From 0ecd552c63a14635b0528f71005666a49aa47718 Mon Sep 17 00:00:00 2001
From: "Knox S. Long" <long@stsci.edu>
Date: Tue, 22 Oct 2024 17:50:51 -0500
Subject: [PATCH 01/22] Small mods to doc setup

---
 docs/documentation_overview.html                 | 16 ++++++++++------
 .../{python_docs.config => sirocco_docs.config}  |  2 +-
 2 files changed, 11 insertions(+), 7 deletions(-)
 rename docs/doxygen/{python_docs.config => sirocco_docs.config} (99%)
diff --git a/docs/documentation_overview.html b/docs/documentation_overview.html
index e63a17837..b9635554d 100644
--- a/docs/documentation_overview.html
+++ b/docs/documentation_overview.html
@@ -5,7 +5,8 @@
     <title>index</title>
   </head>
   <body>
-    <h1>Documentation Overview</h1>
+    <h1>Documentation Overview for Sirocco<br>
+    </h1>
     <p> Note:&nbsp; Documentation is an ongoing effort, and is not
       always up to date. If you notice a problem, please open an<a
         moz-do-not-send="true"
@@ -16,14 +17,15 @@ <h1>Documentation Overview</h1>
     <p> At present the main resources are as follows: </p>
     <ul>
       <li>A draft version of a <a href="cookbook.pdf">cookbook</a>
-        which describes how to install&nbsp; and update Python and run a
-        first model</li>
+        which describes how to install&nbsp; and update Sirocco and run
+        a first model</li>
       <li> A <a href="sphinx/html/index.html">Sphinx</a> repository,
-        which provides the main documentation for Python</li>
+        which provides the main documentation for Sirocco</li>
       <ul>
         <li>A fairly up-to-date version of this documentation may be
           found on Readthedocs <a
-            href="https://agnwinds.readthedocs.io/en/readthedocs/">here</a></li>
+            href="https://sirocco-rt.readthedocs.io/en/latest/"
+            moz-do-not-send="true">here</a></li>
         <li>However, if one wishes to have the latest/local version, or
           the version for a particular branch, one needs to generate the
           html files locally from the source (.rst) files which are
@@ -46,7 +48,7 @@ <h1>Documentation Overview</h1>
       </ul>
       <li> A <a href="pydocs/doc_py_progs.html">pydocs repository</a>
         which provides information about the python scripts contained in
-        $PYTHON/py_progs</li>
+        $SIROCCO/py_progs</li>
       <ul>
         <li>These files are also tracked by git.&nbsp; <br>
         </li>
@@ -75,6 +77,7 @@ <h1>Documentation Overview</h1>
 
 
 
+
         provides information about the source code.</li>
       <ul>
         <li>Like the other documentation, the Doxygen information is not
@@ -104,6 +107,7 @@ <h1>Documentation Overview</h1>
 
 
 
+
             <br>
           </li>
           <li>To get the graphs that show which routines call which
diff --git a/docs/doxygen/python_docs.config b/docs/doxygen/sirocco_docs.config
similarity index 99%
rename from docs/doxygen/python_docs.config
rename to docs/doxygen/sirocco_docs.config
index 9ed957e0e..9ce3de33b 100644
--- a/docs/doxygen/python_docs.config
+++ b/docs/doxygen/sirocco_docs.config
@@ -32,7 +32,7 @@ DOXYFILE_ENCODING      = UTF-8
 # title of most generated pages and in a few other places.
 # The default value is: My Project.
 
-PROJECT_NAME           = "Python MCRT"
+PROJECT_NAME           = "Sirocco MCRT"
 
 # The PROJECT_NUMBER tag can be used to enter a project or revision number. This
 # could be handy for archiving the generated documentation or if some version

From 962b179a8510c98c01b5d874d271200835c3b6c8 Mon Sep 17 00:00:00 2001
From: "Knox S. Long" <long@stsci.edu>
Date: Wed, 23 Oct 2024 13:52:01 -0500
Subject: [PATCH 02/22] Cosmetic change to regression_check

---
 py_progs/regression_check.py | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/py_progs/regression_check.py b/py_progs/regression_check.py
index 0d11f0f01..293f56e58 100755
--- a/py_progs/regression_check.py
+++ b/py_progs/regression_check.py
@@ -321,7 +321,6 @@ def get_other_directory(run1):
     run1
     '''
     x=glob('py*')
-    # x=glob('*/')
     dirs=[]
     modtime=[]
     for one in x:
@@ -330,15 +329,18 @@ def get_other_directory(run1):
             dirs.append(one)
             modtime.append(os.path.getmtime(one))
 
-    # print(dirs)
-    # print(modtime)
 
     dirs=numpy.array(dirs)
     modtime=numpy.array(modtime)
     iorder=numpy.argsort(modtime)
 
-    run2=dirs[iorder[len(dirs)-1]]
-    return run2
+
+    if len(dirs)>1:
+        run2=dirs[iorder[len(dirs)-1]]
+        return run2
+    else:
+        print('Could not find a second set of tests for a comparison')
+        return ''
 
         
 
@@ -366,8 +368,8 @@ def steer(argv):
 
     if run2=='':
         run2=get_other_directory(run1)
-
-
+        if run2=='':
+            return
 
     # Check that we have everthing we need and then run the check
 
@@ -377,6 +379,7 @@ def steer(argv):
     else:
         print('Not able to parse input line:',''.join(argv))
 
+    return
 
 
 
@@ -387,8 +390,6 @@ def steer(argv):
 if __name__ == "__main__":
     import sys
     if len(sys.argv)>1:
-        # doit(int(sys.argv[1]))
-        # doit(sys.argv[1])
         steer(sys.argv)
     else:
         print (__doc__)

From 6cfd1476263c71fd3c4bc0948c0bf6d4456c85cc Mon Sep 17 00:00:00 2001
From: "Knox S. Long" <long@stsci.edu>
Date: Wed, 23 Oct 2024 14:13:37 -0500
Subject: [PATCH 03/22] More mods to regression_check

---
 py_progs/regression_check.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/py_progs/regression_check.py b/py_progs/regression_check.py
index 293f56e58..66419e95b 100755
--- a/py_progs/regression_check.py
+++ b/py_progs/regression_check.py
@@ -320,7 +320,7 @@ def get_other_directory(run1):
     the regression directory that was most recently created that is not
     run1
     '''
-    x=glob('py*')
+    x=glob('py*')+glob('sirocco*')
     dirs=[]
     modtime=[]
     for one in x:
@@ -334,8 +334,10 @@ def get_other_directory(run1):
     modtime=numpy.array(modtime)
     iorder=numpy.argsort(modtime)
 
+    print('xx',dirs)
 
-    if len(dirs)>1:
+
+    if len(dirs)>0:
         run2=dirs[iorder[len(dirs)-1]]
         return run2
     else:

From f6658c579ed782a1e5706c275ae8adbe38b9665f Mon Sep 17 00:00:00 2001
From: jhmatthews <matthews@ast.cam.ac.uk>
Date: Thu, 24 Oct 2024 17:07:20 +0100
Subject: [PATCH 04/22] 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 <https://github.com/agnwinds/data-gen>`_ repository progs/extrapolate\_xs/ 
+The python scripts can be found in the `data-gen <https://github.com/sirocco-rt/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. <http://arti
 
 
 
-Translation to python:
+Translation to SIROCCO:
 ======================
 
 The original data and the translation can be found in py\_verner.  A simple awkscript converts the downloaded data to SIROCCO format.
diff --git a/docs/sphinx/source/atomic/free-free.rst b/docs/sphinx/source/atomic/free-free.rst
index 9cad355e5..af002d421 100644
--- a/docs/sphinx/source/atomic/free-free.rst
+++ b/docs/sphinx/source/atomic/free-free.rst
@@ -14,13 +14,13 @@ The free-free Gaunt factors are taken from  `Sutherland 1998, MNRAS, 300, 321. <
 
 The last file is the one we use to calculate free-free emission, since this in integrated gaunt factor over a population of electrons  with a Boltzmann distribution of velocities.  The other two files could be of use in the future should we wish to have gaunt factor corrections for the heating rates,in which case we should use the gffgu.dat data file. However generally speaking free-free heating is never important and there would be significant overhead in calculating a gaunt factor for each photon.
 
-Translation to python
+Translation to SIROCCO
 =====================
 The file is simply modified by hand to put a label "FF\_GAUNT" at the start of each data line and a hash at the start of each comment line.
 
 Datafile - gffint.dat:
 ======================
-The format of the data file to be read into python is as follows:
+The format of the data file to be read into SIROCCO is as follows:
 
 +----------+------------------------+---------------------------+-----------+-----------+-----------+
 |Label     | :math:`\log(\gamma^2)` |:math:`<g_{ff}(\gamma^2)>` |: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 <https://github.com/agnwinds/python/blob/master/source/setup_disk.c>`_
+  `setup_disk.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_disk.c>`_
+  `setup_disk.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_disk.c>`_
+  `setup_disk.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/python.c>`_
+  `sirocco.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_disk.c>`_
+  `setup_disk.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_disk.c>`_
+  `setup_disk.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_disk.c>`_
+  `setup_disk.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_disk.c>`_
+  `setup_disk.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_disk.c>`_
+  `setup_disk.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_disk.c>`_
+  `setup_disk.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_disk.c>`_
+  `setup_disk.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_disk.c>`_
+  `setup_disk.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/python.c>`_
+  `sirocco.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/python.c>`_
+  `sirocco.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/python.c>`_
+  `sirocco.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_star_bh.c>`_
+  `setup_star_bh.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_domains.c>`_
+  `setup_domains.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/diag.c>`_
+  `diag.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/python.c>`_
+  `sirocco.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/diag.c>`_
+  `diag.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/diag.c>`_
+  `diag.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/diag.c>`_
+  `diag.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/diag.c>`_
+  `diag.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/diag.c>`_
+  `diag.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/diag.c>`_
+  `diag.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/diag.c>`_
+  `diag.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/diag.c>`_
+  `diag.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/diag.c>`_
+  `diag.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/diag.c>`_
+  `diag.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/diag.c>`_
+  `diag.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/diag.c>`_
+  `diag.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/diag.c>`_
+  `diag.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/diag.c>`_
+  `diag.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/diag.c>`_
+  `diag.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/diag.c>`_
+  `diag.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/diag.c>`_
+   `diag.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_domains.c>`_
+  `setup_domains.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/bands.c>`_
+  `bands.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/bands.c>`_
+  `bands.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/bands.c>`_
+  `bands.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/bands.c>`_
+  `bands.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/bands.c>`_
+  `bands.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/bands.c>`_
+  `bands.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_reverb.c>`_
+  `setup_reverb.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_reverb.c>`_
+  `setup_reverb.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_reverb.c>`_
+  `setup_reverb.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_reverb.c>`_
+  `setup_reverb.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_reverb.c>`_
+  `setup_reverb.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_reverb.c>`_
+  `setup_reverb.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_reverb.c>`_
+  `setup_reverb.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_reverb.c>`_
+  `setup_reverb.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_reverb.c>`_
+  `setup_reverb.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_reverb.c>`_
+  `setup_reverb.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_reverb.c>`_
+  `setup_reverb.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_domains.c>`_
+  `setup_domains.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_domains.c>`_
+  `setup_domains.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_line_transfer.c>`_
+  `setup_line_transfer.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_line_transfer.c>`_
+  `setup_line_transfer.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_line_transfer.c>`_
+  `setup_line_transfer.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/>`_ :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 <https://github.com/sirocco-rt/sirocco/blob/master/source/>`_ :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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/>`_ :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 <https://github.com/sirocco-rt/sirocco/blob/master/source/>`_ :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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/corona.c>`_
+  `corona.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/corona.c>`_
+  `corona.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/corona.c>`_
+  `corona.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/corona.c>`_
+  `corona.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/corona.c>`_
+  `corona.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/corona.c>`_
+  `corona.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/homologous.c>`_
+  `homologous.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/homologous.c>`_
+  `homologous.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/homologous.c>`_
+  `homologous.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/homologous.c>`_
+  `homologous.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/homologous.c>`_
+  `homologous.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/hydro_import.c>`_
+  `hydro_import.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/hydro_import.c>`_
+  `hydro_import.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/knigge.c>`_
+  `knigge.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/knigge.c>`_
+  `knigge.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/knigge.c>`_
+  `knigge.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/knigge.c>`_
+  `knigge.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/knigge.c>`_
+  `knigge.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/knigge.c>`_
+  `knigge.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/knigge.c>`_
+  `knigge.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/knigge.c>`_
+  `knigge.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/sv.c>`_
+  `sv.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/sv.c>`_
+  `sv.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/sv.c>`_
+  `sv.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/sv.c>`_
+  `sv.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/sv.c>`_
+  `sv.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/sv.c>`_
+  `sv.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/sv.c>`_
+  `sv.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/sv.c>`_
+  `sv.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/sv.c>`_
+  `sv.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/sv.c>`_
+  `sv.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/sv.c>`_
+  `sv.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/shell_wind.c>`_
+  `shell_wind.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/shell_wind.c>`_
+  `shell_wind.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/shell_wind.c>`_
+  `shell_wind.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/shell_wind.c>`_
+  `shell_wind.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/shell_wind.c>`_
+  `shell_wind.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/shell_wind.c>`_
+  `shell_wind.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/stellar_wind.c>`_
+  `stellar_wind.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/stellar_wind.c>`_
+  `stellar_wind.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/stellar_wind.c>`_
+  `stellar_wind.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/stellar_wind.c>`_
+  `stellar_wind.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/stellar_wind.c>`_
+  `stellar_wind.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/stellar_wind.c>`_
+  `stellar_wind.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_domains.c>`_
+  `setup_domains.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_domains.c>`_
+  `setup_domains.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_domains.c>`_
+  `setup_domains.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_domains.c>`_
+  `setup_domains.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup.c>`_
+  `setup.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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'] <https://github.com/agnwinds/python/blob/master/source/['knigge.c', 'sv.c']>`_
+  `['knigge.c', 'sv.c'] <https://github.com/sirocco-rt/sirocco/blob/master/source/['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 <https://github.com/agnwinds/python/blob/master/source/import.c>`_
+  `import.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/python.c>`_
+  `sirocco.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/python.c>`_
+  `sirocco.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/python.c>`_
+  `sirocco.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_domains.c>`_
+  `setup_domains.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_domains.c>`_
+  `setup_domains.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/setup_domains.c>`_
+  `setup_domains.c <https://github.com/sirocco-rt/sirocco/blob/master/source/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 <https://github.com/agnwinds/python/blob/master/source/filename.c>`_
+  `filename.c <https://github.com/sirocco-rt/sirocco/blob/master/source/filename.c>`_
 
 Parent(s)
   * :ref:`System_type`: `agn`, `binary`

From f84b1d3363dbc4b7ff81ff208a96f3f1bfd93696 Mon Sep 17 00:00:00 2001
From: jhmatthews <matthews@ast.cam.ac.uk>
Date: Thu, 24 Oct 2024 18:00:02 +0100
Subject: [PATCH 05/22] 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 6361872422b94c429de3f42c6d51557885e42284 Mon Sep 17 00:00:00 2001
From: "Knox S. Long" <long@stsci.edu>
Date: Thu, 24 Oct 2024 15:19:07 -0500
Subject: [PATCH 06/22] Clean up of regressions test

* Removal of examples/regress.old
* Removal of unused hydro test from regression
* Documentation
---
 .../other/Diag/Diag.make_ioncycle_tables.rst  |   7 +-
 examples/regress.old/1d_sn.pf                 |  60 ---
 examples/regress.old/README.md                |  33 --
 examples/regress.old/agn_short.pf             |  78 ----
 examples/regress.old/cv.pf                    |  86 ----
 examples/regress.old/cv_boundary_layer.pf     |  90 ----
 examples/regress.old/cv_kur.pf                |  90 ----
 examples/regress.old/cv_reflect.pf            |  86 ----
 examples/regress.old/cv_reflect2.pf           |  86 ----
 .../regress.old/hydro/hdf000002nsh_dw33.zeu   | 409 ------------------
 examples/regress.old/hydro/model_heatcool.dat | 401 -----------------
 .../hydro/model_restart_heatcool.dat          | 401 -----------------
 examples/regress.old/hydro/py_hydro.pf        |  70 ---
 .../regress.old/hydro/py_hydro_restart.pf     |  19 -
 .../regress.old/matom_balmer/balmer_test.pf   |  61 ---
 examples/regress.old/rtheta.pf                |  86 ----
 examples/regress.old/star.pf                  |  61 ---
 examples/regress.old/two.pf                   |  92 ----
 py_progs/regression.py                        | 111 -----
 19 files changed, 6 insertions(+), 2321 deletions(-)
 delete mode 100644 examples/regress.old/1d_sn.pf
 delete mode 100644 examples/regress.old/README.md
 delete mode 100644 examples/regress.old/agn_short.pf
 delete mode 100644 examples/regress.old/cv.pf
 delete mode 100644 examples/regress.old/cv_boundary_layer.pf
 delete mode 100644 examples/regress.old/cv_kur.pf
 delete mode 100644 examples/regress.old/cv_reflect.pf
 delete mode 100644 examples/regress.old/cv_reflect2.pf
 delete mode 100644 examples/regress.old/hydro/hdf000002nsh_dw33.zeu
 delete mode 100644 examples/regress.old/hydro/model_heatcool.dat
 delete mode 100644 examples/regress.old/hydro/model_restart_heatcool.dat
 delete mode 100644 examples/regress.old/hydro/py_hydro.pf
 delete mode 100644 examples/regress.old/hydro/py_hydro_restart.pf
 delete mode 100644 examples/regress.old/matom_balmer/balmer_test.pf
 delete mode 100644 examples/regress.old/rtheta.pf
 delete mode 100644 examples/regress.old/star.pf
 delete mode 100644 examples/regress.old/two.pf

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..4480faf91 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
@@ -1,6 +1,11 @@
 Diag.make_ioncycle_tables
 =========================
-Multi-line description, must keep indentation.
+This diagnostic switch causes a series of astropy-compatable tables being 
+written for each ion cycle into the main diag directory for a run.  The tables are identical to those that can be generated with the separate routine windsave2table, and are intended to provide an overview of the ionization structure and the importance of various physical processes.
+
+Additionally, this option also causes information about the disk into a
+disk diag file after each ionization cycle concerning how much
+energy is hitting the disk as a function of disk radius.  
 
