn; + strcpy (string, "Nphot agn per cell"); + } + else + { + Error ("Unknown choice, try again\n"); + } + } + } + display (string); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".nphot"); + write_array (filename, ochoice); + + } + } + return (0); + + + + +} + + + + + +/************************************************************************** + + + Synopsis: + A summary of the electron temperatures + + Description: + + Arguments: + + Returns: + + Notes: + + History: + + ************************************************************************/ +int +temp_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + char filename[LINELENGTH]; + int nplasma; + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].t_e; + } + } + display ("T_e"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".te"); + write_array (filename, ochoice); + } + return (0); + +} + + + +/************************************************************************** + + + Synopsis: + A summary of the radiation temperatures + + Description: + + Arguments: + + Returns: + + Notes: + + History: + + ************************************************************************/ +int +temp_rad (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + char filename[LINELENGTH]; + int nplasma; + + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].t_r; + } + } + display ("T_rad"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".tr"); + write_array (filename, ochoice); + + } + return (0); + +} + +/************************************************************************** + + + Synopsis: + A summary of the radiative weights + + Description: + + Arguments: + + Returns: + + Notes: + + History: + + ************************************************************************/ + +int +weight_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + int nplasma; + char filename[LINELENGTH]; + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].w; + } + } + display ("Radiative weights"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".w"); + write_array (filename, ochoice); + + } + + return (0); + +} + + + + + +/************************************************************************** + + + Synopsis: + A summary of the velocities + + Description: + + Arguments: + + Returns: + + Notes: + + History: + + ************************************************************************/ +int +velocity_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + double x; + char name[LINELENGTH]; + char filename[LINELENGTH]; + int ichoice; + + ichoice = 0; + + /* Note that EOF actually causes an exit via rdpar */ + while (rdint ("|v|=0;v_x=1,v_y=2,v_z=3,return=other", &ichoice) != EOF) + { + if (ichoice < 0 || ichoice > 3) + return (0); + + if (ichoice == 0) + strcpy (name, "Velocity"); + else if (ichoice == 1) + strcpy (name, "V_x"); + else if (ichoice == 2) + strcpy (name, "V_y"); + else + strcpy (name, "V_z"); + + for (n = 0; n < NDIM2; n++) + { + x = 0; + if (w[n].inwind >= 0) + { + if (ichoice == 0) + x = sqrt (w[n].v[0] * w[n].v[0] + w[n].v[1] * w[n].v[1] + w[n].v[2] * w[n].v[2]); + else if (ichoice == 1) + x = w[n].v[0]; + else if (ichoice == 2) + x = w[n].v[1]; + else + x = w[n].v[2]; + } + aaa[n] = x; + } + display (name); + + + if (ochoice) + { + strcpy (filename, rootname); + + if (ichoice == 0) + { + strcat (filename, ".vel"); + write_array (filename, ochoice); + } + if (ichoice == 1) + { + strcat (filename, ".vx"); + write_array (filename, ochoice); + } + if (ichoice == 2) + { + strcat (filename, ".vy"); + write_array (filename, ochoice); + } + if (ichoice == 3) + { + strcat (filename, ".vz"); + write_array (filename, ochoice); + } + } + } + + return (0); + +} + + + + + + +/************************************************************************** + + + Synopsis: + A summary of the velocities + + Description: + + Arguments: + + Returns: + + Notes: + + History: + + ************************************************************************/ +int +mo_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + int ichoice; + char name[LINELENGTH]; + char filename[LINELENGTH]; + double x; + PlasmaPtr xplasma; + + + ichoice = 0; + + /* Note that EOF actually causes an exit via rdpar */ + while (rdint ("|F_rad|=0;Frac_x=1,Frad_y=2,Frad_z=3,return=other", &ichoice) != EOF) + { + if (ichoice < 0 || ichoice > 4) + return (0); + + if (ichoice == 0) + strcpy (name, "|F_rad|"); + else if (ichoice == 1) + strcpy (name, "F_rad_x"); + else if (ichoice == 2) + strcpy (name, "F_rad_y"); + else + strcpy (name, "F_rad_z"); + + + for (n = 0; n < NDIM2; n++) + { + xplasma = &plasmamain[w[n].nplasma]; + x = 0; + if (w[n].inwind >= 0) + { + if (ichoice == 0) + x = + sqrt (xplasma->dmo_dt[0] * xplasma->dmo_dt[0] + + xplasma->dmo_dt[1] * xplasma->dmo_dt[1] + xplasma->dmo_dt[2] * xplasma->dmo_dt[2]); + else if (ichoice == 1) + x = xplasma->rad_force_es[0]; + else if (ichoice == 2) + x = xplasma->rad_force_es[1]; + else + x = xplasma->rad_force_es[2]; + } + aaa[n] = x; + } + display (name); + + if (ochoice) + { + if (ichoice == 0) + { + strcpy (filename, rootname); + strcat (filename, ".f_rad_mod"); + write_array (filename, ochoice); + } + else if (ichoice == 1) + { + strcpy (filename, rootname); + strcat (filename, ".f_rad_x"); + write_array (filename, ochoice); + } + else if (ichoice == 2) + { + strcpy (filename, rootname); + strcat (filename, ".f_rad_y"); + write_array (filename, ochoice); + } + else + { + strcpy (filename, rootname); + strcat (filename, ".f_rad_z"); + write_array (filename, ochoice); + } + } + + } + return (0); + +} + + + +/************************************************************************** + + + Synopsis: + A summary of the volumes of each cell + + + Description: + + Arguments: + + Returns: + + Notes: + + History: + 080811 ksl Add lines from Stuart's version of this + routine to bring this version into + compliance with it. + + + + ************************************************************************/ +int +vol_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + + char filename[LINELENGTH]; + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + aaa[n] = w[n].vol; + } + } + display ("Volumes"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".vol"); + write_array (filename, ochoice); + } + + return (0); + +} + + +/*********************************************************** + Space Telescope Science Institute + +Synopsis: + wind_element provides a detailed look at a single cell + +Arguments: + + +Returns: + +Description: + + Note that this routine does not calculate anything. It + simple reports on variables in the wind array. + + +Notes: + +History: + 080804 ksl 60b -- Added reporting of partition function + 101106 ksl 69 -- Changed variable name in rdint to make + it more obvious what was being discused here. + +**************************************************************/ + +int +wind_element (w) + WindPtr w; +{ + PlasmaPtr xplasma; + int m, n, i, j, nn, mm; + int first, last; + int ndom; + + ndom = w->ndom; + + + + n = 50; +a:printf ("There are %i wind elements in this model\n", NDIM2); + rdint ("Wind.array.element", &n); + + if (n < 0) + goto b; + else if (n > NDIM2) + { + printf ("No, there are %i wind elements, not %i\n", NDIM2, n); + goto a; + } + + wind_n_to_ij (ndom, n, &i, &j); + xplasma = &plasmamain[w[n].nplasma]; + + Log + ("Element %d (%d,%d) inwind %d plasma cell %d ntot %d nioniz %d nrad %d\n", + n, i, j, w[n].inwind, xplasma->nplasma, xplasma->ntot, xplasma->nioniz, xplasma->nrad); + Log ("xyz %8.2e %8.2e %8.2e vel %8.2e %8.2e %8.2e\n", w[n].x[0], w[n].x[1], w[n].x[2], w[n].v[0], w[n].v[1], w[n].v[2]); + Log ("r theta %12.6e %12.6e \n", w[n].rcen, w[n].thetacen / RADIAN); + + Log ("rho %8.2e nh %8.2e ne %8.2e t_r %8.2e t_e %8.2e w %8.2e vol %8.2e\n", + xplasma->rho, xplasma->rho * rho2nh, xplasma->ne, xplasma->t_r, xplasma->t_e, xplasma->w, w[n].vol); + + if (w[n].inwind < 0) + Log ("\n# Cell is not inwind, expect all zeros to follow\n\n"); + /*70d - added compton - ksl */ + /*70g compton removed from luminosity reporting, it is now a cooling mechanism but does not produce photons + DR cooling also added in to report */ + Log + ("t_e %8.2e lum_tot %8.2e lum_lines %8.2e lum_ff %8.2e lum_rr %8.2e \n", + xplasma->t_e, xplasma->lum_tot_ioniz, xplasma->lum_lines_ioniz, xplasma->lum_ff_ioniz, xplasma->lum_rr_ioniz); + Log + ("t_e %8.2e cool_tot %8.2e lum_lines %8.2e lum_ff %8.2e cool_rr %8.2e cool_comp %8.2e cool_adiab %8.2e cool_DR %8.2e cool_DI %8.2e\n", + xplasma->t_e, xplasma->cool_tot_ioniz + xplasma->cool_comp_ioniz + xplasma->cool_adiabatic_ioniz + xplasma->cool_dr_ioniz, + xplasma->lum_lines_ioniz, xplasma->lum_ff_ioniz, xplasma->cool_rr_ioniz, xplasma->cool_comp_ioniz, xplasma->cool_adiabatic_ioniz, + xplasma->cool_dr_ioniz, xplasma->cool_di_ioniz); + Log ("t_r %8.2e heat_tot %8.2e heat_lines %8.2e heat_ff %8.2e heat_photo %8.2e heat_auger %8.2e heat_comp %8.2e heat_icomp %8.2e\n", + xplasma->t_r, xplasma->heat_tot, xplasma->heat_lines, xplasma->heat_ff, xplasma->heat_photo, xplasma->heat_auger, xplasma->heat_comp, + xplasma->heat_ind_comp); + + + + Log ("Recombination cooling HII>HI %8.2e HeII>HeI %8.2e HeIII>HeII %8.2e Metals %8.2e\n", xplasma->cool_rr_ion[0], + xplasma->cool_rr_ion[2], xplasma->cool_rr_ion[3], xplasma->cool_rr_metals); + Log ("Photoionization heating HI>HII %8.2e HeI>HeII %8.2e HeII>HeIII %8.2e Metals %8.2e\n", xplasma->heat_ion[0], xplasma->heat_ion[2], + xplasma->heat_ion[3], xplasma->heat_z); + + + + Log ("The ratio of rad (total) cooling to heating is %8.2f (%8.2f) \n", + xplasma->lum_tot_ioniz / xplasma->heat_tot, + (xplasma->lum_tot_ioniz + xplasma->cool_adiabatic_ioniz + xplasma->cool_comp_ioniz + xplasma->cool_dr_ioniz) / xplasma->heat_tot); + Log ("Adiabatic cooling %8.2e is %8.2g of total cooling\n", + xplasma->cool_adiabatic_ioniz, + xplasma->cool_adiabatic_ioniz / (xplasma->lum_tot + xplasma->cool_adiabatic + xplasma->cool_comp_ioniz + xplasma->cool_dr_ioniz)); + /*70g NSH compton and DR cooling are now reported seperately. */ + Log ("Compton cooling %8.2e is %8.2g of total cooling\n", + xplasma->cool_comp_ioniz, + xplasma->cool_comp_ioniz / (xplasma->lum_tot + xplasma->cool_adiabatic + xplasma->cool_comp_ioniz + xplasma->cool_dr_ioniz)); + Log ("DR cooling %8.2e is %8.2g of total cooling\n", xplasma->cool_dr_ioniz, + xplasma->cool_dr_ioniz / (xplasma->lum_tot + xplasma->cool_adiabatic + xplasma->cool_comp_ioniz + xplasma->cool_dr_ioniz)); + Log ("Number of ionizing photons in cell nioniz %d\n", xplasma->nioniz); + Log ("Log Ionization parameter in this cell U %4.2f xi %4.2f\n", log10 (xplasma->ip), log10 (xplasma->xi)); //70h NSH computed ionizaion parameter + Log ("ioniz %8.2e %8.2e %8.2e %8.2e %8.2e\n", + xplasma->ioniz[0], xplasma->ioniz[1], xplasma->ioniz[2], xplasma->ioniz[3], xplasma->ioniz[4]); + Log + ("Convergence status: whole %d converging %d t_r %8.2e t_e %8.2e hc %8.2e \n", + xplasma->converge_whole, xplasma->converging, xplasma->converge_t_r, xplasma->converge_t_e, xplasma->converge_hc); + + Log ("Densities:\n"); + for (nn = 0; nn < 5; nn++) + { + first = ele[nn].firstion; + last = first + ele[nn].nions; + Log ("%-5s ", ele[nn].name); + for (m = first; m < last; m++) + Log (" %8.2e", xplasma->density[m]); + Log ("\n"); + } + + + Log ("Partition function:\n"); + for (nn = 0; nn < 5; nn++) + { + first = ele[nn].firstion; + last = first + ele[nn].nions; + Log ("%-5s ", ele[nn].name); + for (m = first; m < last; m++) + Log (" %8.2e", xplasma->partition[m]); + Log ("\n"); + } + + + Log ("Non LTE Level densities:\n"); + + + mm = 0; + for (nn = 0; nn < 10; nn++) + { + while (ion[mm].nlte <= 0 && mm < nions) + mm++; + if (mm == nions) + break; + + first = ion[mm].first_levden; + last = first + ion[mm].nlte; + Log ("ion %3d %3d", ion[mm].z, ion[mm].istate); + for (m = first; m < last; m++) + Log (" %8.2e", xplasma->levden[m]); + Log ("\n"); + mm++; + } + + Log ("Spectral model details:\n"); + for (nn = 0; nn < geo.nxfreq; nn++) + { + Log ("numin= %9.2e (%9.2e) numax= %9.2e (%9.2e) Model= %2d PL_log_w= %9.2e PL_alpha= %9.2e Exp_w= %9.2e EXP_temp= %9.2e\n", + xplasma->fmin_mod[nn], geo.xfreq[nn], xplasma->fmax_mod[nn], geo.xfreq[nn + 1], xplasma->spec_mod_type[nn], xplasma->pl_log_w[nn], + xplasma->pl_alpha[nn], xplasma->exp_w[nn], xplasma->exp_temp[nn]); + } + + Log ("Flux:\n"); + Log ("F_vis_w = %9.2e F_vis_phi = %9.2e F_vis_z = %9.2e \n", xplasma->F_vis[0], xplasma->F_vis[1], xplasma->F_vis[2]); + Log ("F_UV_w = %9.2e F_UV_phi = %9.2e F_UV_z = %9.2e \n", xplasma->F_UV[0], xplasma->F_UV[1], xplasma->F_UV[2]); + Log ("F_Xray_w= %9.2e F_Xray_phi= %9.2e F_Xray_z= %9.2e \n", xplasma->F_Xray[0], xplasma->F_Xray[1], xplasma->F_Xray[2]); + + + + + goto a; + +b:return (0); + +} + + + + + +/************************************************************************** + + + Synopsis: + tau_h_summary (w, rootname, ochoice) + + Description: + + Arguments: + + Returns: + + Notes: + + History: + + ************************************************************************/ +int +tau_h_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + int nplasma; + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = 6.e-18 * plasmamain[nplasma].density[0] * pow (w[n].vol, 0.333); + + } + } + display ("tau_h summary"); + + return (0); + +} + + + + + +/************************************************************************** + + + Synopsis: + coolheat_summary (w, rootname, ochoice) + + Description: + + Arguments: + + Returns: + + Notes: + + History: + + ************************************************************************/ +int +coolheat_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + int nplasma; + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].lum_tot_ioniz / plasmamain[nplasma].heat_tot; + + } + } + display ("Cooling over heating"); + + return (0); + +} + + + + +/************************************************************************** + + + Synopsis: + + complete_file summary produces a standardised set of output files + from the wind_save_file + + Description: + + At present(1409), we we print temperatures, electron densities, + convergence information and ion denisities and fractions for + C III, IV, V, + N IV, V, VI + O V, VI, VII + Si III, IV + + Arguments: + + Returns: + + Notes: + + History: + + 1409 ksl Eliminated several files that were of limited use + to users. The deleted files are printed out if + DEBUG is set. + 1411 JM debug is now deprecated so replaced with FULL_ION_SUMMARTY + + ************************************************************************/ + +#define FULL_ION_SUMMARY 0 + + +int +complete_file_summary (w, root, ochoice) + WindPtr w; + char root[]; + int ochoice; +{ + temp_summary (w, root, ochoice); + temp_rad (w, root, ochoice); + electron_summary (w, root, ochoice); + convergence_summary (w, root, ochoice); + ion_summary (w, 6, 3, 0, root, ochoice); + ion_summary (w, 6, 4, 0, root, ochoice); + ion_summary (w, 6, 5, 0, root, ochoice); + ion_summary (w, 7, 4, 0, root, ochoice); + ion_summary (w, 7, 5, 0, root, ochoice); + ion_summary (w, 7, 6, 0, root, ochoice); + ion_summary (w, 8, 4, 0, root, ochoice); + ion_summary (w, 8, 5, 0, root, ochoice); + ion_summary (w, 8, 6, 0, root, ochoice); + ion_summary (w, 8, 7, 0, root, ochoice); + ion_summary (w, 14, 3, 0, root, ochoice); + ion_summary (w, 14, 4, 0, root, ochoice); + ion_summary (w, 14, 5, 0, root, ochoice); + + ion_summary (w, 6, 3, 1, root, ochoice); + ion_summary (w, 6, 4, 1, root, ochoice); + ion_summary (w, 6, 5, 1, root, ochoice); + ion_summary (w, 7, 4, 1, root, ochoice); + ion_summary (w, 7, 5, 1, root, ochoice); + ion_summary (w, 7, 6, 1, root, ochoice); + ion_summary (w, 8, 4, 1, root, ochoice); + ion_summary (w, 8, 5, 1, root, ochoice); + ion_summary (w, 8, 6, 1, root, ochoice); + ion_summary (w, 8, 7, 1, root, ochoice); + ion_summary (w, 14, 3, 1, root, ochoice); + ion_summary (w, 14, 4, 1, root, ochoice); + ion_summary (w, 14, 5, 1, root, ochoice); + +#if FULL_ION_SUMMARY + ion_summary (w, 6, 3, 2, root, ochoice); + ion_summary (w, 6, 4, 2, root, ochoice); + ion_summary (w, 6, 5, 2, root, ochoice); + ion_summary (w, 7, 4, 2, root, ochoice); + ion_summary (w, 7, 5, 2, root, ochoice); + ion_summary (w, 7, 6, 2, root, ochoice); + ion_summary (w, 8, 4, 2, root, ochoice); + ion_summary (w, 8, 5, 2, root, ochoice); + ion_summary (w, 8, 6, 2, root, ochoice); + ion_summary (w, 8, 7, 2, root, ochoice); + ion_summary (w, 14, 3, 2, root, ochoice); + ion_summary (w, 14, 4, 2, root, ochoice); + ion_summary (w, 14, 5, 2, root, ochoice); + + + ion_summary (w, 6, 3, 3, root, ochoice); + ion_summary (w, 6, 4, 3, root, ochoice); + ion_summary (w, 6, 5, 3, root, ochoice); + ion_summary (w, 7, 4, 3, root, ochoice); + ion_summary (w, 7, 5, 3, root, ochoice); + ion_summary (w, 7, 6, 3, root, ochoice); + ion_summary (w, 8, 4, 3, root, ochoice); + ion_summary (w, 8, 5, 3, root, ochoice); + ion_summary (w, 8, 6, 3, root, ochoice); + ion_summary (w, 8, 7, 3, root, ochoice); + ion_summary (w, 14, 3, 3, root, ochoice); + ion_summary (w, 14, 4, 3, root, ochoice); + ion_summary (w, 14, 5, 3, root, ochoice); +#endif + + return (0); +} + +/* A summary of the regions in the wind */ + +int +wind_reg_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = -9; + if (w[n].inwind >= 0) + { + aaa[n] = w[n].inwind; + + } + } + display ("Regions of the wind"); + + return (0); + +} + + +/* A summary of the dvds_ave */ + +int +dvds_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + char filename[LINELENGTH], suffix[LINELENGTH]; + int n, ichoice; + struct photon p; + //double v1[3] + + + + rdint ("dvds_ave (0) dvdx (1) dvdy (2) dvdz (3) component LoS (4):", &ichoice); + + if (ichoice == 0) + { + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + aaa[n] = w[n].dvds_ave; + + } + } + display ("Average dvds"); + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".dvds"); + write_array (filename, ochoice); + } + } + + else if (ichoice > 0 && ichoice < 4) + { + + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + p.lmn[0] = 0.0; + p.lmn[1] = 0.0; + p.lmn[2] = 0.0; + stuff_v (w[n].xcen, p.x); + + p.lmn[ichoice - 1] = 1.0; + aaa[n] = dvwind_ds_cmf (&p); + + } + } + display ("Average dvds"); + if (ochoice) + { + strcpy (filename, rootname); + sprintf (suffix, ".dvds_%i", ichoice - 1); + strcat (filename, suffix); + write_array (filename, ochoice); + } + } + + else + { + get_los_dvds (w, rootname, ochoice); + } + + + return (0); +} + +/* A summary of inner shell ionization */ +/* NSH - this code removed May 18 +int +inner_shell_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + char filename[LINELENGTH]; + int nplasma; + + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0.0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].gamma_inshl[0]; + } + } + display ("Inner_shell"); + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".is"); + write_array (filename, ochoice); + } + return (0); + +} +*/ + +/* A summary of the Ionization parameter - might not always be present */ + +int +IP_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + char filename[LINELENGTH]; + int nplasma; + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = ((plasmamain[nplasma].ip)); + } + } + display ("Ionization parameter"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".IP"); + write_array (filename, ochoice); + + } + + + /* JM added printout for xi too */ + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = ((plasmamain[nplasma].xi)); + } + } + display ("Xi Ionization parameter"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".xi"); + write_array (filename, ochoice); + + } + + + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = ((plasmamain[nplasma].ip_direct)); + } + } + display ("Log Ionization parameter (direct)"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".IP_direct"); + write_array (filename, ochoice); + + } + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = ((plasmamain[nplasma].ip_scatt)); + } + } + display ("Log Ionization parameter (scattered)"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".IP_scatt"); + write_array (filename, ochoice); + + } + + + + + return (0); + +} + + + +/* A summary of the Sim alpha parameter - might not always be present. + + 1108 ksl Adapted for new version of banded alpha + 1208 nsh Changed names - reference to sim removed to make way for exponential estimators + 1401 nsh Added more information - this now writes out all the spectral model parameters + */ + +int +alpha_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n, m; + char filename[LINELENGTH]; + int nplasma; + char word[LINELENGTH]; + + + + for (m = 0; m < geo.nxfreq; m++) + { + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].pl_alpha[m]; + } + } + + strcpy (word, ""); + sprintf (word, ".pl_alpha%03d", m); + display (word); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, word); + write_array (filename, ochoice); + } + } + + for (m = 0; m < geo.nxfreq; m++) + { + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].pl_log_w[m]; + } + } + + strcpy (word, ""); + sprintf (word, ".pl_log_w%03d", m); + display (word); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, word); + write_array (filename, ochoice); + } + } + + + for (m = 0; m < geo.nxfreq; m++) + { + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].exp_temp[m]; + } + } + + strcpy (word, ""); + sprintf (word, ".exp_temp%03d", m); + display (word); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, word); + write_array (filename, ochoice); + } + } + + for (m = 0; m < geo.nxfreq; m++) + { + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].exp_w[m]; + } + } + + strcpy (word, ""); + sprintf (word, ".exp_w%03d", m); + display (word); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, word); + write_array (filename, ochoice); + } + } + + for (m = 0; m < geo.nxfreq; m++) + { + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].spec_mod_type[m]; + } + } + + strcpy (word, ""); + sprintf (word, ".spec_mod_type%03d", m); + display (word); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, word); + write_array (filename, ochoice); + } + } + + + + for (m = 0; m < geo.nxfreq; m++) + { + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].fmin_mod[m]; + } + } + + strcpy (word, ""); + sprintf (word, ".fmin_mod%03d", m); + display (word); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, word); + write_array (filename, ochoice); + } + } + + for (m = 0; m < geo.nxfreq; m++) + { + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].fmax_mod[m]; + } + } + + strcpy (word, ""); + sprintf (word, ".fmax_mod%03d", m); + display (word); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, word); + write_array (filename, ochoice); + } + } + + return (0); + +} + + + +/* A summary of frequency banded radiation density - a crude spectrum for a cell. + + 1301 nsh Written + */ + +int +J_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int i, n; + char filename[LINELENGTH]; + char number[2], line_number[10]; + int nplasma; + int uplvl, llvl, njump, lu, ll; + struct lines *line_ptr; + + njump = 0; + line_ptr = NULL; + + i = 1; + + rdint ("Band number for J or macro atom J (0), or backup (-1)", &i); + + + while (i >= 0) + { + if (i == 0) + { + printf ("H alpha is 3->2 in this notation!\n"); + rdint ("Upper level macro atom", &uplvl); + rdint ("Lower level macro atom", &llvl); + + /* Convert 'user levels' into actually levels. i.e. 1 is 0! */ + uplvl = uplvl - 1; + llvl = llvl - 1; + + + + /* now we need to find the jbar estimator and line + pointer corresponding to this transition */ + njump = 0; + printf ("Level %i has %i upwards jumps %i downwards jumps\n", llvl + 1, xconfig[llvl].n_bbu_jump, xconfig[llvl].n_bbd_jump); + + while (njump < xconfig[llvl].n_bbu_jump) + { + line_ptr = &line[xconfig[llvl].bbu_jump[njump]]; + lu = line_ptr->nconfigu; + ll = line_ptr->nconfigl; + + if (ll == llvl && lu == uplvl) + break; + njump++; + } + + if (njump >= xconfig[llvl].n_bbu_jump) + { + Error ("Couldn't find this transition, try something else!\n"); + return (0); + } + } + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + if (i == 0) + aaa[n] = macromain[nplasma].jbar_old[xconfig[llvl].bbu_indx_first + njump]; + else + aaa[n] = (plasmamain[nplasma].xj[i]); + } + } + + printf ("Line wavelength is %.2f\n", (VLIGHT / line_ptr->freq) / ANGSTROM); + printf ("Line freq is %8.4e\n", line_ptr->freq); + printf ("njump %i llvl %i uplvl %i nres %i", njump, llvl, uplvl, xconfig[llvl].bbu_jump[njump]); + display ("J in cell"); + //printf ("i=%i", i); + sprintf (number, "%i", i); + + if (ochoice) + { + strcpy (filename, rootname); + if (i == 0) + { + sprintf (line_number, "%ito%i", uplvl, llvl); + strcat (filename, ".Jbar_"); + strcat (filename, line_number); + } + else + { + strcat (filename, ".J_band"); + strcat (filename, number); + } + write_array (filename, ochoice); + + } + + rdint ("Band number for J or macro atom J (0), or backup (-1)", &i); + } + return (0); + +} + + +int +J_scat_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + char filename[LINELENGTH]; + int nplasma; + + + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = (plasmamain[nplasma].j); + } + } + display ("J in cell"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".J_tot"); + write_array (filename, ochoice); + } + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = (plasmamain[nplasma].j_direct); + } + } + display ("J in cell from direct photons"); + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".J_direct"); + write_array (filename, ochoice); + } + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = (plasmamain[nplasma].j_scatt); + } + } + display ("J in cell from scattered photons"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".J_scatt"); + write_array (filename, ochoice); + } + + + return (0); + +} + + + + + + +//Split of photons from different sources in the cell. + +int +phot_split (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + char filename[LINELENGTH]; + int nplasma; + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = (plasmamain[nplasma].ntot_wind); + } + } + display ("Wind photons in cell"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".ntot_wind"); + write_array (filename, ochoice); + + } + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = (plasmamain[nplasma].ntot_agn); + } + } + display ("AGN photons in cell"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".ntot_agn"); + write_array (filename, ochoice); + + } + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = (plasmamain[nplasma].ntot_disk); + } + } + display ("Disk photons in cell"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".ntot_disk"); + write_array (filename, ochoice); + + } + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = (plasmamain[nplasma].ntot_star); + } + } + display ("Stellar photons in cell"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".ntot_star"); + write_array (filename, ochoice); + + } + return (0); + +} + +int +thompson (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + int nplasma; + double ne; + char filename[LINELENGTH]; + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + ne = plasmamain[nplasma].ne; + aaa[n] = (THOMPSON * ne) * pow (w[n].vol, 0.333); + } + } + display ("Thompson optical depths"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".thomp"); + write_array (filename, ochoice); + } + + return (0); + +} + + + +int +nscat_split (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + int nplasma; + char filename[LINELENGTH]; + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].nscat_es; + } + } + display ("Thompson scatters"); + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].nscat_res; + } + } + display ("Resonant scatters"); + + + + + + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".thomp"); + write_array (filename, ochoice); + } + + return (0); + +} + +int +convergence_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + int nplasma; + char filename[LINELENGTH]; + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].converge_whole; + } + } + display ("Convergence (0=converged. Higher numbers indicate one or more convergence tests failed)"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".conv"); + write_array (filename, ochoice); + } + + return (0); + +} + + +/* + + 1112 ksl Write out arrays of use in evaluating what is going on in convergence of + various models +*/ + +int +convergence_all (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + int nplasma; + char filename[LINELENGTH]; + + + + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].converge_t_e; + } + } + display ("t_e Convergence (0=converged)"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".conv_te"); + write_array (filename, ochoice); + } + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].converge_hc; + } + } + display ("hc Convergence (0=converged)"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".conv_hc"); + write_array (filename, ochoice); + } + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].converge_t_r; + } + } + display ("t_r Convergence (0=converged)"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".conv_tr"); + write_array (filename, ochoice); + } + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].converge_whole; + } + } + display ("Convergence (0=converged)"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".conv_whole"); + write_array (filename, ochoice); + } + + + return (0); +} + +/* + + 1306 nsh Write out information pertaining to the models used in each cell/frequency band +*/ + +int +model_bands (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n, m; + int nplasma; + char filename[LINELENGTH]; + char word[LINELENGTH]; + + + + for (m = 0; m < geo.nxfreq; m++) + { + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].nxtot[m]; + } + } + + strcpy (word, ""); + sprintf (word, ".nxtot%03d", m); + display (word); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, word); + write_array (filename, ochoice); + } + } + + if (geo.nxfreq == 0) + { + printf ("This model does not use bands calculating the ionization\n"); + return (0); + } + + + /* Now write out the value of xj in each band */ + + for (m = 0; m < geo.nxfreq; m++) + { + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].xj[m]; + } + } + + strcpy (word, ""); + sprintf (word, ".xj%03d", m); + display (word); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, word); + write_array (filename, ochoice); + } + } + + + /* Now write out the value of xave_freq in each band */ + + for (m = 0; m < geo.nxfreq; m++) + { + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].xave_freq[m]; + } + } + + strcpy (word, ""); + sprintf (word, ".xave_freq%03d", m); + display (word); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, word); + write_array (filename, ochoice); + } + } + + + /* Now write out the value of nxtot in each band */ + + for (m = 0; m < geo.nxfreq; m++) + { + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].nxtot[m]; + } + } + + strcpy (word, ""); + sprintf (word, ".nxtot%03d", m); + display (word); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, word); + write_array (filename, ochoice); + } + } + + return (0); +} + + + +/* A summary of adiabatic cooling */ +int +heatcool_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n; + int nplasma; + char filename[LINELENGTH]; +//OLD float x; + +//OLD x = wind_luminosity (0.0, VERY_BIG, MODE_CMF_TIME); + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].heat_tot; + if (w[n].div_v < 0.0) // add in if it is negative and hence a heating term + { + aaa[n] += -1.0 * (plasmamain[nplasma].cool_adiabatic_ioniz); + } + } + } + display ("Total heating"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".heat_tot"); + write_array (filename, ochoice); + } + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].heat_lines; + } + } + display ("Line heating"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".heat_lines"); + write_array (filename, ochoice); + } + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].heat_ff; + } + } + display ("FF heating"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".heat_ff"); + write_array (filename, ochoice); + } + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].heat_comp; + } + } + display ("Compton heating"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".heat_comp"); + write_array (filename, ochoice); + } + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].heat_ind_comp; + } + } + display ("Induced Compton heating"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".heat_ind_comp"); + write_array (filename, ochoice); + } + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].heat_photo; + } + } + display ("Photo heating"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".heat_photo"); + write_array (filename, ochoice); + } + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].lum_lines_ioniz; + } + } + display ("Line Luminosity"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".lum_lines"); + write_array (filename, ochoice); + } + + + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].cool_adiabatic_ioniz; + } + } + display ("Adiabatic Luminosity"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".cool_adiabatic"); + write_array (filename, ochoice); + } + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].lum_ff_ioniz; + } + } + display ("Free Free Luminosity"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".lum_ff"); + write_array (filename, ochoice); + } + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].cool_comp_ioniz; + } + } + display ("Compton Luminosity"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".cool_comp"); + write_array (filename, ochoice); + } + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].cool_dr_ioniz; + } + } + display ("DR Luminosity"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".cool_dr"); + write_array (filename, ochoice); + } + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].cool_rr_ioniz; + } + } + display ("FB Luminosity"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".cool_rr"); + write_array (filename, ochoice); + } + + + + + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = + plasmamain[nplasma].cool_rr_ioniz + plasmamain[nplasma].cool_dr_ioniz + + plasmamain[nplasma].cool_comp_ioniz + plasmamain[nplasma].lum_ff_ioniz + plasmamain[nplasma].lum_lines_ioniz; + if (w[n].div_v >= 0.0) //only add in if it is treated as a cooling term + { + aaa[n] += plasmamain[nplasma].cool_adiabatic_ioniz; + } + } + } + display ("Total Cooling"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".lum_total"); + write_array (filename, ochoice); + } + + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + nplasma = w[n].nplasma; + aaa[n] = plasmamain[nplasma].lum_tot_ioniz; + } + } + display ("Total Radiating Luminosity"); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".lum_tot"); + write_array (filename, ochoice); + } + + return (0); +} + + + + + + + + +/************************************************************************** + + + Synopsis: + A summary of the important quantites in a given cell. + Primarily used for easily making plots + + Description: + + Arguments: + w WindPtr + rootname filename of root pf + ochoice whether to save to file + + Returns: + + Notes: + + History: + 1411 JM coded + +************************************************************************/ + +int +complete_physical_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n, np; + char filename[LINELENGTH]; + double he1den, he2den, he3den; + double h1den, h2den, c3den, c4den, c5den; + double n5den, o6den, si4den; + int frac_choice; + int ii, jj; + double vtot; + FILE *fptr, *fopen (); + PlasmaPtr xplasma; + int ndom; + + rdint ("Save ions as densities (0) or fractions? (1)", &frac_choice); + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".complete"); + fptr = fopen (filename, "w"); + } + else + printf ("This mode is recommended purely for file output\n"); + + + /* JM 1411 -- First we have to write out some headers so that + astropy can read the output */ + + printf ("n\tnplasma\tinwind\ti\tj\tx\tz\tv\tvx\tvy\tvz\tdvds_ave\tvol\t \ +rho\tne\tte\ttr\tnphot\tw\tave_freq\tIP\tconv\tconv_tr\tconv_te\tconv_hc\t \ +cool_tot\tlum_tot\tlum_rr\tcool_rr\tlum_ff\tlum_lines\tcool_adiabatic\tcool_comp\tcool_dr\tcool_DI \ +heat_tot\theat_photo\theat_auger\theat_lines\theat_ff\theat_comp\theat_ind_comp\theat_shock\t \ +ionH1\tionH2\tionHe1\tionHe2\tionHe3\tionC3\tionC4\tionC5\tionN5\tionO6\tionSi4\n"); + + if (ochoice) + fprintf (fptr, "n\tnplasma\tinwind\ti\tj\tx\tz\tr\ttheta\tv\tvx\tvy\tvz\tdvds_ave\tvol\t \ +rho\tne\tte\ttr\tnphot\tw\tave_freq\tIP\tXi\tconv\tconv_tr\tconv_te\tconv_hc\t \ +cool_tot\tlum_tot\tlum_rr\tcool_rr\tlum_ff\tlum_lines\tcool_adiabatic\tcool_comp\tcool_dr\tcool_DI \t\ +heat_tot\theat_photo\theat_auger\theat_lines\theat_ff\theat_comp\theat_ind_comp\theat_shock\t \ +ionH1\tionH2\tionHe1\tionHe2\tionHe3\tionC3\tionC4\tionC5\tionN5\tionO6\tionSi4\n"); + + + Log ("swind_sub does not work yet\n"); + ndom = 0; + np = 0; + for (n = 0; n < NDIM2; n++) + { + wind_n_to_ij (ndom, n, &ii, &jj); + + if (w[n].inwind >= 0) + { + np = w[n].nplasma; + + vtot = sqrt (w[n].v[0] * w[n].v[0] + w[n].v[1] * w[n].v[1] + w[n].v[2] * w[n].v[2]); + + xplasma = &plasmamain[np]; + + /* find the density of the main ions (or fractions if frac_choice == 1) */ + h1den = get_density_or_frac (xplasma, 1, 1, frac_choice); + h2den = get_density_or_frac (xplasma, 1, 2, frac_choice); + he1den = get_density_or_frac (xplasma, 2, 1, frac_choice); + he2den = get_density_or_frac (xplasma, 2, 2, frac_choice); + he3den = get_density_or_frac (xplasma, 2, 3, frac_choice); + c3den = get_density_or_frac (xplasma, 6, 3, frac_choice); + c4den = get_density_or_frac (xplasma, 6, 4, frac_choice); + c5den = get_density_or_frac (xplasma, 6, 5, frac_choice); + n5den = get_density_or_frac (xplasma, 7, 5, frac_choice); + o6den = get_density_or_frac (xplasma, 8, 6, frac_choice); + si4den = get_density_or_frac (xplasma, 14, 4, frac_choice); + + /* printf("%i %i %i %i %i %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e \ + %8.4e %8.4e %8.4e %i %8.4e %8.4e %8.4e %i %8.4e %8.4e %8.4e %8.4e \ + %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e \ + %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e \ + %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e\n", + n, np, w[n].inwind, ii, jj, w[n].x[0], w[n].x[2], vtot, w[n].v[0], w[n].v[1], w[n].v[2], w[n].dvds_ave, w[n].vol, + plasmamain[np].rho, plasmamain[np].ne, plasmamain[np].t_e, plasmamain[np].t_r, plasmamain[np].ntot, + plasmamain[np].w, plasmamain[np].ave_freq, plasmamain[np].ip, plasmamain[np].converge_whole, + plasmamain[np].converge_t_r, plasmamain[np].converge_t_e, plasmamain[np].converge_hc, + plasmamain[np].cool_tot_ioniz, plasmamain[np].lum_tot, plasmamain[np].cool_rr, + plasmamain[np].lum_ff, plasmamain[np].lum_lines, plasmamain[np].cool_adiabatic, + plasmamain[np].cool_comp, plasmamain[np].cool_dr, plasmamain[np].heat_tot, plasmamain[np].heat_photo, + plasmamain[np].heat_lines , plasmamain[np].heat_ff , plasmamain[np].heat_comp, plasmamain[np].heat_ind_comp, + h1den, h2den, he1den, he2den, he3den, c3den, c4den, c5den, n5den, o6den, si4den); + */ + + if (ochoice) + fprintf (fptr, "%i %i %i %i %i %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e \ + %8.4e %8.4e %8.4e %i %8.4e %8.4e %8.4e %8.4e %i %8.4e %8.4e %8.4e %8.4e \ + %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e\ + %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e \ + %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e %8.4e\n", n, np, w[n].inwind, ii, jj, w[n].x[0], w[n].x[2], w[n].rcen, w[n].thetacen / RADIAN, vtot, w[n].v[0], w[n].v[1], w[n].v[2], w[n].dvds_ave, w[n].vol, plasmamain[np].rho, plasmamain[np].ne, plasmamain[np].t_e, plasmamain[np].t_r, plasmamain[np].ntot, plasmamain[np].w, plasmamain[np].ave_freq, plasmamain[np].ip, plasmamain[np].xi, plasmamain[np].converge_whole, plasmamain[np].converge_t_r, plasmamain[np].converge_t_e, plasmamain[np].converge_hc, plasmamain[np].cool_tot_ioniz + plasmamain[np].cool_comp_ioniz + plasmamain[np].cool_adiabatic_ioniz + plasmamain[np].cool_dr_ioniz, plasmamain[np].lum_tot_ioniz, plasmamain[np].lum_rr_ioniz, plasmamain[np].cool_rr_ioniz, plasmamain[np].lum_ff_ioniz, plasmamain[np].lum_lines_ioniz, plasmamain[np].cool_adiabatic_ioniz, plasmamain[np].cool_comp_ioniz, plasmamain[np].cool_dr_ioniz, plasmamain[np].cool_di_ioniz, plasmamain[np].heat_tot, plasmamain[np].heat_photo, plasmamain[np].heat_auger, plasmamain[np].heat_lines, plasmamain[np].heat_ff, plasmamain[np].heat_comp, plasmamain[np].heat_ind_comp, plasmamain[np].heat_shock, h1den, h2den, he1den, he2den, he3den, c3den, c4den, c5den, n5den, o6den, si4den); + } + else + { + /* if we aren't inwind then print out a load of zeroes */ + + /* printf("%i %i %i %i %i %8.4e %8.4e 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \ + 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \ + 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \ + 0.0 0.0 0.0 0.0 0.0 0.0 \ + 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", + n, np, w[n].inwind, ii, jj, w[n].x[0], w[n].x[2]); + */ + + if (ochoice) + fprintf (fptr, "%i %i %i %i %i %8.4e %8.4e 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \ + 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \ + 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \ + 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \ + 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", n, np, w[n].inwind, ii, jj, w[n].x[0], w[n].x[2]); + } + } + + + if (ochoice) + { + fclose (fptr); + printf ("\nSaved summary of physical quantites in %s, use py_read_output.py to read\n", filename); + } + + return (0); + + +} + +/************************************************************************** + + + Synopsis: + complete_ion_summary outputs a file with all of the ion fractions for a given cell + + + History: + 1602 NSH coded + +************************************************************************/ + + + +int +complete_ion_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; + +{ + char cell[5]; + PlasmaPtr xplasma; + FILE *fptr = NULL, *fopen (); + char filename[LINELENGTH]; + + + int n, mm; + n = 50; +a:printf ("There are %i wind elements in this model\n", NDIM2); + rdint ("Wind.array.element", &n); + + if (n < 0) + goto b; + else if (n > NDIM2) + { + printf ("No, there are %i wind elements, not %i\n", NDIM2, n); + goto a; + } + printf ("OK, using cell %i\n", n); + xplasma = &plasmamain[w[n].nplasma]; + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, "_cell_"); + sprintf (cell, "%04d", n); + strcat (filename, cell); + printf ("opening file %s\n", filename); + + strcat (filename, ".ions"); + printf ("opening file %s\n", filename); + fptr = fopen (filename, "w"); + } + + printf ("ion z n(ion) n(ion)/n(H)\n"); + + + if (ochoice) + fprintf (fptr, "ion z n(ion) n(ion)/n(H)\n"); + + + for (mm = 0; mm < nions; mm++) + { + + printf ("%i %i %e %e\n", mm, ion[mm].z, xplasma->density[mm], xplasma->density[mm] / (xplasma->rho * rho2nh)); + if (ochoice) + { + fprintf (fptr, "%i %i %e %e\n", mm, ion[mm].z, xplasma->density[mm], xplasma->density[mm] / (xplasma->rho * rho2nh)); + } + + } + goto a; + +b:return (0); + +} + + + + + +/************************************************************************** + + + Synopsis: + get_density_or_frac works out the density of an ion element, istate, + in a plasma cell xplasma. + + if frac_choice is 1, return an ion fraction + + History: + 1411 JM coded + +************************************************************************/ + + +double +get_density_or_frac (xplasma, element, istate, frac_choice) + PlasmaPtr xplasma; + int element; + int istate; + int frac_choice; +{ + int nion, nelem; + double nh, density; + + /* find the ion and element in the list */ + nion = find_ion (element, istate); + + nelem = find_element (element); + + /* get density of ion */ + density = xplasma->density[nion]; + + /* we want an ion fraction, not a density, so divide by nh */ + if (frac_choice) + { + nh = xplasma->density[0] + xplasma->density[1]; + density /= ele[nelem].abun * nh; + } + + return (density); +} + + +/************************************************************************** + + + Synopsis: + find_ion is a little routine which finds which number ion in the list corresponds + to element with istate. e.g. for CIV, element = 6 and istate = 4. + + History: + 1411 JM coded + +************************************************************************/ + + +int +find_ion (element, istate) + int element; + int istate; +{ + int nion; + + nion = 0; + + while (nion < nions && !(ion[nion].z == element && ion[nion].istate == istate)) + nion++; + + return nion; +} + +/************************************************************************** + + + Synopsis: + find_ion is a little routine which finds which number element in the list corresponds + to element with z == element. e.g. for CIV, element = 6. + + History: + 1411 JM coded + +************************************************************************/ + + +int +find_element (element) + int element; +{ + int n; + + n = 0; + + while (n < nelements && ele[n].z != element) + n++; + + return n; +} + + + +/************************************************************************** + + + Synopsis: + get_los_dvds finds the gradient along the LoS + of the velocity projected along the same line of sight. + + History: + 1501 JM coded + +************************************************************************/ + +int +get_los_dvds (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + struct photon p; + struct photon ptest; + int n; + double ds, dvds, v1[3], v2[3], xtest[3]; + double lmn[3], diff[3], phase; + int vchoice, sight_choice; + double obs_angle, rzero, r; + char filename[LINELENGTH], suffix[LINELENGTH]; + + int ndom; + + ndom = w->ndom; + + vchoice = 0; + phase = 0; + obs_angle = 80.0; + sight_choice = 0; + + rdint ("use component along LoS (0), or magnitude (1):", &sight_choice); + rdint ("real (0) poloidal (1) or rotational (2) or back(-1):", &vchoice); + + while (vchoice >= 0) + { + rddoub ("angle in deg:", &obs_angle); + rddoub ("phase (0 to 1):", &phase); + + lmn[0] = sin (obs_angle / RADIAN) * cos (-phase * 360. / RADIAN); + lmn[1] = sin (obs_angle / RADIAN) * sin (-phase * 360. / RADIAN); + lmn[2] = cos (obs_angle / RADIAN); + + // if (vchoice == 0) + // strcpy (vstring, ""); + // else if (vchoice == 1) + // strcpy (vstring, "rot"); + // else if (vchoice == 2) + // strcpy (vstring, "pol"); + + + for (n = 0; n < NDIM2; n++) + { + aaa[n] = 0; + if (w[n].inwind >= 0) + { + + stuff_v (w[n].xcen, p.x); + stuff_v (lmn, p.lmn); + stuff_phot (&p, &ptest); + + vsub (p.x, w[n].x, diff); + ds = 0.001 * length (diff); + move_phot (&ptest, ds); + + if (vchoice == 0) + dvds = dvwind_ds_cmf (&p); + + /* next choice is for turning off rotational velocity */ + else if (vchoice == 1) + { + model_velocity (ndom, p.x, v1); + model_velocity (ndom, ptest.x, v2); + v1[1] = 0.0; + v2[1] = 0.0; + + /* calculate the relevant gradient */ + if (sight_choice == 0) + dvds = fabs (dot (v1, p.lmn) - dot (v2, p.lmn)) / ds; + else + dvds = fabs (length (v1) - length (v2)) / ds; + } + + /* next choice is for turning rotational velocity only */ + else + { + r = sqrt (p.x[0] * p.x[0] + p.x[1] * p.x[1]); + rzero = sv_find_wind_rzero (ndom, p.x); + v1[0] = v1[2] = 0.0; + v1[1] = sqrt (GRAV * geo.mstar * rzero) / r; + + + r = sqrt (ptest.x[0] * ptest.x[0] + ptest.x[1] * ptest.x[1]); + rzero = sv_find_wind_rzero (ndom, ptest.x); + v2[0] = v2[2] = 0.0; + v2[1] = sqrt (GRAV * geo.mstar * rzero) / r; + + if (p.x[1] != 0.0) + { + project_from_cyl_xyz (p.x, v1, xtest); + stuff_v (xtest, v1); + } + if (ptest.x[1] != 0.0) + { + project_from_cyl_xyz (ptest.x, v2, xtest); + stuff_v (xtest, v2); + } + + /* calculate the relevant gradient */ + if (sight_choice == 0) + dvds = fabs (dot (v1, p.lmn) - dot (v2, p.lmn)) / ds; + else + dvds = fabs (length (v1) - length (v2)) / ds; + } + + aaa[n] = dvds; + + } + } + + printf ("vchoice %i y coord %8.4e direction cosines %.2f %.2f %.2f\n", vchoice, p.x[1], p.lmn[0], p.lmn[1], p.lmn[2]); + display ("dvds along a LoS"); + + if (ochoice) + { + strcpy (filename, rootname); + sprintf (suffix, ".dv%i_ds_A%.1f_P%.2f", vchoice, obs_angle, phase); + strcat (filename, suffix); + write_array (filename, ochoice); + } + + rdint ("real (0) poloidal (1) or rotational (2) or back(-1):", &vchoice); + + } + + return (0); +} + + +/**********************************************************/ +/** + * @brief Prints grid boundaries to file + * + * @param [in] w Pointer to wind array + * @param [in] rootname Root name of simulation + * @param [in] ochoice Whether or not to write out + * @return 0 + * + * Outputs the boundaries for the grids for each domain + * + * ###Notes### + * 6/15 - Written by SWM +***********************************************************/ +int +grid_summary (WindPtr w, char rootname[], int ochoice) +{ + char filename[LINELENGTH], suffix[LINELENGTH]; + FILE *fopen (), *fptr; + int i, j; + + printf ("Outputting grid boundaries to file.\n"); + + for (j = 0; j < geo.ndomain; j++) + { + strcpy (filename, rootname); + sprintf (suffix, ".dom%d.grid_x.txt", j); + strcat (filename, suffix); + fptr = fopen (filename, "w"); + for (i = 0; i <= zdom[j].ndim; i++) + { + fprintf (fptr, "%10.5g\n", zdom[j].wind_x[i]); + } + fclose (fptr); + + strcpy (filename, rootname); + sprintf (suffix, ".dom%d.grid_z.txt", j); + strcat (filename, suffix); + fptr = fopen (filename, "w"); + for (i = 0; i <= zdom[j].mdim; i++) + { + fprintf (fptr, "%10.5g\n", zdom[j].wind_z[i]); + } + fclose (fptr); + + } + return (0); +} + + + + + + +int +flux_summary (w, rootname, ochoice) + WindPtr w; + char rootname[]; + int ochoice; +{ + int n, np; + char filename[LINELENGTH]; + int ii, jj; + FILE *fptr, *fopen (); +//OLD PlasmaPtr xplasma; + int ndom, m; + + np = 0; + + + if (ochoice) + { + strcpy (filename, rootname); + strcat (filename, ".flux_summary"); + fptr = fopen (filename, "w"); + } + else + printf ("This mode is recommended purely for file output\n"); + + + /* JM 1411 -- First we have to write out some headers so that + astropy can read the output */ + + + + + if (ochoice) + { + fprintf (fptr, "n\tnplasma\tinwind\ti\tj\tx\tz\tr\ttheta "); + + for (m = 0; m < geo.nxfreq; m++) + { + fprintf (fptr, "\tF_w%i\tF_p%i\tF_z%i ", m, m, m); + + } + fprintf (fptr, "\n"); + } + + Log ("swind_sub does not work yet\n"); + ndom = 0; + for (n = 0; n < NDIM2; n++) + { + wind_n_to_ij (ndom, n, &ii, &jj); + + if (w[n].inwind >= 0) + { + np = w[n].nplasma; +//OLD xplasma = &plasmamain[np]; + if (ochoice) + { + fprintf (fptr, "%i %i %i %i %i %8.4e %8.4e %8.4e %8.4e ", n, np, w[n].inwind, ii, jj, w[n].x[0], w[n].x[2], w[n].rcen, + w[n].thetacen / RADIAN); + fprintf (fptr, "%8.4e %8.4e %8.4e ", plasmamain[np].F_vis[0], plasmamain[np].F_vis[1], plasmamain[np].F_vis[2]); + fprintf (fptr, "%8.4e %8.4e %8.4e ", plasmamain[np].F_UV[0], plasmamain[np].F_UV[1], plasmamain[np].F_UV[2]); + fprintf (fptr, "%8.4e %8.4e %8.4e ", plasmamain[np].F_Xray[0], plasmamain[np].F_Xray[1], plasmamain[np].F_Xray[2]); + + + fprintf (fptr, "\n"); + } + + } + else + { + /* if we aren't inwind then print out a load of zeroes */ + + /* printf("%i %i %i %i %i %8.4e %8.4e 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \ + 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \ + 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \ + 0.0 0.0 0.0 0.0 0.0 0.0 \ + 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", + n, np, w[n].inwind, ii, jj, w[n].x[0], w[n].x[2]); + */ + + if (ochoice) + { + fprintf (fptr, "%i %i %i %i %i %8.4e %8.4e 0.0 0.0 ", n, np, -2, ii, jj, w[n].x[0], w[n].x[2]); + for (m = 0; m < geo.nxfreq; m++) + { + fprintf (fptr, "0.0 0.0 0.0 "); + + } + fprintf (fptr, "\n"); + } + } + } + + + if (ochoice) + { + fclose (fptr); + printf ("\nSaved flux details\n"); + } + + return (0); + + +} diff --git a/source/swind_write.c b/source/swind_write.c new file mode 100644 index 000000000..a8590de66 --- /dev/null +++ b/source/swind_write.c @@ -0,0 +1,297 @@ + +/***********************************************************/ +/** @file swind_write.c + * @author ksl + * @date May, 2018 + * + * @brief + * These are subroutines used by swind to write ascii + * files of various variables for use with smongo or normal display + * software. + * + ***********************************************************/ + +#include +#include +#include +#include +#include + +#include "atomic.h" +#include "sirocco.h" +#include "templates.h" + +#define ODIM 256 +int init_write_array = 1; +float aout[ODIM][ODIM]; + + +/**********************************************************/ +/** + * @brief writes out the arrays in a way which can be read easily + * into a routine which can do contouring + * + * @param [in] char filename[] The name of the output file + * @param [in] int choice 0-> don't write array, 1 -> write without intepolation, 2-> write with interpolation + * @return Always returns 0 + * + * @details + * This is a general purpose routine used by swind to write + * ascii data files. If a file is written, it can be written + * at the positions of of the windsave grid, or interpolated + * onto a linear coordinate grid. The latter was intended + * to make it easier to make an "image" of the output file. + * + * ### Notes ### + * The output files are written in a format that should be + * readable with the astropy ascii tables routines. + * + **********************************************************/ + +int +write_array (filename, choice) + char filename[]; + int choice; +{ + //Dynamical allocation is allowed, although I generally avoid it -- 05apr ksl + float r, z; + float rmin, rmax, zmin, zmax; + int ii, jj; + FILE *fopen (), *fptr; + char outfile[LINELENGTH]; + char extra[LINELENGTH]; + + double length (); + double xx[3]; + int i; + int nn, nnn[4], nelem; + double frac[4]; + int ndom, ndim, mdim, nstart, ndomain; + ndom = current_domain; + ndim = zdom[ndom].ndim; + mdim = zdom[ndom].mdim; + nstart = zdom[ndom].nstart; +//OLD float rin[ndim], zin[mdim]; + + + + if (choice == 0) // Then we don't really want output files + { + return (0); + } + +// Open the appropriate file + + strcpy (outfile, filename); + if (geo.ndomain > 1) + { + sprintf (extra, ".%d", current_domain); + strcat (outfile, extra); + } + strcat (outfile, ".dat"); // Add standard extension to filenames, i.e. the one used by tecplot + fptr = fopen (outfile, "w"); + + + /* Write out the header information for the file */ + fprintf (fptr, "# TITLE= \"%s\"\n", outfile); + if (zdom[ndom].coord_type == SPHERICAL) + { + fprintf (fptr, "# Coord_Sys SPHERICAL\n"); + } + else if (zdom[ndom].coord_type == CYLIND) + { + fprintf (fptr, "# Coord_Sys CYLIND\n"); + } + else if (zdom[ndom].coord_type == RTHETA) + { + fprintf (fptr, "# Coord_Sys RTHETA\n"); + } + else if (zdom[ndom].coord_type == CYLVAR) + { + fprintf (fptr, "# Coord_Sys CYLVAR\n"); + } + else + { + Error ("write_array: Unknown coordinaate system type: %d\n", zdom[ndom].coord_type); + } + + +// Put the r and z coord. grid into easier to understand arrays +//OLD for (i = 0; i < ndim; i++) +//OLD { +//OLD zin[i] = wmain[nstart + i].x[2]; +//OLD rin[i] = wmain[nstart + i * mdim].x[0]; +//OLD } + + +/* In swind the filenames are set to start with z if the output coordinates +are linear, and x otherwise. This is not particularly transparent ?? ksl */ + + if (choice == 1) + { // print out the original array elements + + if (zdom[ndom].coord_type == SPHERICAL) + { + + /* JM 1411 -- added header for reading with astropy ascii module */ + fprintf (fptr, "r var inwind i\n"); + + for (i = 0; i < ndim; i++) + { + fprintf (fptr, "%8.2e %8.2e %3d %3d \n", wmain[nstart + i].r, aaa[nstart + i], wmain[nstart + i].inwind, i); + } + } + else + { + + /* JM 1411 -- added header for reading with astropy ascii module */ + fprintf (fptr, "x z var inwind i j\n"); + + for (i = 0; i < ndim * mdim; i++) + { + wind_n_to_ij (ndom, nstart + i, &ii, &jj); + fprintf (fptr, "%8.4e %8.4e %8.5e %3d %3d %3d\n", + wmain[nstart + i].xcen[0], wmain[nstart + i].xcen[2], aaa[nstart + i], wmain[nstart + i].inwind, ii, jj); + + } + } + + } + else if (choice == 2) + { // Then regrid to a linear grid +/* Wind array is organized so that it increments up along z and then over */ + +/* Want linearly spaced output array */ + rmin = zmin = 0; + rmax = zmax = geo.rmax; + + xx[1] = 0.0; + for (ii = 0; ii < ODIM; ii++) + { + xx[0] = r = rmin + ii * (rmax - rmin) / (ODIM - 1); + for (jj = 0; jj < ODIM; jj++) + { + xx[2] = z = zmin + jj * (zmax - zmin) / (ODIM - 1); + if (where_in_wind (xx, &ndomain) == W_ALL_INWIND) + { // Then the position is in the wind region + coord_fraction (ndom, 0, xx, nnn, frac, &nelem); + for (nn = 0; nn < nelem; nn++) + { + aout[ii][jj] += aaa[nnn[nn]] * frac[nn]; + } + + } + } + } + + /* Now print out the array */ + // fprintf (fptr, "VARIABLES= \"X\" \"Z\" \"Var1\" \n"); + // fprintf (fptr, "ZONE I=%d J=%d DATAPACKING=POINT\n", ODIM, ODIM); + fprintf (fptr, "# Resampled outputs\n"); + for (jj = 0; jj < ODIM; jj++) + { + /* JM 1411 -- added header for reading with astropy ascii module */ + fprintf (fptr, "r z var\n"); + + z = zmin + (zmax - zmin) * jj / (ODIM - 1); + for (ii = 0; ii < ODIM; ii++) + { + r = rmin + (rmax - rmin) * ii / (ODIM - 1); + fprintf (fptr, "%8.2e %8.2e %8.2e\n", r, z, aout[ii][jj]); + } + } + /*Finished printing out the array */ + } + else + { + Error ("write_array: Unknown choice %d\n", choice); + } + + fclose (fptr); + return (0); +} + + + + + +/**********************************************************/ +/** + * @brief Print results to the screen + * + * @param [in] char name[] The name associated with the array to be printed + * @return Always returns 0 + * + * @details + * This is a generalized display routine that outputs the results of a query + * to the screen for inspection by the user. + * + * ### Notes ### + * For cylindrical coordinates, one goes up in z, and over in x. To cylindrical coordinates + * we want to display so that each colum represents a constant line on the axis + * w[mdim][ndim]. So for a system with mdim=20 and ndim=25. There are 25 elements + * in the z axis and 20 elements in the xaxis + * + * Therefore a row, constant z, is displayed by by incrementing by 20 or mdim + * + * For rtheta coordinates, one goes around in theta, up in r. The fasted moving + * coordinate is r (when thinking of 1-d versions of the array. Unless we + * are going to write a separate routine, the simplest thing to do + * is to make each column represent a constant r + * + * w[mdim][ndim] So, in spherical polar coorcinates, a system with mdim 20 has + * 20 angles, and 25 radii. + * + * So for spherical polar, constant r is displayed by incrementing by 1. As a result + * it is unclear that one can easily use the same routine for the two situations + * since you seem to be incrementing the opposite axes, since in the one case one + * wants MDIM rows and other case one wants NDIM rows. + * + * It would be possible if you plot theta lines in each row, but what this means + * is that the first row is closest to the z axis + * + **********************************************************/ + +int +display (name) + char name[]; +{ + int i, j, n; +//OLD int ndom, ndim, mdim, nstart; + int ndom, mdim, nstart; + + ndom = current_domain; +//OLD ndim = zdom[ndom].ndim; + mdim = zdom[ndom].mdim; + nstart = zdom[ndom].nstart; + + Log ("Check me now %d %d\n", swind_min, swind_max); + + + Log ("\n %s \n", name); + Log ("z/theta \\x/r"); + for (i = swind_min; i < swind_max; i += swind_delta) + if (zdom[ndom].coord_type == 1 || swind_project == 1) + Log ("%8.2e ", wmain[nstart + i * mdim].x[0]); + else + Log ("%8.2e ", wmain[nstart + i * mdim].rcen); + + Log ("\n"); + + for (j = 0; j < mdim; j++) + { + if (zdom[ndom].coord_type == 1 || swind_project == 1) + Log ("%8.2e ", wmain[nstart + j].x[2]); + else + Log ("%8.2e ", wmain[nstart + j].thetacen); + + for (i = swind_min; i < swind_max; i += swind_delta) + { + n = nstart + i * mdim + j; + Log ("%8.2g ", aaa[n]); + } + Log ("\n"); + } + + return (0); +} From f39d611a0501df27270bef37376a36acf9d0035a Mon Sep 17 00:00:00 2001 From: jhmatthews Date: Fri, 18 Oct 2024 10:40:43 +0100 Subject: [PATCH 11/24] fixed workflow file --- .github/workflows/build.yml | 42 ++++++++++++++++++------------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 0415e19b9..6b784dedf 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -19,7 +19,7 @@ jobs: - name: "Print out environment variables" run: | echo "WORKSPACE IS $GITHUB_WORKSPACE" - echo "PYTHON ENV VARIABLE IS: $PYTHON" + echo "SIROCCO ENV VARIABLE IS: $SIROCCO" build_and_test: name: Build Python and run tests @@ -38,40 +38,40 @@ jobs: - uses: actions/setup-python@v3 with: python-version: '3.x' # Version range - - name: Install Python (the language) dependencies + - name: Install python dependencies run: | python -m pip install --upgrade pip pip install wheel - cd $PYTHON/examples/gh-workflow/ + cd $SIROCCO/examples/gh-workflow/ if [ -f requirements.txt ]; then pip install -r requirements.txt; fi # - name: Compile programs, again # run: | -# cd $PYTHON/source +# cd $SIROCCO/source # make CC=gcc INDENT=no all - - name: Checking Python compiled and can read in inputs correctly + - name: Checking Sirocco compiled and can read in inputs correctly run: | - cd $PYTHON/examples/gh-workflow/ - $PYTHON/bin/Setup_Py_Dir - $PYTHON/bin/py -i cv_macro_benchmark - $PYTHON/bin/py -i cv_standard - $PYTHON/bin/py -i fiducial_agn - $PYTHON/bin/py -i 1d_sn - $PYTHON/bin/py -i -d sv_detailedmode - $PYTHON/bin/py -i agn_ss_2010_modela - $PYTHON/bin/py -i XRB_standard.pf - $PYTHON/bin/py -i ngc5548 - $PYTHON/bin/py -i lamp_post - $PYTHON/bin/py -i cv_standard_import + cd $SIROCCO/examples/gh-workflow/ + $SIROCCO/bin/Setup_Py_Dir + $SIROCCO/bin/sirocco -i cv_macro_benchmark + $SIROCCO/bin/sirocco -i cv_standard + $SIROCCO/bin/sirocco -i fiducial_agn + $SIROCCO/bin/sirocco -i 1d_sn + $SIROCCO/bin/sirocco -i -d sv_detailedmode + $SIROCCO/bin/sirocco -i agn_ss_2010_modela + $SIROCCO/bin/sirocco -i XRB_standard.pf + $SIROCCO/bin/sirocco -i ngc5548 + $SIROCCO/bin/sirocco -i lamp_post + $SIROCCO/bin/sirocco -i cv_standard_import - name: Running unit tests run: | - cd $PYTHON/source/tests + cd $SIROCCO/source/tests make check CC=gcc - name: Running Balmer test case run: | - cd $PYTHON/examples/gh-workflow/ - $PYTHON/bin/py -f -v 1 balmer_test - python $PYTHON/py_progs/balmer_decrement.py balmer_test + cd $SIROCCO/examples/gh-workflow/ + $SIROCCO/bin/sirocco -f -v 1 balmer_test + python $SIROCCO/py_progs/balmer_decrement.py balmer_test From b810c2188b670864dc26e36511edb6569bb833fb Mon Sep 17 00:00:00 2001 From: jhmatthews Date: Fri, 18 Oct 2024 10:51:37 +0100 Subject: [PATCH 12/24] fix workflow env --- .github/workflows/build.