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   "id": "9fa1910a-6db7-4592-986c-e80ac015ac26",
   "metadata": {},
   "source": [
    "# Calculate distances and find neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a293cd46-83e6-4d73-adcc-ee9a6724739f",
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    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "\n",
    "import poligrain as plg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6c19b3-d6a9-4669-b7c8-53894c1ceee6",
   "metadata": {},
   "source": [
    "## Point-to-point distances"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19f3e1b8-51ad-4a72-8413-7cb619c9dee3",
   "metadata": {},
   "source": [
    "### Get PWS dataset"
   ]
  },
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   "execution_count": 2,
   "id": "df67e076-5ca4-43b2-92dc-552d19efc32a",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\n",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
      "\n",
      " 67 10.7M   67 7419k    0     0  14.4M      0 --:--:-- --:--:-- --:--:-- 14.4M\n",
      "100 10.7M  100 10.7M    0     0  16.0M      0 --:--:-- --:--:-- --:--:-- 20.6M\n"
     ]
    }
   ],
   "source": [
    "!curl -OL https://github.com/OpenSenseAction/training_school_opensene_2023/raw/main/data/pws/data_PWS_netCDF_AMS_float.nc"
   ]
  },
  {
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   "execution_count": 3,
   "id": "61b86248-8c91-4a9d-ba26-a3e168d31945",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_pws = xr.open_dataset(\"data_PWS_netCDF_AMS_float.nc\")\n",
    "\n",
    "# fix some issues with this dataset\n",
    "ds_pws[\"time\"] = pd.to_datetime(ds_pws.time.data, unit=\"s\")\n",
    "ds_pws[\"lon\"] = (\"id\", ds_pws.lon.data)\n",
    "ds_pws[\"lat\"] = (\"id\", ds_pws.lat.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf4ef4f3-640a-4c9c-b32f-8af551ee0821",
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   "source": [
    "### Project cooridnates from lon-lat to UTM zone for Europe\n",
    "\n",
    "To do meaningful distance calculations we have to project the lon-lat coordinates first.\n",
    "\n",
    "Info on the UTM zone 32N projection `'EPSG:25832'` that is used can be found [here](https://epsg.io/25832#google_vignette)\n",
    "\n",
    "Note that `plg.spatial.project_coordinates` can also use a different source coordinate system, but its default is WGS 84 (lon and lat in degrees), which is `'EPSG:4326'` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5040ff46-0608-4496-8e08-60ceb42c3464",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_pws.coords[\"x\"], ds_pws.coords[\"y\"] = plg.spatial.project_point_coordinates(\n",
    "    ds_pws.lon, ds_pws.lat, \"EPSG:25832\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "89ea4f3f-e2c2-46ff-885c-e518170ef69b",
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   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 237MB\n",
       "Dimensions:    (time: 219168, id: 134)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2MB 2016-05-01T00:05:00 ... 2018-06-01\n",
       "  * id         (id) &lt;U6 3kB &#x27;ams1&#x27; &#x27;ams2&#x27; &#x27;ams3&#x27; ... &#x27;ams132&#x27; &#x27;ams133&#x27; &#x27;ams134&#x27;\n",
       "    elevation  (id) &lt;U3 2kB ...\n",
       "    lat        (id) float64 1kB 52.31 52.3 52.31 52.35 ... 52.43 52.3 52.26\n",
       "    lon        (id) float64 1kB 4.671 4.675 4.677 4.678 ... 5.036 5.041 5.045\n",
       "    x          (id) float64 1kB 2.049e+05 2.052e+05 ... 2.301e+05 2.301e+05\n",
       "    y          (id) float64 1kB 5.804e+06 5.803e+06 ... 5.802e+06 5.798e+06\n",
       "Data variables:\n",
       "    rainfall   (id, time) float64 235MB ...\n",
       "Attributes:\n",
       "    title:         PWS data from Amsterdam\n",
       "    institution:   Wageningen University and Research, Department of Environm...\n",
       "    history:       Test Version 0.1\n",
       "    references:    https://doi.org/10.1029/2019GL083731\n",
       "    date_created:  2022-10-18 10:32:00\n",
       "    Conventions:   OPENSENSE V0\n",
       "    location:      Amsterdam Metropolitan Area\n",
       "    source:        Netamo\n",
       "    comment:       </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-fcf53fb0-4475-422b-bf75-ef558e29b61c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fcf53fb0-4475-422b-bf75-ef558e29b61c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 219168</li><li><span class='xr-has-index'>id</span>: 134</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-942a6852-5af3-458e-9d37-343568a2864f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-942a6852-5af3-458e-9d37-343568a2864f' class='xr-section-summary' >Coordinates: <span>(7)</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 class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-05-01T00:05:00 ... 2018-06-01</div><input id='attrs-eb86f8a9-76eb-4427-ba0f-927e94912f23' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb86f8a9-76eb-4427-ba0f-927e94912f23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3accdbe5-d902-41e5-b705-5ea333c98923' class='xr-var-data-in' type='checkbox'><label for='data-3accdbe5-d902-41e5-b705-5ea333c98923' 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([&#x27;2016-05-01T00:05:00.000000000&#x27;, &#x27;2016-05-01T00:10:00.000000000&#x27;,\n",
       "       &#x27;2016-05-01T00:15:00.000000000&#x27;, ..., &#x27;2018-05-31T23:50:00.000000000&#x27;,\n",
       "       &#x27;2018-05-31T23:55:00.000000000&#x27;, &#x27;2018-06-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>id</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;ams1&#x27; &#x27;ams2&#x27; ... &#x27;ams133&#x27; &#x27;ams134&#x27;</div><input id='attrs-09558ead-435c-4e30-a919-58452b7c0c08' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09558ead-435c-4e30-a919-58452b7c0c08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97f93b36-f4e2-4ead-815a-c0fd5a35cbaa' class='xr-var-data-in' type='checkbox'><label for='data-97f93b36-f4e2-4ead-815a-c0fd5a35cbaa' 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([&#x27;ams1&#x27;, &#x27;ams2&#x27;, &#x27;ams3&#x27;, &#x27;ams4&#x27;, &#x27;ams5&#x27;, &#x27;ams6&#x27;, &#x27;ams7&#x27;, &#x27;ams8&#x27;, &#x27;ams9&#x27;,\n",
       "       &#x27;ams10&#x27;, &#x27;ams11&#x27;, &#x27;ams12&#x27;, &#x27;ams13&#x27;, &#x27;ams14&#x27;, &#x27;ams15&#x27;, &#x27;ams16&#x27;, &#x27;ams17&#x27;,\n",
       "       &#x27;ams18&#x27;, &#x27;ams19&#x27;, &#x27;ams20&#x27;, &#x27;ams21&#x27;, &#x27;ams22&#x27;, &#x27;ams23&#x27;, &#x27;ams24&#x27;, &#x27;ams25&#x27;,\n",
       "       &#x27;ams26&#x27;, &#x27;ams27&#x27;, &#x27;ams28&#x27;, &#x27;ams29&#x27;, &#x27;ams30&#x27;, &#x27;ams31&#x27;, &#x27;ams32&#x27;, &#x27;ams33&#x27;,\n",
       "       &#x27;ams34&#x27;, &#x27;ams35&#x27;, &#x27;ams36&#x27;, &#x27;ams37&#x27;, &#x27;ams38&#x27;, &#x27;ams39&#x27;, &#x27;ams40&#x27;, &#x27;ams41&#x27;,\n",
       "       &#x27;ams42&#x27;, &#x27;ams43&#x27;, &#x27;ams44&#x27;, &#x27;ams45&#x27;, &#x27;ams46&#x27;, &#x27;ams47&#x27;, &#x27;ams48&#x27;, &#x27;ams49&#x27;,\n",
       "       &#x27;ams50&#x27;, &#x27;ams51&#x27;, &#x27;ams52&#x27;, &#x27;ams53&#x27;, &#x27;ams54&#x27;, &#x27;ams55&#x27;, &#x27;ams56&#x27;, &#x27;ams57&#x27;,\n",
       "       &#x27;ams58&#x27;, &#x27;ams59&#x27;, &#x27;ams60&#x27;, &#x27;ams61&#x27;, &#x27;ams62&#x27;, &#x27;ams63&#x27;, &#x27;ams64&#x27;, &#x27;ams65&#x27;,\n",
       "       &#x27;ams66&#x27;, &#x27;ams67&#x27;, &#x27;ams68&#x27;, &#x27;ams69&#x27;, &#x27;ams70&#x27;, &#x27;ams71&#x27;, &#x27;ams72&#x27;, &#x27;ams73&#x27;,\n",
       "       &#x27;ams74&#x27;, &#x27;ams75&#x27;, &#x27;ams76&#x27;, &#x27;ams77&#x27;, &#x27;ams78&#x27;, &#x27;ams79&#x27;, &#x27;ams80&#x27;, &#x27;ams81&#x27;,\n",
       "       &#x27;ams82&#x27;, &#x27;ams83&#x27;, &#x27;ams84&#x27;, &#x27;ams85&#x27;, &#x27;ams86&#x27;, &#x27;ams87&#x27;, &#x27;ams88&#x27;, &#x27;ams89&#x27;,\n",
       "       &#x27;ams90&#x27;, &#x27;ams91&#x27;, &#x27;ams92&#x27;, &#x27;ams93&#x27;, &#x27;ams94&#x27;, &#x27;ams95&#x27;, &#x27;ams96&#x27;, &#x27;ams97&#x27;,\n",
       "       &#x27;ams98&#x27;, &#x27;ams99&#x27;, &#x27;ams100&#x27;, &#x27;ams101&#x27;, &#x27;ams102&#x27;, &#x27;ams103&#x27;, &#x27;ams104&#x27;,\n",
       "       &#x27;ams105&#x27;, &#x27;ams106&#x27;, &#x27;ams107&#x27;, &#x27;ams108&#x27;, &#x27;ams109&#x27;, &#x27;ams110&#x27;, &#x27;ams111&#x27;,\n",
       "       &#x27;ams112&#x27;, &#x27;ams113&#x27;, &#x27;ams114&#x27;, &#x27;ams115&#x27;, &#x27;ams116&#x27;, &#x27;ams117&#x27;, &#x27;ams118&#x27;,\n",
