diff --git a/deepof/data.py b/deepof/data.py
index 67cce124d6c1a68d98d5a2a03ab322b0c1cc25cc..2f84563cc4306017d2e41ae85562edbfa5ec0844 100644
--- a/deepof/data.py
+++ b/deepof/data.py
@@ -20,6 +20,7 @@ from typing import Dict, List, Tuple, Union
 from multiprocessing import cpu_count
 from sklearn import random_projection
 from sklearn.decomposition import KernelPCA
+from sklearn.impute import SimpleImputer
 from sklearn.manifold import TSNE
 from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncoder
 from tqdm import tqdm
@@ -1049,6 +1050,7 @@ class table_dict(dict):
 
         # noinspection PyUnresolvedReferences
         X = X[np.random.choice(X.shape[0], sample, replace=False), :]
+        X = SimpleImputer(strategy="median").fit_transform(X)
 
         rproj = random_projection.GaussianRandomProjection(n_components=n_components)
         X = rproj.fit_transform(X)
@@ -1063,6 +1065,7 @@ class table_dict(dict):
         performance or visualization reasons"""
 
         X = self.get_training_set()[0]
+        X = SimpleImputer(strategy="median").fit_transform(X)
 
         # Takes care of propagated labels if present
         if self._propagate_labels:
@@ -1084,6 +1087,7 @@ class table_dict(dict):
         performance or visualization reasons"""
 
         X = self.get_training_set()[0]
+        X = SimpleImputer(strategy="median").fit_transform(X)
 
         # Takes care of propagated labels if present
         if self._propagate_labels:
diff --git a/examples/main.ipynb b/examples/main.ipynb
index 3338e2d4f820cbbf180eb035d846c0490f305190..ec666261279a2b701c8896fa7025876abfb77384 100644
--- a/examples/main.ipynb
+++ b/examples/main.ipynb
@@ -252,15 +252,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 109 ms, sys: 13 ms, total: 122 ms\n",
-      "Wall time: 115 ms\n"
+      "CPU times: user 105 ms, sys: 12.1 ms, total: 117 ms\n",
+      "Wall time: 110 ms\n"
      ]
     }
    ],
@@ -269,7 +269,7 @@
     "deepof_main = deepof.data.project(path=os.path.join(\"..\",\"..\",\"Desktop\",\"deepof-data\",\"deepof_multi_topview\"),\n",
     "                                  smooth_alpha=0.99,                                     \n",
     "                                  arena_dims=[380],\n",
-    "                                  #exclude_bodyparts=[\"Tail_1\", \"Tail_2\", \"Tail_tip\", \"Tail_base\"],\n",
+    "                                  # exclude_bodyparts=[\"Tail_1\", \"Tail_2\", \"Tail_tip\", \"Tail_base\"],\n",
     "                                  animal_ids=[\"B\", \"W\"],\n",
     "                                  exp_conditions=aggregated_dset\n",
     "                                 )"
@@ -277,7 +277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -286,12 +286,13 @@
      "text": [
       "Loading trajectories...\n",
       "Smoothing trajectories...\n",
+      "Interpolating outliers...\n",
       "Computing distances...\n",
       "Computing angles...\n",
       "Done!\n",
       "Coordinates of 2 videos across 2 conditions\n",
-      "CPU times: user 1.23 s, sys: 174 ms, total: 1.4 s\n",
-      "Wall time: 1.4 s\n"
+      "CPU times: user 1.74 s, sys: 296 ms, total: 2.03 s\n",
+      "Wall time: 2.07 s\n"
      ]
     }
    ],
@@ -303,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -312,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -336,13 +337,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4c56052297334096b9cd02ba38c2e9e6",
+       "model_id": "65d0668d83514a3baac5567f6477eebe",
        "version_major": 2,
        "version_minor": 0
       },
@@ -377,7 +378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 19,
    "metadata": {
     "scrolled": true
    },
@@ -386,14 +387,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 880 ms, sys: 30.6 ms, total: 911 ms\n",
-      "Wall time: 910 ms\n"
+      "CPU times: user 698 ms, sys: 13.6 ms, total: 711 ms\n",
+      "Wall time: 696 ms\n"
      ]
     }
    ],
    "source": [
     "%%time\n",
-    "deepof_coords = deepof_main.get_coords(center=\"B_Center\", polar=False, speed=0, align=\"B_Spine_1\", align_inplace=True, propagate_labels=False)\n",