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 6b784dedf..0ceda11fd 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -1,4 +1,4 @@ -name: "Python: continuous integration tests" +name: "Sirocco: continuous integration tests" on: push: branches: @@ -9,7 +9,7 @@ on: - "dev" - "main" env: - PYTHON: ${{ github.workspace }} + SIROCCO: ${{ github.workspace }} jobs: greeting: name: Greeting job From 3f1c0138bac113d2743f3b2d70cb4a07f085a8d4 Mon Sep 17 00:00:00 2001 From: jhmatthews Date: Fri, 18 Oct 2024 13:46:40 +0100 Subject: [PATCH 13/24] update checkout in workflow --- .github/workflows/build.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 0ceda11fd..d8ca6f9b1 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -15,7 +15,7 @@ jobs: name: Greeting job runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: "Print out environment variables" run: | echo "WORKSPACE IS $GITHUB_WORKSPACE" From d33ede8c256bb486bdbbd089c82c0e0b4b28ed11 Mon Sep 17 00:00:00 2001 From: jhmatthews Date: Fri, 18 Oct 2024 14:06:30 +0100 Subject: [PATCH 14/24] modified README to point to updated URLs --- README.md | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 7ed2e8c34..a61a8e956 100644 --- a/README.md +++ b/README.md @@ -2,13 +2,14 @@ *Sirocco* (Simulating Ionization and Radiation in Outflows Created by Compact Objects) is a Monte Carlo radiative transfer code which uses the Sobolev approximation. The code was formerly known as Python and renamed in October 2024. It has been developed by Knox Long, Christian Knigge, Stuart Sim, Nick Higginbottom, James Matthews, Sam Manghamm Edward Parkinson, Mandy Hewitt and Nico Scepi. The code has been used for a variety of research projects invovling the winds of cataclysmic variables, young stellar objects, X-ray binaries and AGN. -The code is under active development, but we are looking for beta users to test the code, and potentially use it for their own research. If you are interested in using Sirocco please contact Knox Long via long[at]stsci[dot]edu. +The code is under active development, but we are looking for beta users to test the code, and potentially use it for their own research. If you are interested in using Sirocco please contact Knox Long via long[at]stsci[dot]edu +or James Matthews via james[dot]matthews[at]physics[dot]ox[dot]ac[dot]uk. -Documentation is hosted on [ReadTheDocs](http://agnwinds.readthedocs.io/en/dev/). +Documentation is hosted on [ReadTheDocs](http://sirocco-rt.readthedocs.io/en/dev/). ## CI \& Docs Build Status -[![C/C++ CI](https://github.com/agnwinds/python/actions/workflows/build.yml/badge.svg)](https://github.com/agnwinds/python/actions/workflows/build.yml) +[![C/C++ CI](https://github.com/sirocco-rt/sirocco/actions/workflows/build.yml/badge.svg)](https://github.com/sirocco-rt/sirocco/actions/workflows/build.yml) [![Documentation Status](https://readthedocs.org/projects/agnwinds/badge/?version=latest)](https://agnwinds.readthedocs.io/en/latest/?badge=latest) @@ -16,7 +17,7 @@ Documentation is hosted on [ReadTheDocs](http://agnwinds.readthedocs.io/en/dev/) Sirocco and the various routines associated are set up in a self-contained directory structure. The basic directory structure and the data files that one needs to run Sirocco need to be retrieved and compiled. -If you want to obtain a stable (!) release, go to the [Releases](https://github.com/agnwinds/python/releases) page. +If you want to obtain a stable (!) release, go to the [Releases](https://github.com/sirocco-rt/sirocco/releases) page. If you want to download the latest dev version, you can zip up the git repository by clicking on the zip icon to the right of the GitHub page. Aternatively, you can clone the repository using @@ -41,7 +42,7 @@ Note that the export syntax is for bash- for csh use Atomic data for the current version of Sirocco stored in the directory xdata which is part of the main repository, However, if one wishes to use model stellar spectra to simulate the spectra of the star or disk, one my wish to -also to download various model grids of spectra that have been used in conjunction with Sirocco/Python over the years. These are in a separate [models repository]((https://github.com/agnwinds/xmod). +also to download various model grids of spectra that have been used in conjunction with Sirocco/Python over the years. These are in a separate [models repository]((https://github.com/sirocco-rt/xmod). These can be downloaded as follows: @@ -49,7 +50,7 @@ These can be downloaded as follows: (Previously, both the atomic data and the model grids were stored in a separate repository. Users wishing to run older versions of the code pre-84b may need to download the -[old data repository](https://github.com/agnwinds/data) This repository can be downloaded as follows +[old data repository](https://github.com/sirocco-rt/data) This repository can be downloaded as follows $ cd $SIROCCO; git clone https://github.com/agnwinds/data data @@ -71,6 +72,6 @@ and run the code by typing, e.g. $ sirocco root.pf -Please see the [ReadTheDocs](http://agnwinds.readthedocs.io/en/dev/) or the docs folder for how to use the code. You will need sphinx installed to build the documentation yourself. +Please see the [ReadTheDocs](http://sirocco-rt.readthedocs.io/en/dev/) or the docs folder for how to use the code. You will need sphinx installed to build the documentation yourself. Any comments, email one of the addresses above. From 9689e51df431a307e42e8e6c94bae29c7c67b117 Mon Sep 17 00:00:00 2001 From: jhmatthews Date: Fri, 18 Oct 2024 14:15:31 +0100 Subject: [PATCH 15/24] updated Makefile.in --- Makefile.in | 62 ++++++++++++++++++++++++++--------------------------- 1 file changed, 31 insertions(+), 31 deletions(-) diff --git a/Makefile.in b/Makefile.in index 3ec9a7433..21996bae4 100644 --- a/Makefile.in +++ b/Makefile.in @@ -1,26 +1,26 @@ # -# This is the Makefile for the python related programs +# This is the Makefile for the Sirocco related programs # -# Make sure you have defined $PYTHON as this directory first +# Make sure you have defined $SIROCCO as this directory first # then type: # # ./configure [--with-cuda] # make install # -# If this fails, consult https://github.com/agnwinds/python/wiki/Installing-Python +# If this fails, consult documentation # for manual install. CMAKE = mpicc -GSL_LOCATION = $(PYTHON)/software/gsl-2.6 -CUNIT_LOCATION = $(PYTHON)/software/cunit-3.2.7 +GSL_LOCATION = $(SIROCCO)/software/gsl-2.6 +CUNIT_LOCATION = $(SIROCCO)/software/cunit-3.2.7 INSTALL_LIBS = True ifeq (True, $(INSTALL_LIBS)) # GNU Science library -- math routines MOVE_GSL = \ - mkdir $(PYTHON)/include/gsl; \ - mv $(GSL_LOCATION)/include/gsl/* $(PYTHON)/include/gsl; \ - mv $(GSL_LOCATION)/lib/lib* $(PYTHON)/lib; + mkdir $(SIROCCO)/include/gsl; \ + mv $(GSL_LOCATION)/include/gsl/* $(SIROCCO)/include/gsl; \ + mv $(GSL_LOCATION)/lib/lib* $(SIROCCO)/lib; INSTALL_GSL = \ cd $(GSL_LOCATION); \ ./configure --disable-shared --prefix=$(GSL_LOCATION) cc=gcc CPP=cpp; \ @@ -40,19 +40,19 @@ ifeq (True, $(INSTALL_LIBS)) # CUnit -- unit test framework: built using CMake, so only build if installed ifneq ($(shell which cmake), ) INSTALL_CUNIT = \ - mkdir -p $(PYTHON)/include/CUnit; \ + mkdir -p $(SIROCCO)/include/CUnit; \ cd $(CUNIT_LOCATION); \ mkdir -p build; \ cd build; \ cmake ..; \ cmake --build .; \ - mv CUnit/libcunit.a $(PYTHON)/lib; \ - cp ../CUnit/CUnit/*.h $(PYTHON)/include/CUnit/; \ + mv CUnit/libcunit.a $(SIROCCO)/lib; \ + cp ../CUnit/CUnit/*.h $(SIROCCO)/include/CUnit/; \ cd ..; \ rm -rf build; MAKE_CHECK = \ - cd $(PYTHON)/source; \ + cd $(SIROCCO)/source; \ make check CC=$(CMAKE) else INSTALL_CUNIT = echo 'Unable to install CUnit as CMake (https://cmake.org) is not installed' @@ -65,14 +65,14 @@ else MAKE_CHECK = endif -MAKE_PYTHON = \ - cd $(PYTHON)/source; \ +MAKE_SIROCCO = \ + cd $(SIROCCO)/source; \ make CC=$(CMAKE) INDENT=no all \ -# Compile and install the libraries and Python +# Compile and install the libraries and Sirocco install: - @echo 'Installing Python. the radiative transfer code' - @echo 'Installing in directory '$(PYTHON) + @echo 'Installing Sirocco. the radiative transfer code' + @echo 'Installing in directory '$(SIROCCO) # Then make GSL library @echo 'Installing GSL library...' @@ -84,13 +84,13 @@ install: # Finally, make the latest release @echo 'Making source code...' - $(MAKE_PYTHON) + $(MAKE_SIROCCO) @echo 'all done' -# Install Python for travis -- as above, minus post-installation checks for GSL +# Install Sirocco for travis -- as above, minus post-installation checks for GSL gh_workflow_install: - @echo 'Installing Python. the radiative transfer code' - @echo 'Installing in directory '$(PYTHON) + @echo 'Installing Sirocco. the radiative transfer code' + @echo 'Installing in directory '$(SIROCCO) # Then make GSL library @echo 'Installing GSL library...' @@ -102,12 +102,12 @@ gh_workflow_install: #finally, make the latest release @echo 'Making source code...' - $(MAKE_PYTHON) + $(MAKE_SIROCCO) @echo 'all done' -# Runs Python's unit tests +# Runs Sirocco's unit tests check: - cd $(PYTHON)/source; make check + cd $(SIROCCO)/source; make check # Runs through the install for GSL and post-installation checks gsl: @@ -119,24 +119,24 @@ cunit: @echo 'Installing CUnit unit test framework' $(INSTALL_CUNIT) -# Run the regular clean for the libraries and Python. CUnit doesn't need this, +# Run the regular clean for the libraries and Sirocco. CUnit doesn't need this, # as we delete the CMake build directory after installing it clean: rm -f *.o *~ cd $(GSL_LOCATION); make clean; rm -rf $(CUNIT_LOCATION)/build - cd $(PYTHON)/source/; make clean + cd $(SIROCCO)/source/; make clean -# Runs a more rigorous clean for the libraries and Python. CUnit doesn't need +# Runs a more rigorous clean for the libraries and Sirocco. CUnit doesn't need # this, as we delete the CMake build directory after installing it distclean: rm -f *.o *~ cd $(GSL_LOCATION); make distclean rm -rf $(CUNIT_LOCATION)/build - cd $(PYTHON)/source/; make clean + cd $(SIROCCO)/source/; make clean # Remove just the libraries rm_lib: - rm -rf $(PYTHON)/include/gsl - rm -rf $(PYTHON)/include/CUnit - rm -f $(PYTHON)/lib/lib* + rm -rf $(SIROCCO)/include/gsl + rm -rf $(SIROCCO)/include/CUnit + rm -f $(SIROCCO)/lib/lib* From 15d3e5d4735f717e138b4d9c0f10d211c354ce19 Mon Sep 17 00:00:00 2001 From: jhmatthews Date: Fri, 18 Oct 2024 14:15:58 +0100 Subject: [PATCH 16/24] updated configure --- configure | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/configure b/configure index 47c3970fe..bc320de68 100755 --- a/configure +++ b/configure @@ -1,5 +1,5 @@ #!/bin/sh -# Shell script for configuring Python radiative transfer code compilation +# Shell script for configuring Sirocco radiative transfer code compilation # James Matthews, University of Southampton # Questions: jm8g08@soton.ac.uk # Usage: ./configure From a22578bb4f9fcffc2e2b0d1e3ded7447f9fff759 Mon Sep 17 00:00:00 2001 From: jhmatthews Date: Fri, 18 Oct 2024 14:18:24 +0100 Subject: [PATCH 17/24] updated workflow to remove Python references --- .github/workflows/build.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index d8ca6f9b1..7cc4b0c3f 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -22,7 +22,7 @@ jobs: echo "SIROCCO ENV VARIABLE IS: $SIROCCO" build_and_test: - name: Build Python and run tests + name: Build Sirocco and run tests runs-on: ubuntu-latest needs: greeting steps: @@ -30,9 +30,9 @@ jobs: #- name: "Installing build dependencies" # run: sudo apt update && apt install -y build-essential cmake - - name: Configuring Python + - name: Configuring Sirocco run: ./configure - - name: Build GH Workflow version of Python + - name: Build GH Workflow version of Sirocco run: make gh_workflow_install - uses: actions/setup-python@v3 From 88ea0b9c2f12e094628d2f1cd15f168e0e76cc7f Mon Sep 17 00:00:00 2001 From: jhmatthews Date: Fri, 18 Oct 2024 14:26:16 +0100 Subject: [PATCH 18/24] updated unit test suite for Sirocco --- source/tests/Makefile | 22 +++++++++++----------- source/tests/tests/test_compton.c | 2 +- source/tests/tests/test_define_wind.c | 2 +- source/tests/tests/test_matrix.c | 20 ++++++++++---------- source/tests/tests/test_run_mode.c | 14 +++++++------- source/tests/tests/test_translate.c | 4 ++-- source/tests/unit_test.h | 2 +- source/tests/unit_test_main.c | 2 +- source/tests/unit_test_model.c | 6 +++--- 9 files changed, 37 insertions(+), 37 deletions(-) diff --git a/source/tests/Makefile b/source/tests/Makefile index c9e44c1ec..8ae9f3464 100644 --- a/source/tests/Makefile +++ b/source/tests/Makefile @@ -1,10 +1,10 @@ C = gcc NVCC = MATOM_VER = 1 -TARGET = py_unit_test +TARGET = sirocco_unit_test -I_FLAGS = -I$(PYTHON)/include -L_FLAGS = -L$(PYTHON)/lib +I_FLAGS = -I$(SIROCCO)/include +L_FLAGS = -L$(SIROCCO)/lib C_FLAGS = -O3 -Wall -Wno-deprecated-non-prototype -DMATOM_VER=$(MATOM_VER) CU_FLAGS = -O3 @@ -14,7 +14,7 @@ LIBS = -lstdc++ -lcunit -lgsl -lgslcblas -lm # This is less than ideal, but it's far too complicated and time consuming to # figure out what tests needs which source files. So as we've done in the past, # we'll just include ALL of Python's source -PYTHON_SOURCES = \ +SIROCCO_SOURCES = \ agn.c anisowind.c atomic_extern_init.c atomicdata.c atomicdata_init.c \ atomicdata_sub.c bands.c bb.c bilinear.c brem.c cdf.c charge_exchange.c \ compton.c continuum.c cooling.c corona.c cv.c cylind_var.c cylindrical.c \ @@ -26,7 +26,7 @@ PYTHON_SOURCES = \ macro_gov.c matom.c matom_diag.c matrix_cpu.c matrix_ion.c \ models_extern_init.c para_update.c parse.c partition.c paths.c \ phot_util.c photon2d.c photon_gen.c photon_gen_matom.c pi_rates.c \ - python_extern_init.c radiation.c random.c rdpar.c recipes.c recomb.c \ + sirocco_extern_init.c radiation.c random.c rdpar.c recipes.c recomb.c \ resonate.c reverb.c roche.c rtheta.c run.c saha.c setup.c setup_disk.c \ setup_domains.c setup_files.c setup_line_transfer.c setup_reverb.c \ setup_star_bh.c shell_wind.c signal.c spectra.c spectral_estimators.c \ @@ -51,12 +51,12 @@ TEST_SOURCES = \ tests/test_translate.c # Using absolute paths -PYTHON_SOURCES := $(patsubst %,$(PYTHON)/source/%, $(PYTHON_SOURCES)) -CUDA_SOURCES := $(patsubst %,$(PYTHON)/source/%, $(CUDA_SOURCES)) -TEST_SOURCES := $(patsubst %,$(PYTHON)/source/tests/%, $(TEST_SOURCES)) +SIROCCO_SOURCES := $(patsubst %,$(SIROCCO)/source/%, $(SIROCCO_SOURCES)) +CUDA_SOURCES := $(patsubst %,$(SIROCCO)/source/%, $(CUDA_SOURCES)) +TEST_SOURCES := $(patsubst %,$(SIROCCO)/source/tests/%, $(TEST_SOURCES)) # This is all sources, to be compiled with CC -C_SOURCES := $(TEST_SOURCES) $(PYTHON_SOURCES) +C_SOURCES := $(TEST_SOURCES) $(SIROCCO_SOURCES) # Object files, again keeping C and CUDA sources separate C_OBJS := $(C_SOURCES:.c=.o) @@ -96,10 +96,10 @@ $(C_OBJS): %.o : %.c $(TARGET): $(C_OBJS) $(CUDA_OBJS) $(CC) $(C_OBJS) $(CUDA_OBJS) $(L_FLAGS) $(LIBS) -o $@ - mv $@ $(PYTHON)/bin + mv $@ $(SIROCCO)/bin check: startup $(TARGET) - $(PYTHON)/bin/$(TARGET) + $(SIROCCO)/bin/$(TARGET) clean: rm -f *.o diff --git a/source/tests/tests/test_compton.c b/source/tests/tests/test_compton.c index 7d3b4e6f0..a97b869e4 100644 --- a/source/tests/tests/test_compton.c +++ b/source/tests/tests/test_compton.c @@ -13,7 +13,7 @@ #include #include "../../atomic.h" -#include "../../python.h" +#include "../../sirocco.h" #include "../assert.h" /** ******************************************************************************************************************* diff --git a/source/tests/tests/test_define_wind.c b/source/tests/tests/test_define_wind.c index 3b0211117..911f044bf 100644 --- a/source/tests/tests/test_define_wind.c +++ b/source/tests/tests/test_define_wind.c @@ -19,7 +19,7 @@ #include #include "../../atomic.h" -#include "../../python.h" +#include "../../sirocco.h" #include "../assert.h" #include "../unit_test.h" diff --git a/source/tests/tests/test_matrix.c b/source/tests/tests/test_matrix.c index ab579bff6..ded3ce140 100644 --- a/source/tests/tests/test_matrix.c +++ b/source/tests/tests/test_matrix.c @@ -14,7 +14,7 @@ #include #include "../../atomic.h" -#include "../../python.h" +#include "../../sirocco.h" #include "../assert.h" #define BUFFER_LENGTH 512 @@ -167,8 +167,8 @@ get_solve_matrix_test_data (const char *a_path, const char *b_path, const char * static int call_invert_matrix (const char *test_name) { - const char *python_path = getenv ((const char *) "PYTHON"); - if (python_path == NULL) + const char *sirocco_path = getenv ((const char *) "PYTHON"); + if (sirocco_path == NULL) { CU_FAIL_FATAL ("$PYTHON has not been set"); } @@ -178,8 +178,8 @@ call_invert_matrix (const char *test_name) char matrix_filepath[BUFFER_LENGTH]; char inverse_filepath[BUFFER_LENGTH]; - sprintf (matrix_filepath, "%s/source/tests/test_data/matrix/%s/matrix.txt", python_path, test_name); - sprintf (inverse_filepath, "%s/source/tests/test_data/matrix/%s/inverse.txt", python_path, test_name); + sprintf (matrix_filepath, "%s/source/tests/test_data/matrix/%s/matrix.txt", sirocco_path, test_name); + sprintf (inverse_filepath, "%s/source/tests/test_data/matrix/%s/inverse.txt", sirocco_path, test_name); int matrix_size; const int get_err = get_invert_matrix_test_data (matrix_filepath, inverse_filepath, &matrix, &inverse, &matrix_size); @@ -221,8 +221,8 @@ call_invert_matrix (const char *test_name) int call_solve_matrix (const char *test_name) { - const char *python_path = getenv ((const char *) "PYTHON"); - if (python_path == NULL) + const char *sirocco_path = getenv ((const char *) "PYTHON"); + if (sirocco_path == NULL) { CU_FAIL_FATAL ("$PYTHON has not been set"); } @@ -234,9 +234,9 @@ call_solve_matrix (const char *test_name) char vector_b_filepath[BUFFER_LENGTH]; char vector_x_filepath[BUFFER_LENGTH]; - sprintf (matrix_a_filepath, "%s/source/tests/test_data/matrix/%s/A.txt", python_path, test_name); - sprintf (vector_b_filepath, "%s/source/tests/test_data/matrix/%s/b.txt", python_path, test_name); - sprintf (vector_x_filepath, "%s/source/tests/test_data/matrix/%s/x.txt", python_path, test_name); + sprintf (matrix_a_filepath, "%s/source/tests/test_data/matrix/%s/A.txt", sirocco_path, test_name); + sprintf (vector_b_filepath, "%s/source/tests/test_data/matrix/%s/b.txt", sirocco_path, test_name); + sprintf (vector_x_filepath, "%s/source/tests/test_data/matrix/%s/x.txt", sirocco_path, test_name); int vector_size; const int get_err = diff --git a/source/tests/tests/test_run_mode.c b/source/tests/tests/test_run_mode.c index 6899ba004..13151c28e 100644 --- a/source/tests/tests/test_run_mode.c +++ b/source/tests/tests/test_run_mode.c @@ -17,7 +17,7 @@ #include "../assert.h" #include "../unit_test.h" #include "../../atomic.h" -#include "../../python.h" +#include "../../sirocco.h" /** ******************************************************************************************************************* * @@ -61,12 +61,12 @@ suite_init (void) char windsave_filepath[LINELENGTH]; /* Check that the wind_save was generated in an earlier test */ - const char *python_loc = get_python_env_variable (); - if (python_loc == NULL) + const char *sirocco_loc = get_sirocco_env_variable (); + if (sirocco_loc == NULL) { return EXIT_FAILURE; } - snprintf (windsave_filepath, LINELENGTH, "%s/source/tests/test_data/define_wind/restart_cv.wind_save", python_loc); + snprintf (windsave_filepath, LINELENGTH, "%s/source/tests/test_data/define_wind/restart_cv.wind_save", sirocco_loc); if (access (windsave_filepath, F_OK) == -1) { fprintf (stderr, "Failed to open %s for test\n", windsave_filepath); @@ -90,12 +90,12 @@ suite_teardown (void) char windsave_filepath[LINELENGTH]; /* Delete the test file */ - const char *python_loc = get_python_env_variable (); - if (python_loc == NULL) + const char *sirocco_loc = get_sirocco_env_variable (); + if (sirocco_loc == NULL) { return EXIT_FAILURE; } - snprintf (windsave_filepath, LINELENGTH, "%s/source/tests/test_data/define_wind/restart_cv.wind_save", python_loc); + snprintf (windsave_filepath, LINELENGTH, "%s/source/tests/test_data/define_wind/restart_cv.wind_save", sirocco_loc); int err = remove (windsave_filepath); if (err != 0) { diff --git a/source/tests/tests/test_translate.c b/source/tests/tests/test_translate.c index e3aad4342..708041e69 100644 --- a/source/tests/tests/test_translate.c +++ b/source/tests/tests/test_translate.c @@ -15,9 +15,9 @@ #include "../assert.h" -/* we want to use the same photon and plane structures as defined in python.h */ +/* we want to use the same photon and plane structures as defined in sirocco.h */ #include "../../atomic.h" -#include "../../python.h" +#include "../../sirocco.h" double test_ds_one_photon (struct photon *ptest, int force_positive_z) diff --git a/source/tests/unit_test.h b/source/tests/unit_test.h index 1cd2f0ace..257d52711 100644 --- a/source/tests/unit_test.h +++ b/source/tests/unit_test.h @@ -13,6 +13,6 @@ int cleanup_model (const char *root_name); int setup_model_grid (const char *root_name, const char *atomic_data_location); -const char *get_python_env_variable (void); +const char *get_sirocco_env_variable (void); #endif diff --git a/source/tests/unit_test_main.c b/source/tests/unit_test_main.c index 6c71ef323..e7aff91c1 100644 --- a/source/tests/unit_test_main.c +++ b/source/tests/unit_test_main.c @@ -25,7 +25,7 @@ /* Python logging prototypes */ #include "../atomic.h" -#include "../python.h" +#include "../sirocco.h" /** ******************************************************************************************************************* * diff --git a/source/tests/unit_test_model.c b/source/tests/unit_test_model.c index 3820e0a8b..deab0e02f 100644 --- a/source/tests/unit_test_model.c +++ b/source/tests/unit_test_model.c @@ -13,7 +13,7 @@ #include #include "../atomic.h" -#include "../python.h" +#include "../sirocco.h" #define PATH_SEPARATOR '/' @@ -29,7 +29,7 @@ * ****************************************************************************************************************** */ const char * -get_python_env_variable (void) +get_sirocco_env_variable (void) { const char *env = getenv ("PYTHON"); if (env == NULL) @@ -308,7 +308,7 @@ setup_model_grid (const char *root_name, const char *atomic_data_location) return EXIT_FAILURE; } - zdom = calloc (MAX_DOM, sizeof (domain_dummy)); /* We'll allocate MAX_DOM to follow python */ + zdom = calloc (MAX_DOM, sizeof (domain_dummy)); /* We'll allocate MAX_DOM to follow sirocco */ if (zdom == NULL) { fprintf (stderr, "Unable to allocate space for domain structure\n"); From 7e4429573c82f9e3010ca52fbe2e5e4cc2aba76c Mon Sep 17 00:00:00 2001 From: jhmatthews Date: Fri, 18 Oct 2024 14:31:56 +0100 Subject: [PATCH 19/24] updated unit test suite for SIROCCO env --- source/tests/tests/test_define_wind.c | 36 +++++++++++++-------------- source/tests/tests/test_matrix.c | 8 +++--- source/tests/unit_test.h | 4 +-- source/tests/unit_test_model.c | 28 ++++++++++----------- 4 files changed, 38 insertions(+), 38 deletions(-) diff --git a/source/tests/tests/test_define_wind.c b/source/tests/tests/test_define_wind.c index 911f044bf..3ce15a65e 100644 --- a/source/tests/tests/test_define_wind.c +++ b/source/tests/tests/test_define_wind.c @@ -23,7 +23,7 @@ #include "../assert.h" #include "../unit_test.h" -char *PYTHON_ENV; +char *SIROCCO_ENV; char TEST_CWD[LINELENGTH]; char ATOMIC_DATA_TARGET[LINELENGTH]; char ATOMIC_DATA_DEST[LINELENGTH]; @@ -38,9 +38,9 @@ char ATOMIC_DATA_DEST_DEVELOPER[LINELENGTH]; * * @details * - * This uses the data from $PYTHON/source/tests/test_data/define_wind/agn_macro.pf and - * $PYTHON/source/tests/test_data/define_wind/agn_macro.grid.txt. The latter was created using the Python script in the - * $PYTHON/source/tests/test_data/define_wind directory. + * This uses the data from $SIROCCO/source/tests/test_data/define_wind/agn_macro.pf and + * $SIROCCO/source/tests/test_data/define_wind/agn_macro.grid.txt. The latter was created using the Python script in the + * $SIROCCO/source/tests/test_data/define_wind directory. * * TODO: we don't check anything to do with macro atoms (we don't output this from windsave2table) * @@ -65,7 +65,7 @@ test_sv_agn_macro_wind (void) define_wind (); - snprintf (test_data_filename, LINELENGTH, "%s/source/tests/test_data/define_wind/agn_macro.grid.txt", PYTHON_ENV); + snprintf (test_data_filename, LINELENGTH, "%s/source/tests/test_data/define_wind/agn_macro.grid.txt", SIROCCO_ENV); fp = fopen (test_data_filename, "r"); if (fp == NULL) { @@ -166,9 +166,9 @@ test_sv_agn_macro_wind (void) * * @details * - * This uses the data from $PYTHON/source/tests/test_data/define_wind/cv.pf and - * $PYTHON/source/tests/test_data/define_wind/cv.grid.txt. The latter was created using the Python script in the - * $PYTHON/source/tests/test_data/define_wind directory. + * This uses the data from $SIROCCO/source/tests/test_data/define_wind/cv.pf and + * $SIROCCO/source/tests/test_data/define_wind/cv.grid.txt. The latter was created using the Python script in the + * $SIROCCO/source/tests/test_data/define_wind directory. * * ****************************************************************************************************************** */ @@ -195,7 +195,7 @@ test_sv_cv_wind (void) define_wind (); /* And now we can compare our created grid to the "ground truth" grid */ - snprintf (test_data_filename, LINELENGTH, "%s/source/tests/test_data/define_wind/cv.grid.txt", PYTHON_ENV); + snprintf (test_data_filename, LINELENGTH, "%s/source/tests/test_data/define_wind/cv.grid.txt", SIROCCO_ENV); fp = fopen (test_data_filename, "r"); if (fp == NULL) { @@ -289,7 +289,7 @@ test_sv_cv_wind (void) } /* For the CV model, we want to save the wind_save to use in another test */ - snprintf (windsave_filename, LINELENGTH, "%s/source/tests/test_data/define_wind/restart_cv.wind_save", PYTHON_ENV); + snprintf (windsave_filename, LINELENGTH, "%s/source/tests/test_data/define_wind/restart_cv.wind_save", SIROCCO_ENV); const int err = wind_save (windsave_filename); if (err == 0) { @@ -329,7 +329,7 @@ test_shell_wind (void) define_wind (); /* And now we can compare our created grid to the "ground truth" grid */ - snprintf (test_data_filename, LINELENGTH, "%s/source/tests/test_data/define_wind/shell.grid.txt", PYTHON_ENV); + snprintf (test_data_filename, LINELENGTH, "%s/source/tests/test_data/define_wind/shell.grid.txt", SIROCCO_ENV); fp = fopen (test_data_filename, "r"); if (fp == NULL) { @@ -454,7 +454,7 @@ test_spherical_star_wind (void) define_wind (); /* And now we can compare our created grid to the "ground truth" grid */ - snprintf (test_data_filename, LINELENGTH, "%s/source/tests/test_data/define_wind/star.grid.txt", PYTHON_ENV); + snprintf (test_data_filename, LINELENGTH, "%s/source/tests/test_data/define_wind/star.grid.txt", SIROCCO_ENV); fp = fopen (test_data_filename, "r"); if (fp == NULL) { @@ -593,13 +593,13 @@ suite_init (void) { struct stat sb; - /* Find the PYTHON env var and the directory where tests are called. We need + /* Find the SIROCCO env var and the directory where tests are called. We need * these to create a symbolic link to the atomic data required for the * tests */ - PYTHON_ENV = getenv ("PYTHON"); - if (PYTHON_ENV == NULL) + SIROCCO_ENV = getenv ("SIROCCO"); + if (SIROCCO_ENV == NULL) { - fprintf (stderr, "Failed to find PYTHON environment variable\n"); + fprintf (stderr, "Failed to find SIROCCO environment variable\n"); return EXIT_FAILURE; } if (getcwd (TEST_CWD, LINELENGTH) == NULL) @@ -609,7 +609,7 @@ suite_init (void) } /* Set global variables for atomic data */ - snprintf (ATOMIC_DATA_TARGET, LINELENGTH, "%s/xdata", PYTHON_ENV); + snprintf (ATOMIC_DATA_TARGET, LINELENGTH, "%s/xdata", SIROCCO_ENV); if (!