diff --git a/README.md b/README.md
index 0fbb875..2696803 100644
--- a/README.md
+++ b/README.md
@@ -59,7 +59,7 @@ This requires a conda installation.
 
 Create a dedicated environment (here named `astrom`) with the necessary dependencies:
 
-    conda create --name astrom --yes python=3.7 pandas pip jupyterlab
+    conda create --name astrom --yes python=3.7 pandas=1.2.5 pip jupyterlab pyyaml uncertainties
     
 Activate that environment: 
 
diff --git a/notebooks/1_exoplanet_signatures.ipynb b/notebooks/1_exoplanet_signatures.ipynb
index 2752093..3f6efb5 100644
--- a/notebooks/1_exoplanet_signatures.ipynb
+++ b/notebooks/1_exoplanet_signatures.ipynb
@@ -14,7 +14,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%reload_ext autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,6 +41,7 @@
     "from IPython.display import display\n",
     "import matplotlib as mp\n",
     "import matplotlib.pylab as pl\n",
+    "import numpy as np\n",
     "import pandas as pd\n",
     "\n",
     "from pystrometry.pystrometry import semimajor_axis_barycentre_angular"
@@ -37,9 +49,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/jsahlmann/code/github/JohannesSahlmann/pystrometry\n"
+     ]
+    }
+   ],
    "source": [
     "LOCAL_PATH = os.path.dirname(os.getcwd())\n",
     "TEMPORARY_DIR = os.path.join(LOCAL_PATH, 'temp')\n",
@@ -50,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,9 +187,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jsahlmann/code/github/JohannesSahlmann/pystrometry/pystrometry/pystrometry.py:4078: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  M = (Ggrav * (m2_MJ * MJ_kg)**3.\n",
+      "/Users/jsahlmann/code/github/JohannesSahlmann/pystrometry/pystrometry/pystrometry.py:4048: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  d_pc  = 1. / (plx_mas / 1000.)\n",
+      "/tmp/ipykernel_3510/3769182680.py:53: RuntimeWarning: divide by zero encountered in log10\n",
+      "  table['log10_distance_pc'] = np.log10(table[parameter_mapping['distance_pc']])\n"
+     ]
+    }
+   ],
    "source": [
     "selected_table_name = 'nasa'  \n",
     "table = get_exoplanets(selected_table_name) \n",
@@ -185,9 +218,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3510/3769182680.py:53: RuntimeWarning: divide by zero encountered in log10\n",
+      "  table['log10_distance_pc'] = np.log10(table[parameter_mapping['distance_pc']])\n"
+     ]
+    }
+   ],
    "source": [
     "eod_table = get_exoplanets('eod')  \n",
     "eod_table = set_astrometric_observables(eod_table, 'eod')\n",
@@ -206,9 +248,168 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>period_year</th>\n",
+       "      <th>period_day</th>\n",
+       "      <th>planet_mass_mj</th>\n",
+       "      <th>distance_pc</th>\n",
+       "      <th>parallax_mas</th>\n",
+       "      <th>planet_name</th>\n",
+       "      <th>star_mass_msun</th>\n",
+       "      <th>a_phot_mas</th>\n",
+       "      <th>a_phot_muas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.24</td>\n",
+       "      <td>87.6600</td>\n",
+       "      <td>0.000173</td>\n",
+       "      <td>10</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>Me</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000006</td>\n",
+       "      <td>0.006380</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.62</td>\n",
+       "      <td>226.4550</td>\n",
+       "      <td>0.002564</td>\n",
+       "      <td>10</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>V</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000178</td>\n",
+       "      <td>0.177980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.00</td>\n",
+       "      <td>365.2500</td>\n",
+       "      <td>0.003146</td>\n",
+       "      <td>10</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>E</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000300</td>\n",
+       "      <td>0.300345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.88</td>\n",
+       "      <td>686.6700</td>\n",
+       "      <td>0.000337</td>\n",
+       "      <td>10</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>Ma</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000049</td>\n",
+       "      <td>0.048953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11.86</td>\n",
+       "      <td>4331.8650</td>\n",
+       "      <td>1.000540</td>\n",
+       "      <td>10</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>J</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.496395</td>\n",
+       "      <td>496.395467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>29.46</td>\n",
+       "      <td>10760.2650</td>\n",
+       "      <td>0.298903</td>\n",
+       "      <td>10</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.272114</td>\n",
+       "      <td>272.113525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>84.01</td>\n",
+       "      <td>30684.6525</td>\n",
+       "      <td>0.047195</td>\n",
+       "      <td>10</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>U</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.086414</td>\n",
+       "      <td>86.414409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>164.80</td>\n",
+       "      <td>60193.2000</td>\n",
+       "      <td>0.053488</td>\n",
+       "      <td>10</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>N</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.153471</td>\n",
+       "      <td>153.470970</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   period_year  period_day  planet_mass_mj  distance_pc  parallax_mas  \\\n",
+       "0         0.24     87.6600        0.000173           10         100.0   \n",
+       "1         0.62    226.4550        0.002564           10         100.0   \n",
+       "2         1.00    365.2500        0.003146           10         100.0   \n",
+       "3         1.88    686.6700        0.000337           10         100.0   \n",
+       "4        11.86   4331.8650        1.000540           10         100.0   \n",
+       "5        29.46  10760.2650        0.298903           10         100.0   \n",
+       "6        84.01  30684.6525        0.047195           10         100.0   \n",
+       "7       164.80  60193.2000        0.053488           10         100.0   \n",
+       "\n",
+       "  planet_name  star_mass_msun  a_phot_mas  a_phot_muas  \n",
+       "0          Me               1    0.000006     0.006380  \n",
+       "1           V               1    0.000178     0.177980  \n",
+       "2           E               1    0.000300     0.300345  \n",
+       "3          Ma               1    0.000049     0.048953  \n",
+       "4           J               1    0.496395   496.395467  \n",
+       "5           S               1    0.272114   272.113525  \n",
+       "6           U               1    0.086414    86.414409  \n",
+       "7           N               1    0.153471   153.470970  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "solar_system = pd.DataFrame()\n",
     "\n",
@@ -235,7 +436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,16 +453,534 @@
     "individuals['star_mass_msun'] = [1.108, 0.122, 1.40, 1.36, 1.094, 1.83, 0.754]\n",
     "\n",
     "individuals['a_phot_mas'] = semimajor_axis_barycentre_angular(individuals['star_mass_msun'], individuals['planet_mass_mj'], individuals['period_day'], individuals['parallax_mas'])\n",
-    "individuals['a_phot_muas'] = individuals['a_phot_mas']*1e3\n",
+    "individuals['a_phot_muas'] = individuals['a_phot_mas']*1e3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## HIP-Gaia systems from Li+21: Precise Masses and Orbits for Nine Radial Velocity Exoplanets \n",
+    "#### https://ui.adsabs.harvard.edu/abs/2021arXiv210910422L\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "planet_name = '''HD~29021~b \n",
+    "HD~81040~b \n",
+    "HD~87883~b   \n",
+    "HD~98649~b \n",
+    "HD~106252~b\n",
+    "HD~106515~Ab\n",
+    "HD~106515~B\n",
+    "HD~171238~b \n",
+    "HD~196067~b  \n",
+    "HD~196068\n",
+    "HD~221420~b'''\n",
+    "planet_name = planet_name.split('\\n')\n",
+    "planet_name = [s.replace(' ','').replace('~', ' ') for s in planet_name]\n",
+    "\n",
+    "m1_ms = '''0.86\n",
+    "0.97\n",
+    "0.80\n",
+    "0.97\n",
+    "1.05\n",
+    "0.90\n",
+    "0.90\n",
+    "0.92\n",
+    "1.34\n",
+    "1.34\n",
+    "1.30'''.split('\\n')\n",
+    "\n",
+    "planet_mass_mj = '''4.47\n",
+    "7.24\n",
+    "6.31\n",
+    "9.7\n",
+    "10.0\n",
+    "18.9\n",
+    "904\n",
+    "8.8\n",
+    "12.5\n",
+    "1235\n",
+    "20.6'''.split('\\n')\n",
+    "\n",
+    "period_year = '''3.737\n",
+    "2.7452\n",
+    "8.23\n",
+    "14.74\n",
+    "4.202\n",
+    "9.927\n",
+    "4630\n",
+    "4.148\n",
+    "9.88\n",
+    "44300\n",
+    "27.7'''.split('\\n')\n",
+    "\n",
+    "parallax_mas = '''32.385\n",
+    "29.063\n",
+    "54.668\n",
+    "23.721\n",
+    "26.248\n",
+    "29.315\n",
+    "29.391\n",
+    "22.481\n",
+    "25.033\n",
+    "25.038\n",
+    "32.102'''.split('\\n')\n",
     "\n",
+    "li21 = pd.DataFrame()\n",
+    "li21['planet_name'] = planet_name\n",
+    "li21['star_mass_msun'] = np.array(m1_ms).astype(float)\n",
+    "li21['planet_mass_mj'] = np.array(planet_mass_mj).astype(float)\n",
+    "li21['parallax_mas'] = np.array(parallax_mas).astype(float)\n",
+    "li21['distance_pc'] = 1000./li21['parallax_mas']\n",
+    "li21['ref'] = 'Li+21'\n",
+    "li21['period_year'] = np.array(period_year).astype(float)\n",
+    "li21['period_day'] = li21['period_year']*u.year.to(u.day)\n",
+    "\n",
+    "li21['a_phot_mas'] = semimajor_axis_barycentre_angular(li21['star_mass_msun'], li21['planet_mass_mj'], li21['period_day'], li21['parallax_mas'])\n",
+    "li21['a_phot_muas'] = li21['a_phot_mas']*1e3\n",
+    "\n",
+    "li21 = li21[li21['planet_mass_mj']<100]\n",
+    "# display(li21)\n",
+    "individuals = pd.concat((individuals, li21))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## One system from Feng+21: Optimized modelling of Gaia-Hipparcos astrometry for the detection of the smallest cold Jupiter and confirmation of seven low-mass companions \n",
+    "https://ui.adsabs.harvard.edu/abs/2021MNRAS.507.2856F/abstract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feng21 = pd.DataFrame({'planet_name': 'HD 190360 b', 'ref': 'Feng+21', 'period_year': 7.815, 'planet_mass_mj': 1.8, 'parallax_mas': 62.49, 'star_mass_msun': 1.0}, index=[0])\n",
+    "feng21['period_day'] = feng21['period_year']*u.year.to(u.day)\n",
+    "\n",
+    "feng21['a_phot_mas'] = semimajor_axis_barycentre_angular(feng21['star_mass_msun'], feng21['planet_mass_mj'], feng21['period_day'], feng21['parallax_mas'])\n",
+    "feng21['a_phot_muas'] = feng21['a_phot_mas']*1e3\n",
+    "feng21['distance_pc'] = 1000./feng21['parallax_mas']\n",
+    "individuals = pd.concat((individuals, feng21))\n",
+    "individuals.reset_index(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# individuals.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>planet_name</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>period_year</th>\n",
+       "      <th>period_day</th>\n",
+       "      <th>planet_mass_mj</th>\n",
+       "      <th>distance_pc</th>\n",
+       "      <th>parallax_mas</th>\n",
+       "      <th>star_mass_msun</th>\n",
+       "      <th>a_phot_mas</th>\n",
+       "      <th>a_phot_muas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>HD 33632 Ab</td>\n",
+       "      <td>Currie+20</td>\n",
+       "      <td>91.000000</td>\n",
+       "      <td>33237.75000</td>\n",
+       "      <td>46.000000</td>\n",
+       "      <td>26.560000</td>\n",
+       "      <td>37.650602</td>\n",