+    "deepof_coords = deepof_main.get_coords(polar=False, speed=0, align=\"B_Spine_1\", align_inplace=True, propagate_labels=False)\n",
     "#deepof_dists  = deepof_main.get_distances(propagate_labels=False)\n",
     "#deepof_angles = deepof_main.get_angles(propagate_labels=False)"
    ]
@@ -409,7 +410,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Prototype 1:\n",
     "##### 1) Use a moving average over the univariate x or y positions.\n",
     "##### 2) If either x or y deviate, mark as outlier\n",
     "##### 3) Replace outliers by mean between previous and following (not outlier) points"
@@ -417,20 +417,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig=plt.figure(figsize=(12,8), dpi= 100, facecolor='w', edgecolor='k')\n",
     "\n",
@@ -459,149 +448,445 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(14999,)\n",
-      "(14999, 28)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1296x1080 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "#Visualize error\n",
-    "f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharey=True, figsize=(18, 15))\n",
-    "\n",
-    "test_bpart = deepof_coords[\"Day2Test13\"][\"B_Tail_1\"][\"x\"]\n",
-    "test_ypart = deepof_coords[\"Day2Test13\"][\"B_Tail_1\"][\"y\"]\n",
-    "test_qual  = deepof_main.get_quality()[\"Day2Test13\"]\n",
-    "\n",
-    "print(test_bpart.shape)\n",
-    "print(test_qual.shape)\n",
-    "\n",
-    "out_x = test_bpart-moving_average(test_bpart)\n",
-    "outliers_x = out_x[np.abs(out_x) > 2*np.std(out_x)]\n",
-    "out_y = test_ypart-moving_average(test_ypart)\n",
-    "outliers_y = out_y[np.abs(out_y) > 2*np.std(out_y)]\n",
-    "\n",
-    "smooth_bpart = interpolate_outliers(deepof_coords[\"Day2Test13\"], exclude=\"B_Center\", likelihood=test_qual, limit=15)[\"B_Tail_1\"][\"x\"]\n",
-    "smooth_ypart = interpolate_outliers(deepof_coords[\"Day2Test13\"], exclude=\"B_Center\", likelihood=test_qual, limit=15)[\"B_Tail_1\"][\"y\"]\n",
-    "sout_x = smooth_bpart - moving_average(smooth_bpart)\n",
-    "sout_y = smooth_ypart - moving_average(smooth_ypart)\n",
-    "sout_x = sout_x\n",
-    "sout_y = sout_y\n",
-    "\n",
-    "ax1.axhline(0)\n",
-    "ax1.plot(np.linspace(0,len(test_bpart),len(test_bpart)), test_bpart-moving_average(test_bpart), label=\"X\")\n",
-    "ax1.scatter(np.linspace(0,len(test_bpart),len(test_bpart))[np.abs(out_x) > 2*np.std(out_x)], outliers_x, c=\"red\", zorder=100, label=\"outliers X\")\n",
-    "#ax1.scatter(np.linspace(0,len(test_bpart),len(test_bpart))[test_qual < 0.1], out_x[test_qual < 0.1], c=\"black\", zorder=100, label=\"low conf X\")\n",
-    "ax1.set_xlabel(\"Time\")\n",
-    "ax1.set_ylabel(\"Residuals against moving average\")\n",
-    "\n",
-    "ax2.axhline(0, c=\"orange\")\n",
-    "ax2.plot(np.linspace(0,len(test_ypart),len(test_ypart)), test_ypart-moving_average(test_ypart), label=\"Nose Y\", c=\"orange\")\n",
-    "ax2.scatter(np.linspace(0,len(test_ypart),len(test_ypart))[np.abs(out_y) > 2*np.std(out_y)], outliers_y, c=\"red\", zorder=100)\n",
-    "ax2.set_xlabel(\"Time\")\n",
-    "ax2.set_ylabel(\"Residuals against moving average\")\n",
-    "\n",
-    "ax3.axhline(0)\n",
-    "ax3.plot(np.linspace(0,len(sout_x),len(sout_x)), sout_x, label=\"Nose X\")\n",
-    "ax3.scatter(np.linspace(0,len(test_bpart),len(test_bpart))[np.abs(out_x) > 2*np.std(out_x)], \n",
-    "            sout_x[np.abs(out_x) > 2*np.std(out_x)], c=\"red\", zorder=100)\n",
-    "ax3.set_xlabel(\"Time\")\n",
-    "ax3.set_ylabel(\"Residuals against moving average\")\n",
-    "\n",
-    "ax4.axhline(0, c=\"orange\")\n",
-    "ax4.plot(np.linspace(0,len(sout_y),len(sout_y)), sout_y, label=\"Nose Y\", c=\"orange\")\n",
-    "ax4.scatter(np.linspace(0,len(test_ypart),len(test_ypart))[np.abs(out_y) > 2*np.std(out_y)], \n",
-    "            sout_y[np.abs(out_y) > 2*np.std(out_y)], c=\"red\", zorder=100)\n",
-    "ax4.set_xlabel(\"Time\")\n",
-    "ax4.set_ylabel(\"Residuals against moving average\")\n",
-    "\n",
