diff --git a/docs/notebooks/Tide_statistics.ipynb b/docs/notebooks/Tide_statistics.ipynb
index 57245cd..bf9052e 100644
--- a/docs/notebooks/Tide_statistics.ipynb
+++ b/docs/notebooks/Tide_statistics.ipynb
@@ -8,9 +8,9 @@
     "\n",
     "**This guide demonstrates how to use the [`tide_stats`](../../api/#eo_tides.stats.tide_stats) and  [`pixel_stats`](../../api/#eo_tides.stats.pixel_stats) functions from [`eo_tides.stats`](../../api/#eo_tides.stats) to calculate local tide statistics and identify biases caused by interactions between tidal processes and satellite orbits.**\n",
     "\n",
-    "Complex interactions between temporal tide dynamics and the regular mid-morning overpass timing of sun-synchronous sensors like Landsat or Sentinel-2 mean that satellites often do not observe the entire tidal cycle. \n",
-    "Biases in satellite coverage of the tidal cycle can mean that tidal extremes (e.g. the lowest or highest tides at a location) may either never be captured by satellites, or be over-represented in the satellite EO record. \n",
-    "Local tide dynamics can cause these biases to vary greatly both through time and spatially (Figure 1), making it challenging to consistently analyse and compare coastal processes consistently - particularly for large-scale (e.g. regional or global) analyses.\n",
+    "Complex tide aliasing interactions between temporal tide dynamics and the regular overpass timing of sun-synchronous satellite sensors mean that satellites often do not always observe the entire tidal cycle. \n",
+    "Biases in satellite coverage of the tidal cycle can mean that tidal extremes (e.g. the lowest or highest tides at a location) or particular tidal processes may either never be captured by satellites, or be over-represented in the satellite record. \n",
+    "Local tide dynamics can cause these biases to vary greatly both through time and space, making it challenging to compare coastal processes consistently - particularly for large-scale coastal EO analyses.\n",
     "\n",
     "To ensure that coastal EO analyses are not inadvertently affected by tide biases, it is important to compare how well the tides observed by satellites match the full range of tides at a location.\n",
     "The `tidal_stats` and `pixel_stats` functions compares the subset of tides observed by satellite data against the full range of tides modelled at a regular interval through time (every two hours by default) across the entire time period covered by the satellite dataset.\n",
@@ -136,8 +136,8 @@
     "The `tide_stats` function will return a plain-text summary below, as well as a visual plot that compares the distribution of satellite-observed tides (black dots) against the full range of modelled astronomical tide conditions (blue) using three useful metrics:\n",
     "\n",
     "1. **Spread:** The proportion of the full modelled astronomical tidal range that was observed by satellites. A high value indicating good coverage of the tide range.\n",
-    "2. **Offset high:** The proportion of the highest tides not observed by satellites at any time, as a proportion of the full modelled astronomical tidal range. A high value indicates that the satellite data is biased towards never capturing high tides.\n",
-    "3. **Offset low:** The proportion of the lowest tides not observed by satellites at any time, as a proportion of the full modelled astronomical tidal range. A high value indicates that the satellite data is biased towards never capturing low tides.\n",
+    "2. **High tide offset:** The proportion of the highest tides not observed by satellites at any time, as a proportion of the full modelled astronomical tidal range. A high value indicates that the satellite data is biased towards never capturing high tides.\n",
+    "3. **Low tide offset:** The proportion of the lowest tides not observed by satellites at any time, as a proportion of the full modelled astronomical tidal range. A high value indicates that the satellite data is biased towards never capturing low tides.\n",
     "\n",
     "<div class=\"admonition tip\">\n",
     "    <p class=\"admonition-title\">Tip</p>\n",
@@ -158,25 +158,25 @@
      "text": [
       "Using tide modelling location: 122.21, -18.00\n",
       "Modelling tides with EOT20\n",
+      "Using tide modelling location: 122.21, -18.00\n",
       "Modelling tides with EOT20\n",
       "\n",
       "\n",
-      "🌊 Modelled astronomical tide range: 9.30 metres.\n",
-      "🛰️ Observed tide range: 6.29 metres.\n",
+      "🌊 Modelled astronomical tide range: 9.30 m (-4.60 to 4.70 m).\n",
+      "🛰️ Observed tide range: 6.29 m (-2.36 to 3.93 m).\n",
       "\n",
       "🔴 68% of the modelled astronomical tide range was observed at this location.\n",
-      "🟢 The highest 8% (0.77 metres) of the tide range was never observed.\n",
-      "🔴 The lowest 24% (2.25 metres) of the tide range was never observed.\n",
-      "\n",
-      "🌊 Mean modelled astronomical tide height: -0.00 metres.\n",
-      "🛰️ Mean observed tide height: 0.69 metres.\n",
+      "🟢 The highest 8% (0.77 m) of the tide range was never observed.\n",
+      "🔴 The lowest 24% (2.25 m) of the tide range was never observed.\n",
       "\n",
-      "⬆️ The mean observed tide height was 0.69 metres higher than the mean modelled astronomical tide height.\n"
+      "🌊 Mean modelled astronomical tide height: -0.00 m.\n",
+      "🛰️ Mean observed tide height: 0.69 m.\n",
+      "⬆️ The mean observed tide height was 0.69 m higher than the mean modelled astronomical tide height.\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -198,23 +198,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As we can see in the graph, Sentinel-2 captured a biased proportion of the tide range at this location: only observing ~64% of the tide range, and never observing the lowest 26% of tides.\n",
+    "As we can see in the graph, Sentinel-2 captured a biased proportion of the tide range at this location: only observing ~68% of the tide range, and never observing the lowest 24% of tides.\n",
     "\n",
     "The `tide_stats` function also outputs a `pandas.Series` object containing statistics for the results above, including:\n",
     "\n",
-    "* `y`: latitude used for modelling tide heights\n",
-    "* `x`: longitude used for modelling tide heights\n",
     "* `mot`: mean tide height observed by the satellite (metres)\n",
     "* `mat`: mean modelled astronomical tide height (metres)\n",
-    "* `lot`: minimum tide height observed by the satellite (metres)\n",
-    "* `lat`: minimum tide height from modelled astronomical tidal range (metres)\n",
     "* `hot`: maximum tide height observed by the satellite (metres)\n",
     "* `hat`: maximum tide height from modelled astronomical tidal range (metres)\n",
+    "* `lot`: minimum tide height observed by the satellite (metres)\n",
+    "* `lat`: minimum tide height from modelled astronomical tidal range (metres)\n",
     "* `otr`: tidal range observed by the satellite (metres)\n",
     "* `tr`: modelled astronomical tide range (metres)\n",
     "* `spread`: proportion of the full modelled tidal range observed by the satellite\n",
     "* `offset_low`: proportion of the lowest tides never observed by the satellite\n",
-    "* `offset_high`: proportion of the highest tides never observed by the satellite"
+    "* `offset_high`: proportion of the highest tides never observed by the satellite\n",
+    "* `y`: latitude used for modelling tide heights\n",
+    "* `x`: longitude used for modelling tide heights"
    ]
   },
   {
@@ -225,20 +225,20 @@
     {
      "data": {
       "text/plain": [
-       "y              -18.000\n",
-       "x              122.210\n",
-       "mot              0.691\n",
-       "mat             -0.000\n",
-       "lot             -2.355\n",
-       "lat             -4.604\n",
-       "hot              3.930\n",
-       "hat              4.696\n",
-       "otr              6.285\n",
-       "tr               9.300\n",
-       "spread           0.676\n",
-       "offset_low       0.242\n",
-       "offset_high      0.082\n",
-       "dtype: float64"
+       "mot              0.691000\n",
+       "mat             -0.000000\n",
+       "hot              3.930000\n",
+       "hat              4.696000\n",
+       "lot             -2.355000\n",
+       "lat             -4.604000\n",
+       "otr              6.285000\n",
+       "tr               9.300000\n",
+       "spread           0.676000\n",
+       "offset_low       0.242000\n",
+       "offset_high      0.082000\n",
+       "x              122.209999\n",
+       "y              -18.000000\n",
+       "dtype: float32"
       ]
      },
      "execution_count": 4,
@@ -329,25 +329,25 @@
      "text": [
       "Using tide modelling location: 122.21, -18.00\n",
       "Modelling tides with EOT20\n",
+      "Using tide modelling location: 122.21, -18.00\n",
       "Modelling tides with EOT20\n",
       "\n",
       "\n",
-      "🌊 Modelled astronomical tide range: 9.55 metres.\n",
-      "🛰️ Observed tide range: 6.40 metres.\n",
+      "🌊 Modelled astronomical tide range: 9.55 m (-4.81 to 4.74 m).\n",
+      "🛰️ Observed tide range: 6.40 m (-4.39 to 2.02 m).\n",
       "\n",
       "🔴 67% of the modelled astronomical tide range was observed at this location.\n",
-      "🔴 The highest 28% (2.72 metres) of the tide range was never observed.\n",
-      "🟢 The lowest 4% (0.43 metres) of the tide range was never observed.\n",
+      "🔴 The highest 28% (2.72 m) of the tide range was never observed.\n",
+      "🟢 The lowest 4% (0.43 m) of the tide range was never observed.\n",
       "\n",
-      "🌊 Mean modelled astronomical tide height: -0.00 metres.\n",
-      "🛰️ Mean observed tide height: -1.31 metres.\n",
-      "\n",
-      "⬇️ The mean observed tide height was -1.31 metres lower than the mean modelled astronomical tide height.\n"
+      "🌊 Mean modelled astronomical tide height: -0.00 m.\n",
+      "🛰️ Mean observed tide height: -1.31 m.\n",
+      "⬇️ The mean observed tide height was -1.31 m lower than the mean modelled astronomical tide height.\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -414,8 +414,8 @@
    "metadata": {},
    "source": [
     "We can now run `tide_stats` again.\n",
-    "This time, we pass our satellite name coordinate to the function using the `plot_col=\"satellite_name\"` parameter.\n",
-    "This will plot data from each of our satellites using a different symbol."
+    "This time, we pass our satellite name coordinate to the function using the `plot_var=\"satellite_name\"` parameter.\n",
+    "This will plot data from each of our satellites using a different symbol and colour."
    ]
   },
   {
@@ -429,25 +429,25 @@
      "text": [
       "Using tide modelling location: 122.21, -18.00\n",
       "Modelling tides with EOT20\n",
+      "Using tide modelling location: 122.21, -18.00\n",
       "Modelling tides with EOT20\n",
       "\n",
       "\n",
-      "🌊 Modelled astronomical tide range: 9.30 metres.\n",
-      "🛰️ Observed tide range: 8.32 metres.\n",
+      "🌊 Modelled astronomical tide range: 9.30 m (-4.60 to 4.70 m).\n",
+      "🛰️ Observed tide range: 8.32 m (-4.39 to 3.93 m).\n",
       "\n",
       "🟡 89% of the modelled astronomical tide range was observed at this location.\n",
-      "🟢 The highest 8% (0.77 metres) of the tide range was never observed.\n",
-      "🟢 The lowest 2% (0.22 metres) of the tide range was never observed.\n",