+       "      <td>1.108</td>\n",
+       "      <td>30.438599</td>\n",
+       "      <td>30438.599047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Proxima c</td>\n",
+       "      <td>Kervella+20</td>\n",
+       "      <td>5.201916</td>\n",
+       "      <td>1900.00000</td>\n",
+       "      <td>0.037756</td>\n",
+       "      <td>1.301236</td>\n",
+       "      <td>768.500000</td>\n",
+       "      <td>0.122</td>\n",
+       "      <td>0.337984</td>\n",
+       "      <td>337.983829</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>HD 113337 c</td>\n",
+       "      <td>Xuan+20</td>\n",
+       "      <td>8.665298</td>\n",
+       "      <td>3165.00000</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>36.179450</td>\n",
+       "      <td>27.640000</td>\n",
+       "      <td>1.400</td>\n",
+       "      <td>1.237368</td>\n",
+       "      <td>1237.368314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>HD 38529 c</td>\n",
+       "      <td>Xuan+20</td>\n",
+       "      <td>5.845311</td>\n",
+       "      <td>2135.00000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>42.353140</td>\n",
+       "      <td>23.611000</td>\n",
+       "      <td>1.360</td>\n",
+       "      <td>1.063517</td>\n",
+       "      <td>1063.516641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>pi Men b</td>\n",
+       "      <td>De Rosa+20</td>\n",
+       "      <td>5.719671</td>\n",
+       "      <td>2089.11000</td>\n",
+       "      <td>13.010000</td>\n",
+       "      <td>18.279798</td>\n",
+       "      <td>54.705200</td>\n",
+       "      <td>1.094</td>\n",
+       "      <td>2.031204</td>\n",
+       "      <td>2031.203704</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>beta Pic b</td>\n",
+       "      <td>Brandt+20</td>\n",
+       "      <td>24.312115</td>\n",
+       "      <td>8880.00000</td>\n",
+       "      <td>9.800000</td>\n",
+       "      <td>19.770660</td>\n",
+       "      <td>50.580000</td>\n",
+       "      <td>1.830</td>\n",
+       "      <td>2.645187</td>\n",
+       "      <td>2645.186869</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>eps indi A b</td>\n",
+       "      <td>Feng+19</td>\n",
+       "      <td>45.200000</td>\n",
+       "      <td>16509.30000</td>\n",
+       "      <td>3.250000</td>\n",
+       "      <td>3.639010</td>\n",
+       "      <td>274.800000</td>\n",
+       "      <td>0.754</td>\n",
+       "      <td>13.022748</td>\n",
+       "      <td>13022.747616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0</td>\n",
+       "      <td>HD 29021 b</td>\n",
+       "      <td>Li+21</td>\n",
+       "      <td>3.737000</td>\n",
+       "      <td>1364.93925</td>\n",
+       "      <td>4.470000</td>\n",
+       "      <td>30.878493</td>\n",
+       "      <td>32.385000</td>\n",
+       "      <td>0.860</td>\n",
+       "      <td>0.366760</td>\n",
+       "      <td>366.759633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>HD 81040 b</td>\n",
+       "      <td>Li+21</td>\n",
+       "      <td>2.745200</td>\n",
+       "      <td>1002.68430</td>\n",
+       "      <td>7.240000</td>\n",
+       "      <td>34.408010</td>\n",
+       "      <td>29.063000</td>\n",
+       "      <td>0.970</td>\n",
+       "      <td>0.399980</td>\n",
+       "      <td>399.979988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2</td>\n",
+       "      <td>HD 87883 b</td>\n",
+       "      <td>Li+21</td>\n",
+       "      <td>8.230000</td>\n",
+       "      <td>3006.00750</td>\n",
+       "      <td>6.310000</td>\n",
+       "      <td>18.292237</td>\n",
+       "      <td>54.668000</td>\n",
+       "      <td>0.800</td>\n",
+       "      <td>1.549806</td>\n",
+       "      <td>1549.805893</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>3</td>\n",
+       "      <td>HD 98649 b</td>\n",
+       "      <td>Li+21</td>\n",
+       "      <td>14.740000</td>\n",
+       "      <td>5383.78500</td>\n",
+       "      <td>9.700000</td>\n",
+       "      <td>42.156739</td>\n",
+       "      <td>23.721000</td>\n",
+       "      <td>0.970</td>\n",
+       "      <td>1.339012</td>\n",
+       "      <td>1339.012118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>4</td>\n",
+       "      <td>HD 106252 b</td>\n",
+       "      <td>Li+21</td>\n",
+       "      <td>4.202000</td>\n",
+       "      <td>1534.78050</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>38.098141</td>\n",
+       "      <td>26.248000</td>\n",
+       "      <td>1.050</td>\n",
+       "      <td>0.627770</td>\n",
+       "      <td>627.769762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>5</td>\n",
+       "      <td>HD 106515 Ab</td>\n",
+       "      <td>Li+21</td>\n",
+       "      <td>9.927000</td>\n",
+       "      <td>3625.83675</td>\n",
+       "      <td>18.900000</td>\n",
+       "      <td>34.112229</td>\n",
+       "      <td>29.315000</td>\n",
+       "      <td>0.900</td>\n",
+       "      <td>2.586240</td>\n",
+       "      <td>2586.239510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>7</td>\n",
+       "      <td>HD 171238 b</td>\n",
+       "      <td>Li+21</td>\n",
+       "      <td>4.148000</td>\n",
+       "      <td>1515.05700</td>\n",
+       "      <td>8.800000</td>\n",
+       "      <td>44.482007</td>\n",
+       "      <td>22.481000</td>\n",
+       "      <td>0.920</td>\n",
+       "      <td>0.512288</td>\n",
+       "      <td>512.287682</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>8</td>\n",
+       "      <td>HD 196067 b</td>\n",
+       "      <td>Li+21</td>\n",
+       "      <td>9.880000</td>\n",
+       "      <td>3608.67000</td>\n",
+       "      <td>12.500000</td>\n",
+       "      <td>39.947270</td>\n",
+       "      <td>25.033000</td>\n",
+       "      <td>1.340</td>\n",
+       "      <td>1.124875</td>\n",
+       "      <td>1124.874894</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10</td>\n",
+       "      <td>HD 221420 b</td>\n",
+       "      <td>Li+21</td>\n",
+       "      <td>27.700000</td>\n",
+       "      <td>10117.42500</td>\n",
+       "      <td>20.600000</td>\n",
+       "      <td>31.150707</td>\n",
+       "      <td>32.102000</td>\n",
+       "      <td>1.300</td>\n",
+       "      <td>4.803489</td>\n",
+       "      <td>4803.488719</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0</td>\n",
+       "      <td>HD 190360 b</td>\n",
+       "      <td>Feng+21</td>\n",
+       "      <td>7.815000</td>\n",
+       "      <td>2854.42875</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>16.002560</td>\n",
+       "      <td>62.490000</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.422362</td>\n",
+       "      <td>422.362443</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    index   planet_name          ref  period_year   period_day  \\\n",
+       "0       0   HD 33632 Ab    Currie+20    91.000000  33237.75000   \n",
+       "1       1     Proxima c  Kervella+20     5.201916   1900.00000   \n",
+       "2       2   HD 113337 c      Xuan+20     8.665298   3165.00000   \n",
+       "3       3    HD 38529 c      Xuan+20     5.845311   2135.00000   \n",
+       "4       4      pi Men b   De Rosa+20     5.719671   2089.11000   \n",
+       "5       5    beta Pic b    Brandt+20    24.312115   8880.00000   \n",
+       "6       6  eps indi A b      Feng+19    45.200000  16509.30000   \n",
+       "7       0    HD 29021 b        Li+21     3.737000   1364.93925   \n",
+       "8       1    HD 81040 b        Li+21     2.745200   1002.68430   \n",
+       "9       2    HD 87883 b        Li+21     8.230000   3006.00750   \n",
+       "10      3    HD 98649 b        Li+21    14.740000   5383.78500   \n",
+       "11      4   HD 106252 b        Li+21     4.202000   1534.78050   \n",
+       "12      5  HD 106515 Ab        Li+21     9.927000   3625.83675   \n",
+       "13      7   HD 171238 b        Li+21     4.148000   1515.05700   \n",
+       "14      8   HD 196067 b        Li+21     9.880000   3608.67000   \n",
+       "15     10   HD 221420 b        Li+21    27.700000  10117.42500   \n",
+       "16      0   HD 190360 b      Feng+21     7.815000   2854.42875   \n",
+       "\n",
+       "    planet_mass_mj  distance_pc  parallax_mas  star_mass_msun  a_phot_mas  \\\n",
+       "0        46.000000    26.560000     37.650602           1.108   30.438599   \n",
+       "1         0.037756     1.301236    768.500000           0.122    0.337984   \n",
+       "2        14.000000    36.179450     27.640000           1.400    1.237368   \n",
+       "3        18.000000    42.353140     23.611000           1.360    1.063517   \n",
+       "4        13.010000    18.279798     54.705200           1.094    2.031204   \n",
+       "5         9.800000    19.770660     50.580000           1.830    2.645187   \n",
+       "6         3.250000     3.639010    274.800000           0.754   13.022748   \n",
+       "7         4.470000    30.878493     32.385000           0.860    0.366760   \n",
+       "8         7.240000    34.408010     29.063000           0.970    0.399980   \n",
+       "9         6.310000    18.292237     54.668000           0.800    1.549806   \n",
+       "10        9.700000    42.156739     23.721000           0.970    1.339012   \n",
+       "11       10.000000    38.098141     26.248000           1.050    0.627770   \n",
+       "12       18.900000    34.112229     29.315000           0.900    2.586240   \n",
+       "13        8.800000    44.482007     22.481000           0.920    0.512288   \n",
+       "14       12.500000    39.947270     25.033000           1.340    1.124875   \n",
+       "15       20.600000    31.150707     32.102000           1.300    4.803489   \n",
+       "16        1.800000    16.002560     62.490000           1.000    0.422362   \n",
+       "\n",
+       "     a_phot_muas  \n",
+       "0   30438.599047  \n",
+       "1     337.983829  \n",
+       "2    1237.368314  \n",
+       "3    1063.516641  \n",
+       "4    2031.203704  \n",
+       "5    2645.186869  \n",
+       "6   13022.747616  \n",
+       "7     366.759633  \n",
+       "8     399.979988  \n",
+       "9    1549.805893  \n",
+       "10   1339.012118  \n",
+       "11    627.769762  \n",
+       "12   2586.239510  \n",
+       "13    512.287682  \n",
+       "14   1124.874894  \n",
+       "15   4803.488719  \n",
+       "16    422.362443  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
     "display(individuals)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "# # eps Eri b   Llop-Sayson+21\n",
+    "# # (Mawet+17 ignores the HST astrometry)\n",
+    "# individual_system = df[df['pl_name'].str.contains('eps Eri b').fillna(False)].copy().reset_index()\n",
+    "# individual_system.loc[0,'pl_bmassj'] = [0.66]\n",
+    "# individual_system['ref'] = 'Llop-Sayson+21'\n",
+    "\n",
+    "\n",
+    "# individuals = pd.concat([individuals, individual_system[individuals.columns]])\n",
+    "# individuals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "planet_name;ref\n",
+      "HD 33632 Ab;Currie+20\n",
+      "Proxima c;Kervella+20\n",
+      "HD 113337 c;Xuan+20\n",
+      "HD 38529 c;Xuan+20\n",
+      "pi Men b;De Rosa+20\n",
+      "beta Pic b;Brandt+20\n",
+      "eps indi A b;Feng+19\n",
+      "HD 29021 b;Li+21\n",
+      "HD 81040 b;Li+21\n",
+      "HD 87883 b;Li+21\n",
+      "HD 98649 b;Li+21\n",
+      "HD 106252 b;Li+21\n",
+      "HD 106515 Ab;Li+21\n",
+      "HD 171238 b;Li+21\n",
+      "HD 196067 b;Li+21\n",
+      "HD 221420 b;Li+21\n",
+      "HD 190360 b;Feng+21\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "print(individuals[['planet_name', 'ref']].to_csv(index=False, sep=';'))"
