diff --git a/examples/notebooks/demo_network.ipynb b/examples/notebooks/demo_network.ipynb
new file mode 100644
index 0000000..6332b7b
--- /dev/null
+++ b/examples/notebooks/demo_network.ipynb
@@ -0,0 +1,1155 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Data source: https://figshare.com/ndownloader/files/43053616"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "\n",
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+       ".xr-attrs dd {\n",
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+       "  margin: 0;\n",
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+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
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+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 14MB\n",
+       "Dimensions:    (space: 78582, time: 10)\n",
+       "Coordinates:\n",
+       "    azimuth    (space) int64 629kB dask.array&lt;chunksize=(1000,), meta=np.ndarray&gt;\n",
+       "    lat        (space) float32 314kB dask.array&lt;chunksize=(1000,), meta=np.ndarray&gt;\n",
+       "    lon        (space) float32 314kB dask.array&lt;chunksize=(1000,), meta=np.ndarray&gt;\n",
+       "    range      (space) int64 629kB dask.array&lt;chunksize=(1000,), meta=np.ndarray&gt;\n",
+       "  * time       (time) int64 80B 0 1 2 3 4 5 6 7 8 9\n",
+       "Dimensions without coordinates: space\n",
+       "Data variables:\n",
+       "    amplitude  (space, time) float32 3MB dask.array&lt;chunksize=(1000, 10), meta=np.ndarray&gt;\n",
+       "    complex    (space, time) complex64 6MB dask.array&lt;chunksize=(1000, 10), meta=np.ndarray&gt;\n",
+       "    phase      (space, time) float32 3MB dask.array&lt;chunksize=(1000, 10), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    multi-look:  coarsen-mean</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1979d8d0-a84a-483c-b02f-3055efa3ea1e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1979d8d0-a84a-483c-b02f-3055efa3ea1e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>space</span>: 78582</li><li><span class='xr-has-index'>time</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4685e55c-8994-4fdc-bb62-e44d86a8bda6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4685e55c-8994-4fdc-bb62-e44d86a8bda6' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>azimuth</span></div><div class='xr-var-dims'>(space)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1000,), meta=np.ndarray&gt;</div><input id='attrs-0bbfa73f-8c0a-461a-b872-ea820575507c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0bbfa73f-8c0a-461a-b872-ea820575507c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20a13906-9214-4088-98f9-6385d7b35946' class='xr-var-data-in' type='checkbox'><label for='data-20a13906-9214-4088-98f9-6385d7b35946' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 613.92 kiB </td>\n",
+       "                        <td> 7.81 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (78582,) </td>\n",
+       "                        <td> (1000,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 79 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "  <line x1=\"68\" y1=\"0\" x2=\"68\" y2=\"25\" />\n",
+       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
+       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
+       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >78582</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(space)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1000,), meta=np.ndarray&gt;</div><input id='attrs-dd5f9f98-ac47-40c3-935c-a2375edfb658' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd5f9f98-ac47-40c3-935c-a2375edfb658' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38c5ea9f-b029-460e-a4fa-ca6032e5e811' class='xr-var-data-in' type='checkbox'><label for='data-38c5ea9f-b029-460e-a4fa-ca6032e5e811' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 306.96 kiB </td>\n",
+       "                        <td> 3.91 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (78582,) </td>\n",
+       "                        <td> (1000,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 79 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "\n",
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+       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
+       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n",
+       "  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n",
+       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >78582</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(space)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1000,), meta=np.ndarray&gt;</div><input id='attrs-da8961f1-05ff-4e57-83e1-ae200dac51cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-da8961f1-05ff-4e57-83e1-ae200dac51cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-006e26cc-a602-4ea4-a612-e7c2653b2137' class='xr-var-data-in' type='checkbox'><label for='data-006e26cc-a602-4ea4-a612-e7c2653b2137' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 306.96 kiB </td>\n",
+       "                        <td> 3.91 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (78582,) </td>\n",
+       "                        <td> (1000,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 79 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
+       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
+       "  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n",
+       "  <line x1=\"68\" y1=\"0\" x2=\"68\" y2=\"25\" />\n",
+       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
+       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
+       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n",
+       "  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n",
+       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >78582</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>range</span></div><div class='xr-var-dims'>(space)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1000,), meta=np.ndarray&gt;</div><input id='attrs-d037fb69-c107-43b3-a25b-a09995fd89d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d037fb69-c107-43b3-a25b-a09995fd89d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a284235-4ad5-4465-8ef7-bf7cd37a0b76' class='xr-var-data-in' type='checkbox'><label for='data-1a284235-4ad5-4465-8ef7-bf7cd37a0b76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 613.92 kiB </td>\n",
