From a7e22ec295f30695940047c941d9450283e2296d Mon Sep 17 00:00:00 2001
From: Robert Izzard <r.izzard@surrey.ac.uk>
Date: Sun, 12 Sep 2021 22:28:51 +0200
Subject: [PATCH] add solar-system notebook

clean up some other code
---
 examples/notebook_HRD.ipynb                   |   4 +-
 .../notebook_common_envelope_evolution.ipynb  |   4 +-
 examples/notebook_individual_systems.ipynb    |  47 +--
 ...otebook_luminosity_function_binaries.ipynb |   4 +-
 .../notebook_luminosity_function_single.ipynb |   4 +-
 examples/notebook_solar_system.ipynb          | 372 ++++++++++++++++++
 6 files changed, 405 insertions(+), 30 deletions(-)
 create mode 100644 examples/notebook_solar_system.ipynb

diff --git a/examples/notebook_HRD.ipynb b/examples/notebook_HRD.ipynb
index 52590f8a2..23202ff3a 100644
--- a/examples/notebook_HRD.ipynb
+++ b/examples/notebook_HRD.ipynb
@@ -796,7 +796,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -810,7 +810,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/notebook_common_envelope_evolution.ipynb b/examples/notebook_common_envelope_evolution.ipynb
index 526320ccf..580dbcfba 100644
--- a/examples/notebook_common_envelope_evolution.ipynb
+++ b/examples/notebook_common_envelope_evolution.ipynb
@@ -686,7 +686,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -700,7 +700,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/notebook_individual_systems.ipynb b/examples/notebook_individual_systems.ipynb
index 85aef1e39..50c604385 100644
--- a/examples/notebook_individual_systems.ipynb
+++ b/examples/notebook_individual_systems.ipynb
@@ -8,19 +8,22 @@
     "# Tutorial: Running individual systems with binary_c-python\n",
     "This notebook will show you how to run single systems and analyze their results.\n",
     "\n",
-    "It can be useful to have some functions to quickly run a single system to e.g. inspect what evolutionary steps a specific system goes through, to plot the mass loss evolution of a single star, etc. "
+    "It can be useful to have some functions to quickly run a single system to, for example, inspect what evolutionary steps a specific system goes through, to plot the mass loss evolution of a single system, etc. "
    ]
   },
   {
    "cell_type": "markdown",
    "id": "dd5d9ec7-5791-45f1-afbd-225947e2a583",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
-    "## Single system with run_wrapper\n",
+    "## Single system with run_system_wrapper\n",
+    "\n",
     "The simplest method to run a single system is to use the run_system wrapper. This function deals with setting up the argument string, makes sure all the required parameters are included and handles setting and cleaning up the custom logging functionality (see notebook_custom_logging).\n",
     "\n",
     "As arguments to this function we can add any of the parameters that binary_c itself actually knows, as well as:\n",
-    "- custom_logging_code: string containing a print statement that binary_c can use to print information\n",
+    "- custom_logging_code: string containing a Printf statement that binary_c can use to print information\n",
     "- log_filename: path of the logfile that binary_c generates\n",
     "- parse_function: function that handles parsing the output of binary-c"
    ]
@@ -86,7 +89,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "      TIME      M1       M2   K1  K2           SEP      PER    ECC  R1/ROL1 R2/ROL2  TYPE RANDOM_SEED=17089 RANDOM_COUNT=0\n",
+      "      TIME      M1       M2   K1  K2           SEP      PER    ECC  R1/ROL1 R2/ROL2  TYPE RANDOM_SEED=12122 RANDOM_COUNT=0\n",
       "     0.0000    1.000    0.000  1  15            -1       -1  -1.00   0.000   0.000  \"INITIAL \"\n",
       " 11003.1302    1.000    0.000  2  15            -1       -1  -1.00   0.000   0.000  \"OFF_MS\"\n",
       " 11003.1302    1.000    0.000  2  15            -1       -1  -1.00   0.000   0.000  \"TYPE_CHNGE\"\n",
@@ -225,18 +228,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0        time      mass initial_mass stellar_type\n",
-      "1           0         1            1            1\n",
-      "2           0         1            1            1\n",
