From 865f739bd2139d7afc8e57646c615acb9d64b6f6 Mon Sep 17 00:00:00 2001
From: Harrison Nicholls <harrison.nicholls@physics.ox.ac.uk>
Date: Fri, 20 Sep 2024 10:46:11 +0100
Subject: [PATCH] Version 0.8.3. Now uses F_int rather than T_int for internal
 flux. Updated docs and paper. New notebooks

---
 CITATION.cff                |    4 +-
 Project.toml                |    2 +-
 README.md                   |    2 +-
 codemeta.json               |    4 +-
 docs/paper/paper.md         |    4 +-
 docs/src/usage.md           |    2 +-
 misc/L98-59d.ipynb          | 1844 +++--------------------------------
 res/config/55cnce_chem.toml |    2 +-
 res/config/L-98-59d.toml    |    2 +-
 res/config/condense.toml    |    2 +-
 res/config/default.toml     |    2 +-
 res/config/hotdry.toml      |    2 +-
 src/AGNI.jl                 |    8 +-
 src/atmosphere.jl           |   14 +-
 src/dump.jl                 |    4 +-
 src/load.jl                 |    2 +-
 src/solver.jl               |    6 +-
 17 files changed, 152 insertions(+), 1754 deletions(-)

diff --git a/CITATION.cff b/CITATION.cff
index 33178a36..dfdc24c2 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: 0.8.2
+version: 0.8.3
 doi: 10.xx/xx.xx
-date-released: 2024-09-13
+date-released: 2024-09-20
 url: "https://github.com/nichollsh/AGNI"
diff --git a/Project.toml b/Project.toml
index 6c751f45..233e595e 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 = "0.8.2"
+version = "0.8.3"
 
 [deps]
 ArgParse = "c7e460c6-2fb9-53a9-8c5b-16f535851c63"
diff --git a/README.md b/README.md
index 7d9d4d4f..1b141f99 100644
--- a/README.md
+++ b/README.md
@@ -18,7 +18,7 @@
 </p>
 
 <p align="center">
-  <b>A radiative-convective atmosphere model designed for interior coupling.</b>
+  <b>A radiative-convective model for lava planet atmospheres.</b>
 </p>
 
 
diff --git a/codemeta.json b/codemeta.json
index 6573aa3d..98edf87d 100644
--- a/codemeta.json
+++ b/codemeta.json
@@ -15,9 +15,9 @@
   "datePublished": "2024-09-13",
   "dateModified": "2024-09-13",
   "dateCreated": "2024-09-13",
-  "description": "A radiative-convective model for the atmospheres of rocky planets",
+  "description": "A radiative-convective model for lava planet atmospheres.",
   "keywords": "physics, radiative transfer, exoplanets, astronomy, convection, radiation, planets, atmospheres",
   "license": "GPL v3.0",
   "title": "AGNI",
-  "version": "0.8.2"
+  "version": "0.8.3"
 }
diff --git a/docs/paper/paper.md b/docs/paper/paper.md
index fd086632..e2562841 100644
--- a/docs/paper/paper.md
+++ b/docs/paper/paper.md
@@ -1,5 +1,5 @@
 ---
-title: 'AGNI: A radiative-convective model for the atmospheres of rocky planets'
+title: 'AGNI: A radiative-convective model for lava planet atmospheres.'
 tags:
   - astronomy
   - physics,
@@ -24,7 +24,7 @@ affiliations:
    index: 1
  - name: Kapteyn Astronomical Institute, University of Groningen, P.O. Box 800, 9700 AV Groningen, The Netherlands
    index: 2
-date: 16 September 2024
+date: 20 September 2024
 bibliography: paper.bib
 
 ---
diff --git a/docs/src/usage.md b/docs/src/usage.md
index 71d47fba..a6606331 100644
--- a/docs/src/usage.md
+++ b/docs/src/usage.md
@@ -50,7 +50,7 @@ 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`
+     - 3 : the net flux (up minus down) at each layer is equal to `flux_int`
 