+      "🟢 The highest 8% (0.77 m) of the tide range was never observed.\n",
+      "🟢 The lowest 2% (0.22 m) of the tide range was never observed.\n",
       "\n",
-      "🌊 Mean modelled astronomical tide height: -0.00 metres.\n",
-      "🛰️ Mean observed tide height: 0.10 metres.\n",
-      "\n",
-      "⬆️ The mean observed tide height was 0.10 metres higher than the mean modelled astronomical tide height.\n"
+      "🌊 Mean modelled astronomical tide height: -0.00 m.\n",
+      "🛰️ Mean observed tide height: 0.10 m.\n",
+      "⬆️ The mean observed tide height was 0.10 m higher than the mean modelled astronomical tide height.\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -459,11 +459,16 @@
    "source": [
     "statistics_df = tide_stats(\n",
     "    data=ds_all,\n",
-    "    plot_col=\"satellite_name\",\n",
+    "    plot_var=\"satellite_name\",\n",
     "    directory=directory,\n",
     ")\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -504,29 +509,58 @@
      "text": [
       "Creating reduced resolution 5000 x 5000 metre tide modelling array\n",
       "Modelling tides with EOT20\n",
-      "Computing tide quantiles\n",
       "Returning low resolution tide array\n",
       "Creating reduced resolution 5000 x 5000 metre tide modelling array\n",
       "Modelling tides with EOT20\n",
-      "Computing tide quantiles\n",
-      "Returning low resolution tide array\n",
-      "<xarray.Dataset> Size: 2kB\n",
-      "Dimensions:      (x: 8, y: 8)\n",
+      "Returning low resolution tide array\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/workspaces/eo-tides/.venv/lib/python3.12/site-packages/numpy/lib/_nanfunctions_impl.py:1634: RuntimeWarning: All-NaN slice encountered\n",
+      "  return fnb._ureduce(a,\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reprojecting statistics into original resolution\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/workspaces/eo-tides/.venv/lib/python3.12/site-packages/rasterio/warp.py:387: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.\n",
+      "  dest = _reproject(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.Dataset> Size: 6MB\n",
+      "Dimensions:      (y: 371, x: 356)\n",
       "Coordinates:\n",
-      "  * x            (x) float64 64B 3.975e+05 4.025e+05 ... 4.275e+05 4.325e+05\n",
-      "  * y            (y) float64 64B 8.028e+06 8.022e+06 ... 7.998e+06 7.992e+06\n",
       "    tide_model   <U5 20B 'EOT20'\n",
+      "  * y            (y) float64 3kB 8.015e+06 8.015e+06 ... 8.004e+06 8.004e+06\n",
+      "  * x            (x) float64 3kB 4.11e+05 4.111e+05 ... 4.217e+05 4.217e+05\n",
       "    spatial_ref  int32 4B 32751\n",
       "Data variables:\n",
-      "    hot          (y, x) float32 256B 3.773 3.794 3.844 3.844 ... 3.953 3.953 nan\n",
-      "    hat          (y, x) float32 256B 4.282 4.318 4.408 4.408 ... 4.797 4.797 nan\n",
-      "    lot          (y, x) float32 256B -2.109 -2.128 -2.18 ... -2.418 -2.418 nan\n",
-      "    lat          (y, x) float32 256B -4.222 -4.264 -4.353 ... -4.692 -4.692 nan\n",
-      "    otr          (y, x) float32 256B 5.882 5.922 6.023 6.023 ... 6.372 6.372 nan\n",
-      "    tr           (y, x) float32 256B 8.504 8.582 8.761 8.761 ... 9.49 9.49 nan\n",
-      "    spread       (y, x) float32 256B 0.6917 0.69 0.6875 ... 0.6714 0.6714 nan\n",
-      "    offset_low   (y, x) float32 256B 0.2485 0.2489 0.2481 ... 0.2396 0.2396 nan\n",
-      "    offset_high  (y, x) float32 256B 0.0598 0.06103 0.06443 ... 0.08894 nan\n"
+      "    mot          (y, x) float32 528kB 0.7088 0.7088 0.7088 ... 0.6832 0.6832\n",
+      "    mat          (y, x) float32 528kB -0.0001353 -0.0001353 ... -0.0001289\n",
+      "    hot          (y, x) float32 528kB 3.874 3.874 3.874 ... 3.948 3.948 3.948\n",
+      "    hat          (y, x) float32 528kB 4.494 4.494 4.494 ... 4.773 4.773 4.773\n",
+      "    lot          (y, x) float32 528kB -2.236 -2.236 -2.236 ... -2.402 -2.402\n",
+      "    lat          (y, x) float32 528kB -4.425 -4.425 -4.425 ... -4.672 -4.673\n",
+      "    otr          (y, x) float32 528kB 6.109 6.109 6.109 6.109 ... 6.35 6.35 6.35\n",
+      "    tr           (y, x) float32 528kB 8.919 8.919 8.919 ... 9.445 9.445 9.446\n",
+      "    spread       (y, x) float32 528kB 0.685 0.685 0.685 ... 0.6723 0.6723 0.6723\n",
+      "    offset_low   (y, x) float32 528kB 0.2455 0.2455 0.2455 ... 0.2403 0.2403\n",
+      "    offset_high  (y, x) float32 528kB 0.0695 0.0695 0.0695 ... 0.08738 0.0874\n"
      ]
     }
    ],
@@ -582,7 +616,7 @@
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        "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
        "            &lt;style&gt;\n",
-       "                #map_1fe22147bae3ac9460d15b7a2ce7c75f {\n",
+       "                #map_d5fa050bdb20b49f979cb10ae3a2a470 {\n",
        "                    position: relative;\n",
        "                    width: 100.0%;\n",
        "                    height: 100.0%;\n",
@@ -609,14 +643,14 @@
        "&lt;body&gt;\n",
        "    \n",
        "    \n",
-       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_1fe22147bae3ac9460d15b7a2ce7c75f&quot; &gt;&lt;/div&gt;\n",
+       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_d5fa050bdb20b49f979cb10ae3a2a470&quot; &gt;&lt;/div&gt;\n",
        "        \n",
        "&lt;/body&gt;\n",
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        "    \n",
        "    \n",
-       "            var map_1fe22147bae3ac9460d15b7a2ce7c75f = L.map(\n",
-       "                &quot;map_1fe22147bae3ac9460d15b7a2ce7c75f&quot;,\n",
+       "            var map_d5fa050bdb20b49f979cb10ae3a2a470 = L.map(\n",
+       "                &quot;map_d5fa050bdb20b49f979cb10ae3a2a470&quot;,\n",
        "                {\n",
        "                    center: [0.0, 0.0],\n",
        "                    crs: L.CRS.EPSG3857,\n",
@@ -630,51 +664,51 @@
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        "        \n",
        "    \n",
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+       "            var tile_layer_b768d5cd197255fef7ef5d601bd4f218 = L.tileLayer(\n",
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        "            );\n",
        "        \n",
        "    \n",
-       "            tile_layer_46b4b96a27decab6ced8072eefa77701.addTo(map_1fe22147bae3ac9460d15b7a2ce7c75f);\n",
+       "            tile_layer_b768d5cd197255fef7ef5d601bd4f218.addTo(map_d5fa050bdb20b49f979cb10ae3a2a470);\n",
        "        \n",
        "    \n",
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25Tj7G3bzFyENYxZvUGqKItS8wCaPPqBpPgrbJuRZBX9eaZo4LnLLsS2ILAvz8R6Sdum1/9l70XY 3mpeO44EJ9FrcudJYliEUFruGsI3d1HtFFhp4ghB78yWnn8jzn7Mm5Syv8XOk1UP28apyr9yEdYT 33LXkZC5Rs0DGHbG2km0IXd+Edum+8RBtA2t6wB1uNoqC7HmRdhF7z5UhNQrrdLTbqEJqLtto1Xz FtXv3SAVcB5+TfRXsEdWHZcx9iifd5j3vNLk1vL/uPBCbPdjiWfaNr3jF/V6L8Jan+vaDGbSeQAM zJ3vaI98pfpedYwN41rRn++5UF32FYoeaZWedqvaNpMXYfV9d7Btis9LlV5pP1tl8pEHAW3NJ1gG oeditruflti3P2+K7b6j6qFOve1WsW288dUxnbYNF7IcZ+Mi7AHyPHpdcGfdofaIrS83VlXfLW0H jev25wn0TqscTfq91wKiyHzXe+kZPNk2TjWvjv+GuBg7Mne7ZTIIwaL+/Chbym6VmKr7/5Y6+/MZ FvXnvW27pFVmbZy20IgHbjNw+ec9cuepNlJd48Ixfnpl0xdYsG1alK+mfw2+TH0PI1xr/QaF1f1B 4Gc7a/1r+fPeNltvJa6FN+bobJuZO2EV8f8CAPC//uW/CHvO3z/6N/++31Q9gzgH3sWsmj/f0/vP MGwS7bhu0HD+Tnd/vrVNL7jJz06uJiXv/Iw8ufM91TyAI+Hmxo0b18I//Sf/+sUMkkpPOINU9Nxi YVnGtNuodlwMrr+qHu6zi4udlD8vkSuVVsnlzlNEz40JGKIv4v/P/b/e/H7jxo0bvwX/H5hxIizi Id8sAAAAAElFTkSuQmCC &quot;,\n",
+       "                [[-17.94935113244688, 122.15910248179176], [-18.050847673391925, 122.26097143501093]],\n",
        "                {}\n",
        "            );\n",
        "        \n",
        "    \n",
-       "            image_overlay_60037595c4fdedcdac9d4f96001f2a83.addTo(map_1fe22147bae3ac9460d15b7a2ce7c75f);\n",
+       "            image_overlay_78e76049ddec2a51bb164f64b2b226e1.addTo(map_d5fa050bdb20b49f979cb10ae3a2a470);\n",
        "        \n",
        "    \n",
-       "            map_1fe22147bae3ac9460d15b7a2ce7c75f.fitBounds(\n",
-       "                [[-17.815037709843487, 122.00921674324115], [-18.178111016594787, 122.38538624620925]],\n",
+       "            map_d5fa050bdb20b49f979cb10ae3a2a470.fitBounds(\n",
+       "                [[-17.949595150607788, 122.15983409536503], [-18.05059683051411, 122.26026208264648]],\n",
        "                {}\n",
        "            );\n",
        "        \n",
        "    \n",
-       "            var layer_control_47c4c956e84bfe17f87a9c7e12ebb5b5_layers = {\n",
+       "            var layer_control_347e5095fe8f553789c9f1f464bf18e5_layers = {\n",
        "                base_layers : {\n",
-       "                    &quot;openstreetmap&quot; : tile_layer_46b4b96a27decab6ced8072eefa77701,\n",
+       "                    &quot;openstreetmap&quot; : tile_layer_b768d5cd197255fef7ef5d601bd4f218,\n",
        "                },\n",
        "                overlays :  {\n",
-       "                    &quot;spread&quot; : image_overlay_60037595c4fdedcdac9d4f96001f2a83,\n",
+       "                    &quot;spread&quot; : image_overlay_78e76049ddec2a51bb164f64b2b226e1,\n",
        "                },\n",
        "            };\n",
-       "            let layer_control_47c4c956e84bfe17f87a9c7e12ebb5b5 = L.control.layers(\n",
-       "                layer_control_47c4c956e84bfe17f87a9c7e12ebb5b5_layers.base_layers,\n",
-       "                layer_control_47c4c956e84bfe17f87a9c7e12ebb5b5_layers.overlays,\n",
+       "            let layer_control_347e5095fe8f553789c9f1f464bf18e5 = L.control.layers(\n",
+       "                layer_control_347e5095fe8f553789c9f1f464bf18e5_layers.base_layers,\n",
+       "                layer_control_347e5095fe8f553789c9f1f464bf18e5_layers.overlays,\n",
        "                {&quot;autoZIndex&quot;: true, &quot;collapsed&quot;: true, &quot;position&quot;: &quot;topright&quot;}\n",
-       "            ).addTo(map_1fe22147bae3ac9460d15b7a2ce7c75f);\n",
+       "            ).addTo(map_d5fa050bdb20b49f979cb10ae3a2a470);\n",
        "\n",
        "        \n",
        "&lt;/script&gt;\n",
        "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
       ],
       "text/plain": [
-       "<folium.folium.Map at 0x7f751e620040>"
+       "<folium.folium.Map at 0x74793b4186e0>"
       ]
      },
      "execution_count": 10,
@@ -700,7 +734,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x400 with 6 Axes>"
       ]
@@ -733,7 +767,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -747,7 +781,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.15"
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,
diff --git a/eo_tides/eo.py b/eo_tides/eo.py
index 95429ca..811212c 100644
--- a/eo_tides/eo.py
+++ b/eo_tides/eo.py
@@ -101,6 +101,7 @@ def _pixel_tides_resample(
     resample_method="bilinear",
     dask_chunks=None,
     dask_compute=True,
+    name="tide_height",
 ):
     """Resamples low resolution tides modelled by `pixel_tides` into the
     geobox (e.g. spatial resolution and extent) of the original higher
@@ -125,6 +126,8 @@ def _pixel_tides_resample(
         Whether to compute results of the resampling step using Dask.
         If False, this will return `tides_highres` as a lazy loaded
         Dask-enabled array.
+    name : str, optional
+        The name used for the output array. Defaults to "tide_height".
 