    ]
@@ -270,14 +989,135 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Systems with measured photocentre orbits"
+    "## Systems with fully measured photocentre orbits"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pl_name</th>\n",
+       "      <th>period_year</th>\n",
+       "      <th>pl_bmassj</th>\n",
+       "      <th>a_phot_muas</th>\n",
+       "      <th>ref</th>\n",
+       "      <th>pl_def_refname</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ups And c</td>\n",
+       "      <td>0.660528</td>\n",
+       "      <td>13.980</td>\n",
+       "      <td>629.351182</td>\n",
+       "      <td>McArthur+10</td>\n",
+       "      <td>Curiel et al. 2011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ups And d</td>\n",
+       "      <td>3.494757</td>\n",
+       "      <td>10.250</td>\n",
+       "      <td>1403.608812</td>\n",
+       "      <td>McArthur+10</td>\n",
+       "      <td>Curiel et al. 2011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HD 38529 c</td>\n",
+       "      <td>5.848323</td>\n",
+       "      <td>17.600</td>\n",
+       "      <td>1014.647375</td>\n",
+       "      <td>Benedict+10</td>\n",
+       "      <td>Luhn et al. 2019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>HD 128311 b</td>\n",
+       "      <td>1.240298</td>\n",
+       "      <td>3.789</td>\n",
+       "      <td>288.536056</td>\n",
+       "      <td>McArthur+14</td>\n",
+       "      <td>McArthur et al. 2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>HD 202206 c</td>\n",
+       "      <td>3.449692</td>\n",
+       "      <td>17.900</td>\n",
+       "      <td>716.430919</td>\n",
+       "      <td>Benedict+17</td>\n",
+       "      <td>Benedict &amp;amp; Harrison 2017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>GJ 676 A b</td>\n",
+       "      <td>2.893361</td>\n",
+       "      <td>6.700</td>\n",
+       "      <td>993.248896</td>\n",
+       "      <td>Sahlmann+16</td>\n",
+       "      <td>Stassun et al. 2017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>TVLM 513–46546 b</td>\n",
+       "      <td>0.602327</td>\n",
+       "      <td>0.380</td>\n",
+       "      <td>128.000000</td>\n",
+       "      <td>Curiel+20</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            pl_name  period_year  pl_bmassj  a_phot_muas          ref  \\\n",
+       "0         ups And c     0.660528     13.980   629.351182  McArthur+10   \n",
+       "1         ups And d     3.494757     10.250  1403.608812  McArthur+10   \n",
+       "2        HD 38529 c     5.848323     17.600  1014.647375  Benedict+10   \n",
+       "3       HD 128311 b     1.240298      3.789   288.536056  McArthur+14   \n",
+       "4       HD 202206 c     3.449692     17.900   716.430919  Benedict+17   \n",
+       "5        GJ 676 A b     2.893361      6.700   993.248896  Sahlmann+16   \n",
+       "6  TVLM 513–46546 b     0.602327      0.380   128.000000    Curiel+20   \n",
+       "\n",
+       "                 pl_def_refname  \n",
+       "0            Curiel et al. 2011  \n",
+       "1            Curiel et al. 2011  \n",
+       "2              Luhn et al. 2019  \n",
+       "3          McArthur et al. 2014  \n",
+       "4  Benedict &amp; Harrison 2017  \n",
+       "5           Stassun et al. 2017  \n",
+       "6                           NaN  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "df = table.to_pandas()\n",
     "\n",
@@ -287,31 +1127,26 @@
     "selected_systems['ref'] = 'McArthur+10'\n",
     "\n",
     "#  HD 38529 c\n",
-    "individual_system = df[df['pl_name'].str.contains('HD 38529 c').fillna(False)]\n",
-    "individual_system['pl_bmassj'] = [17.6]\n",
+    "individual_system = df[df['pl_name'].str.contains('HD 38529 c').fillna(False)].copy().reset_index()\n",
+    "individual_system.loc[0, 'pl_bmassj'] = [17.6]\n",
     "individual_system['ref'] = 'Benedict+10'\n",
     "selected_systems = pd.concat([selected_systems, individual_system])\n",
     "\n",
     "#  HD 128311 b\n",
-    "individual_system = df[df['pl_name'].str.contains('HD 128311 b').fillna(False)]\n",
-    "individual_system['pl_bmassj'] = [3.789]\n",
+    "individual_system = df[df['pl_name'].str.contains('HD 128311 b').fillna(False)].copy().reset_index()\n",
+    "individual_system.loc[0, 'pl_bmassj'] = [3.789]\n",
     "individual_system['ref'] = 'McArthur+14'\n",
     "selected_systems = pd.concat([selected_systems, individual_system])\n",
     "\n",
     "#  HD 202206 c\n",
-    "individual_system = df[df['pl_name'].str.contains('HD 202206 c').fillna(False)]\n",
-    "individual_system['ref'] = 'Benedict+17'\n",
+    "individual_system = df[df['pl_name'].str.contains('HD 202206 c').fillna(False)].copy().reset_index()\n",
+    "individual_system.loc[0, 'ref'] = 'Benedict+17'\n",
     "selected_systems = pd.concat([selected_systems, individual_system])\n",
     "\n",
-    "#  eps Eri b (Mawet+17 ignores the HST astrometry)\n",
-    "# individual_system = df[df['pl_name'].str.contains('eps Eri b').fillna(False)]\n",
-    "# individual_system['pl_bmassj'] = [1.55]\n",
-    "# individual_system['ref'] = 'Benedict+06'\n",
-    "# selected_systems = pd.concat([selected_systems, individual_system])\n",
     "\n",
     "#  GJ 676A b\n",
-    "individual_system = df[df['pl_name'].str.contains('GJ 676 A b').fillna(False)]\n",
-    "individual_system['pl_bmassj'] = [6.7]\n",
+    "individual_system = df[df['pl_name'].str.contains('GJ 676 A b').fillna(False)].copy().reset_index()\n",
+    "individual_system.loc[0, 'pl_bmassj'] = [6.7]\n",
     "individual_system['ref'] = 'Sahlmann+16'\n",
     "selected_systems = pd.concat([selected_systems, individual_system])\n",
     "\n",
@@ -329,36 +1164,35 @@
     "individual_system['st_dist'] = individual_system['distance_pc']\n",
     "\n",
     "selected_systems = pd.concat([selected_systems, individual_system])\n",
+    "selected_systems.reset_index(inplace=True)\n",
     "display(selected_systems[['pl_name', 'period_year', 'pl_bmassj', 'a_phot_muas', 'ref', 'pl_def_refname']])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#  df[df['hip_name'].str.contains('HIP 85647').fillna(False)].iloc[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pl_name,ref\n",
+      "ups And c,McArthur+10\n",
+      "ups And d,McArthur+10\n",
+      "HD 38529 c,Benedict+10\n",
+      "HD 128311 b,McArthur+14\n",
+      "HD 202206 c,Benedict+17\n",
+      "GJ 676 A b,Sahlmann+16\n",
+      "TVLM 513–46546 b,Curiel+20\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "print(selected_systems[['pl_name', 'ref']].to_csv(index=False, sep=','))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# np.array(df.columns)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -368,14 +1202,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3510/443072314.py:8: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  eod_df_koi.sort_values('DIST', inplace=True, ascending=True)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'/Users/jsahlmann/code/github/JohannesSahlmann/pystrometry/temp/nasa_True_True_astrometry_signatures.png'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "include_koi = True\n",
     "include_individuals = True\n",
     "include_orbits = True\n",
+    "include_solar_system = True\n",
     "\n",
+    "# make the KOI symbol visible in the legend\n",
+    "eod_df_koi['DIST'].replace(0, np.nan, inplace=True)\n",
+    "eod_df_koi.sort_values('DIST', inplace=True, ascending=True)\n",
     "\n",
     "def annotate_df(row):  \n",
     "    #     https://stackoverflow.com/questions/15910019/annotate-data-points-while-plotting-from-pandas-dataframe\n",
@@ -397,19 +1268,19 @@
     "colormap='viridis'\n",
     "colormap='cubehelix'\n",
     "\n",
-    "\n",
     "fig, ax = pl.subplots()\n",
-    "df.plot(x, y, kind='scatter', logx=True, logy=True, c=parameter_mapping_all['nasa'][colour_by], colormap=colormap, s=30, alpha=0.9, norm=norm, ax=ax, zorder=1, label='Exoplanets')\n",
+    "df.plot(x, y, kind='scatter', logx=True, logy=True, c=parameter_mapping_all['nasa'][colour_by], norm=norm, colormap=colormap, s=30, alpha=0.9, ax=ax, zorder=1, label='Known exoplanets')\n",
     "if include_koi:\n",
-    "    eod_df_koi.plot(x, y, kind='scatter', logx=True, logy=True, c=parameter_mapping_all['eod'][colour_by], colormap=colormap, s=25, marker='s', alpha=0.9, zorder=-50, norm=norm, ax=ax, colorbar=False, label='KOIs')\n",
+    "    eod_df_koi.plot(x, y, kind='scatter', logx=True, logy=True, c=parameter_mapping_all['eod'][colour_by], colormap=colormap, s=25, marker='s', alpha=0.9, zorder=-50, norm=norm, ax=ax, colorbar=False, label='Kepler Objects of Interest')\n",
     "pl.ylim((1e-8*factor, 40*factor))\n",
     "pl.xlim((1e-3, 300))\n",
-    "solar_system.plot(x, y, kind='scatter', logx=True, logy=True, s=200, c=colour_by, ax=ax, norm=norm, colormap=colormap, colorbar=False, marker='^', label='Solar system at 10pc', edgecolor='k')\n",
-    "_ = solar_system.apply(annotate_df, axis=1)\n",
     "if include_orbits:\n",
     "    selected_systems.plot(x, y, kind='scatter', logx=True, logy=True, s=300, c=parameter_mapping_all['nasa'][colour_by], ax=ax, norm=norm, colormap=colormap, colorbar=False, label='Measured orbits',  marker='*', edgecolor='k') #,    \n",
     "if include_individuals:\n",
-    "    individuals.plot(x, y, kind='scatter', logx=True, logy=True, s=200, c=colour_by, ax=ax, norm=norm, colormap=colormap, colorbar=False, label='Hip-Gaia systems',  marker='D', edgecolor='k') #,\n",
+    "    individuals.plot(x, y, kind='scatter', logx=True, logy=True, s=200, c=colour_by, ax=ax, norm=norm, colormap=colormap, colorbar=False, label='Hipparcos-Gaia systems',  marker='D', edgecolor='k', alpha=0.7) #,\n",
+    "if include_solar_system:\n",
+    "    solar_system.plot(x, y, kind='scatter', logx=True, logy=True, s=200, c=colour_by, ax=ax, norm=norm, colormap=colormap, colorbar=False, marker='^', label='Solar system at 10pc', edgecolor='k')\n",
+    "    _ = solar_system.apply(annotate_df, axis=1)\n",
     "pl.xlabel('Orbital period (year)')\n",
     "pl.ylabel('Minimum semimajor axis (micro-arcsec)')\n",
     "ax.grid(ls='--', color='0.8')\n",
@@ -425,8 +1296,8 @@
     "pl.yticks(yticks, ytick_labels)\n",
     "\n",
     "cax = fig.get_axes()[1]\n",
-    "cax.set_ylabel('Distance (pc)')\n",
-    "pl.legend(loc=2, framealpha=0.5)\n",
+    "cax.set_ylabel('Distance from Sun (pc)')\n",
+    "pl.legend(loc=4, framealpha=0.5)\n",
     "\n",
     "pl.text(0.68, 0.03, 'NasaExoplanetArchive & ExoplanetOrbitDatabase', horizontalalignment='left', verticalalignment='top',\n",
     "        transform=pl.gca().transAxes, fontsize=10)\n",
@@ -462,7 +1333,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.8"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
diff --git a/pystrometry/pystrometry.py b/pystrometry/pystrometry.py
index 03cec9a..c2dad08 100644
--- a/pystrometry/pystrometry.py
+++ b/pystrometry/pystrometry.py
@@ -2281,6 +2281,7 @@ def plot(self, argument_dict=None):
                             'horizons_file_seed': None,
                             'frame_omc_description': 'default',
                             'orbit_description': 'default',
+                            'orbit_show_frame_data': True,
                             'scan_angle_definition': 'gaia',
                             'orbit_signal_description': 'default',
                             'ppm_description': 'default',
@@ -2579,9 +2580,17 @@ def insert_ppm_plot(self, orb, argument_dict):
                             offsetDE_mas=orb.offset_delta_mas,
                             horizons_file_seed=argument_dict['horizons_file_seed'])
 
