diff --git a/CITATION.cff b/CITATION.cff
index dc87eccc..aa9800ea 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -5,7 +5,7 @@ authors:
   given-names: "Harrison"
   orcid: "https://orcid.org/0000-0002-8368-4641"
 title: "AGNI"
-version: 0.5.2
+version: 0.5.3
 doi: 10.xx/xx.xx
-date-released: 2024-06-25
+date-released: 2024-07-02
 url: "https://github.com/nichollsh/AGNI"
diff --git a/Project.toml b/Project.toml
index ec04cb97..703f70df 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "AGNI"
 uuid = "ede838c1-9ec3-4ebe-8ae8-da4091b3f21c"
 authors = ["Harrison Nicholls <harrison.nicholls@physics.ox.ac.uk>"]
-version = "0.5.2"
+version = "0.5.3"
 
 [deps]
 ArgParse = "c7e460c6-2fb9-53a9-8c5b-16f535851c63"
diff --git a/docs/src/usage.md b/docs/src/usage.md
index 3feb1bdd..7789d1de 100644
--- a/docs/src/usage.md
+++ b/docs/src/usage.md
@@ -25,24 +25,25 @@ but the model will return an error naming any parameters which are both
 necessary and absent.
 
 Broadly, the configuration files are broken up into four sections:
-* `[planet]` describes general characteristics of the planet and its atmosphere
-* `[files]` lists input/output files
-* `[execution]` describes what the model should do
-* `[plots]` describes which kind of plot to produce
+* `[planet]` -  general characteristics of the planet
+* `[files]` - input/output files and other paths
+* `[composition]` - atmospheric composition and chemistry
+* `[execution]` - what the model should do
+* `[plots]` - which plots should be produced
 
-Some `execution` parameters:
-* `solution_type` tells the model which state to solve for. The allowed values (integers) are...
+Some parameters:
+* `execution.solution_type` tells the model which state to solve for. The allowed values (integers) are...
      - 1 : zero flux divergence at fixed `tmp_surf`
      - 2 : zero flux divergence, with `tmp_surf` set such that the conductive skin (CBL) conserves energy flux
      - 3 : the net upward flux at each layer is equal to `flux_int = sigma * tmp_int^4`
    
-* `solvers` tells the model which solvers to use. This is a list of strings, so multiple solvers can be applied sequentially. An empty string is always appended to the end of this list. Allowed solvers are...
+* `execution.solvers` tells the model which solvers to use. This is a list of strings, so multiple solvers can be applied sequentially. An empty string is always appended to the end of this list. Allowed solvers are...
      - [empty string] : no solving takes place, so the model just calculates fluxes using the initial state
      - `newton` : the Newton-Raphson algorithm is used
      - `gauss`  : the Gauss-Newton algorithm is used 
      - `levenberg` : the Levenberg–Marquardt algorithm is used 
    
-* `initial_state` describes the initial temperature profile applied to the atmosphere. This is a list of strings which are applied in the given order, which allows the user to describe a specific state as required. The descriptors are listed below, some of which take a single argument that needs to immediately follow the descriptor in the list order.
+* `execution.initial_state` describes the initial temperature profile applied to the atmosphere. This is a list of strings which are applied in the given order, which allows the user to describe a specific state as required. The descriptors are listed below, some of which take a single argument that needs to immediately follow the descriptor in the list order.
      - `dry` : integrate the dry adiabatic lapse rate from the surface upwards
      - `str`, `arg` : apply an isothermal stratosphere at `arg` kelvin
      - `iso`, `arg` : set the whole atmosphere to be isothermal at `arg` kelvin
@@ -51,8 +52,8 @@ Some `execution` parameters:
      - `sat` : ensure that no supersaturation occurs at the surface by removing gases as required    
   
     For example, setting `initial_state = ["dry", "sat", "H2O", "str", "180"]` will set T(p) to follow the dry adiabat from the surface, the water condensation curve above that, and then to be isothermal at 180 K until the top of the model.
-   
-* `chem_type` describes the type of chemistry to implement within the model. This is handled externally by FastChem. You must also provide the path to the FastChem installation directory `fastchem_path` in the `[files]` section. The allowed values (integers) are...
+
+* `composition.chem_type` describes the type of chemistry to implement within the model. This is handled externally by FastChem. You must also provide the path to the FastChem installation directory `fastchem_path` in the `[files]` section. The allowed values (integers) are...
      - 0 : Disabled 
      - 1 : Equilibrium, gas phase only
      - 2 : Equilibrium, with condensation (condensates retained)
diff --git a/res/config/55cnce_chem.toml b/res/config/55cnce_chem.toml
index 7cfe5bc2..7b03d9ac 100644
--- a/res/config/55cnce_chem.toml
+++ b/res/config/55cnce_chem.toml
@@ -10,29 +10,25 @@ title = "Roughly 55 Cancri e @ fO2=IW-4"
     albedo_s        = 0.0            
     radius          = 1.1959e7 
     gravity         = 22.304  
-    p_surf          = 854.894   
-    p_top           = 1e-6           
-
-    # Set surface abundances 
-    # First 6 are set by outgassing code 
-    # SO2, FeO2 set to 0.01 to "sprinkle in" some S and Fe 
-    # Others set to zero but allowed to be modelled
-    vmr             = { H2O=0.0017, CO2=0.0, N2=0.0001, H2=0.5837, CO=0.4095, CH4=0.005,    SO2=0.01, FeO2=0.01,     O2=0.00, OH=0.0, H=0.0, SO=0.0, NH3=0.0, NH2=0.0, H2S=0.0, H2SO4=0.0}    
-    condensates     = []               
     tmp_int         = 0.0
     turb_coeff      = 0.001           
-    wind_speed      = 2.0             
-
+    wind_speed      = 2.0      
+           
 [files]
-    clean_output    = true
     input_sf        = "res/spectral_files/nogit/Dayspring/48/Dayspring.sf"   
     input_star      = "res/stellar_spectra/sun.txt"                       
-    input_vmr       = ""                                                     
     output_dir      = "out/"       
     fastchem_path   = "/Users/nichollsh/Projects/fastchem/"
-                                          
+
+[composition]
+    p_top           = 1e-6     
+    p_dict          = {H2O=1.435, CO2=0.11, N2=0.043, H2=499.004, CO=350.044, CH4=4.258,    SO2=0.01, FeO2=0.01}
+    include_all     = true 
+    chemistry       = 1                  
+    condensates     = []               
 
 [execution]
+    clean_output    = true
     verbosity       = 1                  
     max_steps       = 20000              
     max_runtime     = 400                
@@ -46,8 +42,6 @@ title = "Roughly 55 Cancri e @ fO2=IW-4"
     sensible_heat   = true               
     latent_heat     = true               
     convection_type = "mlt"              
-    condensates     = []                 
-    chemistry       = 1                  
     solution_type   = 3                  
     solvers         = ["newton"]         
     dx_max          = 200.0              
diff --git a/res/config/H2_demo.toml b/res/config/H2_demo.toml
deleted file mode 100644
index 6c49bc31..00000000
--- a/res/config/H2_demo.toml
+++ /dev/null
@@ -1,58 +0,0 @@
-# AGNI configuration file 
-title = "Adiabatic H2 demo"                       # Name for this configuration file
-
-[planet]
-    tmp_surf        = 3000.0            # Surface temperature [kelvin]
-    instellation    = 13000.0           # Stellar flux at planet's orbital distance [W m-2]
-    albedo_b        = 0.18             # Pseudo bond-albedo which downscales the stellar flux by 1-this_value
-    s0_fact         = 0.6652            # Stellar flux scale factor which accounts for planetary rotation (c.f. Cronin+13)
-    zenith_angle    = 60.0              # Characteristic zenith angle for incoming stellar radiation [degrees]
-    albedo_s        = 0.0               # Surface albedo
-    radius          = 6.37e6            # Planet radius at the surface [m]
-    gravity         = 9.81              # Gravitational acceleration at the surface [m s-2]
-    p_surf          = 1000.0             # Total surface pressure [bar]
-    p_top           = 1e-5              # Total top-of-atmosphere pressure [bar]
-    vmr             = { H2 = 1.0 }     # Volatile volume mixing ratios
-    condensates     = []                # List of condensable gases
-
-[files]
-    clean_output    = true
-    input_sf        = "res/spectral_files/Dayspring/256/Dayspring.sf"    # Path to SOCRATES spectral file.
-    input_star      = "res/stellar_spectra/sun.txt"                     # Path to stellar spectrum.
-    input_vmr       = ""                                                # Path to input volume mixing ratios. Not required if planet.vmr is passed.
-    output_dir      = "out/"                                            # Path to output directory.
-
-[execution]
-    verbosity       = 1                         # Log level (0: none, 1: normal, 2: debug)
-    max_steps       = 20000                     # Maximum number of solver steps.
-    max_runtime     = 400                       # Maximum wall-clock solver runtime [s].
-    num_levels      = 150                       # Number of model levels.
-    continua        = true                      # Include absorption from continua?
-    rayleigh        = false                      # Include rayleigh scattering?
-    cloud           = false                     # Include water cloud radiative properties?
-    aerosol         = false                     # Include aerosol radiative properties?
-    overlap_method  = 4                         # Method for treating line overlap.
-    thermo_funct    = true                     # Use temperature-dependent thermodynamic properties?
-    sensible_heat   = false                     # Include sensible heat transport at the surface?
-    latent_heat     = false                     # Include heat release from phase change
-    convection_type = ""                        # Convection type (mlt = use mixing length theory).
-    condensates     = []                        # List of volatiles which are allowed to condense.
-    chemistry       = 0                         # Chemistry type (see wiki
-    solution_type   = 0                         # Solution type (see wiki).
-    solvers         = []                        # Ordered list of solvers to apply (see wiki).
-    dx_max          = 200.0                     # Maximum step size [Kelvin], when using nonlinear solvers
-    initial_state   = ["dry"]     # Ordered list of requests describing the initial state of the atmosphere (see wiki).
-    linesearch      = false                     # Use linesearch?
-    converge_atol   = 1.0e-3                    # Convergence criterion on absolute flux divergence [W m-2].
-    converge_rtol   = 1.0e-1                    # Convergence criterion on relative flux divergence [dimensionless].
-
-[plots]
-    at_runtime      = true                      # Make some plots at runtime?
-    temperature     = true                      # Plot temperature profile?
-    fluxes          = true                      # Plot fluxes?
-    contribution    = true                      # Plot spectral contribution function?
-    emission        = true                      # Plot emission spectrum?
-    albedo          = true                      # Plot spectral albedo?
-    mixing_ratios   = true                      # Plot mixing ratios?
-    animate         = false                     # Make animation from runtime plots?
-
diff --git a/res/config/cloud.toml b/res/config/cloud.toml
deleted file mode 100644
index c7cd1190..00000000
--- a/res/config/cloud.toml
+++ /dev/null
@@ -1,58 +0,0 @@
-# AGNI configuration file 
-title = "Test clouds w/ prescribed T(p)"                  
-
-[planet]
-    tmp_surf        = 1900.0       
-    instellation    = 10000.0      
-    albedo_b        = 0.0          
-    s0_fact         = 0.375        
-    zenith_angle    = 48.19        
-    albedo_s        = 0.2          
-    radius          = 6.37e6       
-    gravity         = 9.81         
-    p_surf          = 800.0        
-    p_top           = 1e-6         
-    vmr             = { H2O = 1.0 }
-    condensates     = ["H2O"]
-    tmp_int         = 0.0          
-
-[files]
-    clean_output    = true
-    input_sf        = "res/spectral_files/Frostflow/48/Frostflow.sf" 
-    input_star      = "res/stellar_spectra/sun.txt"                  
-    output_dir      = "out/"                                         
-
-[execution]
-    verbosity       = 1                     
-    max_steps       = 200                   
-    max_runtime     = 200                   
-    num_levels      = 105                   
-    continua        = true                  
-    rayleigh        = false                 
-    cloud           = true                  
-    aerosol         = false                 
-    overlap_method  = 4                     
-    thermo_funct    = true                  
-    sensible_heat   = false                 
-    latent_heat     = false     
-    convection_type = ""                    
-    chemistry       = 0         
-    solution_type   = 1                     
-    solvers         = []                    
-    dx_max          = 200.0                 
-    initial_state   = ["dry","con","H2O"]   
-    linesearch      = false                 
-    converge_atol   = 5.0e-2                
-    converge_rtol   = 1.0e-4                
-
-
-[plots]
-    at_runtime      = true       
-    temperature     = true       
-    fluxes          = true       
-    contribution    = true       
-    emission        = true       
-    albedo          = true       
-    mixing_ratios   = true       
-    animate         = false      
-
diff --git a/res/config/condense.toml b/res/config/condense.toml
index 573c0787..a71f3bc3 100644
--- a/res/config/condense.toml
+++ b/res/config/condense.toml
@@ -1,5 +1,5 @@
 # AGNI configuration file 
-title = "Condensation test"                    # Name for this configuration file
+title = "Condensation test"        
 
 [planet]
     tmp_surf        = 2000.0          
@@ -10,10 +10,6 @@ title = "Condensation test"                    # Name for this configuration fil
     albedo_s        = 0.2             
     radius          = 6.37e6          
     gravity         = 9.81            
-    p_surf          = 7000.0          
-    p_top           = 1e-5            
-    vmr             = { H2O=0.8, CO2=0.1, CH4=0.08, H2=0.02}    
-    condensates     = ["H2O", "CO2"]
     tmp_int         = 0.0            
     turb_coeff      = 1.0e-4
     wind_speed      = 10.0
@@ -27,8 +23,17 @@ title = "Condensation test"                    # Name for this configuration fil
     input_star      = "res/stellar_spectra/sun.txt"                         
     output_dir      = "out/"                                                
 
+[composition]
+    p_surf          = 7000.0          
+    p_top           = 1e-5            
+    vmr_dict        = { H2O=0.8, CO2=0.1, CH4=0.08, H2=0.02}    
+    include_all     = false
+    chemistry       = 0                 
+    condensates     = ["H2O", "CO2"]    
+
 [execution]
-    verbosity       = 1                
+    clean_output    = true             
+    verbosity       = 1
     max_steps       = 300              
     max_runtime     = 200              
     num_levels      = 35               
@@ -41,7 +46,6 @@ title = "Condensation test"                    # Name for this configuration fil
     sensible_heat   = true             
     latent_heat     = true             
     convection_type = "mlt"            
-    chemistry       = 0             
     solution_type   = 1                
     solvers         = ["newton"]       
     dx_max          = 600.0            
diff --git a/res/config/default.toml b/res/config/default.toml
index 2f82eaf6..340c8945 100644
--- a/res/config/default.toml
+++ b/res/config/default.toml
@@ -1,20 +1,18 @@
-# AGNI configuration file 
 # This is the default configuration file - it does not solve for RCE
+# The variables are explained below. If you want to model your own conditions, 
+# try making a copy of this file and then modifying it.
+
 title = "Default"                       # Name for this configuration file
 
 [planet]
     tmp_surf        = 2000.0            # Surface temperature [kelvin]
     instellation    = 44000.0           # Stellar flux at planet's orbital distance [W m-2]
-    albedo_b        = 0.18             # Pseudo bond-albedo which downscales the stellar flux by 1-this_value
+    albedo_b        = 0.18              # Pseudo bond-albedo which downscales the stellar flux by 1-this_value
     s0_fact         = 0.6652            # Stellar flux scale factor which accounts for planetary rotation (c.f. Cronin+13)
     zenith_angle    = 60.0              # Characteristic zenith angle for incoming stellar radiation [degrees]
     albedo_s        = 0.0               # Surface albedo
     radius          = 6.37e6            # Planet radius at the surface [m]
     gravity         = 9.81              # Gravitational acceleration at the surface [m s-2]
-    p_surf          = 270.0             # Total surface pressure [bar]
-    p_top           = 1e-5              # Total top-of-atmosphere pressure [bar]
-    vmr             = { H2O = 1.0 }     # Volatile volume mixing ratios
-    condensates     = []                # List of condensable gases
     skin_d          = 0.01              # Conductive skin thickness [m]. Used when sol_type=2.
     skin_k          = 2.0               # Conductive skin conductivity [W m-1 K-1]. Used when sol_type=2.
     tmp_magma       = 3000.0            # Magma temperature [K]. Used when sol_type=2.
@@ -23,13 +21,21 @@ title = "Default"                       # Name for this configuration file
     wind_speed      = 2.0               # Effective wind speed for sensible heat [m s-1].
 
 [files]
-    clean_output    = true
-    input_sf        = "res/spectral_files/Frostflow/256/Frostflow.sf"    # Path to SOCRATES spectral file.
+    input_sf        = "res/spectral_files/Frostflow/256/Frostflow.sf"   # Path to SOCRATES spectral file.
     input_star      = "res/stellar_spectra/sun.txt"                     # Path to stellar spectrum.
-    input_vmr       = ""                                                # Path to input volume mixing ratios. Not required if planet.vmr is passed.
     output_dir      = "out/"                                            # Path to output directory.
 
