diff --git a/docs/requirements.txt b/docs/requirements.txt
index 024246b..9b69072 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -5,3 +5,4 @@ m2r2
 nbsphinx
 sphinx_design
 furo
+lxml-html-clean
diff --git a/docs/source/examples/notebooks/single_obj.ipynb b/docs/source/examples/notebooks/single_obj.ipynb
index 84ef881..0c4998a 100644
--- a/docs/source/examples/notebooks/single_obj.ipynb
+++ b/docs/source/examples/notebooks/single_obj.ipynb
@@ -10,12 +10,925 @@
   {
    "cell_type": "markdown",
    "metadata": {},
+   "source": [
+    "Here we will do a basic single-objective optimization. This will be done using a Gaussian Process to map input parameters to the objective, and make subsequent recommendations for experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from scipy import stats\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "%matplotlib inline\n",
+    "import matplotlib.lines as mlines\n",
+    "from matplotlib.colors import LinearSegmentedColormap, ListedColormap\n",
+    "import seaborn as sns\n",
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "from glob import glob\n",
+    "\n",
+    "from olympus import Surface\n",
+    "from olympus.campaigns import ParameterSpace, Campaign\n",
+    "from olympus.objects import ParameterContinuous\n",
+    "from atlas.planners.gp.planner import GPPlanner\n",
+    "\n",
+    "sns.set(style='ticks', context='notebook', font_scale=1.2)\n",
+    "from cmcrameri import cm\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## helper functions\n",
+    "# Golem colormap\n",
+    "_reference_colors = ['#008080', '#70a494', '#b4c8a8', '#f6edbd', '#edbb8a', '#de8a5a','#ca562c']\n",
+    "_cmap = LinearSegmentedColormap.from_list('golem', _reference_colors)\n",
+    "_cmap_r = LinearSegmentedColormap.from_list('golem_r', _reference_colors[::-1])\n",
+    "\n",
+    "cmap = cm.nuuk\n",
+    "\n",
+    "def get_golem_colors(n):\n",
+    "    _cmap = plt.get_cmap('golem')\n",
+    "    return [_cmap(x) for x in np.linspace(0, 1, n)]\n",
+    "\n",
+    "def plot_contour(ax, X0, X1, y, xlims, ylims, vlims=[None, None], alpha=0.5, contour_lines=True, contour_labels=True, \n",
+    "                 labels_fs=8, labels_fmt='%d', n_contour_lines=8, contour_color='k', contour_alpha=1, cbar=False, cmap=cmap):\n",
+    "    # background surface\n",
+    "    if contour_lines is True:\n",
+    "        contours = ax.contour(X0, X1, y, n_contour_lines, colors=contour_color, alpha=contour_alpha, linestyles='dashed')\n",
+    "        if contour_labels is True:\n",
+    "            _ = ax.clabel(contours, inline=True, fontsize=labels_fs, fmt=labels_fmt)\n",
+    "    mappable = ax.imshow(y, extent=[xlims[0],xlims[1],ylims[0],ylims[1]], \n",
+    "                         origin='lower', cmap=cmap, alpha=alpha, vmin=vlims[0], vmax=vlims[1])\n",
+    "    \n",
+    "    if cbar is True:\n",
+    "        cbar = plt.colorbar(mappable=mappable, ax=ax, shrink=0.5)\n",
+    "        \n",
+    "    return mappable\n",
+    "\n",
+    "def plot_constr_surface(surface, ax=None, N=100):\n",
+    "    ''' Plot the Olympus surface, N controls the number of points to sample\n",
+    "    '''\n",
+    "    if ax is None:\n",
+    "        fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(5,5))\n",
+    "\n",
+    "    x0 = np.linspace(0,1,N)\n",
+    "    x1 = np.linspace(0,1,N)\n",
+    "    X0, X1 = np.meshgrid(x0,x1)\n",
+    "    X = np.array([X0.flatten(), X1.flatten()]).T\n",
+    "    y = np.array(surface.run(X)).flatten()\n",
+    "    Y = np.reshape(y, newshape=np.shape(X0))\n",
+    "\n",
+    "    _ = plot_contour(ax, X0, X1, Y, xlims=[0,1], ylims=[0,1], alpha=1, contour_lines=True, contour_labels=True, \n",
+    "                 labels_fs=8, labels_fmt='%d', n_contour_lines=8, contour_alpha=0.8, cbar=False, cmap=cmap) #'golem'\n",
+    "    for param in surface.minima:\n",
+    "        x_min = param['params']\n",
+    "        ax.scatter(x_min[0], x_min[1], s=200, marker='*', color='#ffc6ff', zorder=20)\n",
+    "\n",
+    "def plot_constr_surface_with_scatter(ax, surface, data, repeat=0):\n",
+    "\n",
+    "    if ax is None:\n",
+    "        fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(5,5))\n",
+    "    plot_constr_surface(surface, ax=ax, N=100)\n",
+    "\n",
+    "    repeat = 0\n",
+    "    X = data[repeat].loc[:, ['x0', 'x1']]\n",
+    "    #mask = surface.eval_constr(X.to_numpy())\n",
+    "    mask = np.array([True for _ in range(len(X))])\n",
+    "    X_feas = X[mask]\n",
+    "    X_infs = X[~mask]\n",
+    "\n",
+    "    ax.scatter(X_feas['x0'], X_feas['x1'], marker='X', s=100, color='#adb5bd', edgecolor='k', zorder=10)\n",
+    "    ax.scatter(X_infs['x0'], X_infs['x1'], marker='X', s=100, color='white', edgecolor='k', zorder=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Theoretical optimization space"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we will start with a simple optimization surface, the Branin-Hoo surface. The goal is to find the minima of the surface by varying 2 input $(x_0, x_1)$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# initialize the problem space, and visualize it\n",
+    "surface = Surface(kind='Branin')\n",
+    "\n",
+    "fig, axes = plt.subplots(1,1)\n",
+    "plot_constr_surface(surface, axes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set some initial parameters\n",
+    "BUDGET = 30                 # how many measurements are allowed\n",
+    "NUM_INIT_DESIGN = 5         # how many initial measurements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# start a campaign, which keeps track of the measurements and targets\n",
+    "campaign = Campaign()\n",
+    "campaign.set_param_space(surface.param_space)\n",
+    "\n",
+    "planner = GPPlanner(goal='minimize', num_init_design=NUM_INIT_DESIGN) # instantiate Atlas planner\n",
+    "planner.set_param_space(surface.param_space)\n",
+    "\n",
+    "\n",
+    "while len(campaign.observations.get_values()) < BUDGET:\n",