-        pl.plot(ppm_curve[0], ppm_curve[1], 'k-')
+        pl.plot(ppm_curve[0], ppm_curve[1], 'k-', lw=2)
         if self.data_type == '2d':
             pl.plot(self.Xmean_ppm, self.Ymean_ppm, 'ko')
+        elif self.data_type == '1d':
+            # plot the PPM sampling only
+            t_epoch_mjd_2d = np.sort(np.tile(self.t_MJD_epoch, 2))
+            ppm_epoch = orb.ppm(t_epoch_mjd_2d, offsetRA_mas=orb.offset_alphastar_mas,
+                                offsetDE_mas=orb.offset_delta_mas,
+                                horizons_file_seed=argument_dict['horizons_file_seed'])
+            pl.plot(ppm_epoch[0], ppm_epoch[1], marker='o', color='k', mfc='none', ms=5, zorder=-50, ls='none')
+
         plt.annotate('', xy=(np.float(orb.muRA_mas) * argument_dict['arrow_length_factor'] + argument_dict['arrow_offset_x'],
                              np.float(orb.muDE_mas) * argument_dict['arrow_length_factor'] + argument_dict['arrow_offset_y']),
                      xytext=(0. + argument_dict['arrow_offset_x'], 0. + argument_dict['arrow_offset_y']),
@@ -2848,10 +2857,11 @@ def insert_orbit_plot(self, orb, argument_dict):
                 epoch_residual_delta_along_scan = self.mean_cpsi * self.meanResidualX
 