(stat (ATOMIC_DATA_TARGET, &sb) == EXIT_SUCCESS && S_ISDIR (sb.st_mode))) { perror ("Unable to find atomic data directory"); @@ -629,7 +629,7 @@ suite_init (void) } /* Set global variables for atomic data for developers */ - snprintf (ATOMIC_DATA_TARGET_DEVELOPER, LINELENGTH, "%s/zdata", PYTHON_ENV); + snprintf (ATOMIC_DATA_TARGET_DEVELOPER, LINELENGTH, "%s/zdata", SIROCCO_ENV); if (!(stat (ATOMIC_DATA_TARGET_DEVELOPER, &sb) == EXIT_SUCCESS && S_ISDIR (sb.st_mode))) { perror ("Unable to find atomic data directory"); diff --git a/source/tests/tests/test_matrix.c b/source/tests/tests/test_matrix.c index ded3ce140..9bba4ad11 100644 --- a/source/tests/tests/test_matrix.c +++ b/source/tests/tests/test_matrix.c @@ -167,10 +167,10 @@ get_solve_matrix_test_data (const char *a_path, const char *b_path, const char * static int call_invert_matrix (const char *test_name) { - const char *sirocco_path = getenv ((const char *) "PYTHON"); + const char *sirocco_path = getenv ((const char *) "SIROCCO"); if (sirocco_path == NULL) { - CU_FAIL_FATAL ("$PYTHON has not been set"); + CU_FAIL_FATAL ("$SIROCCO has not been set"); } double *matrix; @@ -221,10 +221,10 @@ call_invert_matrix (const char *test_name) int call_solve_matrix (const char *test_name) { - const char *sirocco_path = getenv ((const char *) "PYTHON"); + const char *sirocco_path = getenv ((const char *) "SIROCCO"); if (sirocco_path == NULL) { - CU_FAIL_FATAL ("$PYTHON has not been set"); + CU_FAIL_FATAL ("$SIROCCO has not been set"); } double *matrix_a; diff --git a/source/tests/unit_test.h b/source/tests/unit_test.h index 257d52711..b382db75d 100644 --- a/source/tests/unit_test.h +++ b/source/tests/unit_test.h @@ -8,8 +8,8 @@ * * ****************************************************************************************************************** */ -#ifndef PYTHON_UNIT_TEST_H -#define PYTHON_UNIT_TEST_H +#ifndef SIROCCO_UNIT_TEST_H +#define SIROCCO_UNIT_TEST_H int cleanup_model (const char *root_name); int setup_model_grid (const char *root_name, const char *atomic_data_location); diff --git a/source/tests/unit_test_model.c b/source/tests/unit_test_model.c index deab0e02f..cc1cb1d42 100644 --- a/source/tests/unit_test_model.c +++ b/source/tests/unit_test_model.c @@ -31,10 +31,10 @@ const char * get_sirocco_env_variable (void) { - const char *env = getenv ("PYTHON"); + const char *env = getenv ("SIROCCO"); if (env == NULL) { - fprintf (stderr, "Failed to find PYTHON environment variable\n"); + fprintf (stderr, "Failed to find SIROCCO environment variable\n"); return NULL; } @@ -69,7 +69,7 @@ free_and_null (void **ptr) * * @brief Clean up after a model has run as a test case. * - * @param [in] root_name The root name of the model in $PYTHON/source/tests/test_data/define_wind + * @param [in] root_name The root name of the model in $SIROCCO/source/tests/test_data/define_wind * * @details * @@ -79,7 +79,7 @@ int cleanup_model (const char *root_name) { int n_plasma; - char *PYTHON_ENV; + char *SIROCCO_ENV; char parameter_filepath[LINELENGTH]; PlasmaPtr plasma_cell; @@ -87,14 +87,14 @@ cleanup_model (const char *root_name) (void) root_name; - PYTHON_ENV = getenv ("PYTHON"); - if (PYTHON_ENV == NULL) + SIROCCO_ENV = getenv ("SIROCCO"); + if (SIROCCO_ENV == NULL) { - fprintf (stderr, "Failed to find PYTHON environment variable\n"); + fprintf (stderr, "Failed to find SIROCCO environment variable\n"); return EXIT_FAILURE; } - snprintf (parameter_filepath, LINELENGTH, "%s/source/tests/test_data/define_wind/%s.pf", PYTHON_ENV, files.root); + snprintf (parameter_filepath, LINELENGTH, "%s/source/tests/test_data/define_wind/%s.pf", SIROCCO_ENV, files.root); if (cpar (parameter_filepath) != 1) /* cpar returns 1 when something is "normal" */ { return EXIT_FAILURE; @@ -269,7 +269,7 @@ set_atomic_data_filename (const char *atomic_data_location) * * @brief Initialise all the necessary parameters to define the wind * - * @param [in] root_name The root name of the model in $PYTHON/source/tests/test_data/define_wind + * @param [in] root_name The root name of the model in $SIROCCO/source/tests/test_data/define_wind * @param [in] atomic_data_location The location of atomic data * * @details @@ -284,15 +284,15 @@ int setup_model_grid (const char *root_name, const char *atomic_data_location) { int n_dom; - char *PYTHON_ENV; + char *SIROCCO_ENV; char rdchoice_answer[LINELENGTH]; char rdchoice_choices[LINELENGTH]; char parameter_filepath[LINELENGTH]; - PYTHON_ENV = getenv ("PYTHON"); - if (PYTHON_ENV == NULL) + SIROCCO_ENV = getenv ("SIROCCO"); + if (SIROCCO_ENV == NULL) { - fprintf (stderr, "Failed to find PYTHON environment variable\n"); + fprintf (stderr, "Failed to find SIROCCO environment variable\n"); return EXIT_FAILURE; } @@ -301,7 +301,7 @@ setup_model_grid (const char *root_name, const char *atomic_data_location) /* Set up parameter file, that way we can get all the parameters from that * instead of defining them manually */ strcpy (files.root, root_name); - snprintf (parameter_filepath, LINELENGTH, "%s/source/tests/test_data/define_wind/%s.pf", PYTHON_ENV, files.root); + snprintf (parameter_filepath, LINELENGTH, "%s/source/tests/test_data/define_wind/%s.pf", SIROCCO_ENV, files.root); if (opar (parameter_filepath) != 2) /* opar returns 2 when reading for the parameter file */ { fprintf (stderr, "Unable to read from parameter file %s.pf", files.root); From 39ce287fa12dc3443124846c91c58ee349116e9a Mon Sep 17 00:00:00 2001 From: jhmatthews Date: Fri, 18 Oct 2024 14:37:36 +0100 Subject: [PATCH 20/24] updated SIROCCO env in Python scripts --- py_progs/autogenerate_parameter_docs.py | 14 +++++++------- py_progs/autogenerate_rtd_pages.py | 10 +++++----- py_progs/balmer_decrement.py | 18 +++++++++--------- py_progs/pf_check.py | 14 +++++++------- py_progs/py_error.py | 2 +- py_progs/py_plot_output.py | 2 +- py_progs/py_plot_util.py | 4 ++-- py_progs/regression.py | 20 ++++++++++---------- py_progs/regression_nsh.py | 18 +++++++++--------- py_progs/retro.py | 14 +++++++------- py_progs/test_masterfiles.py | 10 +++++----- py_progs/watchdog.py | 2 +- 12 files changed, 64 insertions(+), 64 deletions(-) diff --git a/py_progs/autogenerate_parameter_docs.py b/py_progs/autogenerate_parameter_docs.py index ab303c2c5..c265ebf44 100755 --- a/py_progs/autogenerate_parameter_docs.py +++ b/py_progs/autogenerate_parameter_docs.py @@ -18,8 +18,8 @@ Print the full text of all .yaml files that would be created to the screen. -w / --write - Move any deprecated parameters to `$PYTHON/parameters/old/`, - then write any new parameters to in `$PYTHON/parameters/` + Move any deprecated parameters to `$SIROCCO/parameters/old/`, + then write any new parameters to in `$SIROCCO/parameters/` Note, this will not over-write any parameters that have changed types but not names e.g. `rdflo('thing')` to `rdint('thing')`. @@ -27,7 +27,7 @@ -h / --help Prints this documentation. -After the program has been run $PYTHON/parameters should contain a yaml file +After the program has been run $SIROCCO/parameters should contain a yaml file for every possible input, and any input that has changed significantly should be in $PYHON/parameters.old One should normally be sure to add the new yaml files to the repository. @@ -58,7 +58,7 @@ The recommendation is to: - * to clean both $PYTHON/parameters/, and $PYTHON/parameters/old/ from your + * to clean both $SIROCCO/parameters/, and $SIROCCO/parameters/old/ from your local directories before using writing files using this routine, and then * to add and comit all of the files that are produced before going on to other stages of activities associated with documentation. @@ -464,9 +464,9 @@ def autogenerate_parameter_docs(): """ Function to autogenerate parameter documentation. """ - input_folder = os.path.join(os.environ["PYTHON"], "source") - output_folder = os.path.join(os.environ["PYTHON"], "docs", "parameters") - output_old_folder = os.path.join(os.environ["PYTHON"], "docs", "parameters", "old") + input_folder = os.path.join(os.environ["SIROCCO"], "source") + output_folder = os.path.join(os.environ["SIROCCO"], "docs", "parameters") + output_old_folder = os.path.join(os.environ["SIROCCO"], "docs", "parameters", "old") input_files = list_input_files(input_folder) existing_documentation = list_existing_documentation(output_folder) diff --git a/py_progs/autogenerate_rtd_pages.py b/py_progs/autogenerate_rtd_pages.py index 4cd33c13e..3961d8d1c 100755 --- a/py_progs/autogenerate_rtd_pages.py +++ b/py_progs/autogenerate_rtd_pages.py @@ -228,11 +228,11 @@ def autogenerate_rtd_pages(output_folder): """ Write the RTD files to disk and add them to git, then run sphinx-build to generate the docs. """ - # html_folder = os.path.join(os.environ["PYTHON"], "docs", "html") - docs_folder = os.path.join(os.environ["PYTHON"], "docs") + # html_folder = os.path.join(os.environ["SIROCCO"], "docs", "html") + docs_folder = os.path.join(os.environ["SIROCCO"], "docs") dox_all = {} - par_folder= os.path.join(os.environ["PYTHON"], "docs", "parameters") + par_folder= os.path.join(os.environ["SIROCCO"], "docs", "parameters") print('Par folder',par_folder) dox_all = read_yaml( @@ -296,8 +296,8 @@ def steer(argv): This is just a steering routine to enable better control of the program ''' - outdir= os.path.join(os.environ["PYTHON"], "docs", "rst", "parameters") - xdir= os.path.join(os.environ["PYTHON"], "docs","sphinx") + outdir= os.path.join(os.environ["SIROCCO"], "docs", "rst", "parameters") + xdir= os.path.join(os.environ["SIROCCO"], "docs","sphinx") i=1 while i< len(argv): diff --git a/py_progs/balmer_decrement.py b/py_progs/balmer_decrement.py index 7560f649d..aec0e153e 100755 --- a/py_progs/balmer_decrement.py +++ b/py_progs/balmer_decrement.py @@ -1,8 +1,8 @@ #!/usr/bin/env python ''' -Runs tests of the Balmer decrement for a one zone thin shell Python model. +Runs tests of the Balmer decrement for a one zone thin shell Sirocco model. -Involves running py_wind on a wind_save file and reading some output files. +Involves running swind on a wind_save file and reading some output files. Compares to Osterbrock values. Usage: @@ -14,7 +14,7 @@ py_wind numpy matplotlib - py_plot_util, py_read_output from $PYTHON/py_progs in the python path + py_plot_util, py_read_output from $SIROCCO/py_progs in the python path Notes: This routine is a routine to check the results of running a one zone @@ -26,8 +26,8 @@ # Do not call this when we're on ReadTheDocs if not os.environ.get('READTHEDOCS'): - PYTHON = os.environ["PYTHON"] - sys.path.append("$PYTHON/py_progs/") + SIROCCO = os.environ["SIROCCO"] + sys.path.append("$SIROCCO/py_progs/") import py_plot_util as util import py_read_output as rd @@ -48,7 +48,7 @@ def BalmerTest(root, plotit=True): print ("Running Balmer Test for run {}...".format(root)) - # create the list of commands to run in py wind + # create the list of commands to run in swind nlevels = 8 cmds = ["1","s","n","i","1","1","1","1","2","0","M","2"] for i in range(nlevels): @@ -57,8 +57,8 @@ def BalmerTest(root, plotit=True): cmds.append("q") # run py wind. pass the command to run for situations in CI where we can't get the path working - py_wind_cmd = "{}/bin/py_wind".format(PYTHON) - isys, logfile_contents = util.run_py_wind(root, cmds=cmds, py_wind_cmd = py_wind_cmd, return_output = True) + swind_cmd = "{}/bin/swind".format(SIROCCO) + isys, logfile_contents = util.run_py_wind(root, cmds=cmds, py_wind_cmd = swind_cmd, return_output = True) print (isys) # these could be in principle be used to check absolute emissivity values @@ -74,7 +74,7 @@ def BalmerTest(root, plotit=True): for i in range(nlevels): ratios[i] = rd.read_pywind("{}.lev{}_emiss.dat".format(root,i+3), mode="1d")[2][1] except FileNotFoundError: - print("Error reading py_wind output. Logfile follows...") + print("Error reading swind output. Logfile follows...") print (logfile_contents) diff --git a/py_progs/pf_check.py b/py_progs/pf_check.py index d137bca98..e515ba07c 100755 --- a/py_progs/pf_check.py +++ b/py_progs/pf_check.py @@ -29,12 +29,12 @@ pf_check_directory_date - The full path names to directories in the `$PYTHON/examples` + The full path names to directories in the `$SIROCCO/examples` do not need to be given, so for example:: pf_check.py beta - will test the parameter files in `$PYTHON/examples/beta`. + will test the parameter files in `$SIROCCO/examples/beta`. After running Python in -i mode for each of the files, the routine diffs the .out.pf file with the original.pf @@ -95,9 +95,9 @@ def travis(): Return a special set of commands when dealing with the travis directory ''' - PYTHON=os.environ['PYTHON'] + SIROCCO=os.environ['SIROCCO'] - x=open('%s/.travis.yml' % (PYTHON)) + x=open('%s/.travis.yml' % (SIROCCO)) z=yaml.load(x) print(z['script']) commands=[] @@ -132,9 +132,9 @@ def doit(directory): if os.path.isdir(directory): xdir=directory else: - # Get the PYTHON environment variable - PYTHON=os.environ['PYTHON'] - ydir='%s/examples/%s' % (PYTHON,directory) + # Get the SIROCCO environment variable + SIROCCO=os.environ['SIROCCO'] + ydir='%s/examples/%s' % (SIROCCO,directory) if os.path.isdir(ydir): xdir=ydir else: diff --git a/py_progs/py_error.py b/py_progs/py_error.py index ce7a13c04..c7362fbff 100755 --- a/py_progs/py_error.py +++ b/py_progs/py_error.py @@ -4,7 +4,7 @@ Post-run parser for error logs from parallel simulation runs. This is the post-processing code to deal with the error logs in -PYTHON's parallel mode. It basically sums the number of errors +SIROCCO's parallel mode. It basically sums the number of errors in the various diagnostic files usage and arguments: diff --git a/py_progs/py_plot_output.py b/py_progs/py_plot_output.py index 18ed12d15..46d960194 100755 --- a/py_progs/py_plot_output.py +++ b/py_progs/py_plot_output.py @@ -98,7 +98,7 @@ def make_spec_plot(s, fname, smooth_factor = 10, angles = True, components = Fal print ("Making a {} by {} plot, {} spectra".format(nx, ny, nspecs)) if with_composite: - lambda_composite, f_composite, errors = np.loadtxt("%s/examples/telfer_qso_composite_hst.asc" % (os.environ["PYTHON"]), unpack=True, comments="#") + lambda_composite, f_composite, errors = np.loadtxt("%s/examples/telfer_qso_composite_hst.asc" % (os.environ["SIROCCO"]), unpack=True, comments="#") for i in range(nspecs): diff --git a/py_progs/py_plot_util.py b/py_progs/py_plot_util.py index 882dd0d63..2b7cc94eb 100755 --- a/py_progs/py_plot_util.py +++ b/py_progs/py_plot_util.py @@ -134,7 +134,7 @@ def read_pywind_smart(filename, return_inwind=False): values = d[2].reshape(xshape, zshape) - # these are the values of inwind PYTHON spits out + # these are the values of inwind SIROCCO spits out inwind = d[3].reshape(xshape, zshape) # create an inwind boolean to use to create mask @@ -211,7 +211,7 @@ def wind_to_masked(d, value_string, return_inwind=False, mode="2d", ignore_parti values = d[value_string].reshape(xshape, zshape) - # these are the values of inwind PYTHON spits out + # these are the values of inwind SIROCCO spits out inwind = d["inwind"].reshape(xshape, zshape) # create an inwind boolean to use to create mask diff --git a/py_progs/regression.py b/py_progs/regression.py index 29b62fe9c..4f2f0d2cd 100755 --- a/py_progs/regression.py +++ b/py_progs/regression.py @@ -17,10 +17,10 @@ the number of processors with which to run (default 3) `-pf_dir test` the directory containing all of the .pf files which will be run - The defaults is `$PYTHON/examples/regress`. One does not need + The defaults is `$SIROCCO/examples/regress`. One does not need to provide the full path name to the directory. The routine doit first searches the current workind directory for the directory and then - looks in `$PYTHON/examples/` + looks in `$SIROCCO/examples/` `-x '-v/c'` Extra switches to be applied to the run, such as using linear Doppler shifts. Note that these will be applied to everything except the @@ -207,7 +207,7 @@ def doit(version='py',pf_dir='',out_dir='',np=3,switches='',outputfile='Summary. Notes: The routine looks for the input directory first as a subdirectory - of the directory from which regression is run, and then in $PYTHON/examples/ + of the directory from which regression is run, and then in $SIROCCO/examples/ History: @@ -235,22 +235,22 @@ def doit(version='py',pf_dir='',out_dir='',np=3,switches='',outputfile='Summary. print(date) - # Get the PYTHON environment variable + # Get the SIROCCO environment variable - PYTHON=os.environ['PYTHON'] - print(PYTHON) + SIROCCO=os.environ['SIROCCO'] + print(SIROCCO) if pf_dir=='': - pf_dir=PYTHON+'/examples/regress' + pf_dir=SIROCCO+'/examples/regress' if os.path.isdir(pf_dir): pf_files=glob(pf_dir+'/*pf') txt_files=glob(pf_dir+'/*.txt') dat_files=glob(pf_dir+'/*.dat') wind_save=glob(pf_dir+'/*.wind_save') - elif os.path.isdir('%s/examples/%s' % (PYTHON,pf_dir)): - pf_files=glob('%s/examples/%s/*pf' % (PYTHON,pf_dir)) - txt_files=glob('%s/examples/%s/*.txt' % (PYTHON,pf_dir)) + elif os.path.isdir('%s/examples/%s' % (SIROCCO,pf_dir)): + pf_files=glob('%s/examples/%s/*pf' % (SIROCCO,pf_dir)) + txt_files=glob('%s/examples/%s/*.txt' % (SIROCCO,pf_dir)) dat_files=glob(pf_dir+'/*.dat') wind_save=glob(pf_dir+'/*.wind_save') else: diff --git a/py_progs/regression_nsh.py b/py_progs/regression_nsh.py index e5234e6bc..fdf89b096 100755 --- a/py_progs/regression_nsh.py +++ b/py_progs/regression_nsh.py @@ -17,10 +17,10 @@ the number of processors with which to run (default 3) -pf_dir test the directory containing all of the .pf files which will be run - The defaults is $PYTHON/examples/regress. One does not need + The defaults is $SIROCCO/examples/regress. One does not need to provide the full path name to the directory. The routine doit first searches the current workind directory for the directory and then - looks in $PYTHON/examples/ + looks in $SIROCCO/examples/ -out_dir foo The directory (below the current working directory) where the tests will run. The defauld is constructed for the version and the data @@ -162,7 +162,7 @@ def doit(version='py',pf_dir='',out_dir='',np=3,outputfile='Summary.txt'): Notes: The routine looks for the input directory first as a subdirectory - of the directory from which regression is run, and then in $PYTHON/examples/ + of the directory from which regression is run, and then in $SIROCCO/examples/ History: @@ -183,18 +183,18 @@ def doit(version='py',pf_dir='',out_dir='',np=3,outputfile='Summary.txt'): print(date) - # Get the PYTHON environment variable + # Get the SIROCCO environment variable - PYTHON=os.environ['PYTHON'] - print(PYTHON) + SIROCCO=os.environ['SIROCCO'] + print(SIROCCO) if pf_dir=='': - pf_dir=PYTHON+'/examples/regress' + pf_dir=SIROCCO+'/examples/regress' if os.path.isdir(pf_dir): pf_files=glob(pf_dir+'/*pf') - elif os.path('%s/%s' % (PYTHON,pf_dir)): - pf_files=glob('%s/%s' % (PYTHON,pf_dir)) + elif os.path('%s/%s' % (SIROCCO,pf_dir)): + pf_files=glob('%s/%s' % (SIROCCO,pf_dir)) else: print('Error: The pf directory %s does not appear to exist' % pf_dir) return diff --git a/py_progs/retro.py b/py_progs/retro.py index 90252f18e..18bd0470e 100755 --- a/py_progs/retro.py +++ b/py_progs/retro.py @@ -191,20 +191,20 @@ def log2table(logfile='commits.txt',masterfile='retro_master.txt'): return SUCCESS -PYTHON_SOURCE_DIRECTORY='' +SIROCCO_SOURCE_DIRECTORY='' def get_python_source_directory(): - global PYTHON_SOURCE_DIRECTORY + global SIROCCO_SOURCE_DIRECTORY result=subprocess.run(['which','py'],capture_output=True,text=True) location=str(result.stdout) x=location.strip() xx=x.replace('bin/py','source/') - PYTHON_SOURCE_DIRECTORY=xx - print('Set Python source directory to :',PYTHON_SOURCE_DIRECTORY) + SIROCCO_SOURCE_DIRECTORY=xx + print('Set Python source directory to :',SIROCCO_SOURCE_DIRECTORY) - return PYTHON_SOURCE_DIRECTORY + return SIROCCO_SOURCE_DIRECTORY def compile_one(commit='a77ec180c017244cb56f41b50178daf81541748a',number=35,print_output=False): @@ -215,9 +215,9 @@ def compile_one(commit='a77ec180c017244cb56f41b50178daf81541748a',number=35,prin # cwd=os.getcwd() - if PYTHON_SOURCE_DIRECTORY=='': + if SIROCCO_SOURCE_DIRECTORY=='': get_python_source_directory() - os.chdir(PYTHON_SOURCE_DIRECTORY) + os.chdir(SIROCCO_SOURCE_DIRECTORY) result=subprocess.run(['git', 'checkout', commit],capture_output=True,text=True) if result.returncode: diff --git a/py_progs/test_masterfiles.py b/py_progs/test_masterfiles.py index 17fd981b3..03a4a77b3 100755 --- a/py_progs/test_masterfiles.py +++ b/py_progs/test_masterfiles.py @@ -5,7 +5,7 @@ needs python to be compiled. Usage: - test_masterfiles.py [PYTHON VERSION] + test_masterfiles.py [SIROCCO VERSION] ''' import py_read_output as rd import subprocess, os, sys @@ -13,7 +13,7 @@ # set env variable # Do not call this when we're on ReadTheDocs if not os.environ.get('READTHEDOCS'): - PYTHON = os.environ["PYTHON"] + SIROCCO = os.environ["SIROCCO"] # change these if you want to test different files. These are all in data/ as of October 2019 macro_files = ['h20', 'h10_hetop_lohe1_standard80', 'h10_standard80', 'h10_hetop_standard80', 'h20_hetop_standard80'] @@ -33,7 +33,7 @@ def run_file(pf_template, masterfile, version=""): rd.write_pf("_tmp.pf", pf) # run process - process = subprocess.run("{}/bin/py{} -i _tmp.pf".format(PYTHON, version), shell=True, stdout=subprocess.PIPE) + process = subprocess.run("{}/bin/py{} -i _tmp.pf".format(SIROCCO, version), shell=True, stdout=subprocess.PIPE) subprocess.run("/bin/rm -f _tmp.sig _tmp.out.pf logfile _tmp.out.pf.old _tmp.pf", shell=True, stdout=subprocess.PIPE) subprocess.run("/bin/rm -rf diag__tmp", shell=True, stdout=subprocess.PIPE) @@ -60,8 +60,8 @@ def run_test(VERSION): run the test. ''' - macro_template = rd.read_pf("{}/examples/extended/cv_macro_benchmark.pf".format(PYTHON)) - std_template = rd.read_pf("{}/examples/basic/cv_standard.pf".format(PYTHON)) + macro_template = rd.read_pf("{}/examples/extended/cv_macro_benchmark.pf".format(SIROCCO)) + std_template = rd.read_pf("{}/examples/basic/cv_standard.pf".format(SIROCCO)) subprocess.run("Setup_Py_Dir") diff --git a/py_progs/watchdog.py b/py_progs/watchdog.py index 425639a86..8e1fa95bd 100755 --- a/py_progs/watchdog.py +++ b/py_progs/watchdog.py @@ -4,7 +4,7 @@ During-run parser for error logs from parallel simulation runs. This is the during-processing watchdog code to deal with the error logs in -PYTHON's parallel mode. Very similar to py_error.py +SIROCCO's parallel mode. Very similar to py_error.py usage and arguments: From a76aa8f0cfd800c4da34a1a9ce36e0af5a30e08f Mon Sep 17 00:00:00 2001 From: James Matthews Date: Mon, 21 Oct 2024 10:25:38 +0100 Subject: [PATCH 21/24] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a61a8e956..08899316a 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ Documentation is hosted on [ReadTheDocs](http://sirocco-rt.readthedocs.io/en/dev [![C/C++ CI](https://github.com/sirocco-rt/sirocco/actions/workflows/build.yml/badge.svg)](https://github.com/sirocco-rt/sirocco/actions/workflows/build.yml) -[![Documentation Status](https://readthedocs.org/projects/agnwinds/badge/?version=latest)](https://agnwinds.readthedocs.io/en/latest/?badge=latest) +[![Documentation Status](https://readthedocs.org/projects/sirocco-rt/badge/?version=latest)](https://sirocco-rt.readthedocs.io/en/latest/?badge=latest) ## Installation From f6658c579ed782a1e5706c275ae8adbe38b9665f Mon Sep 17 00:00:00 2001 From: jhmatthews Date: Thu, 24 Oct 2024 17:07:20 +0100 Subject: [PATCH 22/24] further documentation updates to catch renames --- docs/sphinx/source/atomic/bound_bound.rst | 4 ++-- .../source/atomic/bound_free_topbase.rst | 2 +- docs/sphinx/source/atomic/electron_yields.rst | 2 +- docs/sphinx/source/atomic/elem_ions.rst | 2 +- docs/sphinx/source/atomic/free-free.rst | 4 ++-- docs/sphinx/source/atomic/levels.rst | 4 ++-- docs/sphinx/source/atomic/photon_yields.rst | 2 +- docs/sphinx/source/developer/cuda.rst | 12 +++++------ .../source/developer/programmer_notes.rst | 12 +++++------ docs/sphinx/source/developer/tests.rst | 6 +++--- docs/sphinx/source/input.rst | 2 +- .../parameters/Disk/Disk.T_profile_file.rst | 2 +- .../Disk/Disk.colour_correction.rst | 2 +- .../input/parameters/Disk/Disk.mdot.rst | 2 +- .../Disk/Disk.rad_type_in_final_spectrum.rst | 2 +- .../Disk/Disk.rad_type_to_make_wind.rst | 2 +- .../input/parameters/Disk/Disk.radiation.rst | 2 +- .../input/parameters/Disk/Disk.radmax.rst | 2 +- .../input/parameters/Disk/Disk.radmin.rst | 2 +- .../Disk/Disk.temperature.profile.rst | 2 +- .../input/parameters/Disk/Disk.type.rst | 2 +- .../source/input/parameters/Disk/Disk.z0.rst | 2 +- .../source/input/parameters/Disk/Disk.z1.rst | 2 +- .../parameters/Input_spectra.model_file.rst | 2 +- .../parameters/Spectrum/Spectrum.angle.rst | 2 +- .../Spectrum/Spectrum.live_or_die.rst | 2 +- .../Spectrum/Spectrum.no_observers.rst | 2 +- .../parameters/Spectrum/Spectrum.nwave.rst | 2 +- .../Spectrum/Spectrum.orbit_phase.rst | 2 +- .../Spectrum/Spectrum.select_azimuth.rst | 2 +- .../Spectrum/Spectrum.select_location.rst | 2 +- .../Spectrum.select_photons_by_position.rst | 2 +- .../parameters/Spectrum/Spectrum.select_r.rst | 2 +- .../Spectrum/Spectrum.select_rho.rst | 2 +- .../Spectrum/Spectrum.select_scatters.rst | 2 +- ...ect_specific_no_of_scatters_in_spectra.rst | 2 +- .../parameters/Spectrum/Spectrum.select_z.rst | 2 +- .../parameters/Spectrum/Spectrum.type.rst | 2 +- .../parameters/Spectrum/Spectrum.wavemax.rst | 2 +- .../parameters/Spectrum/Spectrum.wavemin.rst | 2 +- .../source/input/parameters/System_type.rst | 4 ++-- .../central_object/Binary/Binary.mass_sec.rst | 2 +- .../central_object/Binary/Binary.period.rst | 2 +- .../Boundary_layer.luminosity.rst | 2 +- .../Boundary_layer.power_law_cutoff.rst | 2 +- .../Boundary_layer.power_law_index.rst | 2 +- ...ndary_layer.rad_type_in_final_spectrum.rst | 2 +- .../Boundary_layer.rad_type_to_make_wind.rst | 2 +- .../Boundary_layer.radiation.rst | 2 +- .../Boundary_layer/Boundary_layer.temp.rst | 2 +- .../Central_object.blackbody_temp.rst | 2 +- .../Central_object.bremsstrahlung_alpha.rst | 2 +- .../Central_object.bremsstrahlung_temp.rst | 2 +- .../Central_object.bubble_size.rst | 2 +- ...entral_object.cloudy.high_energy_break.rst | 2 +- ...Central_object.cloudy.low_energy_break.rst | 2 +- .../Central_object.geometry_for_source.rst | 2 +- .../Central_object.lamp_post_height.rst | 2 +- .../Central_object.luminosity.rst | 2 +- .../Central_object/Central_object.mass.rst | 2 +- .../Central_object.power_law_cutoff.rst | 2 +- .../Central_object.power_law_index.rst | 2 +- ...tral_object.rad_type_in_final_spectrum.rst | 2 +- .../Central_object.rad_type_to_make_wind.rst | 2 +- .../Central_object.radiation.rst | 2 +- .../Central_object/Central_object.radius.rst | 2 +- .../Central_object/Central_object.temp.rst | 2 +- .../other/Diag/Diag.adjust_grid.rst | 2 +- .../parameters/other/Diag/Diag.angle.rst | 2 +- .../parameters/other/Diag/Diag.extra.rst | 2 +- ....fractional_distance_photon_may_travel.rst | 2 +- .../Diag/Diag.invoke_searchlight_option.rst | 2 +- .../other/Diag/Diag.keep_ioncycle_spectra.rst | 4 ++-- .../Diag/Diag.keep_ioncycle_windsaves.rst | 4 ++-- .../Diag.keep_photoabs_in_final_spectra.rst | 2 +- .../parameters/other/Diag/Diag.location.rst | 2 +- .../Diag.lowest_ion_density_for_photoabs.rst | 2 +- .../other/Diag/Diag.make_ioncycle_tables.rst | 2 +- .../other/Diag/Diag.print_dvds_info.rst | 2 +- .../input/parameters/other/Diag/Diag.r.rst | 2 +- .../other/Diag/Diag.save_cell_statistics.rst | 2 +- .../other/Diag/Diag.save_extract_photons.rst | 2 +- .../other/Diag/Diag.save_photons.rst | 2 +- .../Diag/Diag.track_resonant_scatters.rst | 2 +- ...s_for_emissivities_in_detailed_spectra.rst | 2 +- .../Diag/Diag.use_standard_care_factors.rst | 2 +- ....use_upweighting_of_simple_macro_atoms.rst | 2 +- .../other/Diag/Diag.write_atomicdata.rst | 2 +- .../Photon_sampling.approach.rst | 2 +- .../Photon_sampling.band_boundary.rst | 2 +- .../Photon_sampling.band_min_frac.rst | 2 +- .../Photon_sampling.high_energy_limit.rst | 2 +- .../Photon_sampling.low_energy_limit.rst | 2 +- .../Photon_sampling.nbands.rst | 2 +- .../other/Reverb/Reverb.angle_bins.rst | 2 +- .../other/Reverb/Reverb.disk_type.rst | 2 +- .../other/Reverb/Reverb.dump_cell.rst | 2 +- .../other/Reverb/Reverb.dump_cells.rst | 2 +- .../other/Reverb/Reverb.filter_line.rst | 4 ++-- .../other/Reverb/Reverb.filter_lines.rst | 2 +- .../other/Reverb/Reverb.matom_line.rst | 2 +- .../other/Reverb/Reverb.matom_lines.rst | 2 +- .../other/Reverb/Reverb.path_bins.rst | 2 +- .../parameters/other/Reverb/Reverb.type.rst | 2 +- .../other/Reverb/Reverb.visualisation.rst | 2 +- .../parameters/other/geo/geo.xlog_scale.rst | 2 +- .../parameters/other/geo/geo.zlog_scale.rst | 2 +- .../monochromatic.wavelength.rst | 2 +- .../input/parameters/rt_ion/Atomic_data.rst | 2 +- .../parameters/rt_ion/Ionization_cycles.rst | 2 +- .../input/parameters/rt_ion/Line_transfer.rst | 2 +- .../rt_ion/Matom_transition_mode.rst | 2 +- .../parameters/rt_ion/Photons_per_cycle.rst | 2 +- .../parameters/rt_ion/Spectrum_cycles.rst | 2 +- .../Surface.reflection.or.absorption.rst | 2 +- .../Wind_heating.extra_luminosity.rst | 4 ++-- .../Wind_heating.extra_processes.rst | 4 ++-- .../Wind_heating.kpacket_frac.rst | 2 +- .../wind/Corona/Corona.base_den.rst | 2 +- .../parameters/wind/Corona/Corona.radmax.rst | 2 +- .../parameters/wind/Corona/Corona.radmin.rst | 2 +- .../wind/Corona/Corona.scale_height.rst | 2 +- .../wind/Corona/Corona.vel_frac.rst | 2 +- .../parameters/wind/Corona/Corona.zmax.rst | 2 +- .../Homologous/Homologous.boundary_mdot.rst | 2 +- .../Homologous.density_exponent.rst | 2 +- .../wind/Homologous/Homologous.radmax.rst | 2 +- .../wind/Homologous/Homologous.radmin.rst | 2 +- .../wind/Homologous/Homologous.vbase.rst | 2 +- .../parameters/wind/Hydro/Hydro.file.rst | 2 +- .../parameters/wind/Hydro/Hydro.thetamax.rst | 2 +- .../wind/KWD/KWD.acceleration_exponent.rst | 2 +- .../wind/KWD/KWD.acceleration_length.rst | 2 +- .../input/parameters/wind/KWD/KWD.d.rst | 2 +- .../wind/KWD/KWD.mdot_r_exponent.rst | 2 +- .../input/parameters/wind/KWD/KWD.rmax.rst | 2 +- .../input/parameters/wind/KWD/KWD.rmin.rst | 2 +- .../parameters/wind/KWD/KWD.v_infinity.rst | 2 +- .../input/parameters/wind/KWD/KWD.v_zero.rst | 2 +- .../wind/SV/SV.acceleration_exponent.rst | 2 +- .../wind/SV/SV.acceleration_length.rst | 2 +- .../input/parameters/wind/SV/SV.diskmax.rst | 2 +- .../input/parameters/wind/SV/SV.diskmin.rst | 2 +- .../input/parameters/wind/SV/SV.gamma.rst | 2 +- .../parameters/wind/SV/SV.mdot_r_exponent.rst | 2 +- .../input/parameters/wind/SV/SV.thetamax.rst | 2 +- .../input/parameters/wind/SV/SV.thetamin.rst | 2 +- .../parameters/wind/SV/SV.v_infinity.rst | 2 +- .../input/parameters/wind/SV/SV.v_zero.rst | 2 +- .../parameters/wind/SV/SV.v_zero_mode.rst | 2 +- .../Shell.wind.acceleration_exponent.rst | 2 +- .../wind/Shell/Shell.wind.radmax.rst | 2 +- .../wind/Shell/Shell.wind.radmin.rst | 2 +- .../wind/Shell/Shell.wind.v_at_rmax.rst | 2 +- .../parameters/wind/Shell/Shell.wind_mdot.rst | 2 +- .../wind/Shell/Shell.wind_v_at_rmin.rst | 2 +- .../Stellar_wind.acceleration_exponent.rst | 2 +- .../wind/Stellar_wind/Stellar_wind.mdot.rst | 2 +- .../wind/Stellar_wind/Stellar_wind.radmax.rst | 2 +- .../wind/Stellar_wind/Stellar_wind.radmin.rst | 2 +- .../Stellar_wind/Stellar_wind.v_infinity.rst | 2 +- .../wind/Stellar_wind/Stellar_wind.vbase.rst | 2 +- .../wind/Wind/Wind.coord_system.rst | 2 +- .../Wind/Wind.dim.in.x_or_r.direction.rst | 2 +- .../Wind/Wind.dim.in.z_or_theta.direction.rst | 2 +- .../wind/Wind/Wind.filling_factor.rst | 2 +- .../Wind/Wind.fixed_concentrations_file.rst | 2 +- .../parameters/wind/Wind/Wind.ionization.rst | 2 +- .../input/parameters/wind/Wind/Wind.mdot.rst | 2 +- .../wind/Wind/Wind.model2import.rst | 6 +++--- .../wind/Wind/Wind.number_of_components.rst | 2 +- .../wind/Wind/Wind.old_windfile.rst | 2 +- .../parameters/wind/Wind/Wind.radiation.rst | 2 +- .../parameters/wind/Wind/Wind.radmax.rst | 2 +- .../parameters/wind/Wind/Wind.t.init.rst | 2 +- .../input/parameters/wind/Wind/Wind.type.rst | 4 ++-- .../sphinx/source/input/wind_model_params.rst | 2 +- docs/sphinx/source/installation.rst | 20 +++++++++---------- docs/sphinx/source/reference_rst.txt | 2 +- 179 files changed, 212 insertions(+), 212 deletions(-) diff --git a/docs/sphinx/source/atomic/bound_bound.rst b/docs/sphinx/source/atomic/bound_bound.rst index b823b3ef6..d0e8a7b26 100644 --- a/docs/sphinx/source/atomic/bound_bound.rst +++ b/docs/sphinx/source/atomic/bound_bound.rst @@ -24,7 +24,7 @@ We have also used a line list from Verner in the past Translation to SIROCCO format ============================ -There are several steps to creating the data used in SIROCCO from that in gfall.dat, that are carried out by py_read_kurucz and py_link. The first routine reads the gfall.dat file and creates two output files, a file containing the lines and the associated such as the effective oscillatory strength and a file which contains information about the ion levels. py_read_kurucz chooses only a portion of the Kurucz lines, namely those associated with ions with ionization potentials in a certain range and lines with gf factors exceeding a certain value. The second program py_link attempts to create a model ion with links between the levels and the ions. Both of these routines are driven by .pf files, similar to what are used in python. Examples of the .pf files are in the directory py_kurucz +There are several steps to creating the data used in SIROCCO from that in gfall.dat, that are carried out by py_read_kurucz and py_link. The first routine reads the gfall.dat file and creates two output files, a file containing the lines and the associated such as the effective oscillatory strength and a file which contains information about the ion levels. py_read_kurucz chooses only a portion of the Kurucz lines, namely those associated with ions with ionization potentials in a certain range and lines with gf factors exceeding a certain value. The second program py_link attempts to create a model ion with links between the levels and the ions. Both of these routines are driven by .pf files, similar to what are used in SIROCCO. Examples of the .pf files are in the directory py_kurucz In practice we have not used these data for any SIROCCO publications. At some point early in the AGN project, NSH increased the number of lines, and generated lines\_linked\_ver\_2.dat and levels\_ver\_2.dat. I think this was because there was a small bug which meant the oscillator strength cut that was stated was not that which was applied. @@ -75,6 +75,6 @@ Line data is stored in the data structure **lines** Comments ======== -The version of gfall.dat that is used in SIROCCO is out of date, though whether this affects any of the lines actually used in python is unclear. The website listed above has a link to newer versions of gfall.dat. +The version of gfall.dat that is used in SIROCCO is out of date, though whether this affects any of the lines actually used in SIROCCO is unclear. The website listed above has a link to newer versions of gfall.dat. diff --git a/docs/sphinx/source/atomic/bound_free_topbase.rst b/docs/sphinx/source/atomic/bound_free_topbase.rst index 132f510cb..7464e3b92 100644 --- a/docs/sphinx/source/atomic/bound_free_topbase.rst +++ b/docs/sphinx/source/atomic/bound_free_topbase.rst @@ -119,6 +119,6 @@ This is the shape of a hydrogenic cross-section and whilst it is not accurate for non-hydrogenic ions, it is more realistic (and conservative) than some of the unphysically shallow gradients that were being found. This is also briefly described in section~3.7.2 of Matthews PhD thesis. -The python scripts can be found in the `data-gen `_ repository progs/extrapolate\_xs/ +The python scripts can be found in the `data-gen `_ repository progs/extrapolate\_xs/ with docstrings describing their use. diff --git a/docs/sphinx/source/atomic/electron_yields.rst b/docs/sphinx/source/atomic/electron_yields.rst index 4bc1b901b..929f0050c 100644 --- a/docs/sphinx/source/atomic/electron_yields.rst +++ b/docs/sphinx/source/atomic/electron_yields.rst @@ -38,7 +38,7 @@ The data is linked to the correct inner shell photoionization cross section (and SIROCCO structure ================ -The data is stored in python in the inner_elec_yield structure which contains +The data is stored in SIROCCO in the inner_elec_yield structure which contains - int nion - Index to the ion which was the parent of the inner shell ionization - int z, istate - element and ionization state of parent ion diff --git a/docs/sphinx/source/atomic/elem_ions.rst b/docs/sphinx/source/atomic/elem_ions.rst index f70d49268..7ddc52ccc 100644 --- a/docs/sphinx/source/atomic/elem_ions.rst +++ b/docs/sphinx/source/atomic/elem_ions.rst @@ -10,7 +10,7 @@ This data comes from `Verner, Barthel & Tytler, 1994, ApJ 108, 287. ` |:math:`s_1`|:math:`s_2`|:math:`s_3`| diff --git a/docs/sphinx/source/atomic/levels.rst b/docs/sphinx/source/atomic/levels.rst index a5a2728ff..ab5437533 100644 --- a/docs/sphinx/source/atomic/levels.rst +++ b/docs/sphinx/source/atomic/levels.rst @@ -13,7 +13,7 @@ Level information can be derived from a variety of sources, by: * more commonly, for data abstracted from TopBase or Chianti -Translation to python: +Translation to SIROCCO: ====================== @@ -83,7 +83,7 @@ between ions, and so it includes the ionization energy of the lower level ioniza Note that the radiative rates are not used. The original intention was to use this to define the difference between metastable and normal levels, with the expectation that if the level was metastable it would be put in Boltzmann equilibrium with the ground state. -Right now python uses :math:`10^{15}` seconds, essentially a Hubble time to do this, but this portion of the +Right now SIROCCO uses :math:`10^{15}` seconds, essentially a Hubble time to do this, but this portion of the code is not, according to ss, tested. The primary source for this is usually the NIST database, although similar information is usually available in Chianti. diff --git a/docs/sphinx/source/atomic/photon_yields.rst b/docs/sphinx/source/atomic/photon_yields.rst index d648ae132..a6321cd07 100644 --- a/docs/sphinx/source/atomic/photon_yields.rst +++ b/docs/sphinx/source/atomic/photon_yields.rst @@ -37,7 +37,7 @@ The data is linked to the correct inner shell photoionization cross section (and SIROCCO structure ================ -The data is stored in python in the inner_fluor_yield structure which contains +The data is stored in SIROCCO in the inner_fluor_yield structure which contains - int nion - Index to the ion which was the parent of the inner shell ionization diff --git a/docs/sphinx/source/developer/cuda.rst b/docs/sphinx/source/developer/cuda.rst index 54681b5e8..e7b5a9ec9 100644 --- a/docs/sphinx/source/developer/cuda.rst +++ b/docs/sphinx/source/developer/cuda.rst @@ -241,7 +241,7 @@ The following code exert is an example of using the wrapper function to solve a .. code:: c :caption: The API to solve a linear system hasn't changed - #include "python.h" + #include "sirocco.h" double *populations = malloc(nions * sizeof(*populations)); double *ion_density = malloc(nions * sizeof(*populations)); @@ -269,7 +269,7 @@ Here is an example of using a similar wrapper function to calculate the inverse .. code:: c :caption: The API has changed slightly for calculating the inverse, now that it has a wrapper function - #include "python.h" + #include "sirocco.h" double Q_matrix = malloc(matrix_size * matrix_size * sizeof(double)); double Q_inverse = malloc(matrix_size * matrix_size * sizeof(double)); @@ -383,7 +383,7 @@ The steps for compiling and link GPU and CPU code are outlined below in pseudo-M $(CC) $(C_FLAGS) $(C_SOURCE) -c -o $(C_OBJECTS) # Link the CUDA and C object code and libraries together using the C compiler - $(CC) $(CUDA_OBJECTS) $(C_OBJECTS) -o python $(CUDA_LIBS) $(C_LIBS) + $(CC) $(CUDA_OBJECTS) $(C_OBJECTS) -o sirocco $(CUDA_LIBS) $(C_LIBS) These steps are effectively replicated in the Makefile :code:`$SIROCCO/source/Makefile`, where a deconstructed example is shown below. @@ -409,8 +409,8 @@ shown below. endif # So to compile SIROCCO, we have something which looks vaguely like this. Note that - # we use the CUDA_OBJECTS recipe as a requirement for the python recipe. This CUSOLVER_STATUS_SUCCESS + # we use the CUDA_OBJECTS recipe as a requirement for the sirocco recipe. This CUSOLVER_STATUS_SUCCESS # the CUDA source to be compiled to object code *if* NVCC is defined - python: startup python.o $(python_objects) $(CUDA_OBJECTS) - $(CC) $(CFLAGS) python.o $(python_objects) $(CUDA_OBJECTS) $(kpar_objects) $(LDFLAGS) -o python + sirocco: startup sirocco.o $(sirocco_objects) $(CUDA_OBJECTS) + $(CC) $(CFLAGS) sirocco.o $(sirocco_objects) $(CUDA_OBJECTS) $(kpar_objects) $(LDFLAGS) -o sirocco diff --git a/docs/sphinx/source/developer/programmer_notes.rst b/docs/sphinx/source/developer/programmer_notes.rst index 22fefbc8b..96c053dc4 100644 --- a/docs/sphinx/source/developer/programmer_notes.rst +++ b/docs/sphinx/source/developer/programmer_notes.rst @@ -30,7 +30,7 @@ The main header files are: strengths, photoionization cross-sections, elemental abundances, etc. These data are read in at the beginning of the program (see atomicdata.c and other similarly named routines) -* python.h - This contains the structures and other data that comprise the wind as well +* sirocco.h - This contains the structures and other data that comprise the wind as well as the parameters of the model. (This is fairly well-documented, or should be) @@ -94,7 +94,7 @@ The portions of the routine that are parallelize are: the radiative transfer step in the detailed spectrum phase. -MPI requires intialization. For python this is carried out in python.c. Various subroutines make +MPI requires intialization. For SIROCCO this is carried out in sirocco.c. Various subroutines make use of MPI, and as a result, programmers need to be aware of this fact when they write auxiliary routines that use the various subroutines called by SIROCCO. @@ -128,13 +128,13 @@ In the latest generations of gcc, the standards for extenral variiables have be If one wishes to define an external variable, one must first declare it as eternal, and then one must initialize it outside a specific routine exactly in one place. -The standard convention is that the variables are declared as external in a header file, e.g python.h, -and then intialized in a separate .c file, e.g python_extern_init.c. Unless, a variable is actually +The standard convention is that the variables are declared as external in a header file, e.g sirocco.h, +and then intialized in a separate .c file, e.g sirocco_extern_init.c. Unless, a variable is actually initialized, no space will be allocated for the variable. So if variables are added (or subtracted), one must make a change both in the relavant .h file. -Currently has three.c files atomic_extern_init.c, models_extern_init.c, python_extern_init.c -corresponding to the three main .h files, atommic.h, models.h and python.h +Currently has three.c files atomic_extern_init.c, models_extern_init.c, sirocco_extern_init.c +corresponding to the three main .h files, atommic.h, models.h and sirocco.h diff --git a/docs/sphinx/source/developer/tests.rst b/docs/sphinx/source/developer/tests.rst index aacd80168..bbdfc9738 100644 --- a/docs/sphinx/source/developer/tests.rst +++ b/docs/sphinx/source/developer/tests.rst @@ -151,16 +151,16 @@ implementation. If you need to create your own assertion, these should be kept i } -.. admonition:: Including :code:`python.h` in your tests +.. admonition:: Including :code:`sirocco.h` in your tests - If you need to access various structures or other things defined in :code:`python.h`, it is possible to include + If you need to access various structures or other things defined in :code:`sirocco.h`, it is possible to include the header file in your test source code as in the example below (there are some data structures which depend on values defined in :code:`atomic.h`), .. code:: c #include "../../atomic.h" - #include "../../python.h" + #include "../../sirocco.h" In some situations this might complicate compilation of the unit test. In those cases, it could be better to re-define anything you need in the source file for the unit test. diff --git a/docs/sphinx/source/input.rst b/docs/sphinx/source/input.rst index f0afabcba..1379d7b67 100644 --- a/docs/sphinx/source/input.rst +++ b/docs/sphinx/source/input.rst @@ -3,7 +3,7 @@ Inputs A comprehensive section and lists of all possible parameter inputs for various models. -**Overview** describes how python uses keywords for a .pf parameter file and how a user can create their own parameter files. +**Overview** describes how SIROCCO uses keywords for a .pf parameter file and how a user can create their own parameter files. **System Description** describes the initial system parameters for a model with an example to indicate what .pf output looks like. diff --git a/docs/sphinx/source/input/parameters/Disk/Disk.T_profile_file.rst b/docs/sphinx/source/input/parameters/Disk/Disk.T_profile_file.rst index b04b09c28..f38374d70 100644 --- a/docs/sphinx/source/input/parameters/Disk/Disk.T_profile_file.rst +++ b/docs/sphinx/source/input/parameters/Disk/Disk.T_profile_file.rst @@ -8,7 +8,7 @@ Type String File - `setup_disk.c `_ + `setup_disk.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Disk/Disk.colour_correction.rst b/docs/sphinx/source/input/parameters/Disk/Disk.colour_correction.rst index 97fc2448a..e41ed3e3a 100644 --- a/docs/sphinx/source/input/parameters/Disk/Disk.colour_correction.rst +++ b/docs/sphinx/source/input/parameters/Disk/Disk.colour_correction.rst @@ -11,7 +11,7 @@ Values File - `setup_disk.c `_ + `setup_disk.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Disk/Disk.mdot.rst b/docs/sphinx/source/input/parameters/Disk/Disk.mdot.rst index bc3881274..6c1e743f7 100644 --- a/docs/sphinx/source/input/parameters/Disk/Disk.mdot.rst +++ b/docs/sphinx/source/input/parameters/Disk/Disk.mdot.rst @@ -9,7 +9,7 @@ Unit :math:`M_{\odot}/yr` File - `setup_disk.c `_ + `setup_disk.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Disk/Disk.rad_type_in_final_spectrum.rst b/docs/sphinx/source/input/parameters/Disk/Disk.rad_type_in_final_spectrum.rst index b6178f6a2..96ea0114e 100644 --- a/docs/sphinx/source/input/parameters/Disk/Disk.rad_type_in_final_spectrum.rst +++ b/docs/sphinx/source/input/parameters/Disk/Disk.rad_type_in_final_spectrum.rst @@ -17,7 +17,7 @@ Values File - `python.c `_ + `sirocco.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Disk/Disk.rad_type_to_make_wind.rst b/docs/sphinx/source/input/parameters/Disk/Disk.rad_type_to_make_wind.rst index 7cbbe477b..ebf673e97 100644 --- a/docs/sphinx/source/input/parameters/Disk/Disk.rad_type_to_make_wind.rst +++ b/docs/sphinx/source/input/parameters/Disk/Disk.rad_type_to_make_wind.rst @@ -17,7 +17,7 @@ Values File - `setup_disk.c `_ + `setup_disk.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Disk/Disk.radiation.rst b/docs/sphinx/source/input/parameters/Disk/Disk.radiation.rst index e0a659de5..aa7d76a2c 100644 --- a/docs/sphinx/source/input/parameters/Disk/Disk.radiation.rst +++ b/docs/sphinx/source/input/parameters/Disk/Disk.radiation.rst @@ -6,7 +6,7 @@ Type Boolean(yes/no) File - `setup_disk.c `_ + `setup_disk.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Disk/Disk.radmax.rst b/docs/sphinx/source/input/parameters/Disk/Disk.radmax.rst index bf41115aa..6e76cc749 100644 --- a/docs/sphinx/source/input/parameters/Disk/Disk.radmax.rst +++ b/docs/sphinx/source/input/parameters/Disk/Disk.radmax.rst @@ -14,7 +14,7 @@ Values Greater than 0 File - `setup_disk.c `_ + `setup_disk.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Disk/Disk.radmin.rst b/docs/sphinx/source/input/parameters/Disk/Disk.radmin.rst index 1da159051..af4eec170 100644 --- a/docs/sphinx/source/input/parameters/Disk/Disk.radmin.rst +++ b/docs/sphinx/source/input/parameters/Disk/Disk.radmin.rst @@ -13,7 +13,7 @@ Values Equal to or greater than :ref:`Central_object.radius`. File - `setup_disk.c `_ + `setup_disk.c `_ Parent(s) * :ref:`Disk.type`: :code:`rmin>central.obj.rad` diff --git a/docs/sphinx/source/input/parameters/Disk/Disk.temperature.profile.rst b/docs/sphinx/source/input/parameters/Disk/Disk.temperature.profile.rst index 5abe9f3a0..e5728ec21 100644 --- a/docs/sphinx/source/input/parameters/Disk/Disk.temperature.profile.rst +++ b/docs/sphinx/source/input/parameters/Disk/Disk.temperature.profile.rst @@ -14,7 +14,7 @@ Values File - `setup_disk.c `_ + `setup_disk.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Disk/Disk.type.rst b/docs/sphinx/source/input/parameters/Disk/Disk.type.rst index 14a02c767..3b983623d 100644 --- a/docs/sphinx/source/input/parameters/Disk/Disk.type.rst +++ b/docs/sphinx/source/input/parameters/Disk/Disk.type.rst @@ -17,7 +17,7 @@ Values File - `setup_disk.c `_ + `setup_disk.c `_ Child(ren) diff --git a/docs/sphinx/source/input/parameters/Disk/Disk.z0.rst b/docs/sphinx/source/input/parameters/Disk/Disk.z0.rst index 7118e76b2..b74fe4dab 100644 --- a/docs/sphinx/source/input/parameters/Disk/Disk.z0.rst +++ b/docs/sphinx/source/input/parameters/Disk/Disk.z0.rst @@ -10,7 +10,7 @@ Values Greater than 0 File - `setup_disk.c `_ + `setup_disk.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Disk/Disk.z1.rst b/docs/sphinx/source/input/parameters/Disk/Disk.z1.rst index 098e8fb31..41f798e75 100644 --- a/docs/sphinx/source/input/parameters/Disk/Disk.z1.rst +++ b/docs/sphinx/source/input/parameters/Disk/Disk.z1.rst @@ -11,7 +11,7 @@ Values Greater than 0 File - `setup_disk.c `_ + `setup_disk.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Input_spectra.model_file.rst b/docs/sphinx/source/input/parameters/Input_spectra.model_file.rst index 0ad9dbe77..32391d51d 100644 --- a/docs/sphinx/source/input/parameters/Input_spectra.model_file.rst +++ b/docs/sphinx/source/input/parameters/Input_spectra.model_file.rst @@ -12,7 +12,7 @@ Type String File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.angle.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.angle.rst index fcc69f74c..c618d6696 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.angle.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.angle.rst @@ -14,7 +14,7 @@ Values 0 to 90 degrees, where 0 is normal to the disk and 90 is on the disk plane File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.live_or_die.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.live_or_die.rst index 0ebc3336b..59ca24207 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.live_or_die.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.live_or_die.rst @@ -24,7 +24,7 @@ Values File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.no_observers.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.no_observers.rst index 3378aa925..855ee3d07 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.no_observers.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.no_observers.rst @@ -10,7 +10,7 @@ Values Greater than 0 File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.nwave.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.nwave.rst index adaa79d69..56adfb268 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.nwave.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.nwave.rst @@ -10,7 +10,7 @@ Values Greater than 100 File - `setup.c `_ + `setup.c `_ Parent(s) * :ref:`Spectrum_cycles`: Greater than or equal to 0 diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.orbit_phase.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.orbit_phase.rst index 285b6f727..462237e33 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.orbit_phase.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.orbit_phase.rst @@ -14,7 +14,7 @@ Values Between 0 and 1 File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_azimuth.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_azimuth.rst index 6bab58bb8..e02507cb9 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_azimuth.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_azimuth.rst @@ -16,7 +16,7 @@ Values Between 0, and 360 or -180 to 180 File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_location.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_location.rst index 0fbb6adb1..d77a77fb0 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_location.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_location.rst @@ -23,7 +23,7 @@ Values File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_photons_by_position.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_photons_by_position.rst index ac8f9c979..9a9a0ee9f 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_photons_by_position.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_photons_by_position.rst @@ -19,7 +19,7 @@ Type Boolean (yes/no) File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_r.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_r.rst index 66b5b4707..c65da704f 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_r.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_r.rst @@ -13,7 +13,7 @@ Values Greater than 0 File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_rho.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_rho.rst index 1f6be0e85..38da5b0f0 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_rho.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_rho.rst @@ -16,7 +16,7 @@ Values Condition e.g. greater than 0 or list e.g. [1, 2, 5] File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_scatters.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_scatters.rst index 896b2094c..548042e21 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_scatters.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_scatters.rst @@ -29,7 +29,7 @@ Values Greater than 0 File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_specific_no_of_scatters_in_spectra.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_specific_no_of_scatters_in_spectra.rst index d548bcef6..09c2b8c99 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_specific_no_of_scatters_in_spectra.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_specific_no_of_scatters_in_spectra.rst @@ -15,7 +15,7 @@ Type Boolean (yes/no) File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_z.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_z.rst index d178e35a0..332e13b0d 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_z.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.select_z.rst @@ -16,7 +16,7 @@ Values Within the z range of the model File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.type.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.type.rst index a1c07446e..84446890e 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.type.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.type.rst @@ -19,7 +19,7 @@ Values File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.wavemax.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.wavemax.rst index 548f9e70b..c709041ba 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.wavemax.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.wavemax.rst @@ -13,7 +13,7 @@ Values File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.wavemin.rst b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.wavemin.rst index 639df15e1..23b5f7a35 100644 --- a/docs/sphinx/source/input/parameters/Spectrum/Spectrum.wavemin.rst +++ b/docs/sphinx/source/input/parameters/Spectrum/Spectrum.wavemin.rst @@ -12,7 +12,7 @@ Values Greater than 0 File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/System_type.rst b/docs/sphinx/source/input/parameters/System_type.rst index bd8ad47a2..3f78b917c 100644 --- a/docs/sphinx/source/input/parameters/System_type.rst +++ b/docs/sphinx/source/input/parameters/System_type.rst @@ -20,12 +20,12 @@ Values A system in which the central object is a supermassive black hole. previous - In this case, the user is starting from a previous python run. The user can either continue the + In this case, the user is starting from a previous SIROCCO run. The user can either continue the run or change some parameters associated with radiation sources. File - `python.c `_ + `sirocco.c `_ Child(ren) diff --git a/docs/sphinx/source/input/parameters/central_object/Binary/Binary.mass_sec.rst b/docs/sphinx/source/input/parameters/central_object/Binary/Binary.mass_sec.rst index de82a967f..888975352 100644 --- a/docs/sphinx/source/input/parameters/central_object/Binary/Binary.mass_sec.rst +++ b/docs/sphinx/source/input/parameters/central_object/Binary/Binary.mass_sec.rst @@ -14,7 +14,7 @@ Values Greater than 0 File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Binary/Binary.period.rst b/docs/sphinx/source/input/parameters/central_object/Binary/Binary.period.rst index d714d708d..01b287d0e 100644 --- a/docs/sphinx/source/input/parameters/central_object/Binary/Binary.period.rst +++ b/docs/sphinx/source/input/parameters/central_object/Binary/Binary.period.rst @@ -14,7 +14,7 @@ Values Greater than 0 File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.luminosity.rst b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.luminosity.rst index 51911e9e4..0717bd34e 100644 --- a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.luminosity.rst +++ b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.luminosity.rst @@ -12,7 +12,7 @@ Values Greater than 0 File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.power_law_cutoff.rst b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.power_law_cutoff.rst index 54d058ef7..53b13e239 100644 --- a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.power_law_cutoff.rst +++ b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.power_law_cutoff.rst @@ -14,7 +14,7 @@ Values Greater than 0 File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.power_law_index.rst b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.power_law_index.rst index 4b69d3dbc..2c74db322 100644 --- a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.power_law_index.rst +++ b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.power_law_index.rst @@ -10,7 +10,7 @@ Values Any - must use negative (-) sign for negative indexes. File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_in_final_spectrum.rst b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_in_final_spectrum.rst index f3c94492b..d85125113 100644 --- a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_in_final_spectrum.rst +++ b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_in_final_spectrum.rst @@ -27,7 +27,7 @@ Values File - `python.c `_ + `sirocco.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_to_make_wind.rst b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_to_make_wind.rst index 23d2068a9..d9efd250c 100644 --- a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_to_make_wind.rst +++ b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.rad_type_to_make_wind.rst @@ -24,7 +24,7 @@ Values File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.radiation.rst b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.radiation.rst index 6f812b756..8b73f683f 100644 --- a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.radiation.rst +++ b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.radiation.rst @@ -6,7 +6,7 @@ Type Boolean (yes/no) File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.temp.rst b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.temp.rst index e81b13605..38a88a91b 100644 --- a/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.temp.rst +++ b/docs/sphinx/source/input/parameters/central_object/Boundary_layer/Boundary_layer.temp.rst @@ -12,7 +12,7 @@ Values Greater than 0 File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.blackbody_temp.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.blackbody_temp.rst index c6683ea8d..de42230d1 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.blackbody_temp.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.blackbody_temp.rst @@ -13,7 +13,7 @@ Values Greater than 0 File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.bremsstrahlung_alpha.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.bremsstrahlung_alpha.rst index 6c80b6169..8322a90c1 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.bremsstrahlung_alpha.