+[composition]
+    p_surf          = 270.0                     # Total surface pressure [bar]
+    p_top           = 1e-5                      # Total top-of-atmosphere pressure [bar] 
+    vmr_dict        = { H2O = 1.0}              # Volatile volume mixing ratios (=mole fractions)
+    vmr_path        = ""                        # Path to input volume mixing ratios. Not required if planet.vmr is passed.
+    include_all     = false                     # Track extra gases, even when their mixing ratio is zero
+    chemistry       = 0                         # Chemistry type (see wiki)
+    condensates     = []                        # List of volatiles which are allowed to condense.    
+
 [execution]
+    clean_output    = true                      # Clean the output folder at model startup
     verbosity       = 1                         # Log level (0: none, 1: normal, 2: debug)
     max_steps       = 20000                     # Maximum number of solver steps.
     max_runtime     = 400                       # Maximum wall-clock solver runtime [s].
@@ -43,8 +49,6 @@ title = "Default"                       # Name for this configuration file
     sensible_heat   = false                     # Include sensible heat transport at the surface?
     latent_heat     = false                     # Include heat release from phase change
     convection_type = ""                        # Convection type (mlt = use mixing length theory).
-    condensates     = []                        # List of volatiles which are allowed to condense.
-    chemistry       = 0                         # Chemistry type (see wiki
     solution_type   = 0                         # Solution type (see wiki).
     solvers         = []                        # Ordered list of solvers to apply (see wiki).
     dx_max          = 200.0                     # Maximum step size [Kelvin], when using nonlinear solvers
diff --git a/res/config/hotdry.toml b/res/config/hotdry.toml
index c846d640..3236d2c0 100644
--- a/res/config/hotdry.toml
+++ b/res/config/hotdry.toml
@@ -2,60 +2,65 @@
 title = "Hot and dry"              
 
 [planet]
-    tmp_surf        = 2000.0            # Surface temperature [kelvin]
-    instellation    = 44000.0          # Stellar flux at planet's orbital distance [W m-2]
-    albedo_b        = 0.18              # Pseudo bond-albedo which downscales the stellar flux by 1-this_value
-    s0_fact         = 0.6652           # Stellar flux scale factor which accounts for planetary rotation (c.f. Cronin+13)
-    zenith_angle    = 60.0             # Characteristic zenith angle for incoming stellar radiation [degrees]
-    albedo_s        = 0.0               # Surface albedo
-    radius          = 6.37e6            # Planet radius at the surface [m]
-    gravity         = 9.81              # Gravitational acceleration at the surface [m s-2]
-    p_surf          = 270.0             # Total surface pressure [bar]
-    p_top           = 1e-5              # Total top-of-atmosphere pressure [bar]
-    vmr             = { H2O = 1.0 }     # Volatile volume mixing ratios
-    condensates     = []                
-    tmp_int         = 0.0               # Effective temperature
-    turb_coeff      = 1.0e-4
-    wind_speed      = 10.0
+    tmp_surf        = 2000.0        
+    instellation    = 44000.0       
+    albedo_b        = 0.18          
+    s0_fact         = 0.6652        
+    zenith_angle    = 60.0          
+    albedo_s        = 0.0           
+    radius          = 6.37e6        
+    gravity         = 9.81          
+    tmp_int         = 0.0           
+    turb_coeff      = 1.0e-2
+    wind_speed      = 10.0         
 
 [files]
     clean_output    = true
-    input_sf        = "res/spectral_files/Frostflow/256/Frostflow.sf"    # Path to SOCRATES spectral file.
-    input_star      = "res/stellar_spectra/sun.txt"                     # Path to stellar spectrum.
-    output_dir      = "out/"                                            # Path to output directory.
+    input_sf        = "res/spectral_files/Frostflow/256/Frostflow.sf" 
+    input_star      = "res/stellar_spectra/sun.txt"                   
+    output_dir      = "out/"                                          
+
+[composition]
+    p_surf          = 270.0         
+    p_top           = 1e-5          
+    vmr_dict        = { H2O = 1.0}  
+    vmr_path        = ""            
+    include_all     = false         
+    chemistry       = 0             
+    condensates     = []            
+
 
 [execution]
-    verbosity       = 1                         # Log level (0: none, 1: normal, 2: debug)
-    max_steps       = 200                       # Maximum number of solver steps.
-    max_runtime     = 400                       # Maximum wall-clock solver runtime [s].
-    num_levels      = 35                        # Number of model levels.
-    continua        = true                      # Include absorption from continua?
-    rayleigh        = true                      # Include rayleigh scattering?
-    cloud           = false                     # Include water cloud radiative properties?
-    aerosol         = false                     # Include aerosol radiative properties?
-    overlap_method  = 4                         # Method for treating line overlap.
-    thermo_funct    = true                     # Use temperature-dependent thermodynamic properties?
-    sensible_heat   = true                     # Include sensible heat transport at the surface?
-    latent_heat     = false 
-    convection_type = "mlt"                     # Convection type ("" = no convection, "mlt" = use mixing length theory).
-    condensates     = []                        # List of volatiles which are allowed to condense.
-    chemistry       = 0
-    solution_type   = 3                         # Solution type (see wiki).
-    solvers         = ["newton"]                # Ordered list of solvers to apply (see wiki).
-    dx_max          = 400.0                     # Maximum step size [Kelvin], when using nonlinear solvers
-    initial_state   = ["iso","2300"]       # Ordered list of requests describing the initial state of the atmosphere (see wiki).
-    linesearch      = false                     # Use linesearch?
-    converge_atol   = 1.0e-2                    # Convergence criterion on absolute flux lost [W m-2].
-    converge_rtol   = 1.0e-4                    # Convergence criterion on relative flux lost [dimensionless].
+    clean_output    = true           
+    verbosity       = 1              
+    max_steps       = 200            
+    max_runtime     = 400            
+    num_levels      = 35             
+    continua        = true           
+    rayleigh        = true           
+    cloud           = false          
+    aerosol         = false          
+    overlap_method  = 4              
+    thermo_funct    = true           
+    sensible_heat   = true           
+    latent_heat     = false
+    convection_type = "mlt"          
+    solution_type   = 3              
+    solvers         = ["newton"]     
+    dx_max          = 400.0          
+    initial_state   = ["iso","2300"] 
+    linesearch      = false          
+    converge_atol   = 1.0e-2         
+    converge_rtol   = 1.0e-4         
 
 
 [plots]
-    at_runtime      = true                      # Make some plots at runtime?
-    temperature     = true                      # Plot temperature profile?
-    fluxes          = true                      # Plot fluxes?
-    contribution    = true                      # Plot spectral contribution function?
-    emission        = true                      # Plot emission spectrum?
-    albedo          = true                      # Plot spectral albedo?
-    mixing_ratios   = true                      # Plot mixing ratios?
-    animate         = true                     # Make animation from runtime plots?
+    at_runtime      = true       
+    temperature     = true       
+    fluxes          = true       
+    contribution    = true       
+    emission        = true       
+    albedo          = true       
+    mixing_ratios   = true       
+    animate         = true       
 
diff --git a/res/config/proteus.toml b/res/config/proteus.toml
deleted file mode 100644
index 7a4ea2a4..00000000
--- a/res/config/proteus.toml
+++ /dev/null
@@ -1,69 +0,0 @@
-# AGNI configuration file used when coupling to PROTEUS
-title = "PROTEUS configured"
-
-[planet]
-    p_top           = 1e-05
-    tmp_surf        = 1949.5
-    instellation    = 800.0
-    s0_fact         = 0.375
-    albedo_b        = 0.3
-    zenith_angle    = 48.19
-    albedo_s        = 0.3
-    gravity         = 9.819943999997438
-    radius          = 6371000.0
-    p_surf          = 983.000
-    skin_k          = 2.0
-    skin_d          = 0.01
-    tmp_magma       = 1950.1074604027003
-    # tmp_int         = 1000.0
-    turb_coeff      = 1.0e-3
-    wind_speed      = 10.0
-    condensates     = []
-
-[planet.vmr]
-    H2O = 0.7
-    CO2 = 0.2
-    H2 = 0.0
-    N2 = 0.0
-    CH4 = 0.0
-    CO = 0.1
-
-[files]
-    clean_output    = true
-    output_dir      = "out/"
-    input_sf        = "res/spectral_files/nogit/Dayspring/48/Dayspring.sf"
-    input_star      = "res/stellar_spectra/sun.txt" 
-
-[execution]
-    verbosity       = 1                              
-    max_steps       = 2000
-    max_runtime     = 400    
-    continua        = true   
-    aerosol         = false  
-    overlap_method  = 4      
-    thermo_funct    = true  
-    sensible_heat   = true  
-    latent_heat     = false
-    convection_type = "mlt" 
-    condensates     = [] 
-    solvers         = ["newton"]    
-    dx_max          = 100.0                 
-    chemistry       = 0
-    initial_state   = ["dry","str","1000"] 
-    linesearch      = false
-    converge_atol   = 1.0e-2  
-    converge_rtol   = 2.0e-3  
-    num_levels = 40
-    rayleigh = true
-    cloud = false
-    solution_type = 2
-
-[plots]
-    animate         = false
-    at_runtime = true
-    temperature = true
-    fluxes = true
-    contribution = true
-    emission = true
-    albedo = true
-    mixing_ratios = true
diff --git a/res/config/selsis.toml b/res/config/selsis.toml
index f8edce9a..117329f9 100644
--- a/res/config/selsis.toml
+++ b/res/config/selsis.toml
@@ -9,11 +9,7 @@ title = "Comparison with Selsis+23"
     zenith_angle    = 48.19          
     albedo_s        = 0.0            
     radius          = 6.37e6         
-    gravity         = 9.81           
-    p_surf          = 270.0          
-    p_top           = 1e-5           
-    vmr             = { H2O = 1.0 }  
-    condensates     = ["H2O"]        
+    gravity         = 9.81               
     tmp_int         = 0.0            
     turb_coeff      = 1.0e-4
     wind_speed      = 10.0
@@ -24,7 +20,18 @@ title = "Comparison with Selsis+23"
     input_star      = "res/stellar_spectra/sun.txt"                     
     output_dir      = "out/"                                            
 
+
+[composition]
+    p_surf          = 270.0         
+    p_top           = 1e-5          
+    vmr_dict        = { H2O = 1.0}  
+    vmr_path        = ""            
+    include_all     = false         
+    chemistry       = 0             
+    condensates     = ["H2O"]        
+
 [execution]
+    clean_output    = true 
     verbosity       = 1             
     max_steps       = 200           
     max_runtime     = 400           
diff --git a/res/thermo/standard.txt b/res/thermo/standard.txt
new file mode 100644
index 00000000..ab4846a7
--- /dev/null
+++ b/res/thermo/standard.txt
@@ -0,0 +1,41 @@
+# These files are included in simulations when `include_all = true` in cfg file.
+# They will otherwise be included automatically if provided in cfg file.
+C2H4
+CO
+FeS
+H2O
+H2SO4
+N2
+O2
+OH
+SO
+SiO
+CH4
+CO2
+H2
+H2S
+HCN
+NH3
+OCS
+S2
+S8
+SO2
+SiO2
+O3   
+N2O  
+NO   
+NO2  
+HNO3 
+FeH  
+PH3  
+C2H2 
+NO3  
+N2O5 
+HONO 
+HO2NO2
+H2O2 
+C2H6 
+CH3  
+H2CO 
+HO2  
+
diff --git a/src/AGNI.jl b/src/AGNI.jl
index 3f262f34..cdba3446 100755
--- a/src/AGNI.jl
+++ b/src/AGNI.jl
@@ -184,7 +184,7 @@ module AGNI
 
         # Output folder 
         output_dir = abspath(cfg["files"]["output_dir"])
-        if cfg["files"]["clean_output"] || !(ispath(output_dir) && isdir(output_dir))
+        if cfg["execution"]["clean_output"] || !(ispath(output_dir) && isdir(output_dir))
             rm(output_dir,force=true,recursive=true)
             mkdir(output_dir)
         end 
@@ -216,9 +216,54 @@ module AGNI
         radius::Float64        = cfg["planet"]["radius"]
         zenith::Float64        = cfg["planet"]["zenith_angle"]
         gravity::Float64       = cfg["planet"]["gravity"]
-        p_surf::Float64        = cfg["planet"]["p_surf"]
-        p_top::Float64         = cfg["planet"]["p_top"]
-        condensates::Array{String,1} = cfg["planet"]["condensates"]
+        
+        #    composition stuff 
+        p_top::Float64                  = cfg["composition"]["p_top"]
+        condensates::Array{String,1}    = cfg["composition"]["condensates"]
+        chem_type::Int                  = cfg["composition"]["chemistry"]
+        p_surf::Float64                 = 0.0
+        mf_dict::Dict{String, Float64}  = Dict{String, Float64}()
+        mf_path::String                 = ""
+        use_all_gases::Bool             = cfg["composition"]["include_all"]
+        fastchem_path::String           = ""
+        if haskey(cfg["composition"],"p_surf")
+            # set composition using VMRs + Psurf
+            if haskey(cfg["composition"],"vmr_dict")
+                # from dict in cfg file 
+                mf_dict = cfg["composition"]["vmr_dict"]
+            elseif haskey(cfg["composition"], "vmr_path") 
+                # from csv file to be read-in
+                mf_path = cfg["files"]["input_vmr"]
+            else
+                @error "Misconfiguration: if providing p_surf, must also provide VMRs"
+                exit(1)
+            end 
+            p_surf = cfg["composition"]["p_surf"]
+
+        elseif haskey(cfg["composition"], "p_dict")
+            # set composition from partial pressures (converted to mixing ratios)
+            pp_dict::Dict{String, Float64} = cfg["composition"]["p_dict"]
+            for k in keys(pp_dict)
+                p_surf += pp_dict[k]
+            end 
+            for k in keys(pp_dict)
+                mf_dict[k] = pp_dict[k]/p_surf
+            end 
+
+        else
+            @error "Misconfiguration: must provide either p_dict or p_surf+VMRs"
+            exit(1)
+        end 
+        if chem_type in [1,2,3] 
+            if length(condensates)>0
+                @error "Misconfiguration: FastChem coupling is incompatible with AGNI condensation scheme"
+                exit(1)
+            else
+                fastchem_path = cfg["files"]["fastchem_path"]
+                mkdir(dir_fastchem)
+            end 
+        end 
+
         #    solver stuff 
         spfile_name::String    = cfg["files" ]["input_sf"]
         star_file::String      = cfg["files" ]["input_star"]
@@ -230,7 +275,6 @@ module AGNI
         overlap::Int           = cfg["execution" ]["overlap_method"]
         thermo_funct::Bool     = cfg["execution" ]["thermo_funct"]
         conv_type::String      = cfg["execution" ]["convection_type"]
-        chem_type::Int         = cfg["execution" ]["chemistry"]
         incl_sens::Bool        = cfg["execution" ]["sensible_heat"]
         incl_latent::Bool      = cfg["execution" ]["latent_heat"]
         sol_type::Int          = cfg["execution" ]["solution_type"]
@@ -241,6 +285,7 @@ module AGNI
         conv_atol::Float64     = cfg["execution" ]["converge_atol"]
         conv_rtol::Float64     = cfg["execution" ]["converge_rtol"]
         max_steps::Int         = cfg["execution" ]["max_steps"]
+
         #    plotting stuff 
         plt_run::Bool          = cfg["plots"     ]["at_runtime"]
         plt_tmp::Bool          = cfg["plots"     ]["temperature"]
@@ -253,14 +298,6 @@ module AGNI
         plt_ani = plt_ani && plt_tmp
 
         # Read OPTIONAL configuration options from dict
-        #     mixing ratios can be set either way
-        if haskey(cfg["planet"],"vmr")
-            mf_dict = cfg["planet"]["vmr"]
-            mf_path = nothing
-        else 
-            mf_dict = nothing
-            mf_path = cfg["files"]["input_vmr"]
-        end 
         #     sensible heat at the surface 
         turb_coeff::Float64 = 0.0; wind_speed::Float64 = 0.0
         if incl_sens
@@ -284,16 +321,6 @@ module AGNI
         if sol_type == 4
             target_olr = cfg["planet"]["target_olr"]
         end    
-        #    path to fastchem 
-        fastchem_path::String = ""
-        if chem_type in [1,2,3] 
-            if length(condensates)>0
-                @error "Misconfiguration: FastChem coupling is incompatible with AGNI condensation scheme"
-            else
-                fastchem_path = cfg["files"]["fastchem_path"]
-                mkdir(dir_fastchem)
-            end 
-        end 
 
         # Setup atmosphere
         @info "Setting up"
@@ -304,7 +331,8 @@ module AGNI
                                 tmp_surf, 
                                 gravity, radius,
                                 nlev_centre, p_surf, p_top,
-                                mf_dict=mf_dict, mf_path=mf_path, 
+                                mf_dict, mf_path, 
+                                
                                 condensates=condensates,
                                 flag_gcontinuum=flag_cnt, flag_rayleigh=flag_ray,
                                 flag_cloud=flag_cld, flag_aerosol=flag_aer,
@@ -314,7 +342,8 @@ module AGNI
                                 tmp_int=tmp_int, albedo_s=albedo_s,
                                 thermo_functions=thermo_funct,
                                 C_d=turb_coeff, U=wind_speed,
-                                fastchem_path=fastchem_path
+                                fastchem_path=fastchem_path,
+                                use_all_gases=use_all_gases
                         )
         atmosphere.allocate!(atmos,star_file)
 
@@ -375,6 +404,8 @@ module AGNI
                 @error "Invalid initial state '$str_req'"
                 return false
             end 
+
+            atmosphere.calc_layer_props!(atmos, ignore_errors=true)
             
             # iterate
             idx_req += 1
@@ -478,7 +509,7 @@ module AGNI
         atmosphere.deallocate!(atmos)
 
         # Temp folders
-        if cfg["files"]["clean_output"]
+        if cfg["execution"]["clean_output"]
             @debug "Cleaning output folder"
             # save fastchem outputs 
             if chem_type in [1,2,3]
diff --git a/src/atmosphere.jl b/src/atmosphere.jl
index 85994c0d..0f93d600 100644
--- a/src/atmosphere.jl
+++ b/src/atmosphere.jl
@@ -47,6 +47,7 @@ module atmosphere
         # Directories
         ROOT_DIR::String
         OUT_DIR::String
+        THERMO_DIR::String
 
         # SOCRATES objects
         SOCRATES_VERSION::String
@@ -110,6 +111,7 @@ module atmosphere
         gas_sat::Dict{String, Array{Bool, 1}}       # Layer is saturated or cold-trapped
         gas_dat::Dict{String, phys.Gas_t}           # struct variables containing thermodynamic data
         gas_yield::Dict{String, Array{Float64,1}}   # condensate yield [kg/m^2] at each level (can be negative, representing evaporation)
+        gas_ovmr::Dict{String, Array{Float64,1}}    # original VMR values at model initialisation
         condensates::Array{String, 1}               # List of condensing gases (strings)
         condense_any::Bool                          # length(condensates)>0 ?
         single_component::Bool                      # Does a single gas make up 100% of layer at any point in the column?
@@ -240,11 +242,11 @@ module atmosphere
     - `nlev_centre::Int`                number of model levels.    
     - `p_surf::Float64`                 total surface pressure [bar].    
     - `p_top::Float64`                  total top of atmosphere pressure [bar].    
-    - `mf_dict=nothing`                 dictionary of mole fractions in the format (key,value)=(gas,mf).    
-    - `mf_path=nothing`                 path to file containing mole fractions at each level. 
-    - `condensates::Array{String,1}`    list of condensates (names)
+    - `mf_dict::Dict`                   dictionary of VMRs in the format (key,value)=(gas,mf).    
+    - `mf_path::String`                 path to file containing VMRs at each level. 
 