 * `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
diff --git a/misc/L98-59d.ipynb b/misc/L98-59d.ipynb
index cf57e024..7dfec1a5 100644
--- a/misc/L98-59d.ipynb
+++ b/misc/L98-59d.ipynb
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -29,16 +29,16 @@
     }
    ],
    "source": [
-    "#            Psurf/kbar         Msurf/Me            Rsurf/Re\n",
-    "structure = [55.7995885204057   1.87357329869088    1.26545448273556\n",
-    "             94.9872390766408   1.85080181213859    1.18895003698611\n",
-    "             235.441189355888   1.81201269545852    1.03160242765241\n",
-    "             44.0805809467927   1.8874745283649     1.26809523799334\n",
-    "             81.3022504424403   1.86358543333935    1.19126026978588\n",
-    "             215.825183545351   1.82258865495902    1.03329680311623\n",
-    "             36.606020371988    1.89635484051907    1.26977486802543\n",
-    "             72.7858858655158   1.87155246958974    1.19269414869405\n",
-    "             203.510277091369   1.82923648889318    1.03435826758376]\n",
+    "#            Psurf/kbar         Msurf/Me            Rsurf/Re            Fint/ Wm-2\n",
+    "structure = [55.7995885204057   1.87357329869088    1.26545448273556    0.1\n",
+    "             94.9872390766408   1.85080181213859    1.18895003698611    0.1\n",
+    "             235.441189355888   1.81201269545852    1.03160242765241    0.1\n",
+    "             44.0805809467927   1.8874745283649     1.26809523799334    0.1\n",
+    "             81.3022504424403   1.86358543333935    1.19126026978588    0.1\n",
+    "             215.825183545351   1.82258865495902    1.03329680311623    0.1\n",
+    "             36.606020371988    1.89635484051907    1.26977486802543    0.1\n",
+    "             72.7858858655158   1.87155246958974    1.19269414869405    0.1\n",
+    "             203.510277091369   1.82923648889318    1.03435826758376    0.1    ]\n",
     "nsamps = size(structure)[1]"
    ]
   },
@@ -175,7 +175,7 @@
     "                        nlev_centre, p_surf, p_top,\n",
     "                        mole_fractions, \"\",\n",
     "\n",
-    "                        tmp_int = 0.0,\n",
+    "                        flux_int = 0.0,\n",
     "                        flag_gcontinuum=true,\n",
     "                        flag_rayleigh=true,\n",
     "                        thermo_functions=thermo,\n",
@@ -206,17 +206,17 @@
       "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     tmp_surf = 3000.00 K \n",
       "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     step  resid_med    cost     flux_OLR    max(x)    max(|dx|)   flags \n",
       "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        1  -1.21e+03  1.250e+04  7.778e+04  3.000e+03  3.989e+02  C2-Nr-Ls \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        2  -3.36e+02  3.607e+03  3.128e+04  3.000e+03  3.093e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        3  -6.24e+01  9.809e+02  1.658e+04  3.000e+03  2.077e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        4  -2.91e-03  2.574e+02  1.293e+04  3.000e+03  8.449e+01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        5  +9.13e-04  2.423e+01  1.246e+04  3.000e+03  1.979e+01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        6  +7.14e-05  3.146e-01  1.245e+04  3.000e+03  2.271e+00  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        2  -3.37e+02  3.607e+03  3.128e+04  3.000e+03  3.093e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        3  -6.25e+01  9.809e+02  1.658e+04  3.000e+03  2.077e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        4  -2.90e-03  2.573e+02  1.293e+04  3.000e+03  8.449e+01  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        5  +1.54e-02  2.420e+01  1.246e+04  3.000e+03  1.979e+01  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        6  +5.26e-04  3.068e-01  1.245e+04  3.000e+03  2.270e+00  C2-Nr \n",
       "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     success in 6 steps \n",
       "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     outgoing LW flux   = +1.24e+04 W m-2      \n",
       "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at TOA  = +9.88e+02 W m-2      \n",
       "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at BOA  = +9.88e+02 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     column max loss    = +2.67e-01 W m-2  (+2.70e-02 %)  \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     final cost value   = +3.15e-01 W m-2      \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     column max loss    = +2.56e-01 W m-2  (+2.59e-02 %)  \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     final cost value   = +3.07e-01 W m-2      \n",
       "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     surf temperature   = 3000.000  K         \n",
       "Solver success? true\n"
      ]
@@ -240,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -249,165 +249,82 @@
      "text": [
       "Running model for 9 samples... \n",
       "    sample 1/9 \n",
-      "    Ps=5.6e+09 Pa  ,  Ms=1.1e+25 kg  ,  Rs=8.1e+06 m \n",
+      "    Ps=5.6e+09 Pa  ,  Ms=1.1e+25 kg  ,  Rs=8.1e+06 m  ,  Fi=1.0e-01 \n",
       "[\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]     flux_int = 0.10 W m-2 \n",
       "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     step  resid_med    cost     flux_OLR    max(x)    max(|dx|)   flags \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        1  +1.34e-01  1.268e+02  1.144e+04  2.631e+03  3.698e+02  C2-Nr-Ls \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        2  +1.02e-02  4.003e+01  1.146e+04  2.310e+03  3.209e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        3  -2.17e-03  1.589e+01  1.146e+04  2.058e+03  2.519e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        4  +1.43e-03  5.042e+00  1.146e+04  1.848e+03  2.104e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        5  +7.84e-04  4.596e+00  1.146e+04  1.830e+03  2.158e+01  C2-Nr-Ls \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        6  +5.74e-04  1.436e+00  1.146e+04  1.662e+03  1.679e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        7  +8.24e-04  7.043e-01  1.146e+04  1.546e+03  1.160e+02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        8  +1.82e-05  2.416e-01  1.146e+04  1.480e+03  6.584e+01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        9  +2.12e-06  1.757e-02  1.146e+04  1.460e+03  2.015e+01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     success in 9 steps \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        1  +1.34e-01  1.298e+02  1.144e+04  2.631e+03  3.703e+02  C2-Nr-Ls \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        2  +1.54e-02  4.129e+01  1.146e+04  2.305e+03  3.258e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        3  +1.03e-03  1.745e+01  1.146e+04  2.053e+03  2.516e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        4  +1.61e-03  4.908e+00  1.146e+04  1.848e+03  2.065e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        5  +1.22e-05  1.650e+00  1.146e+04  1.680e+03  1.677e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        6  +9.17e-06  1.511e+00  1.146e+04  1.670e+03  1.264e+01  C2-Nr-Ls \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        7  +9.16e-06  1.511e+00  1.146e+04  1.670e+03  4.090e-03  C2-Nr-Ls \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        8  +9.16e-06  1.511e+00  1.146e+04  1.670e+03  4.089e-03  C2-Nr-Ls \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        9  +9.17e-06  1.511e+00  1.146e+04  1.670e+03  4.088e-03  C2-Nr-Ls \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       10  +9.17e-06  1.511e+00  1.146e+04  1.669e+03  4.087e-03  C2-Nr-Ls \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       11  +9.17e-06  1.511e+00  1.146e+04  1.669e+03  4.087e-03  C2-Nr-Ls \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       12  +6.32e-04  2.835e+06  1.146e+04  1.460e+03  2.853e+02  C2-Nr-X \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       13  +4.02e-04  7.665e+05  1.146e+04  1.756e+03  3.260e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       14  +5.98e-05  1.320e+05  1.146e+04  1.614e+03  1.416e+02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       15  +2.82e-05  2.422e+04  1.146e+04  1.557e+03  5.714e+01  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       16  +1.97e-06  4.598e+03  1.146e+04  1.541e+03  1.628e+01  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       17  +6.03e-08  8.845e+02  1.146e+04  1.539e+03  1.440e+00  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       18  +1.83e-09  1.699e+02  1.146e+04  1.539e+03  3.206e-01  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       19  +1.36e-10  3.155e+01  1.146e+04  1.539e+03  8.753e-02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       20  +7.64e-10  4.579e+00  1.146e+04  1.539e+03  3.312e-02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       21  +1.31e-10  2.839e+00  1.146e+04  1.539e+03  7.791e-03  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       22  +3.36e-11  2.839e+00  1.146e+04  1.539e+03  5.560e-03  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       23  +1.43e-10  2.839e+00  1.146e+04  1.539e+03  6.199e-03  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       24  +1.66e-11  2.838e+00  1.146e+04  1.539e+03  7.069e-03  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       25  +5.78e-11  2.838e+00  1.146e+04  1.539e+03  8.341e-03  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       26  +5.85e-11  2.837e+00  1.146e+04  1.539e+03  1.042e-02  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       27  +9.96e-11  2.834e+00  1.146e+04  1.539e+03  4.361e-02  C2-Nr-X \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       28  +3.57e-07  2.471e-01  1.146e+04  1.494e+03  4.520e+01  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       29  +1.34e-06  5.978e-03  1.146e+04  1.493e+03  1.651e+00  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       30  +1.26e-10  3.682e-04  1.146e+04  1.493e+03  2.453e-01  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]       31  +2.04e-10  2.161e-07  1.146e+04  1.493e+03  9.161e-03  C2-Nr \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     success in 31 steps \n",
       "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     outgoing LW flux   = +1.15e+04 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at TOA  = -2.15e-04 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at BOA  = -2.38e-04 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     column max loss    = +1.09e-02 W m-2  (+1.00e+02 %)  \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     final cost value   = +1.76e-02 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     surf temperature   = 1460.239  K         \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at TOA  = +1.00e-01 W m-2      \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at BOA  = +1.00e-01 W m-2      \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     column max loss    = +1.83e-07 W m-2  (+1.83e-04 %)  \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     final cost value   = +2.16e-07 W m-2      \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     surf temperature   = 1492.887  K         \n",
       "--------------------------------- \n",
       "    sample 2/9 \n",
-      "    Ps=9.5e+09 Pa  ,  Ms=1.1e+25 kg  ,  Rs=7.6e+06 m \n",