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.bremsstrahlung_alpha.rst @@ -11,7 +11,7 @@ Values Any - must use negative (-) sign for negative values. File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.bremsstrahlung_temp.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.bremsstrahlung_temp.rst index b60a0a11e..c393b6168 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.bremsstrahlung_temp.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.bremsstrahlung_temp.rst @@ -14,7 +14,7 @@ Values Greater than 0 File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.bubble_size.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.bubble_size.rst index 46ae13a5c..0f8f6d4ed 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.bubble_size.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.bubble_size.rst @@ -13,7 +13,7 @@ Values Greater than 0 File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) * :ref:`Central_object.geometry_for_source`: ``bubble`` diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.cloudy.high_energy_break.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.cloudy.high_energy_break.rst index e33bb6a48..4efe46b02 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.cloudy.high_energy_break.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.cloudy.high_energy_break.rst @@ -16,7 +16,7 @@ Values Greater than :ref:`Central_object.cloudy.low_energy_break` File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.cloudy.low_energy_break.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.cloudy.low_energy_break.rst index 26208b19a..36367fd6d 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.cloudy.low_energy_break.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.cloudy.low_energy_break.rst @@ -16,7 +16,7 @@ Values Greater than 0 File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.geometry_for_source.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.geometry_for_source.rst index 4223448b9..dfcc31a07 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.geometry_for_source.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.geometry_for_source.rst @@ -24,7 +24,7 @@ Values This model generates isotropic photons. File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.lamp_post_height.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.lamp_post_height.rst index 3d284b08b..71f7048c1 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.lamp_post_height.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.lamp_post_height.rst @@ -13,7 +13,7 @@ Values Greater than 0 File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.luminosity.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.luminosity.rst index 0d62c3de2..ffd74b525 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.luminosity.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.luminosity.rst @@ -13,7 +13,7 @@ Values Greater than 0. File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.mass.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.mass.rst index 5960840db..b1e2df32a 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.mass.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.mass.rst @@ -13,6 +13,6 @@ Values Greater than 0 File - `setup_star_bh.c `_ + `setup_star_bh.c `_ diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.power_law_cutoff.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.power_law_cutoff.rst index fbd336085..1d5a510da 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.power_law_cutoff.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.power_law_cutoff.rst @@ -14,7 +14,7 @@ Values Greater than or equal to 0 File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.power_law_index.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.power_law_index.rst index 14a1b1db0..6dafc2dcc 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.power_law_index.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.power_law_index.rst @@ -10,7 +10,7 @@ Values Greater than 0 File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_in_final_spectrum.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_in_final_spectrum.rst index 5066717dc..a4d2ebb9b 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_in_final_spectrum.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_in_final_spectrum.rst @@ -45,7 +45,7 @@ Values File - `python.c `_ + `sirocco.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_to_make_wind.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_to_make_wind.rst index dc690bc39..12fed7f6b 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_to_make_wind.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.rad_type_to_make_wind.rst @@ -36,7 +36,7 @@ Values File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.radiation.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.radiation.rst index c77a9a3c1..eb210a25d 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.radiation.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.radiation.rst @@ -7,7 +7,7 @@ Type Boolean (yes/no) File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Child(ren) diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.radius.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.radius.rst index 64209c0bc..4fa08bbe5 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.radius.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.radius.rst @@ -14,6 +14,6 @@ Values Greater than 0 File - `setup_star_bh.c `_ + `setup_star_bh.c `_ diff --git a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.temp.rst b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.temp.rst index abcb7d743..123aa93b9 100644 --- a/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.temp.rst +++ b/docs/sphinx/source/input/parameters/central_object/Central_object/Central_object.temp.rst @@ -13,7 +13,7 @@ Values Greater than zero File - `setup_star_bh.c `_ + `setup_star_bh.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.adjust_grid.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.adjust_grid.rst index 91ef46be7..874123cd8 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.adjust_grid.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.adjust_grid.rst @@ -7,7 +7,7 @@ Type Boolean (yes/no) File - `setup_domains.c `_ + `setup_domains.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.angle.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.angle.rst index 8b35c6405..c918a84d4 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.angle.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.angle.rst @@ -13,7 +13,7 @@ Values Greater than 0 File - `diag.c `_ + `diag.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.extra.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.extra.rst index 303800b47..a98d5c0c6 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.extra.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.extra.rst @@ -9,7 +9,7 @@ Type Boolean (yes/no) File - `python.c `_ + `sirocco.c `_ Child(ren) diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.fractional_distance_photon_may_travel.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.fractional_distance_photon_may_travel.rst index dca6bbfda..6a5deb775 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.fractional_distance_photon_may_travel.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.fractional_distance_photon_may_travel.rst @@ -13,7 +13,7 @@ Values 0 to 1 File - `diag.c `_ + `diag.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.invoke_searchlight_option.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.invoke_searchlight_option.rst index daee7b02a..729965e29 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.invoke_searchlight_option.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.invoke_searchlight_option.rst @@ -8,7 +8,7 @@ Type Boolean (yes/no) File - `diag.c `_ + `diag.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.keep_ioncycle_spectra.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.keep_ioncycle_spectra.rst index f3ec5a6a9..742bd9106 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.keep_ioncycle_spectra.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.keep_ioncycle_spectra.rst @@ -3,14 +3,14 @@ Diag.keep_ioncycle_spectra Decide whether or not to keep a copy of the log_spec_tot file after each spectral cycle in order to track the changes as the -code converges. Produces files of format python01.log_spec_tot and so +code converges. Produces files of format sirocco01.log_spec_tot and so on (02,03...) for subsequent cycles. Type Boolean(yes/no) File - `diag.c `_ + `diag.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.keep_ioncycle_windsaves.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.keep_ioncycle_windsaves.rst index 7274288ab..8110fe1f1 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.keep_ioncycle_windsaves.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.keep_ioncycle_windsaves.rst @@ -2,14 +2,14 @@ Diag.keep_ioncycle_windsaves ============================ Decide whether or not to keep a copy of the windsave file after each ionization cycle in order to track the changes as the -code converges. Produces files of format python01.wind_save and so +code converges. Produces files of format sirocco01.wind_save and so on (02,03...) for subsequent cycles. Type Boolean(yes/no) File - `diag.c `_ + `diag.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.keep_photoabs_in_final_spectra.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.keep_photoabs_in_final_spectra.rst index 98af3012a..031ec5e0a 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.keep_photoabs_in_final_spectra.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.keep_photoabs_in_final_spectra.rst @@ -7,7 +7,7 @@ Type Boolean (yes/no) File - `diag.c `_ + `diag.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.location.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.location.rst index 01a9a8934..dfbefe4e9 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.location.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.location.rst @@ -15,7 +15,7 @@ Values The searchlight originates at a user determined location and angle along the disk. File - `diag.c `_ + `diag.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.lowest_ion_density_for_photoabs.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.lowest_ion_density_for_photoabs.rst index 92a93afa6..9cade9cfd 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.lowest_ion_density_for_photoabs.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.lowest_ion_density_for_photoabs.rst @@ -14,7 +14,7 @@ Values Greater than 0 File - `diag.c `_ + `diag.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.make_ioncycle_tables.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.make_ioncycle_tables.rst index 7e8f2fc25..94745945c 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.make_ioncycle_tables.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.make_ioncycle_tables.rst @@ -6,7 +6,7 @@ Type Boolean (yes/no) File - `diag.c `_ + `diag.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.print_dvds_info.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.print_dvds_info.rst index 068d0bd1e..1b1d677a6 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.print_dvds_info.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.print_dvds_info.rst @@ -7,7 +7,7 @@ Type Boolean (yes/no) File - `diag.c `_ + `diag.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.r.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.r.rst index 027a3fdf7..a84427e03 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.r.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.r.rst @@ -13,7 +13,7 @@ Values Greater than 0 File - `diag.c `_ + `diag.c `_ Parent(s) * :ref:`Diag.extra`: :code:`yes` diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.save_cell_statistics.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.save_cell_statistics.rst index cec798138..82b0dc73b 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.save_cell_statistics.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.save_cell_statistics.rst @@ -9,7 +9,7 @@ Type Boolean (yes/no) File - `diag.c `_ + `diag.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.save_extract_photons.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.save_extract_photons.rst index 5d588c20f..62b98323d 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.save_extract_photons.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.save_extract_photons.rst @@ -35,7 +35,7 @@ Type Boolean (yes/no) File - `diag.c `_ + `diag.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.save_photons.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.save_photons.rst index 6d32d7b5a..e466967ea 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.save_photons.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.save_photons.rst @@ -34,7 +34,7 @@ Type Boolean (yes/no) File - `diag.c `_ + `diag.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.track_resonant_scatters.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.track_resonant_scatters.rst index 23827774d..1f7c607f4 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.track_resonant_scatters.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.track_resonant_scatters.rst @@ -24,7 +24,7 @@ Type Boolean (yes/no) File - `diag.c `_ + `diag.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.use_jumps_for_emissivities_in_detailed_spectra.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.use_jumps_for_emissivities_in_detailed_spectra.rst index d4b43a093..3c11cd88c 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.use_jumps_for_emissivities_in_detailed_spectra.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.use_jumps_for_emissivities_in_detailed_spectra.rst @@ -9,7 +9,7 @@ Type Boolean (yes/no) File - `diag.c `_ + `diag.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.use_standard_care_factors.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.use_standard_care_factors.rst index 514c1bb33..be3870c94 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.use_standard_care_factors.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.use_standard_care_factors.rst @@ -9,7 +9,7 @@ Type Boolean (yes/no) File - `diag.c `_ + `diag.c `_ Child(ren) diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.use_upweighting_of_simple_macro_atoms.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.use_upweighting_of_simple_macro_atoms.rst index 86f64dba3..e91f3b903 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.use_upweighting_of_simple_macro_atoms.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.use_upweighting_of_simple_macro_atoms.rst @@ -16,7 +16,7 @@ Type Boolean (yes/no) File - `diag.c `_ + `diag.c `_ Parent(s) * :ref:`Diag.extra`: ``True`` diff --git a/docs/sphinx/source/input/parameters/other/Diag/Diag.write_atomicdata.rst b/docs/sphinx/source/input/parameters/other/Diag/Diag.write_atomicdata.rst index 0863953b2..eb9ddba59 100644 --- a/docs/sphinx/source/input/parameters/other/Diag/Diag.write_atomicdata.rst +++ b/docs/sphinx/source/input/parameters/other/Diag/Diag.write_atomicdata.rst @@ -7,6 +7,6 @@ Type Boolean (yes/no) File - `setup_domains.c `_ + `setup_domains.c `_ diff --git a/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.approach.rst b/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.approach.rst index ea4b58448..e161c7700 100644 --- a/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.approach.rst +++ b/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.approach.rst @@ -37,7 +37,7 @@ Values File - `bands.c `_ + `bands.c `_ Child(ren) diff --git a/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.band_boundary.rst b/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.band_boundary.rst index f3898459b..749f5d5fc 100644 --- a/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.band_boundary.rst +++ b/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.band_boundary.rst @@ -14,7 +14,7 @@ Values Greater than 0, monotonically increasing File - `bands.c `_ + `bands.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.band_min_frac.rst b/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.band_min_frac.rst index 10b773903..90e069891 100644 --- a/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.band_min_frac.rst +++ b/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.band_min_frac.rst @@ -13,7 +13,7 @@ Values Greater than 0 and should sum to less than 1 over all bands File - `bands.c `_ + `bands.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.high_energy_limit.rst b/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.high_energy_limit.rst index 7a31f1a6e..4d246651e 100644 --- a/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.high_energy_limit.rst +++ b/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.high_energy_limit.rst @@ -13,7 +13,7 @@ Values Greater than :ref:`Photon_sampling.low_energy_limit` File - `bands.c `_ + `bands.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.low_energy_limit.rst b/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.low_energy_limit.rst index 269c97bf7..d26c0af54 100644 --- a/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.low_energy_limit.rst +++ b/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.low_energy_limit.rst @@ -14,7 +14,7 @@ Values Greater than 0 File - `bands.c `_ + `bands.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.nbands.rst b/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.nbands.rst index 1af323989..22273a905 100644 --- a/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.nbands.rst +++ b/docs/sphinx/source/input/parameters/other/Photon_sampling/Photon_sampling.nbands.rst @@ -11,7 +11,7 @@ Values Greater than 0 File - `bands.c `_ + `bands.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.angle_bins.rst b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.angle_bins.rst index 7d4273e8e..ce9099c92 100644 --- a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.angle_bins.rst +++ b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.angle_bins.rst @@ -11,7 +11,7 @@ Values Greater than 0 File - `setup_reverb.c `_ + `setup_reverb.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.disk_type.rst b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.disk_type.rst index cb0b8892c..47c555139 100644 --- a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.disk_type.rst +++ b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.disk_type.rst @@ -34,7 +34,7 @@ Values File - `setup_reverb.c `_ + `setup_reverb.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.dump_cell.rst b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.dump_cell.rst index f306414b9..234709917 100644 --- a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.dump_cell.rst +++ b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.dump_cell.rst @@ -18,7 +18,7 @@ Values File - `setup_reverb.c `_ + `setup_reverb.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.dump_cells.rst b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.dump_cells.rst index 4ab164a46..931789614 100644 --- a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.dump_cells.rst +++ b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.dump_cells.rst @@ -11,7 +11,7 @@ Values Greater than or equal to 0 File - `setup_reverb.c `_ + `setup_reverb.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.filter_line.rst b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.filter_line.rst index e6ebc0897..f2010bd04 100644 --- a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.filter_line.rst +++ b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.filter_line.rst @@ -1,7 +1,7 @@ Reverb.filter_line ================== Line number of one line to include in the output ``.delay_dump`` file. This is -the python internal line number. It can be found using either the macro-atom +the sirocco internal line number. It can be found using either the macro-atom mode (which prints out the line number once it's found one) or by doing an exploratory run with :ref:`reverb.filter_lines` = -1, then looking through the delay dump file for photons of the right wavelength to see what their line is. This @@ -18,7 +18,7 @@ Values Any valid line index File - `setup_reverb.c `_ + `setup_reverb.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.filter_lines.rst b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.filter_lines.rst index feb2f6c57..ffc31edca 100644 --- a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.filter_lines.rst +++ b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.filter_lines.rst @@ -29,7 +29,7 @@ Values File - `setup_reverb.c `_ + `setup_reverb.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.matom_line.rst b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.matom_line.rst index 9cc2bb0f9..fa80e5677 100644 --- a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.matom_line.rst +++ b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.matom_line.rst @@ -14,7 +14,7 @@ Values File - `setup_reverb.c `_ + `setup_reverb.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.matom_lines.rst b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.matom_lines.rst index 2c3730fde..89fdfc167 100644 --- a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.matom_lines.rst +++ b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.matom_lines.rst @@ -13,7 +13,7 @@ Values Greater than or equal to 0 File - `setup_reverb.c `_ + `setup_reverb.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.path_bins.rst b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.path_bins.rst index 0812c170f..d90a868e5 100644 --- a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.path_bins.rst +++ b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.path_bins.rst @@ -15,7 +15,7 @@ Values Greater than 0 File - `setup_reverb.c `_ + `setup_reverb.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.type.rst b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.type.rst index 1728e1da2..356700b6d 100644 --- a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.type.rst +++ b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.type.rst @@ -41,7 +41,7 @@ Values File - `setup_reverb.c `_ + `setup_reverb.c `_ Child(ren) diff --git a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.visualisation.rst b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.visualisation.rst index f90be3126..76d3ae058 100644 --- a/docs/sphinx/source/input/parameters/other/Reverb/Reverb.visualisation.rst +++ b/docs/sphinx/source/input/parameters/other/Reverb/Reverb.visualisation.rst @@ -26,7 +26,7 @@ Values File - `setup_reverb.c `_ + `setup_reverb.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/geo/geo.xlog_scale.rst b/docs/sphinx/source/input/parameters/other/geo/geo.xlog_scale.rst index f29ac63d0..b1230502f 100644 --- a/docs/sphinx/source/input/parameters/other/geo/geo.xlog_scale.rst +++ b/docs/sphinx/source/input/parameters/other/geo/geo.xlog_scale.rst @@ -9,7 +9,7 @@ Unit cm File - `setup_domains.c `_ + `setup_domains.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/geo/geo.zlog_scale.rst b/docs/sphinx/source/input/parameters/other/geo/geo.zlog_scale.rst index 93bc92647..f1c16e24e 100644 --- a/docs/sphinx/source/input/parameters/other/geo/geo.zlog_scale.rst +++ b/docs/sphinx/source/input/parameters/other/geo/geo.zlog_scale.rst @@ -9,7 +9,7 @@ Unit cm File - `setup_domains.c `_ + `setup_domains.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/other/monochromatic/monochromatic.wavelength.rst b/docs/sphinx/source/input/parameters/other/monochromatic/monochromatic.wavelength.rst index b16226205..7dcb38c60 100644 --- a/docs/sphinx/source/input/parameters/other/monochromatic/monochromatic.wavelength.rst +++ b/docs/sphinx/source/input/parameters/other/monochromatic/monochromatic.wavelength.rst @@ -12,7 +12,7 @@ Values Greater than 0 File - `setup.c `_ + `setup.c `_ Parent(s) :ref:`Central_object.rad_type_in_final_spectrum`: :code:`mono` diff --git a/docs/sphinx/source/input/parameters/rt_ion/Atomic_data.rst b/docs/sphinx/source/input/parameters/rt_ion/Atomic_data.rst index 16c98c809..009e27970 100644 --- a/docs/sphinx/source/input/parameters/rt_ion/Atomic_data.rst +++ b/docs/sphinx/source/input/parameters/rt_ion/Atomic_data.rst @@ -7,7 +7,7 @@ Type String File - `setup_line_transfer.c `_ + `setup_line_transfer.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/rt_ion/Ionization_cycles.rst b/docs/sphinx/source/input/parameters/rt_ion/Ionization_cycles.rst index 5ff5cc6be..9cb50b6e5 100644 --- a/docs/sphinx/source/input/parameters/rt_ion/Ionization_cycles.rst +++ b/docs/sphinx/source/input/parameters/rt_ion/Ionization_cycles.rst @@ -10,6 +10,6 @@ Values Greater than 0 File - `setup.c `_ + `setup.c `_ diff --git a/docs/sphinx/source/input/parameters/rt_ion/Line_transfer.rst b/docs/sphinx/source/input/parameters/rt_ion/Line_transfer.rst index 18cb94085..6d0504c9e 100644 --- a/docs/sphinx/source/input/parameters/rt_ion/Line_transfer.rst +++ b/docs/sphinx/source/input/parameters/rt_ion/Line_transfer.rst @@ -60,7 +60,7 @@ Values File - `setup_line_transfer.c `_ + `setup_line_transfer.c `_ Child(ren) diff --git a/docs/sphinx/source/input/parameters/rt_ion/Matom_transition_mode.rst b/docs/sphinx/source/input/parameters/rt_ion/Matom_transition_mode.rst index 1ddd69995..a5b2266b5 100644 --- a/docs/sphinx/source/input/parameters/rt_ion/Matom_transition_mode.rst +++ b/docs/sphinx/source/input/parameters/rt_ion/Matom_transition_mode.rst @@ -15,7 +15,7 @@ Values correspond to transitions that generate k-packets. Transitions between levels of the macro atom via collisions with the thermal pool correspond to internal transitions of the absorbing state machine. The advantage of an absorbing state machine is that the new r-packet is not generated probabilistically but through several matrix inversions. All of the macro atoms and the thermal pool are treated simultaneously. File - `setup_line_transfer.c `_ + `setup_line_transfer.c `_ Parent(s) * :ref:`Line_transfer` diff --git a/docs/sphinx/source/input/parameters/rt_ion/Photons_per_cycle.rst b/docs/sphinx/source/input/parameters/rt_ion/Photons_per_cycle.rst index 3570bc2b2..4bd19697c 100644 --- a/docs/sphinx/source/input/parameters/rt_ion/Photons_per_cycle.rst +++ b/docs/sphinx/source/input/parameters/rt_ion/Photons_per_cycle.rst @@ -9,6 +9,6 @@ Values Greater than 0 File - `setup.c `_ + `setup.c `_ diff --git a/docs/sphinx/source/input/parameters/rt_ion/Spectrum_cycles.rst b/docs/sphinx/source/input/parameters/rt_ion/Spectrum_cycles.rst index a289f62c2..e34ff8d59 100644 --- a/docs/sphinx/source/input/parameters/rt_ion/Spectrum_cycles.rst +++ b/docs/sphinx/source/input/parameters/rt_ion/Spectrum_cycles.rst @@ -14,7 +14,7 @@ Type Integer File - `setup.c `_ + `setup.c `_ Child(ren) diff --git a/docs/sphinx/source/input/parameters/rt_ion/Surface.reflection.or.absorption.rst b/docs/sphinx/source/input/parameters/rt_ion/Surface.reflection.or.absorption.rst index dff5cd61a..744eddfc9 100644 --- a/docs/sphinx/source/input/parameters/rt_ion/Surface.reflection.or.absorption.rst +++ b/docs/sphinx/source/input/parameters/rt_ion/Surface.reflection.or.absorption.rst @@ -17,6 +17,6 @@ Values File - `setup.c `_ + `setup.c `_ diff --git a/docs/sphinx/source/input/parameters/rt_ion/Wind_heating/Wind_heating.extra_luminosity.rst b/docs/sphinx/source/input/parameters/rt_ion/Wind_heating/Wind_heating.extra_luminosity.rst index f10e2b704..58cd30916 100644 --- a/docs/sphinx/source/input/parameters/rt_ion/Wind_heating/Wind_heating.extra_luminosity.rst +++ b/docs/sphinx/source/input/parameters/rt_ion/Wind_heating/Wind_heating.extra_luminosity.rst @@ -1,7 +1,7 @@ Wind_heating.extra_luminosity ============================= -This options allows the user to determine the shock factor. The shock heating is defined initally as a luminosity to be added to wind but is immediately converted to a luminosity per unit volume. Since nearly all systems that we are dealing with have a star, we initialize the amount of extra heating as a fraction of the stellar luminosity. See `cooling.c `_ :code:`shock_heating`. +This options allows the user to determine the shock factor. The shock heating is defined initally as a luminosity to be added to wind but is immediately converted to a luminosity per unit volume. Since nearly all systems that we are dealing with have a star, we initialize the amount of extra heating as a fraction of the stellar luminosity. See `cooling.c `_ :code:`shock_heating`. This is a very special option put in place for modelling FU Ori stars, and should be used with extreme caution. The default value is calculated as a function of the star's radius and temperature. @@ -16,7 +16,7 @@ Values Greater than 0 File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/rt_ion/Wind_heating/Wind_heating.extra_processes.rst b/docs/sphinx/source/input/parameters/rt_ion/Wind_heating/Wind_heating.extra_processes.rst index 2d4e41e1a..9f0e0a355 100644 --- a/docs/sphinx/source/input/parameters/rt_ion/Wind_heating/Wind_heating.extra_processes.rst +++ b/docs/sphinx/source/input/parameters/rt_ion/Wind_heating/Wind_heating.extra_processes.rst @@ -16,7 +16,7 @@ Values nonthermal This is a very special option put in place for modelling FU Ori stars, and should be used with extreme caution. - This mode enables shock heating into the thermal balance of the wind. The shock heating is defined initally as a luminosity to be added to wind but is immediately converted to a luminosity per unit volume (:ref:`Wind_heating.extra_luminosity`). Since nearly all systems that we are dealing with have a star, we initialize the amount of extra heating as a fraction of the stellar luminosity. See `cooling.c `_ :code:`shock_heating`. + This mode enables shock heating into the thermal balance of the wind. The shock heating is defined initally as a luminosity to be added to wind but is immediately converted to a luminosity per unit volume (:ref:`Wind_heating.extra_luminosity`). Since nearly all systems that we are dealing with have a star, we initialize the amount of extra heating as a fraction of the stellar luminosity. See `cooling.c `_ :code:`shock_heating`. both This is a very special option put in place for modelling FU Ori stars, and should be used with extreme caution. @@ -25,7 +25,7 @@ Values File - `setup.c `_ + `setup.c `_ Child(ren) diff --git a/docs/sphinx/source/input/parameters/rt_ion/Wind_heating/Wind_heating.kpacket_frac.rst b/docs/sphinx/source/input/parameters/rt_ion/Wind_heating/Wind_heating.kpacket_frac.rst index d41eb2ce7..252a9d158 100644 --- a/docs/sphinx/source/input/parameters/rt_ion/Wind_heating/Wind_heating.kpacket_frac.rst +++ b/docs/sphinx/source/input/parameters/rt_ion/Wind_heating/Wind_heating.kpacket_frac.rst @@ -12,7 +12,7 @@ Values Greater than 0 File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Corona/Corona.base_den.rst b/docs/sphinx/source/input/parameters/wind/Corona/Corona.base_den.rst index c8fefa4e5..2daea6b52 100644 --- a/docs/sphinx/source/input/parameters/wind/Corona/Corona.base_den.rst +++ b/docs/sphinx/source/input/parameters/wind/Corona/Corona.base_den.rst @@ -12,7 +12,7 @@ Values Greater than 0 File - `corona.c `_ + `corona.