     Optional arguments:
+    - `condensates::Array{String,1}`    list of condensates (names)
     - `albedo_s::Float64`               surface albedo.   
     - `tmp_floor::Float64`              temperature floor [K].   
     - `C_d::Float64`                    turbulent heat exchange coefficient [dimensionless].   
@@ -254,7 +256,7 @@ module atmosphere
     - `skin_k::Float64`                 skin thermal conductivity [W m-1 K-1].   
     - `overlap_method::Int`             gaseous overlap scheme (2: rand overlap, 4: equiv extinct, 8: ro+resort+rebin).   
     - `target_olr::Float64`             target OLR [W m-2] for sol_type==4.   
-    - `tmp_int::Float64`                planet's effective brightness temperature [K] for sol_type==3.   
+    - `tmp_int::Float64`                planet's effective (or internal) brightness temperature [K] for sol_type==3.   
     - `all_channels::Bool`              use all channels available for RT?   
     - `flag_rayleigh::Bool`             include rayleigh scattering?   
     - `flag_gcontinuum::Bool`           include generalised continuum absorption?   
@@ -263,6 +265,7 @@ module atmosphere
     - `flag_cloud::Bool`                include clouds?   
     - `thermo_functions::Bool`          use temperature-dependent thermodynamic properties   
     - `fastchem_path::String`           path to FastChem folder (empty string => disabled)   
+    - `use_all_gases::Bool`             store information on all supported gases, incl those not provided in cfg
 
     Returns:
         Nothing
@@ -273,9 +276,9 @@ module atmosphere
                     instellation::Float64, s0_fact::Float64, albedo_b::Float64, zenith_degrees::Float64,
                     tmp_surf::Float64,
                     gravity::Float64, radius::Float64,
-                    nlev_centre::Int, p_surf::Float64, p_top::Float64;
-                    mf_dict=                    nothing,
-                    mf_path =                   nothing,
+                    nlev_centre::Int, p_surf::Float64, p_top::Float64,
+                    mf_dict::Dict{String, Float64}, mf_path::String;
+
                     condensates::Array{String,1} = String[],
                     albedo_s::Float64 =         0.0,
                     tmp_floor::Float64 =        2.0,
@@ -294,7 +297,8 @@ module atmosphere
                     flag_aerosol::Bool =        false,
                     flag_cloud::Bool =          false,
                     thermo_functions::Bool =    true,
-                    fastchem_path::String =     ""
+                    fastchem_path::String =     "",
+                    use_all_gases::Bool =       false
                     )
 
         if !isdir(OUT_DIR) && !isfile(OUT_DIR)
@@ -302,7 +306,7 @@ module atmosphere
         end
 
         # Code versions 
-        atmos.AGNI_VERSION = "0.5.2"
+        atmos.AGNI_VERSION = "0.5.3"
         atmos.SOCRATES_VERSION = readchomp(joinpath(ENV["RAD_DIR"],"version"))
         @debug "AGNI VERSION = $(atmos.AGNI_VERSION)"
         @debug "Using SOCRATES at $(ENV["RAD_DIR"])"
@@ -322,6 +326,7 @@ module atmosphere
         # Set the parameters (and make sure that they're reasonable)
         atmos.ROOT_DIR =        abspath(ROOT_DIR)
         atmos.OUT_DIR =         abspath(OUT_DIR)
+        atmos.THERMO_DIR  =     joinpath(atmos.ROOT_DIR, "res", "thermo")
         atmos.spectral_file =   abspath(spfile)
         atmos.all_channels =    all_channels
         atmos.overlap_method =  overlap_method
@@ -359,7 +364,8 @@ module atmosphere
         atmos.skin_k =          max(1.0e-6,skin_k)
 
         if p_top > p_surf 
-            error("p_top must be less than p_surf")
+            @error "p_top must be less than p_surf"
+            return false
         end
 
         atmos.p_toa =           p_top * 1.0e+5 # Convert bar -> Pa
@@ -406,23 +412,26 @@ module atmosphere
         atmos.cloud_val_f   = 0.8     # 100% of the cell "area" is cloud
 
         # Read mole fractions
-        if isnothing(mf_dict) && isnothing(mf_path)
-            error("No mole fractions provided")
+        if !isempty(mf_path) && !isempty(mf_dict)
+            @error "VMRs provided twice"
+            return false
         end
 
-        if !isnothing(mf_dict) && !isnothing(mf_path)
-            error("Mole fractions provided twice")
+        if isempty(mf_path) && isempty(mf_dict)
+            @error "VMRs not provided"
+            return false
         end
 
-        mf_source::Int = 0  # source for mf (0: dict, 1: file)
-        if isnothing(mf_dict) && !isnothing(mf_path)
-            mf_source = 1
+        mf_source::Int = 1  # source for mf (0: dict, 1: file)
+        if isempty(mf_path)
+            mf_source = 0
         end
         
         # The values will be stored in a dict of arrays
         atmos.gas_names =   Array{String}(undef, 0)           # list of names (String)
         atmos.gas_dat =     Dict{String, phys.Gas_t}()        # dict of data structures (phys.Gas_t)
         atmos.gas_vmr  =    Dict{String, Array{Float64,1}}()  # dict of VMR arrays (Float)
+        atmos.gas_ovmr  =   Dict{String, Array{Float64,1}}()  # ^ backup of initial values
         atmos.gas_sat  =    Dict{String, Array{Bool, 1}}()    # dict for saturation/coldtrapping (Bool) 
         atmos.gas_yield =   Dict{String, Array{Float64,1}}()  # dict of condensate yield values (Float [kg])
         atmos.gas_num   =   0                                 # number of gases 
@@ -452,7 +461,8 @@ module atmosphere
         if mf_source == 1
             # check file 
             if !isfile(mf_path)
-                error("Could not read file '$mf_path'")
+                @error "Could not read VMR file '$mf_path'"
+                return false
             end
             @info "Composition set by file"
 
@@ -511,16 +521,47 @@ module atmosphere
 
         end # end read VMR from file 
 
+        # add extra gases if required 
+        if use_all_gases
+
+            # for each gas in the file
+            open(joinpath(atmos.THERMO_DIR, "standard.txt"), "r") do hdl 
+                for gas in readlines(hdl)
+                    # comment line 
+                    if isempty(gas) || occursin("#",gas)
+                        continue 
+                    end 
+                    gas = strip(gas)
+                    
+                    # duplicate
+                    if gas in atmos.gas_names
+                        continue 
+                    end 
+
+                    # add gas
+                    atmos.gas_vmr[gas] = zeros(Float64, atmos.nlev_c)
+                    push!(atmos.gas_names, gas)
+                    atmos.gas_num += 1
+                end
+            end 
+        end 
+
+        # backup mixing ratios from current state 
+        for k in keys(atmos.gas_vmr)
+            atmos.gas_ovmr[k] = zeros(Float64, atmos.nlev_c)
+            atmos.gas_ovmr[k][:] .= atmos.gas_vmr[k][:]
+        end 
+
         # store condensates 
         for c in condensates
             push!(atmos.condensates, c)
         end 
 
-        # Cannot have n_gas==n_cond AND n_cond>1, because it will overspecify
+        # Cannot have n_gas==n_cond, because it will overspecify
         #   the total pressure within condensing regions
-        if (length(atmos.condensates) == atmos.gas_soc_num) && (length(atmos.condensates)>1)
-            error("There must be at least one non-condensable gas")
-            return 
+        if (length(atmos.condensates) == atmos.gas_num) && (atmos.gas_num > 1)
+            @error "There must be at least one non-condensable gas"
+            return false
         end 
 
         # Validate condensate names 
@@ -543,7 +584,8 @@ module atmosphere
 
         # Check that we actually stored some values
         if atmos.gas_num == 0
-            error("No mole fractions were stored")
+            @error "No gases were stored"
+            return false
         end
 
         # Normalise VMRs at each level
@@ -571,9 +613,9 @@ module atmosphere
 
         # Load gas thermodynamic data 
         for g in atmos.gas_names 
-            atmos.gas_dat[g] = phys.load_gas(g, atmos.thermo_funct)
+            atmos.gas_dat[g] = phys.load_gas(atmos.THERMO_DIR, g, atmos.thermo_funct)
             if (g in atmos.condensates) && (atmos.gas_dat[g].stub)
-                error("No thermodynamic data found for condensable gas $g")
+                @warn "No thermodynamic data found for condensable gas $g"
             end
         end 
 
@@ -593,6 +635,7 @@ module atmosphere
             atmos.fastchem_path = abspath(fastchem_path)
             if !isdir(fastchem_path)
                 @error "Could not find fastchem folder at '$(fastchem_path)'"
+                return 
             end 
             atmos.fastchem_work = joinpath(atmos.OUT_DIR, "fastchem/")
             
@@ -600,6 +643,7 @@ module atmosphere
             atmos.fastchem_flag = isfile(joinpath(fastchem_path,"fastchem")) 
             if !atmos.fastchem_flag 
                 @error "Could not find fastchem executable inside '$(atmos.fastchem_path)' "
+                return 
             else 
                 @info "Found FastChem executable"
             end 
@@ -614,31 +658,6 @@ module atmosphere
         return nothing
     end # end function setup 
 
-    """
-    **Get the mole fraction of a gas within the atmosphere.**
-
-    Arguments:
-    - `atmos::Atmos_t`          the atmosphere struct instance to be used.
-    - `gas::String`             name of the gas (e.g. "H2O").
-    - `lvl::Int`                model level to measure mole fraction
-
-    Returns:
-    - `x::Float64`              mole fraction of `gas`.
-    """
-    function get_x(atmos::atmosphere.Atmos_t, gas::String, lvl::Int)::Float64
-
-        if gas in atmos.gas_names
-            if (lvl >= 1) && (lvl <= atmos.nlev_c)
-                return atmos.gas_vmr[gas][lvl]
-            else
-                error("Invalid level provided ($lvl)") 
-            end 
-        else
-            @error "Invalid gas $gas queried in get_x()" 
-            return 0.0
-        end 
-    end
-
     """
     **Calculate properties within each layer of the atmosphere (e.g. density, mmw).**
 
@@ -1068,9 +1087,11 @@ module atmosphere
         # For now, they are just stored inside the atmos struct
 
         # Print info on the gases
-        @info "Allocated atmosphere with composition"
+        @info "Allocated atmosphere with composition:"
         gas_flags::String = ""
-        for g in atmos.gas_names 
+        g::String = ""
+        for i in 1:atmos.gas_num
+            g = atmos.gas_names[i]
             gas_flags = ""
             if !(g in atmos.gas_soc_names)  # flag as included in radtrans
                 gas_flags *= "NO_OPACITY "
@@ -1084,7 +1105,7 @@ module atmosphere
             if !isempty(gas_flags)
                 gas_flags = "($(gas_flags[1:end-1]))"
             end 
-            @info @sprintf("    %6.2e %-6s %s", atmos.gas_vmr[g][end], g, gas_flags)
+            @info @sprintf("    %3d %-6s %6.2e %s", i, g, atmos.gas_vmr[g][end], gas_flags)
         end 
  
 
@@ -1302,8 +1323,10 @@ module atmosphere
                         N_g[i] += d[e]
                     end 
                 end 
-                # will be normalised in later code 
-                N_g *= get_x(atmos, gas, atmos.nlev_c) * atmos.p[end] / (phys.k_B * atmos.tmp[end])  # gas contribution
+                # Get gas abundance from original VMR value, since the running 
+                #    one will be updated using FastChem's output. These will 
+                #    be normalised later in this function.
+                N_g *= atmos.gas_ovmr[gas][atmos.nlev_c] * atmos.p[end] / (phys.k_B * atmos.tmp[end])  # gas contribution
                 N_t += N_g  
             end 
 
diff --git a/src/phys.jl b/src/phys.jl
index abf77f9a..c9c97ee4 100644
--- a/src/phys.jl
+++ b/src/phys.jl
@@ -234,12 +234,14 @@ module phys
         ("H"  , "#0000ff"),
         ("C"  , "#ff0000"),
         ("O"  , "#00ff00"),
-        ("N"  , "#ffff00"),
+        ("N"  , "#eeaa00"),
         ("S" ,  "#ff22ff"),
+        ("P" ,  "#33ccff"),
 
         # refractory elements 
         ("Fe" , "#888888"),
         ("Si" , "#aa2277"),
+        ("Mg" , "#996633"),
     ])
 
     """
@@ -399,13 +401,13 @@ module phys
     """
     Load gas data into a new struct  
     """
-    function load_gas(formula::String, tmp_dep::Bool)::Gas_t
+    function load_gas(thermo_dir::String, formula::String, tmp_dep::Bool)::Gas_t
 
         @debug ("Loading data for gas $formula")
 
         # Clean input and get file path
         formula = String(strip(formula))
-        fpath = abspath(joinpath(dirname(abspath(@__FILE__)), "..", "res", "thermo", "$formula.nc" ))
+        fpath = joinpath(thermo_dir, "$formula.nc" )
 
         # Initialise struct 
         gas = Gas_t()
diff --git a/src/plotting.jl b/src/plotting.jl
index 68092714..71b7f234 100644
--- a/src/plotting.jl
+++ b/src/plotting.jl
@@ -145,7 +145,10 @@ module plotting
     function plot_vmr(atmos::atmosphere.Atmos_t, fname::String; 
                             dpi::Int=250,
                             size_x::Int=500, size_y::Int=400)
-        
+
+        # X-axis minimum allowed left-hand-side limit (log units)
+        minmin_x::Float64 = -8
+
         arr_P = atmos.p .* 1.0e-5 # Convert Pa to bar
         ylims  = (arr_P[1]/1.5, arr_P[end]*1.5)
         yticks = 10.0 .^ round.(Int,range( log10(ylims[1]), stop=log10(ylims[2]), step=1))
@@ -154,22 +157,44 @@ module plotting
         plt = plot(ylims=ylims, yticks=yticks, dpi=dpi, legend=:outertopright, size=(size_x,size_y))
 
         # Plot log10 mole fractions for each gas
-        xminmin::Float64 = -8
-        xmin::Float64 = -2
-        this_min::Float64 = 0.0
-        for gas in atmos.gas_names
-            # get VMR
-            x_arr = log10.(clamp.(atmos.gas_vmr[gas][:],1e-100, 1e100))
-            this_min = minimum(x_arr)
-            if this_min > -90
-                xmin = min(xmin, this_min)
-            end
+        gas_xsurf::Array = zeros(Float64, atmos.gas_num)
+        gas::String = ""
+        for i in 1:atmos.gas_num 
+            gas = atmos.gas_names[i]
+
+            # store surface value
+            gas_xsurf[i] = log10(clamp(atmos.gas_vmr[gas][end],1e-100, 1e100))
+        end
+
+        num_plotted::Int = 0
+        arr_x::Array{Float64, 1} = zeros(Float64, atmos.nlev_c)
+        min_x::Float64 = -3
+        for i in reverse(sortperm(gas_xsurf))
+            # Plot gases in order of descending abundance, so that the legend 
+            #    shows the most interesting gases at the top of the list.
+
+            # Avoid plotting too many gases, since this makes it unreadable
+            if num_plotted > 20
+                break 
+            end 
+
+            # Get data
+            gas = atmos.gas_names[i]
+            arr_x[:] .= atmos.gas_vmr[gas][:]
+            if minimum(arr_x) < 1e-90 
+                continue 
+            end 
+            arr_x[:] .= log10.(arr_x[:])
+
+            plot!(arr_x, arr_P,  label=atmos.gas_dat[gas].plot_label, 
+                    lw=2.5, linealpha=0.7, color=atmos.gas_dat[gas].plot_color)
+
+            num_plotted += 1
 
-            # plot gas
-            plot!(x_arr, arr_P, label=phys.pretty_name(gas), lw=2.5, linealpha=0.7, color=phys.pretty_color(gas))
+            min_x = min(min_x, minimum(arr_x))
         end
 
-        xlims  = (max(xmin-0.1, xminmin), 0.1)
+        xlims  = (max(min_x, minmin_x)-0.1, 0.1)
         xticks = round.(Int,range( xlims[1], stop=0, step=1))
 
         # Set figure properties
diff --git a/src/setpt.jl b/src/setpt.jl
index edcdcfe9..a52f148a 100644
--- a/src/setpt.jl
+++ b/src/setpt.jl
@@ -109,7 +109,6 @@ module setpt
         atmos.tmpl[:] .= itp.(log10.(atmos.pl[:]))  # Cell edges 
         atmos.tmp[:]  .= itp.(log10.(atmos.p[:]))   # Cell centres 
 
-        atmosphere.calc_layer_props!(atmos)
         return nothing
     end
 
@@ -162,7 +161,6 @@ module setpt
         # Close file 
         close(ds)
 
-        atmosphere.calc_layer_props!(atmos)
         return nothing
     end # end load_ncdf
 
@@ -174,7 +172,6 @@ module setpt
         atmos.tmpl[:] .= set_tmp 
         atmos.tmp[:]  .= set_tmp
 
-        atmosphere.calc_layer_props!(atmos)
         return nothing
     end 
 
@@ -186,7 +183,6 @@ module setpt
         atmos.tmpl[:] .+= delta 
         atmos.tmp[:]  .+= delta
 
-        atmosphere.calc_layer_props!(atmos)
         return nothing
     end 
 
@@ -200,9 +196,6 @@ module setpt
         # Set surface 
         atmos.tmpl[end] = atmos.tmp_surf
 
-        # Thermodynamics etc 
-        atmosphere.calc_layer_props!(atmos)
-
         # Lapse rate dT/dp 
         grad::Float64 = 0.0
 
@@ -230,8 +223,6 @@ module setpt
             atmos.tmpl[i] = max(atmos.tmpl[i], atmos.tmp_floor)
         end 
 
-        # Thermodynamics at new temperature profile 
-        atmosphere.calc_layer_props!(atmos)
 
         return nothing
     end 
@@ -260,7 +251,6 @@ module setpt
             atmos.tmpl[1] = strat_tmp 
         end
 
-        atmosphere.calc_layer_props!(atmos)
         return nothing
     end
 
@@ -283,7 +273,6 @@ module setpt
         # Set cell-centres 
         atmos.tmp[1:end] .= 0.5 .* (atmos.tmpl[1:end-1] + atmos.tmpl[2:end])
 
-        atmosphere.calc_layer_props!(atmos)
         return nothing
     end
 
@@ -335,7 +324,6 @@ module setpt
 
         # Generate new pressure grid 
         atmosphere.generate_pgrid!(atmos)
-        atmosphere.calc_layer_props!(atmos)
         
         return nothing
     end
@@ -379,9 +367,6 @@ module setpt
         # Set cell-edge temperatures
         atmosphere.set_tmpl_from_tmp!(atmos)
 
-        # calculate properties 
-        atmosphere.calc_layer_props!(atmos)
-
         return nothing
     end
 
diff --git a/src/solver.jl b/src/solver.jl
index 55e409ac..64e89830 100644
--- a/src/solver.jl
+++ b/src/solver.jl
@@ -491,9 +491,9 @@ module solver
                 @debug "        chemistry"
                 fc_retcode = atmosphere.chemistry_eq!(atmos, chem_type, false)
                 if fc_retcode == 0
-                    stepflags *= "Cs-"
+                    stepflags *= "Cs-"  # chemistry success 
                 else 
-                    stepflags *= "Cf-"
+                    stepflags *= "Cf-"  # chemistry failure
                     step_ok = false
                 end
             end 
diff --git a/test/runtests.jl b/test/runtests.jl
index 77580943..77c9efea 100755
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -45,7 +45,7 @@ passing = true
 # -------------
 @info " "
 @info "Testing heat capacity functions"
-data_H2O::phys.Gas_t = phys.load_gas("H2O", true)
+data_H2O::phys.Gas_t = phys.load_gas("res/thermo/", "H2O", true)
 c_expt::Array{Float64, 1} = [4.975, 35.22, 41.27 , 51.20 , 55.74 , 59.40 ]     # Expected values of cp [J mol-1 K-1]
 t_test::Array{Float64, 1} = [10.0,  500.0, 1000.0, 2000.0, 3000.0, 5000.0]     # Tested values of temperature 
 cp_pass = true 
@@ -92,7 +92,7 @@ atmosphere.setup!(atmos, ROOT_DIR, output_dir,
                          tmp_surf,
                          gravity, radius,
                          nlev_centre, p_surf, p_top,
-                         mf_dict=mf_dict
+                         mf_dict, ""
                  )
 atmosphere.allocate!(atmos,"")
 