       "       &#x27;ams119&#x27;, &#x27;ams120&#x27;, &#x27;ams121&#x27;, &#x27;ams122&#x27;, &#x27;ams123&#x27;, &#x27;ams124&#x27;, &#x27;ams125&#x27;,\n",
       "       &#x27;ams126&#x27;, &#x27;ams127&#x27;, &#x27;ams128&#x27;, &#x27;ams129&#x27;, &#x27;ams130&#x27;, &#x27;ams131&#x27;, &#x27;ams132&#x27;,\n",
       "       &#x27;ams133&#x27;, &#x27;ams134&#x27;], dtype=&#x27;&lt;U6&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-333ca976-ae6f-4c60-8127-cc4b0f19a8bf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-333ca976-ae6f-4c60-8127-cc4b0f19a8bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cc5d40d-c28a-4358-9ed5-894dbb69129c' class='xr-var-data-in' type='checkbox'><label for='data-3cc5d40d-c28a-4358-9ed5-894dbb69129c' 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>[134 values with dtype=&lt;U3]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>52.31 52.3 52.31 ... 52.3 52.26</div><input id='attrs-7b40c65a-e6e7-4d59-81d4-4602da804f67' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b40c65a-e6e7-4d59-81d4-4602da804f67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd5ecc3a-6b46-4bbf-83bb-2cfc24d31a3f' class='xr-var-data-in' type='checkbox'><label for='data-cd5ecc3a-6b46-4bbf-83bb-2cfc24d31a3f' 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([52.305896  , 52.302044  , 52.308427  , 52.3460125 , 52.3185718 ,\n",
       "       52.29778   , 52.296925  , 52.2934974 , 52.29419571, 52.25049431,\n",
       "       52.24811396, 52.26693085, 52.260747  , 52.38318474, 52.2609028 ,\n",
       "       52.3795406 , 52.3742245 , 52.385828  , 52.351625  , 52.27627871,\n",
       "       52.25582208, 52.43208637, 52.34437   , 52.279142  , 52.36049   ,\n",
       "       52.277776  , 52.37934869, 52.3554281 , 52.43358697, 52.355287  ,\n",
       "       52.35273245, 52.3456794 , 52.355926  , 52.27835152, 52.27971491,\n",
       "       52.283123  , 52.436889  , 52.431829  , 52.28069   , 52.2926813 ,\n",
       "       52.3876584 , 52.36617   , 52.3155607 , 52.3561545 , 52.353684  ,\n",
       "       52.35675837, 52.34473808, 52.344777  , 52.324336  , 52.311689  ,\n",
       "       52.3743658 , 52.3267374 , 52.311614  , 52.368619  , 52.365304  ,\n",
       "       52.3114207 , 52.3360639 , 52.36694431, 52.345668  , 52.361872  ,\n",
       "       52.3774156 , 52.3544442 , 52.37659878, 52.30656433, 52.347122  ,\n",
       "       52.347146  , 52.364318  , 52.420592  , 52.3922401 , 52.37126378,\n",
       "       52.384832  , 52.2956804 , 52.3383171 , 52.3702157 , 52.304167  ,\n",
       "       52.3477135 , 52.3474113 , 52.35736482, 52.42248595, 52.3630175 ,\n",
       "       52.296284  , 52.430344  , 52.37586384, 52.385221  , 52.40346573,\n",
       "       52.352911  , 52.313202  , 52.4287962 , 52.291897  , 52.291065  ,\n",
       "       52.3438503 , 52.2958859 , 52.342303  , 52.370533  , 52.29106666,\n",
       "       52.38675   , 52.389641  , 52.29942216, 52.356894  , 52.42842   ,\n",
       "       52.349484  , 52.37919928, 52.360095  , 52.35515544, 52.37839   ,\n",
       "       52.36428   , 52.39224409, 52.40145   , 52.3182354 , 52.3340836 ,\n",
       "       52.33424189, 52.3416245 , 52.33705723, 52.33636389, 52.337291  ,\n",
       "       52.271513  , 52.3819577 , 52.269474  , 52.334801  , 52.353509  ,\n",
       "       52.300295  , 52.2996697 , 52.32541   , 52.26782176, 52.326555  ,\n",
       "       52.36057803, 52.247674  , 52.427009  , 52.262895  , 52.313112  ,\n",
       "       52.30725   , 52.4314243 , 52.301688  , 52.2634545 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.671 4.675 4.677 ... 5.041 5.045</div><input id='attrs-84d0f25f-fc33-4954-a934-a496ee71c436' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-84d0f25f-fc33-4954-a934-a496ee71c436' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d602a803-1aeb-481c-beaf-cfa75bc2c449' class='xr-var-data-in' type='checkbox'><label for='data-d602a803-1aeb-481c-beaf-cfa75bc2c449' 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([4.670664  , 4.67494   , 4.677275  , 4.6775537 , 4.6785928 ,\n",
       "       4.678602  , 4.678811  , 4.6914236 , 4.69311403, 4.69746144,\n",
       "       4.70325708, 4.71668363, 4.721024  , 4.7266384 , 4.7294838 ,\n",
       "       4.7443698 , 4.7464102 , 4.750101  , 4.756874  , 4.76169092,\n",
       "       4.76310423, 4.77926731, 4.78131   , 4.783426  , 4.789773  ,\n",
       "       4.795101  , 4.80228758, 4.8053591 , 4.80548646, 4.80853   ,\n",
       "       4.80870933, 4.8096695 , 4.815386  , 4.81591776, 4.81805215,\n",
       "       4.820963  , 4.823946  , 4.830253  , 4.831394  , 4.8382181 ,\n",
       "       4.8407331 , 4.84121   , 4.8443933 , 4.8457529 , 4.846606  ,\n",
       "       4.84722229, 4.85252214, 4.852834  , 4.857523  , 4.861786  ,\n",
       "       4.86189724, 4.8644629 , 4.866243  , 4.866419  , 4.868215  ,\n",
       "       4.870087  , 4.8724196 , 4.87465899, 4.876575  , 4.877924  ,\n",
       "       4.8779407 , 4.8789889 , 4.88051044, 4.88236411, 4.885412  ,\n",
       "       4.885653  , 4.886345  , 4.887069  , 4.8906832 , 4.89147718,\n",
       "       4.8918962 , 4.8930799 , 4.8951159 , 4.8951679 , 4.896876  ,\n",
       "       4.897039  , 4.8970442 , 4.89851619, 4.89944615, 4.8997121 ,\n",
       "       4.899845  , 4.900383  , 4.90048094, 4.900914  , 4.90278912,\n",
       "       4.903877  , 4.905173  , 4.9058957 , 4.906561  , 4.90671   ,\n",
       "       4.9072082 , 4.909413  , 4.90966   , 4.911188  , 4.91129446,\n",
       "       4.9164974 , 4.91687   , 4.91849311, 4.919527  , 4.91978   ,\n",
       "       4.923808  , 4.92543972, 4.925732  , 4.92613967, 4.928533  ,\n",
       "       4.928972  , 4.92969803, 4.94701   , 4.9542801 , 4.95708   ,\n",
       "       4.95919931, 4.9594646 , 4.96209728, 4.96230016, 4.96429   ,\n",
       "       4.964432  , 4.9651068 , 4.966738  , 4.969634  , 4.971247  ,\n",
       "       4.971332  , 4.9713795 , 4.974268  , 4.97452145, 4.978757  ,\n",
       "       4.97953569, 4.994072  , 4.996017  , 5.028072  , 5.03076   ,\n",
       "       5.032035  , 5.0356554 , 5.041156  , 5.0450199 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.049e+05 2.052e+05 ... 2.301e+05</div><input id='attrs-e381cb33-8196-4dfe-b03b-cc550720b606' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e381cb33-8196-4dfe-b03b-cc550720b606' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b29a6e56-8031-4544-8085-dec5f9ad990e' class='xr-var-data-in' type='checkbox'><label for='data-b29a6e56-8031-4544-8085-dec5f9ad990e' 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'><dt><span>projection :</span></dt><dd>EPSG:25832</dd></dl></div><div class='xr-var-data'><pre>array([204889.65032949, 205155.36294111, 205356.90100492, 205625.818236  ,\n",
       "       205514.11138754, 205376.54947783, 205385.1103298 , 206221.90677765,\n",
       "       206341.73778652, 206348.97691677, 206728.62908122, 207768.49236614,\n",
       "       208023.80696091, 209211.56827161, 208601.81532398, 210393.69016351,\n",
       "       210497.67044197, 210824.66618811, 211061.92518383, 210898.62198394,\n",
       "       210861.7616915 , 213108.98307981, 212678.04484145, 212399.08743477,\n",
       "       213358.60797536, 213186.21605642, 214332.08526108, 214386.58341132,\n",
       "       214900.02173575, 214601.48196273, 214597.21101653, 214617.08621134,\n",
       "       215072.21245107, 214609.17897794, 214763.46266675, 214983.80881446,\n",
       "       216175.38144685, 216571.33744103, 215679.30871151, 216221.26643397,\n",
       "       217000.45393709, 216895.3276126 , 216788.35550101, 217140.41031959,\n",
       "       217182.68142942, 217244.27455898, 217528.25921204, 217549.73816343,\n",
       "       217738.68322661, 217948.53311193, 218355.16068503, 218226.62375731,\n",
       "       218251.70197953, 218626.21879273, 218727.33842969, 218512.35835985,\n",
       "       218827.73163455, 219176.23631166, 219171.60278514, 219366.21870206,\n",
       "       219465.96880331, 219391.58091357, 219635.60029513, 219318.02725748,\n",
       "       219782.3936605 , 219798.95134484, 219954.75997552, 220360.36434395,\n",
       "       220426.58111969, 220347.91072304, 220462.21829609, 219979.42775539,\n",
       "       220387.36704529, 220592.39314061, 220291.68722484, 220577.62168174,\n",
       "       220576.06788123, 220739.08949791, 221213.4827266 , 220856.14866896,\n",
       "       220444.29614069, 221326.76921385, 220989.52218702, 221078.02528932,\n",
       "       221320.65151721, 221075.86349581, 220913.94101684, 221691.57230315,\n",
       "       220874.39709389, 220879.31681202, 221245.54361144, 221093.87479775,\n",
       "       221402.72283187, 221684.37242388, 221191.80687893, 222147.54824242,\n",
