diff --git a/examples/notebook_luminosity_function_single.ipynb b/examples/notebook_luminosity_function_single.ipynb
index 0a19202d3d6b54cc27b089c742e1a194a226587a..8746ed3a8f2e8b7c92356d88700b96c0612d80c0 100644
--- a/examples/notebook_luminosity_function_single.ipynb
+++ b/examples/notebook_luminosity_function_single.ipynb
@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "aba3fe4e-18f2-4bb9-8e5c-4c6007ab038b",
    "metadata": {},
    "outputs": [],
@@ -127,6 +127,7 @@
     "resolution = {\"M_1\": 40} # start with resolution = 10, and increase later if you want \"more accurate\" data\n",
     "massrange = (0.07, 100.0) # we work with stars of mass 0.07 to 100 Msun\n",
     "total_probability = 1.0 # theoretical integral of the mass probability density function over all masses    \n",
+    "\n",
     "# distribution binwidths : \n",
     "# (log10) luminosity distribution\n",
     "binwidth = { 'luminosity' : 0.5 }"
@@ -142,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "47979841-2c26-4b26-8945-603d013dc93a",
    "metadata": {},
    "outputs": [],
@@ -187,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "0c986215-93b1-4e30-ad79-f7c397e9ff7d",
    "metadata": {},
    "outputs": [],
@@ -231,18 +232,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 7,
    "id": "fd197154-a8ce-4865-8929-008d3483101a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "adding: parse_function=<function parse_function at 0x7f6920fd2430> to grid_options\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# import the bin_data function so we can construct finite-resolution probability distributions\n",
     "# import the datalinedict to make a dictionary from each line of data from binary_c\n",
@@ -267,6 +260,7 @@
     "            # bin the log10(luminosity) to the nearest 0.1dex\n",
     "            binned_log_luminosity = bin_data(math.log10(linedata['luminosity']),\n",
     "                                             binwidth['luminosity'])\n",
+    "\n",
     "            # append the data to the results_dictionary \n",
     "            self.grid_results['luminosity distribution'][binned_log_luminosity] += linedata['probability'] \n",
     "    \n",
@@ -294,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 8,
    "id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
    "metadata": {
     "tags": []
@@ -304,7 +298,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "adding: verbosity=0 to grid_options\n",
       "Do dry run? True\n",
       "Doing dry run to calculate total starcount and probability\n",
       "Grid has handled 39 stars with a total probability of 1\n",
@@ -317,11 +310,11 @@
       "Do join of subprocesses ...\n",
       "Joined subprocesses.\n",
       "**********************************************************\n",
-      "*  Population-f9b28e4ed6ec4a67b17cd86c5a43c41c finished! *\n",
+      "*  Population-6afcef10590f48f3b089a6e5bffc70c4 finished! *\n",
       "*               The total probability is 1.              *\n",
-      "*  It took a total of 3.99s to run 39 systems on 2 cores *\n",
-      "*                   = 7.98s of CPU time.                 *\n",
-      "*              Maximum memory use 343.570 MB             *\n",
+      "*  It took a total of 1.11s to run 39 systems on 2 cores *\n",
+      "*                   = 2.22s of CPU time.                 *\n",
+      "*              Maximum memory use 335.164 MB             *\n",
       "**********************************************************\n",
       "\n",
       "No failed systems were found in this run.\n",
@@ -356,7 +349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 9,
    "id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
    "metadata": {},
    "outputs": [
@@ -364,7 +357,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'population_id': 'f9b28e4ed6ec4a67b17cd86c5a43c41c', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.9999999999999999, 'total_count': 39, 'start_timestamp': 1655508316.7679594, 'end_timestamp': 1655508320.7581806, 'time_elapsed': 3.9902212619781494, 'total_mass_run': 1951.365, 'total_probability_weighted_mass_run': 50.035, 'zero_prob_stars_skipped': 0}\n"
+      "{'population_id': '6afcef10590f48f3b089a6e5bffc70c4', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 1.0, 'total_count': 39, 'start_timestamp': 1655551739.2635226, 'end_timestamp': 1655551740.371832, 'time_elapsed': 1.108309268951416, 'total_mass_run': 1951.3650000000002, 'total_probability_weighted_mass_run': 50.035, 'zero_prob_stars_skipped': 0}\n"
      ]
     }
    ],
@@ -374,7 +367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 10,
    "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
    "metadata": {},
    "outputs": [
@@ -384,7 +377,7 @@
        "[None]"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -451,7 +444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 11,
    "id": "1f37d2c0-1108-4ab9-a309-20b1e6b6e3fd",
    "metadata": {},
    "outputs": [],
@@ -465,7 +458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 12,
    "id": "6f4463e8-1935-45f2-8c5f-e7b215f8dc47",
    "metadata": {},
    "outputs": [
@@ -485,11 +478,11 @@
       "Do join of subprocesses ...\n",
       "Joined subprocesses.\n",
       "**********************************************************\n",
-      "*  Population-1d1d556abeae4549aa28f9740807dc84 finished! *\n",
+      "*  Population-452bcf6eb93e4e2493019ec93ee250df finished! *\n",
       "*           The total probability is 0.211729.           *\n",
-      "*  It took a total of 3.90s to run 39 systems on 2 cores *\n",
-      "*                   = 7.80s of CPU time.                 *\n",
-      "*              Maximum memory use 519.211 MB             *\n",
+      "*  It took a total of 1.27s to run 39 systems on 2 cores *\n",
+      "*                   = 2.54s of CPU time.                 *\n",
+      "*              Maximum memory use 468.883 MB             *\n",
       "**********************************************************\n",
       "\n",
       "No failed systems were found in this run.\n",
@@ -509,7 +502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 13,
    "id": "cfe45a9e-1121-43b6-b6b6-4de6f8946a18",
    "metadata": {},
    "outputs": [
@@ -519,7 +512,7 @@
        "[None]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -582,26 +575,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 14,
    "id": "5956f746-e3b9-4912-b75f-8eb0af66d3f6",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Failed to rename grid variable M_1 to lnM_1.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [27]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Rename the old variable (M_1) because we want it to be called lnM_1 now\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mpopulation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrename_grid_variable\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mM_1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlnM_1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.9.9/envs/dev_binarycpython3.9.9/lib/python3.9/site-packages/binarycpython/utils/population_extensions/gridcode.py:965\u001b[0m, in \u001b[0;36mgridcode.rename_grid_variable\u001b[0;34m(self, oldname, newname)\u001b[0m\n\u001b[1;32m    963\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    964\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed to rename grid variable \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m to \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(oldname, newname)\n\u001b[0;32m--> 965\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n",
-      "\u001b[0;31mValueError\u001b[0m: Failed to rename grid variable M_1 to lnM_1."