+       "                        <td> 7.81 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (78582,) </td>\n",
+       "                        <td> (1000,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 79 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
+       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
+       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
+       "  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"0\" x2=\"36\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
+       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
+       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
+       "  <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"25\" />\n",
+       "  <line x1=\"68\" y1=\"0\" x2=\"68\" y2=\"25\" />\n",
+       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
+       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
+       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n",
+       "  <line x1=\"106\" y1=\"0\" x2=\"106\" y2=\"25\" />\n",
+       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >78582</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9</div><input id='attrs-93e49cc2-74d6-42ce-aec0-19d56bc63784' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93e49cc2-74d6-42ce-aec0-19d56bc63784' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afa23ab9-fff5-4236-a517-5450f314bd42' class='xr-var-data-in' type='checkbox'><label for='data-afa23ab9-fff5-4236-a517-5450f314bd42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d9d8efdc-d6eb-41c7-9f37-cd16bfb5772a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d9d8efdc-d6eb-41c7-9f37-cd16bfb5772a' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>amplitude</span></div><div class='xr-var-dims'>(space, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1000, 10), meta=np.ndarray&gt;</div><input id='attrs-74cf4bd4-fab3-4db5-8d23-13c179a68538' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74cf4bd4-fab3-4db5-8d23-13c179a68538' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b49885c1-d090-48bc-99bc-8f004d31d5d2' class='xr-var-data-in' type='checkbox'><label for='data-b49885c1-d090-48bc-99bc-8f004d31d5d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 3.00 MiB </td>\n",
+       "                        <td> 39.06 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (78582, 10) </td>\n",
+       "                        <td> (1000, 10) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 79 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"25\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"25\" y2=\"12\" />\n",
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+       "  <line x1=\"0\" y1=\"36\" x2=\"25\" y2=\"36\" />\n",
+       "  <line x1=\"0\" y1=\"44\" x2=\"25\" y2=\"44\" />\n",
+       "  <line x1=\"0\" y1=\"50\" x2=\"25\" y2=\"50\" />\n",
+       "  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n",
+       "  <line x1=\"0\" y1=\"62\" x2=\"25\" y2=\"62\" />\n",
+       "  <line x1=\"0\" y1=\"68\" x2=\"25\" y2=\"68\" />\n",
+       "  <line x1=\"0\" y1=\"74\" x2=\"25\" y2=\"74\" />\n",
+       "  <line x1=\"0\" y1=\"82\" x2=\"25\" y2=\"82\" />\n",
+       "  <line x1=\"0\" y1=\"88\" x2=\"25\" y2=\"88\" />\n",
+       "  <line x1=\"0\" y1=\"94\" x2=\"25\" y2=\"94\" />\n",
+       "  <line x1=\"0\" y1=\"100\" x2=\"25\" y2=\"100\" />\n",
+       "  <line x1=\"0\" y1=\"106\" x2=\"25\" y2=\"106\" />\n",
+       "  <line x1=\"0\" y1=\"113\" x2=\"25\" y2=\"113\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >10</text>\n",
+       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">78582</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>complex</span></div><div class='xr-var-dims'>(space, time)</div><div class='xr-var-dtype'>complex64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1000, 10), meta=np.ndarray&gt;</div><input id='attrs-49b51f29-06e9-49b8-afb1-eef3056635c3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-49b51f29-06e9-49b8-afb1-eef3056635c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58fc81bf-9b0b-455c-b865-eceb007dc25b' class='xr-var-data-in' type='checkbox'><label for='data-58fc81bf-9b0b-455c-b865-eceb007dc25b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 6.00 MiB </td>\n",
+       "                        <td> 78.12 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (78582, 10) </td>\n",
+       "                        <td> (1000, 10) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 79 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> complex64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"25\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"25\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"25\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"36\" x2=\"25\" y2=\"36\" />\n",
+       "  <line x1=\"0\" y1=\"44\" x2=\"25\" y2=\"44\" />\n",
+       "  <line x1=\"0\" y1=\"50\" x2=\"25\" y2=\"50\" />\n",
+       "  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n",
+       "  <line x1=\"0\" y1=\"62\" x2=\"25\" y2=\"62\" />\n",
+       "  <line x1=\"0\" y1=\"68\" x2=\"25\" y2=\"68\" />\n",
+       "  <line x1=\"0\" y1=\"74\" x2=\"25\" y2=\"74\" />\n",
+       "  <line x1=\"0\" y1=\"82\" x2=\"25\" y2=\"82\" />\n",
+       "  <line x1=\"0\" y1=\"88\" x2=\"25\" y2=\"88\" />\n",
+       "  <line x1=\"0\" y1=\"94\" x2=\"25\" y2=\"94\" />\n",
+       "  <line x1=\"0\" y1=\"100\" x2=\"25\" y2=\"100\" />\n",
+       "  <line x1=\"0\" y1=\"106\" x2=\"25\" y2=\"106\" />\n",
+       "  <line x1=\"0\" y1=\"113\" x2=\"25\" y2=\"113\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >10</text>\n",
+       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">78582</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>phase</span></div><div class='xr-var-dims'>(space, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1000, 10), meta=np.ndarray&gt;</div><input id='attrs-cf9e250d-89e3-4901-81ed-8853f1be16ae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf9e250d-89e3-4901-81ed-8853f1be16ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be26ba97-788d-429c-be59-f410f97ed62b' class='xr-var-data-in' type='checkbox'><label for='data-be26ba97-788d-429c-be59-f410f97ed62b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 3.00 MiB </td>\n",