             frame_residual_color = '0.8'
-            pl.plot(phi1_model_frame + frame_residual_alphastar_along_scan,
-                    phi2_model_frame + frame_residual_delta_along_scan, marker='o',
-                    color=frame_residual_color, ms=4, mfc=frame_residual_color,
-                    mec=frame_residual_color, ls='')
+            if argument_dict['orbit_show_frame_data']:
+                pl.plot(phi1_model_frame + frame_residual_alphastar_along_scan,
+                        phi2_model_frame + frame_residual_delta_along_scan, marker='o',
+                        color=frame_residual_color, ms=4, mfc=frame_residual_color,
+                        mec=frame_residual_color, ls='')
             pl.plot(phi1_model_epoch + epoch_residual_alphastar_along_scan,
                     phi2_model_epoch + epoch_residual_delta_along_scan, marker='o', color='k',
                     ms=5, ls='')
@@ -4247,33 +4257,60 @@ def RadialVelocitiesKepler(alpha_mps,beta_mps,delta_mps,theta_rad):
 
 
 def EllipticalRectangularCoordinates(ecc, E_rad):
-# /*
-#  * DOCUMENT
-#  *   EllipticalRectangularCoordinates(ecc,E_rad)
-#  *
-#  *   It computes the ellipses of the orbit for  \f$  i=0\f$  and \f$  \Omega=0\f$
-#  *
-#  *
-#  *   - INPUT
-#  *       - omega_rad Longitude of periastron expressed in radian
-#  *       - ecc Eccentricity
-#  *       - Tp_day Time of passage at periastron (julian date-2400000)
-#  *       - P_day Period of the orbit
-#  *       - t_day Date/time of the observations  (julian date-2400000)
-#  *
-#  *    OUTPUT
-#  *       Position on the sky. Needs the Thieles-Innes coef
-#  *
-#  *
-#  *
-#  *  SEE ALSO EccentricAnomaly
-#  */
-  X = np.cos(E_rad) - ecc
-  Y = np.sqrt(1.-ecc**2)*np.sin(E_rad)
-  return np.array([X,Y])
-
-
-def geometric_elements(thiele_innes_parameters):
+    # /*
+    #  * DOCUMENT
+    #  *   EllipticalRectangularCoordinates(ecc,E_rad)
+    #  *
+    #  *   It computes the ellipses of the orbit for  \f$  i=0\f$  and \f$  \Omega=0\f$
+    #  *
+    #  *
+    #  *   - INPUT
+    #  *       - omega_rad Longitude of periastron expressed in radian
+    #  *       - ecc Eccentricity
+    #  *       - Tp_day Time of passage at periastron (julian date-2400000)
+    #  *       - P_day Period of the orbit
+    #  *       - t_day Date/time of the observations  (julian date-2400000)
+    #  *
+    #  *    OUTPUT
+    #  *       Position on the sky. Needs the Thieles-Innes coef
+    #  *
+    #  *
+    #  *
+    #  *  SEE ALSO EccentricAnomaly
+    #  */
+    X = np.cos(E_rad) - ecc
+    Y = np.sqrt(1.-ecc**2)*np.sin(E_rad)
+    return np.array([X,Y])
+
+
+def adjust_omega_OMEGA(omega_rad, OMEGA_rad):
+    """ Return omega and OMEGA in ranges of [0,2pi) and [0,pi)
+
+    Parameters
+    ----------
+    omega_rad
+    OMEGA_rad
+
+    Returns
+    -------
+
+    """
+
+    if OMEGA_rad < 0.:
+        OMEGA_rad += np.pi
+        omega_rad += np.pi
+        if omega_rad > 2 * np.pi:
+            omega_rad -= 2 * np.pi
+    elif omega_rad < 0.:
+        omega_rad += 2 * np.pi
+
+    return omega_rad, OMEGA_rad
+# def adjust_geometric_element_ranges(omega_rad, OMEGA_rad):
+#     """Return vectorised version of adjust_omega_OMEGA."""
+#     return np.vectorize(adjust_omega_OMEGA)
+
+
+def geometric_elements(thiele_innes_parameters, post_process=False):
     """Return geometrical orbit elements a, omega, OMEGA, i.
 