     Returns
     -------
@@ -145,7 +148,11 @@ def _pixel_tides_resample(
         how=gbox,
         chunks=dask_chunks,
         resampling=resample_method,
-    ).rename("tide_height")
+    )
+
+    # Set output name
+    if name is not None:
+        tides_highres = tides_highres.rename(name)
 
     # Optionally process and load into memory with Dask
     if dask_compute:
@@ -373,7 +380,7 @@ def pixel_tides(
         `data` has a geographic CRS (e.g. degree units).
     resample_method : str, optional
         If resampling is requested (see `resample` above), use this
-        resampling method when converting from low resolution to high
+        resampling method when resampling from low resolution to high
         resolution pixels. Defaults to "bilinear"; valid options include
         "nearest", "cubic", "min", "max", "average" etc.
     dask_chunks : tuple of float, optional
@@ -385,7 +392,7 @@ def pixel_tides(
         `(2048, 2048)`.
     dask_compute : bool, optional
         Whether to compute results of the resampling step using Dask.
-        If False, `tides_highres` will be returned as a Dask array.
+        If False, `tides_highres` will be returned as a Dask-enabled array.
     **model_tides_kwargs :
         Optional parameters passed to the `eo_tides.model.model_tides`
         function. Important parameters include `cutoff` (used to
diff --git a/eo_tides/model.py b/eo_tides/model.py
index a2e4a6a..2930bfe 100644
--- a/eo_tides/model.py
+++ b/eo_tides/model.py
@@ -21,7 +21,7 @@
 import pyTMD
 from tqdm import tqdm
 
-from .utils import DatetimeLike, _set_directory, _standardise_models, _standardise_time, idw, list_models
+from .utils import DatetimeLike, _set_directory, _standardise_models, _standardise_time, idw
 
 
 def _parallel_splits(
@@ -486,7 +486,7 @@ def model_tides(
         - "spline": scipy bivariate spline interpolation
         - "bilinear": quick bilinear interpolation
     extrapolate : bool, optional
-        Whether to extrapolate tides for x and y coordinates outside of
+        Whether to extrapolate tides into x and y coordinates outside of
         the valid tide modelling domain using nearest-neighbor.
     cutoff : float, optional
         Extrapolation cutoff in kilometers. The default is None, which
@@ -544,7 +544,7 @@ def model_tides(
     time = _standardise_time(time)
 
     # Validate input arguments
-    assert time is not None, "Times for modelling tides muyst be provided via `time`."
+    assert time is not None, "Times for modelling tides must be provided via `time`."
     assert method in ("bilinear", "spline", "linear", "nearest")
     assert output_units in (
         "m",
@@ -555,6 +555,8 @@ def model_tides(
         "long",
         "wide",
     ), "Output format must be either 'long' or 'wide'."
+    assert np.issubdtype(x.dtype, np.number), "`x` must contain only valid numeric values, and must not be None."
+    assert np.issubdtype(y.dtype, np.number), "`y` must contain only valid numeric values, and must not be None.."
     assert len(x) == len(y), "x and y must be the same length."
     if mode == "one-to-one":
         assert len(x) == len(time), (
diff --git a/eo_tides/stats.py b/eo_tides/stats.py
index 660f0ac..ed0d000 100644
--- a/eo_tides/stats.py
+++ b/eo_tides/stats.py
@@ -8,44 +8,101 @@
 import numpy as np
 import pandas as pd
 import xarray as xr
-from scipy import stats
 
 # Only import if running type checking
 if TYPE_CHECKING:
-    import xarray as xr
     from odc.geo.geobox import GeoBox
 
-from .eo import _standardise_inputs, pixel_tides, tag_tides
-from .model import model_tides
+from .eo import _pixel_tides_resample, _resample_chunks, _standardise_inputs, pixel_tides, tag_tides
 from .utils import DatetimeLike
 
 
-def _plot_biases(
-    all_tides_df,
-    obs_tides_da,
-    lat,
-    lot,
-    hat,
-    hot,
-    offset_low,
-    offset_high,
-    spread,
-    plot_col,
-    obs_linreg,
-    obs_x,
-    all_timerange,
+def _tide_statistics(obs_tides, all_tides, min_max_q=(0.0, 1.0), dim="time"):
+    # Calculate means of observed and modelled tides
+    mot = obs_tides.mean(dim=dim)
+    mat = all_tides.mean(dim=dim)
+
+    # Identify highest and lowest observed tides
+    obs_tides_q = obs_tides.quantile(q=min_max_q, dim=dim).astype("float32")
+    lot = obs_tides_q.isel(quantile=0, drop=True)
+    hot = obs_tides_q.isel(quantile=-1, drop=True)
+
+    # Identify highest and lowest modelled tides
+    all_tides_q = all_tides.quantile(q=min_max_q, dim=dim).astype("float32")
+    lat = all_tides_q.isel(quantile=0, drop=True)
+    hat = all_tides_q.isel(quantile=-1, drop=True)
+
+    # Calculate tidal range
+    otr = hot - lot
+    tr = hat - lat
+
+    # Calculate Bishop-Taylor et al. 2018 tidal metrics
+    spread = otr / tr
+    offset_low_m = lot - lat
+    offset_high_m = hat - hot
+    offset_low = offset_low_m / tr
+    offset_high = offset_high_m / tr
+
+    # Combine into a single dataset
+    stats_ds = xr.merge(
+        [
+            mot.rename("mot"),
+            mat.rename("mat"),
+            hot.rename("hot"),
+            hat.rename("hat"),
+            lot.rename("lot"),
+            lat.rename("lat"),
+            otr.rename("otr"),
+            tr.rename("tr"),
+            spread.rename("spread"),
+            offset_low.rename("offset_low"),
+            offset_high.rename("offset_high"),
+        ],
+        compat="override",
+    )
+
+    return stats_ds
+
+
+def _stats_plain_english(mot, mat, hot, hat, lot, lat, otr, tr, spread, offset_low, offset_high):
+    # Plain text descriptors
+    mean_diff = "higher" if mot > mat else "lower"
+    mean_diff_icon = "⬆️" if mot > mat else "⬇️"
+    spread_icon = "🟢" if spread >= 0.9 else "🟡" if 0.7 < spread <= 0.9 else "🔴"
+    low_tide_icon = "🟢" if offset_low <= 0.1 else "🟡" if 0.1 <= offset_low < 0.2 else "🔴"
+    high_tide_icon = "🟢" if offset_high <= 0.1 else "🟡" if 0.1 <= offset_high < 0.2 else "🔴"
+
+    # Print summary
+    print(f"\n\n🌊 Modelled astronomical tide range: {tr:.2f} m ({lat:.2f} to {hat:.2f} m).")
+    print(f"🛰️ Observed tide range: {otr:.2f} m ({lot:.2f} to {hot:.2f} m).\n")
+    print(f"{spread_icon} {spread:.0%} of the modelled astronomical tide range was observed at this location.")
+    print(
+        f"{high_tide_icon} The highest {offset_high:.0%} ({offset_high * tr:.2f} m) of the tide range was never observed."
+    )
+    print(
+        f"{low_tide_icon} The lowest {offset_low:.0%} ({offset_low * tr:.2f} m) of the tide range was never observed.\n"
+    )
+    print(f"🌊 Mean modelled astronomical tide height: {mat:.2f} m.")
+    print(f"🛰️ Mean observed tide height: {mot:.2f} m.")
+    print(
+        f"{mean_diff_icon} The mean observed tide height was {mot - mat:.2f} m {mean_diff} than the mean modelled astronomical tide height."
+    )
+
+
+def _stats_figure(
+    all_tides_da, obs_tides_da, hot, hat, lot, lat, spread, offset_low, offset_high, plot_var, point_col=None
 ):
     """
     Plot tide bias statistics as a figure, including both
     satellite observations and all modelled tides.
     """
 
-    # Create plot and add all time and observed tide data
+    # Create plot and add all modelled tides
     fig, ax = plt.subplots(figsize=(10, 6))
-    all_tides_df.reset_index(["x", "y"]).tide_height.plot(ax=ax, alpha=0.4, label="Modelled tides")
+    all_tides_da.plot(ax=ax, alpha=0.4, label="Modelled tides")
 
-    # Look through custom column values if provided
-    if plot_col is not None:
+    # Loop through custom variable values if provided
+    if plot_var is not None:
         # Create a list of marker styles
         markers = [
             "o",
@@ -65,23 +122,32 @@ def _plot_biases(
             "|",
             "_",
         ]
-        for i, value in enumerate(np.unique(plot_col)):
-            obs_tides_da.sel(time=plot_col == value).plot.line(
+
+        # Sort values to allow correct grouping
+        obs_tides_da = obs_tides_da.sortby("time")
+        plot_var = plot_var.sortby("time")
+
+        # Iterate and plot each group
+        for i, (label, group) in enumerate(obs_tides_da.groupby(plot_var)):
+            group.plot.line(
                 ax=ax,
                 linewidth=0.0,
-                color="black",
+                color=point_col,
                 marker=markers[i % len(markers)],
-                markersize=4,
-                label=value,
+                label=label,
+                markeredgecolor="black",
+                markeredgewidth=0.6,
             )
+
     # Otherwise, plot all data at once
     else:
         obs_tides_da.plot.line(
             ax=ax,
             marker="o",
             linewidth=0.0,
-            color="black",
-            markersize=3.5,
+            color="black" if point_col is None else point_col,
+            markeredgecolor="black",
+            markeredgewidth=0.6,
             label="Satellite observations",
         )
 