+       "                        <td> 39.06 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (78582, 10) </td>\n",
+       "                        <td> (1000, 10) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 79 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"25\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"25\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"25\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"36\" x2=\"25\" y2=\"36\" />\n",
+       "  <line x1=\"0\" y1=\"44\" x2=\"25\" y2=\"44\" />\n",
+       "  <line x1=\"0\" y1=\"50\" x2=\"25\" y2=\"50\" />\n",
+       "  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n",
+       "  <line x1=\"0\" y1=\"62\" x2=\"25\" y2=\"62\" />\n",
+       "  <line x1=\"0\" y1=\"68\" x2=\"25\" y2=\"68\" />\n",
+       "  <line x1=\"0\" y1=\"74\" x2=\"25\" y2=\"74\" />\n",
+       "  <line x1=\"0\" y1=\"82\" x2=\"25\" y2=\"82\" />\n",
+       "  <line x1=\"0\" y1=\"88\" x2=\"25\" y2=\"88\" />\n",
+       "  <line x1=\"0\" y1=\"94\" x2=\"25\" y2=\"94\" />\n",
+       "  <line x1=\"0\" y1=\"100\" x2=\"25\" y2=\"100\" />\n",
+       "  <line x1=\"0\" y1=\"106\" x2=\"25\" y2=\"106\" />\n",
+       "  <line x1=\"0\" y1=\"113\" x2=\"25\" y2=\"113\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >10</text>\n",
+       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">78582</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-c72b475a-5708-4038-a935-5fd84c5f1622' class='xr-section-summary-in' type='checkbox'  ><label for='section-c72b475a-5708-4038-a935-5fd84c5f1622' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-10a7d0a8-facf-47c8-bab6-97cc57bc9c6b' class='xr-index-data-in' type='checkbox'/><label for='index-10a7d0a8-facf-47c8-bab6-97cc57bc9c6b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=&#x27;int64&#x27;, name=&#x27;time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aa11c2c7-9099-468e-8a1b-5025b159f6f8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aa11c2c7-9099-468e-8a1b-5025b159f6f8' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>multi-look :</span></dt><dd>coarsen-mean</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 14MB\n",
+       "Dimensions:    (space: 78582, time: 10)\n",
+       "Coordinates:\n",
+       "    azimuth    (space) int64 629kB dask.array<chunksize=(1000,), meta=np.ndarray>\n",
+       "    lat        (space) float32 314kB dask.array<chunksize=(1000,), meta=np.ndarray>\n",
+       "    lon        (space) float32 314kB dask.array<chunksize=(1000,), meta=np.ndarray>\n",
+       "    range      (space) int64 629kB dask.array<chunksize=(1000,), meta=np.ndarray>\n",
+       "  * time       (time) int64 80B 0 1 2 3 4 5 6 7 8 9\n",
+       "Dimensions without coordinates: space\n",
+       "Data variables:\n",
+       "    amplitude  (space, time) float32 3MB dask.array<chunksize=(1000, 10), meta=np.ndarray>\n",
+       "    complex    (space, time) complex64 6MB dask.array<chunksize=(1000, 10), meta=np.ndarray>\n",
+       "    phase      (space, time) float32 3MB dask.array<chunksize=(1000, 10), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    multi-look:  coarsen-mean"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load data.\n",
+    "stm_points = xr.open_zarr('../../stm.zarr')\n",
+    "stm_points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coordinates:\n",
+      "    azimuth  (space) int64 629kB dask.array<chunksize=(1000,), meta=np.ndarray>\n",
+      "    lat      (space) float32 314kB dask.array<chunksize=(1000,), meta=np.ndarray>\n",
+      "    lon      (space) float32 314kB dask.array<chunksize=(1000,), meta=np.ndarray>\n",
+      "    range    (space) int64 629kB dask.array<chunksize=(1000,), meta=np.ndarray>\n",
+      "  * time     (time) int64 80B 0 1 2 3 4 5 6 7 8 9\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(stm_points.coords)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stm_points_sparse = stm_points.isel(space=slice(0, 78000, 500))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7ff11dc7fad0>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(stm_points_sparse['lon'].values, stm_points_sparse['lat'].values, s=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "from pydepsi.network import generate_arcs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "156\n",
+      "442\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Generate a Delaunay network with long edges removed.\n",
+    "coordinates, arcs_delaunay = generate_arcs(stm_points_sparse, method='delaunay', max_length=0.05)\n",
+    "print(len(coordinates))\n",
+    "print(len(arcs_delaunay))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize results.\n",
+    "xx = np.hstack([coordinates[list(p),0] for p in arcs_delaunay])\n",
+    "yy = np.hstack([coordinates[list(p),1] for p in arcs_delaunay])\n",
+    "\n",
+    "for i in range(0, len(xx), 2):\n",
+    "    plt.plot(xx[i:i+2], yy[i:i+2], 'tab:blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "156\n",
+      "797\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Generate a 'redundant' network with long edges removed.\n",
+    "# A redundant network tries to create min_links arcs per node, spread evenly over num_partitions based on orientation.\n",
+    "coordinates, arcs_redundant = generate_arcs(stm_points_sparse, method='redundant', max_length=0.05, min_links=8, num_partitions=4)\n",
+    "print(len(coordinates))\n",
+    "print(len(arcs_redundant))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize results.\n",
+    "xx = np.hstack([coordinates[list(p),0] for p in arcs_redundant])\n",
+    "yy = np.hstack([coordinates[list(p),1] for p in arcs_redundant])\n",
+    "\n",
+    "for i in range(0, len(xx), 2):\n",
+    "    plt.plot(xx[i:i+2], yy[i:i+2], 'tab:blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pydepsi-dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pydepsi/network.py b/pydepsi/network.py
new file mode 100644
index 0000000..626d5fa
--- /dev/null
+++ b/pydepsi/network.py
@@ -0,0 +1,163 @@
+"""Module for creating networks from STM points."""