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Rename the old variable (M_1) because we want it to be called lnM_1 now\n",
-    "population.rename_grid_variable(\"M_1\",\"lnM_1\")"
+    "population.rename_grid_variable(\"M_1\", \"lnM_1\")"
    ]
   },
   {
@@ -614,7 +594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "108d470a-bb21-40b0-8387-2caa7ab0f923",
    "metadata": {},
    "outputs": [],
@@ -635,10 +615,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "fb8db646-f3d0-4ccd-81ba-7fde23f29c79",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Do dry run? True\n",
+      "Doing dry run to calculate total starcount and probability\n",
+      "Grid has handled 39 stars with a total probability of 0.991317\n",
+      "**********************************\n",
+      "*             Dry run            *\n",
+      "*      Total starcount is 39     *\n",
+      "*  Total probability is 0.991317 *\n",
+      "**********************************\n",
+      "\n",
+      "Do join of subprocesses ...\n",
+      "Joined subprocesses.\n",
+      "**********************************************************\n",
+      "*  Population-ec5d853278c743a3acdb8ab290f641df finished! *\n",
+      "*           The total probability is 0.991317.           *\n",
+      "*  It took a total of 1.01s to run 39 systems on 2 cores *\n",
+      "*                   = 2.02s of CPU time.                 *\n",
+      "*              Maximum memory use 475.789 MB             *\n",
+      "**********************************************************\n",
+      "\n",
+      "No failed systems were found in this run.\n",
+      "Do analytics\n",
+      "Added analytics to metadata\n"
+     ]
+    }
+   ],
    "source": [
     "# Clean and re-evolve the population \n",
     "population.clean()\n",
@@ -658,10 +667,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "68ee1e56-21e5-48f4-b74c-50e48685ae94",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZxRJf1xjtsGTTMZJSMkp5hRCirJyd7bukDls9gLNXEIsQPs9YM8moJeXmGWsmeVpObh5nJBGKSqjETg1KqSeAJ+yKJiml3nCwaxRQDfjKhbEJ4ZOa1qlOt6tqsX73aTbuOcPg4xeIr1e99BdeIYvFypqdicz67SAX0rK5uX88Q7u5ZjJZIXxBaTWkFOCI7QeMmRqOFPk5DKwCXgIed0eQQviaUX2b4O9nWzPJA4Nl/zhynomTN/L1gj1cSDNWwp2z6iDnLma69X2F8KQSa0ha6ynAFACl1CHg71rrnzwRmBC+LKZ6CNd0rc8v645w8ORFvp6/hyFd6lOvZrhL3+fUuXRmrNjP1n1/toZXCw3gYnoO2bkWZv12kPuGXeXS9xTCW5wZhyQLwghh57qrG7Jq+0kupuewemciq3cmEl+3Ov061KFLi5oE+Jd/abDUjBzmrTnM8i3HybMYta/AADPXdWvINV0b8PnPu9myN4m1CacY1LkejWrLAoKi4itzQlJKRQM1tdZ/2JU1Bp4CagBTtdaLXB+iEL4pJMifv47rwIyV+9l18BxWYP+JC+w/cYHvl+6jZ5s4+ravQ1x0WJmPmZtnYcXWE/y0+hBptnFOJqBHm9qM7NOUqIggAMb0a8r2/WfJs1iZtmw/z9zaAZNJhj6Lis2ZFWPfBZoDXaFgGfNVQB3b9rFKqQFa699cG6IQvqt+zXCeurk9SSkZ/Lb9ZEGNKS0zl8Ubj7F44zFaNIikX4e6dGweW3DfqSir1cr2/clMW7Gf0+fSC8pV/UjGDWxGw9oRhfavVSOUgZ3qsXjjMfSxFLbuO0vH5peNWxeiQnEmIXUHvrF7PhYjGV2HsTDfEuAZQBKSqHJiI0MY1bcpN/VqzJa9SazceoI9R1MA2HM0hT1HU6gWGkDvdnXo064OsZEhBa89evoS05bv548j5wvKakaFcHP/eDo0iym25nNDz0as2ZlIWmYu01fsp23T6GITnhAVgTMJqRZwzO75tcAmrfVCAKXUZIzmOyGqLH8/M11b1qJry1okJqfx67aTBUnjYnoOv6w7wvx1R2jVpAa92sSx69A5Vu9ILBjgFxrkz409GzGgU71Sk0tYcAA39mrM90v3ceZ8Bsu3nGBIl/ruP0kh3MSZhJQDhNg97wtMtnueAkRfeUjC5WT9Ca+Iiw5j3MBmjOzThE36DCu3nmT/iQtYgYSD50g4eK5gX7PJRP+OdbmxZyMiQgPL/B79O9Rl+ebjnD6fwbw1h+jRujbhIQFuOBsh3M+ZhLQXGKWU+hC4AaMjwzK77fWBc45eKERVFhjgR4/WcfRoHcfxM6ms3HaCtQmnyMzOA6Bd02huHhDvVOeHfP5+Zm7uH8/7s3aSlpnLvDWHuWVQM1efghAe4UxC+hCjRnQeCAUOUjgh9QZ2uiwy4XrSCcvr6tUM5/YhitH9mrJt/1lqRATTvH7kFR2zfbMYWjSIZM/RFJZvOU7/jnWpXSPUNQEL4UFlvgOqtZ4K3IWRhL4FrtVa50BBl/BIYLobYnQ5pVSQUuprpdRxpdQFpdQKpVQrb8clqo7gQH+uvqr2FScjsM2vN6AZJiDPYmXGiv1XfEwhvMGZGhJa628o3NMuvzwZ6OSqoDzAH6OGdzWQiDFf3xxA2jpEhdSwdgQ92tRmzc5TbN13lj1HztOiYZS3wxLCKVWyj6jWOk1r/ZrW+rjWOg/4AGhqq+kJUSGN7NOUwADjv/S05fuxuHl+PSFczakakjsopeIwaijdgM5AONBfa73Swb5BwETgDowZxrcDL2itlxXd10ndgTO2mp4QFVJURBBDuzbgpzWHOXL6EusSTtGzTZy3wxKizHyhhqSAZ4F6wI5S9p0MTMC4h/UExoKAC5RS3cv95kpFAp8Bz5f3GEL4imu7NaR6uNFtfNZvB8my9eQToiLweg0J2AzEaK2TlVLDgdmOdlJKdQXGARO01pNsZVOBBOBtoI/dvisxxkk58jet9Tu2/YKBucDPWmtZy0lUeEGBfozq05Sv5v/B+UtZLNpwlBt7ybzIomLwekLSWl8q466jMQbnfmH32kyl1JfAG0qpOK11oq28X2kHU0r5AT9gzD7xtLNxC+GrerSpzdLNxzh6OpX564/Qu12dgklZy8NqtbJl71kOJl6gf4e6xFQPKf1FQpRDsU12SqmDSqkb7Z6/rJRq7ZmwHOoA7NFapxYp34Axwqa9k8f7HAgG7tZay91fUWmYbd3AAbJzLMz+7WC5j3Xw5EX+77stfDh7Jwt+P8rEyZvYYzfnnhCuVNI9pAaA/RTDrwJt3RpNyeIwumgXlV9Wx8E2h5RSDYG7MZr1ziulUm0/va88TCG8r2XDKNrHxwCwZmciR0+XtSHCcO5iJp/N28XrUzex//iFgvLUjBze+WEbSzcdc/squaLqKanJ7gTQpkiZN/8CQ4AsB+WZdtvLRGt9BBfMWxAdXfLqoLGxESVu9xQ/2ySdwUEBbonJV87T3SraeT4wqi2P/msFeRYrs1Yd4vUHe5S6ZlJGVi6LNh9n9soDZOcYHSL8/czc2LsJcTFhfDZnJzm5Fv63dB9JF7N4aFTbK1qI0Jsq2u+zvCrSeZaUkOYCzyilhvLnHHUvKqXuL+E1Vq31QJdFV1gG4KghPNhuu0clJ6disTjO0bGxESQlOfet1F3y8iwAZGbluDwmXzpPd6qI5xlkMiZfXbr5ODv2n2XpusO0bxbjcF+LxcqanYnMWX2I85f+/N7XWcUyul9TakYZUxE9e2tHPpi1g5TUbJZsOMrBEyk8MqINkeHlv0flDRXx91kevnaeZrOpxC/yJSWkZzHmrRsENMSoHcVizGPnDYkYzXZF5Zed9GAsQlQIN/ZqzNqEU6Rn5TJtxX5aN6lx2bIWfxw5z7Rl+zh65s/bs41qRzBuYLPLpjZqUqcaL4/vwoezd3LgxEUOnLjIxMkbeXRkW5rUkWXUxZUpNiFprTOAV2w/KKUswJNa6/95KLaitgFPKKXCi3Rs6GZ73O75kITwbeEhAdzQsxHTlhsr0a7ceoJBnY01k06dS2fGiv1s3Xe2YP/o6sGM6N2Yq1vVxlxM815keBDP3NKRbxdrVu1IJCU1m7e+28JdQ5UMxBVXxJmBsXcDa90VSBn8CAQA9+UX2GZuuBtYo7WWGlIxrLIgUpU2oGM9atpWqJ27+hBnUjL4fuk+XvpifUEyCgwwM7x3Yz75+0B6tI4rNhnlC/A3M/7aFtw2uDlmk4ncPAtf/vIH3y/dR57F4vZzEpVTmcchaa2n5P/bNudb/mi7Q1c65Y5S6kXbP1vaHu9QSvUCUrTWH9jef71SagbwT9t0QwcwZh9vCIy/kvevKmT1iaopwN/MmP5N+XB2AmmZuTz36TryO8iZMMYtjezTlKiIIIID/SnrHQeTycTATvWoGxPGR3MSSM3IYcmmYxxPSuWh4a1loUDhNKcGxiql2gHvAb2KlK8CHtdalzb1T3FeK/L8HtvjEYyJT/Pdadv3Toy57HYA12mt15TzfYWoEjo2j6V5versPX6hIBmp+pGMG9iMhrWvrBdWi4ZRvHxXZ96ftZNjZ1L548h5Jk7eyOOj2lKvZsk9UYWwV+aEZBsUuxqjV9tcYJdtUyuMFWRXKaV6aK13FXOIYmmty/TlXWudCfzN9iOEKCOTycTtQxT/nr6NsOAARvZpQodmMaV2Ay+rmMgQnr+9E1/N/4ONe85w9kImb3yzmfuGtaSTqumS9xCVnzM1pIkYU/f0LFoTsiWr32z7jHJdeEIIV6lXM5z/PNLTZUmoqKBAPx68qRUNaoUz69eDZOXk8eHsBG7o0Yibejcu9b6UEM50augDfOioWU5rnQB8RPETmgohfIC7kpH98a/v3ojHR7clJMgYMDtv7WE+mLmTjKxct763qPicSUhhwKkStifa9hFCVHHt4mN48c7O1KphDFvctv8sr0/dxOlz6V6OTPgyZxLSQWBYCduH2fYRQgjiosN46c5OtGliLMScmJzOa1M2kXBQ1sEUjjlzD2kq8H9Kqf8BbwB7bOUtgeeAIcDfXRueEKIiCw0O4InRbZn120Hm/36E9Kxc/jtjO2P6xXNN1/pub0IUFYszCekdoCPGInljMVZrBaOWZQKmA/92aXRCiArPbDYxul9T6tcM5+v5f5Cda2H6iv0cPXOJ8UNbEBhQMSdnFa7nzMDYPGCsUuoLYDh/Dow9CMzRWi91fXhCiMqi21W1qF0jlA9m7SD5Yha/7zpNYnI6j41sQ41qwaUfQFR6Tq8Yq7VeAixxQyxCiEquYe0IXhrfhY9mJ7D3WApHTl1i4uSNPDyizWUTuYqqx5lODUIIccWqhQby9Lj2DOhYF4CL6Tn86/utrNx2wsuRCW+ThCSE8Dh/PzO3D1GMv7YFfmYTeRYrUxdqpi7S5ObJ5KxVlSQkIYTX9GlXh2du7UC1sEAAVm49wTvfb+ViWraXIxPeIAmpCrDK6hPChzWrF8nLd3WmkW2S173HLzBxykaOnPKdlU6FZ0hCEkJ4XY1qwfz9to50b1UbgHMXs/i/bzfz++6SJocRlU2ZEpJSKkQpdadSqlvpewvfJYMQhe8KDPDjvmEtGTcgHpMJsnMtfPbTbmas2I/FItX8qqCsNaQs4HOggxtjEUJUcSaTiSFdG/DUze0JCzZGpSxYf5RJP24nLTPHy9EJdytTQtJaW4BjQDX3hiOEENCqcQ1euqszdWOM+ZoTDp7j9SmbOHk2zcuRCXdy5h7SFIylxYPcFYwQQuSrGRXK83d0okOzGABOn8/g9amb2LbvrJcjE+7izEwNa4GRwDal1EfAPuCyueS11r+5KDYhRBUXEuTPIyPbMG/NYeauPkRmdh7vz9zB8N6NGdajkUzOWsk4k5Dspwt6Fyh6l9FkK5OZEoUQLmM2mbipV2Pq1wzn8593k5Wdx+xVhzh6JpV7r29JcKDTM6AJH+XMb/Jut0UhhBCl6Ng8lhfv6MT7M3dyJiWDzTqJ0+fSeWxUW2IjQ7wdnnABZ2b7nuLOQIQQojR1Y8N58a7OfDo3gV2Hz3M8KY2Jkzfy0PDWXNWohrfDE1dIBsYKISqU8JAAnry5Hdd0rQ9AWmYu/5m2nSUbj2GVaUkqNKcaX5VS