@@ -95,15 +161,6 @@ def _plot_biases(
     )
     ax.set_title("")
 
-    # Add linear regression line
-    if obs_linreg is not None:
-        ax.plot(
-            obs_tides_da.time.isel(time=[0, -1]),
-            obs_linreg.intercept + obs_linreg.slope * obs_x[[0, -1]],
-            "r",
-            label="fitted line",
-        )
-
     # Add horizontal lines for spread/offsets
     ax.axhline(lot, color="black", linestyle=":", linewidth=1)
     ax.axhline(hot, color="black", linestyle=":", linewidth=1)
@@ -113,17 +170,17 @@ def _plot_biases(
     # Add text annotations for spread/offsets
     ax.annotate(
         f"    High tide\n    offset ({offset_high:.0%})",
-        xy=(all_timerange.max(), np.mean([hat, hot])),
+        xy=(all_tides_da.time.max(), np.mean([hat, hot])),
         va="center",
     )
     ax.annotate(
         f"    Spread\n    ({spread:.0%})",
-        xy=(all_timerange.max(), np.mean([lot, hot])),
+        xy=(all_tides_da.time.max(), np.mean([lot, hot])),
         va="center",
     )
     ax.annotate(
         f"    Low tide\n    offset ({offset_low:.0%})",
-        xy=(all_timerange.max(), np.mean([lat, lot])),
+        xy=(all_tides_da.time.max(), np.mean([lat, lot])),
     )
 
     # Remove top right axes and add labels
@@ -145,23 +202,25 @@ def tide_stats(
     tidepost_lon: float | None = None,
     plain_english: bool = True,
     plot: bool = True,
-    plot_col: str | None = None,
+    plot_var: str | None = None,
+    point_col: str | None = None,
     modelled_freq: str = "3h",
-    linear_reg: bool = False,
     min_max_q: tuple = (0.0, 1.0),
     round_stats: int = 3,
-    **model_tides_kwargs,
+    **tag_tides_kwargs,
 ) -> pd.Series:
     """
     Takes a multi-dimensional dataset and generate tide statistics
     and satellite-observed tide bias metrics, calculated based on
-    every timestep in the satellte data and the geographic centroid
+    every timestep in the satellite data and the geographic centroid
     of the imagery.
 
     By comparing the subset of tides observed by satellites
     against the full astronomical tidal range, we can evaluate
     whether the tides observed by satellites are biased
-    (e.g. fail to observe either the highest or lowest tides).
+    (e.g. fail to observe either the highest or lowest tides) due
+    to tide aliasing interactions with sun-synchronous satellite
+    overpasses.
 
     For more information about the tidal statistics computed by this
     function, refer to Figure 8 in Bishop-Taylor et al. 2018:
@@ -181,10 +240,13 @@ def tide_stats(
         be used to provide a custom set of times. Accepts any format
         that can be converted by `pandas.to_datetime()`. For example:
         `time=pd.date_range(start="2000", end="2001", freq="5h")`
-    model : str, optional
-        The tide model to use to model tides. Defaults to "EOT20";
-        for a full list of available/supported models, run
-        `eo_tides.model.list_models`.
+    model : str or list of str, optional
+        The tide model (or list of models) to use to model tides.
+        If a list is provided, the resulting statistics will be
+        returned as a `pandas.Dataframe`; otherwise a `pandas.Series`.
+        Defaults to "EOT20"; specify "all" to use all models available
+        in `directory`. For a full list of available and supported
+        models, run `eo_tides.model.list_models`.
     directory : str, optional
         The directory containing tide model data files. If no path is
         provided, this will default to the environment variable
@@ -198,26 +260,25 @@ def tide_stats(
         location.
     plain_english : bool, optional
         An optional boolean indicating whether to print a plain english
-        version of the tidal statistics to the screen. Defaults to True.
+        version of the tidal statistics to the screen. Defaults to True;
+        only supported when a single tide model is passed to `model`.
     plot : bool, optional
         An optional boolean indicating whether to plot how satellite-
         observed tide heights compare against the full tidal range.
-        Defaults to True.
-    plot_col : str, optional
+        Defaults to True; only supported when a single tide model is
+        passed to `model`.
+    plot_var : str, optional
         Optional name of a coordinate, dimension or variable in the array
         that will be used to plot observations with unique symbols.
         Defaults to None, which will plot all observations as circles.
+    point_col : str, optional
+        Colour used to plot points on the graph. Defaults to None which
+        will automatically select colours.
     modelled_freq : str, optional
         An optional string giving the frequency at which to model tides
         when computing the full modelled tidal range. Defaults to '3h',
         which computes a tide height for every three hours across the
         temporal extent of `data`.
-    linear_reg: bool, optional
-        Whether to return linear regression statistics that assess
-        whether satellite-observed tides show any decreasing  or
-        increasing trends over time. This may indicate whether your
-        satellite data may produce misleading trends based on uneven
-        sampling of the local tide regime.
     min_max_q : tuple, optional
         Quantiles used to calculate max and min observed and modelled
         astronomical tides. By default `(0.0, 1.0)` which is equivalent
@@ -226,17 +287,15 @@ def tide_stats(
     round_stats : int, optional
         The number of decimal places used to round the output statistics.
         Defaults to 3.
-    **model_tides_kwargs :
-        Optional parameters passed to the `eo_tides.model.model_tides`
-        function. Important parameters include `cutoff` (used to
-        extrapolate modelled tides away from the coast; defaults to
-        `np.inf`), `crop` (whether to crop tide model constituent files
-        on-the-fly to improve performance) etc.
+    **tag_tides_kwargs :
+        Optional parameters passed to the `eo_tides.eo.tag_tides`
+        function that is used to model tides for each observed and
+        modelled timestep.
 
     Returns
     -------
-    stats_df : pandas.Series
-        A `pandas.Series` containing the following statistics:
+    stats_df : pandas.Series or pandas.Dataframe
+        A pandas object containing the following statistics:
 
         - `y`: latitude used for modelling tide heights
         - `x`: longitude used for modelling tide heights
@@ -251,158 +310,92 @@ def tide_stats(
         - `spread`: proportion of the full modelled tidal range observed by the satellite
         - `offset_low`: proportion of the lowest tides never observed by the satellite
         - `offset_high`: proportion of the highest tides never observed by the satellite
-
-        If `linear_reg = True`, the output will also contain:
-
-        - `observed_slope`: slope of any relationship between observed tide heights and time
-        - `observed_pval`: significance/p-value of any relationship between observed tide heights and time
     """
+
     # Standardise data inputs, time and models
-    gbox, time_coords = _standardise_inputs(data, time)
+    gbox, obs_times = _standardise_inputs(data, time)
 
-    # Verify that only one tide model is provided
-    if isinstance(model, list):
-        raise Exception("Only single tide models are supported by `tide_stats`.")
+    # Generate range of times covering entire period of satellite record
+    assert obs_times is not None
+    all_times = pd.date_range(
+        start=obs_times.min().item(),
+        end=obs_times.max().item(),
+        freq=modelled_freq,
+    )
 
     # If custom tide modelling locations are not provided, use the
     # dataset centroid
     if not tidepost_lat or not tidepost_lon:
         tidepost_lon, tidepost_lat = gbox.geographic_extent.centroid.coords[0]
 
-    # Model tides for each observation in the supplied xarray object
-    assert time_coords is not None
+    # Model tides for observed timesteps
     obs_tides_da = tag_tides(
         gbox,
-        time=time_coords,
+        time=obs_times,
         model=model,
         directory=directory,
         tidepost_lat=tidepost_lat,  # type: ignore
         tidepost_lon=tidepost_lon,  # type: ignore
-        return_tideposts=True,
-        **model_tides_kwargs,
+        **tag_tides_kwargs,
     )
-    if isinstance(data, (xr.Dataset, xr.DataArray)):
-        obs_tides_da = obs_tides_da.reindex_like(data)
 
-    # Generate range of times covering entire period of satellite record
-    all_timerange = pd.date_range(
-        start=time_coords.min().item(),
-        end=time_coords.max().item(),
-        freq=modelled_freq,
-    )
-
-    # Model tides for each timestep
-    all_tides_df = model_tides(
-        x=tidepost_lon,  # type: ignore
-        y=tidepost_lat,  # type: ignore
-        time=all_timerange,
+    # Model tides for all modelled timesteps
+    all_tides_da = tag_tides(
+        gbox,
+        time=all_times,
         model=model,
         directory=directory,
-        crs="EPSG:4326",
-        **model_tides_kwargs,
+        tidepost_lat=tidepost_lat,  # type: ignore
+        tidepost_lon=tidepost_lon,  # type: ignore
+        **tag_tides_kwargs,
     )
 
-    # Get coarse statistics on all and observed tidal ranges
-    obs_mean = obs_tides_da.mean().item()
-    all_mean = all_tides_df.tide_height.mean()
-    obs_min, obs_max = obs_tides_da.quantile(min_max_q).values
-    all_min, all_max = all_tides_df.tide_height.quantile(min_max_q).values
-
-    # Calculate tidal range
-    obs_range = obs_max - obs_min
-    all_range = all_max - all_min
-
-    # Calculate Bishop-Taylor et al. 2018 tidal metrics
-    spread = obs_range / all_range
-    low_tide_offset_m = abs(all_min - obs_min)
-    high_tide_offset_m = abs(all_max - obs_max)
-    low_tide_offset = low_tide_offset_m / all_range
-    high_tide_offset = high_tide_offset_m / all_range
-
-    # Plain text descriptors
-    mean_diff = "higher" if obs_mean > all_mean else "lower"
-    mean_diff_icon = "⬆️" if obs_mean > all_mean else "⬇️"
-    spread_icon = "🟢" if spread >= 0.9 else "🟡" if 0.7 < spread <= 0.9 else "🔴"
-    low_tide_icon = "🟢" if low_tide_offset <= 0.1 else "🟡" if 0.1 <= low_tide_offset < 0.2 else "🔴"
-    high_tide_icon = "🟢" if high_tide_offset <= 0.1 else "🟡" if 0.1 <= high_tide_offset < 0.2 else "🔴"
+    # Calculate statistics
+    stats_ds = _tide_statistics(obs_tides_da, all_tides_da, min_max_q=min_max_q)
 
-    # Extract x (time in decimal years) and y (distance) values
-    obs_x = (
-        obs_tides_da.time.dt.year + ((obs_tides_da.time.dt.dayofyear - 1) / 365) + ((obs_tides_da.time.dt.hour) / 24)
-    )
-    obs_y = obs_tides_da.values.astype(np.float32)
+    # Convert to pandas and add tide post coordinates
+    stats_df = stats_ds.to_pandas().astype("float32")
+    stats_df["x"] = tidepost_lon
+    stats_df["y"] = tidepost_lat
 