-      "[\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    cost     flux_OLR    max(x)    max(|dx|)   flags \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        1  +2.57e-04  3.928e+01  1.147e+04  1.459e+03  3.215e+01  C2-Nr-Ls \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        2  +1.90e-05  1.167e-01  1.146e+04  1.460e+03  2.258e+00  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        3  +7.72e-08  5.707e-04  1.146e+04  1.460e+03  4.174e-02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     success in 3 steps \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     outgoing LW flux   = +1.15e+04 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at TOA  = -5.58e-06 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at BOA  = -1.43e-05 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     column max loss    = +2.24e-04 W m-2  (+1.00e+02 %)  \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     final cost value   = +5.71e-04 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     surf temperature   = 1459.691  K         \n",
-      "--------------------------------- \n",
-      "    sample 3/9 \n",
-      "    Ps=2.4e+10 Pa  ,  Ms=1.1e+25 kg  ,  Rs=6.6e+06 m \n",
-      "[\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    cost     flux_OLR    max(x)    max(|dx|)   flags \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        1  +9.70e-04  7.951e+01  1.148e+04  1.461e+03  3.782e+01  C2-Nr-Ls \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        2  +5.02e-04  1.114e+00  1.146e+04  1.461e+03  1.686e+00  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        3  +4.05e-07  1.291e-03  1.146e+04  1.461e+03  4.348e-02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     success in 3 steps \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     outgoing LW flux   = +1.15e+04 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at TOA  = +5.17e-05 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at BOA  = -3.81e-07 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     column max loss    = +5.04e-04 W m-2  (+1.00e+02 %)  \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     final cost value   = +1.29e-03 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     surf temperature   = 1460.827  K         \n",
-      "--------------------------------- \n",
-      "    sample 4/9 \n",
-      "    Ps=4.4e+09 Pa  ,  Ms=1.1e+25 kg  ,  Rs=8.1e+06 m \n",
-      "[\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    cost     flux_OLR    max(x)    max(|dx|)   flags \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        1  +2.93e-04  1.752e+02  1.159e+04  1.458e+03  8.087e+01  C2-Nr-Ls \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        2  +5.54e-04  1.094e+01  1.146e+04  1.459e+03  1.076e+01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        3  -5.31e-06  4.874e-03  1.146e+04  1.459e+03  3.708e-01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     success in 3 steps \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     outgoing LW flux   = +1.15e+04 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at TOA  = +1.54e-03 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at BOA  = -3.97e-06 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     column max loss    = +1.77e-03 W m-2  (+9.98e+01 %)  \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     final cost value   = +4.87e-03 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     surf temperature   = 1459.358  K         \n",
-      "--------------------------------- \n",
-      "    sample 5/9 \n",
-      "    Ps=8.1e+09 Pa  ,  Ms=1.1e+25 kg  ,  Rs=7.6e+06 m \n",
-      "[\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    cost     flux_OLR    max(x)    max(|dx|)   flags \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        1  +3.20e-03  5.011e+01  1.149e+04  1.460e+03  4.289e+01  C2-Nr-Ls \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        2  +1.42e-04  1.839e+00  1.146e+04  1.460e+03  3.964e+00  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        3  +1.66e-06  6.250e-04  1.146e+04  1.460e+03  5.572e-02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     success in 3 steps \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     outgoing LW flux   = +1.15e+04 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at TOA  = -2.27e-05 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at BOA  = -1.08e-06 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     column max loss    = +2.39e-04 W m-2  (+1.00e+02 %)  \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     final cost value   = +6.25e-04 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     surf temperature   = 1459.772  K         \n",
-      "--------------------------------- \n",
-      "    sample 6/9 \n",
-      "    Ps=2.2e+10 Pa  ,  Ms=1.1e+25 kg  ,  Rs=6.6e+06 m \n",
+      "    Ps=9.5e+09 Pa  ,  Ms=1.1e+25 kg  ,  Rs=7.6e+06 m  ,  Fi=1.0e-01 \n",
       "[\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    cost     flux_OLR    max(x)    max(|dx|)   flags \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        1  +5.26e-05  1.111e+02  1.149e+04  1.461e+03  4.594e+01  C2-Nr-Ls \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        2  +3.08e-05  1.258e+00  1.146e+04  1.461e+03  3.035e+00  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        3  -1.05e-06  1.963e-03  1.146e+04  1.461e+03  2.866e-02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     success in 3 steps \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     outgoing LW flux   = +1.15e+04 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at TOA  = -6.48e-04 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at BOA  = -1.73e-06 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     column max loss    = +1.79e-03 W m-2  (+1.00e+02 %)  \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     final cost value   = +1.96e-03 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     surf temperature   = 1460.674  K         \n",
-      "--------------------------------- \n",
-      "    sample 7/9 \n",
-      "    Ps=3.7e+09 Pa  ,  Ms=1.1e+25 kg  ,  Rs=8.1e+06 m \n",
-      "[\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    cost     flux_OLR    max(x)    max(|dx|)   flags \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        1  +1.45e-02  2.188e+02  1.161e+04  1.458e+03  8.988e+01  C2-Nr-Ls \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        2  -1.35e-03  2.302e+01  1.145e+04  1.460e+03  1.359e+01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        3  +2.43e-05  4.008e-02  1.146e+04  1.459e+03  7.202e-01  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     success in 3 steps \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     outgoing LW flux   = +1.15e+04 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at TOA  = +1.85e-02 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at BOA  = +1.59e-04 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     column max loss    = +3.87e-02 W m-2  (+1.00e+02 %)  \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     final cost value   = +4.01e-02 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     surf temperature   = 1459.358  K         \n",
-      "--------------------------------- \n",
-      "    sample 8/9 \n",
-      "    Ps=7.3e+09 Pa  ,  Ms=1.1e+25 kg  ,  Rs=7.6e+06 m \n",
-      "[\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    cost     flux_OLR    max(x)    max(|dx|)   flags \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        1  +2.39e-04  1.086e+02  1.148e+04  1.460e+03  5.092e+01  C2-Nr-Ls \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        2  +4.95e-05  2.519e+00  1.146e+04  1.460e+03  4.009e+00  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        3  +5.96e-08  1.664e-03  1.146e+04  1.460e+03  7.629e-02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     success in 3 steps \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     outgoing LW flux   = +1.15e+04 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at TOA  = +1.89e-04 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at BOA  = -6.11e-07 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     column max loss    = +8.27e-04 W m-2  (+1.00e+02 %)  \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     final cost value   = +1.66e-03 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     surf temperature   = 1459.819  K         \n",
-      "--------------------------------- \n",
-      "    sample 9/9 \n",
-      "    Ps=2.0e+10 Pa  ,  Ms=1.1e+25 kg  ,  Rs=6.6e+06 m \n",
-      "[\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    cost     flux_OLR    max(x)    max(|dx|)   flags \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        1  +1.76e-03  1.322e+02  1.150e+04  1.461e+03  5.327e+01  C2-Nr-Ls \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        2  -4.87e-06  2.269e+00  1.145e+04  1.462e+03  4.929e+00  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]        3  -5.49e-07  1.064e-03  1.145e+04  1.462e+03  7.699e-02  C2-Nr \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     success in 3 steps \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     outgoing LW flux   = +1.15e+04 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at TOA  = -2.88e-05 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     total flux at BOA  = +1.01e-05 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     column max loss    = +5.24e-04 W m-2  (+1.00e+02 %)  \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     final cost value   = +1.06e-03 W m-2      \n",
-      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     surf temperature   = 1461.798  K         \n",
-      "--------------------------------- \n",
-      "Done!\n"
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     flux_int = 0.10 W m-2 \n",
+      "[\u001b[32m\u001b[1m INFO  \u001b[21m\u001b[0m]     step  resid_med    cost     flux_OLR    max(x)    max(|dx|)   flags \n"
+     ]
+    },
+    {
+     "ename": "MethodError",
+     "evalue": "MethodError: no method matching getindex(::Nothing, ::Int64)",
+     "output_type": "error",
+     "traceback": [
+      "MethodError: no method matching getindex(::Nothing, ::Int64)\n",
+      "\n",
+      "Stacktrace:\n",
+      " [1] radtrans!(atmos::AGNI.atmosphere.Atmos_t, lw::Bool; calc_cf::Bool)\n",
+      "   @ AGNI.energy ~/AGNI/src/energy.jl:233\n",
+      " [2] radtrans!\n",
+      "   @ ~/AGNI/src/energy.jl:35 [inlined]\n",
+      " [3] calc_fluxes!(atmos::AGNI.atmosphere.Atmos_t, latent::Bool, convect::Bool, sens_heat::Bool, conduct::Bool; convect_sf::Float64, latent_sf::Float64, calc_cf::Bool)\n",
+      "   @ AGNI.energy ~/AGNI/src/energy.jl:700\n",
+      " [4] calc_fluxes!\n",
+      "   @ ~/AGNI/src/energy.jl:666 [inlined]\n",
+      " [5] (::AGNI.solver.var\"#_fev!#3\"{Int64, Bool, Bool, Bool, Bool, AGNI.atmosphere.Atmos_t, AGNI.solver.var\"#_set_tmps!#2\"{Int64, AGNI.atmosphere.Atmos_t}})(x::Vector{Float64}, resid::Vector{Float64})\n",
+      "   @ AGNI.solver ~/AGNI/src/solver.jl:244\n",
+      " [6] (::AGNI.solver.var\"#_calc_jac_res!#4\"{Float64, AGNI.solver.var\"#_fev!#3\"{Int64, Bool, Bool, Bool, Bool, AGNI.atmosphere.Atmos_t, AGNI.solver.var\"#_set_tmps!#2\"{Int64, AGNI.atmosphere.Atmos_t}}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}})(x::Vector{Float64}, jacob::Matrix{Float64}, resid::Vector{Float64}, central::Bool, order::Int64, which::Vector{Bool})\n",
+      "   @ AGNI.solver ~/AGNI/src/solver.jl:317\n",
+      " [7] solve_energy!(atmos::AGNI.atmosphere.Atmos_t; sol_type::Int64, chem_type::Int64, convect::Bool, sens_heat::Bool, conduct::Bool, latent::Bool, dx_max::Float64, max_steps::Int64, max_runtime::Float64, fdw::Float64, fdc::Bool, fdo::Int64, method::Int64, ls_method::Int64, easy_start::Bool, ls_increase::Float64, detect_plateau::Bool, perturb_all::Bool, modplot::Int64, save_frames::Bool, modprint::Int64, plot_jacobian::Bool, conv_atol::Float64, conv_rtol::Float64)\n",
+      "   @ AGNI.solver ~/AGNI/src/solver.jl:570\n",
+      " [8] solve_energy!\n",
+      "   @ ~/AGNI/src/solver.jl:103 [inlined]\n",