@@ -100,7 +100,7 @@ dct_e::Dict{String, Float64} = mf_dict
 dct_o::Dict{String, Float64} = Dict()
 sp_pass = true
 for k in keys(dct_e)
-    dct_o[k] = atmosphere.get_x(atmos, k, 25)
+    dct_o[k] = atmos.gas_vmr[k][20]
     global sp_pass = sp_pass && ( abs(dct_o[k]-dct_e[k]) < 1.0e-6 )
 end
 @info "Expected values = $(dct_e)"
@@ -141,7 +141,7 @@ atmosphere.setup!(atmos, ROOT_DIR, output_dir,
                          tmp_surf,
                          gravity, radius,
                          nlev_centre, p_surf, p_top,
-                         mf_dict=mf_dict,
+                         mf_dict,"",
                          flag_gcontinuum=false,
                          flag_rayleigh=false,
                          overlap_method=2
@@ -202,7 +202,7 @@ atmosphere.setup!(atmos, ROOT_DIR, output_dir,
                          tmp_surf,
                          gravity, radius,
                          nlev_centre, p_surf, p_top,
-                         mf_dict=mf_dict,
+                         mf_dict,"",
                          flag_gcontinuum=true,
                          flag_rayleigh=false,
                          overlap_method=4, 
@@ -212,6 +212,7 @@ atmosphere.allocate!(atmos,joinpath(ROOT_DIR,"res/stellar_spectra/sun.txt"))
 setpt.prevent_surfsupersat!(atmos)
 setpt.dry_adiabat!(atmos)
 setpt.saturation!(atmos, "H2O")
+atmosphere.calc_layer_props!(atmos)
 energy.radtrans!(atmos, true)
 