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Corona/Corona.radmax.rst b/docs/sphinx/source/input/parameters/wind/Corona/Corona.radmax.rst index c309bb850..bccf5b79a 100644 --- a/docs/sphinx/source/input/parameters/wind/Corona/Corona.radmax.rst +++ b/docs/sphinx/source/input/parameters/wind/Corona/Corona.radmax.rst @@ -13,7 +13,7 @@ Values Greater than :ref:`Central_object.radius` File - `corona.c `_ + `corona.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Corona/Corona.radmin.rst b/docs/sphinx/source/input/parameters/wind/Corona/Corona.radmin.rst index c52974bfa..5949f824c 100644 --- a/docs/sphinx/source/input/parameters/wind/Corona/Corona.radmin.rst +++ b/docs/sphinx/source/input/parameters/wind/Corona/Corona.radmin.rst @@ -13,7 +13,7 @@ Values Greater than :ref:`Central_object.radius` File - `corona.c `_ + `corona.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Corona/Corona.scale_height.rst b/docs/sphinx/source/input/parameters/wind/Corona/Corona.scale_height.rst index ec3840452..bdcb1fdc3 100644 --- a/docs/sphinx/source/input/parameters/wind/Corona/Corona.scale_height.rst +++ b/docs/sphinx/source/input/parameters/wind/Corona/Corona.scale_height.rst @@ -13,7 +13,7 @@ Values Greater than 0 File - `corona.c `_ + `corona.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Corona/Corona.vel_frac.rst b/docs/sphinx/source/input/parameters/wind/Corona/Corona.vel_frac.rst index 4a3522995..067539a2d 100644 --- a/docs/sphinx/source/input/parameters/wind/Corona/Corona.vel_frac.rst +++ b/docs/sphinx/source/input/parameters/wind/Corona/Corona.vel_frac.rst @@ -17,7 +17,7 @@ Values Any, 0 implies no radial velocity. File - `corona.c `_ + `corona.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Corona/Corona.zmax.rst b/docs/sphinx/source/input/parameters/wind/Corona/Corona.zmax.rst index 775a26433..1e390655e 100644 --- a/docs/sphinx/source/input/parameters/wind/Corona/Corona.zmax.rst +++ b/docs/sphinx/source/input/parameters/wind/Corona/Corona.zmax.rst @@ -14,7 +14,7 @@ Values Greater than that the radius of the central object File - `corona.c `_ + `corona.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.boundary_mdot.rst b/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.boundary_mdot.rst index a8c1b44d6..1a2a8d40e 100644 --- a/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.boundary_mdot.rst +++ b/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.boundary_mdot.rst @@ -15,7 +15,7 @@ Values Greater than 0 File - `homologous.c `_ + `homologous.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.density_exponent.rst b/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.density_exponent.rst index 1159605df..45794aa43 100644 --- a/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.density_exponent.rst +++ b/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.density_exponent.rst @@ -10,7 +10,7 @@ Values Greater than 0 for a density that declines with radius File - `homologous.c `_ + `homologous.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.radmax.rst b/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.radmax.rst index 927900d32..4b6b701e0 100644 --- a/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.radmax.rst +++ b/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.radmax.rst @@ -14,7 +14,7 @@ Values Greater than :ref:`Homologous.radmin` File - `homologous.c `_ + `homologous.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.radmin.rst b/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.radmin.rst index 5394a186b..780f975e0 100644 --- a/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.radmin.rst +++ b/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.radmin.rst @@ -14,7 +14,7 @@ Values Greater than or equal to :ref:`Central_object.radius` File - `homologous.c `_ + `homologous.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.vbase.rst b/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.vbase.rst index cfdce11b6..c7aec0425 100644 --- a/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.vbase.rst +++ b/docs/sphinx/source/input/parameters/wind/Homologous/Homologous.vbase.rst @@ -13,7 +13,7 @@ Values Greater than 0 File - `homologous.c `_ + `homologous.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Hydro/Hydro.file.rst b/docs/sphinx/source/input/parameters/wind/Hydro/Hydro.file.rst index 4ec613508..0c3e1d965 100644 --- a/docs/sphinx/source/input/parameters/wind/Hydro/Hydro.file.rst +++ b/docs/sphinx/source/input/parameters/wind/Hydro/Hydro.file.rst @@ -6,7 +6,7 @@ Type String File - `hydro_import.c `_ + `hydro_import.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Hydro/Hydro.thetamax.rst b/docs/sphinx/source/input/parameters/wind/Hydro/Hydro.thetamax.rst index 86ecdf768..7017aa96f 100644 --- a/docs/sphinx/source/input/parameters/wind/Hydro/Hydro.thetamax.rst +++ b/docs/sphinx/source/input/parameters/wind/Hydro/Hydro.thetamax.rst @@ -20,7 +20,7 @@ Values File - `hydro_import.c `_ + `hydro_import.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/KWD/KWD.acceleration_exponent.rst b/docs/sphinx/source/input/parameters/wind/KWD/KWD.acceleration_exponent.rst index 171956fb4..84dfda4fb 100644 --- a/docs/sphinx/source/input/parameters/wind/KWD/KWD.acceleration_exponent.rst +++ b/docs/sphinx/source/input/parameters/wind/KWD/KWD.acceleration_exponent.rst @@ -14,7 +14,7 @@ Values Greater than 0 File - `knigge.c `_ + `knigge.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/KWD/KWD.acceleration_length.rst b/docs/sphinx/source/input/parameters/wind/KWD/KWD.acceleration_length.rst index d51465769..4e5771ed4 100644 --- a/docs/sphinx/source/input/parameters/wind/KWD/KWD.acceleration_length.rst +++ b/docs/sphinx/source/input/parameters/wind/KWD/KWD.acceleration_length.rst @@ -13,7 +13,7 @@ Values Greater than 0 File - `knigge.c `_ + `knigge.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/KWD/KWD.d.rst b/docs/sphinx/source/input/parameters/wind/KWD/KWD.d.rst index 25c827db6..9f409e4fa 100644 --- a/docs/sphinx/source/input/parameters/wind/KWD/KWD.d.rst +++ b/docs/sphinx/source/input/parameters/wind/KWD/KWD.d.rst @@ -18,7 +18,7 @@ Values Greater than 0 File - `knigge.c `_ + `knigge.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/KWD/KWD.mdot_r_exponent.rst b/docs/sphinx/source/input/parameters/wind/KWD/KWD.mdot_r_exponent.rst index a046ef50a..28acd41fe 100644 --- a/docs/sphinx/source/input/parameters/wind/KWD/KWD.mdot_r_exponent.rst +++ b/docs/sphinx/source/input/parameters/wind/KWD/KWD.mdot_r_exponent.rst @@ -16,7 +16,7 @@ Values Greater than or equal to 0 File - `knigge.c `_ + `knigge.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/KWD/KWD.rmax.rst b/docs/sphinx/source/input/parameters/wind/KWD/KWD.rmax.rst index 7d5393ac0..bcc553663 100644 --- a/docs/sphinx/source/input/parameters/wind/KWD/KWD.rmax.rst +++ b/docs/sphinx/source/input/parameters/wind/KWD/KWD.rmax.rst @@ -13,7 +13,7 @@ Values Greater than :ref:`KWD.rmin` File - `knigge.c `_ + `knigge.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/KWD/KWD.rmin.rst b/docs/sphinx/source/input/parameters/wind/KWD/KWD.rmin.rst index f597a2617..cccdebc5c 100644 --- a/docs/sphinx/source/input/parameters/wind/KWD/KWD.rmin.rst +++ b/docs/sphinx/source/input/parameters/wind/KWD/KWD.rmin.rst @@ -13,7 +13,7 @@ Values Greater than 1 File - `knigge.c `_ + `knigge.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/KWD/KWD.v_infinity.rst b/docs/sphinx/source/input/parameters/wind/KWD/KWD.v_infinity.rst index a9b54fbb3..874f149f8 100644 --- a/docs/sphinx/source/input/parameters/wind/KWD/KWD.v_infinity.rst +++ b/docs/sphinx/source/input/parameters/wind/KWD/KWD.v_infinity.rst @@ -17,7 +17,7 @@ Values Greater than 0 File - `knigge.c `_ + `knigge.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/KWD/KWD.v_zero.rst b/docs/sphinx/source/input/parameters/wind/KWD/KWD.v_zero.rst index e44e67f95..b43ec38f9 100644 --- a/docs/sphinx/source/input/parameters/wind/KWD/KWD.v_zero.rst +++ b/docs/sphinx/source/input/parameters/wind/KWD/KWD.v_zero.rst @@ -15,7 +15,7 @@ Values Greater than 0 File - `knigge.c `_ + `knigge.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/SV/SV.acceleration_exponent.rst b/docs/sphinx/source/input/parameters/wind/SV/SV.acceleration_exponent.rst index 75c107cd6..936e688db 100644 --- a/docs/sphinx/source/input/parameters/wind/SV/SV.acceleration_exponent.rst +++ b/docs/sphinx/source/input/parameters/wind/SV/SV.acceleration_exponent.rst @@ -17,7 +17,7 @@ Values Greater than 0 File - `sv.c `_ + `sv.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/SV/SV.acceleration_length.rst b/docs/sphinx/source/input/parameters/wind/SV/SV.acceleration_length.rst index 0d69ec18b..f7e80ac27 100644 --- a/docs/sphinx/source/input/parameters/wind/SV/SV.acceleration_length.rst +++ b/docs/sphinx/source/input/parameters/wind/SV/SV.acceleration_length.rst @@ -18,7 +18,7 @@ Values Greater than 0 File - `sv.c `_ + `sv.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/SV/SV.diskmax.rst b/docs/sphinx/source/input/parameters/wind/SV/SV.diskmax.rst index 5f09c7ce0..b61b5a22e 100644 --- a/docs/sphinx/source/input/parameters/wind/SV/SV.diskmax.rst +++ b/docs/sphinx/source/input/parameters/wind/SV/SV.diskmax.rst @@ -15,7 +15,7 @@ Values Greater than or equal to :ref:`sv.diskmin` (inner radius disk wind) File - `sv.c `_ + `sv.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/SV/SV.diskmin.rst b/docs/sphinx/source/input/parameters/wind/SV/SV.diskmin.rst index 5d74e63fc..bdaa9d206 100644 --- a/docs/sphinx/source/input/parameters/wind/SV/SV.diskmin.rst +++ b/docs/sphinx/source/input/parameters/wind/SV/SV.diskmin.rst @@ -15,7 +15,7 @@ Values Greater than or equal to :ref:`Central_object.radius` File - `sv.c `_ + `sv.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/SV/SV.gamma.rst b/docs/sphinx/source/input/parameters/wind/SV/SV.gamma.rst index bbad96526..f3a212d15 100644 --- a/docs/sphinx/source/input/parameters/wind/SV/SV.gamma.rst +++ b/docs/sphinx/source/input/parameters/wind/SV/SV.gamma.rst @@ -10,7 +10,7 @@ Values Greater than 0 File - `sv.c `_ + `sv.c `_ Parent(s) * :ref:`Wind.type`: ``SV`` diff --git a/docs/sphinx/source/input/parameters/wind/SV/SV.mdot_r_exponent.rst b/docs/sphinx/source/input/parameters/wind/SV/SV.mdot_r_exponent.rst index 16093395d..15e6797fd 100644 --- a/docs/sphinx/source/input/parameters/wind/SV/SV.mdot_r_exponent.rst +++ b/docs/sphinx/source/input/parameters/wind/SV/SV.mdot_r_exponent.rst @@ -14,7 +14,7 @@ Values Greater than or equal to 0. 0 sets a uniform mass loss rate. File - `sv.c `_ + `sv.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/SV/SV.thetamax.rst b/docs/sphinx/source/input/parameters/wind/SV/SV.thetamax.rst index 4f5f80806..8c0306884 100644 --- a/docs/sphinx/source/input/parameters/wind/SV/SV.thetamax.rst +++ b/docs/sphinx/source/input/parameters/wind/SV/SV.thetamax.rst @@ -14,7 +14,7 @@ Values Greater than :ref:`sv.thetamin` File - `sv.c `_ + `sv.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/SV/SV.thetamin.rst b/docs/sphinx/source/input/parameters/wind/SV/SV.thetamin.rst index e837ab1e9..9a1e429cb 100644 --- a/docs/sphinx/source/input/parameters/wind/SV/SV.thetamin.rst +++ b/docs/sphinx/source/input/parameters/wind/SV/SV.thetamin.rst @@ -14,7 +14,7 @@ Values Greater than 0 File - `sv.c `_ + `sv.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/SV/SV.v_infinity.rst b/docs/sphinx/source/input/parameters/wind/SV/SV.v_infinity.rst index 3dbff82e3..0accfe45e 100644 --- a/docs/sphinx/source/input/parameters/wind/SV/SV.v_infinity.rst +++ b/docs/sphinx/source/input/parameters/wind/SV/SV.v_infinity.rst @@ -18,7 +18,7 @@ Values Greater than 0 File - `sv.c `_ + `sv.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/SV/SV.v_zero.rst b/docs/sphinx/source/input/parameters/wind/SV/SV.v_zero.rst index 82e011124..9b44f07f6 100644 --- a/docs/sphinx/source/input/parameters/wind/SV/SV.v_zero.rst +++ b/docs/sphinx/source/input/parameters/wind/SV/SV.v_zero.rst @@ -15,7 +15,7 @@ Values Greater than 0 File - `sv.c `_ + `sv.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/SV/SV.v_zero_mode.rst b/docs/sphinx/source/input/parameters/wind/SV/SV.v_zero_mode.rst index 006e4c3cb..f7dbddbd4 100644 --- a/docs/sphinx/source/input/parameters/wind/SV/SV.v_zero_mode.rst +++ b/docs/sphinx/source/input/parameters/wind/SV/SV.v_zero_mode.rst @@ -16,7 +16,7 @@ Values File - `sv.c `_ + `sv.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind.acceleration_exponent.rst b/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind.acceleration_exponent.rst index f120ed6aa..58b9adbd5 100644 --- a/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind.acceleration_exponent.rst +++ b/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind.acceleration_exponent.rst @@ -13,7 +13,7 @@ Values Greater than or equal to 0 File - `shell_wind.c `_ + `shell_wind.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind.radmax.rst b/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind.radmax.rst index 299e81384..f4022a874 100644 --- a/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind.radmax.rst +++ b/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind.radmax.rst @@ -13,7 +13,7 @@ Values Greater than :ref:`Shell.wind.radmin` File - `shell_wind.c `_ + `shell_wind.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind.radmin.rst b/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind.radmin.rst index 00c3ce927..05b4391ef 100644 --- a/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind.radmin.rst +++ b/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind.radmin.rst @@ -13,7 +13,7 @@ Values Greater than :ref:`Central_object.radius` File - `shell_wind.c `_ + `shell_wind.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind.v_at_rmax.rst b/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind.v_at_rmax.rst index a6e94fd21..660e4f69b 100644 --- a/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind.v_at_rmax.rst +++ b/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind.v_at_rmax.rst @@ -15,7 +15,7 @@ Values Greater than or equal to 0 File - `shell_wind.c `_ + `shell_wind.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind_mdot.rst b/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind_mdot.rst index 94d2e59d2..71e4b423b 100644 --- a/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind_mdot.rst +++ b/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind_mdot.rst @@ -15,7 +15,7 @@ Values Greater than 0 File - `shell_wind.c `_ + `shell_wind.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind_v_at_rmin.rst b/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind_v_at_rmin.rst index 137b05999..c8409cb15 100644 --- a/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind_v_at_rmin.rst +++ b/docs/sphinx/source/input/parameters/wind/Shell/Shell.wind_v_at_rmin.rst @@ -15,7 +15,7 @@ Values Greater than or equal to 0 File - `shell_wind.c `_ + `shell_wind.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.acceleration_exponent.rst b/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.acceleration_exponent.rst index 7777f361a..44446faf5 100644 --- a/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.acceleration_exponent.rst +++ b/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.acceleration_exponent.rst @@ -13,7 +13,7 @@ Values Greater than or equal to 0 File - `stellar_wind.c `_ + `stellar_wind.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.mdot.rst b/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.mdot.rst index 1cb8a7757..39d0d0aa8 100644 --- a/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.mdot.rst +++ b/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.mdot.rst @@ -14,7 +14,7 @@ Values Greater than 0 File - `stellar_wind.c `_ + `stellar_wind.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.radmax.rst b/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.radmax.rst index f3c29637f..a7a9cf849 100644 --- a/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.radmax.rst +++ b/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.radmax.rst @@ -13,7 +13,7 @@ Values Greater than or equal to :ref:`Stellar_wind.radmin` File - `stellar_wind.c `_ + `stellar_wind.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.radmin.rst b/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.radmin.rst index dce79fefd..b1206f8cc 100644 --- a/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.radmin.rst +++ b/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.radmin.rst @@ -13,7 +13,7 @@ Values Greater than or equal to :ref:`Central_object.radius` File - `stellar_wind.c `_ + `stellar_wind.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.v_infinity.rst b/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.v_infinity.rst index 101ff17f7..c73de847a 100644 --- a/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.v_infinity.rst +++ b/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.v_infinity.rst @@ -16,7 +16,7 @@ Values Greater than 0 File - `stellar_wind.c `_ + `stellar_wind.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.vbase.rst b/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.vbase.rst index 7ee105a4e..2bd2c42ca 100644 --- a/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.vbase.rst +++ b/docs/sphinx/source/input/parameters/wind/Stellar_wind/Stellar_wind.vbase.rst @@ -13,7 +13,7 @@ Values Greater than 0 File - `stellar_wind.c `_ + `stellar_wind.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Wind/Wind.coord_system.rst b/docs/sphinx/source/input/parameters/wind/Wind/Wind.coord_system.rst index 4f858cf91..d924b7672 100644 --- a/docs/sphinx/source/input/parameters/wind/Wind/Wind.coord_system.rst +++ b/docs/sphinx/source/input/parameters/wind/Wind/Wind.coord_system.rst @@ -20,7 +20,7 @@ Values File - `setup_domains.c `_ + `setup_domains.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Wind/Wind.dim.in.x_or_r.direction.rst b/docs/sphinx/source/input/parameters/wind/Wind/Wind.dim.in.x_or_r.direction.rst index 54c79c1e9..576e6ebb2 100644 --- a/docs/sphinx/source/input/parameters/wind/Wind/Wind.dim.in.x_or_r.direction.rst +++ b/docs/sphinx/source/input/parameters/wind/Wind/Wind.dim.in.x_or_r.direction.rst @@ -17,7 +17,7 @@ Values Greater than or equal to 4, to allow for boundaries. File - `setup_domains.c `_ + `setup_domains.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Wind/Wind.dim.in.z_or_theta.direction.rst b/docs/sphinx/source/input/parameters/wind/Wind/Wind.dim.in.z_or_theta.direction.rst index 4a660ba91..49d5ae80b 100644 --- a/docs/sphinx/source/input/parameters/wind/Wind/Wind.dim.in.z_or_theta.direction.rst +++ b/docs/sphinx/source/input/parameters/wind/Wind/Wind.dim.in.z_or_theta.direction.rst @@ -17,7 +17,7 @@ Values Greater than 0 File - `setup_domains.c `_ + `setup_domains.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Wind/Wind.filling_factor.rst b/docs/sphinx/source/input/parameters/wind/Wind/Wind.filling_factor.rst index d9df15076..22dce676b 100644 --- a/docs/sphinx/source/input/parameters/wind/Wind/Wind.filling_factor.rst +++ b/docs/sphinx/source/input/parameters/wind/Wind/Wind.filling_factor.rst @@ -11,7 +11,7 @@ Values :math:`0\lt f\le1`, where 1 is a fully smooth wind. File - `setup_domains.c `_ + `setup_domains.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Wind/Wind.fixed_concentrations_file.rst b/docs/sphinx/source/input/parameters/wind/Wind/Wind.fixed_concentrations_file.rst index d9b356e40..f94f9dc47 100644 --- a/docs/sphinx/source/input/parameters/wind/Wind/Wind.fixed_concentrations_file.rst +++ b/docs/sphinx/source/input/parameters/wind/Wind/Wind.fixed_concentrations_file.rst @@ -23,7 +23,7 @@ Type String File - `setup.c `_ + `setup.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Wind/Wind.ionization.rst b/docs/sphinx/source/input/parameters/wind/Wind/Wind.ionization.rst index 4454b1a76..4db3ac068 100644 --- a/docs/sphinx/source/input/parameters/wind/Wind/Wind.ionization.rst +++ b/docs/sphinx/source/input/parameters/wind/Wind/Wind.ionization.rst @@ -51,7 +51,7 @@ Values File - `setup.c `_ + `setup.c `_ Child(ren) diff --git a/docs/sphinx/source/input/parameters/wind/Wind/Wind.mdot.rst b/docs/sphinx/source/input/parameters/wind/Wind/Wind.mdot.rst index b5c9b2866..66968a5b9 100644 --- a/docs/sphinx/source/input/parameters/wind/Wind/Wind.mdot.rst +++ b/docs/sphinx/source/input/parameters/wind/Wind/Wind.mdot.rst @@ -12,7 +12,7 @@ Values Greater than 0 File - `['knigge.c', 'sv.c'] `_ + `['knigge.c', 'sv.c'] `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Wind/Wind.model2import.rst b/docs/sphinx/source/input/parameters/wind/Wind/Wind.model2import.rst index 17a6f5ca9..09681b9b2 100644 --- a/docs/sphinx/source/input/parameters/wind/Wind/Wind.model2import.rst +++ b/docs/sphinx/source/input/parameters/wind/Wind/Wind.model2import.rst @@ -1,7 +1,7 @@ Wind.model2import ================= -The name of a file to containing a generic model to read in to python from an ascii file. (Note -that this is not the same as reading in a model generated by python, but is intended to allow +The name of a file to containing a generic model to read in to sirocco from an ascii file. (Note +that this is not the same as reading in a model generated by sirocco, but is intended to allow one to read in a generic model in a variety of formats with only a limited amount of information required). @@ -9,7 +9,7 @@ Type String File - `import.c `_ + `import.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Wind/Wind.number_of_components.rst b/docs/sphinx/source/input/parameters/wind/Wind/Wind.number_of_components.rst index b7963ba53..df70c2e0d 100644 --- a/docs/sphinx/source/input/parameters/wind/Wind/Wind.number_of_components.rst +++ b/docs/sphinx/source/input/parameters/wind/Wind/Wind.number_of_components.rst @@ -13,7 +13,7 @@ Values Greater than 0 File - `python.c `_ + `sirocco.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Wind/Wind.old_windfile.rst b/docs/sphinx/source/input/parameters/wind/Wind/Wind.old_windfile.rst index 4b7c1a308..82c82e4a2 100644 --- a/docs/sphinx/source/input/parameters/wind/Wind/Wind.old_windfile.rst +++ b/docs/sphinx/source/input/parameters/wind/Wind/Wind.old_windfile.rst @@ -9,7 +9,7 @@ Type String File - `python.c `_ + `sirocco.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Wind/Wind.radiation.rst b/docs/sphinx/source/input/parameters/wind/Wind/Wind.radiation.rst index 9790841e4..ba7c34c20 100644 --- a/docs/sphinx/source/input/parameters/wind/Wind/Wind.radiation.rst +++ b/docs/sphinx/source/input/parameters/wind/Wind/Wind.radiation.rst @@ -6,6 +6,6 @@ Type Boolean (yes/no) File - `python.c `_ + `sirocco.c `_ diff --git a/docs/sphinx/source/input/parameters/wind/Wind/Wind.radmax.rst b/docs/sphinx/source/input/parameters/wind/Wind/Wind.radmax.rst index 2d2adb406..f1a7a0cda 100644 --- a/docs/sphinx/source/input/parameters/wind/Wind/Wind.radmax.rst +++ b/docs/sphinx/source/input/parameters/wind/Wind/Wind.radmax.rst @@ -13,7 +13,7 @@ Values Greater than :ref:`Central_object.radius` and any minimum wind radii in the system. File - `setup_domains.c `_ + `setup_domains.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Wind/Wind.t.init.rst b/docs/sphinx/source/input/parameters/wind/Wind/Wind.t.init.rst index 5dfbe5980..c99aef9e2 100644 --- a/docs/sphinx/source/input/parameters/wind/Wind/Wind.t.init.rst +++ b/docs/sphinx/source/input/parameters/wind/Wind/Wind.t.init.rst @@ -13,7 +13,7 @@ Values Greater than 0 File - `setup_domains.c `_ + `setup_domains.c `_ Parent(s) diff --git a/docs/sphinx/source/input/parameters/wind/Wind/Wind.type.rst b/docs/sphinx/source/input/parameters/wind/Wind/Wind.type.rst index e1fc40403..e44a28912 100644 --- a/docs/sphinx/source/input/parameters/wind/Wind/Wind.type.rst +++ b/docs/sphinx/source/input/parameters/wind/Wind/Wind.type.rst @@ -17,7 +17,7 @@ Values A homologous expansion law useful for simulating SNe. (:ref:`Homologous`) hydro - A special purpose mode used by the python collaboration for importing models from Zeus and Pluto. (Depreciated) (:ref:`Hydro`) + A special purpose mode used by the sirocco.collaboration for importing models from Zeus and Pluto. (Depreciated) (:ref:`Hydro`) imported A general purpose mode for importing a wind from an ascii file (see also :doc:`SIROCCO Script documentation <../wind_models/importing_models>`). @@ -33,7 +33,7 @@ Values File - `setup_domains.c `_ + `setup_domains.c `_ Parent(s) diff --git a/docs/sphinx/source/input/wind_model_params.rst b/docs/sphinx/source/input/wind_model_params.rst index 6d484c8dd..752ba94fb 100644 --- a/docs/sphinx/source/input/wind_model_params.rst +++ b/docs/sphinx/source/input/wind_model_params.rst @@ -25,7 +25,7 @@ If the number of components exceeds 1, then the remaining questions relating to A stellar-wind model. A fairly standard parameterization of a spherical outflow for a hot star. :ref:`hydro` - A special purpose mode used by the python collaboration for importing models from Zeus and Pluto. (Depreciated) + A special purpose mode used by the sirocco collaboration for importing models from Zeus and Pluto. (Depreciated) :ref:`Corona` A simple model for a corona above the disk. diff --git a/docs/sphinx/source/installation.rst b/docs/sphinx/source/installation.rst index d195f388d..41891656b 100644 --- a/docs/sphinx/source/installation.rst +++ b/docs/sphinx/source/installation.rst @@ -1,7 +1,7 @@ Getting Started ############### -What machines will python run on? SIROCCO is written C. We have regularly run SIROCCO on various linux systems as well as a variity of Mac machines. +SIROCCO is written in C. We have regularly run SIROCCO on various linux systems as well as a variity of Mac machines. It is compiled using mpicc/gcc, but it can be compiled simply with gcc. It uses the Gnu Scientific Libraries (gsl) @@ -57,10 +57,10 @@ Instead, one can pull in changes and recompile the source code by running which will compile the main program. The program plus full set of auxiliary programs (such as windsave2table and py_wind, see below) can be compiled using :code:`make all`. -Running python +Running SIROCCO ============== -To run python you need to add the following to your $PATH variable: +To run SIROCCO you need to add the following to your $PATH variable: .. code:: bash @@ -103,23 +103,23 @@ Auxiliary programs There are two programs that are useful for extracting information about models * windsave2table generates a series of astropy tables that can be used to inspect elements of the various models, including densities of specific ions -* sirocco_wind is a mainly interactive routine that prints similar infomation to the screen. +* swind is a mainly interactive routine that prints similar infomation to the screen. The two files are run as follows .. code:: bash $ windsave2table root - $ sirocco_wind root + $ swind root Brief descriptions of command line options for running these routines can obtained using a -h switch SIROCCO scripts -------------- -There are a number of Python, the programming language scripts, that can be used to plot results +There are a number of python scripts that can be used to plot results from a SIROCCO run. These are not particularly well documented and many have been developed -for looking at various aspects of the code. A few may require Python packages to be installed. +for looking at various aspects of the code. A few may require python packages to be installed. However, a number are likely to be useful. To make use of these scripts one should add @@ -140,7 +140,7 @@ the electron and temperature structrue of the wind is, as well as quick plots of Directory structure ------------------- -The python directory structure is fairly simple: +The SIROCCO directory structure is fairly simple: source Location of source code @@ -158,13 +158,13 @@ bin The location of the executables. (It is a good idea to put this directory in your path) software - This directory contains libraries which are used in in python that must be recompiled when creating an installation on a new machine, primarily Bill Pence's cfitsio package and the GNU scientific library gsl + This directory contains libraries which are used in SIROCCO that must be recompiled when creating an installation on a new machine, primarily Bill Pence's cfitsio package and the GNU scientific library gsl py_progs python programs for helping analyse the code. We recommend adding this directory to your PATH and PYTHON_PATH environment variables. examples - A directory with a few examples of python runs. (Note that the input files will have changed and so one may not be able to run these examples without some changes in the input files.) + A directory with a few examples of SIROCCO runs. (Note that the input files will have changed and so one may not be able to run these examples without some changes in the input files.) Please help by reporting bugs in installation --------------------------------------------- diff --git a/docs/sphinx/source/reference_rst.txt b/docs/sphinx/source/reference_rst.txt index 7fcf9f1e7..ef1750e67 100644 --- a/docs/sphinx/source/reference_rst.txt +++ b/docs/sphinx/source/reference_rst.txt @@ -27,7 +27,7 @@ Values * :ref:`KWD.rmax` File - `filename.c `_ + `filename.c `_ Parent(s) * :ref:`System_type`: `agn`, `binary` From f84b1d3363dbc4b7ff81ff208a96f3f1bfd93696 Mon Sep 17 00:00:00 2001 From: jhmatthews Date: Thu, 24 Oct 2024 18:00:02 +0100 Subject: [PATCH 23/24] changed sirocco_wind --- docs/sphinx/source/output/model.