+    "    samples = planner.recommend(campaign.observations) # ask planner for batch of parameters\n",
+    "    for sample in samples:\n",
+    "        measurement = surface.run(sample) # measure Branin-Hoo function\n",
+    "        campaign.add_observation(sample, measurement) # tell planner about most recent observation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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>x0</th>\n",
+       "      <th>x1</th>\n",
+       "      <th>obj</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.482116</td>\n",
+       "      <td>0.123662</td>\n",
+       "      <td>5.634700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.274550</td>\n",
+       "      <td>0.539074</td>\n",
+       "      <td>16.444189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.451501</td>\n",
+       "      <td>0.455776</td>\n",
+       "      <td>18.650442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.326579</td>\n",
+       "      <td>0.457993</td>\n",
+       "      <td>20.053193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.223969</td>\n",
+       "      <td>0.944575</td>\n",
+       "      <td>36.476601</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         x0        x1        obj\n",
+       "0  0.482116  0.123662   5.634700\n",
+       "1  0.274550  0.539074  16.444189\n",
+       "2  0.451501  0.455776  18.650442\n",
+       "3  0.326579  0.457993  20.053193\n",
+       "4  0.223969  0.944575  36.476601"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# to access the measurements, we can gather the results from the campaign\n",
+    "params = campaign.get_params()      # the parameters\n",
+    "objs = campaign.get_values()        # the objective value\n",
+    "\n",
+    "# put into a pandas dataframe for convenience\n",
+    "df_gp = pd.DataFrame({'x0': params[:, 0].flatten(), 'x1': params[:, 1].flatten(), 'obj': objs[:].flatten()})\n",
+    "df_gp.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also look at the cumulative best value as a function of measurements made. As a baseline, we can also do random sampling strategy, to see comparatively how the `GPPlanner` reduces the number of measurements are required."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #051923; text-decoration-color: #051923\">───────────────────────────────────────────────────────────────────────────────────────────────────────────────────</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;2;5;25;35m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #05a6fb; text-decoration-color: #05a6fb; font-weight: bold\">                                                                                                                   </span>\n",
+       "<span style=\"color: #05a6fb; text-decoration-color: #05a6fb; font-weight: bold\">                                                 Welcome to ATLAS!                                                 </span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;38;2;5;166;251m                                                 \u001b[0m\u001b[1;38;2;5;166;251m                 \u001b[0m\u001b[1;38;2;5;166;251m                                                 \u001b[0m\n",
+       "\u001b[1;38;2;5;166;251m                                                 \u001b[0m\u001b[1;38;2;5;166;251mWelcome to ATLAS!\u001b[0m\u001b[1;38;2;5;166;251m                                                 \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #006494; text-decoration-color: #006494\">                                                Made with 💕 in 🇨🇦                                                 </span>\n",
+       "<span style=\"color: #006494; text-decoration-color: #006494\">                                                                                                                   </span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;2;0;100;148m                                                \u001b[0m\u001b[38;2;0;100;148mMade with 💕 in 🇨🇦\u001b[0m\u001b[38;2;0;100;148m                                                 \u001b[0m\n",
+       "\u001b[38;2;0;100;148m                                                \u001b[0m\u001b[38;2;0;100;148m                  \u001b[0m\u001b[38;2;0;100;148m                                                 \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #051923; text-decoration-color: #051923\">───────────────────────────────────────────────────────────────────────────────────────────────────────────────────</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;2;5;25;35m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #05a6fb; text-decoration-color: #05a6fb\">───────────────────────────── Initial design phase ─────────────────────────────</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;2;5;166;251m───────────────────────────── Initial design phase ─────────────────────────────\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m1\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m2\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m3\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m4\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m5\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m6\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m7\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m8\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m9\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m10\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m11\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m12\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m13\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m14\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">15</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m15\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m16\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">17</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m17\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