 Type
   Boolean (yes/no)
diff --git a/examples/regress.old/1d_sn.pf b/examples/regress.old/1d_sn.pf
deleted file mode 100644
index 4a4f1532c..000000000
--- a/examples/regress.old/1d_sn.pf
+++ /dev/null
@@ -1,60 +0,0 @@
-System_type(star,cv,bh,agn,previous)                 star
-
-### Parameters for the Central Object
-Central_object.mass(msol)                  0.8
-Central_object.radius(cm)                  1.23552e15
-Central_object.radiation(yes,no)                  yes
-Central_object.rad_type_to_make_wind(bb,models)                   bb
-Central_object.temp                        10672.1
-
-### Parameters for the Disk (if there is one)
-Disk.type(none,flat,vertically.extended)                 none
-
-### Parameters for Boundary Layer or the compact object in an X-ray Binary or AGN
-Boundary_layer.radiation(yes,no)                   no
-
-### Parameters describing the various winds or coronae in the system
-Wind.radiation(yes,no)                          yes
-Wind.number_of_components                  1
-Wind.type(SV,star,hydro,corona,kwd,homologous,shell,imported)           homologous
-Wind.coord_system(spherical,cylindrical,polar,cyl_var)            spherical
-Wind.dim.in.x_or_r.direction               30
-
-### Parameters associated with photon number, cycles,ionization and radiative transfer options
-Photons_per_cycle                          80000
-Ionization_cycles                          20
-Spectrum_cycles                            10
-Wind.ionization(on.the.spot,ML93,LTE_tr,LTE_te,fixed,matrix_bb,matrix_pow)                 ml93
-Line_transfer(pure_abs,pure_scat,sing_scat,escape_prob,thermal_trapping,macro_atoms,macro_atoms_thermal_trapping)          escape_prob
-Atomic_data                                data/standard80_sn_kurucz.dat
-Surface.reflection.or.absorption(reflect,absorb,thermalized.rerad)               absorb
-Wind_heating.extra_processes(none,adiabatic,nonthermal,both)                 none
-
-### Parameters for Domain 0
-Homologous.boundary_mdot(msol/yr)          26.8
-Homologous.radmin(cm)                      1.23552e15
-Homologous.radmax(cm)                      2.2464e15
-Homologous.vbase(cm)                       1.1e9
-Homologous.density_exponent                7.0
-Wind.t.init                                10000.0
-Wind.filling_factor(1=smooth,<1=clumped)   1
-
-### Parameters defining the spectra seen by observers
-
-Central_object.rad_type_in_final_spectrum(bb,models,uniform)                   bb
-
-### The minimum and maximum wavelengths in the final spectra
-Spectrum.wavemin(Angstroms)                200
-Spectrum.wavemax(Angstroms)                20000
-
-### The observers and their location relative to the system
-Spectrum.no_observers                      1
-Spectrum.angle(0=pole)                     45
-Spectrum.live_or_die(live.or.die,extract)              extract
-Spectrum.type(flambda,fnu,basic)                  fnu
-
-### Parameters for Reverberation Modeling (if needed)
-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)                    cv
diff --git a/examples/regress.old/README.md b/examples/regress.old/README.md
deleted file mode 100644
index 2f35f17bb..000000000
--- a/examples/regress.old/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
-### Regress
-
-The .pf files in this directory are intended for testing out
-various verisons of python using the script regression.py which 
-is in py_progs.  
-
-Note that in some cases, there may have been changes to the .pf
-files that are used to create particular models.
-
-An example of this, is that beginning with python_82a the treatment
-of reflection is read in has changed.  
-
-So for example, in 82a
-
-Surface.reflection.or.absorption(0=no.rerad,1=high.albedo,2=thermalized.rerad)                    0
-
-sets the reflection, whereas earlier (when only disk reflection is allowed it is set by
-
-Disk.illumination.treatment(0=no.rerad,1=high.albedo,2=thermalized.rerad,3=analytic)   0
-
-To handle this, there are likely variations in the .pf files here, from the ones that would
-be produced from the same models if one had entered everything interactively.
-
-
-Ideally, the history of these changes would be documented here:
-
-82a - Since reflection in 82a refers both to the star and disk, the variable  
-
-Surface.reflection is now always requested, where as in earlier versions of python it was 
-not needed, in situations where there was no disk.  AS a result, this rdpar line has been added
-to 1d_sn.pf and star.pf
-
-
diff --git a/examples/regress.old/agn_short.pf b/examples/regress.old/agn_short.pf
deleted file mode 100644
index 5e991cb82..000000000
--- a/examples/regress.old/agn_short.pf
+++ /dev/null
@@ -1,78 +0,0 @@
-System_type(star,cv,bh,agn,previous)                  agn
-
-### Parameters for the Central Object
-Central_object.mass(msol)                  1e9
-Central_object.radius(cm)                  8.85667e+14
-
-### Parameters for the Disk (if there is one)
-Disk.type(none,flat,vertically.extended)                 flat
-Disk.radiation(yes,no)                          yes
-Disk.rad_type_to_make_wind(bb,models)                   bb
-Disk.temperature.profile(standard,readin)             standard
-Disk.mdot(msol/yr)                         5
-Disk.radmax(cm)                            1e17
-
-### Parameters for Boundary Layer or the compact object in an X-ray Binary or AGN
-Central_object.radiation(yes,no)                  yes
-Central_object.rad_type_to_make_wind(bb,models,power,cloudy,brems)                power
-Central_object.luminosity(ergs/s)          1e43
-Central_object.power_law_index             -0.9
-Central_object.geometry_for_source(sphere,lamp_post)               sphere
-
-### Parameters describing the various winds or coronae in the system
-Wind.radiation(yes,no)                          yes
-Wind.number_of_components                  1
-Wind.type(SV,star,hydro,corona,kwd,homologous,shell,imported)                   sv
-Wind.coord_system(spherical,cylindrical,polar,cyl_var)          cylindrical
-Wind.dim.in.x_or_r.direction               30
-Wind.dim.in.z_or_theta.direction           30
-
-### Parameters associated with photon number, cycles,ionization and radiative transfer options
-Photons_per_cycle                          2000000
-Ionization_cycles                          3
-Spectrum_cycles                            2
-Wind.ionization(on.the.spot,ML93,LTE_tr,LTE_te,fixed,matrix_bb,matrix_pow)           matrix_pow
-Line_transfer(pure_abs,pure_scat,sing_scat,escape_prob,thermal_trapping,macro_atoms,macro_atoms_thermal_trapping)          escape_prob
-Atomic_data                                data/standard80_reduced.dat
-Surface.reflection.or.absorption(reflect,absorb,thermalized.rerad)               absorb
-Wind_heating.extra_processes(none,adiabatic,nonthermal,both)            adiabatic
-
-### Parameters for Domain 0
-Wind.mdot(msol/yr)                         5
-SV.diskmin(units_of_rstar)                 50
-SV.diskmax(units_of_rstar)                 100
-SV.thetamin(deg)                           70
-SV.thetamax(deg)                           82
-SV.mdot_r_exponent                         0
-SV.v_infinity(in_units_of_vescape          1
-SV.acceleration_length(cm)                 1e18
-SV.acceleration_exponent                   1.0
-SV.gamma(streamline_skew;1=usually)        1
-SV.v_zero_mode(fixed,sound_speed)                fixed
-SV.v_zero(cm/s)                            6e5
-Wind.radmax(cm)                            1e19
-Wind.t.init                                1e5
-Wind.filling_factor(1=smooth,<1=clumped)   1
-
-### Parameters defining the spectra seen by observers
-
-Disk.rad_type_in_final_spectrum(bb,models,uniform)                   bb
-Central_object.rad_type_in_final_spectrum(bb,models,power,cloudy,brems)                power
-
-### The minimum and maximum wavelengths in the final spectra
-Spectrum.wavemin(Angstroms)                200
-Spectrum.wavemax(Angstroms)                2600
-
-### The observers and their location relative to the system
-Spectrum.no_observers                      3
-Spectrum.angle(0=pole)                     20
-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
-
-### Parameters for Reverberation Modeling (if needed)
-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
diff --git a/examples/regress.old/cv.pf b/examples/regress.old/cv.pf
deleted file mode 100644
index fd0050598..000000000
--- a/examples/regress.old/cv.pf
+++ /dev/null
@@ -1,86 +0,0 @@
-System_type(star,cv,bh,agn,previous)                   cv
-
-### Parameters for the Central Object
-Central_object.mass(msol)                  0.8
-Central_object.radius(cm)                  7e+08
-Central_object.radiation(yes,no)                  yes
-Central_object.rad_type_to_make_wind(bb,models)                   bb
-Central_object.temp                        40000
-Binary.mass_sec(msol)                      0.6
-Binary.period(hr)                          5.57
-
-### Parameters for the Disk (if there is one)
-Disk.type(none,flat,vertically.extended)                 flat
-Disk.radiation(yes,no)                          yes
-Disk.rad_type_to_make_wind(bb,models)                   bb
-Disk.temperature.profile(standard,readin)             standard
-Disk.mdot(msol/yr)                         1e-8
-Disk.radmax(cm)                            2.4e+10
-
-### Parameters for Boundary Layer or the compact object in an X-ray Binary or AGN
-Boundary_layer.radiation(yes,no)                   no
-
-### Parameters describing the various winds or coronae in the system
-Wind.radiation(yes,no)                          yes
-Wind.number_of_components                  1
-Wind.type(SV,star,hydro,corona,kwd,homologous,shell,imported)                   sv
-Wind.coord_system(spherical,cylindrical,polar,cyl_var)          cylindrical
-Wind.dim.in.x_or_r.direction               30
-Wind.dim.in.z_or_theta.direction           30
-
-### Parameters associated with photon number, cycles,ionization and radiative transfer options
-Photons_per_cycle                          100000
-Ionization_cycles                          10
-Spectrum_cycles                            10
-Wind.ionization(on.the.spot,ML93,LTE_tr,LTE_te,fixed,matrix_bb,matrix_pow)            matrix_bb
-Line_transfer(pure_abs,pure_scat,sing_scat,escape_prob,thermal_trapping,macro_atoms,macro_atoms_thermal_trapping)     thermal_trapping
-Atomic_data                                data/standard80_reduced.dat
-Surface.reflection.or.absorption(reflect,absorb,thermalized.rerad)               absorb
-Wind_heating.extra_processes(none,adiabatic,nonthermal,both)            adiabatic
-
-### Parameters for Domain 0
-Wind.mdot(msol/yr)                         1e-9
-SV.diskmin(units_of_rstar)                 4
-SV.diskmax(units_of_rstar)                 12
-SV.thetamin(deg)                           20
-SV.thetamax(deg)                           65
-SV.mdot_r_exponent                         0
-SV.v_infinity(in_units_of_vescape          3
-SV.acceleration_length(cm)                 7e10
-SV.acceleration_exponent                   1.5
-SV.gamma(streamline_skew;1=usually)        1
-SV.v_zero_mode(fixed,sound_speed)                fixed
-SV.v_zero(cm/s)                            6e5
-Wind.radmax(cm)                            1e+12
-Wind.t.init                                40000
-Wind.filling_factor(1=smooth,<1=clumped)   1
-
-### Parameters defining the spectra seen by observers
-
-Central_object.rad_type_in_final_spectrum(bb,models,uniform)                   bb
-Disk.rad_type_in_final_spectrum(bb,models,uniform)                   bb
-
-### The minimum and maximum wavelengths in the final spectra
-Spectrum.wavemin(Angstroms)                800
-Spectrum.wavemax(Angstroms)                1850
-
-### The observers and their location relative to the system
-Spectrum.no_observers                      5
-Spectrum.angle(0=pole)                     10
-Spectrum.angle(0=pole)                     27.5
-Spectrum.angle(0=pole)                     45
-Spectrum.angle(0=pole)                     62.5
-Spectrum.angle(0=pole)                     80
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.live_or_die(live.or.die,extract)              extract
-Spectrum.type(flambda,fnu,basic)              flambda
-
-### Parameters for Reverberation Modeling (if needed)
-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)                    cv
diff --git a/examples/regress.old/cv_boundary_layer.pf b/examples/regress.old/cv_boundary_layer.pf
deleted file mode 100644
index 91b43919c..000000000
--- a/examples/regress.old/cv_boundary_layer.pf
+++ /dev/null
@@ -1,90 +0,0 @@
-System_type(star,cv,bh,agn,previous)                   cv
-
-### Parameters for the Central Object
-Central_object.mass(msol)                  0.8
-Central_object.radius(cm)                  7e+08
-Central_object.radiation(yes,no)                  yes
-Central_object.rad_type_to_make_wind(bb,models)                   bb
-Central_object.temp                        40000
-Binary.mass_sec(msol)                      0.6
-Binary.period(hr)                          5.57
-
-### Parameters for the Disk (if there is one)
-Disk.type(none,flat,vertically.extended)                 flat
-Disk.radiation(yes,no)                          yes
-Disk.rad_type_to_make_wind(bb,models)                   bb
-Disk.temperature.profile(standard,readin)             standard
-Disk.mdot(msol/yr)                         1e-8
-Disk.radmax(cm)                            2.4e+10
-
-### Parameters for Boundary Layer or the compact object in an X-ray Binary or AGN
-Boundary_layer.radiation(yes,no)                  yes
-Boundary_layer.rad_type_to_make_wind(bb,models,power)                   bb
-Boundary_layer.luminosity(ergs/s)          4.77818e+34
-Boundary_layer.temp(K)                     100000
-
-### Parameters describing the various winds or coronae in the system
-Wind.radiation(yes,no)                          yes
-Wind.number_of_components                  1
-Wind.type(SV,star,hydro,corona,kwd,homologous,shell,imported)                   sv
-Wind.coord_system(spherical,cylindrical,polar,cyl_var)          cylindrical
-Wind.dim.in.x_or_r.direction               30
-Wind.dim.in.z_or_theta.direction           30
-
-### Parameters associated with photon number, cycles,ionization and radiative transfer options
-Photons_per_cycle                          100000
-Ionization_cycles                          10
-Spectrum_cycles                            10
-Wind.ionization(on.the.spot,ML93,LTE_tr,LTE_te,fixed,matrix_bb,matrix_pow)            matrix_bb
-Line_transfer(pure_abs,pure_scat,sing_scat,escape_prob,thermal_trapping,macro_atoms,macro_atoms_thermal_trapping)     thermal_trapping
-Atomic_data                                data/standard80_reduced.dat
-Surface.reflection.or.absorption(reflect,absorb,thermalized.rerad)               absorb
-Wind_heating.extra_processes(none,adiabatic,nonthermal,both)            adiabatic
-
-### Parameters for Domain 0
-Wind.mdot(msol/yr)                         1e-9
-SV.diskmin(units_of_rstar)                 4
-SV.diskmax(units_of_rstar)                 12
-SV.thetamin(deg)                           20
-SV.thetamax(deg)                           65
-SV.mdot_r_exponent                         0
-SV.v_infinity(in_units_of_vescape          3
-SV.acceleration_length(cm)                 7e10
-SV.acceleration_exponent                   1.5
-SV.gamma(streamline_skew;1=usually)        1
-SV.v_zero_mode(fixed,sound_speed)                fixed
-SV.v_zero(cm/s)                            6e5
-Wind.radmax(cm)                            1e+12
-Wind.t.init                                40000
-Wind.filling_factor(1=smooth,<1=clumped)   1
-
-### Parameters defining the spectra seen by observers
-
-Central_object.rad_type_in_final_spectrum(bb,models,uniform)                   bb
-Disk.rad_type_in_final_spectrum(bb,models,uniform)                   bb
-Boundary_layer.rad_type_in_final_spectrum(bb,models,uniform)                   bb
-
-### The minimum and maximum wavelengths in the final spectra
-Spectrum.wavemin(Angstroms)                800
-Spectrum.wavemax(Angstroms)                1850
-
-### The observers and their location relative to the system
-Spectrum.no_observers                      5
-Spectrum.angle(0=pole)                     10
-Spectrum.angle(0=pole)                     27.5
-Spectrum.angle(0=pole)                     45
-Spectrum.angle(0=pole)                     62.5
-Spectrum.angle(0=pole)                     80
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.live_or_die(live.or.die,extract)              extract
-Spectrum.type(flambda,fnu,basic)              flambda
-
-### Parameters for Reverberation Modeling (if needed)
-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)                    cv
diff --git a/examples/regress.old/cv_kur.pf b/examples/regress.old/cv_kur.pf
deleted file mode 100644
index c4b51a9e7..000000000
--- a/examples/regress.old/cv_kur.pf
+++ /dev/null
@@ -1,90 +0,0 @@
-System_type(star,cv,bh,agn,previous)                   cv
-
-### Parameters for the Central Object
-Central_object.mass(msol)                  0.8
-Central_object.radius(cm)                  7e+08
-Central_object.radiation(yes,no)                  yes
-Central_object.rad_type_to_make_wind(bb,models)               models
-Input_spectra.model_file                   data/kurucz91.ls
-Central_object.temp                        40000
-Binary.mass_sec(msol)                      0.6
-Binary.period(hr)                          5.57
-
-### Parameters for the Disk (if there is one)
-Disk.type(none,flat,vertically.extended)                 flat
-Disk.radiation(yes,no)                          yes
-Disk.rad_type_to_make_wind(bb,models)               models
-Input_spectra.model_file                   data/kurucz91.ls
-Disk.temperature.profile(standard,readin)             standard
-Disk.mdot(msol/yr)                         1e-8
-Disk.radmax(cm)                            2.4e+10
-
-### Parameters for Boundary Layer or the compact object in an X-ray Binary or AGN
-Boundary_layer.radiation(yes,no)                   no
-
-### Parameters describing the various winds or coronae in the system
-Wind.radiation(yes,no)                          yes
-Wind.number_of_components                  1
-Wind.type(SV,star,hydro,corona,kwd,homologous,shell,imported)                   sv
-Wind.coord_system(spherical,cylindrical,polar,cyl_var)          cylindrical
-Wind.dim.in.x_or_r.direction               30
-Wind.dim.in.z_or_theta.direction           30
-
-### Parameters associated with photon number, cycles,ionization and radiative transfer options
-Photons_per_cycle                          100000
-Ionization_cycles                          10
-Spectrum_cycles                            10
-Wind.ionization(on.the.spot,ML93,LTE_tr,LTE_te,fixed,matrix_bb,matrix_pow)            matrix_bb
-Line_transfer(pure_abs,pure_scat,sing_scat,escape_prob,thermal_trapping,macro_atoms,macro_atoms_thermal_trapping)     thermal_trapping
-Atomic_data                                data/standard80_reduced.dat
-Surface.reflection.or.absorption(reflect,absorb,thermalized.rerad)               absorb
-Wind_heating.extra_processes(none,adiabatic,nonthermal,both)            adiabatic
-
-### Parameters for Domain 0
-Wind.mdot(msol/yr)                         1e-9
-SV.diskmin(units_of_rstar)                 4
-SV.diskmax(units_of_rstar)                 12
-SV.thetamin(deg)                           20
-SV.thetamax(deg)                           65
-SV.mdot_r_exponent                         0
-SV.v_infinity(in_units_of_vescape          3
-SV.acceleration_length(cm)                 7e10
-SV.acceleration_exponent                   1.5
-SV.gamma(streamline_skew;1=usually)        1
-SV.v_zero_mode(fixed,sound_speed)                fixed
-SV.v_zero(cm/s)                            6e5
-Wind.radmax(cm)                            1e+12
-Wind.t.init                                40000
-Wind.filling_factor(1=smooth,<1=clumped)   1
-
-### Parameters defining the spectra seen by observers
-
-Central_object.rad_type_in_final_spectrum(bb,models,uniform)               models
-Input_spectra.model_file                   data/kurucz91.ls
-Disk.rad_type_in_final_spectrum(bb,models,uniform)               models
-Input_spectra.model_file                   data/kurucz91.ls
-
-### The minimum and maximum wavelengths in the final spectra
-Spectrum.wavemin(Angstroms)                800
-Spectrum.wavemax(Angstroms)                1850
-
-### The observers and their location relative to the system
-Spectrum.no_observers                      5
-Spectrum.angle(0=pole)                     10
-Spectrum.angle(0=pole)                     27.5
-Spectrum.angle(0=pole)                     45
-Spectrum.angle(0=pole)                     62.5
-Spectrum.angle(0=pole)                     80
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.live_or_die(live.or.die,extract)              extract
-Spectrum.type(flambda,fnu,basic)              flambda
-
-### Parameters for Reverberation Modeling (if needed)
-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)                    cv
diff --git a/examples/regress.old/cv_reflect.pf b/examples/regress.old/cv_reflect.pf
deleted file mode 100644
index 36d8f3d65..000000000
--- a/examples/regress.old/cv_reflect.pf
+++ /dev/null
@@ -1,86 +0,0 @@
-System_type(star,cv,bh,agn,previous)                   cv
-
-### Parameters for the Central Object
-Central_object.mass(msol)                  0.8
-Central_object.radius(cm)                  7e+08
-Central_object.radiation(yes,no)                  yes
-Central_object.rad_type_to_make_wind(bb,models)                   bb
-Central_object.temp                        40000
-Binary.mass_sec(msol)                      0.6
-Binary.period(hr)                          5.57
-
-### Parameters for the Disk (if there is one)
-Disk.type(none,flat,vertically.extended)                 flat
-Disk.radiation(yes,no)                          yes
-Disk.rad_type_to_make_wind(bb,models)                   bb
-Disk.temperature.profile(standard,readin)             standard
-Disk.mdot(msol/yr)                         1e-8
-Disk.radmax(cm)                            2.4e+10
-
-### Parameters for Boundary Layer or the compact object in an X-ray Binary or AGN
-Boundary_layer.radiation(yes,no)                   no
-
-### Parameters describing the various winds or coronae in the system
-Wind.radiation(yes,no)                          yes
-Wind.number_of_components                  1
-Wind.type(SV,star,hydro,corona,kwd,homologous,shell,imported)                   sv
-Wind.coord_system(spherical,cylindrical,polar,cyl_var)          cylindrical
-Wind.dim.in.x_or_r.direction               30
-Wind.dim.in.z_or_theta.direction           30
-
-### Parameters associated with photon number, cycles,ionization and radiative transfer options
-Photons_per_cycle                          100000
-Ionization_cycles                          10
-Spectrum_cycles                            10
-Wind.ionization(on.the.spot,ML93,LTE_tr,LTE_te,fixed,matrix_bb,matrix_pow)            matrix_bb
-Line_transfer(pure_abs,pure_scat,sing_scat,escape_prob,thermal_trapping,macro_atoms,macro_atoms_thermal_trapping)     thermal_trapping
-Atomic_data                                data/standard80_reduced.dat
-Surface.reflection.or.absorption(reflect,absorb,thermalized.rerad)              reflect
-Wind_heating.extra_processes(none,adiabatic,nonthermal,both)            adiabatic
-
-### Parameters for Domain 0
-Wind.mdot(msol/yr)                         1e-9
-SV.diskmin(units_of_rstar)                 4
-SV.diskmax(units_of_rstar)                 12
-SV.thetamin(deg)                           20
-SV.thetamax(deg)                           65
-SV.mdot_r_exponent                         0
-SV.v_infinity(in_units_of_vescape          3
-SV.acceleration_length(cm)                 7e10
-SV.acceleration_exponent                   1.5
-SV.gamma(streamline_skew;1=usually)        1
-SV.v_zero_mode(fixed,sound_speed)                fixed
-SV.v_zero(cm/s)                            6e5
-Wind.radmax(cm)                            1e+12
-Wind.t.init                                40000
-Wind.filling_factor(1=smooth,<1=clumped)   1
-
-### Parameters defining the spectra seen by observers
-
-Central_object.rad_type_in_final_spectrum(bb,models,uniform)                   bb
-Disk.rad_type_in_final_spectrum(bb,models,uniform)                   bb
-
-### The minimum and maximum wavelengths in the final spectra
-Spectrum.wavemin(Angstroms)                800
-Spectrum.wavemax(Angstroms)                1850
-
-### The observers and their location relative to the system
-Spectrum.no_observers                      5
-Spectrum.angle(0=pole)                     10
-Spectrum.angle(0=pole)                     27.5
-Spectrum.angle(0=pole)                     45
-Spectrum.angle(0=pole)                     62.5
-Spectrum.angle(0=pole)                     80
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.live_or_die(live.or.die,extract)              extract
-Spectrum.type(flambda,fnu,basic)              flambda
-
-### Parameters for Reverberation Modeling (if needed)
-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)                    cv
diff --git a/examples/regress.old/cv_reflect2.pf b/examples/regress.old/cv_reflect2.pf
deleted file mode 100644
index 80785b4b1..000000000
--- a/examples/regress.old/cv_reflect2.pf
+++ /dev/null
@@ -1,86 +0,0 @@
-System_type(star,cv,bh,agn,previous)                   cv
-
-### Parameters for the Central Object
-Central_object.mass(msol)                  0.8
-Central_object.radius(cm)                  7e+08
-Central_object.radiation(yes,no)                  yes
-Central_object.rad_type_to_make_wind(bb,models)                   bb
-Central_object.temp                        40000
-Binary.mass_sec(msol)                      0.6
-Binary.period(hr)                          5.57
-
-### Parameters for the Disk (if there is one)
-Disk.type(none,flat,vertically.extended)                 flat
-Disk.radiation(yes,no)                          yes
-Disk.rad_type_to_make_wind(bb,models)                   bb
-Disk.temperature.profile(standard,readin)             standard
-Disk.mdot(msol/yr)                         1e-8
-Disk.radmax(cm)                            2.4e+10
-
-### Parameters for Boundary Layer or the compact object in an X-ray Binary or AGN
-Boundary_layer.radiation(yes,no)                   no
-
-### Parameters describing the various winds or coronae in the system
-Wind.radiation(yes,no)                          yes
-Wind.number_of_components                  1
-Wind.type(SV,star,hydro,corona,kwd,homologous,shell,imported)                   sv
-Wind.coord_system(spherical,cylindrical,polar,cyl_var)          cylindrical
-Wind.dim.in.x_or_r.direction               30
-Wind.dim.in.z_or_theta.direction           10
-
-### Parameters associated with photon number, cycles,ionization and radiative transfer options
-Photons_per_cycle                          100000
-Ionization_cycles                          10
-Spectrum_cycles                            10
-Wind.ionization(on.the.spot,ML93,LTE_tr,LTE_te,fixed,matrix_bb,matrix_pow)            matrix_bb
-Line_transfer(pure_abs,pure_scat,sing_scat,escape_prob,thermal_trapping,macro_atoms,macro_atoms_thermal_trapping)     thermal_trapping
-Atomic_data                                data/standard80_reduced.dat
-Surface.reflection.or.absorption(reflect,absorb,thermalized.rerad)    thermalized.rerad
-Wind_heating.extra_processes(none,adiabatic,nonthermal,both)            adiabatic
-
-### Parameters for Domain 0
-Wind.mdot(msol/yr)                         1e-9
-SV.diskmin(units_of_rstar)                 4
-SV.diskmax(units_of_rstar)                 12
-SV.thetamin(deg)                           20
-SV.thetamax(deg)                           65
-SV.mdot_r_exponent                         0
-SV.v_infinity(in_units_of_vescape          3
-SV.acceleration_length(cm)                 7e10
-SV.acceleration_exponent                   1.5
-SV.gamma(streamline_skew;1=usually)        1
-SV.v_zero_mode(fixed,sound_speed)                fixed
-SV.v_zero(cm/s)                            6e5
-Wind.radmax(cm)                            1e+12
-Wind.t.init                                40000
-Wind.filling_factor(1=smooth,<1=clumped)   1
-
-### Parameters defining the spectra seen by observers
-
-Central_object.rad_type_in_final_spectrum(bb,models,uniform)                   bb
-Disk.rad_type_in_final_spectrum(bb,models,uniform)                   bb
-
-### The minimum and maximum wavelengths in the final spectra
-Spectrum.wavemin(Angstroms)                800
-Spectrum.wavemax(Angstroms)                1850
-
-### The observers and their location relative to the system
-Spectrum.no_observers                      5
-Spectrum.angle(0=pole)                     10
-Spectrum.angle(0=pole)                     27.5
-Spectrum.angle(0=pole)                     45
-Spectrum.angle(0=pole)                     62.5
-Spectrum.angle(0=pole)                     80
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.live_or_die(live.or.die,extract)              extract
-Spectrum.type(flambda,fnu,basic)              flambda
-
-### Parameters for Reverberation Modeling (if needed)
-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)                    cv
diff --git a/examples/regress.old/hydro/hdf000002nsh_dw33.zeu b/examples/regress.old/hydro/hdf000002nsh_dw33.zeu
deleted file mode 100644
index 85b370336..000000000
--- a/examples/regress.old/hydro/hdf000002nsh_dw33.zeu
+++ /dev/null
@@ -1,409 +0,0 @@
-# This is a file generated by hydro_to_python
-# We can put any number of comments in behind # signs
-# By default, the order of coordinates are 
-#				r, theta phi for spherical polars
-# 		                x,y,z        for carteisan
-#                         or w, z, phi    for cylindrical
-# Coordinate_system spol
-# Dimensions 2
-ir r_cent r_edge itheta theta_cent theta_edge v_r v_theta v_phi density temperature
-000 04.348375e+10 02.412338e+10 0 06.121442e-02 07.188807e-09 -9.184150e+06 01.229772e+04 02.312647e+07 02.891734e-19 02.514272e+07
-001 08.259169e+10 06.284411e+10 0 06.121442e-02 07.188807e-09 -1.763110e+07 01.482375e+04 04.940696e+07 02.297492e-21 02.354382e+07
-002 01.224818e+11 01.023393e+11 0 06.121442e-02 07.188807e-09 -1.708392e+07 02.088710e+05 02.539087e+07 01.035081e-21 01.827968e+07
-003 01.631697e+11 01.426243e+11 0 06.121442e-02 07.188807e-09 -1.624921e+07 01.002890e+05 02.730597e+07 06.449711e-22 01.397102e+07
-004 02.046713e+11 01.837150e+11 0 06.121442e-02 07.188807e-09 -1.492461e+07 02.942582e+04 01.930618e+07 04.580795e-22 01.096156e+07
-005 02.470030e+11 02.256276e+11 0 06.121442e-02 07.188807e-09 -2.271791e+07 02.026959e+06 03.039249e+02 02.589104e-22 06.831776e+06
-006 02.901813e+11 02.683784e+11 0 06.121442e-02 07.188807e-09 -3.892676e+07 02.388736e+06 02.004508e+03 01.609801e-22 02.625931e+06
-007 03.342232e+11 03.119842e+11 0 06.121442e-02 07.188807e-09 -4.991727e+07 01.713959e+06 05.571645e+03 01.159641e-22 01.095161e+06
-008 03.791460e+11 03.564621e+11 0 06.121442e-02 07.188807e-09 -4.968571e+07 01.365665e+06 01.014407e+04 01.003669e-22 07.363503e+05
-009 04.249671e+11 04.018296e+11 0 06.121442e-02 07.188807e-09 -4.428315e+07 02.126963e+06 01.908765e+04 01.000000e-22 05.426289e+05
-010 04.717048e+11 04.481044e+11 0 06.121442e-02 07.188807e-09 -3.968612e+07 03.943591e+06 07.027256e+04 01.000000e-22 04.272502e+05
-011 05.193771e+11 04.953047e+11 0 06.121442e-02 07.188807e-09 -3.499816e+07 04.829349e+06 04.371887e+05 01.000000e-22 04.077300e+05
-012 05.680029e+11 05.434490e+11 0 06.121442e-02 07.188807e-09 -3.002433e+07 03.543156e+06 02.385100e+06 01.000000e-22 05.001635e+05
-013 06.176012e+11 05.925561e+11 0 06.121442e-02 07.188807e-09 -2.492747e+07 02.781290e+06 07.631120e+06 01.000000e-22 05.804273e+05
-014 06.681915e+11 06.426454e+11 0 06.121442e-02 07.188807e-09 -1.996851e+07 01.002630e+06 01.566110e+07 01.000000e-22 06.695052e+05
-015 07.197936e+11 06.937365e+11 0 06.121442e-02 07.188807e-09 -1.549277e+07 05.343340e+05 01.972119e+07 01.000000e-22 06.826081e+05
-016 07.724277e+11 07.458494e+11 0 06.121442e-02 07.188807e-09 -1.173732e+07 03.404781e+05 02.226895e+07 01.000000e-22 06.311863e+05
-017 08.261146e+11 07.990046e+11 0 06.121442e-02 07.188807e-09 -8.378144e+06 01.754470e+05 02.395437e+07 01.000000e-22 05.674570e+05
-018 08.808751e+11 08.532229e+11 0 06.121442e-02 07.188807e-09 -5.151664e+06 01.790850e+05 02.484868e+07 01.000000e-22 04.913601e+05
-019 09.367309e+11 09.085255e+11 0 06.121442e-02 07.188807e-09 -1.777908e+06 02.801267e+04 02.676175e+07 01.000000e-22 04.418570e+05
-000 04.348375e+10 02.412338e+10 1 01.805825e-01 01.224288e-01 -3.502176e+07 07.278491e+06 04.851545e+07 03.855789e-19 02.775268e+07
-001 08.259169e+10 06.284411e+10 1 01.805825e-01 01.224288e-01 -7.273296e+07 05.491996e+06 06.659177e+07 06.792328e-19 01.231628e+07
-002 01.224818e+11 01.023393e+11 1 01.805825e-01 01.224288e-01 -5.920822e+07 02.193190e+06 07.647921e+07 01.049893e-18 01.145864e+07
-003 01.631697e+11 01.426243e+11 1 01.805825e-01 01.224288e-01 -3.710248e+07 01.450688e+06 07.820432e+07 01.501759e-18 01.208287e+07
-004 02.046713e+11 01.837150e+11 1 01.805825e-01 01.224288e-01 -2.930090e+07 01.927452e+06 07.923295e+07 01.930071e-18 01.204617e+07
-005 02.470030e+11 02.256276e+11 1 01.805825e-01 01.224288e-01 -2.475896e+07 03.758780e+06 08.220313e+07 02.772744e-18 01.203282e+07
-006 02.901813e+11 02.683784e+11 1 01.805825e-01 01.224288e-01 -1.954964e+07 03.548465e+06 08.125323e+07 04.162095e-18 01.152024e+07
-007 03.342232e+11 03.119842e+11 1 01.805825e-01 01.224288e-01 -1.512463e+07 02.572220e+06 07.926659e+07 05.919688e-18 01.099338e+07
-008 03.791460e+11 03.564621e+11 1 01.805825e-01 01.224288e-01 -1.189364e+07 02.011985e+06 07.735658e+07 08.166349e-18 01.055494e+07
-009 04.249671e+11 04.018296e+11 1 01.805825e-01 01.224288e-01 -9.171535e+06 02.573373e+06 07.524942e+07 01.135279e-17 09.966623e+06
-010 04.717048e+11 04.481044e+11 1 01.805825e-01 01.224288e-01 -5.620327e+06 04.078270e+06 07.237161e+07 01.779879e-17 08.467733e+06
-011 05.193771e+11 04.953047e+11 1 01.805825e-01 01.224288e-01 01.377487e+06 04.367942e+06 06.710736e+07 03.985778e-17 06.381866e+06
-012 05.680029e+11 05.434490e+11 1 01.805825e-01 01.224288e-01 09.831467e+06 02.519320e+06 06.205117e+07 07.490886e-17 05.642759e+06
-013 06.176012e+11 05.925561e+11 1 01.805825e-01 01.224288e-01 01.620925e+07 01.388372e+06 05.758348e+07 01.122101e-16 05.353488e+06
-014 06.681915e+11 06.426454e+11 1 01.805825e-01 01.224288e-01 02.078695e+07 -6.348318e+05 05.367827e+07 01.513933e-16 04.966976e+06
-015 07.197936e+11 06.937365e+11 1 01.805825e-01 01.224288e-01 02.408721e+07 -1.245621e+06 05.023462e+07 01.887134e-16 04.633960e+06
-016 07.724277e+11 07.458494e+11 1 01.805825e-01 01.224288e-01 02.673092e+07 -1.486153e+06 04.722739e+07 02.194083e-16 04.360637e+06
-017 08.261146e+11 07.990046e+11 1 01.805825e-01 01.224288e-01 02.915706e+07 -1.682366e+06 04.442483e+07 02.430325e-16 04.077800e+06
-018 08.808751e+11 08.532229e+11 1 01.805825e-01 01.224288e-01 03.119739e+07 -1.575592e+06 04.227820e+07 02.544053e-16 03.858841e+06
-019 09.367309e+11 09.085255e+11 1 01.805825e-01 01.224288e-01 03.202357e+07 -1.711572e+06 03.865975e+07 02.834691e-16 03.650502e+06
-000 04.348375e+10 02.412338e+10 2 02.939822e-01 02.387362e-01 -2.566237e+07 01.592248e+07 01.242865e+08 01.443025e-18 02.655861e+07
-001 08.259169e+10 06.284411e+10 2 02.939822e-01 02.387362e-01 -3.193923e+07 06.975607e+06 01.348681e+08 09.371025e-18 02.014613e+07
-002 01.224818e+11 01.023393e+11 2 02.939822e-01 02.387362e-01 -9.474553e+06 02.707755e+06 01.191232e+08 03.461838e-17 01.771694e+07
-003 01.631697e+11 01.426243e+11 2 02.939822e-01 02.387362e-01 -4.015032e+06 01.450687e+06 01.061132e+08 09.637100e-17 01.505176e+07
-004 02.046713e+11 01.837150e+11 2 02.939822e-01 02.387362e-01 02.259920e+06 01.178586e+06 08.901185e+07 02.354408e-16 01.180489e+07
-005 02.470030e+11 02.256276e+11 2 02.939822e-01 02.387362e-01 08.665411e+06 06.609082e+05 07.818625e+07 03.833805e-16 01.059807e+07
-006 02.901813e+11 02.683784e+11 2 02.939822e-01 02.387362e-01 01.281072e+07 -1.404258e+05 07.023913e+07 05.489139e-16 09.465515e+06
-007 03.342232e+11 03.119842e+11 2 02.939822e-01 02.387362e-01 01.578288e+07 -6.733548e+05 06.388216e+07 07.259122e-16 08.485154e+06
-008 03.791460e+11 03.564621e+11 2 02.939822e-01 02.387362e-01 01.831434e+07 -1.078974e+06 05.845615e+07 08.960698e-16 07.677328e+06
-009 04.249671e+11 04.018296e+11 2 02.939822e-01 02.387362e-01 02.055590e+07 -1.399965e+06 05.366852e+07 01.042917e-15 07.008778e+06
-010 04.717048e+11 04.481044e+11 2 02.939822e-01 02.387362e-01 02.257868e+07 -1.852332e+06 04.942792e+07 01.160307e-15 06.430332e+06
-011 05.193771e+11 04.953047e+11 2 02.939822e-01 02.387362e-01 02.439883e+07 -2.627474e+06 04.574034e+07 01.257108e-15 05.883649e+06
-012 05.680029e+11 05.434490e+11 2 02.939822e-01 02.387362e-01 02.593724e+07 -3.345183e+06 04.256478e+07 01.334207e-15 05.386590e+06
-013 06.176012e+11 05.925561e+11 2 02.939822e-01 02.387362e-01 02.720672e+07 -3.812642e+06 03.982555e+07 01.390071e-15 04.954668e+06
-014 06.681915e+11 06.426454e+11 2 02.939822e-01 02.387362e-01 02.828520e+07 -4.103576e+06 03.745308e+07 01.423909e-15 04.581428e+06
-015 07.197936e+11 06.937365e+11 2 02.939822e-01 02.387362e-01 02.923570e+07 -4.270476e+06 03.537904e+07 01.435402e-15 04.259798e+06
-016 07.724277e+11 07.458494e+11 2 02.939822e-01 02.387362e-01 03.012540e+07 -4.306372e+06 03.356739e+07 01.426438e-15 03.981125e+06
-017 08.261146e+11 07.990046e+11 2 02.939822e-01 02.387362e-01 03.112581e+07 -4.315122e+06 03.188999e+07 01.397631e-15 03.718052e+06
-018 08.808751e+11 08.532229e+11 2 02.939822e-01 02.387362e-01 03.244672e+07 -4.132360e+06 03.056562e+07 01.329224e-15 03.462214e+06
-019 09.367309e+11 09.085255e+11 2 02.939822e-01 02.387362e-01 03.320862e+07 -4.150559e+06 02.839153e+07 01.291897e-15 03.202794e+06
-000 04.348375e+10 02.412338e+10 3 04.017120e-01 03.492283e-01 -2.687668e+06 01.253929e+07 01.708008e+08 09.836080e-18 02.882643e+07
-001 08.259169e+10 06.284411e+10 3 04.017120e-01 03.492283e-01 -3.238291e+06 01.675885e+06 01.288743e+08 02.703113e-16 01.999890e+07
-002 01.224818e+11 01.023393e+11 3 04.017120e-01 03.492283e-01 02.017979e+06 01.304796e+05 09.952364e+07 08.691234e-16 01.406519e+07
-003 01.631697e+11 01.426243e+11 3 04.017120e-01 03.492283e-01 07.493940e+06 -1.026031e+06 08.315524e+07 01.472782e-15 01.129697e+07
-004 02.046713e+11 01.837150e+11 3 04.017120e-01 03.492283e-01 01.138120e+07 -2.239994e+06 07.316438e+07 02.073490e-15 09.299885e+06
-005 02.470030e+11 02.256276e+11 3 04.017120e-01 03.492283e-01 01.420438e+07 -2.920618e+06 06.578884e+07 02.630247e-15 07.996288e+06
-006 02.901813e+11 02.683784e+11 3 04.017120e-01 03.492283e-01 01.651590e+07 -3.342911e+06 05.953547e+07 03.091809e-15 07.081088e+06
-007 03.342232e+11 03.119842e+11 3 04.017120e-01 03.492283e-01 01.835466e+07 -3.746886e+06 05.404266e+07 03.427916e-15 06.413852e+06
-008 03.791460e+11 03.564621e+11 3 04.017120e-01 03.492283e-01 01.985687e+07 -4.062853e+06 04.923484e+07 03.636991e-15 05.877828e+06
-009 04.249671e+11 04.018296e+11 3 04.017120e-01 03.492283e-01 02.112735e+07 -4.247676e+06 04.508040e+07 03.735328e-15 05.420695e+06
-010 04.717048e+11 04.481044e+11 3 04.017120e-01 03.492283e-01 02.213580e+07 -4.452036e+06 04.156770e+07 03.729416e-15 05.051462e+06
-011 05.193771e+11 04.953047e+11 3 04.017120e-01 03.492283e-01 02.292057e+07 -4.680616e+06 03.864388e+07 03.682030e-15 04.717958e+06
-012 05.680029e+11 05.434490e+11 3 04.017120e-01 03.492283e-01 02.359886e+07 -4.885724e+06 03.621876e+07 03.596091e-15 04.413730e+06
-013 06.176012e+11 05.925561e+11 3 04.017120e-01 03.492283e-01 02.418819e+07 -5.028583e+06 03.418786e+07 03.489965e-15 04.141012e+06
-014 06.681915e+11 06.426454e+11 3 04.017120e-01 03.492283e-01 02.472752e+07 -5.102452e+06 03.245391e+07 03.372117e-15 03.896370e+06
-015 07.197936e+11 06.937365e+11 3 04.017120e-01 03.492283e-01 02.527183e+07 -5.147638e+06 03.093652e+07 03.244685e-15 03.672956e+06
-016 07.724277e+11 07.458494e+11 3 04.017120e-01 03.492283e-01 02.588291e+07 -5.136487e+06 02.959249e+07 03.104466e-15 03.465208e+06
-017 08.261146e+11 07.990046e+11 3 04.017120e-01 03.492283e-01 02.666889e+07 -5.114410e+06 02.831841e+07 02.944987e-15 03.262277e+06
-018 08.808751e+11 08.532229e+11 3 04.017120e-01 03.492283e-01 02.776201e+07 -5.023676e+06 02.728841e+07 02.740896e-15 03.056545e+06
-019 09.367309e+11 09.085255e+11 3 04.017120e-01 03.492283e-01 02.839974e+07 -5.147647e+06 02.555293e+07 02.585643e-15 02.863556e+06
-000 04.348375e+10 02.412338e+10 4 05.040552e-01 04.541957e-01 -8.698766e+05 05.031326e+06 01.665090e+08 01.667658e-16 02.428723e+07
-001 08.259169e+10 06.284411e+10 4 05.040552e-01 04.541957e-01 02.633692e+05 -8.856378e+04 01.109053e+08 02.542911e-15 01.431002e+07
-002 01.224818e+11 01.023393e+11 4 05.040552e-01 04.541957e-01 04.282734e+06 -1.508505e+06 08.949648e+07 04.123132e-15 01.124850e+07
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-019 09.367309e+11 09.085255e+11 14 01.284320e+00 01.254468e+00 01.823113e+07 -6.643064e+06 02.557333e+07 02.781171e-14 01.742275e+06
-000 04.348375e+10 02.412338e+10 15 01.342533e+00 01.314173e+00 -7.936674e+02 01.526559e+03 01.275100e+08 01.684436e-12 07.782934e+06
-001 08.259169e+10 06.284411e+10 15 01.342533e+00 01.314173e+00 04.351476e+05 -8.433788e+05 01.021283e+08 09.283943e-13 05.199734e+06
-002 01.224818e+11 01.023393e+11 15 01.342533e+00 01.314173e+00 05.987186e+05 -4.575055e+05 07.844682e+07 07.843960e-13 03.768332e+06
-003 01.631697e+11 01.426243e+11 15 01.342533e+00 01.314173e+00 06.699973e+05 -8.414789e+05 07.041466e+07 04.907773e-13 03.493751e+06
-004 02.046713e+11 01.837150e+11 15 01.342533e+00 01.314173e+00 06.183318e+05 -1.506430e+06 06.230019e+07 04.171599e-13 02.956226e+06
-005 02.470030e+11 02.256276e+11 15 01.342533e+00 01.314173e+00 03.446083e+05 -1.325604e+06 05.462679e+07 03.190607e-13 02.784706e+06
-006 02.901813e+11 02.683784e+11 15 01.342533e+00 01.314173e+00 01.787272e+06 -1.983505e+06 04.882139e+07 02.460860e-13 02.615234e+06
-007 03.342232e+11 03.119842e+11 15 01.342533e+00 01.314173e+00 04.655534e+06 -3.121114e+06 04.482402e+07 01.972501e-13 02.434372e+06
-008 03.791460e+11 03.564621e+11 15 01.342533e+00 01.314173e+00 07.484432e+06 -4.216539e+06 04.221192e+07 01.630877e-13 02.277283e+06
-009 04.249671e+11 04.018296e+11 15 01.342533e+00 01.314173e+00 09.640076e+06 -4.959930e+06 04.018640e+07 01.371991e-13 02.157038e+06
-010 04.717048e+11 04.481044e+11 15 01.342533e+00 01.314173e+00 01.113582e+07 -5.430442e+06 03.842270e+07 01.165730e-13 02.065534e+06
-011 05.193771e+11 04.953047e+11 15 01.342533e+00 01.314173e+00 01.227410e+07 -5.751046e+06 03.681623e+07 09.965442e-14 01.991626e+06
-012 05.680029e+11 05.434490e+11 15 01.342533e+00 01.314173e+00 01.317339e+07 -5.919770e+06 03.532994e+07 08.572594e-14 01.932598e+06
-013 06.176012e+11 05.925561e+11 15 01.342533e+00 01.314173e+00 01.378638e+07 -5.989858e+06 03.395402e+07 07.418464e-14 01.884334e+06
-014 06.681915e+11 06.426454e+11 15 01.342533e+00 01.314173e+00 01.412657e+07 -6.029204e+06 03.268627e+07 06.445697e-14 01.842830e+06
-015 07.197936e+11 06.937365e+11 15 01.342533e+00 01.314173e+00 01.430132e+07 -6.052826e+06 03.151560e+07 05.612764e-14 01.805407e+06
-016 07.724277e+11 07.458494e+11 15 01.342533e+00 01.314173e+00 01.445873e+07 -6.066484e+06 03.043891e+07 04.894863e-14 01.770222e+06
-017 08.261146e+11 07.990046e+11 15 01.342533e+00 01.314173e+00 01.472492e+07 -6.093370e+06 02.939547e+07 04.255232e-14 01.736979e+06
-018 08.808751e+11 08.532229e+11 15 01.342533e+00 01.314173e+00 01.520456e+07 -6.167544e+06 02.854698e+07 03.703460e-14 01.684116e+06
-019 09.367309e+11 09.085255e+11 15 01.342533e+00 01.314173e+00 01.550990e+07 -6.326554e+06 02.710729e+07 03.113296e-14 01.695578e+06
-000 04.348375e+10 02.412338e+10 16 01.397835e+00 01.370894e+00 -4.884442e+02 01.120642e+03 01.291784e+08 02.473823e-12 06.427032e+06
-001 08.259169e+10 06.284411e+10 16 01.397835e+00 01.370894e+00 02.695675e+05 -6.617172e+05 01.023092e+08 01.347114e-12 04.303300e+06
-002 01.224818e+11 01.023393e+11 16 01.397835e+00 01.370894e+00 03.491592e+05 -2.942142e+05 07.914146e+07 01.076725e-12 03.182434e+06
-003 01.631697e+11 01.426243e+11 16 01.397835e+00 01.370894e+00 03.096310e+05 -7.513534e+05 07.120189e+07 06.356460e-13 03.060694e+06
-004 02.046713e+11 01.837150e+11 16 01.397835e+00 01.370894e+00 05.243723e+04 -1.031283e+06 06.177803e+07 05.188157e-13 02.665098e+06
-005 02.470030e+11 02.256276e+11 16 01.397835e+00 01.370894e+00 01.044698e+06 -1.510095e+06 05.429351e+07 03.908320e-13 02.483762e+06
-006 02.901813e+11 02.683784e+11 16 01.397835e+00 01.370894e+00 04.075722e+06 -2.643462e+06 04.937912e+07 02.994788e-13 02.322608e+06
-007 03.342232e+11 03.119842e+11 16 01.397835e+00 01.370894e+00 06.903898e+06 -3.725527e+06 04.651021e+07 02.385705e-13 02.183410e+06
-008 03.791460e+11 03.564621e+11 16 01.397835e+00 01.370894e+00 08.947466e+06 -4.582987e+06 04.432203e+07 01.956189e-13 02.068835e+06
-009 04.249671e+11 04.018296e+11 16 01.397835e+00 01.370894e+00 01.034813e+07 -5.037351e+06 04.234488e+07 01.626928e-13 01.981536e+06
-010 04.717048e+11 04.481044e+11 16 01.397835e+00 01.370894e+00 01.114987e+07 -5.248924e+06 04.049369e+07 01.369351e-13 01.914283e+06
-011 05.193771e+11 04.953047e+11 16 01.397835e+00 01.370894e+00 01.134834e+07 -5.349442e+06 03.877680e+07 01.164698e-13 01.860172e+06
-012 05.680029e+11 05.434490e+11 16 01.397835e+00 01.370894e+00 01.120462e+07 -5.402217e+06 03.720685e+07 09.949393e-14 01.813612e+06
-013 06.176012e+11 05.925561e+11 16 01.397835e+00 01.370894e+00 01.104189e+07 -5.397128e+06 03.576198e+07 08.560317e-14 01.774612e+06
-014 06.681915e+11 06.426454e+11 16 01.397835e+00 01.370894e+00 01.098357e+07 -5.385740e+06 03.442320e+07 07.399114e-14 01.739128e+06
-015 07.197936e+11 06.937365e+11 16 01.397835e+00 01.370894e+00 01.104444e+07 -5.380236e+06 03.317627e+07 06.428331e-14 01.706596e+06
-016 07.724277e+11 07.458494e+11 16 01.397835e+00 01.370894e+00 01.119770e+07 -5.400783e+06 03.202145e+07 05.596554e-14 01.674521e+06
-017 08.261146e+11 07.990046e+11 16 01.397835e+00 01.370894e+00 01.146302e+07 -5.457180e+06 03.092012e+07 04.867067e-14 01.643460e+06
-018 08.808751e+11 08.532229e+11 16 01.397835e+00 01.370894e+00 01.188764e+07 -5.582876e+06 03.001690e+07 04.238939e-14 01.598690e+06
-019 09.367309e+11 09.085255e+11 16 01.397835e+00 01.370894e+00 01.214814e+07 -5.817418e+06 02.855391e+07 03.573852e-14 01.601705e+06
-000 04.348375e+10 02.412338e+10 17 01.450372e+00 01.424778e+00 -1.420436e+02 08.130508e+02 01.304631e+08 03.570019e-12 05.254808e+06
-001 08.259169e+10 06.284411e+10 17 01.450372e+00 01.424778e+00 03.879890e+05 -5.517018e+05 01.025367e+08 01.857796e-12 03.629036e+06
-002 01.224818e+11 01.023393e+11 17 01.450372e+00 01.424778e+00 02.218542e+05 -1.464146e+05 07.966081e+07 01.396757e-12 02.766795e+06
-003 01.631697e+11 01.426243e+11 17 01.450372e+00 01.424778e+00 09.814808e+05 -8.750532e+05 07.153352e+07 07.983038e-13 02.691495e+06
-004 02.046713e+11 01.837150e+11 17 01.450372e+00 01.424778e+00 02.772646e+06 -9.218845e+05 06.143279e+07 06.276674e-13 02.397216e+06
-005 02.470030e+11 02.256276e+11 17 01.450372e+00 01.424778e+00 04.168692e+06 -1.914775e+06 05.485711e+07 04.751487e-13 02.202509e+06
-006 02.901813e+11 02.683784e+11 17 01.450372e+00 01.424778e+00 06.106572e+06 -3.082290e+06 05.160372e+07 03.634208e-13 02.063770e+06
-007 03.342232e+11 03.119842e+11 17 01.450372e+00 01.424778e+00 07.822624e+06 -3.881913e+06 04.910308e+07 02.885856e-13 01.950487e+06
-008 03.791460e+11 03.564621e+11 17 01.450372e+00 01.424778e+00 08.432585e+06 -4.255933e+06 04.673142e+07 02.354015e-13 01.861711e+06
-009 04.249671e+11 04.018296e+11 17 01.450372e+00 01.424778e+00 08.103888e+06 -4.466304e+06 04.452954e+07 01.945879e-13 01.790644e+06
-010 04.717048e+11 04.481044e+11 17 01.450372e+00 01.424778e+00 07.654368e+06 -4.520102e+06 04.248840e+07 01.633896e-13 01.732775e+06
-011 05.193771e+11 04.953047e+11 17 01.450372e+00 01.424778e+00 07.366754e+06 -4.498585e+06 04.059786e+07 01.387137e-13 01.682358e+06
-012 05.680029e+11 05.434490e+11 17 01.450372e+00 01.424778e+00 07.274720e+06 -4.510092e+06 03.886520e+07 01.187581e-13 01.635561e+06
-013 06.176012e+11 05.925561e+11 17 01.450372e+00 01.424778e+00 07.274846e+06 -4.512616e+06 03.728362e+07 01.024427e-13 01.592930e+06
-014 06.681915e+11 06.426454e+11 17 01.450372e+00 01.424778e+00 07.306996e+06 -4.522278e+06 03.584277e+07 08.884457e-14 01.553928e+06
-015 07.197936e+11 06.937365e+11 17 01.450372e+00 01.424778e+00 07.356109e+06 -4.526262e+06 03.452583e+07 07.734851e-14 01.518790e+06
-016 07.724277e+11 07.458494e+11 17 01.450372e+00 01.424778e+00 07.430842e+06 -4.545418e+06 03.331931e+07 06.749766e-14 01.486509e+06
-017 08.261146e+11 07.990046e+11 17 01.450372e+00 01.424778e+00 07.550102e+06 -4.598248e+06 03.219846e+07 05.891816e-14 01.455389e+06
-018 08.808751e+11 08.532229e+11 17 01.450372e+00 01.424778e+00 07.753004e+06 -4.722067e+06 03.123620e+07 05.135270e-14 01.421352e+06
-019 09.367309e+11 09.085255e+11 17 01.450372e+00 01.424778e+00 07.881920e+06 -4.963576e+06 02.986873e+07 04.395194e-14 01.399862e+06
-000 04.348375e+10 02.412338e+10 18 01.500282e+00 01.475968e+00 03.914823e+02 06.081125e+02 01.301407e+08 04.733614e-12 04.476966e+06
-001 08.259169e+10 06.284411e+10 18 01.500282e+00 01.475968e+00 08.154852e+05 -5.352094e+05 01.041738e+08 02.385833e-12 03.153710e+06
-002 01.224818e+11 01.023393e+11 18 01.500282e+00 01.475968e+00 06.763638e+05 04.833146e+04 07.847703e+07 01.679126e-12 02.498479e+06
-003 01.631697e+11 01.426243e+11 18 01.500282e+00 01.475968e+00 02.173492e+06 -1.277798e+06 07.341564e+07 09.979069e-13 02.309883e+06
-004 02.046713e+11 01.837150e+11 18 01.500282e+00 01.475968e+00 05.332006e+06 -1.095035e+06 06.460842e+07 07.627535e-13 02.097168e+06
-005 02.470030e+11 02.256276e+11 18 01.500282e+00 01.475968e+00 05.980604e+06 -1.764265e+06 05.842669e+07 05.822213e-13 01.918470e+06
-006 02.901813e+11 02.683784e+11 18 01.500282e+00 01.475968e+00 05.476392e+06 -2.565066e+06 05.458060e+07 04.518874e-13 01.774943e+06
-007 03.342232e+11 03.119842e+11 18 01.500282e+00 01.475968e+00 04.521042e+06 -3.043359e+06 05.152994e+07 03.623543e-13 01.660072e+06
-008 03.791460e+11 03.564621e+11 18 01.500282e+00 01.475968e+00 03.797217e+06 -3.105196e+06 04.870096e+07 02.986176e-13 01.572166e+06
-009 04.249671e+11 04.018296e+11 18 01.500282e+00 01.475968e+00 03.490577e+06 -3.172880e+06 04.611354e+07 02.501184e-13 01.495005e+06
-010 04.717048e+11 04.481044e+11 18 01.500282e+00 01.475968e+00 03.355139e+06 -3.192024e+06 04.380361e+07 02.124064e-13 01.428565e+06
-011 05.193771e+11 04.953047e+11 18 01.500282e+00 01.475968e+00 03.288332e+06 -3.188716e+06 04.175474e+07 01.822683e-13 01.370466e+06
-012 05.680029e+11 05.434490e+11 18 01.500282e+00 01.475968e+00 03.239960e+06 -3.181044e+06 03.993280e+07 01.576058e-13 01.318866e+06
-013 06.176012e+11 05.925561e+11 18 01.500282e+00 01.475968e+00 03.199590e+06 -3.156027e+06 03.829941e+07 01.371444e-13 01.273109e+06
-014 06.681915e+11 06.426454e+11 18 01.500282e+00 01.475968e+00 03.175100e+06 -3.125402e+06 03.682224e+07 01.199452e-13 01.231942e+06
-015 07.197936e+11 06.937365e+11 18 01.500282e+00 01.475968e+00 03.168802e+06 -3.092183e+06 03.547609e+07 01.053377e-13 01.194448e+06
-016 07.724277e+11 07.458494e+11 18 01.500282e+00 01.475968e+00 03.180152e+06 -3.066913e+06 03.424234e+07 09.281145e-14 01.159591e+06
-017 08.261146e+11 07.990046e+11 18 01.500282e+00 01.475968e+00 03.209079e+06 -3.063228e+06 03.310591e+07 08.195634e-14 01.125908e+06
-018 08.808751e+11 08.532229e+11 18 01.500282e+00 01.475968e+00 03.264631e+06 -3.107039e+06 03.207381e+07 07.246885e-14 01.090973e+06
-019 09.367309e+11 09.085255e+11 18 01.500282e+00 01.475968e+00 03.300701e+06 -3.264260e+06 03.092161e+07 06.388402e-14 01.050745e+06
-000 04.348375e+10 02.412338e+10 19 01.547697e+00 01.524599e+00 00.000000e+00 02.663664e+02 01.460011e+08 05.669693e-12 04.017891e+06
-001 08.259169e+10 06.284411e+10 19 01.547697e+00 01.524599e+00 00.000000e+00 -2.738287e+05 01.059379e+08 02.880828e-12 02.773190e+06
-002 01.224818e+11 01.023393e+11 19 01.547697e+00 01.524599e+00 00.000000e+00 07.884642e+04 08.699290e+07 01.816268e-12 02.414086e+06
-003 01.631697e+11 01.426243e+11 19 01.547697e+00 01.524599e+00 00.000000e+00 -7.499634e+05 07.537022e+07 01.273235e-12 01.895614e+06
-004 02.046713e+11 01.837150e+11 19 01.547697e+00 01.524599e+00 00.000000e+00 -5.859431e+05 06.729627e+07 09.513897e-13 01.746460e+06
-005 02.470030e+11 02.256276e+11 19 01.547697e+00 01.524599e+00 00.000000e+00 -7.479970e+05 06.125879e+07 07.420409e-13 01.567780e+06
-006 02.901813e+11 02.683784e+11 19 01.547697e+00 01.524599e+00 00.000000e+00 -9.830128e+05 05.651774e+07 05.969089e-13 01.408141e+06
-007 03.342232e+11 03.119842e+11 19 01.547697e+00 01.524599e+00 00.000000e+00 -1.127862e+06 05.266247e+07 04.915052e-13 01.288181e+06
-008 03.791460e+11 03.564621e+11 19 01.547697e+00 01.524599e+00 00.000000e+00 -1.175160e+06 04.944431e+07 04.121866e-13 01.198841e+06
-009 04.249671e+11 04.018296e+11 19 01.547697e+00 01.524599e+00 00.000000e+00 -1.200319e+06 04.670269e+07 03.507999e-13 01.122759e+06
-010 04.717048e+11 04.481044e+11 19 01.547697e+00 01.524599e+00 00.000000e+00 -1.199378e+06 04.432864e+07 03.021994e-13 01.057878e+06
-011 05.193771e+11 04.953047e+11 19 01.547697e+00 01.524599e+00 00.000000e+00 -1.184574e+06 04.224528e+07 02.629918e-13 01.000597e+06
-012 05.680029e+11 05.434490e+11 19 01.547697e+00 01.524599e+00 00.000000e+00 -1.163624e+06 04.039655e+07 02.308579e-13 09.486079e+05
-013 06.176012e+11 05.925561e+11 19 01.547697e+00 01.524599e+00 00.000000e+00 -1.138061e+06 03.874052e+07 02.041638e-13 09.008261e+05
-014 06.681915e+11 06.426454e+11 19 01.547697e+00 01.524599e+00 00.000000e+00 -1.110010e+06 03.724509e+07 01.817291e-13 08.562944e+05
-015 07.197936e+11 06.937365e+11 19 01.547697e+00 01.524599e+00 00.000000e+00 -1.080920e+06 03.588522e+07 01.626818e-13 08.142985e+05
-016 07.724277e+11 07.458494e+11 19 01.547697e+00 01.524599e+00 00.000000e+00 -1.052732e+06 03.464102e+07 01.463654e-13 07.741662e+05
-017 08.261146e+11 07.990046e+11 19 01.547697e+00 01.524599e+00 00.000000e+00 -1.030521e+06 03.349650e+07 01.322772e-13 07.349152e+05
-018 08.808751e+11 08.532229e+11 19 01.547697e+00 01.524599e+00 00.000000e+00 -1.022599e+06 03.243862e+07 01.200267e-13 06.952701e+05
-019 09.367309e+11 09.085255e+11 19 01.547697e+00 01.524599e+00 00.000000e+00 -1.057130e+06 03.145662e+07 01.093065e-13 06.517910e+05
diff --git a/examples/regress.old/hydro/model_heatcool.dat b/examples/regress.old/hydro/model_heatcool.dat
deleted file mode 100644
index 374c7a317..000000000
--- a/examples/regress.old/hydro/model_heatcool.dat
+++ /dev/null
@@ -1,401 +0,0 @@
-i j rcen thetacen vol temp xi ne heat_xray heat_comp heat_lines heat_ff cool_comp cool_lines cool_ff rho n_h
-0 0 4.348375e+10 6.121442e-02 7.341598e+30 2.514272e+07 2.156970e+11 1.474256e+05 1.389626e-14 1.992033e-06 0.000000e+00 3.388392e-20 3.728033e-06 8.636771e-15 2.408823e-13 2.891734e-19 1.219690e+05
-0 1 4.348375e+10 1.805825e-01 2.047723e+31 2.775268e+07 1.404272e+11 1.965748e+05 1.910641e-14 2.302256e-06 0.000000e+00 5.560969e-20 4.807500e-06 1.491664e-14 4.482960e-13 3.855789e-19 1.626314e+05
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-16 19 7.724277e+11 1.547697e+00 1.841214e+34 7.741662e+05 3.349896e+02 7.460164e+10 3.383263e-02 7.352861e-04 0.000000e+00 1.656000e-09 4.506122e-05 1.977001e-02 1.227158e-02 1.463654e-13 6.173474e+10
-17 0 8.261146e+11 6.121442e-02 3.481657e+33 5.674570e+05 1.867267e+12 5.098174e+01 0.000000e+00 2.006860e-12 0.000000e+00 4.122878e-27 8.647319e-14 0.000000e+00 4.953076e-21 1.000000e-22 4.217851e+01
-17 1 8.261146e+11 1.805825e-01 9.711058e+33 4.077800e+06 6.937561e+05 1.239022e+08 3.805108e-08 4.540739e-06 0.000000e+00 1.269858e-15 1.381875e-06 1.331660e-08 7.388860e-08 2.430325e-16 1.025075e+08
-17 2 8.261146e+11 2.939822e-01 1.488500e+34 3.718052e+06 1.302875e+05 7.125356e+08 1.286532e-06 2.688997e-05 0.000000e+00 4.475216e-14 7.718817e-06 4.446936e-07 2.344290e-06 1.397631e-15 5.894999e+08
-17 3 8.261146e+11 4.017120e-01 1.908170e+34 3.262277e+06 5.624624e+04 1.501400e+09 6.520807e-06 5.178855e-05 0.000000e+00 2.228453e-13 1.299682e-05 1.999334e-06 9.801600e-06 2.944987e-15 1.242152e+09
-17 4 8.261146e+11 5.040552e-01 2.239336e+34 3.044335e+06 3.445318e+04 2.412570e+09 1.713728e-05 8.118414e-05 0.000000e+00 6.453571e-13 1.911537e-05 5.207374e-06 2.451323e-05 4.732252e-15 1.995993e+09
-17 5 8.261146e+11 6.012813e-01 2.491819e+34 2.860464e+06 2.177278e+04 3.499920e+09 3.389155e-05 1.050486e-04 0.000000e+00 1.394042e-12 2.385455e-05 1.106214e-05 5.012075e-05 6.865114e-15 2.895603e+09
-17 6 8.261146e+11 6.936460e-01 2.675504e+34 2.742188e+06 1.529979e+04 4.655985e+09 6.748866e-05 1.370727e-04 0.000000e+00 2.402349e-12 2.867541e-05 1.977829e-05 8.697745e-05 9.132767e-15 3.852065e+09
-17 7 8.261146e+11 7.813926e-01 2.799928e+34 2.650358e+06 1.212006e+04 5.835092e+09 1.053524e-04 1.633428e-04 0.000000e+00 3.812634e-12 3.424083e-05 3.141135e-05 1.344529e-04 1.144565e-14 4.827604e+09
-17 8 8.261146e+11 8.647518e-01 2.874037e+34 2.529281e+06 9.214864e+03 7.140185e+09 1.625404e-04 1.930338e-04 0.000000e+00 5.861997e-12 3.753626e-05 4.781218e-05 1.969778e-04 1.400566e-14 5.907378e+09
-17 9 8.261146e+11 9.439430e-01 2.906023e+34 2.373623e+06 6.810615e+03 8.675575e+09 2.414508e-04 2.141226e-04 0.000000e+00 8.597744e-12 3.849928e-05 7.290752e-05 2.826500e-04 1.701747e-14 7.177715e+09
-17 10 8.261146e+11 1.019175e+00 2.903261e+34 2.208241e+06 5.224607e+03 1.040455e+10 3.370127e-04 2.327958e-04 0.000000e+00 1.372694e-11 3.927082e-05 1.093021e-04 3.931135e-04 2.040904e-14 8.608228e+09
-17 11 8.261146e+11 1.090645e+00 2.872346e+34 2.058582e+06 4.267034e+03 1.234618e+10 4.758137e-04 2.549532e-04 3.719617e-17 1.943799e-11 4.192487e-05 1.626957e-04 5.356655e-04 2.421780e-14 1.021471e+10
-17 12 8.261146e+11 1.158541e+00 2.818909e+34 1.943932e+06 3.116497e+03 1.450520e+10 6.621169e-04 2.758646e-04 0.000000e+00 2.487193e-11 4.047789e-05 2.407736e-04 7.198315e-04 2.845301e-14 1.200105e+10
-17 13 8.261146e+11 1.223043e+00 2.747922e+34 1.859026e+06 2.534413e+03 1.677309e+10 8.512886e-04 2.912785e-04 0.000000e+00 3.511265e-11 4.184700e-05 3.527832e-04 9.424754e-04 3.290186e-14 1.387751e+10
-17 14 8.261146e+11 1.284320e+00 2.663590e+34 1.795738e+06 2.009755e+03 1.918018e+10 1.136869e-03 3.026165e-04 2.426398e-14 4.215938e-11 4.198993e-05 5.010197e-04 1.212418e-03 3.762377e-14 1.586914e+10
-17 15 8.261146e+11 1.342533e+00 2.569586e+34 1.736979e+06 1.780508e+03 2.169263e+10 1.602929e-03 3.503729e-04 0.000000e+00 5.252775e-11 4.574824e-05 6.849534e-04 1.526757e-03 4.255232e-14 1.794793e+10
-17 16 8.261146e+11 1.397835e+00 2.468709e+34 1.643460e+06 1.286836e+03 2.481138e+10 2.031164e-03 3.336369e-04 6.895119e-16 7.420309e-11 4.160546e-05 1.088355e-03 1.945486e-03 4.867067e-14 2.052856e+10
-17 17 8.261146e+11 1.450372e+00 2.363591e+34 1.455389e+06 9.575405e+02 3.003487e+10 3.351340e-03 3.516868e-04 0.000000e+00 1.166920e-10 3.949804e-05 2.022459e-03 2.694892e-03 5.891816e-14 2.485080e+10
-17 18 8.261146e+11 1.500282e+00 2.256219e+34 1.125908e+06 6.543940e+02 4.177738e+10 8.334195e-03 4.770322e-04 0.000000e+00 2.898342e-10 4.042565e-05 5.320792e-03 4.613131e-03 8.195634e-14 3.456796e+10
-17 19 8.261146e+11 1.547697e+00 2.148096e+34 7.349152e+05 3.194673e+02 6.742020e+10 2.813733e-02 5.725338e-04 0.000000e+00 1.282747e-09 3.334851e-05 1.603201e-02 9.770361e-03 1.322772e-13 5.579255e+10
-18 0 8.808751e+11 6.121442e-02 4.037574e+33 4.913601e+05 1.640441e+12 5.098174e+01 0.000000e+00 1.766879e-12 0.000000e+00 4.475690e-27 6.580997e-14 0.000000e+00 4.612269e-21 1.000000e-22 4.217851e+01
-18 1 8.808751e+11 1.805825e-01 1.126162e+34 3.858841e+06 5.830731e+05 1.297002e+08 4.286131e-08 4.187082e-06 0.000000e+00 1.334906e-15 1.205108e-06 1.468196e-08 7.900955e-08 2.544053e-16 1.073044e+08
-18 2 8.808751e+11 2.939822e-01 1.726169e+34 3.462214e+06 1.204691e+05 6.776604e+08 1.228791e-06 2.243526e-05 0.000000e+00 3.967910e-14 6.006197e-06 4.050921e-07 2.052218e-06 1.329224e-15 5.606468e+08
-18 3 8.808751e+11 4.017120e-01 2.212848e+34 3.056545e+06 5.317336e+04 1.397351e+09 5.935695e-06 4.244360e-05 0.000000e+00 1.883062e-13 9.974217e-06 1.744296e-06 8.239141e-06 2.740896e-15 1.156069e+09
-18 4 8.808751e+11 5.040552e-01 2.596891e+34 2.858090e+06 3.281709e+04 2.231708e+09 1.531333e-05 6.629204e-05 0.000000e+00 5.364485e-13 1.462984e-05 4.487537e-06 2.037303e-05 4.377494e-15 1.846362e+09
-18 5 8.808751e+11 6.012813e-01 2.889688e+34 2.702430e+06 2.087598e+04 3.212690e+09 2.977180e-05 8.476672e-05 0.000000e+00 1.120839e-12 1.820984e-05 9.381856e-06 4.113531e-05 6.301714e-15 2.657969e+09
-18 6 8.808751e+11 6.936460e-01 3.102703e+34 2.608966e+06 1.477313e+04 4.240902e+09 5.776200e-05 1.098344e-04 0.000000e+00 1.895088e-12 2.185040e-05 1.651120e-05 7.051387e-05 8.318584e-15 3.508655e+09
-18 7 8.808751e+11 7.813926e-01 3.246993e+34 2.528996e+06 1.165850e+04 5.298584e+09 8.919951e-05 1.291637e-04 0.000000e+00 2.969784e-12 2.591942e-05 2.608060e-05 1.084733e-04 1.039329e-14 4.383735e+09
-18 8 8.808751e+11 8.647518e-01 3.332935e+34 2.412104e+06 8.933256e+03 6.489221e+09 1.392727e-04 1.545203e-04 0.000000e+00 4.597848e-12 2.865465e-05 3.981087e-05 1.593407e-04 1.272879e-14 5.368814e+09
-18 9 8.808751e+11 9.439430e-01 3.370029e+34 2.261080e+06 6.593954e+03 7.884329e+09 2.058384e-04 1.710817e-04 0.000000e+00 6.684693e-12 2.932798e-05 6.089376e-05 2.282500e-04 1.546543e-14 6.523087e+09
-18 10 8.808751e+11 1.019175e+00 3.366825e+34 2.098978e+06 5.032551e+03 9.432476e+09 2.824006e-04 1.841830e-04 0.000000e+00 1.071379e-11 2.955958e-05 9.119749e-05 3.155580e-04 1.850229e-14 7.803990e+09
-18 11 8.808751e+11 1.090645e+00 3.330974e+34 1.958578e+06 4.171819e+03 1.108517e+10 3.940768e-04 2.010673e-04 3.003341e-18 1.489335e-11 3.144138e-05 1.332083e-04 4.219186e-04 2.174428e-14 9.171413e+09
-18 12 8.808751e+11 1.158541e+00 3.269005e+34 1.854500e+06 3.103550e+03 1.285131e+10 5.382759e-04 2.152201e-04 0.000000e+00 1.844422e-11 3.017506e-05 1.916893e-04 5.527322e-04 2.520880e-14 1.063270e+10
-18 13 8.808751e+11 1.223043e+00 3.186683e+34 1.780874e+06 2.540969e+03 1.473058e+10 6.764854e-04 2.259835e-04 0.000000e+00 2.541158e-11 3.099850e-05 2.752878e-04 7.124424e-04 2.889531e-14 1.218761e+10
-18 14 8.808751e+11 1.284320e+00 3.088886e+34 1.729550e+06 2.028575e+03 1.673893e+10 8.913708e-04 2.327723e-04 1.483397e-15 3.003322e-11 3.108915e-05 3.850061e-04 9.073069e-04 3.283505e-14 1.384933e+10
-18 15 8.808751e+11 1.342533e+00 2.979872e+34 1.684116e+06 1.794201e+03 1.887976e+10 1.243638e-03 2.674365e-04 0.000000e+00 3.685495e-11 3.385269e-05 5.237156e-04 1.139813e-03 3.703460e-14 1.562064e+10
-18 16 8.808751e+11 1.397835e+00 2.862889e+34 1.598690e+06 1.313546e+03 2.160931e+10 1.582187e-03 2.574824e-04 0.000000e+00 5.173184e-11 3.132903e-05 8.225746e-04 1.456633e-03 4.238939e-14 1.787921e+10
-18 17 8.808751e+11 1.450372e+00 2.740986e+34 1.421352e+06 9.626642e+02 2.617818e+10 2.601994e-03 2.672773e-04 0.000000e+00 8.027836e-11 2.944128e-05 1.549689e-03 2.024543e-03 5.135270e-14 2.165980e+10
-18 18 8.808751e+11 1.500282e+00 2.616469e+34 1.090973e+06 6.518088e+02 3.694102e+10 6.825540e-03 3.714930e-04 0.000000e+00 2.094220e-10 3.050698e-05 4.192597e-03 3.552768e-03 7.246885e-14 3.056628e+10
-18 19 8.808751e+11 1.547697e+00 2.491083e+34 6.952701e+05 3.070036e+02 6.117551e+10 2.399037e-02 4.538792e-04 0.000000e+00 1.004021e-09 2.497504e-05 1.305148e-02 7.827838e-03 1.200267e-13 5.062547e+10
-19 0 9.367309e+11 6.121442e-02 4.657230e+33 4.418570e+05 1.443386e+12 5.098174e+01 0.000000e+00 1.550540e-12 0.000000e+00 4.642749e-27 5.209605e-14 0.000000e+00 4.374180e-21 1.000000e-22 4.217851e+01
-19 1 9.367309e+11 1.805825e-01 1.298997e+34 3.650502e+06 4.633134e+05 1.445174e+08 5.589192e-08 4.136564e-06 0.000000e+00 1.597238e-15 1.124604e-06 1.833438e-08 9.563327e-08 2.834691e-16 1.195630e+08
-19 2 9.367309e+11 2.939822e-01 1.991089e+34 3.202794e+06 1.098209e+05 6.586303e+08 1.225301e-06 1.931948e-05 0.000000e+00 3.726261e-14 4.784366e-06 3.854792e-07 1.870404e-06 1.291897e-15 5.449029e+08
-19 3 9.367309e+11 4.017120e-01 2.552460e+34 2.863556e+06 4.980743e+04 1.318200e+09 5.542041e-06 3.536687e-05 0.000000e+00 1.642135e-13 7.789438e-06 1.563251e-06 7.114698e-06 2.585643e-15 1.090586e+09
-19 4 9.367309e+11 5.040552e-01 2.995442e+34 2.701184e+06 3.106068e+04 2.085876e+09 1.386496e-05 5.483292e-05 0.000000e+00 4.520453e-13 1.143697e-05 3.943956e-06 1.733841e-05 4.091446e-15 1.725711e+09
-19 5 9.367309e+11 6.012813e-01 3.333176e+34 2.585222e+06 2.006548e+04 2.955466e+09 2.586098e-05 6.876748e-05 0.000000e+00 8.959985e-13 1.415808e-05 7.980046e-06 3.410351e-05 5.797173e-15 2.445161e+09
-19 6 9.367309e+11 6.936460e-01 3.578882e+34 2.518069e+06 1.433771e+04 3.858041e+09 4.904177e-05 8.837622e-05 0.000000e+00 1.465503e-12 1.695045e-05 1.372475e-05 5.740118e-05 7.567601e-15 3.191901e+09
-19 7 9.367309e+11 7.813926e-01 3.745317e+34 2.449706e+06 1.134168e+04 4.819991e+09 7.558943e-05 1.039102e-04 0.000000e+00 2.276277e-12 2.020374e-05 2.168663e-05 8.854463e-05 9.454525e-15 3.987777e+09
-19 8 9.367309e+11 8.647518e-01 3.844448e+34 2.343714e+06 8.626398e+03 5.923335e+09 1.173114e-04 1.242370e-04 0.000000e+00 3.529854e-12 2.239715e-05 3.339189e-05 1.310072e-04 1.161880e-14 4.900636e+09
-19 9 9.367309e+11 9.439430e-01 3.887235e+34 2.214766e+06 6.403510e+03 7.178715e+09 1.729748e-04 1.378184e-04 0.000000e+00 5.064248e-12 2.313763e-05 5.081516e-05 1.874143e-04 1.408135e-14 5.939303e+09
-19 10 9.367309e+11 1.019175e+00 3.883540e+34 2.088401e+06 4.915942e+03 8.543186e+09 2.318816e-04 1.477147e-04 0.000000e+00 7.848300e-12 2.356614e-05 7.515520e-05 2.582505e-04 1.675791e-14 7.068236e+09
-19 11 9.367309e+11 1.090645e+00 3.842187e+34 1.966669e+06 4.114746e+03 9.918663e+09 3.130039e-04 1.587403e-04 0.000000e+00 1.046719e-11 2.493241e-05 1.069074e-04 3.384400e-04 1.945611e-14 8.206297e+09
-19 12 9.367309e+11 1.158541e+00 3.770707e+34 1.867256e+06 3.125175e+03 1.129152e+10 4.142436e-04 1.670531e-04 2.832011e-16 1.248484e-11 2.361407e-05 1.473607e-04 4.280748e-04 2.214916e-14 9.342185e+09
-19 13 9.367309e+11 1.223043e+00 3.675751e+34 1.795388e+06 2.592879e+03 1.271402e+10 4.997125e-04 1.720856e-04 0.000000e+00 1.649208e-11 2.376785e-05 2.031883e-04 5.327627e-04 2.493964e-14 1.051917e+10
-19 14 9.367309e+11 1.284320e+00 3.562945e+34 1.742275e+06 2.110104e+03 1.417811e+10 6.342340e-04 1.732457e-04 0.000000e+00 1.895458e-11 2.337303e-05 2.712913e-04 6.531906e-04 2.781171e-14 1.173056e+10
-19 15 9.367309e+11 1.342533e+00 3.437200e+34 1.695578e+06 1.901914e+03 1.587120e+10 8.793718e-04 1.996921e-04 5.816005e-18 2.280950e-11 2.552212e-05 3.600123e-04 8.080949e-04 3.113296e-14 1.313142e+10
-19 16 9.367309e+11 1.397835e+00 3.302263e+34 1.601705e+06 1.358447e+03 1.821884e+10 1.120553e-03 1.904093e-04 1.753533e-15 3.243325e-11 2.309916e-05 5.762888e-04 1.036382e-03 3.573852e-14 1.507397e+10
-19 17 9.367309e+11 1.450372e+00 3.161652e+34 1.399862e+06 9.957917e+02 2.240551e+10 1.949459e-03 2.021454e-04 0.000000e+00 5.265716e-11 2.195421e-05 1.116118e-03 1.472510e-03 4.395194e-14 1.853827e+10
-19 18 9.367309e+11 1.500282e+00 3.018025e+34 1.050745e+06 6.479764e+02 3.256479e+10 5.429283e-03 2.880510e-04 0.000000e+00 1.533121e-10 2.272309e-05 3.283945e-03 2.711459e-03 6.388402e-14 2.694533e+10
-19 19 9.367309e+11 1.547697e+00 2.873396e+34 6.517910e+05 2.972287e+02 5.571098e+10 2.075373e-02 3.650458e-04 0.000000e+00 8.113403e-10 1.879986e-05 1.071265e-02 6.288530e-03 1.093065e-13 4.610385e+10
diff --git a/examples/regress.old/hydro/model_restart_heatcool.dat b/examples/regress.old/hydro/model_restart_heatcool.dat
deleted file mode 100644
index 03b69723b..000000000
--- a/examples/regress.old/hydro/model_restart_heatcool.dat
+++ /dev/null
@@ -1,401 +0,0 @@
-i j rcen thetacen vol temp xi ne heat_xray heat_comp heat_lines heat_ff cool_comp cool_lines cool_ff rho n_h
-0 0 4.348375e+10 6.121442e-02 7.341598e+30 2.514272e+07 1.878746e+11 1.474256e+05 9.590528e-15 1.572381e-06 0.000000e+00 3.870631e-20 3.220878e-06 8.636771e-15 2.408823e-13 2.891734e-19 1.219690e+05
-0 1 4.348375e+10 1.805825e-01 2.047723e+31 2.775268e+07 1.490348e+11 1.965748e+05 1.974025e-14 2.363578e-06 0.000000e+00 5.583661e-20 5.036525e-06 1.491664e-14 4.482960e-13 3.855789e-19 1.626314e+05
-0 2 4.348375e+10 2.939822e-01 3.138727e+31 2.655861e+07 3.951055e+10 7.356793e+05 2.638584e-13 8.805474e-06 0.000000e+00 7.609158e-19 1.791157e-05 2.931627e-13 6.152045e-12 1.443025e-18 6.086464e+05
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-17 0 8.261146e+11 6.121442e-02 3.481657e+33 5.674570e+05 1.697786e+12 5.098174e+01 0.000000e+00 1.730556e-12 0.000000e+00 4.490118e-27 7.826507e-14 0.000000e+00 4.953076e-21 1.000000e-22 4.217851e+01
-17 1 8.261146e+11 1.805825e-01 9.711058e+33 4.077800e+06 7.076131e+05 1.239022e+08 3.782403e-08 4.440254e-06 0.000000e+00 1.262528e-15 1.402553e-06 1.331601e-08 7.388856e-08 2.430325e-16 1.025075e+08
-17 2 8.261146e+11 2.939822e-01 1.488500e+34 3.718052e+06 1.274369e+05 7.125356e+08 1.251330e-06 2.686843e-05 0.000000e+00 4.575536e-14 7.563762e-06 4.447081e-07 2.344287e-06 1.397631e-15 5.894999e+08
-17 3 8.261146e+11 4.017120e-01 1.908170e+34 3.262277e+06 5.426046e+04 1.501399e+09 5.898230e-06 4.944465e-05 0.000000e+00 2.287881e-13 1.261837e-05 1.998363e-06 9.801406e-06 2.944987e-15 1.242152e+09
-17 4 8.261146e+11 5.040552e-01 2.239336e+34 3.044335e+06 3.460265e+04 2.412570e+09 1.730641e-05 8.152938e-05 0.000000e+00 6.406579e-13 1.926716e-05 5.207436e-06 2.451330e-05 4.732252e-15 1.995993e+09
-17 5 8.261146e+11 6.012813e-01 2.491819e+34 2.860464e+06 2.196324e+04 3.499921e+09 3.713673e-05 1.070706e-04 0.000000e+00 1.456182e-12 2.417597e-05 1.106590e-05 5.012115e-05 6.865114e-15 2.895603e+09
-17 6 8.261146e+11 6.936460e-01 2.675504e+34 2.742188e+06 1.573392e+04 4.655985e+09 6.830664e-05 1.403400e-04 0.000000e+00 2.384114e-12 2.942954e-05 1.976758e-05 8.697791e-05 9.132767e-15 3.852065e+09
-17 7 8.261146e+11 7.813926e-01 2.799928e+34 2.650358e+06 1.200866e+04 5.835094e+09 1.046502e-04 1.651131e-04 0.000000e+00 3.877103e-12 3.411180e-05 3.140225e-05 1.344545e-04 1.144565e-14 4.827604e+09
-17 8 8.261146e+11 8.647518e-01 2.874037e+34 2.529281e+06 9.215450e+03 7.140180e+09 1.523820e-04 1.879173e-04 0.000000e+00 5.946448e-12 3.722497e-05 4.783156e-05 1.969734e-04 1.400566e-14 5.907378e+09
-17 9 8.261146e+11 9.439430e-01 2.906023e+34 2.373623e+06 6.746322e+03 8.675569e+09 2.256730e-04 2.072369e-04 0.000000e+00 8.700300e-12 3.794298e-05 7.300786e-05 2.826433e-04 1.701747e-14 7.177715e+09
-17 10 8.261146e+11 1.019175e+00 2.903261e+34 2.208241e+06 5.263129e+03 1.040456e+10 3.595750e-04 2.396761e-04 0.000000e+00 1.259876e-11 3.990786e-05 1.088393e-04 3.931235e-04 2.040904e-14 8.608228e+09
-17 11 8.261146e+11 1.090645e+00 2.872346e+34 2.058582e+06 4.061168e+03 1.234618e+10 4.629693e-04 2.504405e-04 0.000000e+00 1.977862e-11 4.008241e-05 1.639314e-04 5.356719e-04 2.421780e-14 1.021471e+10
-17 12 8.261146e+11 1.158541e+00 2.818909e+34 1.943932e+06 3.262330e+03 1.450520e+10 6.839660e-04 2.757084e-04 8.663767e-13 2.653952e-11 4.202158e-05 2.403950e-04 7.198165e-04 2.845301e-14 1.200105e+10
-17 13 8.261146e+11 1.223043e+00 2.747922e+34 1.859026e+06 2.611224e+03 1.677313e+10 9.497403e-04 3.133965e-04 1.252395e-12 3.535722e-11 4.317418e-05 3.445842e-04 9.425463e-04 3.290186e-14 1.387751e+10
-17 14 8.261146e+11 1.284320e+00 2.663590e+34 1.795738e+06 2.178392e+03 1.918022e+10 1.292439e-03 3.258916e-04 0.000000e+00 4.348106e-11 4.499272e-05 4.894907e-04 1.212498e-03 3.762377e-14 1.586914e+10
-17 15 8.261146e+11 1.342533e+00 2.569586e+34 1.736979e+06 1.714981e+03 2.169258e+10 1.445307e-03 3.270122e-04 2.218012e-13 5.256978e-11 4.380455e-05 7.065332e-04 1.526667e-03 4.255232e-14 1.794793e+10
-17 16 8.261146e+11 1.397835e+00 2.468709e+34 1.643460e+06 1.316808e+03 2.481141e+10 2.136152e-03 3.454059e-04 0.000000e+00 7.509714e-11 4.241020e-05 1.066901e-03 1.945553e-03 4.867067e-14 2.052856e+10
-17 17 8.261146e+11 1.450372e+00 2.363591e+34 1.455389e+06 9.985322e+02 3.003496e+10 3.568027e-03 3.680861e-04 0.000000e+00 1.148140e-10 4.144021e-05 1.932282e-03 2.695102e-03 5.891816e-14 2.485080e+10
-17 18 8.261146e+11 1.500282e+00 2.256219e+34 1.125908e+06 5.892142e+02 4.177717e+10 7.928874e-03 4.272469e-04 0.000000e+00 2.814826e-10 3.686140e-05 5.436587e-03 4.612581e-03 8.195634e-14 3.456796e+10
-17 19 8.261146e+11 1.547697e+00 2.148096e+34 7.349152e+05 3.146133e+02 6.741971e+10 2.940457e-02 6.045729e-04 0.000000e+00 1.282146e-09 3.307533e-05 1.598228e-02 9.769042e-03 1.322772e-13 5.579255e+10
-18 0 8.808751e+11 6.121442e-02 4.037574e+33 4.913601e+05 1.489167e+12 5.098174e+01 0.000000e+00 1.521337e-12 0.000000e+00 4.877265e-27 5.945072e-14 0.000000e+00 4.612269e-21 1.000000e-22 4.217851e+01
-18 1 8.808751e+11 1.805825e-01 1.126162e+34 3.858841e+06 5.932227e+05 1.297002e+08 4.318249e-08 4.092460e-06 0.000000e+00 1.321652e-15 1.220336e-06 1.468121e-08 7.900950e-08 2.544053e-16 1.073044e+08
-18 2 8.808751e+11 2.939822e-01 1.726169e+34 3.462214e+06 1.178065e+05 6.776604e+08 1.192142e-06 2.240382e-05 0.000000e+00 4.061225e-14 5.887183e-06 4.051031e-07 2.052216e-06 1.329224e-15 5.606468e+08
-18 3 8.808751e+11 4.017120e-01 2.212848e+34 3.056545e+06 5.144407e+04 1.397350e+09 5.355618e-06 4.054263e-05 0.000000e+00 1.931747e-13 9.706007e-06 1.743446e-06 8.238962e-06 2.740896e-15 1.156069e+09
-18 4 8.808751e+11 5.040552e-01 2.596891e+34 2.858090e+06 3.285920e+04 2.231708e+09 1.538036e-05 6.625539e-05 0.000000e+00 5.295803e-13 1.470046e-05 4.487409e-06 2.037306e-05 4.377494e-15 1.846362e+09
-18 5 8.808751e+11 6.012813e-01 2.889688e+34 2.702430e+06 2.106175e+04 3.212691e+09 3.275075e-05 8.685659e-05 0.000000e+00 1.179634e-12 1.846867e-05 9.385915e-06 4.113576e-05 6.301714e-15 2.657969e+09
-18 6 8.808751e+11 6.936460e-01 3.102703e+34 2.608966e+06 1.518871e+04 4.240903e+09 5.831882e-05 1.121354e-04 0.000000e+00 1.872127e-12 2.240857e-05 1.650108e-05 7.051425e-05 8.318584e-15 3.508655e+09
-18 7 8.808751e+11 7.813926e-01 3.246993e+34 2.528996e+06 1.163304e+04 5.298587e+09 8.939142e-05 1.316546e-04 0.000000e+00 3.018200e-12 2.598775e-05 2.606233e-05 1.084753e-04 1.039329e-14 4.383735e+09
-18 8 8.808751e+11 8.647518e-01 3.332935e+34 2.412104e+06 8.898148e+03 6.489216e+09 1.280534e-04 1.493112e-04 0.000000e+00 4.652480e-12 2.828509e-05 3.984661e-05 1.593361e-04 1.272879e-14 5.368814e+09
-18 9 8.808751e+11 9.439430e-01 3.370029e+34 2.261080e+06 6.519819e+03 7.884323e+09 1.917319e-04 1.656002e-04 0.000000e+00 6.790632e-12 2.887383e-05 6.100898e-05 2.282438e-04 1.546543e-14 6.523087e+09
-18 10 8.808751e+11 1.019175e+00 3.366825e+34 2.098978e+06 5.115093e+03 9.432487e+09 3.077762e-04 1.921115e-04 0.000000e+00 9.815142e-12 3.031395e-05 9.062277e-05 3.155695e-04 1.850229e-14 7.803990e+09
-18 11 8.808751e+11 1.090645e+00 3.330974e+34 1.958578e+06 3.972450e+03 1.108517e+10 3.837001e-04 1.972497e-04 6.006314e-18 1.504624e-11 3.009362e-05 1.344589e-04 4.219212e-04 2.174428e-14 9.171413e+09
-18 12 8.808751e+11 1.158541e+00 3.269005e+34 1.854500e+06 3.239232e+03 1.285130e+10 5.522596e-04 2.155209e-04 0.000000e+00 1.989526e-11 3.126296e-05 1.913566e-04 5.527224e-04 2.520880e-14 1.063270e+10
-18 13 8.808751e+11 1.223043e+00 3.186683e+34 1.780874e+06 2.623371e+03 1.473061e+10 7.573299e-04 2.429916e-04 0.000000e+00 2.558720e-11 3.206288e-05 2.686084e-04 7.124933e-04 2.889531e-14 1.218761e+10
-18 14 8.808751e+11 1.284320e+00 3.088886e+34 1.729550e+06 2.196668e+03 1.673897e+10 1.011233e-03 2.503134e-04 0.000000e+00 3.077870e-11 3.329665e-05 3.767374e-04 9.073635e-04 3.283505e-14 1.384933e+10
-18 15 8.808751e+11 1.342533e+00 2.979872e+34 1.684116e+06 1.721351e+03 1.887971e+10 1.114799e-03 2.474504e-04 0.000000e+00 3.668281e-11 3.224882e-05 5.410492e-04 1.139740e-03 3.703460e-14 1.562064e+10
-18 16 8.808751e+11 1.397835e+00 2.862889e+34 1.598690e+06 1.330087e+03 2.160932e+10 1.630726e-03 2.641824e-04 1.045582e-17 5.162224e-11 3.159292e-05 8.151286e-04 1.456663e-03 4.238939e-14 1.787921e+10
-18 17 8.808751e+11 1.450372e+00 2.740986e+34 1.421352e+06 9.976497e+02 2.617826e+10 2.737644e-03 2.808716e-04 0.000000e+00 7.927254e-11 3.072794e-05 1.488224e-03 2.024687e-03 5.135270e-14 2.165980e+10
-18 18 8.808751e+11 1.500282e+00 2.616469e+34 1.090973e+06 5.840155e+02 3.694081e+10 6.336338e-03 3.296430e-04 0.000000e+00 2.029625e-10 2.764164e-05 4.296317e-03 3.552281e-03 7.246885e-14 3.056628e+10
-18 19 8.808751e+11 1.547697e+00 2.491083e+34 6.952701e+05 3.002936e+02 6.117495e+10 2.437137e-02 4.770851e-04 0.000000e+00 9.864152e-10 2.459739e-05 1.305784e-02 7.826503e-03 1.200267e-13 5.062547e+10
-19 0 9.367309e+11 6.121442e-02 4.657230e+33 4.418570e+05 1.318090e+12 5.098174e+01 0.000000e+00 1.346781e-12 0.000000e+00 5.037235e-27 4.732358e-14 0.000000e+00 4.374180e-21 1.000000e-22 4.217851e+01
-19 1 9.367309e+11 1.805825e-01 1.298997e+34 3.650502e+06 4.714725e+05 1.445174e+08 5.553173e-08 4.041920e-06 0.000000e+00 1.578007e-15 1.139122e-06 1.833306e-08 9.563318e-08 2.834691e-16 1.195630e+08
-19 2 9.367309e+11 2.939822e-01 1.991089e+34 3.202794e+06 1.069978e+05 6.586303e+08 1.187671e-06 1.918785e-05 0.000000e+00 3.819488e-14 4.672996e-06 3.854852e-07 1.870400e-06 1.291897e-15 5.449029e+08
-19 3 9.367309e+11 4.017120e-01 2.552460e+34 2.863556e+06 4.828723e+04 1.318199e+09 5.008325e-06 3.385812e-05 0.000000e+00 1.680758e-13 7.593383e-06 1.562552e-06 7.114533e-06 2.585643e-15 1.090586e+09
-19 4 9.367309e+11 5.040552e-01 2.995442e+34 2.701184e+06 3.103605e+04 2.085876e+09 1.398306e-05 5.483159e-05 0.000000e+00 4.455719e-13 1.147277e-05 3.943938e-06 1.733845e-05 4.091446e-15 1.725711e+09
-19 5 9.367309e+11 6.012813e-01 3.333176e+34 2.585222e+06 2.025047e+04 2.955467e+09 2.850858e-05 7.050013e-05 0.000000e+00 9.439457e-13 1.435931e-05 7.983323e-06 3.410394e-05 5.797173e-15 2.445161e+09
-19 6 9.367309e+11 6.936460e-01 3.578882e+34 2.518069e+06 1.476126e+04 3.858041e+09 4.912631e-05 8.997666e-05 0.000000e+00 1.448501e-12 1.739132e-05 1.371725e-05 5.740134e-05 7.567601e-15 3.191901e+09
-19 7 9.367309e+11 7.813926e-01 3.745317e+34 2.449706e+06 1.131862e+04 4.819993e+09 7.512774e-05 1.059769e-04 0.000000e+00 2.322709e-12 2.026688e-05 2.166941e-05 8.854618e-05 9.454525e-15 3.987777e+09
-19 8 9.367309e+11 8.647518e-01 3.844448e+34 2.343714e+06 8.610784e+03 5.923331e+09 1.092024e-04 1.201309e-04 0.000000e+00 3.589321e-12 2.215124e-05 3.341599e-05 1.310041e-04 1.161880e-14 4.900636e+09
-19 9 9.367309e+11 9.439430e-01 3.887235e+34 2.214766e+06 6.338692e+03 7.178710e+09 1.609961e-04 1.338675e-04 0.000000e+00 5.119516e-12 2.279620e-05 5.089640e-05 1.874102e-04 1.408135e-14 5.939303e+09
-19 10 9.367309e+11 1.019175e+00 3.883540e+34 2.088401e+06 4.989268e+03 8.543194e+09 2.517568e-04 1.532501e-04 2.576098e-18 7.160849e-12 2.412410e-05 7.471669e-05 2.582590e-04 1.675791e-14 7.068236e+09
-19 11 9.367309e+11 1.090645e+00 3.842187e+34 1.966669e+06 3.931616e+03 9.918664e+09 3.074404e-04 1.563186e-04 0.000000e+00 1.054570e-11 2.395390e-05 1.077462e-04 3.384433e-04 1.945611e-14 8.206297e+09
-19 12 9.367309e+11 1.158541e+00 3.770707e+34 1.867256e+06 3.237949e+03 1.129152e+10 4.219140e-04 1.668099e-04 2.400746e-15 1.350934e-11 2.431846e-05 1.474600e-04 4.280645e-04 2.214916e-14 9.342185e+09
-19 13 9.367309e+11 1.223043e+00 3.675751e+34 1.795388e+06 2.675727e+03 1.271405e+10 5.610748e-04 1.841311e-04 0.000000e+00 1.657602e-11 2.453635e-05 1.982465e-04 5.328018e-04 2.493964e-14 1.051917e+10
-19 14 9.367309e+11 1.284320e+00 3.562945e+34 1.742275e+06 2.293436e+03 1.417814e+10 7.266192e-04 1.869063e-04 0.000000e+00 1.956357e-11 2.510337e-05 2.651409e-04 6.532336e-04 2.781171e-14 1.173056e+10
-19 15 9.367309e+11 1.342533e+00 3.437200e+34 1.695578e+06 1.804530e+03 1.587117e+10 7.797818e-04 1.841585e-04 0.000000e+00 2.296977e-11 2.408542e-05 3.743802e-04 8.080379e-04 3.113296e-14 1.313142e+10
-19 16 9.367309e+11 1.397835e+00 3.302263e+34 1.601705e+06 1.394968e+03 1.821887e+10 1.180794e-03 1.968014e-04 0.000000e+00 3.208754e-11 2.357653e-05 5.641831e-04 1.036422e-03 3.573852e-14 1.507397e+10
-19 17 9.367309e+11 1.450372e+00 3.161652e+34 1.399862e+06 1.034748e+03 2.240557e+10 2.025143e-03 2.130022e-04 0.000000e+00 5.331054e-11 2.297543e-05 1.074292e-03 1.472606e-03 4.395194e-14 1.853827e+10
-19 18 9.367309e+11 1.500282e+00 3.018025e+34 1.050745e+06 5.822176e+02 3.256462e+10 5.120833e-03 2.560579e-04 0.000000e+00 1.464510e-10 2.063193e-05 3.354673e-03 2.711111e-03 6.388402e-14 2.694533e+10
-19 19 9.367309e+11 1.547697e+00 2.873396e+34 6.517910e+05 2.901175e+02 5.571043e+10 2.078913e-02 3.826839e-04 0.000000e+00 7.811429e-10 1.848488e-05 1.072990e-02 6.287407e-03 1.093065e-13 4.610385e+10
diff --git a/examples/regress.old/hydro/py_hydro.pf b/examples/regress.old/hydro/py_hydro.pf
deleted file mode 100644
index 3f657a42e..000000000
--- a/examples/regress.old/hydro/py_hydro.pf
+++ /dev/null
@@ -1,70 +0,0 @@
-System_type(star,cv,bh,agn,previous)                  agn
-
-### Parameters for the Central Object
-Central_object.mass(msol)                  7
-Central_object.radius(cm)                  7e+08
-
-### Parameters for the Disk (if there is one)
-Disk.type(none,flat,vertically.extended)                 flat
-Disk.radiation(yes,no)                           no
-Disk.temperature.profile(standard,readin)             standard
-Disk.mdot(msol/yr)                         1e-9
-Disk.radmax(cm)                            1.0e+12
-
-### Parameters for Boundary Layer or the compact object in an X-ray Binary or AGN
-Central_object.radiation(yes,no)                  yes
-Central_object.rad_type_to_make_wind(bb,models,power,cloudy,brems)                brems
-Central_object.luminosity(ergs/s)          1.7698e37
-Central_object.bremsstrahlung_temp(K)      5.6e7
-Central_object.bremsstrahlung_alpha        0.0
-Central_object.geometry_for_source(sphere,lamp_post)               sphere
-
-### Parameters describing the various winds or coronae in the system
-Wind.radiation(yes,no)                           no
-Wind.number_of_components                  1
-Wind.type(SV,star,hydro,corona,kwd,homologous,shell,imported)                hydro
-Wind.coord_system(spherical,cylindrical,polar,cyl_var)          cylindrical
-Wind.dim.in.x_or_r.direction               30
-Wind.dim.in.z_or_theta.direction           30
-
-### Parameters associated with photon number, cycles,ionization and radiative transfer options
-Photons_per_cycle                          100000
-Ionization_cycles                          2
-Spectrum_cycles                            1
-Wind.ionization(on.the.spot,ML93,LTE_tr,LTE_te,fixed,matrix_bb,matrix_pow)           matrix_pow
-Line_transfer(pure_abs,pure_scat,sing_scat,escape_prob,thermal_trapping,macro_atoms,macro_atoms_thermal_trapping)          escape_prob
-Atomic_data                                data/standard80_reduced.dat
-Surface.reflection.or.absorption(reflect,absorb,thermalized.rerad)              reflect
-Wind_heating.extra_processes(none,adiabatic,nonthermal,both)                 none
-
-### Parameters for Domain 0
-Hydro.file                                 hdf000002nsh_dw33.zeu
-Hydro.thetamax(degrees:negative_means_no_maximum)   -1
-Wind.t.init                                40000
-Wind.filling_factor(1=smooth,<1=clumped)   1
-
-### Parameters defining the spectra seen by observers
-
-Central_object.rad_type_in_final_spectrum(bb,models,power,cloudy,brems)                brems
-
-### The minimum and maximum wavelengths in the final spectra
-Spectrum.wavemin(Angstroms)                     850
-Spectrum.wavemax(Angstroms)                    1850
-
-### The observers and their location relative to the system
-Spectrum.no_observers                             4
-Spectrum.angle(0=pole)                           10
-Spectrum.angle(0=pole)                           30
-Spectrum.angle(0=pole)                           60
-Spectrum.angle(0=pole)                           80
-Spectrum.live_or_die(live.or.die,extract)              extract
-Spectrum.type(flambda,fnu,basic)              flambda
-
-### Parameters for Reverberation Modeling (if needed)
-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)                    logarithmic
-Photon_sampling.nbands                     10
-Photon_sampling.low_energy_limit(eV)       1.03333
-Photon_sampling.high_energy_limit(eV)      50000
diff --git a/examples/regress.old/hydro/py_hydro_restart.pf b/examples/regress.old/hydro/py_hydro_restart.pf
deleted file mode 100644
index 62bb3d4df..000000000
--- a/examples/regress.old/hydro/py_hydro_restart.pf
+++ /dev/null
@@ -1,19 +0,0 @@
-
-### Parameters associated with photon number, cycles,ionization and radiative transfer options
-Photons_per_cycle                          100000
-Ionization_cycles                          3
-Spectrum_cycles                            3
-Wind.ionization(on.the.spot,ML93,LTE_tr,LTE_te,fixed,matrix_bb,matrix_pow)           matrix_pow
-Line_transfer(pure_abs,pure_scat,sing_scat,escape_prob,thermal_trapping,macro_atoms,macro_atoms_thermal_trapping)          escape_prob
-Wind.radiation(yes,no)                           no
-Surface.reflection.or.absorption(reflect,absorb,thermalized.rerad)              reflect
-Wind_heating.extra_processes(none,adiabatic,nonthermal,both)                 none
-
-### Parameters for Reverberation Modeling (if needed)
-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)                    logarithmic
-Photon_sampling.nbands                     10
-Photon_sampling.low_energy_limit(eV)       1.03333
-Photon_sampling.high_energy_limit(eV)      50000
diff --git a/examples/regress.old/matom_balmer/balmer_test.pf b/examples/regress.old/matom_balmer/balmer_test.pf
deleted file mode 100644
index 8c7c0159a..000000000
--- a/examples/regress.old/matom_balmer/balmer_test.pf
+++ /dev/null
@@ -1,61 +0,0 @@
-System_type(0=star,1=binary,2=agn,3=previous)   0
-
-### Parameters for the Central Object
-Central_object.mass(msol)                  0.8
-Central_object.radius(cm)                  1e10
-Central_object.radiation(y=1)              1
-Central_object.rad_type_to_make_wind(0=bb,1=models)   0
-Central_object.temp                        10000
-
-### Parameters for the Disk (if there is one)
-Disk.type(0=no.disk,1=standard.flat.disk,2=vertically.extended.disk)   0
-
-### Parameters for BL or AGN
-Boundary_layer.radiation(y=1)              0
-
-### Parameters descibing the various winds or coronae in the system
-Wind_radiation(y=1)                        1
-Wind.number_of_components                  1
-Wind_type(0=SV,1=Star,3=Hydro,4=corona,5=knigge,6=homologous,7=yso,9=shell,10=imported)   9
-Wind.coord_system(0=spherical,1=cylindrical,2=spherical_polar,3=cyl_var)   0
-Wind.dim.in.x_or_r.direction               4
-
-### Parameters associated with photon number, cycles,ionization and radiative transfer options
-Photons_per_cycle                          10000
-Ionization_cycles                          20
-Spectrum_cycles                            1
-Wind_ionization(0=on.the.spot,1=LTE(tr),2=fixed,3=recalc_bb,4=LTE(t_e),8=matrix_bb,9=matrix_pow)   3
-Line_transfer(0=pure.abs,1=pure.scat,2=sing.scat,3=escape.prob,4=anisotryopic,5=thermal_trapping,6=macro_atoms,7=macro_atoms+aniso.scattering)   7
-Atomic_data                                data/h20
-Surface.reflection.or.absorption(0=no.rerad,1=high.albedo,2=thermalized.rerad)   0
-Thermal_balance_options(0=everything.on,1=no.adiabatic)   1
-
-### Parameters for Domain 0
-Shell.wind_mdot(msol/yr)                   1e-20
-Shell.wind.radmin(cm)                      1e10
-Shell.wind.radmax(cm)                       1.1e+10
-Shell.wind_v_at_rmin(cm)                   1
-Shell.wind.v_at_rmax(cm)                   2
-Shell.wind.acceleration_exponent           1
-Wind.t.init                                10000
-Wind.filling_factor(1=smooth,<1=clumped)   1
-
-### Parameters defining the spectra seen by observers
-
-Central_object.rad_type_in_final_spectrum(0=bb,1=models,2=uniform)   0
-
-### The minimum and maximum wavelengths in the final spectra
-Spectrum.wavemin(Angstroms)                3000
-Spectrum.wavemax(Angstroms)                7000
-
-### The observers and their location relative to the system
-Spectrum.no_observers                      1
-Spectrum.angle(0=pole)                     45
-Spectrum.live_or_die(0=live.or.die,extract=anything_else)   1
-Spectrum.type(flambda(1),fnu(2),basic(other)   2
-
-### Parameters for Reverberation Modeling (if needed)
-Reverb.type(0=off,1=photon,2=wind,3=matom)   0
-
-### Other parameters
-Photon_sampling.approach(0=T,1=(f1,f2),2=cv,3=yso,4=user_defined,5=cloudy_test,6=wide,7=AGN,8=logarithmic)   3
diff --git a/examples/regress.old/rtheta.pf b/examples/regress.old/rtheta.pf
deleted file mode 100644
index 2273f675f..000000000
--- a/examples/regress.old/rtheta.pf
+++ /dev/null
@@ -1,86 +0,0 @@
-System_type(star,cv,bh,agn,previous)                   cv
-
-### Parameters for the Central Object
-Central_object.mass(msol)                  0.8
-Central_object.radius(cm)                  7e+08
-Central_object.radiation(yes,no)                  yes
-Central_object.rad_type_to_make_wind(bb,models)                   bb
-Central_object.temp                        40000
-Binary.mass_sec(msol)                      0.6
-Binary.period(hr)                          5.57
-
-### Parameters for the Disk (if there is one)
-Disk.type(none,flat,vertically.extended)                 flat
-Disk.radiation(yes,no)                          yes
-Disk.rad_type_to_make_wind(bb,models)                   bb
-Disk.temperature.profile(standard,readin)             standard
-Disk.mdot(msol/yr)                         1e-8
-Disk.radmax(cm)                            2.4e+10
-
-### Parameters for Boundary Layer or the compact object in an X-ray Binary or AGN
-Boundary_layer.radiation(yes,no)                   no
-
-### Parameters describing the various winds or coronae in the system
-Wind.radiation(yes,no)                          yes
-Wind.number_of_components                  1
-Wind.type(SV,star,hydro,corona,kwd,homologous,shell,imported)                   sv
-Wind.coord_system(spherical,cylindrical,polar,cyl_var)                polar
-Wind.dim.in.x_or_r.direction               30
-Wind.dim.in.z_or_theta.direction           30
-
-### Parameters associated with photon number, cycles,ionization and radiative transfer options
-Photons_per_cycle                          100000
-Ionization_cycles                          10
-Spectrum_cycles                            10
-Wind.ionization(on.the.spot,ML93,LTE_tr,LTE_te,fixed,matrix_bb,matrix_pow)            matrix_bb
-Line_transfer(pure_abs,pure_scat,sing_scat,escape_prob,thermal_trapping,macro_atoms,macro_atoms_thermal_trapping)     thermal_trapping
-Atomic_data                                data/standard80_reduced.dat
-Surface.reflection.or.absorption(reflect,absorb,thermalized.rerad)               absorb
-Wind_heating.extra_processes(none,adiabatic,nonthermal,both)            adiabatic
-
-### Parameters for Domain 0
-Wind.mdot(msol/yr)                         1e-9
-SV.diskmin(units_of_rstar)                 4
-SV.diskmax(units_of_rstar)                 12
-SV.thetamin(deg)                           20
-SV.thetamax(deg)                           65
-SV.mdot_r_exponent                         0
-SV.v_infinity(in_units_of_vescape          3
-SV.acceleration_length(cm)                 7e10
-SV.acceleration_exponent                   1.5
-SV.gamma(streamline_skew;1=usually)        1
-SV.v_zero_mode(fixed,sound_speed)                fixed
-SV.v_zero(cm/s)                            6e5
-Wind.radmax(cm)                            1e+12
-Wind.t.init                                40000
-Wind.filling_factor(1=smooth,<1=clumped)   1
-
-### Parameters defining the spectra seen by observers
-
-Central_object.rad_type_in_final_spectrum(bb,models,uniform)                   bb
-Disk.rad_type_in_final_spectrum(bb,models,uniform)                   bb
-
-### The minimum and maximum wavelengths in the final spectra
-Spectrum.wavemin(Angstroms)                800
-Spectrum.wavemax(Angstroms)                1850
-
-### The observers and their location relative to the system
-Spectrum.no_observers                      5
-Spectrum.angle(0=pole)                     10
-Spectrum.angle(0=pole)                     27.5
-Spectrum.angle(0=pole)                     45
-Spectrum.angle(0=pole)                     62.5
-Spectrum.angle(0=pole)                     80
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.orbit_phase(0=inferior_conjunction)   0.5
-Spectrum.live_or_die(live.or.die,extract)              extract
-Spectrum.type(flambda,fnu,basic)              flambda
-
-### Parameters for Reverberation Modeling (if needed)
-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)                    cv
diff --git a/examples/regress.old/star.pf b/examples/regress.old/star.pf
deleted file mode 100644
index 25fd3772e..000000000
--- a/examples/regress.old/star.pf
+++ /dev/null
@@ -1,61 +0,0 @@
-System_type(star,cv,bh,agn,previous)                 star
-
-### Parameters for the Central Object
-Central_object.mass(msol)                  52.5
-Central_object.radius(cm)                  1.32e12
-Central_object.radiation(yes,no)                  yes
-Central_object.rad_type_to_make_wind(bb,models)                   bb
-Central_object.temp                        42000
-
-### Parameters for the Disk (if there is one)
-Disk.type(none,flat,vertically.extended)                 none
-
-### Parameters for Boundary Layer or the compact object in an X-ray Binary or AGN
-Boundary_layer.radiation(yes,no)                   no
-
-### Parameters describing the various winds or coronae in the system
-Wind.radiation(yes,no)                          yes
-Wind.number_of_components                  1
-Wind.type(SV,star,hydro,corona,kwd,homologous,shell,imported)                 star
-Wind.coord_system(spherical,cylindrical,polar,cyl_var)            spherical
-Wind.dim.in.x_or_r.direction               30
-
-### Parameters associated with photon number, cycles,ionization and radiative transfer options
-Photons_per_cycle                          100000
-Ionization_cycles                          20
-Spectrum_cycles                            20
-Wind.ionization(on.the.spot,ML93,LTE_tr,LTE_te,fixed,matrix_bb,matrix_pow)                 ml93
-Line_transfer(pure_abs,pure_scat,sing_scat,escape_prob,thermal_trapping,macro_atoms,macro_atoms_thermal_trapping)          escape_prob
-Atomic_data                                data/standard80_reduced.dat
-Surface.reflection.or.absorption(reflect,absorb,thermalized.rerad)               absorb
-Wind_heating.extra_processes(none,adiabatic,nonthermal,both)            adiabatic
-
-### Parameters for Domain 0
-Stellar_wind.mdot(msol/yr)                 5.1e-6
-Stellar_wind.radmin(cm)                    1.32e12
-Stellar_wind.radmax(cm)                    1e13
-Stellar_wind.vbase(cm)                     2e+07
-Stellar_wind.v_infinity(cm)                2.25e+08
-Stellar_wind.acceleration_exponent         1
-Wind.t.init                                40000
-Wind.filling_factor(1=smooth,<1=clumped)   1
-
-### Parameters defining the spectra seen by observers
-
-Central_object.rad_type_in_final_spectrum(bb,models,uniform)                   bb
-
-### The minimum and maximum wavelengths in the final spectra
-Spectrum.wavemin(Angstroms)                850
-Spectrum.wavemax(Angstroms)                1850
-
-### The observers and their location relative to the system
-Spectrum.no_observers                      1
-Spectrum.angle(0=pole)                     45
-Spectrum.live_or_die(live.or.die,extract)              extract
-Spectrum.type(flambda,fnu,basic)              flambda
-
-### Parameters for Reverberation Modeling (if needed)
-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)                    cv
diff --git a/examples/regress.old/two.pf b/examples/regress.old/two.pf
deleted file mode 100644
index 5c3a6b2f1..000000000
--- a/examples/regress.old/two.pf
+++ /dev/null
@@ -1,92 +0,0 @@
-System_type(star,cv,bh,agn,previous)                 star
-
-### Parameters for the Central Object
-Central_object.mass(msol)                  0.8
-Central_object.radius(cm)                  7e+08
-Central_object.radiation(yes,no)                  yes
-Central_object.rad_type_to_make_wind(bb,models)                   bb
-Central_object.temp                        40000
-
-### Parameters for the Disk (if there is one)
-Disk.type(none,flat,vertically.extended)                 flat
-Disk.radiation(yes,no)                          yes
-Disk.rad_type_to_make_wind(bb,models)                   bb
-Disk.temperature.profile(standard,readin)             standard
-Disk.mdot(msol/yr)                         1e-08
-Disk.radmax(cm)                            2.4e+10
-
-### Parameters for Boundary Layer or the compact object in an X-ray Binary or AGN
-Boundary_layer.radiation(yes,no)                   no
-
-### Parameters describing the various winds or coronae in the system
-Wind.radiation(yes,no)                          yes
-Wind.number_of_components                  2
-Wind.type(SV,star,hydro,corona,kwd,homologous,shell,imported)                 star
-Wind.coord_system(spherical,cylindrical,polar,cyl_var)          cylindrical
-Wind.dim.in.x_or_r.direction               30
-Wind.dim.in.z_or_theta.direction           30
-Wind.type(SV,star,hydro,corona,kwd,homologous,shell,imported)                   sv
-Wind.coord_system(spherical,cylindrical,polar,cyl_var)          cylindrical
-Wind.dim.in.x_or_r.direction               30
-Wind.dim.in.z_or_theta.direction           30
-
-### Parameters associated with photon number, cycles,ionization and radiative transfer options
-Photons_per_cycle                          100000
-Ionization_cycles                          2
-Spectrum_cycles                            2
-Wind.ionization(on.the.spot,ML93,LTE_tr,LTE_te,fixed,matrix_bb,matrix_pow)                 ml93
-Line_transfer(pure_abs,pure_scat,sing_scat,escape_prob,thermal_trapping,macro_atoms,macro_atoms_thermal_trapping)     thermal_trapping
-Atomic_data                                data/standard80_reduced.dat
-Surface.reflection.or.absorption(reflect,absorb,thermalized.rerad)               absorb
-Wind_heating.extra_processes(none,adiabatic,nonthermal,both)            adiabatic
-
-### Parameters for Domain 0
-Stellar_wind.mdot(msol/yr)                 1e-8
-Stellar_wind.radmin(cm)                    7e+08
-Stellar_wind.radmax(cm)                    1e12
-Stellar_wind.vbase(cm)                     2e+07
-Stellar_wind.v_infinity(cm)                3e+08
-Stellar_wind.acceleration_exponent         1
-Wind.t.init                                40000
-Wind.filling_factor(1=smooth,<1=clumped)   1
-
-### Parameters for Domain 1
-Wind.mdot(msol/yr)                         1e-09
-SV.diskmin(units_of_rstar)                 4
-SV.diskmax(units_of_rstar)                 12
-SV.thetamin(deg)                           20
-SV.thetamax(deg)                           65
-SV.mdot_r_exponent                         0
-SV.v_infinity(in_units_of_vescape          3
-SV.acceleration_length(cm)                 7e+10
-SV.acceleration_exponent                   1.5
-SV.gamma(streamline_skew;1=usually)        1
-SV.v_zero_mode(fixed,sound_speed)                fixed
-SV.v_zero(cm/s)                            6e5
-Wind.radmax(cm)                            1e+12
-Wind.t.init                                30000
-Wind.filling_factor(1=smooth,<1=clumped)   1
-
-### Parameters defining the spectra seen by observers
-
-Central_object.rad_type_in_final_spectrum(bb,models,uniform)                   bb
-Disk.rad_type_in_final_spectrum(bb,models,uniform)                   bb
-
-### The minimum and maximum wavelengths in the final spectra
-Spectrum.wavemin(Angstroms)                850
-Spectrum.wavemax(Angstroms)                1850
-
-### The observers and their location relative to the system
-Spectrum.no_observers                      4
-Spectrum.angle(0=pole)                     10
-Spectrum.angle(0=pole)                     30
-Spectrum.angle(0=pole)                     60
-Spectrum.angle(0=pole)                     80
-Spectrum.live_or_die(live.or.die,extract)              extract
-Spectrum.type(flambda,fnu,basic)              flambda
-
-### Parameters for Reverberation Modeling (if needed)
-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)                    cv
diff --git a/py_progs/regression.py b/py_progs/regression.py
index 4f2f0d2cd..f6c70ec63 100755
--- a/py_progs/regression.py
+++ b/py_progs/regression.py
@@ -349,28 +349,6 @@ def doit(version='py',pf_dir='',out_dir='',np=3,switches='',outputfile='Summary.
     # placed here.  The special tests should generally be self
     # contained, that is to say one_liners here.
 	