+
+import logging
+import math
+
+import numpy as np
+from scipy.spatial import Delaunay
+
+logger = logging.getLogger(__name__)
+
+
+def generate_arcs(stm_points, method="delaunay", x="lon", y="lat", max_length=None, min_links=12, num_partitions=8):
+    """Generate a network from a list of STM points.
+
+    The network is undirected and without self-loops.
+
+    Args:
+    ----
+        stm_points: Xarray.Dataset, input Space-Time Matrix.
+        method: method to form the network; either "delaunay" or "redundant".
+        x: str, first coordinate used to describe a point.
+        y: str, second coordinate used to describe a point.
+        max_length: float, maximum length of any generated arc or None.
+        min_links: int, minimum number of arcs per node, limited by max_length. Only used for the redundant method.
+        num_partitions: int, number of partitions to split the nodes into based on orientation from the current node.
+          Only used for the redundant method.
+
+    Returns:
+    -------
+        coordinates: list, [x, y] point coordinates extracted from stm_points.
+        arcs: list of pairs, point indices describing the adjacent nodes. The pairs are sorted, as is the list.
+    """
+    if method == "redundant":
+        if min_links <= 0:
+            logger.error(f"min_links must be strictly positive (currently: {min_links})")
+            return
+        if num_partitions <= 0:
+            logger.error(f"num_partitions must be strictly positive (currently: {num_partitions})")
+            return
+
+    # Collect point coordinates.
+    indexes = [stm_points[coord] for coord in [x, y]]
+    coordinates = np.column_stack(indexes)
+
+    arcs = None
+
+    # Create network arcs.
+    if method == "delaunay":
+        arcs = _generate_arcs_delaunay(coordinates, max_length)
+    elif method == "redundant":
+        arcs = _generate_arcs_redundant(coordinates, max_length, min_links, num_partitions)
+
+    return coordinates, arcs
+
+
+def _get_distance(s, t):
+    """Calculate the distance between two points.
+
+    Args:
+    ----
+        s: the source point.
+        t: the target point.
+
+    Returns:
+    -------
+        The distance between the two points.
+    """
+    # TODO(tvl) More complex distance functions can be implemented here.
+    #
+    # For example, network generation gives better results for square Euclidean distances.
+    # Non-square coordinate systems (like non-square image coordinates) may distort the circle properties of Delaunay
+    # networks into ellipses.
+    # Non-Euclidean coordinate systems (like angular lat-lon systems) may distort these same properties depending on
+    # the distance to a pole.
+    #
+    # Coordinate system transformations may be done on the STM before generating the network.
+    # However, there may be cases where it is impossible or undesirable to transform the point coordinates in the STM.
+    # In such a case, coordinates may be transformed inside this function.
+
+    return math.dist(s, t)
+
+
+def _generate_arcs_delaunay(coordinates, max_length=None):
+    # Create network and collect neighbors.
+    network = Delaunay(coordinates)
+    neighbors_ptr, neighbors_idx = network.vertex_neighbor_vertices
+
+    # Convert ptr and idx arrays into list of sorted index pairs.
+    arcs = []
+    for s in range(len(neighbors_ptr) - 1):
+        for t in range(neighbors_ptr[s], neighbors_ptr[s + 1]):
+            length = _get_distance(coordinates[int(s)], coordinates[neighbors_idx[t]])
+            if max_length is None or length <= max_length:
+                arcs.append(tuple(sorted([int(s), int(neighbors_idx[t])])))
+
+    # Remove duplicates and make the list canonical.
+    arcs = sorted(list(set(arcs)))
+
+    return arcs
+
+
+def _generate_arcs_redundant(coordinates, max_length=None, min_links=12, num_partitions=8):
+    # Create a network with at least min_links arcs per node.
+    # Arcs are created ordered by length.
+    # However, the orientations around the node are split into num_partitions partitions;
+    # each partition can only get an (x+1)th arc if every other partition either
+    # already has x arcs connected or already has all allowed arcs connected
+    # (e.g. there are no more nodes in that partition, or they are all too far away).
+    # Note that a node may get less than min_links arcs if there are not enough neighbors within max_length.
+
+    arcs = []
+    indices = range(len(coordinates))
+    for cur_index in indices:
+        # Calculate per node, the partition they are in and the distance from the current node.
+        partitions = [
+            int(math.floor(num_partitions * (0.5 + math.atan2(coordinate[1], coordinate[0]) / math.tau)))
+            for coordinate in coordinates - coordinates[cur_index]
+        ]
+        partitions[cur_index] = num_partitions + 1  # Separate the current node into its own partition.
+        distances = [_get_distance(coordinates[cur_index], coordinate) for coordinate in coordinates]
+
+        # Create a list of tuples with the partition, distance, and index, sorted by partition and then distance.
+        values = np.array(sorted(list(zip(partitions, distances, indices, strict=False))))
+
+        # Collect the nearest min_links neighbors per partition and discard the partition of the current node.