-      "3       1e-06         1            1            1\n",
-      "4       2e-06         1            1            1\n",
-      "5       3e-06         1            1            1\n",
-      "...       ...       ...          ...          ...\n",
-      "1617  12461.8  0.546683            1            6\n",
-      "1618  12461.9  0.517749            1           11\n",
-      "1619  13461.9  0.517749            1           11\n",
-      "1620  14461.9  0.517749            1           11\n",
-      "1621    15000  0.517749            1           11\n",
+      "0             time      mass initial_mass stellar_type\n",
+      "1              0.0       1.0          1.0          1.0\n",
+      "2              0.0       1.0          1.0          1.0\n",
+      "3         0.000001       1.0          1.0          1.0\n",
+      "4         0.000002       1.0          1.0          1.0\n",
+      "5         0.000003       1.0          1.0          1.0\n",
+      "...            ...       ...          ...          ...\n",
+      "1617  12461.819616  0.546683          1.0          6.0\n",
+      "1618  12461.945773  0.517749          1.0         11.0\n",
+      "1619  13461.945773  0.517749          1.0         11.0\n",
+      "1620  14461.945773  0.517749          1.0         11.0\n",
+      "1621       15000.0  0.517749          1.0         11.0\n",
       "\n",
       "[1621 rows x 4 columns]\n"
      ]
@@ -358,7 +361,7 @@
       "Creating and loading custom logging functionality\n",
       "Running binary_c M_1 10 api_log_filename_prefix /tmp/binary_c_python/notebooks/notebook_individual_systems\n",
       "Cleaning up the custom logging stuff. type: single\n",
-      "Removed /tmp/binary_c_python/custom_logging/libcustom_logging_8967553693ac4e11a49c42d4eef773e8.so\n",
+      "Removed /tmp/binary_c_python/custom_logging/libcustom_logging_6d4ed30fa6684630885c08523336d45f.so\n",
       "EXAMPLE_MASSLOSS             0.000000000000e+00 10 0 10 1\n",
       "EXAMPLE_MASSLOSS             0.000000000000e+00 10 10 10 1\n",
       "EXAMPLE_MASSLOSS             1.000000000000e-06 10 10 10 1\n",
@@ -460,13 +463,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "adding: parse_function=<function object_parse_function at 0x7fb4d41ebbf8> to grid_options\n",
+      "adding: parse_function=<function object_parse_function at 0x14938275de50> to grid_options\n",
       "<<<< Warning: Key does not match previously known parameter:                     adding: output_dir=/tmp/binary_c_python/notebooks/notebook_individual_systems to custom_options >>>>\n",
       "adding: api_log_filename_prefix=/tmp/binary_c_python/notebooks/notebook_individual_systems to BSE_options\n",
       "Creating and loading custom logging functionality\n",
       "Running binary_c M_1 10 api_log_filename_prefix /tmp/binary_c_python/notebooks/notebook_individual_systems\n",
       "Cleaning up the custom logging stuff. type: single\n",
-      "Removed /tmp/binary_c_python/custom_logging/libcustom_logging_5d7779e8190e4b79b10c7e6a44cb0e7e.so\n",
+      "Removed /tmp/binary_c_python/custom_logging/libcustom_logging_3bc2f679982a4df6a2b83d382e83d724.so\n",
       "[['time', 'mass', 'initial_mass', 'stellar_type'], [0.0, 10.0, 0.0, 10.0, 1.0], [0.0, 10.0, 10.0, 10.0, 1.0], [1e-06, 10.0, 10.0, 10.0, 1.0]]\n",
       "dict_keys(['time', 'mass', 'initial_mass', 'stellar_type'])\n"
      ]
@@ -496,7 +499,7 @@
     "## Single system via API functionality\n",
     "It is possible to construct your own functionality to run a single system by directly calling the API function to run a system. Under the hood all the other functions and wrappers actually use this API.\n",
     "\n",
-    "There are less failsafes for this method, so this make sure the input is correct and binary_c knows all the arguments you pass in.\n",
+    "There are fewer failsafes for this method, so this make sure the input is correct and binary_c knows all the arguments you pass in.\n",
     "\n",
     "for more details on this API function see `notebook_api_functions`"
    ]
@@ -519,7 +522,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "SINGLE_STAR_LIFETIME 15 14.9947\n",
+      "SINGLE_STAR_LIFETIME 15 14.9927\n",