-    #  Run a special test on hydro
-
-    # htest=0
-    # if os.path.exists(pf_dir+'/hydro'):
-    #     print("Hydro directory exists")
-    #     hydro_dir=pf_dir+'/hydro'
-    #     hydro_files=glob(hydro_dir+'/*')
-		
-    #     htest=1
-		
-    # if htest:
-    #     for one in hydro_files:
-    #         shutil.copy(one,'../'+out_dir)
-    #     py_hydro(version,outputfile)
-
-    py_hydro(version, pf_dir, outputfile)
-
-    # run a special macro-atom test -- XXX needs fixing 
-    # balmer_decrement.BalmerTest(glob(pf_dir+'/matom_balmer/balmer_test.pf', plotit=True)
-
-
-    # Return to the place where the code was made from
     os.chdir(cwd)
     return out_dir
 	
@@ -413,95 +391,6 @@ def run_cmds(commands,root_names,outputfile):
 	
 	
 	
-def py_hydro(version,pf_dir,outputfile):
-	
-    '''
-    This is a self contained script to perform specialised tests of
-    the py_hydro models. We need to run on one processor, otherwise
-    the test of the heatcool wont work - but its quick!
-
-    Notes:
-
-        This routine is run from within the current working directory,
-        which is the directory created earlier be doit
-
-    History:
-
-        1801  nsh
-            Development started
-        1802  ksl
-            Modified this routine so that it is more standalone
-            than previously.  Special regression tests need to
-            be isolated from the internal logic of doit so they
-            can be added/removed easily.
-        2908 kdl
-            Eliminated the checks for a difference in heating and
-            and cooling rates.  If this is important, a check of
-            these should be done in regression_checks, and the
-            check should be made between this run and a previous
-            run, not one against a file made in the distant past.
-    '''
-
-    out_dir=os.getcwd()
-
-    htest=0
-    hydro_dir=pf_dir+'/hydro'
-    if os.path.exists(hydro_dir):
-        print("\nHydro directory exists")
-        hydro_files=glob(hydro_dir+'/*')
-        htest=1
-    else:
-        print('The Hydro directory %s doues not appear to exist' % hydro_dir)
-        return
-		
-    if htest:
-        for one in hydro_files:
-            shutil.copy(one,out_dir)
-
-    root_name=['py_hydro']
-    hydro_command=['%s %s  >%s.stdout.txt' % (version,root_name[0]+'.pf',root_name[0])]
-    run_cmds(hydro_command,root_name,outputfile)
-	
-    cmd='cp py_hydro.wind_save py_hydro_restart.wind_save'
-    subprocess.Popen(cmd,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)
-
-    cmd='cp py_hydro.spec_save py_hydro_restart.spec_save'
-    subprocess.Popen(cmd,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)
-	
-#OLD    cmd='diff py_heatcool.dat model_heatcool.dat'
-#OLD    proc=subprocess.Popen(cmd,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)
-#OLD    stdout,stderr=proc.communicate()
-	
-#OLD    if len(stdout):
-#OLD        string="py_heatcool.dat has changed from model - important to investigate"
-#OLD    else:
-#OLD        string="py_heatcool.dat is unchanged - test passed"
-#OLD    print(string)
-#OLD    f1=open('Summary.txt','a')
-#OLD    f1.write('%s\n'% string)
-#OLD    f1.close()
-	
-		
-    root_name=['py_hydro_restart']
-    # hydro_command=['%s -z -r %s  >%s.stdout.txt' % (version,root_name[0]+'.pf',root_name[0])]
-    hydro_command=['%s -r %s  >%s.stdout.txt' % (version,root_name[0]+'.pf',root_name[0])]
-    run_cmds(hydro_command,root_name,outputfile)
-	
-#OLD    cmd='diff py_heatcool.dat model_restart_heatcool.dat'
-#OLD    proc=subprocess.Popen(cmd,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)
-#OLD    stdout,stderr=proc.communicate()
-	
-#OLD    if len(stdout):
-#OLD        string="restart py_heatcool.dat has changed from model - important to investigate"
-#OLD    else:
-#OLD        string="restart py_heatcool.dat is unchanged - test passed"
-#OLD    print(string)
-#OLD    f1=open('Summary.txt','a')
-#OLD    f1.write('%s\n'% string)
-#OLD    f1.close()
-
-
-    return
 
 
 

From 6dc99289d90352d7bf7a73b2d7f5f6049fd7e31e Mon Sep 17 00:00:00 2001
From: jhmatthews <matthews@ast.cam.ac.uk>
Date: Fri, 25 Oct 2024 09:26:55 +0100
Subject: [PATCH 07/22] 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 <http://cdsweb.u-strasbg.fr/topbase/topbase.h
 
 
 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.
 
@@ -64,12 +64,12 @@ 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
-================
+==========================
 
 The data are stored in the Topbase_phot stucture which can be found in atomic.h
 
 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`
 
diff --git a/docs/sphinx/source/atomic/bound_free_verner.rst b/docs/sphinx/source/atomic/bound_free_verner.rst
index 1e27207fd..eac72aeeb 100644
--- a/docs/sphinx/source/atomic/bound_free_verner.rst
+++ b/docs/sphinx/source/atomic/bound_free_verner.rst
@@ -16,7 +16,7 @@ There are three sources for this data
 
 
 Translation to SIROCCO format
-============================
+======================================
 
 **Tabulation Process**
 
@@ -62,7 +62,7 @@ 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
-================
+==========================
 
 Where the data is stored internally in SIROCCO
 
diff --git a/docs/sphinx/source/atomic/direct_ionization.rst b/docs/sphinx/source/atomic/direct_ionization.rst
index 950801fe8..061fd0be7 100644
--- a/docs/sphinx/source/atomic/direct_ionization.rst
+++ b/docs/sphinx/source/atomic/direct_ionization.rst
@@ -11,7 +11,7 @@ The data comes directly from `Dere 2006, A&A, 466, 771 <https://www.aanda.org/ar
 
 
 Translation to SIROCCO format
-============================
+======================================
 
 
 The data table is downloaded in its entirety  from the data table associated with the paper. All that happens is that the table is saved to a text file, and the keyword DI_DERE is just prepended to each row.
@@ -41,7 +41,7 @@ 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
-================
+==========================
 
 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 929f0050c..ae20f9426 100644
--- a/docs/sphinx/source/atomic/electron_yields.rst
+++ b/docs/sphinx/source/atomic/electron_yields.rst
@@ -10,7 +10,7 @@ Source
 This data comes from `Kaastra and Mewe 1993, A&A, 97, 443 <http://articles.adsabs.harvard.edu/full/1993A%26AS...97..443K>`_ . 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. <http://arti
 
 
 Translation to SIROCCO:
-======================
+================================
 
 The original data and the translation can be found in py\_verner.  A simple awkscript converts the downloaded data to SIROCCO format.
 
@@ -93,7 +93,7 @@ Note that only evident changed is the label, but in this case the number of nlte
 
 
 SIROCCO structure:
-=================
+===========================
 This data is held in SIROCCO in various fields in structures **elements** and **ions**.
 
 Comments:
diff --git a/docs/sphinx/source/atomic/free-free.rst b/docs/sphinx/source/atomic/free-free.rst
index af002d421..dbb39c038 100644
--- a/docs/sphinx/source/atomic/free-free.rst
+++ b/docs/sphinx/source/atomic/free-free.rst
@@ -15,7 +15,7 @@ The free-free Gaunt factors are taken from  `Sutherland 1998, MNRAS, 300, 321. <
 The last file is the one we use to calculate free-free emission, since this in integrated gaunt factor over a population of electrons  with a Boltzmann distribution of velocities.  The other two files could be of use in the future should we wish to have gaunt factor corrections for the heating rates,in which case we should use the gffgu.dat data file. However generally speaking free-free heating is never important and there would be significant overhead in calculating a gaunt factor for each photon.
 
 Translation to SIROCCO
-=====================
+===============================
 The file is simply modified by hand to put a label "FF\_GAUNT" at the start of each data line and a hash at the start of each comment line.
 
 Datafile - gffint.dat:
@@ -49,7 +49,7 @@ where
 :math:`\Delta=\log(x)-\log(\gamma^2)`
 
 SIROCCO structure
-================
+==========================
 This data is held internally in SIROCCO in the structure **gaunt_total** which has members
 
 - log_gsqrd
diff --git a/docs/sphinx/source/atomic/levels.rst b/docs/sphinx/source/atomic/levels.rst
index ab5437533..824b2a97e 100644
--- a/docs/sphinx/source/atomic/levels.rst
+++ b/docs/sphinx/source/atomic/levels.rst
@@ -14,7 +14,7 @@ Level information can be derived from a variety of sources, by:
 
 
 Translation to SIROCCO:
-======================
+================================
 
 
 
@@ -94,7 +94,7 @@ Since they quote J, one converts to g = 2J+1
 The ionization potential is not used, as it is redundant with the excitation energy which is, and the last column giving the configuration is also for information only.
 
 SIROCCO structure:
-=================
+===========================
 This data is held in SIROCCO in various fields in structure **config**.
 
 Comments:
diff --git a/docs/sphinx/source/atomic/photon_yields.rst b/docs/sphinx/source/atomic/photon_yields.rst
index a6321cd07..99d93d558 100644
--- a/docs/sphinx/source/atomic/photon_yields.rst
+++ b/docs/sphinx/source/atomic/photon_yields.rst
@@ -9,7 +9,7 @@ Source
 This data comes from `Kaastra and Mewe 1993, A&A, 97, 443 <http://articles.adsabs.harvard.edu/full/1993A%26AS...97..443K>`_ . 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

From 521ebe25bb3b080bdfe7cde3ed27f9f5d6e0034a Mon Sep 17 00:00:00 2001
From: James Matthews <matthews@ast.cam.ac.uk>
Date: Fri, 25 Oct 2024 11:54:42 +0100
Subject: [PATCH 08/22] logo with frame README

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index e00521dcd..395fade9b 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-<img src="https://github.com/user-attachments/assets/1f4b0865-251d-407f-821a-021f7a0beac8" alt="drawing" style="width:400px;"/>
+<img src="https://github.com/user-attachments/assets/1f262fb8-5f67-42df-ac5f-68ac7f792e15" alt="drawing" style="width:400px;"/>
 
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.13969075.svg)](https://doi.org/10.5281/zenodo.13969075)
 [![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)

From 91ef9bfacd61a88a3cb8c842ab46da919f616fef Mon Sep 17 00:00:00 2001
From: jhmatthews <matthews@ast.cam.ac.uk>
Date: Fri, 25 Oct 2024 13:21:10 +0100
Subject: [PATCH 09/22] further sphinx updates and typo fixes

---
 docs/sphinx/source/images/logo.png | Bin 0 -> 114262 bytes
 docs/sphinx/source/index.rst       |  63 ++++++++++++++++++++++++-----
 2 files changed, 53 insertions(+), 10 deletions(-)
 create mode 100644 docs/sphinx/source/images/logo.png

diff --git a/docs/sphinx/source/images/logo.png b/docs/sphinx/source/images/logo.png
new file mode 100644
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diff --git a/docs/sphinx/source/index.rst b/docs/sphinx/source/index.rst
index ae60aca32..87803a75c 100644
--- a/docs/sphinx/source/index.rst
+++ b/docs/sphinx/source/index.rst
@@ -7,15 +7,15 @@
 SIROCCO - Simulating Ionization and Radiation in Outflows Created by Compact Objects
 ####################################################################################
 
---------------------------------------
-Radiative transfer and ionisation code
---------------------------------------
+figure:: ../images/logo.png
 
 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
+winds in disk systems.  It was formerly known as Python, and originally written by
 `Long and Knigge (2002) <https://ui.adsabs.harvard.edu/abs/2002ApJ...579..725L/abstract>`_ 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.
+also been used to simulate the spectra of systems ranging from young stellar objects to AGN. 
+SIROCCO is named after the `Sirocco wind <https://en.wikipedia.org/wiki/Sirocco>`_, and also 
+stands for Simulating Ionization and Radiation in Outflows Created by Compact Objects. 
 
 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 <https://github.com/sirocco-rt/sirocco>`_. Issues
@@ -23,18 +23,61 @@ regarding the code and suggestions for improvement should be reported there.  We
 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.
 
-
--------------
-Documentation
--------------
+---------------------------------------
+Documentation \& Publications
+---------------------------------------
 
 Various documentation exists:
 
 * A :doc:`Quick Guide <quick>` describing how to install and run SIROCCO (in a fairly mechanistic fashion).
+* More detailed documentation on this site
+* A code release paper, submitted in October 2024
+* Various PhD theses that describe 
 
 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 <meta>`.
 
+The following papers have used SIROCCO: 
+
+- Parkinson, E. J., Knigge, C., Dai, L., Thomsen, L. L., Matthews, J. H., & Long, K. S. (2024). *arXiv*, arXiv:2408.16371. DOI: [10.48550/arXiv.2408.16371](https://doi.org/10.48550/arXiv.2408.16371), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2024arXiv240816371P)
+
+- Tampo, Y., Knigge, C., Long, K. S., Matthews, J. H., & Segura, N. C. (2024). *MNRAS*, 532, 1199. DOI: [10.1093/mnras/stae1557](https://doi.org/10.1093/mnras/stae1557), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2024MNRAS.532.1199T)
+
+- Higginbottom, N., Scepi, N., Knigge, C., Long, K. S., Matthews, J. H., & Sim, S. A. (2024). *MNRAS*, 527, 9236. DOI: [10.1093/mnras/stad3830](https://doi.org/10.1093/mnras/stad3830), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.9236H)
+
+- Matthews, J. H., Strong-Wright, J., Knigge, C., Hewett, P., Temple, M. J., Long, K. S., Rankine, A. L., et al. (2023). *MNRAS*, 526, 3967. DOI: [10.1093/mnras/stad2895](https://doi.org/10.1093/mnras/stad2895), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.3967M)
+
+- Koljonen, K. I. I., Long, K. S., Matthews, J. H., & Knigge, C. (2023). *MNRAS*, 521, 4190. DOI: [10.1093/mnras/stad809](https://doi.org/10.1093/mnras/stad809), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2023MNRAS.521.4190K)
+
+- Parkinson, E. J., Knigge, C., Matthews, J. H., Long, K. S., Higginbottom, N., & Sim, S. A. (2022). *MNRAS*, 510, 5426. DOI: [10.1093/mnras/stac027](https://doi.org/10.1093/mnras/stac027), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2022MNRAS.510.5426P)
+
+- Middleton, M. J., Higginbottom, N., Knigge, C., Khan, N., & Wiktorowicz, G. (2022). *MNRAS*, 509, 1119. DOI: [10.1093/mnras/stab2991](https://doi.org/10.1093/mnras/stab2991), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.1119M)
+
+- Parkinson, E. J., Knigge, C., Long, K. S., Matthews, J. H., Higginbottom, N., Sim, S. A., & Hewitt, H. A. (2020). *MNRAS*, 494, 4914. DOI: [10.1093/mnras/staa1060](https://doi.org/10.1093/mnras/staa1060), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.4914P)
+
+- Matthews, J. H., Knigge, C., Higginbottom, N., Long, K. S., Sim, S. A., Mangham, S. W., Parkinson, E. J., et al. (2020). *MNRAS*, 492, 5540. DOI: [10.1093/mnras/staa136](https://doi.org/10.1093/mnras/staa136), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2020MNRAS.492.5540M)
+
+- Higginbottom, N., Knigge, C., Sim, S. A., Long, K. S., Matthews, J. H., & Hewitt, H. A. (2020). *MNRAS*, 492, 5271. DOI: [10.1093/mnras/staa209](https://doi.org/10.1093/mnras/staa209), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2020MNRAS.492.5271H)
+
+- Charles, P., Matthews, J. H., Buckley, D. A. H., Gandhi, P., Kotze, E., & Paice, J. (2019). *MNRAS*, 489, L47. DOI: [10.1093/mnrasl/slz120](https://doi.org/10.1093/mnrasl/slz120), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2019MNRAS.489L..47C)
+
+- Mangham, S. W., Knigge, C., Williams, P., Horne, K., Pancoast, A., Matthews, J. H., Long, K. S., et al. (2019). *MNRAS*, 488, 2780. DOI: [10.1093/mnras/stz1713](https://doi.org/10.1093/mnras/stz1713), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.2780M)
+
+- Higginbottom, N., Knigge, C., Long, K. S., Matthews, J. H., & Parkinson, E. J. (2019). *MNRAS*, 484, 4635. DOI: [10.1093/mnras/stz310](https://doi.org/10.1093/mnras/stz310), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.4635H)
+
+- Milliner, K., Matthews, J. H., Long, K. S., & Hartmann, L. (2019). *MNRAS*, 483, 1663. DOI: [10.1093/mnras/sty3197](https://doi.org/10.1093/mnras/sty3197), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2019MNRAS.483.1663M)
+
+- Higginbottom, N., Knigge, C., Long, K. S., Matthews, J. H., Sim, S. A., & Hewitt, H. A. (2018). *MNRAS*, 479, 3651. DOI: [10.1093/mnras/sty1599](https://doi.org/10.1093/mnras/sty1599), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2018MNRAS.479.3651H)
+
+- Mangham, S. W., Knigge, C., Matthews, J. H., Long, K. S., Sim, S. A., & Higginbottom, N. (2017). *MNRAS*, 471, 4788. DOI: [10.1093/mnras/stx1863](https://doi.org/10.1093/mnras/stx1863), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2017MNRAS.471.4788M)
+
+- Higginbottom, N., Proga, D., Knigge, C., & Long, K. S. (2017). *ApJ*, 836, 42. DOI: [10.3847/1538-4357/836/1/42](https://doi.org/10.3847/1538-4357/836/1/42), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2017ApJ...836...42H)
+
+- Matthews, J. H. (2016). *PhDT*. DOI: [10.5281/zenodo.1256805](https://doi.org/10.5281/zenodo.1256805), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2016PhDT.......348M)
+
+- Matthews, J. H., Knigge, C., Long, K. S., Sim, S. A., Higginbottom, N., & Mangham, S
+
+
 -------
 Authors
 -------
@@ -54,7 +97,7 @@ Nick Higginbottom
   Department of Physics and Astronomy, University of Southampton, Southampton, SO17 1BJ, UK
 
 James Matthews
-  Institute of Astronomy, University of Cambridge, Cambridge, CB3 0HA, UK
+  Department of Physics, Astrophysics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UK
 
 Sam Mangham
   Department of Physics and Astronomy, University of Southampton, Southampton, SO17 1BJ, UK

From ef5d200e6018aee3d02fc18d405864b082172620 Mon Sep 17 00:00:00 2001
From: jhmatthews <matthews@ast.cam.ac.uk>
Date: Fri, 25 Oct 2024 16:32:07 +0100
Subject: [PATCH 10/22] trying to include pysi in docs

---
 docs/sphinx/source/index.rst                  |  50 +----
 docs/sphinx/source/plotting.rst               |  13 ++
 .../source/plotting/plot_spectrum.ipynb       | 181 ++++++++++++++----
 docs/sphinx/source/quick.rst                  |   1 +
 4 files changed, 169 insertions(+), 76 deletions(-)

diff --git a/docs/sphinx/source/index.rst b/docs/sphinx/source/index.rst
index 87803a75c..39bc0ed3a 100644
--- a/docs/sphinx/source/index.rst
+++ b/docs/sphinx/source/index.rst
@@ -19,7 +19,7 @@ stands for Simulating Ionization and Radiation in Outflows Created by Compact Ob
 
 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 <https://github.com/sirocco-rt/sirocco>`_. Issues
-regarding the code and suggestions for improvement should be reported there.  We actively encourage other to make use of
+regarding the code and suggestions for improvement should be reported there.  We actively encourage others 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.
 
@@ -30,52 +30,18 @@ Documentation \& Publications
 Various documentation exists:
 
 * A :doc:`Quick Guide <quick>` describing how to install and run SIROCCO (in a fairly mechanistic fashion).
-* More detailed documentation on this site
+* More detailed documentation on this site and in the docs/sphinx/ folder of the repository. 
 * A code release paper, submitted in October 2024
-* Various PhD theses that describe 
+* Various PhD theses that describe the code in more detail: 
+    * Higginbottom, N (2014): `Modelling accretion disk winds in quasars <https://eprints.soton.ac.uk/368584/>`_, 
+    * Matthews, J. (2016): `Disc Winds Matter: Modelling Accretion And Outflow On All Scales <https://ui.adsabs.harvard.edu/abs/2016PhDT.......348M/abstract>`_, 
+    * Mangham, S. (2019): `The reverberation signatures of accretion disk winds in active galactic nuclei <https://eprints.soton.ac.uk/437717/>`_, 
+    * Parkinson, E. (2022): `Shining lights, even in death: modelling the optical and ultraviolet emission from tidal disruption events <https://eprints.soton.ac.uk/457482/>`_ . 
 
 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 <meta>`.
 
-The following papers have used SIROCCO: 
-
-- Parkinson, E. J., Knigge, C., Dai, L., Thomsen, L. L., Matthews, J. H., & Long, K. S. (2024). *arXiv*, arXiv:2408.16371. DOI: [10.48550/arXiv.2408.16371](https://doi.org/10.48550/arXiv.2408.16371), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2024arXiv240816371P)
-
-- Tampo, Y., Knigge, C., Long, K. S., Matthews, J. H., & Segura, N. C. (2024). *MNRAS*, 532, 1199. DOI: [10.1093/mnras/stae1557](https://doi.org/10.1093/mnras/stae1557), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2024MNRAS.532.1199T)
-
-- Higginbottom, N., Scepi, N., Knigge, C., Long, K. S., Matthews, J. H., & Sim, S. A. (2024). *MNRAS*, 527, 9236. DOI: [10.1093/mnras/stad3830](https://doi.org/10.1093/mnras/stad3830), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.9236H)
-
-- Matthews, J. H., Strong-Wright, J., Knigge, C., Hewett, P., Temple, M. J., Long, K. S., Rankine, A. L., et al. (2023). *MNRAS*, 526, 3967. DOI: [10.1093/mnras/stad2895](https://doi.org/10.1093/mnras/stad2895), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.3967M)
-
-- Koljonen, K. I. I., Long, K. S., Matthews, J. H., & Knigge, C. (2023). *MNRAS*, 521, 4190. DOI: [10.1093/mnras/stad809](https://doi.org/10.1093/mnras/stad809), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2023MNRAS.521.4190K)
-
-- Parkinson, E. J., Knigge, C., Matthews, J. H., Long, K. S., Higginbottom, N., & Sim, S. A. (2022). *MNRAS*, 510, 5426. DOI: [10.1093/mnras/stac027](https://doi.org/10.1093/mnras/stac027), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2022MNRAS.510.5426P)
-
-- Middleton, M. J., Higginbottom, N., Knigge, C., Khan, N., & Wiktorowicz, G. (2022). *MNRAS*, 509, 1119. DOI: [10.1093/mnras/stab2991](https://doi.org/10.1093/mnras/stab2991), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.1119M)
-
-- Parkinson, E. J., Knigge, C., Long, K. S., Matthews, J. H., Higginbottom, N., Sim, S. A., & Hewitt, H. A. (2020). *MNRAS*, 494, 4914. DOI: [10.1093/mnras/staa1060](https://doi.org/10.1093/mnras/staa1060), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.4914P)
-
-- Matthews, J. H., Knigge, C., Higginbottom, N., Long, K. S., Sim, S. A., Mangham, S. W., Parkinson, E. J., et al. (2020). *MNRAS*, 492, 5540. DOI: [10.1093/mnras/staa136](https://doi.org/10.1093/mnras/staa136), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2020MNRAS.492.5540M)
-
-- Higginbottom, N., Knigge, C., Sim, S. A., Long, K. S., Matthews, J. H., & Hewitt, H. A. (2020). *MNRAS*, 492, 5271. DOI: [10.1093/mnras/staa209](https://doi.org/10.1093/mnras/staa209), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2020MNRAS.492.5271H)
-
-- Charles, P., Matthews, J. H., Buckley, D. A. H., Gandhi, P., Kotze, E., & Paice, J. (2019). *MNRAS*, 489, L47. DOI: [10.1093/mnrasl/slz120](https://doi.org/10.1093/mnrasl/slz120), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2019MNRAS.489L..47C)
-
-- Mangham, S. W., Knigge, C., Williams, P., Horne, K., Pancoast, A., Matthews, J. H., Long, K. S., et al. (2019). *MNRAS*, 488, 2780. DOI: [10.1093/mnras/stz1713](https://doi.org/10.1093/mnras/stz1713), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.2780M)
-
-- Higginbottom, N., Knigge, C., Long, K. S., Matthews, J. H., & Parkinson, E. J. (2019). *MNRAS*, 484, 4635. DOI: [10.1093/mnras/stz310](https://doi.org/10.1093/mnras/stz310), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.4635H)
-
-- Milliner, K., Matthews, J. H., Long, K. S., & Hartmann, L. (2019). *MNRAS*, 483, 1663. DOI: [10.1093/mnras/sty3197](https://doi.org/10.1093/mnras/sty3197), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2019MNRAS.483.1663M)
-
-- Higginbottom, N., Knigge, C., Long, K. S., Matthews, J. H., Sim, S. A., & Hewitt, H. A. (2018). *MNRAS*, 479, 3651. DOI: [10.1093/mnras/sty1599](https://doi.org/10.1093/mnras/sty1599), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2018MNRAS.479.3651H)
-
-- Mangham, S. W., Knigge, C., Matthews, J. H., Long, K. S., Sim, S. A., & Higginbottom, N. (2017). *MNRAS*, 471, 4788. DOI: [10.1093/mnras/stx1863](https://doi.org/10.1093/mnras/stx1863), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2017MNRAS.471.4788M)
-
-- Higginbottom, N., Proga, D., Knigge, C., & Long, K. S. (2017). *ApJ*, 836, 42. DOI: [10.3847/1538-4357/836/1/42](https://doi.org/10.3847/1538-4357/836/1/42), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2017ApJ...836...42H)
-
-- Matthews, J. H. (2016). *PhDT*. DOI: [10.5281/zenodo.1256805](https://doi.org/10.5281/zenodo.1256805), ADS: [Link](https://ui.adsabs.harvard.edu/abs/2016PhDT.......348M)
-
-- Matthews, J. H., Knigge, C., Long, K. S., Sim, S. A., Higginbottom, N., & Mangham, S
+`This ADS library <https://ui.adsabs.harvard.edu/public-libraries/dRea2kBFSCmzAji_7yTK9Q>`_ contains a list of publications using SIROCCO to date. 
 
 
 -------
diff --git a/docs/sphinx/source/plotting.rst b/docs/sphinx/source/plotting.rst
index 517d09983..a1ae1821b 100644
--- a/docs/sphinx/source/plotting.rst
+++ b/docs/sphinx/source/plotting.rst
@@ -3,6 +3,19 @@ Plotting \& Processing Outputs
 
 SIROCCO produces a large number of files in both binary and ascii format. Tools exist to examine the binary files.
 
+PySi 
+==============
+PySi, short for PySIROCCO, is a python package designed to interface with the SIROCCO code.
+It can be installed from its `own Github repository <https://github.com/sirocco-rt/pysi>`_ 
+under the sirocco-rt organisation. We recommend installing this package for reading code outputs.:
+isntructions to do so can be found in the README of that repository.
+
+.. admonition :: Warning to users
+
+These tutorials are not complete, and our approach to interfacing 
+with the code has not been uniform across the collaboration. Nevertheless, these 
+tutorials should give you the basic tools needed to look at spectra and wind properties, 
+with more detail on python scripts and packages provided in :ref:`the API documentation <py_progs>`. 
 
 .. toctree::
    :glob:
diff --git a/docs/sphinx/source/plotting/plot_spectrum.ipynb b/docs/sphinx/source/plotting/plot_spectrum.ipynb
index 8948163f6..dc8337357 100644
--- a/docs/sphinx/source/plotting/plot_spectrum.ipynb
+++ b/docs/sphinx/source/plotting/plot_spectrum.ipynb
@@ -14,53 +14,177 @@
     "    \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 ```$SIROCCO/py_progs```. In this example, I will assume py_progs has been added to ```$PATH``` and to ```$PYTHONPATH```. ```plot_spec.py``` can be run from the command line using\n",
+    "One simple to make a quick look spectrum plot is using the ```plot_spec.py``` routine in ```$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",
-    "where the flags control the minimum and maximum wavelengths. Alternatively, it can be run from within python by doing:"
+    "where the flags control the minimum and maximum wavelengths. An alternative route is to use the pysi package, or work directly with the raw data. We briefly give examples of both of these approaches here. \n",
+    "\n",
+    "## Plotting spectra with pysi\n",
+    "\n",
+    "First we will set up the basic modules to import, then make a few different style plots. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.rcParams[\"text.usetex\"] = \"True\"\n",
+    "from pysi.spec import Spectrum\n",
+    "import pysi \n",
+    "import numpy as np "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We started by creating a plot directly using the Spectrum class. In this case we make a linear plot of the CV model spectrum at 62 degrees. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('10', '28', '45', '62', '80')\n"
+     ]
+    },
     {
      "data": {
+      "image/png": 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       "text/plain": [
-       "'cv_test/cv_standard.png'"
+       "<Figure size 1200x500 with 1 Axes>"
       ]
      },
-     "execution_count": 9,
      "metadata": {},
-     "output_type": "execute_result"
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a plot directly using the Spectrum class\n",
+    "# in this case we make a linear plot of the CV model spectrum at 62 degrees. \n",
+    "s = Spectrum(\"cv_test/cv_standard\")\n",
+    "print (s[\"inclinations\"])\n",
+    "fig, ax = s.plot(\"62\", ax_scale=\"linlin\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also make a log-log plot, with all axes plotted on the same figure. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
+      "image/png": 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       "text/plain": [
-       "'cv_test/cv_standard.png'"
+       "<Figure size 1200x500 with 1 Axes>"
       ]
      },
-     "execution_count": 9,
      "metadata": {},
-     "output_type": "execute_result"
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,1, figsize=(12, 5))\n",
+    "for i in s[\"inclinations\"]:\n",
+    "    s.plot(i, fig=fig, ax=ax, ax_scale=\"loglog\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also look at the total angle averaged spectrum using the log_spec_tot file. For example, we can look at the total emitted spectrum (spectrum that escapes to infinity) by first switching spectrum type, then plotting. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
-       "<Figure size 900x600 with 1 Axes>"
+       "<Figure size 1200x500 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "s.set_spectrum(\"spec_tot\")\n",
+    "_ = s.plot(\"Emitted\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the spec_tot spectrum, again most of the keys correspond to columns in the spectrum. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_keys(['units', 'spectral_axis', 'distance', 'Freq.', 'Lambda', 'Created', 'WCreated', 'Emitted', 'CenSrc', 'Disk', 'Wind', 'HitSurf', 'columns', 'inclinations', 'num_inclinations'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print (s[\"spec_tot\"].keys()) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some important ones are:\n",
+    "* **Created:** total spectrum of all of the photons paakets as created, that is before having been translated through the wind\n",
+    "* **WCreated:** spectrum of the photons that are created in the wind before translation\n",
+    "* **Emitted:** is the emergent spectrun after the photons have been translated through the wind\n",
+    "* **CenSrc:** is the emergent spectrum from photons bundles originating on the Star or BL,\n",
+    "* **Disk:** spectrum due to photons starting in the disk\n",
+    "* **Wind:** spectrum due to photons starting in the wind\n",
+    "* **HitSurf:** photons that did not escape the system but ran into a boundary\n",
+    "\n",
+    "**Units:** The units of the spec_tot file are typical $L_\\nu$, i.e. monochromatic or specific luminosity in CGS units. \n",
+    "\n",
+    "We can plot multiple columns on a log log plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 900x600 with 1 Axes>"
+       "<Figure size 1200x500 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -68,18 +192,16 @@
     }
    ],
    "source": [
-    "%matplotlib inline\n",
-    "import plot_spec\n",
-    "wmin, wmax = 850,1800\n",
-    "smooth = 21\n",
-    "fname = \"cv_test/cv_standard\"\n",
-    "plot_spec.do_all_angles(fname, smooth, wmin, wmax)"
+    "fig, ax = plt.subplots(1,1, figsize=(12, 5))\n",
+    "for i in [\"Emitted\", \"Created\"]:\n",
+    "    s.plot(i, fig=fig, ax=ax, ax_scale=\"loglog\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Reading data without pysi \n",
     "You may, however, wish to get more direct access to the data, which can be done easily by reading in the ```cv_standard.spec``` file, for example using ```astropy```. In the next code block, we read in the spectrum file and print out the columns."
    ]
   },
@@ -114,16 +236,7 @@
     "* **Freq.:** frequency in Hz\n",
     "* **Lambda:** wavelength in Angstroms\n",
     "\n",
-    "The next set of columns correspond to:\n",
-    "* **Created:** total spectrum of all of the photons paakets as created, that is before having been translated through the wind\n",
-    "* **WCreated:** spectrum of the photons that are created in the wind before translation\n",
-    "* **Emitted:** is the emergent spectrun after the photons have been translated through the wind\n",
-    "* **CenSrc:** is the emergent spectrum from photons bundles originating on the Star or BL,\n",
-    "* **Disk:** spectrum due to photons starting in the disk\n",
-    "* **Wind:** spectrum due to photons starting in the wind\n",
-    "* **HitSurf:** photons that did not escape the system but ran into a boundary\n",
-    "\n",
-    "The remaining columns show the spectrum extracted at various angles, where A45P0.50 denotes an inclination of 45 degrees with respect to the polar axis, and a phase of 0.50 relative to inferior conjunction. Phase only matters if a companion is present. \n",
+    "The next set of columns correspond to the same as the spec_tot columns above, but only over the specific wavelength range requested.  The remaining columns show the spectrum extracted at various angles, where A45P0.50 denotes an inclination of 45 degrees with respect to the polar axis, and a phase of 0.50 relative to inferior conjunction. Phase only matters if a companion is present. \n",
     "\n",
     "**Units:** The units depend on whether flambda or fnu has been requested by the user, but correspond to CGS units either in per Angstrom or per Hz.\n",
     "\n",
@@ -247,9 +360,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/docs/sphinx/source/quick.rst b/docs/sphinx/source/quick.rst
index 1e9fb6576..bc3239921 100644
--- a/docs/sphinx/source/quick.rst
+++ b/docs/sphinx/source/quick.rst
@@ -12,3 +12,4 @@ the inputs correspond to a plausible model of an astrophysical system.
 * :ref:`Creating the input file for SIROCCO <Inputs>`  -- Simple instructions how to set up a model interactively
 * :ref:`The files produced by SIROCCO <Outputs>`  -- A quick look at the output files
 * :ref:`Evaluation of the results <Evaluation>` -- A discussion of whether a model has run as required, or not
+* :ref:`Plotting \& Processing Outputs <plotting>` -- Some tutorials for plotting and processing outputs with PySi and other tools.
\ No newline at end of file

From bdc84e6b0e8c8e48a5435a8a8c618abdb3fcdb79 Mon Sep 17 00:00:00 2001
From: jhmatthews <matthews@ast.cam.ac.uk>
Date: Fri, 25 Oct 2024 16:56:52 +0100
Subject: [PATCH 11/22] further mods to plot spectrum docs