+        partitions_diff = values[1:, 0] - values[:-1, 0]
+        separators = np.where(partitions_diff > 0)[0]
+        partitions = np.split(values, separators + 1)
+        partitions = partitions[: len(partitions) - 1]
+        partitions = [partition[:min_links] for partition in partitions]
+
+        # Collect the neighbor 'hierarchies'.
+        # Each hierarchy contains the nth nearest neighbor from all partitions.
+        neighbor_hierarchies = [[] for _ in range(min_links)]
+        count = 0
+        for n in range(min_links):
+            # Break early if we have gathered enough neighbors.
+            if min_links <= count:
+                break
+            for partition in partitions:
+                # Note that we do not break inside this loop,
+                # because we want the nth nearest neighbors from all partitions.
+                if n < len(partition) and (max_length is None or partition[n][1] <= max_length):
+                    neighbor_hierarchies[n].append(partition[n])
+                    count = count + 1
+
+        # Sort hierarchies per partition by distance to the current node.
+        neighbor_hierarchies = [
+            sorted(hierarchy, key=lambda x: x[1]) for hierarchy in neighbor_hierarchies if len(hierarchy) != 0
+        ]
+
+        # Add sorted arcs to at least min_links neighbors.
+        cur_arcs = [
+            tuple(sorted([cur_index, int(neighbor[2])])) for hierarchy in neighbor_hierarchies for neighbor in hierarchy
+        ]
+        cur_arcs = cur_arcs[:min_links]
+
+        arcs.extend(cur_arcs)
+
+    # Remove duplicates and make the list canonical.
+    arcs = sorted(list(set(arcs)))
+
+    return arcs
diff --git a/tests/data/stm_sparse.zarr/.zattrs b/tests/data/stm_sparse.zarr/.zattrs
new file mode 100644
index 0000000..bed8730
--- /dev/null
+++ b/tests/data/stm_sparse.zarr/.zattrs
@@ -0,0 +1,3 @@
+{
+    "multi-look": "coarsen-mean"
+}
\ No newline at end of file
diff --git a/tests/data/stm_sparse.zarr/.zgroup b/tests/data/stm_sparse.zarr/.zgroup
new file mode 100644
index 0000000..3b7daf2
--- /dev/null
+++ b/tests/data/stm_sparse.zarr/.zgroup
@@ -0,0 +1,3 @@
+{
+    "zarr_format": 2
+}
\ No newline at end of file
diff --git a/tests/data/stm_sparse.zarr/.zmetadata b/tests/data/stm_sparse.zarr/.zmetadata
new file mode 100644
index 0000000..c5c43c5
--- /dev/null
+++ b/tests/data/stm_sparse.zarr/.zmetadata
@@ -0,0 +1,223 @@
+{
+    "metadata": {
+        ".zattrs": {
+            "multi-look": "coarsen-mean"
+        },
+        ".zgroup": {
+            "zarr_format": 2
+        },
+        "amplitude/.zarray": {
+            "chunks": [
+                156,
+                10
+            ],
+            "compressor": {
+                "blocksize": 0,
+                "clevel": 5,
+                "cname": "lz4",
+                "id": "blosc",
+                "shuffle": 1
+            },
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+            "filters": null,
+            "order": "C",
+            "shape": [
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+                10
+            ],
+            "zarr_format": 2
+        },
+        "amplitude/.zattrs": {
+            "_ARRAY_DIMENSIONS": [
+                "space",
+                "time"
+            ],
+            "coordinates": "azimuth lat lon range"
+        },
+        "azimuth/.zarray": {
+            "chunks": [
+                156
+            ],
+            "compressor": {
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+                "clevel": 5,
+                "cname": "lz4",
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+                "shuffle": 1
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+            "filters": null,
+            "order": "C",
+            "shape": [
+                156
+            ],
+            "zarr_format": 2
+        },
+        "azimuth/.zattrs": {
+            "_ARRAY_DIMENSIONS": [
+                "space"
+            ]
+        },
+        "complex/.zarray": {
+            "chunks": [
+                156,
+                10
+            ],
+            "compressor": {
+                "blocksize": 0,
+                "clevel": 5,
+                "cname": "lz4",
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+            "filters": null,
+            "order": "C",
+            "shape": [
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+                10
+            ],
+            "zarr_format": 2
+        },
+        "complex/.zattrs": {
+            "_ARRAY_DIMENSIONS": [
+                "space",