9YF/YKwOWxMYqrVerpSKBd4GPtZab3R9mEII8Sc/s5mxA5pRv2Y4kxcYE7J+v2wfR89c4s5rFAH+ciu7IipzDUkp1RjYBIwCdmHXeUFrnQR0Bu5zdYBCCFGcHq3jeO72jkRFGKNR1uw8xdv/28r5S1lejkyUhzNNdm9gLFveGriNy+ehmQ/0clFcQghRJo3jqvHyXZ2Jr1sdgIMnLzJxykYOnLjg5ciEs5xJSIOAj7TWx7i8yzfAEaCeS6ISQggnVA8P4m+3dKBPuzoAXEjN5u3/bWHVjpNejkw4w5mEVA1ILGF7IOVYEl0IIVwhwN/MXUMVdwxpjp/ZRG6ela/n7+G7JXtl0b8KwpmEdAxoVcL2q4H9VxaOEEKUn8lkon/Hejw9rj3hIQEALNt8nP9M28aldFn0z9c5k5BmAfcopVrblVkBlFKjgDHAdBfGJlxMZlkRVYVqEMXL4zvToGY4AHuOpvDalE0cPS2L/vkyZzs1HAfWA99iJKO/K6XWYSSi7cC/XR6hEEKUQ0z1EJ67oxNdW9YE4OyFTN78djMb95zxcmSiOGVOSFrri0B34AuMLt4mYDCggI+A/lrrTHcEKYQQ5REU4McDN7ZidL+mmIDsHAsfz0ngmwV/YJFBtD7HqU4ItqT0BPCEbTCsCUjSWstvVgjhk0wmE9dd3ZB6sWF8+tNuMrJymb50L/pQMvff0IrQYOmL5SvKPXWQ1jpJa31GkpEQoiJo2zSGl+7qTFx0KADbDyTzxjebOHXuslV0hJc4/dVAKXUzMAJoYis6CMzWWkuHBiGET6tdI5QX7ujMlMWajbtPk5iczmtTNvHgTa1o0yTa2+FVec5MHRSmlFoCfA+MBZrZfsYC3yullimlwtwTphBCuEZosD8v3t2NYT0aApCRlcuk6dtZ8PsRmZzVy5ztZTcQeB+oo7WuobWuAdSxlfW37SOEED7NbDYxsk9THhremsAAM1ZgxsoDfDZvN1k5ed4Or8pypsluLDBDa/2kfaHW+hTwpFKqrm2fJy9/qRBC+J4uLWpSKyqE92fuJPliJut3nyYxOY3HRrYlunqwt8OrcpydOmhFCduX2/YRQogKo0GtCF4e35kWDSIBOHo6lYlTNrL3WIpX46qKnElIOzDuGRWnGbDzysIRQgjPiwgN5Kmx7RnYyZgf+lJ6Dv/6fisrtp7wcmRVizMJ6UXgfqXUDUU3KKVuwlgL6XlXBeZOSqmPlVKJSqmLSqmdSqlh3o5JCOFd/n5mbhvcnLuva4G/n4k8i5VvFmmmLNwjk7N6SLH3kJRSXzkoPgTMUUpp4A9bWUuM2Rp2YqyTtNzVQbrBu8AErXWmUqozsFQp1Vhrfd7bgQkhvKt32zrUiQ7jg9k7uZCaza/bTnLibBqPjGhD9bBAb4dXqZXUqWF8Cdta2H7stQXaAPdeYUxup7XeU6QoCIgDJCEJIWhatzov39WFD2bt5FDiRfYfv8DEyRt5dGQbGsfJrXJ3KTYhaa3LPYtDWSml4jCmIuqGMT9eOMaceCsd7BsETATuAKIwJnN9QWu9rJzv/RFwNxAM/MyfNb5KR4ZWCOG8qIgg/n5bB6Yu1KxJOMX5S1m89d0Wxg9tQffWtb0dXqXk9qRTCgU8i7HS7I5S9p0MTMCYafwJjOXUFyilupfnjbXWD2MkwMHAUpkCSQhRVIC/H/dc35JbBjbDbDKRk2vh8593M335fiwWuWS4mrdnFdwMxGitk5VSw4HZjnZSSnUFxmHc95lkK5sKJABvA33s9l0J9C3m/f6mtX4n/4nWOg/j/tETSqk9WutFV3xGPkyWQxLCeSaTicFd6lM3NoyP5ySQlpnLwg1HOZaUyoM3tSIsOMDbIVYaTiUkpVQP4BGMLt7RXH6Ns2qtm5b1eFrrsq6WNRrIwVj6Iv+1mUqpL4E3lFJxWutEW3m/sr6/HX+gzHELIaqeqxrV4KXxXfhg5g6OJ6Wx69A5Xpu8icdGtaFubLi3w6sUypyQlFL3A58A2YAGjrorKAc6AHu01qlFyjdgJMX2QGJZDqSUCgeGA3OATOAmjGmPnnFNqEKIyqpmZAjP39GJL3/5g806iTMpGbz+zWb+MuwqOjSP9XZ4FZ4zNaTngW3ANVrrs+4Jp1hxgKMRavlJqI4Tx7IC9wAfYCSz/cAtWmsZ1CuEKFVwoD8PD2/Nz2sPM3vVIbKy83h/1k6G92rMsJ6NMJukcby8nElItYB/eSEZAYQAWQ7KM+22l4nWOg0Y4IqgoqNLrqbHxka44m2umJ+f8R8kKDjALTH5ynm6m5xn5XKl53nP8La0io/l3//bQkZWLnNWH2LviQv07ViPbq1qE129zJclt6pIv09nEtIfGN2tvSEDY6xQUcF22z0uOTm12J42sbERJCWV9RaZe+XlGTFmZea4PCZfOk93kvOsXFx1nk1qhfP8HZ14f+YOzpzPYPehc+w+dI6PZ+6gSZ1qdGgWQ8fmscRFe2dlHl/7fZrNphK/yDuTkN4A3ldKfa21PnnFkTknEaPZrqj8Mk/HI4QQANSNCeOluzozd/UhNu05Q0pqNgAHT17k4MmLzPz1IHHRoXRsHkuHZrE0iouQZr1ilDkhaa1nKaVCgd1KqbnAYaDowiFWrfVrLowv3zbgCaVUeJGODd1sj9vd8J5CCFEmYcEB3DqoOeMGNuNw4iW27ktiy94kEpON5dETk9P5Zd0Rfll3hKiIINrbak6qfiT+ft4eDuo7nOll1xxjpoRqGLMlOGIF3JGQfgSexpjAdZItniCMmRbWeKHGJoQQlzGbTDSpU40mdaoxqm9TEpPT2LI3ia37znLw5EUAzl/KYsWWE6zYcoLQIH/axUfTuUVN2sfHYKriNSdnmuw+AmpizJKwChfN+6aUetH2z5a2xzuUUr2AFK31BwBa6/VKqRnAP23TDR0A7gIaUvKce0II4TVx0WFc3z2M67s34vylLLbZak57jqaQZ7GSnpXLul2nWbfrNIM61ePWwc29HbJXOZOQumP0snvfxTEUrVHdY3s8gtE1O9+dtn3vxOhcsQO4Tmu9xsXxCCGEy0VFBNG/Yz36d6xHemYO2w8ks3VvEjsPniMrJ4+lm49TNzaMvu3rejtUr3EmIV0AklwdgNa6THVUrXUm8DfbjxBCVFihwQF0b1Wb7q1qc+5iJhOnbOJiWjbfLt5L7RqhqAbe6tDsXc7cTZsOjHRXIEIIURXVqBbMoyPaFCwK+OHsBM6meGUki9c5U0P6FJiilJoDvIexWF/RXnZorT05pZAQQlR48fWqc8c1iq/n7yE1I4f3Zu7k+Ts6Ehzo7fmvPcuZGtIuoBNwI7AEY8qdQw5+hM+RafKF8HW929ZhSJf6ABxPSuWLn//AUsUWM3Mm/U5ErmwVW9XuUSqEzxvTvyknz6aRcOgcW/YmMXfVIUb0aeLtsDzGmYGxr7oxDiGEqPL8zGYevKkVr03dzOlz6cxbe5i6sWF0bVnL26F5hAwRFkIIHxIaHMDjo9oQEmTUF7765Q+OnPKd+ejcyZmZGvqUvhdorX8rfzhCCCHiosN46KZW/HfGdrJzLbw3cwcv39WZ6uGO5piuPJy5h7SSst1D8itfKEIIIfK1bhLN2P7x/LB8P+cvZfHB7J08c0tHAvwrb8OWMwnp7mJe3xRj+p7DGF3DhRBCuMDgLvU5npTG6p2JHDhxkamL9nDPdS0r7Zx3znRqmFLcNqXUv4AtLolICCEEACaTiTuuUZw6l87+ExdYs/MU9WPDGdK1gbdDcwuX1P201ueBL4BnXHE8IYQQhgB/M4+MbENUhHH/aNqK/SQcTPZyVO7hysbI80DV6TAvhBAeUj0skMdHtSXQ34zVCh/P3UVicpq3w3I5lyQkpVQwxhpJp1xxPCGEEIU1rB3BvcOuAiAjK5f3Zu4kLTPHy1G5ljPdvr8qZlMNjKUpYpGZuIUQwm26tKjJ8R6NmLf2MKfPpfPJ3F08OaYtfubK0fPOmV5244spPwfsBSZorf93xREJIYQo1k29G3PirLES7a5D55ix4gDjBjbzdlgu4Uwvu8qRgoUQogIzm0zcN6wlb36TwfGkVJZsPMagTvWIiQzxdmhXTJKMEEJUMMGB/tx7fUvAmK1g7a7KcfteElIVIFO0C1H5NKwdQf2a4QCsTTiFtRIsVVFik51S6icnj2fVWt90BfEINzLJ+hNCVCo928Txw7J9nDmfwf4TF2hWL9LbIV2R0u4hDXPyeBU/RQshRAVx9VW1mLFiP3kWK2t2JlbuhFSWjgxKqb7AP4EuQKKL4hJCCFGKamGBtGkSzbb9Z9m45wy3DGpOUEDFnd+63PeQlFKtlVK/AMsBBbwEVI6+h0IIUUH0bFMbgIysPLbuTfJyNFfGmXFIACil6gOvAbcBecB7wOta68o5uZIQQviwdvExhIcEkJqRw5qEU1zdqra3Qyo3Z2ZqiAJeAB4GgoDvgRe11ofdE5oQQojS+PuZ6dayFsu2HGf3oXOcu5hJjWrB3g6rXMpyjyhIKfUscAB4ClgFdNJa3y7JSAghvK9nW6NWZAXWVeAxSaaS+q4rpe4FXgXqYKx39Het9TLPhObTGgGHkpNTsVgcf36xsRGcPJnMpUsp5OZmY7HkeTRAe+cuZWGxWAkO8CM8NMClxzabzVgsFpce0xfJeVYule08rVa4kJpFrsWKn9lEZHgQJpPj8/Tz8yc8PJKQkDCPx2k2m4iODgdojLGoayGlNdl9jpF0NwHTgXZKqXYl7G/VWv+3fKFWLikpKZw/f4bw8OoEBdXAbPbz2iqPOeZU8vIshIcEuHx6EX9/M7m5lec/dnHkPCuXynieoalZnL+UBUBUdCjBgf6XnafVaiUnJ5uUFKPzgzeSUknKcg/JhNGlu0sZ9rUCkpCApKSzREbGEBhYMdtyhRAVS1hIQEFCSs3IJTjw8su7yWQiMDCIyMhYLlw4W+ESUn+PRFEJZWdnU716kLfDEEJUEf5+ZkKC/MnIyiU9MwdLteKvPwEBgeTl5XowurIpbWDsr54KpDLyVhOdEKJqCg8JICMrF4vFSkZmLoHFDJL11WuTTK4qhBCVREiwP2azkWxSMyrearKSkIQQopIwm0yEBhs9aTOycsnNq1jTi0pCElXe6NE38MYbr3rlvR999C88+uhfvPLe+RITT9KrV2fmz5/nM+//xhuvMnr0DV5776FD+7n9vYt7/ysVHvLnnZhL6dkuO64nOD11kKi6EhNPMmbMjaXu9/zzr3DddYUvJsnJZxk58nosFgszZsyjdu3Lpzd59NG/sG3bFho3bsI330y/bPvy5Ut5+eW/A/Dee5/QsWPngm3bt29j6tSvOHBgHxcvXiAyMor4+OYMGnQNQ4YMdfZUq7zff1/Lrl07uffeB7wdSpklJOxk/fq13HzzrURERHg7nEI8+XkGBfgVdPe+lJZNeLC/z94zKkoSUlVyhX+TkZFRvPTSxEJlZrMJi8WKxWLh/ff/S0ZGOs2aqcteu2zZYoKDg/Hz82fp0oXcfvt4h+8RGBjIoUMH2b9/H/HxhefqXbJkIYGBQWRnZxUqX758Ka+88hzNmjVnzJhxRERUIzHxJNu2bWHevNk+nZD++98PvR0CtWvHsWzZGvz9/7wcrF+/jhkzvvdaQnr22RedHri6e/dOvv76c6677oYyJyRH5+4OxX2e7nh/k8lEeEgAKZeyyM7JIzvXUmFmAJeEJMosJCSEa665rlBZ/jexL7/8lIsXL/Doo0/SrFnzy167ZMlCevXqS2BgIEuWLCo2ITVo0Ij09DSWLl1UKCGlpqayfv1aunfvya+/rij0mq+++owmTZry6aeTCQgoPBPF+fPnynm2nlE0Xm8wmUwEBfnWEAV3J4i8vDyys3MIDAz06rm767PPT0hgdG6oKAlJ7iGJK7Zt2xamTv2K7t17MnbsbZdtP3bsKH/8sZuBA4cwcOAQDhzYx8GDB4o93sCBQ1i2bHGhJZlXrlyGyWSid+9+l+1/8uRxrrqqtcOLe1RUDafP58svP6VXr86Xlc+fP49evTqTmHiyoGz06Bt47rm/snHj79xzz20MGNCTe+65jd27EwBYsOBnxo0byYABPXj00b9w8uSJQscseg9py5ZN9OrVmV9/Xc7kyV8wfPi1DBjQgyeeeIjjx49dFtOyZYu5++5bGTCgB8OGDeb//m8iKSkphfY5duwoL7zwN2688RoGDOjBiBHX8corz5Gamgpcfh/jjTdeZcaM7wHo1atzwU96ejqDBvVi0qR3Lovj2LGj9OrVmVmzZpT42V66dIk33niVa67py9Ch/Xj99VdITb102X6O7iEtXbqIe+65ncGD+zBkSF/uvHMs06cbcX755ae8995/ABgz5saCmPN/V716debdd//NggU/c+uto+jT52oSEnaUeA/n+PFjPPnkwwwa1IuRI6/nu++mFNqe/7vasmVTofKyfp6O9s23adMGHnroHgYO7MnQof158cVnL/vbyb/Xdfr0KZ59dgKDB/dm2LBBfPDBJExYCQ4yknpaRk6FWd5cakjiily4cIGJE18iKqoGL7zwD4dt1UuWLCQiohpdu16NyWSiRo1olixZyAMPPOLwmIMHD+Wbb74mIWEHbdoYM1UtXbqIHj16Exp6+cjyWrVqs2nTBpKSzhAbW9O1J1gGR44c5vXXX2H48NEMGRLCt99O4ZlnJvDgg4/w3XdTuemmkaSnp/Hdd1N4++3Xeffdj0s95pQpX2I2+3HrrXdy6dJFvv/+G/7xjxf5/PM/L4rz58/jzTf/QatWbXjoocc5c+Y0M2dO448/dvH551MJCgoiJyeHp556DD8/M2PH3kr16tU5ffo0a9euJjX1EuHh4Ze99003jSQ5OZkNG9YVaqINDQ2lT5/+rFixhMcem4Cf35/fuhcvXoC/vz8DBw4u9pysVivPPfdXduzYxvDho2jYsBG//baS119/tdTPY+PG33n11Rfo27c/N944gry8PA4fPsTOndu5+eZb6Nt3ACdPHmfRogU8/vhTVK8eCRjNzPbHWL58MSNGjKF69WrExMQU+365ubn89a+P0a5dBx566HHWrl3Nxx+/D8Btt91Varz2ivs8iz/X9Tz99OPUr9+Q++57iPT0NGbM+J6HHrqXyZO/Jyrqz3PKzc3lqacepU2bdjzyyBNs3LieH374lrp16zF46E1k2sYkpWflEhbs/dp4aSQhiSvy5psTOXs2iUmTPiIyMtLhPkuWLKRPn34FzTD9+g1g6dJF/OUvDztMYE2aNKVp03iWLFlImzbtSE4+y9atm5k48S2Hx7/ttrt4663XGDt2OG3atKNt2/Z06dKN1q3bYja7vxHg6NEjfPbZZK66qjUAsbG1eOWV5/jww/f44YdZBRfH3Nxcvvnma06fPkWtWiWvWZObm8tXX00p+MyqVavOu+++w8GD+2nSJJ7c3Fw+/vh94uOb8/77nxIYGAiAUi149dUXmDdvNqNHj+Pw4YMkJp7g88+n0LJlq4Ljl3RvqHXrtjRs2IgNG9Zd1kR7zTXXsXjxArZs2UiXLlcXlC9ZspBu3boXnKsjq1f/yrZtW3jssQkFNenhw0fz+OMPlvhZAKxdu4bGjZvwxhv/crg9Pr4ZSrVk0aIF9O7dj7i4Opftc+zYUb75ZjoNGjQsaGq2r+3ay8zMpFevvjz22AQARowYzYQJjzJ58peMGDGG0NDQUmPOV9Ln6chHH71LZGQkH3/8ZcG9sG7devDgg3fz7beTC2LKj3Po0Ou54467AePzvOee2/j557ncdNMoTGYTVouV1PQcSUi+TinVHVgDPK+1dny1c7GDJy8yb80hMrM9N/t3Vk4eVqsVPz8zAX7GBTo40I8bejamSZ1q5T7uzJnT+fXXFYwff1+hHm/29uzZzbFjR5kw4ZmCsoEDr2HWrBns3Lmdtm3bO3zdoEHXMH369zzxxNMsX76UkJAQunfvye+/r71s32HDbiI2tibTpn3Hli2b2Lx5I19//Tl169bjpZdeo3XrNuU+x7Jo2rRZQTICaNXK+HevXn0KXaCvuspICImJJ0tNSNdff2Oh+yjt2rUH4OTJEzRpEs+ePbs5f/4c99//UEEyAhgwYDAffvgua9euYfTocYSFGTWgNWtWER/f/IrvWXXu3JXo6BgWL15YkJB2707g+PFj3H//wyW+dt26NQQEBHDTTaMKyvz8/Bg1aizbt28t8bXh4eGcOXOaXbsSCj5fZ3Xs2JkGDRqWef+RI8cU/NtsNjNixCg2b97Ajh3buPrqHuWKoTRnz55l37693HHH3YU6ZrRu3YZWrdqwbt3qQgkJjBqYvbZtO7Bo0XzMZhMRIQFcTMsmIzuXvDwLfn6+fZemyiYkpZQZYyLYjZ583yWbjrH9gG8srhsS5M9fbmxV+o4OHDiwnw8/fJf27Ttw9933F7vf4sULCA0No3bt2gX3QGrUqEFkZCSLFy8sNiENHDiETz/9kE2bNthqWP0LXXiL6tatO926dSczMxOt/2DZssXMnTuLZ555ku+++7FQM4erFU0u+UmgZs1aDssvXbro9DEjIqrZXmvcbzl1KhHgsgus2WymXr36nD5tbK9Tpy5jx97G5MlfMG3a/+jQoSM9evRmyJChDps/S+Pn58fgwUOZN282Tz/9HEFBQSxevJCwsDB69epd4mtPnTpFTExNgoMLTzhcliQxcuQYVqxYygMPjCcuri5dunSlf/9BdOnSrcyxO6o1FcfPz++y/evVawDAqVOOa1WuUNzvFaBhw0YsXbq4UFlISCjVqlUvVBYREVHwNxYRFsjFtGywQmpmLtXDiv8/5AuqbEIC/gKsB6qXtqMrDe5cn8ysXJ+oIQ3uUr98x8vK5JVXnic4OJiJE98sdC/BnsViYdmyJaSnp3HrraMv275y5VKefPJphz2q6tSpS6tWbfjmm6/ZvTuB++4rvVkHIDg4mHbtOtCuXQeqV4/k668/5/ff13DttcPKfH7Fjdkobk2r4r51FtdcWJb7y2az48+0PDenH3tsAtdffwOrVv3Khg2/85//vM3UqV/x6adfl+ue29Ch1/PDD9+ydu0q+vTpz/LlS+jbdwBBQe6b2T4qqgZff/0/Nmz4nd9/X8vvv6/lp59mc/31N/Lccy+X6Riujq/4vxPPLWtRWo0nJMgffz8zuXkW0jJyJCGVRCkVBzwBdAM6A+FAf631Sgf7BgETgTuAKGA78EJ5FgxUSkUDTwJXA5PKF335NKlTjSfGlLSklOsdO2NbDyk0gJjqV74e0qRJ/+bw4YO89da/qVmzVrHrymzevJHk5LM88MCj1KtXr9C2pKQzvPfef9iw4Xd69Ojl8PWDB1/DpEnvEBVVg06dyrL6SWEtWlwFGM0gzrCvjdg3m5w65TsrcdauHQcY96/at+9YUG61Wjl+/BiNGzcttH+TJvE0aRLPXXfdy65dCTzwwHjmzJnJ/fc/5PD4JY2jjI9vRtOmzVi8eCGhoWGcO5fMkCHXliHm2mzduonMzMxCtaSjR4+U+lowusj37Nmbnj17Y7Va+e9//8msWTO48857qFu3Hlc80M5OXl4eiYknbcc1HD9+FIBatYzPPv/vJL+3Yr78Wo69so5Ltf+9FnX06BGHA8pLEx7655ikrJw8n+4C7u0GRQU8C9QDdpSy72RgAvAtRhKzAAts94Gc9QYwSWudUo7XVmkrViy13TAfS69efUvcd8mShYSHh3PLLbfTv/+gQj9jxtxCVFQNlixZWOzrBw68hrvvvp8JE54pthYGRhdZR9atWwOUrUnIXv5FaPv2LQVlGRkZLFjws1PHcacWLa4iKqoGc+b8SE7On5NorlixjKSkM/To0ROAtLRUcnMLLzPQpElT/Pz8yM4uflqZ4GDji0t+E2FRQ4dez/r1a5k9ewYxMbHF3kO01717T3Jycpg7d2ZBWV5eHjNnTiv1tRcupBR6bjKZaNrUGKeWlWWMtwkJMWJ21I28POy7sFssFmbPnklISAjt2nUAjOTh5+dX6O8EYPbsy7u+l/Z55ouJiaFZs+bMnz+vUKLbvTuBhIQddO/u+MtbSew7M6T5+ISr3m6y2wzEaK2TlVLDgdmOdlJKdQXGARO01pNsZVOBBOBtoI/dviuB4q6UfwOWYSw26LjPsSjW2bNJvP32G4SEhBIf36zgxmnRZdzr1q1Hs2aK335bQZcuVztskjOZTHTv3pMVK5aSkZFRcDGxFxUVVaaZAp577q/ExdWhZ88+1K1bl4yMTDZtWs+aNato2fIqevYs+d5GUV27Xk2tWrV5663XuOWWw5jNfsyf/xORkVGcPu0btSR/f38eeugx3nzzHzz22AMMGjSEM2dO8+OP02jSpCk33DACgM2bN/Hf//6Tfv0G0qBBQyyWPBYtWoDJZKJv3wHFHl+plgBMmvQvunXrjtlsZtCgawq2Dx48lE8+eZ/Vq39j3Ljby9SbsWfPPrRp044PP3yXEyeO07BhY377bcVlNQxH3nrrdS5dukjHjp2pWbMmp08b59qsWXMaNWpsi7kFAJ999hEDBw7B39+fnj37OPzbKk1wcDCrV//KpUsXUaola9euZvPmDTzwwKMFPezCw8Pp128gP/44DTBRt2491q5dxfnz5y87Xmmfp72HH36Cp