">18</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m18\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">19</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m19\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m20\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">21</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m21\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m22\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">23</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m23\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m24\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">25</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m25\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">26</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m26\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">27</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m27\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m28\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m29\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ATLAS <span style=\"font-weight: bold\">[</span>INFO<span style=\"font-weight: bold\">]</span> Generating <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> initial design points <span style=\"font-weight: bold\">(</span>batch <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">)</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "ATLAS \u001b[1m[\u001b[0mINFO\u001b[1m]\u001b[0m Generating \u001b[1;36m1\u001b[0m initial design points \u001b[1m(\u001b[0mbatch \u001b[1;36m30\u001b[0m/\u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>x0</th>\n",
+       "      <th>x1</th>\n",
+       "      <th>obj</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.873209</td>\n",
+       "      <td>0.433764</td>\n",
+       "      <td>31.916190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.881381</td>\n",
+       "      <td>0.637830</td>\n",
+       "      <td>69.296376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.870583</td>\n",
+       "      <td>0.097367</td>\n",
+       "      <td>8.058218</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.122221</td>\n",
+       "      <td>0.703558</td>\n",
+       "      <td>3.576440</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.506949</td>\n",
+       "      <td>0.500807</td>\n",
+       "      <td>24.606605</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         x0        x1        obj\n",
+       "0  0.873209  0.433764  31.916190\n",
+       "1  0.881381  0.637830  69.296376\n",
+       "2  0.870583  0.097367   8.058218\n",
+       "3  0.122221  0.703558   3.576440\n",
+       "4  0.506949  0.500807  24.606605"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# run another campaign, but this time, the initial design will be the entire budget\n",
+    "# the initial design uses a random uniform sampling strategy\n",
+    "campaign = Campaign()\n",
+    "campaign.set_param_space(surface.param_space)\n",
+    "\n",
+    "planner = GPPlanner(goal='minimize', num_init_design=BUDGET) # instantiate Atlas planner\n",
+    "planner.set_param_space(surface.param_space)\n",
+    "\n",
+    "\n",
+    "while len(campaign.observations.get_values()) < BUDGET:\n",
+    "    samples = planner.recommend(campaign.observations) # ask planner for batch of parameters\n",
+    "    for sample in samples:\n",
+    "        measurement = surface.run(sample) # measure Branin-Hoo function\n",
+    "        campaign.add_observation(sample, measurement) # tell planner about most recent observation\n",
+    "\n",
+    "# to access the measurements, we can gather the results from the campaign\n",
+    "params = campaign.get_params()      # the parameters\n",
+    "objs = campaign.get_values()        # the objective value\n",
+    "\n",
+    "# put into a pandas dataframe for convenience\n",
+    "df_random = pd.DataFrame({'x0': params[:, 0].flatten(), 'x1': params[:, 1].flatten(), 'obj': objs[:].flatten()})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, when we plot the values, we can see that the GPPlanners are more focused in measurements, clustering around the minima of the Branin-Hoo function in this parameter space. We also see the Random sampling method does not achieve as low of an objective as the GPPlanners, and the estimates are more homogeneous distributed in the parameter space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Evaluations')"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1,3, figsize=(15,4))\n",
+    "\n",
+    "# plot GP planner\n",
+    "plot_constr_surface_with_scatter(axes[0], surface, [df_gp], repeat=0)\n",
+    "axes[0].set_title('GP planner on Branin-Hoo')\n",
+    "axes[0].set_xlabel(r'$x_0$', fontsize=15)\n",
+    "axes[0].set_ylabel(r'$x_1$', fontsize=15)\n",
+    "\n",
+    "# plot the random sampling\n",
+    "plot_constr_surface_with_scatter(axes[1], surface, [df_random], repeat=0)\n",
+    "axes[1].set_title('Random sampling on Branin-Hoo')\n",
+    "axes[1].set_xlabel(r'$x_0$', fontsize=15)\n",
+    "axes[1].set_ylabel(r'$x_1$', fontsize=15)\n",
+    "\n",
+    "# process the dataframes\n",
+    "df_gp['Planner'] = 'GP'\n",
+    "df_gp['Best value'] = df_gp['obj'].cummin()\n",
+    "df_random['Planner'] = 'Random'\n",
+    "df_random['Best value'] = df_random['obj'].cummin()\n",
+    "all_df = pd.concat([df_gp, df_random])\n",
+    "\n",
+    "# plot the best trace\n",
+    "sns.lineplot(data=all_df, x=all_df.index, y='Best value', hue='Planner', ax=axes[2])\n",
+    "axes[2].set_yscale('log')\n",
+    "axes[2].set_xlabel('Evaluations')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "atlas",
+   "language": "python",
+   "name": "atlas"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.20"
   },
   "orig_nbformat": 4
  },
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 8ee2a61..1412511 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -4,7 +4,7 @@ Welcome to Atlas' documentation!
 
