From 86330e067bbb6b580db0796324a2cf42401e2c73 Mon Sep 17 00:00:00 2001
From: David Hendriks <davidhendriks93@gmail.com>
Date: Fri, 13 Sep 2019 10:57:18 +0100
Subject: [PATCH] removed stuff that didnt really belong here

---
 ...inary_full_evolution_plot-checkpoint.ipynb | 162 ---------------
 .../single_star_full-checkpoint.ipynb         | 141 --------------
 .../binary_full_evolution_plot.ipynb          | 184 ------------------
 david_calculations/single_star_full.ipynb     | 143 --------------
 david_results/s.png                           | Bin 12833 -> 0 bytes
 5 files changed, 630 deletions(-)
 delete mode 100644 david_calculations/.ipynb_checkpoints/binary_full_evolution_plot-checkpoint.ipynb
 delete mode 100644 david_calculations/.ipynb_checkpoints/single_star_full-checkpoint.ipynb
 delete mode 100644 david_calculations/binary_full_evolution_plot.ipynb
 delete mode 100644 david_calculations/single_star_full.ipynb
 delete mode 100644 david_results/s.png

diff --git a/david_calculations/.ipynb_checkpoints/binary_full_evolution_plot-checkpoint.ipynb b/david_calculations/.ipynb_checkpoints/binary_full_evolution_plot-checkpoint.ipynb
deleted file mode 100644
index 541ad219b..000000000
--- a/david_calculations/.ipynb_checkpoints/binary_full_evolution_plot-checkpoint.ipynb
+++ /dev/null
@@ -1,162 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os, sys, time\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "from collections import defaultdict\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "# sys.path.append('../')\n",
-    "import binary_c\n",
-    "\n",
-    "\n",
-    "from binaryc_python_utils.functions import create_arg_string, parse_output, run_system\n",
-    "\n",
-    "result_dir = '../david_results/'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Run binaryc command"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Took 1.60s to run single system\n",
-      "The following keys are present in the results:\n",
-      "dict_keys(['t', 'mass_1', 'zams_mass_1', 'mass_2', 'zams_mass_2', 'stellar_type_1', 'prev_stellar_type_1', 'stellar_type_2', 'prev_stellar_type_2', 'radius_1', 'radius_2', 'roche_radius_1', 'roche_radius_2', 'core_mass_1', 'core_mass_2', 'luminosity_1', 'luminosity_2', 'omega_1', 'omega_2', 'metallicity', 'probability', 'separation', 'eccentricity', 'period', 'dtm', 'core_radius_1', 'core_radius_2', 'teff_1', 'teff_2', 'drdt_1', 'drdt_2', 'vwind_1', 'vwind_2', 'tm_1', 'tm_2', 'tn_1', 'tn_2', 'tkh_1', 'tk_2', 'angular_momentum_1', 'angular_momentum_2', 'he_core_mass_1', 'he_core_mass_2', 'CO_core_mass_1', 'CO_core_mass_2', 'GB_core_mass_1', 'GB_core_mass_2', 'v_eq_1', 'v_eq_2', 'v_eq_ratio_1', 'v_eq_ratio_2'])\n"
-     ]
-    }
-   ],
-   "source": [
-    "start = time.time()\n",
-    "output = run_system(M_1=10, M_2=20, separation=0, orbital_period=100, max_evolution_time=15000)\n",
-    "result = parse_output(output, 'DAVID_BINARY_ANALYSIS')\n",
-    "stop = time.time()\n",
-    "print(\"Took {:.2f}s to run single system\".format(stop-start))\n",
-    "print(\"The following keys are present in the results:\\n{}\".format(result.keys()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "18351949\n"
-     ]
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# Cast the data into a dataframe. \n",
-    "df = pd.DataFrame.from_dict(result, dtype=np.float64)\n",
-    "#### Now do whatever you want with it: \n",
-    "\n",
-    "# Get last change moment\n",
-    "last_st_1 = df['stellar_type_1'].unique()[-1]\n",
-    "last_stellar_type_change_time_1 = df[df.stellar_type_1==last_st_1]['t'].iloc[0]\n",