       "\n"
      ]
     }
diff --git a/examples/notebook_luminosity_function_binaries.ipynb b/examples/notebook_luminosity_function_binaries.ipynb
index e50463e6e..51f866085 100644
--- a/examples/notebook_luminosity_function_binaries.ipynb
+++ b/examples/notebook_luminosity_function_binaries.ipynb
@@ -679,7 +679,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -693,7 +693,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/notebook_luminosity_function_single.ipynb b/examples/notebook_luminosity_function_single.ipynb
index cdae316f9..acab6b2d0 100644
--- a/examples/notebook_luminosity_function_single.ipynb
+++ b/examples/notebook_luminosity_function_single.ipynb
@@ -703,7 +703,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -717,7 +717,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/notebook_solar_system.ipynb b/examples/notebook_solar_system.ipynb
new file mode 100644
index 000000000..57c0842fc
--- /dev/null
+++ b/examples/notebook_solar_system.ipynb
@@ -0,0 +1,372 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ddc06da3-fc68-4c6f-8067-14ea862b964d",
+   "metadata": {},
+   "source": [
+    "## Solar system using the API functionality\n",
+    "We use the API interface to construct a model of the Solar system."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56886792-d379-4eac-b0d4-54508edb39c7",
+   "metadata": {},
+   "source": [
+    "First we must construct the argument string that we pass to binary_c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ec48125c-6bf5-48f4-9357-8261800b5d8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "s 0 0x2295520 (nil) (null)\n",
+      "s 1 0x22955b8 0x2293e10 Venus\n",
+      "s 2 0x2295650 0x22938f0 Earth\n",
+      "s 3 0x22956e8 0x239ad10 Mars\n",
+      "s 4 0x2295780 0x239ad30 Jupiter\n",
+      "s 5 0x2295818 0x239ad50 Saturn\n",
+      "s 6 0x22958b0 0x2292fa0 Uranus\n",
+      "s 7 0x2295948 0x239ad70 Neptune\n",
+      "s 8 0x22959e0 0x22948c0 Pluto\n",
+      "Shift 0x22955b8 (0x2293e10) to 0x2295520 ((nil))\n",
+      "s 0 0x2295520 name=0x2293e10=Venus\n",
+      "s 1 0x22955b8 name=0x22938f0=Earth\n",
+      "s 2 0x2295650 name=0x239ad10=Mars\n",
+      "s 3 0x22956e8 name=0x239ad30=Jupiter\n",
+      "s 4 0x2295780 name=0x239ad50=Saturn\n",
+      "s 5 0x2295818 name=0x2292fa0=Uranus\n",
+      "s 6 0x22958b0 name=0x239ad70=Neptune\n",
+      "s 7 0x2295948 name=0x22948c0=Pluto\n",
+      "realloc 8\n",
+      "s 0 0x2295520 (nil) (null)\n",
+      "s 1 0x22955b8 0x22938f0 Earth\n",
+      "s 2 0x2295650 0x239ad10 Mars\n",
+      "s 3 0x22956e8 0x239ad30 Jupiter\n",
+      "s 4 0x2295780 0x239ad50 Saturn\n",
+      "s 5 0x2295818 0x2292fa0 Uranus\n",
+      "s 6 0x22958b0 0x239ad70 Neptune\n",
+      "s 7 0x2295948 0x22948c0 Pluto\n",
+      "Shift 0x22955b8 (0x22938f0) to 0x2295520 ((nil))\n",
+      "s 0 0x2295520 name=0x22938f0=Earth\n",
+      "s 1 0x22955b8 name=0x239ad10=Mars\n",
+      "s 2 0x2295650 name=0x239ad30=Jupiter\n",
+      "s 3 0x22956e8 name=0x239ad50=Saturn\n",
+      "s 4 0x2295780 name=0x2292fa0=Uranus\n",
+      "s 5 0x2295818 name=0x239ad70=Neptune\n",
+      "s 6 0x22958b0 name=0x22948c0=Pluto\n",
+      "realloc 7\n",
+      "s 0 0x2295520 (nil) (null)\n",
+      "s 1 0x22955b8 0x239ad10 Mars\n",
+      "s 2 0x2295650 0x239ad30 Jupiter\n",
+      "s 3 0x22956e8 0x239ad50 Saturn\n",
+      "s 4 0x2295780 0x2292fa0 Uranus\n",
+      "s 5 0x2295818 0x239ad70 Neptune\n",
+      "s 6 0x22958b0 0x22948c0 Pluto\n",
+      "Shift 0x22955b8 (0x239ad10) to 0x2295520 ((nil))\n",
+      "s 0 0x2295520 name=0x239ad10=Mars\n",
+      "s 1 0x22955b8 name=0x239ad30=Jupiter\n",
+      "s 2 0x2295650 name=0x239ad50=Saturn\n",
+      "s 3 0x22956e8 name=0x2292fa0=Uranus\n",
+      "s 4 0x2295780 name=0x239ad70=Neptune\n",
+      "s 5 0x2295818 name=0x22948c0=Pluto\n",
+      "realloc 6\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from binarycpython.utils.functions import temp_dir\n",
+    "from binarycpython import _binary_c_bindings\n",
+    "\n",
+    "TMP_DIR = temp_dir(\"notebooks\", \"notebook_solar_system\")\n",
+    "M_1 = 1.0  # Msun\n",
+    "metallicity = 0.02\n",