---
 .../source/plotting/plot_spectrum.ipynb       | 82 +++++++------------
 1 file changed, 30 insertions(+), 52 deletions(-)

diff --git a/docs/sphinx/source/plotting/plot_spectrum.ipynb b/docs/sphinx/source/plotting/plot_spectrum.ipynb
index dc8337357..87e251f00 100644
--- a/docs/sphinx/source/plotting/plot_spectrum.ipynb
+++ b/docs/sphinx/source/plotting/plot_spectrum.ipynb
@@ -18,8 +18,13 @@
     "\n",
     "    plot_spec.py [-wmin 850 -wmax 1850 -smooth 21] cv_standard\n",
     "    \n",
-    "where the flags control the minimum and maximum wavelengths. An alternative route is to use the pysi package, or work directly with the raw data. We briefly give examples of both of these approaches here. \n",
-    "\n",
+    "where the flags control the minimum and maximum wavelengths. An alternative route is to use the pysi package, or work directly with the raw data. We briefly give examples of both of these approaches here. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## Plotting spectra with pysi\n",
     "\n",
     "First we will set up the basic modules to import, then make a few different style plots. "
@@ -27,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -69,8 +74,6 @@
     }
    ],
    "source": [
-    "# Create a plot directly using the Spectrum class\n",
-    "# in this case we make a linear plot of the CV model spectrum at 62 degrees. \n",
     "s = Spectrum(\"cv_test/cv_standard\")\n",
     "print (s[\"inclinations\"])\n",
     "fig, ax = s.plot(\"62\", ax_scale=\"linlin\")"
@@ -85,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -114,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -142,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -162,6 +165,7 @@
    "metadata": {},
    "source": [
     "Some important ones are:\n",
+    "\n",
     "* **Created:** total spectrum of all of the photons paakets as created, that is before having been translated through the wind\n",
     "* **WCreated:** spectrum of the photons that are created in the wind before translation\n",
     "* **Emitted:** is the emergent spectrun after the photons have been translated through the wind\n",
@@ -177,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -207,14 +211,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['Freq.', 'Lambda', 'Created', 'WCreated', 'Emitted', 'CenSrc', 'Disk', 'Wind', 'HitSurf', 'Scattered', 'A10P0.50', 'A28P0.50', 'A45P0.50', 'A62P0.50', 'A80P0.50']\n",
       "['Freq.', 'Lambda', 'Created', 'WCreated', 'Emitted', 'CenSrc', 'Disk', 'Wind', 'HitSurf', 'Scattered', 'A10P0.50', 'A28P0.50', 'A45P0.50', 'A62P0.50', 'A80P0.50']\n"
      ]
     }
@@ -222,7 +225,7 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import astropy.io.ascii as io\n",
-    "\n",
+    "fname = \"cv_test/cv_standard\"\n",
     "s = io.read(\"{}.spec\".format(fname))\n",
     "\n",
     "print (s.colnames)"
@@ -245,42 +248,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f77ba8acc10>]"
+       "[<matplotlib.lines.Line2D at 0x14024da50>]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f77ba8acc10>]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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VSkNEREowVGARjL5Yly76vFJ32YG8Ww9EEFNZ5XkOFR3HUUREpsDAQqf+d35+C3KUea7E4f/5pZVep2fXc60NEZHRGCqwCMYhmu7kFinTv6Kkogr/WbQXaZl5iqzPk0AkyLKv4Sksq0SPl5aix0tLVd8uERFJY6jAglzlFlXg0zVHcNWHf6u+rUDUDPyaloX03GIAwP7sQgBAuV3ziFhoqXS8+eaS/Rj19moUlAXnDLRERGpiYEGK8SWuyDhTgju/2oKtx85Jfs/0ea6dWovLq7DjeJ7PQUROQRl+TTuBymrPfTgAYNbKQziUU4SvNxzzbWNERAZmqNlNjdMQoq7SCs99EgLpvu+2YteJAlkTmJWLdOC86sO/cTCnyOdyjHtvLc4UV+DYmRI8cEl7Se+ptvKIIyJyxhoLEyosV6cKX25TiFUQsOtEgSLb9ieoAIAz53OGrNiXo0RxiIhMy1CBBTv/a0tu5803luz3cTtySa9ZkLNuHm9ERK4MFViwYto/R077d9efXVCmUEnU8+Jvuz2+zqGpRET+MVRgQdLkFJSLPv+Vn50R3SX7UlpqRp7PQ4u/XH/U4+uMK4iI/GO6wCLjTIn3hQyusCz402KfzC/DmoO5WheDiIicGCuwkHAT+9rifeqXQ+fcVfcHU4Kxaz7egPdXHFR8vWpPpEZEZHTGCiyk4HXDEE7klaqyXh4eRET+MV9gQW4vnsFTX6EedxUW5VXVLnOS1C5bUlEVVLU9RERqMlRgIfDSKAmr+90TGzJrtQro9fIypLywxCEzpyAAWXml6PL8Etz8302BLCYRkW4ZKvMmScO4Qp6SymqUnM9WerrQcUTNvNTjAIC17EhKRATAYDUW5B/W5ssLuiwW7jMiImeGCiyknOR5s+6pj4Uxr5JyLv5ya3OMuceIiHxnqMCCpAmWppBN6WcDvk2xPhbuOmayrwoRkSsGFqRb2zPztC6Ci7u/3mp7LAgCm0KIiJwYKrDgOd4/vEh6r83ZeSI/MAUhIgpShgosSCpW4dcqrxLPTeH4nPj+slgshu2XQkTkK9MNN2W7uIeU3oEtRsC4+1yHcgox6u01uKF/su05OX0srFaj7jEiIt8ZqsaCVfnSuB0VYpL9d/BUIRZsO4GPVh4GAHy/KdP2mpy4861lB0yzz4iIpDJdjQW5d/ycOWZ+vfSdNQCA6Ejvh7/VKmDJ7lNuX2dcQUTkSFaNxezZs9G9e3fExMQgJiYGgwYNwp9//qlW2Ugl7pqDcosqAlwSdRWUVQJwX0NTWO59+vh5207gsZ+2K1gqIiJjkxVYJCUlYebMmdiyZQu2bNmCkSNH4sorr8Tu3bvVKp8s7EgnjfumEH3tv1UHcvx6f/cXl+JQTqGso8I56Pr7EFN1ExHJIaspZPz48Q7/f/XVVzF79mxs3LgRXbt2VbRgamHXzeDx96Ezfq9jrl3/CSlkB1c6C8aIiLTmc+fN6upqzJ07F8XFxRg0aJDb5crLy1FQUODwR9pyOyrEgNfIOeuPoqLK6n3B89YezMWMP/dKXt6Au4yIyC+yA4udO3eifv36iIyMxD333IP58+ejS5cubpefMWMGYmNjbX/Jyclul/WXES+MahAbUgkAVgPuwGqrgHUymzM+WX3E9vhMsbH6nRARqU12YNGxY0ekpaVh48aNuPfeezFlyhTs2bPH7fLTp09Hfn6+7S8zU17VNCnPbHks/LHmwGmPrxswFiMi8ovs4aYRERFo164dAKBv377YvHkz3nvvPXzyySeiy0dGRiIyMtK/UkrE3FfS8GJIRERq8TtBliAIKC8vV6IsfuMFUxr3fSy4A4mIyD+yaiyefvppjBs3DsnJySgsLMTcuXOxatUqLF68WK3ykQrYFKKcjUdcR67kFtUE2nH1A1NTR0SkJ7ICi1OnTmHy5Mk4efIkYmNj0b17dyxevBiXXnqpWuWjQGJk4UDKtO3HzjpmK62stqLvK8sBAAdeGYeIMENlzSci8kpWYPHFF1+oVQ5FSLkuCqhJ0xwSYt4OGe5GhZCjKz/82+sypwsdmwGLyi5k88wvrUSTaNZaEJG5mO526vftWRj97hpUVkvPbUDa2XUiX5PtllVWe19IBCt9iMjsjBVYSOx8eCinCNsy8tQti44FUx+LLUfParLdTs/51m/IvgMsRykRkRkZK7AgSdzFX+m5xYEtiATuJkzTK/tde+JcqWblICLSCgMLIpVc+eHfWM9JzIjIZAwVWOixKp/8UyRhanM9u/Hzf7QuAhFRQBkqsMgt4rwOUgRT68IbS/ZrXQRZxJqZ8kp4XBKReRgqsHh/xUGtixAUgimwCDaCSL1ZbV4LIiIzMFRgQaRHVdYLwcauE/m4dc4m7Msu0LBERETqkT0JGRH57uqP1qOi2orUjDxsf2G01sUhIlKcYWosDpwqlLU8mwNIFV56EFecT8yWX1oZgMIQEQWeYQKLdQflDevjRJ6kBl8Oq7mbMvDqwj2Kzy779rIDuPbjDSiv8i2LKBGRLwwTWBBpLb/Et1qIp+btxGdr07H56DlFy/P+ioPYdPQsft9+0uuyW4+dxasL96C0gkEIEfnHtIGF0neHRMPeWOnX+wt8aB7ZdSIfM/7ci8Iy9++VMi/Ov2ZvwGdr0/HBXxxZRUT+YedNE+LspurIL62U1cTmPMvu8XMlHpYWd8UH6wAAJeXV+PdVKbLf7+zIaf2ldSei4GLaGguiQNqf7dq5uNuLSzDLrobgxd/32B5XWwWs3JeDM0XlLu8Tw+GrRKQXDCxMiCNiAk+sqaK4ohpvLj0guvx3/xzDrV9uxtj31qpdNMVYrWxeJCIGFkQBIfeSu2T3KQDA6UJpNRZae3/FQfR9dTkyzshvziEiYzFMYMF7JdKz5XtOBWQ7G4+cwdvLDqBKQodNJb297ADOFlfg9SX7ArpdItIfwwQWRHo2f9sJnC2WPhmZr81V13+6Ee+vOIifth73uFxxeRWmfpeKhTu8D0WV448dJzF71WFF10lEwcW0gUVhWXBPx03BJaewHL3/vUzVbSzYdsL2+OgZz6M7Pl1zBAt3nMTU71IVL8dri1lrQWRmpg0svliXrnURiBT10A9pkpfNtRttUs1Ol0SkIMMEFnITXpWZOM0xB4Wop6RCmZowSwCH7rR9epHq27BaBVz78QY88P021bdFRNoyTGBxqqBM6yIQ4asNxzTZrlLpwAWVukHvzirApqNn8dv2LJRUVOGgzEkDiSh4GCawCHAn+KDEPAPqU6zGwt8V2H3Vfx/KxZT/bkLmWe2Gglbb1SiOeXcNLn1nDTYcPqNZeYhIPYYJLNS60zISdlhVnx6noPljx0msPnAaj/603euyx86USJpbxB+ZZ0sBAIt2KjsihYj0wTCBha/KKs3X14KZN9WjRlyx60S+IuvJkdBcuC+7ELd9uVmR7RGROZk2sNiWkQcAsOrxFpNMzz74q51oLFDWHswN6PaIyFgME1j4OmNnbqH0pEVE3uglTt2VJV7LcTS3WPWmDqnYfElkTKafNj3teJ7WRdAA20LUotTF0t9v6O9Drh0jj54pwfA3V6F/60ZoH1/fzy3II3c4OBEFL+PUWPBa6R33kfoUun6qmcdiU/pZ1dZNRGSYwIKIiIi0x8DChEJYc6EapSr8+RURUbAyTGDBuT+kiwoP1boIRERkUIYJLEiC87fT+7ILtC2HganVSfGT1ZyKnIiCg+kDCzP1Vr/u0w0AgAfnpmlbEANTrCnEqS1kxp/GnopcEATcMmcTJn/xj6l+k0RGZPrAwkz2ZddM/FRepY88BkZklr4R6w/l4o7/bcHJ/FJJy4uFCvbxQ0FpFVbtP421B3Nx2m5KdyIKPqbPY0GkT/oOUW78/B8AQHlVNb6+fYDf67OfpCyEY8eJghprLIgUpFZTiF5l54vPP/Lu8gNe5+Gx/4z2qfU/WnkYl723FgVllYqUkYgCi4GFGbEJW7eCYVK84vILs+S6u/i/u/wgPl97RPI67QOL//6djj0nC/DfAIz0EgQBMxbtxbf/HFN9W0RmYfrAQs0Mh0Ryvb/iIAB9N4RU2PXRKat0319n/6kij+ux72Mh1l8zEHOabD+ej0/WHMEz83epvi0iszB9YGFKer5qBTl/d+26Q/qZWfTl3/fg3m+2uozSUKPCS6tZhgtFalzKKquDouaISK/YedNkFu44qXURDM0orUx3frUFy/acAgDszipASvNY0eVKK5S5AGs1wtR5u5XVVnR5fjHCQkOw7+WxCGGaWiLZTF9jYbYx81+sk97uTfL5ezjtOF4z3bnaLXTf/pPh8fXaoAIAqq3uP1SFxOYKb/slEL9CQRDw4+ZMW4I4QRBw//fbHJY5XVgOq1DT3FMShLUWqw+cxn3fbsUZDtklDbHGwmRSM/IQEWb6eFLXWj21UOsi6IJF4Ta7hTtP4olfdgAAjs68HFuPnUN+qWNTiH2AMy/1OG4e1ErRMqhtyn83AQAiw0LxznU9tS0MmRavMEQKOlPMO8Va/oYFgsL1GDtP5Dv831uiuOd/3a3o9gNJauIyIjUwsDAjc7X+BNTfh85oXQTFGeZwMcwH8W73iQK0mb4Q6w7qpzMwmYfpAwsONyXyz6drfJsgbf62E5jzdzrySioULpHyvtl4LCB5NZRSWF4FqwDc9MU/WheFTMj0gUWlCefNkNrhjghwbdI45xQI/GeRlAnSXKsLSiqq8dLve9xOiqd0HwtfKyzKKqvx7IJdePmPPcgNwk6Rk7/4BwdOFWpdDDIR0wcW32/y3DueiC44cKoQT/y8Q9F1rj5wWtH1Ka3KblSMUsNrA2ntwVzccr5TJ1EgmH5UCGdSJPKs9rJ67ccbsOnoWU3L4os5f6dj67FzaBoT5XG57zdlYMNh4/WRAYAsN3O6EKnB9IEFUbAoq6xGVHhowLf78A9pWPnYcNlBxW/bs3BxhyYY1qGJ12WtHnJl+Oul3/cAAJrGRDo879zQMn3eTpf35hSWoW6EMU6TS3ZnI6+kAtf1a6F1UcjgTN8UQhQsai+QgZaeW+zzex/9aTuunv231+U++Oug12U2pZ/FZ2uO+JzU7lSBY+2klLXMTz1hmAz4d3+9FU/+shMZZ0q0LgoZHAMLoiARrP2BMs96z6nw45bjXpe59pMNeHXRXizela1EsSQz2sAx5863REozfWBhsozeFOTOFgfnRUHJ39lRiXfc+aWVeGjuNtHXcgrKJNVE8PRAJB8DC546KIjc9Lm58hLkFpVj5/F87wuKeHPJfixIyxJ97bpPN/pTLCLywBi9kvzAGgsKJntOFuBEXimaN6ijdVFUZ7EAfV9ZDgD4bdpFst+flee+CUZqvxHnWg2eL4i8M32NxfFzzKlPwWXn8TytiyBbhQ+J6Owv4loNAxWgfKIuIqMzfWBBROqb5GdqaV86UCrV6VLJ5tIXft2FK2et8ynQIgoWDCyIgowW1fGemhWk0GMTQmF5lddlXJpC/Awy/rfhGLYfz8eKvaf8Wg+Rnpm+jwVRsNHiGn3lh95zUSht7uZMScuVVlRj8hf/YFiHJnjgkvaS1/+QmzlK7H2+Lh0hPlR9fLPxGBrXi8C4bs1EX1cxHxiR5lhjQRRktLj7P10Y+NT39hN+2fdzcL7O/5x6HFuOncPbyw7IWn9ppfd5P04XluPVRXtlrTc9txjPLtiFe79NdbsMR6ORkTGwIKKg4hxYlbsNELTpdBmsuUaIlCIrsJgxYwb69euH6OhoxMfH46qrrsL+/fvVKhsREQB4nPZbj/03iMxMVmCxevVqTJ06FRs3bsSyZctQVVWF0aNHo7jY97kESFvTRrTTuggkkxmr0X/aeiHlt3NTiLv9sZwdJIk0Iavz5uLFix3+P2fOHMTHx2Pr1q0YNmyYogUjInFmv0N3/vyB3B/228ovrcRbS/djYq/m6NWioc/rITIav/pY5OfXpNpt1KiR22XKy8tRUFDg8Ef6YbQJlsyA1yTPcgrL8NyCXaqs237fz1i0F19tOIaJH61XZVtEwcrnwEIQBDzyyCMYMmQIUlJS3C43Y8YMxMbG2v6Sk5N93SSpgHdOFGxcm0IcPfrjdny98Zgq256XehzD31iJ9YdzJQ+HFcOfHRmZz4HFtGnTsGPHDnz//fcel5s+fTry8/Ntf5mZvv8YiagmqDezUwVlDv933h27Tvg2aZkUH/x1CEfPlODGz8w1GRyRHD4lyLr//vvx22+/Yc2aNUhKSvK4bGRkJCIjI30qHKmPTSEUbKqdskvllToO79Q+7NK+BERakhVYCIKA+++/H/Pnz8eqVavQunVrtcpFAcK4goKNfQ1FYVklPll9RLvCBKGn5u3ENX2SUD8qDNf2ZdM0KU9WYDF16lR89913+PXXXxEdHY3s7GwAQGxsLOrUMf40zobEKougY/KWEOzLLoDVKiAkxIIDp4pcXs8rqQx4mT5fewQ3DWyJqPBQ2IfrgiDAIvIb07I5a+/JArz8xx4AQH5JJXacyMfMq7uhXiRneCBlyOpjMXv2bOTn52P48OFo1qyZ7e+HH35Qq3xERA42Hz2Ht5bpKzHfKwv34rXF+1ye13sQ+Oqivfh9exY+WX1Y66KQgcgKLARBEP275ZZbVCoeETkzY4IsZx+u1N+F8Kctx12e8/WbqrYKAa3VOF0U+LlgyLg4VwhRkNH7XbBZXQgE/PuCKqqsGPHmqqAbebJi7ykMe30lth47p3VRSGMMLMyOV6mgw6/Mnn52RnFFNd5bfhCnCy+MUpFa6/DFunRc+eHfyC+txM4T+cg4W4INR86oVVQR/ve1uv1/W5BxtgQ3fxFcAREpj711iIgU8s7yAwgLseu8KfF9/z7fmfKzNUcwolMTn7ZdWW316X01lAvQpExHT8bGGguz46iQoHXG5O3i2zPztC6CqCq7PBuCUHPBzy91P1IlPffCJI5lldU4fq7Up+1+8Nchn96nNLFRMGQurLEwOS1OAUkN6/h88qQL95bnSio8Lmd0V374N365d5DDc2U6u1vu8Oyftsebnr5EdJkRb66yPZ637QQ+X5fu07Z+9CPFuJ5UWwWUVVZz+GsQY40FBVxKYqzWRQhqF9rteWfovA9m/uk65FMv+v9nhddlzhYHf7Do71E5/oN16PrCEtPXyAUzBhZEQUY/3RX1wHFvfLn+qDbFkElvHXCVLI+/LSF7TtbMgL36wGkFSkNaYGBBFGx0dlEifdBTfhNBEDz2KyFjY2BBAce+Xf6pTWZk9llOg1lxRRVKK5TtD+LP4aDkb9ICC+75Zit6vLRU1ZlmSb8YWJiclBPKw6M6qF8QkqykogoAsHDnSY1LQr56Zv4udH5+MX5NO+Fxuez8Mo+v2/MnzFQ6Rl2y+xQA/5qmeAMSvBhYkFcpzWO0LgLZsZzvHne6kJ3bgt2Dc9M8vj5wxgocPFUoaV1aHA9nisox8899OHLadTI4QB/dizPPluD37VmwWlnDFygcz2Ny0VHhWheBfMQ7OnNYuucU2jeN1roYoh77aTtW7j+NrzccvfCkzo7Loa+vBFCTKv1ffZI0Lo05sMbC5G7on6x1EUim9YdzAVyouSBjC3QAKQgCyquk9f9IzcgDUJPOXIyegt/NR89qXQTTYGAhQ8vGdbUuguIiw0JlLT8uJcHvberpZBOMak/m3I+klLl2ybUe+XE7Oj67GMfPlXh9n9gxaHF47PtBqnTgzN9L4DCwCEJX92qOuPoRiqwrRMKPrXVcPdvjTgnsb6EXITxTmkKga6bmb6vpUPr1xmNulxEEAe8sO4C8EtchpfaHpb4OUV0VxtAYWAShK3s1R50IeTUN7lgsFmx+ZpTHZdo0qY+vbuuPPx8cqsg21fbvK7tqXQSioHPft1tFJzIrLKvEA99vw/I9p2zPLdl9Cu+tOKh4GbyNkvGHvoIcY2NgYRA9kmLxxZS+aFg3HIPbNpb13ibRkV6XGdahCTo3U6a2Qq07sOv6JuPfV6VgQs/mqqyfSAuBuiAu2pmNhTsuDGH+ZPURXPvJBry19AB+256FO77aAqCmtmLp7my367H/fcstu7dRMv5gXBE4HBVip0uzGFs6WTF6OjCdL87f3zUQdSPCsOmZUQgLsaD19EUalcw7tTIEdkmMweSBLVFYZo6Mf9UcPqe71NhqCOR5J9dpfo5N6WexKd2x0+P3mzIxb5t6NQu1lA6oWGMROKyxsHPrRa20LoIkFgCRYeJfXXhoSECnLQ6x1JSlU4L2w+HMduLg7I+ktA9Xep56fcXeU3h6/k4Za9Tnj/L7TRlY4qHWhfzDwMKOt5uf+lH6OZF30Mm49t+mDcGOF0c79Pn4x8300LU4TFIZUjreUvALZMB8TqQzpr3b/7fF6zrsy/v9pgyXGg858r2Ux5f1Hc0txvR5O3H311sVXTddwMDCnpfI4t3regakGJJodFGxP2n0SG6Azs1iEBkW6lCcpjFRASnLzKu7Yfak3rb/m6FanByZ4SsPtkC8xCmnxbWfbLA9tloFPPrjdny25ojX9UyftxM9Xl6KdQdzFSnXNxsz0OPlpfhzF2sq1MbAwo63tv928b7VElzdW9nOhHqp8l9w32CE+nLbrFD5r+/fAuO6NVNmZUFIL8eBlswQTGr9PStZM7b+8Bn8knocry7a63XZ2gDlvRUHlCsAgFl/KT+ahRwxsLATVDkanE6oWpxgA9mXg4i0oWS+lOLzE+iRsTGwsJPSPFaV9apRlanWyApfyQkyuig0bJWIU8err0ql0UfvLpdWE8GvOPgwsPBTbB1tJvHSW7trvFMujLev7SG63ONjOuKOoa1VLQvPQ0TAxzf19r5QgNmftd5dfhAFPg4N/317Fsa8swaHnWZVfen33XjkhzQGnBpjYGFHX5dq9yyw6K6wkwe1dPj/1b3FZxGcOqKd7PlJpPJWafLk2E74z8RuuMYgMxzqLbjUAlN5uDc2RT/9jxbuOIn7vt3q0rEzp6DMp/Xd//027D9ViCd+3gGgpubqtcX7MOfvo5i37QSOninBrhP5eHaBnKGx8p0rrlB1/cGKgYVB+HJ+bduknveFJHKXV8MXfz16sWLrshcfHYkbB7TAvcPbqrL+QKsQSb9MxmOEvkxTv0vFop3Z+Hj1YYfn/a1YqA1UUjPyMHvVhXVXW6244oN1+GZjhn8b8ODztUfQ69/L8MW6dJfXqq0Ccgp9C5qMgIGFTBEKXkDFXNUz0esyYucZX6r+fDlhRYW7+/zKnfzCQtTdx22a1Fd1/YGw5ehZfCphyJ7RnSku975QkAv+sOKCcyXu7/BL3Uy9vuXYOXy06hCOnC7CxI/+xoq9F+Ys2XuyABVVVuS5rFfaXhMEwe12vXllYc3Iln//scfltSn/3YT+r67A1mPmnKqdgYUdKdfZtU+MkD0XhxztA5j4ytPHvaF/C9HnJw1oid4tGuDJsZ3UKZSKpMZRcfW9z52itX8v9D5czwxe/t31pG40BqiwsDlV4BgI/m43N8k3HmZTfX3xfox8azW2ZeS5JOl6cO42n2s+ps/bic7PL8ZeD1M5+GLdoZrcG2rWmOgZAwuZmsZEYWSneK2LgTCnweW+/K48vedfbnJv1IsMw7z7LnJpTgiGLJDSAwvxKemNdII3ipxC49dY+CO5UR2ti+DR+ysOIjXjHACgsNy3oah/7sp2OZdJ/a3O3ZwJwHMq85KKKlhldOaZl3rc5f/eUqUbDQOLABibkqD4Om8fou7ICrm6JzVA/9aNPCYD692igctzdcLV6cgpxt/OjuufGqmbnvaMcczD6N/11R+tR1W1VZfjSnMKytDl+SW4/tONOHamGJlnS7y+55Eft7v8/40l+xWvFdEzBhYqe2hUe4zqHI+HR3XAxF4XLrqfTu7j8zotABrWdbyr1vo3GRpiwY93D8Lb1/a0Pde8gePdklj/FCUn0goPVfdwbhZbR1c97ckc/Om8qfV5QapP1hzxa4SPcx8zT3tMbDPu9vGinTVNNZuOnsXFb6zC0NdX1gRBTiZ+9Dde+n23xzLml4oPrc04U4Klu7MNNUSWgYUdJXtft2lSD7teGoOHRnWAxWLBg6Pa4//shjmO7upHLYYlOE4YKx8bju3PjxZ9bc4t/dCxaTTm3NLP5TV3X0ObOPFRLA9e0h59Wza0BW7u9o2nr/fxMR1tj+Xs27oRgatxIZJrjD/nmQBauOOkX0n/nN+5L7vQvwKdJ5YcTGw01raMPMz5+6hP2xj2xkrc9fVW/LUvx6f36xEDCx/0bdVI0nL11ZrWWuz358NvUu0q1oiwEMTWvZBAzL4pYkSneCx5eBi6JUnLdrr2iRGIj7nQqdK+WeXhSzvg53sHI8pLs0qLRnXdvjZ1RDtJ5bA3pF0clj2iztBYb9jfwzz8+a7tA2Y9s1j8y0mSne84tPO+b1P9LFGNapFCWWARrbXw1+aj5xRfp1YYWPigZ3ID/HTPIGyYPlJW9ZWatQxKp/j2dBHWQrJTeebeNUj2Onq1aChpOan7UoDg0twTKIwrzEPqdz2sQxOX57wF23oidhGX6oXfPDdD+Kpa5KRtsUhPc27W36lKt9TG109irYUqAnC0xsdE4fdpQ1AvUp8nJjXziQRDMxOZh9TDMTE2StVyqGl3VgF2Z2nbubGovApWQUBM1IVa1upqeSeD7zf5PrxUb/M/+YOBhUrErv1KVV9bYHE5CNW4GEptppDKSD8cIr2x7yM2aYB4HhoS9/v2LPy+PQsAsP+VsYgMC0Vqxjm8tUzelO3T5/mRQtxAp0c2hfhJ6XS74aFSM8Ypullj8XHf1HYOvby7vJEfWiTUKi73LVsgGVnNgd+8QR08c3lnjcuiX87zlTjLLarJ4nn1R+v93pZZT9MMLPyk1BChbs1jkdI8BpMGtPS6bOdmrtk56+q0yUJJA9uol/EUAH65dzA+vqkPpo5oh/dv6IWmMZ4DhtrOqPPvG+x2md+mXeTynKdcH1LtP6VMr3fSP6mnmNrlbuifjLoRrIz2lQUQSRHuG1l98BTZoj7w6FOJ3IPk/pHtJA9BDRPJ1+DLjKH+dJbSwr3D26Jx/Uhc3N61k5oSGtaLsCUzm9AjERN6JKLVUwvdLl/btOPcsbTWpV2aontSA5fn6/GkTzJIvTjVLmaEScu09Nrifejf2n0fOkGQ3qwt5xTLPBbkk5aNlRlpodRp40husUJrCozIsFBMHtgSLRTaj1J0bhYTsG2ZRY/kBloXIahIvdxYDXRh0tKvaVl4Zv4ut68LEERnNBVf1pxMHViI3bE/eEl71baX1LAu5t41EEseGgbAMTOl3ANQ7vJD2sXJfIfy9Hrea+0m8RbgXxDn7r0dEpSfaO6x0R0wvKNjTc4jl3bAJ14yvDawyzOipvbxF2aU5f20OrYfzwPAHCeB8MaS/ZKWs6+F2J6Zh7LKC/07svPLcPuXm+2WdXzvU7/swGXvrUVZZXXQ1WaYOrDYnZXv8pzcTHVyqx0HtmmMjucvLCsfGy7rvf4w04gMuUNRr++X7NN25M49EhUegtWPD0cTlTp7fnZzX4f/1wkPRYM64oHD0PZxeGBkOzx3eRdVyuIshFc7n0mtTj9wqggAsCndnFN1B4qca7z9sjP+3Ieb/7vJ9v9n5u/ECrtsm5+vS3cIPOZuzsSekwXo9Nxih/cFA1MHFkqwjyT/uH+IrPfaXwCDLCDVtToRoXjv+p6Sl78oQLU5jepGoGXjeqrdUYaHhmDdkyMcngtxM+1sl2YxeGR0R1XzgdhrVO/C3DaMMeSRe7ea7tTEqWYtLHnmfENnH/SddMoWCgCfrz0iup61B3Ntj/dkFeCnLZm6rsUwdWCRekzZFKopzS/kffDn3Dl5oPeRIXJZlc9Aq2tX9pQ28qJFo7oO35szTxdBPdYCJTV07H/ibTr7QF3kuyRe6Ktiv8lAzm5rFs5f6UOjGFgo6d3l0nNbiF37zxbXjDgR6xPz5lLv677s/bV4/OcdWL5Xv3OLmDqwOFVYLmm5+OhIXNqlqcqluXCQjewcr/jaY+pwJIKYTl76O+j4psDBiE7ix4zbJghL7T/+RxbTvMyzckP/ZDxod3Gzbz5MCOJskYEi9xh0bp7lKBFlfbZWWsdNADgtco3ZcPiMIuXYp+Np2E19tRG7m3O+C72+XzJendjN651foMmtBtMiiZOzILlG+6RHcgNsz8zTbPtdE8VrXdwFFrVDdpU4rh8b0xEbj5zBFjc1gA+N6uCQJtl+kzr7WemS3Jox7lP9cPebAKTPN1JLEAQs2plt+7+e40VTBxbe7ta2PDsKjetFMOIPMD3tbk9lsT9+xC7Q00Y63snXnkYC9fGiwkMQKlKwX+4djD4tayZkC8S+DnfKu+JpmzcPaomvNhxTuUTBRXatmY5+P+Se3OHBS3afwtTvLszaqufrkqmbQrx9L3H1IwP25dkfY/o9XIKX2nMniH1nYsmxAmH6uE4Y3LYxrumbjKhw1594bVABiJ+cXrkqxes2Yp1GmzTzMMtr7RaSGtYs09tu+86u7JnoddtmI+XGliNBgo/VzRe78Yh4U8nmo47f8ZtL9+u2A6e5AwsV163m1+1LrBNM0ycr/VtJalgHr07sJvqaP3GjfRW1nABU7VPB3Re3xXd3DkRUeCjaNqmP6/slY0KPRIzo2ATf3THAYVnnUq96bDhuktB5uJ1dXgoAeGG892GrKx8bjt0vjXFoFnE3akUtr070HjTpjZQ7203pFy5GvDHRtz0n8zF/23HRKdkB4PpPN4o+f/xcicP/BQFoPX0RXvxtt+6yKJu6KURPde5qHha3DG7lkKDIbKQ2Z8h9r+N6vNPi5sJisWDmv7q7fT0+xrHzZCsPycLsfXvHAMz8cx8Gt62Zv8VTH57afRgeGoLw0BDERF047fRMboBDOUWStqmESQNaesyqqEe1NT2elNrlPzhVIK1TOmnjw5WHvS6zcr/riI80N324vlx/FJ2bReO6fvqZ0ZY1FkGqsYzOmC9O6KpiSaRr4WZODS07ljas53vmSfugRIkYNUyDHsI9kxsgpXnNUFAptQ4AcM/FbREVHooXJ3SVNL+Nc0B1Td9kjOrcFP++KgXPXeF9m0+O7SSpXFINba99Flo5nJudxNh30tVr9ThJd+uczS7PeQoY1ys00kQp5g4sVDyPy1213HOBlJONw/rlrV5Rc+8aiGv6JOFZN1M5Oyd1Uvp7Edu3s27shWEdmuDxMZ4vWncPawsAGNPV83Bj55qPsSIX3NqmE3cfb0IPx/4Fz1wWmKmv/7h/KPa+PBa3XtRa0vJ3DJW2XC3nqvyo8FB8PqUvJg9sidg64Q59PsTcMriVrO158/FNffDlrf0UXaeapPx2g/kmiZTx1tL9mDBrHUq9TAsfCKZuClmy+5TLcwz2lTewTWOPU55r0f/jiu6JuKK7946C43skomdyAyR66JwIuAZD79/QS1I5GteLwJli71M0b3rmEvR/dYWkdfqiToT076CujGUBuUGt6yVS6UCzXmQYhndUPldMrTuHtpaV60ARMndS05hINpkYzAd/HQIA/LQ1EzcPaqVpWUxdY7FXRwlG1M7i6OnCTp4lN6orOmyzoV2aamf2qbJrs0v2bek6FfMPdw/C0PZxmH/fYI9liI8O3kRS3joftm1yoV+H2CiWYAv2nwnQ/CtyfH17f9vjlOYx+OfpURqWhpT2a1qW7XFltfY/GFMHFsFiwdSL/F6Hpxk8SZ6Pb+qNoe3j8Jxd046nn/KiB4figUvai45IaBdfH1/fPgC9Wjg2B4RYgBGdapJYNfIQwKjlj/uHYJSPGWCbxUZhw/SRF57wcp575rIuuGlgC/xy72B0aRbjMjTYPuge0LoRxqUkYFiHJs6rcXF592ayyu1N3/NNNs61Uar3AZdynbCLvmJEmkmHtr+wv5TItkrkCQMLPz04qgMA4P/6JPm1Hk93ZaE6Gr0STJ4aV9N/YubV7kdF+GJsSjN8ffsAlxEV7rSOq4dHLu2ABnVrAoSubuYmsT8ELBYL2sVHY+0TI1z6oARCSvNYfHBDb1nvmX/fYAzr0AT/u60/6kZcaGX1dl2MrRuOV67qhj4tG8JiseDVid3QsemFVOuCAHwxpS8eG90Bc+8aiNk39UFRWaXX8nx4o7zye/PzvYNx5D+XufSFEQSge5L7+WYCrZuHuW/I+PRwtTB1Hwsl3HZRKwzv2AStG6tXI+AcVxj9jkOppGT3XNwWt13UOjAzeMqofWzeoA6WPTwMsXUd7yzte/PXPk62G0kT6HZxuV9DrxYN8dVtNVXuVquAuhGhqKoWfKpxsd+2AOCSzk1xSecLHWhTM/Jkr1MJtXk3RnWOd5gEalxKM+w4nq9JmQDYglbA2KnzyTs93IeyxsJPFosFbZvU9zvRT2KDC3e/SlxYa6uKxUYnmEmgpgWXq33TaI/9JsTy3TzhZQSLnoSEWJD63KXY8eJol5TeUjS1qw2qq8Pkbj2TG9geR4aFqFJjIafmwVvnYqJAYo2FSiLCpJ0M/3dbf6SfLkIfkY59/ph1Yy/8tTcHo1SflZXU0FdkCKYe7kTk8Ge0T3io3TwsfgTtr1yVgmcXKJ8Qyz74/+aOAejXqhE+vqmPLSPp7Em9ce+3qe7eLouUjt2OtV2KbJaClB5OE/q8ndNQk2j/kjW9c10PtGhUF+9c10PS8hd3aIJbJOYPkCMmKhxX9WqO+pHBFzuaNcGP/af25S5fadoGMspsfGAbaQF7t+axPifO6teqZhtjUxJsgcW4bs2w6IGhDsulPneprPXK2f9yMjqrPQKNtKWHycmC76qjsqYxUfjs5r6oF+nb3dbEXkmY2Mu/jpxkTt7iqUDHW+768kRKrI3za9sBPjdGhIVgXEozrD2YK/q6Lwm1nC/gao7uYbBAeqL9bZEOXdqlKQa3Da60v0biHHFfasLmHB3cdLgY1bkpdr80RjSnh9KmnE/wc1E7f/OvSCvrnUNbe9znzgm1QiR8QWKB4CWd4tE1MUZSmTytx9l3/2TIWqev3rpGWk0smZvswGLNmjUYP348EhMTYbFYsGDBAhWKFXh3ykxTTIHz6eQ+WhdBlnoyM1OK0UNrkPO1MzoqDPUC1LQ2pH0c1j81Ev+7tb/3hf3Uv1UjjE1xzHnxze0XZoFtLtIx8sYBLdCiUV3cMUTeeePzKX3xx/1DHJ6b2Ks5Lu/WDDOudpyBV0745jhXhOeDp3E935p737+hFyb2au7Te53dNayNIushV3q4KZEdWBQXF6NHjx6YNWuWGuXRzI0DvE8VTdrQQ5uhHJMkTDseDLTe64kN6iDMz74mzWK95xpp1sB1mcb1PTdbxNYJx+rHh+NZCZOo2bNYLA7Hc1R4CGb+qxs+nNRb9ZEdc27phyHt4lwCGDEjO7kmR5vQI1GRae6Hto/T/NgyMj3sW9m3H+PGjcO4cePUKAudV1pR5fB/lzwWejhyyJHdd3J9v2SfVuEt9bXWgvGwqxcZhvqRYSgqr/K+sB0pX4W3gNd+He6Go35wQ2/RPivPXNYZf+zIEnmHd+7KPqJTPEaIBAxiGsic5FCOTyb3wXsrDqq2ftPTwQVC9T4W5eXlKCgocPgjz77blKl1EcgPbZrU93sdOjg3BF1NkTtJDaXVBCj9ae07VP5yr+e5YJzdaddUoEW8qdYmR3Rs4pCVlYxJ9cBixowZiI2Ntf0lJ/t2N2cmZZXaT3urpWC8nDWVmN7bEy3mBJEliL6YWBl33GIX7jZNlM2k69PwYR8Du9pU9oEmJYCbc77PjNGzB2tJD3tW9cBi+vTpyM/Pt/1lZurzblwPX4Y7av0I3V3I4ur7l8vDH2/8n7LzegRKokg7vVy1ORD0IjTE4tA5cbyEaeb1Qs7IFbG7c3+Se2mtvd08K2r4V2/x4fRiOXO+vWOAyJKkJj1UNKoeWERGRiImJsbhj/TB/fGnXVt/s9jgTE2sxERxPZIa2B6L3UUHakSGvWev6ILdL43BwgeGYHhH7zOK6kUghsR6o1QThha/RnfBAwC8LhL8148Mw1vXug5Fvaid+LB9PVz8SD1s7DIxdz9uOVn8lBasJxwpeQ286WE3/4SYdvHqTXTnSb3IMHRNDK4ZMx+8pL3s9yh97En5GXnaZKB/CrWdXDslRKNDU8fas2vczN781W390TquHpIa1oHFYsH6p0Zi8My/AlFcv7SOq4f03GKti6EKPTQzya6xKCoqQlpaGtLS0gAA6enpSEtLQ0ZGYBK0kJLED0CzptT2h9I3yGIXuXbx6lZxG8mgthcSa907vC0A4LJu4hPy1R7vl3dPRNOYSFzZU5kmn2D7Hd12USv8cu9gzL/vIofntzw7Cm/YJcayP9b7t26E5EZ1bR19pQ6ZtT+8EyUMCVbS0oeHoYXdrMFGo4ebM9k1Flu2bMGIESNs/3/kkUcAAFOmTMGXX36pWMECTQ9fRqC5+8xang4tUKYjZKApMb6flGNfg3Rlz+bo3aKh14te/cgwrH/qEsWaUeIlHMdSzjsBC1AsFvQ5P/ldYXml7WnnZj6LxYK9L49FldWqSF+UQP92OjSN1v3Qbn88/+su3NC/haZlkB1YDB8+POgi8WDj/DNTK+hxN/rEqmlbiD7ax+VSoimEpBvSLg7rDonP6wG41iAle7hDtT/alTz2mjeog49v6o2YKN9yQgT6kKqoskpetk5EKADfgwqpn+1fvZPwS+pxn7fjjpEvYZXVAs4UlaOxhp3wOVeIibm7GFZWa/er00P7oC+UDoa6NGMnZ0+8ZdSUcxy1iRPvu1L7nfZs0UDyupyNTWmGwW46MAJw6LsiNd+GWga39XdeFunsvx+x09DITvH4bdpFGNZBnTmb3E3a1l5nI7N8peW9IcDAwsbIEaw79j/oWwa3sj2+pi9nZ5VLTt4ETxY9MBQPjGyHR0Z3UGR9RvWkl1wNUu6If75nEO4c2hr3DW8n+vrSh4dh2oh2ePWqFF+K6NGWZ0fhr0cvRoJdgNS2SX3MntQbv9w7SPHtefPj3YMcpo0PZIAvtq1r+yahe1IDWUnaHrikPf569GJJy1rdVM7cfXFbyduTI9B9OrSu9GVgcZ6earIDlcLbfrX2CYEm9NAuX4Gevgc5rumbhLFdE/DqRP8uQl0SY/DI6I7MTuhFw7qek4lJabfv26oRnrm8y/lqfVdtm9THY2M6ooGXbfkirn6kaIbWcd2aoU/LRg7PBeKep3/rRm4v4mpsf2CbC7UjYputvdGzTy0+sE0j1wXt3DWsDVo1ljZySo0+Fr1aNMCTY8UD3qhw3y61a58Y4X0hEVpnzTVMYNEk2r/2pGCtgveH/cFn/1jrzpvBKDIsFB9P7oNJnMxOF4L1OLKn5Wewvy6p0aduSPs4fHP7AGyYPhLxIufu2i0ObR+Hmwe1xIyru+EVLzVHFjiWu7Yjqhg1znHVVgGt48RrJko9ZFPumhiDx8d0FH3NU98gT7Q+/g0TWBiZ1ChcLvuDz/4GT8vOm3F+BohEADvTSjF1RFuHf+3Vs6sx87f2rDYgePoyx7v5Ie3j0Cy2Dt66pqdL/47aWMZiseDlK1NwQ/8WaNukPsalJGDSAPERDxaL4w3SY6NdL9b/mdjt/Podz3HRkWE4/J/L3A5JlqJa5LxZe8Pb3S75nbPJA1uil4d+PFJmo3Wmdcs+61t1yLn2JCo8FJFhISh36rX9xZS+eGreTrxzbU/ftmO3GfttanFQ/veWvsgtrEBbBSbwIuPzFjYYKa6QU2HQv7Xn5gJ7j43uiGv6JKNlY9e74joRofjx7kEQBMFtU5E7bZvUw+HTxbablZsGtsT47omIrSveD6lF47r47s6BaPXUQttzYp0rLRYLZt/UBwDw7T++5U26undzAK6dGz+4sRdCQyweg6i3rumBR3/a7vZ1scBi3ZMjUFpRjdeX7Hf7vhCLxWMfjBv6t8D0eTvdvi5G6+G0rLHQIbEDtLVdz/XaY+aSzk2x6elLMKS9rz2nxXtma3FQjuzUFNf6ON04kTMjBBZS28nt777ljOywWCxoFVfP7Xb6t26EAW3kjxT5Yko/XN69GX6/f4jtOXdBhTs9vWShFeN8Q+Zu5AcAJDjlGFHijFdlFVzS7keGhbr00YmrH+lQe9O/dSMkNfTc5HF592ayyqL1YATDBBb+7kg9nYi2H8+TvKwanXS0PiiJ/GWmPlP2v1efZlFVWKu4evjwxt5+pYH3dqH1Ve2+emFCF1zapanX5Zc+POzCe70sW20VMMhNIGb/HW1+5hLcNawt0p6/FH89ejFanb9pnDZCfHQSAHxwfS+vZXXYnsaNIdofhToRo9BwQbXYH5hKxRL26wnRuMaCpAmmicDU5O03oPVwO62YuW+JnI8eHx2Fz27ua/t/10TXvDEN6oajg91MseGhnjeQ2CAKYaEhGNPVc8BSezPYoG6Ew8igBy5pj49v6oMwkYNX7te6J6tA3hsUxsDiPKXyEAQT+2N1Qo/mtsd6iCsu7yav6s8szHvZkEcHh7CCPH+agzlFtsdiFyUjeve6nogKD8HYrh46W4rsNucL9PbnR2PdkyMQH32haSQirOay6FyDUDciDK//y3Fm19f/1R1f3dYf41IS8Mb/uc7u6rEwTiLCQjA2JQEN67kOb7avmXYeQfLe9T1dlr9lzmav21MTO2/qkOi4bhVOlfZrtO+gpYeTcluDZMBTmtbj0/XC236o0jB7rFKkftNj3l1je9w0wBN6aeWqXs0xvkcijp8rweLd2ZLf53zYxNYNd+n/seqx4diUfhZXOPVrEAQB1/ZLxptL9yOnsBwAbP3ChnVQribReW4WZ56G0eoFayx0qFms59S+StUouLu70UNTSLfmwTVNd6CY5IbUJ/YpseWOZDCKYT535A4+3tLoO5/Fnr28MyLDvB8XiQ3q4KpezRHmpr/K3LsGYmKv5lj+yDDR10XLIuOU6u03Xic81Da6BdDH+dqZYQKLdvHq5HrQgtg4bTU6o4W5aTPUwyRzozrH473re2LZw9J/vObg+p3Vj2TFI+B4oTFS06acn6OZOq1K0Smhpo/EnFv74Y6hbfxaV+3suG2a1Mc71/VEu/ho0eX8H0jg+TtMiI3C23YpBgQBfmf8VZphzkgDWjfGxiNntS6GIiLDXOM9NZpCmkaLV5u6y6MfSBaLBVf2bO59QZNxPucMbR+Hp7zMm2EWRgomAGkd9nadyHd6kzplCVYLpl6EzLMlaN9UPAiQ4vs7B+JEXilS/KhFlRNs9GnZECfySl1qLn6bdhEKy6rQ1GmorFW4EEDphWECi7uGtcHh00UYl9IMU79L1bo4/hE5o6hRiTDezZwgDesZ6wRtZF/fPkDrIujGO9f1xKM/bsf9I90P2zOaN5c6Jl5iF5wLBKEmuaA/QQUADJI56+sNA1pg6Z5T6O3jrLj/vjIFyY3qYGIvxxsrd9k7BUFAaIi+Gh8ME1jUiwzDrBt7AwCmfqdxYfzk7dygVO2F80RN713fE+m5xejdQv+dg8yKfSzEdU2MQdsm9bFg6kVaFyWgcovKtS4CORnRMR6rHhtuazoB5J2zY+uG4/Ex0mshBUF/o4EME1gYSaDuOpo3cKxSY9OD/rENXVxUuHE7a3q6JO064ZivIJiPjmv6JOGnrcc9JoqSQ8skUa3iAtfnr1eLBqjU2SgoBhY6JHbxuHd4Wzzy43a3r/uiW/MGiqyHAodV3eKMuFvMFkTOuLobpgxuhS7NXJNVuWOfEKz2YcO64ThXUulx4i8j2PT0JcgpLEf7ptE4cKpQ6+I4YGChQw1E8ur3ayV9ciEyLgYW5ImGExP7LSw0RHYHyaSGdXBJp3jUjQyzDSXd+PQlKK+yIiZKP33F7h/ZHn/uysakAS0VW2d8TBTiz3fkdM642kTjWaIZWOiQtxkKlariiwzXV4cf8s5sd7Gkj0y4emWxWPDFLf0cnosMC5WUryKQkhvVRdrzo73m3vBV8waOuY/6apxEi1cWHRKbSEiNO1U9RfQkEeMKUYasyfHlMzEI0S21ggqgJiFc6nOX2tJ9a500i4GFDhnyJEmkoroRrHwFtJ/VkrTTqF4EGp6fol3rJjFTBRYRIomnzETKNMGkc7xuiHrlKn1lHtSKuzTUZA61lSJaZ0821VG444XR2P7CaK2L4ZVaFRaD2shL9EIULJIb1dW6CKqRUwvB9O7mVpubqFrjKgtTHYVR4aFBMd5dLFe8fbriMB+zrPFm1wDYTGYa/KpJrtrRIVo3hZgqsAgWYieU6KhwzL1rIMJDLaZv0jGzDvHRWIiTWheDiHSotiWMnTfJhbvOmwPbNEaflsxnYWa3XNRK6yJQgHG4KUl1ocaCgQU5Ya4CcsdoM3iSexwdRnKVV9VMTf33oTOaloNNITqk1glF657CRKSMjDMlWLybTWLk6PftWVoXAYBBAwuLhdWHYpgQiyj4iJ3Kxr63BiUV1Q7Pzbm1n8iSZCbllVatiwDAoE0h9w1vq3UR/GJfYzF7Um/F1ntVL85eSsYx2sR5WZyDCsA1rTOZj9Z9K2oZMrDwdTimXtj3sWjTpL5i6+VoEjKSK3okal0EVcnta6WTawppqI/Gc4TU4pVGh+xrLNiBi0hcQ5FZgM2sQ1PlbkIoOP2rT5LtcU5hmWblMHxg8d71PVEnPBRfBlH7o8XNYyK6YEi7ONwxpDXevKaH1kVRldRO12KJ9chc7Cc6c55KPZAM2XlzYJvGeG/FQQDAlT2bY3z3RFuq02Bgf4LguYJInMViwbNXdNG6GKrhb5/kso9BwzXsEmDIwGJQ28b4/s6BaB1XDwCCKqgAnGspgqvsRESkDfvardBQ1lgoblDb4J1wKyTEgks6xSO/tBJtzgdHRETu9NVJpz3Slv0cIWEa3lAbNrAIdl/c0g+CILDdlMik5Pz0+7Ziqn9yHG6qZWBh+M6bYnq3aKB1ESRhUEFERFLZBxahDCwC6+vbB2hdBCIixQii+TnJbJIa1rU91vLG1JRNIfUiTfmxySCio8JQWFaldTEoQJxHmx4/V6JNQUj3YuuEY/XjwzVPhsgrLBGRDrnLvHnrnM2SlyXzadlY+w7/pmwKISIKVgdzilyeY1MI6QkDCyIiHZMUNDCuIB1hYGFS9SJCtS4CEXnAQWEUrBhYALi4QxOtixAwwzvWfNabB7fStiDkM15vyFm1lVUWpB/svAnYUn+bwexJfbAt8xz6MaFO0OrVoiFWHziNqHDeF1CN+JhIrYtAZMPAAsDork21LkLA1IkIxeC2cVoXg/zw1rU98NmaI7i2X7LWRaEAkDK56c2DWqleDiKpGFhAH8NziKSKqx+J6Zd11roYpIHKaqvo81Hh7DNF+sG6VCKiIFBZbUX/V5drXQwirxhYgJ3hiEi/aptCvtl4DOdKKl1ef4a1V6QzDCyIiHTIea6HfScLRZe7smdiIIpDJJlpA4uOTaNtj6uqOVSLiPRt89Gzos/H1AkPcEmIPDNtYLFg6kW2x80b1tGwJERE3h3JLXZ5bmKv5uy4Sbpj2lEhdSJCkT7jMlgFbeetJyLyxFN9auN6EQErB5FUpg0sgJo2zFDGFESkQ/anprLKatFlbh/aOjCFIZLB1IEFEZHebc/Mw8Ec8Y6bzWLZjEv6w8CCiEjHvt54TPT5p8Z1CnBJiKQxbedNIiI98za76egu5pmKgIILAwsioiDUpkl9rYtAJIqBBRFRkBljookTKfgwsCAi0qFQD20hb1/bM3AFIZLJp8Dio48+QuvWrREVFYU+ffpg7dq1SpeLiMjUTuaXuX2tXiT73ZN+yQ4sfvjhBzz00EN45plnsG3bNgwdOhTjxo1DRkaGGuUjIjKlPScLRJ9f8ejFAS4JkTyyA4u3334bt99+O+644w507twZ7777LpKTkzF79mw1ykdEZEp1IxxTdfdq0QBHZ16Otuy0STonK7CoqKjA1q1bMXr0aIfnR48ejfXr14u+p7y8HAUFBQ5/RETk2e6Xxjj8/3+39deoJETyyAoscnNzUV1djaZNHXskN23aFNnZ2aLvmTFjBmJjY21/ycnJvpeWiMgkLBYL1j4xAvHRkXj9/7ojJoqzmFJw8KnzpsWpt7IgCC7P1Zo+fTry8/Ntf5mZmb5skojIdJIb1cWmZ0bh2r68IaPgIatrcVxcHEJDQ11qJ3JyclxqMWpFRkYiMjLS9xISERFR0JBVYxEREYE+ffpg2bJlDs8vW7YMgwcPVrRgREREFHxkD4Z+5JFHMHnyZPTt2xeDBg3Cp59+ioyMDNxzzz1qlI+IiIiCiOzA4rrrrsOZM2fw8ssv4+TJk0hJScGiRYvQsmVLNcpHREREQcQiCIIQyA0WFBQgNjYW+fn5iImJCeSmiYiIyEdSr9+cK4SIiIgUw8CCiIiIFMPAgoiIiBTDwIKIiIgUw8CCiIiIFMPAgoiIiBTDwIKIiIgUw8CCiIiIFCM786a/avNxFRQUBHrTRERE5KPa67a3vJoBDywKCwsBAMnJnAaYiIgo2BQWFiI2Ntbt6wFP6W21WpGVlYXo6GhYLBbF1ltQUIDk5GRkZmYyVbiCuF/Vwf2qDu5XdXC/qiPY9qsgCCgsLERiYiJCQtz3pAh4jUVISAiSkpJUW39MTExQfEHBhvtVHdyv6uB+VQf3qzqCab96qqmoxc6bREREpBgGFkRERKQYwwQWkZGReOGFFxAZGal1UQyF+1Ud3K/q4H5VB/erOoy6XwPeeZOIiIiMyzA1FkRERKQ9BhZERESkGAYWREREpBgGFkRERKQYXQcWVVVVePbZZ9G6dWvUqVMHbdq0wcsvvwyr1WpbRhAEvPjii0hMTESdOnUwfPhw7N6922E95eXluP/++xEXF4d69ephwoQJOH78eKA/jmbWrFmD8ePHIzExERaLBQsWLHB4Xal9eO7cOUyePBmxsbGIjY3F5MmTkZeXp/Kn046n/VpZWYknn3wS3bp1Q7169ZCYmIibb74ZWVlZDuvgfnXl7Xi1d/fdd8NiseDdd991eJ771ZWU/bp3715MmDABsbGxiI6OxsCBA5GRkWF7nfvVkbd9WlRUhGnTpiEpKQl16tRB586dMXv2bIdljLhPdR1YvPbaa/j4448xa9Ys7N27F6+//jreeOMNfPDBB7ZlXn/9dbz99tuYNWsWNm/ejISEBFx66aW2OUkA4KGHHsL8+fMxd+5crFu3DkVFRbjiiitQXV2txccKuOLiYvTo0QOzZs0SfV2pfXjjjTciLS0NixcvxuLFi5GWlobJkyer/vm04mm/lpSUIDU1Fc899xxSU1Mxb948HDhwABMmTHBYjvvVlbfjtdaCBQvwzz//IDEx0eU17ldX3vbr4cOHMWTIEHTq1AmrVq3C9u3b8dxzzyEqKsq2DPerI2/79OGHH8bixYvxzTffYO/evXj44Ydx//3349dff7UtY8h9KujY5ZdfLtx2220Oz1199dXCTTfdJAiCIFitViEhIUGYOXOm7fWysjIhNjZW+PjjjwVBEIS8vDwhPDxcmDt3rm2ZEydOCCEhIcLixYsD8Cn0BYAwf/582/+V2od79uwRAAgbN260LbNhwwYBgLBv3z6VP5X2nPermE2bNgkAhGPHjgmCwP0qhbv9evz4caF58+bCrl27hJYtWwrvvPOO7TXuV+/E9ut1111nO7eK4X71TGyfdu3aVXj55Zcdnuvdu7fw7LPPCoJg3H2q6xqLIUOGYMWKFThw4AAAYPv27Vi3bh0uu+wyAEB6ejqys7MxevRo23siIyNx8cUXY/369QCArVu3orKy0mGZxMREpKSk2JYxM6X24YYNGxAbG4sBAwbYlhk4cCBiY2O5n8/Lz8+HxWJBgwYNAHC/+spqtWLy5Ml4/PHH0bVrV5fXuV/ls1qtWLhwITp06IAxY8YgPj4eAwYMcKja536Vb8iQIfjtt99w4sQJCIKAlStX4sCBAxgzZgwA4+5TXQcWTz75JG644QZ06tQJ4eHh6NWrFx566CHccMMNAIDs7GwAQNOmTR3e17RpU9tr2dnZiIiIQMOGDd0uY2ZK7cPs7GzEx8e7rD8+Pp77GUBZWRmeeuop3HjjjbbJhrhfffPaa68hLCwMDzzwgOjr3K/y5eTkoKioCDNnzsTYsWOxdOlSTJw4EVdffTVWr14NgPvVF++//z66dOmCpKQkREREYOzYsfjoo48wZMgQAMbdpwGf3VSOH374Ad988w2+++47dO3aFWlpaXjooYeQmJiIKVOm2JZznn5dEASvU7JLWcZMlNiHYstzP9d05Lz++uthtVrx0UcfeV2e+9W9rVu34r333kNqaqrsz8/96l5th/grr7wSDz/8MACgZ8+eWL9+PT7++GNcfPHFbt/L/ere+++/j40bN+K3335Dy5YtsWbNGtx3331o1qwZRo0a5fZ9wb5PdV1j8fjjj+Opp57C9ddfj27dumHy5Ml4+OGHMWPGDABAQkICALhEbTk5ObY78ISEBFRUVODcuXNulzEzpfZhQkICTp065bL+06dPm3o/V1ZW4tprr0V6ejqWLVvmMDUy96t8a9euRU5ODlq0aIGwsDCEhYXh2LFjePTRR9GqVSsA3K++iIuLQ1hYGLp06eLwfOfOnW2jQrhf5SktLcXTTz+Nt99+G+PHj0f37t0xbdo0XHfddXjzzTcBGHef6jqwKCkpQUiIYxFDQ0Nt0XXr1q2RkJCAZcuW2V6vqKjA6tWrMXjwYABAnz59EB4e7rDMyZMnsWvXLtsyZqbUPhw0aBDy8/OxadMm2zL//PMP8vPzTbufa4OKgwcPYvny5WjcuLHD69yv8k2ePBk7duxAWlqa7S8xMRGPP/44lixZAoD71RcRERHo168f9u/f7/D8gQMH0LJlSwDcr3JVVlaisrLS4zXMsPtUix6jUk2ZMkVo3ry58Mcffwjp6enCvHnzhLi4OOGJJ56wLTNz5kwhNjZWmDdvnrBz507hhhtuEJo1ayYUFBTYlrnnnnuEpKQkYfny5UJqaqowcuRIoUePHkJVVZUWHyvgCgsLhW3btgnbtm0TAAhvv/22sG3bNtvoBKX24dixY4Xu3bsLGzZsEDZs2CB069ZNuOKKKwL+eQPF036trKwUJkyYICQlJQlpaWnCyZMnbX/l5eW2dXC/uvJ2vDpzHhUiCNyvYrzt13nz5gnh4eHCp59+Khw8eFD44IMPhNDQUGHt2rW2dXC/OvK2Ty+++GKha9euwsqVK4UjR44Ic+bMEaKiooSPPvrItg4j7lNdBxYFBQXCgw8+KLRo0UKIiooS2rRpIzzzzDMOJ2ar1Sq88MILQkJCghAZGSkMGzZM2Llzp8N6SktLhWnTpgmNGjUS6tSpI1xxxRVCRkZGoD+OZlauXCkAcPmbMmWKIAjK7cMzZ84IkyZNEqKjo4Xo6Ghh0qRJwrlz5wL0KQPP035NT08XfQ2AsHLlSts6uF9deTtenYkFFtyvrqTs1y+++EJo166dEBUVJfTo0UNYsGCBwzq4Xx1526cnT54UbrnlFiExMVGIiooSOnbsKLz11luC1Wq1rcOI+5TTphMREZFidN3HgoiIiIILAwsiIiJSDAMLIiIiUgwDCyIiIlIMAwsiIiJSDAMLIiIiUgwDCyIiIlIMAwsiIiJSDAMLIiIiUgwDCyIiIlIMAwsiIiJSDAMLIiIiUsz/A5OPf0gINO2mAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -304,32 +287,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The Created luminosity was  4.478638412507001e+34\n",
-      "The emitted luminosity was  3.90951228511005e+34\n",
       "The Created luminosity was  4.478638412507001e+34\n",
       "The emitted luminosity was  3.90951228511005e+34\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ]
@@ -340,8 +311,15 @@
    ],
    "source": [
     "import plot_tot\n",
-    "plot_tot.doit(fname, smooth)"
+    "plot_tot.doit(fname, smooth=10)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 2b1c47460f6c6472815120bb93686f9a95edffb6 Mon Sep 17 00:00:00 2001
From: jhmatthews <matthews@ast.cam.ac.uk>
Date: Fri, 25 Oct 2024 17:03:33 +0100
Subject: [PATCH 12/22] minor tweaks

---
 docs/sphinx/source/plotting.rst                 | 2 +-
 docs/sphinx/source/plotting/plot_spectrum.ipynb | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/sphinx/source/plotting.rst b/docs/sphinx/source/plotting.rst
index a1ae1821b..2891a1f66 100644
--- a/docs/sphinx/source/plotting.rst
+++ b/docs/sphinx/source/plotting.rst
@@ -4,7 +4,7 @@ Plotting \& Processing Outputs
 SIROCCO produces a large number of files in both binary and ascii format. Tools exist to examine the binary files.
 