     Parameters
@@ -4296,31 +4333,36 @@ def geometric_elements(thiele_innes_parameters):
     q = A * G - B * F
 
     a_mas = np.sqrt(p + np.sqrt(p ** 2 - q ** 2))
-    # i_rad = math.acos(q/(a_mas**2.))
-    # omega_rad = (math.atan2(B-F,A+G)+math.atan2(-B-F,A-G))/2.;
-    # OMEGA_rad = (math.atan2(B-F,A+G)-math.atan2(-B-F,A-G))/2.;
-
     i_rad = np.arccos(q / (a_mas ** 2.))
+
     omega_rad = (np.arctan2(B - F, A + G) + np.arctan2(-B - F, A - G)) / 2.
     OMEGA_rad = (np.arctan2(B - F, A + G) - np.arctan2(-B - F, A - G)) / 2.
 
     i_deg = np.rad2deg(i_rad)
+    if post_process:
+        # adjust the value ranges
+        index_1 = np.where(OMEGA_rad < 0.)[0]
+        index_2 = np.where((OMEGA_rad >= 0.) & (omega_rad < 0.))[0]
+
+        OMEGA_rad[index_1] += np.pi
+        omega_rad[index_1] += np.pi
+
+        omega_1 = omega_rad[index_1]
+        omega_1[omega_1 > 2 * np.pi] -= 2 * np.pi
+        omega_rad[index_1] = omega_1
+
+        omega_rad[index_2] += 2 * np.pi
+        # assert np.all(OMEGA_rad - np.minimum(OMEGA_rad, np.pi) == 0)
+        # OMEGA_rad = np.minimum(OMEGA_rad, np.pi)
+        # omega_rad = np.minimum(np.maximum(0., omega_rad), 2*np.pi)
+
     omega_deg = np.rad2deg(omega_rad)
     OMEGA_deg = np.rad2deg(OMEGA_rad)
-    # OMEGA_deg = np.rad2deg(np.unwrap(OMEGA_rad))
 
     if np.any(np.isnan(a_mas)):
         index = np.where(np.isnan(a_mas))[0]
         raise RuntimeError('nan detected: {} occurrences'.format(len(index)))
 
-    # if isinstance(omega_deg, (list, tuple, np.ndarray)):
-    #     index = np.where(omega_deg < 0.)[0]
-    #     omega_deg[index] += 180.
-    #
-    # if isinstance(OMEGA_deg, (list, tuple, np.ndarray)):
-    #     index = np.where(OMEGA_deg < 0.)[0]
-    #     OMEGA_deg[index] += 180.
-
     geometric_parameters = np.array([a_mas, omega_deg, OMEGA_deg, i_deg])
     return geometric_parameters
 