-    # Compute linear regression
-    obs_linreg = stats.linregress(x=obs_x, y=obs_y)
+    # Convert coordinates to index if dataframe
+    if isinstance(stats_df, pd.DataFrame):
+        stats_df = stats_df.set_index(["x", "y"], append=True)
 
-    if plain_english:
-        print(f"\n\n🌊 Modelled astronomical tide range: {all_range:.2f} metres.")
-        print(f"🛰️ Observed tide range: {obs_range:.2f} metres.\n")
-        print(f"{spread_icon} {spread:.0%} of the modelled astronomical tide range was observed at this location.")
-        print(
-            f"{high_tide_icon} The highest {high_tide_offset:.0%} ({high_tide_offset_m:.2f} metres) of the tide range was never observed."
-        )
-        print(
-            f"{low_tide_icon} The lowest {low_tide_offset:.0%} ({low_tide_offset_m:.2f} metres) of the tide range was never observed.\n"
-        )
-        print(f"🌊 Mean modelled astronomical tide height: {all_mean:.2f} metres.")
-        print(f"🛰️ Mean observed tide height: {obs_mean:.2f} metres.\n")
-        print(
-            f"{mean_diff_icon} The mean observed tide height was {obs_mean - all_mean:.2f} metres {mean_diff} than the mean modelled astronomical tide height."
-        )
+    # If a series, print and plot summaries
+    else:
+        if plain_english:
+            _stats_plain_english(
+                mot=stats_df.mot,
+                mat=stats_df.mat,
+                hot=stats_df.hot,
+                hat=stats_df.hat,
+                lot=stats_df.lot,
+                lat=stats_df.lat,
+                otr=stats_df.otr,
+                tr=stats_df.tr,
+                spread=stats_df.spread,
+                offset_low=stats_df.offset_low,
+                offset_high=stats_df.offset_high,
+            )
 
-        if linear_reg:
-            if obs_linreg.pvalue > 0.01:
-                print("➖ Observed tides showed no significant trends over time.")
-            else:
-                obs_slope_desc = "decreasing" if obs_linreg.slope < 0 else "increasing"
-                print(
-                    f"⚠️ Observed tides showed a significant {obs_slope_desc} trend over time (p={obs_linreg.pvalue:.3f}, {obs_linreg.slope:.2f} metres per year)"
-                )
-
-    if plot:
-        _plot_biases(
-            all_tides_df=all_tides_df,
-            obs_tides_da=obs_tides_da,
-            lat=all_min,
-            lot=obs_min,
-            hat=all_max,
-            hot=obs_max,
-            offset_low=low_tide_offset,
-            offset_high=high_tide_offset,
-            spread=spread,
-            plot_col=data[plot_col] if plot_col else None,
-            obs_linreg=obs_linreg if linear_reg else None,
-            obs_x=obs_x,
-            all_timerange=all_timerange,
-        )
+        if plot:
+            _stats_figure(
+                all_tides_da=all_tides_da,
+                obs_tides_da=obs_tides_da,
+                hot=stats_df.hot,
+                hat=stats_df.hat,
+                lot=stats_df.lot,
+                lat=stats_df.lat,
+                spread=stats_df.spread,
+                offset_low=stats_df.offset_low,
+                offset_high=stats_df.offset_high,
+                plot_var=data[plot_var] if plot_var else None,
+                point_col=point_col,
+            )
 
-    # Export pandas.Series containing tidal stats
-    output_stats = {
-        "y": tidepost_lat,
-        "x": tidepost_lon,
-        "mot": obs_mean,
-        "mat": all_mean,
-        "lot": obs_min,
-        "lat": all_min,
-        "hot": obs_max,
-        "hat": all_max,
-        "otr": obs_range,
-        "tr": all_range,
-        "spread": spread,
-        "offset_low": low_tide_offset,
-        "offset_high": high_tide_offset,
-    }
-
-    if linear_reg:
-        output_stats.update({
-            "observed_slope": obs_linreg.slope,
-            "observed_pval": obs_linreg.pvalue,
-        })
-
-    # Return pandas data
-    stats_df = pd.Series(output_stats).round(round_stats)
-    return stats_df
+    # Return in Pandas format
+    return stats_df.round(round_stats)
 
 
 def pixel_stats(
@@ -410,26 +403,31 @@ def pixel_stats(
     time: DatetimeLike | None = None,
     model: str | list[str] = "EOT20",
     directory: str | os.PathLike | None = None,
-    resample: bool = False,
+    resample: bool = True,
     modelled_freq: str = "3h",
     min_max_q: tuple[float, float] = (0.0, 1.0),
+    resample_method: str = "bilinear",
+    dask_chunks: tuple[float, float] | None = None,
+    dask_compute: bool = True,
     extrapolate: bool = True,
     cutoff: float = 10,
     **pixel_tides_kwargs,
 ) -> xr.Dataset:
     """
-    Takes a multi-dimensional dataset and generate two-dimensional
+    Takes a multi-dimensional dataset and generate spatial
     tide statistics and satellite-observed tide bias metrics,
-    calculated based on every timestep in the satellte data and
+    calculated based on every timestep in the satellite data and
     modelled into the spatial extent of the imagery.
 
     By comparing the subset of tides observed by satellites
     against the full astronomical tidal range, we can evaluate
     whether the tides observed by satellites are biased
-    (e.g. fail to observe either the highest or lowest tides).
+    (e.g. fail to observe either the highest or lowest tides)
+    due to tide aliasing interactions with sun-synchronous satellite
+    overpasses.
 
     Compared to `tide_stats`, this function models tide metrics
-    spatially to produce a two-dimensional output.
+    spatially to produce a two-dimensional output for each statistic.
 
     For more information about the tidal statistics computed by this
     function, refer to Figure 8 in Bishop-Taylor et al. 2018:
@@ -439,7 +437,7 @@ def pixel_stats(
     ----------
     data : xarray.Dataset or xarray.DataArray or odc.geo.geobox.GeoBox
         A multi-dimensional dataset or GeoBox pixel grid that will
-        be used to calculate 2D tide statistics. If `data`
+        be used to calculate spatial tide statistics. If `data`
         is an xarray object, it should include a "time" dimension.
         If no "time" dimension exists or if `data` is a GeoBox,
         then times must be passed using the `time` parameter.
@@ -452,7 +450,7 @@ def pixel_stats(
     model : str or list of str, optional
         The tide model (or list of models) to use to model tides.
         If a list is provided, a new "tide_model" dimension will be
-        added to the `xarray.DataArray` outputs. Defaults to "EOT20";
+        added to the `xarray.Dataset` output. Defaults to "EOT20";
         specify "all" to use all models available in `directory`.
         For a full list of available and supported models, run
         `eo_tides.model.list_models`.
@@ -465,9 +463,9 @@ def pixel_stats(
         (<https://geoscienceaustralia.github.io/eo-tides/setup/>).
     resample : bool, optional
         Whether to resample tide statistics back into `data`'s original
-        higher resolution grid. Defaults to False, which will return
-        lower-resolution statistics that are typically sufficient for
-        most purposes.
+        higher resolution grid. Set this to `False` if you want to return
+        lower-resolution tide statistics (which can be useful for
+        assessing tide biases across large spatial extents).
     modelled_freq : str, optional
         An optional string giving the frequency at which to model tides
         when computing the full modelled tidal range. Defaults to '3h',
@@ -476,10 +474,25 @@ def pixel_stats(
     min_max_q : tuple, optional
         Quantiles used to calculate max and min observed and modelled
         astronomical tides. By default `(0.0, 1.0)` which is equivalent
-        to minimum and maximum; to use a softer threshold that is more
-        robust to outliers, use e.g. `(0.1, 0.9)`.
+        to minimum and maximum; for a softer threshold that is more
+        robust to outliers use e.g. `(0.1, 0.9)`.
+    resample_method : str, optional
+        If resampling is requested (see `resample` above), use this
+        resampling method when resampling from low resolution to high
+        resolution pixels. Defaults to "bilinear"; valid options include
+        "nearest", "cubic", "min", "max", "average" etc.
+    dask_chunks : tuple of float, optional
+        Can be used to configure custom Dask chunking for the final
+        resampling step. By default, chunks will be automatically set
+        to match y/x chunks from `data` if they exist; otherwise chunks
+        will be chosen to cover the entire y/x extent of the dataset.
+        For custom chunks, provide a tuple in the form `(y, x)`, e.g.
+        `(2048, 2048)`.
+    dask_compute : bool, optional
+        Whether to compute results of the resampling step using Dask.
+        If False, `stats_ds` will be returned as a Dask-enabled array.
     extrapolate : bool, optional
-        Whether to extrapolate tides for x and y coordinates outside of
+        Whether to extrapolate tides into x and y coordinates outside of
         the valid tide modelling domain using nearest-neighbor. Defaults
         to True.
     cutoff : float, optional
@@ -494,6 +507,8 @@ def pixel_stats(
     stats_ds : xarray.Dataset
         An `xarray.Dataset` containing the following statistics as two-dimensional data variables:
 
+        - `mot`: mean tide height observed by the satellite (metres)
+        - `mat`: mean modelled astronomical tide height (metres)
         - `lot`: minimum tide height observed by the satellite (metres)
         - `lat`: minimum tide height from modelled astronomical tidal range (metres)
         - `hot`: maximum tide height observed by the satellite (metres)
@@ -505,90 +520,62 @@ def pixel_stats(
         - `offset_high`: proportion of the highest tides never observed by the satellite
 
     """
+
     # Standardise data inputs, time and models
-    gbox, time_coords = _standardise_inputs(data, time)
+    gbox, obs_times = _standardise_inputs(data, time)
+    dask_chunks = _resample_chunks(data, dask_chunks)
     model = [model] if isinstance(model, str) else model
 
-    # Model observed tides
-    assert time_coords is not None
-    obs_tides = pixel_tides(
+    # Generate range of times covering entire period of satellite record
+    assert obs_times is not None
+    all_times = pd.date_range(
+        start=obs_times.min().item(),
+        end=obs_times.max().item(),
+        freq=modelled_freq,
+    )
+
+    # Model tides for observed timesteps
+    obs_tides_da = pixel_tides(
         gbox,
-        time=time_coords,
-        resample=False,
+        time=obs_times,
         model=model,
         directory=directory,
-        calculate_quantiles=min_max_q,
+        resample=False,
         extrapolate=extrapolate,
         cutoff=cutoff,
         **pixel_tides_kwargs,
     )
 
-    # Generate times covering entire period of satellite record
-    all_timerange = pd.date_range(
-        start=time_coords.min().item(),
-        end=time_coords.max().item(),
-        freq=modelled_freq,
-    )
-
-    # Model all tides
-    all_tides = pixel_tides(
+    # Model tides for all modelled timesteps
+    all_tides_da = pixel_tides(
         gbox,
-        time=all_timerange,
+        time=all_times,
         model=model,
         directory=directory,
-        calculate_quantiles=min_max_q,
         resample=False,
         extrapolate=extrapolate,
         cutoff=cutoff,
         **pixel_tides_kwargs,
     )
 