       "       222191.06239045, 221734.90609071, 222166.09447989, 222632.62192143,\n",
       "       222410.97979633, 222708.38516791, 222608.47225361, 222605.25032245,\n",
       "       222913.73430372, 222855.18800279, 223079.7946497 , 224314.54440248,\n",
       "       224290.7737958 , 224580.05580203, 224725.3551023 , 224789.30950394,\n",
       "       224940.18065344, 224949.68826494, 225090.93600014, 224692.42358812,\n",
       "       225423.8899139 , 224837.03428915, 225439.37382491, 225665.16761953,\n",
       "       225341.20215928, 225340.56584031, 225696.77061425, 225357.60541976,\n",
       "       226009.59435346, 226273.07687814, 226566.88672878, 227804.21015855,\n",
       "       228979.59042386, 229469.34772042, 229520.44396499, 230524.72755462,\n",
       "       230108.07662248, 230138.95066968])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.804e+06 5.803e+06 ... 5.798e+06</div><input id='attrs-6b97e9c0-6dbd-497d-9f99-d2057ea902ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b97e9c0-6dbd-497d-9f99-d2057ea902ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e15260bf-d1cf-465a-b636-17fc271bdcff' class='xr-var-data-in' type='checkbox'><label for='data-e15260bf-d1cf-465a-b636-17fc271bdcff' 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'><dt><span>projection :</span></dt><dd>EPSG:25832</dd></dl></div><div class='xr-var-data'><pre>array([5803891.49555336, 5803445.90777479, 5804145.85080152,\n",
       "       5808322.29833911, 5805268.06302283, 5802957.04435908,\n",
       "       5802861.16080338, 5802428.87646955, 5802499.62784277,\n",
       "       5797624.6120552 , 5797336.53463638, 5799373.69615578,\n",
       "       5798668.83777567, 5812255.22073917, 5798652.04895386,\n",
       "       5811778.93559801, 5811179.86338354, 5812454.83096064,\n",
       "       5808626.00214559, 5800231.87831993, 5797952.44624591,\n",
       "       5817480.23564355, 5807722.16732955, 5800463.49739538,\n",
       "       5809480.34526123, 5800265.30872799, 5811526.98884112,\n",
       "       5808855.98163987, 5817543.15732629, 5808827.76991094,\n",
       "       5808543.1133459 , 5807755.34830493, 5808871.74242426,\n",
       "       5800246.94773232, 5800390.07475107, 5800757.42299891,\n",
       "       5817837.45105025, 5817250.22530232, 5800445.93566708,\n",
       "       5801752.02036354, 5812299.32496272, 5809908.90833957,\n",
       "       5804270.96207065, 5808777.84272946, 5808499.89482605,\n",
       "       5808839.21504936, 5807482.3714007 , 5807485.47853178,\n",
       "       5805195.03605123, 5803772.61784142, 5810739.06674608,\n",
       "       5805434.89274087, 5803746.90522612, 5810082.65669919,\n",
       "       5809707.18156238, 5803710.44777068, 5806440.60645054,\n",
       "       5809864.4473536 , 5807492.00617738, 5809287.94301037,\n",
       "...\n",
       "       5807640.06260072, 5807606.45085924, 5808707.16499843,\n",
       "       5815942.27071673, 5809330.88039236, 5801912.47750117,\n",
       "       5816812.1376436 , 5810755.87551754, 5811794.32050762,\n",
       "       5813815.12308711, 5808191.38803088, 5803772.44856706,\n",
       "       5816618.82253672, 5801398.89469411, 5801305.83724815,\n",
       "       5807171.3732937 , 5801831.28624132, 5806989.92661323,\n",
       "       5810122.01611095, 5801288.34084952, 5811904.2265107 ,\n",
       "       5812224.15137179, 5802189.39098031, 5808573.84475261,\n",
       "       5816523.57371793, 5807733.71431602, 5811030.54407721,\n",
       "       5808905.82458415, 5808355.18734573, 5810928.71740993,\n",
       "       5809358.58980476, 5812464.2492441 , 5813421.34381575,\n",
       "       5804143.55772464, 5805894.5534306 , 5805904.07603057,\n",
       "       5806723.71057343, 5806205.99669543, 5806128.15304154,\n",
       "       5806223.64079871, 5798911.28845222, 5811185.66229929,\n",
       "       5798675.86380139, 5805926.54427502, 5807999.99222574,\n",
       "       5802084.43383269, 5802014.74564239, 5804865.05537271,\n",
       "       5798462.6303236 , 5804975.3115962 , 5808754.35581588,\n",
       "       5796148.98797518, 5816076.58239877, 5797713.06071847,\n",
       "       5803285.17148981, 5802628.77479764, 5816418.70756985,\n",
       "       5801976.4206223 , 5797711.91648581])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6ddd1af4-1142-4148-a315-94961d5c61e7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6ddd1af4-1142-4148-a315-94961d5c61e7' class='xr-section-summary' >Data variables: <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-var-name'><span>rainfall</span></div><div class='xr-var-dims'>(id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-448256f0-96d4-4f5d-9353-c389803de11a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-448256f0-96d4-4f5d-9353-c389803de11a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9a5ffec-1185-4978-9cbf-700665297d8e' class='xr-var-data-in' type='checkbox'><label for='data-d9a5ffec-1185-4978-9cbf-700665297d8e' 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'><dt><span>name :</span></dt><dd>rainfall</dd><dt><span>long_name :</span></dt><dd>rainfall amount per time unit</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>coverage_contant_type :</span></dt><dd>physicalMeasurement</dd></dl></div><div class='xr-var-data'><pre>[29368512 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c6ec8086-8805-448e-a178-98aa21fec5e9' class='xr-section-summary-in' type='checkbox'  ><label for='section-c6ec8086-8805-448e-a178-98aa21fec5e9' class='xr-section-summary' >Indexes: <span>(2)</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-5490de9b-9f57-419d-b84a-432251bb93c1' class='xr-index-data-in' type='checkbox'/><label for='index-5490de9b-9f57-419d-b84a-432251bb93c1' 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(DatetimeIndex([&#x27;2016-05-01 00:05:00&#x27;, &#x27;2016-05-01 00:10:00&#x27;,\n",
       "               &#x27;2016-05-01 00:15:00&#x27;, &#x27;2016-05-01 00:20:00&#x27;,\n",
       "               &#x27;2016-05-01 00:25:00&#x27;, &#x27;2016-05-01 00:30:00&#x27;,\n",
       "               &#x27;2016-05-01 00:35:00&#x27;, &#x27;2016-05-01 00:40:00&#x27;,\n",
       "               &#x27;2016-05-01 00:45:00&#x27;, &#x27;2016-05-01 00:50:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2018-05-31 23:15:00&#x27;, &#x27;2018-05-31 23:20:00&#x27;,\n",
       "               &#x27;2018-05-31 23:25:00&#x27;, &#x27;2018-05-31 23:30:00&#x27;,\n",
       "               &#x27;2018-05-31 23:35:00&#x27;, &#x27;2018-05-31 23:40:00&#x27;,\n",
       "               &#x27;2018-05-31 23:45:00&#x27;, &#x27;2018-05-31 23:50:00&#x27;,\n",
       "               &#x27;2018-05-31 23:55:00&#x27;, &#x27;2018-06-01 00:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=219168, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-83bcc75c-37f8-4857-805e-2a1208c92028' class='xr-index-data-in' type='checkbox'/><label for='index-83bcc75c-37f8-4857-805e-2a1208c92028' 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([&#x27;ams1&#x27;, &#x27;ams2&#x27;, &#x27;ams3&#x27;, &#x27;ams4&#x27;, &#x27;ams5&#x27;, &#x27;ams6&#x27;, &#x27;ams7&#x27;, &#x27;ams8&#x27;, &#x27;ams9&#x27;,\n",
       "       &#x27;ams10&#x27;,\n",
       "       ...\n",
       "       &#x27;ams125&#x27;, &#x27;ams126&#x27;, &#x27;ams127&#x27;, &#x27;ams128&#x27;, &#x27;ams129&#x27;, &#x27;ams130&#x27;, &#x27;ams131&#x27;,\n",
       "       &#x27;ams132&#x27;, &#x27;ams133&#x27;, &#x27;ams134&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;id&#x27;, length=134))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e0a165c0-0158-4d20-a16f-530cfb17af4f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e0a165c0-0158-4d20-a16f-530cfb17af4f' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>PWS data from Amsterdam</dd><dt><span>institution :</span></dt><dd>Wageningen University and Research, Department of Environmental Sciences</dd><dt><span>history :</span></dt><dd>Test Version 0.1</dd><dt><span>references :</span></dt><dd>https://doi.org/10.1029/2019GL083731</dd><dt><span>date_created :</span></dt><dd>2022-10-18 10:32:00</dd><dt><span>Conventions :</span></dt><dd>OPENSENSE V0</dd><dt><span>location :</span></dt><dd>Amsterdam Metropolitan Area</dd><dt><span>source :</span></dt><dd>Netamo</dd><dt><span>comment :</span></dt><dd></dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 237MB\n",
       "Dimensions:    (time: 219168, id: 134)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2MB 2016-05-01T00:05:00 ... 2018-06-01\n",
       "  * id         (id) <U6 3kB 'ams1' 'ams2' 'ams3' ... 'ams132' 'ams133' 'ams134'\n",
       "    elevation  (id) <U3 2kB ...\n",
       "    lat        (id) float64 1kB 52.31 52.3 52.31 52.35 ... 52.43 52.3 52.26\n",