diff --git a/pystrometry/tests/test_du437_tools.py b/pystrometry/tests/test_du437_tools.py
new file mode 100644
index 0000000..5de9270
--- /dev/null
+++ b/pystrometry/tests/test_du437_tools.py
@@ -0,0 +1,49 @@
+import numpy as np
+from numpy.testing import assert_allclose
+
+from ..utils.du437_tools import geometric_elements_with_uncertainties
+
+def test_geometric_elements_with_uncertainties():
+    """Test computation of uncertainties in geometric elements from ABFG+covariance matrix."""
+    absolute_tolerance = 3e-5  # this relatively high tolerance was necessary because of rounding errors in the test inputs
+
+    # first test case
+    thiele_innes_parameters = np.array([0.5887957934067639, -0.783542139419248, -0.586541897649227, -0.4380942241437222])
+    thiele_innes_parameters_errors  = np.array([0.14288834024020794, 0.10684726126075933, 0.13904500248228854, 0.1772629684907557])
+    correlation_matrix_ti = np.array([[1.0, 0.977777963152516, 0.9843118342310587, -0.9705972306140258],
+                                      [0.977777963152516, 1.0, 0.9854463019218139, -0.9847018411767419],
+                                      [0.9843118342310587, 0.9854463019218139, 1.0, -0.984254484163135],
+                                      [-0.9705972306140258, -0.9847018411767419, -0.984254484163135, 1.0]])
+
+    ge1, ge1_err = geometric_elements_with_uncertainties(thiele_innes_parameters,
+                                                         thiele_innes_parameters_errors,
+                                                         correlation_matrix_ti, post_process=True)
+
+    ge1_reference = np.array([0.980117, 3.146510, 2.218900, 2.414238])
+    ge1_reference[1:] = np.rad2deg(ge1_reference[1:])
+    ge1_err_reference = np.array([0.018120, 0.220922, 0.066129, 0.042308])
+    ge1_err_reference[1:] = np.rad2deg(ge1_err_reference[1:])
+
+    assert_allclose(ge1, ge1_reference, atol=absolute_tolerance)
+    assert_allclose(ge1_err, ge1_err_reference, atol=absolute_tolerance)
+
+    # second test case
+    thiele_innes_parameters = np.array([-2.961570196500256, -2.422183719010767, 1.211375364690141, -1.4678757108790645])
+    thiele_innes_parameters_errors  = np.array([.07764212692792022, 0.09451361935572963, 0.1961760927350047, 0.15440786748261082])
+    correlation_matrix_ti = np.array([[1.0, -0.9135449715394732, 0.9211753621269316, 0.9399225380071006],
+                                      [-0.9135449715394732, 1.0, -0.9033380261771987, -0.9188459402029718],
+                                      [0.9211753621269316, -0.9033380261771987, 1.0, 0.9868011084831556],
+                                      [0.9399225380071006, -0.9188459402029718, 0.9868011084831556, 1.0]])
+
+    ge1, ge1_err = geometric_elements_with_uncertainties(thiele_innes_parameters,
+                                                         thiele_innes_parameters_errors,
+                                                         correlation_matrix_ti, post_process=True)
+
+    ge1_reference = np.array([3.825959, 3.144507, 0.684094, 1.050160])
+    ge1_reference[1:] = np.rad2deg(ge1_reference[1:])
+    ge1_err_reference = np.array([0.024898, 0.067359, 0.014764, 0.007025])
+    ge1_err_reference[1:] = np.rad2deg(ge1_err_reference[1:])
+
+    assert_allclose(ge1, ge1_reference, atol=absolute_tolerance)
+    assert_allclose(ge1_err, ge1_err_reference, atol=absolute_tolerance)
+
diff --git a/pystrometry/tests/test_pystrometry.py b/pystrometry/tests/test_pystrometry.py
index 40b816e..62a8375 100644
--- a/pystrometry/tests/test_pystrometry.py
+++ b/pystrometry/tests/test_pystrometry.py
@@ -86,6 +86,47 @@ def test_thiele_innes():
     assert_allclose(OMEGA, geometric_parameters[2], atol=absolute_tolerance)
     assert_allclose(i, geometric_parameters[3], atol=absolute_tolerance)
 
+    geometric_parameters = geometric_elements(thiele_innes_parameters, post_process=True)
+    assert_allclose(a, geometric_parameters[0], atol=absolute_tolerance)
+    assert_allclose(omega, geometric_parameters[1], atol=absolute_tolerance)
+    assert_allclose(OMEGA, geometric_parameters[2], atol=absolute_tolerance)
+    assert_allclose(i, geometric_parameters[3], atol=absolute_tolerance)
+
+def test_geometric_elements():
+    """Test conversion between Thiele Innes constants and geometric elements."""
+
+    n_grid = 20
+    a_mas = np.linspace(0.1, 100, n_grid)
+    omega_deg = np.linspace(0.1, 360, n_grid, endpoint=False)
+    OMEGA_deg = np.linspace(0.1, 179, n_grid, endpoint=False)
+    i_deg = np.linspace(0.5, 180, n_grid, endpoint=False)
+
+    a_mesh, omega_mesh, OMEGA_mesh, i_mesh = np.meshgrid(a_mas, omega_deg, OMEGA_deg, i_deg)
+    a = a_mesh.flatten()
+    omega = omega_mesh.flatten()
+    OMEGA = OMEGA_mesh.flatten()
+    i = i_mesh.flatten()
+
+    # input_array = np.array([a_mas, omega_deg, OMEGA_deg, i_deg])
+    input_array = np.array([a, omega, OMEGA, i])
+
+    thiele_innes_parameters = thiele_innes_constants(input_array)
+
+    absolute_tolerance = 1e-6
+
+    # assert that Thiele-Innes are the same for the degenerate orbit configuration
+    thiele_innes_parameters_alternative = thiele_innes_constants(np.array([a, omega-180, OMEGA-180, i]))
+    assert_allclose(thiele_innes_parameters,  thiele_innes_parameters_alternative, atol=absolute_tolerance)
+
+    # use with post_process=True to have omega in [0, 180) and OMEGA in [0, 360)
+    geometric_parameters = geometric_elements(thiele_innes_parameters, post_process=True)
+    assert_allclose(a, geometric_parameters[0], atol=absolute_tolerance)
+    assert_allclose(omega, geometric_parameters[1], atol=absolute_tolerance)
+    assert_allclose(OMEGA, geometric_parameters[2], atol=absolute_tolerance)
+    assert_allclose(i, geometric_parameters[3], atol=absolute_tolerance)
+
+
+
 
 def test_orbit_computation(verbose=False):
     """Perform basic checks on single Keplerian systems.
diff --git a/pystrometry/utils/archives.py b/pystrometry/utils/archives.py
index 9794b4d..6662460 100644
--- a/pystrometry/utils/archives.py
+++ b/pystrometry/utils/archives.py
@@ -13,7 +13,8 @@
 
 
 def get_gaiadr_data(analysis_dataset_name, data_dir, source_id_array=None, gaia_data_release='dr3int5',
-                    overwrite_query=False, gaia_table_name='gaia_source', shared_user_name='dr3int5'):
+                    overwrite_query=False, gaia_table_name='gaia_source', shared_user_name=None,
+                    gacs_connection=None):
     """Query a Gaia archive table by source_id. Only data corresponding to source_id_array are returned.
 
     Parameters
@@ -37,13 +38,23 @@ def get_gaiadr_data(analysis_dataset_name, data_dir, source_id_array=None, gaia_
 
 
         if 'int' in gaia_data_release:
-            gaia = TapPlus(url="http://geapre.esac.esa.int/tap-server/tap")
+            if gacs_connection is None:
+                gaia = TapPlus(url="http://geapre.esac.esa.int/tap-server/tap")
+            else:
+                gaia = gacs_connection
             if getattr(gaia, '_TapPlus__isLoggedIn') is False:
                 gaia.login()
+            if shared_user_name is None:
+                shared_user_name = gaia_data_release
             table_name = 'user_{}'.format(shared_user_name)
         else:
             gaia = Gaia
-            table_name = '{}'.format(gaia_data_release)
+            if shared_user_name is not None:
+                if getattr(gaia, '_TapPlus__isLoggedIn') is False:
+                    gaia.login()
+                table_name = 'user_{}'.format(shared_user_name)
+            else:
+                table_name = '{}'.format(gaia_data_release)
 
         if source_id_array is not None:
             input_table_name = '{}_source_id'.format(analysis_dataset_name)
diff --git a/pystrometry/utils/du437_tools.py b/pystrometry/utils/du437_tools.py
index dd8b84a..737be99 100644
--- a/pystrometry/utils/du437_tools.py
+++ b/pystrometry/utils/du437_tools.py
@@ -13,9 +13,12 @@
 from astropy.table import Table
 from astropy.time import Time
 import numpy as np
+import pandas as pd
 import pylab as pl
 
 from .. import gaia_astrometry, pystrometry
+# from ..pystrometry.utils.du437_tools import geometric_elements_with_uncertainties, correlation_matrix, \
+#     covariance_matrix
 
 from uncertainties import unumpy as unp
 from uncertainties import correlated_values_norm
@@ -26,6 +29,111 @@
     print('universal_helpers not available')
 