-    # # Calculate means
-    # TODO: Find way to make this work with `calculate_quantiles`
-    # mot = obs_tides.mean(dim="time")
-    # mat = all_tides.mean(dim="time")
-
-    # Calculate min and max tides
-    lot = obs_tides.isel(quantile=0)
-    hot = obs_tides.isel(quantile=-1)
-    lat = all_tides.isel(quantile=0)
-    hat = all_tides.isel(quantile=-1)
-
-    # Calculate tidal range
-    otr = hot - lot
-    tr = hat - lat
-
-    # Calculate Bishop-Taylor et al. 2018 tidal metrics
-    spread = otr / tr
-    offset_low_m = abs(lat - lot)
-    offset_high_m = abs(hat - hot)
-    offset_low = offset_low_m / tr
-    offset_high = offset_high_m / tr
+    # Calculate statistics
+    stats_lowres = _tide_statistics(obs_tides_da, all_tides_da, min_max_q=min_max_q)
 
-    # Combine into a single dataset
-    stats_ds = (
-        xr.merge(
-            [
-                # mot.rename("mot"),
-                # mat.rename("mat"),
-                hot.rename("hot"),
-                hat.rename("hat"),
-                lot.rename("lot"),
-                lat.rename("lat"),
-                otr.rename("otr"),
-                tr.rename("tr"),
-                spread.rename("spread"),
-                offset_low.rename("offset_low"),
-                offset_high.rename("offset_high"),
-            ],
-            compat="override",
-        )
-        .drop_vars("quantile")
-        .odc.assign_crs(crs=gbox.crs)
-    )
+    # Assign CRS and geobox to allow reprojection
+    stats_lowres = stats_lowres.odc.assign_crs(crs=gbox.crs)
 
-    # Optionally resample into the original pixel grid of `data`
+    # Reproject statistics into original high resolution grid
     if resample:
-        stats_ds = stats_ds.odc.reproject(how=gbox, resample_method="bilinear")
+        print("Reprojecting statistics into original resolution")
+        stats_highres = _pixel_tides_resample(
+            stats_lowres,
+            gbox,
+            resample_method,
+            dask_chunks,
+            dask_compute,
+            None,
+        )
+        return stats_highres
 
-    return stats_ds
+    print("Returning low resolution statistics array")
+    return stats_lowres
diff --git a/pyproject.toml b/pyproject.toml
index 719ff5d..fb13230 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -45,7 +45,7 @@ dependencies = [
     "psutil>=5.8.0",
     "pyogrio>=0.7.0",
     "pyproj>=3.6.1",
-    "pyTMD==2.1.8",
+    "pyTMD==2.2.0",
     "scikit-learn>=1.4.0",
     "scipy>=1.11.2",
     "shapely>=2.0.6",
diff --git a/tests/test_stats.py b/tests/test_stats.py
index 06b2033..04cde0a 100644
--- a/tests/test_stats.py
+++ b/tests/test_stats.py
@@ -10,66 +10,98 @@
 
 # Run test for multiple modelled frequencies
 @pytest.mark.parametrize(
-    "modelled_freq",
+    "modelled_freq, tidepost_lon, tidepost_lat",
     [
-        ("2h"),  # Model tides every two hours
-        ("120min"),  # Model tides every 120 minutes
+        ("2h", None, None),  # Model tides every two hours
+        ("120min", None, None),  # Model tides every 120 minutes
+        ("2h", 122.218, -18.001),  # Custom tidepost
     ],
 )
-def test_tidal_stats(satellite_ds, modelled_freq):
+def test_tidal_stats(satellite_ds, modelled_freq, tidepost_lon, tidepost_lat):
     # Calculate tidal stats
     tidal_stats_df = tide_stats(
         satellite_ds,
         modelled_freq=modelled_freq,
+        tidepost_lon=tidepost_lon,
+        tidepost_lat=tidepost_lat,
     )
 
     # Compare outputs to expected results (within 2% or 0.02 m)
     expected_results = pd.Series({
-        "tidepost_lat": -18.001,
-        "tidepost_lon": 122.218,
-        "observed_mean_m": -0.417,
-        "all_mean_m": -0.005,
-        "observed_min_m": -2.141,
-        "all_min_m": -4.321,
-        "observed_max_m": 1.674,
-        "all_max_m": 4.259,
-        "observed_range_m": 3.814,
-        "all_range_m": 8.580,
+        "mot": -0.417,
+        "mat": -0.005,
+        "hot": 1.674,
+        "hat": 4.259,
+        "lot": -2.141,
+        "lat": -4.321,
+        "otr": 3.814,
+        "tr": 8.580,
         "spread": 0.445,
-        "low_tide_offset": 0.254,
-        "high_tide_offset": 0.301,
+        "offset_low": 0.254,
+        "offset_high": 0.301,
+        "x": 122.218,
+        "y": -18.001,
     })
     assert np.allclose(tidal_stats_df, expected_results, atol=0.02)
 
-    # Test linear regression
-    tidal_stats_linreg_df = tide_stats(
+
+# Run test for one or multiple model inputs
+@pytest.mark.parametrize(
+    "models",
+    [
+        (["EOT20"]),
+        (["EOT20", "GOT5.5"]),
+    ],
+)
+def test_tidal_stats_models(satellite_ds, models):
+    # Calculate tidal stats
+    tidal_stats_df = tide_stats(
         satellite_ds,
-        modelled_freq=modelled_freq,
-        linear_reg=True,
+        model=models,
     )
 
-    # Compare outputs to expected results (within 2% or 0.02 m)
-    expected_results = pd.Series({
-        "tidepost_lat": -18.001,
-        "tidepost_lon": 122.218,
-        "observed_mean_m": -0.417,
-        "all_mean_m": -0.005,
-        "observed_min_m": -2.141,
-        "all_min_m": -4.321,
-        "observed_max_m": 1.674,
-        "all_max_m": 4.259,
-        "observed_range_m": 3.814,
-        "all_range_m": 8.580,
-        "spread": 0.445,
-        "low_tide_offset": 0.254,
-        "high_tide_offset": 0.301,
-        "observed_slope": 6.952,
-        "observed_pval": 0.573,
-    })
-    assert np.allclose(tidal_stats_linreg_df, expected_results, atol=0.02)
+    # If multiple models, verify data is a pandas.DataFrame with expected rows
+    if len(models) > 1:
+        assert isinstance(tidal_stats_df, pd.DataFrame)
+        assert len(tidal_stats_df.index) == len(models)
+        assert models == tidal_stats_df.index.get_level_values("tide_model").tolist()
 
+    # If just one, verify data is a pandas.Series
+    else:
+        assert isinstance(tidal_stats_df, pd.Series)
 
-# Run test for multiple modelled frequencies
+
+# Test if plotting a custom variable runs without errors
+def test_tide_stats_plotvar(satellite_ds):
+    # Test on custom coordinate
+    satellite_ds_withcoords = satellite_ds.assign_coords(coord=("time", [1, 1, 2, 2, 3, 4, 5]))
+    tide_stats(
+        satellite_ds_withcoords,
+        plot_var="coord",
+    )
+
+    # Test on custom data variable
+    satellite_ds_withvar = satellite_ds.assign(var=("time", [1, 1, 2, 2, 3, 4, 5]))
+    tide_stats(
+        satellite_ds_withvar,
+        plot_var="var",
+    )
+
+    # Test configuring color when plotting variable
+    tide_stats(
+        satellite_ds_withvar,
+        plot_var="var",
+        point_col="red",
+    )
+
+    # Test when not plotting variable
+    tide_stats(
+        satellite_ds_withvar,
+        point_col="red",
+    )
+
+
+# Run test for multiple models and with resampling on and off
 @pytest.mark.parametrize(
     "models, resample",
     [
@@ -89,7 +121,7 @@ def test_pixel_stats(satellite_ds, models, resample):
     assert stats_ds.odc.spatial_dims == satellite_ds.odc.spatial_dims
 
     # Verify vars are as expected
-    expected_vars = ["hat", "hot", "lat", "lot", "otr", "tr", "spread", "offset_low", "offset_high"]
+    expected_vars = ["mot", "mat", "hot", "hat", "lot", "lat", "otr", "tr", "spread", "offset_low", "offset_high"]
     assert set(expected_vars) == set(stats_ds.data_vars)
 