 PySi 
-==============
+#####################################
 PySi, short for PySIROCCO, is a python package designed to interface with the SIROCCO code.
 It can be installed from its `own Github repository <https://github.com/sirocco-rt/pysi>`_ 
 under the sirocco-rt organisation. We recommend installing this package for reading code outputs.:
diff --git a/docs/sphinx/source/plotting/plot_spectrum.ipynb b/docs/sphinx/source/plotting/plot_spectrum.ipynb
index 87e251f00..cd8cb958e 100644
--- a/docs/sphinx/source/plotting/plot_spectrum.ipynb
+++ b/docs/sphinx/source/plotting/plot_spectrum.ipynb
@@ -25,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Plotting spectra with pysi\n",
+    "## Plotting spectra with pysi \n",
     "\n",
     "First we will set up the basic modules to import, then make a few different style plots. "
    ]
@@ -166,7 +166,7 @@
    "source": [
     "Some important ones are:\n",
     "\n",
-    "* **Created:** total spectrum of all of the photons paakets as created, that is before having been translated through the wind\n",
+    "* **Created:** total spectrum of all of the photons packets as created, that is before having been translated through the wind\n",
     "* **WCreated:** spectrum of the photons that are created in the wind before translation\n",
     "* **Emitted:** is the emergent spectrun after the photons have been translated through the wind\n",
     "* **CenSrc:** is the emergent spectrum from photons bundles originating on the Star or BL,\n",
@@ -254,7 +254,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x14024da50>]"
+       "[<matplotlib.lines.Line2D at 0x146ba43a0>]"
       ]
      },
      "execution_count": 8,

From 61de6db73712339e981421886341f261f33128a6 Mon Sep 17 00:00:00 2001
From: jhmatthews <matthews@ast.cam.ac.uk>
Date: Fri, 25 Oct 2024 17:09:30 +0100
Subject: [PATCH 13/22] minor tweaks

---
 docs/sphinx/source/plotting.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/sphinx/source/plotting.rst b/docs/sphinx/source/plotting.rst
index 2891a1f66..38b77b5ad 100644
--- a/docs/sphinx/source/plotting.rst
+++ b/docs/sphinx/source/plotting.rst
@@ -4,7 +4,7 @@ Plotting \& Processing Outputs
 SIROCCO produces a large number of files in both binary and ascii format. Tools exist to examine the binary files.
 
 PySi 
-#####################################
+====================
 PySi, short for PySIROCCO, is a python package designed to interface with the SIROCCO code.
 It can be installed from its `own Github repository <https://github.com/sirocco-rt/pysi>`_ 
 under the sirocco-rt organisation. We recommend installing this package for reading code outputs.:

From c9caa1b64a37adb3cfb06f76a7f9397e820f1338 Mon Sep 17 00:00:00 2001
From: jhmatthews <matthews@ast.cam.ac.uk>
Date: Mon, 28 Oct 2024 09:13:10 +0000
Subject: [PATCH 14/22] fix sirocco logo

---
 docs/sphinx/source/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/sphinx/source/index.rst b/docs/sphinx/source/index.rst
index 39bc0ed3a..152ce8300 100644
--- a/docs/sphinx/source/index.rst
+++ b/docs/sphinx/source/index.rst
@@ -7,7 +7,7 @@
 SIROCCO - Simulating Ionization and Radiation in Outflows Created by Compact Objects
 ####################################################################################
 
-figure:: ../images/logo.png
+figure:: images/logo.png
 
 SIROCCO is a Monte-Carlo radiative transfer code designed to simulate the spectrum of biconical (or spherical)
 winds in disk systems.  It was formerly known as Python, and originally written by

From fa7eb6211a483ad16fc8a0af82ddcfd4b0ea24ea Mon Sep 17 00:00:00 2001
From: jhmatthews <matthews@ast.cam.ac.uk>
Date: Mon, 28 Oct 2024 09:19:53 +0000
Subject: [PATCH 15/22] logo fix

---
 docs/sphinx/source/index.rst | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/sphinx/source/index.rst b/docs/sphinx/source/index.rst
index 152ce8300..12a6e4c6c 100644
--- a/docs/sphinx/source/index.rst
+++ b/docs/sphinx/source/index.rst
@@ -7,7 +7,8 @@
 SIROCCO - Simulating Ionization and Radiation in Outflows Created by Compact Objects
 ####################################################################################
 
-figure:: images/logo.png
+.. figure:: images/logo.png
+    :width: 300px
 
 SIROCCO is a Monte-Carlo radiative transfer code designed to simulate the spectrum of biconical (or spherical)
 winds in disk systems.  It was formerly known as Python, and originally written by

From b618ede30701fc2b97b39a1a3013b410b93c5e95 Mon Sep 17 00:00:00 2001
From: jhmatthews <matthews@ast.cam.ac.uk>
Date: Mon, 28 Oct 2024 09:51:58 +0000
Subject: [PATCH 16/22] changed unit tests Makefile and error tolerance
 variable

---
 source/tests/Makefile                 |  34 ++---
 source/tests/tests/test_define_wind.c | 190 +++++++++++++-------------
 source/tests/unit_test.h              |   4 +-
 3 files changed, 108 insertions(+), 120 deletions(-)

diff --git a/source/tests/Makefile b/source/tests/Makefile
index 8ae9f3464..1587e73f5 100644
--- a/source/tests/Makefile
+++ b/source/tests/Makefile
@@ -1,3 +1,13 @@
+include $(SIROCCO)/source/Makefile 
+# 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. 
+# This is done by including the sirocco_source from the main Makefile 
+# note that for some reason, get_models.c cannot be included in the sources
+# in that Makefile, so we have to add it in
+SIROCCO_SOURCES = $(sirocco_source) get_models.c
+
+
 C = gcc
 NVCC =
 MATOM_VER = 1
@@ -11,30 +21,6 @@ CU_FLAGS = -O3
 INCLUDES =
 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
-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  \
-	density.c diag.c dielectronic.c direct_ion.c disk.c disk_init.c  \
-	disk_photon_gen.c emission.c estimators_macro.c estimators_simple.c  \
-	extract.c frame.c gradv.c gridwind.c homologous.c hydro_import.c import.c  \
-	import_calloc.c import_cylindrical.c import_rtheta.c import_spherical.c  \
-	ionization.c knigge.c levels.c lines.c macro_accelerate.c macro_gen_f.c  \
-	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  \
-	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  \
-	spherical.c stellar_wind.c sv.c synonyms.c time.c trans_phot.c  \
-	vvector.c walls.c wind.c wind2d.c wind_sum.c wind_updates2d.c wind_util.c  \
-	windsave.c windsave2table_sub.c xlog.c xtest.c zeta.c get_models.c \
-	rdpar_init.c define_wind.c communicate_wind.c communicate_plasma.c \
-	communicate_macro.c communicate_spectra.c
 
 # This is sources to be compiled with NVCC
 CUDA_SOURCES = \
diff --git a/source/tests/tests/test_define_wind.c b/source/tests/tests/test_define_wind.c
index 3ce15a65e..1bd6e5383 100644
--- a/source/tests/tests/test_define_wind.c
+++ b/source/tests/tests/test_define_wind.c
@@ -111,10 +111,10 @@ test_sv_agn_macro_wind (void)
     plasma_cell = &plasmamain[wind_cell->nplasma];
 
     /* cell positions */
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->x[0], x, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->x[2], z, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->xcen[0], xcen, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->xcen[2], zcen, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->x[0], x, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->x[2], z, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->xcen[0], xcen, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->xcen[2], zcen, TEST_TOLERANCE);
     CU_ASSERT_EQUAL_FATAL (wind_cell->inwind, inwind);
 
     /* The default behaviour of Python's output tools (e.g. windsave2table) is
@@ -126,34 +126,34 @@ test_sv_agn_macro_wind (void)
     }
 
     /* velocities */
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[0], v_x, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[1], v_y, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[2], v_z, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[0], v_x, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[1], v_y, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[2], v_z, TEST_TOLERANCE);
     /* velocity gradients */
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][0], dv_x_dx, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][1], dv_x_dy, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][2], dv_x_dz, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][0], dv_y_dx, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][1], dv_y_dy, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][2], dv_y_dz, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][0], dv_z_dx, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][1], dv_z_dy, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][2], dv_z_dz, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->div_v, div_v, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->dvds_max, dvds_max, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->xgamma, gamma, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][0], dv_x_dx, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][1], dv_x_dy, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][2], dv_x_dz, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][0], dv_y_dx, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][1], dv_y_dy, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][2], dv_y_dz, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][0], dv_z_dx, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][1], dv_z_dy, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][2], dv_z_dz, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->div_v, div_v, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->dvds_max, dvds_max, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->xgamma, gamma, TEST_TOLERANCE);
 
     /* Some things (plasma properties) are stored in plasma cells */
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->rho, rho, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->ne, ne, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->t_e, t_e, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->t_r, t_r, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->rho, rho, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->ne, ne, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->t_e, t_e, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->t_r, t_r, TEST_TOLERANCE);
 
     /* Ion abundances are tested in their number density relative to Hydrogen.
      * This is the default output option in windsave2table */
     const double n_h = rho2nh * plasma_cell->rho;
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->density[0] / (n_h * ele[0].abun), h1, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->density[8] / (n_h * ele[2].abun), c4, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->density[0] / (n_h * ele[0].abun), h1, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->density[8] / (n_h * ele[2].abun), c4, TEST_TOLERANCE);
   }
 
   fclose (fp);
@@ -243,10 +243,10 @@ test_sv_cv_wind (void)
     plasma_cell = &plasmamain[wind_cell->nplasma];
 
     /* cell positions */
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->x[0], x, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->x[2], z, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->xcen[0], xcen, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->xcen[2], zcen, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->x[0], x, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->x[2], z, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->xcen[0], xcen, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->xcen[2], zcen, TEST_TOLERANCE);
     CU_ASSERT_EQUAL_FATAL (wind_cell->inwind, inwind);
 
     /* The default behaviour of Python's output tools (e.g. windsave2table) is
@@ -258,34 +258,34 @@ test_sv_cv_wind (void)
     }
 
     /* velocities */
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[0], v_x, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[1], v_y, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[2], v_z, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[0], v_x, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[1], v_y, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[2], v_z, TEST_TOLERANCE);
     /* velocity gradients */
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][0], dv_x_dx, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][1], dv_x_dy, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][2], dv_x_dz, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][0], dv_y_dx, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][1], dv_y_dy, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][2], dv_y_dz, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][0], dv_z_dx, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][1], dv_z_dy, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][2], dv_z_dz, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->div_v, div_v, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->dvds_max, dvds_max, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->xgamma, gamma, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][0], dv_x_dx, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][1], dv_x_dy, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][2], dv_x_dz, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][0], dv_y_dx, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][1], dv_y_dy, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][2], dv_y_dz, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][0], dv_z_dx, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][1], dv_z_dy, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][2], dv_z_dz, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->div_v, div_v, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->dvds_max, dvds_max, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->xgamma, gamma, TEST_TOLERANCE);
 
     /* Some things (plasma properties) are stored in plasma cells */
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->rho, rho, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->ne, ne, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->t_e, t_e, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->t_r, t_r, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->rho, rho, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->ne, ne, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->t_e, t_e, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->t_r, t_r, TEST_TOLERANCE);
 
     /* Ion abundances are tested in their number density relative to Hydrogen.
      * This is the default output option in windsave2table */
     const double n_h = rho2nh * plasma_cell->rho;
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->density[0] / (n_h * ele[0].abun), h1, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->density[8] / (n_h * ele[2].abun), c4, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->density[0] / (n_h * ele[0].abun), h1, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->density[8] / (n_h * ele[2].abun), c4, TEST_TOLERANCE);
   }
 
   /* For the CV model, we want to save the wind_save to use in another test */
@@ -379,8 +379,8 @@ test_shell_wind (void)
     plasma_cell = &plasmamain[wind_cell->nplasma];
 
     /* cell positions */
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->r, r, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->rcen, rcen, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->r, r, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->rcen, rcen, TEST_TOLERANCE);
     CU_ASSERT_EQUAL_FATAL (wind_cell->inwind, inwind);
 
     /* The default behaviour of Python's output tools (e.g. windsave2table) is
@@ -392,33 +392,33 @@ test_shell_wind (void)
     }
 
     /* velocities */
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[0], v_x, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[1], v_y, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[2], v_z, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[0], v_x, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[1], v_y, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[2], v_z, TEST_TOLERANCE);
     /* velocity gradients */
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][0], dv_x_dx, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][1], dv_x_dy, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][2], dv_x_dz, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][0], dv_y_dx, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][1], dv_y_dy, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][2], dv_y_dz, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][0], dv_z_dx, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][1], dv_z_dy, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][2], dv_z_dz, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->div_v, div_v, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->dvds_max, dvds_max, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->xgamma, gamma, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][0], dv_x_dx, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][1], dv_x_dy, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][2], dv_x_dz, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][0], dv_y_dx, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][1], dv_y_dy, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][2], dv_y_dz, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][0], dv_z_dx, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][1], dv_z_dy, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][2], dv_z_dz, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->div_v, div_v, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->dvds_max, dvds_max, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->xgamma, gamma, TEST_TOLERANCE);
 
     /* Some things (plasma properties) are stored in plasma cells */
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->rho, rho, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->ne, ne, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->t_e, t_e, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->t_r, t_r, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->rho, rho, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->ne, ne, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->t_e, t_e, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->t_r, t_r, TEST_TOLERANCE);
 
     /* Ion abundances are tested in their number density relative to Hydrogen.
      * This is the default output option in windsave2table */
     const double n_h = rho2nh * plasma_cell->rho;
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->density[0] / (n_h * ele[0].abun), h1, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->density[0] / (n_h * ele[0].abun), h1, TEST_TOLERANCE);
   }
 
   fclose (fp);
@@ -503,8 +503,8 @@ test_spherical_star_wind (void)
     plasma_cell = &plasmamain[wind_cell->nplasma];
 
     /* cell positions */
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->r, r, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->rcen, rcen, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->r, r, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->rcen, rcen, TEST_TOLERANCE);
     CU_ASSERT_EQUAL_FATAL (wind_cell->inwind, inwind);
 
     /* The default behaviour of Python's output tools (e.g. windsave2table) is
@@ -516,34 +516,34 @@ test_spherical_star_wind (void)
     }
 
     /* velocities */
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[0], v_x, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[1], v_y, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[2], v_z, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[0], v_x, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[1], v_y, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v[2], v_z, TEST_TOLERANCE);
     /* velocity gradients */
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][0], dv_x_dx, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][1], dv_x_dy, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][2], dv_x_dz, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][0], dv_y_dx, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][1], dv_y_dy, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][2], dv_y_dz, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][0], dv_z_dx, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][1], dv_z_dy, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][2], dv_z_dz, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->div_v, div_v, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->dvds_max, dvds_max, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->xgamma, gamma, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][0], dv_x_dx, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][1], dv_x_dy, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[0][2], dv_x_dz, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][0], dv_y_dx, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][1], dv_y_dy, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[1][2], dv_y_dz, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][0], dv_z_dx, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][1], dv_z_dy, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->v_grad[2][2], dv_z_dz, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->div_v, div_v, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->dvds_max, dvds_max, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (wind_cell->xgamma, gamma, TEST_TOLERANCE);
 
     /* Some things (plasma properties) are stored in plasma cells */
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->rho, rho, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->ne, ne, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->t_e, t_e, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->t_r, t_r, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->rho, rho, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->ne, ne, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->t_e, t_e, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->t_r, t_r, TEST_TOLERANCE);
 
     /* Ion abundances are tested in their number density relative to Hydrogen.
      * This is the default output option in windsave2table */
     const double n_h = rho2nh * plasma_cell->rho;
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->density[0] / (n_h * ele[0].abun), h1, FRACTIONAL_ERROR);
-    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->density[8] / (n_h * ele[2].abun), c4, FRACTIONAL_ERROR);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->density[0] / (n_h * ele[0].abun), h1, TEST_TOLERANCE);
+    CU_ASSERT_DOUBLE_FRACTIONAL_EQUAL_FATAL (plasma_cell->density[8] / (n_h * ele[2].abun), c4, TEST_TOLERANCE);
   }
 
   fclose (fp);
diff --git a/source/tests/unit_test.h b/source/tests/unit_test.h
index b382db75d..1c4e0290f 100644
--- a/source/tests/unit_test.h
+++ b/source/tests/unit_test.h
@@ -1,6 +1,6 @@
 /** ********************************************************************************************************************
  *
- *  @file load_model.c
+ *  @file unit_test.h
  *  @author Edward J. Parkinson (e.parkinson@soton.ac.uk)
  *  @date Jan 2024
  *
@@ -8,6 +8,8 @@
  *
  * ****************************************************************************************************************** */
 
+#define TEST_TOLERANCE 0.01
+
 #ifndef SIROCCO_UNIT_TEST_H
 #define SIROCCO_UNIT_TEST_H
 

From 97c45b38fb9165a0addf90fde3b6188f286fb537 Mon Sep 17 00:00:00 2001
From: jhmatthews <matthews@ast.cam.ac.uk>
Date: Mon, 28 Oct 2024 10:15:17 +0000
Subject: [PATCH 17/22] tweaks to plotting path

---
 .../sphinx/source/py_progs/{plotting.rst => plotting_scripts.rst} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename docs/sphinx/source/py_progs/{plotting.rst => plotting_scripts.rst} (100%)

diff --git a/docs/sphinx/source/py_progs/plotting.rst b/docs/sphinx/source/py_progs/plotting_scripts.rst
similarity index 100%
rename from docs/sphinx/source/py_progs/plotting.rst
rename to docs/sphinx/source/py_progs/plotting_scripts.rst

From ea4fabcfcebcde974a3f68bddd348bbce6e10d69 Mon Sep 17 00:00:00 2001
From: jhmatthews <matthews@ast.cam.ac.uk>
Date: Mon, 28 Oct 2024 16:59:29 +0000
Subject: [PATCH 18/22] added better tutorials using PySi

---
 docs/sphinx/source/plotting.rst               |   5 +-
 docs/sphinx/source/plotting/Convergence.ipynb | 243 +++++++++++++
 .../source/plotting/plot_spectrum.ipynb       |  53 ++-
 docs/sphinx/source/plotting/plot_wind.ipynb   | 334 +++++++++++-------
 4 files changed, 499 insertions(+), 136 deletions(-)
 create mode 100644 docs/sphinx/source/plotting/Convergence.ipynb

diff --git a/docs/sphinx/source/plotting.rst b/docs/sphinx/source/plotting.rst
index 38b77b5ad..160bc9554 100644
--- a/docs/sphinx/source/plotting.rst
+++ b/docs/sphinx/source/plotting.rst
@@ -10,7 +10,7 @@ It can be installed from its `own Github repository <https://github.com/sirocco-
 under the sirocco-rt organisation. We recommend installing this package for reading code outputs.:
 isntructions to do so can be found in the README of that repository.
 
-.. admonition :: Warning to users
+.. admonition :: Warning to users! 
 
 These tutorials are not complete, and our approach to interfacing 
 with the code has not been uniform across the collaboration. Nevertheless, these 
@@ -22,4 +22,5 @@ with more detail on python scripts and packages provided in :ref:`the API docume
    