+      " [9] top-level scope\n",
+      "   @ ~/AGNI/misc/jl_notebook_cell_df34fa98e69747e1a8f8a730347b8e2f_X12sdnNjb2RlLXJlbW90ZQ==.jl:25"
      ]
     }
    ],
@@ -425,11 +342,13 @@
     "    Ps = row[1] * 1e3 * 1e5 # convert kbar -> bar -> Pa\n",
     "    Ms = row[2] * M_earth\n",
     "    Rs = row[3] * R_earth\n",
+    "    Fi = row[4]\n",
     "\n",
     "    atmos.rp = Rs\n",
     "    atmos.p_boa = Ps\n",
     "    atmos.grav_surf = 6.67e-11 * Ms / Rs^2\n",
-    "    @printf(\"    Ps=%.1e Pa  ,  Ms=%.1e kg  ,  Rs=%.1e m \\n\",Ps,Ms,Rs)\n",
+    "    atmos.flux_int = Fi\n",
+    "    @printf(\"    Ps=%.1e Pa  ,  Ms=%.1e kg  ,  Rs=%.1e m  ,  Fi=%.1e \\n\",Ps,Ms,Rs,Fi)\n",
     "\n",
     "    atmosphere.generate_pgrid!(atmos)\n",
     "\n",
@@ -442,8 +361,8 @@
     "                                    dx_max=400.0,        # Smaller steps\n",
     "                                    ls_method=1 ,\n",
     "                                    save_frames=false, modplot=0,\n",
-    "                                    modprint=1, perturb_all=false,\n",
-    "                                    conv_atol=0.1\n",
+    "                                    modprint=1, perturb_all=true,\n",
+    "                                    conv_atol=1.0e-4\n",
     "                                    )\n",
     "\n",
     "    # Store result\n",
@@ -455,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -475,1028 +394,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
-     "data": {
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"/home/n/nichollsh/AGNI/out/profiles.pdf\""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "BoundsError",
+     "evalue": "BoundsError: attempt to access 0-element Vector{AGNI.atmosphere.Atmos_t} at index [1]",
+     "output_type": "error",
+     "traceback": [
+      "BoundsError: attempt to access 0-element Vector{AGNI.atmosphere.Atmos_t} at index [1]\n",
+      "\n",
+      "Stacktrace:\n",
+      " [1] getindex(A::Vector{AGNI.atmosphere.Atmos_t}, i1::Int64)\n",
+      "   @ Base ./essentials.jl:13\n",
+      " [2] top-level scope\n",
+      "   @ ~/AGNI/misc/jl_notebook_cell_df34fa98e69747e1a8f8a730347b8e2f_X14sdnNjb2RlLXJlbW90ZQ==.jl:15"
+     ]
     }
    ],
    "source": [
@@ -1529,524 +442,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
-     "data": {
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rS2E3xfO+PX99qePdD/z7W3La8mtnPJqet5Qy5YWeaIgkXnC3drXv3t/0mnH59Nplq6KKHwAAAAAAIH4UFRWF74Rxww03KPyCHo/n73+HctrAMAlKxXbPPfc888wzQ3Xs3n///Z/+9KeJ2e/4Y8+ePb29vYGm0WhUJAtVpbS0dP369e+//35gyc6dO9evX282m++444477rgDIeR2uxsbGy9fvtzb28txnMFgyMzMLCwsrKmpCT0ZYXzT12NPx0rrRWKysVislg2rEUI2NKlCo48kiYoUowyp2XAC9k9Y8CFWaQrkSC+jxHsDOTtp1opihwGXG0V+KJ+Px+9SZKrmOKWnMBgKSyVKYQ48A+nG41AogVAkxwgGB/q0UgzRpE9OaUwkthO1hc5kIEQpyao5bBQMmWI0vu697mP7S6tmBJqd/c02VKsYZWGSTCI0s4TCx7NhnlOWdCI9ppGNwhidQhgN2DkmchbLWC/zrsFuXEEy9DrGuMEJiM2MKYKSNOfG1frFgiwWKy/KdSWNRj3VSiB5qMgj0kVeNUqlOkUCJDMX6YHFSAxoySWToUolQghd2v7X+VPvRMEJVwS36O+jLVOGmxS7wFKZ4NiSE573mc90MBlV+EJHZ920jfeOboM1C5cghL74eNdcZqopc7ZiVhtlzKutetS///LBtk9XbX5cse7eXVtLBFNhzmzWMvWmikcj26EoeXtcrob63ksz192bnVdWhMqiG34DAAAAAABISh588MF/+Id/aG9vDyw5d+6cjvEASUWCBMLKysqNGzfu2LEDIXT48OG6urpVq1YlZtfjjF27iAmqd955Z0FBgVZnnEcffRQXCHft2vXCCy/QtPwvy2q1zp07d+7cubEKdXzQ2nxhNiP7X7lIEmlGBOaloxiEEENqVHg+TFHyBQs4fjLljiC4AxczRcdSIGFps9yQhk14g36XopuPj8CZjnn4CAchafkSDQakjUAKhM7e3sKiUtWeirubYkWRqEEYtUJmZsx405YTRSZhwrwYZ3FuWvoUvHlVGCjHT76hCOhkEwiJY8ukhSmbnDO5CGHCvWGcCoQMnjpY9CM0VoHQThEnwiQrlCGMGhNeu1H06Z65UeJd1IhAyBig3jiAEELmdGyqEudcccf9+sUCJC/Lbrvbt7eRSSsdalKm/D1vvrxy82O6BqUnde+8sSx32fTS4IKdkrfvVHsh7eSd88V8NJLV3GCrPLL1pQhTaI5j3J+fsObU4kt8jtN5t986xs0uXXs3Qmjvrq21ttnGjFmK2XW0Zeryim949zYc6jlcNGO26/SJivyZabbKZVM2RboDwe0fuNzac9ZZVLh4+S05qGgZWjXGmAEAAAAAAJIKmqaXLl26ffv2wBJcLAQmOAkSCBFCv/nNbz799NOhAoRPPvnkgQMHJmKyyjHz3nvv4c3IddZVq1ZRFCWN2NTa29uPHz++aNFYk9QlgCHjKUJo586dV65olPRDyGazfec734n5SeXs7kBy7R7k48OnGI0EwkOGKISQgfyvy2E5SEXBEyzgKByEuECIYlqDjcKVsJHEpy7Orejm8YcXCCUkBD4k4SBkCEWQNoVKkSqSmUI9bmUkAVhSc+VEbYEwepXOaCCCLKmMojCnIod8qQAAACAASURBVEtqtLuOiuz0Urkh8gtvuw8hJJL7ZKLXR+OKiRQIM/ILtXoOMbW8Gp2XmwZSux034KmDJSEGN6LZN69D16SAfTbbNnns25xoMLitU/AgFIWTOB7wfofBNGnoNaWdDQ+YONS988by4gcCTY/rUjq6Ucd4gGTm7LW982aUBpoL8+frF4ue9PXY/YeOLp9yr0r+eX/fwZadNz/0xFBmhtZ3/lZUfFfgzRvK7u3rses+U0RHOv++O7fwFnwJ52owrZwdq+3ffPcWhNDerX9aUnIbbSF9fRTFppUtTytFPgpVRTbbVXBz7tY2+zl7RtqStXeyaF5ez5TCcFkrAAAAgJizf//+v//975J2oZm2trZExgMA45i8PKIQSWC8HQAS9yO4pKRk27Ztd955p8/nu3jx4vr163fs2FFSUpKwAMYBTqfz8uXL+JLlyyMtlp6Xl1ddXX3hwoXAklOnTqWEQHjq1KmhFx999NFHH30UoueRI0d++ctfRrVxh8OBEMrKytLqMGDvIgRCztvQ0BDVLlQpwX/8UHRDQwMtEaKNh+cCOypWEwO8nA+PpIAfkG1TtPH8+TNsjHxUpXixPdE/tNNeThnSoN8d4sgMDAz4fL5aSb7eKYoN9E8jHYQeQQixKSuZOPT6tSuWBnUTLU0KhF7skCKEChHhIAzeY09PT3d3t9Wq7tMqYHCBUHQ43Q5npCfGJNzCp7brUePxeAYHB/v6+gKmwDKzrPpI/t7rHQPXOzo5sjojJUkxjCFafD6fy+Wy2+3syKBMLnFsEWWwhQ2vTJoWGMhjaWO8Pw7P8w6HIz093RScdixulDFyxkhRCHW5qSKKYm9vb1pamsUiH94cwRPISGky5et4Gmhx5coVt9s9NLUoCSlj5YFggXc1N/QhhOx2u8lkstl00OcK/H3ybAvadPjQ/uyciHIM9PX1MQyTkZFKpsOwj+8kZOhROBR5YiiXiNvphc7TWQ3ReYW7urrsdrvWozA5CX4UJj/Bj8LEY5m3CLkGAzntzRmz9tV9PKlomlZ/v9/vdDozMjKMxpQp/Tv0+FY8CnGuHPp4deUDaHKQ400S+jr3dpYUFC5cGXhWekqnF7ntyDjyIDBkeg991DBDq7zAKBFFMckfhUPQZ06UkoZL0Xv9tLk3uznGp3Rh7aoW5O08/NKS8o2USZHAI9wlL/Kiv7vfUX+6p2Hq0rUoy4iKFuQgNPSdwqMwMTidTkEQ8JopyU9XV1dfX5/ZnEpzEOFRmBhS91GY4H+yIXA6nWvXrvV6I5r/2tPTE+94AGB8o3j+FhaGmY4PTBwS+ui99dZbd+/eff/99/f09Bw/fnzu3LnPPvvs448/npMDlXIior6+Hp9Ww7JsTU1NiP4KamtrcYEQf53MMEzQBF4NzGZz5J3xjYdYy0T+1XQL/vQod6GKJGH+P4phGMbAEBejiEXF84PBvzd9Am/DIvH6XdhoNHX9SlPpjBqEEM/7Wg99PienNCermjbmIdHzecP28uW3RR4qhQmEougfiopnlX+QBjhvnvaRYRiGZVnJJ2CVF5nAB1TkhExLzwvxjYgS4SCUeF6rs5EmDqkfSXhPgXQQBm+EZVmaVlk+/C5hrNSMQRWFfzTakzYELMuyLMswzNBfwY62K2W26YF3vZ5rDJODEFLMEWKw7yLxsCMEYiC1bTEtI4IaqCKPRlZn2KjvA6NAEXMiwJzBouBmmFBG22AoigqOWfQ76JFahqwxh2GSrgwhTdOJPs5RgX0LAudiGBtCaChgXWJ2errlBwFF2RvP5y2NyBjKjBC30GJP2Md3EjL0KExkzAW52K9ESaAqqqPde+hHYXKieBSmBMGPwsTDMNa+lqPZk1YNt2nDDWL+0SsXp5bNUu2fDDGPAq2YO9qa53KZZbO/FVyXWvS0fXr1kxmLVyueuzZb+r6T21fMfiKQDSK/cM2pU9sqb1gaw4Apikr2RyFCA0f3zq78Gr5E8vfsc58uLYsiwUZUFC1b18z3Sac+rZh2JwrjmBclb1d/f/2Z7oZJC1eYcqyosKwUlQX3g0dhYkjFmOFRmBhS8bGSijEjXf7JamM0GiOPJIXOZwBIQiRJOnjwIL5kyZIlWp2BiUaCBEK73R7Icvu9733v+eefH5oI+Y//+I/PPfdcbW1tbW3tpEmTolUKaZp+/HFlQfJxjELSmzJlSlSzq6ZNI2YBp4pAWFNTc+DAAYTQxo0bQ9y8MjIyvv71r6elpWl1UGVo9kSIE89++DO86Re58vLyqHahiqdVrgQrIVReXn7xxGG8A2M2B3bUc/Fy8NR9nkZ4JJdPnSBW97gkzl3ULhjTZ5TP+Tb2TtqaqgfZcsJUHhq+W55gIgm+8vIKhJDEudEg0c3HoBBHxul0er1e6aKchpSi2UD/3ktX8c7zFy9Jz8jU2tTl08QnTTMZtPZb/yVxSFmDCe9pb2iV36OonMwsRVYom81mNBq1Nu65elZuiHxUZ0XvJTxFBjVtWnGs7J5ut9vlcuXn5w/9dO46/Bmqujnwbmd/c0X5IoTQ1eNkkT+GiclZPTq8Xm9/f39ubm7gbjbY4pTfjuyK4+3yKjSt+a3FCp7n7XZ7ZmZmIqcS873ytEqed5eXz4lqdVEUu7u709PTcSfQYMuXpkCSLtY2bZotVqdirHC5XJMnT540aZLegajQ0d6Cq7Zev6O8fB5CKD093Ww262JBOHF0L55zLY+mIrwWenp6WJZNLQtC2Md3EjL0KIywdPTY6e7sYCzydyp6ry9aviLajVitVjP2gyQlUDwKU4LgR6EuHGq+UCuJgcLMpsw5y8XKznOfG2+szZ+knF/s9/v7+vqys7NTyzZht9sVj0KE0Mk3X108/V7KFvQnQvQ1X9k14/6HNiD1/5vl5U95PjttyBzRUCl2Zf4NlpheL6IoDg4OJu2jECF0aOtLtTO+TgirvPOLgWOLbrw57o/Cqll9PXbXgXeLJq9FxNQ9CXEO90DjpZ7z+YtvKimvzkPFq1GYUojwKEwMDoeD53lFirMkx2q1Wq1WeBTGmyR5FEZF6j4KE/xPNjRffPHFBx98ECLF6O9///uhLKOpdbsDgGTjrbfeunbtWqCZn5+/du1aHeMBkooEPXrb2tq+/e1vq77l9Xr379+/f//+UWyWZdkJJRAq8r9Fm6BV0f/ixYsxiCn+UCPTctetW/ed73wnwXs3k9kvvbEqGIdVoaMoGiHEUuTFyMqJ4jz+geC/qjwZiYsj9DrW6yls8xmz1erHGLIOfLj9ptvuU3lLFcyHJwq+oRcV1TX8cT+eyUe0hs+nJxGmPXmaGIMXTZTEEOogQkggc2SLvKDVkyUdhMhINAWRSLNp72mPqmwMReOZV3ntjipIZDXEPntf8LhbTJieMQVvXhUGh/JeiaQvlg6usqMrNH7RSZrfL4HIB04oKqY1OJOEjvaWPLo40PRHUPIzElyeDlPWSKkeijn08c7lG+6PyZYnAvVHv8Cruw34enX/w9pPE/+r80ypNMoJxJzmT95bWP1IoNnTe7YIleoWDZAKLFl7p3fPRdaGDYLTpklTbkMtA+f3/mXWAw/rF1q8OPTxe/ONFXMqVT4a57xw0dI7//6HQm/hLN2xQKpA1PBvD0P6jENbX1qy5YnYx5qU7HnjpZsqv0bUaxTch3o/q154s/ZKsSQ7Lzf7K/c0NpxPv3Q1O7Oa9/e12s8PFBbUrrg1A+UvRLF0cwIAAADxoKamJnRqtG3btkEZQgAYIydOnPjud7+LL3nmmWe0su4DE5CUmU8EoKBkwVOmTNHqqUpxcTHeTK3U/3qhEAiFGOVhEPEqdIhGCLFkilHGJN+mPbwneAsCObFuYES6GyKNNpgzqrT2nuNS2aAmlCxVCgK2IrnH3OLpKByShGlytJZAGEYQEskOEs9p9WRp4suiDMQUOYVAOOCI7nKgMYFQEv0hegaj+AiegXjldcy0lWJ75ZfctXnoJcUSR4ZJsgcBTYivmt8vDn5q0eNRIGw6fRxvuv1OrZ5R0TnQhTeNfX0x2ewEwTjowpsOb2y+lLFQNIuYFJJpTaXp+UDMqconMkPWe6FqCxCeL3oOISHoVyJrm1H2EHfw+sGtf9IjqLjA876WHdtrs9ewtqCqgfzA8fr/tayumb9kZdjtLFq17nobUSK9tuzevh57DENNWup2vH5T+UOIlv8pING378r2xE82qpgxa9Id643Lp1nXzK9+YEvtijBmQQAAAAAAgAlCR0fHz372s+XLl9vt8g/UDRs2fP/739cxKiDZSBnzPoAQGhggXCM2W3jPFo4i/aZia4AqJjLhHpsWm1Q5hIeMohFCDGPAOxjTsDSAagKh0Ux8mz4Dsfq0rDKkXaKsOFtTO1TA8z7cQcgpBEJ5F9LMeQvCbk2SlLLo8Ct8ZAGFEQgVDkJKFLV6suQhNVmI60UgbXxeFzHWHxbcphatQEiWP0QuZ7wEQtYkeyIlf69lJJmk0UCc1TSVXAIhhanyhKgcAkxHpChDiI4pCufoQ/lyc4CLzd27lyEMZ5OsUZhogRwDcUtxRHkfiAcz5tzAH3MHjCwWU7a+8QD6YsuYITcE95pNj+gWCpA6rL7vG431ZzKbOrInrUBkcgvKlL+46pv+fZcPdO1dfd839IowJtTt+MuySauKp30l6B1xsPeYq2rSjTdGkaImb/06dNKOjCPPUEOm54vd2XfdEZtYk5UjdR8un3InmdiTP9i4dfVDE8U9CQAAAAC60+XoP9HUpBjdApIWA8MurKzISU+P+ZYvXbq0Y8cOfInD4ejp6Tl16tSXX34pCMQZsnnz5ldffTWFclADCQAEwlRicJBIIxlt0nCFd5jneZ/PZzIlV8WpZMPEEgc5Iz82qSBJqYxCQSlGrelysrpBtYe9MZ3IHWfLJ+ykGVlEfbKWlu2lxWsRO6xuWtIiTU7bXH+uDM0NNHGBUBKxfKsix7LBdRKViLgbDHsUUbhAKGkKfkMIiFDXaO1U9SyhOyJLJnHEeNKaJnjdoferYCwCoYiIz+jxeLV6jhGKwtPDYh/QQDgIaSa5fhZQFHZshYiOrSj6Ax+JosehQGgVifPcxfu0ekbF7BVr0TUpUDcoKy0uqW7HK5lmIhmyMW+yXpEQ8G5kGL6CWCOkGJ24HPr4vdqc2wJNn6uJReHn8QAAQqiiugZV1+z/YNsNpkpj5iyithxCtHXqitKH3J+dOsldKZ9/o15BjhpXv4M+dnr5tAeD8+hI/p4DLe+teujxUJnu1bBYrPsvv75kxqNopCxCweRbDn64a9ltd8ci5GTk3IlDN5gWIhabKCOJZxq33gzqIAAAAAAkiv9+/4Nf7/gbx0dX8gbQF7PR+E8Pbd6yelVsN3v+/Pkf//jHYbvV1NT89Kc/vf9+qCwDKEmQQFhcXPzf//3fMd/sRJO7FZ6/aAXC4P4DAwMgEIbGQPrwSspmxmSzRJLJIQchqWpk58t2IT+rInhkFxEJY6tvuBE1SIGBCWTABzdE86Jaf+NVY+bskXezW5vqS8qrw8bZ3Xa5LF8WCH1+WWQShAFMffIjFIFASIg9skZFKDrh6vnxpFwawkGoPKS5RLY9xXb8/mjSriKyNKMYncKnSDEq+KPTJqMAKwkjYcIqw5AOQpRcNQhx8VUUI1LCJAk7tcajQJhOCvAeOoyOHiEFhcV8qxMxwxs3mwpistkJgtWM+/OkecvX6BYKhsA5GcOwLkgbYmN5B1KRAqcHYVUxm3tO14BACETDUJbIurdeWVK4nE0rI9+kjZmzF4tVzhOHr07LK585W3ULSciRt15ZXH4/KixXviFy3R2fZd26etXSUda2X77lW566s4b0kZ/WFLvAXBZyjRSmq6Ot0l2E8IegJDW1bFuw+Zv6BQUAAAAAE4svLtQ//9bbekcBRI3X7//pa3+ZV1Y2e1qkzo2YkJmZ+S//8i9PPvkkE6PKWcA4I0ECYW5u7re+9a3E7Gscw3GE5ynaq5pllV+3369/SrQkhyVSjIrZebFJwScGOQgValZuvmxGSStQGbWfMrUUb6ZnZPKSB6klV5S8XcUl5c1H3y4JCIQU3X5kXyQCod/Zjyc29PKyEtbjaChMG67awrmvsii8VUWQcPFPI8VoOIFQIC2DLEVp9fz/7L1pmBvVmf59atPSLan3dru9tffdBhsb2zS2CZglbMEsCWTCGpyQZLJNMrxZhsmQGSdXmITMNUlIIMkQ8jdMEsIW4gxLwGATbIzxBsYL7bSN3e5u96J9q+39oG7VOaUqqSTVInU/v0860lGdR1WlOlLd534e1S5taSdmXzIYRIlF5mTAzHlisQ5C8jMKySK1ScNgijGR1ZYl1wpUWopRXHw16M6UBawbNQZ/7tS4iJUKbE2x9gZdpPQw7R0RCBkXZKQsAhe+DkNMer3m5wkpAZ4PMmj0WkcXXrcBjFXam7FEArLEzl2k3xcAdFl/050IoT1P/Grp9GsRR/7So9lAywWBSPz4H/8w8aNXer2Ve8HZ9uwTcyh/c9PSlZjJL4sQ+/ue+IELrr6+zFEOolPLpJnZVUqcf96bjz+8+pax9v83Eg7VHU3StR34k72n/zL3hlsciggAAAAAxiNb33rL6RCAEpFk+S9vv22zQBgKhb7whS888MADX/3qVz/72c+6XK7C7wHGExV2XxjIi6qIYCpVXJa5ZFLtcyq2iuE4hKUxKaWQdmUcGd8URUXCIZpMs4nfZ5k+bwlSIYsaN2J0pJRI5AOE0AlE2NRm1U03EidLatJxTCBs2nBJ+OwbKD2UDr13vD6W81YNJBHbGkX1954afYgJQnIhgZBs0rLuRYwmsrZKqj3Gi6RAWGxaBmzjglCcg1Ak06iKaXPSReZC4Q5CTJN21xBffJquMEWNyIxqaOcQGi01BlNn13CE+DTBvHv96bRSpxqxPsGk5KXjAQb354lWyfzFEk8MKA2afftv25yKBHCQRCLO1ih/OOXU2fnnLHcwHqDaWX7zXfzSmg9PPKNxrWNqpnZcx+2P73n8V06EpksiEd++5RfBF94Qdg52Tr6xddLltGeiWh0UE+9/sMWzdvYFl5erDiKEVq6/vPfMS/gzK2ZsjIRD5W+5chCEFLPnBEOqg+GzOyZfO2aTqQIAAABAZSJKutV2gMpHLNaiUIja2toZGNOnT29oaKByFsadOHHiS1/60vnnn3/kyBFzAwCqHRAIqwmVnpcr+OUnkSD+1VMUpVIcgVxoBi81x+fpWRSqJJOx6DCDuaZUdfha2yYjWTV5aM0lOokujw4dQwhddP1teIeAPye9khY1NKG1JLA94PXWNF6+jl3dWvORpQvPXWVka4JM7MDB3r6RR5g4WlggJHdOnvJ5DIMbE9UpGQXZaC3DXHp7uhHmuhOE4nKESmQwkmBZQWksSFyTdruJtUJUBTsIBWPpWwVcR6w0vdMMOA53EMqz5iwwa8uR2BmlQTFvvvAns7Y85qEZZQIVhYiDkeAEE4N4M9Z91KlIAAfZ8/T/Q7SS/yAYet/BYICxgddbM33jDT1Np4f7tqHc38Nc/dK5t6V3dG/73186EZ3CB0cPHfzdbxKvHODelVbPu8vXfD6ZdX8UWU4G9x9zHV388dtMHL1u/TrEB5U2Vx/ett3E7TtO9K9vuwLEL5Bk6EDj5esdCgcAAAAAxi8blkH5gGqFoijTD98ll1zShXH8+PGhoaFIJLJ9+/ZvfOMbEydOxDvv27dv/fr1hw8fNjcGoKqpsPvCQF5Uep5K8CuIqn9NTc14K+JYAhR2i41Sq3SlI5KbCg8Nqexu6jeo3IGSRiSSpoVFFjvWbxgZNNGbfZr2tCUShWWtGobIRZkqT3kRROKOUjQ4ciObdBAWUGFVn5zRzyeZf5eq1ltxxYhkHx4l7rmninQQSqTGKYuW1ZTGPpSECaI1AeJmGV1JOTmHBwbxicmgoY3UEemsOXXMwFBYrmPzrMwIob5YH950jy2vg7VgFWoFPuxgIDgDaaJWcSMDZYbHI4ubCZPxoeBxpyIBxhhTZ85r+egle2Pbk8H9ub+saO/kztm3JV49uOMvT9oc2JsvPXfymadS2493hGfOn/VJrm4BovUrtfOhnUcf9V283HRnrT9Q99bxp/FnJky8eNe2reaO4hS9z2/1NZ+PPyPEPmBWFy5YAAAAAACA6Vy0dMmmKy7PtYgBFQ5D01+7fuOyWbNsGKu2trazs3Pz5s1dXV1f+9rX8LOlt7f3+uuvL1ZWAMYwYzAV2xhG5SAcHBzU66mJqj/kFzUCTeMOQtNKNqqq0MWjEZrCqn/JuQIhj9+MzjEUIoSQKMRzNS45ebatvSPzOBjpasomBaK5XX96LFNaJg8ejri5LBM2pqJR1epLxkZvqeMCYSGbpmrXMPrCHiEQ5uxSniKeyVPLMJfYUD/CMj6my0sxSlnnIMSUNvyUq60hyghVlEB44oN3F3EXZJu8scyNPHkIerq7WtsmmxyZozB4MVTzVioghIbIZSJtXihDaIiTXYfbaeU3fTIVrBA/vlhHHMFGrzl1c4HqojZbchghJMYvuMlMjxQArFh/KULolSf/Z01rJ1urSllPcYH5q/xzhl/Y3juhxurctq898ciiupl1DQtXNFyBGgv9kJMFIXbyZP8e36oLOlfdbVFIa265O/Hqe1xg7kib5pbI0yway04++OPvOjqIRKxSoic8x9dawbUnAQAAAGBs8/99/KYbLrxwb9cHgphzCxGoSDiGWTl3ztTWVpvH9Xq9DzzwwKxZs+655x55NH3aoUOHfvjDH37729+2ORigMgGBsJqYOpUoYXry5Mmi3n7ixIk8WwM0oXCBsJC5zTgqB2EqGUN0M/aEenaXpRRx20PLQiQKcS7nyXDkWBMaUUq6Qiea2pSXOjRzLpF4WEIR9LW06fU0gqrsn5AcrVyIZ8IslGJUJsWsPAIhovM5CAUypShbjEhGpQjVKlFk+THV0ady9WAzEIQUwj6UiCmvDc1NwgllP1dUitHY2T7UrjSTvKF9qzJxRgbPmhuV45A1Ss0UCBevuxydkhEaucDU10zM3x/IcHz/7vZpikAYSQxViBB3zoUb0DEpuzjADwLh+OP1xx9eM1dZ/RMPHQqwhtKAA0BRdF7zyeHh4Q9e/vXKGddQrmbiNYrxN6/286me557/IHpGqvW1TJ1uMB19Qfp7T334ygtzJ5zj9s26YM4dhd8gxpPRD4707Z1y0WWNzTPqBuv8fn/hd5XBAerEcmlGNn8+F5j7t8cfXnPLJksHtZR9//s/i2Z/kngqPdhdPzCnbZlDEQEAAAAAgBBCs9onzmqHv/CAIT7zmc9s3759y5Yt2Wd+8pOf3HvvvRyXey8ZGHeAQFhNzJtHZHEpViBU9Z8/f74JMY11KAorj2eZg1BIJigvbndTCwCSmMD1K00VLS1Ec7MpHR3+YDW6KPN4xvpL0RnlxvGEhrk53dW4WWJd8LQ5i/R6GoEnPzWVHpWssKJxklhgJ8ukksfqF5yjqLy7lFxozhaTbtdFFixMCMWdGGoHoTVJIYYGzjYiRdCV1OeMoglVlEDIJ4gEiSnRUIpRnsxeK8QrpSCcWeC5jpF5KxUQQi0T2oQTYcSMfNPdnhYTNz6G8aQITXo4He5wKBIV/kAdL4QodsTQ6HKDJXTcsbSFmKkPnD3QiUAgBKxixY23cy7Xnsd/tXTmdYgNEK/R7tZJl48skJaQsEdASEJiGklpSYyJfEQU4mkhluKjiXQ8KsRjYjpFUVxDw5I1l/gD6kVs+3a+5vnw9LSWcxprpzfONWCK5cOxyOFDZ99bcdOtPnbZcrQMISQIliV1xzh//Ud7nnu+ddLl2WdWztgYCYdyP1RVsOPxR1bNuZWoSyLE9qT2nL/6o84FBQAAAAAAABTNfffdhwuEfX19b7/99urVqx0MCagQQCCsJlSSXjAY7O/vbzXsTT5KFk5TyY2ANphxRzKmVRhBICUiMZ2iavPlw1QJhEhLIIzzkYDqKVmcPlqAECHU1t7Bd5+h3CMCAOctnIORxWsQymLLhPIchGT6UFqR2XCjWyGxjSHUrDzpMSlcO8yp2ighQpbj6CKWzHjIzokiLYAqgZCW9TqWxWBvHy4QiirXqSwjKisQVlCKUYYnTpKkMfE1QX43VRsZA1A0vlLB5E8npYfp0RRhjKvR3I2PVRo4IqVouJD12VaEGBoVCBmuPn9fYOxRG1Dyi8pCbBXkFwWsZ/ktd53t6+V3/V/rxI8gLPdGDjRiPIjx0FyA9kzkENKuE3hMFqQYknhZTMpSQhQSLFe3yLMGzSiYRFSUEj29g3tPedg1l19Th9asRmtK/1Rl4F9/IToURNkrMFcf2rbVf801jgRTDr093atmfRzPSIHExK7+Fy64+ibnggIAAAAAAABKYc6cObNnzz527Fj2md27d4NACKBKEAgTicTQ0FAkEonFYoV7k1AUtWzZOMpt0tHR4fV68SKif/vb3z72sY8Zea8oijt37sSfqRYHYXa17+OPP75v377M45qamuuvv76zs9Py4bE0lZJkmkAokXqVyPOESJNjdxOFBC5JaSoEiZT6GySl+rMFCEf6xLprRgVCxPre2vZ/K9dfjvRh8RSjMo+QW79vYdLkp+aQjDKZMLFVyWIhByFesBDlTQ1KOAiRepfKLkLkY6giLoZehtgPYjHuQ4SQJBECoUXqXCwyjDD/pyiqzhlFlqRRBdW1dhOJYVHKmBiWIuVP15ir1E1amU0WCNPpQY930kiDrRWEFMuW9U0fD9R5CGeed+IUpyLJReCDnGdk5RDFQLHh8cXrjz+yZq6SdDERfh/yiwL20DKhDV1z1cE9O6cOSrWNy1FZa48oRLsR7aZYH0U413SQUnykq2tgr+ecFbPOWzAVTXe8iII/UPfm8YdXYMl+2yZuKPiruwLpff3V5tmfUtpSekf3HwqWMAcAAACAkhFF8b//tF3NQgAAIABJREFU+7/ff//97DOqYkkAAJTD9OnTcYGwv7/fwWCAysEZgTCZTD7zzDN/+tOf9uzZc+zYMdXtcuOwLMuPOadIHmiavvDCC1988cXsM2+88YZBgfDAgQPhcDjbZBjGDnXNDPbv3595sGPHjh07dmSff+SRR44cOeJ2l3UjOxgMohzBBqeeUuqUiEJyYGCgnOGUTZFSmZBK4mqWLIuqgWghhi+ylmQ+N5IQH1U9Ew4dQwNEEcEzQ0fnN67INj29ffk/kQ93EEpCyR8/Go0mk8k0qeIwiBoYGOg9/eE8anH2SUFK5x+Flwnth6Zpvf512M0pWVLvUoHMMcpQjKrD4OBgMBjU3LibJVbHpzm2qD3Dk0efRsis8yqRSESjUYqiaJoODZ4lBELy8NUjRTag6OLiN5dUKpW5NDEMgxDiyDuBKVkyEltCICYCN7L2E4miODQ0JAhCmdcf49QTDsIC3xFNJEkaGhpKp9PxeFz1Ujp2xlO3ZKRBMa8/+8SSdVeUEayZBINBl8uVOTcqihoX5syTpanzFmcPytDQkMfjSadNS0ldLHTyLOefM9JgPK//5Y8LVqzL/5ahoSGWZe1JvmcWBafvCiQzFdJFrikpiqUtC/Hm3t69Cwdm6XU2wtDQkN5UWLHgU6HTsRhFNRVWBTzPB4NBSZLwsiUTp83ip6HXX/v96qZzOf9cZOkKJD4UDR0+0H9w+vqPeqe3t6F2VOgHVWb61pwKTWf2pRv5d49w/tF8/jS3WJ5U2vTt4FTY0TAHb+488ttFl19v5FM4PhWWAEyF9hAOh6trJyOYCu1izEyFFY79/2SL5Xe/+91XvvIVzZfOnj1rczAAMPZQfffHlaoC5MFugVAUxQcffHDz5s3Dw8M2Dz02uPbaa3GB8Omnn/7BD35gpIbZM888gzfXrl3b3NxsfnwWoPfDxefzud1uli3rHM68XW8jiUQcXwEtSOkyh1M2ReajYyRELLWWRMK6h1BSINyBslYkMVH9X+vI0LGFLGGxDdUTrpdpTbPlvJ+Ixq1yYppl86SNygfDMCzL8mTpPo5iWJYNDvahOkwgFFPevCFxbmLP0IjWPSiYF01Goqqby0Nsh2NYVQd2lNwNu8mj422YUNSJISJiPzCy/kcokmzMNE3LZHJOXub9xCjKrQQKUWYFUAKiKLIsmzlDEEIu8g8hj2QjsQk0cQ30MJzVnyjP6WEJ2EVeKulCJEmSXswfRvvwRNW1qaSD54MKu/ezYTg3JhBKSY9HyTiKn8+O8GGoO9ByQbZZMxQsGEzF7uc85J++K5PMiWFpzLV1SgFCWYgtvGJjmcPZELPp4FOh07EYRTUVVgWZaUUz5mUXX51CaM+uvzADvX7WU8vV1rh9bs7vYmtptoZhvBTtQYwb0a7ijYaSnOg/O7z/ULL/vA3XIbRwCVpY+E0Ydl7udkWPdNbOyFYr4PzzDr/wP4uuLC45Z57p2wZqfdOVhhhfdNXHDb6x6s5nBFOhXVRjzDAV2sMYmwormQq/3DU26ta8cLlKvB8FAECWvr4+vDlhwgSnIgEqClunhOHh4SuvvPLNN9+0c9Axxsc+9rEvfOEL8qjQ0tXV9dprr61fvz7/u0RRfPTRR1XbsSZA81m0aNH27dsRQrfccsu6dSM2CIqiNmzYUL7GmVlxWV+vXaXpTHdXC1LunPNCYoJOz2I5SUplLE3jKUZlJKhCOs4TK50lSd0BISTXkKnkZHH2JVequnVedo2wK4jYkZ41NR1c3k/EU8q4spyurzda8FIFTdPJZPI0R/w/cbNcfX09ShHGR15KT8wbkq+OqLTI0Kze4RMQtr5MVu+xuhbis+RuJ5VKxeNxzY2LDCEQzlmwSC8GTWTyfxrL0EW9PQ8ul4um6fr6epqmGVWdS0nCRxGkWDa3KUUxZgVQAslkEiFUX1+f+YtylqxdRHEuI7HRXqIgnIf1WPqJBEEQRbGurs7j0S6fZP6ItPI1kaR0CZ9OkiSe5/1+f01NjeqlkIs4ISfWNjt4Pqjw+Xx1dXWVE08WnlWOiCzE6+uVep/pdNrj8QQC6pqwtnHASyxhntowpeAOFASBZXWvpZVJ/um7MslMhdbF/PoTj6yZg+UXDR1qbi63nkQikbA0ZivAp0KnYzGKaiqsCtLptCzL9fX1enfrOi8rXHKvt6f7+IED6eF+jyz7WFctV1vD+dycn+NqGbaGYWooxoNolyzLYrLnxNm96ekzFneuakeT20uKOTN9a06FVrD+qo09f/pza/tlI22KWjXjugRN+QN1xjciSZJTU2FvT7fPo+xpPv5hfb1ROdbxqbAEYCq0DUHQ+CdbySQSiXS6lJ/fDgJToT0UnAorEPv/yRbLzTff3NTU1N3dnX1m8+bNmSyjdXVFTKAAAOQSjUYPHjyIP9PW1qbXGRhX2Df1xmKxK664YteuXbaNOCZpb2+/4IIL8Eyb3//+9wsKhL/97W9PnjyZbXIct3HjRositI7Ozs5NmzbZOeJw72nUsjTbTAtJs7YsklUGWSTjNVbknBqEcZ5wEEpaRchaps/CXGFISvW1tmkUYeHjp7nASL4jytPc33uqtW2yXpwUptaUX4JRIsvLcbQLIcTHiY/GCwXs7V6fD/+YdJ615xS2SyX1LvXVNyJsZIYuIisIy2I/pmUpzw7URFI5CClL/rZRIpnGVm0wVWKgrAmgNDjyQDDG/rd4yWWGXKlW1woGK4YqmlYMNcPSdVejE1L2ElTvhR+IhaFYZUGGKEQ4VEE7be01twhvxxE18i0I1FZQfUTAUs5pWoQ39w2+uxZBwXmgomlr71BVy9aDQ3VzUXVUT8fxr+tE7wVRNiu1qyH4yvP+Klmm+fdtLzVjNU37g0emFenXBAAAAIASuPTSS/HmI488AmUIAcAUnnzyyUQigT+zejX8YQQQslMg/N73vqepDk6bNm3hwoWNjY0lrOWsovVQJvK1r30NFwhfeOGFRx999Pbbb9frf+bMmX/6p3/Cn7n55psnTy5O0hifpGNDqEVppsy7Ly+QDkKaohCNF8xTJwuNkVmhRS2tbu7ic9E+AY3WMgyFjrYgDYGwP3Rk0qhAiBB99JW/tN5yt26gDCYQlv3xGTJbLEexCCGK/GhpqUC1En+gHgWxbdL6FzHSlKl6sam5vXSBEC/NKAsIFSdHSSoHIWXJdZgmzzFe1hcI6Qoq88CyxIFweQ2tPW9q70AR7F1shS6HLA1BSBG5js0WCBuam4SuEGJHXJhuT0v+/gBCCNHKOSakQw4GoomcGqI8I5ol64acIeOFmroFSkOMr7nxVudiAQAAIYT8gbo3//7ICkxmmzjpsre2/d/K9Zc7GJVBZtdPx5vH08FpToUCAAAAAAAAlMfg4OA3vvEN/JklS5ZMnz5drz8wrrBJIOzv7//Rj36EP+P1er/61a/eeeedM2bMsCeGMcM111yzdu3a119/PfvMZz/72dra2htvvDG385kzZ6688sqhoaHsM16v9zvf+Y4NcY4B6DShXSVFEx2ERPpHBtGEmpXjIEzShNgjCBoqGsu6h86+Gmi9ECGExEQXl9S8zf/3xNAkrDnZo5vhHSGEKNy3VO7HV4k9LMMhhBjS6JbOqaSowusjBELDDkL1Zhuam4RuPlvajc4jNOZAlGZUC29G3k+TrcI1REuAJU4ZxKs9lOTLFQNLEQKht85QMp+OGTPRPjl7NFm6Qiuul0b38a4Oak62yZt3Icoi8UF6VCBkXHmvCQBC7+3dOZc+L9tMpIO+PL2dIJk44x0VCBHn/+DooVlzFuR9B1D17Hj84VVz78w2Y6F369g1DsYDAECG1bfcndh2iPOPzuM0t0guLUOq3QQCs5SGlL5w4yediwUAAAAAAABACCGe5++///4vf/nLTU1Nxt/V19d32WWX9fb24k/eddddZkcHVCs2CYRbt27FTayTJk16+eWX582bZ8/odrJ58+annnrKSM9bb721trY29/n58+f/9re/zfNGiqJ+9rOfrVy5Mh4fqQ+XSqVuvvnm11577ctf/vKsWSP/5WKx2KOPPvq9733v9OnT+Nvvv/9+WCBgEBepoSTFAtkvjaPSlFiKwlOMohw1C9X6ibfrZPtsvOKivz3+SKu7LjppwqoNV2n2qZ8+G2+6ubxeK0w2K1+WqG0kakZmBEIXIhS+dI44qqKhqQGdUpp53W/5UowihBAlI5QVCItw0dG4/qSV7rUADCEQMnk0zjJgycShgkRo0gj3F1oTQGmwZHbQpraJxt7lFlAKjZ5LLDOmBMKhnpMdfkUgTFsgEKZTAx7v6MoBtjaRiHu9dtRnqlL6jx2aO1MRCCOJ4UozXZ4NdU9tOHe0RZ3ZvR0EwjHPkubFeHP/2ffWIhAIAaAi2M8fP0+ajkaTVbj883c8/khnngQeFUAiEec8SrluMdHjZmfl6Q8AAAAAAADYgCRJ//7v//7jH//41ltvveGGG9auXcsw+e7pRSKRRx999F//9V+Hh4fx5xcvXvy5z33O4mCBqsEmgfCll17KPqYo6o9//OOYVAcRQidPntyzZ4+RnocPHy55lIULF/7iF7+49dZb5dG7/KIo/vSnP/3pT3/a2to6ZcqU4eHhkydPCoJaZ7rhhhtU6UaBPHhIV1lSNs10RWo1iKEY3O4m5ZjS5q/sRKeVImHB+JBewas1hW43tHfMRJhkzOknYzzZdbgdKfcCeCGef8sFaWhpQWeVJktzCCEXOZPxhQRClnULKJ3dFfmEPTrfLkUIIUnM9qFRESlGKUx/kuXiBUKWkK8sEgg5ijAmipTqpFOaFKqgXM0M6SBsazecDFkS0Oi5xIwtgTAeGkbYCgFey0BcJuF4r6d+tN4qxbzz8rMXXH2T6aOMGXwiMRcMp8JORaLHh+kgnmB6mq9VtyswVqipx2qDifE1N0F+UQCoFFZtuKr3+b80T9ww0qaoVdM/FgmH/IE6R+PKx1vPbLlgjpIZdTh0rA2BQAgAAAAAAFARRKPRn/3sZz/72c+am5tXrly5ZMmShQsXNjQ0BAIBr9cbDAaHhoYOHz78zjvvvPjii6q6gwghv9//61//mmXtKzwHVDg2nQp4RdnLL7/8/PPPt2fcMcw//MM/JBKJz33ucyoVsL+/v7+/X/MtGzdu3LJlC0VZks9wTOIhZQbBvF0nk5IQQ9H5BcLWtsl9e15omvARRFFy6qwwt3QvSGvbZOG0IrCxtK5A2HP8g/ZG5V5AqmyBsK19Mi4Q0jSHEHIzxFWIZwzsZFkeNf7lTTGKeRMlbZ+fIpIVlWKUohWXm1x8QTiGLLNnUS1Vltwz6lOKcBBWkEBIE+eDxLLGpT5FWqbp4qpCVjrxGN4yMddxlv5YP64gcdGIblcAoQYXYemOsBU3qy7ZsBGdUNaUNPk6HA0HsJztW36xep6SHyYWhPyiAFBZuFetRB+EEDeqCLoahl/9k//a6xwNKh8zfcRaxGPhk3prEwEAAAAAAACnGBgY2Lp169atW42/paam5rnnnjvvvPMKdwXGDTbdFx4YGMg+vvTSS+0ZdMxz9913//Wvf50zZ07BnrW1tQ888MCTTz7pco2t++YW4yazHdK1puXck0nxhqUZ/Mso5aYYRWjClZdtP/m/O4/8T+/kdNukKWUNj+XbzJOMMRkeIJp8ubIEy7pxTY7JpBgltRyRMuLkU8QtitIX9vJqrgghhFWCzCs05m5YCVKSirZzseTXkLbmOsyS3kpZbbXEHISVJBAyFLZzNBPD6oFpwPTYqkHIkPshZV6u4yxBjpC42mqgDGE+Al6iNGZDR+FZ2GYamptQOpRturyGUvUC1cvS1qV4c//Au05FAgCAJg3NTbuPE0Uo2iddtnvbi07FU5DmOqwkgSzOu+Ra52IBAAAAAAAAzGHNmjW7du1av36904EAlYVNDkLc5TZ16tQ8PaudZcuW3XjjjeVsYcaMGcY7r1279uDBg1u2bPnVr361c+dOUVTfT58xY8YnPvGJz3/+8+3t7eVENT5xs168WdNgXpa23Cp0mEgj6pS1u2jjpxBCwWAwN3lsccgCGs2oyeh7reg0EUZcUHvSSyEnqyfHEIogV+szspXsIz1xKxIOeZFS41PS1lQwF10xNQgRpmLKYvECIVn3sSjzonE4mtixMkd+QMxBWFEpRsm9UYRAKEt8VuOi6CISxlY+bnWu4/K+/losXXc1bjir94JPIB9eV4PSkMWlKzudi0UXPtXHjcZJuRqgruQYRhBS3sB8pS3G19x0m3PhAACgzepb7k6+9j7rGxXeKNdCqXLzP7OeSdnHcvJsywTDKd8BAAAAAAAAy+A47vvf//6zzz67a9cuSZIKv2GUc8899wtf+MLtt99uURozoKqxSSBsbGzs6urKPB7bKS43bdq0adMmO0d0uVx33HHHHXfcEQqFDh48eOrUqXA47PF4Wltb58+fP23aNDuDGWO4yPp8k2eaV3uDTJzoJm18OvkwzQOr80cxugKhl9SNEoIZUeGmPSYjEBKf3RuoV78lF0nOZg/VEwj7ek93IMXWI2juUiwYqjiBULlyimLRuqmrhrhNz1gzNzPkJ6I54kDLSFIuxJXkIKQwMUwuzkGoyGbU2EoxqiqGmjatFqpCQ3OT0BVC7Iim7vZW7i3LSoDjAkpDTCJUiY7V4XB3q3+02DPF7Hz+8YtuvCPvO4BqZefvH1s1Vzm40eC79ZBfFAAqkr3pYyukDjS6jMkVWLh9yy8u/ORnnI0ql23PPtE5WVnwGol2NSIQCAEAAAAAAJyHpul777333nvv7evr27FjxxtvvLFnz57Dhw9rlhtrb29ftmzZ8uXLr7vuuqVLl+Z2AIAMNgmECxYs2L17d+axXoU8oEzq6uo6OyvRx1C9kPX5pLb2DtO27CIcTiolUrAghSCBbEhK8ZI2rBRtii6BFYqjOIQQRwZQP8GI1RVTGZG2sBcdOos4RSDUNmXiQmlRF0NMsBGkojOv1gbqEFZUziIHIUsmX+W8pDUTE0dRJS3aoDB3JoWKWAwlyensqTDGHIQeUsUX2WLEbMNI/DA9KhAyuEMOyIFhlRqEshBDqC5PZ6c4GT2Dy7ztVCWqmIApLG1ZgjcPDL63FoFACACVyOoN1/Q9/39NEy8ZaVPU6unXVaDDe4pA/NI4PnysEa1zKhgAAAAAAAAglwkTJlx//fXXX399phkOhwcGBoaHh2VZ9nq9gUCgrq4uEAjk3wgAZLDJOHLJJZdkH7/99tv2DAoAZULU5yvKzFQIjrwRwJG5TEXZzLFywbNi0pSuQOghbY4CY4bogpv2GAYhxJDix6QpHYW3gYtbOva7RCSEN7U1V+yYGncQnuw6jJCiqPFC0QKhL+DHmxbVIGTUuVtVPwuwA1FJKUZxByEq5osg48Ug81SmrEJc5HfE5TWShrdo0qlBpcH6Eom4FaO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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"/home/n/nichollsh/AGNI/out/emission.pdf\""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "BoundsError",
+     "evalue": "BoundsError: attempt to access 0-element Vector{AGNI.atmosphere.Atmos_t} at index [1]",
+     "output_type": "error",
+     "traceback": [
+      "BoundsError: attempt to access 0-element Vector{AGNI.atmosphere.Atmos_t} at index [1]\n",
+      "\n",
+      "Stacktrace:\n",
+      " [1] getindex(A::Vector{AGNI.atmosphere.Atmos_t}, i1::Int64)\n",
+      "   @ Base ./essentials.jl:13\n",
+      " [2] top-level scope\n",
+      "   @ ~/AGNI/misc/jl_notebook_cell_df34fa98e69747e1a8f8a730347b8e2f_X15sdnNjb2RlLXJlbW90ZQ==.jl:10"
+     ]
     }
    ],
    "source": [
@@ -2086,22 +497,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Writing index 1 \n",
-      "Writing index 2 \n",
-      "Writing index 3 \n",
-      "Writing index 4 \n",
-      "Writing index 5 \n",
-      "Writing index 6 \n",
-      "Writing index 7 \n",
-      "Writing index 8 \n",
-      "Writing index 9 \n",
       "Done \n"
      ]
     }
diff --git a/res/config/55cnce_chem.toml b/res/config/55cnce_chem.toml
index 014ddc98..6ac6796e 100644
--- a/res/config/55cnce_chem.toml
+++ b/res/config/55cnce_chem.toml
@@ -10,7 +10,7 @@ title = "Roughly 55 Cancri e @ fO2=IW"
     surface_material= "res/surface_albedos/basalt_tuff.dat"
     radius          = 1.1959e7
     gravity         = 22.304
-    tmp_int         = 0.0
+    flux_int        = 0.0
     turb_coeff      = 0.001
     wind_speed      = 2.0
 
