diff --git a/binaryc_python_utils/functions.py b/binaryc_python_utils/functions.py
index ace2c62cbb8fca0b9d7178da1d0d2d809d1a2324..eb22fda38b84e1fa5f9771bba5ccefb7693504c4 100644
--- a/binaryc_python_utils/functions.py
+++ b/binaryc_python_utils/functions.py
@@ -7,7 +7,6 @@ def create_arg_string(arg_dict):
     """
     Function that creates the arg string
     """
-
     arg_string = '' 
     for key in arg_dict.keys():
         arg_string += "{key} {value} ".format(key=key, value=arg_dict[key])
@@ -27,17 +26,26 @@ def get_defaults():
             key, value = default.split(' = ')
 
             # Filter out NULLS (not compiled anyway)
-            if not value=='NULL':
-                default_dict[key] = value
+            if not value in ['NULL', 'Function']:
+                if not value=='':
+                    default_dict[key] = value
     return default_dict
 
+def get_arg_keys():
+    """
+    Function that return the list of possible keys to give in the arg string
+    """
+
+    return get_defaults().keys()
+
 def run_system(**kwargs):
     """
     Wrapper to run a system with settings 
     """
 
     # Load default args
-    physics_args = get_defaults()
+    args = get_defaults()
+    # args = {}
 
     # For example
     # physics_args['M_1'] = 20
@@ -46,11 +54,12 @@ def run_system(**kwargs):
 
     # Use kwarg value to override defaults and add new args
     for key in kwargs.keys():
-        physics_args[key] = kwargs[key]
+        args[key] = kwargs[key]
 
     # Construct arguments string and final execution string
-    arg_string = create_arg_string(physics_args)
+    arg_string = create_arg_string(args)
     arg_string = f'binary_c {arg_string}' 
+    # print(arg_string)
 
     # Run it and get output
     buffer = ""
@@ -58,7 +67,6 @@ def run_system(**kwargs):
 
     return output
 
-
 def parse_output(output, selected_header):
     """
     Function that parses output of binaryc when it is construction like this:
@@ -98,5 +106,4 @@ def parse_output(output, selected_header):
         for key in keys:
             final_values_dict[key].append(value_dict[key])
 
-    return final_values_dict
-
+    return final_values_dict
\ No newline at end of file
diff --git a/david_calculations/.ipynb_checkpoints/single_star_full-checkpoint.ipynb b/david_calculations/.ipynb_checkpoints/single_star_full-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..30c50c50e881ec48ddea6d58d863dbb22eda7dc2
--- /dev/null
+++ b/david_calculations/.ipynb_checkpoints/single_star_full-checkpoint.ipynb
@@ -0,0 +1,133 @@
+{
+ "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": 4,
+   "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.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/single_star_full.ipynb b/david_calculations/single_star_full.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..30c50c50e881ec48ddea6d58d863dbb22eda7dc2
--- /dev/null
+++ b/david_calculations/single_star_full.ipynb
@@ -0,0 +1,133 @@
+{
+ "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": 4,
+   "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.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_results/s.png b/david_results/s.png
new file mode 100644
index 0000000000000000000000000000000000000000..99edac26ddbed3c3483a289314d03d838c750ffc
Binary files /dev/null and b/david_results/s.png differ
diff --git a/tests_david/testing_automatic_log_readout.py b/tests_david/testing_automatic_log_readout.py
index 5cb370e2bf2adb945b5dfca8e65d51fce3d1ecd2..5e8f636c5552e64858772186d5226e0dfd570a51 100644
--- a/tests_david/testing_automatic_log_readout.py
+++ b/tests_david/testing_automatic_log_readout.py
@@ -9,7 +9,6 @@ import pandas as pd
 import binary_c
 
 
-from binaryc_python_utils.defaults import physics_defaults
 from binaryc_python_utils.functions import create_arg_string, parse_output, run_system
 
 """
@@ -31,13 +30,15 @@ print("The following keys are present in the results:\n{}".format(result.keys())
 
 # Cast the data into a dataframe. 
 df = pd.DataFrame.from_dict(result, dtype=np.float64)
-print(df)
 
-
-# sliced_df = df[df.t < 1000] # Cut off late parts of evolution
+sliced_df = df[df.t < 1000] # Cut off late parts of evolution
 # print(sliced_df["t"])
 
-# plt.plot(sliced_df['t'], sliced_df['radius'])
-# plt.xlabel('Time (Myr)')
-# plt.ylabel('Radius (Rsol)')
-# plt.show()
\ No newline at end of file
+
+
+
+
+plt.plot(sliced_df['omega'], sliced_df['radius'])
+plt.xlabel('Time (Myr)')
+plt.ylabel('omega (Rsol)')
+plt.show()
\ No newline at end of file