+    "max_evolution_time = 15000  # Myr. You need to include this argument.\n",
+    "api_log_filename_prefix = TMP_DIR,\n",
+    "orbiting_objects = \"\"\"\\\n",
+    "orbiting_object name=Mercury,M=1MMercury,orbital_separation=1AMercury,orbits=star1,orbital_eccentricity=0.206 \\\n",
+    "orbiting_object name=Venus,M=1MVenus,orbital_separation=1AVenus,orbits=star1,orbital_eccentricity=0.007 \\\n",
+    "orbiting_object name=Earth,M=1MEarth,orbital_separation=1AEarth,orbits=star1,orbital_eccentricity=0.017 \\\n",
+    "orbiting_object name=Mars,M=1MMars,orbital_separation=1AMars,orbits=star1,orbital_eccentricity=0.093 \\\n",
+    "orbiting_object name=Jupiter,M=1MJupiter,orbital_separation=1AJupiter,orbits=star1,orbital_eccentricity=0.048 \\\n",
+    "orbiting_object name=Saturn,M=1MSaturn,orbital_separation=1ASaturn,orbits=star1,orbital_eccentricity=0.056 \\\n",
+    "orbiting_object name=Uranus,M=1MUranus,orbital_separation=1AUranus,orbits=star1,orbital_eccentricity=0.047 \\\n",
+    "orbiting_object name=Neptune,M=1MNeptune,orbital_separation=1ANeptune,orbits=star1,orbital_eccentricity=0.009 \\\n",
+    "orbiting_object name=Pluto,M=1MPluto,orbital_separation=1APluto,orbital_eccentricity=0.2444,orbits=star1,orbital_eccentricity=0.244 \\\n",
+    "orbiting_objects_log 1\n",
+    "\"\"\"\n",
+    "# Here we set up the argument string that is passed to the bindings\n",
+    "\n",
+    "argstring = \"\"\"\n",
+    "binary_c  M_1 {M_1} metallicity {metallicity} max_evolution_time {max_evolution_time} api_log_filename_prefix {api_log_filename_prefix} {orbiting_objects} \n",
+    "\"\"\".format(\n",
+    "    M_1=M_1,\n",
+    "    metallicity=metallicity,\n",
+    "    max_evolution_time=max_evolution_time,\n",
+    "    api_log_filename_prefix=TMP_DIR,\n",
+    "    orbiting_objects=orbiting_objects\n",
+    ").strip()\n",
+    "\n",
+    "\n",
+    "output = _binary_c_bindings.run_system(argstring)\n",
+    "#print(output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55c8ea24-0fc0-452c-8121-1e7667433479",
+   "metadata": {},
+   "source": [
+    "There is a lot of data here! Let's split it into a set of lists of lists, one for each planet, and let's select only objects that are still orbiting their central star."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "323aad96-408d-404a-a56f-da98d51844dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "import pandas as pd\n",
+    "import math\n",
+    "#pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
+    "data = {}\n",
+    "for line in output.splitlines():\n",
+    "    #print (line)\n",
+    "    match = re.search('Object (\\d+) (\\S+)',line)\n",
+    "    if match:\n",
+    "        number = match.group(1)\n",
+    "        name = match.group(2)\n",
+    "        if not name in data:\n",
+    "            data[name] = []\n",
+    "        x = line.split()\n",
+    "        if x[4] == 'CS1':\n",
+    "            x.pop(0) # remove first element of the list \"Object\" - this is superfluous\n",
+    "            x.pop(0) # remove second element of the list \"index\" - this is superfluous\n",
+    "            x.pop(0) # remove third element of the list \"name\" - this is superfluous (it's the dict key)\n",
+    "            x.pop(1) # remove fourth element \"CS1\" - this is superfluous (we select this already) \n",
+    "            # x[0] = math.log10((max(float(x[0]),1e-6)))\n",
+    "            for i in range(0,10):\n",
+    "                x[i] = float(x[i])\n",
+    "            data[name].append(x)            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2b99ae0-2e5f-49b1-bb27-1b65ef926e72",
+   "metadata": {},
+   "source": [
+    "Now convert this data to Pandas dataframes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5be09aa5-02cf-4db5-97eb-a4aefbddfac6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataframes = {}\n",
+    "for planet in data:\n",
+    "    dataframes[planet] = pd.DataFrame(data[planet], \n",
+    "                                      columns=['time',\n",
+    "                                               'mass',\n",
+    "                                               'angular momentum',\n",