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/sphinx/source/output/model.rst b/docs/sphinx/source/output/model.rst index 813057808..6f56e8188 100644 --- a/docs/sphinx/source/output/model.rst +++ b/docs/sphinx/source/output/model.rst @@ -25,9 +25,9 @@ windsave2table There are varrious options for how much data is to be printed out. A summary of these can be obtained with code:`windsave2table -h` -sirocco_wind - Executed from the command line with :code:`sirocco_wind rootname` +swind + Executed from the command line with :code:`swind rootname` Allows the user to query for information about the model interactively. The results can be written to ascii files for future reference - Again, there are various options, and a summary can be obtained with :code:`sirocco_wind -h` + Again, there are various options, and a summary can be obtained with :code:`swind -h` From 6dc99289d90352d7bf7a73b2d7f5f6049fd7e31e Mon Sep 17 00:00:00 2001 From: jhmatthews Date: Fri, 25 Oct 2024 09:26:55 +0100 Subject: [PATCH 24/24] continuing to make sphinx changes --- docs/sphinx/requirements.txt | 2 +- docs/sphinx/source/atomic/bound_bound.rst | 4 ++-- ...und_bound_electron_collision_strengths.rst | 4 ++-- .../source/atomic/bound_free_topbase.rst | 6 +++--- .../source/atomic/bound_free_verner.rst | 4 ++-- .../source/atomic/direct_ionization.rst | 4 ++-- docs/sphinx/source/atomic/electron_yields.rst | 4 ++-- docs/sphinx/source/atomic/elem_ions.rst | 4 ++-- docs/sphinx/source/atomic/free-free.rst | 4 ++-- docs/sphinx/source/atomic/levels.rst | 4 ++-- docs/sphinx/source/atomic/photon_yields.rst | 4 ++-- docs/sphinx/source/conf.py | 4 ++-- docs/sphinx/source/developer/cuda.rst | 2 +- docs/sphinx/source/py_progs.rst | 4 ++-- .../py_progs/MakeMacro/MacroCombine.rst | 19 +------------------ .../source/py_progs/MakeMacro/MakeMacro.rst | 19 +------------------ .../source/py_progs/MakeMacro/RedoPhot.rst | 19 +------------------ .../py_progs/development/CheckAtomic.rst | 19 +------------------ .../source/py_progs/development/add_param.rst | 19 +------------------ .../autogenerate_parameter_docs.rst | 19 +------------------ .../development/autogenerate_rtd_pages.rst | 19 +------------------ .../source/py_progs/development/dox.rst | 19 +------------------ .../source/py_progs/development/dox_check.rst | 19 +------------------ .../py_progs/development/init_extern.rst | 19 +------------------ .../py_progs/development/make_rtheta.rst | 19 +------------------ .../source/py_progs/development/pf_check.rst | 19 +------------------ .../source/py_progs/development/retro.rst | 19 +------------------ .../py_progs/development/run_indent.rst | 19 +------------------ .../py_progs/development/update_param.rst | 19 +------------------ .../source/py_progs/regression/regression.rst | 19 +------------------ .../py_progs/regression/regression_check.rst | 19 +------------------ .../py_progs/regression/regression_nsh.rst | 19 +------------------ .../py_progs/regression/regression_plot.rst | 19 +------------------ .../sphinx/source/py_progs/running/ModSum.rst | 19 +------------------ .../py_progs/running/cloudy_pl_loop.rst | 19 +------------------ .../source/py_progs/running/pf_grid.rst | 19 +------------------ .../source/py_progs/running/py79_pl_loop.rst | 19 +------------------ .../py_progs/running/python_pl_loop.rst | 19 +------------------ .../source/py_progs/running/run_many.rst | 19 +------------------ .../source/py_progs/testing/CompareAtomic.rst | 19 +------------------ .../py_progs/testing/balmer_decrement.rst | 19 +------------------ .../source/py_progs/testing/compare_ion.rst | 19 +------------------ .../source/py_progs/testing/grid_check.rst | 19 +------------------ .../source/py_progs/testing/run_check.rst | 19 +------------------ .../py_progs/testing/test_masterfiles.rst | 19 +------------------ .../source/py_progs/testing/xcompare.rst | 19 +------------------ .../source/py_progs/utilities/import_1d.rst | 19 +------------------ .../source/py_progs/utilities/import_cyl.rst | 19 +------------------ .../py_progs/utilities/import_rtheta.rst | 19 +------------------ .../sphinx/source/py_progs/utilities/kpar.rst | 19 +------------------ .../source/py_progs/utilities/py_classes.rst | 19 +------------------ .../source/py_progs/utilities/py_error.rst | 19 +------------------ .../py_progs/utilities/py_plot_util.rst | 19 +------------------ .../py_progs/utilities/py_read_output.rst | 19 +------------------ .../source/py_progs/utilities/watchdog.rst | 19 +------------------ .../source/py_progs/utilities/xhtml.rst | 19 +------------------ 56 files changed, 69 insertions(+), 783 deletions(-) diff --git a/docs/sphinx/requirements.txt b/docs/sphinx/requirements.txt index 8fbcc9df9..3158573ab 100644 --- a/docs/sphinx/requirements.txt +++ b/docs/sphinx/requirements.txt @@ -12,4 +12,4 @@ sqlalchemy jinja2==3.1.4 sphinx_gallery pyhdf -sphinx-autoapi==3.1.1 \ No newline at end of file +sphinx-autoapi==3.1.1 diff --git a/docs/sphinx/source/atomic/bound_bound.rst b/docs/sphinx/source/atomic/bound_bound.rst index d0e8a7b26..b4089ae36 100644 --- a/docs/sphinx/source/atomic/bound_bound.rst +++ b/docs/sphinx/source/atomic/bound_bound.rst @@ -23,7 +23,7 @@ We have also used a line list from Verner in the past Translation to SIROCCO format -============================ +====================================== There are several steps to creating the data used in SIROCCO from that in gfall.dat, that are carried out by py_read_kurucz and py_link. The first routine reads the gfall.dat file and creates two output files, a file containing the lines and the associated such as the effective oscillatory strength and a file which contains information about the ion levels. py_read_kurucz chooses only a portion of the Kurucz lines, namely those associated with ions with ionization potentials in a certain range and lines with gf factors exceeding a certain value. The second program py_link attempts to create a model ion with links between the levels and the ions. Both of these routines are driven by .pf files, similar to what are used in SIROCCO. Examples of the .pf files are in the directory py_kurucz In practice we have not used these data for any SIROCCO publications. At some point early in the AGN project, NSH increased the number of lines, and generated lines\_linked\_ver\_2.dat and levels\_ver\_2.dat. I think this was because there was a small bug which meant the oscillator strength cut that was stated was not that which was applied. @@ -69,7 +69,7 @@ one needs to index everything self-consistentl SIROCCO structure -================ +================================ Line data is stored in the data structure **lines** diff --git a/docs/sphinx/source/atomic/bound_bound_electron_collision_strengths.rst b/docs/sphinx/source/atomic/bound_bound_electron_collision_strengths.rst index 88c9da405..73448f504 100644 --- a/docs/sphinx/source/atomic/bound_bound_electron_collision_strengths.rst +++ b/docs/sphinx/source/atomic/bound_bound_electron_collision_strengths.rst @@ -20,7 +20,7 @@ These values of :math:`\Upsilon` simply replace :math:`\Omega`. In the asbsence of data in this format, the Van Regemorter approximation is utilized. Translation to SIROCCO format -============================ +====================================== It is necessary to link each line in our line list with the relevant electron collision strength. This is achieved using the python script "coll_stren_lookup.py" which first reads in the "lines_linked_ver_2.py" line list, then attempts to work out which lines are which by comparing the energy and the oscillator strength of the line. If these match to within a factor of 10% then the code logs this as a possible match. If better matches come along, then the code adopts those instead. @@ -67,7 +67,7 @@ and So, to get :math:`\Upsilon` for a given T, one converts T to x via the correct equation, then linearly interpolate between values of :math:`y(x)`, then convert back to :math:`\Upsilon`. SIROCCO structure -================ +========================== The data is stored in SIROCCO in the Coll\_stren structure which has memebers diff --git a/docs/sphinx/source/atomic/bound_free_topbase.rst b/docs/sphinx/source/atomic/bound_free_topbase.rst index 7464e3b92..5f2a56ce4 100644 --- a/docs/sphinx/source/atomic/bound_free_topbase.rst +++ b/docs/sphinx/source/atomic/bound_free_topbase.rst @@ -9,7 +9,7 @@ Obtained from The `Opacity project `_ . The data is downloaded from the vizier site linked and put into a file called "electron_yield.data" Translation to SIROCCO format -============================ +====================================== The translation takes place using the python script "kaastra_2_py.py" which takes the saved raw data file "electron_yield.data" and compares it line by line to the inner shell cross section data in "vy_innershell_tab.data"(see above). The n shell and l subshell to which each record applies is coded in the KM data and needs to be decoded. This is what the script does, and all the script then does is output the yield data into a new file "kaastra_electron_yield.data" which contains the n and l cross reference. @@ -36,7 +36,7 @@ The data is linked to the correct inner shell photoionization cross section (and SIROCCO structure -================ +========================== The data is stored in SIROCCO in the inner_elec_yield structure which contains diff --git a/docs/sphinx/source/atomic/elem_ions.rst b/docs/sphinx/source/atomic/elem_ions.rst index 7ddc52ccc..18db3a9ab 100644 --- a/docs/sphinx/source/atomic/elem_ions.rst +++ b/docs/sphinx/source/atomic/elem_ions.rst @@ -11,7 +11,7 @@ This data comes from `Verner, Barthel & Tytler, 1994, ApJ 108, 287. `_ . The data is downloaded from the vizier site linked and put into a file called "fluorescent\_yield.data" Translation to SIROCCO format -============================ +====================================== The translation takes place using the python script "kaastra_2_py.py". All identical to electron yield, but input file is "fluorescent_yield.data" and output is "kaastra_fluorescent_yield.data" @@ -35,7 +35,7 @@ The data is linked to the correct inner shell photoionization cross section (and SIROCCO structure -================ +========================== The data is stored in SIROCCO in the inner_fluor_yield structure which contains diff --git a/docs/sphinx/source/conf.py b/docs/sphinx/source/conf.py index 99081aedb..f868d1a13 100755 --- a/docs/sphinx/source/conf.py +++ b/docs/sphinx/source/conf.py @@ -87,9 +87,9 @@ # built documents. # # The short X.Y version. -version = '88' +version = '0.1' # The full version, including alpha/beta/rc tags. -release = '88a' +release = '0.1' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. diff --git a/docs/sphinx/source/developer/cuda.rst b/docs/sphinx/source/developer/cuda.rst index e7b5a9ec9..03e9261f8 100644 --- a/docs/sphinx/source/developer/cuda.rst +++ b/docs/sphinx/source/developer/cuda.rst @@ -411,6 +411,6 @@ shown below. # So to compile SIROCCO, we have something which looks vaguely like this. Note that # we use the CUDA_OBJECTS recipe as a requirement for the sirocco recipe. This CUSOLVER_STATUS_SUCCESS # the CUDA source to be compiled to object code *if* NVCC is defined - sirocco: startup sirocco.o $(sirocco_objects) $(CUDA_OBJECTS) + sirocco: startup sirocco.o $(sirocco_objects) $(CUDA_OBJECTS) $(CC) $(CFLAGS) sirocco.o $(sirocco_objects) $(CUDA_OBJECTS) $(kpar_objects) $(LDFLAGS) -o sirocco diff --git a/docs/sphinx/source/py_progs.rst b/docs/sphinx/source/py_progs.rst index 3523167e5..144526f96 100644 --- a/docs/sphinx/source/py_progs.rst +++ b/docs/sphinx/source/py_progs.rst @@ -1,7 +1,7 @@ -SIROCCO Scripts +Python Scripts ################################ -There are several SIROCCO scripts written to prepare input for +There are several python scripts written to prepare input for and analyse the output of SIROCCO. Some of the more useful scripts/modules are documented below. diff --git a/docs/sphinx/source/py_progs/MakeMacro/MacroCombine.rst b/docs/sphinx/source/py_progs/MakeMacro/MacroCombine.rst index 34c24f0be..9d43dcc83 100644 --- a/docs/sphinx/source/py_progs/MakeMacro/MacroCombine.rst +++ b/docs/sphinx/source/py_progs/MakeMacro/MacroCombine.rst @@ -4,11 +4,6 @@ .. automodule:: MacroCombine - - - - - .. rubric:: Functions .. autosummary:: @@ -25,16 +20,4 @@ steer write_phot xguess - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/MakeMacro/MakeMacro.rst b/docs/sphinx/source/py_progs/MakeMacro/MakeMacro.rst index f1564ef29..4270e74bb 100644 --- a/docs/sphinx/source/py_progs/MakeMacro/MakeMacro.rst +++ b/docs/sphinx/source/py_progs/MakeMacro/MakeMacro.rst @@ -4,11 +4,6 @@ .. automodule:: MakeMacro - - - - - .. rubric:: Functions .. autosummary:: @@ -23,16 +18,4 @@ make_phot print_elvlc write_phot - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/MakeMacro/RedoPhot.rst b/docs/sphinx/source/py_progs/MakeMacro/RedoPhot.rst index 47a60f86a..d01b1aa50 100644 --- a/docs/sphinx/source/py_progs/MakeMacro/RedoPhot.rst +++ b/docs/sphinx/source/py_progs/MakeMacro/RedoPhot.rst @@ -4,11 +4,6 @@ .. automodule:: RedoPhot - - - - - .. rubric:: Functions .. autosummary:: @@ -19,16 +14,4 @@ redo_one steer write_phot_tab - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/development/CheckAtomic.rst b/docs/sphinx/source/py_progs/development/CheckAtomic.rst index 14f0dd443..faf7c739d 100644 --- a/docs/sphinx/source/py_progs/development/CheckAtomic.rst +++ b/docs/sphinx/source/py_progs/development/CheckAtomic.rst @@ -4,11 +4,6 @@ .. automodule:: CheckAtomic - - - - - .. rubric:: Functions .. autosummary:: @@ -20,16 +15,4 @@ get_level_tab get_line_tab xread - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/development/add_param.rst b/docs/sphinx/source/py_progs/development/add_param.rst index dad8c838f..6f72db64d 100644 --- a/docs/sphinx/source/py_progs/development/add_param.rst +++ b/docs/sphinx/source/py_progs/development/add_param.rst @@ -4,11 +4,6 @@ .. automodule:: add_param - - - - - .. rubric:: Functions .. autosummary:: @@ -17,16 +12,4 @@ doit read_file read_table - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/development/autogenerate_parameter_docs.rst b/docs/sphinx/source/py_progs/development/autogenerate_parameter_docs.rst index 7d00c9706..7bb51feb1 100644 --- a/docs/sphinx/source/py_progs/development/autogenerate_parameter_docs.rst +++ b/docs/sphinx/source/py_progs/development/autogenerate_parameter_docs.rst @@ -4,11 +4,6 @@ .. automodule:: autogenerate_parameter_docs - - - - - .. rubric:: Functions .. autosummary:: @@ -23,16 +18,4 @@ read_parameters should_use_block yaml_output - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/development/autogenerate_rtd_pages.rst b/docs/sphinx/source/py_progs/development/autogenerate_rtd_pages.rst index c34e55ecf..c11be0ac7 100644 --- a/docs/sphinx/source/py_progs/development/autogenerate_rtd_pages.rst +++ b/docs/sphinx/source/py_progs/development/autogenerate_rtd_pages.rst @@ -4,11 +4,6 @@ .. automodule:: autogenerate_rtd_pages - - - - - .. rubric:: Functions .. autosummary:: @@ -20,16 +15,4 @@ write_header_by_level write_rst write_str_indent - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/development/dox.rst b/docs/sphinx/source/py_progs/development/dox.rst index 4c22a375d..99cd3ed81 100644 --- a/docs/sphinx/source/py_progs/development/dox.rst +++ b/docs/sphinx/source/py_progs/development/dox.rst @@ -4,11 +4,6 @@ .. automodule:: dox - - - - - .. rubric:: Functions .. autosummary:: @@ -21,16 +16,4 @@ read_file read_table steer - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/development/dox_check.rst b/docs/sphinx/source/py_progs/development/dox_check.rst index 15ee07ce8..05f709448 100644 --- a/docs/sphinx/source/py_progs/development/dox_check.rst +++ b/docs/sphinx/source/py_progs/development/dox_check.rst @@ -4,26 +4,9 @@ .. automodule:: dox_check - - - - - .. rubric:: Functions .. autosummary:: doit - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/development/init_extern.rst b/docs/sphinx/source/py_progs/development/init_extern.rst index 8e6f4e35b..0471365eb 100644 --- a/docs/sphinx/source/py_progs/development/init_extern.rst +++ b/docs/sphinx/source/py_progs/development/init_extern.rst @@ -4,26 +4,9 @@ .. automodule:: init_extern - - - - - .. rubric:: Functions .. autosummary:: doit - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/development/make_rtheta.rst b/docs/sphinx/source/py_progs/development/make_rtheta.rst index 495d5049d..ba4bae1ee 100644 --- a/docs/sphinx/source/py_progs/development/make_rtheta.rst +++ b/docs/sphinx/source/py_progs/development/make_rtheta.rst @@ -4,11 +4,6 @@ .. automodule:: make_rtheta - - - - - .. rubric:: Functions .. autosummary:: @@ -16,16 +11,4 @@ doit gen_model get_inputs - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/development/pf_check.rst b/docs/sphinx/source/py_progs/development/pf_check.rst index d95114600..ae67b491a 100644 --- a/docs/sphinx/source/py_progs/development/pf_check.rst +++ b/docs/sphinx/source/py_progs/development/pf_check.rst @@ -4,11 +4,6 @@ .. automodule:: pf_check - - - - - .. rubric:: Functions .. autosummary:: @@ -16,16 +11,4 @@ doit steer travis - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/development/retro.rst b/docs/sphinx/source/py_progs/development/retro.rst index e6065bf74..320cf555a 100644 --- a/docs/sphinx/source/py_progs/development/retro.rst +++ b/docs/sphinx/source/py_progs/development/retro.rst @@ -4,11 +4,6 @@ .. automodule:: retro - - - - - .. rubric:: Functions .. autosummary:: @@ -26,16 +21,4 @@ run_many run_one xsmooth - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/development/run_indent.rst b/docs/sphinx/source/py_progs/development/run_indent.rst index 27ace212a..c80439bae 100644 --- a/docs/sphinx/source/py_progs/development/run_indent.rst +++ b/docs/sphinx/source/py_progs/development/run_indent.rst @@ -4,11 +4,6 @@ .. automodule:: run_indent - - - - - .. rubric:: Functions .. autosummary:: @@ -18,16 +13,4 @@ doit get_gnu steer - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/development/update_param.rst b/docs/sphinx/source/py_progs/development/update_param.rst index 1a17cb127..b863a59a7 100644 --- a/docs/sphinx/source/py_progs/development/update_param.rst +++ b/docs/sphinx/source/py_progs/development/update_param.rst @@ -4,11 +4,6 @@ .. automodule:: update_param - - - - - .. rubric:: Functions .. autosummary:: @@ -17,16 +12,4 @@ get_root_from_filepath main update_parameter - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/regression/regression.rst b/docs/sphinx/source/py_progs/regression/regression.rst index b82979b6b..fe9492c76 100644 --- a/docs/sphinx/source/py_progs/regression/regression.rst +++ b/docs/sphinx/source/py_progs/regression/regression.rst @@ -4,11 +4,6 @@ .. automodule:: regression - - - - - .. rubric:: Functions .. autosummary:: @@ -19,16 +14,4 @@ run_cmds steer sum_errors - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/regression/regression_check.rst b/docs/sphinx/source/py_progs/regression/regression_check.rst index 79765e935..418ba6d8f 100644 --- a/docs/sphinx/source/py_progs/regression/regression_check.rst +++ b/docs/sphinx/source/py_progs/regression/regression_check.rst @@ -4,11 +4,6 @@ .. automodule:: regression_check - - - - - .. rubric:: Functions .. autosummary:: @@ -19,16 +14,4 @@ read_file read_table steer - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/regression/regression_nsh.rst b/docs/sphinx/source/py_progs/regression/regression_nsh.rst index a81caeed2..b1cc2075c 100644 --- a/docs/sphinx/source/py_progs/regression/regression_nsh.rst +++ b/docs/sphinx/source/py_progs/regression/regression_nsh.rst @@ -4,11 +4,6 @@ .. automodule:: regression_nsh - - - - - .. rubric:: Functions .. autosummary:: @@ -19,16 +14,4 @@ run_cmds steer sum_errors - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/regression/regression_plot.rst b/docs/sphinx/source/py_progs/regression/regression_plot.rst index 2f7ea0883..3518c78de 100644 --- a/docs/sphinx/source/py_progs/regression/regression_plot.rst +++ b/docs/sphinx/source/py_progs/regression/regression_plot.rst @@ -4,11 +4,6 @@ .. automodule:: regression_plot - - - - - .. rubric:: Functions .. autosummary:: @@ -21,16 +16,4 @@ read_file read_table xsmooth - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/running/ModSum.rst b/docs/sphinx/source/py_progs/running/ModSum.rst index 47369434a..8d5c33632 100644 --- a/docs/sphinx/source/py_progs/running/ModSum.rst +++ b/docs/sphinx/source/py_progs/running/ModSum.rst @@ -4,11 +4,6 @@ .. automodule:: ModSum - - - - - .. rubric:: Functions .. autosummary:: @@ -18,16 +13,4 @@ get_pf get_status make_master - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/running/cloudy_pl_loop.rst b/docs/sphinx/source/py_progs/running/cloudy_pl_loop.rst index 467aefb39..bb8c2fe65 100644 --- a/docs/sphinx/source/py_progs/running/cloudy_pl_loop.rst +++ b/docs/sphinx/source/py_progs/running/cloudy_pl_loop.rst @@ -3,21 +3,4 @@ .. automodule:: cloudy_pl_loop - - - - - - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/running/pf_grid.rst b/docs/sphinx/source/py_progs/running/pf_grid.rst index 8521da79e..b9f7e827c 100644 --- a/docs/sphinx/source/py_progs/running/pf_grid.rst +++ b/docs/sphinx/source/py_progs/running/pf_grid.rst @@ -4,11 +4,6 @@ .. automodule:: pf_grid - - - - - .. rubric:: Functions .. autosummary:: @@ -21,16 +16,4 @@ export_results get_input log_ints - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/running/py79_pl_loop.rst b/docs/sphinx/source/py_progs/running/py79_pl_loop.rst index 21dd10b8d..9362f8892 100644 --- a/docs/sphinx/source/py_progs/running/py79_pl_loop.rst +++ b/docs/sphinx/source/py_progs/running/py79_pl_loop.rst @@ -3,21 +3,4 @@ .. automodule:: py79_pl_loop - - - - - - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/running/python_pl_loop.rst b/docs/sphinx/source/py_progs/running/python_pl_loop.rst index 176a2277b..5fd12010f 100644 --- a/docs/sphinx/source/py_progs/running/python_pl_loop.rst +++ b/docs/sphinx/source/py_progs/running/python_pl_loop.rst @@ -3,21 +3,4 @@ .. automodule:: python_pl_loop - - - - - - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/running/run_many.rst b/docs/sphinx/source/py_progs/running/run_many.rst index fa03f4c8a..3f21d9fb7 100644 --- a/docs/sphinx/source/py_progs/running/run_many.rst +++ b/docs/sphinx/source/py_progs/running/run_many.rst @@ -4,11 +4,6 @@ .. automodule:: run_many - - - - - .. rubric:: Functions .. autosummary:: @@ -18,16 +13,4 @@ read_file run_one steer - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/testing/CompareAtomic.rst b/docs/sphinx/source/py_progs/testing/CompareAtomic.rst index 976e08d58..ef8cf5849 100644 --- a/docs/sphinx/source/py_progs/testing/CompareAtomic.rst +++ b/docs/sphinx/source/py_progs/testing/CompareAtomic.rst @@ -4,11 +4,6 @@ .. automodule:: CompareAtomic - - - - - .. rubric:: Functions .. autosummary:: @@ -16,16 +11,4 @@ compare_files doit xread - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/testing/balmer_decrement.rst b/docs/sphinx/source/py_progs/testing/balmer_decrement.rst index 1055b557c..7da0da699 100644 --- a/docs/sphinx/source/py_progs/testing/balmer_decrement.rst +++ b/docs/sphinx/source/py_progs/testing/balmer_decrement.rst @@ -4,26 +4,9 @@ .. automodule:: balmer_decrement - - - - - .. rubric:: Functions .. autosummary:: BalmerTest - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/testing/compare_ion.rst b/docs/sphinx/source/py_progs/testing/compare_ion.rst index 568d559f5..b6cf6ad11 100644 --- a/docs/sphinx/source/py_progs/testing/compare_ion.rst +++ b/docs/sphinx/source/py_progs/testing/compare_ion.rst @@ -4,11 +4,6 @@ .. automodule:: compare_ion - - - - - .. rubric:: Functions .. autosummary:: @@ -16,16 +11,4 @@ compare_ion edge xsmooth - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/testing/grid_check.rst b/docs/sphinx/source/py_progs/testing/grid_check.rst index 847ef8d41..233ac295b 100644 --- a/docs/sphinx/source/py_progs/testing/grid_check.rst +++ b/docs/sphinx/source/py_progs/testing/grid_check.rst @@ -4,27 +4,10 @@ .. automodule:: grid_check - - - - - .. rubric:: Functions .. autosummary:: doit read_file - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/testing/run_check.rst b/docs/sphinx/source/py_progs/testing/run_check.rst index 11190557d..26c1ec30f 100644 --- a/docs/sphinx/source/py_progs/testing/run_check.rst +++ b/docs/sphinx/source/py_progs/testing/run_check.rst @@ -4,11 +4,6 @@ .. automodule:: run_check - - - - - .. rubric:: Functions .. autosummary:: @@ -23,16 +18,4 @@ steer windsave2table xwindsave2table - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/testing/test_masterfiles.rst b/docs/sphinx/source/py_progs/testing/test_masterfiles.rst index 362c21668..b4e8fa318 100644 --- a/docs/sphinx/source/py_progs/testing/test_masterfiles.rst +++ b/docs/sphinx/source/py_progs/testing/test_masterfiles.rst @@ -4,11 +4,6 @@ .. automodule:: test_masterfiles - - - - - .. rubric:: Functions .. autosummary:: @@ -16,16 +11,4 @@ check_run run_file run_test - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/testing/xcompare.rst b/docs/sphinx/source/py_progs/testing/xcompare.rst index 36794de4a..b0c381024 100644 --- a/docs/sphinx/source/py_progs/testing/xcompare.rst +++ b/docs/sphinx/source/py_progs/testing/xcompare.rst @@ -4,11 +4,6 @@ .. automodule:: xcompare - - - - - .. rubric:: Functions .. autosummary:: @@ -22,16 +17,4 @@ windsave2table xplot xsmooth - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/utilities/import_1d.rst b/docs/sphinx/source/py_progs/utilities/import_1d.rst index 829e9ad65..ca29a52e1 100644 --- a/docs/sphinx/source/py_progs/utilities/import_1d.rst +++ b/docs/sphinx/source/py_progs/utilities/import_1d.rst @@ -4,11 +4,6 @@ .. automodule:: import_1d - - - - - .. rubric:: Functions .. autosummary:: @@ -16,16 +11,4 @@ doit read_file read_table - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/utilities/import_cyl.rst b/docs/sphinx/source/py_progs/utilities/import_cyl.rst index f58164d9b..cb3f66a6c 100644 --- a/docs/sphinx/source/py_progs/utilities/import_cyl.rst +++ b/docs/sphinx/source/py_progs/utilities/import_cyl.rst @@ -4,11 +4,6 @@ .. automodule:: import_cyl - - - - - .. rubric:: Functions .. autosummary:: @@ -16,16 +11,4 @@ doit read_file read_table - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/utilities/import_rtheta.rst b/docs/sphinx/source/py_progs/utilities/import_rtheta.rst index 4990c8b44..45a8454e0 100644 --- a/docs/sphinx/source/py_progs/utilities/import_rtheta.rst +++ b/docs/sphinx/source/py_progs/utilities/import_rtheta.rst @@ -4,11 +4,6 @@ .. automodule:: import_rtheta - - - - - .. rubric:: Functions .. autosummary:: @@ -16,16 +11,4 @@ doit read_file read_table - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/utilities/kpar.rst b/docs/sphinx/source/py_progs/utilities/kpar.rst index 11f3077d4..df68d32c7 100644 --- a/docs/sphinx/source/py_progs/utilities/kpar.rst +++ b/docs/sphinx/source/py_progs/utilities/kpar.rst @@ -4,11 +4,6 @@ .. automodule:: kpar - - - - - .. rubric:: Functions .. autosummary:: @@ -18,16 +13,4 @@ rdpar read_file read_table - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/utilities/py_classes.rst b/docs/sphinx/source/py_progs/utilities/py_classes.rst index 35e2bea3e..592f0a696 100644 --- a/docs/sphinx/source/py_progs/utilities/py_classes.rst +++ b/docs/sphinx/source/py_progs/utilities/py_classes.rst @@ -4,15 +4,6 @@ .. automodule:: py_classes - - - - - - - - - .. rubric:: Classes .. autosummary:: @@ -23,12 +14,4 @@ line specclass spectotclass - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/utilities/py_error.rst b/docs/sphinx/source/py_progs/utilities/py_error.rst index 18c5092fa..b4715acd2 100644 --- a/docs/sphinx/source/py_progs/utilities/py_error.rst +++ b/docs/sphinx/source/py_progs/utilities/py_error.rst @@ -4,26 +4,9 @@ .. automodule:: py_error - - - - - .. rubric:: Functions .. autosummary:: doit - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/utilities/py_plot_util.rst b/docs/sphinx/source/py_progs/utilities/py_plot_util.rst index c60fc8102..0cc415137 100644 --- a/docs/sphinx/source/py_progs/utilities/py_plot_util.rst +++ b/docs/sphinx/source/py_progs/utilities/py_plot_util.rst @@ -4,11 +4,6 @@ .. automodule:: py_plot_util - - - - - .. rubric:: Functions .. autosummary:: @@ -21,16 +16,4 @@ run_py_wind smooth wind_to_masked - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/utilities/py_read_output.rst b/docs/sphinx/source/py_progs/utilities/py_read_output.rst index a44e3616f..9300c42ba 100644 --- a/docs/sphinx/source/py_progs/utilities/py_read_output.rst +++ b/docs/sphinx/source/py_progs/utilities/py_read_output.rst @@ -4,11 +4,6 @@ .. automodule:: py_read_output - - - - - .. rubric:: Functions .. autosummary:: @@ -23,16 +18,4 @@ setpars thinshell_read write_pf - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/utilities/watchdog.rst b/docs/sphinx/source/py_progs/utilities/watchdog.rst index 7de8e6517..99c420b64 100644 --- a/docs/sphinx/source/py_progs/utilities/watchdog.rst +++ b/docs/sphinx/source/py_progs/utilities/watchdog.rst @@ -4,27 +4,10 @@ .. automodule:: watchdog - - - - - .. rubric:: Functions .. autosummary:: help strip_error - - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/sphinx/source/py_progs/utilities/xhtml.rst b/docs/sphinx/source/py_progs/utilities/xhtml.rst index 956a267b4..33bb336e0 100644 --- a/docs/sphinx/source/py_progs/utilities/xhtml.rst +++ b/docs/sphinx/source/py_progs/utilities/xhtml.rst @@ -4,11 +4,6 @@ .. automodule:: xhtml - - - - - .. rubric:: Functions .. autosummary:: @@ -26,16 +21,4 @@ preformat table test - - - - - - - - - - - - - + \ No newline at end of file