     # Verify tide models are correct
diff --git a/tests/testing.ipynb b/tests/testing.ipynb
index c6bdabf..2b37d22 100644
--- a/tests/testing.ipynb
+++ b/tests/testing.ipynb
@@ -7,7 +7,7 @@
    "outputs": [],
    "source": [
     "!pip install uv==0.5.0\n",
-    "!pip install -e .. --quiet\n"
+    "!pip install -e .. --quiet"
    ]
   },
   {
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,216 +128,26 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Run tests"
+    "cd .."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 3,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/home/jovyan/Robbi/eo-tides\n"
-     ]
-    }
-   ],
    "source": [
-    "cd .."
+    "## Run tests"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m============================= test session starts ==============================\u001b[0m\n",
-      "platform linux -- Python 3.10.15, pytest-8.3.3, pluggy-1.5.0 -- /env/bin/python3.10\n",
-      "cachedir: .pytest_cache\n",
-      "rootdir: /home/jovyan/Robbi/eo-tides\n",
-      "configfile: pyproject.toml\n",
-      "plugins: anyio-4.6.2.post1, nbval-0.11.0, dependency-0.6.0, cov-6.0.0\n",
-      "collected 40 items / 39 deselected / 1 selected                                \u001b[0m\u001b[1m\n",
-      "\n",
-      "tests/test_model.py::test_model_tides_ensemble \u001b[31mFAILED\u001b[0m\u001b[31m                    [100%]\u001b[0m\n",
-      "\n",
-      "=================================== FAILURES ===================================\n",
-      "\u001b[31m\u001b[1m__________________________ test_model_tides_ensemble ___________________________\u001b[0m\n",
-      "\n",
-      "    \u001b[0m\u001b[94mdef\u001b[39;49;00m \u001b[92mtest_model_tides_ensemble\u001b[39;49;00m():\u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[90m# Input params\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
-      "        good_hamtide11 = -\u001b[94m17.58549\u001b[39;49;00m, \u001b[94m123.59414\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
-      "        good_eot20 = -\u001b[94m17.1611\u001b[39;49;00m, \u001b[94m123.3406\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
-      "        y = [good_eot20[\u001b[94m0\u001b[39;49;00m], good_hamtide11[\u001b[94m0\u001b[39;49;00m]]\u001b[90m\u001b[39;49;00m\n",
-      "        x = [good_eot20[\u001b[94m1\u001b[39;49;00m], good_hamtide11[\u001b[94m1\u001b[39;49;00m]]\u001b[90m\u001b[39;49;00m\n",
-      "    \u001b[90m\u001b[39;49;00m\n",
-      "        times = pd.date_range(\u001b[33m\"\u001b[39;49;00m\u001b[33m2020\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33m2021\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, periods=\u001b[94m2\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n",
-      "    \u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[90m# Default, only ensemble requested\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
-      "        modelled_tides_df = model_tides(\u001b[90m\u001b[39;49;00m\n",
-      "            x=x,\u001b[90m\u001b[39;49;00m\n",
-      "            y=y,\u001b[90m\u001b[39;49;00m\n",
-      "            time=times,\u001b[90m\u001b[39;49;00m\n",
-      "            model=\u001b[33m\"\u001b[39;49;00m\u001b[33mensemble\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
-      "            ensemble_models=ENSEMBLE_MODELS,\u001b[90m\u001b[39;49;00m\n",
-      "        )\u001b[90m\u001b[39;49;00m\n",
-      "    \u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[94massert\u001b[39;49;00m modelled_tides_df.index.names == [\u001b[33m\"\u001b[39;49;00m\u001b[33mtime\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33mx\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33my\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m]\u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[94massert\u001b[39;49;00m modelled_tides_df.columns.tolist() == [\u001b[33m\"\u001b[39;49;00m\u001b[33mtide_model\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33mtide_height\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m]\u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[94massert\u001b[39;49;00m \u001b[96mall\u001b[39;49;00m(modelled_tides_df.tide_model == \u001b[33m\"\u001b[39;49;00m\u001b[33mensemble\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n",
-      "    \u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[90m# Default, ensemble + other models requested\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
-      "        models = [\u001b[33m\"\u001b[39;49;00m\u001b[33mEOT20\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33mHAMTIDE11\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33mensemble\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m]\u001b[90m\u001b[39;49;00m\n",
-      "        modelled_tides_df = model_tides(\u001b[90m\u001b[39;49;00m\n",
-      "            x=x,\u001b[90m\u001b[39;49;00m\n",
-      "            y=y,\u001b[90m\u001b[39;49;00m\n",
-      "            time=times,\u001b[90m\u001b[39;49;00m\n",
-      "            model=models,\u001b[90m\u001b[39;49;00m\n",
-      "            ensemble_models=ENSEMBLE_MODELS,\u001b[90m\u001b[39;49;00m\n",
-      "        )\u001b[90m\u001b[39;49;00m\n",
-      "    \u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[94massert\u001b[39;49;00m modelled_tides_df.index.names == [\u001b[33m\"\u001b[39;49;00m\u001b[33mtime\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33mx\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33my\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m]\u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[94massert\u001b[39;49;00m modelled_tides_df.columns.tolist() == [\u001b[33m\"\u001b[39;49;00m\u001b[33mtide_model\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33mtide_height\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m]\u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[94massert\u001b[39;49;00m \u001b[96mset\u001b[39;49;00m(modelled_tides_df.tide_model) == \u001b[96mset\u001b[39;49;00m(models)\u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[94massert\u001b[39;49;00m np.allclose(\u001b[90m\u001b[39;49;00m\n",
-      "            modelled_tides_df.tide_height.values,\u001b[90m\u001b[39;49;00m\n",
-      "            [\u001b[90m\u001b[39;49;00m\n",
-      "                \u001b[94m0.094\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
-      "                -\u001b[94m3.202\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
-      "                \u001b[94m0.409\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
-      "                -\u001b[94m3.098\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
-      "                \u001b[94m0.803\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
-      "                \u001b[94m0.664\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
-      "                \u001b[94m0.989\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
-      "                \u001b[94m1.011\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
-      "                \u001b[94m0.449\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
-      "                -\u001b[94m1.269\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
-      "                \u001b[94m0.699\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
-      "                -\u001b[94m1.043\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
-      "            ],\u001b[90m\u001b[39;49;00m\n",
-      "            atol=\u001b[94m0.02\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
-      "        )\u001b[90m\u001b[39;49;00m\n",
-      "    \u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[90m# One-to-one mode\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
-      "        modelled_tides_df = model_tides(\u001b[90m\u001b[39;49;00m\n",
-      "            x=x,\u001b[90m\u001b[39;49;00m\n",
-      "            y=y,\u001b[90m\u001b[39;49;00m\n",
-      "            time=times,\u001b[90m\u001b[39;49;00m\n",
-      "            model=models,\u001b[90m\u001b[39;49;00m\n",
-      "            mode=\u001b[33m\"\u001b[39;49;00m\u001b[33mone-to-one\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
-      "            ensemble_models=ENSEMBLE_MODELS,\u001b[90m\u001b[39;49;00m\n",
-      "        )\u001b[90m\u001b[39;49;00m\n",
-      "    \u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[94massert\u001b[39;49;00m modelled_tides_df.index.names == [\u001b[33m\"\u001b[39;49;00m\u001b[33mtime\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33mx\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33my\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m]\u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[94massert\u001b[39;49;00m modelled_tides_df.columns.tolist() == [\u001b[33m\"\u001b[39;49;00m\u001b[33mtide_model\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33mtide_height\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m]\u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[94massert\u001b[39;49;00m \u001b[96mset\u001b[39;49;00m(modelled_tides_df.tide_model) == \u001b[96mset\u001b[39;49;00m(models)\u001b[90m\u001b[39;49;00m\n",
-      "    \u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[90m# Wide mode, default\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
-      "        modelled_tides_df = model_tides(\u001b[90m\u001b[39;49;00m\n",
-      "            x=x,\u001b[90m\u001b[39;49;00m\n",
-      "            y=y,\u001b[90m\u001b[39;49;00m\n",
-      "            time=times,\u001b[90m\u001b[39;49;00m\n",
-      "            model=models,\u001b[90m\u001b[39;49;00m\n",
-      "            output_format=\u001b[33m\"\u001b[39;49;00m\u001b[33mwide\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
-      "            ensemble_models=ENSEMBLE_MODELS,\u001b[90m\u001b[39;49;00m\n",
-      "        )\u001b[90m\u001b[39;49;00m\n",
-      "    \u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[90m# Check that expected models exist, and that ensemble is approx average\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[90m# of other two models\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[94massert\u001b[39;49;00m \u001b[96mset\u001b[39;49;00m(modelled_tides_df.columns) == \u001b[96mset\u001b[39;49;00m(models)\u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[94massert\u001b[39;49;00m np.allclose(\u001b[90m\u001b[39;49;00m\n",
-      "            \u001b[94m0.5\u001b[39;49;00m * (modelled_tides_df.EOT20 + modelled_tides_df.HAMTIDE11),\u001b[90m\u001b[39;49;00m\n",
-      "            modelled_tides_df.ensemble,\u001b[90m\u001b[39;49;00m\n",
-      "        )\u001b[90m\u001b[39;49;00m\n",
-      "    \u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[90m# Wide mode, top n == 1\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
-      "        modelled_tides_df = model_tides(\u001b[90m\u001b[39;49;00m\n",
-      "            x=x,\u001b[90m\u001b[39;49;00m\n",
-      "            y=y,\u001b[90m\u001b[39;49;00m\n",
-      "            time=times,\u001b[90m\u001b[39;49;00m\n",
-      "            model=models,\u001b[90m\u001b[39;49;00m\n",
-      "            output_format=\u001b[33m\"\u001b[39;49;00m\u001b[33mwide\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
-      "            ensemble_top_n=\u001b[94m1\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
-      "            ensemble_models=ENSEMBLE_MODELS,\u001b[90m\u001b[39;49;00m\n",
-      "        )\u001b[90m\u001b[39;49;00m\n",
-      "    \u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[90m# Check that expected models exist, and that ensemble is equal to at\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[90m# least one of the other models\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[94massert\u001b[39;49;00m \u001b[96mset\u001b[39;49;00m(modelled_tides_df.columns) == \u001b[96mset\u001b[39;49;00m(models)\u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[94massert\u001b[39;49;00m \u001b[96mall\u001b[39;49;00m(\u001b[90m\u001b[39;49;00m\n",
-      "            (modelled_tides_df.EOT20 == modelled_tides_df.ensemble)\u001b[90m\u001b[39;49;00m\n",
-      "            | (modelled_tides_df.HAMTIDE11 == modelled_tides_df.ensemble)\u001b[90m\u001b[39;49;00m\n",
-      "        )\u001b[90m\u001b[39;49;00m\n",
-      "    \u001b[90m\u001b[39;49;00m\n",
-      "        \u001b[90m# Check that correct model is the closest at each row\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
-      "        closer_model = modelled_tides_df.apply(\u001b[90m\u001b[39;49;00m\n",
-      "            \u001b[94mlambda\u001b[39;49;00m row: (\u001b[90m\u001b[39;49;00m\n",
-      "                \u001b[33m\"\u001b[39;49;00m\u001b[33mEOT20\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m \u001b[94mif\u001b[39;49;00m \u001b[96mabs\u001b[39;49;00m(row[\u001b[33m\"\u001b[39;49;00m\u001b[33mensemble\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m] - row[\u001b[33m\"\u001b[39;49;00m\u001b[33mEOT20\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m]) < \u001b[96mabs\u001b[39;49;00m(row[\u001b[33m\"\u001b[39;49;00m\u001b[33mensemble\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m] - row[\u001b[33m\"\u001b[39;49;00m\u001b[33mHAMTIDE11\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m]) \u001b[94melse\u001b[39;49;00m \u001b[33m\"\u001b[39;49;00m\u001b[33mHAMTIDE11\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
-      "            ),\u001b[90m\u001b[39;49;00m\n",
-      "            axis=\u001b[94m1\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
-      "        ).tolist()\u001b[90m\u001b[39;49;00m\n",
-      ">       \u001b[94massert\u001b[39;49;00m closer_model == [\u001b[33m\"\u001b[39;49;00m\u001b[33mEOT20\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33mHAMTIDE11\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33mEOT20\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33mHAMTIDE11\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m]\u001b[90m\u001b[39;49;00m\n",
-      "\u001b[1m\u001b[31mE       AssertionError: assert ['EOT20', 'EO...T20', 'EOT20'] == ['EOT20', 'HA..., 'HAMTIDE11']\u001b[0m\n",
-      "\u001b[1m\u001b[31mE         \u001b[0m\n",
-      "\u001b[1m\u001b[31mE         At index 1 diff: \u001b[0m\u001b[33m'\u001b[39;49;00m\u001b[33mEOT20\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[90m\u001b[39;49;00m != \u001b[0m\u001b[33m'\u001b[39;49;00m\u001b[33mHAMTIDE11\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\u001b[0m\n",
-      "\u001b[1m\u001b[31mE         \u001b[0m\n",
-      "\u001b[1m\u001b[31mE         Full diff:\u001b[0m\n",
-      "\u001b[1m\u001b[31mE         \u001b[0m\u001b[90m \u001b[39;49;00m [\u001b[90m\u001b[39;49;00m\u001b[0m\n",
-      "\u001b[1m\u001b[31mE         \u001b[90m \u001b[39;49;00m     'EOT20',\u001b[90m\u001b[39;49;00m\u001b[0m\n",
-      "\u001b[1m\u001b[31mE         \u001b[91m-     'HAMTIDE11',\u001b[39;49;00m\u001b[90m\u001b[39;49;00m...\u001b[0m\n",
-      "\u001b[1m\u001b[31mE         \u001b[0m\n",
-      "\u001b[1m\u001b[31mE         ...Full output truncated (5 lines hidden), use '-vv' to show\u001b[0m\n",
-      "\n",
-      "\u001b[1m\u001b[31mtests/test_model.py\u001b[0m:377: AssertionError\n",
-      "----------------------------- Captured stdout call -----------------------------\n",
-      "Running ensemble tide modelling\n",
-      "Modelling tides with EOT20, HAMTIDE11 in parallel (models: 2, splits: 1)\n",
-      "Interpolating model rankings using IDW interpolation \n",
-      "Combining models into single ensemble model\n",
-      "Running ensemble tide modelling\n",
-      "Modelling tides with EOT20, HAMTIDE11 in parallel (models: 2, splits: 1)\n",
-      "Interpolating model rankings using IDW interpolation \n",
-      "Combining models into single ensemble model\n",
-      "Running ensemble tide modelling\n",
-      "Modelling tides with EOT20, HAMTIDE11 in parallel (models: 2, splits: 1)\n",
-      "Interpolating model rankings using IDW interpolation \n",
-      "Combining models into single ensemble model\n",
-      "Running ensemble tide modelling\n",
-      "Modelling tides with EOT20, HAMTIDE11 in parallel (models: 2, splits: 1)\n",
-      "Interpolating model rankings using IDW interpolation \n",
-      "Combining models into single ensemble model\n",
-      "Converting to a wide format dataframe\n",
-      "Running ensemble tide modelling\n",
-      "Modelling tides with EOT20, HAMTIDE11 in parallel (models: 2, splits: 1)\n",
-      "Interpolating model rankings using IDW interpolation \n",
-      "Combining models into single ensemble model\n",
-      "Converting to a wide format dataframe\n",
-      "----------------------------- Captured stderr call -----------------------------\n",
-      "100%|██████████| 2/2 [00:00<00:00, 14.59it/s]\n",
-      "100%|██████████| 2/2 [00:00<00:00, 16.97it/s]\n",
-      "100%|██████████| 2/2 [00:00<00:00, 17.09it/s]\n",
-      "100%|██████████| 2/2 [00:00<00:00, 17.76it/s]\n",
-      "100%|██████████| 2/2 [00:00<00:00, 17.48it/s]\n",
-      "\u001b[33m=============================== warnings summary ===============================\u001b[0m\n",
-      "<frozen importlib._bootstrap>:241\n",
-      "  <frozen importlib._bootstrap>:241: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility. Expected 16 from C header, got 96 from PyObject\n",
-      "\n",
-      "-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n",
-      "\u001b[36m\u001b[1m=========================== short test summary info ============================\u001b[0m\n",
-      "\u001b[31mFAILED\u001b[0m tests/test_model.py::\u001b[1mtest_model_tides_ensemble\u001b[0m - AssertionError: assert ['EOT20', 'EO...T20', 'EOT20'] == ['EOT20', 'HA..., ...\n",
-      "\u001b[31m================= \u001b[31m\u001b[1m1 failed\u001b[0m, \u001b[33m39 deselected\u001b[0m, \u001b[33m1 warning\u001b[0m\u001b[31m in 13.68s\u001b[0m\u001b[31m =================\u001b[0m\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "!export EO_TIDES_TIDE_MODELS=./tests/data/tide_models && pytest tests/test_model.py --verbose -k test_model_tides_ensemble"
    ]
@@ -360,6 +170,90 @@
     "!export EO_TIDES_TIDE_MODELS=./tests/data/tide_models && pytest tests --verbose"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Update `tide_stats`\n",
+    "\n",
+    "\n",
+    "Aim: internal `_tide_statistics` function that takes a stack of input observed and modelled tides in _both_ pandas and xarray format, and returns statistics in corresponding format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pytest\n",
+    "\n",
+    "from eo_tides.stats import pixel_stats, tide_stats\n",
+    "\n",
+    "GAUGE_X = 122.2183\n",
+    "GAUGE_Y = -18.0008\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = [\"EOT20\", \"GOT5.5\"]\n",
+    "\n",
+    "# Calculate tidal stats\n",
+    "tidal_stats_df = tide_stats(\n",
+    "    satellite_ds,\n",
+    "    model=models,\n",
+    "    directory=\"./tests/data/tide_models\",\n",
+    ")\n",
+    "\n",
+    "# assert isinstance(tidal_stats_df, pd.Series)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "satellite_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stats_ds = pixel_stats(\n",
+    "    satellite_ds,\n",
+    "    model=models,\n",
+    "    resample=False,\n",
+    "    directory=\"./tests/data/tide_models\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(stats_ds.data_vars)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -369,21 +263,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "AssertionError",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[8], line 122\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;66;03m# Check that correct model is the closest at each row\u001b[39;00m\n\u001b[1;32m    116\u001b[0m closer_model \u001b[38;5;241m=\u001b[39m modelled_tides_df\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m    117\u001b[0m     \u001b[38;5;28;01mlambda\u001b[39;00m row: (\n\u001b[1;32m    118\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEOT20\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mabs\u001b[39m(row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensemble\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m-\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEOT20\u001b[39m\u001b[38;5;124m\"\u001b[39m]) \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mabs\u001b[39m(row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensemble\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m-\u001b[39m row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHAMTIDE11\u001b[39m\u001b[38;5;124m\"\u001b[39m]) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHAMTIDE11\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    119\u001b[0m     ),\n\u001b[1;32m    120\u001b[0m     axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m    121\u001b[0m )\u001b[38;5;241m.\u001b[39mtolist()\n\u001b[0;32m--> 122\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m closer_model \u001b[38;5;241m==\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEOT20\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHAMTIDE11\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEOT20\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHAMTIDE11\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    124\u001b[0m \u001b[38;5;66;03m# # Check values are expected\u001b[39;00m\n\u001b[1;32m    125\u001b[0m \u001b[38;5;66;03m# assert np.allclose(modelled_tides_df.ensemble, [0.09, 0.98, -3.20, 1.01], atol=0.02)\u001b[39;00m\n\u001b[1;32m    126\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    178\u001b[0m \u001b[38;5;66;03m#     \"ensemble-mean\",\u001b[39;00m\n\u001b[1;32m    179\u001b[0m \u001b[38;5;66;03m# ])\u001b[39;00m\n",
-      "\u001b[0;31mAssertionError\u001b[0m: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "dir_path = \"./tests/data/tide_models\"\n",
     "\n",
@@ -570,94 +452,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "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></th>\n",
-       "      <th>tide_model</th>\n",
-       "      <th>EOT20</th>\n",
-       "      <th>HAMTIDE11</th>\n",
-       "      <th>ensemble</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>time</th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">2020-01-01</th>\n",
-       "      <th>123.34060</th>\n",
-       "      <th>-17.16110</th>\n",
-       "      <td>0.094431</td>\n",
-       "      <td>0.802646</td>\n",
-       "      <td>0.094431</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>123.59414</th>\n",
-       "      <th>-17.58549</th>\n",
-       "      <td>0.409453</td>\n",
-       "      <td>0.988605</td>\n",
-       "      <td>0.409453</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">2021-01-01</th>\n",
-       "      <th>123.34060</th>\n",
-       "      <th>-17.16110</th>\n",
-       "      <td>-3.201904</td>\n",
-       "      <td>0.664067</td>\n",
-       "      <td>-3.201904</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>123.59414</th>\n",
-       "      <th>-17.58549</th>\n",
-       "      <td>-3.098302</td>\n",
-       "      <td>1.011445</td>\n",
-       "      <td>-3.098302</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "tide_model                         EOT20  HAMTIDE11  ensemble\n",
-       "time       x         y                                       \n",
-       "2020-01-01 123.34060 -17.16110  0.094431   0.802646  0.094431\n",
-       "           123.59414 -17.58549  0.409453   0.988605  0.409453\n",
-       "2021-01-01 123.34060 -17.16110 -3.201904   0.664067 -3.201904\n",
-       "           123.59414 -17.58549 -3.098302   1.011445 -3.098302"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "modelled_tides_df"
    ]
@@ -678,23 +475,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "rank_EOT20         0\n",
-       "rank_HAMTIDE11    87\n",
-       "geometry           0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model_ranks_gdf.isnull().sum(axis=0)"
    ]
@@ -728,17 +511,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Interpolating model rankings using IDW interpolation \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from eo_tides.utils import idw\n",
     "\n",
@@ -775,104 +550,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "rank_EOT20         0\n",
-       "rank_HAMTIDE11    87\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model_ranks_gdf[model_ranking_cols].isnull()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "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></th>\n",
-       "      <th></th>\n",
-       "      <th>rank</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>tide_model</th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">EOT20</th>\n",
-       "      <th>123.34060</th>\n",
-       "      <th>-17.16110</th>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>123.59414</th>\n",
-       "      <th>-17.58549</th>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">HAMTIDE11</th>\n",
-       "      <th>123.34060</th>\n",
-       "      <th>-17.16110</th>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>123.59414</th>\n",
-       "      <th>-17.58549</th>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                rank\n",
-       "tide_model x         y              \n",
-       "EOT20      123.34060 -17.16110   1.0\n",
-       "           123.59414 -17.58549   2.0\n",
-       "HAMTIDE11  123.34060 -17.16110   2.0\n",
-       "           123.59414 -17.58549   1.0"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "ensemble_ranks_df"
    ]
@@ -2774,7 +2463,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -2788,7 +2477,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.15"
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,
diff --git a/uv.lock b/uv.lock
index 3aea1b4..ccd8704 100644
--- a/uv.lock
+++ b/uv.lock
@@ -782,7 +782,7 @@ requires-dist = [
     { name = "pyogrio", specifier = ">=0.7.0" },
     { name = "pyproj", specifier = ">=3.6.1" },
     { name = "pystac-client", marker = "extra == 'notebooks'", specifier = ">=0.8.3" },
-    { name = "pytmd", specifier = "==2.1.8" },
+    { name = "pytmd", specifier = "==2.2.0" },
     { name = "scikit-learn", specifier = ">=1.4.0" },
     { name = "scipy", specifier = ">=1.11.2" },
     { name = "shapely", specifier = ">=2.0.6" },
@@ -2725,7 +2725,7 @@ wheels = [
 