-    "for ax in [ax1, ax2, ax3, ax4]:\n",
-    "    ax.set_xlim(6000, 8000)\n",
+    "def rolling_speed(dframe: pd.DatetimeIndex, window: int = 3, rounds: int = 3, deriv: int = 1, center: str = None, shift: int = 2,\n",
+    "                  typ: str = \"coords\") -> pd.DataFrame:\n",
     "\n",
-    "plt.show()"
+    "    original_shape = dframe.shape\n",
+    "    if center:\n",
+    "        body_parts = [bp for bp in dframe.columns.levels[0] if bp != center]\n",
+    "    else:\n",
+    "        try:\n",
+    "            body_parts = dframe.columns.levels[0]\n",
+    "        except AttributeError:\n",
+    "            body_parts = dframe.columns\n",
+    "\n",
+    "    speeds = pd.DataFrame\n",
+    "\n",
+    "    for der in range(deriv):\n",
+    "\n",
+    "        features = (2 if der == 0 and typ == \"coords\" else 1)\n",
+    "\n",
+    "        distances = (\n",
+    "            np.concatenate(\n",
+    "                [\n",
+    "                    np.array(dframe).reshape([-1, features], order=\"C\"),\n",
+    "                    np.array(dframe.shift(shift)).reshape([-1, features], order=\"C\"),\n",
+    "                ],\n",
+    "                axis=1,\n",
+    "            )\n",
+    "            / shift\n",
+    "        )\n",
+    "\n",
+    "        distances = np.array(compute_dist(distances))\n",
+    "        distances = distances.reshape(\n",
+    "            [\n",
+    "                original_shape[0],\n",
+    "                (original_shape[1] // 2 if typ == \"coords\" else original_shape[1]),\n",
+    "            ],\n",
+    "            order=\"C\",\n",
+    "        )\n",
+    "        distances = pd.DataFrame(distances, index=dframe.index)\n",
+    "        speeds = np.round(distances.rolling(window).mean(), rounds)\n",
+    "        dframe = speeds\n",
+    "\n",
+    "    speeds.columns = body_parts\n",
+    "\n",
+    "    return speeds"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 11 µs, sys: 1e+03 ns, total: 12 µs\n",
-      "Wall time: 14.1 µs\n"
-     ]
+     "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>bodyparts</th>\n",
+       "      <th>B_Center</th>\n",
+       "      <th>B_Left_bhip</th>\n",
+       "      <th>B_Left_ear</th>\n",
+       "      <th>B_Left_fhip</th>\n",
+       "      <th>B_Nose</th>\n",
+       "      <th>B_Right_bhip</th>\n",
+       "      <th>B_Right_ear</th>\n",
+       "      <th>B_Right_fhip</th>\n",
+       "      <th>B_Spine_1</th>\n",
+       "      <th>B_Spine_2</th>\n",
+       "      <th>...</th>\n",
+       "      <th>W_Nose</th>\n",
+       "      <th>W_Right_bhip</th>\n",
+       "      <th>W_Right_ear</th>\n",
+       "      <th>W_Right_fhip</th>\n",
+       "      <th>W_Spine_1</th>\n",
+       "      <th>W_Spine_2</th>\n",
+       "      <th>W_Tail_1</th>\n",
+       "      <th>W_Tail_2</th>\n",
+       "      <th>W_Tail_base</th>\n",
+       "      <th>W_Tail_tip</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.408</td>\n",
+       "      <td>0.945</td>\n",
+       "      <td>0.900</td>\n",
+       "      <td>0.996</td>\n",
+       "      <td>1.827</td>\n",
+       "      <td>0.379</td>\n",
+       "      <td>1.283</td>\n",
+       "      <td>0.495</td>\n",
+       "      <td>0.929</td>\n",
+       "      <td>0.206</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.668</td>\n",
+       "      <td>3.221</td>\n",
+       "      <td>4.153</td>\n",
+       "      <td>4.058</td>\n",
+       "      <td>3.805</td>\n",
+       "      <td>2.394</td>\n",
+       "      <td>1.490</td>\n",
+       "      <td>2.816</td>\n",
+       "      <td>1.851</td>\n",
+       "      <td>3.907</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14992</th>\n",
+       "      <td>0.065</td>\n",
+       "      <td>0.101</td>\n",
+       "      <td>0.137</td>\n",
+       "      <td>0.082</td>\n",
+       "      <td>0.093</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>0.043</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>0.066</td>\n",