 .. image:: _static/atlas_logo.png
 
-**Atlas**  is ...::
+**Atlas**  is an chemistry and materials science adaptive experimentation package. ::
 
     from olympus import Surface, Campaign
     from atlas.planners.gp.planner import GPPlanner
diff --git a/docs/source/install.rst b/docs/source/install.rst
index 6ca35ce..7d94fc9 100644
--- a/docs/source/install.rst
+++ b/docs/source/install.rst
@@ -1,26 +1,24 @@
 Installation
 ============
 
-We recommend to install **Olympus** with ``pip``::
+To install Atlas, we recommend installing from from source ::
 
-    pip install olymp
+    git clone https://github.com/aspuru-guzik-group/atlas.git
+    cd atlas
+    pip install -e .
+    pip install -r requirements.txt
 
-Alternatively, you can install **Olympus** with ``conda``::
+Atlas works hand-in-hand with Olympus, which can be installed from source. Specifically, the ``olympus-atlas`` branch for compatibility with Atlas. ::
 
-    conda install -c conda-forge  olymp
-
-Finally, you can clone the GitHub repo and install it from source::
-
-    git clone git@github.com:aspuru-guzik-group/olympus.git
+    git clone olympus-atlas --single-branch https://github.com/aspuru-guzik-group/olympus.git
     cd olympus
-    python setup.py install
-
+    python install -e .
 
 Dependencies
 ------------
 The installation only requires:
 
-* ``python >= 3.6``
+* ``python >= 3.9``
 * ``numpy``
 * ``pandas``