       "    lon        (id) float64 1kB 4.671 4.675 4.677 4.678 ... 5.036 5.041 5.045\n",
       "    x          (id) float64 1kB 2.049e+05 2.052e+05 ... 2.301e+05 2.301e+05\n",
       "    y          (id) float64 1kB 5.804e+06 5.803e+06 ... 5.802e+06 5.798e+06\n",
       "Data variables:\n",
       "    rainfall   (id, time) float64 235MB ...\n",
       "Attributes:\n",
       "    title:         PWS data from Amsterdam\n",
       "    institution:   Wageningen University and Research, Department of Environm...\n",
       "    history:       Test Version 0.1\n",
       "    references:    https://doi.org/10.1029/2019GL083731\n",
       "    date_created:  2022-10-18 10:32:00\n",
       "    Conventions:   OPENSENSE V0\n",
       "    location:      Amsterdam Metropolitan Area\n",
       "    source:        Netamo\n",
       "    comment:       "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_pws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "62725b6d-cd00-4c38-aee7-a5cb909c340d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(ds_pws.x, ds_pws.y);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfb4ba61-c9c3-480d-b003-0f346181cf0f",
   "metadata": {},
   "source": [
    "### Distance matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00a6b3cc-7132-428e-a836-88390ab2a8e7",
   "metadata": {},
   "source": [
    "#### Calculate distance matrix\n",
    "\n",
    "Beware that the distance matrix can become quite large when using large datasets, e.g. 800 MB if you use 10,000 stations against 10,000 stations from a country-wide PWS dataset, and calculation can take tens of seconds. We might add the option to calculate a sparse distance matrix only up the certain distances later, which reduced size and computation time signficantly.\n",
    "\n",
    "For the typicall use case to find nearby stations, you can just use the nearest neighbor lookup described below for large datasets. The distance matrix can still be quite nice and handy for small regional datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d6c43dad-aab5-4a7b-b89c-0b80c6492460",
   "metadata": {},
   "outputs": [
    {
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       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  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",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (id: 134, id_neighbor: 134)&gt; Size: 144kB\n",
       "array([[    0.        ,   518.79828487,   531.99603941, ...,\n",
       "        28532.23134009, 25291.03668667, 25994.5064412 ],\n",
       "       [  518.79828487,     0.        ,   728.38027968, ...,\n",
       "        28493.8273213 , 24995.94592241, 25633.14869253],\n",
       "       [  531.99603941,   728.38027968,     0.        , ...,\n",
       "        28000.75903421, 24846.06853717, 25603.62271956],\n",
       "       ...,\n",
       "       [28532.23134009, 28493.8273213 , 28000.75903421, ...,\n",
       "            0.        , 14448.29575676, 18710.76846276],\n",
       "       [25291.03668667, 24995.94592241, 24846.06853717, ...,\n",
       "        14448.29575676,     0.        ,  4264.61589559],\n",
       "       [25994.5064412 , 25633.14869253, 25603.62271956, ...,\n",
       "        18710.76846276,  4264.61589559,     0.        ]])\n",
       "Coordinates:\n",
       "  * id           (id) &lt;U6 3kB &#x27;ams1&#x27; &#x27;ams2&#x27; &#x27;ams3&#x27; ... &#x27;ams133&#x27; &#x27;ams134&#x27;\n",
       "  * id_neighbor  (id_neighbor) &lt;U6 3kB &#x27;ams1&#x27; &#x27;ams2&#x27; ... &#x27;ams133&#x27; &#x27;ams134&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>id</span>: 134</li><li><span class='xr-has-index'>id_neighbor</span>: 134</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-03968c1e-d97a-409b-b9dc-e55bbf81205f' class='xr-array-in' type='checkbox' checked><label for='section-03968c1e-d97a-409b-b9dc-e55bbf81205f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 518.8 532.0 4.492e+03 ... 4.956e+03 1.871e+04 4.265e+03 0.0</span></div><div class='xr-array-data'><pre>array([[    0.        ,   518.79828487,   531.99603941, ...,\n",
       "        28532.23134009, 25291.03668667, 25994.5064412 ],\n",
       "       [  518.79828487,     0.        ,   728.38027968, ...,\n",
       "        28493.8273213 , 24995.94592241, 25633.14869253],\n",
       "       [  531.99603941,   728.38027968,     0.        , ...,\n",
       "        28000.75903421, 24846.06853717, 25603.62271956],\n",
       "       ...,\n",
       "       [28532.23134009, 28493.8273213 , 28000.75903421, ...,\n",
       "            0.        , 14448.29575676, 18710.76846276],\n",
       "       [25291.03668667, 24995.94592241, 24846.06853717, ...,\n",
       "        14448.29575676,     0.        ,  4264.61589559],\n",
       "       [25994.5064412 , 25633.14869253, 25603.62271956, ...,\n",
       "        18710.76846276,  4264.61589559,     0.        ]])</pre></div></div></li><li class='xr-section-item'><input id='section-ed885349-adcd-4788-b65e-96bcc4ec779c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ed885349-adcd-4788-b65e-96bcc4ec779c' class='xr-section-summary' >Coordinates: <span>(2)</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 class='xr-has-index'>id</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;ams1&#x27; &#x27;ams2&#x27; ... &#x27;ams133&#x27; &#x27;ams134&#x27;</div><input id='attrs-cb00cf8e-aa58-48c3-95ba-0a77d4b0e68e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cb00cf8e-aa58-48c3-95ba-0a77d4b0e68e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-314ac649-0fbe-4270-9bba-1c4049a0af82' class='xr-var-data-in' type='checkbox'><label for='data-314ac649-0fbe-4270-9bba-1c4049a0af82' 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([&#x27;ams1&#x27;, &#x27;ams2&#x27;, &#x27;ams3&#x27;, &#x27;ams4&#x27;, &#x27;ams5&#x27;, &#x27;ams6&#x27;, &#x27;ams7&#x27;, &#x27;ams8&#x27;, &#x27;ams9&#x27;,\n",
       "       &#x27;ams10&#x27;, &#x27;ams11&#x27;, &#x27;ams12&#x27;, &#x27;ams13&#x27;, &#x27;ams14&#x27;, &#x27;ams15&#x27;, &#x27;ams16&#x27;, &#x27;ams17&#x27;,\n",
       "       &#x27;ams18&#x27;, &#x27;ams19&#x27;, &#x27;ams20&#x27;, &#x27;ams21&#x27;, &#x27;ams22&#x27;, &#x27;ams23&#x27;, &#x27;ams24&#x27;, &#x27;ams25&#x27;,\n",
       "       &#x27;ams26&#x27;, &#x27;ams27&#x27;, &#x27;ams28&#x27;, &#x27;ams29&#x27;, &#x27;ams30&#x27;, &#x27;ams31&#x27;, &#x27;ams32&#x27;, &#x27;ams33&#x27;,\n",
       "       &#x27;ams34&#x27;, &#x27;ams35&#x27;, &#x27;ams36&#x27;, &#x27;ams37&#x27;, &#x27;ams38&#x27;, &#x27;ams39&#x27;, &#x27;ams40&#x27;, &#x27;ams41&#x27;,\n",
       "       &#x27;ams42&#x27;, &#x27;ams43&#x27;, &#x27;ams44&#x27;, &#x27;ams45&#x27;, &#x27;ams46&#x27;, &#x27;ams47&#x27;, &#x27;ams48&#x27;, &#x27;ams49&#x27;,\n",
       "       &#x27;ams50&#x27;, &#x27;ams51&#x27;, &#x27;ams52&#x27;, &#x27;ams53&#x27;, &#x27;ams54&#x27;, &#x27;ams55&#x27;, &#x27;ams56&#x27;, &#x27;ams57&#x27;,\n",
       "       &#x27;ams58&#x27;, &#x27;ams59&#x27;, &#x27;ams60&#x27;, &#x27;ams61&#x27;, &#x27;ams62&#x27;, &#x27;ams63&#x27;, &#x27;ams64&#x27;, &#x27;ams65&#x27;,\n",
       "       &#x27;ams66&#x27;, &#x27;ams67&#x27;, &#x27;ams68&#x27;, &#x27;ams69&#x27;, &#x27;ams70&#x27;, &#x27;ams71&#x27;, &#x27;ams72&#x27;, &#x27;ams73&#x27;,\n",
       "       &#x27;ams74&#x27;, &#x27;ams75&#x27;, &#x27;ams76&#x27;, &#x27;ams77&#x27;, &#x27;ams78&#x27;, &#x27;ams79&#x27;, &#x27;ams80&#x27;, &#x27;ams81&#x27;,\n",
       "       &#x27;ams82&#x27;, &#x27;ams83&#x27;, &#x27;ams84&#x27;, &#x27;ams85&#x27;, &#x27;ams86&#x27;, &#x27;ams87&#x27;, &#x27;ams88&#x27;, &#x27;ams89&#x27;,\n",
       "       &#x27;ams90&#x27;, &#x27;ams91&#x27;, &#x27;ams92&#x27;, &#x27;ams93&#x27;, &#x27;ams94&#x27;, &#x27;ams95&#x27;, &#x27;ams96&#x27;, &#x27;ams97&#x27;,\n",
       "       &#x27;ams98&#x27;, &#x27;ams99&#x27;, &#x27;ams100&#x27;, &#x27;ams101&#x27;, &#x27;ams102&#x27;, &#x27;ams103&#x27;, &#x27;ams104&#x27;,\n",
       "       &#x27;ams105&#x27;, &#x27;ams106&#x27;, &#x27;ams107&#x27;, &#x27;ams108&#x27;, &#x27;ams109&#x27;, &#x27;ams110&#x27;, &#x27;ams111&#x27;,\n",
       "       &#x27;ams112&#x27;, &#x27;ams113&#x27;, &#x27;ams114&#x27;, &#x27;ams115&#x27;, &#x27;ams116&#x27;, &#x27;ams117&#x27;, &#x27;ams118&#x27;,\n",
       "       &#x27;ams119&#x27;, &#x27;ams120&#x27;, &#x27;ams121&#x27;, &#x27;ams122&#x27;, &#x27;ams123&#x27;, &#x27;ams124&#x27;, &#x27;ams125&#x27;,\n",
       "       &#x27;ams126&#x27;, &#x27;ams127&#x27;, &#x27;ams128&#x27;, &#x27;ams129&#x27;, &#x27;ams130&#x27;, &#x27;ams131&#x27;, &#x27;ams132&#x27;,\n",