+                "time"
+            ],
+            "coordinates": "azimuth lat lon range"
+        },
+        "lat/.zarray": {
+            "chunks": [
+                156
+            ],
+            "compressor": {
+                "blocksize": 0,
+                "clevel": 5,
+                "cname": "lz4",
+                "id": "blosc",
+                "shuffle": 1
+            },
+            "dtype": "<f4",
+            "fill_value": "NaN",
+            "filters": null,
+            "order": "C",
+            "shape": [
+                156
+            ],
+            "zarr_format": 2
+        },
+        "lat/.zattrs": {
+            "_ARRAY_DIMENSIONS": [
+                "space"
+            ]
+        },
+        "lon/.zarray": {
+            "chunks": [
+                156
+            ],
+            "compressor": {
+                "blocksize": 0,
+                "clevel": 5,
+                "cname": "lz4",
+                "id": "blosc",
+                "shuffle": 1
+            },
+            "dtype": "<f4",
+            "fill_value": "NaN",
+            "filters": null,
+            "order": "C",
+            "shape": [
+                156
+            ],
+            "zarr_format": 2
+        },
+        "lon/.zattrs": {
+            "_ARRAY_DIMENSIONS": [
+                "space"
+            ]
+        },
+        "phase/.zarray": {
+            "chunks": [
+                156,
+                10
+            ],
+            "compressor": {
+                "blocksize": 0,
+                "clevel": 5,
+                "cname": "lz4",
+                "id": "blosc",
+                "shuffle": 1
+            },
+            "dtype": "<f4",
+            "fill_value": "NaN",
+            "filters": null,
+            "order": "C",
+            "shape": [
+                156,
+                10
+            ],
+            "zarr_format": 2
+        },
+        "phase/.zattrs": {
+            "_ARRAY_DIMENSIONS": [
+                "space",
+                "time"
+            ],
+            "coordinates": "azimuth lat lon range"
+        },
+        "range/.zarray": {
+            "chunks": [
+                156
+            ],
+            "compressor": {
+                "blocksize": 0,
+                "clevel": 5,
+                "cname": "lz4",
+                "id": "blosc",
+                "shuffle": 1
+            },
+            "dtype": "<i8",
+            "fill_value": null,
+            "filters": null,
+            "order": "C",
+            "shape": [
+                156
+            ],
+            "zarr_format": 2
+        },
+        "range/.zattrs": {
+            "_ARRAY_DIMENSIONS": [
+                "space"
+            ]
+        },
+        "time/.zarray": {
+            "chunks": [
+                10
+            ],
+            "compressor": {
+                "blocksize": 0,
+                "clevel": 5,
+                "cname": "lz4",
+                "id": "blosc",
+                "shuffle": 1
+            },
+            "dtype": "<i8",
+            "fill_value": null,
+            "filters": null,
+            "order": "C",
+            "shape": [
+                10
+            ],
+            "zarr_format": 2
+        },
+        "time/.zattrs": {
+            "_ARRAY_DIMENSIONS": [
+                "time"
+            ]
+        }
+    },
+    "zarr_consolidated_format": 1
+}
\ No newline at end of file
diff --git a/tests/data/stm_sparse.zarr/amplitude/.zarray b/tests/data/stm_sparse.zarr/amplitude/.zarray
new file mode 100644
index 0000000..2ed23d0
--- /dev/null
+++ b/tests/data/stm_sparse.zarr/amplitude/.zarray
@@ -0,0 +1,22 @@
+{
+    "chunks": [
+        156,
+        10
+    ],
+    "compressor": {
+        "blocksize": 0,
+        "clevel": 5,
+        "cname": "lz4",
+        "id": "blosc",
+        "shuffle": 1
+    },
+    "dtype": "<f4",
+    "fill_value": "NaN",
+    "filters": null,
+    "order": "C",
+    "shape": [
+        156,
+        10
+    ],
+    "zarr_format": 2
+}
\ No newline at end of file
diff --git a/tests/data/stm_sparse.zarr/amplitude/.zattrs b/tests/data/stm_sparse.zarr/amplitude/.zattrs
new file mode 100644
index 0000000..6f2f543
--- /dev/null
+++ b/tests/data/stm_sparse.zarr/amplitude/.zattrs
@@ -0,0 +1,7 @@
+{
+    "_ARRAY_DIMENSIONS": [
+        "space",
+        "time"
+    ],
+    "coordinates": "azimuth lat lon range"
+}
\ No newline at end of file
diff --git a/tests/data/stm_sparse.zarr/amplitude/0.0 b/tests/data/stm_sparse.zarr/amplitude/0.0
new file mode 100644
index 0000000..fbdee9b
Binary files /dev/null and b/tests/data/stm_sparse.zarr/amplitude/0.0 differ