 val_e = [270.0, 290.0]
@@ -252,7 +253,7 @@ atmosphere.setup!(atmos, ROOT_DIR, output_dir,
                          tmp_surf,
                          gravity, radius,
                          nlev_centre, p_surf, p_top,
-                         mf_dict=mf_dict,
+                         mf_dict, "",
                          flag_gcontinuum=true,
                          flag_rayleigh=true,
                          overlap_method=2
@@ -299,7 +300,7 @@ atmosphere.setup!(atmos, ROOT_DIR, output_dir,
                          tmp_surf,
                          gravity, radius,
                          nlev_centre, p_surf, p_top,
-                         mf_dict=mf_dict,
+                         mf_dict, "",
                          flag_gcontinuum=true,
                          flag_rayleigh=false,
                          overlap_method=2,
@@ -307,6 +308,7 @@ atmosphere.setup!(atmos, ROOT_DIR, output_dir,
                  )
 atmosphere.allocate!(atmos,joinpath(ROOT_DIR,"res/stellar_spectra/trappist-1.txt"))
 setpt.isothermal!(atmos, 300.0)
+atmosphere.calc_layer_props!(atmos)
 atmos.flux_tot[:] .= 0.0
 energy.radtrans!(atmos, true)
 energy.radtrans!(atmos, false)
diff --git a/tutorials/01_canonical-runaway.ipynb b/tutorials/01_canonical-runaway.ipynb
index 2a4303b0..2bcd8d70 100644
--- a/tutorials/01_canonical-runaway.ipynb
+++ b/tutorials/01_canonical-runaway.ipynb
@@ -34,8 +34,9 @@
        "\"/Users/nichollsh/Projects/AGNI/socrates\""
       ]
      },
+     "execution_count": 1,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -48,7 +49,16 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling IJuliaExt [2f4121a4-3b3a-5ce6-9c5e-1f2673ce168a]\n",
+      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling AGNI [ede838c1-9ec3-4ebe-8ae8-da4091b3f21c]\n"
+     ]
+    }
+   ],
    "source": [
     "# Import system packages\n",
     "using Printf\n",
@@ -83,8 +93,9 @@
        "\"/Users/nichollsh/Projects/AGNI/out/\""
       ]
      },
+     "execution_count": 3,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -135,7 +146,7 @@
     "                        1700.0,\n",
     "                        gravity, radius,\n",
     "                        nlev_centre, p_surf, p_top,\n",
-    "                        mf_dict=mole_fractions,\n",
+    "                        mole_fractions, \"\",\n",
     "                        flag_gcontinuum=true,\n",
     "                        thermo_functions=false\n",
     "                        )\n",
@@ -212,6 +223,7 @@
     "    setpt.prevent_surfsupersat!(atmos)\n",
     "    setpt.dry_adiabat!(atmos)\n",
     "    setpt.saturation!(atmos, \"H2O\")\n",
+    "    atmosphere.calc_layer_props!(atmos)\n",
     "\n",
     "    # Calculate LW fluxes \n",
     "    energy.radtrans!(atmos, true)\n",
@@ -269,6 +281,13 @@
    "execution_count": 9,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Making plot\n"
+     ]
+    },
     {
      "data": {
       "image/png": 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",
@@ -276,560 +295,553 @@
        "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n",
        "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"700\" height=\"500\" viewBox=\"0 0 2800 2000\">\n",
        "<defs>\n",
-       "  <clipPath id=\"clip970\">\n",
+       "  <clipPath id=\"clip760\">\n",
        "    <rect x=\"0\" y=\"0\" width=\"2800\" height=\"2000\"/>\n",
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364.226,1582.49 366.009,1581.59 366.009,1580.69 366.009,1579.78 366.009,1578.88 366.009,1577.97 366.009,1577.07 366.009,1576.17 366.009,1575.26 367.791,1574.36 367.791,1573.46 367.791,1572.55 367.791,1571.65 367.791,1570.74 367.791,1569.84 367.791,1568.94 367.791,1568.03 369.574,1567.13 369.574,1566.23 369.574,1565.32 369.574,1564.42 369.574,1563.52 369.574,1562.61 369.574,1561.71 369.574,1560.8 371.356,1559.9 371.356,1559 371.356,1558.09 371.356,1557.19 371.356,1556.29 371.356,1555.38 371.356,1554.48 371.356,1553.58 373.138,1552.67 373.138,1551.77 373.138,1550.87 373.138,1549.96 373.138,1549.06 373.138,1548.15 373.138,1547.25 373.138,1546.35 374.921,1545.44 374.921,1544.54 374.921,1543.64 374.921,1542.73 374.921,1541.83 374.921,1540.93 374.921,1540.02 374.921,1539.12 376.704,1538.21 376.704,1537.31 376.704,1536.41 376.704,1535.5 376.704,1534.6 376.704,1533.7 376.704,1532.79 376.704,1531.89 378.486,1530.99 378.486,1530.08 378.486,1529.18 378.486,1528.27 378.486,1527.37 378.486,1526.47 378.486,1525.56 378.486,1524.66 380.268,1523.76 380.268,1522.85 380.268,1521.95 380.268,1521.05 380.268,1520.14 380.268,1519.24 380.268,1518.33 380.268,1517.43 380.268,1516.53 380.268,1515.62 382.051,1514.72 382.051,1513.82 382.051,1512.91 382.051,1512.01 382.051,1511.11 382.051,1510.2 382.051,1509.3 382.051,1508.39 382.051,1507.49 383.834,1506.59 383.834,1505.68 383.834,1504.78 383.834,1503.88 383.834,1502.97 383.834,1502.07 383.834,1501.16 385.616,1500.26 385.616,1499.36 385.616,1498.45 385.616,1497.55 385.616,1496.65 385.616,1495.74 385.616,1494.84 387.399,1493.94 387.399,1493.03 387.399,1492.13 387.399,1491.23 387.399,1490.32 387.399,1489.42 387.399,1488.51 389.181,1487.61 389.181,1486.71 389.181,1485.8 389.181,1484.9 389.181,1484 389.181,1483.09 389.181,1482.19 390.964,1481.29 390.964,1480.38 390.964,1479.48 390.964,1478.58 390.964,1477.67 390.964,1476.77 392.746,1475.86 392.746,1474.96 392.746,1474.06 392.746,1473.15 392.746,1472.25 392.746,1471.35 394.529,1470.44 394.529,1469.54 394.529,1468.64 394.529,1467.73 394.529,1466.83 394.529,1465.92 394.529,1465.02 396.311,1464.12 396.311,1463.21 396.311,1462.31 396.311,1461.41 396.311,1460.5 396.311,1459.6 398.094,1458.7 398.094,1457.79 398.094,1456.89 398.094,1455.99 398.094,1455.08 398.094,1454.18 399.876,1453.28 399.876,1452.37 399.876,1451.47 399.876,1450.56 399.876,1449.66 399.876,1448.76 401.659,1447.85 401.659,1446.95 401.659,1446.05 401.659,1445.14 401.659,1444.24 401.659,1443.33 403.441,1442.43 403.441,1441.53 403.441,1440.62 403.441,1439.72 403.441,1438.82 403.441,1437.91 405.224,1437.01 405.224,1436.11 405.224,1435.2 405.224,1434.3 405.224,1433.4 407.006,1432.49 407.006,1431.59 407.006,1430.69 407.006,1429.78 407.006,1428.88 407.006,1427.97 408.789,1427.07 408.789,1426.17 408.789,1425.26 408.789,1424.36 408.789,1423.46 410.571,1422.55 410.571,1421.65 410.571,1420.75 410.571,1419.84 412.354,1418.94 412.354,1418.04 412.354,1417.13 414.136,1416.23 414.136,1415.33 414.136,1414.42 415.919,1413.52 415.919,1412.62 415.919,1411.71 417.701,1410.81 417.701,1409.91 417.701,1409 419.484,1408.1 419.484,1407.2 419.484,1406.29 421.266,1405.39 421.266,1404.49 421.266,1403.58 423.049,1402.68 423.049,1401.78 423.049,1400.87 424.831,1399.97 424.831,1399.06 426.614,1398.16 426.614,1397.26 426.614,1396.35 428.396,1395.45 428.396,1394.55 428.396,1393.64 428.396,1392.74 430.179,1391.84 430.179,1390.93 430.179,1390.03 431.961,1389.13 431.961,1388.22 431.961,1387.32 433.744,1386.42 433.744,1385.51 433.744,1384.61 435.526,1383.71 435.526,1382.8 435.526,1381.9 437.309,1381 437.309,1380.09 439.091,1379.19 439.091,1378.29 439.091,1377.38 439.091,1376.48 440.874,1375.58 440.874,1374.67 440.874,1373.77 442.656,1372.87 442.656,1371.96 442.656,1371.06 442.656,1370.16 444.439,1369.25 444.439,1368.35 444.439,1367.45 444.439,1366.54 446.221,1365.64 446.221,1364.74 446.221,1363.83 446.221,1362.93 448.004,1362.03 448.004,1361.12 448.004,1360.22 448.004,1359.31 449.786,1358.41 449.786,1357.51 449.786,1356.6 451.569,1355.7 451.569,1354.8 451.569,1353.89 451.569,1352.99 453.351,1352.09 453.351,1351.18 453.351,1350.28 453.351,1349.38 455.134,1348.47 455.134,1347.57 455.134,1346.67 455.134,1345.76 456.916,1344.86 456.916,1343.96 456.916,1343.05 456.916,1342.15 458.699,1341.25 458.699,1340.34 458.699,1339.44 460.481,1338.54 462.264,1337.63 462.264,1336.73 464.046,1336.73 464.046,1335.83 465.829,1334.92 467.611,1334.02 469.394,1333.12 471.176,1332.22 471.176,1331.31 472.959,1330.41 474.741,1329.51 476.524,1328.6 478.306,1328.61 480.089,1327.7 481.871,1326.8 483.654,1325.9 485.436,1325.9 487.219,1325 489.001,1324.09 490.784,1323.19 492.566,1322.29 494.349,1321.39 496.131,1321.39 497.914,1320.48 499.696,1320.49 501.479,1319.58 503.261,1319.58 505.044,1319.59 506.826,1318.68 508.609,1318.68 510.392,1317.78 512.174,1317.78 513.956,1317.78 515.739,1317.78 517.522,1316.88 519.304,1316.88 521.087,1316.88 522.869,1315.98 524.652,1315.98 526.434,1315.98 528.217,1315.99 529.999,1315.08 531.782,1315.08 533.564,1315.08 535.347,1315.09 537.129,1314.18 538.912,1314.18 540.694,1314.19 542.477,1314.19 544.259,1314.19 544.259,1313.28 546.042,1313.29 547.824,1313.29 549.607,1313.29 551.389,1313.29 553.172,1313.29 554.954,1313.29 556.737,1312.39 558.519,1312.39 560.302,1312.39 562.084,1312.39 563.867,1312.39 565.649,1312.39 567.432,1312.4 569.214,1311.49 570.997,1311.49 572.779,1311.5 574.562,1311.5 576.344,1311.5 578.127,1311.5 579.909,1311.5 581.692,1311.5 583.474,1311.5 585.257,1311.5 587.039,1311.5 588.822,1310.6 590.604,1310.6 592.387,1310.6 594.169,1310.61 595.952,1310.61 597.734,1310.61 599.517,1310.61 601.299,1310.61 603.082,1310.61 604.864,1310.61 606.647,1310.61 608.429,1310.61 610.212,1310.62 611.994,1309.71 613.777,1309.71 615.559,1309.72 617.342,1309.72 619.124,1309.72 620.907,1309.72 622.689,1309.72 624.472,1309.72 626.254,1309.72 628.037,1309.72 629.819,1309.72 631.602,1309.73 633.384,1309.73 635.167,1309.73 636.949,1309.73 638.732,1309.73 640.514,1309.73 642.297,1308.83 644.08,1308.83 645.862,1308.83 647.644,1308.83 649.427,1308.83 651.21,1308.83 652.992,1308.84 654.774,1308.84 656.557,1308.84 658.339,1308.84 660.122,1308.84 661.905,1308.84 663.687,1308.84 665.47,1308.84 667.252,1308.85 669.035,1308.85 670.817,1308.85 672.6,1308.85 674.382,1308.85 676.165,1308.85 677.947,1308.85 679.73,1308.85 681.512,1308.85 683.295,1308.86 685.077,1308.86 686.86,1307.95 688.642,1307.96 690.425,1307.96 692.207,1307.96 693.99,1307.96 695.772,1307.96 697.555,1307.96 699.337,1307.96 701.12,1307.96 702.902,1307.96 704.685,1307.97 706.467,1307.97 708.25,1307.97 710.032,1307.97 711.815,1307.97 713.597,1307.97 715.38,1307.97 717.162,1307.97 718.945,1307.98 720.727,1307.98 722.51,1307.98 724.292,1307.98 726.075,1307.98 727.857,1307.98 729.64,1307.98 731.422,1307.98 733.205,1307.98 734.987,1307.99 736.77,1307.99 738.552,1307.99 740.335,1307.99 742.117,1307.99 743.9,1307.99 745.682,1307.99 747.465,1307.99 749.247,1308 751.03,1307.09 752.812,1307.09 754.595,1307.09 756.377,1307.1 758.16,1307.1 759.942,1307.1 761.725,1307.1 763.507,1307.1 765.29,1307.1 767.072,1307.1 768.855,1307.1 770.637,1307.11 772.42,1307.11 774.202,1307.11 775.985,1307.11 777.768,1307.11 779.55,1307.11 781.332,1307.11 783.115,1307.11 784.897,1307.11 786.68,1307.12 788.463,1307.12 790.245,1307.12 792.027,1307.12 793.81,1307.12 795.593,1307.12 797.375,1307.12 799.158,1307.12 800.94,1307.13 802.723,1307.13 804.505,1307.13 806.288,1307.13 808.07,1307.13 809.853,1307.13 811.635,1307.13 813.418,1307.13 815.2,1307.13 816.983,1307.14 818.765,1307.14 820.548,1307.14 822.33,1307.14 824.112,1307.14 825.895,1307.14 827.678,1307.14 829.46,1307.14 831.243,1307.14 833.025,1307.15 834.808,1307.15 836.59,1307.15 838.373,1307.15 840.155,1307.15 841.938,1307.15 843.72,1307.15 845.503,1307.15 847.285,1307.16 849.068,1307.16 850.85,1307.16 852.633,1307.16 854.415,1307.16 856.198,1307.16 857.98,1307.16 859.763,1307.16 861.545,1307.16 863.328,1307.17 865.11,1307.17 866.893,1307.17 868.675,1307.17 870.458,1307.17 872.24,1307.17 874.023,1307.17 875.805,1307.17 877.588,1307.17 879.37,1307.18 881.153,1307.18 882.935,1307.18 884.718,1307.18 886.5,1307.18 888.283,1307.18 890.065,1307.18 891.848,1307.18 893.63,1307.19 895.413,1307.19 897.195,1307.19 898.978,1307.19 900.76,1307.19 902.543,1307.19 904.325,1307.19 906.108,1307.19 907.89,1307.19 909.673,1307.2 911.455,1307.2 913.238,1307.2 915.02,1307.2 916.803,1307.2 918.585,1307.2 920.368,1307.2 922.15,1307.2 923.933,1307.21 925.715,1307.21 927.498,1307.21 929.28,1307.21 931.063,1307.21 932.845,1307.21 934.628,1307.21 936.411,1307.21 938.193,1307.21 939.976,1307.22 941.758,1307.22 943.54,1307.22 945.323,1307.22 947.106,1307.22 948.888,1307.22 950.671,1307.22 952.453,1307.22 954.236,1307.22 956.018,1307.23 957.801,1307.23 959.583,1307.23 961.366,1307.23 963.148,1307.23 964.931,1307.23 966.713,1307.23 968.495,1307.23 970.278,1307.24 972.061,1307.24 973.843,1307.24 975.626,1307.24 977.408,1307.24 979.191,1307.24 980.973,1307.24 982.756,1307.24 984.538,1307.24 986.321,1307.25 988.103,1307.25 989.886,1307.25 991.668,1307.25 993.451,1307.25 995.233,1307.25 997.016,1307.25 998.798,1307.25 1000.58,1307.26 1002.36,1307.26 1004.15,1307.26 1005.93,1307.26 1007.71,1307.26 1009.49,1307.26 1011.28,1307.26 1013.06,1307.26 1014.84,1307.26 1016.62,1307.27 1018.41,1307.27 1020.19,1307.27 1021.97,1307.27 1023.75,1307.27 1025.54,1307.27 1027.32,1307.27 1029.1,1307.27 1030.88,1307.27 1032.67,1307.28 1034.45,1307.28 1036.23,1307.28 1038.01,1307.28 1039.8,1307.28 1041.58,1307.28 1043.36,1307.28 1045.14,1307.28 1046.93,1307.29 1048.71,1307.29 1050.49,1307.29 1052.27,1307.29 1054.06,1307.29 1055.84,1307.29 1057.62,1307.29 1059.4,1307.29 1061.19,1307.29 1062.97,1307.3 1064.75,1307.3 1066.53,1307.3 1068.32,1307.3 1070.1,1307.3 1071.88,1307.3 1073.66,1307.3 1075.45,1307.3 1077.23,1307.31 1079.01,1307.31 1080.79,1307.31 1082.58,1307.31 1084.36,1307.31 1086.14,1307.31 1087.92,1307.31 1089.71,1307.31 1091.49,1307.31 1093.27,1307.32 1095.05,1307.32 1096.84,1307.32 1098.62,1307.32 1100.4,1307.32 1102.18,1307.32 1103.97,1307.32 1105.75,1307.32 1107.53,1307.32 1109.31,1307.33 1111.1,1307.33 1112.88,1307.33 1114.66,1307.33 1116.44,1307.33 1118.23,1307.33 1120.01,1307.33 1121.79,1307.33 1123.57,1307.34 1125.36,1307.34 1127.14,1307.34 1128.92,1307.34 1130.7,1307.34 1132.49,1307.34 1134.27,1307.34 1136.05,1307.34 1137.83,1307.34 1139.62,1307.35 1141.4,1307.35 1143.18,1307.35 1144.96,1307.35 1146.75,1307.35 1148.53,1307.35 1150.31,1307.35 1152.09,1307.35 1153.88,1307.36 1155.66,1307.36 1157.44,1307.36 1159.22,1307.36 1161.01,1307.36 1162.79,1307.36 1164.57,1307.36 1166.35,1307.36 1168.14,1307.36 1169.92,1307.37 1171.7,1307.37 1173.48,1307.37 1175.27,1307.37 1177.05,1307.37 1178.83,1307.37 1180.61,1307.37 1182.4,1307.37 1184.18,1307.37 1185.96,1307.38 1187.74,1307.38 1189.53,1307.38 1191.31,1307.38 1193.09,1307.38 1194.87,1307.38 1196.66,1307.38 1198.44,1307.38 1200.22,1307.39 1202,1307.39 1203.79,1307.39 1205.57,1307.39 1207.35,1307.39 1209.13,1307.39 1210.92,1307.39 1212.7,1307.39 1214.48,1307.39 1216.26,1307.4 1218.05,1307.4 1219.83,1307.4 1221.61,1307.4 1223.39,1307.4 1225.18,1307.4 1226.96,1307.4 1228.74,1307.4 1230.52,1307.4 1232.31,1307.41 1234.09,1307.41 1235.87,1307.41 1237.65,1307.41 1239.44,1307.41 1241.22,1307.41 1243,1307.41 1244.78,1307.41 1246.57,1307.42 1248.35,1307.42 1250.13,1307.42 1251.91,1307.42 1253.7,1307.42 1255.48,1307.42 1257.26,1307.42 1259.04,1307.42 1260.83,1307.42 1262.61,1307.43 1264.39,1307.43 1266.17,1307.43 1267.96,1307.43 1269.74,1307.43 1271.52,1307.43 1273.3,1307.43 1275.09,1307.43 1276.87,1307.44 1278.65,1307.44 1280.43,1307.44 1282.22,1307.44 1284,1307.44 1285.78,1307.44 1287.56,1307.44 1289.35,1307.44 1291.13,1307.44 1292.91,1307.45 1294.69,1307.45 1296.48,1307.45 1298.26,1307.45 1300.04,1307.45 1301.82,1307.45 1303.61,1307.45 1305.39,1307.45 1307.17,1307.45 1308.95,1307.46 1310.74,1307.46 1312.52,1307.46 1314.3,1307.46 1316.08,1307.46 1317.87,1307.46 1319.65,1307.46 1321.43,1307.46 1323.21,1307.47 1325,1307.47 1326.78,1307.47 1328.56,1307.47 1330.34,1307.47 1332.13,1307.47 1333.91,1307.47 1335.69,1307.47 1337.47,1307.47 1339.26,1307.48 1341.04,1307.48 1342.82,1307.48 1344.6,1307.48 1346.39,1307.48 1348.17,1307.48 1349.95,1307.48 1351.73,1307.48 1353.52,1307.49 1355.3,1307.49 1357.08,1307.49 1358.86,1307.49 1360.65,1307.49 1362.43,1307.49 1364.21,1307.49 1365.99,1307.49 1367.78,1307.49 1369.56,1307.5 1371.34,1307.5 1373.12,1307.5 1374.91,1307.5 1376.69,1307.5 1378.47,1307.5 1380.25,1307.5 1382.04,1307.5 1383.82,1307.5 1385.6,1307.51 1387.38,1307.51 1389.17,1307.51 1390.95,1307.51 1392.73,1307.51 1394.51,1307.51 1396.3,1307.51 1398.08,1307.51 1399.86,1307.52 1401.64,1307.52 1403.43,1307.52 1405.21,1307.52 1406.99,1307.52 1408.77,1307.52 1410.56,1307.52 1412.34,1307.52 1414.12,1307.52 1415.9,1307.53 1417.69,1307.53 1419.47,1307.53 1421.25,1307.53 1423.03,1307.53 1424.82,1307.53 1426.6,1307.53 1428.38,1307.53 1430.16,1307.54 1431.95,1307.54 1433.73,1307.54 1435.51,1307.54 1437.29,1307.54 1439.08,1307.54 1440.86,1307.54 1442.64,1307.54 1444.42,1307.54 1446.21,1307.55 1447.99,1307.55 1449.77,1307.55 1451.55,1307.55 1453.34,1307.55 1455.12,1307.55 1456.9,1307.55 1458.68,1307.55 1460.47,1307.55 1462.25,1307.56 1464.03,1307.56 1465.81,1307.56 1467.6,1307.56 1469.38,1307.56 1471.16,1307.56 1472.94,1307.56 1474.73,1307.56 1476.51,1307.57 1478.29,1307.57 1480.07,1307.57 1481.86,1307.57 1483.64,1307.57 1485.42,1307.57 1487.2,1307.57 1488.99,1307.57 1490.77,1307.57 1492.55,1307.58 1494.33,1307.58 1496.12,1307.58 1497.9,1307.58 1499.68,1307.58 1501.47,1307.58 1503.25,1307.58 1505.03,1307.58 1506.81,1307.58 1508.6,1307.59 1510.38,1307.59 1512.16,1307.59 1513.94,1307.59 1515.72,1307.59 1517.51,1307.59 1519.29,1307.59 1521.07,1307.59 1522.86,1307.6 1524.64,1307.6 1526.42,1307.6 1528.2,1307.6 1529.99,1307.6 1531.77,1307.6 1533.55,1307.6 1535.33,1307.6 1537.12,1307.6 1538.9,1307.61 1540.68,1307.61 1542.46,1307.61 1544.25,1307.61 1546.03,1307.61 1547.81,1307.61 1549.59,1307.61 1551.38,1307.61 1553.16,1307.62 1554.94,1307.62 1556.72,1307.62 1558.51,1307.62 1560.29,1307.62 1562.07,1307.62 1563.85,1307.62 1565.64,1307.62 