-    "last_st_2 = df['stellar_type_2'].unique()[-1]\n",
-    "last_stellar_type_change_time_2 = df[df.stellar_type_2==last_st_2]['t'].iloc[0]\n",
-    "last_change_time = last_stellar_type_change_time_1 if last_stellar_type_change_time_1>last_stellar_type_change_time_2 else last_stellar_type_change_time_2\n",
-    "\n",
-    "\n",
-    "# slice to get that last time\n",
-    "sliced_df = df[df.t < last_change_time] # Cut off late parts of evolution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(sliced_df['t'], sliced_df['mass_1'], label='mass_1')\n",
-    "plt.plot(sliced_df['t'], sliced_df['mass_2'], label='mass_2')\n",
-    "plt.xlabel('Time (Myr)')\n",
-    "plt.ylabel('Radius (Rsol)')\n",
-    "# plt.yscale('log')\n",
-    "plt.legend()\n",
-    "plt.savefig(os.path.join(result_dir, 's'))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(sliced_df['t'], sliced_df['period'], label='period')\n",
-    "plt.plot(sliced_df['t'], sliced_df['mass_2'], label='mass_2')\n",
-    "plt.xlabel('Time (Myr)')\n",
-    "plt.ylabel('Radius (Rsol)')\n",
-    "# plt.yscale('log')\n",
-    "plt.legend()\n",
-    "plt.savefig(os.path.join(result_dir, 's'))\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/david_calculations/.ipynb_checkpoints/single_star_full-checkpoint.ipynb b/david_calculations/.ipynb_checkpoints/single_star_full-checkpoint.ipynb
deleted file mode 100644
index 080265bf5..000000000
--- a/david_calculations/.ipynb_checkpoints/single_star_full-checkpoint.ipynb
+++ /dev/null
@@ -1,141 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os, sys, time\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "from collections import defaultdict\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "# sys.path.append('../')\n",
-    "import binary_c\n",
-    "\n",
-    "\n",
-    "from binaryc_python_utils.functions import create_arg_string, parse_output, run_system\n",
-    "\n",
-    "result_dir = '../david_results/'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Execute command and parse args"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Took 0.20s to run single system\n",
-      "The following keys are present in the results:\n",
-      "dict_keys(['t', 'mass', 'zams_mass', 'stellar_type', 'prev_stellar_type', 'metallicity', 'probability', 'dM_in_timestep', 'mdot', 'core_mass', 'core_radius', 'luminosity', 'radius', 'dt', 'dtm', 'Teff', 'omega', 'vwind', 'drdt', 'tm', 'tn', 'tkh', 'angular_momentum', 'he_core_mass', 'CO_core_mass', 'GB_core_mass', 'v_eq', 'v_eq_ratio'])\n"
-     ]
-    }
-   ],
-   "source": [
-    "start = time.time()\n",
-    "output = run_system(M_1=10, M_2=20, separation=0, orbital_period=100000000000)\n",
-    "result = parse_output(output, 'DAVID_SINGLE_ANALYSIS')\n",
-    "stop = time.time()\n",
-    "print(\"Took {:.2f}s to run single system\".format(stop-start))\n",
-    "print(\"The following keys are present in the results:\\n{}\".format(result.keys()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Cast data into pandas framework and do analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#### Now do whatever you want with it: \n",
-    "\n",
-    "# Cast the data into a dataframe. \n",
-    "df = pd.DataFrame.from_dict(result, dtype=np.float64)\n",
-    "\n",
-    "# Get last change moment\n",
-    "last_st = df['stellar_type'].unique()[-1]\n",
-    "last_stellar_type_change_time_1 = df[df.stellar_type==last_st]['t'].iloc[0]\n",
-    "\n",
-    "# slice to get that last time\n",
-    "sliced_df = df[df.t < last_stellar_type_change_time_1] # Cut off late parts of evolution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(sliced_df['t'], sliced_df['radius'], label='radius')\n",