       "       &#x27;ams133&#x27;, &#x27;ams134&#x27;], dtype=&#x27;&lt;U6&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>id_neighbor</span></div><div class='xr-var-dims'>(id_neighbor)</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;ams1&#x27; &#x27;ams2&#x27; ... &#x27;ams133&#x27; &#x27;ams134&#x27;</div><input id='attrs-be8d62d9-8666-43c0-8d3e-25bc75bf5ee6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be8d62d9-8666-43c0-8d3e-25bc75bf5ee6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6f81192-075c-49ab-aeb5-6a14762ea64e' class='xr-var-data-in' type='checkbox'><label for='data-c6f81192-075c-49ab-aeb5-6a14762ea64e' 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([&#x27;ams1&#x27;, &#x27;ams2&#x27;, &#x27;ams3&#x27;, &#x27;ams4&#x27;, &#x27;ams5&#x27;, &#x27;ams6&#x27;, &#x27;ams7&#x27;, &#x27;ams8&#x27;, &#x27;ams9&#x27;,\n",
       "       &#x27;ams10&#x27;, &#x27;ams11&#x27;, &#x27;ams12&#x27;, &#x27;ams13&#x27;, &#x27;ams14&#x27;, &#x27;ams15&#x27;, &#x27;ams16&#x27;, &#x27;ams17&#x27;,\n",
       "       &#x27;ams18&#x27;, &#x27;ams19&#x27;, &#x27;ams20&#x27;, &#x27;ams21&#x27;, &#x27;ams22&#x27;, &#x27;ams23&#x27;, &#x27;ams24&#x27;, &#x27;ams25&#x27;,\n",
       "       &#x27;ams26&#x27;, &#x27;ams27&#x27;, &#x27;ams28&#x27;, &#x27;ams29&#x27;, &#x27;ams30&#x27;, &#x27;ams31&#x27;, &#x27;ams32&#x27;, &#x27;ams33&#x27;,\n",
       "       &#x27;ams34&#x27;, &#x27;ams35&#x27;, &#x27;ams36&#x27;, &#x27;ams37&#x27;, &#x27;ams38&#x27;, &#x27;ams39&#x27;, &#x27;ams40&#x27;, &#x27;ams41&#x27;,\n",
       "       &#x27;ams42&#x27;, &#x27;ams43&#x27;, &#x27;ams44&#x27;, &#x27;ams45&#x27;, &#x27;ams46&#x27;, &#x27;ams47&#x27;, &#x27;ams48&#x27;, &#x27;ams49&#x27;,\n",
       "       &#x27;ams50&#x27;, &#x27;ams51&#x27;, &#x27;ams52&#x27;, &#x27;ams53&#x27;, &#x27;ams54&#x27;, &#x27;ams55&#x27;, &#x27;ams56&#x27;, &#x27;ams57&#x27;,\n",
       "       &#x27;ams58&#x27;, &#x27;ams59&#x27;, &#x27;ams60&#x27;, &#x27;ams61&#x27;, &#x27;ams62&#x27;, &#x27;ams63&#x27;, &#x27;ams64&#x27;, &#x27;ams65&#x27;,\n",
       "       &#x27;ams66&#x27;, &#x27;ams67&#x27;, &#x27;ams68&#x27;, &#x27;ams69&#x27;, &#x27;ams70&#x27;, &#x27;ams71&#x27;, &#x27;ams72&#x27;, &#x27;ams73&#x27;,\n",
       "       &#x27;ams74&#x27;, &#x27;ams75&#x27;, &#x27;ams76&#x27;, &#x27;ams77&#x27;, &#x27;ams78&#x27;, &#x27;ams79&#x27;, &#x27;ams80&#x27;, &#x27;ams81&#x27;,\n",
       "       &#x27;ams82&#x27;, &#x27;ams83&#x27;, &#x27;ams84&#x27;, &#x27;ams85&#x27;, &#x27;ams86&#x27;, &#x27;ams87&#x27;, &#x27;ams88&#x27;, &#x27;ams89&#x27;,\n",
       "       &#x27;ams90&#x27;, &#x27;ams91&#x27;, &#x27;ams92&#x27;, &#x27;ams93&#x27;, &#x27;ams94&#x27;, &#x27;ams95&#x27;, &#x27;ams96&#x27;, &#x27;ams97&#x27;,\n",
       "       &#x27;ams98&#x27;, &#x27;ams99&#x27;, &#x27;ams100&#x27;, &#x27;ams101&#x27;, &#x27;ams102&#x27;, &#x27;ams103&#x27;, &#x27;ams104&#x27;,\n",
       "       &#x27;ams105&#x27;, &#x27;ams106&#x27;, &#x27;ams107&#x27;, &#x27;ams108&#x27;, &#x27;ams109&#x27;, &#x27;ams110&#x27;, &#x27;ams111&#x27;,\n",
       "       &#x27;ams112&#x27;, &#x27;ams113&#x27;, &#x27;ams114&#x27;, &#x27;ams115&#x27;, &#x27;ams116&#x27;, &#x27;ams117&#x27;, &#x27;ams118&#x27;,\n",
       "       &#x27;ams119&#x27;, &#x27;ams120&#x27;, &#x27;ams121&#x27;, &#x27;ams122&#x27;, &#x27;ams123&#x27;, &#x27;ams124&#x27;, &#x27;ams125&#x27;,\n",
       "       &#x27;ams126&#x27;, &#x27;ams127&#x27;, &#x27;ams128&#x27;, &#x27;ams129&#x27;, &#x27;ams130&#x27;, &#x27;ams131&#x27;, &#x27;ams132&#x27;,\n",
       "       &#x27;ams133&#x27;, &#x27;ams134&#x27;], dtype=&#x27;&lt;U6&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3f45ea39-95eb-456d-b338-ff36af56d127' class='xr-section-summary-in' type='checkbox'  ><label for='section-3f45ea39-95eb-456d-b338-ff36af56d127' class='xr-section-summary' >Indexes: <span>(2)</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>id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-905ea96a-3d58-44ac-8a79-3b6b48a35bf3' class='xr-index-data-in' type='checkbox'/><label for='index-905ea96a-3d58-44ac-8a79-3b6b48a35bf3' 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([&#x27;ams1&#x27;, &#x27;ams2&#x27;, &#x27;ams3&#x27;, &#x27;ams4&#x27;, &#x27;ams5&#x27;, &#x27;ams6&#x27;, &#x27;ams7&#x27;, &#x27;ams8&#x27;, &#x27;ams9&#x27;,\n",
       "       &#x27;ams10&#x27;,\n",
       "       ...\n",
       "       &#x27;ams125&#x27;, &#x27;ams126&#x27;, &#x27;ams127&#x27;, &#x27;ams128&#x27;, &#x27;ams129&#x27;, &#x27;ams130&#x27;, &#x27;ams131&#x27;,\n",
       "       &#x27;ams132&#x27;, &#x27;ams133&#x27;, &#x27;ams134&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;id&#x27;, length=134))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>id_neighbor</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-554178b9-2f6e-4389-9c32-8d52c649d949' class='xr-index-data-in' type='checkbox'/><label for='index-554178b9-2f6e-4389-9c32-8d52c649d949' 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([&#x27;ams1&#x27;, &#x27;ams2&#x27;, &#x27;ams3&#x27;, &#x27;ams4&#x27;, &#x27;ams5&#x27;, &#x27;ams6&#x27;, &#x27;ams7&#x27;, &#x27;ams8&#x27;, &#x27;ams9&#x27;,\n",
       "       &#x27;ams10&#x27;,\n",
       "       ...\n",
       "       &#x27;ams125&#x27;, &#x27;ams126&#x27;, &#x27;ams127&#x27;, &#x27;ams128&#x27;, &#x27;ams129&#x27;, &#x27;ams130&#x27;, &#x27;ams131&#x27;,\n",
       "       &#x27;ams132&#x27;, &#x27;ams133&#x27;, &#x27;ams134&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;id_neighbor&#x27;, length=134))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eed5098e-8e55-4d3f-86da-2ad81676b333' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eed5098e-8e55-4d3f-86da-2ad81676b333' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (id: 134, id_neighbor: 134)> Size: 144kB\n",
       "array([[    0.        ,   518.79828487,   531.99603941, ...,\n",
       "        28532.23134009, 25291.03668667, 25994.5064412 ],\n",
       "       [  518.79828487,     0.        ,   728.38027968, ...,\n",
       "        28493.8273213 , 24995.94592241, 25633.14869253],\n",
       "       [  531.99603941,   728.38027968,     0.        , ...,\n",
       "        28000.75903421, 24846.06853717, 25603.62271956],\n",
       "       ...,\n",
       "       [28532.23134009, 28493.8273213 , 28000.75903421, ...,\n",
       "            0.        , 14448.29575676, 18710.76846276],\n",
       "       [25291.03668667, 24995.94592241, 24846.06853717, ...,\n",
       "        14448.29575676,     0.        ,  4264.61589559],\n",
       "       [25994.5064412 , 25633.14869253, 25603.62271956, ...,\n",
       "        18710.76846276,  4264.61589559,     0.        ]])\n",
       "Coordinates:\n",
       "  * id           (id) <U6 3kB 'ams1' 'ams2' 'ams3' ... 'ams133' 'ams134'\n",
       "  * id_neighbor  (id_neighbor) <U6 3kB 'ams1' 'ams2' ... 'ams133' 'ams134'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_matrix = plg.spatial.calc_point_to_point_distances(\n",
    "    ds_pws,\n",
    "    ds_pws,\n",
    ")\n",
    "\n",
    "distance_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5096061c-0b8b-40a9-9410-e07ce5b7dd57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distance_matrix.plot(cbar_kwargs={\"label\": \"distance in meters\"})\n",
    "plt.xticks([])\n",
    "plt.yticks([]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "373a9d88-957f-4cfa-89aa-746f656a5e80",
   "metadata": {},
   "source": [
    "#### Plot locations with distance to selected station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dbc068dd-291f-4f39-b2cc-c0782e6dd17a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pws_id = \"ams63\"\n",
    "\n",
    "sc = plt.scatter(ds_pws.x, ds_pws.y, c=distance_matrix.sel(id=pws_id) / 1e3)\n",
    "plt.scatter(ds_pws.sel(id=pws_id).x, ds_pws.sel(id=pws_id).y, c=\"r\", marker=\"x\")\n",
    "plt.colorbar(sc, label=\"distance in km\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "628309ea-bfda-410d-a561-761a9ee8d424",
   "metadata": {},
   "source": [
    "#### Select neighbors within certain range from distance matrix\n",
    "\n",
    "Note that this can be done more easily with the nearest neighbor lookup shown below. But you might findet having the distance matrix and getting the neigbors from there a nicer solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "648586fc-0dfc-4725-9f15-b76a0a2de7d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ams41', 'ams42', 'ams51', 'ams54', 'ams55', 'ams58', 'ams60',\n",
       "       'ams61', 'ams62', 'ams63', 'ams67', 'ams69', 'ams70', 'ams71',\n",
       "       'ams74', 'ams78', 'ams80', 'ams83', 'ams84', 'ams94', 'ams96',\n",
       "       'ams97'], dtype='<U6')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pws_id = \"ams63\"\n",
    "\n",