 .. nbgallery::
    plotting/plot_spectrum
-   plotting/plot_wind
\ No newline at end of file
+   plotting/plot_wind
+   plotting/Convergence
\ No newline at end of file
diff --git a/docs/sphinx/source/plotting/Convergence.ipynb b/docs/sphinx/source/plotting/Convergence.ipynb
new file mode 100644
index 000000000..3ebca9846
--- /dev/null
+++ b/docs/sphinx/source/plotting/Convergence.ipynb
@@ -0,0 +1,243 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b2c4c22a-2b37-4822-8460-b458b0c6a957",
+   "metadata": {},
+   "source": [
+    "# Thermal Balance and Convergence\n",
+    "\n",
+    "As described under [Evaluation](../output/evaluation.rst) and in the release paper, basic information about convergence is printed out to the user. This information can also be accessed through the same files used to plot wind properties, and inspected on a cell by cell basis. this notebook describes how to assess convergence in a bit more detail and look at the heating and cooling processes in the wind. As with [Plotting Wind Properties](plot_wind), we will assume we have run the ```cv_test``` model and run ```windsave2table``` on this model to produce the basic output files. We then start by setting up the basic ```Wind``` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "06453c38-6479-4abc-9d2b-406cb7a63a1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pysi \n",
+    "from pysi.sim import model\n",
+    "root = \"cv_standard\"\n",
+    "directory = \"cv_test/\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed562fe6-60ab-42b5-91f7-9b58f2264f5a",
+   "metadata": {},
+   "source": [
+    "## Basic convergence statistics \n",
+    "A function ```model.model_convergence``` using the ```.diag.``` file to get basic convergence information, either total convergence or on a cycle by cycle basis. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0eab09cb-e124-4767-acd3-a967ff65b19b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.803\n"
+     ]
+    }
+   ],
+   "source": [
+    "conv = model.model_convergence(root, fp=directory)\n",
+    "print (conv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7bdf9b9b-665a-4803-b46b-5a77f0fd163e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.139, 0.015, 0.066, 0.277, 0.467, 0.46, 0.54, 0.599, 0.62, 0.628, 0.657, 0.562, 0.657, 0.708, 0.81, 0.81, 0.81, 0.73, 0.62, 0.803]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pysi.sim import model\n",
+    "conv = model.model_convergence(root, fp=directory, return_per_cycle=True)\n",
+    "print (conv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4af2557f-221e-4f12-8f93-499dc4b8dff5",
+   "metadata": {},
+   "source": [
+    "We can also plot this trivially, or using an inbuilt function as follows: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d1a097e1-193f-4280-bf69-161100046167",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = model.plot_model_convergence(root, fp=directory, display=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a2b6ad8-d719-43f9-8860-05613dced539",
+   "metadata": {},
+   "source": [
+    "## More detailed convergence\n",
+    "We can investigate the final convergence in more detail using the ```Wind``` class, which contains information on a cell-by-cell basis. Models with ```converge == 0``` are converged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "823376ab-46ab-459e-86f8-30d65d0f69b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Version: UNKNOWN\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pysi.wind import Wind\n",
+    "wind = Wind(root = root, directory = directory)\n",
+    "fig, ax = wind.plot_parameter(\"converge\", vmin=0, vmax=3, log_p = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0501f83-0888-4a0d-a119-2782ff85f9e3",
+   "metadata": {},
+   "source": [
+    "Sometimes it's useful to see how the convergence depends on temperature and photon number, which we do using the wind class directly. In this case it's not particularly useful, because the model just needs to run for a bit longer with a few more photons, but in some cases it shows clearly where the trouble is. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f0dfcc31-4f85-40b2-ab68-6403ab0c1d9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(wind[\"t_e\"], wind[\"ntot\"], c=wind[\"converge\"])\n",
+    "plt.loglog()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0bbf783-7b06-4e93-9b4e-1ecacd0a5ee8",
+   "metadata": {},
+   "source": [
+    "## Showing errors\n",
+    "Finally we can use the ```model_errors``` function to print out the error summary. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3ec0fab5-5b11-4c81-af49-66271fdb760b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total errors reported from 1 of 1 processes for cv_test/cv_standard, which exited successfully:\n",
+      "       7 -- getatomic_data: line input f odd (may be OK if Macro): %s\n",
+      "       2 -- get_atomicdata: (Case default) Could not interpret line %d in file %s: %s %d\n",
+      "       1 -- Ignored %d inner shell cross sections because no matching yields\n",
+      "       1 -- get_wind_params: zdom[ndom].rmax = 0 for wind type %d\n",
+      "       1 -- zero_find: function not bracketed x_lo %e -> %e, x_hi %e -> %e\n",
+      "       1 -- roche_width : zero_find failure x=%6.2e rho %.2e phi(l2) %.2e\n",
+      "       1 -- wind2d: Cell %3d (%2d,%2d) in domain %d has %d corners in wind, but zero volume\n",
+      "       1 -- check_grid: velocity changes by >1,000 km/s in %i cells\n",
+      "       1 -- check_grid: optical depth may be high in %i\n",
+      "       1 -- check_grid: some cells have large changes. Consider modifying zlog_scale or grid dims\n"
+     ]
+    }
+   ],
+   "source": [
+    "_ = model.model_errors(root=root, directory=directory, print_errors=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/sphinx/source/plotting/plot_spectrum.ipynb b/docs/sphinx/source/plotting/plot_spectrum.ipynb
index cd8cb958e..5d830a3e9 100644
--- a/docs/sphinx/source/plotting/plot_spectrum.ipynb
+++ b/docs/sphinx/source/plotting/plot_spectrum.ipynb
@@ -140,13 +140,41 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the spec_tot spectrum, again most of the keys correspond to columns in the spectrum. "
+    "This spectrum is pretty noisy, so we can smooth it. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "s.smooth_all_spectra(boxcar_width=20)\n",
+    "_ = s.plot(\"Emitted\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the spec_tot spectrum, again most of the keys correspond to columns in the spectrum. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -181,12 +209,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1200x500 with 1 Axes>"
       ]
@@ -197,7 +225,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots(1,1, figsize=(12, 5))\n",
-    "for i in [\"Emitted\", \"Created\"]:\n",
+    "for i in [\"Emitted\", \"Created\", \"HitSurf\", \"Wind\", \"Disk\"]:\n",
     "    s.plot(i, fig=fig, ax=ax, ax_scale=\"loglog\")"
    ]
   },
@@ -211,7 +239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -248,16 +276,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x146ba43a0>]"
+       "[<matplotlib.lines.Line2D at 0x1748bd1b0>]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -287,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -313,13 +341,6 @@
     "import plot_tot\n",
     "plot_tot.doit(fname, smooth=10)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/sphinx/source/plotting/plot_wind.ipynb b/docs/sphinx/source/plotting/plot_wind.ipynb
index 7562aeba8..a2816022e 100644
--- a/docs/sphinx/source/plotting/plot_wind.ipynb
+++ b/docs/sphinx/source/plotting/plot_wind.ipynb
@@ -16,15 +16,18 @@
     "\n",
     "    windsave2table cv_standard\n",
     "\n",
-    "which will create a series of ascii files containing key variables in the wind cells. We will use these ascii files for our plots. \n",
-    "\n",
-    "## Make A Basic Quick Look Wind Plot\n",
+    "which will create a series of ascii files containing key variables in the wind cells. We will use these ascii files for our plots. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Making wind plots using PySi\n",
     "\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",
+    "We will start by demonstrating how to use PySi to plot data from the wind files. In particular we will plot some key variables in the wind, followed by some ion fractions. \n",
     "\n",
-    "    plot_wind.py cv_standard t_e\n",
-    "    \n",
-    "where the second argument is the variable to plot. Alternatively, it can be run from within a python script by doing (where we are now assuming you are running this code from one directory above cv_test):"
+    "PySi works by setting up a ```Wind``` class which reads in and stores all the useful data and attributes from the model. This class can be used to inspect data or to plot it directly. "
    ]
   },
   {
@@ -33,42 +36,28 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "'cv_test/cv_standard_log_t_e.png'"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Version: UNKNOWN\n"
+     ]
     }
    ],
    "source": [
-    "%matplotlib inline\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\")"
+    "import pysi \n",
+    "from pysi.wind import Wind\n",
+    "root = \"cv_standard\"\n",
+    "directory = \"cv_test/\"\n",
+    "wind = Wind(root = root, directory = directory)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## More detailed/customisable plots\n",
-    "\n",
-    "You may, however, wish to get more direct access to the data, which can be done easily by reading in the ```cv_standard.master.txt``` file, for example using ```astropy```. In the next code block, we read in the data file and print out the columns."
+    "The data can be plotted easily using the ```plot_parameter``` method. This method returns Figure and Axes objects so the plot can be modified easily. "
    ]
   },
   {
@@ -77,39 +66,67 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "['x', 'z', 'xcen', 'zcen', 'i', 'j', 'inwind', 'converge', 'v_x', 'v_y', 'v_z', 'vol', 'rho', 'ne', 't_e', 't_r', 'h1', 'he2', 'c4', 'n5', 'o6', 'dmo_dt_x', 'dmo_dt_y', 'dmo_dt_z', 'ip', 'xi', 'ntot', 'nrad', 'nioniz']\n"
+      "/Users/matthewsj/.virtualenvs/sirocco/lib/python3.10/site-packages/pysi/wind/model/plot.py:484: RuntimeWarning: divide by zero encountered in log10\n",
+      "  parameter_points = numpy.log10(parameter_points)\n"
      ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import astropy.io.ascii as io\n",
-    "\n",
-    "data = io.read(fname)\n",
-    "\n",
-    "print (data.colnames)"
+    "fig, ax = wind.plot_parameter(\"t_e\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot = wind.plot_parameter(\"t_e\", \"linlin\")\n",
+    "plt.xlim(0,2e11)\n",
+    "_ = plt.ylim(0,2e11)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " The ```py_plot_util``` script in py_progs comes with a handy guide to the main columns in the .master.txt file and returns a dictionary containing the description for all variables."
+    "The ```get_windsave_descriptions``` method in the wind class comes provides a handy guide to the main columns in the .master.txt file, which can be plotted using the above methods, or accessed directly as, e.g., ```wind[\"ne\"]```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "no description for column x\n",
+      "x          --  left-hand lower cell corner x-coordinate, cm\n",
       "z          --  left-hand lower cell corner z-coordinate, cm\n",
       "xcen       --  cell centre x-coordinate, cm\n",
       "zcen       --  cell centre z-coordinate, cm\n",
@@ -137,40 +154,34 @@
       "xi         --  xi ionization parameter\n",
       "ntot       --  total photons passing through cell\n",
       "nrad       --  total wind photons produced in cell\n",
-      "nioniz     --  total ionizing photons passing through cell\n"
+      "nioniz     --  total ionizing photons passing through cell\n",
+      "None\n"
      ]
     }
    ],
    "source": [
-    "import py_plot_util as util\n",
-    "descr = util.get_windsave_descriptions(data)"
+    "print (wind.get_windsave_descriptions())"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```py_plot_util``` also contains some routines for reshaping and masking arrays and so on. One of the most useful for plotting is the ```wind_to_masked``` function which turns the raw 1D flattened data into a masked 2D array with the right shape which can be easily used with ```pcolormesh``` and so on. Here's an example plot of the electron density in the model."
+    "We can also use the multiplot command to generate multiple plots for specified wind parameters. \n",
+    "This method creates subplots to visualize the specified wind parameters using either 1D or 2D \n",
+    "representation based on the coordinate system."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/matthewsj/.mpi_temp/ipykernel_31972/816639112.py:2: RuntimeWarning: divide by zero encountered in log10\n",
-      "  plt.pcolormesh(x,z, np.log10(ne))\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
+       "<Figure size 800x600 with 8 Axes>"
       ]
      },
      "metadata": {},
@@ -178,67 +189,53 @@
     }
    ],
    "source": [
-    "x, z, ne, inwind = util.wind_to_masked(data, value_string=\"ne\", return_inwind=True)\n",
-    "plt.pcolormesh(x,z, np.log10(ne))\n",
-    "plt.loglog()\n",
-    "plt.xlim(1e9,1e12)\n",
-    "plt.ylim(1e8,1e12)\n",
-    "cbar = plt.colorbar()"
+    "fig, ax = wind.multiplot( (\"ne\", \"t_e\", \"v_z\", \"ntot\") , \"loglog\", nrows = 2, ncols = 2)\n",
+    "fig.tight_layout(pad=0.05)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This procedure can be used to plot any of the variables in the masterfile and is a good starting point for delving into the properties of the wind. \n",
-    "\n",
-    "To make a simple multi-panel plot of wind properties, you can use some of the routines in ```py_plot_output```. The example below plots all the variables passed in an array and saves the file as ```cv_standard_wind.png```"
+    "We can also check all the possible things to plot, some of which are intuitive and some of which aren't! "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/matthewsj/opt/anaconda3/lib/python3.9/site-packages/astropy/table/table.py:3474: FutureWarning: elementwise == comparison failed and returning scalar instead; this will raise an error or perform elementwise comparison in the future.\n",
-      "  result = self.as_array() == other\n",
-      "/Users/matthewsj/winds/python/py_progs/py_plot_output.py:301: RuntimeWarning: divide by zero encountered in log10\n",
-      "  p.pcolormesh(x,z,np.log10(v))\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "7363719978102.189\n",
-      "12772.992700729927\n",
-      "37305.83941605839\n",
-      "1214.8115635036497\n",
-      "11854.087591240876\n",
-      "145555532.84671533\n",
-      "5.267621167883212e-06\n",
-      "0.41242660718248175\n"
+      "dict_keys(['x', 'z', 'xcen', 'zcen', 'i', 'j', 'inwind', 'converge', 'v_x', 'v_y', 'v_z', 'vol', 'rho', 'ne', 't_e', 't_r', 'h1', 'he2', 'c4', 'n5', 'o6', 'dmo_dt_x', 'dmo_dt_y', 'dmo_dt_z', 'ip', 'xi', 'ntot', 'nrad', 'nioniz', 'w', 'ave_freq', 'J', 'J_direct', 'J_scatt', 'lum_tot', 'heat_tot', 'heat_comp', 'heat_line', 'heat_ff', 'heat_phot', 'heat_auge', 'cool_tot', 'cool_comp', 'lum_lines', 'cool_dr', 'lum_ff', 'lum_rr', 'cool_rr', 'cool_adia', 'heat_shoc', 'dv_x_dx', 'dv_y_dx', 'dv_z_dx', 'dv_x_dy', 'dv_y_dy', 'dv_z_dy', 'dv_x_dz', 'dv_y_dz', 'dv_z_dz', 'div_v', 'dvds_max', 'gamma', 'dfudge', 't_e_old', 'dt_e', 'dt_e_old', 't_r_old', 'heat_tot_', 'gain', 'macro_bf_', 'H_i01_frac', 'H_i02_frac', 'He_i01_frac', 'He_i02_frac', 'He_i03_frac', 'C_i01_frac', 'C_i02_frac', 'C_i03_frac', 'C_i04_frac', 'C_i05_frac', 'C_i06_frac', 'C_i07_frac', 'N_i01_frac', 'N_i02_frac', 'N_i03_frac', 'N_i04_frac', 'N_i05_frac', 'N_i06_frac', 'N_i07_frac', 'N_i08_frac', 'O_i01_frac', 'O_i02_frac', 'O_i03_frac', 'O_i04_frac', 'O_i05_frac', 'O_i06_frac', 'O_i07_frac', 'O_i08_frac', 'O_i09_frac', 'Na_i01_frac', 'Na_i02_frac', 'Na_i03_frac', 'Na_i04_frac', 'Na_i05_frac', 'Na_i06_frac', 'Na_i07_frac', 'Na_i08_frac', 'Na_i09_frac', 'Na_i10_frac', 'Na_i11_frac', 'Na_i12_frac', 'Si_i01_frac', 'Si_i02_frac', 'Si_i03_frac', 'Si_i04_frac', 'Si_i05_frac', 'Si_i06_frac', 'Si_i07_frac', 'Si_i08_frac', 'Si_i09_frac', 'Si_i10_frac', 'Si_i11_frac', 'Si_i12_frac', 'Si_i13_frac', 'Si_i14_frac', 'Si_i15_frac', 'Fe_i01_frac', 'Fe_i02_frac', 'Fe_i03_frac', 'Fe_i04_frac', 'Fe_i05_frac', 'Fe_i06_frac', 'Fe_i07_frac', 'Fe_i08_frac', 'Fe_i09_frac', 'Fe_i10_frac', 'Fe_i11_frac', 'Fe_i12_frac', 'Fe_i13_frac', 'Fe_i14_frac', 'Fe_i15_frac', 'Fe_i16_frac', 'Fe_i17_frac', 'Fe_i18_frac', 'Fe_i19_frac', 'Fe_i20_frac', 'Fe_i21_frac', 'Fe_i22_frac', 'Fe_i23_frac', 'Fe_i24_frac', 'Fe_i25_frac', 'Fe_i26_frac', 'Fe_i27_frac', 'spec_freq', 'spec_flux', 'model_freq', 'model_flux', 'v_l', 'v_rot', 'v_r'])\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
+    }
+   ],
+   "source": [
+    "print (wind.things_read_in)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting ions using PySi \n",
+    "\n",
+    "PySi also allows one to plot ion fractions or densities (fractions by default), which are accessed through strings like ```Fe_i01_frac```, using astronomical notation for the ion stage. An example multiplot of the first six ionic stages of Carbon would be as follows. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
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WITMzEydPnkRGRga2bt2KHj164NVXX0VqaqqzPwoREdnAzCciOdj4V7Fp06Zhy5YtqKiogF6vR1JSEp599lkMGTLE1VUjIiIHY+YTkRxs/BMRERERaQTH/BMRERERaQQb/0REREREGsFFvmwwm80oLy9HUFCQ7GWxicixJEnC+fPnER0dDS8v+/os6urq0NDQIPt4X19f+Pn52VtFUgHmPZF7cFbmazXv2fi3oby83GrZbyJyndLSUvTo0UP28XV1dbisZyAqTphkvycyMhIlJSWa/EHQOuY9kXtROvO1mvds/NsQFBQE4OIfwEuXPSci56iqqkJMTIzl76NcDQ0NqDhhQsnunjAE2e49qjpvxmUJP6OhoUFzPwbEvCdyF87IfC3nvaoa/7fccgu2bt2K0aNH4/333wdwMcSnTJmCEydOwMfHB3PnzsWkSZNkn7P51q/BYOCPAZGLtXcoRufAi5stJs595lEcnfnMeyL3omTmaznvVfXA74wZM/Dmm29a7fPx8cHSpUuxf/9+bNy4EQ899BBqampcVEMicgUzJNkbeQ5mPhG1hnkvpqrGf3JycotbRFFRURg0aBCAi2O7wsLCcObMGRfUjohcxWzH/5HnYOYTUWuY92Ju0/j/8ssvMXbsWERHR0On02Ht2rUtjjEajejVqxf8/PyQmJiIHTt22HWN3bt3w2Qy8YEuIo0xSZLsjZyDmU9ESmHei7lN47+mpgYDBw6E0WhstXzNmjXIyclBXl4e9uzZg4EDByItLQ0nTpyQdf4zZ85g6tSpWLFihSOrTUQegMN+3A8zn4iUwrwXc5sHftPT05Gent5m+ZIlS5CVlYVp06YBAJYtW4b169dj5cqVyM3NFZ67vr4e48ePR25uLkaMGOHQehOR+2uCGY0yjyPnYOYTkVLkZL6W895tev5FGhoasHv3bqSmplr2eXl5ITU1Fdu3bxe+V5IkZGZmYtSoUZgyZYrNa9XX16OqqspqIyLPxmE/nsVZmc+8J1In5r2Y2/T8i5w6dQomkwkRERFW+yMiInDw4EHL69TUVHz//feoqalBjx498N5778FkMmHNmjUYMGCAZUzpP/7xD/Tv37/Vay1cuBALFixQ7LMQkfOZf93kHEeu56zMZ94TqZOczNdy3ntE41+uTZs2tbrfbJb/n3j27NnIycmxvG5eaIKIPJcJEkwyxnfKOYbcR0czn3lPpE5yMl/Lee8Rjf+wsDB4e3ujsrLSan9lZSUiIyMdei29Xg+9Xu/QcxKRa5kkeQu6aHnRF3firMxn3hOpk5zM13Lee8SYf19fXyQkJKCgoMCyz2w2o6CgAElJSYpc02g0Ij4+HsOGDVPk/ETkPGY7NnI9Z2c+855IXZj3Ym7T819dXY3i4mLL65KSEhQWFiI0NBSxsbHIyclBRkYGhg4diuHDh2Pp0qWoqamxzAThaNnZ2cjOzkZVVRWCg4MVuQYROYcZOphge5l4s4xjyDHcKfOZ90TqIifztZz3btP437VrF1JSUiyvm8dhZmRkID8/H5MnT8bJkycxb948VFRUYNCgQdiwYUOLB8KIiC5lli5uco4j52DmE5FS5GS+lvNeJ0kanutIwGg0wmg0wmQy4fDhwzh37hwMBoOrq0WkSc09svb+PWx+37f7IhEYZHuUY/V5MxL7VfDvu8Yw74ncizMyX8t57xFj/l0hOzsb+/fvx86dO11dFSLqoEbJS/ZG2sO8J1IX5r2Y2wz7ISJSiknmmH85xxARkXuTk/lazns2/olI9UzwgknGjU6TE+pCRETKkpP5Ws577d7zsIFTvxGphyTpYJaxSZJ2e4K0jHlPpC5yMl/Lec/Gfxs4BpRIPZpvAcvZlLR+/XokJibC398fISEhGD9+vKLXI3mY90Tq4g5578447IdIpdKvmi0sbwrpLCz/fNscR1bHpUySF0wyHu5ScsXHf/3rX8jKysKzzz6LUaNGoampCXv37lXugkSkGWnDFgjL67v6Ccu3bpjlyOq4nJzM1/IKv2z8E5HqmaGDWcaNTjOU+TVoamrCjBkzsGjRItx1112W/fHx8Ypcj4hIy+RkvlJ57wk47IeIVM/Vw3727NmDsrIyeHl5YfDgwYiKikJ6ejp7/omIFMBhP2Js/LeBD4ARqUej5C17Ay4uFPPbrb6+vkPX/+mnnwAA8+fPx5w5c7Bu3TqEhIQgOTkZZ86c6fDno45h3hOpiz15r0Vs/LeBD4ARqYf512nfbG3Nt4ljYmIQHBxs2RYuXNjqeXNzc6HT6YTbwYMHYTabAQBPPPEEJkyYgISEBKxatQo6nQ7vvfee074Hah3znkhd5GS+nKGgasUx/0SkevIf+L04BrS0tNRquXe9Xt/q8TNnzkRmZqbwnHFxcTh+/DgA6zH+er0ecXFxOHbsmM16ERGRfPIe+NXumH82/olI9cwye3maHwAzGAxWjf+2hIeHIzw83OZxCQkJ0Ov1OHToEEaOHAkAaGxsxNGjR9GzZ0+b7yciIvnkZL6WH/hl45+IVM8k6WCSsaCLnGPaw2Aw4L777kNeXh5iYmLQs2dPLFq0CAAwadIkRa5JRKRVcjJfqbz3BGz8E7mp9O4PiA8IDhIWN4WK5/FvCuwkLP/dLYvF7/cTB+f2f84UljuTnKXeLx6nXE/QokWL4OPjgylTpqC2thaJiYnYvHkzQkJCFLsmEXmG9MsfE5abu4jzvD7MX1jeECR+uHXYtCXCcgAw2cj8Pa89bPMcziIn85XMe3fHxn8bjEYjjEYjTCaTq6tCRB1klrxgljHm36zgGNBOnTph8eLFWLxY/I8qcj7mPZG6yMl8JfPe3Wn3UWcbOPsDkXrImelH7t0BUh/mPZG6MO/F2PNPRKpnhrzxnWblq0JERAqTk/lazns2/olI9RolH/hItuOuUbt3gYmIVENO5ms579n4JyLVM0MHs4yl3OUcQ0RE7k1O5ms579n4JyLVk7/Il3bHgBIRqYW8Rb60m/fa/eQ2GI1GxMfHY9iwYa6uChF1EB/4JRHmPZG6MO/F2PPfhuzsbGRnZ6OqqgrBwcGurg6pUIfn8Q8JEJY3BPsKy01+NnpFbM3j/477zONvi1nSwSzngV8NL/qiZcx7UpqtefxNIYHC8oaufsLy+i7iefyb/MXZZmvdFgD47lX3mcffFjmZr+W8Z+OfiFTPLLOXx9Zy8ERE5P7kZL6W8167n5yINKN5wRc5GxEReTZX5v3Ro0dx11134bLLLoO/vz969+6NvLw8NDQ0KHK99mDPPxGpngk6mGTM7CDnGCIicm9yMl+pvD948CDMZjOWL1+OPn36YO/evcjKykJNTY3brPDOxj8RqV6j5AVvSTwm9uJxWl72hYhIHeRkvlJ5P2bMGIwZM8byOi4uDocOHcJrr73Gxj8RkbPIvcXLYT9ERJ5PTuY7M+/PnTuH0NBQp13PFjb+iUj1OM8/EZF22DPPf1VVldV+vV4PvV7vsLoUFxfj5Zdfdptef4AP/BKRBki/rvZoa5M45p+IyOPJyfzmvI+JiUFwcLBlW7hwYavnzM3NhU6nE24HDx60ek9ZWRnGjBmDSZMmISsrS/HPLRd7/ttgNBphNBphMplcXRXyUB2exz+0s7C8PtTGPP6+Nh52sjHP/zdv5QjLPQl7/kmEeU8dZXMe/67iefzrQ8Xz+Nd1FTfXTDY6qm3N41/4iufM4S+HPT3/paWlMBgMlv1t9frPnDkTmZmZwnPGxcVZ/nd5eTlSUlIwYsQIrFixQmbNnYON/zZw0Rci9eAiXyTCvCdSF3sW+TIYDFaN/7aEh4cjPDxc1vXLysqQkpKChIQErFq1Cl5e7tWxxMY/Eame3KXctbzcOxGRWsjJfKXyvqysDMnJyejZsycWL16MkydPWsoiIyMVuaa92PgnItVjzz8RkXbY0/PvaJ9//jmKi4tRXFyMHj16WJVJkqTINe3Fbi4iUr1GyVv2RkREns2VeZ+ZmQlJklrd3AV7/olI9djzT0SkHa7s+fcEbPwTkepJMhf5kjjbDxGRx5OT+VrOezb+iUj1TNDBJGMOfznHEBGRe5OT+VrOezb+idohPSrb9kEh4ikDbc7j39XWPP42ZjLQi4NNTfP422KW5N3iNbvPkEwiciPpcY8Iy03dxFNF1nft2Dz+tubptzXPf+HL6prH3xY5ma/lvGfjn4hUzyxz2I+cY4iIyL3JyXwt5712PzkRaYatZd5/uynl8OHDuPnmmxEWFgaDwYCRI0diy5Ytil2PiEirXJ337o6NfyJSPZOkk70p5aabbkJTUxM2b96M3bt3Y+DAgbjppptQUVGh2DWJiLTI1Xnv7tj4b4PRaER8fDyGDRvm6qoQUQc13wKWsynh1KlTOHLkCHJzczFgwABcfvnl+Otf/4oLFy5g7969ilyT5GPeE6mLK/PeE2j3k9uQnZ2N/fv3Y+fOna6uChF1kAleaJJsb0ot9961a1dceeWVePPNN1FTU4OmpiYsX74c3bp1Q0JCgiLXJPmY90TqIifzlcp7T8AHfolI9exd5Kuqqspqv16vh15vYzoNAZ1Oh02bNmH8+PEICgqCl5cXunXrhg0bNiAkJKTd5yUiopa4yJeYdv/ZQ0SaYe+wn5iYGAQHB1u2hQsXtnre3Nxc6HQ64Xbw4EFIkoTs7Gx069YNX331FXbs2IHx48dj7NixOH78uDO/CiIi1eOwHzH2/BO1Ij3yL+IDQrvYPEdjmHge/4YuHZvH/+v3ZtqsA11kb89/aWkpDIb/zdvdVq//zJkzkZmZKTxnXFwcNm/ejHXr1uGXX36xnPfVV1/F559/jtWrVyM3N1fmJyEiJaT3Es+Db+omvkNXH+4vLK8L8RaWN/qL82nPcm3N099R7PkXY+OfiFRP7rRuzccYDAarxn9bwsPDER4ebvO4CxcuAAC8vKz/Qefl5QWz2Wzz/UREJJ+czOdUn0REKtbcCyRnU0JSUhJCQkKQkZGB77//HocPH8ajjz6KkpIS3HjjjYpck4hIq1yZ956AjX8iUj1XN/7DwsKwYcMGVFdXY9SoURg6dCj+85//4KOPPsLAgQMVuSYRkVax8S/GYT9EpHr2jvlXwtChQ/HZZ58pdn4iIrqIY/7F2PgnItUzSTroZMzsoOUVH4mI1EJO5ms579n4JyLVc4eefyIicg72/Iux8U9EqsfGPxGRdrDxL8bGPxGpHhv/RETawca/GBv/pEo3+N4uLPcOCRaWS2GhwvLGcPECXgDQaOgkLDfpuYiXs7DxT6RuaZ2nCsu9QsWLdJmiugrLa6PFi3jVB4sX8TLpuYiXM7HxL8bGPxGpniTpIMkIejnHEBGRe5OT+VrOe1XN83/LLbcgJCQEEydOlLWfiLShebVHORt5DmY+EbWGeS+mqsb/jBkz8Oabb8reT0Ta4OpFvkgZzHwiag3zXkxVjf/k5GQEBQXJ3k9E2tB8C1jORp6DmU9ErWHei7lN4//LL7/E2LFjER0dDZ1Oh7Vr17Y4xmg0olevXvDz80NiYiJ27Njh/IoSkccxmb1kb+QczHwiUgrzXsxtPnlNTQ0GDhwIo9HYavmaNWuQk5ODvLw87NmzBwMHDkRaWhpOnDjh5JoSkaeRZN4C1nJPkLMx84lIKXIyX8t57zaz/aSnpyM9Pb3N8iVLliArKwvTpk0DACxbtgzr16/HypUrkZub67B61NfXo76+3vK6qqrKYecmIteQAEiSvOPIOdwh85n3ROokJ/O1nPdu0/gXaWhowO7duzF79mzLPi8vL6SmpmL79u0OvdbChQuxYMECh56THO/33pOF5T7h4jmbpXDxnM9NoeJ5/Js62/6r0+QnvrG27V3O4+8sZuigkzGzg5Znf3Anzsp85r3nsLl2S2Q3YbkpWrx2S1038Tz+jYHiefyb/DiPvzuRk/laznu3GfYjcurUKZhMJkRERFjtj4iIQEVFheV1amoqJk2ahE8++QQ9evSw/Ei0tb81s2fPxrlz5yxbaWmpMh+KiJyGD/x6FmdlPvOeSJ2Y92Ie0fMv16ZNm+za3xq9Xg+9Xu+oKhGRGzBLOui4wq/qdDTzmfdE6iQn87Wc9x7R+A8LC4O3tzcqKyut9ldWViIyMtJFtSIiTyFJMsf8a3kQqBth5hNRR8jJfC3nvUcM+/H19UVCQgIKCgos+8xmMwoKCpCUlKTINY1GI+Lj4zFs2DBFzk9EzsNhP57F2ZnPvCdSF+a9mNv0/FdXV6O4uNjyuqSkBIWFhQgNDUVsbCxycnKQkZGBoUOHYvjw4Vi6dClqamosM0E4WnZ2NrKzs1FVVYXg4GBFrkFEzmEyewEy5nTW8rzPzuZOmc+8J1IXOZmv5bx3m8b/rl27kJKSYnmdk5MDAMjIyEB+fj4mT56MkydPYt68eaioqMCgQYOwYcOGFg+EERFdisN+3A8zn4iUwmE/Ym7T+E9OToZk47/E9OnTMX36dKfUx2g0wmg0wmQyOeV6RKSciz8Etm/xavnHwNncKfOZ90TqIifztZz3btP4dze8Dexav/eaJCz36RYuLJe6ied0bgwTz+PfGCT+q9Gkt327kPP4uw+54zu1PAZUy5j3rmcz87tHC8tNkeLMr40KEJbXG8SZ3uRvYx7/ZZzH353IyXwt5z0b/0SkehLkreao4Y4gIiLVkJP5Ws57Nv6JSPXY809EpB3s+RfT7qPONnDqNyIVkezYSHOY90Qq4yZ5X19fj0GDBkGn06GwsFD5C8rExn8bsrOzsX//fuzcudPVVSGijpI757OGe4K0jHlPpDJukvePPfYYoqPFz6u4Ahv/RKR6zdO+ydmU8swzz2DEiBEICAhAly5dWj3m2LFjuPHGGxEQEIBu3brh0UcfRVNTk3KVIiJSIVfnPQB8+umn2LhxIxYvXqzshdqBY/6JSPUksxckGQu6yDmmvRoaGjBp0iQkJSXhjTfeaFFuMplw4403IjIyEtu2bcPx48cxdepUdOrUCc8++6xi9SIiUhs5ma9k3ldWViIrKwtr165FQIB4pilXYOOfiFTPHRb5WrBgAQAgPz+/1fKNGzdi//792LRpEyIiIjBo0CA89dRTmDVrFubPnw9fX1/lKkdEpCL2LPJVVVVltV+v10Ov13fg2hIyMzNx3333YejQoTh69Gi7z6UU2Y3/f//733af/Pe//z38/f3tfp874KIvyrrB93ZhuU9EN2G5FNFVWN7QVfwvbVOAt7i8k3gsIOfw9zB2zvXp6B8DObZv347+/ftbrWCblpaG+++/H/v27cPgwYMVvf6ltJT5zHtl/d57ss1jbM3j3xQjXtulrpv472djZ3Evr7mTsJjz+HsaO+b6jImJsdqdl5eH+fPntzg8NzcXzz33nPCUBw4cwMaNG3H+/HnMnj1bfn2dTHbjf/z48XadWKfT4ciRI4iLi7O3Tm6Bi74QqYe9U33K/TFwpIqKCquGPwDL64qKCkWv3RotZT7znkhd7Jnqs7S0FAaDwbK/rY6emTNnIjMzU3jOuLg4bN68Gdu3b29xnqFDh+LPf/4zVq9eLeMTKMuuYT8VFRXo1k3cI9ssKCioXRUiIlKEHUN65P4YyO0J6tu3r/yLuxFmPhF5LJmZbzAYrPK+LeHh4QgPF9+BAoC//e1vePrppy2vy8vLkZaWhjVr1iAxMVFepRQmu/GfkZFh1+3cO+64Q9aXSUSkNHt7/uX+GMjtCZIjMjISO3bssNpXWVlpKXM2Zj4ReSpXLvIVGxtr9TowMBAA0Lt3b/To0UORa9pLduN/1apVdp34tddes7syRESKsHPMv1xye4LkSEpKwjPPPIMTJ05Yets///xzGAwGxMfHO+Qa9mDmE5HHsmPMvxZxth8i0gDdr5uc45Rx7NgxnDlzBseOHYPJZLKs9tinTx8EBgbihhtuQHx8PKZMmYLnn38eFRUVmDNnDrKzsxV/2JiISF3kZL5zFnXs1asXJKUXFbBTuxv/dXV1KCoqwokTJ2A2m63Kxo0b1+GKuRpnfyBSEfOvm5zjFDJv3jyrB72aZ+/ZsmULkpOT4e3tjXXr1uH+++9HUlISOnfujIyMDDz55JPKVcoOas585j2RysjJfAXz3t21q/G/YcMGTJ06FadOnWpRptPpVBGgnP2BSEXkLuWu4HLv+fn5bc7x36xnz5745JNPFKtDe6k985n3RCojJ/MVzHt3167G/wMPPIBJkyZh3rx5LaamIwKA9Ij7heXe0eI/N+Yw8Q9wY4ifsLzJxjz+TXrxnM+cx19d3GGRL0/GzCeRMcF3Cst9YrvbPEdTd/HaLXVh4kXuGgPFmd/oL27o7X6d8/iriT2LfGlRuxr/lZWVyMnJ4Y8AEXkGhR741QpmPhF5FD7wKyTu/mzDxIkTsXXrVgdXhYhIIc23gOVs1AIzn4g8CvNeqF09/6+88gomTZqEr776Cv3790enTtbrYj/44IMOqRwRkSPopIubnOOoJWY+EXkSOZmv5bxvV+P/n//8JzZu3Ag/Pz9s3boVOt3//vWk0+lU8UPA2R+IVITDfjpE7ZnPvCdSGQ77EWpX4/+JJ57AggULkJubCy+vdo0ccnuc/YFIRdxgth9PpvbMZ94TqQxn+xFqV+O/oaEBkydPVuWPABGpkBvM8+/JmPlE5FE4z79Qu5I8IyMDa9ascXRdiIiUIdmxUQvMfCLyKMx7oXb1/JtMJjz//PP47LPPMGDAgBYPfy1ZssQhlSP3lX7FLGG51C1UWG4K8heWNwV1EpY3dhbP6fyfDx4RlpPGcNhPhzDztS09Zob4gB5RwuKGiECb12gIFmd+vUHcV7ljdY7Na5CGcNiPULsa/z/88INlafq9e/c6tEJERI7G2X46hplPRJ6Es/2Itavxv2XLFkfXg4hIOZztp0OY+UTkUTjbj1C7xvwvXLgQK1eubLF/5cqVeO655zpcKSIich/MfCIi9WhX43/58uXo27dvi/39+vXDsmXLOlwpIiJH0uF/t4GFm6sr6qaY+UTkSWRlvqsr6ULtavxXVFQgKqrlAz7h4eE4fvx4hyvlDoxGI+Lj4zFs2DBXV4WIOkruUu8afgBMRO2Zz7wnUhnmvVC7Gv8xMTH4+uuvW+z/+uuvER0d3eFKuYPs7Gzs378fO3fudHVViKijONVnh6g985n3RCrDvBdq1wO/WVlZeOihh9DY2IhRo0YBAAoKCvDYY49h5syZDq0gEVFH6cwXNznHUUvMfCLyJHIyX8t5367G/6OPPorTp0/jL3/5CxoaGgAAfn5+mDVrFmbPnu3QChIRdRhn++kQZj4ReRTO9iPUrsa/TqfDc889h7lz5+LAgQPw9/fH5ZdfDr1e7+j6kYukJeQJy6XQzsJyU4B4wZamzuI/eo0B4hFp29awt5HswMZ/hzDz1S39ylxhuTkiRFje0FW8aGODwXZToz5YPP5619+5iBfZgY1/oXY1/psFBgbyASkicntc5MsxmPlE5Am4yJeY7Ad+i4qKYDbLHyC1b98+NDU1tatSREQOxdl+7MbMJyKPxbwXkt34Hzx4ME6fPi37xElJSTh27Fi7KkVE5FCc7cduzHwi8ljMeyHZw34kScLcuXMREBAg6/jmh8KIiFyNw37sx8wnIk/FYT9ishv/1113HQ4dOiT7xElJSfD3Fz8ERETkFHzg127MfCLyWHzgV0h243/r1q0KVoOISEEy5/mHhud9vhQzn4g8lpzM13Det2uFXyIij+IGY/6feeYZjBgxAgEBAejSpUuL8u+//x5/+tOfEBMTA39/f1x11VV46aWXlKsQEZFaccy/UIem+lQzo9EIo9EIk8nk6qoo4vfXPi0sNweJ5+9uCrAxT3+gt7i8s/gp+52rOKczOY47jPlvaGjApEmTkJSUhDfeeKNF+e7du9GtWze89dZbiImJwbZt23DPPffA29sb06dPV65ipPq8HzNonrC8KSJIWN4Q4issr+sizvuGINuzqhS+8rDNY4jk4ph/MTb+25CdnY3s7GxUVVUhODjY1dUhIg+3YMECAEB+fn6r5XfeeafV67i4OGzfvh0ffPABG/8KY94TkZa0a9jP3/72N5SXlzu6LkREyrBz2E9VVZXVVl9f74pa49y5cwgNDXXJtX+LmU9EHoXDfoTa1fh/6KGH8Lvf/Q6lpaVW+xsaGrB7926HVIyIyFGabwHL2QAgJiYGwcHBlm3hwoVOr/O2bduwZs0a3HPPPU6/9qWY+UTkSezJey1q9wO/qampuP76661+DH755RcMHz7cIRUjInIoO3qBSktLce7cOcs2e/bsVk+Zm5sLnU4n3A4ePGh3Vffu3Yubb74ZeXl5uOGGG+x+vxKY+UTkUdjr36Z2jfnX6XR46qmn0K1bN1x//fX44osvEBMTA+DiwjBERG7Fznn+DQYDDAaDzcNnzpyJzMxM4TFxcXEyLvw/+/fvx+jRo3HPPfdgzpw5dr1XKcx8IvIonOdfqEMP/D711FPQ6XSWHwNfX1/odLaf6icicialZvsJDw9HeHh4+yrVin379mHUqFHIyMjAM88847DzOgozn4g8AWf7EWtX4/+3PT1PPvmk5cfg//7v/xxWMSIiR9HJXORL1kJg7XTs2DGcOXMGx44dg8lkQmFhIQCgT58+CAwMxN69ezFq1CikpaUhJycHFRUVAABvb2+H/gOjPZj5RORJ5GS+knnv7trV+H/mmWfQuXNny+vmKezGjh3rmFpRh6VeJ+41bArqJC4PEM/bXN9F/LhIk7+4N/A7I+d0Jieyc9iPEubNm4fVq1dbXg8ePBgAsGXLFiQnJ+P999/HyZMn8dZbb+Gtt96yHNezZ08cPXpUuYrJwMx3b2mD84TlDd06i8uDxU2BC93Eed8oPj32Ps+8JyfjsB+hdj3wO3v2bKsfAuDij8EDDzyAoCDxYiFERE7nBiv85ufnQ5KkFltycjIAYP78+a2Wu7rhDzDzicjDcKpPoXbP9tOaOXPm4OzZs448JRFRh9k71SfJw8wnInfEvBfjCr9EpH5uMOyHiIichMN+hNj4JyL1Y+OfiEg72PgXcuiwHyIid8RhP0RE2uEOeb9+/XokJibC398fISEhGD9+vLIXtAN7/olI/djzT0SkHS7u+f/Xv/6FrKwsPPvssxg1ahSampqwd+9e5S5oJ1X1/N9yyy0ICQnBxIkTrfavW7cOV155JS6//HL8/e9/d1HtiMhVmud8lrORZ2DeE1FbXJn3TU1NmDFjBhYtWoT77rsPV1xxBeLj4/HHP/5RmQu2g6p6/mfMmIE777zTai7tpqYm5OTkYMuWLQgODkZCQgJuueUWdO3a1YU17bhRo/8qLG8K8RWWN3YW/7uvzsY8/jbndV7EeZ3JjbDnX3W0lPc3DH9SWF4fJQ7kuq7in/raMPG6LA1dhMU4mMe8JzdjR89/VVWV1W69Xg+9Xt/uS+/ZswdlZWXw8vLC4MGDUVFRgUGDBmHRokW4+uqr231eR1JVz39ycnKLOad37NiBfv36oXv37ggMDER6ejo2btzoohoSkSvo7NjIMzDviagt9uR9TEwMgoODLdvChQs7dO2ffvoJwMW1W+bMmYN169YhJCQEycnJOHPmTIfO7Shu0/j/8ssvMXbsWERHR0On02Ht2rUtjjEajejVqxf8/PyQmJiIHTt22DxveXk5unfvbnndvXt3lJWVObLqROTu3GCRL/of5j0RKcqOvC8tLcW5c+cs2+zZs1s9ZW5uLnQ6nXA7ePAgzOaL44meeOIJTJgwAQkJCVi1ahV0Oh3ee+89hT+4PG7T+K+pqcHAgQNhNBpbLV+zZg1ycnKQl5eHPXv2YODAgUhLS8OJEyecXFMi8jSc7ce9MO+JSEn25L3BYLDa2hryM3PmTBw4cEC4xcXFISoqCgAQHx9vea9er0dcXByOHTum+GeXw23G/KenpyM9Pb3N8iVLliArKwvTpk0DACxbtgzr16/HypUrkZub2+b7oqOjrXp+ysrKMHz48DaPr6+vR319veX1pWPBiMgDccy/W2HeE5GiFJjtJzw8HOHh4TaPS0hIgF6vx6FDhzBy5EgAQGNjI44ePYqePXvad1GFuE3Pv0hDQwN2796N1NRUyz4vLy+kpqZi+/btwvcOHz4ce/fuRVlZGaqrq/Hpp58iLS2tzeMXLlxoNfYrJibGYZ+DiFyIQ348AvOeiBzCRXlvMBhw3333IS8vDxs3bsShQ4dw//33AwAmTZqk3IXt4DY9/yKnTp2CyWRCRESE1f6IiAgcPHjQ8jo1NRXff/89ampq0KNHD7z33ntISkrCCy+8gJSUFJjNZjz22GPCmR9mz56NnJwcy+uqqir+IBB5OLlDejjsx/WY90TUUXIyX8m8X7RoEXx8fDBlyhTU1tYiMTERmzdvRkhIiHIXtYNHNP7l2rRpU6v7x40bh3Hjxsk6R0eneCIiN8RhP6rDvCeiNrl4ka9OnTph8eLFWLx4sXIX6QCPaPyHhYXB29sblZWVVvsrKysRGRnpolop6/o/PC8sbwrtJCyvN4gnLawPsTGvc7CwGI1BbCWR55C7oAsX+XI9Leb97699WlheH+UvLK+JsDGPv41hynVh4jw3d2kSn4DIzcjJfC3nvUeM+ff19UVCQgIKCgos+8xmMwoKCpCUlKTINY1GI+Lj4zFs2DBFzk9EzsPZfjwH856IOop5L+Y2Pf/V1dUoLi62vC4pKUFhYSFCQ0MRGxuLnJwcZGRkYOjQoRg+fDiWLl2Kmpoay2wQjpadnY3s7GxUVVUhONhGNzgRuTcO+3ErzHsiUpSLh/24O7dp/O/atQspKSmW180PYWVkZCA/Px+TJ0/GyZMnMW/ePMtSyRs2bGjxUBgRUQts/LsV5j0RKYqNfyG3afwnJydDksT/JaZPn47p06c7pT5GoxFGoxEmk8kp1yMi5XC2H/fCvCciJbl6th935xFj/l0hOzsb+/fvx86dO11dFSLqKLlLvWv4x0DLmPdEKsO8F3Kbnn8iIqXoJAk6Gz3NzccREZFnk5P5Ws57Nv6JSP045p+ISDs45l+Ijf82uHoMaF1X8X+a+mAb8/R3EZ+/PlT8p74pWDyv8893PSa+AJEb4Tz/JOLqvL8QbWMe/0jxCN3abuLz14eLP1enrnXC8p8mzRFfgMjNcJ5/MY75bwPHgBKpB+f5JxHmPZG6MO/F2PgnIvVzgwd+n3nmGYwYMQIBAQHo0qWL8NjTp0+jR48e0Ol0OHv2rHKVIiJSIz7wK8TGPxGpnjv0/Dc0NGDSpEm4//77bR571113YcCAAcpVhohIxVyd9+6OjX8iUj836PlfsGABHn74YfTv31943GuvvYazZ8/ikUceUa4yRERqxp5/ITb+22A0GhEfH49hw4a5uipE5ACe0Au0f/9+PPnkk3jzzTfh5cV4dhbmPZH6uHveuxJ/XdrAB8CIVESS5G8AqqqqrLb6+nrFq1hfX48//elPWLRoEWJjYxW/Hv0P855IZezIey1i45+IVM/eMf8xMTEIDg62bAsXLmz1vLm5udDpdMLt4MGDsuo4e/ZsXHXVVbjjjjsc9bGJiDSJY/7FOM8/EamezgToZHR16H6dDr20tBQGg8GyX6/Xt3r8zJkzkZmZKTxnXFycrDpu3rwZP/zwA95//30AgPRrr1RYWBieeOIJLFiwQNZ5iIi0Tk7m61yzrIdbYOPfBYZNW2LzmNpI8SJe9V3F728MFv+p9g5pEJb/fNvj4gsQeRK5D3f9eozBYLBq/LclPDwc4eHhHapas3/961+ora21vN65cyfuvPNOfPXVV+jdu7dDrkGuMWLyC8Ly87HiVsqFaPEfXlNYo7A8uGu1sPz7m54SlhN5HDmZz55/upSrV3wkIseRe4tXydvAx44dw5kzZ3Ds2DGYTCYUFhYCAPr06YPAwMAWDfxTp04BAK666iqb6wJQxzDvidRFTuZredgPx/y3gQ+AEamInQ/8KmHevHkYPHgw8vLyUF1djcGDB2Pw4MHYtWuXYtckeZj3RCrDB36F2PgnItVzh0W+8vPzIUlSiy05ObnV45OTkyFJEnv9iYjs5Oq8d3cc9kNE6mfnmH8iIvJgHPMvxMY/EameO4z5JyIi5+CYfzE2/olI/eSO79TwGFAiItWQk/kazns2/olI9djzT0SkHez5F2Pjvw1KTv1W3UM8hz8A1HcV/6k0dxHP6xzQpVZYvn/8fJt1IFILnfniJuc40h6lp/o8d5l4bo3aaPEfPF23emF5ZMh5Yfk3aa2vUE2kVnIyX8t5z9l+2sCp34hUxCzJ30hzmPdEKsO8F2LPPxGpH2f7ISLSDs72I8TGPxGpng4yx/wrXhMiIlKanMzXct6z8U9E6sfZfoiItIOz/Qix8U9EqsfZfoiItIOz/Yix8U9E6scx/0RE2sEx/0Js/BOR6ukkCToZt3jlHENERO5NTuZrOe/Z+FfAVfNeFJbXh9ueXFbq2iAsDw8Vz+vcJ+SUzWsQaYXOJEEn4x6vzqTdHwNqv6sfFWd+bYw4872jxOuy9Oj6i7D8qi6VwnIirZGT+VrOe87z3waj0Yj4+HgMGzbM1VUhoo6S7NhIc5j3RCrDvBdi478NXPSFSEWaZ36Qs5HmMO+JVIZ5L8RhP0Skepzth4hIOzjbjxgb/0Skfpznn4hIOzjPvxAb/0SkejrzxU3OcURE5NnkZL6W855j/olI/Tjmn4hIO1yc94cPH8bNN9+MsLAwGAwGjBw5Elu2bFHsevZi45+I1I+z/RARaYeL8/6mm25CU1MTNm/ejN27d2PgwIG46aabUFFRodxF7cBhPwqoszGPv1e3OpvniO56Tlh+dchxYfmyhH/YvAaRVnCRL1JSbZT4z42tefxjw84Iy4d1PSYs/+uA94XlRFrjykW+Tp06hSNHjuCNN97AgAEDAAB//etf8eqrr2Lv3r2IjIxU5Lr2YM8/EamfWQJMMjYzG/9ERB5PTuYrlPddu3bFlVdeiTfffBM1NTVoamrC8uXL0a1bNyQkJChyTXux55+IVI89/0RE2mFPz39VVZXVfr1eD71e3/5r63TYtGkTxo8fj6CgIHh5eaFbt27YsGEDQkJC2n1eR2LPPxGpnwSZD4C5uqJERNRhsjL/4qExMTEIDg62bAsXLmz1lLm5udDpdMLt4MGDkCQJ2dnZ6NatG7766ivs2LED48ePx9ixY3H8uHjItrOw55+I1M8N5vl/5plnsH79ehQWFsLX1xdnz55t9bj8/HwsWbIEhw8fhsFgwKRJk2A0GhWrFxGR6tgxz39paSkMBoNld1u9/jNnzkRmZqbwlHFxcdi8eTPWrVuHX375xXLeV199FZ9//jlWr16N3Nxc+Z9DIWz8t8FoNMJoNMJkMrm6KkTUUWYAOpnHKaShoQGTJk1CUlIS3njjjVaPWbJkCV544QUsWrQIiYmJqKmpwdGjR5WrFAFg3hOpjpzM/zXvDQaDVeO/LeHh4QgPD7d53IULFwAAXl7Wg2u8vLxgNrvH4gJs/LchOzsb2dnZqKqqQnBwsKurQ0Qd4A5j/hcsWADgYs9+a3755RfMmTMHH3/8MUaPHm3Z3zxbBCmHeU+kLq6c7ScpKQkhISHIyMjAvHnz4O/vj9dffx0lJSW48cYbFbmmvTjmn4jUzwMW+fr8889hNptRVlaGq666Cj169MAf//hHlJaWuqxOREQeyYV5HxYWhg0bNqC6uhqjRo3C0KFD8Z///AcfffQRBg4cqMg17cWefwXowuqF5VE25vAHbM/jPyDwv3bViUjT7Bzz7+jZH+T46aefYDab8eyzz+Kll15CcHAw5syZg9///vcoKiqCr6+voten9jNHiDM/JvSssHxwaJm4POBne6tEpG12jPlXwtChQ/HZZ58pdv6OYs8/EamfnDn+mzc4fvYHOcxmMxobG/G3v/0NaWlpuOaaa/DPf/4TR44ccatl4YmI3J4dea9F7PknItWzd8y/o2d/kCMqKgoAEB8fb9kXHh6OsLAwHDsmXuGViIj+x5Vj/j0BG/9EpH52Dvtx9OwPclx77bUAgEOHDqFHjx4AgDNnzuDUqVPo2bOnQ65BRKQJLh724+7Y+Cci9TNLgE5G0Cu03DsAHDt2DGfOnMGxY8dgMplQWFgIAOjTpw8CAwNxxRVX4Oabb8aMGTOwYsUKGAwGzJ49G3379kVKSopi9SIiUh05ma9g3rs7Nv6JSP3cYJGvefPmYfXq1ZbXgwcPBgBs2bIFycnJAIA333wTDz/8MG688UZ4eXnh+uuvx4YNG9CpUyfF6kVEpDrs+Rdi45+INEDutG7K/Rjk5+e3Ocd/M4PBgDfeeKPNRcCIiEgOOZnPxj8RkXq5Qc8/ERE5CXv+hdj4b4de+c8Jy7tFVgvL+3Y5YfMaV3UWz/Mf0+m0zXMQ0a/MEmT18mh4DCi1rdfyxcLybj3PC8vjQyqE5f0DxAu5Me+J7CQn8zWc92z8E5H6SeaLm5zjiIjIs8nJfA3nPRv/RKR+JpmNf7N2fwyIiFRDTuZrOO/Z+Cci9eOYfyIi7eCYfyEvV1fAGRYvXox+/frh6quvxltvveXq6hCRs0n434+BcHN1RamjmPdEJC/zXV1J11F9z/8PP/yAd955B7t374YkSUhJScFNN92ELl26uLpqROQs7PnXBOY9EQFgz78Nqu/5P3DgAJKSkuDn5wd/f38MHDgQGzZscHW1iMiZzGb5G3ks5j0RAWDe2+D2jf8vv/wSY8eORXR0NHQ6HdauXdviGKPRiF69esHPzw+JiYnYsWOHpezqq6/G1q1bcfbsWfzyyy/YunUrysrKnPgJiMjlZA35kbsQGCmFeU9EDsG8F3L7xn9NTQ0GDhwIo9HYavmaNWuQk5ODvLw87NmzBwMHDkRaWhpOnLg4l358fDwefPBBjBo1CrfeeiuuueYaeHt7O/MjEJGrsfHvEZj3ROQQzHshtx/zn56ejvT09DbLlyxZgqysLEybNg0AsGzZMqxfvx4rV65Ebm4uAODee+/FvffeCwC4++67cfnll7d5vvr6etTX11teV1VVtTjGz1DfYt9v9TT8Iiy/zP+UsBywvahLN2/xojJE9Btc5MsjuGPeA0Cn0DphvW1l/uX+4oUde3US/yZ09RJfn4guwUW+hNy+51+koaEBu3fvRmpqqmWfl5cXUlNTsX37dsu+5l6hQ4cOYceOHUhLS2vznAsXLkRwcLBli4mJUe4DEJFTSGYTJJOMzWxydVWpDcx7IpJLVuZrOO/dvudf5NSpUzCZTIiIiLDaHxERgYMHD1pe33zzzTh37hw6d+6MVatWwcen7Y89e/Zs5OTkWF5XVVXxB4HI00kye/41fBvY3THviUg2OZmv4bz36Ma/XL/tFbJFr9dDr9crWBsicjqzGdDJmNlBw8u9qwXznohkZb6G896jG/9hYWHw9vZGZWWl1f7KykpERka6qFZE5HbY8+/xmPdEJBt7/oU8esy/r68vEhISUFBQYNlnNptRUFCApKSkDp3baDQiPj4ew4YN62g1icjFJLNZ9kbuiXlPRHIx78Xcvue/uroaxcXFltclJSUoLCxEaGgoYmNjkZOTg4yMDAwdOhTDhw/H0qVLUVNTY5kNor2ys7ORnZ2NqqoqBAcHd/RjEJErseffIzDvicgh2PMv5PaN/127diElJcXyuvnhrIyMDOTn52Py5Mk4efIk5s2bh4qKCgwaNAgbNmxo8VAYEWmYWQJ0bPy7O+Y9ETmEnMzXcN67feM/OTkZko3/QNOnT8f06dMdel2j0Qij0QiTqeVUUCGBF4Tv7dn5jLA8otM5m9fvZWOe/yGxx2yeg4h+JUkA5Dzwq90fA3fgjnkPAF06mPk9fMV5Hu1TLSy/vEe5sJyILiEn8zWc9x495l9J2dnZ2L9/P3bu3OnqqhBRB8ma4//XjbSHeU+kLsx7Mbfv+Sci6ijJLEGSMezHVq8zERG5PzmZr+W8Z+PfhuY/HL9d9r2ppr6twwEA9dWNwvJaU5PN61Y3im9XtbUMPZEaNf95b29YN0n1suZ0boL47y6pW2t5DwCmCx3L/AuSuIexuoF5T/Rbzsh8Lec9G/9taB4D2tDQAAB2rfq4S6lKWeGMFKQ958+ft2s2Fl9fX0RGRuI/FZ/Ifk9kZCR8fX3bUz3yUB3JewD4TolKWWHekzYpnflazXudpOX7HjKYzWaUl5cjKCgIOp0Ow4YNkzUuVM5xbR1jz/5L9/32dfNS9aWlpTAYDDbr3BFyv5eOvtfWsaJyOd+fnH3O+l499Ttta39H/qxKkoTz588jOjoaXl72PapUV1dnadTJ4evrCz8/P7uuQerQ3rwHmE1KvNcTssmRPPV7VeLPqrMyX6t5z55/G7y8vNCjRw/La29vb1kBIOe4to6xZ/+l+1o7xmAwKB5acr+Xjr7X1rGicjnfnz37lP5ePfU7bWt/R/+stnf+dT8/P02GO9mvvXkv51hmk/3v9ZRschRP/V6V+rPKzFcOZ/uxU3Z2tsOOa+sYe/Zfuk9u/RytI9e15722jhWVy/n+7NmnNE/9Ttva7y5/VonkYja1jtmkDE/9Xt35zyq1jsN+VKx5tcpz584p3mOhJfxeHY/fKVHH8e+R4/E7VQa/V9diz7+K6fV65OXlQa/Xu7oqqsLv1fH4nRJ1HP8eOR6/U2Xwe3Ut9vwTEREREWkEe/6JiIiIiDSCjX8iIiIiIo1g45+IiIiISCPY+CciIiIi0gg2/jXmlltuQUhICCZOnGi1f926dbjyyitx+eWX4+9//7uLaueZ2vpO29pPtrX23ZWWliI5ORnx8fEYMGAA3nvvPRfWkMj9Me8dj3mvDGa+c3G2H43ZunUrzp8/j9WrV+P9998HADQ1NSE+Ph5btmxBcHAwEhISsG3bNnTt2tXFtfUMrX2nov1kW2vf3fHjx1FZWYlBgwahoqICCQkJOHz4MDp37uzi2hK5J+a94zHvlcHMdy72/GtMcnIygoKCrPbt2LED/fr1Q/fu3REYGIj09HRs3LjRRTX0PK19p6L9ZFtr311UVBQGDRoEAIiMjERYWBjOnDnjgtoReQbmveMx75XBzHcuNv49yJdffomxY8ciOjoaOp0Oa9eubXGM0WhEr1694Ofnh8TEROzYscPmecvLy9G9e3fL6+7du6OsrMyRVXdbSn2nWuaM73T37t0wmUyIiYlxUK2J3Avz3vGY98pg5nseNv49SE1NDQYOHAij0dhq+Zo1a5CTk4O8vDzs2bMHAwcORFpaGk6cOOHkmnoOfqeOp/R3eubMGUydOhUrVqxwZLWJ3AqzyfH4nSqDme+BJPJIAKQPP/zQat/w4cOl7Oxsy2uTySRFR0dLCxcutDpuy5Yt0oQJEyyvv/76a2n8+PGW1zNmzJDefvttZSruxhz5ndrarxWO/k7r6uqk3/3ud9Kbb76pWJ2J3A3z3vGY98pg5nsG9vyrRENDA3bv3o3U1FTLPi8vL6SmpmL79u3C9w4fPhx79+5FWVkZqqur8emnnyItLU3pKru9jnyn1LqOfKeSJCEzMxOjRo3ClClTlK4qkdti3jse814ZzHz35OPqCpBjnDp1CiaTCREREVb7IyIicPDgQcvr1NRUfP/996ipqUGPHj3w3nvvISkpCS+88AJSUlJgNpvx2GOPceYHdPw7bWu/lnXkOzWZTFizZg0GDBhgGVP6j3/8A/3793fmRyByOea94zHvlcHMd09s/GvMpk2bWt0/btw4jBs3zsm1UYe2vtO29pNtbX13ZrPZyTUh8lzMe8dj3iuDme9cHPajEmFhYfD29kZlZaXV/srKSkRGRrqoVp6N36nj8Tsl6jj+PXI8fqfK4Pfqntj4VwlfX18kJCSgoKDAss9sNqOgoIC3HtuJ36nj8Tsl6jj+PXI8fqfK4Pfqnjjsx4NUV1ejuLjY8rqkpASFhYUIDQ1FbGwscnJykJGRgaFDh2L48OFYunQpampqMG3aNBfW2r3xO3U8fqdEHce/R47H71QZ/F49kKunGyL5tmzZIgFosWVkZFiOefnll6XY2FjJ19dXGj58uPTNN9+4rsIegN+p4/E7Jeo4/j1yPH6nyuD36nl0kiRJzvhHBhERERERuRbH/BMRERERaQQb/0REREREGsHGPxERERGRRrDxT0RERESkEWz8ExERERFpBBv/REREREQawcY/EREREZFGsPFPRERERKQRbPwTEREREWkEG/9ERERERBrBxj95vOTkZOh0Ouh0OhQWFip6rczMTMu11q5dq+i1iIioJWY+Ucew8U+qkJWVhePHj+Pqq69W9DovvfQSjh8/rug1iIhIjJlP1H4+rq4AkSMEBAQgMjJS8esEBwcjODhY8esQEVHbmPlE7ceef3I7//znP+Hv72/V2zJt2jQMGDAA586dk30es9mM559/Hn369IFer0dsbCyeeeYZS3lycjIeeOABPPTQQwgJCUFERARef/111NTUYNq0aQgKCkKfPn3w6aefOvTzERHR/zDziZyLjX9yO7fddhuuuOIKPPvsswCAvLw8bNq0CZ9++qldPTCzZ8/GX//6V8ydOxf79+/HO++8g4iICKtjVq9ejbCwMOzYsQMPPPAA7r//fkyaNAkjRozAnj17cMMNN2DKlCm4cOGCQz8jERFdxMwncjKJyA19/PHHkl6vl55++mkpJCRE2rt3b5vHXn/99dKMGTOs9lVVVUl6vV56/fXXhe8bOXKk5XVTU5PUuXNnacqUKZZ9x48flwBI27dvt3ovAOnDDz+070MREVGrmPlEzsMx/+SWbrrpJsTHx+PJJ5/Exo0b0a9fP7vef+DAAdTX12P06NHC4wYMGGD5397e3ujatSv69+9v2dfca3TixAm7rk9ERPIx84mch8N+yC1t2LABBw8ehMlkanHbVg5/f39Zx3Xq1MnqtU6ns9qn0+kAXBxLSkREymDmEzkPG//kdvbs2YM//vGPeOONNzB69GjMnTvX7nNcfvnl8Pf3R0FBgQI1JCIiR2HmEzkXh/2QWzl69ChuvPFGPP744/jTn/6EuLg4JCUlYc+ePRgyZIjs8/j5+WHWrFl47LHH4Ovri2uvvRYnT57Evn37cNdddyn4CYiISC5mPpHzsfFPbuPMmTMYM2YMbr75ZuTm5gIAEhMTkZ6ejscffxwbNmyw63xz586Fj48P5s2bh/LyckRFReG+++5ToupERGQnZj6Ra+gkSZJcXQmijkhOTsagQYOwdOlSp11Tp9Phww8/xPjx4512TSIiYuYTdRTH/JMqvPrqqwgMDMQPP/yg6HXuu+8+BAYGKnoNIiISY+YTtR97/snjlZWVoba2FgAQGxsLX19fxa514sQJVFVVAQCioqLQuXNnxa5FREQtMfOJOoaNfyIiIiIijeCwHyIiIiIijWDjn4iIiIhII9j4JyIiIiLSCDb+iYiIiIg0go1/IiIiIiKNYOOfiIiIiEgj2PgnIiIiItIINv6JiIiIiDSCjX8iIiIiIo1g45+IiIiISCPY+CciIiIi0gg2/omIiIiINIKNfyIiIiIijWDjn4iIiIhII9j4JyIiIiLSCDb+iYiIiIg0go1/IiIiIiKNYOPfzeXn50On0+Ho0aOuropszz//PPr27Quz2ezqqliprq6Gl5cXXnzxRVdXxW5msxlPP/00evfujU6dOqF3795Oue6yZcsQGxuL+vp6p1yPSOuY+Y7DzLcfM18b2Pgnh6qqqsJzzz2HWbNmwcur9T9eBw4cwP33348+ffogICAAQUFBuPbaa/Haa6+hqalJsbrt3bsXkiRhwIABAID6+nrMmjUL0dHR8Pf3R2JiIj7//PMW79u3bx8mTZqEuLg4BAQEICwsDNdddx0+/vhjxep6qVdffRXz5s3DrbfeipUrV2L58uVOuW5mZiYaGhqcdj0i8ixqzPxLPfPMM9DpdLj66qsVq+ulmPmkKInc2qpVqyQAUklJiaurIsuLL74oGQwGqba2ttXyZcuWSb6+vlJERIT0yCOPSCtWrJAWLVokXXfddRIAadGiRYrVzWQySbW1tZLZbJYkSZJuu+02ycfHR3rkkUek5cuXS0lJSZKPj4/01VdfWb1v/fr1UlpamjR//nxpxYoV0tKlS6Xf/e53EgBp+fLlitX3t4YMGSLdcMMNTrnWpR577DGpZ8+elu+NiJTDzHec9mb+b5WWlkoBAQFS586dpX79+ilW10sx80lJbPy7OU/7IRgwYIB0xx13tFr29ttvSzqdTrrpppuk6urqVss/++wzpasoSZIkffvtty1+eGpra6XevXtLSUlJNt/f1NQkDRw4ULryyiuVrKalXt7e3tLTTz8t61hH27VrlwRAKigocPi5icgaM18Z7c38yZMnS6NGjZKuv/56pzX+mfmkNDb+3VxrPwR79uyRxowZIwUFBUmdO3eWRo0aJW3fvr3Fe7ds2SIlJCRIer1eiouLk5YtWybl5eVJSt3w+emnnyQAUn5+fouy48ePS126dJH69esn1dXVKXJ9W1JTU6URI0ZIkiRJjz76qOTt7S2dO3fO6phnn31WAiAdO3bM5vluuukmKSIiQpG6NrvzzjslAFbbNddcI0nSxc+TlJQkffnll9J1110n+fn5Sffcc4+0f/9+6d5775WuuOIKyd/fX4qIiJBuv/12qby8vNVr7Nq1S5owYYIUHh4u6fV66corr5QWLFhgdUxoaKj04IMPKvpZiYiZ70gdzfwvvvhC8vb2loqKipzW+GfmkzP4OGl0ETnIvn378Lvf/Q4GgwGPPfYYOnXqhOXLlyM5ORlffPEFEhMTAQDfffcdxowZg6ioKCxYsAAmkwlPPvkkwsPDFavbtm3bAABDhgxpUfbSSy/h7Nmz+Oc//wm9Xq9YHUSKiopw6623Arj4/VxxxRUwGAxWxwwfPhwAUFhYiJiYGKuympoa1NbW4ty5c/j3v/+NTz/9FJMnT1a0zn/+858t/41feuklhIaGomfPnpbP06VLF4wfPx5ZWVm4/fbb0adPH6xcuRIlJSWYMmUKIiMjUVRUhOXLl6OyshKbNm2yOv+HH36I2267DXFxcXj00UcRGBiInTt3Wv5bNhsyZAi+/vprRT8rEbXEzG+/jmS+yWTCAw88gLvvvhv9+/d3Wp2Z+eQUrv7XB4ld2gs0fvx4ydfXV/rxxx8tx5SXl0tBQUHSddddZ9k3duxYKSAgQCorK7PsO3LkiOTj46NYL9CcOXMkANL58+dblEVHR0sxMTGSyWRS5Nq2VFZWSgCkV199VZIkSerXr580atSoFsft27dPAiAtW7asRdm9995r6Ynx8vKSJk6cKJ05c0bxuj/++ONS586drb675s8TGBgoHThwwOr4mpqaFueYM2eO5OXlZXWL+NChQ1JAQIB0yy23tLh1fGlP3T333CP5+/s74uMQkQAz3zE6mvmvvPKKFBwcLJ04cUKSJMmpw36Y+aQ09vx7EJPJhI0bN2L8+PGIi4uz7I+KisLtt9+O119/HVVVVejcuTM2bdqEW265BdHR0Zbj+vTpg/T0dMVmqTl9+jR8fHwQGBhotf/HH39EeXk57rvvvjZng2iL2WxGQ0ODrGP1ej10Ol2rZUVFRQBg6cGpra1ttTfKz8/PUn6phx56CBMnTkR5eTneffddmEwmYd0cWfd+/fpZfXfNn+fxxx9H3759rY4PCAiw/O+zZ8+iqakJISEhMJvNVjNrzJs3D35+fsjPz7d87t/W57dCQkJQW1uLCxcuWJ2fiJTDzBdTKvNPnz6NefPmYe7cuXbdOWHmk6fgVJ8e5OTJk7hw4QKuvPLKFmVXXXUVzGYzSktLceLECdTW1qJPnz4tjmtt32uvvYYhQ4agU6dOmD9/fotr3njjjejcuTOuvPJKFBQU2F3v0tJSALD68ZLryy+/hL+/v6zt0KFDbZ7nhx9+APC/HwJ/f/9W5zGuq6uzlF+qb9++SE1NxdSpU7Fu3TpUV1dj7NixkCRJ0bp///33lqnqLv08lw47MplMWLlyJYYMGYKAgACEhIQgPDwcM2fORFhYmOVHur6+Hh9//DHuuOOOFrfBW9P8Gdv6sSIix2Pmuybz58yZg9DQUDzwwAMuqTszn5TGnn9CVFQU5s+fj3feeadFWXZ2NiIjI3Hy5Els2rQJf/zjH3HkyBGEhoa2OLZr165oamrC+fPnERQUZNnf3PPQ2Nhod9369u2LVatWyf4cbSkqKkJsbCyCg4Mtx5aVlbU47vjx4wBg1XvWlokTJ+Lee+/F4cOHW/1xdkTdz549i9LS0hZjTouKihAVFdXix3Xq1Kl49913kZGRgYceeghhYWHw9fXFgw8+aHWNn376CRcuXEBCQoKs+v3yyy8ICAho9R9FRORZmPn/c2nmHzlyBCtWrMDSpUtRXl5uOa6urg6NjY04evQoDAZDq98HM588BRv/HiQ8PBwBAQGt9hgcPHgQXl5eiImJQefOneHn54fi4uIWx7W2b/z48QCATz75xGp/dXU11q5di59++gkBAQEYN24c+vfvj48++gjTpk1rcZ7mW5ElJSVWvRbNPU979+6V/2F/FRkZiczMTLvfd6mioiKrOg0aNAhbtmxBVVWVVS/It99+aym3pfk28blz51otd0Tdm2/1XtoLVFRUhIEDB1rt279/P9555x28+OKLeOihhyz7jx8/jsOHD2PMmDEt6i63V6ekpARXXXVVez4CEbUTM7/92pv5ZWVlMJvNePDBB/Hggw+2OO9ll12GGTNmYOnSpYrUnZlPzsBhPx7E29sbN9xwAz766COrpd8rKyvxzjvvYOTIkTAYDPD29kZqairWrl1r1XNRXFyMTz/9VPb1jhw5gsDAQPTo0cOyr3///ti3b1+rxyclJQEAdu3aZbW/V69eGDZsGN577z3s2bOnxfvOnz+PF154QXa97GUymbB//36rMJ04cSJMJhNWrFhh2VdfX49Vq1YhMTHRataHEydOtDhnY2Mj3nzzTfj7+yM+Pl6xun///fcArH8Imj/PpT8E//3vfwFY3+ZvbGzE3XffDZPJ1OqP86UzQTS/51J79uzBiBEjOvBJiMhezPz26UjmX3311fjwww9bbP369UNsbCw+/PBD3HXXXYrVnZlPzsCefw/z9NNP4/PPP8fIkSPxl7/8BT4+Pli+fDnq6+vx/PPPW46bP38+Nm7ciGuvvRb3338/TCYTXnnlFVx99dUoLCyUda3q6uoWYwMNBgNOnz7d6vFxcXG4+uqrsWnTJtx5551WZa+//jquv/56XHvttbjzzjvRv39/NDU1Yc+ePfjggw8wcuRIzJw5074vQ6YjR46grq7O6jZqYmIiJk2ahNmzZ+PEiRPo06cPVq9ejaNHj+KNN96wev+9996LqqoqXHfddejevTsqKirw9ttv4+DBg3jhhRdaPOzmSEVFRejevbvVLebmz3PpD8GgQYMQEBCAGTNmoKSkBLW1tfjHP/4Bb29vALA63mAwIDMzE/n5+aivr0dycjLOnz+PLVu24MYbb0R2drbl2N27d+PMmTO4+eabFfucRNQ6Zr79OpL5YWFhljsjv9Xc099amSMx88kpXDzbENnQ1oIvaWlpUmBgoBQQECClpKRI27Zta/HegoICafDgwZKvr6/Uu3dv6e9//7s0c+ZMyc/Pr9Vr3XvvvVJeXp7VdUJCQqyOmT59ujRz5sw267tkyRIpMDBQunDhQouy4uJiadq0aVL37t2lTp06SREREdI111wjzZ8/XyouLrbxTbTfu+++KwGQ9u3bZ7W/trZWeuSRR6TIyEhJr9dLw4YNkzZs2NDi/f/85z+l1NRUKSIiQvLx8ZFCQkKk1NRU6aOPPlKszs2GDx8upaenW+1r/jx79+5tcfy6deukvn37Sn5+ftKAAQOkVatWSfPnz5d8fHxaTOVWU1MjPfHEE9Lll18u6fV6KSoqSpowYUKL/xazZs2SYmNjudQ7kRMw8zuuo5nfGmdN9cnMJ2fQSVIbU5WQKo0fPx779u3DkSNHWpTdd999iIyMtMz+UF1djdDQUJSUlKB79+4AgJSUFEydOrXV8Z/AxfHvcXFxeP755xW9NUrOUV9fj169eiE3NxczZsxwdXWIyE7MfLIHM18bOOZfxS6dq/7IkSP45JNPkJycbLW/qakJdXV1MJlMVv87MDAQN998M/Ly8lBbW4t169ahqKhIeCswODgYjz32GBYtWgSz2azExyInWrVqFTp16oT77rvP1VUhIhuY+dRRzHxtYM+/ikVFRSEzMxNxcXH4+eef8dprr6G+vh7fffcdLr/8cstx8+fPx4IFC6zeu2rVKmRmZuLkyZPIyMjA1q1b0aNHD7z66qtITU119kchIiIbmPlEJAcb/yo2bdo0bNmyBRUVFdDr9UhKSsKzzz6LIUOGuLpqRETkYMx8IpKDjX8iIiIiIo3gmH8iIiIiIo1g45+IiIiISCPY+CciIiIi0giu8GuD2WxGeXk5goKCoNPpXF0dIk2SJAnnz59HdHQ0vLzs67Ooq6tDQ0OD7ON9fX3h5+dnbxVJBZj3RO7BWZmv1bxn49+G8vJyxMTEuLoaRASgtLQUPXr0kH18XV0dLusZiIoTJtnviYyMRElJiSZ/ELSOeU/kXpTOfK3mPRv/NgQFBQG4+AfQYDC4uDZE2lRVVYWYmBjL30e5GhoaUHHChOJdMTAE2e49qjpvRp+hpWhoaNDcjwEx74nchTMyX8t5r6rG/y233IKtW7di9OjReP/99wFcDPEpU6bgxIkT8PHxwdy5czFp0iTZ52y+9WswGPhjQORi7R2KERikQ2CQ7feawaEensTRmc+8J3IvSma+lvNeVQ/8zpgxA2+++abVPh8fHyxduhT79+/Hxo0b8dBDD6GmpsZFNSQiVzDb8X/kOZj5RNQa5r2Yqhr/ycnJLW4RRUVFYdCgQQAuju0KCwvDmTNnXFA7InIVkyTJ3shzMPOJqDXMezG3afx/+eWXGDt2LKKjo6HT6bB27doWxxiNRvTq1Qt+fn5ITEzEjh077LrG7t27YTKZ+EAXkcaYIcneyDmY+USkFOa9mNs0/mtqajBw4EAYjcZWy9esWYOcnBzk5eVhz549GDhwINLS0nDixAlZ5z9z5gymTp2KFStWOLLaROQBzJBgkrFp+cfA2Zj5RKQUOZmv5bx3mwd+09PTkZ6e3mb5kiVLkJWVhWnTpgEAli1bhvXr12PlypXIzc0Vnru+vh7jx49Hbm4uRowYYfPY+vp6y+uqqio7PgURuSO5vTxa/jFwNnfIfOY9kTrJyXwt573b9PyLNDQ0YPfu3UhNTbXs8/LyQmpqKrZv3y58ryRJyMzMxKhRozBlyhSb11q4cCGCg4MtG28XE3k+jvn3LM7KfOY9kTox78Xcpudf5NSpUzCZTIiIiLDaHxERgYMHD1pep6am4vvvv0dNTQ169OiB9957DyaTCWvWrMGAAQMsY0r/8Y9/oH///q1ea/bs2cjJybG8bp5rlog8VyMkNMro5ZFzDCnPWZnPvCdSJzmZr+W894jGv1ybNm1qdb/ZLH86J71eD71e76gqEZEbMEkXNznHkefoaOYz74nUSU7maznvPWLYT1hYGLy9vVFZWWm1v7KyEpGRkYpc02g0Ij4+HsOGDVPk/ETkPGY7NnI9Z2c+855IXZj3Yh7R+Pf19UVCQgIKCgos+8xmMwoKCpCUlKTINbOzs7F//37s3LlTkfMTkfOYoYNJxqblFR/dibMzn3lPpC5yMl/Lee82w36qq6tRXFxseV1SUoLCwkKEhoYiNjYWOTk5yMjIwNChQzF8+HAsXboUNTU1lpkgiIjaYpYubnKOI+dg5hORUuRkvpbz3m0a/7t27UJKSorldfNDWBkZGcjPz8fkyZNx8uRJzJs3DxUVFRg0aBA2bNjQ4oEwIqJLNff0yDmOnIOZT0RKkZP5Ws57t2n8JycnQ7Ix7dL06dMxffp0p9THaDTCaDTCZDI55XpEpBw2/t2PO2U+855IXdj4F/OIMf+uwDGgROphlnSyN9Ie5j2RujDvxdym55+ISCns+Sci0g72/Iux8U9EqtckeaNRsn2js0nDPUFERGohJ/O1nPcc9tMGzvtMpB5ypvmUe3eA1Id5T6QuzHsx9vy3ITs7G9nZ2aiqqkJwcLCrq0Nkt2v+/IKw/Hys+N/++xY+7MjquJRJ8oJJRs+/lld81DLmPXm6I/+NFpaHeonzr2v0fx1ZHZeTk/lazns2/olI9czQwSzjRqcZGv41ICJSCTmZr+W8Z+OfiFSPD/wSEWkHH/gVY+O/DZz3mUg95A/70W5PkJYx74nURd6wH+3mPR/4bQPnfSZSj4u3gOVtpD3MeyJ1Yd6LsfFPRKpnhhdMMjY5zwUQEZF7k5P5Sub9M888gxEjRiAgIABdunRR7DrtxWE/RKR6jZIPGiVvGcdptyeIiEgt5GS+knnf0NCASZMmISkpCW+88YZi12kvNv6JSPVMkg4mGUEv5xgiInJvcjJfybxfsGABACA/P1+xa3QEG/9Ebmp0ykJheXWMXlh+vpf4lmZdmPhhp175zwnLfYMahOWHJ8wVljtT821e28cp9wDYmTNn8MADD+Djjz+Gl5cXJkyYgJdeegmBgYFtvic5ORlffPGF1b57770Xy5YtU6yeROR8O4/1EpZ39aoXlod6iXu5A3SdhOVNFX2E5QDgZSNDvSIP2zyHs8jJ/Oa8r6qqstqv1+uh14t/Xz0dB7i2gSs+EqmHWfKSvSnlz3/+M/bt24fPP/8c69atw5dffol77rnH5vuysrJw/Phxy/b8888rVketYt4TqYs9eR8TE4Pg4GDLtnChuONNDdjz3wau+EikHq7u+T9w4AA2bNiAnTt3YujQoQCAl19+GX/4wx+wePFiREe3vTpnQEAAIiMjFakXXcS8J1IXe3r+S0tLYTAYLPvb6vXPzc3Fc8+J74gfOHAAffv2tbO2zsfGPxGpnhnyxneaf/3/jr4NvH37dnTp0sXS8AeA1NRUeHl54dtvv8Utt9zS5nvffvttvPXWW4iMjMTYsWMxd+5cBAQEtLsuRERqJyfzm/PeYDBYNf7bMnPmTGRmZgqPiYuLk1dBF2Pjn4hUzyxzWrfmY2JiYqz25+XlYf78+e2+fkVFBbp162a1z8fHB6GhoaioqGjzfbfffjt69uyJ6OhoFBUVYdasWTh06BA++OCDdteFiEjt5GS+vVN9hoeHIzw8vCPVchts/BOR6slf4ffiMY6+Ddxev30moH///oiKisLo0aPx448/onfv3u0+LxGRmslb4Ve5Z7yOHTuGM2fO4NixYzCZTCgsLAQA9OnTRzjJg7Ow8U9Eqid3NcfmYxx9GzgyMhInTpyw2t/U1IQzZ87YNZ4/MTERAFBcXMzGPxFRG+RkvpIr/M6bNw+rV6+2vB48eDAAYMuWLUhOTlbsunKx8U9Eqtcg+cBbsh13DXY+7yv3NnBSUhLOnj2L3bt3IyEhAQCwefNmmM1mS4Nejubeo6ioKPsqSkSkIXIy3968t0d+fr7bzvEPsPHfJqPRCKPRCJPJ5OqqkErZmse/qpf4AdOa7uJei9oIs7BcCm4UlvsFiufxP3jrPGG5OzFLOpjlPPCr0KIvV111FcaMGYOsrCwsW7YMjY2NmD59Om677TbLTD9lZWUYPXo03nzzTQwfPhw//vgj3nnnHfzhD39A165dUVRUhIcffhjXXXcdBgwYoEg9tYp5T0rr6Dz+wTbm8Q/U+QrLvXU25uiXMf7dnebxt0VO5iuV956A8/y3ITs7G/v378fOnTtdXRUi6iDzr9O+2drsfQDMHm+//Tb69u2L0aNH4w9/+ANGjhyJFStWWMobGxtx6NAhXLhwAQDg6+uLTZs24YYbbkDfvn0xc+ZMTJgwAR9//LFiddQq5j2RusjJfCXz3t2x55+IVE/uAl5KLvIVGhqKd955p83yXr16QZL+dx86Jiamxeq+RERkm5zMVzLv3R0b/0SkeiboYJLxcJecY4iIyL3JyXwt5z0b/0Skeu7Q809ERM7Bnn8xNv6JSPVMkNfLw8c9iYg8n5zM13Les/FPRKrHnn8iIu1gz78YG/9EpHpNkjcaJfFUeRePE0+PSkRE7k9O5ms579n4bwPnfaaOGjX6r8Ly85eJ5/Gv7mFjHv/u4j+bukDxPP5BQXXC8h/GLRCWexI5S703H0faw7ynjtpzLFZY3tVLnMcdnce/k65jzTlPmsNfDjmZr+W81+4nt4HzPhOpR/OCL3I20h7mPZG6MO/F2PNPRKrXvKiLnOOIiMizycl8Lec9G/9EpHpye3m03BNERKQWcjJfy3nPxj8RqZ5Z5lLuWl7unYhILeRkvpbzno1/IlI9k6SDSUYvj5xjiIjIvcnJfC3nPRv/RKR6HPZDRKQdHPYjxsY/EameJHORL0nDU78REamFnMzXct6z8U9Eqtco6aCTEfSNGu4JIiJSCzmZr+W8Z+OfqB1Gpyy0eYytRbzO97S1iFeTsNzHxiJeAQH1wnI1LeJli5yl3puPIyK61M5jvYTlthbxCvUSN7f8dZ2E5VzEyz5yMl/Lec/GPxGpnhk6mCFjzL+MY4iIyL3JyXwt5z0b/0Skepzth4hIOzjbjxgb/0Skehz2Q0SkHRz2I6bdT26D0WhEfHw8hg0b5uqqEFEHmaGzTP0m3DR8G1jLmPdE6iIr8xXK+6NHj+Kuu+7CZZddBn9/f/Tu3Rt5eXloaGhQ5HrtwZ7/NmRnZyM7OxtVVVUIDg52dXWIqAMkmWP+JTb+NYl5T6QucjJfqbw/ePAgzGYzli9fjj59+mDv3r3IyspCTU0NFi9erMg17cXGPxGpHhf5IiLSDlcu8jVmzBiMGTPG8jouLg6HDh3Ca6+95jaNfw77ISLVazJ7y96U8swzz2DEiBEICAhAly5dZL1HkiTMmzcPUVFR8Pf3R2pqKo4cOaJYHYmI1MDVeX+pc+fOITQ01GnXs4U9/0St+P21TwvLz/UNsHkOW/P410WL5/HvZBDP01/8xzk260AXucNUnw0NDZg0aRKSkpLwxhtvyHrP888/j7/97W9YvXo1LrvsMsydOxdpaWnYv38//Pz8FKsrkdZ8+/NlwvJwb3EeKz2Pv9bm6e8oe6b6rKqqstqv1+uh14vX6bFHcXExXn75Zbfp9QfY809EGiDrYV+ZQ4Paa8GCBXj44YfRv39/WcdLkoSlS5dizpw5uPnmmzFgwAC8+eabKC8vx9q1axWrJxGRp7Mn72NiYhAcHGzZFi5sfRHP3Nxc6HQ64Xbw4EGr95SVlWHMmDGYNGkSsrKyFP/ccrHnn4hUzxPH/JeUlKCiogKpqamWfcHBwUhMTMT27dtx2223ubB2RETuy54x/6WlpTAYDJb9bfX6z5w5E5mZmcJzxsXFWf53eXk5UlJSMGLECKxYsUJmzZ2DjX8iUj17G/9K3waWo6KiAgAQERFhtT8iIsJSRkRELdnT+DcYDFaN/7aEh4cjPDxc1vXLysqQkpKChIQErFq1Cl5e7jXQxr1qQ0SkAHuH/Sh5G5iIiJTlymGeZWVlSE5ORmxsLBYvXoyTJ0+ioqLCrTpt2PNPRKonQd7DvNKv/1+p28D2iIyMBABUVlYiKirKsr+yshKDBg1q1zmJiLRATuZLwtL2+/zzz1FcXIzi4mL06NHD+pqSUle1Dxv/RKR69g77UeI2sL0uu+wyREZGoqCgwNLYr6qqwrfffov7779fkWsSEamBK+f5z8zMtNkp5Goc9kNEqtdk9pK9KeXYsWMoLCzEsWPHYDKZUFhYiMLCQlRXV1uO6du3Lz788EMAgE6nw0MPPYSnn34a//73v/HDDz9g6tSpiI6Oxvjx4xWrJxGRp3N13rs79vyTKqWH3ycsN/WOFpafjQ8Ullf1tt1jUB/RKCz3CRSXcx5/x3GH2X7mzZuH1atXW14PHjwYALBlyxYkJycDAA4dOoRz585ZjnnsscdQU1ODe+65B2fPnsXIkSOxYcMGzvFPdIlPSq4Wlnf1qhGWR3jXCctDvcTz9OttzNPPefydy5U9/56AjX8iUj1J0kGSEfRyjmmv/Px85Ofn27i+9XhQnU6HJ598Ek8++aRi9SIiUhs5ma9k3rs7Nv6JSPXcYYVfIiJyDntW+NUiVQ14uuWWWxASEoKJEyfK2k9E2uAOK/yS4zHziag1zHsxVTX+Z8yYgTfffFP2fiLShuZbwHI28hzMfCJqDfNeTFWN/+TkZAQFBcneT0TawJ5/dWLmE1FrmPdibtP4//LLLzF27FhER0dDp9Nh7dq1LY4xGo3o1asX/Pz8kJiYiB07dji/okTkcdjz736Y+USkFOa9mNs0/mtqajBw4EAYjcZWy9esWYOcnBzk5eVhz549GDhwINLS0nDixAkn15SIPI0ksxdIyz8GzsbMJyKlyMl8Lee928z2k56ejvT09DbLlyxZgqysLEybNg0AsGzZMqxfvx4rV65Ebm6uw+pRX1+P+vp6y+uqqiqHnZscJy0wQ1huHnC5sPyXqwKE5VWXiUOhIaxJWA4AvoYGYfmRSZzH31lM0AEygt6k4dkfnM0dMp957zk+/mmAsLybN+fxp/+Rk/laznu36fkXaWhowO7du5GammrZ5+XlhdTUVGzfvt2h11q4cCGCg4MtW0xMjEPPT0TOx2E/nsVZmc+8J1In5r2YRzT+T506BZPJhIiICKv9ERERqKiosLxOTU3FpEmT8Mknn6BHjx6WH4m29rdm9uzZOHfunGUrLS1V5kMRkdPwgV/P4qzMZ94TqRPzXsxthv04wqZNm+za3xq9Xg+9Xu+oKhGRG5Cki5uc48hzdDTzmfdE6iQn87Wc9x7R8x8WFgZvb29UVlZa7a+srERkZKQi1zQajYiPj8ewYcMUOT8ROQ+H/XgWZ2c+855IXZj3Yh7R+Pf19UVCQgIKCgos+8xmMwoKCpCUlKTINbOzs7F//37s3LlTkfMTkfOw8e9ZnJ35zHsidWHei7nNsJ/q6moUFxdbXpeUlKCwsBChoaGIjY1FTk4OMjIyMHToUAwfPhxLly5FTU2NZSYIIqK2mCUddDKCXstjQJ2NmU9ESpGT+VrOe7dp/O/atQspKSmW1zk5OQCAjIwM5OfnY/LkyTh58iTmzZuHiooKDBo0CBs2bGjxQBgR0aU45t/9MPOJSCkc8y/mNo3/5ORkSDb+S0yfPh3Tp093Sn2MRiOMRiNMJpNTrkfWfu81SViuG9pfWH72SvE8/ucutzGPf7dGYbneUC8sB4DDE+faPIacw2zWQWe2PcrRbNZuT5CzuVPmM+9d74MfBwvLu/ucF5ZzHn/6LTmZr+W894gx/67AMaBE6iHZsZH2MO+J1IV5L+Y2Pf9EREqR+3CXlh8AIyJSCzmZr+W8Z+OfiNRPbjePlruCiIjUQk7mazjvOeynDZz3mUhF5E77puGeIC1j3hOpDPNeiI3/NnAMKJF6NM/8IGcj7WHeE6mLq/N+3LhxiI2NhZ+fH6KiojBlyhSUl5crd0E7sfFPRKrHRb6IiLTD1XmfkpKCd999F4cOHcK//vUv/Pjjj5g4caJi17MXx/wTkfrJvcXLxj8RkeeTk/kK5v3DDz9s+d89e/ZEbm4uxo8fj8bGRnTqJJ521hlkN/7//e9/233y3//+9/D397f7fe6A8z4r6/fek4XlXkP6CctPDQwSllfFia/fGCL+7+rt1yQs5xz+nsUdFvl65plnsH79ehQWFsLX1xdnz561+Z7MzEysXr3aal9aWho2bNigUC3/R0uZz7xXlq05/AGgu89ZYXm0j3ge/2Bdx+bx99aJB0JwHn/PYs8iX1VVVVb79Xo99Hq9w+py5swZvP322xgxYoRbNPwBOxr/48ePt+vEOp0OR44cQVycjVaYm8rOzkZ2djaqqqoQHBzs6uoQUQdIZh0kGQu6yDmmvRoaGjBp0iQkJSXhjTfekP2+MWPGYNWqVZbXjvxREtFS5jPvidRFTuY3l8fExFjtz8vLw/z58ztch1mzZuGVV17BhQsXcM0112DdunUdPqej2DXmv6KiAmazWdYWECBeYZWIyKlcvOLLggUL8PDDD6N/f/Hq1JfS6/WIjIy0bCEhIQrVsCVmPhF5LJl5X1painPnzlm22bNnt3q63Nxc6HQ64Xbw4EHL8Y8++ii+++47bNy4Ed7e3pg6darNVc2dRXbPf0ZGhl23c++44w4YDIZ2VYqIyJHsXeRL6dvA9ti6dSu6deuGkJAQjBo1Ck8//TS6du2q+HWZ+UTkqexZ5MtgMMjKrpkzZyIzM1N4zG/vfIaFhSEsLAxXXHEFrrrqKsTExOCbb75BUlKS7Q+gMNmN/9/edpbjtddes7syRESKsHORL6VuA9trzJgxuPXWW3HZZZfhxx9/xOOPP4709HRs374d3t7eil6bmU9EHkuBRb7Cw8MRHh7eruqYzWYAQH19fbve72ic7YeINED36ybnuIu3gX/bE9RWr39ubi6ee+454RkPHDiAvn37yq2oldtuu83yv/v3748BAwagd+/e2Lp1K0aPHt2ucxIRqZ+czFfmGa9vv/0WO3fuxMiRIxESEoIff/wRc+fORe/evd2i1x/oQOO/rq4ORUVFOHHihOVfNM3GjRvX4Yq5Gmd/IFIRO3v+lboN3FFxcXEICwtDcXGx0xv/as585j2RyijQ8y9XQEAAPvjgA+Tl5aGmpgZRUVEYM2YM5syZ47Lho5dqV+N/w4YNmDp1Kk6dOtWiTKfTqSJAOfsDkYrY2fiXqyO3gdvjv//9L06fPo2oqCinXRNQf+Yz74lUxoWN//79+2Pz5s3KnNxB2rXC7wMPPIBJkybh+PHjLWZ88PQfASJSoeYFX+RsCjl27BgKCwtx7NgxmEwmFBYWorCwENXV1ZZj+vbtiw8//BAAUF1djUcffRTffPMNjh49ioKCAtx8883o06cP0tLSFKtna5j5RORRXJz37q5dPf+VlZXIyclBRESEo+tDKpEecb+wXDdMvIjXyUHiRbyqY4TFaAwVN0i8ghqE5T/d/rj4AuRRJPPFTc5xSpk3b57Vgl2DB19c+GjLli1ITk4GABw6dAjnzp0DAHh7e6OoqAirV6/G2bNnER0djRtuuAFPPfWU028dM/NJxNYiXpE+52yew9WLePlEFgvLybPIyXwl897dtavxP3HiRGzduhW9e/d2dH2IiBxPbi+Pgj1B+fn5yM/PF1/+N3NA+/v747PPPlOsPvZg5hORR5GT+ez5t88rr7yCSZMm4auvvkL//v1bLFf84IMPOqRyRESOoJMubnKOo5aY+UTkSeRkvpbzvl2N/3/+85/YuHEj/Pz8sHXrVuh0//vXk06n4w8BEbkXhR741QpmPhF5FBc+8OsJ2tX4f+KJJ7BgwQLk5ubCy6tdzwy7PU79RqQibjDsx5OpPfOZ90Qqw2E/Qu1K8YaGBkyePFmVPwLNsrOzsX//fuzcudPVVSGijpLs2KgFtWc+855IZZj3Qu1K8oyMDKxZs8bRdSEiUgYb/x3CzCcij8K8F2rXsB+TyYTnn38en332GQYMGNDi4a8lS5Y4pHJERA7BMf8dwswnIo/CMf9C7Wr8//DDD5Y5qvfu3evQCpFnSL9ilrC8bmAvYfmZq3yF5bXdxNevj2gSlv9896PiE5C2cMx/hzDztc3WPP7dfc4Ky2N86m1eI7CD8/jro36yeQ3SEI75F2pX43/Lli2OrgcRkWJ05oubnOOoJWY+EXkSOZmv5bxv15j/hQsXYuXKlS32r1y5Es8991yHK0VERO6DmU9EpB7tavwvX74cffv2bbG/X79+WLZsWYcrRUTkSDr8b9EX4ebqiropZj4ReRJZme/qSrpQuxr/FRUViIqKarE/PDwcx48f73Cl3IHRaER8fDyGDRvm6qoQUUc1j/+Us1ELas985j2RyjDvhdrV+I+JicHXX3/dYv/XX3+N6OjoDlfKHXDeZyIV4VSfHaL2zGfeE6kM816oXQ/8ZmVl4aGHHkJjYyNGjRoFACgoKMBjjz2GmTNnOrSCREQdxqk+O4SZT0QehVN9CrWr8f/oo4/i9OnT