diff --git a/res/config/L-98-59d.toml b/res/config/L-98-59d.toml
index 79f721c5..aa6a43d7 100644
--- a/res/config/L-98-59d.toml
+++ b/res/config/L-98-59d.toml
@@ -10,7 +10,7 @@ title = "L 98-59 d"
     surface_material= "res/surface_albedos/lunar_marebasalt.dat"
     radius          = 6.59051e6
     gravity         = 16.73
-    tmp_int         = 0.0
+    flux_int        = 0.0
     turb_coeff      = 0.001
     wind_speed      = 2.0
 
diff --git a/res/config/condense.toml b/res/config/condense.toml
index 8786a616..22717dcd 100644
--- a/res/config/condense.toml
+++ b/res/config/condense.toml
@@ -11,7 +11,7 @@ title = "Condensation test"
     albedo_s        = 0.2
     radius          = 6.37e6
     gravity         = 9.81
-    tmp_int         = 0.0
+    flux_int        = 0.0
     turb_coeff      = 1.0e-4
     wind_speed      = 10.0
     skin_k          = 2.0
diff --git a/res/config/default.toml b/res/config/default.toml
index ef87b18e..6d3f8f38 100644
--- a/res/config/default.toml
+++ b/res/config/default.toml
@@ -17,7 +17,7 @@ title = "Default"                       # Name for this configuration file
     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.
-    tmp_int         = 0.0               # Planet's effective interior temperature [K]. Used when sol_type=3.
+    flux_int        = 0.0               # Planet's internal flux [W m-2]. Used when sol_type=3.
     turb_coeff      = 0.001             # Turbulent exchange coefficient for sensible heat.
     wind_speed      = 2.0               # Effective wind speed for sensible heat [m s-1].
 