1567.42,1307.62 1569.2,1307.63 1570.98,1307.63 1572.77,1307.63 1574.55,1307.63 1576.33,1307.63 1578.11,1307.63 1579.9,1307.63 1581.68,1307.63 1583.46,1307.63 1585.24,1307.64 1587.03,1307.64 1588.81,1307.64 1590.59,1307.64 1592.37,1307.64 1594.16,1307.64 1595.94,1307.64 1597.72,1307.64 1599.5,1307.65 1601.29,1307.65 1603.07,1307.65 1604.85,1307.65 1606.63,1307.65 1608.42,1307.65 1610.2,1307.65 1611.98,1307.65 1613.76,1307.65 1615.55,1307.66 1617.33,1307.66 1619.11,1307.66 1620.89,1307.66 1622.68,1307.66 1624.46,1307.66 1626.24,1307.66 1628.02,1307.66 1629.81,1307.67 1631.59,1307.67 1633.37,1307.67 1635.15,1307.67 1636.94,1307.67 1638.72,1307.67 1640.5,1307.67 1642.28,1307.67 1644.07,1307.67 1645.85,1307.68 1647.63,1307.68 1649.41,1307.68 1651.2,1307.68 1652.98,1307.68 1654.76,1307.68 1656.54,1307.68 1658.33,1307.68 1660.11,1307.69 1661.89,1307.69 1663.67,1307.69 1665.46,1307.69 1667.24,1307.69 1669.02,1307.69 1670.8,1307.69 1672.59,1307.69 1674.37,1307.69 1676.15,1307.7 1677.93,1307.7 1679.72,1307.7 1681.5,1307.7 1683.28,1307.7 1685.06,1307.7 1686.85,1307.7 1688.63,1307.7 1690.41,1307.7 1692.19,1307.71 1693.98,1307.71 1695.76,1307.71 1697.54,1307.71 1699.32,1307.71 1701.11,1307.71 1702.89,1307.71 1704.67,1307.71 1706.45,1307.72 1708.24,1307.72 1710.02,1307.72 1711.8,1307.72 1713.58,1307.72 1715.37,1307.72 1717.15,1307.72 1718.93,1307.72 1720.71,1307.72 1722.5,1307.73 1724.28,1307.73 1726.06,1307.73 1727.84,1307.73 1729.63,1307.73 1731.41,1307.73 1733.19,1307.73 1734.97,1307.73 1736.76,1307.73 1738.54,1307.74 1740.32,1307.74 1742.1,1307.74 1743.89,1307.74 1745.67,1307.74 1747.45,1307.74 1749.23,1307.74 1751.02,1307.74 1752.8,1307.75 1754.58,1307.75 1756.36,1307.75 1758.15,1307.75 1759.93,1307.75 1761.71,1307.75 1763.49,1307.75 1765.28,1307.75 1767.06,1307.75 1768.84,1307.76 1770.62,1307.76 1772.41,1307.76 1774.19,1307.76 1775.97,1307.76 1777.75,1307.76 1779.54,1307.76 1781.32,1307.76 1783.1,1307.76 1784.88,1307.77 1786.67,1307.77 1788.45,1307.77 1790.23,1307.77 1792.01,1307.77 1793.8,1307.77 1795.58,1307.77 1797.36,1307.77 1799.14,1307.78 1800.93,1307.78 1802.71,1306.87 1804.49,1306.88 1806.27,1306.88 1808.06,1306.88 1809.84,1306.88 1811.62,1306.88 1813.4,1306.88 1815.19,1306.88 1816.97,1306.88 1818.75,1306.88 1820.53,1306.89 1822.32,1306.89 1824.1,1306.89 1825.88,1306.89 1827.66,1306.89 1829.45,1306.89 1831.23,1306.89 1833.01,1306.89 1834.79,1306.89 1836.58,1306.9 1838.36,1306.9 1840.14,1306.9 1841.92,1306.9 1843.71,1306.9 1845.49,1306.9 1847.27,1306.9 1849.05,1306.9 1850.84,1306.91 1852.62,1306.91 1854.4,1306.91 1856.18,1306.91 1857.97,1306.91 1859.75,1306.91 1861.53,1306.91 1863.31,1306.91 1865.1,1306.91 1866.88,1306.92 1868.66,1306.92 1870.44,1306.92 1872.23,1306.92 1874.01,1306.92 1875.79,1306.92 1877.57,1306.92 1879.36,1306.92 1881.14,1306.93 1882.92,1306.93 1884.7,1306.93 1886.49,1306.93 1888.27,1306.93 1890.05,1306.93 1891.83,1306.93 1893.62,1306.93 1895.4,1306.93 1897.18,1306.94 1898.96,1306.94 1900.75,1306.03 1902.53,1306.04 1904.31,1306.04 1906.09,1306.04 1907.88,1306.04 1909.66,1306.04 1911.44,1306.04 1913.22,1306.04 1915.01,1306.04 1916.79,1306.04 1918.57,1306.05 1920.35,1306.05 1922.14,1306.05 1923.92,1306.05 1925.7,1306.05 1927.48,1306.05 1929.27,1306.05 1931.05,1306.05 1932.83,1306.06 1934.61,1306.06 1936.4,1306.06 1938.18,1306.06 1939.96,1306.06 1941.74,1306.06 1943.53,1306.06 1945.31,1306.06 1947.09,1306.06 1948.87,1306.07 1950.66,1306.07 1952.44,1306.07 1954.22,1306.07 1956,1306.07 1957.79,1306.07 1959.57,1306.07 1961.35,1306.07 1963.13,1306.07 1964.92,1306.08 1966.7,1306.08 1968.48,1306.08 1970.26,1306.08 1972.05,1306.08 1973.83,1306.08 1975.61,1306.08 1977.39,1306.08 1979.18,1306.09 1980.96,1306.09 1982.74,1306.09 1984.52,1306.09 1986.31,1306.09 1988.09,1306.09 1989.87,1306.09 1991.65,1305.19 1993.44,1305.19 1995.22,1305.19 1997,1305.19 1998.78,1305.19 2000.57,1305.2 2002.35,1305.2 2004.13,1305.2 2005.91,1305.2 2007.7,1305.2 2009.48,1305.2 2011.26,1305.2 2013.04,1305.2 2014.83,1305.2 2016.61,1305.21 2018.39,1305.21 2020.17,1305.21 2021.96,1305.21 2023.74,1305.21 2025.52,1305.21 2027.3,1305.21 2029.09,1305.21 2030.87,1305.22 2032.65,1305.22 2034.43,1305.22 2036.22,1305.22 2038,1305.22 2039.78,1305.22 2041.56,1305.22 2043.35,1305.22 2045.13,1305.22 2046.91,1305.23 2048.69,1305.23 2050.48,1305.23 2052.26,1305.23 2054.04,1304.33 2055.82,1304.33 2057.61,1304.33 2059.39,1304.33 2061.17,1304.33 2062.95,1304.33 2064.74,1304.33 2066.52,1304.33 2068.3,1304.34 2070.08,1304.34 2071.87,1304.34 2073.65,1304.34 2075.43,1304.34 2077.21,1304.34 2079,1303.44 2080.78,1303.44 2082.56,1303.44 2084.34,1303.44 2086.13,1303.44 2087.91,1303.44 2089.69,1303.45 2091.47,1302.54 2093.26,1302.54 2095.04,1302.55 2096.82,1302.55 2098.6,1301.64 2100.39,1301.65 2102.17,1301.65 2103.95,1301.65 2105.73,1300.75 2107.52,1300.75 2109.3,1300.75 2111.08,1299.84 2112.86,1299.85 2114.65,1299.85 2116.43,1299.85 2118.21,1298.95 2119.99,1298.95 2121.78,1298.95 2123.56,1298.95 2125.34,1298.05 2127.12,1298.05 2128.91,1298.05 2130.69,1298.05 2132.47,1297.15 2134.25,1297.15 2136.04,1297.15 2137.82,1297.15 2139.6,1296.25 2141.38,1296.25 2143.17,1296.25 2144.95,1296.25 2146.73,1295.35 2148.51,1295.35 2150.3,1295.35 2152.08,1295.35 2153.86,1295.35 2155.64,1294.45 2157.43,1294.45 2159.21,1294.45 2160.99,1294.45 2162.77,1293.55 2164.56,1293.55 2166.34,1293.55 2168.12,1293.56 2169.9,1292.65 2171.69,1292.65 2173.47,1292.66 2175.25,1292.66 2177.03,1291.75 2178.82,1291.75 2180.6,1291.76 2182.38,1291.76 2184.16,1290.85 2185.95,1290.86 2187.73,1290.86 2189.51,1290.86 2191.29,1289.96 2193.08,1289.96 2194.86,1289.96 2196.64,1289.96 2198.42,1289.06 2200.21,1289.06 2201.99,1289.06 2203.77,1289.06 2205.56,1288.16 2207.34,1288.16 2209.12,1288.16 2210.9,1288.16 2212.69,1287.26 2214.47,1287.26 2216.25,1287.26 2218.03,1287.26 2219.81,1286.36 2221.6,1286.36 2223.38,1286.36 2225.16,1286.36 2226.95,1285.46 2228.73,1285.46 2230.51,1285.46 2232.29,1285.46 2234.08,1285.46 2235.86,1284.56 2237.64,1284.56 2239.42,1284.56 2241.21,1284.57 2242.99,1283.66 2244.77,1283.66 2246.55,1283.67 2248.34,1283.67 2250.12,1282.76 2251.9,1282.76 2253.68,1282.77 2255.47,1281.86 2257.25,1281.86 2259.03,1281.87 2260.81,1281.87 2262.6,1280.96 2264.38,1280.97 2266.16,1280.97 2267.94,1280.97 2269.73,1280.97 2271.51,1280.07 2273.29,1280.07 2275.07,1280.07 2276.86,1280.07 2278.64,1279.17 2280.42,1279.17 2282.2,1279.17 2283.99,1279.17 2285.77,1278.27 2287.55,1278.27 2289.33,1278.27 2291.12,1278.27 2292.9,1277.37 2294.68,1277.37 2296.46,1277.37 2298.25,1276.47 2300.03,1276.47 2301.81,1276.47 2303.59,1275.57 2305.38,1275.57 2307.16,1274.67 2308.94,1274.67 2308.94,1273.76 2310.72,1272.86 2312.51,1272.86 2314.29,1271.96 2314.29,1271.06 2316.07,1271.06 2317.85,1270.16 2319.64,1270.16 2319.64,1269.25 2321.42,1268.35 2323.2,1268.35 2324.98,1267.45 2324.98,1266.54 2326.77,1266.55 2328.55,1265.64 2330.33,1264.74 2332.11,1264.74 2332.11,1263.84 2333.9,1262.94 2335.68,1262.94 2337.46,1262.03 2339.24,1261.13 2341.03,1260.23 2342.81,1259.33 2342.81,1258.42 2344.59,1257.52 2346.37,1256.62 2348.16,1255.71 2349.94,1254.81 2351.72,1253.91 2351.72,1253.01 2353.5,1252.1 2355.29,1251.2 2355.29,1250.3 2357.07,1249.39 2358.85,1248.49 2358.85,1247.59 2360.63,1246.68 2362.42,1245.78 2364.2,1244.88 2364.2,1243.98 2365.98,1243.07 2367.76,1242.17 2369.55,1241.27 2371.33,1240.37 2371.33,1239.46 2373.11,1238.56 2374.89,1237.66 2376.68,1236.75 2378.46,1235.85 2380.24,1234.95 2382.02,1234.05 2383.81,1233.14 2385.59,1232.24 2387.37,1231.34 2387.37,1230.43 2389.15,1229.53 2390.94,1228.63 2390.94,1227.73 2390.94,1226.82 2392.72,1225.92 2392.72,1225.02 2394.5,1224.11 2394.5,1223.21 2396.28,1222.31 2396.28,1221.4 2398.07,1220.5 2398.07,1219.6 2398.07,1218.69 2399.85,1217.79 2399.85,1216.89 2401.63,1215.98 2401.63,1215.08 2403.41,1214.18 2403.41,1213.27 2405.2,1212.37 2405.2,1211.47 2406.98,1210.56 2406.98,1209.66 2408.76,1208.76 2408.76,1207.85 2410.54,1206.95 2410.54,1206.05 2412.33,1205.14 2412.33,1204.24 2414.11,1203.34 2414.11,1202.43 2415.89,1201.53 2415.89,1200.63 2417.67,1199.73 2417.67,1198.82 2419.46,1197.92 2421.24,1197.02 2423.02,1196.11 2423.02,1195.21 2424.8,1194.31 2426.59,1193.41 2426.59,1192.5 2426.59,1191.6 2428.37,1190.69 2428.37,1189.79 2430.15,1188.89 2430.15,1187.98 2430.15,1187.08 2431.93,1186.18 2431.93,1185.27 2431.93,1184.37 2433.72,1183.47 2433.72,1182.56 2433.72,1181.66 2435.5,1180.76 2435.5,1179.85 2435.5,1178.95 2437.28,1178.05 2437.28,1177.14 2437.28,1176.24 2437.28,1175.34 2439.06,1174.43 2439.06,1173.53 2439.06,1172.63 2439.06,1171.72 2440.85,1170.82 2440.85,1169.92 2440.85,1169.01 2440.85,1168.11 2442.63,1167.21 2442.63,1166.3 2442.63,1165.4 2442.63,1164.49 2444.41,1163.59 2444.41,1162.69 2444.41,1161.78 2446.19,1160.88 2446.19,1159.98 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1319.65,1307.46 1321.43,1307.46 1323.21,1307.47 1325,1307.47 1326.78,1307.47 1328.56,1307.47 1330.34,1307.47 1332.13,1307.47 1333.91,1307.47 1335.69,1307.47 1337.47,1307.47 1339.26,1307.48 1341.04,1307.48 1342.82,1307.48 1344.6,1307.48 1346.39,1307.48 1348.17,1307.48 1349.95,1307.48 1351.73,1307.48 1353.52,1307.49 1355.3,1307.49 1357.08,1307.49 1358.86,1307.49 1360.65,1307.49 1362.43,1307.49 1364.21,1307.49 1365.99,1307.49 1367.78,1307.49 1369.56,1307.5 1371.34,1307.5 1373.12,1307.5 1374.91,1307.5 1376.69,1307.5 1378.47,1307.5 1380.25,1307.5 1382.04,1307.5 1383.82,1307.5 1385.6,1307.51 1387.38,1307.51 1389.17,1307.51 1390.95,1307.51 1392.73,1307.51 1394.51,1307.51 1396.3,1307.51 1398.08,1307.51 1399.86,1307.52 1401.64,1307.52 1403.43,1307.52 1405.21,1307.52 1406.99,1307.52 1408.77,1307.52 1410.56,1307.52 1412.34,1307.52 1414.12,1307.52 1415.9,1307.53 1417.69,1307.53 1419.47,1307.53 1421.25,1307.53 1423.03,1307.53 1424.82,1307.53 1426.6,1307.53 1428.38,1307.53 1430.16,1307.54 1431.95,1307.54 1433.73,1307.54 1435.51,1307.54 1437.29,1307.54 1439.08,1307.54 1440.86,1307.54 1442.64,1307.54 1444.42,1307.54 1446.21,1307.55 1447.99,1307.55 1449.77,1307.55 1451.55,1307.55 1453.34,1307.55 1455.12,1307.55 1456.9,1307.55 1458.68,1307.55 1460.47,1307.55 1462.25,1307.56 1464.03,1307.56 1465.81,1307.56 1467.6,1307.56 1469.38,1307.56 1471.16,1307.56 1472.94,1307.56 1474.73,1307.56 1476.51,1307.57 1478.29,1307.57 1480.07,1307.57 1481.86,1307.57 1483.64,1307.57 1485.42,1307.57 1487.2,1307.57 1488.99,1307.57 1490.77,1307.57 1492.55,1307.58 1494.33,1307.58 1496.12,1307.58 1497.9,1307.58 1499.68,1307.58 1501.47,1307.58 1503.25,1307.58 1505.03,1307.58 1506.81,1307.58 1508.6,1307.59 1510.38,1307.59 1512.16,1307.59 1513.94,1307.59 1515.72,1307.59 1517.51,1307.59 1519.29,1307.59 1521.07,1307.59 1522.86,1307.6 1524.64,1307.6 1526.42,1307.6 1528.2,1307.6 1529.99,1307.6 1531.77,1307.6 1533.55,1307.6 1535.33,1307.6 1537.12,1307.6 1538.9,1307.61 1540.68,1307.61 1542.46,1307.61 1544.25,1307.61 1546.03,1307.61 1547.81,1307.61 1549.59,1307.61 1551.38,1307.61 1553.16,1307.62 1554.94,1307.62 1556.72,1307.62 1558.51,1307.62 1560.29,1307.62 1562.07,1307.62 1563.85,1307.62 1565.64,1307.62 1567.42,1307.62 1569.2,1307.63 1570.98,1307.63 1572.77,1307.63 1574.55,1307.63 1576.33,1307.63 1578.11,1307.63 1579.9,1307.63 1581.68,1307.63 1583.46,1307.63 1585.24,1307.64 1587.03,1307.64 1588.81,1307.64 1590.59,1307.64 1592.37,1307.64 1594.16,1307.64 1595.94,1307.64 1597.72,1307.64 1599.5,1307.65 1601.29,1307.65 1603.07,1307.65 1604.85,1307.65 1606.63,1307.65 1608.42,1307.65 1610.2,1307.65 1611.98,1307.65 1613.76,1307.65 1615.55,1307.66 1617.33,1307.66 1619.11,1307.66 1620.89,1307.66 1622.68,1307.66 1624.46,1307.66 1626.24,1307.66 1628.02,1307.66 1629.81,1307.67 1631.59,1307.67 1633.37,1307.67 1635.15,1307.67 1636.94,1307.67 1638.72,1307.67 1640.5,1307.67 1642.28,1307.67 1644.07,1307.67 1645.85,1307.68 1647.63,1307.68 1649.41,1307.68 1651.2,1307.68 1652.98,1307.68 1654.76,1307.68 1656.54,1307.68 1658.33,1307.68 1660.11,1307.69 1661.89,1307.69 1663.67,1307.69 1665.46,1307.69 1667.24,1307.69 1669.02,1307.69 1670.8,1307.69 1672.59,1307.69 1674.37,1307.69 1676.15,1307.7 1677.93,1307.7 1679.72,1307.7 1681.5,1307.7 1683.28,1307.7 1685.06,1307.7 1686.85,1307.7 1688.63,1307.7 1690.41,1307.7 1692.19,1307.71 1693.98,1307.71 1695.76,1307.71 1697.54,1307.71 1699.32,1307.71 1701.11,1307.71 1702.89,1307.71 1704.67,1307.71 1706.45,1307.72 1708.24,1307.72 1710.02,1307.72 1711.8,1307.72 1713.58,1307.72 1715.37,1307.72 1717.15,1307.72 1718.93,1307.72 1720.71,1307.72 1722.5,1307.73 1724.28,1307.73 1726.06,1307.73 1727.84,1307.73 1729.63,1307.73 1731.41,1307.73 1733.19,1307.73 1734.97,1307.73 1736.76,1307.73 1738.54,1307.74 1740.32,1307.74 1742.1,1307.74 1743.89,1307.74 1745.67,1307.74 1747.45,1307.74 1749.23,1307.74 1751.02,1307.74 1752.8,1307.75 1754.58,1307.75 1756.36,1307.75 1758.15,1307.75 1759.93,1307.75 1761.71,1307.75 1763.49,1307.75 1765.28,1307.75 1767.06,1307.75 1768.84,1307.76 1770.62,1307.76 1772.41,1307.76 1774.19,1307.76 1775.97,1307.76 1777.75,1307.76 1779.54,1307.76 1781.32,1307.76 1783.1,1307.76 1784.88,1307.77 1786.67,1307.77 1788.45,1307.77 1790.23,1307.77 1792.01,1307.77 1793.8,1307.77 1795.58,1307.77 1797.36,1307.77 1799.14,1307.78 1800.93,1307.78 1802.71,1306.87 1804.49,1306.88 1806.27,1306.88 1808.06,1306.88 1809.84,1306.88 1811.62,1306.88 1813.4,1306.88 1815.19,1306.88 1816.97,1306.88 1818.75,1306.88 1820.53,1306.89 1822.32,1306.89 1824.1,1306.89 1825.88,1306.89 1827.66,1306.89 1829.45,1306.89 1831.23,1306.89 1833.01,1306.89 1834.79,1306.89 1836.58,1306.9 1838.36,1306.9 1840.14,1306.9 1841.92,1306.9 1843.71,1306.9 1845.49,1306.9 1847.27,1306.9 1849.05,1306.9 1850.84,1306.91 1852.62,1306.91 1854.4,1306.91 1856.18,1306.91 1857.97,1306.91 1859.75,1306.91 1861.53,1306.91 1863.31,1306.91 1865.1,1306.91 1866.88,1306.92 1868.66,1306.92 1870.44,1306.92 1872.23,1306.92 1874.01,1306.92 1875.79,1306.92 1877.57,1306.92 1879.36,1306.92 1881.14,1306.93 1882.92,1306.93 1884.7,1306.93 1886.49,1306.93 1888.27,1306.93 1890.05,1306.93 1891.83,1306.93 1893.62,1306.93 1895.4,1306.93 1897.18,1306.94 1898.96,1306.94 1900.75,1306.03 1902.53,1306.04 1904.31,1306.04 1906.09,1306.04 1907.88,1306.04 1909.66,1306.04 1911.44,1306.04 1913.22,1306.04 1915.01,1306.04 1916.79,1306.04 1918.57,1306.05 1920.35,1306.05 1922.14,1306.05 1923.92,1306.05 1925.7,1306.05 1927.48,1306.05 1929.27,1306.05 1931.05,1306.05 1932.83,1306.06 1934.61,1306.06 1936.4,1306.06 1938.18,1306.06 1939.96,1306.06 1941.74,1306.06 1943.53,1306.06 1945.31,1306.06 1947.09,1306.06 1948.87,1306.07 1950.66,1306.07 1952.44,1306.07 1954.22,1306.07 1956,1306.07 1957.79,1306.07 1959.57,1306.07 1961.35,1306.07 1963.13,1306.07 1964.92,1306.08 1966.7,1306.08 1968.48,1306.08 1970.26,1306.08 1972.05,1306.08 1973.83,1306.08 1975.61,1306.08 1977.39,1306.08 1979.18,1306.09 1980.96,1306.09 1982.74,1306.09 1984.52,1306.09 1986.31,1306.09 1988.09,1306.09 1989.87,1306.09 1991.65,1305.19 1993.44,1305.19 1995.22,1305.19 1997,1305.19 1998.78,1305.19 2000.57,1305.2 2002.35,1305.2 2004.13,1305.2 2005.91,1305.2 2007.7,1305.2 2009.48,1305.2 2011.26,1305.2 2013.04,1305.2 2014.83,1305.2 2016.61,1305.21 2018.39,1305.21 2020.17,1305.21 2021.96,1305.21 2023.74,1305.21 2025.52,1305.21 2027.3,1305.21 2029.09,1305.21 2030.87,1305.22 2032.65,1305.22 2034.43,1305.22 2036.22,1305.22 2038,1305.22 2039.78,1305.22 2041.56,1305.22 2043.35,1305.22 2045.13,1305.22 2046.91,1305.23 2048.69,1305.23 2050.48,1305.23 2052.26,1305.23 2054.04,1304.33 2055.82,1304.33 2057.61,1304.33 2059.39,1304.33 2061.17,1304.33 2062.95,1304.33 2064.74,1304.33 2066.52,1304.33 2068.3,1304.34 2070.08,1304.34 2071.87,1304.34 2073.65,1304.34 2075.43,1304.34 2077.21,1304.34 2079,1303.44 2080.78,1303.44 2082.56,1303.44 2084.34,1303.44 2086.13,1303.44 2087.91,1303.44 2089.69,1303.45 2091.47,1302.54 2093.26,1302.54 2095.04,1302.55 2096.82,1302.55 2098.6,1301.64 2100.39,1301.65 2102.17,1301.65 2103.95,1301.65 2105.73,1300.75 2107.52,1300.75 2109.3,1300.75 2111.08,1299.84 2112.86,1299.85 2114.65,1299.85 2116.43,1299.85 2118.21,1298.95 2119.99,1298.95 2121.78,1298.95 2123.56,1298.95 2125.34,1298.05 2127.12,1298.05 2128.91,1298.05 2130.69,1298.05 2132.47,1297.15 2134.25,1297.15 2136.04,1297.15 2137.82,1297.15 2139.6,1296.25 2141.38,1296.25 2143.17,1296.25 2144.95,1296.25 2146.73,1295.35 2148.51,1295.35 2150.3,1295.35 2152.08,1295.35 2153.86,1295.35 2155.64,1294.45 2157.43,1294.45 2159.21,1294.45 2160.99,1294.45 2162.77,1293.55 2164.56,1293.55 2166.34,1293.55 2168.12,1293.56 2169.9,1292.65 2171.69,1292.65 2173.47,1292.66 2175.25,1292.66 2177.03,1291.75 2178.82,1291.75 2180.6,1291.76 2182.38,1291.76 2184.16,1290.85 2185.95,1290.86 2187.73,1290.86 2189.51,1290.86 2191.29,1289.96 2193.08,1289.96 2194.86,1289.96 2196.64,1289.96 2198.42,1289.06 2200.21,1289.06 2201.99,1289.06 2203.77,1289.06 2205.56,1288.16 2207.34,1288.16 2209.12,1288.16 2210.9,1288.16 2212.69,1287.26 2214.47,1287.26 2216.25,1287.26 2218.03,1287.26 2219.81,1286.36 2221.6,1286.36 2223.38,1286.36 2225.16,1286.36 2226.95,1285.46 2228.73,1285.46 2230.51,1285.46 2232.29,1285.46 2234.08,1285.46 2235.86,1284.56 2237.64,1284.56 2239.42,1284.56 2241.21,1284.57 2242.99,1283.66 2244.77,1283.66 2246.55,1283.67 2248.34,1283.67 2250.12,1282.76 2251.9,1282.76 2253.68,1282.77 2255.47,1281.86 2257.25,1281.86 2259.03,1281.87 2260.81,1281.87 2262.6,1280.96 2264.38,1280.97 2266.16,1280.97 2267.94,1280.97 2269.73,1280.97 2271.51,1280.07 2273.29,1280.07 2275.07,1280.07 2276.86,1280.07 2278.64,1279.17 2280.42,1279.17 2282.2,1279.17 2283.99,1279.17 2285.77,1278.27 2287.55,1278.27 2289.33,1278.27 2291.12,1278.27 2292.9,1277.37 2294.68,1277.37 2296.46,1277.37 2298.25,1276.47 2300.03,1276.47 2301.81,1276.47 2303.59,1275.57 2305.38,1275.57 2307.16,1274.67 2308.94,1274.67 2308.94,1273.76 2310.72,1272.86 2312.51,1272.86 