 
+@pd.api.extensions.register_dataframe_accessor("nss")
+class NssDataFrame:
+    """Extend the pandas DataFrame class for NSS tables.
+
+    References
+    ----------
+        https://pandas.pydata.org/pandas-docs/stable/development/extending.html#extending-subclassing-pandas
+        https://pandas.pydata.org/pandas-docs/stable/development/extending.html#extending-extension-types
+
+    """
+
+
+    def __init__(self, pandas_obj):
+        """Set _obj attribute and validate input dataframe."""
+        self._obj = pandas_obj
+
+    def add_geometric_elements(self):
+        self._obj = pd.concat([self._obj, self._obj.apply(lambda x: pystrometry.geometric_elements(np.array(
+            [x['a_thiele_innes'], x['b_thiele_innes'], x['f_thiele_innes'], x['g_thiele_innes']])),
+                 axis=1, result_type='expand').rename(
+            columns={0: 'p1_a1_mas', 1: 'p1_omega_deg', 2: 'p1_OMEGA_deg', 3: 'p1_incl_deg'})], axis='columns')
+        return self._obj
+
+    def add_geometric_elements_unc(self):
+        """ Add GE with uncertainties assuming Gaussian distributions."""
+
+        def compute_ge(corr_vec, a,b,f,g,sa,sb,sf,sg):
+            # Extract the submatrix of the correlation matrix corresponding to the thiele innes parameters
+            correlation_matrix_ti = correlation_matrix(corr_vec)[5:9, 5:9]
+            thiele_innes_parameters = np.array([a,b,f,g])
+            thiele_innes_parameters_errors = np.array([sa,sb,sf,sg])
+            ge1, ge1_err = geometric_elements_with_uncertainties(thiele_innes_parameters,
+                                                                 thiele_innes_parameters_errors,
+                                                                 correlation_matrix_ti,
+                                                                 post_process=True,
+                                                                 return_angles_in_deg=True)
+            return np.concatenate((ge1, ge1_err))
+
+        if ('p1_a1_mas' not in self._obj.columns) and ('sigma_p1_a1_mas' not in self._obj.columns):
+            tmp = self._obj.apply(lambda x: compute_ge(x['corr_vec'], x['a_thiele_innes'], x['b_thiele_innes'], x['f_thiele_innes'], x['g_thiele_innes'],
+                                                       x['a_thiele_innes_error'], x['b_thiele_innes_error'], x['f_thiele_innes_error'], x['g_thiele_innes_error']),
+                                  axis=1, result_type='expand').rename(
+                columns={0: 'p1_a1_mas', 1: 'p1_omega_deg', 2: 'p1_OMEGA_deg', 3: 'p1_incl_deg',
+                         4: 'sigma_p1_a1_mas', 5: 'sigma_p1_omega_deg', 6: 'sigma_p1_OMEGA_deg', 7: 'sigma_p1_incl_deg'})
+            tmp['p1_a1_div_sigma_a1_mas'] = tmp['p1_a1_mas']/tmp['sigma_p1_a1_mas']
+
+            self._obj['sigma_p1_period_day'] = self._obj['period_error']
+            self._obj['sigma_p1_ecc'] = self._obj['eccentricity_error']
+
+            self._obj = pd.concat([self._obj, tmp], axis='columns')
+        else:
+            print('Columns already exist!')
+        return self._obj
+
+
+    def add_companion_mass_estimate(self, m1_MS=1., delta_mag=None):
+        """Add m2_MJ column to dataframe."""
+
+        if 'p1_a1_mas' not in self._obj.columns:
+            self.add_geometric_elements()
+
+        a_m = pystrometry.convert_from_angular_to_linear(self._obj['p1_a1_mas'], self._obj['parallax'])
+        m2_kg = pystrometry.pjGet_m2(m1_MS * pystrometry.MS_kg, a_m, self._obj['period'])
+        self._obj['m2_MJ'] = m2_kg / pystrometry.MJ_kg
+
+        return self._obj
+
+    def add_absolute_magnitude(self, column='phot_g_mean_mag'):
+        self._obj[f'absolute_{column}'] = self._obj[column] + 5 * np.log10(self._obj['parallax']) - 10
+        return self._obj
+
+    def add_colour(self):
+        self._obj['bp_rp'] = self._obj['phot_bp_mean_mag'] - self._obj['phot_rp_mean_mag']
+        return self._obj
+
+    def plot_cmd(self, label=None, title=None):
+        """Plot colour magnitude diagram."""
+
+        if 'absolute_phot_g_mean_mag' not in self._obj.columns:
+            self.add_absolute_magnitude()
+        if 'bp_rp' not in self._obj.columns:
+            self.add_colour()
+
+
+        colour_cutoff = 3.5
+        x = np.linspace(-1, colour_cutoff, 100)
+
+        def cutoff(x):
+            return 5.5 + 3 * x
+
+        # fig = pl.figure()
+        ax = pl.gca()
+
+        self._obj.plot('bp_rp', 'absolute_phot_g_mean_mag', kind='scatter', ax=ax, c='k', label=label)  # , c='parallax'
+        pl.title('{} ({} sources)'.format(title, len(self._obj)))
+        ax.invert_yaxis()
+        pl.plot(x, cutoff(x), 'k-')
+        pl.plot([colour_cutoff, colour_cutoff], [cutoff(colour_cutoff), 0], 'k-')
+
+        pl.xlabel('$G_\mathrm{BP}-G_\mathrm{RP}$')
+        pl.ylabel('$M_G$')
+
+        pl.show()
+
+
 def apply_elimination_cuts(table, selection_cuts, parameter_mapping):
     """Eliminate rows in astropy table based on input parameters.
 
@@ -702,7 +810,8 @@ def show_best_solution(file, out_dir):
                         name_seed='best_solution', units=units)
 
 
-def geometric_elements_with_uncertainties(thiele_innes_parameters, thiele_innes_parameters_errors=None, correlation_matrix=None):
+def geometric_elements_with_uncertainties(thiele_innes_parameters, thiele_innes_parameters_errors=None, correlation_matrix=None,
+                                          post_process=False, return_angles_in_deg=True):
     """
     Return geometrical orbit elements a, omega, OMEGA, i. If errors are not given
     they are assumed to be 0 and correlation matrix is set to identity.
@@ -758,10 +867,19 @@ def geometric_elements_with_uncertainties(thiele_innes_parameters, thiele_innes_
     omega_rad = (unp.arctan2(B - F, A + G) + unp.arctan2(-B - F, A - G)) / 2.
     OMEGA_rad = (unp.arctan2(B - F, A + G) - unp.arctan2(-B - F, A - G)) / 2.
 
-    # Convert radians to degrees
-    i_deg = i_rad * 180 / np.pi
-    omega_deg = omega_rad * 180 / np.pi
-    OMEGA_deg = OMEGA_rad * 180 / np.pi
+    if post_process:
+        # convert angles to nominal ranges
+        omega_rad, OMEGA_rad = pystrometry.adjust_omega_OMEGA(omega_rad, OMEGA_rad)
+
+    if return_angles_in_deg:
+        # Convert radians to degrees
+        i_deg = i_rad * 180 / np.pi
+        omega_deg = omega_rad * 180 / np.pi
+        OMEGA_deg = OMEGA_rad * 180 / np.pi
+    else:
+        i_deg = i_rad
+        omega_deg = omega_rad
+        OMEGA_deg = OMEGA_rad
 
     # Extract nominal values and standard deviations
     geometric_parameters = np.array([unp.nominal_values(a_mas), 
diff --git a/setup.cfg b/setup.cfg
index a268bd4..26fbe43 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -43,7 +43,7 @@ github_project = https://github.com/Johannes-Sahlmann/pystrometry
 install_requires = astropy, linearfit, matplotlib, scipy, astroquery, sympy
 
 # version should be PEP440 compatible (https://www.python.org/dev/peps/pep-0440/)
-version = 0.3.0
+version = 0.3.dev
 # Note: you will also need to change this in your package's __init__.py
 minimum_python_version = 3.7