    "max_distance = 3e3  # this is in meteres\n",
    "\n",
    "distances = distance_matrix.sel(id=pws_id)\n",
    "selected_neighbor_ids = ds_pws.id.data[distances < max_distance]\n",
    "selected_neighbor_ids"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d54da1c-8f8f-4706-94eb-0aa34cdfbf7f",
   "metadata": {},
   "source": [
    "### Nearest neighbor lookup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d32106c-adb8-4258-b91c-b8e6e30d7b1f",
   "metadata": {},
   "source": [
    "This uses `scipy.spatial.KDTree` for fast lookup of nearest neighbors and distance calculation. In the resulting `xarray.Dataset` the IDs of the neighboring stations are `None` for the cases where their distance is larger than `max_distance`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe8edb0f-9dce-4514-8ddf-c647450e558f",
   "metadata": {},
   "source": [
    "#### Get N closest points for two point datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3a347638-cb91-4248-a0f1-a658d8564e19",
   "metadata": {},
   "outputs": [],
   "source": [
    "closest_neigbors = plg.spatial.get_closest_points_to_point(\n",
    "    ds_points=ds_pws,\n",
    "    ds_points_neighbors=ds_pws,\n",
    "    max_distance=max_distance,\n",
    "    n_closest=25,\n",
    "    id_var_points=\"id\",\n",
    "    id_var_neighbors=\"id\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "143cf39c-78b7-4858-aa50-37a3901b49d1",
   "metadata": {},
   "outputs": [
    {
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       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  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",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 57kB\n",
       "Dimensions:      (id: 134, n_closest: 25)\n",
       "Coordinates:\n",
       "  * id           (id) &lt;U6 3kB &#x27;ams1&#x27; &#x27;ams2&#x27; &#x27;ams3&#x27; ... &#x27;ams133&#x27; &#x27;ams134&#x27;\n",
       "Dimensions without coordinates: n_closest\n",
       "Data variables:\n",
       "    distance     (id, n_closest) float64 27kB 0.0 518.8 532.0 ... inf inf inf\n",
       "    neighbor_id  (id, n_closest) object 27kB &#x27;ams1&#x27; &#x27;ams2&#x27; &#x27;ams3&#x27; ... None None</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-c99f856d-daad-40f6-9df9-c5516b19e9e3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c99f856d-daad-40f6-9df9-c5516b19e9e3' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>id</span>: 134</li><li><span>n_closest</span>: 25</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6b1ffab3-e4c5-4481-9f01-574814120b34' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6b1ffab3-e4c5-4481-9f01-574814120b34' class='xr-section-summary' >Coordinates: <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-var-name'><span class='xr-has-index'>id</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;ams1&#x27; &#x27;ams2&#x27; ... &#x27;ams133&#x27; &#x27;ams134&#x27;</div><input id='attrs-4e1e5485-7d27-4b32-af88-984603fe199b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e1e5485-7d27-4b32-af88-984603fe199b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f46dbff-3713-468d-9f69-b574b8d0c6d2' class='xr-var-data-in' type='checkbox'><label for='data-9f46dbff-3713-468d-9f69-b574b8d0c6d2' 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([&#x27;ams1&#x27;, &#x27;ams2&#x27;, &#x27;ams3&#x27;, &#x27;ams4&#x27;, &#x27;ams5&#x27;, &#x27;ams6&#x27;, &#x27;ams7&#x27;, &#x27;ams8&#x27;, &#x27;ams9&#x27;,\n",
       "       &#x27;ams10&#x27;, &#x27;ams11&#x27;, &#x27;ams12&#x27;, &#x27;ams13&#x27;, &#x27;ams14&#x27;, &#x27;ams15&#x27;, &#x27;ams16&#x27;, &#x27;ams17&#x27;,\n",
       "       &#x27;ams18&#x27;, &#x27;ams19&#x27;, &#x27;ams20&#x27;, &#x27;ams21&#x27;, &#x27;ams22&#x27;, &#x27;ams23&#x27;, &#x27;ams24&#x27;, &#x27;ams25&#x27;,\n",
       "       &#x27;ams26&#x27;, &#x27;ams27&#x27;, &#x27;ams28&#x27;, &#x27;ams29&#x27;, &#x27;ams30&#x27;, &#x27;ams31&#x27;, &#x27;ams32&#x27;, &#x27;ams33&#x27;,\n",
       "       &#x27;ams34&#x27;, &#x27;ams35&#x27;, &#x27;ams36&#x27;, &#x27;ams37&#x27;, &#x27;ams38&#x27;, &#x27;ams39&#x27;, &#x27;ams40&#x27;, &#x27;ams41&#x27;,\n",
       "       &#x27;ams42&#x27;, &#x27;ams43&#x27;, &#x27;ams44&#x27;, &#x27;ams45&#x27;, &#x27;ams46&#x27;, &#x27;ams47&#x27;, &#x27;ams48&#x27;, &#x27;ams49&#x27;,\n",
       "       &#x27;ams50&#x27;, &#x27;ams51&#x27;, &#x27;ams52&#x27;, &#x27;ams53&#x27;, &#x27;ams54&#x27;, &#x27;ams55&#x27;, &#x27;ams56&#x27;, &#x27;ams57&#x27;,\n",
       "       &#x27;ams58&#x27;, &#x27;ams59&#x27;, &#x27;ams60&#x27;, &#x27;ams61&#x27;, &#x27;ams62&#x27;, &#x27;ams63&#x27;, &#x27;ams64&#x27;, &#x27;ams65&#x27;,\n",
       "       &#x27;ams66&#x27;, &#x27;ams67&#x27;, &#x27;ams68&#x27;, &#x27;ams69&#x27;, &#x27;ams70&#x27;, &#x27;ams71&#x27;, &#x27;ams72&#x27;, &#x27;ams73&#x27;,\n",
       "       &#x27;ams74&#x27;, &#x27;ams75&#x27;, &#x27;ams76&#x27;, &#x27;ams77&#x27;, &#x27;ams78&#x27;, &#x27;ams79&#x27;, &#x27;ams80&#x27;, &#x27;ams81&#x27;,\n",
       "       &#x27;ams82&#x27;, &#x27;ams83&#x27;, &#x27;ams84&#x27;, &#x27;ams85&#x27;, &#x27;ams86&#x27;, &#x27;ams87&#x27;, &#x27;ams88&#x27;, &#x27;ams89&#x27;,\n",
       "       &#x27;ams90&#x27;, &#x27;ams91&#x27;, &#x27;ams92&#x27;, &#x27;ams93&#x27;, &#x27;ams94&#x27;, &#x27;ams95&#x27;, &#x27;ams96&#x27;, &#x27;ams97&#x27;,\n",
       "       &#x27;ams98&#x27;, &#x27;ams99&#x27;, &#x27;ams100&#x27;, &#x27;ams101&#x27;, &#x27;ams102&#x27;, &#x27;ams103&#x27;, &#x27;ams104&#x27;,\n",
       "       &#x27;ams105&#x27;, &#x27;ams106&#x27;, &#x27;ams107&#x27;, &#x27;ams108&#x27;, &#x27;ams109&#x27;, &#x27;ams110&#x27;, &#x27;ams111&#x27;,\n",
       "       &#x27;ams112&#x27;, &#x27;ams113&#x27;, &#x27;ams114&#x27;, &#x27;ams115&#x27;, &#x27;ams116&#x27;, &#x27;ams117&#x27;, &#x27;ams118&#x27;,\n",
       "       &#x27;ams119&#x27;, &#x27;ams120&#x27;, &#x27;ams121&#x27;, &#x27;ams122&#x27;, &#x27;ams123&#x27;, &#x27;ams124&#x27;, &#x27;ams125&#x27;,\n",
       "       &#x27;ams126&#x27;, &#x27;ams127&#x27;, &#x27;ams128&#x27;, &#x27;ams129&#x27;, &#x27;ams130&#x27;, &#x27;ams131&#x27;, &#x27;ams132&#x27;,\n",
       "       &#x27;ams133&#x27;, &#x27;ams134&#x27;], dtype=&#x27;&lt;U6&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-41525c66-3102-4121-b150-2b0b1eb87aa3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-41525c66-3102-4121-b150-2b0b1eb87aa3' class='xr-section-summary' >Data variables: <span>(2)</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>distance</span></div><div class='xr-var-dims'>(id, n_closest)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 518.8 532.0 ... inf inf inf</div><input id='attrs-1fb014ce-967b-4795-b750-0fb382d92683' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1fb014ce-967b-4795-b750-0fb382d92683' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-274b61cc-1738-4919-a71a-1cccd7847ad6' class='xr-var-data-in' type='checkbox'><label for='data-274b61cc-1738-4919-a71a-1cccd7847ad6' 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.        ,  518.79828487,  531.99603941, ...,           inf,\n",
       "                  inf,           inf],\n",
       "       [   0.        ,  518.79828487,  536.57331581, ...,           inf,\n",
       "                  inf,           inf],\n",
       "       [   0.        ,  531.99603941,  728.38027968, ...,           inf,\n",
       "                  inf,           inf],\n",
       "       ...,\n",
       "       [   0.        , 2741.94539242,           inf, ...,           inf,\n",
       "                  inf,           inf],\n",
       "       [   0.        ,  877.99664591, 1456.29785536, ...,           inf,\n",
       "                  inf,           inf],\n",
       "       [   0.        , 1159.36081048,           inf, ...,           inf,\n",
       "                  inf,           inf]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>neighbor_id</span></div><div class='xr-var-dims'>(id, n_closest)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;ams1&#x27; &#x27;ams2&#x27; &#x27;ams3&#x27; ... None None</div><input id='attrs-dc5caf79-84ad-4a10-8b25-c5ba99e621bf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc5caf79-84ad-4a10-8b25-c5ba99e621bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c217367d-5432-4b57-9a18-8f901f0c1bdc' class='xr-var-data-in' type='checkbox'><label for='data-c217367d-5432-4b57-9a18-8f901f0c1bdc' 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([[&#x27;ams1&#x27;, &#x27;ams2&#x27;, &#x27;ams3&#x27;, ..., None, None, None],\n",
       "       [&#x27;ams2&#x27;, &#x27;ams1&#x27;, &#x27;ams6&#x27;, ..., None, None, None],\n",
       "       [&#x27;ams3&#x27;, &#x27;ams1&#x27;, &#x27;ams2&#x27;, ..., None, None, None],\n",