2314.29,1271.96 2314.29,1271.06 2316.07,1271.06 2317.85,1270.16 2319.64,1270.16 2319.64,1269.25 2321.42,1268.35 2323.2,1268.35 2324.98,1267.45 2324.98,1266.54 2326.77,1266.55 2328.55,1265.64 2330.33,1264.74 2332.11,1264.74 2332.11,1263.84 2333.9,1262.94 2335.68,1262.94 2337.46,1262.03 2339.24,1261.13 2341.03,1260.23 2342.81,1259.33 2342.81,1258.42 2344.59,1257.52 2346.37,1256.62 2348.16,1255.71 2349.94,1254.81 2351.72,1253.91 2351.72,1253.01 2353.5,1252.1 2355.29,1251.2 2355.29,1250.3 2357.07,1249.39 2358.85,1248.49 2358.85,1247.59 2360.63,1246.68 2362.42,1245.78 2364.2,1244.88 2364.2,1243.98 2365.98,1243.07 2367.76,1242.17 2369.55,1241.27 2371.33,1240.37 2371.33,1239.46 2373.11,1238.56 2374.89,1237.66 2376.68,1236.75 2378.46,1235.85 2380.24,1234.95 2382.02,1234.05 2383.81,1233.14 2385.59,1232.24 2387.37,1231.34 2387.37,1230.43 2389.15,1229.53 2390.94,1228.63 2390.94,1227.73 2390.94,1226.82 2392.72,1225.92 2392.72,1225.02 2394.5,1224.11 2394.5,1223.21 2396.28,1222.31 2396.28,1221.4 2398.07,1220.5 2398.07,1219.6 2398.07,1218.69 2399.85,1217.79 2399.85,1216.89 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870.458,1307.17 872.24,1307.17 874.023,1307.17 875.805,1307.17 877.588,1307.17 879.37,1307.18 881.153,1307.18 882.935,1307.18 884.718,1307.18 886.5,1307.18 888.283,1307.18 890.065,1307.18 891.848,1307.18 893.63,1307.19 895.413,1307.19 897.195,1307.19 898.978,1307.19 900.76,1307.19 902.543,1307.19 904.325,1307.19 906.108,1307.19 907.89,1307.19 909.673,1307.2 911.455,1307.2 913.238,1307.2 915.02,1307.2 916.803,1307.2 918.585,1307.2 920.368,1307.2 922.15,1307.2 923.933,1307.21 925.715,1307.21 927.498,1307.21 929.28,1307.21 931.063,1307.21 932.845,1307.21 934.628,1307.21 936.411,1307.21 938.193,1307.21 939.976,1307.22 941.758,1307.22 943.54,1307.22 945.323,1307.22 947.106,1307.22 948.888,1307.22 950.671,1307.22 952.453,1307.22 954.236,1307.22 956.018,1307.23 957.801,1307.23 959.583,1307.23 961.366,1307.23 963.148,1307.23 964.931,1307.23 966.713,1307.23 968.495,1307.23 970.278,1307.24 972.061,1307.24 973.843,1307.24 975.626,1307.24 977.408,1307.24 979.191,1307.24 980.973,1307.24 982.756,1307.24 984.538,1307.24 986.321,1307.25 988.103,1307.25 989.886,1307.25 991.668,1307.25 993.451,1307.25 995.233,1307.25 997.016,1307.25 998.798,1307.25 1000.58,1307.26 1002.36,1307.26 1004.15,1307.26 1005.93,1307.26 1007.71,1307.26 1009.49,1307.26 1011.28,1307.26 1013.06,1307.26 1014.84,1307.26 1016.62,1307.27 1018.41,1307.27 1020.19,1307.27 1021.97,1307.27 1023.75,1307.27 1025.54,1307.27 1027.32,1307.27 1029.1,1307.27 1030.88,1307.27 1032.67,1307.28 1034.45,1307.28 1036.23,1307.28 1038.01,1307.28 1039.8,1307.28 1041.58,1307.28 1043.36,1307.28 1045.14,1307.28 1046.93,1307.29 1048.71,1307.29 1050.49,1307.29 1052.27,1307.29 1054.06,1307.29 1055.84,1307.29 1057.62,1307.29 1059.4,1307.29 1061.19,1307.29 1062.97,1307.3 1064.75,1307.3 1066.53,1307.3 1068.32,1307.3 1070.1,1307.3 1071.88,1307.3 1073.66,1307.3 1075.45,1307.3 1077.23,1307.31 1079.01,1307.31 1080.79,1307.31 1082.58,1307.31 1084.36,1307.31 1086.14,1307.31 1087.92,1307.31 1089.71,1307.31 1091.49,1307.31 1093.27,1307.32 1095.05,1307.32 1096.84,1307.32 1098.62,1307.32 1100.4,1307.32 1102.18,1307.32 1103.97,1307.32 1105.75,1307.32 1107.53,1307.32 1109.31,1307.33 1111.1,1307.33 1112.88,1307.33 1114.66,1307.33 1116.44,1307.33 1118.23,1307.33 1120.01,1307.33 1121.79,1307.33 1123.57,1307.34 1125.36,1307.34 1127.14,1307.34 1128.92,1307.34 1130.7,1307.34 1132.49,1307.34 1134.27,1307.34 1136.05,1307.34 1137.83,1307.34 1139.62,1307.35 1141.4,1307.35 1143.18,1307.35 1144.96,1307.35 1146.75,1307.35 1148.53,1307.35 1150.31,1307.35 1152.09,1307.35 1153.88,1307.36 1155.66,1307.36 1157.44,1307.36 1159.22,1307.36 1161.01,1307.36 1162.79,1307.36 1164.57,1307.36 1166.35,1307.36 1168.14,1307.36 1169.92,1307.37 1171.7,1307.37 1173.48,1307.37 1175.27,1307.37 1177.05,1307.37 1178.83,1307.37 1180.61,1307.37 1182.4,1307.37 1184.18,1307.37 1185.96,1307.38 1187.74,1307.38 1189.53,1307.38 1191.31,1307.38 1193.09,1307.38 1194.87,1307.38 1196.66,1307.38 1198.44,1307.38 1200.22,1307.39 1202,1307.39 1203.79,1307.39 1205.57,1307.39 1207.35,1307.39 1209.13,1307.39 1210.92,1307.39 1212.7,1307.39 1214.48,1307.39 1216.26,1307.4 1218.05,1307.4 1219.83,1307.4 1221.61,1307.4 1223.39,1307.4 1225.18,1307.4 1226.96,1307.4 1228.74,1307.4 1230.52,1307.4 1232.31,1307.41 1234.09,1307.41 1235.87,1307.41 1237.65,1307.41 1239.44,1307.41 1241.22,1307.41 1243,1307.41 1244.78,1307.41 1246.57,1307.42 1248.35,1307.42 1250.13,1307.42 1251.91,1307.42 1253.7,1307.42 1255.48,1307.42 1257.26,1307.42 1259.04,1307.42 1260.83,1307.42 1262.61,1307.43 1264.39,1307.43 1266.17,1307.43 1267.96,1307.43 1269.74,1307.43 1271.52,1307.43 1273.3,1307.43 1275.09,1307.43 1276.87,1307.44 1278.65,1307.44 1280.43,1307.44 1282.22,1307.44 1284,1307.44 1285.78,1307.44 1287.56,1307.44 1289.35,1307.44 1291.13,1307.44 1292.91,1307.45 1294.69,1307.45 1296.48,1307.45 1298.26,1307.45 1300.04,1307.45 1301.82,1307.45 1303.61,1307.45 1305.39,1307.45 1307.17,1307.45 1308.95,1307.46 1310.74,1307.46 1312.52,1307.46 1314.3,1307.46 1316.08,1307.46 1317.87,1307.46 1319.65,1307.46 1321.43,1307.46 1323.21,1307.47 1325,1307.47 1326.78,1307.47 1328.56,1307.47 1330.34,1307.47 1332.13,1307.47 1333.91,1307.47 1335.69,1307.47 1337.47,1307.47 1339.26,1307.48 1341.04,1307.48 1342.82,1307.48 1344.6,1307.48 1346.39,1307.48 1348.17,1307.48 1349.95,1307.48 1351.73,1307.48 1353.52,1307.49 1355.3,1307.49 1357.08,1307.49 1358.86,1307.49 1360.65,1307.49 1362.43,1307.49 1364.21,1307.49 1365.99,1307.49 1367.78,1307.49 1369.56,1307.5 1371.34,1307.5 1373.12,1307.5 1374.91,1307.5 1376.69,1307.5 1378.47,1307.5 1380.25,1307.5 1382.04,1307.5 1383.82,1307.5 1385.6,1307.51 1387.38,1307.51 1389.17,1307.51 1390.95,1307.51 1392.73,1307.51 1394.51,1307.51 1396.3,1307.51 1398.08,1307.51 1399.86,1307.52 1401.64,1307.52 1403.43,1307.52 1405.21,1307.52 1406.99,1307.52 1408.77,1307.52 1410.56,1307.52 1412.34,1307.52 1414.12,1307.52 1415.9,1307.53 1417.69,1307.53 1419.47,1307.53 1421.25,1307.53 1423.03,1307.53 1424.82,1307.53 1426.6,1307.53 1428.38,1307.53 1430.16,1307.54 1431.95,1307.54 1433.73,1307.54 1435.51,1307.54 1437.29,1307.54 1439.08,1307.54 1440.86,1307.54 1442.64,1307.54 1444.42,1307.54 1446.21,1307.55 1447.99,1307.55 1449.77,1307.55 1451.55,1307.55 1453.34,1307.55 1455.12,1307.55 1456.9,1307.55 1458.68,1307.55 1460.47,1307.55 1462.25,1307.56 1464.03,1307.56 1465.81,1307.56 1467.6,1307.56 1469.38,1307.56 1471.16,1307.56 1472.94,1307.56 1474.73,1307.56 1476.51,1307.57 1478.29,1307.57 1480.07,1307.57 1481.86,1307.57 1483.64,1307.57 1485.42,1307.57 1487.2,1307.57 1488.99,1307.57 1490.77,1307.57 1492.55,1307.58 1494.33,1307.58 1496.12,1307.58 1497.9,1307.58 1499.68,1307.58 1501.47,1307.58 1503.25,1307.58 1505.03,1307.58 1506.81,1307.58 1508.6,1307.59 1510.38,1307.59 1512.16,1307.59 1513.94,1307.59 1515.72,1307.59 1517.51,1307.59 1519.29,1307.59 1521.07,1307.59 1522.86,1307.6 1524.64,1307.6 1526.42,1307.6 1528.2,1307.6 1529.99,1307.6 1531.77,1307.6 1533.55,1307.6 1535.33,1307.6 1537.12,1307.6 1538.9,1307.61 1540.68,1307.61 1542.46,1307.61 1544.25,1307.61 1546.03,1307.61 1547.81,1307.61 1549.59,1307.61 1551.38,1307.61 1553.16,1307.62 1554.94,1307.62 1556.72,1307.62 1558.51,1307.62 1560.29,1307.62 1562.07,1307.62 1563.85,1307.62 1565.64,1307.62 1567.42,1307.62 1569.2,1307.63 1570.98,1307.63 1572.77,1307.63 1574.55,1307.63 1576.33,1307.63 1578.11,1307.63 1579.9,1307.63 1581.68,1307.63 1583.46,1307.63 1585.24,1307.64 1587.03,1307.64 1588.81,1307.64 1590.59,1307.64 1592.37,1307.64 1594.16,1307.64 1595.94,1307.64 1597.72,1307.64 1599.5,1307.65 1601.29,1307.65 1603.07,1307.65 1604.85,1307.65 1606.63,1307.65 1608.42,1307.65 1610.2,1307.65 1611.98,1307.65 1613.76,1307.65 1615.55,1307.66 1617.33,1307.66 1619.11,1307.66 1620.89,1307.66 1622.68,1307.66 1624.46,1307.66 1626.24,1307.66 1628.02,1307.66 1629.81,1307.67 1631.59,1307.67 1633.37,1307.67 1635.15,1307.67 1636.94,1307.67 1638.72,1307.67 1640.5,1307.67 1642.28,1307.67 1644.07,1307.67 1645.85,1307.68 1647.63,1307.68 1649.41,1307.68 1651.2,1307.68 1652.98,1307.68 1654.76,1307.68 1656.54,1307.68 1658.33,1307.68 1660.11,1307.69 1661.89,1307.69 1663.67,1307.69 1665.46,1307.69 1667.24,1307.69 1669.02,1307.69 1670.8,1307.69 1672.59,1307.69 1674.37,1307.69 1676.15,1307.7 1677.93,1307.7 1679.72,1307.7 1681.5,1307.7 1683.28,1307.7 1685.06,1307.7 1686.85,1307.7 1688.63,1307.7 1690.41,1307.7 1692.19,1307.71 1693.98,1307.71 1695.76,1307.71 1697.54,1307.71 1699.32,1307.71 1701.11,1307.71 1702.89,1307.71 1704.67,1307.71 1706.45,1307.72 1708.24,1307.72 1710.02,1307.72 1711.8,1307.72 1713.58,1307.72 1715.37,1307.72 1717.15,1307.72 1718.93,1307.72 1720.71,1307.72 1722.5,1307.73 1724.28,1307.73 1726.06,1307.73 1727.84,1307.73 1729.63,1307.73 1731.41,1307.73 1733.19,1307.73 1734.97,1307.73 1736.76,1307.73 1738.54,1307.74 1740.32,1307.74 1742.1,1307.74 1743.89,1307.74 1745.67,1307.74 1747.45,1307.74 1749.23,1307.74 1751.02,1307.74 1752.8,1307.75 1754.58,1307.75 1756.36,1307.75 1758.15,1307.75 1759.93,1307.75 1761.71,1307.75 1763.49,1307.75 1765.28,1307.75 1767.06,1307.75 1768.84,1307.76 1770.62,1307.76 1772.41,1307.76 1774.19,1307.76 1775.97,1307.76 1777.75,1307.76 1779.54,1307.76 1781.32,1307.76 1783.1,1307.76 1784.88,1307.77 1786.67,1307.77 1788.45,1307.77 1790.23,1307.77 1792.01,1307.77 1793.8,1307.77 1795.58,1307.77 1797.36,1307.77 1799.14,1307.78 1800.93,1307.78 1802.71,1306.87 1804.49,1306.88 1806.27,1306.88 1808.06,1306.88 1809.84,1306.88 1811.62,1306.88 1813.4,1306.88 1815.19,1306.88 1816.97,1306.88 1818.75,1306.88 1820.53,1306.89 1822.32,1306.89 1824.1,1306.89 1825.88,1306.89 1827.66,1306.89 1829.45,1306.89 1831.23,1306.89 1833.01,1306.89 1834.79,1306.89 1836.58,1306.9 1838.36,1306.9 1840.14,1306.9 1841.92,1306.9 1843.71,1306.9 1845.49,1306.9 1847.27,1306.9 1849.05,1306.9 1850.84,1306.91 1852.62,1306.91 1854.4,1306.91 1856.18,1306.91 1857.97,1306.91 1859.75,1306.91 1861.53,1306.91 1863.31,1306.91 1865.1,1306.91 1866.88,1306.92 1868.66,1306.92 1870.44,1306.92 1872.23,1306.92 1874.01,1306.92 1875.79,1306.92 1877.57,1306.92 1879.36,1306.92 1881.14,1306.93 1882.92,1306.93 1884.7,1306.93 1886.49,1306.93 1888.27,1306.93 1890.05,1306.93 1891.83,1306.93 1893.62,1306.93 1895.4,1306.93 1897.18,1306.94 1898.96,1306.94 1900.75,1306.03 1902.53,1306.04 1904.31,1306.04 1906.09,1306.04 1907.88,1306.04 1909.66,1306.04 1911.44,1306.04 1913.22,1306.04 1915.01,1306.04 1916.79,1306.04 1918.57,1306.05 1920.35,1306.05 1922.14,1306.05 1923.92,1306.05 1925.7,1306.05 1927.48,1306.05 1929.27,1306.05 1931.05,1306.05 1932.83,1306.06 1934.61,1306.06 1936.4,1306.06 1938.18,1306.06 1939.96,1306.06 1941.74,1306.06 1943.53,1306.06 1945.31,1306.06 1947.09,1306.06 1948.87,1306.07 1950.66,1306.07 1952.44,1306.07 1954.22,1306.07 1956,1306.07 1957.79,1306.07 1959.57,1306.07 1961.35,1306.07 1963.13,1306.07 1964.92,1306.08 1966.7,1306.08 1968.48,1306.08 1970.26,1306.08 1972.05,1306.08 1973.83,1306.08 1975.61,1306.08 1977.39,1306.08 1979.18,1306.09 1980.96,1306.09 1982.74,1306.09 1984.52,1306.09 1986.31,1306.09 1988.09,1306.09 1989.87,1306.09 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982.756,1307.24 984.538,1307.24 986.321,1307.25 988.103,1307.25 989.886,1307.25 991.668,1307.25 993.451,1307.25 995.233,1307.25 997.016,1307.25 998.798,1307.25 1000.58,1307.26 1002.36,1307.26 1004.15,1307.26 1005.93,1307.26 1007.71,1307.26 1009.49,1307.26 1011.28,1307.26 1013.06,1307.26 1014.84,1307.26 1016.62,1307.27 1018.41,1307.27 1020.19,1307.27 1021.97,1307.27 1023.75,1307.27 1025.54,1307.27 1027.32,1307.27 1029.1,1307.27 1030.88,1307.27 1032.67,1307.28 1034.45,1307.28 1036.23,1307.28 1038.01,1307.28 1039.8,1307.28 1041.58,1307.28 1043.36,1307.28 1045.14,1307.28 1046.93,1307.29 1048.71,1307.29 1050.49,1307.29 1052.27,1307.29 1054.06,1307.29 1055.84,1307.29 1057.62,1307.29 1059.4,1307.29 1061.19,1307.29 1062.97,1307.3 1064.75,1307.3 1066.53,1307.3 1068.32,1307.3 1070.1,1307.3 1071.88,1307.3 1073.66,1307.3 1075.45,1307.3 1077.23,1307.31 1079.01,1307.31 1080.79,1307.31 1082.58,1307.31 1084.36,1307.31 1086.14,1307.31 1087.92,1307.31 1089.71,1307.31 1091.49,1307.31 1093.27,1307.32 1095.05,1307.32 1096.84,1307.32 1098.62,1307.32 1100.4,1307.32 1102.18,1307.32 1103.97,1307.32 1105.75,1307.32 1107.53,1307.32 1109.31,1307.33 1111.1,1307.33 1112.88,1307.33 1114.66,1307.33 1116.44,1307.33 1118.23,1307.33 1120.01,1307.33 1121.79,1307.33 1123.57,1307.34 1125.36,1307.34 1127.14,1307.34 1128.92,1307.34 1130.7,1307.34 1132.49,1307.34 1134.27,1307.34 1136.05,1307.34 1137.83,1307.34 1139.62,1307.35 1141.4,1307.35 1143.18,1307.35 1144.96,1307.35 1146.75,1307.35 1148.53,1307.35 1150.31,1307.35 1152.09,1307.35 1153.88,1307.36 1155.66,1307.36 1157.44,1307.36 1159.22,1307.36 1161.01,1307.36 1162.79,1307.36 1164.57,1307.36 1166.35,1307.36 1168.14,1307.36 1169.92,1307.37 1171.7,1307.37 1173.48,1307.37 1175.27,1307.37 1177.05,1307.37 1178.83,1307.37 1180.61,1307.37 1182.4,1307.37 1184.18,1307.37 1185.96,1307.38 1187.74,1307.38 1189.53,1307.38 1191.31,1307.38 1193.09,1307.38 1194.87,1307.38 1196.66,1307.38 1198.44,1307.38 1200.22,1307.39 1202,1307.39 1203.79,1307.39 1205.57,1307.39 1207.35,1307.39 1209.13,1307.39 1210.92,1307.39 1212.7,1307.39 1214.48,1307.39 1216.26,1307.4 1218.05,1307.4 1219.83,1307.4 1221.61,1307.4 1223.39,1307.4 1225.18,1307.4 1226.96,1307.4 1228.74,1307.4 1230.52,1307.4 1232.31,1307.41 1234.09,1307.41 1235.87,1307.41 1237.65,1307.41 1239.44,1307.41 1241.22,1307.41 1243,1307.41 1244.78,1307.41 1246.57,1307.42 1248.35,1307.42 1250.13,1307.42 1251.91,1307.42 1253.7,1307.42 1255.48,1307.42 1257.26,1307.42 1259.04,1307.42 1260.83,1307.42 1262.61,1307.43 1264.39,1307.43 1266.17,1307.43 1267.96,1307.43 1269.74,1307.43 1271.52,1307.43 1273.3,1307.43 1275.09,1307.43 1276.87,1307.44 1278.65,1307.44 1280.43,1307.44 1282.22,1307.44 1284,1307.44 1285.78,1307.44 1287.56,1307.44 1289.35,1307.44 1291.13,1307.44 1292.91,1307.45 1294.69,1307.45 1296.48,1307.45 1298.26,1307.45 1300.04,1307.45 1301.82,1307.45 1303.61,1307.45 1305.39,1307.45 1307.17,1307.45 1308.95,1307.46 1310.74,1307.46 1312.52,1307.46 1314.3,1307.46 1316.08,1307.46 1317.87,1307.46 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1431.95,1307.54 1433.73,1307.54 1435.51,1307.54 1437.29,1307.54 1439.08,1307.54 1440.86,1307.54 1442.64,1307.54 1444.42,1307.54 1446.21,1307.55 1447.99,1307.55 1449.77,1307.55 1451.55,1307.55 1453.34,1307.55 1455.12,1307.55 1456.9,1307.55 1458.68,1307.55 1460.47,1307.55 1462.25,1307.56 1464.03,1307.56 1465.81,1307.56 1467.6,1307.56 1469.38,1307.56 1471.16,1307.56 1472.94,1307.56 1474.73,1307.56 1476.51,1307.57 1478.29,1307.57 1480.07,1307.57 1481.86,1307.57 1483.64,1307.57 1485.42,1307.57 1487.2,1307.57 1488.99,1307.57 1490.77,1307.57 1492.55,1307.58 1494.33,1307.58 1496.12,1307.58 1497.9,1307.58 1499.68,1307.58 1501.47,1307.58 1503.25,1307.58 1505.03,1307.58 1506.81,1307.58 1508.6,1307.59 1510.38,1307.59 1512.16,1307.59 1513.94,1307.59 1515.72,1307.59 1517.51,1307.59 1519.29,1307.59 1521.07,1307.59 1522.86,1307.6 1524.64,1307.6 1526.42,1307.6 1528.2,1307.6 1529.99,1307.6 1531.77,1307.6 1533.55,1307.6 1535.33,1307.6 1537.12,1307.6 1538.9,1307.61 1540.68,1307.61 1542.46,1307.61 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1654.76,1307.68 1656.54,1307.68 1658.33,1307.68 1660.11,1307.69 1661.89,1307.69 1663.67,1307.69 1665.46,1307.69 1667.24,1307.69 1669.02,1307.69 1670.8,1307.69 1672.59,1307.69 1674.37,1307.69 1676.15,1307.7 1677.93,1307.7 1679.72,1307.7 1681.5,1307.7 1683.28,1307.7 1685.06,1307.7 1686.85,1307.7 1688.63,1307.7 1690.41,1307.7 1692.19,1307.71 1693.98,1307.71 1695.76,1307.71 1697.54,1307.71 1699.32,1307.71 1701.11,1307.71 1702.89,1307.71 1704.67,1307.71 1706.45,1307.72 1708.24,1307.72 1710.02,1307.72 1711.8,1307.72 1713.58,1307.72 1715.37,1307.72 1717.15,1307.72 1718.93,1307.72 1720.71,1307.72 1722.5,1307.73 1724.28,1307.73 1726.06,1307.73 1727.84,1307.73 1729.63,1307.73 1731.41,1307.73 1733.19,1307.73 1734.97,1307.73 1736.76,1307.73 1738.54,1307.74 1740.32,1307.74 1742.1,1307.74 1743.89,1307.74 1745.67,1307.74 1747.45,1307.74 1749.23,1307.74 1751.02,1307.74 1752.8,1307.75 1754.58,1307.75 1756.36,1307.75 1758.15,1307.75 1759.93,1307.75 1761.71,1307.75 1763.49,1307.75 1765.28,1307.75 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       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Making plot\n"
-     ]
     }
    ],
    "source": [
diff --git a/tutorials/02_convergence.ipynb b/tutorials/02_convergence.ipynb
index d295bd33..672fa3e6 100644
--- a/tutorials/02_convergence.ipynb
+++ b/tutorials/02_convergence.ipynb
@@ -53,6 +53,7 @@
     "import AGNI.atmosphere as atmosphere\n",
     "import AGNI.solver as nl\n",
     "import AGNI.plotting as plotting\n",
+    "import AGNI.setpt as setpt\n",
     "\n",
     "# Normal logging from AGNI module\n",
     "AGNI.setup_logging(\"\",1)"
@@ -72,13 +73,13 @@
    "outputs": [],
    "source": [
     "# Configuration options\n",
-    "instellation    = 1000.0  # Solar flux [W m-2]\n",
+    "instellation    = 1300.0  # Solar flux [W m-2]\n",
     "gravity         = 9.81\n",
     "radius          = 6.0e6\n",
-    "nlev_centre     = 60\n",
+    "nlev_centre     = 55\n",
     "p_surf          = 700.0     # bar\n",
     "t_surf          = 2000.0\n",
-    "p_top           = 1e-6      # bar \n",
+    "p_top           = 1e-5      # bar \n",
     "mole_fractions  = Dict([(\"H2O\", 1.0)])\n",
     "\n",
     "spectral_file = joinpath(ROOT_DIR,\"res/spectral_files/Dayspring/16/Dayspring.sf\")\n",
@@ -115,8 +116,8 @@
      "text": [
       "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m] Composition set by dict \n",