+       "      <td>0.022</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.214</td>\n",
+       "      <td>9.329</td>\n",
+       "      <td>8.414</td>\n",
+       "      <td>8.990</td>\n",
+       "      <td>8.744</td>\n",
+       "      <td>8.055</td>\n",
+       "      <td>6.141</td>\n",
+       "      <td>5.656</td>\n",
+       "      <td>8.027</td>\n",
+       "      <td>6.413</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14993</th>\n",
+       "      <td>0.074</td>\n",
+       "      <td>0.137</td>\n",
+       "      <td>0.104</td>\n",
+       "      <td>0.100</td>\n",
+       "      <td>0.135</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>0.044</td>\n",
+       "      <td>0.064</td>\n",
+       "      <td>0.056</td>\n",
+       "      <td>0.058</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.604</td>\n",
+       "      <td>9.509</td>\n",
+       "      <td>8.175</td>\n",
+       "      <td>8.065</td>\n",
+       "      <td>8.281</td>\n",
+       "      <td>7.195</td>\n",
+       "      <td>5.392</td>\n",
+       "      <td>4.261</td>\n",
+       "      <td>6.759</td>\n",
+       "      <td>4.122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14994</th>\n",
+       "      <td>0.133</td>\n",
+       "      <td>0.154</td>\n",
+       "      <td>0.104</td>\n",
+       "      <td>0.185</td>\n",
+       "      <td>0.260</td>\n",
+       "      <td>0.116</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>0.112</td>\n",
+       "      <td>0.067</td>\n",
+       "      <td>0.220</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.714</td>\n",
+       "      <td>10.788</td>\n",
+       "      <td>9.827</td>\n",
+       "      <td>9.076</td>\n",
+       "      <td>9.048</td>\n",
+       "      <td>6.941</td>\n",
+       "      <td>5.902</td>\n",
+       "      <td>5.574</td>\n",
+       "      <td>6.276</td>\n",
+       "      <td>5.227</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14995</th>\n",
+       "      <td>0.169</td>\n",
+       "      <td>0.187</td>\n",
+       "      <td>0.090</td>\n",
+       "      <td>0.231</td>\n",
+       "      <td>0.332</td>\n",
+       "      <td>0.119</td>\n",
+       "      <td>0.116</td>\n",
+       "      <td>0.131</td>\n",
+       "      <td>0.077</td>\n",
+       "      <td>0.331</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.066</td>\n",
+       "      <td>9.562</td>\n",
+       "      <td>9.760</td>\n",
+       "      <td>9.003</td>\n",
+       "      <td>8.580</td>\n",
+       "      <td>6.321</td>\n",
+       "      <td>5.921</td>\n",
+       "      <td>7.031</td>\n",
+       "      <td>5.709</td>\n",
+       "      <td>6.799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14996</th>\n",
+       "      <td>0.129</td>\n",
+       "      <td>0.124</td>\n",
+       "      <td>0.063</td>\n",
+       "      <td>0.192</td>\n",
+       "      <td>0.245</td>\n",
+       "      <td>0.105</td>\n",
+       "      <td>0.094</td>\n",
+       "      <td>0.108</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>0.290</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.396</td>\n",
+       "      <td>6.008</td>\n",
+       "      <td>6.721</td>\n",
+       "      <td>6.226</td>\n",
+       "      <td>5.812</td>\n",
+       "      <td>4.409</td>\n",
+       "      <td>4.058</td>\n",
+       "      <td>5.714</td>\n",
+       "      <td>4.057</td>\n",
+       "      <td>5.215</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>14997 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "bodyparts  B_Center  B_Left_bhip  B_Left_ear  B_Left_fhip  B_Nose  \\\n",
+       "0               NaN          NaN         NaN          NaN     NaN   \n",
+       "1               NaN          NaN         NaN          NaN     NaN   \n",
+       "2               NaN          NaN         NaN          NaN     NaN   \n",
+       "3               NaN          NaN         NaN          NaN     NaN   \n",
+       "4             0.408        0.945       0.900        0.996   1.827   \n",
+       "...             ...          ...         ...          ...     ...   \n",