+Mtf/oKGhgYAgJ+fH2bNmoXZs2c7tIJERB3VPMZTznHUEjOfiDyJnMzXct63q/Gv0+nw3HPPYe7cuThw4AD8/f1x+eWXQ6/XO7p+5CJpCXnC8gv9woXlZ3t7C8trI8R/65oiGoTlP0/NFZYTWWHPf4cw89Xto58GCcu7+5wTltuaxz/YSzyHPwDobczz7xNZbPMcRBbs+RdqV+O/WWBgIB+QIiK3x3n+HYOZT0SegPP8i8l+4LeoqAhms/xvat++fWhqEq/CSkTkFJztx27MfCLyWMx7IdmN/8GDB+P06dOyT5yUlIRjx461q1JERA7F2X7sxswnIo/FvBeSPexHkiTMnTsXAQEBso5vfiiMiMjV+MCv/Zj5ROSp3OWB3/r6eiQmJuL777/Hd999h0GDBil/URlkN/6vu+46HDp0SPaJk5KS4O/v365KERE5FB/4tRszn4g8lps88PvYY48hOjoa33//vfIXs4Psxv/WrVsVrAYRkYJk9vyz8f8/zHwi8lhyMl/hvP/000+xceNG/Otf/8Knn36q7MXs1K4VfomIPIqLx/wfPXoUd911Fy677DL4+/ujd+/eyMvLszlUpq6uDtnZ2ejatSsCAwMxYcIEVFZWKlNJIiK1cPGY/8rKSmRlZeEf//iH7KGTztShqT7Jc6Ve/6yw/Hy/YGF5VU/xU/K10SZhuV/EBWF58a3zhOVEdnHxsJ+DBw/CbDZj+fLl6NOnD/bu3YusrCzU1NRg8eLFbb7v4Ycfxvr16/Hee+8hODgY06dPx6233oqvv/5amYqSKn1ScrWwvJeNefyjfcSzPgXqxOs92JrDHwC8Ig/bPIZINjuG/VRVVVnt1uv1HVrDRJIkZGZm4r777sPQoUNx9OjRdp9LKez5b4PRaER8fDzntCZSgeaHv+RsShgzZgxWrVqFG264AXFxcRg3bhweeeQRfPDBB22+59y5c3jjjTewZMkSjBo1CgkJCVi1ahW2bduGb775RpmKahTznkhd7Mn7mJgYBAcHW7aFCxe2es7c3FzodDrhdvDgQbz88ss4f/68W69+3q6e/7/97W+YOHEioqOjHV0ft5GdnY3s7GxUVVUhOFjcC05Ebs4NH/g9d+4cQkND2yzfvXs3GhsbkZqaatnXt29fxMbGYvv27bjmmmucUU0A6s985j2RytjR819aWgqDwWDZ3Vav/8yZM5GZmSk8ZVxcHDZv3ozt27e3OM/QoUPx5z//GatXr7ZRMeW1q/H/0EMP4aWXXsLWrVsRExNj2d/Q0IAffvgBCQkJDqsgEVFH2TvVp6NvA1+quLgYL7/8snDIT0VFBXx9fdGlSxer/REREaioqHBYXeRg5hORJ7Fnqk+DwWDV+G9LeHg4wsPDbR73t7/9DU8//bTldXl5OdLS0rBmzRokJibafL8ztHvYT2pqKq6//nqUlpZa9v3yyy8YPny4QypGRORQdjz85ejbwL9VVlaGMWPGYNKkScjKylLikyqCmU9EHsVFD/vGxsbi6quvtmxXXHEFAKB3797o0aOHche2Q7t6/nU6HZ566il069YN119/Pb744gtLb5Akca48InIzdg77cfRt4Gbl5eVISUnBiBEjsGLFCuH7IiMj0dDQgLNnz1r1/ldWViIyMlL8ORyMmU9EHsVN5vl3Vx2a7eepp56CTqez/Bj4+vpCpxPPAkNE5Gz2Dvtx9G1g4GKPf0pKiuXBXS8v8Y3XhIQEdOrUCQUFBZgwYQIA4NChQzh27BiSkpJkXdPRmPlE5AncZYVfAOjVq5fbdZK0q/H/2w/x5JNPWn4M/u///s9hFSMichgXP/BbVlaG5ORk9OzZE4sXL8bJkyctZc29+GVlZRg9ejTefPNNDB8+HMHBwbjrrruQk5OD0NBQGAwGPPDAA0hKSnLqw74AM5+IPAx7/oXa1fh/5pln0LlzZ8vrBQsWAADGjh3rmFpRh/3+2qeF5Wf6dxaWV9sYlma6XDxPf4+u4nmj/5P6vPgCRA5kb8+/o33++ecoLi5GcXFxizGfzQ3rxsZGHDp0CBcu/O/v1osvvggvLy9MmDAB9fX1SEtLw6uvvqpMJQWY+e7t458GCMt7d/pFWB7uLT5/iJe/sNzLxuODnMOfnM2dev7dUbsa/63NXbpgwQJ06tRJOHsFEZFLuLjnPzMz0+azAa3dGvbz84PRaITRaFSmYjIx84nIo7DnX8ihi3zNmTMHZ8+edeQpiYg6TGeWv5F8zHwickfMe7EOPfBLROQR3HCRLyIiUgh7/oXY+Cci9WPjn4hIO9j4F2Ljn4hUz9UP/BIRkfPwgV8xNv6JSP3Y809EpB3s+Rdi45+IVI89/0RE2sGefzFVNf5vueUWbN26FaNHj8b7779v2b9u3TrMnDkTZrMZs2bNwt133+3CWjrG6JSFwvLTQ8Tz+J/vJf5Tr+9dJSwfFlUmLH878XVhOZFTsedfdbSU97bn8T8tLI/2Fk/sF+ilF5ZzHn/yOOz5F3LoVJ+uNmPGDLz55ptW+5qampCTk4PNmzfju+++w6JFi3D6tDgoiUhlJDs28gjMeyJqE/NeSFWN/+TkZAQFBVnt27FjB/r164fu3bsjMDAQ6enp2Lhxo4tqSESu0HwLWM5GnoF5T0RtYd6LuU3j/8svv8TYsWMRHR0NnU6HtWvXtjjGaDSiV69e8PPzQ2JiInbs2GHzvOXl5ejevbvldffu3VFWJh6yQkTqwsa/e2HeE5GSmPdibtP4r6mpwcCBA9tcxn7NmjXIyclBXl4e9uzZg4EDByItLQ0nTpxwck2JyONw2I9bYd4TkaKY90Ju0/hPT0/H008/jVtuuaXV8iVLliArKwvTpk1DfHw8li1bhoCAAKxcuVJ43ujoaKuen7KyMkRHR7d5fH19Paqqqqw2IlIB/hC4DeY9ESmOed8mt2n8izQ0NGD37t1ITU217PPy8kJqaiq2b98ufO/w4cOxd+9elJWVobq6Gp9++inS0tLaPH7hwoUIDg62bDExMQ77HETkGhz24zmY90TUUcx7MY9o/J86dQomkwkRERFW+yMiIlBRUWF5nZqaikmTJuGTTz5Bjx49sH37dvj4+OCFF15ASkoKBg0ahJkzZ6Jr165tXmv27Nk4d+6cZSstLVXscxGRk3DYj8dg3hNRhzHvhVQ1z/+mTZta3T9u3DiMGzdO1jn0ej30evGcx0TkWbjIl/ow74moLVzkS8wjGv9hYWHw9vZGZWWl1f7KykpERkYqck2j0Qij0QiTyaTI+W0Z9fu/CstPD/ATlp+7Slzvy644LixPj9wvLE8L3CssJ3Ircnt5NPxj4C60mPcdXcSrh7e3sDzQS/x7YYZZWH7CVC0sV+a/ClEHyMl8Dee9Rwz78fX1RUJCAgoKCiz7zGYzCgoKkJSUpMg1s7OzsX//fuzcuVOR8xOR83DMv+dg3hNRRzHvxdym57+6uhrFxcWW1yUlJSgsLERoaChiY2ORk5ODjIwMDB06FMOHD8fSpUtRU1ODadOmubDWROQRzL9uco4jxTHviUhRcjJfw3nvNo3/Xbt2ISUlxfI6JycHAJCRkYH8/HxMnjwZJ0+exLx581BRUYFBgwZhw4YNLR4KIyK6FMf8uxfmPREpiWP+xdym8Z+cnAxJEv+XmD59OqZPn+6U+rh6DCgRORDH/LsV5j0RKcrFY/579eqFn3/+2WrfwoULkZubq9xF7eA2jX93k52djezsbFRVVSE4ONjV1SGiDtBJEnQ2GpvNx5H2MO+J1EVO5iud908++SSysrIsr4OCghS9nj3Y+Cci9WPPPxGRdrjBbD9BQUGKzVDWUR4x2w8RUUe4erafo0eP4q677sJll10Gf39/9O7dG3l5eWhoaBC+Lzk5GTqdzmq77777lKkkEZFK2JP3VVVVVlt9fb1D6vDXv/4VXbt2xeDBg7Fo0SI0NTU55LyOwJ7/Nrh6DOiJQeKFZ6r7ixsNCX1+Fpb/OWK7sHyU/ylheZdoroRJHsTFPf8HDx6E2WzG8uXL0adPH+zduxdZWVmoqanB4sWLhe/NysrCk08+aXkdEBCgTCU1zNV5b2se/54+4p9qf53496JWEjdm9tR3Epb/rle5sJzI7djR8x8TE2O1Oy8vD/Pnz+/Q5R988EEMGTIEoaGh2LZtG2bPno3jx49jyZIlHTqvo7Dx3waOASVSD1fP9jNmzBiMGTPG8jouLg6HDh3Ca6+9ZrPxHxAQ4La3jtWCeU+kLvbM9lNaWgqDwWDZ39aq37m5uXjuueeE5zxw4AD69u1rmcEMAAYMGABfX1/ce++9WLhwoVusKs7GPxGpns58cZNzHHDxNvBv6fV6hwf2uXPnEBoaavO4t99+G2+99RYiIyMxduxYzJ07l73/REQCcjK/udxgMFg1/tsyc+ZMZGZmCo+Ji4trdX9iYiKamppw9OhRXHnllTavpTQ2/olI/ewc9qPEbeDfKi4uxssvv2yz1//2229Hz549ER0djaKiIsyaNQuHDh3CBx984LC6EBGpjgIP/IaHhyM8PLxd1SksLISXlxe6devWrvc7Ghv/bXD1GFAicix7hvQocRu4WVlZGcaMGYNJkyZZTQPXmnvuucfyv/v374+oqCiMHj0aP/74I3r37i3no5AMzHsi9XHVIl7bt2/Ht99+i5SUFAQFBWH79u14+OGHcccddyAkJMQ1lboEG/9t4BhQIhWRpIubnOOg3G3g8vJypKSkYMSIEVixYoXt+lwiMTERwMU7B2z8Ow7znkhl5GS+QvP86/V6/N///R/mz5+P+vp6XHbZZXj44YetngNwNTb+iUj1lHrg157bwGVlZUhJSUFCQgJWrVoFLy/7Z1ouLCwEAERFRdn9XiIirbDngV9HGzJkCL755htlTu4gnOefiNRPsmNTQFlZGZKTkxEbG4vFixfj5MmTqKioQEVFhdUxffv2xY4dOwAAP/74I5566ins3r0bR48exb///W9MnToV1113HQYMGKBMRYmI1MCFee8J2PPvAv0fedHmMbXDLgjLJ/YtEpbf2mWnsDzBV3x9fRTn8Sf1sHe2H0f7/PPPUVxcjOLiYvTo0cOqTPr11nNjYyMOHTqECxcu/t339fXFpk2bsHTpUtTU1CAmJgYTJkzAnDlzlKkkKeaH0h7C8jgb8/jrdeJ5+H8xi38vPqruJSy/84r/CMuJPI09s/1oERv/RKR+Ll7kKzMz0+azAb169bL8QwC4OOPQF198oUyFiIjUTIHZftSEw37aYDQaER8fj2HDhrm6KkTUQXKXenfV7BDkWsx7InVh3oux8d+G7Oxs7N+/Hzt3iofPEJH705kl2RtpD/OeSF2Y92Ic9kNE6ufiYT9EROREHPYjxMY/EameUlN9EhGR+3HlVJ+egI1/IlI/Oxf5IiIiD+bCRb48ARv/RKR67PknItIO9vyLsfHvAvVJ1TaPmXLlLmH5n23M43+ZT4Cw3Cey2GYdiFSDY/7JhXp6i+fWsDWP/3+baoTl758XL/r2yFWfCcuJVIdj/oU4208bOPUbkXpwqk8SYd4TqQvzXoyN/zZw6jciFTFL8jfSHOY9kcow74U47IeIVE8nyVvKXcs9QUREaiEn87Wc92z8E5H6cbYfIiLt4Gw/Qmz8E5HqcbYfIiLt4Gw/Ymz8E5H6cbYfIiLt4Gw/Qmz8E5Hq6SQJOhm3eOUcQ0RE7k1O5ms579n4V0Cv/OeE5X8cXGTzHLbm8e/tE2hXnYg0zfzrJuc4IjtVlEULy0O8/ITltubxf/X0SGH5e3uHCMsfuUpYTKQ+cjJfw3nPqT7bwHmfidSjuRdIzkbaw7wnUhfmvRgb/23gvM9EKiLZsZHmMO+JVIZ5L8RhP0SkejqzBJ2MBV3kHENERO5NTuZrOe/Z809E6tc857OcjYiIPJsb5P369euRmJgIf39/hISEYPz48Ypezx7s+Sci1dOZZa7wq+EHwIiI1EJO5iuZ9//617+QlZWFZ599FqNGjUJTUxP27t2r3AXtxMY/EakfV/glItIOF67w29TUhBkzZmDRokW46667LPvj4+MVuV57cNgPEamfGzzwO27cOMTGxsLPzw9RUVGYMmUKysvLhe+pq6tDdnY2unbtisDAQEyYMAGVlZXKVZKISA1cmPd79uxBWVkZvLy8MHjwYERFRSE9PZ09/2qX1PdHYfldoV/bPEdH5/H3ijzcofcTqYk7LPKVkpKCxx9/HFFRUSgrK8MjjzyCiRMnYtu2bW2+5+GHH8b69evx3nvvITg4GNOnT8ett96Kr7+2nSHkPMFeemH5CXOtsPyNX64Rlr+7Y7iw/GjWo8JyIq2xZ5Gvqqoqq/16vR56vfjvtMhPP/0EAJg/fz6WLFmCXr164YUXXkBycjIOHz6M0NDQdp/bUdjzT0Tq5wYP/D788MO45ppr0LNnT4wYMQK5ubn45ptv0NjY2Orx586dwxtvvIElS5Zg1KhRSEhIwKpVq7Bt2zZ88803itWTiMjj2ZH3MTExCA4OtmwLFy5s9ZS5ubnQ6XTC7eDBgzCbLz5M8MQTT2DChAmW7NbpdHjvvfec9hWIsOefiNRPgrzVHJ005P/MmTN4++23MWLECHTq1KnVY3bv3o3GxkakpqZa9vXt2xexsbHYvn07rrlG3FtMRKRZcjL/17wvLS2FwWCw7G6r13/mzJnIzMwUnjIuLg7Hjx8HYD3GX6/XIy4uDseOHbNVc6dg45+IVM/eYT+Ovg3cbNasWXjllVdw4cIFXHPNNVi3bl2bx1ZUVMDX1xddunSx2h8REYGKiooO14WISK3sGfZjMBisGv9tCQ8PR3h4uM3jEhISoNfrcejQIYwcORIA0NjYiKNHj6Jnz54yaq88DvshIvUzS4DZLGNT5jZws0cffRTfffcdNm7cCG9vb0ydOhUSZxgiInIsWZmvTPYaDAbcd999yMvLw8aNG3Ho0CHcf//9AIBJkyYpck17seefiNTPDEAn8zg4/jZws7CwMISFheGKK67AVVddhZiYGHzzzTdISkpq8b7IyEg0NDTg7NmzVr3/lZWViIyMlPFhiIg0Sk7mKzjP/6JFi+Dj44MpU6agtrYWiYmJ2Lx5M0JCQpS7qB3Y+G+D0WiE0WiEyWRydVWIqIPsHfbj6NvArWl+KKy+vr7V8oSEBHTq1AkFBQWYMGECAODQoUM4duxYq/9YoPZj3hOpiz3DfpTQqVMnLF68GIsXL1bsGh3BYT9tyM7Oxv79+7Fz505XV4WIOsrFs/18++23eOWVV1BYWIiff/4Zmzdvxp/+9Cf07t3b0pAvKytD3759sWPHDgBAcHAw7rrrLuTk5GDLli3YvXs3pk2bhqSkJD7s62DMeyKVcfHsbu6OPf9EpH4uXuE3ICAAH3zwAfLy8lBTU4OoqCiMGTMGc+bMsQwpamxsxKFDh3DhwgXL+1588UV4eXlhwoQJqK+vR1paGl599VVF6khEpBouXOHXE7Dxr4A7I74SlvfuFOCkmhARAJc3/vv374/NmzcLj+nVq1eLh3/9/PwsQ1LIfdVLTcLyD873E5a/VSRexCt4H3+qiezCxr8QE4WI1M/OB36JiMiDufiBX3fHxj8RqZ69D/wSEZHncvUDv+6OjX8iUj+TGbK6eUwa7goiIlILOZmv4bxn45+I1M/FY/6JiMiJOOZfiI1/ItIAudO6affHgIhIPeRkvnbzno1/IlI/9vwTEWkHe/6F2PgnIvUzS5DVy2PW7o8BEZFqyMl8Dec9G//tkFs0UVj+eFiNsNwLfh2uQ6ONeaX1Hb4CkYpI5oubnOOILlF7/DJh+fcN/sLyN46MEJb7HxD/JgSW8c8lkV3kZL6G856NfyJSPw77ISLSDg77EWLjn4jUj8N+iIi0g8N+hLxcXQFnWLx4Mfr164err74ab731lqurQ0TO1twLJGcjj8a8JyLmvZjqe/5/+OEHvPPOO9i9ezckSUJKSgpuuukmdOnSxdVVIyJnMUuQtciXhnuC1IB5T0QA5GW+hvNe9T3/Bw4cQFJSEvz8/ODv74+BAwdiw4YNrq4WETmT2Sx/I4/FvCciAMx7G9y+8f/ll19i7NixiI6Ohk6nw9q1a1scYzQa0atXL/j5+SExMRE7duywlF199dXYunUrzp49i19++QVbt25FWVmZEz8BEbkch/14BOY9ETkE817I7Rv/NTU1GDhwIIxGY6vla9asQU5ODvLy8rBnzx4MHDgQaWlpOHHiBAAgPj4eDz74IEaNGoVbb70V11xzDby9vZ35EYjI1dj49wjMeyJyCOa9kNuP+U9PT0d6enqb5UuWLEFWVhamTZsGAFi2bBnWr1+PlStXIjc3FwBw77334t577wUA3H333bj88ss7VKcbDD8IywO9lJ/HvwkmYTnn+Sf6Dc724xHcMe8B4ILUKCx//4x4Hv/q4i7C8tDj4j93+jPi6xPRJTjbj5Db9/yLNDQ0YPfu3UhNTbXs8/LyQmpqKrZv327Z19wrdOjQIezYsQNpaWltnrO+vh5VVVVWGxF5Nkkyy97IPTHviUgu5r2Y2/f8i5w6dQomkwkRERFW+yMiInDw4EHL65tvvhnnzp1D586dsWrVKvj4tP2xFy5ciAULFihWZyJyAUmS18uj4dvA7o55T0Syycl8Dee9Rzf+5fptr5Ats2fPRk5OjuV1VVUVYmJilKgWETmLJHPYj4Z/DNSCeU9EsjJfw3nv0Y3/sLAweHt7o7Ky0mp/ZWUlIiMj23VOvV4PvZ4j5olUxWQCdOLnZAAAkoxjyCWY90Qkm5zMVyjvt27dipSUlFbLduzYgWHDhilyXXt49Jh/X19fJCQkoKCgwLLPbDajoKAASUlJHTq30WhEfHy8W/xHIqKOkcxm2Ru5J+Y9EcnlyrwfMWIEjh8/brXdfffduOyyyzB06FBFrmkvt+/5r66uRnFxseV1SUkJCgsLERoaitjYWOTk5CAjIwNDhw7F8OHDsXTpUtTU1Fhmg2iv7OxsZGdno6qqCsHBwR39GETkShz24xGY90TkEC4c9uPr62t1N7KxsREfffQRHnjgAeh0OkWuaS+3b/zv2rXL6vZJ8/jMjIwM5OfnY/LkyTh58iTmzZuHiooKDBo0CBs2bGjxUBgRaZhZAnSubfyPGzcOhYWFOHHiBEJCQpCamornnnsO0dHRbb4nOTkZX3zxhdW+e++9F8uWLVOsnq7EvCcih5CT+b/m/aWzfDl6OOC///1vnD59usOdFI6kkyR2dbXGaDTCaDTCZDLh8OHDOHfuHAwGAwCgoqztH2sA6OYdKCw3w/atpgvmBmG5IfqYzXMQqUVzj+xv/x7a875RvpPgo+tk8/gmqRGbG96z+zpyvPjii0hKSkJUVBTKysrwyCOPAAC2bdvW5nuSk5NxxRVX4Mknn7TsCwgIcHjdtE6U9wDwQ2kP4fv/uCtLWO61Q/zfq9secd5v+WyWsJxIbZyR+c15f6m8vDzMnz/f3iq36Q9/+AMA4JNPPnHYOTvK7Xv+XYW3gYnUQzJLkGT0/CvZF/Lwww9b/nfPnj2Rm5uL8ePHo7GxEZ06tf0jFRAQ0O4HWkke5j2RusjJ/Oa8Ly0ttfoHRlu9/rm5uXjuueeE5zxw4AD69u1ref3f//4Xn332Gd599125VXcKNv6JSP0kMyDjjht+XfRF6dvAZ86cwdtvv40RI0YIG/4A8Pbbb+Ott95CZGQkxo4di7lz5yIgIMBhdSEiUh05mf9r3hsMBll3F2bOnInMzEzhMXFxcVavV61aha5du2LcuHE2z+9MbPwTkerZ2/N/6VzvjroNPGvWLLzyyiu4cOECrrnmGqxbt054/O23346ePXsiOjoaRUVFmDVrFg4dOoQPPvigw3UhIlIre3r+5QoPD0d4eLj8OkgSVq1ahalTp9rs5HE2Nv5tkFp5IOT8efG/Jv28xXPHmmXMOnLB1hRUXIaeNKT57197h+U0SfWWXh7hcWgEoNxt4EcffRR33XUXfv75ZyxYsABTp07FunXr2pwB4p577rH87/79+yMqKgqjR4/Gjz/+iN69e9v8PGSf1vIeAKptZL7pQp34vPW+wvKmJvGY/0vrQ6R2zsj85rxXyubNm1FSUoK7775b0eu0Bx/4bUPzA2ANDQ348ccfXV0dIsLFRnmPHuKHL3+rrq4Ol112GSoqKmS/JzIyEiUlJfDz87N57MmTJ3H69GnhMXFxcfD1bdn4++9//4uYmBhs27ZN9jz1NTU1CAwMxIYNG5CWlibrPWQb857IPSmd+fbkvb1uv/12/Pzzz/j6668dfu6OYuPfBrPZjPLycgQFBUGn02HYsGHYuXOnzffJOa6tY+zZf+m+375uXqr+0l5MJcj9Xjr6XlvHisrlfH9y9jnre/XU77St/R35sypJEs6fP4/o6Gh4edm3NmFdXR0aGsQ9q7/1/+3de1xUZf4H8M+AcpO7IhcFDTERE0EUwm6gJKhpmlltqWD+MF3UEjbFSvCSeV2zdclLqdimZe2WZRdTEWtXSRQXTVE2TIMQ0CJBULnMnN8fLrOOMDNnYM5cP+/X67xeznOec84zR/jOl2ee8zx2dnaSfBDcrbS0FL169UJubi5iYmJEHXPkyBE8+OCDOHXqFEJDQ6VtoBVqb7wHGJukONYcYpM+met9leJn1VAx31Dx3tRw2I8WNjY2Kn912traigoAYuqpq6NL+d1lbdUR+zBLR4i9Lx09VltdTfvF3D9dyqS+r+Z6T9WVd/Rntb2zsDg4OBg9uB87dgzHjx/Hgw8+CA8PD1y4cAGLFi1Cnz59lL3+5eXlGDFiBN577z1ERkbiwoUL2LVrF0aPHo2uXbvi9OnTmDdvHh5++GEm/hJpb7wXU5exSfdjzSU26Yu53lepflbNOeabOt3+nCKkpKTorZ66OrqU310mtn361pHr6nKstrqa9ou5f7qUSc1c76m6clP5WTUGJycnfPLJJxgxYgT69euH6dOnIzQ0FN9++63yeYKmpiYUFxfjxo0bAG73SB08eBAjR45EcHAw0tLSMHHiROzdu9eYb8WqMDa1jbFJGuZ6X035Z5XaxmE/Fqy9i2SQZryv+sd7StRx/D3SP95TafC+Ghd7/i2Yvb09MjMz9To/OfG+SoH3lKjj+Hukf7yn0uB9NS72/BMRERERWQn2/BMRERERWQkm/0REREREVoLJPxERERGRlWDyT0RERERkJZj8W5kJEybAw8MDTz75pEr5F198gX79+qFv37549913jdQ686TunqorJ+3aundlZWWIiYlBSEgIQkND8fHHHxuxhUSmj/Fe/xjvpcGYb1ic7cfKHD58GNevX8eOHTvw97//HQDQ3NyMkJAQ5Obmws3NDRERETh69Ci6du1q5Naah7buqaZy0q6te1dRUYGqqiqEhYWhsrISERER+M9//oMuXboYubVEponxXv8Y76XBmG9Y7Pm3MjExMXBxcVEpy8/Px4ABA9CjRw84Oztj1KhR2L9/v5FaaH7auqeaykm7tu6dr68vwsLCAAA+Pj7o1q0bqqurjdA6IvPAeK9/jPfSYMw3LCb/ZuS7777D2LFj4efnB5lMhj179rSqk5WVhd69e8PBwQFRUVHIz8/Xet7Lly+jR48eytc9evRAeXm5PptusqS6p9bMEPe0oKAAcrkc/v7+emo1kWlhvNc/xntpMOabHyb/ZqS+vh6DBg1CVlZWm/t3796N1NRUZGZm4uTJkxg0aBDi4+Nx5coVA7fUfPCe6p/U97S6uhpTp07Fli1b9NlsIpPC2KR/vKfSYMw3QwKZJQDCp59+qlIWGRkppKSkKF/L5XLBz89PWLFihUq93NxcYeLEicrXR44cEcaPH698/eKLLwo7d+6UpuEmTJ/3VFu5tdD3Pb1165bw0EMPCe+9955kbSYyNYz3+sd4Lw3GfPPAnn8L0djYiIKCAsTFxSnLbGxsEBcXh7y8PI3HRkZG4syZMygvL0ddXR2+/vprxMfHS91kk9eRe0pt68g9FQQBSUlJGD58OKZMmSJ1U4lMFuO9/jHeS4Mx3zR1MnYDSD9+/fVXyOVyeHt7q5R7e3vj/PnzytdxcXE4deoU6uvr0bNnT3z88ceIjo7Gn//8Z8TGxkKhUGD+/Pmc+QEdv6fqyq1ZR+6pXC7H7t27ERoaqhxT+re//Q0DBw405FsgMjrGe/1jvJcGY75pYvJvZQ4ePNhm+bhx4zBu3DgDt8YyqLun6spJO3X3TqFQGLglROaL8V7/GO+lwZhvWBz2YyG6desGW1tbVFVVqZRXVVXBx8fHSK0yb7yn+sd7StRx/D3SP95TafC+miYm/xbCzs4OERERyMnJUZYpFArk5OTwq8d24j3VP95Too7j75H+8Z5Kg/fVNHHYjxmpq6tDSUmJ8vXFixdRWFgIT09PBAQEIDU1FYmJiRgyZAgiIyOxfv161NfXY9q0aUZstWnjPdU/3lOijuPvkf7xnkqD99UMGXu6IRIvNzdXANBqS0xMVNbZsGGDEBAQINjZ2QmRkZHC999/b7wGmwHeU/3jPSXqOP4e6R/vqTR4X82PTBAEwRB/ZBARERERkXFxzD8RERERkZVg8k9EREREZCWY/BMRERERWQkm/0REREREVoLJPxERERGRlWDyT0RERERkJZj8ExERERFZCSb/RERERERWgsk/EREREZGVYPJPRERERGQlmPyT2YuJiYFMJoNMJkNhYaGk10pKSlJea8+ePZJei4iIWmPMJ+oYJv9kEZKTk1FRUYH77rtP0uu89dZbqKiokPQaRESkGWM+Uft1MnYDiPTByckJPj4+kl/Hzc0Nbm5ukl+HiIjUY8wnaj/2/JPJ+eCDD+Do6KjS2zJt2jSEhoaipqZG9HkUCgVWr16NoKAg2NvbIyAgAMuXL1fuj4mJwZw5c/DSSy/Bw8MD3t7eeOedd1BfX49p06bBxcUFQUFB+Prrr/X6/oiI6H8Y84kMi8k/mZxnnnkG9957L9544w0AQGZmJg4ePIivv/5apx6YhQsXYuXKlVi0aBGKioqwa9cueHt7q9TZsWMHunXrhvz8fMyZMwezZs3CpEmTMGzYMJw8eRIjR47ElClTcOPGDb2+RyIiuo0xn8jABCITtHfvXsHe3l54/fXXBQ8PD+HMmTNq6z7yyCPCiy++qFJWW1sr2NvbC++8847G4x588EHl6+bmZqFLly7ClClTlGUVFRUCACEvL0/lWADCp59+qtubIiKiNjHmExkOx/yTSXrssccQEhKCpUuXYv/+/RgwYIBOx587dw4NDQ0YMWKExnqhoaHKf9va2qJr164YOHCgsqyl1+jKlSs6XZ+IiMRjzCcyHA77IZO0b98+nD9/HnK5vNXXtmI4OjqKqte5c2eV1zKZTKVMJpMBuD2WlIiIpMGYT2Q4TP7J5Jw8eRJPPfUUtm7dihEjRmDRokU6n6Nv375wdHRETk6OBC0kIiJ9YcwnMiwO+yGTcunSJYwZMwavvPIK/vCHPyAwMBDR0dE4efIkBg8eLPo8Dg4OWLBgAebPnw87Ozs88MADuHr1Ks6ePYvp06dL+A6IiEgsxnwiw2PyTyajuroaCQkJePzxx5Geng4AiIqKwqhRo/DKK69g3759Op1v0aJF6NSpEzIyMnD58mX4+vpi5syZUjSdiIh0xJhPZBwyQRAEYzeCqCNiYmIQFhaG9evXG+yaMpkMn376KcaPH2+waxIREWM+UUdxzD9ZhLfffhvOzs744YcfJL3OzJkz4ezsLOk1iIhIM8Z8ovZjzz+ZvfLycty8eRMAEBAQADs7O8mudeXKFdTW1gIAfH190aVLF8muRURErTHmE3UMk38iIiIiIivBYT9ERERERFaCyT8RERERkZVg8k9EREREZCWY/BMRERERWQkm/0REREREVoLJPxERERGRlWDyT0RERERkJZj8ExERERFZCSb/RERERERWgsk/EREREZGVYPJPRERERGQlmPwTEREREVkJJv9ERERERFaCyT8RERERkZVg8k9EREREZCWY/BMRERERWQkm/0REREREVoLJv4nLzs6GTCbDpUuXjN0U0VavXo3g4GAoFApjN0VFXV0dbGxs8Oabbxq7KTpTKBR4/fXX0adPH3Tu3Bl9+vQxyHU3bdqEgIAANDQ0GOR6RNaOMV9/GPN1x5hvHZj8k17V1tZi1apVWLBgAWxs2v7xOnfuHGbNmoWgoCA4OTnBxcUFDzzwADZu3Ijm5mbJ2nbmzBkIgoDQ0FAAQENDAxYsWAA/Pz84OjoiKioKBw4caHXc4cOHIZPJ2ty+//57ydp7p7fffhsZGRl44oknsG3bNmzevNkg101KSkJjY6PBrkdE5sUSY36LkydPYty4cfD09ISTkxPuu+8+/OUvf5GsvXdizCcpdTJ2A8iybNu2Dc3NzfjDH/7Q5v7Nmzdj7ty58PDwwJQpU3DvvfeipqYGe/fuxR//+EfU19fjT3/6kyRti4yMxM2bN2Fvbw/gdpD7+9//jpdeegl9+/ZFdnY2Ro8ejdzcXDz44IOtjp87dy6GDh2qUhYUFCRJW++2fft2PProo1izZo1BrtfCwcEBiYmJWLduHebMmQOZTGbQ6xORabPUmL9//36MHTsW4eHhWLRoEZydnXHhwgX88ssvkrT1boz5JCmBTNr27dsFAMLFixeN3RRRQkNDhcmTJ7e5b+fOnYJMJhMee+wxoa6urs3933zzjdRNFARBEI4dOyYAENasWaMsu3nzptCnTx8hOjpapW5ubq4AQPj4448N0ra73bx5U7C1tRVef/11UXX17cSJEwIAIScnR+/nJiJVjPnS0CXm19TUCN7e3sKECRMEuVxukPbdiTGfpMbk38S19UFw8uRJISEhQXBxcRG6dOkiDB8+XMjLy2t1bG5urhARESHY29sLgYGBwqZNm4TMzExBqr/5fvrpJwGAkJ2d3WpfRUWF4O7uLgwYMEC4deuWJNfXJi4uThg2bJggCILw8ssvC7a2tkJNTY1KnTfeeEMAIJSWlirL7kz+a2trhaamJoO1+fnnnxcAqGz333+/8v1ER0cL3333nfDwww8LDg4OwowZM4SioiLhhRdeEO69917B0dFR8Pb2Fp599lnh8uXLbV7jxIkTwsSJEwUvLy/B3t5e6Nevn7BkyRKVOp6ensLcuXMlf79E1o4xX3/aG/M3btwoABCKiooEQRCEuro6g/0RwJhPhsBhP2bm7NmzeOihh+Dq6or58+ejc+fO2Lx5M2JiYvDtt98iKioKAPDvf/8bCQkJ8PX1xZIlSyCXy7F06VJ4eXlJ1rajR48CAAYPHtxq31tvvYVr167hgw8+UH4Fa2inT5/GE088AeD2/bn33nvh6uqqUicyMhIAUFhYCH9/f5V906ZNQ11dHWxtbfHQQw9hzZo1GDJkiKRtfu6555T/x2+99RY8PT3Rq1cv5ftxd3fH+PHjkZycjGeffRZBQUHYtm0bLl68iClTpsDHxwenT5/G5s2bUVVVhYMHD6qc/9NPP8UzzzyDwMBAvPzyy3B2dsbx48eV/5ctBg8ejCNHjkj6XomoNcb89mtvzD948CBcXV1RXl6O8ePH4z//+Q+6dOmCKVOm4M0334SDg4NkbWbMJ4Mw9l8fpNndvUDjx48X7OzshAsXLijrXL58WXBxcREefvhhZdnYsWMFJycnoby8XFn2448/Cp06dZKsF+i1114TAAjXr19vtc/Pz0/w9/c3yleogiAIVVVVAgDh7bffFgRBEAYMGCAMHz68Vb2zZ88KAIRNmzYpy44cOSJMnDhR2Lp1q/DZZ58JK1asELp27So4ODgIJ0+elLztr7zyitClSxeVe9fyfpydnYVz586p1K+vr291jtdee02wsbFR+Yq4uLhYcHJyEiZMmNDqq+O7e+pmzJghODo66uPtEJEGjPn60ZGYHxoaKjg5OQlOTk7CnDlzhH/84x/CnDlzBADCM888I3nbGfNJauz5NyNyuRz79+/H+PHjERgYqCz39fXFs88+i3feeQe1tbXo0qULDh48iAkTJsDPz09ZLygoCKNGjcLevXslad9vv/2GTp06wdnZWaX8woULuHz5MmbOnKl2Ngh1FAoFGhsbRdW1t7dX+3DS6dOnAQADBw4EAJWHwO7U0qNz8+ZNZdmwYcMwbNgw5etx48bhySefRGhoKBYuXIh9+/ZJ3vYBAwao3LuW9/PKK68gODhYpb6Tk5Py39euXUNzczM8PDygUChUZtbIyMiAg4MDsrOzW/Vk3X1vPDw8cPPmTdy4cUPl/EQkHcZ8zaSK+XV1dbhx4wZmzpypnN3niSeeUM6Cs3TpUvTt21fStjPmk5Q41acZuXr1Km7cuIF+/fq12te/f38oFAqUlZXhypUruHnzZpsz0bRVtnHjRgwePBidO3fG4sWLW11zzJgx6NKlC/r164ecnByd211WVgYAKh9eYn333XdwdHQUtRUXF6s9zw8//ADgfx8Ejo6Obc5jfOvWLeV+TYKCgvD4448jNzcXcrlc0rafOnVKOVXd3e/n6aefVimXy+XYtm0bBg8eDCcnJ3h4eMDLywtpaWno1q2b8kO6oaEBe/fuxeTJk1t9Dd4WQRAAgDM/EBkQY75xYn7Lv++ewejZZ58FAOTl5UnadsZ8khp7/gm+vr5YvHgxdu3a1WpfSkoKfHx8cPXqVRw8eBBPPfUUfvzxR3h6eraq27VrVzQ3N+P69etwcXFRlrf0PDQ1NenctuDgYGzfvl30+1Dn9OnTCAgIgJubm7JueXl5q3oVFRUAoNJ7po6/vz8aGxtRX1/fZjDVR9uvXbuGsrIy5QdYi9OnT8PX17fVh+vUqVPx0UcfITExES+99BK6desGOzs7zJ07V+UaP/30E27cuIGIiAhR7fv999/h5OSk9Y8iIjJ9jPn/01bM9/Pzw9mzZ+Ht7a1St3v37gBux0Op2s6YT4bA5N+MeHl5wcnJqc0eg/Pnz8PGxgb+/v7o0qULHBwcUFJS0qpeW2Xjx48HAHz11Vcq5XV1ddizZw9++uknODk5Ydy4cRg4cCA+++wzTJs2rdV5Wr6KvHjxokqvRUvP05kzZ8S/2f/y8fFBUlKSzsfd7fTp0yptCgsLQ25uLmpra1US92PHjin3a/PTTz/BwcGh1VfeLfTR9paveu/uBTp9+jQGDRqkUlZUVIRdu3bhzTffxEsvvaQsr6iowH/+8x8kJCQoy1q+4hbbq3Px4kX079+/PW+BiNqJMb/9OhLzIyIicODAAZSXl6t863L58mUAUPsQNWM+mQsO+zEjtra2GDlyJD777DOVpd+rqqqwa9cuPPjgg3B1dYWtrS3i4uKwZ88eZbACbn8IfP3116Kv9+OPP8LZ2Rk9e/ZUlg0cOBBnz55ts350dDQA4MSJEyrlvXv3xtChQ/Hxxx/j5MmTrY67fv06/vznP4tul67kcjmKiopUgumTTz4JuVyOLVu2KMsaGhqwfft2REVFqcz0c/Xq1VbnPHXqFD7//HOMHDlS5zGtujh16hQA1Q+Clvdz9wdBy+Izd37N39TUhP/7v/+DXC5v88P57pkgWo6528mTJ1WeeyAi6THmt09HY/5TTz0FANi6davKed9991106tQJMTExkrWdMZ8MgT3/Zub111/HgQMH8OCDD+KPf/wjOnXqhM2bN6OhoQGrV69W1lu8eDH279+PBx54ALNmzYJcLsdf//pX3HfffSgsLBR1rbq6ulbDWVxdXfHbb7+1WT8wMBD33XcfDh48iOeff15l3zvvvINHHnkEDzzwAJ5//nkMHDgQzc3NOHnyJD755BM8+OCDSEtL0+1miPTjjz/i1q1bKl+jRkVFYdKkSVi4cCGuXLmCoKAg7NixA5cuXWoV8J9++mk4Ojpi2LBh6N69O4qKirBlyxY4OTlh5cqVkrS5xenTp9GjRw+Vr9xb3s/dHwRhYWFwcnLCiy++iIsXL+LmzZv429/+BltbWwBQqe/q6oqkpCRkZ2ejoaEBMTExuH79OnJzczFmzBikpKQo6xYUFKC6uhqPP/64pO+ViFpjzNddR2N+eHg4nn/+eeXqxY888ggOHz6Mjz/+GAsXLhQ1LLS9GPPJIIw93RBppm7Bl/j4eMHZ2VlwcnISYmNjhaNHj7Y6NicnRwgPDxfs7OyEPn36CO+++66QlpYmODg4tHmtF154QcjMzFS5joeHh0qd2bNnC2lpaWrbu27dOsHZ2Vm4ceNGq30lJSXCtGnThB49egidO3cWvL29hfvvv19YvHixUFJSouVOtN9HH30kABDOnj2rUn7z5k3hT3/6k+Dj4yPY29sLQ4cOFfbt29fq+LfeekuIjIwUPD09hU6dOgm+vr7C5MmThR9//FGyNreIjIwURo0apVLW8n7OnDnTqv4XX3whBAcHCw4ODkJoaKiwfft2YfHixUKnTp1aTeVWX18vvPrqq0Lfvn0Fe3t7wdfXV5g4cWKr/4sFCxYIAQEBgkKh0P8bJCIVjPkd19GYLwiC0NjYKCxevFjo1auX0LlzZyEoKEh48803JWtzC8Z8MgSZIPz3kW6yCuPHj8fZs2fx448/tto3c+ZM+Pj4KGd/qKurg6enJy5evIgePXoAAGJjYzF16tQ2x38CQE1NDQIDA7F69WpMnz5dsvdBhtHQ0IDevXsjPT0dL774orGbQ0Q6YswnXTDmWweO+bdgd85bDNz+6vCrr75qNV6xubkZt27dglwuV/m3s7MzHn/8cWRmZuLmzZv44osvcPr0aY1fBbq5uWH+/PlYs2YNFAqFFG+LDGj79u3o3LkzZs6caeymEJEWjPnUUYz51oE9/xbM19cXSUlJCAwMxM8//4yNGzeioaEB//73v1UWKFm8eDGWLFmicuz27duRlJSEq1evIjExEYcPH0bPnj3x9ttvIy4uztBvhYiItGDMJyIxmPxbsGnTpiE3NxeVlZWwt7dHdHQ03njjDQwePNjYTSMiIj1jzCcyDcuXL8eXX36JwsJC2NnZ4dq1a63qHD9+HOnp6SgoKIBMJkNkZCRWr17d6sHuO926dQtpaWn48MMP0dDQgPj4eLz99tut1qTQhsk/EREREZGeZGZmwt3dHb/88gu2bt3aKvmvq6tDr169MG7cOKSnp6O5uRmZmZn417/+hbKyMnTu3LnN886aNQtffvklsrOz4ebmhtmzZ8PGxgZHjhzRqX1M/omIiIiI9Cw7OxsvvfRSq+T/xIkTGDp0KEpLS5VrTPzwww8IDQ3Fjz/+qLJ2Q4uamhp4eXlh165dePLJJwHcXuyvf//+yMvLw/333y+6XXzgl4iIiIjIQPr164euXbti69ataGxsxM2bN7F161b0798fvXv3bvOYgoICNDU1qTyDExwcjICAAOTl5el0fS7ypYVCocDly5fh4uIiellsItIvQRBw/fp1+Pn56byi8q1bt9DY2Ci6vp2dHRwcHHRtIlkAxnsi02ComC8IQqvfdXt7e9jb2+t0TV25uLjg8OHDGD9+PJYtWwYA6Nu3L7755ht06tR2al5ZWQk7Ozu4u7urlHt7e6OyslKn6zP51+Ly5csqy34TkfGUlZWhZ8+eouvfunUL9/RyRuUVuehjfHx8cPHiRf4BYIUY74lMi9Qx39nZGXV1dSplmZmZyrUv7pSeno5Vq1ZpPN+5c+cQHBys9bo3b97E9OnT8cADD+CDDz6AXC7H2rVrMWbMGBw/fhyOjo6i2t9eTP61cHFxAXD7B/DuZc+JyDBqa2vh7++v/H0Uq7GxEZVX5LhY0AuuLtp7j2qvK3BPxM9obGxk8m+FGO+JTIMhYn5LvL/7911dr39aWhqSkpI0njMwMFBUO3ft2oVLly4hLy9P+c3Grl274OHhgc8++wzPPPNMq2N8fHzQ2NiIa9euqfT+V1VVwcfHR9R1W1hU8j9hwgQcPnwYI0aMwN///ncAt4P4lClTcOXKFXTq1AmLFi3CpEmTRJ+z5esgV1dXfhgQGVl7h2J0cb69aSPn9AdmRd8xn/GeyLRIGfNb4r3Y33cvLy94eXm1qz13u3HjBmxsbFTeX8trdYvlRUREoHPnzsjJycHEiRMBAMXFxSgtLUV0dLRO17eoB35ffPFFvPfeeyplnTp1wvr161FUVIT9+/fjpZdeQn19vZFaSETGoIAgeiPzwZhPRG0xdrwvLS1FYWEhSktLIZfLUVhYiMLCQuUQo0cffRS///47UlJScO7cOZw9exbTpk1Dp06dEBsbCwAoLy9HcHAw8vPzAdxeTXv69OlITU1Fbm4uCgoKMG3aNERHR+s00w9gYT3/MTExOHz4sEqZr68vfH19Adz+yqRbt26orq5Gly5djNBCIjKGJkGOJhGzGjcJbfe4kGlizCeitoiJ+VLG+4yMDOzYsUP5Ojw8HACQm5uLmJgYBAcHY+/evViyZAmio6NhY2OD8PBw7Nu3Txm/mpqaUFxcjBs3bijP8+abb8LGxgYTJ05UWeRLVybT8//dd99h7Nix8PPzg0wmw549e1rVycrKQu/eveHg4ICoqCjlX0NiFRQUQC6X84EuIivDnn/Tw5hPRFIxdrzPzs6GIAittpiYGGWdRx99FP/6179w7do1VFdXIycnR6UHv3fv3q2OcXBwQFZWFqqrq1FfX49PPvlE5/H+gAkl//X19Rg0aBCysrLa3L97926kpqYiMzMTJ0+exKBBgxAfH48rV66IOn91dTWmTp2KLVu26LPZRGQGFBAgF7Ex+TccxnwikoqYmG/N8d5khv2MGjUKo0aNUrt/3bp1SE5OxrRp0wAAmzZtwpdffolt27YhPT1d47kbGhowfvx4pKenY9iwYVrrNjQ0KF/X1tbq8C6IyBSJ7eWx5g8DQzOFmM94T2SZxMR8a473JtPzr0ljYyMKCgpUVjWzsbFBXFyc1lXNBEFAUlIShg8fjilTpmi91ooVK+Dm5qbc+HUxkfmTC4LojYzPUDGf8Z7IMjHea2YWyf+vv/4KuVwOb29vlfK7VzWLi4vDpEmT8NVXX6Fnz57Iy8vDkSNHsHv3buzZswdhYWEICwvDDz/8oPZaCxcuRE1NjXIrKyuT7H0RkWEodNjI+AwV8xnviSwT471mJjPsRx8OHjzYZrm6OVPbYohlnYnIsFrGeIqpR+ajozGf8Z7IMomJ+dYc782i579bt26wtbVFVVWVSnl7VjUTKysrCyEhIRg6dKgk5yciw5EL4jcyPkPHfMZ7IsvCeK+ZWST/dnZ2iIiIQE5OjrJMoVAgJydH51XNxEpJSUFRURGOHz8uyfmJyHA47Me8GDrmM94TWRbGe81MZthPXV0dSkpKlK8vXryIwsJCeHp6IiAgAKmpqUhMTMSQIUMQGRmJ9evXo76+XjkTBBGROs2CDE2C9mXim0XUIf1gzCciqYiJ+dYc700m+T9x4oRySWMASE1NBQAkJiYiOzsbTz/9NK5evYqMjAxUVlYiLCwM+/bta/VAGBHR3eSQQQ7tgV5MHdIPxnwikoqYmG/N8d5kkv+YmBgIWqZdmj17NmbPnm2Q9mRlZSErKwtyudwg1yMi6TD5Nz2mFPMZ74ksC5N/zcxizL8xcAwokeVQCDLRG1kfxnsiy8J4r5nJ9PwTEUmFPf9ERNaDPf+aMfknIosnhw3kIr7o5KAPIiLzJybmW3O857AfNTjvM5HlEER+BSxY8dfA1ozxnsiyiIn51hzv2fOvRkpKClJSUlBbWws3NzdjN4dIZ82VQRr3Nwma+z0cfS/qszlGxWE/pAnjPZm7wL/8uUPH/zQ3TU8tMQ0c9qMZk38isnhNgi2aBFsR9az5i2AiIssgJuZbc7xn8k9EFo89/0RE1oM9/5ox+SciiycXbCAXRDzwq2XeeSIiMn1iYr41x3s+8KsGHwAjshwKyERvZH0Y74ksi7Hj/fLlyzFs2DA4OTnB3d29zTrHjx/HiBEj4O7uDg8PD8THx+PUqVMazxsTEwOZTKayzZw5U+f2MflXg4u+EFkOxX+nfdO2KRgSrRLjPZFlERPzpYz3jY2NmDRpEmbNmtXm/rq6OiQkJCAgIADHjh3Dv/71L7i4uCA+Ph5NTU0az52cnIyKigrltnr1ap3bx2E/RGTxOOyHiMh6GHvYz5IlSwAA2dnZbe4/f/48qqursXTpUvj7+wMAMjMzERoaip9//hlBQepn63NycoKPj0+H2sduLiKyeIr/9vKI2aRw6dIlTJ8+Hffccw8cHR3Rp08fZGZmorGxUeNx+vqKl4jImhgz3ovRr18/dO3aFVu3bkVjYyNu3ryJrVu3on///ujdu7fGY3fu3Ilu3brhvvvuw8KFC3Hjxg2dr8+efyITpai8V/N+KDTu1zaNWYPQrHF/3eUeGvffEjRf379Hhcb9hiQXZJCLWNBFTJ32OH/+PBQKBTZv3oygoCCcOXMGycnJqK+vx9q1azUem5ycjKVLlypfOzk5SdJGIjKejs7TbwrXN6W1AsTE/Jb9tbW1KuX29vawt7eXrG0A4OLigsOHD2P8+PFYtmwZAKBv37745ptv0KmT+tT82WefRa9eveDn54fTp09jwYIFKC4uxieffKLT9Zn8q5GVlYWsrCzI5dY7DyyRpWgSOomc51+a5D8hIQEJCQnK14GBgSguLsbGjRu1Jv/6+IqXNGO8J7IsYmJ+S7xvGXbTIjMzE4sXL25VPz09HatWrdJ4znPnziE4OFhr+27evInp06fjgQcewAcffAC5XI61a9dizJgxOH78OBwdHds8bsaMGcp/Dxw4EL6+vhgxYgQuXLiAPn36aL1uCyb/anDFRyLL0fKAl/Z6t8eAGqInqKamBp6enlrr7dy5E++//z58fHwwduxYLFq0iL3/esZ4T2RZxMT8lnhfVlYGV1dXZbm6WJ+WloakpCSN5wwMDBTVvl27duHSpUvIy8uDjY2NsszDwwOfffYZnnnmGVHniYqKAgCUlJQw+SciupMC4ob0tAxkEtsT1F4lJSXYsGGD1l5/fX3FS0RkTcTE/JZ47+rqqpL8q+Pl5QUvL6+ONw7AjRs3YGNjA5nsf21sea1QaB5Se6fCwkIAgK+vr07XZ/JPRBZP7MNdLXXE9gS152vg8vJyJCQkYNKkSUhOTtZ4rL6+4iUisiZiYr6UD/yWlpaiuroapaWlkMvlyiQ9KCgIzs7OePTRR/Hyyy8jJSUFc+bMgUKhwMqVK9GpUyfExsYCuP1ZMWLECLz33nuIjIzEhQsXsGvXLowePRpdu3bF6dOnMW/ePDz88MMIDQ3VqX1M/onI4omf6vN2HbE9Qbp+DXz58mXExsZi2LBh2LJli9bz3629X/ESEVkTcVN9Spf8Z2RkYMeOHcrX4eHhAIDc3FzExMQgODgYe/fuxZIlSxAdHQ0bGxuEh4dj3759yl78pqYmFBcXK2fzsbOzw8GDB7F+/XrU19fD398fEydOxGuvvaZz+5j8E5HFE7uao64rPuryNXB5eTliY2MRERGB7du3K8d56qK9X/ESEVkTMTFfyhV+s7Oz1c7x3+LRRx/Fo48+qnZ/7969IdyxFoG/vz++/fZbvbSP8/wTkcVr6QUSs0mhvLwcMTExCAgIwNq1a3H16lVUVlaisrJSpU5wcDDy8/MBABcuXMCyZctQUFCAS5cu4fPPP8fUqVPb9RUvEZE1MWa8Nwfs+VeDU78RWQ7xs/1I82Fw4MABlJSUoKSkBD179lTZ19KzI+VXvKQZ4z2RZRE324/1Jv8yQeB69pq0TP1WU1MjagwwkVjGXsTrlpbjm6A5NBhyEa/2/h62HLf6+ENwdNbe13Gzrhnzh/6Tv+9WivGepGLsRbz0wZCLeBki5ltzvGfPPxFZvGahE5oE7eGumV0hRERmT0zMt+Z4z+SfiCyeHDLIRTzcJaYOERGZNjEx35rjPZN/IrJ4CsEGChEPd4mpQ0REpk1MzLfmeM/kn4gsnhzienn4uCcRkfkTE/OtOd4z+Scii8eefyIi68Gef82Y/BORxdN1hV8iIjJfxl7h19Qx+SciiyeIXOFXsOIHwIiILIWYmG/N8Z7Jvxpc9IU6Sts8/k1a5uFXaJln/4bQpOX8mtcJuKVliY/ePQ03j7/U2PNPmjDeU0eZ+zz+hpzD3xDY86+Z9b5zLVJSUlBUVITjx48buylE1EFNgq3ojawP4z2RZWG814w9/0Rk8RSCDApB+1e8YuoQEZFpExPzrTneM/knIoungA0UIr7oFFOHiIhMm5iYb83xnsk/EVk8uSCDXEQvj5g6RERk2sTEfGuO90z+icjicdgPEZH14LAfzZj8E5HFE0Qu8iVY8ewPRESWQkzMt+Z4z+SfiCyeHDKtS7231CMiIvMmJuZbc7xn8k/UDtrm8Ae0z+PfDM1zit8SNO/XNo9/k5Z1AixpHn9tFIK4r3gVmm8ZEVkpzuNvXsTEfCnj/fLly/Hll1+isLAQdnZ2uHbtWqs6OTk5WLRoEX744Qd06dIFiYmJWL58OTp1Up+a37p1C2lpafjwww/R0NCA+Ph4vP322/D29tapfdb7nQcRWQ3Ff78CFrMREZF5M3a8b2xsxKRJkzBr1qw29586dQqjR49GQkIC/v3vf2P37t34/PPPkZ6ervG88+bNw969e/Hxxx/j22+/xeXLl/HEE0/o3D72/BORxWsSbCATEeibmPwTEZk9MTFfyni/ZMkSAEB2dnab+3fv3o3Q0FBkZGQAAIKCgrB69Wo89dRTyMzMhIuLS6tjampqsHXrVuzatQvDhw8HAGzfvh39+/fH999/j/vvv190+/hJR0QWjz3/RETWQ5d4X1tbq7I1NDRI3r6GhgY4ODiolDk6OuLWrVsoKCho85iCggI0NTUhLi5OWRYcHIyAgADk5eXpdH1+0qmRlZWFkJAQDB061NhNIaIOUkCmnPpN42bFD4BZM8Z7IssiKub/N977+/vDzc1Nua1YsULy9sXHx+Po0aP44IMPIJfLUV5ejqVLlwIAKirafh6vsrISdnZ2cHd3Vyn39vZGZWWlTtdn8q9GSkoKioqKcPz4cWM3hYg6SMDtQK9tEyRM/nv37g2ZTKayrVy5UuMxt27dQkpKCrp27QpnZ2dMnDgRVVVVkrXRWjHeE1kWMTG/Jd6XlZWhpqZGuS1cuLDNc6anp7eK4Xdv58+fF9W+kSNHYs2aNZg5cybs7e1x7733YvTo0QAAGxvpU3OO+Scii2cqi3wtXboUycnJytdtjeu807x58/Dll1/i448/hpubG2bPno0nnngCR44ckbSdRETmTJdFvlxdXeHq6qr1nGlpaUhKStJYJzAwUHQbU1NTMW/ePFRUVMDDwwOXLl3CwoUL1Z7Dx8cHjY2NuHbtmkrvf1VVFXx8fERfF2DyT0RWQOx4fqnH/Lu4uIgO0vp8uIuIyJqIifm6xnsvLy94eXl1pFmtyGQy+Pn5AQA++OAD+Pv7Y/DgwW3WjYiIQOfOnZGTk4OJEycCAIqLi1FaWoro6Gidrsvkn6gN2ubx1zaHPyD9PP4+PS5rbQPdZio9/ytXrsSyZcsQEBCAZ599FvPmzVM7p7O2h7uY/BPpj6nP429t8/R3lC49/1IoLS1FdXU1SktLIZfLUVhYCOD2rD7Ozs4AgDVr1iAhIQE2Njb45JNPsHLlSnz00UewtbUFAJSXl2PEiBF47733EBkZCTc3N0yfPh2pqanw9PSEq6sr5syZg+joaJ0/D5j8E5HFaxnjKaYecHv2hzvZ29vD3t6+Q22YO3cuBg8eDE9PTxw9ehQLFy5ERUUF1q1b12Z9fT7cRURkTcTEfCkneMjIyMCOHTuUr8PDwwEAubm5iImJAQB8/fXXWL58ORoaGjBo0CB89tlnGDVqlPKYpqYmFBcX48aNG8qyN998EzY2Npg4caLKIl+6YvJPRBavWWEDmUL7V7zN/63j7++vUp6ZmYnFixe3qp+eno5Vq1ZpPOe5c+cQHByM1NRUZVloaCjs7OzwwgsvYMWKFR3+w4KIiP5HTMxvFvGZ0F7Z2dlq5/hvcejQIY37e/fuDUFQXYbYwcEBWVlZyMrK6lD7mPwTkcXTddhPWVmZygNg6pLzjjwAFhUVhebmZly6dAn9+vVrtV+fD3cREVkTYw/7MXVM/onI4uma/Iud/aEjD4AVFhbCxsYG3bt3b3O/Ph/uIiKyJkz+NWPyT0QWT4C48Z2C1hrtk5eXh2PHjiE2NhYuLi7Iy8vDvHnzMHnyZHh4eACQ9uEuIiJrIibmSxXvzQGTfyKyeMae7cfe3h4ffvghFi9ejIaGBtxzzz2YN2+eynMAUj7cRURkTdjzrxmTfyKyeMZO/gcPHozvv/9eYx0pH+4iIrImTP41Y/JPFknbPP3aaJvHX9sc/gDn8Tclxk7+iUhapj5Pvzacx1+/mPxrxuSfiCwek38iIuvB5F8zJv9EZPEEQQZBRKAXU4eIiEybmJhvzfFeuhUOjGDChAnw8PDAk08+KaqciKxDs2AjeiPzwZhPRG1hvNfMot75iy++iPfee090ORFZh5ZeIDEbmQ/GfCJqC+O9ZhaV/MfExMDFxUV0ORFZh5bxn2I2Mh+M+UTUFsZ7zUwm+f/uu+8wduxY+Pn5QSaTYc+ePa3qZGVloXfv3nBwcEBUVBTy8/MN31AiMjvs+Tc9jPlEJBXGe81MJvmvr6/HoEGD1M5nvXv3bqSmpiIzMxMnT57EoEGDEB8fjytXrhi4pURkbgSRvUDW/GFgaIz5RCQVMTHfmuO9ycz2M2rUKIwaNUrt/nXr1iE5ORnTpk0DAGzatAlffvkltm3bhvT0dL21o6GhAQ0NDcrXtbW1ejs36Y+x5/HXNof/7WtwHn9TIQAQRKzlbs3LvRuaKcR8xnvzwXn8SRdiYr41x3uT6fnXpLGxEQUFBYiLi1OW2djYIC4uDnl5eXq91ooVK+Dm5qbc/P399Xp+IjI8BWSiNzI+Q8V8xnsiy8R4r5lZJP+//vor5HI5vL29Vcq9vb1RWVmpfB0XF4dJkybhq6++Qs+ePZUfEurK27Jw4ULU1NQot7KyMmneFBEZDMf8mxdDxXzGeyLLxHivmckM+9GHgwcP6lTeFnt7e9jb2+urSURkAuQKGaDQHujlIuqQ6ehozGe8J7JMYmK+Ncd7s+j579atG2xtbVFVVaVSXlVVBR8fH0mumZWVhZCQEAwdOlSS8xOR4bDn37wYOuYz3hNZFmPH++XLl2PYsGFwcnKCu7t7m3VycnIwbNgwuLi4wMfHBwsWLEBzs+bnEWNiYiCTyVS2mTNn6tw+s0j+7ezsEBERgZycHGWZQqFATk4OoqOjJblmSkoKioqKcPz4cUnOT0SGw+TfvBg65jPeE1kWY8f7xsZGTJo0CbNmzWpz/6lTpzB69GgkJCTg3//+N3bv3o3PP/9c1GQGycnJqKioUG6rV6/WuX0mM+ynrq4OJSUlytcXL15EYWEhPD09ERAQgNTUVCQmJmLIkCGIjIzE+vXrUV9fr5wJgohIHYUgg0xEoLfmRV8MjTGfiKQiJuZLGe+XLFkCAMjOzm5z/+7duxEaGoqMjAwAQFBQEFavXo2nnnoKmZmZGhcpdHJy6vA3oCaT/J84cQKxsbHK16mpqQCAxMREZGdn4+mnn8bVq1eRkZGByspKhIWFYd++fa0eCCMiupsgiJzq05rnfjMwxnwikoqYmG/MeN/Q0AAHBweVMkdHR9y6dQsFBQWIiYlRe+zOnTvx/vvvw8fHB2PHjsWiRYvg5OSk0/VNJvmPiYmBoOV/Yvbs2Zg9e7ZB2pOVlYWsrCzI5drncyci03b7g0B7Lw+Tf8MxpZjPeE9kWcTE/Jbwc/f6HoaYCCA+Ph7r16/HBx98gKeeegqVlZVYunQpAKCiokLtcc8++yx69eoFPz8/nD59GgsWLEBxcTE++eQTna5vMsm/qUlJSUFKSgpqa2vh5uZm7OZYHVNfxKtBywJeAODHRbxMhtjxnRzzb50Y742Pi3iRPomJ+S37717fIzMzE4sXL25VPz09HatWrdJ4znPnziE4OFhr+0aOHIk1a9Zg5syZmDJlCuzt7bFo0SL885//hI2N+sdxZ8yYofz3wIED4evrixEjRuDChQvo06eP1uu2YPJPRBZPgLjVHNnxT0Rk/sTE/Jb9ZWVlcHV1VZar6/VPS0tDUlKSxnMGBgaKbmNqairmzZuHiooKeHh44NKlS1i4cKFO54iKigIAlJSUMPknIrqToJBBEDGns5g6RERk2sTE/Jb9rq6uKsm/Ol5eXvDy8tJL+1rIZDL4+fkBAD744AP4+/tj8ODBoo8vLCwEAPj6+up0XSb/anAMKJEFETutG4f9WCXGeyILIybmSxjvS0tLUV1djdLSUsjlcmWSHhQUBGdnZwDAmjVrkJCQABsbG3zyySdYuXIlPvroI9ja2gIAysvLMWLECLz33nuIjIzEhQsXsGvXLowePRpdu3bF6dOnMW/ePDz88MMIDQ3VqX1mMc+/MXDeZyLL0TLzg5hNCocPH261MEvLpinG6GtBF9KM8Z7Ishgz3gNARkYGwsPDkZmZibq6OoSHhyM8PBwnTpxQ1vn666/x0EMPYciQIfjyyy/x2WefYfz48cr9TU1NKC4uxo0bNwDcXv/k4MGDGDlyJIKDg5GWloaJEydi7969OrePPf9EZPGM/cDvsGHDWs3gsGjRIuTk5GDIkCEaj01OTlbOAgFA5yndiIisjS4P/EohOztb7Rz/LQ4dOqRxf+/evVVmRPP398e3336rj+Yx+SciKyDIxH3FK9GHgZ2dncqiLE1NTfjss88wZ84cyGSar6mPBV2IiKyKmJhvxcM8OeyHiCyesYf93O3zzz/Hb7/9Jmq12p07d6Jbt2647777sHDhQuVXwERE1DZTivemSHTP/+eff67zyR999FE4OjrqfJwp4ANg0jL1efybtMzjzzn8zYyOc31KvejL1q1bER8fj549e2qsp68FXdrDmmI+4720zH0Of4Dz+JsdXeb6tEKik/87H0IQQyaT4ccff9RpvlJTwkVfiCyHrmP+pVz05ZdffsE333yDjz76SGt79LWgS3tYU8xnvCeyLMYe82/qdBrzX1lZie7du4uq6+Li0q4GERFJQodeHikXfdm+fTu6du2KcePGiW/Qf7V3QZf2YswnIrNlxT372ohO/hMTE3X6Onfy5MmiFk0gIpKarot8SbXoiyAI2L59O6ZOnYrOnTuLPq5Fexd0aQ/GfCIyV7os8mWNRD/wu337dp16djZu3Ihu3bq1q1FERPol02GTzqFDh3Dx4kX83//9X6t95eXlCA4ORn5+PgDgwoULWLZsGQoKCnDp0iV8/vnnmDp1arsWdGkPxnwiMl/Gj/emjFN9qsEHwIgsiI4P/Epl69atGDZsmMozAC3ULeiyfv161NfXw9/fHxMnTsRrr70mbSOtEOM9kYXhA78atTv5v3XrFk6fPo0rV65AoVCdGaU9Y1lNDR8AI7IgJpL879q1S+0+KRd00QdLjvmM90QWhsm/Ru1K/vft24epU6fi119/bbVPJpOx94SITIuRF/kyd4z5RGRWuMiXRu1K/ufMmYNJkyYhIyMD3t7e+m4TWQBzn8ffh/P4WxSxC7pY86IvmjDmkyacx59MjZiYb83xvl3Jf1VVFVJTU/khQETmwUSG/ZgrxnwiMisc9qOR6Nl+7vTkk0/i8OHDem4KEZFEWr4CFrNRK4z5RGRWGO81alfP/1//+ldMmjQJ//znPzFw4MBW81XPnTtXL40jItIHmeL2JqYetcaYT0TmREzMt+Z4367k/4MPPsD+/fvh4OCAw4cPQyb7319PMpnMIj4IOPUbkQXhA78dYukxn/GeyMLwgV+N2pX8v/rqq1iyZAnS09NhY9OukUMmj1O/EVkQjvnvEEuP+Yz3RBaGY/41alfy39jYiKefftoiPwSIyAIx+e8QxnwiMitM/jVqVyRPTEzE7t279d0WIiJpCDps1ApjPhGZFcZ7jdrV8y+Xy7F69Wp88803CA0NbfXw17p16/TSODJdpj6Pf1e/X3RuE1kwjvnvEMZ862YO8/hznn5SYcQx/5cuXcKyZctw6NAhVFZWws/PD5MnT8arr74KOzs7Zb3Tp08jJSUFx48fh5eXF+bMmYP58+drPHdpaSlmzZqF3NxcODs7IzExEStWrECnTrql8+1K/n/44QeEh4cDAM6cOdOeUxARGYxMuL2JqUetMeYTkTkRE/Olivfnz5+HQqHA5s2bERQUhDNnziA5ORn19fVYu3YtAKC2thYjR45EXFwcNm3ahB9++AHPP/883N3dMWPGjDbPK5fLMWbMGPj4+ODo0aOoqKjA1KlT0blzZ7zxxhs6tbFdyX9ubm57DiMiMg6O+e8QxnwiMitGHPOfkJCAhIQE5evAwEAUFxdj48aNyuR/586daGxsxLZt22BnZ4cBAwagsLAQ69atU5v879+/H0VFRTh48CC8vb0RFhaGZcuWYcGCBVi8eLHKtwratGvM/4oVK7Bt27ZW5du2bcOqVavac0oiIjJRjPlEZKlqa2tVtoaGBr1fo6amBp6ensrXeXl5ePjhh1US9vj4eBQXF+P3339v8xx5eXkYOHCgykrr8fHxqK2txdmzZ3VqT7uS/82bNyM4OLhV+YABA7Bp06b2nJKISDIyQQaZQsTGMf9tYswnInMiKub/N977+/vDzc1Nua1YsUKvbSkpKcGGDRvwwgsvKMsqKytVkngAyteVlZVtnqc9x6jTruS/srISvr6+rcq9vLxQUVHRnlOanKysLISEhGDo0KHGbgoRdRRn++kQS4/5jPdEFkaHeF9WVoaamhrltnDhwjZPmZ6eDplMpnE7f/68yjHl5eVISEjApEmTkJycLNGb1V27xvz7+/vjyJEjuOeee1TKjxw5Aj8/P700zNi46AuRBeGY/w6x9JjPeE9kYXQY8+/q6gpXV1etp0xLS0NSUpLGOoGBgcp/X758GbGxsRg2bBi2bNmiUs/HxwdVVVUqZS2vfXx82jy3j48P8vPzdTpGnXYl/8nJyXjppZfQ1NSE4cOHAwBycnIwf/58pKVxui0iMi2c7adjGPOJyJxIMduPl5cXvLy8RNUtLy9HbGwsIiIisH379lYLJEZHR+PVV19FU1OTcurkAwcOoF+/fvDw8GjznNHR0Vi+fDmuXLmC7t27K49xdXVFSEiITu+lXcn/yy+/jN9++w1//OMf0djYCABwcHDAggUL1H5dQuZF6nn8G7Tt1zLPv5dfuc5tIivGnv8OYcy3bJzHnyyOEWf7KS8vR0xMDHr16oW1a9fi6tWryn0tPfTPPvsslixZgunTp2PBggU4c+YM3nrrLbz55pvKup9++ikWLlyoHEo0cuRIhISEYMqUKVi9ejUqKyvx2muvISUlBfb29jq1sV3Jv0wmw6pVq7Bo0SKcO3cOjo6O6Nu3r84XJyIyCCb/HcKYT0RmxYjJ/4EDB1BSUoKSkhL07NlT9ZLC7Yu6ublh//79SElJQUREBLp164aMjAyVaT5rampQXFysfG1ra4svvvgCs2bNQnR0NLp06YLExEQsXbpU5za2K/lv4ezszAekiMjkcdiPfjDmE5E5MOYiX0lJSVqfDQCA0NBQ/POf/9TpPL169cJXX33VwRbqMNvP6dOnoVAoRJ/47NmzaG7WPLSDiMggWpZ6F7MRAMZ8IjJjjPcaiU7+w8PD8dtvv4k+cXR0NEpLS9vVKCIifZIpxG9SWb58OYYNGwYnJye4u7u3Wae0tBRjxoyBk5MTunfvjpdffllrQl1dXY3nnnsOrq6ucHd3x/Tp01FXV9fh9jLmE5G5Mna8N3Wih/0IgoBFixbByclJVP2Wh8KIiIzOBMb8NzY2YtKkSYiOjsbWrVtb7ZfL5RgzZgx8fHxw9OhRVFRUYOrUqejcuTPeeOMNted97rnnUFFRgQMHDqCpqQnTpk3DjBkzsGvXrg61lzGfiMyWEcf8mwPRyf/DDz+s8uCBNtHR0XB0dGxXo4iI9ErkmH8pPwyWLFkCAMjOzm5z//79+1FUVISDBw/C29sbYWFhWLZsGRYsWIDFixerLAPf4ty5c9i3bx+OHz+OIUOGAAA2bNiA0aNHY+3atR2ag58xn4jMlpiYz+Rfu8OHD0vYDCIiCZlAz782eXl5GDhwoMry7fHx8Zg1axbOnj2L8PDwNo9xd3dXJv4AEBcXBxsbGxw7dgwTJkxod3sY84nIbLHnX6MOzfZD5kvbPP4KaB4M1yRonodf2zz+N7Qc79fjssb9RDrRMfmvra1VKba3t5d8WsvKykqVxB+A8nVlZaXaY1oWe2nRqVMneHp6qj2GrA/n8Serw+RfI9EP/FqbrKwshISEcFo7IgvQMu2bmA0A/P394ebmptxWrFjR5nnT09Mhk8k0bi0LtJDpYrwnsiy6xHtrxJ5/NVJSUpCSkoLa2lq4ubkZuzlEZEBlZWVwdXVVvlbX65+WlqZ1PufAwEBR1/Tx8UF+fr5KWVVVlXKfumOuXLmiUtbc3Izq6mq1x1BrjPdEZE3alfz/5S9/wZNPPtmhh8mIiAxGx2E/rq6uKsm/Ol5eXvDy8upQ01pER0dj+fLluHLlinIoz4EDB+Dq6oqQkBC1x1y7dg0FBQWIiIgAABw6dAgKhQJRUVF6aRfAmE9EZobDfjRq17Cfl156CQ899BDKyspUyhsbG1FQUKCXhhER6Yuuw36kUFpaisLCQpSWlkIul6OwsBCFhYXKOflHjhyJkJAQTJkyBadOncI333yD1157DSkpKcpvHvLz8xEcHIzy8nIAQP/+/ZGQkIDk5GTk5+fjyJEjmD17Np555hm9JuqM+URkTowd701du8f8x8XF4ZFHHlH5MPj9998RGRmpl4YREemNAEAhYpPwwyAjIwPh4eHIzMxEXV0dwsPDER4ejhMnTgAAbG1t8cUXX8DW1hbR0dGYPHkypk6diqVLlyrPcePGDRQXF6OpqUlZtnPnTgQHB2PEiBEYPXo0HnzwQWzZskXv7WfMJyKzISbmW3Hy365hPzKZDMuWLUP37t3xyCOP4Ntvv4W/vz+A2wvDEBGZErG9PFL2BGVnZ6ud479Fr1698NVXX6ndHxMT0yrGenp6dnhBL20Y84nInIiJ+dbc89+hB36XLVsGmUym/DCws7ODTCbTV9uIiPTDDOb5NweM+URkFjjmX6N2Jf939vQsXbpU+WHw4Ycf6q1hRET6Ygo9/+aMMZ+IzAl7/jVrV/K/fPlydOnSRfm6Zdn6sWPH6qdV1GFSL+JVJzRq3H9dofn8gT0rNO4n0iv2/HcIY75pM/VFvLiAFxkce/41alfyv3DhwlZlS5YsQefOnbF27doON4qISK+Y/HcIYz4RmRUm/xrpdYXf1157DdeuXdPnKYmIOswUpvq0RIz5RGSKGO814wq/RGT52PNPRGQ92POvkV57/omITJFMIX4jIiLzZsx4f+nSJUyfPh333HMPHB0d0adPH2RmZqKxUfVZydOnT+Ohhx6Cg4MD/P39sXr1au3vSyZrtbVn4gX2/BOR5WPPPxGR9TBiz//58+ehUCiwefNmBAUF4cyZM0hOTkZ9fb3yGana2lqMHDkScXFx2LRpE3744Qc8//zzcHd3x4wZMzSef/v27UhISFC+dnd317mNTP6JyOJxqk8iIuthzKk+ExISVJLzwMBAFBcXY+PGjcrkf+fOnWhsbMS2bdtgZ2eHAQMGoLCwEOvWrdOa/Lu7u8PHx6dDbbSoYT8TJkyAh4cHnnzySZXyL774Av369UPfvn3x7rvvGql1RGQ0gg4bmQXGeyJSS4d4X1tbq7I1NDTovTk1NTXw9PRUvs7Ly8PDDz8MOzs7ZVl8fDyKi4vx+++/azxXSkoKunXrhsjISGzbtq1dq6xbVM//iy++iOeffx47duxQljU3NyM1NRW5ublwc3NDREQEJkyYgK5duxqxpR3X0Xn8G4QmjfuvKzTvr1ZoXtUzxJ/z+JMJ4bAfi2NN8Z7z+BPpSIdhP/7+/irFmZmZWLx4sd6aUlJSgg0bNqhMi1xZWYl77rlHpZ63t7dyn4eHR5vnWrp0KYYPHw4nJyfs378ff/zjH1FXV4e5c+fq1CaL6vmPiYmBi4uLSll+fj4GDBiAHj16wNnZGaNGjcL+/fuN1EIiMgaZDhuZB8Z7IlJHl3hfVlaGmpoa5dbWuiYAkJ6e3uYDt3du58+fVzmmvLwcCQkJmDRpEpKTkzv8vhYtWoQHHngA4eHhWLBgAebPn481a9bofB6TSf6/++47jB07Fn5+fpDJZNizZ0+rOllZWejduzccHBwQFRWF/Px8ree9fPkyevTooXzdo0cPlJeX67PpRGTqOOzHpDDeE5GkdIj3rq6uKpu9vX2bp0xLS8O5c+c0boGBgcr6ly9fRmxsLIYNG4YtW7aonMvHxwdVVVUqZS2vdRnPHxUVhV9++UXnoUomk/zX19dj0KBByMrKanP/7t27kZqaiszMTJw8eRKDBg1CfHw8rly5YuCWEpG54SJfpoXxnoikJEW89/LyQnBwsMatZQx/eXk5YmJiEBERge3bt8PGRjXdjo6OxnfffYempv8NsT5w4AD69eundshPWwoLC+Hh4aH2DxZ1TCb5HzVqFF5//XVMmDChzf3r1q1DcnIypk2bhpCQEGzatAlOTk7Ytm2bxvP6+fmp9PyUl5fDz89Pbf2GhoZWD38QkZkTAChEbEz+DYLxnogkJSbmSxTvWxL/gIAArF27FlevXkVlZSUqKyuVdZ599lnY2dlh+vTpOHv2LHbv3o233noLqampyjqffvopgoODla/37t2Ld999F2fOnEFJSQk2btyIN954A3PmzNG5jSaT/GvS2NiIgoICxMXFKctsbGwQFxeHvLw8jcdGRkbizJkzKC8vR11dHb7++mvEx8errb9ixQq4ubkpt7sfBCEi88Oef/PBeE9EHWXMeH/gwAGUlJQgJycHPXv2hK+vr3Jr4ebmhv379+PixYuIiIhAWloaMjIyVKb5rKmpQXFxsfJ1586dkZWVhejoaISFhWHz5s1Yt24dMjMzdW6jWcz28+uvv0IulyufhG7h7e2t8nBFXFwcTp06hfr6evTs2RMff/wxoqOj8ec//xmxsbFQKBSYP3++xpkfFi5cqPKXV21tLT8QiMwdZ/sxG4z3RNRhRlzkKykpCUlJSVrrhYaG4p///Kfo89y9fkBHmEXyL9bBgwfbLB83bhzGjRsn6hz29vY6j50iItPGRb4sD+M9EaljzEW+zIFZJP/dunWDra1tm09Gd3SVM3WysrKQlZUFuVwuyfm16eg8/jcUjRr3V2uZx7+suYvG/T82ar7vIRr3EhkYe/7NhjXGe1Ofx5/I7Bix598cmMWYfzs7O0RERCAnJ0dZplAokJOTg+joaEmumZKSgqKiIhw/flyS8xOR4XDMv/lgvCeijmK818xkev7r6upQUlKifH3x4kUUFhbC09MTAQEBSE1NRWJiIoYMGYLIyEisX78e9fX1mDZtmhFbTURmgT3/JoXxnogkxZ5/jUym5//EiRMIDw9HeHg4ACA1NRXh4eHIyMgAADz99NNYu3YtMjIyEBYWhsLCQuzbt6/VQ2FERK2YwCJfy5cvx7Bhw+Dk5AR3d/dW+0+dOoU//OEP8Pf3h6OjI/r374+33npL63l79+7dapXJlStXSvAO9IfxnogkxUUdNTKZnv+YmBgIgub/idmzZ2P27NkGaY+xx4ASkf6YwgO/jY2NmDRpEqKjo7F169ZW+wsKCtC9e3e8//778Pf3x9GjRzFjxgzY2tpqjXtLly5VWTrexcVF7+3XJ8Z7IpISH/jVzGSSf1OTkpKClJQU1NbWws3NzdjNIaIOkCkEyBTaI72YOu21ZMkSAEB2dnab+59//nmV14GBgcjLy8Mnn3yiNQl2cXGR7GFYa8B4T2RZxMR8KeO9qTOZYT9ERJLRcdjP3au+NjQ0GKPVqKmpgaenp9Z6K1euRNeuXREeHo41a9agubnZAK0jIjJRHPajEXv+icji6Trs5+6FnjIzM7F48WL9N0yDo0ePYvfu3fjyyy811ps7dy4GDx4MT09PHD16FAsXLkRFRQXWrVtnoJYSEZkWDvvRjMm/GsYeA9rRefyrFJp7/n5q8tC4P/e65pn6V4b+XeN+IpOi42w/ZWVlcHV1VRarWwgqPT0dq1at0njKc+fOITg4WGRDbztz5gwef/xxZGZmYuTIkRrr3rlCbWhoKOzs7PDCCy9gxYoVXMBKJGPHe1P309w0YzeBSDec7UcjJv9qcAwokeXQteff1dVVJflXJy0tTesy7oGBgSJa+D9FRUUYMWIEZsyYgddee02nYwEgKioKzc3NuHTpEvr166fz8daI8Z7IsrDnXzMm/0Rk+SSa59/LywteXl7taVGbzp49i+HDhyMxMRHLly9v1zkKCwthY2OD7t27661dRERmhT3/GjH5JyKLZwpTfZaWlqK6uhqlpaWQy+UoLCwEAAQFBcHZ2RlnzpzB8OHDER8fj9TUVFRWVgIAbG1tlX9g5OfnY+rUqcjJyUGPHj2Ql5eHY8eOITY2Fi4uLsjLy8O8efMwefJkeHhoHtpHRGSp2POvGZN/IrJ8JrDCb0ZGBnbs2KF83bLAVW5uLmJiYvD3v/8dV69exfvvv4/3339fWa9Xr164dOkSAODGjRsoLi5GU1MTgNvPInz44YdYvHgxGhoacM8992DevHkqzwEQEVkd9vxrxORfDT4ARmRBBHHz/EPLwlMdkZ2drXaOfwBYvHix1hmF7l4ca/Dgwfj+++/11ELrxXhPZGHExHwJ472p4zz/aqSkpKCoqAjHjx83dlOIqINavgIWs5H1YbwnsiyM95qx55+ILJ8JDPshIiID4bAfjZj8G0FzZZDWOtrm8a+Qa57H/2yjt8b9n/4aoXH/36Le1bifyJzIFLc3MfWI9C3wL382dhM04jz+ZGnExHyp4v2lS5ewbNkyHDp0CJWVlfDz88PkyZPx6quvws7ODgBw69YtzJw5EwUFBTh37hwee+wx7NmzR+u5q6urMWfOHOzduxc2NjaYOHEi3nrrLTg7O+vURib/RGT52PNPRGQ9jNjzf/78eSgUCmzevBlBQUE4c+YMkpOTUV9fj7Vr1wIA5HI5HB0dMXfuXPzjH/8Qfe7nnnsOFRUVOHDgAJqamjBt2jTMmDEDu3bt0qmNTP7V4ANgRJbDFKb6JNPFeE9kWYw51WdCQgISEhKUrwMDA1FcXIyNGzcqk/8uXbpg48aNAIAjR47g2rVrWs977tw57Nu3D8ePH8eQIUMAABs2bMDo0aOxdu1a+Pn5iW4jH/hVgw+AEVkQQRC/kdVhvCeyMCYW72tqauDp6dmhc+Tl5cHd3V2Z+ANAXFwcbGxscOzYMZ3OxZ5/IrJ47PknIrIeuvT819bWqpTb29vD3t5eb20pKSnBhg0blL3+7VVZWdlq5fZOnTrB09NTuSikWOz5JyLLJ+iwERGRedMh3vv7+8PNzU25rVixos1TpqenQyaTadzOnz+vckx5eTkSEhIwadIkJCcnS/RmdceefyKyeDKFuEW+RC0ERkREJk1MzG/ZX1ZWBldXV2W5ul7/tLQ0JCUlaTxnYGCg8t+XL19GbGwshg0bhi1btohsuXo+Pj64cuWKSllzczOqq6vh4+Oj07mY/BORxeOwHyIi66HLsB9XV1eV5F8dLy8veHl5ibp+eXk5YmNjERERge3bt8PGpuMDbaKjo3Ht2jUUFBQgIuL2dO2HDh2CQqFAVFSUTudi8m8E2ubwB7TP4//9rd4a939+NUzj/n8Me1trG4gsBqf6JCvGefzJ6hhxqs/y8nLExMSgV69eWLt2La5evarcd2cPfVFRERobG1FdXY3r16+jsLAQABAWFgYAyM/Px9SpU5GTk4MePXqgf//+SEhIQHJyMjZt2oSmpibMnj0bzzzzjE4z/QBM/tXi1G9EloM9/6QJ4z2RZTHmVJ8HDhxASUkJSkpK0LNnT5V9wh0zDI0ePRo///yz8nV4eLhKnRs3bqC4uBhNTU3KOjt37sTs2bMxYsQI5SJff/nLX3RuI5N/NVJSUpCSkoLa2lq4ubkZuzlE1BEK4fYmph5ZHcZ7IgsjJuZLFO+TkpK0PhsA3F4JWJOYmBiVPxYAwNPTU+cFvdrC5J+ILB+H/RARWQ8jDvsxB0z+icjiySBy2I/kLSEiIqmJifnWHO+Z/BOR5RO7miNX+CUiMn9iYr4Vx3sm/0Rk8WSK25uYekREZN7ExHxrjvdM/onI4skEATIRvTxi6hARkWkTE/OtOd4z+Sciy6f47yamHhERmTcxMd+K4z2TfwncrLhH4/5fFU0a9wNA/q1eGve/98v9Gvf/dKaH5gsM09oEIovBnn+SUuBf/mzsJhDRHdjzr1nH1xu2UFlZWQgJCcHQoUON3RQi6ihBh42sDuM9kYVhvNeIyb8aKSkpKCoqwvHjx43dFCLqqJaZH8RsElm+fDmGDRsGJycnuLu7t1lHJpO12j788EON562ursZzzz0HV1dXuLu7Y/r06airq5PgHVguxnsiC2PkeG/qmPwTkcVrWepdzCaVxsZGTJo0CbNmzdJYb/v27aioqFBu48eP11j/ueeew9mzZ3HgwAF88cUX+O677zBjxgw9tpyIyLwYO96bOo75JyLLZwLz/C9ZsgQAkJ2drbGeu7s7fHx8RJ3z3Llz2LdvH44fP44hQ4YAADZs2IDRo0dj7dq18PPz61CbiYjMEuf514g9/0Rk8VrmfBazAUBtba3K1tDQYLC2pqSkoFu3boiMjMS2bdsgaPiAysvLg7u7uzLxB4C4uDjY2Njg2LFjhmguEZHJ0SXeWyP2/BOR5VMItzcx9QD4+/urFGdmZmLx4sUSNEzV0qVLMXz4cDg5OWH//v344x//iLq6OsydO7fN+pWVlejevbtKWadOneDp6YnKykrJ20tEZJLExHwxnwkWisk/EVk8Xaf6LCsrg6urq7Lc3t6+zfrp6elYtWqVxnOeO3cOwcHBotq5aNEi5b/Dw8NRX1+PNWvWqE3+iYioNU71qRmTfwnUKDQPESho0D4O96NKzVPO/XzcX+P+i/Pnab0GkdXQccy/q6urSvKvTlpaGpKSkjTWCQwMFNPCNkVFRWHZsmVoaGho8w8QHx8fXLlyRaWsubkZ1dXVop8bIPP309w0YzeByLRwzL9GTP6JyPIJELeao46fBV5eXvDy8mpPi0QpLCyEh4eH2m8eoqOjce3aNRQUFCAiIgIAcOjQISgUCkRFRUnWLiIikyYm5ltv7s/kn4gsnyms8FtaWorq6mqUlpZCLpejsLAQABAUFARnZ2fs3bsXVVVVuP/+++Hg4IADBw7gjTfewJ/+9CflOfLz8zF16lTk5OSgR48e6N+/PxISEpCcnIxNmzahqakJs2fPxjPPPMOZfojIanHYj2ZM/onI8gkQOexHuiZkZGRgx44dytfh4eEAgNzcXMTExKBz587IysrCvHnzIAgCgoKCsG7dOiQnJyuPuXHjBoqLi9HU1KQs27lzJ2bPno0RI0bAxsYGEydOxF/+8hfp3ggRkakTE/OtN/fnVJ9EZAVMYIXf7OxsCILQaouJiQEAJCQk4N///jeuX7+Ouro6FBYW4oUXXoCNzf/CdExMDARBQO/evZVlnp6e2LVrF65fv46amhps27YNzs7Okr0PIiKTZ8R4f+nSJUyfPh333HMPHB0d0adPH2RmZqKxsVFZ59atW0hKSsLAgQPRqVMnrYs5tujdu3erVeBXrlypcxvZ869GVlYWsrKyIJfLjd0UIuooBQCZyHpkdRjviSyMmJgvUbw/f/48FAoFNm/ejKCgIJw5cwbJycmor6/H2rVrAQByuRyOjo6YO3cu/vGPf+h0/qVLl6p8I+zi4qJzG5n8q5GSkoKUlBTU1tbCzc3N2M0hog6QKRSQiVjRRaZg9m+NGO+JLIuYmC9VvE9ISEBCQoLydWBgIIqLi7Fx40Zl8t+lSxds3LgRAHDkyBFcu3ZN9PldXFw6PJsbh/0QkeUzgWE/RERkIDrEe0Os6F5TUwNPT0+9nGvlypXo2rUrwsPDsWbNGjQ3N+t8Dvb8S+CnZgeN+7/4LUzrOc6c6q1xv1cRkxQi0XSc55+IiMyYDvP8S72ie0lJCTZs2KDs9e+IuXPnYvDgwfD09MTRo0excOFCVFRUYN26dTqdh8k/EVk+jvknIrIeOoz5l3JF9/LyciQkJGDSpEkq4/TbKzU1Vfnv0NBQ2NnZ4YUXXsCKFSvUtrstTP6JyOKZwjz/RERkGLrM8y/Viu6XL19GbGwshg0bhi1btmhvdDtERUWhubkZly5dQr9+/UQfx+SfiCwfh/0QEVkPHYb9iKXLiu7l5eWIjY1FREQEtm/frjJlsz4VFhbCxsYG3bt31+k4Jv9EZPkUAiATEegVTP6JiMyemJgvUbwvLy9HTEwMevXqhbVr1+Lq1avKfXfO0lNUVITGxkZUV1fj+vXrylXfw8LCALRe0T0vLw/Hjh1DbGwsXFxckJeXh3nz5mHy5Mnw8PDQqY1M/onI8rHnn4jIekjQ8y/WgQMHUFJSgpKSEvTs2fOuS/7vmqNHj8bPP/+sfN2y6ntLnbtXdLe3t8eHH36IxYsXo6GhAffccw/mzZun8hyAWEz+icjyCQpAzJzOAp/4JSIye2JivkTxPikpSeuzAcDtlYA1aVnRvcXgwYPx/fffd7B1tzH5JyLLpxAAcNgPEZFVEBPzrTjeM/lvh8vlfhr3H7txn8b9357vq/Ua7sWaHw7pUtGo9RxE9F+CQlwvD3v+qQ2Bf/mzsZtARLoQE/OtON4z+Sciy8cx/0RE1sOIY/7NAZN/IrJ8HPZDRGQ9OOxHIyb/RGT52PNPRGQ92POvkTSrDpiYtWvXYsCAAbjvvvvw/vvvG7s5RGRoAv73YaBxM3ZDqaMY74lIXMw3diONx+J7/n/44Qfs2rULBQUFEAQBsbGxeOyxx+Du7m7sphGRobDn3yow3hMRAPb8a2HxPf/nzp1DdHQ0HBwc4OjoiEGDBmHfvn3GbhYRGZJCIX4js8V4T0QAGO+1MPnk/7vvvsPYsWPh5+cHmUyGPXv2tKqTlZWF3r17w8HBAVFRUcjPz1fuu++++3D48GFcu3YNv//+Ow4fPozy8nIDvgMiMjom/2aB8Z6I9ILxXiOTH/ZTX1+PQYMG4fnnn8cTTzzRav/u3buRmpqKTZs2ISoqCuvXr0d8fDyKi4vRvXt3hISEYO7cuRg+fDjc3Nxw//33w9bWtkNtKpfbady/70qIxv0OP9lrvYbzZbnG/XbVt7Seg4j+i7P9mAVTjPdEZIY4249GJt/zP2rUKLz++uuYMGFCm/vXrVuH5ORkTJs2DSEhIdi0aROcnJywbds2ZZ0XXngBJ0+eRG5uLjp37oy+fdUvstXQ0IDa2lqVjYjMmyAoRG9kPIz3RKQPjPeamXzyr0ljYyMKCgoQFxenLLOxsUFcXBzy8vKUZVeuXAEAFBcXIz8/H/Hx8WrPuWLFCri5uSk3f39/6d4AERmGINzu5dG2SfgA2PLlyzFs2DA4OTm1+QBqdnY2ZDJZm1tLDGtL7969W9VfuXKlZO/DWBjviUg0MTHfih/4NflhP5r8+uuvkMvl8Pb2Vin39vbG+fPnla8ff/xx1NTUoEuXLti+fTs6dVL/thcuXIjU1FTl69raWn4gEJk7QeSwHwk/DBobGzFp0iRER0dj69atrfY//fTTSEhIUClLSkrCrVu30L17d43nXrp0KZKTk5WvXVxc9NNoE8J4T0SiiYn5TP4t2529QtrY29vD3l77mHwiMiMKBSAT8RWvhF8DL1myBMDtHv62ODo6wtHRUfn66tWrOHToUJt/KNzNxcUFPj4+emmnuWO8JyJRMZ/DfsxTt27dYGtri6qqKpXyqqqqDn8QZmVlISQkBEOHDu3QeYjIBIha4Ot/XwPfPQ68oaHB4E1+77334OTkhCeffFJr3ZUrV6Jr164IDw/HmjVr0NzcbIAWGhbjPRGJpkO8t0Zmnfzb2dkhIiICOTk5yjKFQoGcnBxER0d36NwpKSkoKirC8ePHO9pMIjIyQaEQvQGAv7+/yljwFStWGLzNW7duxbPPPqvybUBb5s6diw8//BC5ubl44YUX8MYbb2D+/PkGaqXhMN4TkVi6xHtrZPLDfurq6lBSUqJ8ffHiRRQWFsLT0xMBAQFITU1FYmIihgwZgsjISKxfvx719fWYNm2aEVtNRCZFrtuwn7KyMri6uiqL1Q0NSU9Px6pVqzSe8ty5cwgODhbfVtweunLu3Dn87W9/01r3zjHroaGhsLOzwwsvvIAVK1aY3ZAWxnsi0gsxMd+Kh/2YfPJ/4sQJxMbGKl+3fNAlJiYiOzsbTz/9NK5evYqMjAxUVlYiLCwM+/bta/VQmD792Kj53MWXfDXu96zSuBsA4FiheR7/b45naj8JEd0mCADEJP+3vwZ2dXVVSf7VSUtLQ1JSksY6gYGBIhqo6t1330VYWBgiIiJ0PjYqKgrNzc24dOkS+vXrp/PxxmSK8d4U/DQ3zdhNIDIvYmK+RMN+Ll26hGXLluHQoUOorKyEn58fJk+ejFdffRV2drfXiTp8+DDefPNN5Ofno7a2Fn379sXLL7+M5557TuO5S0tLMWvWLOTm5sLZ2RmJiYlYsWKFxokN2mLyyX9MTAwELf9Bs2fPxuzZs/V63aysLGRlZUEu17zYFhGZPkEhQJBpD/TaYs3dvLy84OXl1d5mtamurg4fffRRu4caFRYWwsbGRusMQaaI8Z6I9EFMzNc13ot1/vx5KBQKbN68GUFBQThz5gySk5NRX1+PtWvXAgCOHj2K0NBQLFiwAN7e3vjiiy8wdepUuLm54bHHHmvzvHK5HGPGjIGPjw+OHj2KiooKTJ06FZ07d8Ybb7yhUxtNPvk3lpSUFKSkpKC2thZubm7Gbg4RdYSggLief+m+Bi4tLUV1dTVKS0shl8tRWFgIAAgKCoKzs7Oy3u7du9Hc3IzJkye3Okd+fj6mTp2KnJwc9OjRA3l5eTh27BhiY2Ph4uKCvLw8zJs3D5MnT4aHh4dk78XSMN4TWRgxMV+ieJ+QkKAybXNgYCCKi4uxceNGZfL/yiuvqBzz4osvYv/+/fjkk0/UJv/79+9HUVERDh48CG9vb4SFhWHZsmVYsGABFi9erPxWQQyzfuCXiEgMQSGI3qSSkZGB8PBwZGZmoq6uDuHh4QgPD8eJEydU6m3duhVPPPFEmwuB3bhxA8XFxWhqagJw+1mEDz/8EI888ggGDBiA5cuXY968ediyZYtk74OIyNQZO97fraamBp6enh2qk5eXh4EDB6oMc4yPj0dtbS3Onj2rU3vY86+FcMfUfy1uXNf81bDipubx+vJG7X9tNjdrPgeXoSdr0vLz3t6vaZuFBlG9PM1oatf5xcjOzlY7x/+djh49qnbf3cNiBg8ejO+//14fzSO0He8BQHFLczyWGuM9WRtDxPyWeH/375e+1/8oKSnBhg0blL3+bfnoo49w/PhxbN68WW2dysrKNhc5bNmnE4Ha9Ne//lXo37+/0KdPn5Zl4rhx42bkraysTKff45s3bwo+Pj46XcPHx0e4efOmRJGFTBHjPTduprlJHfOdnZ1blWVmZrZ57gULFmg937lz51SO+eWXX4Q+ffoI06dPV9vmQ4cOCU5OTsKOHTs0vrfk5GRh5MiRKmX19fUCAOGrr74Sd4P+SyYIVrzKgQgKhQKXL1+Gi4sLZDIZhg4dKmouaDH11NXRpfzusjtftyxVf/e0hVIQe186eqy2upr2i7l/YsoMdV/N9Z6qK+/Iz6ogCLh+/Tr8/PxgY6PbaMVbt26hsbFRdH07Ozs4ODjodA2yDO2N9wBjkxTHmkNs0idzva9S/KwaKuYLggCZTKZSpq7n/+rVq/jtt980ni8wMFA59v7y5cuIiYnB/fffj+zs7Dbfx7fffosxY8Zg3bp1mDFjhsZzZ2Rk4PPPP1c+Lwbcng45MDAQJ0+eRHh4uMbj78RhP1rY2NigZ8+eyte2traiAoCYeurq6FJ+d1lbdcROW9gRYu9LR4/VVlfTfjH3T5cyqe+rud5TdeUd/Vlt74OYDg4OTOZJlPbGezF1GZt0P9ZcYpO+mOt9lepn1dRivi6zu5WXlyM2NhYRERHYvn17m4n/4cOH8dhjj2HVqlVaE38AiI6OxvLly3HlyhXlbG4HDhyAq6srQkJCdHovfOBXRykpKXqrp66OLuV3l4ltn7515Lq6HKutrqb9Yu6fLmVSM9d7qq7cVH5WicRibGobY5M0zPW+mvLPqjGUl5cjJiYGAQEBWLt2La5evYrKykqVcfm5ubkYM2YM5s6di4kTJyr3V1dXK+t8+umnKgtEjhw5EiEhIZgyZQpOnTqFb775Bq+99hpSUlJ0fkaBw34sWMu0dTU1NZL3WFgT3lf94z0l6jj+Hukf76k0LPm+Zmdnq111vCXlTkpKwo4dO1rtf+SRR3D48GGV89yZpv/888+YNWsWDh8+jC5duiAxMRErV67UeZEv9vxbMHt7e2RmZur1qXXifZUC7ylRx/H3SP94T6Vhyfc1KSkJgiC0ubXIzs5uc39L4n/nee7Uq1cvfPXVV7hx4wauXr2KtWvX6pz4A+z5JyIiIiKyGuz5JyIiIiKyEkz+iYiIiIisBJN/IiIiIiIrweSfiIiIiMhKMPm3MhMmTICHhweefPJJlfIvvvgC/fr1Q9++ffHuu+8aqXXmSd09VVdO2rV178rKyhATE4OQkBCEhobi448/NmILiUwf473+Md5LgzHfsDjbj5U5fPgwrl+/jh07duDvf/87AKC5uRkhISHIzc2Fm5sbIiIicPToUXTt2tXIrTUPbd1TTeWkXVv3rqKiAlVVVQgLC0NlZSUiIiLwn//8B126dDFya4lME+O9/jHeS4Mx37DY829lYmJi4OLiolKWn5+PAQMGoEePHnB2dsaoUaOwf/9+I7XQ/LR1TzWVk3Zt3TtfX1+EhYUBAHx8fNCtWzeV1RCJSBXjvf4x3kuDMd+wmPybke+++w5jx46Fn58fZDIZ9uzZ06pOVlYWevfuDQcHB0RFRSE/P1/reS9fvowePXooX/fo0QPl5eX6bLrJkuqeWjND3NOCggLI5XL4+/vrqdVEpoXxXv8Y76XBmG9+mPybkfr6egwaNAhZWVlt7t+9ezdSU1ORmZmJkydPYtCgQYiPj8eVK1cM3FLzwXuqf1Lf0+rqakydOhVbtmzRZ7OJTApjk/7xnkqDMd8MCWSWAAiffvqpSllkZKSQkpKifC2XywU/Pz9hxYoVKvVyc3OFiRMnKl8fOXJEGD9+vPL1iy++KOzcuVOahpswfd5TbeXWQt/39NatW8JDDz0kvPfee5K1mcjUMN7rH+O9NBjzzQN7/i1EY2MjCgoKEBcXpyyzsbFBXFwc8vLyNB4bGRmJM2fOoLy8HHV1dfj6668RHx8vdZNNXkfuKbWtI/dUEAQkJSVh+PDhmDJlitRNJTJZjPf6x3gvDcZ809TJ2A0g/fj1118hl8vh7e2tUu7t7Y3z588rX8fFxeHUqVOor69Hz5498fHHHyM6Ohp//vOfERsbC4VCgfnz53PmB3T8nqort2YduadyuRy7d+9GaGiockzp3/72NwwcONCQb4HI6Bjv9Y/xXhqM+aaJyb+VOXjwYJvl48aNw7hx4wzcGsug7p6qKyft1N07hUJh4JYQmS/Ge/1jvJcGY75hcdiPhejWrRtsbW1RVVWlUl5VVQUfHx8jtcq88Z7qH+8pUcfx90j/eE+lwftqmpj8Wwg7OztEREQgJydHWaZQKJCTk8OvHtuJ91T/eE+JOo6/R/rHeyoN3lfTxGE/ZqSurg4lJSXK1xcvXkRhYSE8PT0REBCA1NRUJCYmYsiQIYiMjMT69etRX1+PadOmGbHVpo33VP94T4k6jr9H+sd7Kg3eVzNk7OmGSLzc3FwBQKstMTFRWWfDhg1CQECAYGdnJ0RGRgrff/+98RpsBnhP9Y/3lKjj+Hukf7yn0uB9NT8yQRAEQ/yRQURERERExsUx/0REREREVoLJPxERERGRlWDyT0RERERkJZj8ExERERFZCSb/RERERERWgsk/EREREZGVYPJPRERERGQlmPwTEREREVkJJv9ERERERFaCyT8RERERkZVg8k9mLyYmBjKZDDKZDIWFhZJeKykpSXmtPXv2SHotIiJqjTGfqGOY/JNFSE5ORkVFBe677z5Jr/PWW2+hoqJC0msQEZFmjPlE7dfJ2A0g0gcnJyf4+PhIfh03Nze4ublJfh0iIlKPMZ+o/djzTybngw8+gKOjo0pvy7Rp0xAaGoqamhrR51EoFFi9ejWCgoJgb2+PgIAALF++XLk/JiYGc+bMwUsvvQQPDw94e3vjnXfeQX19PaZNmwYXFxcEBQXh66+/1uv7IyKi/2HMJzIsJv9kcp555hnce++9eOONNwAAmZmZOHjwIL7++mudemAWLlyIlStXYtGiRSgqKsKuXbvg7e2tUmfHjh3o1q0b8vPzMWfOHMyaNQuTJk3CTYHAhgAAAqBJREFUsGHDcPLkSYwcORJTpkzBjRs39PoeiYjoNsZ8IgMTiEzQ3r17BXt7e+H1118XPDw8hDNnzqit+8gjjwgvvviiSlltba1gb28vvPPOOxqPe/DBB5Wvm5ubhS5dughTpkxRllVUVAgAhLy8PJVjAQiffvqpbm+KiIjaxJhPZDgc808m6bHHHkNISAiWLl2K/fv3Y8CAATodf+7cOTQ0NGDEiBEa64WGhir/bWtri65du2LgwIHKspZeoytXruh0fSIiEo8xn8hwOOyHTNK+fftw/vx5yOXyVl/biuHo6CiqXufOnVVey2QylTKZTAbg9lhSIiKSBmM+keEw+SeTc/LkSTz11FPYunUrRowYgUWLFul8jr59+8LR0RE5OTkStJCIiPSFMZ/IsDjsh0zKpUuXMGbMGLzyyiv4wx/+gMDAQERHR+PkyZMYPHiw6PM4ODhgwYIFmD9/Puzs7PDAAw/g6tWrOHv2LKZPny7hOyAiIrEY84kMj8k/mYzq6mokJCTg8ccfR3p6OgAgKioKo0aNwiuvvIJ9+/bpdL5FixahU6dOyMjIwOXLl+Hr64uZM2dK0XQiItIRYz6RccgEQRCM3QiijoiJiUFYWBjWr19vsGvKZDJ8+umnGD9+vMGuSUREjPlEHcUx/2QR3n77bTg7O+OHH36Q9DozZ86Es7OzpNcgIiLNGPOJ2o89/2T2ysvLcfPmTQBAQEAA7OzsJLvWlStXUFtbCwDw9fVFly5dJLsWERG1xphP1DFM/omIiIiIrASH/RARERERWQkm/0REREREVoLJPxERERGRlWDyT0RERERkJZj8ExERERFZCSb/RERERERWgsk/EREREZGVYPJPRERERGQlmPwTEREREVkJJv9ERERERFbi/wEAICgjkDHTiQAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 800x1200 with 16 Axes>"
+       "<Figure size 800x1000 with 12 Axes>"
       ]
      },
      "metadata": {},
@@ -246,43 +243,118 @@
     }
    ],
    "source": [
-    "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) )"
+    "ions_to_plot = [\"C_i{:02d}_frac\".format(i+1) for i in range(6)] \n",
+    "fig, ax = wind.multiplot(ions_to_plot, \"loglog\", nrows = 3, ncols = 2, figsize=(8,10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All the ions in the simulation can be viewed using the ```ions_read_in``` list. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['H_i01_frac', 'H_i02_frac', 'He_i01_frac', 'He_i02_frac', 'He_i03_frac', 'C_i01_frac', 'C_i02_frac', 'C_i03_frac', 'C_i04_frac', 'C_i05_frac', 'C_i06_frac', 'C_i07_frac', 'N_i01_frac', 'N_i02_frac', 'N_i03_frac', 'N_i04_frac', 'N_i05_frac', 'N_i06_frac', 'N_i07_frac', 'N_i08_frac', 'O_i01_frac', 'O_i02_frac', 'O_i03_frac', 'O_i04_frac', 'O_i05_frac', 'O_i06_frac', 'O_i07_frac', 'O_i08_frac', 'O_i09_frac', 'Na_i01_frac', 'Na_i02_frac', 'Na_i03_frac', 'Na_i04_frac', 'Na_i05_frac', 'Na_i06_frac', 'Na_i07_frac', 'Na_i08_frac', 'Na_i09_frac', 'Na_i10_frac', 'Na_i11_frac', 'Na_i12_frac', 'Si_i01_frac', 'Si_i02_frac', 'Si_i03_frac', 'Si_i04_frac', 'Si_i05_frac', 'Si_i06_frac', 'Si_i07_frac', 'Si_i08_frac', 'Si_i09_frac', 'Si_i10_frac', 'Si_i11_frac', 'Si_i12_frac', 'Si_i13_frac', 'Si_i14_frac', 'Si_i15_frac', 'Fe_i01_frac', 'Fe_i02_frac', 'Fe_i03_frac', 'Fe_i04_frac', 'Fe_i05_frac', 'Fe_i06_frac', 'Fe_i07_frac', 'Fe_i08_frac', 'Fe_i09_frac', 'Fe_i10_frac', 'Fe_i11_frac', 'Fe_i12_frac', 'Fe_i13_frac', 'Fe_i14_frac', 'Fe_i15_frac', 'Fe_i16_frac', 'Fe_i17_frac', 'Fe_i18_frac', 'Fe_i19_frac', 'Fe_i20_frac', 'Fe_i21_frac', 'Fe_i22_frac', 'Fe_i23_frac', 'Fe_i24_frac', 'Fe_i25_frac', 'Fe_i26_frac', 'Fe_i27_frac']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print (wind.ions_read_in)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Plotting Ion Populations \n",
+    "## More direct data access\n",
     "\n",
-    "Ion populations outputted from ```windsave2table``` are stored in files like ```cv_standard.C.frac.txt```, where the letter before frac denotes the element. Plots of the C III ion fraction can thus be made through commands like the following, where strings like ```i05``` index the ion for each file. \n"
+    "We recommend using PySi where possible. You may, however, wish to get more direct access to the data, which can be done easily by reading in the ```cv_standard.master.txt``` file, for example using ```astropy```. In the next code block, we read in the data file and print out the columns."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/Users/matthewsj/.mpi_temp/ipykernel_31972/4163463376.py:3: RuntimeWarning: divide by zero encountered in log10\n",
-      "  plt.pcolormesh(x,z, np.log10(c3_frac))\n"
+      "['x', 'z', 'xcen', 'zcen', 'i', 'j', 'inwind', 'converge', 'v_x', 'v_y', 'v_z', 'vol', 'rho', 'ne', 't_e', 't_r', 'h1', 'he2', 'c4', 'n5', 'o6', 'dmo_dt_x', 'dmo_dt_y', 'dmo_dt_z', 'ip', 'xi', 'ntot', 'nrad', 'nioniz']\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import astropy.io.ascii as io\n",
+    "\n",
+    "fname = f\"{directory}{root}.master.txt\"\n",
+    "data = io.read(fname)\n",
+    "\n",
+    "print (data.colnames)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```py_plot_util``` also contains some routines for reshaping and masking arrays and so on. One of the most useful for plotting is the ```wind_to_masked``` function which turns the raw 1D flattened data into a masked 2D array with the right shape which can be easily used with ```pcolormesh``` and so on. Here's an example plot of the electron density in the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "NameError",
+     "evalue": "name 'util' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m x, z, ne, inwind \u001b[38;5;241m=\u001b[39m \u001b[43mutil\u001b[49m\u001b[38;5;241m.\u001b[39mwind_to_masked(data, value_string\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mne\u001b[39m\u001b[38;5;124m\"\u001b[39m, return_inwind\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mpcolormesh(x,z, np\u001b[38;5;241m.\u001b[39mlog10(ne))\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mloglog()\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'util' is not defined"
+     ]
     }
    ],
+   "source": [
+    "x, z, ne, inwind = util.wind_to_masked(data, value_string=\"ne\", return_inwind=True)\n",
+    "plt.pcolormesh(x,z, np.log10(ne))\n",
+    "plt.loglog()\n",
+    "plt.xlim(1e9,1e12)\n",
+    "plt.ylim(1e8,1e12)\n",
+    "cbar = plt.colorbar()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This procedure can be used to plot any of the variables in the masterfile and is a good starting point for delving into the properties of the wind if not using PySi. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ion populations outputted from ```windsave2table``` are stored in files like ```cv_standard.C.frac.txt```, where the letter before frac denotes the element. Plots of the C III ion fraction can thus be made through commands like the following, where strings like ```i05``` index the ion for each file. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "carbon_ion = io.read(\"cv_test/cv_standard.C.frac.txt\")\n",
     "x, z, c3_frac, inwind = util.wind_to_masked(carbon_ion, value_string=\"i03\", return_inwind=True)\n",
@@ -292,6 +364,32 @@
     "plt.ylim(1e8,1e12)\n",
     "cbar = plt.colorbar()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 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 ```$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",
+    "where the second argument is the variable to plot. Alternatively, it can be run from within a python script by doing (where we are now assuming you are running this code from one directory above cv_test):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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\")"
+   ]
   }
  ],
  "metadata": {
@@ -310,9 +408,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