       "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m] Inserting stellar spectrum \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m] Allocated atmosphere with composition \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     1.00e+00 H2O    \n"
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m] Allocated atmosphere with composition: \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       1 H2O    1.00e+00  \n"
      ]
     }
    ],
@@ -129,11 +130,12 @@
     "                        t_surf,\n",
     "                        gravity, radius,\n",
     "                        nlev_centre, p_surf, p_top,\n",
-    "                        mf_dict=mole_fractions,\n",
+    "                        mole_fractions, \"\",\n",
     "                        flag_gcontinuum=true,\n",
     "                        thermo_functions=true\n",
     "                        )\n",
-    "atmosphere.allocate!(atmos, star_file)"
+    "atmosphere.allocate!(atmos, star_file)\n",
+    "setpt.isothermal!(atmos, 2000.0)"
    ]
   },
   {
@@ -157,57 +159,59 @@
       "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     sol_type = 3 \n",
       "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     tmp_int  = 0.00 K \n",
       "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     f_int    = 0.00 W m-2 \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     step  resid_med  resid_2nm  flux_OLR   xvals_med  xvals_max  |dx|_max   flags \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        1  -4.83e+01  7.380e+07  1.015e+04  +6.51e+02  +9.52e+02  3.000e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        2  -5.29e+01  7.900e+08  1.008e+04  +6.50e+02  +1.21e+03  3.000e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        3  -5.54e+01  7.904e+08  9.949e+03  +6.48e+02  +1.43e+03  3.000e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        4  -6.42e+01  7.739e+08  9.710e+03  +6.45e+02  +1.64e+03  3.000e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        5  -7.20e+01  7.254e+08  9.037e+03  +6.36e+02  +1.93e+03  3.000e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        6  -6.55e+01  6.632e+08  8.309e+03  +6.27e+02  +2.01e+03  3.000e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        7  -5.69e+01  5.106e+08  6.510e+03  +6.02e+02  +2.25e+03  3.000e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        8  -2.30e+01  1.635e+08  3.096e+03  +5.41e+02  +1.98e+03  3.000e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        9  -9.36e+00  4.488e+07  1.469e+03  +4.54e+02  +2.23e+03  3.000e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       10  -2.13e+00  8.020e+06  8.024e+02  +4.13e+02  +2.11e+03  1.309e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       11  -1.74e+00  7.481e+06  3.574e+02  +4.12e+02  +2.08e+03  3.000e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       12  +4.32e-01  6.307e+06  6.903e+02  +4.61e+02  +2.06e+03  3.000e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       13  +1.82e-01  2.598e+06  7.667e+02  +4.43e+02  +2.02e+03  2.957e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       14  +7.38e-02  1.172e+06  6.988e+02  +4.43e+02  +2.01e+03  7.785e+01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       15  +5.13e-02  4.604e+05  6.812e+02  +4.27e+02  +2.00e+03  9.743e+01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       16  +5.43e-02  1.888e+07  4.938e+02  +4.13e+02  +1.98e+03  3.000e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       17  +3.74e-02  4.196e+08  5.773e+02  +4.16e+02  +2.08e+03  1.692e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       18  +4.19e-01  2.443e+08  6.172e+02  +4.16e+02  +2.19e+03  3.000e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       19  +6.00e-02  6.168e+07  6.559e+02  +4.16e+02  +1.90e+03  3.000e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       20  -6.40e-02  1.170e+08  6.605e+02  +4.16e+02  +1.80e+03  1.351e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       21  +9.69e-03  2.116e+07  6.602e+02  +4.15e+02  +1.74e+03  8.323e+01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       22  -1.15e-02  4.038e+06  6.628e+02  +4.15e+02  +1.73e+03  2.895e+01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       23  -3.19e-03  8.261e+05  6.664e+02  +4.15e+02  +1.72e+03  1.219e+01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       24  -6.16e-05  1.865e+05  6.667e+02  +4.15e+02  +1.72e+03  3.365e+00  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       25  -9.21e-04  4.957e+04  6.664e+02  +4.15e+02  +1.72e+03  5.385e+00  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       26  +1.33e-03  1.637e+04  6.667e+02  +4.15e+02  +1.72e+03  1.889e+00  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       27  +2.20e-04  6.592e+03  6.667e+02  +4.15e+02  +1.72e+03  1.723e+00  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       28  +2.01e-04  2.862e+03  6.667e+02  +4.15e+02  +1.72e+03  1.577e+00  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       29  +1.91e-05  1.334e+03  6.666e+02  +4.15e+02  +1.72e+03  4.693e+00  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       30  +6.46e-05  6.422e+02  6.667e+02  +4.15e+02  +1.72e+03  1.325e+00  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       31  +8.52e-05  3.227e+02  6.667e+02  +4.15e+02  +1.72e+03  1.223e+00  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       32  +4.09e-05  1.589e+02  6.667e+02  +4.15e+02  +1.72e+03  1.130e+00  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       33  +4.13e-05  8.014e+01  6.667e+02  +4.15e+02  +1.72e+03  1.045e+00  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       34  +5.91e-05  4.022e+01  6.667e+02  +4.15e+02  +1.72e+03  9.682e-01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       35  +3.47e-05  2.137e+01  6.667e+02  +4.15e+02  +1.72e+03  8.978e-01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       36  +2.73e-05  1.172e+01  6.667e+02  +4.15e+02  +1.72e+03  8.333e-01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       37  +1.31e-05  6.518e+00  6.667e+02  +4.15e+02  +1.72e+03  7.739e-01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       38  +6.31e-06  3.673e+00  6.667e+02  +4.15e+02  +1.72e+03  7.164e-01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       39  +3.07e-06  2.111e+00  6.667e+02  +4.15e+02  +1.72e+03  6.668e-01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       40  +1.73e-06  1.275e+00  6.667e+02  +4.15e+02  +1.72e+03  6.208e-01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       41  +9.73e-07  8.598e-01  6.667e+02  +4.15e+02  +1.72e+03  5.783e-01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       42  -2.22e-07  5.621e-02  6.666e+02  +4.15e+02  +1.72e+03  1.669e+01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       43  -2.93e-07  1.279e-03  6.667e+02  +4.15e+02  +1.72e+03  9.945e-01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     success \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     outgoing LW flux   = +6.67e+02 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at TOA  = -1.73e-04 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at BOA  = +2.08e-09 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     column max. loss   = +1.23e-03 W m-2  (+1.16e+02 %)  \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     final cost value   = +1.28e-03 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     surf temperature   = 1716.089  K         \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     step  resid_med    cost     flux_OLR   xvals_med  xvals_max  |dx|_max   flags \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        1  -1.01e+02  6.407e+05  6.416e+05  +1.87e+03  +1.95e+03  2.000e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        2  -1.46e+02  4.463e+05  4.471e+05  +1.73e+03  +1.93e+03  2.000e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        3  -9.88e+01  3.206e+05  3.215e+05  +1.60e+03  +1.92e+03  2.000e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        4  -1.33e+02  2.335e+05  2.343e+05  +1.48e+03  +1.91e+03  2.000e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        5  -9.97e+01  1.678e+05  1.687e+05  +1.38e+03  +1.92e+03  2.000e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        6  -1.11e+02  1.164e+05  1.173e+05  +1.27e+03  +1.92e+03  2.000e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        7  -9.08e+01  1.685e+06  7.662e+04  +1.17e+03  +1.92e+03  2.000e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        8  -1.38e+02  1.007e+07  5.390e+04  +1.08e+03  +1.92e+03  2.000e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        9  -9.03e+01  3.867e+07  3.148e+04  +9.74e+02  +1.90e+03  2.000e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       10  -7.80e+01  4.640e+07  1.971e+04  +8.99e+02  +1.86e+03  2.000e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       11  -4.12e+01  3.319e+07  6.857e+03  +7.87e+02  +1.81e+03  1.832e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       12  -2.59e+01  6.403e+06  2.602e+03  +7.03e+02  +1.81e+03  1.559e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       13  -2.13e+00  1.745e+06  1.240e+03  +5.89e+02  +1.80e+03  1.317e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       14  -4.92e-01  3.291e+05  9.356e+02  +5.23e+02  +1.80e+03  1.258e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       15  -7.52e-02  8.207e+04  9.347e+02  +5.07e+02  +1.79e+03  2.000e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       16  -2.52e-01  4.416e+04  9.120e+02  +5.08e+02  +1.79e+03  8.939e+01  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       17  -5.74e-02  6.089e+04  8.274e+02  +5.08e+02  +1.79e+03  2.000e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       18  +2.23e-02  2.188e+04  8.412e+02  +5.05e+02  +1.78e+03  6.859e+01  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       19  +6.94e-02  5.929e+03  8.618e+02  +5.07e+02  +1.78e+03  5.030e+01  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       20  -4.48e-03  1.243e+03  8.653e+02  +5.07e+02  +1.78e+03  2.562e+01  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       21  +2.18e-05  2.427e+02  8.666e+02  +5.07e+02  +1.78e+03  6.786e+00  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       22  +2.96e-04  4.743e+01  8.591e+02  +5.07e+02  +1.78e+03  1.097e+01  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       23  -1.46e-05  9.317e+00  8.666e+02  +5.07e+02  +1.78e+03  5.021e+00  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       24  -2.93e-07  5.519e+00  8.667e+02  +5.07e+02  +1.78e+03  3.608e-01  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       25  -2.52e-07  4.392e+00  8.667e+02  +5.07e+02  +1.78e+03  1.012e-01  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       26  -4.77e-08  3.871e+00  8.667e+02  +5.07e+02  +1.78e+03  4.715e-02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       27  -3.72e-08  3.575e+00  8.667e+02  +5.07e+02  +1.78e+03  4.680e-02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       28  -5.54e-09  3.043e+00  8.667e+02  +5.07e+02  +1.78e+03  4.647e-02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       29  -5.94e-09  2.443e+00  8.667e+02  +5.07e+02  +1.78e+03  4.614e-02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       30  -3.69e-08  1.885e+00  8.666e+02  +5.07e+02  +1.78e+03  9.546e+00  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       31  -1.58e-08  1.409e+00  8.667e+02  +5.07e+02  +1.78e+03  5.209e-01  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       32  -2.28e-08  1.029e+00  8.667e+02  +5.07e+02  +1.78e+03  5.015e-02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       33  -9.57e-09  7.394e-01  8.667e+02  +5.07e+02  +1.78e+03  5.589e-03  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       34  -3.29e-10  5.254e-01  8.667e+02  +5.07e+02  +1.78e+03  3.991e-04  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       35  -4.13e-10  3.705e-01  8.667e+02  +5.07e+02  +1.78e+03  5.929e-05  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       36  +3.80e-10  2.599e-01  8.667e+02  +5.07e+02  +1.78e+03  1.202e-05  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       37  -3.08e-10  1.816e-01  8.667e+02  +5.07e+02  +1.78e+03  8.448e-06  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       38  +1.97e-10  1.265e-01  8.667e+02  +5.07e+02  +1.78e+03  5.908e-06  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       39  -1.87e-10  8.799e-02  8.667e+02  +5.07e+02  +1.78e+03  4.120e-06  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       40  +1.62e-11  6.111e-02  8.667e+02  +5.07e+02  +1.78e+03  3.409e-06  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       41  +8.88e-11  4.241e-02  8.667e+02  +5.07e+02  +1.78e+03  1.992e-06  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       42  -4.39e-11  2.941e-02  8.667e+02  +5.07e+02  +1.78e+03  1.382e-06  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       43  -2.51e-10  2.039e-02  8.667e+02  +5.07e+02  +1.78e+03  9.585e-07  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       44  +2.59e-11  1.413e-02  8.667e+02  +5.07e+02  +1.78e+03  4.174e-06  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       45  +7.80e-11  9.788e-03  8.667e+02  +5.07e+02  +1.78e+03  4.191e-06  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     success in 45 steps \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     outgoing LW flux   = +8.67e+02 W m-2      \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at TOA  = +2.80e-09 W m-2      \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at BOA  = -3.04e-10 W m-2      \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     column max. loss   = +9.79e-03 W m-2  (+2.09e+06 %)  \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     final cost value   = +9.79e-03 W m-2      \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     surf temperature   = 1781.244  K         \n",
       "Solver success? true\n"
      ]
     }
@@ -217,7 +221,7 @@
     "                                    sol_type=3,         # Tell the model to solve for a state with zero net flux transport\n",
     "                                    sens_heat=true,     # Include sensible heat transport\n",
     "                                    method=1,           # Use the Newton-Raphson method\n",
-    "                                    dx_max=300.0,       # Allow large step sizes because of the poor initial guess\n",
+    "                                    dx_max=200.0,       # Allow large step sizes because of the poor initial guess\n",
     "                                    linesearch=false,   # Disable Linesearch\n",
     "                                    modplot=1,          # Disable live-plotting \n",
     "                                    save_frames=false,  # ^\n",
@@ -235,7 +239,7 @@
     "The following lines print information at each solver step. These columns are...\n",
     "* Step number\n",
     "* Median of all residuals\n",
-    "* Norm of all residuals (this is what the solver is attempting to minimise - the 'cost')\n",
+    "* The \"cost\" at this step, which the solver is attempting to minimise\n",
     "* Outgoing longwave radiation at the top of the atmosphere \n",
     "* Median of all x-values (temperatures)\n",
     "* Maximum of all x-values\n",
@@ -260,190 +264,130 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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      "execution_count": 7,
@@ -462,7 +406,7 @@
    "source": [
     "There are a few notable features:\n",
     "* An isothermal layer is formed near the surface. The atmosphere is optically thick in this region, so a small temperature gradient is required in order to satisfy the zero-flux condition.\n",
-    "* Convection occurs between ~30 bar and ~1 bar.\n",
+    "* Convection occurs between ~100 bar and ~1 bar.\n",
     "* The atmosphere is purely radiative above 1 bar, with an inversion formed at pressures less than 0.1 mbar.\n",
     "* The temperature becomes very small in the upper atmosphere, because condensation is  disabled.\n",
     "\n",
@@ -477,322 +421,224 @@
    "outputs": [
     {
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       ]
      },
      "execution_count": 8,
@@ -809,7 +655,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This plot shows the upward (right) and downward (left) flux components. Note the log scale on the x-axis.   \n",
+    "This plot shows the upward (right side) and downward (left side) flux components. Note the log scale on the x-axis.   \n",
     "We can see that the convective and radiative fluxes add to zero in the region of dry convection.    \n",
     "Rayleigh scattering is turned off here, so the upward SW flux is zero."
    ]
diff --git a/tutorials/03_rce-runaway.ipynb b/tutorials/03_rce-runaway.ipynb
index 9a1d3f46..2c567f71 100644
--- a/tutorials/03_rce-runaway.ipynb
+++ b/tutorials/03_rce-runaway.ipynb
@@ -138,7 +138,7 @@
     "                        t_surf,  \n",
     "                        gravity, radius,\n",
     "                        nlev_centre, p_surf, p_top,\n",
-    "                        mf_dict=mole_fractions,\n",
+    "                        mole_fractions, \"\",\n",
     "                        condensates=condensates,\n",
     "                        flag_gcontinuum=true,\n",
     "                        thermo_functions=false,\n",