-    "plt.plot(sliced_df['t'], sliced_df['omega'], label='Omega')\n",
-    "plt.plot(sliced_df['t'], sliced_df['v_eq'], label='Equatorial velocity')\n",
-    "plt.xlabel('Time (Myr)')\n",
-    "plt.ylabel('Radius (Rsol)')\n",
-    "plt.yscale('log')\n",
-    "plt.legend()\n",
-    "plt.savefig(os.path.join(result_dir, 's'))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/david_calculations/binary_full_evolution_plot.ipynb b/david_calculations/binary_full_evolution_plot.ipynb
deleted file mode 100644
index 6d90484f4..000000000
--- a/david_calculations/binary_full_evolution_plot.ipynb
+++ /dev/null
@@ -1,184 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os, sys, time\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "from collections import defaultdict\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "# sys.path.append('../')\n",
-    "import binary_c\n",
-    "\n",
-    "\n",
-    "from binaryc_python_utils.functions import create_arg_string, parse_output, run_system\n",
-    "\n",
-    "result_dir = '../david_results/'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Run binaryc command"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Took 1.60s to run single system\n",
-      "The following keys are present in the results:\n",
-      "dict_keys(['t', 'mass_1', 'zams_mass_1', 'mass_2', 'zams_mass_2', 'stellar_type_1', 'prev_stellar_type_1', 'stellar_type_2', 'prev_stellar_type_2', 'radius_1', 'radius_2', 'roche_radius_1', 'roche_radius_2', 'core_mass_1', 'core_mass_2', 'luminosity_1', 'luminosity_2', 'omega_1', 'omega_2', 'metallicity', 'probability', 'separation', 'eccentricity', 'period', 'dtm', 'core_radius_1', 'core_radius_2', 'teff_1', 'teff_2', 'drdt_1', 'drdt_2', 'vwind_1', 'vwind_2', 'tm_1', 'tm_2', 'tn_1', 'tn_2', 'tkh_1', 'tk_2', 'angular_momentum_1', 'angular_momentum_2', 'he_core_mass_1', 'he_core_mass_2', 'CO_core_mass_1', 'CO_core_mass_2', 'GB_core_mass_1', 'GB_core_mass_2', 'v_eq_1', 'v_eq_2', 'v_eq_ratio_1', 'v_eq_ratio_2'])\n"
-     ]
-    }
-   ],
-   "source": [
-    "start = time.time()\n",
-    "output = run_system(M_1=10, M_2=20, separation=0, orbital_period=100, max_evolution_time=15000)\n",
-    "result = parse_output(output, 'DAVID_BINARY_ANALYSIS')\n",
-    "stop = time.time()\n",
-    "print(\"Took {:.2f}s to run single system\".format(stop-start))\n",
-    "print(\"The following keys are present in the results:\\n{}\".format(result.keys()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "18351949\n"
-     ]
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# Cast the data into a dataframe. \n",
-    "df = pd.DataFrame.from_dict(result, dtype=np.float64)\n",
-    "#### Now do whatever you want with it: \n",
-    "\n",
-    "# Get last change moment\n",
-    "last_st_1 = df['stellar_type_1'].unique()[-1]\n",
-    "last_stellar_type_change_time_1 = df[df.stellar_type_1==last_st_1]['t'].iloc[0]\n",
-    "last_st_2 = df['stellar_type_2'].unique()[-1]\n",
-    "last_stellar_type_change_time_2 = df[df.stellar_type_2==last_st_2]['t'].iloc[0]\n",
-    "last_change_time = last_stellar_type_change_time_1 if last_stellar_type_change_time_1>last_stellar_type_change_time_2 else last_stellar_type_change_time_2\n",
-    "\n",
-    "\n",
-    "# slice to get that last time\n",
-    "sliced_df = df[df.t < last_change_time] # Cut off late parts of evolution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(sliced_df['t'], sliced_df['mass_1'], label='mass_1')\n",
-    "plt.plot(sliced_df['t'], sliced_df['mass_2'], label='mass_2')\n",