diff --git a/res/config/hotdry.toml b/res/config/hotdry.toml
index f5e1575f..fc558bed 100644
--- a/res/config/hotdry.toml
+++ b/res/config/hotdry.toml
@@ -11,7 +11,7 @@ title = "Hot and dry"
     albedo_s        = 0.0
     radius          = 6.37e6
     gravity         = 9.81
-    tmp_int         = 0.0
+    flux_int        = 0.0
     turb_coeff      = 1.0e-2
     wind_speed      = 10.0
 
diff --git a/src/AGNI.jl b/src/AGNI.jl
index fa416c35..e61b6875 100755
--- a/src/AGNI.jl
+++ b/src/AGNI.jl
@@ -32,7 +32,7 @@ module AGNI
     import .plotting
     import .energy
     import .solver
-    import .realgas 
+    import .realgas
 
     # Export
     # export atmosphere
@@ -328,9 +328,9 @@ module AGNI
             tmp_magma   = cfg["planet"]["tmp_magma"]
         end
         #     effective temperature case
-        tmp_int::Float64 = 0.0
+        flux_int::Float64 = 0.0
         if sol_type == 3
-            tmp_int = cfg["planet"]["tmp_int"]
+            flux_int = cfg["planet"]["flux_int"]
         end
         #     target OLR case
         target_olr::Float64 = 0.0
@@ -358,7 +358,7 @@ module AGNI
                                 overlap_method=overlap,
                                 skin_d=skin_d, skin_k=skin_k, tmp_magma=tmp_magma,
                                 target_olr=target_olr,
-                                tmp_int=tmp_int,
+                                flux_int=flux_int,
                                 surface_material=surface_mat,
                                 albedo_s=albedo_s,
                                 thermo_functions=thermo_funct,
diff --git a/src/atmosphere.jl b/src/atmosphere.jl
index a5dfd86c..d33bf1a8 100644
--- a/src/atmosphere.jl
+++ b/src/atmosphere.jl
@@ -70,7 +70,6 @@ module atmosphere
         surf_e_arr::Array{Float64,1}    # Spectral surface emissivity
 