       "       ...,\n",
       "       [&#x27;ams132&#x27;, &#x27;ams128&#x27;, None, ..., None, None, None],\n",
       "       [&#x27;ams133&#x27;, &#x27;ams131&#x27;, &#x27;ams130&#x27;, ..., None, None, None],\n",
       "       [&#x27;ams134&#x27;, &#x27;ams129&#x27;, None, ..., None, None, None]], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f01482a6-cdec-417f-860a-377fd31fc691' class='xr-section-summary-in' type='checkbox'  ><label for='section-f01482a6-cdec-417f-860a-377fd31fc691' 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>id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ad5d6a5a-55a9-4be2-92d0-09394b1840ae' class='xr-index-data-in' type='checkbox'/><label for='index-ad5d6a5a-55a9-4be2-92d0-09394b1840ae' 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([&#x27;ams1&#x27;, &#x27;ams2&#x27;, &#x27;ams3&#x27;, &#x27;ams4&#x27;, &#x27;ams5&#x27;, &#x27;ams6&#x27;, &#x27;ams7&#x27;, &#x27;ams8&#x27;, &#x27;ams9&#x27;,\n",
       "       &#x27;ams10&#x27;,\n",
       "       ...\n",
       "       &#x27;ams125&#x27;, &#x27;ams126&#x27;, &#x27;ams127&#x27;, &#x27;ams128&#x27;, &#x27;ams129&#x27;, &#x27;ams130&#x27;, &#x27;ams131&#x27;,\n",
       "       &#x27;ams132&#x27;, &#x27;ams133&#x27;, &#x27;ams134&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;id&#x27;, length=134))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b8a817f5-825f-4d74-b25d-e8a2eb4ee406' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b8a817f5-825f-4d74-b25d-e8a2eb4ee406' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 57kB\n",
       "Dimensions:      (id: 134, n_closest: 25)\n",
       "Coordinates:\n",
       "  * id           (id) <U6 3kB 'ams1' 'ams2' 'ams3' ... 'ams133' 'ams134'\n",
       "Dimensions without coordinates: n_closest\n",
       "Data variables:\n",
       "    distance     (id, n_closest) float64 27kB 0.0 518.8 532.0 ... inf inf inf\n",
       "    neighbor_id  (id, n_closest) object 27kB 'ams1' 'ams2' 'ams3' ... None None"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closest_neigbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "89a1fe81-aed6-4336-bcb3-647479951b4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "closest_neigbors.isel(id=slice(50, 70)).distance.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa742e69-799c-4546-9383-b546fc8c745b",
   "metadata": {},
   "source": [
    "One interesting thing might be the distance of the closest station, which is at index 1 of `n_closest` in our case because at index 0 is each station itself.\n",
    "\n",
    "Note that there is no data point if there is not neigbor within `max_distance`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6aa1081b-6249-4eab-898f-0c49dba0e32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "closest_neigbors.isel(n_closest=1).distance.plot(figsize=(10, 4))\n",
    "plt.xticks([])\n",
    "plt.ylabel(\"distance to closest station in meters\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a698bf-a895-4807-bac2-4060f015a7a2",
   "metadata": {},
   "source": [
    "#### Get IDs of closest points"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "248c605b-f8c0-46b1-a742-f4dccd458c78",
   "metadata": {},
   "source": [
    "We can also get the station IDs of the closest neighbors. Note that they are different for each target station ID and they are sorted by ascending distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aca2d717-bceb-4cb5-83c0-184984eaf6d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "html[data-theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  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",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;neighbor_id&#x27; (n_closest: 22)&gt; Size: 176B\n",
       "array([&#x27;ams63&#x27;, &#x27;ams61&#x27;, &#x27;ams70&#x27;, &#x27;ams58&#x27;, &#x27;ams71&#x27;, &#x27;ams74&#x27;, &#x27;ams51&#x27;,\n",
       "       &#x27;ams54&#x27;, &#x27;ams83&#x27;, &#x27;ams67&#x27;, &#x27;ams55&#x27;, &#x27;ams60&#x27;, &#x27;ams84&#x27;, &#x27;ams69&#x27;,\n",
       "       &#x27;ams80&#x27;, &#x27;ams94&#x27;, &#x27;ams78&#x27;, &#x27;ams62&#x27;, &#x27;ams96&#x27;, &#x27;ams97&#x27;, &#x27;ams42&#x27;,\n",
       "       &#x27;ams41&#x27;], dtype=object)\n",
       "Coordinates:\n",
       "    id       &lt;U6 24B &#x27;ams63&#x27;\n",
       "Dimensions without coordinates: n_closest</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'neighbor_id'</div><ul class='xr-dim-list'><li><span>n_closest</span>: 22</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3a5b7935-d1a3-4041-a455-1c114eb2de61' class='xr-array-in' type='checkbox' checked><label for='section-3a5b7935-d1a3-4041-a455-1c114eb2de61' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>&#x27;ams63&#x27; &#x27;ams61&#x27; &#x27;ams70&#x27; &#x27;ams58&#x27; ... &#x27;ams96&#x27; &#x27;ams97&#x27; &#x27;ams42&#x27; &#x27;ams41&#x27;</span></div><div class='xr-array-data'><pre>array([&#x27;ams63&#x27;, &#x27;ams61&#x27;, &#x27;ams70&#x27;, &#x27;ams58&#x27;, &#x27;ams71&#x27;, &#x27;ams74&#x27;, &#x27;ams51&#x27;,\n",
       "       &#x27;ams54&#x27;, &#x27;ams83&#x27;, &#x27;ams67&#x27;, &#x27;ams55&#x27;, &#x27;ams60&#x27;, &#x27;ams84&#x27;, &#x27;ams69&#x27;,\n",
       "       &#x27;ams80&#x27;, &#x27;ams94&#x27;, &#x27;ams78&#x27;, &#x27;ams62&#x27;, &#x27;ams96&#x27;, &#x27;ams97&#x27;, &#x27;ams42&#x27;,\n",
       "       &#x27;ams41&#x27;], dtype=object)</pre></div></div></li><li class='xr-section-item'><input id='section-3dde110f-59b6-4e09-b8d8-e093f66ba7a7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3dde110f-59b6-4e09-b8d8-e093f66ba7a7' class='xr-section-summary' >Coordinates: <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-var-name'><span>id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;ams63&#x27;</div><input id='attrs-4161ca29-369b-4950-b269-b42e6e554b19' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4161ca29-369b-4950-b269-b42e6e554b19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8c9fc64-d5f6-41d7-bae8-fa7f8df36c4a' class='xr-var-data-in' type='checkbox'><label for='data-b8c9fc64-d5f6-41d7-bae8-fa7f8df36c4a' 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(&#x27;ams63&#x27;, dtype=&#x27;&lt;U6&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fe7497bc-f66a-4fe7-ac88-aed0e8f4eb62' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fe7497bc-f66a-4fe7-ac88-aed0e8f4eb62' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-a0050520-62de-4720-9447-b5bb5b411b39' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a0050520-62de-4720-9447-b5bb5b411b39' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'neighbor_id' (n_closest: 22)> Size: 176B\n",
       "array(['ams63', 'ams61', 'ams70', 'ams58', 'ams71', 'ams74', 'ams51',\n",
       "       'ams54', 'ams83', 'ams67', 'ams55', 'ams60', 'ams84', 'ams69',\n",
       "       'ams80', 'ams94', 'ams78', 'ams62', 'ams96', 'ams97', 'ams42',\n",
       "       'ams41'], dtype=object)\n",
       "Coordinates:\n",
       "    id       <U6 24B 'ams63'\n",
       "Dimensions without coordinates: n_closest"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pws_id = \"ams63\"\n",
    "\n",
    "neighbor_ids = closest_neigbors.sel(id=pws_id).neighbor_id.dropna(dim=\"n_closest\")\n",
    "neighbor_ids"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "badcd997-4b4c-4326-b9b8-0e588dd1ccf6",
   "metadata": {},
   "source": [
    "These should be the same neighboring station IDs as derive from the distance matrix above, since we have chosen the same ID as target station. This is, however, only true if `n_closets` does not cut off the list for `get_closest_points_to_point()`. For the chosen values in this example the two sets of neighboring IDs should be the same. Let's check that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7cdc8509-13b8-449a-bd90-ccffeaec10db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(selected_neighbor_ids) == set(neighbor_ids.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29066302-5191-440a-9893-c885a4257aa8",
   "metadata": {},
   "source": [
    "#### Plot stations and mark closest neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0e611962-55b9-4bf8-8e44-8dc24f35b310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.set_aspect(\"equal\")\n",
    "\n",
    "ax.scatter(ds_pws.x, ds_pws.y, c=\"C1\", s=30)\n",
    "\n",
    "neighbor_ids = closest_neigbors.sel(id=pws_id).neighbor_id.dropna(dim=\"n_closest\")\n",
    "ds_pws_neighbors = ds_pws.sel(id=neighbor_ids)\n",
    "ax.scatter(ds_pws_neighbors.x, ds_pws_neighbors.y, c=\"C0\", s=30)\n",
    "\n",
    "ax.scatter(\n",