-    "plt.xlabel('Time (Myr)')\n",
-    "plt.ylabel('Radius (Rsol)')\n",
-    "# plt.yscale('log')\n",
-    "plt.legend()\n",
-    "plt.savefig(os.path.join(result_dir, 's'))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(sliced_df['t'], sliced_df['period'], label='period')\n",
-    "plt.plot(sliced_df['t'], sliced_df['omega_1'], label='omega_1')\n",
-    "plt.plot(sliced_df['t'], sliced_df['omega_2'], label='omega_2')\n",
-    "\n",
-    "plt.xlabel('Time (Myr)')\n",
-    "plt.ylabel('Radius (Rsol)')\n",
-    "plt.yscale('log')\n",
-    "plt.legend()\n",
-    "plt.savefig(os.path.join(result_dir, 's'))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/david_calculations/single_star_full.ipynb b/david_calculations/single_star_full.ipynb
deleted file mode 100644
index 33e45e38d..000000000
--- a/david_calculations/single_star_full.ipynb
+++ /dev/null
@@ -1,143 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os, sys, time\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "from collections import defaultdict\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "# sys.path.append('../')\n",
-    "import binary_c\n",
-    "\n",
-    "\n",
-    "from binaryc_python_utils.functions import create_arg_string, parse_output, run_system\n",
-    "\n",
-    "result_dir = '../david_results/'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Execute command and parse args"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Took 0.21s to run single system\n",
-      "The following keys are present in the results:\n",
-      "dict_keys(['t', 'mass', 'zams_mass', 'stellar_type', 'prev_stellar_type', 'metallicity', 'probability', 'dM_in_timestep', 'mdot', 'core_mass', 'core_radius', 'luminosity', 'radius', 'dt', 'dtm', 'Teff', 'omega', 'vwind', 'drdt', 'tm', 'tn', 'tkh', 'angular_momentum', 'he_core_mass', 'CO_core_mass', 'GB_core_mass', 'v_eq', 'v_eq_ratio'])\n",
-      "28\n"
-     ]
-    }
-   ],
-   "source": [
-    "start = time.time()\n",
-    "output = run_system(M_1=10, M_2=20, separation=0, orbital_period=100000000000)\n",
-    "result = parse_output(output, 'DAVID_SINGLE_ANALYSIS')\n",
-    "stop = time.time()\n",
-    "print(\"Took {:.2f}s to run single system\".format(stop-start))\n",
-    "print(\"The following keys are present in the results:\\n{}\".format(result.keys()))\n",
-    "print(len(result))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Cast data into pandas framework and do analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#### Now do whatever you want with it: \n",
-    "\n",
-    "# Cast the data into a dataframe. \n",
-    "df = pd.DataFrame.from_dict(result, dtype=np.float64)\n",
-    "\n",
-    "# Get last change moment\n",
-    "last_st = df['stellar_type'].unique()[-1]\n",
-    "last_stellar_type_change_time_1 = df[df.stellar_type==last_st]['t'].iloc[0]\n",
-    "\n",
-    "# slice to get that last time\n",
-    "sliced_df = df[df.t < last_stellar_type_change_time_1] # Cut off late parts of evolution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(sliced_df['t'], sliced_df['radius'], label='radius')\n",
-    "plt.plot(sliced_df['t'], sliced_df['omega'], label='Omega')\n",
-    "plt.plot(sliced_df['t'], sliced_df['v_eq'], label='Equatorial velocity')\n",
-    "plt.xlabel('Time (Myr)')\n",
-    "plt.ylabel('Radius (Rsol)')\n",
-    "plt.yscale('log')\n",
-    "plt.legend()\n",
-    "plt.savefig(os.path.join(result_dir, 's'))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/david_results/s.png b/david_results/s.png
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