diff --git a/tests/data/stm_sparse.zarr/azimuth/.zarray b/tests/data/stm_sparse.zarr/azimuth/.zarray
new file mode 100644
index 0000000..25f0c4a
--- /dev/null
+++ b/tests/data/stm_sparse.zarr/azimuth/.zarray
@@ -0,0 +1,20 @@
+{
+    "chunks": [
+        156
+    ],
+    "compressor": {
+        "blocksize": 0,
+        "clevel": 5,
+        "cname": "lz4",
+        "id": "blosc",
+        "shuffle": 1
+    },
+    "dtype": "<i8",
+    "fill_value": null,
+    "filters": null,
+    "order": "C",
+    "shape": [
+        156
+    ],
+    "zarr_format": 2
+}
\ No newline at end of file
diff --git a/tests/data/stm_sparse.zarr/azimuth/.zattrs b/tests/data/stm_sparse.zarr/azimuth/.zattrs
new file mode 100644
index 0000000..a7e8266
--- /dev/null
+++ b/tests/data/stm_sparse.zarr/azimuth/.zattrs
@@ -0,0 +1,5 @@
+{
+    "_ARRAY_DIMENSIONS": [
+        "space"
+    ]
+}
\ No newline at end of file
diff --git a/tests/data/stm_sparse.zarr/azimuth/0 b/tests/data/stm_sparse.zarr/azimuth/0
new file mode 100644
index 0000000..a24b93c
Binary files /dev/null and b/tests/data/stm_sparse.zarr/azimuth/0 differ
diff --git a/tests/data/stm_sparse.zarr/complex/.zarray b/tests/data/stm_sparse.zarr/complex/.zarray
new file mode 100644
index 0000000..76292e0
--- /dev/null
+++ b/tests/data/stm_sparse.zarr/complex/.zarray
@@ -0,0 +1,22 @@
+{
+    "chunks": [
+        156,
+        10
+    ],
+    "compressor": {
+        "blocksize": 0,
+        "clevel": 5,
+        "cname": "lz4",
+        "id": "blosc",
+        "shuffle": 1
+    },
+    "dtype": "<c8",
+    "fill_value": null,
+    "filters": null,
+    "order": "C",
+    "shape": [
+        156,
+        10
+    ],
+    "zarr_format": 2
+}
\ No newline at end of file
diff --git a/tests/data/stm_sparse.zarr/complex/.zattrs b/tests/data/stm_sparse.zarr/complex/.zattrs
new file mode 100644
index 0000000..6f2f543
--- /dev/null
+++ b/tests/data/stm_sparse.zarr/complex/.zattrs
@@ -0,0 +1,7 @@
+{
+    "_ARRAY_DIMENSIONS": [
+        "space",
+        "time"
+    ],
+    "coordinates": "azimuth lat lon range"
+}
\ No newline at end of file
diff --git a/tests/data/stm_sparse.zarr/complex/0.0 b/tests/data/stm_sparse.zarr/complex/0.0
new file mode 100644
index 0000000..55c7141
Binary files /dev/null and b/tests/data/stm_sparse.zarr/complex/0.0 differ
diff --git a/tests/data/stm_sparse.zarr/lat/.zarray b/tests/data/stm_sparse.zarr/lat/.zarray
new file mode 100644
index 0000000..39360c0
--- /dev/null
+++ b/tests/data/stm_sparse.zarr/lat/.zarray
@@ -0,0 +1,20 @@
+{
+    "chunks": [
+        156
+    ],
+    "compressor": {
+        "blocksize": 0,
+        "clevel": 5,
+        "cname": "lz4",
+        "id": "blosc",
+        "shuffle": 1
+    },
+    "dtype": "<f4",
+    "fill_value": "NaN",
+    "filters": null,
+    "order": "C",
+    "shape": [
+        156
+    ],
+    "zarr_format": 2
+}
\ No newline at end of file
diff --git a/tests/data/stm_sparse.zarr/lat/.zattrs b/tests/data/stm_sparse.zarr/lat/.zattrs
new file mode 100644
index 0000000..a7e8266
--- /dev/null
+++ b/tests/data/stm_sparse.zarr/lat/.zattrs
@@ -0,0 +1,5 @@
+{
+    "_ARRAY_DIMENSIONS": [
+        "space"
+    ]
+}
\ No newline at end of file
diff --git a/tests/data/stm_sparse.zarr/lat/0 b/tests/data/stm_sparse.zarr/lat/0
new file mode 100644
index 0000000..e6a24dc
Binary files /dev/null and b/tests/data/stm_sparse.zarr/lat/0 differ
diff --git a/tests/data/stm_sparse.zarr/lon/.zarray b/tests/data/stm_sparse.zarr/lon/.zarray
new file mode 100644
index 0000000..39360c0
--- /dev/null
+++ b/tests/data/stm_sparse.zarr/lon/.zarray
@@ -0,0 +1,20 @@
+{
+    "chunks": [
+        156
+    ],
+    "compressor": {
+        "blocksize": 0,
+        "clevel": 5,
+        "cname": "lz4",
+        "id": "blosc",
+        "shuffle": 1
+    },
+    "dtype": "<f4",
+    "fill_value": "NaN",
+    "filters": null,
+    "order": "C",
+    "shape": [
+        156
+    ],
+    "zarr_format": 2
+}
\ No newline at end of file
diff --git a/tests/data/stm_sparse.zarr/lon/.zattrs b/tests/data/stm_sparse.zarr/lon/.zattrs
new file mode 100644
index 0000000..a7e8266
--- /dev/null
+++ b/tests/data/stm_sparse.zarr/lon/.zattrs
@@ -0,0 +1,5 @@
+{
+    "_ARRAY_DIMENSIONS": [
+        "space"
+    ]
+}
\ No newline at end of file
diff --git a/tests/data/stm_sparse.zarr/lon/0 b/tests/data/stm_sparse.zarr/lon/0
new file mode 100644
index 0000000..7dbbcc3
Binary files /dev/null and b/tests/data/stm_sparse.zarr/lon/0 differ
diff --git a/tests/data/stm_sparse.zarr/phase/.zarray b/tests/data/stm_sparse.zarr/phase/.zarray
new file mode 100644
index 0000000..2ed23d0
--- /dev/null