         tmp_surf::Float64               # Surface brightness temperature [K]
-        tmp_int::Float64                # Effective temperature of the planet [K]
         grav_surf::Float64              # Surface gravity [m s-2]
         overlap_method::Int             # Absorber overlap method to be used
 
@@ -269,7 +268,7 @@ 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 (or internal) brightness temperature [K] for sol_type==3.
+    - `flux_int::Float64`               planet's internal flux 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?
@@ -302,7 +301,7 @@ module atmosphere
                     skin_k::Float64 =           2.0,
                     overlap_method::Int =       4,
                     target_olr::Float64 =       0.0,
-                    tmp_int::Float64 =          0.0,
+                    flux_int::Float64 =         0.0,
                     all_channels::Bool  =       true,
                     flag_rayleigh::Bool =       false,
                     flag_gcontinuum::Bool =     false,
@@ -319,7 +318,7 @@ module atmosphere
         end
 
         # Code versions
-        atmos.AGNI_VERSION = "0.8.2"
+        atmos.AGNI_VERSION = "0.8.3"
         atmos.SOCRATES_VERSION = readchomp(joinpath(ENV["RAD_DIR"],"version"))
         @debug "AGNI VERSION = $(atmos.AGNI_VERSION)"
         @debug "Using SOCRATES at $(ENV["RAD_DIR"])"
@@ -356,7 +355,6 @@ module atmosphere
         atmos.nlev_c         =  nlev_centre
         atmos.nlev_l         =  atmos.nlev_c + 1
         atmos.tmp_surf =        max(tmp_surf, atmos.tmp_floor)
-        atmos.tmp_int =         tmp_int
         atmos.grav_surf =       max(1.0e-7, gravity)
         atmos.zenith_degrees =  max(min(zenith_degrees,89.8), 0.2)
         atmos.surface_material= surface_material
@@ -367,7 +365,7 @@ module atmosphere
         atmos.toa_heating =     atmos.instellation * (1.0 - atmos.albedo_b) *
                                     s0_fact * cosd(atmos.zenith_degrees)
 
-        atmos.flux_int =        phys.sigma * (atmos.tmp_int)^4.0
+        atmos.flux_int =        flux_int
         atmos.target_olr =      max(1.0e-20,target_olr)
 
         atmos.C_d =             max(0,C_d)
@@ -425,8 +423,8 @@ module atmosphere
         atmos.cloud_arr_f   = zeros(Float64, atmos.nlev_c)
 
         # Phase change timescales [seconds]
-        atmos.phs_tau_mix = 1.0e4   # mixed composition case
-        atmos.phs_tau_sgl = 1.0e4   # single gas case
+        atmos.phs_tau_mix = 1.0e5   # mixed composition case
+        atmos.phs_tau_sgl = 1.0e5   # single gas case
 
         # Hardcoded cloud properties
         atmos.cond_alpha    = 0.0     # 0% of condensate is retained (i.e. complete rainout)
diff --git a/src/dump.jl b/src/dump.jl
index f811ce57..9bf17996 100644
--- a/src/dump.jl
+++ b/src/dump.jl
@@ -126,7 +126,7 @@ module dump
             # Scalar quantities
             #    Create variables
             var_tmp_surf =  defVar(ds, "tmp_surf",      Float64, (), attrib = OrderedDict("units" => "K"))      # Surface brightness temperature [K]
-            var_tmp_int =   defVar(ds, "tmp_int",       Float64, (), attrib = OrderedDict("units" => "K"))      # Effective temperature [K]
+            var_flux_int =  defVar(ds, "flux_int",      Float64, (), attrib = OrderedDict("units" => "W m-2"))  # Internal flux [W m-2]
             var_inst =      defVar(ds, "instellation",  Float64, (), attrib = OrderedDict("units" => "W m-2"))  # Solar flux at TOA
             var_s0fact =    defVar(ds, "inst_factor",   Float64, ())                                            # Scale factor applied to instellation
             var_albbond =   defVar(ds, "bond_albedo",   Float64, ())                                            # Bond albedo used to scale-down instellation
@@ -150,7 +150,7 @@ module dump
 
             #     Store data
             var_tmp_surf[1] =   atmos.tmp_surf
-            var_tmp_int[1] =    atmos.tmp_int
+            var_flux_int[1] =   atmos.flux_int
             var_inst[1] =       atmos.instellation
             var_s0fact[1] =     atmos.s0_fact
             var_albbond[1] =    atmos.albedo_b
diff --git a/src/load.jl b/src/load.jl
index 3809eb21..dff19d31 100644
--- a/src/load.jl
+++ b/src/load.jl
@@ -59,7 +59,7 @@ module load
             # ----------------------
             # Load scalar quantities
             atmos.tmp_surf =        ds["tmp_surf"][]        # Surface brightness temperature [K]
-            atmos.tmp_int  =        ds["tmp_int"][]         # Effective temperature [K]
+            atmos.flux_int  =       ds["flux_int"][]        # Internal flux [W m-2]
             atmos.instellation =    ds["instellation"][]    # Solar flux
             atmos.s0_fact =         ds["inst_factor"][]     # Scale factor applied to instellation
             atmos.albedo_b =        ds["bond_albedo"][]     # Bond albedo used to scale-down instellation
diff --git a/src/solver.jl b/src/solver.jl
index 6f6cdb5a..0980e659 100644
--- a/src/solver.jl
+++ b/src/solver.jl
@@ -73,7 +73,7 @@ module solver
 
     Arguments:
     - `atmos::Atmos_t`                  the atmosphere struct instance to be used.
-    - `sol_type::Int`                   solution type, 1: tmp_surf | 2: skin | 3: tmp_int | 4: tgt_olr
+    - `sol_type::Int`                   solution type, 1: tmp_surf | 2: skin | 3: flux_int | 4: tgt_olr
     - `chem_type::Int`                  chemistry type (see wiki)
     - `convect::Bool`                   include convection
     - `sens_heat::Bool`                 include sensible heating
@@ -263,7 +263,6 @@ module solver
             elseif (sol_type == 3)
                 # Zero loss
                 resid[2:end] .= atmos.flux_dif[1:end]
-                # Total flux at TOA is equal to sigma*tmp_int^4
                 resid[1] = atmos.flux_tot[1] - atmos.flux_int
 
             elseif (sol_type == 4)
@@ -401,8 +400,7 @@ module solver
             @info @sprintf("    skin_d   = %.2f m",         atmos.skin_d)
             @info @sprintf("    skin_k   = %.2f W K-1 m-1", atmos.skin_k)
         elseif (sol_type == 3)
-            @info @sprintf("    tmp_int  = %.2f K",     atmos.tmp_int)
-            @info @sprintf("    f_int    = %.2f W m-2", atmos.flux_int)
+            @info @sprintf("    flux_int = %.2f W m-2", atmos.flux_int)
         elseif (sol_type == 4)
             @info @sprintf("    tgt_olr  = %.2f W m-2", atmos.target_olr)
         end