+    "                                               'separation',\n",
+    "                                               'period',\n",
+    "                                               'eccentricity',\n",
+    "                                               'temperature',\n",
+    "                                               'angular frequency',\n",
+    "                                               'spin of central object',\n",
+    "                                               'in disc'],\n",
+    "                                     )\n",
+    "    #print (dataframes['Earth'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a15227d-b69f-4668-8ad8-3e9bfbaa0ee9",
+   "metadata": {},
+   "source": [
+    "We now make a plot of the separation (distance from the object to the Sun) as a function of time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ff18274d-0ed7-42cc-8830-b2261a893c6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[None]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# set up seaborn for use in the notebook\n",
+    "sns.set(rc={'figure.figsize':(20,10)})\n",
+    "sns.set_context(\"notebook\",\n",
+    "                font_scale=1.5,\n",
+    "                rc={\"lines.linewidth\":2.5})\n",
+    "\n",
+    "x = 'time'\n",
+    "y = 'separation'\n",
+    "for planet in dataframes:\n",
+    "    #print (dataframes[planet])\n",
+    "    df = dataframes[planet]\n",
+    "    p = sns.lineplot(data=df,\n",
+    "                     sort=False,\n",
+    "                     x=x,\n",
+    "                     y=y,\n",
+    "                     estimator=None,\n",
+    "                     ci=None,\n",
+    "                     legend='full'\n",
+    "                    )\n",
+    "    xx = df.head(1).loc[0,x]\n",
+    "    yy = df.head(1).loc[0,y]\n",
+    "    p.text(xx,yy,planet)\n",
+    "    \n",
+    "p.set_xlabel(\"time/Myr\")\n",
+    "p.set_ylabel(\"separation/Rsun\")\n",
+    "#p.set(xlim=(0,5)) # might be necessary?\n",
+    "p.set(yscale=\"log\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ab65543-b864-41d9-98e6-10a5ef051a22",
+   "metadata": {},
+   "source": [
+    "The inner objects are swallowed by the Sun when it becomes a red giant. Earth survives, although tides mess with its orbit somewhat. Jupiter is pushed out beyond the orbits of Saturn and Uranus, and this simple model assumes they are ejected in the interaction because Jupiter is (far) more massive. There are options to detect when its orbit is too close to Neptune, and hence possibly eject Neptune, but I'll let you explore these.\n",
+    "\n",
+    "We now construct a plot of the temperature of each planet vs time. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "13214947-a209-4695-a6e2-692614af05dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[None]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# set up seaborn for use in the notebook\n",
+    "sns.set(rc={'figure.figsize':(20,10)})\n",
+    "sns.set_context(\"notebook\",\n",
+    "                font_scale=1.5,\n",
+    "                rc={\"lines.linewidth\":2.5})\n",
+    "\n",
+    "x = 'time'\n",
+    "y = 'temperature'\n",
+    "for planet in dataframes:\n",
+    "    df = dataframes[planet]\n",
+    "    p = sns.lineplot(data=df,\n",
+    "                     sort=False,\n",
+    "                     x=x,\n",
+    "                     y=y,\n",
+    "                     estimator=None,\n",
+    "                     ci=None,\n",
+    "                     legend='full'\n",
+    "                    )\n",
+    "    xx = df.head(1).loc[0,x]\n",
+    "    yy = df.head(1).loc[0,y]\n",
+    "    p.text(xx,yy,planet)\n",
+    "    \n",
+    "p.set_xlabel(\"time/Myr\")\n",
+    "p.set_ylabel(\"Temperature/K\")\n",
+    "p.set(ylim=(30,3000)) # might be necessary?\n",
+    "p.set(yscale=\"log\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8feac6a0-4a91-4600-8b3b-fab149ffb640",
+   "metadata": {},
+   "source": [
+    "It gets a little toasty on Earth in the not too distant future!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22df65d7-1c77-4e9c-b188-955219377014",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
-- 
GitLab