+++ b/tests/data/stm_sparse.zarr/phase/.zarray
@@ -0,0 +1,22 @@
+{
+    "chunks": [
+        156,
+        10
+    ],
+    "compressor": {
+        "blocksize": 0,
+        "clevel": 5,
+        "cname": "lz4",
+        "id": "blosc",
+        "shuffle": 1
+    },
+    "dtype": "<f4",
+    "fill_value": "NaN",
+    "filters": null,
+    "order": "C",
+    "shape": [
+        156,
+        10
+    ],
+    "zarr_format": 2
+}
\ No newline at end of file
diff --git a/tests/data/stm_sparse.zarr/phase/.zattrs b/tests/data/stm_sparse.zarr/phase/.zattrs
new file mode 100644
index 0000000..6f2f543
--- /dev/null
+++ b/tests/data/stm_sparse.zarr/phase/.zattrs
@@ -0,0 +1,7 @@
+{
+    "_ARRAY_DIMENSIONS": [
+        "space",
+        "time"
+    ],
+    "coordinates": "azimuth lat lon range"
+}
\ No newline at end of file
diff --git a/tests/data/stm_sparse.zarr/phase/0.0 b/tests/data/stm_sparse.zarr/phase/0.0
new file mode 100644
index 0000000..833ab15
Binary files /dev/null and b/tests/data/stm_sparse.zarr/phase/0.0 differ
diff --git a/tests/data/stm_sparse.zarr/range/.zarray b/tests/data/stm_sparse.zarr/range/.zarray
new file mode 100644
index 0000000..25f0c4a
--- /dev/null
+++ b/tests/data/stm_sparse.zarr/range/.zarray
@@ -0,0 +1,20 @@
+{
+    "chunks": [
+        156
+    ],
+    "compressor": {
+        "blocksize": 0,
+        "clevel": 5,
+        "cname": "lz4",
+        "id": "blosc",
+        "shuffle": 1
+    },
+    "dtype": "<i8",
+    "fill_value": null,
+    "filters": null,
+    "order": "C",
+    "shape": [
+        156
+    ],
+    "zarr_format": 2
+}
\ No newline at end of file
diff --git a/tests/data/stm_sparse.zarr/range/.zattrs b/tests/data/stm_sparse.zarr/range/.zattrs
new file mode 100644
index 0000000..a7e8266
--- /dev/null
+++ b/tests/data/stm_sparse.zarr/range/.zattrs
@@ -0,0 +1,5 @@
+{
+    "_ARRAY_DIMENSIONS": [
+        "space"
+    ]
+}
\ No newline at end of file
diff --git a/tests/data/stm_sparse.zarr/range/0 b/tests/data/stm_sparse.zarr/range/0
new file mode 100644
index 0000000..02c2933
Binary files /dev/null and b/tests/data/stm_sparse.zarr/range/0 differ
diff --git a/tests/data/stm_sparse.zarr/time/.zarray b/tests/data/stm_sparse.zarr/time/.zarray
new file mode 100644
index 0000000..f24b055
--- /dev/null
+++ b/tests/data/stm_sparse.zarr/time/.zarray
@@ -0,0 +1,20 @@
+{
+    "chunks": [
+        10
+    ],
+    "compressor": {
+        "blocksize": 0,
+        "clevel": 5,
+        "cname": "lz4",
+        "id": "blosc",
+        "shuffle": 1
+    },
+    "dtype": "<i8",
+    "fill_value": null,
+    "filters": null,
+    "order": "C",
+    "shape": [
+        10
+    ],
+    "zarr_format": 2
+}
\ No newline at end of file
diff --git a/tests/data/stm_sparse.zarr/time/.zattrs b/tests/data/stm_sparse.zarr/time/.zattrs
new file mode 100644
index 0000000..5a25782
--- /dev/null
+++ b/tests/data/stm_sparse.zarr/time/.zattrs
@@ -0,0 +1,5 @@
+{
+    "_ARRAY_DIMENSIONS": [
+        "time"
+    ]
+}
\ No newline at end of file
diff --git a/tests/data/stm_sparse.zarr/time/0 b/tests/data/stm_sparse.zarr/time/0
new file mode 100644
index 0000000..db12d08
Binary files /dev/null and b/tests/data/stm_sparse.zarr/time/0 differ
diff --git a/tests/test_network.py b/tests/test_network.py
new file mode 100644
index 0000000..b8e0806
--- /dev/null
+++ b/tests/test_network.py
@@ -0,0 +1,39 @@
+"""test_network.py"""
+
+import pytest
+import xarray as xr
+
+from pydepsi.network import generate_arcs
+
+
+@pytest.fixture
+def stm_sparse():
+    # A sparse STM.
+    return xr.open_zarr("tests/data/stm_sparse.zarr")
+
+
+class TestNetwork:
+    def test_generate_arcs_fail(self, stm_sparse):
+        # Test min_links must be strictly positive.
+        result = generate_arcs(stm_sparse, method="redundant", min_links=0)
+        assert result is None
+
+        # Test num_partitions must be strictly positive.
+        result = generate_arcs(stm_sparse, method="redundant", num_partitions=0)
+        assert result is None
+
+    def test_generate_arcs_delaunay(self, stm_sparse):
+        # Generate a Delaunay network with long edges removed.
+        coordinates, arcs = generate_arcs(stm_sparse, method="delaunay", max_length=0.05)
+
+        assert len(coordinates) == 156
+        assert len(arcs) == 442
+
+    def test_generate_arcs_redundant(self, stm_sparse):
+        # Generate a 'redundant' network with long edges removed.
+        coordinates, arcs = generate_arcs(
+            stm_sparse, method="redundant", max_length=0.05, min_links=8, num_partitions=4
+        )
+
+        assert len(coordinates) == 156
+        assert len(arcs) == 797