+       "14992         0.065        0.101       0.137        0.082   0.093   \n",
+       "14993         0.074        0.137       0.104        0.100   0.135   \n",
+       "14994         0.133        0.154       0.104        0.185   0.260   \n",
+       "14995         0.169        0.187       0.090        0.231   0.332   \n",
+       "14996         0.129        0.124       0.063        0.192   0.245   \n",
+       "\n",
+       "bodyparts  B_Right_bhip  B_Right_ear  B_Right_fhip  B_Spine_1  B_Spine_2  ...  \\\n",
+       "0                   NaN          NaN           NaN        NaN        NaN  ...   \n",
+       "1                   NaN          NaN           NaN        NaN        NaN  ...   \n",
+       "2                   NaN          NaN           NaN        NaN        NaN  ...   \n",
+       "3                   NaN          NaN           NaN        NaN        NaN  ...   \n",
+       "4                 0.379        1.283         0.495      0.929      0.206  ...   \n",
+       "...                 ...          ...           ...        ...        ...  ...   \n",
+       "14992             0.098        0.043         0.098      0.066      0.022  ...   \n",
+       "14993             0.075        0.044         0.064      0.056      0.058  ...   \n",
+       "14994             0.116        0.098         0.112      0.067      0.220  ...   \n",
+       "14995             0.119        0.116         0.131      0.077      0.331  ...   \n",
+       "14996             0.105        0.094         0.108      0.062      0.290  ...   \n",
+       "\n",
+       "bodyparts  W_Nose  W_Right_bhip  W_Right_ear  W_Right_fhip  W_Spine_1  \\\n",
+       "0             NaN           NaN          NaN           NaN        NaN   \n",
+       "1             NaN           NaN          NaN           NaN        NaN   \n",
+       "2             NaN           NaN          NaN           NaN        NaN   \n",
+       "3             NaN           NaN          NaN           NaN        NaN   \n",
+       "4           3.668         3.221        4.153         4.058      3.805   \n",
+       "...           ...           ...          ...           ...        ...   \n",
+       "14992       8.214         9.329        8.414         8.990      8.744   \n",
+       "14993       8.604         9.509        8.175         8.065      8.281   \n",
+       "14994      10.714        10.788        9.827         9.076      9.048   \n",
+       "14995      10.066         9.562        9.760         9.003      8.580   \n",
+       "14996       6.396         6.008        6.721         6.226      5.812   \n",
+       "\n",
+       "bodyparts  W_Spine_2  W_Tail_1  W_Tail_2  W_Tail_base  W_Tail_tip  \n",
+       "0                NaN       NaN       NaN          NaN         NaN  \n",
+       "1                NaN       NaN       NaN          NaN         NaN  \n",
+       "2                NaN       NaN       NaN          NaN         NaN  \n",
+       "3                NaN       NaN       NaN          NaN         NaN  \n",
+       "4              2.394     1.490     2.816        1.851       3.907  \n",
+       "...              ...       ...       ...          ...         ...  \n",
+       "14992          8.055     6.141     5.656        8.027       6.413  \n",
+       "14993          7.195     5.392     4.261        6.759       4.122  \n",
+       "14994          6.941     5.902     5.574        6.276       5.227  \n",
+       "14995          6.321     5.921     7.031        5.709       6.799  \n",
+       "14996          4.409     4.058     5.714        4.057       5.215  \n",
+       "\n",
+       "[14997 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "%%time\n",
-    "# Mask, remove and interpolate outliers\n",
-    "\n",
-    "def moving_average(time_series, N=5):\n",
-    "    return np.convolve(time_series, np.ones(N)/N, mode='same')\n",
-    "\n",
-    "def mask_outliers(time_series, likelihood, likelihood_tolerance, lag, n_std, mode):\n",
-    "    \n",
-    "    moving_avg_x = moving_average(time_series[\"x\"], lag)\n",
-    "    moving_avg_y = moving_average(time_series[\"y\"], lag)\n",
-    "    \n",
-    "    residuals_x = time_series[\"x\"] - moving_avg_x\n",
-    "    residuals_y = time_series[\"y\"] - moving_avg_y\n",
-    "\n",
-    "    outlier_mask_x = np.abs(residuals_x) > n_std * np.std(residuals_x)\n",
-    "    outlier_mask_y = np.abs(residuals_y) > n_std * np.std(residuals_y)\n",
-    "    outlier_mask_l = likelihood < likelihood_tolerance\n",
-    "    \n",
-    "    if mode == \"and\":\n",
-    "        return outlier_mask_x & outlier_mask_y & outlier_mask_l\n",
-    "    elif mode == \"or\":\n",
-    "        return (outlier_mask_x | outlier_mask_y) & outlier_mask_l\n",
-    "    return None\n",
-    "\n",
-    "def full_outlier_mask(experiment, likelihood, likelihood_tolerance, exclude, lag, n_std, mode):\n",
-    "    \n",
-    "    body_parts = experiment.columns.levels[0]\n",
-    "    full_mask = experiment.copy()\n",
-    "    for bpart in body_parts:\n",
-    "        if bpart != exclude:\n",
-    "            mask = mask_outliers(experiment[bpart], likelihood[bpart], likelihood_tolerance, lag, n_std, mode)\n",
-    "            full_mask.loc[:, (bpart, \"x\")] = mask\n",
-    "            full_mask.loc[:, (bpart, \"y\")] = mask\n",
-    "            continue\n",
-    "        \n",
-    "    return full_mask\n",
-    "\n",
-    "def interpolate_outliers(experiment, likelihood, exclude, likelihood_tolerance=0.75, lag=5, n_std=2, mode=\"or\", limit=5):\n",
-    "    \n",
-    "    exp = experiment.copy()\n",
-    "    mask = full_outlier_mask(experiment, likelihood, likelihood_tolerance, exclude, lag, n_std, mode)\n",
-    "    exp[mask] = np.nan\n",
-    "    exp.interpolate(method=\"linear\", limit=limit, limit_direction=\"both\", inplace=True)\n",
-    "    \n",
-    "    return exp\n",
-    "\n",
-    "# full_outlier_mask(deepof_coords[\"Day2Test13\"], deepof_main.get_quality()[\"Day2Test13\"], exclude=\"B_Center\", likelihood_tolerance=0.75,\n",
-    "#                  lag=5, n_std=2, mode=\"or\").sum()"
+    "rolling_speed(deepof_main.get_coords()[\"Day2Test14\"], deriv=1)"
    ]
   },
   {
diff --git a/tests/test_pose_utils.py b/tests/test_pose_utils.py
index 81a6b422100b040fab7c140272a10987d0a1beac..2b253c130b792d59dd3c42f4a76530a49b98f213 100644
--- a/tests/test_pose_utils.py
+++ b/tests/test_pose_utils.py
@@ -137,23 +137,16 @@ def test_climb_wall(center, axes, angle, tol):
                 "y4",
                 "X5",
                 "y5",
-                "X6",
-                "y6",
-                "X7",
-                "y7",
-                "X8",
-                "y8",
             ],
             dtype=float,
             elements=st.floats(min_value=-20, max_value=20),
         ),
     ),
     tol_forward=st.floats(min_value=0.01, max_value=4.98),
-    tol_spine=st.floats(min_value=0.01, max_value=4.98),
     tol_speed=st.floats(min_value=0.01, max_value=4.98),
     animal_id=st.text(min_size=0, max_size=15, alphabet=string.ascii_lowercase),
 )
-def test_huddle(pos_dframe, tol_forward, tol_spine, tol_speed, animal_id):
+def test_huddle(pos_dframe, tol_forward, tol_speed, animal_id):
 
     _id = animal_id
     if animal_id != "":
@@ -162,14 +155,11 @@ def test_huddle(pos_dframe, tol_forward, tol_spine, tol_speed, animal_id):
     idx = pd.MultiIndex.from_product(
         [
             [
-                _id + "Left_ear",
-                _id + "Right_ear",
+                _id + "Left_bhip",
+                _id + "Right_bhip",
                 _id + "Left_fhip",
                 _id + "Right_fhip",
-                _id + "Spine_1",
                 _id + "Center",
-                _id + "Spine_2",
-                _id + "Tail_base",
             ],
             ["X", "y"],
         ],
@@ -180,7 +170,6 @@ def test_huddle(pos_dframe, tol_forward, tol_spine, tol_speed, animal_id):
         pos_dframe,
         pos_dframe.xs("X", level="coords", axis=1, drop_level=True),
         tol_forward,
-        tol_spine,
         tol_speed,
         animal_id,
     )