 [[package]]
 name = "pytmd"
-version = "2.1.8"
+version = "2.2.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "lxml" },
@@ -2737,9 +2737,9 @@ dependencies = [
     { name = "setuptools-scm" },
     { name = "timescale" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/98/c6/2c07b885cd62a8198d98bfb090d5a2fc7be86739f6d1d9f51a026cf4faec/pytmd-2.1.8.tar.gz", hash = "sha256:2bfd5e8428843937c6fbcf0fb023221f049ef4af8475a736d17e5736f4f890dc", size = 6356387 }
+sdist = { url = "https://files.pythonhosted.org/packages/89/4d/a9984a6e590961c631289be0b05a7336ec0aafb390493b0b378143ad04ef/pytmd-2.2.0.tar.gz", hash = "sha256:b18aba6d2d78e69a17b3cf1937acc5524e8795af250e29585c920a5443c4af7d", size = 6354754 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/fc/bc/49378fc92b8427a7569ea7ca5235da191cf85719fc50aba23499590c8cb4/pyTMD-2.1.8-py3-none-any.whl", hash = "sha256:300537e50569f00e6b4b61618bee82fab26898ea0a939632c9cd1e08cb55797b", size = 6457745 },
+    { url = "https://files.pythonhosted.org/packages/03/8f/48d5b64a0c6555becc89e76f3be5985e645dde754432d99848d9899f878c/pyTMD-2.2.0-py3-none-any.whl", hash = "sha256:7f727a138a9b7028ca12e033e99f406688ab9f013e6e9f7a78bff48d4393c184", size = 6456124 },
 ]
 
 [[package]]