From 65b8d2251b80701059184deafa5afd8d4fc2b752 Mon Sep 17 00:00:00 2001
From: "Knox S. Long" <long@stsci.edu>
Date: Mon, 28 Oct 2024 14:48:58 -0500
Subject: [PATCH 19/22] Clean up names of executables, replase Setup_Py_Dir

---
 bin/{Setup_Py_Dir => Setup_Sirocco_Dir} |  0
 source/Makefile                         | 14 +++++++-------
 source/setup.c                          | 10 +++++-----
 source/windsave.c                       |  7 ++++---
 4 files changed, 16 insertions(+), 15 deletions(-)
 rename bin/{Setup_Py_Dir => Setup_Sirocco_Dir} (100%)

diff --git a/bin/Setup_Py_Dir b/bin/Setup_Sirocco_Dir
similarity index 100%
rename from bin/Setup_Py_Dir
rename to bin/Setup_Sirocco_Dir
diff --git a/source/Makefile b/source/Makefile
index ce5d1b643..c7657e86b 100644
--- a/source/Makefile
+++ b/source/Makefile
@@ -272,43 +272,43 @@ run_indent:
 swind: startup $(swind_objects) $(CUDA_OBJECTS)
 	$(CC) $(CFLAGS) $(swind_objects) $(CUDA_OBJECTS) $(LDFLAGS) -o swind
 	cp $@ $(BIN)
-	mv $@ $(BIN)/swind$(VERSION)
+	mv $@ $(BIN)/swind-$(VERSION)
 	@if [ $(INDENT) = yes ] ; then  ../py_progs/run_indent.py -changed ; fi
 
 windsave2table: startup $(table_objects) $(CUDA_OBJECTS)
 	$(CC) $(CFLAGS) $(table_objects) $(CUDA_OBJECTS) $(LDFLAGS) -o windsave2table
 	cp $@ $(BIN)
-	mv $@ $(BIN)/windsave2table$(VERSION)
+	mv $@ $(BIN)/windsave2table-$(VERSION)
 	@if [ $(INDENT) = yes ] ; then  ../py_progs/run_indent.py -changed ; fi
 
 windsave2fits: startup $(windsave2fits_objects) $(CUDA_OBJECTS)
 	$(CC) $(CFLAGS) $(windsave2fits_objects) $(CUDA_OBJECTS) $(LDFLAGS) -lcfitsio -o windsave2fits 
 	cp $@ $(BIN)
-	mv $@ $(BIN)/windsave2fits$(VERSION)
+	mv $@ $(BIN)/windsave2fits-$(VERSION)
 	@if [ $(INDENT) = yes ] ; then  ../py_progs/run_indent.py -changed ; fi
 
 rad_hydro_files: startup $(rhf_objects) $(CUDA_OBJECTS)
 	$(CC) $(CFLAGS) $(rhf_objects) $(CUDA_OBJECTS) $(LDFLAGS) -o rad_hydro_files
 	cp $@ $(BIN)
-	mv $@ $(BIN)/rad_hydro_files$(VERSION)
+	mv $@ $(BIN)/rad_hydro_files-$(VERSION)
 	@if [ $(INDENT) = yes ] ; then  ../py_progs/run_indent.py -changed ; fi
 
 modify_wind: startup $(modify_wind_objects) $(CUDA_OBJECTS)
 	$(CC) $(CFLAGS) $(modify_wind_objects) $(CUDA_OBJECTS) $(LDFLAGS) -o modify_wind
 	cp $@ $(BIN)
-	mv $@ $(BIN)/modify_wind$(VERSION)
+	mv $@ $(BIN)/modify_wind-$(VERSION)
 	@if [ $(INDENT) = yes ] ; then  ../py_progs/run_indent.py -changed ; fi
 
 inspect_wind: startup $(inspect_wind_objects) $(CUDA_OBJECTS)
 	$(CC) $(CFLAGS) $(inspect_wind_objects) $(CUDA_OBJECTS) $(LDFLAGS)  -o inspect_wind
 	cp $@ $(BIN)
-	mv $@ $(BIN)/inspect_wind$(VERSION)
+	mv $@ $(BIN)/inspect_wind-$(VERSION)
 	@if [ $(INDENT) = yes ] ; then  ../py_progs/run_indent.py -changed ; fi
 
 sirocco_optd: startup $(sirocco_optd_obj) $(CUDA_OBJECTS)
 	$(CC) $(CFLAGS) $(sirocco_optd_obj) $(CUDA_OBJECTS) $(LDFLAGS) -o $@
 	cp $@ $(BIN)
-	mv $@ $(BIN)/sirocco_optd$(VERSION)
+	mv $@ $(BIN)/sirocco_optd-$(VERSION)
 	@if [ $(INDENT) = yes ] ; then  ../py_progs/run_indent.py -changed ; fi
 
 # unit_tests allows one to test low level routines, but one must be aware that one needs to define
diff --git a/source/setup.c b/source/setup.c
index 144f8ff45..88be4a170 100644
--- a/source/setup.c
+++ b/source/setup.c
@@ -906,7 +906,7 @@ setup_dfudge ()
 void
 setup_atomic_data (const char *atomic_filename)
 {
-  int rc;                       // Return code from running Setup_Py_Dir
+  int rc;                       // Return code from running Setup_Sirocco_Dir
   struct stat file_stat;        // Used to check the atomic data exists
   char answer[LINELENGTH];
 
@@ -924,19 +924,19 @@ setup_atomic_data (const char *atomic_filename)
   /*
    * Check that geo.atomic_filename exists - i.e. that the directory is readable
    * and in the directory Python is being executed from. If it isn't - then
-   * try to run Setup_Py_Dir. If both fail, then warn the user and exit Python
+   * try to run Setup_Sirocco_Dir. If both fail, then warn the user and exit Python
    */
 
   if (stat (atomic_filename, &file_stat))
   {
     Log ("Unable to open atomic masterfile %s\n", atomic_filename);
-    Log ("Running Setup_Py_Dir to try and fix the situation\n");
+    Log ("Running Setup_Sirocco_Dir to try and fix the situation\n");
     if (rank_global == 0)
     {
-      rc = system ("Setup_Py_Dir");
+      rc = system ("Setup_Sirocco_Dir");
       if (rc)
       {
-        Error ("Unable to open %s and run Setup_Py_Dir\n", atomic_filename);
+        Error ("Unable to open %s and run Setup_Sirocco_Dir\n", atomic_filename);
         Exit (1);
       }
     }
diff --git a/source/windsave.c b/source/windsave.c
index a14f2e97f..00528521a 100644
--- a/source/windsave.c
+++ b/source/windsave.c
@@ -215,7 +215,7 @@ wind_read (filename)
 
   if (stat (geo.atomic_filename, &file_stat))
   {
-    if (system ("Setup_Py_Dir"))
+    if (system ("Setup_Sirocco_Dir"))
     {
       Error ("Unable to open %s or create link for atomic data\n", geo.atomic_filename);
       Exit (1);
@@ -498,8 +498,9 @@ spec_read (filename)
     Exit (EXIT_FAILURE);
   }
 
-  Log ("Reading specfile %s with %d spectra and %d wavelength bins, created with sirocco version %s and currently using sirocco version %s\n",
-       filename, nspectra, NWAVE_EXTRACT, version, VERSION);
+  Log
+    ("Reading specfile %s with %d spectra and %d wavelength bins, created with sirocco version %s and currently using sirocco version %s\n",
+     filename, nspectra, NWAVE_EXTRACT, version, VERSION);
 
   /* First allocate space */
 

From 80c1d018b45237e3b6cf9b2b07c03c448bdd4638 Mon Sep 17 00:00:00 2001
From: "Knox S. Long" <long@stsci.edu>
Date: Mon, 28 Oct 2024 15:07:16 -0500
Subject: [PATCH 20/22] Update build.yml

---
 .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 7cc4b0c3f..2552c2ea6 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -53,7 +53,7 @@ jobs:
     - name: Checking Sirocco compiled and can read in inputs correctly
       run: |
         cd $SIROCCO/examples/gh-workflow/
-        $SIROCCO/bin/Setup_Py_Dir
+        $SIROCCO/bin/Setup_Sirocco_Dir
         $SIROCCO/bin/sirocco -i cv_macro_benchmark
         $SIROCCO/bin/sirocco -i cv_standard
         $SIROCCO/bin/sirocco -i fiducial_agn

From b074c9e91ea436096c2f30aab81d7b2892de17a1 Mon Sep 17 00:00:00 2001
From: James Matthews <matthews@ast.cam.ac.uk>
Date: Mon, 28 Oct 2024 20:25:10 +0000
Subject: [PATCH 21/22] Update installation.rst w/ Setup_Sirocco_Dir

---
 docs/sphinx/source/installation.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/sphinx/source/installation.rst b/docs/sphinx/source/installation.rst
index 41891656b..5f3c0d33b 100644
--- a/docs/sphinx/source/installation.rst
+++ b/docs/sphinx/source/installation.rst
@@ -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.
 

From b5c3e9a4c86a2aba67b1ed6cf58a2f3abd398974 Mon Sep 17 00:00:00 2001
From: James Matthews <matthews@ast.cam.ac.uk>
Date: Mon, 28 Oct 2024 20:25:41 +0000
Subject: [PATCH 22/22] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 395fade9b..f0599e06c 100644
--- a/README.md
+++ b/README.md
@@ -63,7 +63,7 @@ To run Sirocco you need to add the following to your $PATH variable:
 
 You can then setup your symbolic links by running 
 
-    $ Setup_Py_Dir
+    $ Setup_Sirocco_Dir
 
 and run the code by typing, e.g.
 