    "    ds_pws.sel(id=pws_id).x, ds_pws.sel(id=pws_id).y, color=\"r\", s=30, marker=\"x\"\n",
    ")\n",
    "\n",
    "# Plot a circel with max_distance to see if the selections fits\n",
    "an = np.linspace(0, 2 * np.pi, 100)\n",
    "ax.plot(\n",
    "    ds_pws.x.sel(id=pws_id).data + max_distance * np.cos(an),\n",
    "    ds_pws.y.sel(id=pws_id).data + max_distance * np.sin(an),\n",
    "    color=\"k\",\n",
    "    linestyle=\"--\",\n",
    "    linewidth=0.5,\n",
    ")\n",
    "plt.title(\"\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afe12088-441b-4190-8663-c87e6116040c",
   "metadata": {},
   "source": [
    "Note that not all stations within the circle will be marked in case that `n_closest` cuts off the list of nearby stations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38133013-8cea-427c-9e16-c67a3d20345e",
   "metadata": {},
   "source": [
    "## Line-to-points distances\n",
    "This example shows how to use plg.spatial.get_closest_points_to_line to find points close to CMLs. We use CMLs from the OpenMerge dataset. The full dataset contains raingauges, but they where not unzippet and thus not readily available. We instead modify some of the CMLs so they look like raingauges by using the CML midpoints."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daf3434d-6e12-4963-8cfb-b813428d9f5a",
   "metadata": {},
   "source": [
    "### Download CML data from training school example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3000655b-6f30-4c55-bda8-b1968d93d759",
   "metadata": {},
   "outputs": [],
   "source": [
    "!curl -OL https://github.com/OpenSenseAction/training_school_opensene_2023/raw/main/data/cml/openMRG_example.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e844f0-6d41-444d-ac3a-25f00eb480db",
   "metadata": {},
   "source": [
    "### Remove unnecessary data and turn some CMLs into points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "910eea67-f29e-451a-ac25-00224766350c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load downloaded CML data\n",
    "ds_cmls = xr.open_dataset(\"./openMRG_example.nc\")\n",
    "\n",
    "# We only need metadata, so we drop tsl and rsl here\n",
    "ds_cmls = ds_cmls.drop_vars([\"tsl\", \"rsl\"]).drop_dims(\"time\")\n",
    "\n",
    "# Select some cmls, these are used as rain gauges\n",
    "gauges_ind = np.arange(0, ds_cmls.cml_id.size, 20)\n",
    "\n",
    "\n",
    "# Get cml indices that is not rain gauges\n",
    "cml_ind = np.arange(0, ds_cmls.cml_id.size)\n",
    "cml_ind = cml_ind[~np.isin(cml_ind, gauges_ind)]\n",
    "\n",
    "# Update both datasets with new non-overlapping indices\n",
    "ds_gauges = ds_cmls.isel(cml_id=gauges_ind)\n",
    "ds_cmls = ds_cmls.isel(cml_id=cml_ind)\n",
    "\n",
    "# Restructure ds_gauges so it has PWS standard names:\n",
    "ds_gauges = ds_gauges.drop_dims(\"sublink_id\").rename({\"cml_id\": \"id\"})\n",
    "\n",
    "# Use midpoint as gauge point\n",
    "ds_gauges.coords[\"lat\"] = (ds_gauges.site_0_lat + ds_gauges.site_1_lat) / 2\n",
    "ds_gauges.coords[\"lon\"] = (ds_gauges.site_0_lon + ds_gauges.site_1_lon) / 2\n",
    "ds_gauges = ds_gauges.drop_vars(\n",
    "    [\n",
    "        \"site_0_lat\",\n",
    "        \"site_1_lat\",\n",
    "        \"site_0_lon\",\n",
    "        \"site_1_lon\",\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Reformat ids to str, a more common format\n",
    "ds_gauges.coords[\"id\"] = ds_gauges.id.astype(str)\n",
    "ds_cmls.coords[\"cml_id\"] = ds_cmls.cml_id.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d2eac80-8521-4d2a-ae73-856f0f35851d",
   "metadata": {},
   "source": [
    "### Project cooridnates from lon-lat to UTM zone for Europe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0346a3dc-2bb9-4c7e-bbc7-662748d30394",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Project coordinates for rain gauges\n",
    "ds_gauges.coords[\"x\"], ds_gauges.coords[\"y\"] = plg.spatial.project_point_coordinates(\n",
    "    ds_gauges.lon, ds_gauges.lat, \"EPSG:25832\"\n",
    ")\n",
    "\n",
    "# Project coordinates for CMLs\n",
    "(\n",
    "    ds_cmls.coords[\"site_0_x\"],\n",
    "    ds_cmls.coords[\"site_0_y\"],\n",
    ") = plg.spatial.project_point_coordinates(\n",
    "    ds_cmls.site_0_lon, ds_cmls.site_0_lat, \"EPSG:25832\"\n",
    ")\n",
    "(\n",
    "    ds_cmls.coords[\"site_1_x\"],\n",
    "    ds_cmls.coords[\"site_1_y\"],\n",
    ") = plg.spatial.project_point_coordinates(\n",
    "    ds_cmls.site_1_lon, ds_cmls.site_1_lat, \"EPSG:25832\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8353fefa-45c7-4b05-b73d-fd842f34850d",
   "metadata": {},
   "source": [
    "### Nearest neighbor lookup\n",
    "Uses KDTree for fast neighbour lookup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "628511e3-f55a-4528-a927-b94f44efb6cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_distance = 20000  # in meters due to the projection EPSG:25832\n",
    "closest_neigbors = plg.spatial.get_closest_points_to_line(\n",
    "    ds_cmls, ds_gauges, max_distance=max_distance, n_closest=25\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cd9b357-f160-4302-a1c6-db7e1f450b8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "closest_neigbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c74fcb2-1e30-4a0a-b3f1-f265f422656b",
   "metadata": {},
   "outputs": [],
   "source": [
    "closest_neigbors.isel(cml_id=slice(50, 70)).distance.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08ffb8bc-ce33-48c3-abaf-2062f1d73645",
   "metadata": {},
   "source": [
    "We can get the nearest neighbour by selecting the first element in n_closest. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7caf5e8d-4ffd-4623-85ac-31aff7cf1340",
   "metadata": {},
   "outputs": [],
   "source": [
    "closest_neigbors.isel(n_closest=0).distance.plot(figsize=(10, 4))\n",
    "plt.xticks([])\n",
    "plt.ylabel(\"distance to closest station in meters\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd04d2fb-6a00-43a6-82d0-acc0c9e4dfec",
   "metadata": {},
   "source": [
    "We can also get the IDs of the closest gauges of a CML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ce65795-38b6-41b3-ab7b-601a3d413293",
   "metadata": {},
   "outputs": [],
   "source": [
    "gauge_id = \"10002\"\n",
    "neighbor_ids = closest_neigbors.sel(cml_id=gauge_id).neighbor_id.dropna(dim=\"n_closest\")\n",
    "neighbor_ids"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d26740f2-eb73-4664-8075-0a83f806a7a7",
   "metadata": {},
   "source": [
    "#### Plot CMLs that are close to rain gauges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fdd36ea-d82c-470f-a271-1239e2c3dfa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "gauge_ids = [13, 16]  # [13, 16] # Select only 2 rain gauges\n",
    "max_distance = 5500  # Distance in meters\n",
    "closest_neigbors = plg.spatial.get_closest_points_to_line(\n",
    "    ds_cmls, ds_gauges.isel(id=gauge_ids), max_distance=max_distance, n_closest=1\n",
    ").isel(n_closest=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b10d4b58-e8a7-42c9-90ac-3553f261450b",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.set_aspect(\"equal\")\n",
    "\n",
    "# Plot all CMLs\n",
    "for cml_id in ds_cmls.cml_id:\n",
    "    plt.plot(\n",
    "        [ds_cmls.sel(cml_id=cml_id).site_0_x, ds_cmls.sel(cml_id=cml_id).site_1_x],\n",
    "        [ds_cmls.sel(cml_id=cml_id).site_0_y, ds_cmls.sel(cml_id=cml_id).site_1_y],\n",
    "        \"C0\",\n",
    "    )\n",
    "\n",
    "# Plot CMLs close to selected gauges\n",
    "nearby_cml_id = closest_neigbors.neighbor_id.dropna(dim=\"cml_id\").cml_id\n",
    "for cml_id in nearby_cml_id:\n",
    "    plt.plot(\n",
    "        [ds_cmls.sel(cml_id=cml_id).site_0_x, ds_cmls.sel(cml_id=cml_id).site_1_x],\n",
    "        [ds_cmls.sel(cml_id=cml_id).site_0_y, ds_cmls.sel(cml_id=cml_id).site_1_y],\n",
    "        \"C1\",\n",
    "    )\n",
    "\n",
    "# Plot selected rain gauges\n",
    "ax.scatter(ds_gauges.isel(id=gauge_ids).x, ds_gauges.isel(id=gauge_ids).y, c=\"C2\", s=30)\n",
    "\n",
    "# Plot a circel with max_distance to see if the selections fits\n",
    "an = np.linspace(0, 2 * np.pi, 100)\n",
    "for gauge_id in gauge_ids:\n",
    "    ax.plot(\n",
    "        ds_gauges.x.isel(id=gauge_id).data + max_distance * np.cos(an),\n",
    "        ds_gauges.y.isel(id=gauge_id).data + max_distance * np.sin(an),\n",
    "        color=\"k\",\n",
    "        linestyle=\"--\",\n",
    "        linewidth=0.5,\n",
    "    )\n",
    "    plt.title(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a55b716",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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