59+nIceuofrr7+RtLQ0Zsz4gejomGIHlJckwN9McKAfmdl5pGbkEBUR5LNTCXk1IWmty/pVZjSQA3xh99pMpdSXwBtKqTitdaKtvF9JB1JKPYlRMzuhlALjHlKuUqqR1rpsNyqqqKNHjxR8+3zrrdeL3e/aa4fRs2dvUlNTi22OA+jZszfz589j1apfr2h6n2effZFVq35l+fIlnD2bhNVq3IO68857uP32u5we9e/v78+bb77Dv//9Fl988Qk1akQzbtythIVF8Oab/yh3nK523XU3EBgYyHffTeHDD98lLCyMwYOH8uCDjxWM/o+Pb0bXrlezdu0q5s6dRXBwMPHxzXjnnfdK7H3Yu3dfxoy5hSVLFrJ48QKsVmuhC2hMTAydOnW1dWUuvbkOjHtqb7/9H959998sWjQfk8lEz559ePTRJ7n77ssHVNu75ppr+emn2cye/SOpqZeoUSOaAQMGcc89fylIhs2bt+CBBx5h1qwZrF+/zjZv4k/lSkj+/v78+9/v8847/8eyZYuJiKjGAw88yh13jC+034QJz5CXl8vcuTMJCAhkwIBBPPzwE9x559hC+5X2edrr0qUb77zzHl9++SmfffYxgYEBdO7clYcffqLcnXPCQwLIzM4z1knKyi2YEdzXmHxlBK9dDemye0hKqSVALa112yLlA4GlwHVa6wVlfJ9QwL6v87vAPuBfWusLZQy3EXAoOTn1stpBvjNnjlGzZvk6Dbiaq+8h2csfz1HZyXle7plnJpCYeMLhRLi+rqr9Pi0WK8fOpGK1WgkN9qdmVCinTh2hdm3nmrOvlNlsIjo6HKAxcPiyeD0aTfnFAScclOffPSxzf06tdTqQnv9cKZUBpDqRjISo8s6cOc369WuL7RQhfIvZbCI02J+0jBzSs3LJ82BPQGdUlIQUAmQ5KM+0214uWuvx5X2tLdM7dOaM8e3El5hMJrfE5Gvn6S5ynsbA3O3btzFnzkyCgoIYPnxEhf1cKmrczso/z8jwIKNTgxUysvMwm83ExvrWMh0VJSFlAI6mxA222+5xJTXZAT7XJGC1Wl0eU1Vr+qjsSjvPTZs28eab/6B27TheeOEfhIdXr5CfS1X8ffr7mfDzM5OXZ+FiajZmi4WkJNf0SCwruyY7hypKQkrEaLYrKr/MfUOnhRAFrrvuhssWXxQVQ/46SRdSjTFJ/iV8mfaWilJn3Qa0UEoVTa3584Zs92w4QghR8dgvb57lwdliyqqiJKQfgQDgvvwC28wNdwNrtNZSQxJCiFIE+PsRFGgMMs/KyfO5zg1eb7JTSr1o+2dL2+MdSqleQIrW+gMArfV6pdQM4J+26YYOAHcBDYHxHg65zKxWq88OQBNCVE3hIQFkZeWSZ7Gy69B52jaN9nZIBbyekIDXijy/x/Z4BPjArvxO2753YsxltwNj/NEat0dYDoGBgeTkZBEY6L4JJ4UQwllhwQEkc4nUzDxOnEqRhGRPa12mKoTWOhNj6p+/uTci14iNjeHEiUTCwqoTHByC2ewntSUhhFcZPW2zSb10juXbztOsQa3SX+RBXk9IlVVkZCTp6XmkpqaQlnah2KULPOHSxSwsViu5GX7kZrh2yhCz2ezR6fa9Rc6zcqnK5+nn58/8jWc5cCqLZg28FFgxJCG5UUBAIFFRzq8342r/mrmG85ey6N02jruvi3fpsWNjIzw+lsEb5Dwrl6p+ngdP7/NCNKWrKL3shBBCVHKSkIQQQvgESUhCCCF8giQkIYQQPkESkhBCCJ8gvezKxw+MmWtLUtp2T4mJDCHA30y1sEC3xOQr5+lucp6VS1U+z9ioEHJyjEU7Pfk52L2Xn6PtPrNibAXTC1jl7SCEEKKC6g2sLlooCal8goAuGMti+N6UuUII4Zv8MJYN2oiDRVclIQkhhPAJ0qlBCCGET5CEJIQQwidIQhJCCOETJCEJIYTwCZKQhBBC+ARJSEIIIXyCJCQhhBA+QRKSEEIInyBz2XmBUuoj4CFgrtZ6uJfDcRml1EDgdqAnUA9jJotlwMta61PejK08lFJBwETgDiAK2A68oLVe5tXAXEgp1QUYD/QHGgLJwFrgRa31fi+G5nZKqWeAt4HtWuv2Xg7HpWy/11eBHkAAcAD4r9Z6shfDKpXUkDxMKdUWuA/I9HYsbvA20BeYDTwOTAPGAVuUUt5fy915k4EJwLfAE4AFWKCU6u7NoFzsWWAksBTjHD8D+gFblVItvRiXWymlagMvAmnejsXVlFLXAmswEtFLwF8xfr/1vRlXWUgNyfPexbjADfB2IG7wFLBaa23JL1BKLQR+BR7G+MZWISilumIk0wla60m2sqlAAkbi7eO96FzqP8CtWuvs/AKl1DRgJ0ayGu+luNztLWATxpfySO+G4jpKqeoYX6Q+1lo/4eVwnCY1JA9SSo3BmJT1BW/H4g5a69/sk1F+GXAOqGjftkcDOcAX+QVa60zgS6CXUirOW4G5ktZ6rX0yspXtA3ZR8X5nZWL7snE7xheoyuZWjAT7MoBSKkIpVWHW2ZCE5CFKqRDgHeBtrXWit+PxFKVUOBAOnPV2LE7qAOzRWqcWKd8AmID2Ho/IQ2wXsFpUvN9ZqWzn9j4wRWu9zcvhuMMgYA9wnVLqGHAROKeUeksp5XANIl8iTXae8wzGhewdbwfiYU8CgcB0L8fhrDjghIPy/C8TdTwYi6fdBtSlctbk7wSuAoZ7OQ53ice4VzQZ+CewFRiG0fwajPH/0WdJQnKSUsqMcYEtla2JB6VUA4w/iPu11hluDM9lynOeDo7RB3gF+F5r/asLw/OEEBys18KfnVFCPBiLxyilWgAfYiye9o2Xw3EppVQExr2jtypxK0U4Ro/Qv2ut37aVzbK1VDyslHpda+2zNV9psnNeHyCjLD9KqRjba/6FcZP4fx6PtvzKc54FbBe22Rhdpe/3UMyulIGxEGNRwXbbKxVbz7NfgPPAmKL3AyuBF4FsjI4clVX+3+X3Rcq/w+h119Wz4ThHakjO2wPcXcZ9LymlOgE3YzSDNFRK5W/zB0KVUo2AZK31JVcHeoWcOk/7J0qp+sBiIAW4XmtdEbvWJmI02xWVX3bSg7G4na131gKgOtCzIo4bK4mtE8qTGN2ga9n9PwwGAm3/Dy9orc97JUDXSQRaAaeLlOc/j/JsOM6RhOQk23/UyWXd33ZxBuMbSlF1gUMYg2Q/ueLgXMjZ88ynlIrGSEZBwACtddH/GBXFNuAJpVR4kY4N3WyP2z0fknsopYKBeUBzYKDWWns5JHeohdEE/bbtp6hDtvK/ezIoN9iM0bGhLnDQrrye7THJ4xE5QRKS+60HRjgo/wzjP8H/YVz8KjylVBgwH+M/Q/8KPtL/R+BpjEHMk6Bg5oa7gTVa60pRQ7L1vJoGdAdu0lr/7uWQ3OUQjv8fvg6EYQyA3uvRiNxjBsb96nuxdUqx9Sy8D2MQsE//fk1Wq9XbMVRJSqnDwLZKNnXQHOAm4CtgRZHNp7XWSzwe1BVQSk3H6I31X4ypV+7CGEfWX2u9xouhuYxSahLGDA3zuLwnZKrWeo6nY/IkpdRKILIyTR2klJqCMd3Vl8AW4HrbzzNa6395M7bSSA1JuFJ72+M9th97vwIVKiFhdBF+zfYYBewArqssycimve3xBtuPvSPAHE8GI1zifuAoxheouzCa7h7UWn/q1ajKQGpIQgghfIJ0+xZCCOETJCEJIYTwCZKQhBBC+ARJSEIIIXyCJCQhhBA+QRKSEEIInyAJSQghhE+QhCSEEMInSEISQgjhEyQhiQpFKdVPKWVVSo33dixXSinVWimVq5Qa7O1YqgKl1E1KqWylVDNvxyIck4QkhPf8B2Pm8EJz/CmlatiS7gIvxeU2SqkPlFInbDNQF7dP/peOp1353lrruRgLZTpafkL4AElIQniBUqo7MBjHq5d2tD1u8VxE7mdLQsOBuVprb02i+S4wQinVykvvL0ogCUkI73gYOIuxflRRlTIhYSzdURfvziA+C0gHHvRiDKIYsvyEqBSUUjHAP4AbMVYHPQ38BLystU4usm8j4N8YNRQw1m560vZ4WGvdz82x+mPUFH7WWuc42KWyJqQRGMvaF10ry2O01qlKqVXAaOAxb8UhHJMakqjwlFLVgbUYS8EvwkguC23PVyulIuz2jQZWYaz9Mxljdc00jItkmIdC7gSEAxuK2d4ROK+1PuSheDxlBDC/mCTsSeuA2kqpFl6OQxQhNSRRGTwDNAMe0Vp/lF+olNoGfGDb/pKt+FmgHnC71vo7W9nHSql/An/zULxX2R4PFN1gS57xeLEW4Q5KqZaAAl70diz8+bm3AvZ4MxBRmCQkURmMAJKAz4qUfwq8Ytuen5BuABKB74vs+w6eS0ixtsdzDrZ1AExUvua64UAmRs3VpZRStTFW9W0BnAFmaK03l/CS/Cbcmq6ORVwZSUiiMmgMbNJa59oXaq1zlVJ7+fOeTP6+G7TWliL7nlFKpdiXKaVuBh7HWOb7rNa6UZHt/hj3ou7AaP6eiVFLyywl3vweZo66PneyPRabkJRSvQFHXcIDAT+ttV+R/T/CaL7sobVeV2TbSqAvxtLsC+zKbwamAb9orYfZypoD/wR6AsHY7tNprZ8qLlY7I4ClWuvUMuxbZkqpUcAA4EvgfYwkc6tSajTwfDG9+fI/d1ku28dIQhKieOcxmvxqARMcbH8e6A+0AbIxOlH8EyOJlSTJ9ljDwbZSOzRorVdh3IMqoJSqA2yyxWtfHgLcilEbuw/j/klRe4B7KZzk7uPy5qxfMHqp3Y7RU60pRnIqkVKqHtAZuL+0fZ2hlGoNNNJaP2JXfAT4P6XU1Rg96T528NL8zz3JwTbhRdKpQVQGBwFlq7EUsD1vbtue7zAQr5QyF9m3JhBpX6a1XqK1/gHjIufIfcCbWusTWusk4FVgvFLKr5j98yXYHh3NGNARSAX2lnKMAkqpIIxEsVpr/WaRzWMAC0ZHj5uVUuFcbjowQCkVazteQ4xa4Ry794jBuLf1idY6VWtt0Vrv01pPLkOIwzFqIz+V9ZzKaCSOx3Ghtf4diCn6N2ETb3tMcLBNeJEkJFEZzMG4L3NfkfL7beWz7crmAXHALUX2dWpWAKVUJFAf2GZXvAWIABqV8vKtwEXg6iLHDMW48b/NyYGjn2A0oY13sO0+4AeMpJMDjHOwzyWMz+hO2/N7gf8BWfk7aK3PAn8AXymlximlmjoR3wiMZOnqGkl6/ueklOqulDqrlLLvNLEZ4wtJUVcDp7XW2sXxiCskTXaiMvgnRk3gQ6VUR4wLfgeMC6u2bc/3NkYT1tdKqa4YzVK9gR4YA1XLmgjyu5Kn2JWlFNnmkNY6Tyk1CxiulArSWudf+NsBfkCwUurvDl6aqrUu2iT3ODAM6KK1Ti+yrTnGuf1Na52llPoB4zP5wsGxvwS+UEpNwkhs1wOjiuzTDyNxPw9cpZQ6Cvxdaz29uHNVStUA+mD0dHTWQKVUsIPys1rrT4qUtQOiKdyEeJEivwtbDbE38FU54hFuJjUkUeFprS9gXIg+Ba4D3rM9fgL00lpfstv3LNAL+Bm4ByNBhWHcCzIBGWV82/xjVrcriyyyrSQf2/YfZleWf/+oM/B/Dn7G2B9AKdXfFv8YrfVhB+9xH7BHa73e9nwycLWjaXO01msxzv9V4JTWeqeDfc5orZ/RWrfFuA/zEfC/UsbzDMP44ju7hH2KMxR4zcHPk7btIXb7foFRE7vLrqwdsK/IMUcBoRh/K8LHSA1JVCha65U46J1maw562PZT2jEOYdx/KGAbMBsNHC1jHClKqWMY91rym346YCSjw2V4/QalVP4g3pm2sg+BD8vy/rbZJqZj1H5WOtgegNEEV10pdarI5nsBRz3jvsSoTT5UhvgvAu8opZ6j5PE8I4DtxSTM4o69Esc9EIs6rJTqobVea+thOSd/g22wdCOtddGu9U8As7XWcv/IB0lCElWOUipEa120JpTfRLbEbj8/IMD2Y7I1H1ntmti+AJ6zTUWTg1G7mKy1zitjKH8FtiulhmitFzsRfyjGxfenok14dm4AojCSpP1F+XbgWaXU37XW2UVe8xnGPbG1Dt4zCmOc1ncYHS7MGE17oRj3aoqzDvi8xBMqv2+Bd5VSYfYzpiulGmPUpAolXaXUcKA1MNZN8YgrJAlJVEXzlVJHMDohmIGBGE1Layk88ecdwNd2zzMwetw1sj1/E4gBdtmO8yPGTBBlorXeRfn+D47CaI5qrpRydHG9CqO57nut9W77DUqpT4AXMHq+Fbr3Y6v1LC3mPbOB2hg95WphdHjYBdxYUu1Ha/3P4rZdKa21VSn1JPCoUuo+jN6EZoyBz08UncNQaz0HY6yW8FEmq1XGhomqRSn1V4zmrEYY9yGOY3Sb/of9/SYhhGdJQhJCCOETpJedEEIInyAJSQghhE+QhCSEEMInSEISQgjhEyQhCSGE8AmSkIQQQvgESUhCCCF8giQkIYQQPuH/AeELPwJ3Fj4SAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# plot luminosity distribution\n",
     "ldist = population.grid_results['luminosity distribution']\n",
@@ -707,14 +729,6 @@
     "* What about evolved stars? Here we consider only the *zero-age* main sequnece. What about other main-sequence stars? What about stars in later phases of stellar evolution?\n",
     "* Binary stars! (see notebook_luminosity_function_binaries.ipynb)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ef00d82c-dc36-4a94-b4be-98f58649644e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {