diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index ed9defc8..0fbc33c9 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -35,6 +35,7 @@ jobs:
           pip install pytest-cov
           pip install pytest-dependency
           pip install codacy-coverage
+          pip install mfpymake
           pip install -e .
           
       - name: Lint with flake8
diff --git a/examples/01_basic_model.ipynb b/examples/01_basic_model.ipynb
index feb94fd9..c796c321 100644
--- a/examples/01_basic_model.ipynb
+++ b/examples/01_basic_model.ipynb
@@ -47,7 +47,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "nlmod version: 0.2.0\n"
+      "nlmod version: 0.2.1\n"
      ]
     }
    ],
@@ -153,12 +153,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:nlmod.mdims.mgrid:get active cells (idomain) from bottom DataArray\n",
+      "INFO:nlmod.mdims.mlayers:get active cells (idomain) from bottom DataArray\n",
       "INFO:nlmod.mdims.mgrid:get first active modellayer for each cell in idomain\n",
-      "INFO:nlmod.mdims.mgrid:using top and bottom from model layers dataset for modflow model\n",
-      "INFO:nlmod.mdims.mgrid:replace nan values for inactive layers with dummy value\n",
-      "INFO:nlmod.mdims.mgrid:add kh and kv from model layer dataset to modflow model\n",
-      "INFO:nlmod.mdims.mgrid:nan values at the northsea are filled using the bathymetry from jarkus\n",
+      "INFO:nlmod.mdims.mlayers:using top and bottom from model layers dataset for modflow model\n",
+      "INFO:nlmod.mdims.mlayers:replace nan values for inactive layers with dummy value\n",
+      "INFO:nlmod.mdims.mlayers:add kh and kv from model layer dataset to modflow model\n",
+      "INFO:nlmod.mdims.mlayers:nan values at the northsea are filled using the bathymetry from jarkus\n",
       "INFO:nlmod.cache:using cached data -> sea_model_ds.nc\n",
       "INFO:nlmod.cache:using cached data -> bathymetry_model_ds.nc\n",
       "INFO:nlmod.mdims.mgrid:get first active modellayer for each cell in idomain\n"
@@ -268,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -632,7 +632,7 @@
        "  * x                   (x) float64 9.505e+04 9.515e+04 ... 1.048e+05 1.05e+05\n",
        "  * y                   (y) float64 5e+05 4.998e+05 ... 4.942e+05 4.94e+05\n",
        "  * layer               (layer) object 'AAOP' 'NASC' 'ONAWA' ... 'PZWAz4' 'MSz1'\n",
-       "Data variables: (12/18)\n",
+       "Data variables: (12/17)\n",
        "    time_steps          (time) int32 0\n",
        "    idomain             (layer, y, x) int32 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1\n",
        "    first_active_layer  (y, x) int32 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0\n",
@@ -640,26 +640,26 @@
        "    top                 (y, x) float64 0.0 0.0 0.0 0.0 ... 2.29 1.49 0.5 0.37\n",
        "    kh                  (layer, y, x) float64 10.0 10.0 10.0 ... 6.1 6.11 6.11\n",
        "    ...                  ...\n",
+       "    rws_oppwater_area   (y, x) float64 1e+04 1e+04 1e+04 1e+04 ... 0.0 0.0 0.0\n",
        "    rws_oppwater_cond   (y, x) float64 1e+03 1e+03 1e+03 1e+03 ... 0.0 0.0 0.0\n",
        "    rws_oppwater_peil   (y, x) float64 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
        "    ahn                 (y, x) float64 nan nan nan nan ... 1.971 nan nan 0.2378\n",
        "    chd                 (layer, y, x) int32 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1\n",
-       "    recharge            (y, x) float64 0.000606 0.000606 ... 0.0007697 0.0007697\n",
-       "    test                int32 1\n",
-       "Attributes: (12/27)\n",
-       "    model_name:                        IJmuiden\n",
-       "    mfversion:                         mf6\n",
-       "    model_dataset_created_on:          20211210_12:59:57\n",
-       "    exe_name:                          c:\\users\\oebbe\\02_python\\nlmod\\nlmod\\m...\n",
-       "    model_ws:                          model1\n",
-       "    figdir:                            model1\\figure\n",
-       "    ...                                ...\n",
-       "    fill_value_kh:                     1.0\n",
-       "    fill_value_kv:                     0.1\n",
-       "    surface_drn_cond:                  1000\n",
-       "    model_dataset_written_to_disk_on:  20211210_13:00:02\n",
-       "    model_data_written_to_disk_on:     20211210_13:00:09\n",
-       "    model_ran_on:                      20211210_13:00:10</pre><div class='xr-wrap' hidden><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-e4bde375-be62-4cc5-8163-e357821b9b1f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e4bde375-be62-4cc5-8163-e357821b9b1f' 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>: 1</li><li><span class='xr-has-index'>x</span>: 100</li><li><span class='xr-has-index'>y</span>: 60</li><li><span class='xr-has-index'>layer</span>: 40</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6c39b5b4-8ab0-4b0a-8f9c-968439f36ba5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6c39b5b4-8ab0-4b0a-8f9c-968439f36ba5' class='xr-section-summary' >Coordinates: <span>(4)</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'>2015-01-01</div><input id='attrs-dd4930f2-5a99-4aa2-908e-d6afb6adf3b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd4930f2-5a99-4aa2-908e-d6afb6adf3b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e014b88-aa5d-40dd-9554-62b515b59f78' class='xr-var-data-in' type='checkbox'><label for='data-2e014b88-aa5d-40dd-9554-62b515b59f78' 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;2015-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.505e+04 9.515e+04 ... 1.05e+05</div><input id='attrs-6da2f399-61bd-4751-a744-db1a23847456' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6da2f399-61bd-4751-a744-db1a23847456' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a62dc06b-e713-48fb-92aa-95a5c847a680' class='xr-var-data-in' type='checkbox'><label for='data-a62dc06b-e713-48fb-92aa-95a5c847a680' 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([ 95050.,  95150.,  95250.,  95350.,  95450.,  95550.,  95650.,  95750.,\n",
+       "    recharge            (y, x) float64 0.0003688 0.0003688 ... 0.0003688\n",
+       "Attributes: (12/24)\n",
+       "    model_name:                IJmuiden\n",
+       "    mfversion:                 mf6\n",
+       "    model_dataset_created_on:  20220315_15:56:45\n",
+       "    exe_name:                  c:\\users\\oebbe\\02_python\\nlmod\\nlmod\\mdims\\..\\...\n",
+       "    model_ws:                  model1\n",
+       "    figdir:                    model1\\figure\n",
+       "    ...                        ...\n",
+       "    delc:                      100.0\n",
+       "    nodata:                    -999\n",
+       "    anisotropy:                10\n",
+       "    fill_value_kh:             1.0\n",
+       "    fill_value_kv:             0.1\n",
+       "    surface_drn_cond:          1000</pre><div class='xr-wrap' hidden><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-10b5ccfe-d54f-4f1c-9a6d-8813f32e76fe' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-10b5ccfe-d54f-4f1c-9a6d-8813f32e76fe' 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>: 1</li><li><span class='xr-has-index'>x</span>: 100</li><li><span class='xr-has-index'>y</span>: 60</li><li><span class='xr-has-index'>layer</span>: 40</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f0d99081-279f-4595-9da8-5a04ecc6e681' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f0d99081-279f-4595-9da8-5a04ecc6e681' class='xr-section-summary' >Coordinates: <span>(4)</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'>2015-01-01</div><input id='attrs-e70fd338-6f4c-4c10-8dca-d9649502cd81' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e70fd338-6f4c-4c10-8dca-d9649502cd81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a71866dd-b302-46de-bd0b-ee46262e3c9b' class='xr-var-data-in' type='checkbox'><label for='data-a71866dd-b302-46de-bd0b-ee46262e3c9b' 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;2015-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.505e+04 9.515e+04 ... 1.05e+05</div><input id='attrs-5ee03c84-14fb-4b67-8ddc-71dc8865872e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ee03c84-14fb-4b67-8ddc-71dc8865872e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c48383a1-f7eb-40ea-a3b2-0fbe191cabf3' class='xr-var-data-in' type='checkbox'><label for='data-c48383a1-f7eb-40ea-a3b2-0fbe191cabf3' 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([ 95050.,  95150.,  95250.,  95350.,  95450.,  95550.,  95650.,  95750.,\n",
        "        95850.,  95950.,  96050.,  96150.,  96250.,  96350.,  96450.,  96550.,\n",
        "        96650.,  96750.,  96850.,  96950.,  97050.,  97150.,  97250.,  97350.,\n",
        "        97450.,  97550.,  97650.,  97750.,  97850.,  97950.,  98050.,  98150.,\n",
@@ -671,19 +671,19 @@
        "       102250., 102350., 102450., 102550., 102650., 102750., 102850., 102950.,\n",
        "       103050., 103150., 103250., 103350., 103450., 103550., 103650., 103750.,\n",
        "       103850., 103950., 104050., 104150., 104250., 104350., 104450., 104550.,\n",
-       "       104650., 104750., 104850., 104950.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5e+05 4.998e+05 ... 4.94e+05</div><input id='attrs-4ee38a64-ddc2-4d1d-b273-a1eb5482dbd7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ee38a64-ddc2-4d1d-b273-a1eb5482dbd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d79eb0ea-b8a3-42fc-b88b-accc78a8dbce' class='xr-var-data-in' type='checkbox'><label for='data-d79eb0ea-b8a3-42fc-b88b-accc78a8dbce' 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([499950., 499850., 499750., 499650., 499550., 499450., 499350., 499250.,\n",
+       "       104650., 104750., 104850., 104950.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5e+05 4.998e+05 ... 4.94e+05</div><input id='attrs-5cf42d81-ecfe-4a40-82d4-584e812caa63' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5cf42d81-ecfe-4a40-82d4-584e812caa63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-207a9446-f7e2-4bea-8a94-44f6ace0edca' class='xr-var-data-in' type='checkbox'><label for='data-207a9446-f7e2-4bea-8a94-44f6ace0edca' 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([499950., 499850., 499750., 499650., 499550., 499450., 499350., 499250.,\n",
        "       499150., 499050., 498950., 498850., 498750., 498650., 498550., 498450.,\n",
        "       498350., 498250., 498150., 498050., 497950., 497850., 497750., 497650.,\n",
        "       497550., 497450., 497350., 497250., 497150., 497050., 496950., 496850.,\n",
        "       496750., 496650., 496550., 496450., 496350., 496250., 496150., 496050.,\n",
        "       495950., 495850., 495750., 495650., 495550., 495450., 495350., 495250.,\n",
        "       495150., 495050., 494950., 494850., 494750., 494650., 494550., 494450.,\n",
-       "       494350., 494250., 494150., 494050.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>layer</span></div><div class='xr-var-dims'>(layer)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;AAOP&#x27; &#x27;NASC&#x27; ... &#x27;PZWAz4&#x27; &#x27;MSz1&#x27;</div><input id='attrs-a57c478a-4c97-482e-b795-f77adf137184' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a57c478a-4c97-482e-b795-f77adf137184' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3f986b3-ed53-4698-b8f0-4a573d052d91' class='xr-var-data-in' type='checkbox'><label for='data-f3f986b3-ed53-4698-b8f0-4a573d052d91' 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;AAOP&#x27;, &#x27;NASC&#x27;, &#x27;ONAWA&#x27;, &#x27;NAZA&#x27;, &#x27;NAWA&#x27;, &#x27;NAWO&#x27;, &#x27;NAWOVE&#x27;, &#x27;NIBA&#x27;,\n",
+       "       494350., 494250., 494150., 494050.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>layer</span></div><div class='xr-var-dims'>(layer)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;AAOP&#x27; &#x27;NASC&#x27; ... &#x27;PZWAz4&#x27; &#x27;MSz1&#x27;</div><input id='attrs-f1cbd695-8f12-4207-b701-42ebe7b10b18' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1cbd695-8f12-4207-b701-42ebe7b10b18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f63ac76d-79db-4d29-a212-096c10e447e2' class='xr-var-data-in' type='checkbox'><label for='data-f63ac76d-79db-4d29-a212-096c10e447e2' 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;AAOP&#x27;, &#x27;NASC&#x27;, &#x27;ONAWA&#x27;, &#x27;NAZA&#x27;, &#x27;NAWA&#x27;, &#x27;NAWO&#x27;, &#x27;NAWOVE&#x27;, &#x27;NIBA&#x27;,\n",
        "       &#x27;BXWISIKO&#x27;, &#x27;BX&#x27;, &#x27;KRBXDE&#x27;, &#x27;BXz4&#x27;, &#x27;KRz3&#x27;, &#x27;EEz1&#x27;, &#x27;EEk1&#x27;, &#x27;EEz2&#x27;,\n",
        "       &#x27;EEk2&#x27;, &#x27;EEz3&#x27;, &#x27;DRz1&#x27;, &#x27;DRUIk1&#x27;, &#x27;DRz2&#x27;, &#x27;DRGIk1&#x27;, &#x27;DRz3&#x27;, &#x27;URz2&#x27;,\n",
        "       &#x27;URz3&#x27;, &#x27;URz4&#x27;, &#x27;URk3&#x27;, &#x27;URz5&#x27;, &#x27;STz1&#x27;, &#x27;STk1&#x27;, &#x27;STz2&#x27;, &#x27;APz1&#x27;,\n",
        "       &#x27;PZWAz1&#x27;, &#x27;WAk1&#x27;, &#x27;PZWAz2&#x27;, &#x27;WAk2&#x27;, &#x27;PZWAz3&#x27;, &#x27;PZc&#x27;, &#x27;PZWAz4&#x27;, &#x27;MSz1&#x27;],\n",
-       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-73de4404-7687-493c-9a1e-9e5a64a47b4e' class='xr-section-summary-in' type='checkbox'  ><label for='section-73de4404-7687-493c-9a1e-9e5a64a47b4e' class='xr-section-summary' >Data variables: <span>(18)</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>time_steps</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-33c07ca5-e7ac-4d18-800e-38947fef6389' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33c07ca5-e7ac-4d18-800e-38947fef6389' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9da57333-3757-4842-9cb8-313cf716eb7b' class='xr-var-data-in' type='checkbox'><label for='data-9da57333-3757-4842-9cb8-313cf716eb7b' 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])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>idomain</span></div><div class='xr-var-dims'>(layer, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1</div><input id='attrs-f93c2e8e-31ba-4bd5-bf46-90c50de8efa8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f93c2e8e-31ba-4bd5-bf46-90c50de8efa8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9fbbd95-900f-4d49-9c0a-71fdb60942b2' class='xr-var-data-in' type='checkbox'><label for='data-f9fbbd95-900f-4d49-9c0a-71fdb60942b2' 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([[[ 1,  1,  1, ...,  1,  1,  1],\n",
+       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d15eceb-1983-4e64-ae4a-b756596f9035' class='xr-section-summary-in' type='checkbox'  ><label for='section-8d15eceb-1983-4e64-ae4a-b756596f9035' class='xr-section-summary' >Data variables: <span>(17)</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>time_steps</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-88033fe5-fd40-41d4-befb-b28cde381a0f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-88033fe5-fd40-41d4-befb-b28cde381a0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1111429-f261-49c8-a257-d521d350e789' class='xr-var-data-in' type='checkbox'><label for='data-e1111429-f261-49c8-a257-d521d350e789' 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])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>idomain</span></div><div class='xr-var-dims'>(layer, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1</div><input id='attrs-a01c29be-45e0-4e7a-8c2c-99643e6a029b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a01c29be-45e0-4e7a-8c2c-99643e6a029b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cab40f2-a461-4a8a-9386-9d2c6a984904' class='xr-var-data-in' type='checkbox'><label for='data-3cab40f2-a461-4a8a-9386-9d2c6a984904' 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([[[ 1,  1,  1, ...,  1,  1,  1],\n",
        "        [ 1,  1,  1, ...,  1,  1,  1],\n",
        "        [ 1,  1,  1, ...,  1,  1,  1],\n",
        "        ...,\n",
@@ -723,13 +723,13 @@
        "        ...,\n",
        "        [-1, -1, -1, ...,  1,  1,  1],\n",
        "        [-1, -1, -1, ...,  1,  1,  1],\n",
-       "        [-1, -1, -1, ...,  1,  1,  1]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>first_active_layer</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-48235c49-9eb2-441c-84ed-1c8b5f3776cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-48235c49-9eb2-441c-84ed-1c8b5f3776cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fff55dfd-1008-4217-aee5-5bb88590721b' class='xr-var-data-in' type='checkbox'><label for='data-fff55dfd-1008-4217-aee5-5bb88590721b' 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, 0, 0, ..., 0, 0, 0],\n",
+       "        [-1, -1, -1, ...,  1,  1,  1]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>first_active_layer</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-f4275a11-5eb4-4fa3-a782-a25733812317' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4275a11-5eb4-4fa3-a782-a25733812317' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de3335fc-a7e0-407e-a7ca-7d9b7a63814d' class='xr-var-data-in' type='checkbox'><label for='data-de3335fc-a7e0-407e-a7ca-7d9b7a63814d' 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, 0, 0, ..., 0, 0, 0],\n",
        "       [0, 0, 0, ..., 0, 0, 0],\n",
        "       [0, 0, 0, ..., 0, 0, 0],\n",
        "       ...,\n",
        "       [0, 0, 0, ..., 0, 0, 0],\n",
        "       [0, 0, 0, ..., 0, 0, 0],\n",
-       "       [0, 0, 0, ..., 0, 0, 0]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bot</span></div><div class='xr-var-dims'>(layer, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-13.07 -13.07 ... -218.2 -218.5</div><input id='attrs-1f45f7d9-5b0a-4a70-a71c-3e9bf74b8624' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f45f7d9-5b0a-4a70-a71c-3e9bf74b8624' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ba7e96e-633e-483b-8543-393c379c575a' class='xr-var-data-in' type='checkbox'><label for='data-9ba7e96e-633e-483b-8543-393c379c575a' 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>source :</span></dt><dd>REGIS/geotop</dd><dt><span>regis_url :</span></dt><dd>http://www.dinodata.nl:80/opendap/REGIS/REGIS.nc</dd><dt><span>geotop_url :</span></dt><dd>http://www.dinodata.nl/opendap/GeoTOP/geotop.nc</dd><dt><span>date :</span></dt><dd>20211210</dd><dt><span>units :</span></dt><dd>mNAP</dd></dl></div><div class='xr-var-data'><pre>array([[[ -13.07      ,  -13.07      ,  -13.07      , ...,\n",
+       "       [0, 0, 0, ..., 0, 0, 0]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bot</span></div><div class='xr-var-dims'>(layer, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-13.07 -13.07 ... -218.2 -218.5</div><input id='attrs-ae1f4faf-02aa-4201-91d9-f34a58a49473' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae1f4faf-02aa-4201-91d9-f34a58a49473' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4467f89b-bdc6-4700-81d0-c9a0b4c575f5' class='xr-var-data-in' type='checkbox'><label for='data-4467f89b-bdc6-4700-81d0-c9a0b4c575f5' 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>source :</span></dt><dd>REGIS/geotop</dd><dt><span>regis_url :</span></dt><dd>http://www.dinodata.nl:80/opendap/REGIS/REGIS.nc</dd><dt><span>geotop_url :</span></dt><dd>http://www.dinodata.nl/opendap/GeoTOP/geotop.nc</dd><dt><span>date :</span></dt><dd>20211220</dd><dt><span>units :</span></dt><dd>mNAP</dd></dl></div><div class='xr-var-data'><pre>array([[[ -13.07      ,  -13.07      ,  -13.07      , ...,\n",
        "            3.        ,    3.        ,    3.        ],\n",
        "        [ -13.07      ,  -13.07      ,  -13.07      , ...,\n",
        "            2.5       ,    2.5       ,    2.5       ],\n",
@@ -769,7 +769,7 @@
        "        [ -10.91      ,  -10.91      ,  -10.91      , ...,\n",
        "         -218.27000427, -218.55000305, -218.8500061 ],\n",
        "        [-200.77000427, -200.77000427, -200.77000427, ...,\n",
-       "         -217.88999939, -218.19999695, -218.47000122]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>top</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 1.49 0.5 0.37</div><input id='attrs-8b3eeb3f-8663-402f-9800-492ef6dcc9be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b3eeb3f-8663-402f-9800-492ef6dcc9be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d70b9fd-94f8-47d1-9ec6-abf2f9d96e6e' class='xr-var-data-in' type='checkbox'><label for='data-7d70b9fd-94f8-47d1-9ec6-abf2f9d96e6e' 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>source :</span></dt><dd>REGIS/geotop</dd><dt><span>regis_url :</span></dt><dd>http://www.dinodata.nl:80/opendap/REGIS/REGIS.nc</dd><dt><span>geotop_url :</span></dt><dd>http://www.dinodata.nl/opendap/GeoTOP/geotop.nc</dd><dt><span>date :</span></dt><dd>20211210</dd><dt><span>units :</span></dt><dd>mNAP</dd></dl></div><div class='xr-var-data'><pre>array([[0.        , 0.        , 0.        , ..., 3.5       , 3.5       ,\n",
+       "         -217.88999939, -218.19999695, -218.47000122]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>top</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 1.49 0.5 0.37</div><input id='attrs-a30ba364-afe0-4a81-b279-5a46b9b4a271' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a30ba364-afe0-4a81-b279-5a46b9b4a271' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92905060-09e8-4729-b8a8-c078e1514978' class='xr-var-data-in' type='checkbox'><label for='data-92905060-09e8-4729-b8a8-c078e1514978' 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>source :</span></dt><dd>REGIS/geotop</dd><dt><span>regis_url :</span></dt><dd>http://www.dinodata.nl:80/opendap/REGIS/REGIS.nc</dd><dt><span>geotop_url :</span></dt><dd>http://www.dinodata.nl/opendap/GeoTOP/geotop.nc</dd><dt><span>date :</span></dt><dd>20211220</dd><dt><span>units :</span></dt><dd>mNAP</dd></dl></div><div class='xr-var-data'><pre>array([[0.        , 0.        , 0.        , ..., 3.5       , 3.5       ,\n",
        "        3.5       ],\n",
        "       [0.        , 0.        , 0.        , ..., 3.        , 3.        ,\n",
        "        3.36999989],\n",
@@ -781,7 +781,7 @@
        "       [0.        , 0.        , 0.        , ..., 1.5       , 0.88999999,\n",
        "        0.        ],\n",
        "       [0.        , 0.        , 0.        , ..., 1.49000001, 0.5       ,\n",
-       "        0.37      ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kh</span></div><div class='xr-var-dims'>(layer, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0 10.0 10.0 ... 6.1 6.11 6.11</div><input id='attrs-c94928fd-8982-488c-b3ed-1bd224bc2698' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c94928fd-8982-488c-b3ed-1bd224bc2698' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27d00495-3bf9-4fc9-8a5c-6f1ca8335aef' class='xr-var-data-in' type='checkbox'><label for='data-27d00495-3bf9-4fc9-8a5c-6f1ca8335aef' 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>source :</span></dt><dd>REGIS/geotop</dd><dt><span>regis_url :</span></dt><dd>http://www.dinodata.nl:80/opendap/REGIS/REGIS.nc</dd><dt><span>geotop_url :</span></dt><dd>http://www.dinodata.nl/opendap/GeoTOP/geotop.nc</dd><dt><span>date :</span></dt><dd>20211210</dd><dt><span>units :</span></dt><dd>m/day</dd></dl></div><div class='xr-var-data'><pre>array([[[1.00000000e+01, 1.00000000e+01, 1.00000000e+01, ...,\n",
+       "        0.37      ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kh</span></div><div class='xr-var-dims'>(layer, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0 10.0 10.0 ... 6.1 6.11 6.11</div><input id='attrs-64d8b349-03d5-4b89-b876-80278afb1442' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64d8b349-03d5-4b89-b876-80278afb1442' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbb860f5-ab93-4330-904b-197783ace3fd' class='xr-var-data-in' type='checkbox'><label for='data-dbb860f5-ab93-4330-904b-197783ace3fd' 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>source :</span></dt><dd>REGIS/geotop</dd><dt><span>regis_url :</span></dt><dd>http://www.dinodata.nl:80/opendap/REGIS/REGIS.nc</dd><dt><span>geotop_url :</span></dt><dd>http://www.dinodata.nl/opendap/GeoTOP/geotop.nc</dd><dt><span>date :</span></dt><dd>20211220</dd><dt><span>units :</span></dt><dd>m/day</dd></dl></div><div class='xr-var-data'><pre>array([[[1.00000000e+01, 1.00000000e+01, 1.00000000e+01, ...,\n",
        "         5.00000000e+00, 5.00000000e+00, 5.00000000e+00],\n",
        "        [1.00000000e+01, 1.00000000e+01, 1.00000000e+01, ...,\n",
        "         5.00000000e+00, 5.00000000e+00, 5.00000000e+00],\n",
@@ -821,7 +821,7 @@
        "        [1.00000000e+00, 1.00000000e+00, 1.00000000e+00, ...,\n",
        "         6.09999990e+00, 6.09999990e+00, 6.09999990e+00],\n",
        "        [6.11999989e+00, 6.11999989e+00, 6.11999989e+00, ...,\n",
-       "         6.09999990e+00, 6.11000013e+00, 6.11000013e+00]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kv</span></div><div class='xr-var-dims'>(layer, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0 10.0 10.0 ... 0.61 0.611 0.611</div><input id='attrs-018319a5-4cd6-434b-a9ce-0b40b85b02c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-018319a5-4cd6-434b-a9ce-0b40b85b02c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6be5a1bc-251b-4bc0-be02-14233380b3d2' class='xr-var-data-in' type='checkbox'><label for='data-6be5a1bc-251b-4bc0-be02-14233380b3d2' 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>source :</span></dt><dd>REGIS/geotop</dd><dt><span>regis_url :</span></dt><dd>http://www.dinodata.nl:80/opendap/REGIS/REGIS.nc</dd><dt><span>geotop_url :</span></dt><dd>http://www.dinodata.nl/opendap/GeoTOP/geotop.nc</dd><dt><span>date :</span></dt><dd>20211210</dd><dt><span>units :</span></dt><dd>m/day</dd></dl></div><div class='xr-var-data'><pre>array([[[1.00000000e+01, 1.00000000e+01, 1.00000000e+01, ...,\n",
+       "         6.09999990e+00, 6.11000013e+00, 6.11000013e+00]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kv</span></div><div class='xr-var-dims'>(layer, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0 10.0 10.0 ... 0.61 0.611 0.611</div><input id='attrs-cf9afc03-8c2d-4634-a1e2-67b79745ae67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf9afc03-8c2d-4634-a1e2-67b79745ae67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-008051d0-ca60-4453-b904-ee49096e15c0' class='xr-var-data-in' type='checkbox'><label for='data-008051d0-ca60-4453-b904-ee49096e15c0' 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>source :</span></dt><dd>REGIS/geotop</dd><dt><span>regis_url :</span></dt><dd>http://www.dinodata.nl:80/opendap/REGIS/REGIS.nc</dd><dt><span>geotop_url :</span></dt><dd>http://www.dinodata.nl/opendap/GeoTOP/geotop.nc</dd><dt><span>date :</span></dt><dd>20211220</dd><dt><span>units :</span></dt><dd>m/day</dd></dl></div><div class='xr-var-data'><pre>array([[[1.00000000e+01, 1.00000000e+01, 1.00000000e+01, ...,\n",
        "         1.25000000e+00, 1.25000000e+00, 1.25000000e+00],\n",
        "        [1.00000000e+01, 1.00000000e+01, 1.00000000e+01, ...,\n",
        "         1.25000000e+00, 1.25000000e+00, 1.25000000e+00],\n",
@@ -861,19 +861,19 @@
        "        [1.00000000e-01, 1.00000000e-01, 1.00000000e-01, ...,\n",
        "         6.09999990e-01, 6.09999990e-01, 6.09999990e-01],\n",
        "        [6.11999989e-01, 6.11999989e-01, 6.11999989e-01, ...,\n",
-       "         6.09999990e-01, 6.11000013e-01, 6.11000013e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>northsea</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 0 0 0 0 0 0 0 0</div><input id='attrs-e92e9fdd-925c-407b-bb6c-1541d97e3120' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e92e9fdd-925c-407b-bb6c-1541d97e3120' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a430ac0c-2215-4630-9142-1b69c72028c2' class='xr-var-data-in' type='checkbox'><label for='data-a430ac0c-2215-4630-9142-1b69c72028c2' 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([[1, 1, 1, ..., 0, 0, 0],\n",
+       "         6.09999990e-01, 6.11000013e-01, 6.11000013e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>northsea</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 0 0 0 0 0 0 0 0</div><input id='attrs-b4dcc5d1-2366-4830-bd8e-70131404e9e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b4dcc5d1-2366-4830-bd8e-70131404e9e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a9921bd-87ce-4cd1-9c63-7ffcecb993cf' class='xr-var-data-in' type='checkbox'><label for='data-3a9921bd-87ce-4cd1-9c63-7ffcecb993cf' 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([[1, 1, 1, ..., 0, 0, 0],\n",
        "       [1, 1, 1, ..., 0, 0, 0],\n",
        "       [1, 1, 1, ..., 0, 0, 0],\n",
        "       ...,\n",
        "       [1, 1, 1, ..., 0, 0, 0],\n",
        "       [1, 1, 1, ..., 0, 0, 0],\n",
-       "       [1, 1, 1, ..., 0, 0, 0]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bathymetry</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-13.07 -13.07 -13.07 ... nan nan</div><input id='attrs-5bb1b960-38ef-4c12-9ac3-04e9a3c35e5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bb1b960-38ef-4c12-9ac3-04e9a3c35e5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eebedfbf-94bd-40e0-8cc0-e45ce2d15d4b' class='xr-var-data-in' type='checkbox'><label for='data-eebedfbf-94bd-40e0-8cc0-e45ce2d15d4b' 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>source :</span></dt><dd>20211209</dd><dt><span>url :</span></dt><dd>http://opendap.deltares.nl/thredds/dodsC/opendap/rijkswaterstaat/jarkus/grids/catalog.nc</dd><dt><span>units :</span></dt><dd>mNAP</dd></dl></div><div class='xr-var-data'><pre>array([[-13.07, -13.07, -13.07, ...,    nan,    nan,    nan],\n",
+       "       [1, 1, 1, ..., 0, 0, 0]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bathymetry</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9db4a951-347d-4fa2-a231-7f106c68f915' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9db4a951-347d-4fa2-a231-7f106c68f915' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cacac11c-5001-4d46-a58d-3c44647f24a7' class='xr-var-data-in' type='checkbox'><label for='data-cacac11c-5001-4d46-a58d-3c44647f24a7' 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>source :</span></dt><dd>20211220</dd><dt><span>url :</span></dt><dd>http://opendap.deltares.nl/thredds/dodsC/opendap/rijkswaterstaat/jarkus/grids/catalog.nc</dd><dt><span>units :</span></dt><dd>mNAP</dd></dl></div><div class='xr-var-data'><pre>array([[-13.07, -13.07, -13.07, ...,    nan,    nan,    nan],\n",
        "       [-13.07, -13.07, -13.07, ...,    nan,    nan,    nan],\n",
        "       [-13.07, -13.07, -13.07, ...,    nan,    nan,    nan],\n",
        "       ...,\n",
        "       [-10.9 , -10.9 , -10.9 , ...,    nan,    nan,    nan],\n",
        "       [-10.91, -10.91, -10.91, ...,    nan,    nan,    nan],\n",
-       "       [-10.95, -10.91, -10.91, ...,    nan,    nan,    nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thickness</span></div><div class='xr-var-dims'>(layer, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>13.07 13.07 13.07 ... 0.0 0.0 0.0</div><input id='attrs-7e5eaa62-219c-428a-a3ff-a9e6b6eaa1cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e5eaa62-219c-428a-a3ff-a9e6b6eaa1cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a4238e2-12c3-45fb-8edc-91ba9c717b48' class='xr-var-data-in' type='checkbox'><label for='data-1a4238e2-12c3-45fb-8edc-91ba9c717b48' 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>source :</span></dt><dd>REGIS/geotop</dd><dt><span>regis_url :</span></dt><dd>http://www.dinodata.nl:80/opendap/REGIS/REGIS.nc</dd><dt><span>geotop_url :</span></dt><dd>http://www.dinodata.nl/opendap/GeoTOP/geotop.nc</dd><dt><span>date :</span></dt><dd>20211210</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[[13.07      , 13.07      , 13.07      , ...,  0.5       ,\n",
+       "       [-10.95, -10.91, -10.91, ...,    nan,    nan,    nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thickness</span></div><div class='xr-var-dims'>(layer, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>13.07 13.07 13.07 ... 0.0 0.0 0.0</div><input id='attrs-b68c28f3-37c9-4ebf-b572-a2fa32030a0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b68c28f3-37c9-4ebf-b572-a2fa32030a0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46a0c3e9-4167-4a26-95b3-f0559a6784d2' class='xr-var-data-in' type='checkbox'><label for='data-46a0c3e9-4167-4a26-95b3-f0559a6784d2' 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>source :</span></dt><dd>REGIS/geotop</dd><dt><span>regis_url :</span></dt><dd>http://www.dinodata.nl:80/opendap/REGIS/REGIS.nc</dd><dt><span>geotop_url :</span></dt><dd>http://www.dinodata.nl/opendap/GeoTOP/geotop.nc</dd><dt><span>date :</span></dt><dd>20211220</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[[13.07      , 13.07      , 13.07      , ...,  0.5       ,\n",
        "          0.5       ,  0.5       ],\n",
        "        [13.07      , 13.07      , 13.07      , ...,  0.5       ,\n",
        "          0.5       ,  0.86999989],\n",
@@ -913,7 +913,7 @@
        "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
        "          0.        ,  0.        ],\n",
        "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
-       "          0.        ,  0.        ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>starting_head</span></div><div class='xr-var-dims'>(layer, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0</div><input id='attrs-9fdd9506-0dce-4cb7-86e1-1eb5674acfd9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9fdd9506-0dce-4cb7-86e1-1eb5674acfd9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ad31c55-5177-4a8c-a364-2452c295608c' class='xr-var-data-in' type='checkbox'><label for='data-6ad31c55-5177-4a8c-a364-2452c295608c' 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>units :</span></dt><dd>mNAP</dd></dl></div><div class='xr-var-data'><pre>array([[[1., 1., 1., ..., 1., 1., 1.],\n",
+       "          0.        ,  0.        ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>starting_head</span></div><div class='xr-var-dims'>(layer, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0</div><input id='attrs-f3c54a81-55d5-4ccc-81e0-e658c0a2c121' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3c54a81-55d5-4ccc-81e0-e658c0a2c121' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2adb3b06-ac46-41d0-b2cf-1f00b95ac936' class='xr-var-data-in' type='checkbox'><label for='data-2adb3b06-ac46-41d0-b2cf-1f00b95ac936' 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>units :</span></dt><dd>mNAP</dd></dl></div><div class='xr-var-data'><pre>array([[[1., 1., 1., ..., 1., 1., 1.],\n",
        "        [1., 1., 1., ..., 1., 1., 1.],\n",
        "        [1., 1., 1., ..., 1., 1., 1.],\n",
        "        ...,\n",
@@ -953,31 +953,31 @@
        "        ...,\n",
        "        [1., 1., 1., ..., 1., 1., 1.],\n",
        "        [1., 1., 1., ..., 1., 1., 1.],\n",
-       "        [1., 1., 1., ..., 1., 1., 1.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rws_oppwater_area</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+04 1e+04 1e+04 ... 0.0 0.0 0.0</div><input id='attrs-b2a77722-690d-438d-9af6-e061ab6910e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2a77722-690d-438d-9af6-e061ab6910e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd684db2-6184-4133-9554-ffa383d95e44' class='xr-var-data-in' type='checkbox'><label for='data-dd684db2-6184-4133-9554-ffa383d95e44' 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>units :</span></dt><dd>m2</dd><dt><span>source :</span></dt><dd>RWS</dd><dt><span>date :</span></dt><dd>20211203</dd></dl></div><div class='xr-var-data'><pre>array([[10000., 10000., 10000., ...,     0.,     0.,     0.],\n",
+       "        [1., 1., 1., ..., 1., 1., 1.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rws_oppwater_area</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9ed774f8-2bb1-4bfe-9b47-dcf8a6a89953' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ed774f8-2bb1-4bfe-9b47-dcf8a6a89953' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e886bea3-3b60-46c2-8cfc-625675504da0' class='xr-var-data-in' type='checkbox'><label for='data-e886bea3-3b60-46c2-8cfc-625675504da0' 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>units :</span></dt><dd>m2</dd><dt><span>source :</span></dt><dd>RWS</dd><dt><span>date :</span></dt><dd>20211220</dd></dl></div><div class='xr-var-data'><pre>array([[10000., 10000., 10000., ...,     0.,     0.,     0.],\n",
        "       [10000., 10000., 10000., ...,     0.,     0.,     0.],\n",
        "       [10000., 10000., 10000., ...,     0.,     0.,     0.],\n",
        "       ...,\n",
        "       [10000., 10000., 10000., ...,     0.,     0.,     0.],\n",
        "       [10000., 10000., 10000., ...,     0.,     0.,     0.],\n",
-       "       [10000., 10000., 10000., ...,     0.,     0.,     0.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rws_oppwater_cond</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 1e+03 1e+03 ... 0.0 0.0 0.0</div><input id='attrs-9cca7cb5-6413-4bf8-ad78-509f11046499' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9cca7cb5-6413-4bf8-ad78-509f11046499' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-385daf19-99ed-45ca-ab7e-19c0922c03bf' class='xr-var-data-in' type='checkbox'><label for='data-385daf19-99ed-45ca-ab7e-19c0922c03bf' 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>units :</span></dt><dd>m2/day</dd><dt><span>source :</span></dt><dd>RWS</dd><dt><span>date :</span></dt><dd>20211203</dd></dl></div><div class='xr-var-data'><pre>array([[1000., 1000., 1000., ...,    0.,    0.,    0.],\n",
+       "       [10000., 10000., 10000., ...,     0.,     0.,     0.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rws_oppwater_cond</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 1e+03 1e+03 ... 0.0 0.0 0.0</div><input id='attrs-1b1d9654-2dd2-42de-8db8-42674e9e9e1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b1d9654-2dd2-42de-8db8-42674e9e9e1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58461806-c8ff-4701-9328-4c8d1b151439' class='xr-var-data-in' type='checkbox'><label for='data-58461806-c8ff-4701-9328-4c8d1b151439' 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>units :</span></dt><dd>m2/day</dd><dt><span>source :</span></dt><dd>RWS</dd><dt><span>date :</span></dt><dd>20211220</dd></dl></div><div class='xr-var-data'><pre>array([[1000., 1000., 1000., ...,    0.,    0.,    0.],\n",
        "       [1000., 1000., 1000., ...,    0.,    0.,    0.],\n",
        "       [1000., 1000., 1000., ...,    0.,    0.,    0.],\n",
        "       ...,\n",
        "       [1000., 1000., 1000., ...,    0.,    0.,    0.],\n",
        "       [1000., 1000., 1000., ...,    0.,    0.,    0.],\n",
-       "       [1000., 1000., 1000., ...,    0.,    0.,    0.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rws_oppwater_peil</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-a79422cf-7fdd-4a11-9286-d119d900ea2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a79422cf-7fdd-4a11-9286-d119d900ea2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a206b7c9-0a8c-4c61-a00a-d56fc3f73509' class='xr-var-data-in' type='checkbox'><label for='data-a206b7c9-0a8c-4c61-a00a-d56fc3f73509' 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>units :</span></dt><dd>mNAP</dd><dt><span>source :</span></dt><dd>RWS</dd><dt><span>date :</span></dt><dd>20211203</dd></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [1000., 1000., 1000., ...,    0.,    0.,    0.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rws_oppwater_peil</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-f8265a22-eed1-49c6-9157-307cd066d5dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8265a22-eed1-49c6-9157-307cd066d5dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e475b548-6899-4354-9d1f-dee40415f49b' class='xr-var-data-in' type='checkbox'><label for='data-e475b548-6899-4354-9d1f-dee40415f49b' 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>units :</span></dt><dd>mNAP</dd><dt><span>source :</span></dt><dd>RWS</dd><dt><span>date :</span></dt><dd>20211220</dd></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., ..., 0., 0., 0.],\n",
        "       [0., 0., 0., ..., 0., 0., 0.],\n",
        "       [0., 0., 0., ..., 0., 0., 0.],\n",
        "       ...,\n",
        "       [0., 0., 0., ..., 0., 0., 0.],\n",
        "       [0., 0., 0., ..., 0., 0., 0.],\n",
-       "       [0., 0., 0., ..., 0., 0., 0.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ahn</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan 0.2378</div><input id='attrs-050e1021-b27f-4cce-b1bb-271cdf6030aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-050e1021-b27f-4cce-b1bb-271cdf6030aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5e651e9-4193-43b1-a5ac-6c00cfcbfc0e' class='xr-var-data-in' type='checkbox'><label for='data-f5e651e9-4193-43b1-a5ac-6c00cfcbfc0e' 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>source :</span></dt><dd>ahn3_5m_dtm</dd><dt><span>url :</span></dt><dd>https://geodata.nationaalgeoregister.nl/ahn3/wcs?request=GetCapabilities&amp;service=WCS</dd><dt><span>date :</span></dt><dd>20211209</dd><dt><span>units :</span></dt><dd>mNAP</dd></dl></div><div class='xr-var-data'><pre>array([[    nan,     nan,     nan, ..., 3.407  , 3.172  ,     nan],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ahn</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan 0.2378</div><input id='attrs-db11441b-a025-4993-9076-1628779378b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db11441b-a025-4993-9076-1628779378b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d37829c2-871c-4974-9286-64a0d817bf74' class='xr-var-data-in' type='checkbox'><label for='data-d37829c2-871c-4974-9286-64a0d817bf74' 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>source :</span></dt><dd>ahn3_5m_dtm</dd><dt><span>url :</span></dt><dd>https://geodata.nationaalgeoregister.nl/ahn3/wcs?request=GetCapabilities&amp;service=WCS</dd><dt><span>date :</span></dt><dd>20211220</dd><dt><span>units :</span></dt><dd>mNAP</dd></dl></div><div class='xr-var-data'><pre>array([[    nan,     nan,     nan, ..., 3.407  , 3.172  ,     nan],\n",
        "       [    nan,     nan,     nan, ...,     nan,     nan, 2.99225],\n",
        "       [    nan,     nan,     nan, ...,     nan, 2.68275,     nan],\n",
        "       ...,\n",
        "       [    nan,     nan,     nan, ..., 2.74175, 1.03975,     nan],\n",
        "       [    nan,     nan,     nan, ...,     nan, 0.81975, 0.058  ],\n",
-       "       [    nan,     nan,     nan, ...,     nan,     nan, 0.23775]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chd</span></div><div class='xr-var-dims'>(layer, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1</div><input id='attrs-bb0e9064-691e-424f-ae0e-9fa2cfebee04' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb0e9064-691e-424f-ae0e-9fa2cfebee04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b62e1933-7cd6-4ee9-bdec-3b80b8a3a21b' class='xr-var-data-in' type='checkbox'><label for='data-b62e1933-7cd6-4ee9-bdec-3b80b8a3a21b' 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([[[1, 1, 1, ..., 1, 1, 1],\n",
+       "       [    nan,     nan,     nan, ...,     nan,     nan, 0.23775]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chd</span></div><div class='xr-var-dims'>(layer, y, x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1</div><input id='attrs-344d4208-c6f1-4d7e-95b3-0d55689337d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-344d4208-c6f1-4d7e-95b3-0d55689337d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85208eb6-0503-4484-acca-0d4803e468c4' class='xr-var-data-in' type='checkbox'><label for='data-85208eb6-0503-4484-acca-0d4803e468c4' 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([[[1, 1, 1, ..., 1, 1, 1],\n",
        "        [1, 0, 0, ..., 0, 0, 1],\n",
        "        [1, 0, 0, ..., 0, 0, 1],\n",
        "        ...,\n",
@@ -1017,13 +1017,13 @@
        "        ...,\n",
        "        [0, 0, 0, ..., 0, 0, 1],\n",
        "        [0, 0, 0, ..., 0, 0, 1],\n",
-       "        [0, 0, 0, ..., 1, 1, 1]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>recharge</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.000606 0.000606 ... 0.0007697</div><input id='attrs-f6823c4a-cc22-42f5-958b-0115bfa34474' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6823c4a-cc22-42f5-958b-0115bfa34474' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9c90257-900b-4449-aea2-98bdd159672c' class='xr-var-data-in' type='checkbox'><label for='data-a9c90257-900b-4449-aea2-98bdd159672c' 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>source :</span></dt><dd>KNMI</dd><dt><span>date :</span></dt><dd>20211206</dd><dt><span>units :</span></dt><dd>m/day</dd></dl></div><div class='xr-var-data'><pre>array([[0.000606, 0.000606, 0.000606, ..., 0.000606, 0.000606, 0.000606],\n",
-       "       [0.000606, 0.000606, 0.000606, ..., 0.000606, 0.000606, 0.000606],\n",
-       "       [0.000606, 0.000606, 0.000606, ..., 0.000606, 0.000606, 0.000606],\n",
+       "        [0, 0, 0, ..., 1, 1, 1]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>recharge</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0003688 0.0003688 ... 0.0003688</div><input id='attrs-f0c3bb9b-1dba-48c3-b9d0-b586e8837b11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0c3bb9b-1dba-48c3-b9d0-b586e8837b11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c78144f-8ad8-42f1-98fc-92f119590acc' class='xr-var-data-in' type='checkbox'><label for='data-1c78144f-8ad8-42f1-98fc-92f119590acc' 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>source :</span></dt><dd>KNMI</dd><dt><span>date :</span></dt><dd>20211220</dd><dt><span>units :</span></dt><dd>m/day</dd></dl></div><div class='xr-var-data'><pre>array([[0.000369, 0.000369, 0.000369, ..., 0.000369, 0.000369, 0.000369],\n",
+       "       [0.000369, 0.000369, 0.000369, ..., 0.000369, 0.000369, 0.000369],\n",
+       "       [0.000369, 0.000369, 0.000369, ..., 0.000369, 0.000369, 0.000369],\n",
        "       ...,\n",
-       "       [0.000879, 0.000879, 0.000879, ..., 0.00077 , 0.00077 , 0.00077 ],\n",
-       "       [0.000879, 0.000879, 0.000879, ..., 0.00077 , 0.00077 , 0.00077 ],\n",
-       "       [0.000879, 0.000879, 0.000879, ..., 0.00077 , 0.00077 , 0.00077 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>test</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-4b092cf7-625c-4ae0-b0c1-9cc8d34d1c89' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b092cf7-625c-4ae0-b0c1-9cc8d34d1c89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2b5f541-dbf2-485f-8681-4af3ee4d6676' class='xr-var-data-in' type='checkbox'><label for='data-f2b5f541-dbf2-485f-8681-4af3ee4d6676' 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(1)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b0cf62e5-ef6e-4a2e-a00a-a9701ae173a0' class='xr-section-summary-in' type='checkbox'  ><label for='section-b0cf62e5-ef6e-4a2e-a00a-a9701ae173a0' class='xr-section-summary' >Attributes: <span>(27)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>model_name :</span></dt><dd>IJmuiden</dd><dt><span>mfversion :</span></dt><dd>mf6</dd><dt><span>model_dataset_created_on :</span></dt><dd>20211210_12:59:57</dd><dt><span>exe_name :</span></dt><dd>c:\\users\\oebbe\\02_python\\nlmod\\nlmod\\mdims\\..\\..\\bin\\mf6.exe</dd><dt><span>model_ws :</span></dt><dd>model1</dd><dt><span>figdir :</span></dt><dd>model1\\figure</dd><dt><span>cachedir :</span></dt><dd>model1\\cache</dd><dt><span>time_units :</span></dt><dd>DAYS</dd><dt><span>start_time :</span></dt><dd>2015-01-01 00:00:00</dd><dt><span>nper :</span></dt><dd>1</dd><dt><span>perlen :</span></dt><dd>1825</dd><dt><span>nstp :</span></dt><dd>1</dd><dt><span>tsmult :</span></dt><dd>1.0</dd><dt><span>steady_start :</span></dt><dd>0</dd><dt><span>steady_state :</span></dt><dd>1</dd><dt><span>gridtype :</span></dt><dd>structured</dd><dt><span>extent :</span></dt><dd>[ 95000. 105000. 494000. 500000.]</dd><dt><span>delr :</span></dt><dd>100.0</dd><dt><span>delc :</span></dt><dd>100.0</dd><dt><span>nodata :</span></dt><dd>-999</dd><dt><span>anisotropy :</span></dt><dd>10</dd><dt><span>fill_value_kh :</span></dt><dd>1.0</dd><dt><span>fill_value_kv :</span></dt><dd>0.1</dd><dt><span>surface_drn_cond :</span></dt><dd>1000</dd><dt><span>model_dataset_written_to_disk_on :</span></dt><dd>20211210_13:00:02</dd><dt><span>model_data_written_to_disk_on :</span></dt><dd>20211210_13:00:09</dd><dt><span>model_ran_on :</span></dt><dd>20211210_13:00:10</dd></dl></div></li></ul></div></div>"
+       "       [0.000369, 0.000369, 0.000369, ..., 0.000369, 0.000369, 0.000369],\n",
+       "       [0.000369, 0.000369, 0.000369, ..., 0.000369, 0.000369, 0.000369],\n",
+       "       [0.000369, 0.000369, 0.000369, ..., 0.000369, 0.000369, 0.000369]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a250ed3-86de-4a38-90be-3cb6d4591a6d' class='xr-section-summary-in' type='checkbox'  ><label for='section-5a250ed3-86de-4a38-90be-3cb6d4591a6d' class='xr-section-summary' >Attributes: <span>(24)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>model_name :</span></dt><dd>IJmuiden</dd><dt><span>mfversion :</span></dt><dd>mf6</dd><dt><span>model_dataset_created_on :</span></dt><dd>20220315_15:56:45</dd><dt><span>exe_name :</span></dt><dd>c:\\users\\oebbe\\02_python\\nlmod\\nlmod\\mdims\\..\\..\\bin\\mf6.exe</dd><dt><span>model_ws :</span></dt><dd>model1</dd><dt><span>figdir :</span></dt><dd>model1\\figure</dd><dt><span>cachedir :</span></dt><dd>model1\\cache</dd><dt><span>time_units :</span></dt><dd>DAYS</dd><dt><span>start_time :</span></dt><dd>2015-01-01 00:00:00</dd><dt><span>nper :</span></dt><dd>1</dd><dt><span>perlen :</span></dt><dd>1825</dd><dt><span>nstp :</span></dt><dd>1</dd><dt><span>tsmult :</span></dt><dd>1.0</dd><dt><span>steady_start :</span></dt><dd>0</dd><dt><span>steady_state :</span></dt><dd>1</dd><dt><span>gridtype :</span></dt><dd>structured</dd><dt><span>extent :</span></dt><dd>[ 95000. 105000. 494000. 500000.]</dd><dt><span>delr :</span></dt><dd>100.0</dd><dt><span>delc :</span></dt><dd>100.0</dd><dt><span>nodata :</span></dt><dd>-999</dd><dt><span>anisotropy :</span></dt><dd>10</dd><dt><span>fill_value_kh :</span></dt><dd>1.0</dd><dt><span>fill_value_kv :</span></dt><dd>0.1</dd><dt><span>surface_drn_cond :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -1033,7 +1033,7 @@
        "  * x                   (x) float64 9.505e+04 9.515e+04 ... 1.048e+05 1.05e+05\n",
        "  * y                   (y) float64 5e+05 4.998e+05 ... 4.942e+05 4.94e+05\n",
        "  * layer               (layer) object 'AAOP' 'NASC' 'ONAWA' ... 'PZWAz4' 'MSz1'\n",
-       "Data variables: (12/18)\n",
+       "Data variables: (12/17)\n",
        "    time_steps          (time) int32 0\n",
        "    idomain             (layer, y, x) int32 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1\n",
        "    first_active_layer  (y, x) int32 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0\n",
@@ -1041,29 +1041,29 @@
        "    top                 (y, x) float64 0.0 0.0 0.0 0.0 ... 2.29 1.49 0.5 0.37\n",
        "    kh                  (layer, y, x) float64 10.0 10.0 10.0 ... 6.1 6.11 6.11\n",
        "    ...                  ...\n",
+       "    rws_oppwater_area   (y, x) float64 ...\n",
        "    rws_oppwater_cond   (y, x) float64 1e+03 1e+03 1e+03 1e+03 ... 0.0 0.0 0.0\n",
        "    rws_oppwater_peil   (y, x) float64 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
        "    ahn                 (y, x) float64 nan nan nan nan ... 1.971 nan nan 0.2378\n",
        "    chd                 (layer, y, x) int32 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1\n",
-       "    recharge            (y, x) float64 0.000606 0.000606 ... 0.0007697 0.0007697\n",
-       "    test                int32 1\n",
-       "Attributes: (12/27)\n",
-       "    model_name:                        IJmuiden\n",
-       "    mfversion:                         mf6\n",
-       "    model_dataset_created_on:          20211210_12:59:57\n",
-       "    exe_name:                          c:\\users\\oebbe\\02_python\\nlmod\\nlmod\\m...\n",
-       "    model_ws:                          model1\n",
-       "    figdir:                            model1\\figure\n",
-       "    ...                                ...\n",
-       "    fill_value_kh:                     1.0\n",
-       "    fill_value_kv:                     0.1\n",
-       "    surface_drn_cond:                  1000\n",
-       "    model_dataset_written_to_disk_on:  20211210_13:00:02\n",
-       "    model_data_written_to_disk_on:     20211210_13:00:09\n",
-       "    model_ran_on:                      20211210_13:00:10"
+       "    recharge            (y, x) float64 0.0003688 0.0003688 ... 0.0003688\n",
+       "Attributes: (12/24)\n",
+       "    model_name:                IJmuiden\n",
+       "    mfversion:                 mf6\n",
+       "    model_dataset_created_on:  20220315_15:56:45\n",
+       "    exe_name:                  c:\\users\\oebbe\\02_python\\nlmod\\nlmod\\mdims\\..\\...\n",
+       "    model_ws:                  model1\n",
+       "    figdir:                    model1\\figure\n",
+       "    ...                        ...\n",
+       "    delc:                      100.0\n",
+       "    nodata:                    -999\n",
+       "    anisotropy:                10\n",
+       "    fill_value_kh:             1.0\n",
+       "    fill_value_kv:             0.1\n",
+       "    surface_drn_cond:          1000"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1084,14 +1084,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:nlmod.util:write script 2021121001_basic_model.ipynb to model workspace\n",
+      "INFO:nlmod.util:write script 2022031501_basic_model.ipynb to model workspace\n",
       "INFO:nlmod.util:write model dataset to cache\n",
       "INFO:nlmod.util:write modflow files to model workspace\n"
      ]
@@ -1127,7 +1127,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "FloPy is using the following  executable to run the model: c:\\users\\oebbe\\02_python\\nlmod\\nlmod\\mfpackages\\..\\..\\bin\\mf6.exe\n",
+      "FloPy is using the following executable to run the model: c:\\users\\oebbe\\02_python\\nlmod\\nlmod\\mfpackages\\..\\..\\bin\\mf6.exe\n",
       "                                   MODFLOW 6\n",
       "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
       "                            VERSION 6.2.1 02/18/2021\n",
@@ -1148,15 +1148,15 @@
       "and distribution information.\n",
       "\n",
       " \n",
-      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2021/12/10 13:00:09\n",
+      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2022/03/15 15:57:16\n",
       " \n",
       " Writing simulation list file: mfsim.lst\n",
       " Using Simulation name file: mfsim.nam\n",
       " \n",
       "    Solving:  Stress period:     1    Time step:     1\n",
       " \n",
-      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2021/12/10 13:00:10\n",
-      " Elapsed run time:  1.923 Seconds\n",
+      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2022/03/15 15:57:20\n",
+      " Elapsed run time:  4.554 Seconds\n",
       " \n",
       " Normal termination of simulation.\n"
      ]
@@ -1177,7 +1177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -1215,7 +1215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1232,7 +1232,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1008x792 with 8 Axes>"
       ]
@@ -1281,7 +1281,7 @@
   },
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/examples/02_surface_water.ipynb b/examples/02_surface_water.ipynb
index 9fa2183c..893dcbc7 100644
--- a/examples/02_surface_water.ipynb
+++ b/examples/02_surface_water.ipynb
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 1,
    "id": "a13eab81",
    "metadata": {},
    "outputs": [],
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 2,
    "id": "be54846a",
    "metadata": {},
    "outputs": [
@@ -60,7 +60,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "nlmod version: 0.2.0\n"
+      "nlmod version: 0.2.1\n"
      ]
     }
    ],
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 3,
    "id": "411c7323",
    "metadata": {},
    "outputs": [
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 4,
    "id": "7780e8fb",
    "metadata": {},
    "outputs": [
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 5,
    "id": "17d75fcb",
    "metadata": {},
    "outputs": [
@@ -205,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 6,
    "id": "34ff7b0a",
    "metadata": {},
    "outputs": [],
@@ -223,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 7,
    "id": "9bc8b375",
    "metadata": {},
    "outputs": [],
@@ -238,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 8,
    "id": "4b48338c",
    "metadata": {},
    "outputs": [
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 9,
    "id": "b35ea38e",
    "metadata": {},
    "outputs": [
@@ -286,13 +286,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:nlmod.mdims.mgrid:get active cells (idomain) from bottom DataArray\n",
+      "INFO:nlmod.mdims.mlayers:get active cells (idomain) from bottom DataArray\n",
       "INFO:nlmod.mdims.mgrid:get first active modellayer for each cell in idomain\n",
-      "INFO:nlmod.mdims.mgrid:using top and bottom from model layers dataset for modflow model\n",
-      "INFO:nlmod.mdims.mgrid:replace nan values for inactive layers with dummy value\n",
-      "INFO:nlmod.mdims.mgrid:add kh and kv from model layer dataset to modflow model\n",
-      "INFO:nlmod.mdims.mgrid:kv and kh both undefined in layer 0\n",
-      "INFO:nlmod.mdims.mgrid:kv and kh both undefined in layer 0\n",
+      "INFO:nlmod.mdims.mlayers:using top and bottom from model layers dataset for modflow model\n",
+      "INFO:nlmod.mdims.mlayers:replace nan values for inactive layers with dummy value\n",
+      "INFO:nlmod.mdims.mlayers:add kh and kv from model layer dataset to modflow model\n",
+      "INFO:nlmod.mdims.mlayers:kv and kh both undefined in layer 0\n",
+      "INFO:nlmod.mdims.mlayers:kv and kh both undefined in layer 0\n",
       "INFO:nlmod.mdims.mgrid:get first active modellayer for each cell in idomain\n"
      ]
     }
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 10,
    "id": "9b4a90b5",
    "metadata": {},
    "outputs": [
@@ -355,7 +355,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Intersecting with grid: 100%|█████████████████████████████████████████████████████| 3883/3883 [00:31<00:00, 123.64it/s]\n"
+      "Intersecting with grid: 100%|██████████████████████████████████████████████████████| 3883/3883 [00:52<00:00, 73.95it/s]\n"
      ]
     }
    ],
@@ -373,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 11,
    "id": "82a551e9",
    "metadata": {},
    "outputs": [
@@ -414,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 12,
    "id": "ded567ef",
    "metadata": {},
    "outputs": [
@@ -525,7 +525,7 @@
        "334  POLYGON ((116202.937 436620.942, 116200.000 43...  (107, 6)  "
       ]
      },
-     "execution_count": 40,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -547,7 +547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 13,
    "id": "3628d275",
    "metadata": {},
    "outputs": [
@@ -602,7 +602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 14,
    "id": "ffa7f59b",
    "metadata": {},
    "outputs": [
@@ -632,7 +632,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 15,
    "id": "572e51e5",
    "metadata": {},
    "outputs": [],
@@ -650,7 +650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 16,
    "id": "03f980b9",
    "metadata": {},
    "outputs": [
@@ -658,13 +658,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Aggregate surface water data: 100%|███████████████████████████████████████████████| 9777/9777 [00:51<00:00, 188.66it/s]\n"
+      "Aggregate surface water data:   0%|                                                           | 0/9777 [00:00<?, ?it/s]INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n",
+      "Aggregate surface water data: 100%|███████████████████████████████████████████████| 9777/9777 [01:07<00:00, 145.89it/s]\n"
      ]
     }
    ],
    "source": [
-    "celldata = nlmod.mfpackages.surface_water.aggregate_surface_water(\n",
-    "    sfw_grid, \"area_weighted\")"
+    "celldata = nlmod.mfpackages.surface_water.aggregate_surface_water(sfw_grid, \"area_weighted\")"
    ]
   },
   {
@@ -677,7 +677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 17,
    "id": "0f6b8d06",
    "metadata": {},
    "outputs": [
@@ -799,7 +799,7 @@
        "  9  -1.85  347.101131 -2.15  347.101131"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -834,7 +834,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 18,
    "id": "d927545f-83a4-4911-b884-37a306637ab4",
    "metadata": {},
    "outputs": [
@@ -853,7 +853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 19,
    "id": "095b3323",
    "metadata": {},
    "outputs": [
@@ -861,7 +861,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Building stress period data RIV: 100%|████████████████████████████████████████████| 8753/8753 [00:38<00:00, 226.00it/s]\n"
+      "Building stress period data RIV: 100%|████████████████████████████████████████████| 8753/8753 [01:06<00:00, 131.01it/s]\n"
      ]
     }
    ],
@@ -879,7 +879,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 20,
    "id": "37144d27",
    "metadata": {},
    "outputs": [
@@ -898,7 +898,7 @@
        " [(0, 0, 9), -1.85, 347.1011307303384, -2.15]]"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -919,7 +919,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 21,
    "id": "35478e93",
    "metadata": {},
    "outputs": [],
@@ -937,7 +937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 22,
    "id": "11da46a0",
    "metadata": {},
    "outputs": [
@@ -972,7 +972,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 23,
    "id": "414f388f",
    "metadata": {},
    "outputs": [
@@ -980,7 +980,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:nlmod.util:write script 2021121002_surface_water.ipynb to model workspace\n",
+      "INFO:nlmod.util:write script 2022031502_surface_water.ipynb to model workspace\n",
       "INFO:nlmod.util:write model dataset to cache\n",
       "INFO:nlmod.util:write modflow files to model workspace\n"
      ]
@@ -1014,7 +1014,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "FloPy is using the following  executable to run the model: c:\\users\\oebbe\\02_python\\nlmod\\nlmod\\mfpackages\\..\\..\\bin\\mf6.exe\n",
+      "FloPy is using the following executable to run the model: c:\\users\\oebbe\\02_python\\nlmod\\nlmod\\mfpackages\\..\\..\\bin\\mf6.exe\n",
       "                                   MODFLOW 6\n",
       "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
       "                            VERSION 6.2.1 02/18/2021\n",
@@ -1035,15 +1035,15 @@
       "and distribution information.\n",
       "\n",
       " \n",
-      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2021/12/10 12:55:54\n",
+      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2022/03/15 16:00:49\n",
       " \n",
       " Writing simulation list file: mfsim.lst\n",
       " Using Simulation name file: mfsim.nam\n",
       " \n",
       "    Solving:  Stress period:     1    Time step:     1\n",
       " \n",
-      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2021/12/10 12:55:57\n",
-      " Elapsed run time:  3.235 Seconds\n",
+      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2022/03/15 16:00:58\n",
+      " Elapsed run time:  9.620 Seconds\n",
       " \n",
       " Normal termination of simulation.\n"
      ]
@@ -1065,7 +1065,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 24,
    "id": "8594c562",
    "metadata": {},
    "outputs": [],
@@ -1084,7 +1084,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 25,
    "id": "301690ff",
    "metadata": {},
    "outputs": [],
@@ -1103,7 +1103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 26,
    "id": "8b9e2c74",
    "metadata": {},
    "outputs": [
@@ -1141,13 +1141,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 27,
    "id": "282abb74",
    "metadata": {},
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 720x216 with 2 Axes>"
       ]
@@ -1181,7 +1181,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/examples/03_local_grid_refinement.ipynb b/examples/03_local_grid_refinement.ipynb
index f00944ad..1619bf43 100644
--- a/examples/03_local_grid_refinement.ipynb
+++ b/examples/03_local_grid_refinement.ipynb
@@ -2188,7 +2188,7 @@
   },
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2202,7 +2202,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.9.4"
   },
   "widgets": {
    "state": {},
diff --git a/examples/04_modifying_layermodels.ipynb b/examples/04_modifying_layermodels.ipynb
index a085012e..2d8b963d 100644
--- a/examples/04_modifying_layermodels.ipynb
+++ b/examples/04_modifying_layermodels.ipynb
@@ -2591,7 +2591,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2605,7 +2605,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.4"
   }
  },
  "nbformat": 4,
diff --git a/examples/05_caching.ipynb b/examples/05_caching.ipynb
index e60d06c7..8b041f9e 100644
--- a/examples/05_caching.ipynb
+++ b/examples/05_caching.ipynb
@@ -34,7 +34,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "nlmod version: 0.2.0\n"
+      "nlmod version: 0.2.1\n"
      ]
     }
    ],
@@ -77,8 +77,8 @@
      "output_type": "stream",
      "text": [
       "model5\n",
-      "model5/figure\n",
-      "model5/cache\n"
+      "model5\\figure\n",
+      "model5\\cache\n"
      ]
     }
    ],
@@ -2688,7 +2688,7 @@
   },
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2702,7 +2702,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.9.4"
   },
   "widgets": {
    "state": {},
diff --git a/examples/06_compare_layermodels.ipynb b/examples/06_compare_layermodels.ipynb
index 6d447a29..46290e6c 100644
--- a/examples/06_compare_layermodels.ipynb
+++ b/examples/06_compare_layermodels.ipynb
@@ -513,7 +513,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/examples/07_resampling.ipynb b/examples/07_resampling.ipynb
index a1d56603..8938a7b6 100644
--- a/examples/07_resampling.ipynb
+++ b/examples/07_resampling.ipynb
@@ -21,7 +21,8 @@
     "2. [Structured grid to fine structured grid](#2)\n",
     "3. [Structured grid to locally refined grid](#3)\n",
     "4. [Locally refined grid to structured grid](#4)\n",
-    "5. [Fill nan values](#5)"
+    "5. [Fill nan values](#5)\n",
+    "6. [Vector to grid](#6)"
    ]
   },
   {
@@ -34,13 +35,19 @@
     "from nlmod.mdims import resample\n",
     "import numpy as np\n",
     "import xarray as xr\n",
+    "import flopy\n",
     "\n",
     "from matplotlib.patches import Polygon\n",
     "from matplotlib.collections import PatchCollection\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
+    "import geopandas as gpd\n",
+    "from shapely.geometry import LineString, Point\n",
+    "from shapely.geometry import Polygon as shp_polygon\n",
     "from scipy.interpolate import RectBivariateSpline\n",
-    "import logging"
+    "import logging\n",
+    "\n",
+    "from IPython.display import display"
    ]
   },
   {
@@ -52,7 +59,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "nlmod version: 0.2.0\n"
+      "nlmod version: 0.2.1\n"
      ]
     }
    ],
@@ -92,7 +99,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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2FPlNZlZxFb6m+A1Ja4DvAD8GPhkRlwGnkHVzmx0nstZlV0TcmNv1ENlACun1wZT/cOD7wDUR8eNa5tTF3ilpZirz0toxdWVdADyWAriZ7eU61X0ucp/ieODLEfGrfGJE9Ek6p8VxpwGXAOslrUtp1wI3AMskzQVeBi5M+64AjgWuk3RdSjszPdXiMuB7wAHAw2mDLOgulbSZrIV4UYHfY2Z7g6rOaImIv2ixr6vFvidpvsjCrAb5rweub1LWM8D0Bum76A+qZjaSVDUompkNt7K6xkU4KJpZNXmJUzOzfp1qKfrJ22ZWTW2+JUfSKEnPSvr7VvncUjSz6innmuJ8sunGh7bK5JaimVVTG1uKaWLIF4D/OVBetxTNrJJU/CGz4+uea7AoIhbV5fkvwL8BDhmoMAdFM9vbtVz3OU0y2RYRayR9dqDCHBTNrJrad03xNOBLks4G9gcOlfR3EfEnjTL7mqKZVU/Bec9FBmMi4pqImBQRU8imAj/WLCCCW4pmVlWe0WJmllNCUIyIVcCqVnkcFM2scsSgRp/bykHRzKrHD4QwM6vjoGhmluOgaGbWz91nM7M8B0UzsyQ8+mxmtju3FM3M+vmaoplZnoOimVkyyKUG2slB0cwqR7j7bGa2GwdFM7M8B0Uzs5yRtu6zpMmSHpfUJWmjpPkp/QhJKyRtSq9jU/q4lP8tSbfUlbVK0vOS1qXtyJS+n6R7JW2WtFrSlLJ+j5kNozY+eVvS/pJ+Kum5FIv+Xav8ZS5H0ANcFRHTgJnA5ZKOBxYAKyNiKrAyfQbYBVwHXN2kvIsj4sS0bUtpc4HXI+JY4CZgYUm/xcyGW/uWOH0XOCMiPgWcCMyWNLNZ5tKCYkRsiYi16f1OskWoJwLnAktStiXAeSnP2xHxJFlwLCpf1nJgliQNvfZm1mnqK7YNJDJvpY9j0tY0nA7LNcXUrT0JWA0cFRFbIAucta5wAd+V1AvcB1wfEUEWZF9JZfVIegMYB2yv+/55wLza55tPvmdoP2iEezSWd7oKlfeFYzZ2ugoj3iBGnwdc91nSKGANcCzwNxGxullhpQdFSQeTBbIrI+LNPWzIXRwRr0o6JJV1CXAH2e1M9T5wKtMJWpTqE71bpu5JHT4URk3YxO89fE2nq1FZT5/1bQCfoxZq52hIBnfzdst1nwEiohc4UdLhwAOSpkfEhkZ5S13iVNIYsiB2Z0Tcn5K3SpqQ9k8AtjU7viYiXk2vO4G7gFPTrm5gciprNHAYsKOdv8HMOqR91xT7i4z4f2QLV81ulqfM0WcBi4GuiLgxt+shYE56Pwd4cIByRksan96PAc4BahE+X9YFZOu5dmgg38zapTajpU2jzx9JLUQkHQB8DvhFs/xldp9PI+vmrpe0LqVdC9wALJM0F3gZuLB2gKSXgEOBfSWdB5wJ/Ap4JAXEUcCjwG3pkMXAUkmbyVqIF5X4e8xsGKmvbe2bCcCSdF1xH2BZRPx9s8ylBcU0ktzsAuKsJsdMaZL/lCb5d5ELqmY2QrTxgRAR8TOygd5CPKPFzCrJc5/NzPIcFM3M+rmlaGaW56BoZpZ4NT8zs35+8raZWb0OzcNwUDSzSnJL0cysxqv5mZntzgMtZmY5DopmZjWBB1rMzPI80GJmluegaGaW6eTN26UuR2BmtkciUF+xbSDN1qBvxi1FM6um9rUUa2vQr02L362RtCIift4os4OimVVSu7rPaUnl2rLKOyXV1qB3UDSzvUQAxddoGXDd55q6NegbclA0s2pq47rP8ME16Jvlc1A0s0pq5+hzkzXoG3JQNLNKatcSpy3WoG/It+SYWfXEILaB1dagP0PSurSd3SyzW4pmVjnZzdvtaSkOsAb9Bzgomlk1+Sk5Zmb92tVSHCwHRTOrng4+ebu0gZZm8w0lHSFphaRN6XVsSh+X8r8l6Za6sk6RtF7SZkk3p9EkJO0n6d6UvjrdmGlme732zX0erDJHn2vzDacBM4HLJR0PLABWRsRUYGX6DLALuA64ukFZtwLzgKlpm53S5wKvR8SxwE3AwpJ+i5kNt4hiW5uVFhQjYktErE3vdwK1+YbnAktStiXAeSnP22mUaFe+HEkTgEMj4qmICOCO2jF1ZS0HZtVakWa2F4tsOYIiW7sNy32KdfMNj0oTtGsTtY8c4PCJQHfuc3dKq+17JZXVA7wBjGtbxc2sczrUUix9oKV+vuEeNOQaHRAF9uXrMI+s+21me4uRNtACTecbbk1d4lrXeNsAxXQDk3KfJwGv5fZNTmWNBg4DdtQXEBGLImJGkUnjZlYN6usrtLVbmaPPzeYbPgTMSe/nAA+2Kid1sXdKmpnKvDR3TL6sC4DH0nVHM9ubBdnN20W2Niuz+1ybb7he0rqUdi1wA7BM0lzgZeDC2gGSXgIOBfaVdB5wZno67mXA94ADgIfTBlnQXSppM1kL8aISf4+ZDRMRI+/m7QHmG85qcsyUJunPANMbpO8iF1TNbAQZaUHRzGxIHBTNzJLaNcUOcFA0s0oqY2S5CD9k1swqqOCN2wW62JJul7RN0oYi3+ygaGbVE7RzRsv36H9ewoDcfTazampT7zkinhjME7QcFM2skkbcfYpmZkNSPCiOl/RM7vOiiFi0p1/roGhm1RMBvYX7z9vb+VwDB0Uzq6YOdZ89+mxm1dS+W3LuBp4CjpPUnZ670JRbimZWPQG0af2ViPjqYPI7KJpZBQVEZ2a0OCiaWfUEgxloaSsHRTOrJt+naGaW46BoZlZTzkp9RTgomln1BNChR4c5KJpZNbmlaGZWM6hpfm3loGhm1RMQvk/RzCynTTNaBstB0cyqydcUzcySCI8+m5ntxi1FM7OaIHp7O/LNDopmVj1tfHTYYDkomlk1deiWnNKevC1psqTHJXVJ2ihpfko/QtIKSZvS69jcMddI2izpeUl/nEtfldLWpe3IlL6fpHvTMasHs4yhmVVXANEXhbYiJM1OMWSzpAWt8pa5HEEPcFVETANmApdLOh5YAKyMiKnAyvSZtO8i4ASyhav/u6RRufIujogT07Ytpc0FXo+IY4GbgIUl/h4zGy6RHjJbZBtAiiN/A5wFHA98NcWbhkrrPkfEFmBLer9TUhcwETgX+GzKtgRYBXwzpd8TEe8Cv5S0GTiVbG2FZs4FvpXeLwdukaSI1sNWoyZs2oNf9OHx9Fnf7nQVKs/nqHxtHGg5FdgcES8CSLqHLHb8vFHmYbmmmLq1JwGrgaNSwCQittS6wmQB8ye5w7pTWs13JfUC9wHXp8A3EXglldUj6Q1gHLC97vvnAfPSx3eBDe37dUM2nrr6VkDV6uT6tFa1+gAcN5SDd/L6I4/G8vEFs+8/wLrP78eJpBv4dLPCSg+Kkg4mC2RXRsSbkppmbZBWa/FdHBGvSjoklXUJcMcAx/QnZCdoUarPM+1cI3aoqlYfqF6dXJ/WqlYfyOo0lOMjYna76kLBOFFT6hKnksaQBbE7I+L+lLxV0oS0fwJQuz7YDUzOHT4JeA0gIl5NrzuBu8iaw7sdI2k0cBiwo6zfY2Z7paaxpZEyR58FLAa6IuLG3K6HgDnp/RzgwVz6RWlE+WhgKvBTSaMljU9ljgHOob/7my/rAuCxga4nmtmHztPAVElHS9qXbED3oWaZy+w+n0bWzV0vaV1Kuxa4AViWFqR+GbgQICI2SlpGdvGzB7g8InolHQQ8kgLiKOBR4LZU3mJgaRqU2UH2YweyaOAsw6pq9YHq1cn1aa1q9YEK1SmNN1wBPEIWQ26PiI3N8ssNKzOzfqVeUzQz29s4KJqZ5Yy4oChpvqQNaWrhlSntW5JezU0TPDuXv+HUwiHW4XZJ2yRtyKXtyfTGUyStT/tuVov7mdpVH0lTJP02d67+dpjqc2H6b9YnaUZd/k6cn4b1GY7z06JO/1HSLyT9TNIDkg7P7evEOWpYn+E6R6WJiBGzAdPJRqYPJBtEepRsFPtbwNUN8h8PPAfsBxwNvACMakM9TgdOBjbk0r4DLEjvFwALB6oD8FPg98nus3oYOGsY6jMln6+unDLrM43sht9VwIwi/406VJ/Sz0+LOp0JjE7vF1bgb6hZfYblHJW1jbSW4jTgJxHxTkT0AD8Czm+R//2phRHxS6A2tXBIIuIJPni/5Llk0xpJr+e1qoOyezgPjYinIvtruiN3TJn1aajs+kREV0Q83yB7R85Pi/o01M76tKjTD9PfNWSzvyal9506R83q01C7z1FZRlpQ3ACcLmmcpAOBs+m/afOK1My/Pdd1bTT9Jz+1sJ12m94I5Kc3NqrDxPS+rLo1qw/A0ZKelfQjSZ/J1bPM+jTTqfPTShXOz78ga2nVvrvT5yhfH6jGOdojI+p5ihHRJWkhsAJ4i6xL0QPcCvwV2dSevwL+M9l/xEFN/ylJszp0qm5bgI9FxG8knQL8L0kndLA+Pj91JP052d/1nbWkJt89LHVqUJ+On6OhGGktRSJicUScHBGnkzX3N0XE1ojojWwh2dtoME0waTn9Z4gGO72xm927I+2uW8P6pC7Yb9L7NWTXpz4xDPVpplPnp6FOnx9Jc8hmdV2cuqDQwXPUqD6dPkdDNeKCovofQPsx4MvA3bX/8yfns/s0wQ9MLSypaoOa3pi6tDslzUwjdJfmjimtPpI+ovQcS0nHpPq8OAz1aVXPTpyfhjp5fiTNJnvM3pci4p3cro6co2b1qeDf0OB0eqSn3Rvwv8mmCj4HzEppS4H1wM/I/oAm5PL/Odm/ZM/TppEw4G6yLsR7ZP86ziV7pNlKYFN6PWKgOgAzyAL4C8AtpBlIZdYH+AqwMZ2/tcAXh6k+56f37wJbgUc6fH4a1mc4zk+LOm0mu3a4Lm1/2+Fz1LA+w3WOyto8zc/MLGfEdZ/NzIbCQdHMLMdB0cwsx0HRzCzHQdHMLMdB0cwsx0HRzCzHQdEqSdLvpQd47C/pIGXPNpze6XrZyOebt62yJF0P7A8cAHRHxLc7XCX7EHBQtMpSthzl08Au4A8iorfDVbIPAXefrcqOAA4GDiFrMZqVzi1FqyxJDwH3kD1if0JEXNHhKtmHwIh6yKyNHJIuBXoi4q70GKr/I+mMiHis03Wzkc0tRTOzHF9TNDPLcVA0M8txUDQzy3FQNDPLcVA0M8txUDQzy3FQNDPL+f+siAZire0zXgAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -123,7 +130,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -150,7 +157,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -223,7 +230,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEKCAYAAACFekfkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAdVUlEQVR4nO3df/BddX3n8eeLBEHkh0CETUMUOrAOP9yCZCktrdMaROuqgMIsOxTS3cxkq7gNO+AWcVk7U5wFa2F1nLWNGzWkiDABF6YdRBJAF1ZjIUST+JVJFIVAShphIaAg+ea1f5zPJYcv936/95vc870nX18P58y993M+53M/9+Trm8+P8zlHtomIiMo+w65ARESbJChGRNQkKEZE1CQoRkTUJChGRNQkKEZE1DQWFCXNlXSvpBFJGyQtLumHSbpb0sbyemhJP03S2rJ9X9K5tbLuk/RIbf8RJX0/STdL2iRptaSjm/o9EbH3krRY0voSiy4dN29T1ylKmg3Mtr1G0kHAQ8A5wJ8AT9u+RtIVwKG2/1zSAcCvbO8ox34f+I3y+T7gctsPjvmOjwD/yvafSroAONf2v23kB0XEXknSScDXgNOAXwHfAD5se2O3/I21FG1vsb2mvN8OjABzgLOBZSXbMqpAie1f2N5R0vcH+onW9bJWAPMlaSA/ICKmi+OB79ZizLeAc3tlnjkVNSrd2lOA1cCRtrdAFTg7XeGS77eBLwFvAS6qBUmAL0saBW4FrnbVxJ0DPF7K2iHpWeBwYNuY718ELCofTx38L4yIMbbZftPuHvzuP3yDf/70aF95H/rBSxuAF2tJS2wvqX1eD3xK0uHAL4H3Aq/qddY1HhQlHUgVyC61/dx4DTnbq4ETJR0PLJN0p+0XgQttP1G64bcCFwE3AN0Ke00Ls5ygJaU+Puazn9nTnzWtPbr4ct79tk8Muxqtdde6T3Gmzht2NVptpVf8bE+O//nTo3zvrjf3lXfG7I0v2p7Xa7/tEUnXAncDz1MNze3olb/R2WdJ+1IFsRtt31aSnypjhp1xx61jj7M9ArwAnFQ+P1FetwNfpRobANgMzC1lzQQOAZ5u6vdExNQwsLPP//VVnr3U9tttv4MqRnQdT4RmZ58FLAVGbF9X23UHsKC8XwDcXvIfUwIbkt4CvBX4qaSZkmaV9H2B91E1h8eWdR5wj3OHi4i9njEve7SvrR+1K1beDHwQuKlX3ia7z2dQdXPXSVpb0q4ErgFukbQQeAw4v+z7PeAKSS8DO4GP2N4m6Q3AXSUgzgBWAl8sxywFlkvaRBX9L2jw90TEFOq3FdinW8uY4svAJbaf6ZWxsaBo+366j/kBzO+SfzmwvEv6C/SYHCnjjed32xcRey9jRgfY6bP9+/3mnZLZ54iIydrZ11V5g5egGBGtY2A0QTEiYpe0FCMiCgMvD+lCkgTFiGgd43SfIyJeYRgd0hXHCYoR0TrVipbhSFCMiBYSoz0vc25WgmJEtE410ZKgGBEBdK5TTFCMiHjFzrQUIyIqaSlGRNQYMTqkh40mKEZEK6X7HBFRGPErzxjKdycoRkTrVBdvp/scEfGKTLRERBS2GPVwWorD+daIiAnsRH1t/ZD0nyVtkLRe0k2S9u+VN0ExIlqnmmiZ2dc2EUlzgD8D5tk+ieoBeD0fcpfuc0S0TgMTLTOB15enhR4APDlexoiI1hkd0HWKtp+Q9BmqRyr/Evim7W/2yp/uc0S0TmdFSz8bMEvSg7VtUb0sSYcCZwPHAL8BvEHSH/f67rQUI6KVdvY/+7zN9rxx9p8JPGr7nwEk3Qb8LvB33TInKEZE61Q3hBhYR/Yx4HRJB1B1n+cDD/bKnKAYEa1jxMsDWuZne7WkFcAaYAfwMLCkV/4ExYhoHZuBXrxt+5PAJ/vJ29hEi6S5ku6VNFIumlxc0g+TdLekjeX10JJ+mqS1Zfu+pHNrZZ0qaZ2kTZI+J0klfT9JN5f01ZKObur3RMRU6u/C7X4v3p6MJmefdwCX2T4eOB24RNIJwBXAKtvHAavKZ4D1VBdXngy8B/hbSZ2W7BeARcBxZXtPSV8IPGP7WOB64NoGf09ETBFTtRT72QatsaBoe4vtNeX9dmAEmEM1Nb6sZFsGnFPy/ML2jpK+P9V5QdJs4GDb37Ft4IbOMWPKWgHM77QiI2LvNolLcgZqSq5TLN3aU4DVwJG2t0AVOIEjavl+W9IGYB3wpyVIzgE214rbXNIor4+XsnYAzwKHN/pjIqJxRux0f9ugNT7RIulA4FbgUtvPjdeQs70aOFHS8cAySXdC10EDd4ofZ1+9Douout8RsReoHnE6nHngRluKkvalCog32r6tJD9VusSdrvHWscfZHgFeAE6iahkeVdt9FLvWLW4G5payZgKHAE93KW+J7XkTXOAZEa0hRvvcBq3J2WcBS4ER29fVdt0BLCjvFwC3l/zHdCZWJL0FeCvw09LF3i7p9FLmxZ1jxpR1HnBPGXeMiL2YqVa09LMNWpPt0zOAi4B1ktaWtCuBa4BbJC2kutL8/LLv94Aryl0sdgIfsb2t7Psw8BXg9cCdZYMq6C6XtImqhdjzdkARsXeZdnfetn0/3cf8oFpmMzb/cmB5j7IepOpKj01/kV1BNSKmCVuNtAL7kRUtEdE61URLnuYXEVEM7xktCYoR0TrVRMs0G1OMiNgTTaxW6UeCYkS0TmdFyzAkKEZEKw34wVV9S1CMiNax4eWdCYoREUCn+5ygGBHximm3oiUiYncN85KcPPc5IlpIA7shhKS31h51slbSc5Iu7ZU/LcWIaKVBPX/F9iPAyQCSZgBPAF/vlT9BMSJap5p9bmTt83zgx7Z/1itDgmJEtM4kL96eJan+cPsltns91/kC4KbxCktQjIhWmkT3eVs/d9WX9DrgA8DHx8uXoBgRrdPQ7PMfAWtsPzVepgTFiGilBi7e/ndM0HWGBMWIaCFb7BhgUJR0APAu4D9OlDdBMSJaaZDdZ9u/oM9nwicoRkTr5CazERFjJChGRBS5yWxExBiDWuY3WQmKEdE6NuzITWYjInZJ9zkiosiYYkTEGE5QjIjYZVgTLY2NZEqaK+leSSOSNkhaXNIPk3S3pI3l9dCS/i5JD0laV17fWSvrPkmP1O6ce0RJ30/SzZI2SVot6eimfk9ETB27GlPsZxu0Jqd3dgCX2T4eOB24RNIJwBXAKtvHAavKZ4BtwPttvw1YACwfU96Ftk8u29aSthB4xvaxwPXAtQ3+noiYMmJ05z59bYPWWFC0vcX2mvJ+OzACzAHOBpaVbMuAc0qeh20/WdI3APtL2m+Cr6mXtQKYL2k4be6IGChbfW2DNiVjiqVbewqwGjjS9haoAmenKzzGh4CHbb9US/uypFHgVuBq26YKso+XsnZIepZq0fe2Md+/CFjU+fzo4ssH9dOmJSHuWvepYVejtaR9WOkVw67GLvvsAzt3DrsWAzWt1z5LOpAqkF1q+7mJGnKSTqTqBp9VS77Q9hOSDiplXQTcAF1HYv2ahOrW5EtK+T7jg3+1Oz/l18YDt32MM3XesKvRWiu9gjb9DT1w28c45rOfGXY1XmWPGx6uxhWHodFLxiXtSxXEbrR9W0l+StLssn82sLWW/yiqp2xdbPvHnXTbT5TX7cBXgdPKrs3A3HLsTOAQ4Okmf1NETI2dqK9t0JqcfRawFBixfV1t1x1UEymU19tL/jcC/wB83PYDtXJmSppV3u8LvA9Y36Ws84B7Src6IvZiHuJES5Pd5zOournrJK0taVcC1wC3SFoIPAacX/Z9FDgWuErSVSXtLOAF4K4SEGcAK4Evlv1LgeWSNlG1EC9o8PdExBQaVvOmsaBo+366j/lB9ezVsfmvBq7ukf/UHt/xIruCakRMI8Na0TKc21BERIzDHuwlOZLeKGmFpB+VBSW/0ytvlvlFRCsN+JKczwLfsH1eef7zAb0yJihGRCsNakxR0sHAO4A/qcr1r4Bf9cqfoBgRrWPEzv5nlmdJerD2eUm5NrnjN4F/ploA8lvAQ8Bi2y90KyxjihHRSu5zA7bZnlfblowpaibwduALtk+huqLlCnpIUIyI9hnsRMtmYLPt1eXzCqog2VWCYkS00ySaiuMWY/8T8Likt5ak+cAPe+XPmGJEtNKAr1P8T8CNZeb5J8C/75UxQTEiWsfAzp2DC4q21wLz+smboBgR7WNgut46LCJid0y7tc8REXskQTEioqOZRw30I0ExItopLcWIiMLgAc4+T0aCYkS0VIJiRMQu6T5HRNQkKEZEFLl4OyLi1XLxdkREXWafIyJ2UVqKERFFn/dKbEKCYkS0kDLREhHxKmkpRkTU7BzO1yYoRkT7DPE6xcYeXCVprqR7JY1I2iBpcUk/TNLdkjaW10NL+rskPSRpXXl9Z62sU0v6Jkmfk6SSvp+km0v6aklHN/V7ImJqyf1tfZUl/bTEkLVjnhH9Gk0+zW8HcJnt44HTgUsknUD1vNVVto8DVrHr+avbgPfbfhuwAFheK+sLwCLguLK9p6QvBJ6xfSxwPXBtg78nIqbSgJ7mV/OHtk+2Pe6zWiYMipI+2mnNTYbtLbbXlPfbgRFgDnA2sKxkWwacU/I8bPvJkr4B2L+0BGcDB9v+jm0DN3SOGVPWCmB+pxUZEbE7+hlT/BfAP0paA3wJuKsEp76Vbu0pwGrgSNtboAqcko7ocsiHgIdtvyRpDtXDrDs2UwVXyuvjpawdkp4FDqdqdda/fxFVSzNiIB647WPDrsIrhHh08eXDrsbATeLi7VljusRLbC8Zk8fANyUZ+Nsu+18xYVC0/V8lXQWcRfWs1M9LugVYavvHEx0v6UDgVuBS289N1JCTdCJVN/isTlK3avWxr/4blgBLSvlDmuiP6WSS7YJGSeJMnTfsarzKSq/YswLMZJb5bZuoSwycYfvJ0gi7W9KPbH+7W8a+xhRLy/CfyrYDOBRYIenT4x0naV+qgHij7dtK8lOlS0x53VrLfxTwdeDiWsDdDBxVK/Yo4Mnavrnl2JnAIcDT/fymiGi5AY4pdobmbG+lijGn9crbz5jin0l6CPg08ADwNtsfBk6l6ub2Ok7AUmDE9nW1XXdQTaRQXm8v+d8I/APwcdsP1H7MFmC7pNNLmRd3jhlT1nnAPZPt2kdEOw1q9lnSGyQd1HlP1Qtd3yt/P2OKs4AP2v5ZPdH2TknvG+e4M4CLgHWS1pa0K4FrgFskLQQeA84v+z4KHAtcVbrrAGeVyP5h4CvA64E7ywZV0F0uaRNVC/GCPn5PROwNBte8ORL4ehm6mwl81fY3emXuZ0zxv42zb2ScfffT+yEL87vkvxq4ukdZDwIndUl/kV1BNSKmkwEFRds/AX6r3/xZ0RIRrTOZC7MHLUExItopN5mNiNglLcWIiLoExYiIImOKERFjJChGROyiId1ktslbh0VE7HXSUoyIdkr3OSKiyERLRMQYCYoRETUJihERFTG82ecExYhon4wpRkSMkaAYEVGToBgRsUu6zxERdUMKilnmFxHt42r2uZ+tX5JmSHpY0t+Ply9BMSLaaYCPOC0WAz2fK9WRoBgRrTSoR5zCK8+U/zfA/5oob8YUI6Kd+m8FzpL0YO3zEttLxuT5H8B/AQ6aqLAExYhon8l1jbfZntdrZ3k+/VbbD0n6g4kKS1CMiNYRA70k5wzgA5LeC+wPHCzp72z/cbfMGVOMiFYa1Jii7Y/bPsr20cAFwD29AiKkpRgRbZWLtyMiahoIirbvA+4bL09j3WdJcyXdK2lE0gZJi0v6YZLulrSxvB5a0g8v+Z+X9PkxZd0n6RFJa8t2REnfT9LNkjZJWi3p6KZ+T0RMoT67zk0sBWxyTHEHcJnt44HTgUsknQBcAayyfRywqnwGeBG4Cri8R3kX2j65bFtL2kLgGdvHAtcD1zb0WyJiqg3+4u2+NBYUbW+xvaa83051Jfkc4GxgWcm2DDin5HnB9v1UwbFf9bJWAPMlac9rHxHDNuhlfv2akjHF0q09BVgNHGl7C1SBs9MV7sOXJY0CtwJX2zZVkH28lLVD0rPA4cC2Md+/CFjU+fzAbR/bsx80zc2cAStHVwy7Gu21zz607b+9Kz39/r2m7V1yJB1IFcgutf3cbv4xXWj7CUkHlbIuAm6gupxprNecynJ1+5JSH5+p83anDr82Vo6uYHTLccOuRmvNmL2RYz77mWFXo9UeXdxrFKxPDXWN+9HodYqS9qUKYjfavq0kPyVpdtk/G9ja6/gO20+U1+3AV4HTyq7NwNxS1kzgEODpQf6GiBiS6TamWMb2lgIjtq+r7boDWFDeLwBun6CcmZJmlff7Au8D1ncp6zyqizKH9N+XiBiUzoqWYcw+N9l9PoOqm7tO0tqSdiVwDXCLpIXAY8D5nQMk/RQ4GHidpHOAs4CfAXeVgDgDWAl8sRyyFFguaRNVC/GCBn9PREwh7RxO+6axoFhmknsNIM7vcczRPfKf2iP/i9SCakRME0McU8yKlohopWk7+xwRsVsSFCMidklLMSKiLkExIqJwM0v4+pGgGBGtM+A7b09KgmJEtNOQ1mEkKEZEK6WlGBHRkYu3IyJebVATLZL2B74N7EcV81bY/mSv/AmKEdFKA5x9fgl4p+3nyz0U7pd0p+3vdsucoBgR7WMGNtFS7pz1fPm4b9l6Fp7nPkdEK03i1mGzJD1Y2xa9pixpRrlb11bgbture31vWooR0U79NxS32Z43blH2KHCypDcCX5d0ku313fKmpRgRrdPUTWZt/z+q5z6/p1eeBMWIaB8b7exvm4ikN5UWIpJeD5wJ/KhX/nSfI6KdBned4mxgmaQZVA3BW2z/fa/MCYoR0UqDWtFi+wdUj1juS4JiRLSPgen2jJaIiD2SZX4REbvkhhARETXT7hGnERG7LXfJiYjYpbp4Oy3FiIhd8oyWiIhd0lKMiOgY4phiY2ufJc2VdK+kEUkbJC0u6YdJulvSxvJ6aEk/vOR/XtLnx5R1qqR1kjZJ+pwklfT9JN1c0ldLOrqp3xMRU2lwa58nq8kbQuwALrN9PHA6cImkE4ArgFW2jwNWlc8ALwJXAZd3KesLwCLguLJ17nCxEHjG9rHA9cC1Df2WiJhqdn/bgDUWFG1vsb2mvN8OjABzgLOBZSXbMuCckucF2/dTBcdXSJoNHGz7O+UOujd0jhlT1gpgfqcVGRF7MVePI+hnG7QpuXVY6daeAqwGjrS9BarACRwxweFzgM21z5tLWmff46WsHcCzwOEDq3hEDM+QWoqNT7RIOhC4FbjU9nO70ZDrdoD72FevwyKq7ndE7C2m20QLQHly1q3AjbZvK8lPlS5xp2u8dYJiNgNH1T4fBTxZ2ze3lDUTOAR4emwBtpfYnjfRLcsjoj20c2df26A1OfssYCkwYvu62q47gAXl/QLg9vHKKV3s7ZJOL2VeXDumXtZ5wD1l3DEi9mamuni7n23Amuw+nwFcBKwrT9ECuBK4BrhF0kLgMeD8zgGSfgocDLxO0jnAWbZ/CHwY+ArweuDOskEVdJdL2kTVQrygwd8TEVNEePpdvF1mknsNIM7vcczRPdIfBE7qkv4itaAaEdPIkIJiHlwVEe00oNnnXgtJeskyv4hon86Y4mB0FpKskXQQ8JCku8vQ3GskKEZEKw1qZrlM1naujd4uqbOQJEExIvYWzVyYPWYhSVcJihHRPmYyQXGWpAdrn5fYXjI209iFJL0KS1CMiHbqv/e8baKFGT0WknSVoBgRrTSo6xTHWUjSVS7JiYh2GtwNIToLSd4paW3Z3tsrc1qKEdE+NowObPZ5vIUkr5GgGBHtNN2W+UVE7JEExYiIwkADz1/pR4JiRLSQwcN58HOCYkS0jxnYRMtkJShGRDtlTDEioiZBMSKio5kbQvQjQTEi2sdAAw+l6keCYkS0U1qKEREdg1vmN1kJihHRPgbnOsWIiJqsaImIqMmYYkREYWf2OSLiVdJSjIjoMB4dHco3JyhGRPvk1mEREWMM6ZKcxh5cJWmupHsljUjaIGlxST9M0t2SNpbXQ2vHfFzSJkmPSHp3Lf2+ktZ56MwRJX0/STeXY1aXB11HxF7OgHe6r20ikr4kaauk9f18d5NP89sBXGb7eOB04BJJJwBXAKtsHwesKp8p+y4ATgTeA/xPSTNq5V1o++SybS1pC4FnbB8LXA9c2+DviYip4nKT2X62iX2FKqb0pbGgaHuL7TXl/XZgBJgDnA0sK9mWAeeU92cDX7P9ku1HgU3AaRN8Tb2sFcD88ozXiNjLeXS0r23CcuxvA0/3+71TMqZYurWnAKuBI21vgSpwdrrCVAHzu7XDNpe0ji9LGgVuBa627bL/8VLWDknPAocD28Z8/yJgUfn40kqv6KsZPUVmMaa+LTBrxuyNbapT287RrEcXX96q+tCu8wPw1j05eDvP3LXSK2b1mX1/SQ/WPi+xvWR3v7vxoCjpQKpAdqnt58ZpyHXb0RkwuND2E5IOKmVdBNwwwTG7EqoTtKTU50Hb8yb3K5rTtvpA++qU+oyvbfWBqk57crztvru7g9bkmCKS9qUKYjfavq0kPyVpdtk/G+iMD24G5tYOPwp4EsD2E+V1O/BVdnWrXzlG0kzgECbRTI6IGKvJ2WcBS4ER29fVdt0BLCjvFwC319IvKDPKxwDHAd+TNFPSrFLmvsD7gPVdyjoPuKd0qyMidkuT3eczqLq56yStLWlXAtcAt0haCDwGnA9ge4OkW4AfUs1cX2J7VNIbgLtKQJwBrAS+WMpbCiyXtImqhXhBH/Xa7bGGhrStPtC+OqU+42tbfaBFdZJ0E/AHwCxJm4FP2l7aM38aVhERuzQ6phgRsbdJUIyIqJl2QVHSYknry9LCS0vaX0h6orZM8L21/F2XFu5hHV6zrGg3lzeeKmld2fe53b0wfTL1kXS0pF/WztXfTFF9zi//ZjslzRuTfxjnp2t9puL8jFOnv5L0I0k/kPR1SW+s7RvGOepan6k6R42xPW024CSqmekDqCaRVlLNYv8FcHmX/CcA3wf2A44BfgzMGEA93gG8HVhfS/s0cEV5fwVw7UR1AL4H/A7V9Zh3An80BfU5up5vTDlN1ud4qgt+7wPm9fNvNKT6NH5+xqnTWcDM8v7aFvwN9arPlJyjprbp1lI8Hviu7V/Y3gF8Czh3nPy7s7RwQu6+rGhSyxtVXcN5sO3vuPpruqF2TJP16arp+tgesf1Il+xDOT/j1KerQdZnnDp9s/xdQ7X666jyfljnqFd9uhr0OWrKdAuK64F3SDpc0gHAe9l1QfhHSzP/S7Wu6yvLBIuxSwsH6VXLG4H68sZudZhT3jdVt171AThG0sOSviXp92v1bLI+vQzr/IynDefnP1C1tDrfPexzVK8PtOMc7ZZpdT9F2yOSrgXuBp6n6lLsAL4A/CXVEsC/BP6a6h+xr2WCDetVh2HVbQvwZts/l3Qq8L8lnTjE+uT8jCHpE1R/1zd2knp895TUqUt9hn6O9sR0aylie6ntt9t+B1Vzf6Ptp2yPunqQ7BfpskyweGVpYQMmu7xxM6/ujgy6bl3rU7pgPy/vH6Ian/qXU1CfXoZ1froa9vmRtIBqVdeFpQsKQzxH3eoz7HO0p6ZdUNSuG9C+GfggcFPn//zFubx6meBrlhY2VLVJLW8sXdrtkk4vM3QX145prD6S3qRyH0tJv1nq85MpqM949RzG+elqmOdH0nuAPwc+YPsXtV1DOUe96tPCv6HJGfZMz6A34P9QLRX8PjC/pC0H1gE/oPoDml3L/wmq/5I9woBmwoCbqLoQL1P913Eh1S3NVgEby+thE9UBmEcVwH8MfJ6yAqnJ+gAfAjaU87cGeP8U1efc8v4l4CngriGfn671mYrzM06dNlGNHa4t298M+Rx1rc9UnaOmtizzi4iomXbd54iIPZGgGBFRk6AYEVGToBgRUZOgGBFRk6AYEVGToBgRUZOgGK0k6V+XG3jsL+kNqu5teNKw6xXTXy7ejtaSdDWwP/B6YLPt/z7kKsWvgQTFaC1JrwP+EXgR+F3bo0OuUvwaSPc52uww4EDgIKoWY0Tj0lKM1pJ0B/A1qlvsz7b90SFXKX4NTKubzMb0IeliYIftr5bbUP1fSe+0fc+w6xbTW1qKERE1GVOMiKhJUIyIqElQjIioSVCMiKhJUIyIqElQjIioSVCMiKj5/4WqCnwrtVwgAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -285,7 +292,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -333,7 +340,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -381,7 +388,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -422,7 +429,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -470,7 +477,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -518,7 +525,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -567,7 +574,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -616,7 +623,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -670,7 +677,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -744,7 +751,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -797,7 +804,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -850,7 +857,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -892,7 +899,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -923,7 +930,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -962,7 +969,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -993,7 +1000,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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p+/0eYAtwIHACcH632fnAS7ptflxV27v1ezEaL06SjcAjq+rzVVXABTP77HKsS4BjZ3o6JGkhO2u3JS/TxsyW1LqWMntV/gp0l+mOBK4BDqiqbTAKbGD/se2elmQzcAPw6i6cDwS2jh1ua7eO7uet3bG2Az/A8YaStCxmtiTNr/dJhEkeAVwKnFFVP5yvs6GqrgGenORw4Pwkl8OsA1dq5vDzvDfehtMYXU6UJGDmpvx2fu7KzJbUotYyu9ce6CR7MArii6pq5pYgd3SX+GYu9d25635VtQW4FziCUe/FQWNvHwTc3v2+FTi4O9buwD7A3bMcb1NVHbWYJ9BIGo6WJqS0wMyW1LKWMrvPu3AEOA/YUlVvG3vrMuCU7vdTgI922z9+ZgJKkl8Angh8q7tkeE+SY7pjvnJmn12O9TLgqm7MnSTNa+am/Etdpo2ZLallrWV2n0M4ng68ArghyfXdujcC5wIXJzkV+A5wYvfeM4CzkvwE2An8blXd1b33GuB9wEOBy7sFRmF/YZJbGPVinNTj55GkaWZmS9Ii9VZAV9XnmH28G8Cxs2x/IXDhHMe6ltGlwV3X38cDYS5JE5nGu2kslZktqXUtZbZPIpQ0TFM6FEOSplJjmW0BLWmQCqZ2MqAkTZvWMtsCWtJgtdSbIUmaX0uZ3c5gEkmSJGkdsAda0iDN3BJJktS+1jLbAlrSYLUUxpKk+bWU2RbQkgaptcfCSpLm1lpmW0BLGqyWZnRLkubXUmY7iVCSJEmagD3Qkoap2hpPJ0maR2OZbQEtaZBam9EtSZpba5ltAS1psFoKY0nS/FrKbMdAS5IkSROwB1rSILV2SyRJ0txay2wLaEmDVQ2FsSRpfi1ltgW0pMFq6Z6ikqT5tZTZFtCSBqkauyWSJGlurWW2kwglSZKkCVhASxqsqix5WYwkj0pySZKbkmxJ8s96/kiSNLVaymyHcEgaqFWZ0f3nwCeq6mVJHgI8rO8TStJ0aiuzLaAlDVafM7qTPBJ4JvA7o3PVPwL/2NsJJWnKtZTZFtCSBmkFHgu7X5Jrx15vqqpNY6+fAPwD8N4kvwJcB5xeVfcu56SSNEStZbZjoCVpae6qqqPGlk27vL878KvAu6rqSOBe4KxVb6UkCVY4sy2gJQ1TjW6LtNRlEbYCW6vqmu71JYzCWZI0qcYy2wJa0mDtJEteFlJVfw/cmuSJ3apjgb/t8/NI0jRrKbMdAy1pkIpVeSzsvwYu6mZzfwN4Vd8nlKRp1FpmW0BLUk+q6nrgqLVuhyRpYZNktgW0pIFalXuKSpJWRFuZbQEtabAWObFEktSAljLbAlrSYK3CeDpJ0gppKbMtoCUN0ujWRu2EsSRpbq1ltrexkyRJkibQWwGd5OAkn06yJcnmJKd36x+T5IokN3c/H92tf26S65Lc0P189tixrk7ytSTXd8v+3fo9k3woyS1JrklySF+fR9L02VlZ8jJtzGxJrWsps/vsgd4OnFlVhwPHAK9N8iRGj0W8sqoOA67kgcck3gW8qKp+GTgFuHCX451cVU/plju7dacC36uqQ4G3A2/t8fNImjI9P9VqvTGzJTWtpczubQx0VW0DtnW/35NkC3AgcALwrG6z84GrgddX1ZfHdt8M7JVkz6q6f57TnAC8qfv9EuCdSVI1pX/eJK2olsbTrbWWM3vDxpsn+zB6EL/DlfGJm85d6yYMWkuZvSqTCLvLdEcC1wAHdEFNVW2bubS3i98GvrxLEL83yQ7gUuCcLnAPBG7tjrU9yQ+AfRn1jIyf/zTgtJnXBsnyhN38Dpcp2a35IE52o2rnWjejN0WaCuOWtJbZktRaZvdeQCd5BKMAPaOqfpjM/+GTPJnRZb3nja0+uapuS7J3d6xXABfArA83f1BPRlVtAjZ1x6/n5GVL+SjqfKou4bhfOmvhDTWnT9x0Ls88vu2r15+9/PXroo1aWWb2dPlUXQJgZi/TTIfHL775bWvckvXr7/7499e6CSuq17twJNmDUXheVFUf7lbfkWRj9/5G4M6x7Q8CPgK8sqq+PrO+qm7rft4DfAA4untrK3Bwt+/uwD7A3X1+JknTo5axTCMzW1LLWsrsPu/CEeA8YEtVjf+T7TJGE07ofn602/5RwMeAN1TV34wdZ/ck+3W/7wG8ELhxlmO9DLjK8c+SFqW7p+hSl2ljZktqWmOZ3ecQjqczumx3Q5Lru3VvBM4FLk5yKvAd4MTuvdcBhwJnJzm7W/c84F7gk10QbwA+Bbyne/884MIktzDqxTipx88jadpYuo0zsyW1raHM7vMuHJ9j9vFuAMfOsv05wDlzbP/UOc5xHw+EuSRpicxsSVo8H+UtabCmcSiGJE2rljLbAlrSYDn6VpLWj5Yy2wJa0iAVbfVmSJLm1lpmW0BLGqYCGgpjSdI8GsvsXu8DLUmSJE0be6AlDVZL4+kkSfNrKbMtoCUNV0NhLElaQEOZbQEtaaCm84mCkjSd2spsC2hJw9VQb4YkaQENZbaTCCVJkqQJ2AMtaZiqrXuKSpLm0VhmW0BLGq6GLgdKkhbQUGZbQEsasHZ6MyRJC2knsy2gJaknSb4F3APsALZX1VFr2yJJ0lwmyWwLaEnDtTqXA3+jqu5alTNJ0jRrKLMtoCUNV0Pj6SRJC2gos72NnaRhKqCy9GXxZ/nrJNclOa2/DyNJU66xzLYHWtJg1fJ6M/ZLcu3Y601VtWmXbZ5eVbcn2R+4IslNVfXZZZ1Vkgaqpcy2gJakpblroUmBVXV79/POJB8BjgYsoCVp9a1oZjuEQ9Jw1TKWBSR5eJK9Z34HngfcuNIfQZIGo6HMtgda0nD1+1SrA4CPJIFR1n6gqj7R5wklaao1lNkW0JIGKz3O6K6qbwC/0t8ZJGlYWspsC2hJw7TIy3qSpAY0ltmOgZYkSZImYA+0pIGa6N6gkqQ11VZmW0BLGq6GLgdKkhbQUGZbQEsarobCWJK0gIYy2zHQkiRJ0gTsgZY0XA31ZkiSFtBQZltASxqmoqkJKZKkeTSW2RbQkgarz5vyS5JWVkuZ3dsY6CQHJ/l0ki1JNic5vVv/mCRXJLm5+/nobv1zk1yX5Ibu57PHjvXUbv0tSd6R7jmLSfZM8qFu/TVJDunr80iaQrWMZcqY2ZKa11Bm9zmJcDtwZlUdDhwDvDbJk4CzgCur6jDgyu41wF3Ai6rql4FTgAvHjvUu4DTgsG45rlt/KvC9qjoUeDvw1h4/jyRNMzNbkhZpwQI6yetmehwmUVXbqupL3e/3AFuAA4ETgPO7zc4HXtJt8+Wqur1bvxnYq+ut2Ag8sqo+X1UFXDCzzy7HugQ4dqanQ5KGaim5bWZL0uItpgf654AvJrk4yXFLCbvuMt2RwDXAAVW1DUaBDew/yy6/DXy5qu5nFOBbx97b2q2j+3lrd6ztwA+AfSdtn6RhSi19adyyctvMltSiljJ7wUmEVfW/JTkbeB7wKuCdSS4Gzquqry+0f5JHAJcCZ1TVDxfK8SRPZnRZ73kzq2Zr1iLeGz/maYwuJ0rN+Ozlr1/rJsxvw27tt3G5GprRvZKWk9tmtqRmNZTZi7oLR1VVkr8H/p7ROLlHA5ckuaKq/miu/ZLswSiIL6qqD3er70iysaq2dZf67hzb/iDgI8Arx0J+K3DQ2GEPAm4fe+9gYGuS3YF9gLtnaf8mYFN3jvb7jjQIo6vb7UrCr13+hrVuxry+ePxblr7zlE4GnLGU3DazJTWrscxezBjo30tyHfB/An8D/HJVvQZ4KqPLdnPtF+A8YEtVvW3srcsYTTih+/nRbvtHAR8D3lBVfzOzcXfJ8J4kx3THfOXMPrsc62XAVdV6VSJJPVtKbpvZkrR4i+mB3g/4rar69vjKqtqZ5IXz7Pd04BXADUmu79a9ETgXuDjJqcB3gBO7914HHAqc3V16BHheVd0JvAZ4H/BQ4PJugVHYX5jkFka9GCct4vNI0sj0lm5LyW0zW1LbGsrsxYyB/uN53tsyz3ufY/bxbgDHzrL9OcA5cxzrWuCIWdbfxwNhLkkTmdbBAUvJbTNbUutaymyfRChpuBoKY0nSAhrK7D4fpCJJkiRNHXugJQ1XQ70ZkqQFNJTZFtCSBmmdPBBFkkR7mW0BLWm4GropvyRpAQ1ltgW0pOFqqDdDkrSAhjLbSYSSJEnSBOyBljRYLY2nkyTNr6XMtoCWNFwNhbEkaQENZbYFtKRhWqUZ3Uk2ANcCt1XVXI/RliTNp7HMdgy0JPXrdGDWx2dLkpqzqMy2gJY0XLWMZRGSHAS8APi/V7bhkjRADWW2QzgkDVf/lwP/L+CPgL17P5MkTbuGMtseaEmDNfNkq6UswH5Jrh1bTvuZYycvBO6squvW4rNJ0rRpKbPtgZakpbmrqo6a5/2nAy9O8nxgL+CRSd5fVf9idZonSRqzopltD7Qk9aCq3lBVB1XVIcBJwFUWz5LUpkkz2x5oScPV0D1FJUkLaCizLaAlDdMq3VMUoKquBq5enbNJ0hRqLLMtoCUNV0O9GZKkBTSU2RbQkoaroTCWJC2gocx2EqEkSZI0AXugJQ1SWL3xdJKk5Wktsy2gJQ1XQ2EsSVpAQ5ltAS1pmFZxRrckaZkay2zHQEuSJEkTsAda0nA11JshSVpAQ5ltAS1puBoKY0nSAhrKbAtoSYPV0ng6SdL8WspsC2hJw9VQGEuSFtBQZjuJUJIkSZqAPdCShqloqjdDkjSPxjLbAlrSYLU0nk6SNL+WMru3IRxJDk7y6SRbkmxOcnq3/jFJrkhyc/fz0d36fbvtf5Tknbsc6+okX0tyfbfs363fM8mHktyS5Jokh/T1eSRNoVrGMmXMbEnNayiz+xwDvR04s6oOB44BXpvkScBZwJVVdRhwZfca4D7gbOAP5jjeyVX1lG65s1t3KvC9qjoUeDvw1p4+i6QplFr6MoXMbElNaymzexvCUVXbgG3d7/ck2QIcCJwAPKvb7HzgauD1VXUv8Lkkh05wmhOAN3W/XwK8M0mqajr/vElST1rO7E/VJROcQrP5xE3nrnUTpsLf/fHvr3UT1IhVGQPdXaY7ErgGOKALaqpq28ylvUV4b5IdwKXAOV3gHgjc2h1re5IfAPsCd+1y/tOA02ZeG8bLs2GDYbxs2Y0ka92KBX3x+LesdRP65T+1Z9VaZksS0FRm915AJ3kEowA9o6p+uMSi4eSqui3J3t2xXgFcAMx2sAd9vVW1CdjUtad+7fI3LKUN6nzx+LfwsW88ea2bsa694AmbOfLVb1vrZqx7X373MnqDpnQs83KZ2dNl5h/BZvbyvOAJmwHwAvfSLbvTqLHM7vU+0En2YBSeF1XVh7vVdyTZ2L2/Ebhzrv1nVNVt3c97gA8AR3dvbQUO7o61O7APcPdKfgZJ0ynLXKaRmS2pVa1ldp934QhwHrClqsa72i4DTul+PwX46ALH2T3Jft3vewAvBG6c5VgvA65y/LMkTc7MlqTF63MIx9MZXba7Icn13bo3AucCFyc5FfgOcOLMDkm+BTwSeEiSlwDPA74NfLIL4g3Ap4D3dLucB1yY5BZGvRgn9fh5JE0bS7dxZraktjWU2X3eheNzzN1rfuwc+xwyx/ZPnWP7+xgLc0maxJTejm5JzGxJrWsps30SoaThaiiMJUkLaCizLaAlDVdDYSxJWkBDmd3rXTgkSZKkaWMPtKRhmt5HckvS9Gkssy2gJQ1XQ2EsSVpAQ5ltAS1psPrszUiyF/BZYE9GWXtJVf1Jf2eUpOnWUmZbQEsarn57M+4Hnl1VP+ruify5JJdX1Rd6PaskTauGMtsCWpJ60D1h70fdyz26paELkJKkGZNmtnfhkDRYqaUvwH5Jrh1bTnvQ8ZMN3VP97gSuqKprVvcTStL0aCmz7YGWNEzFcvuD76qqo+Y9RdUO4ClJHgV8JMkRVXXjss4qSUPUWGbbAy1puGoZyySnqfo+cDVw3Eo0W5IGqaHMtoCWpB4keWzXi0GShwLPAW5a00ZJkmY1aWY7hEPSIIXeb8q/ETg/yQZGnRUXV9Vf9XpGSZpSrWW2BbSk4eoxjKvqq8CR/Z1Bkgamocy2gJY0WCnvKidJ60VLmW0BLWmYlj+jW5K0WhrLbCcRSpIkSROwB1rSYPU8IUWStIJaymwLaEnD1VAYS5IW0FBmW0BLGqyWejMkSfNrKbMtoCUNV0NhLElaQEOZ7SRCSZIkaQL2QEsapmrrcqAkaR6NZbYFtKThaiiMJUkLaCizLaAlDVJoqzdDkjS31jLbMdCSJEnSBOyBljRc1VB3hiRpfg1ltgW0pMFq6XKgJGl+LWW2BbSkYSqampAiSZpHY5ltAS1psLJzrVsgSVqsljLbSYSSJEnSBHoroJMcnOTTSbYk2Zzk9G79Y5JckeTm7ueju/X7dtv/KMk7dznWU5PckOSWJO9Ikm79nkk+1K2/JskhfX0eSVOolrFMGTNbUvMayuw+e6C3A2dW1eHAMcBrkzwJOAu4sqoOA67sXgPcB5wN/MEsx3oXcBpwWLcc160/FfheVR0KvB14a0+fRdIUSi19mUJmtqSmtZTZvRXQVbWtqr7U/X4PsAU4EDgBOL/b7HzgJd0291bV5xiF8k8l2Qg8sqo+X1UFXDCzzy7HugQ4dqanQ5LmVYxuibTUZcqY2ZKa1lhmr8okwu4y3ZHANcABVbUNRoGdZP8Fdj8Q2Dr2emu3bua9W7tjbU/yA2Bf4K6Va72kaTWlPcnLZmZLalFLmd17AZ3kEcClwBlV9cMldDbMtkMt4r3xNpzG6HKiJGkeZrYkLazXu3Ak2YNREF9UVR/uVt/RXeKbudR35wKH2QocNPb6IOD2sfcO7o61O7APcPeuB6iqTVV1VFUdtdTPImkKNTQhpQVmtqSmNZTZfd6FI8B5wJaqetvYW5cBp3S/nwJ8dL7jdJcO70lyTHfMV47tM36slwFXdWPuJGleoa0JKWvNzJbUstYyu88hHE8HXgHckOT6bt0bgXOBi5OcCnwHOHFmhyTfAh4JPCTJS4DnVdXfAq8B3gc8FLi8W2AU9hcmuYVRL8ZJPX4eSdNkSicDLoOZLaldjWV2bwV0Nzt7rsFzx86xzyFzrL8WOGKW9fcxFuaSpKUxsyVp8XyUt6TBmsahGJI0rVrKbB/lLWm4epyQMteT/SRJS9RQZtsDLWmweu7NmHmy35eS7A1cl+SKboywJGlCLWW2BbSkYSpgZ39p3N2NYuYBJPckmXmynwW0JE2qscy2gJakpdkvybVjrzdV1abZNtzlyX6SpNW3opltAS1puJbXmXHXYh70seuT/ZZ1RkkasoYy2wJa0mD1PaN7jif7SZKWoKXMtoCWNFw93pR/nif7SZKWoqHM9jZ2kgar58fCzjzZ79lJru+W5/f6gSRpirWU2fZAS1IPFniynySpIZNmtgW0pGFa5M31JUkNaCyzLaAlDVKA9DieTpK0clrLbAtoScO1c60bIElatIYy20mEkiRJ0gTsgZY0WC1dDpQkza+lzLaAljRMjU1IkSTNo7HMtoCWNFDV6035JUkrqa3MtoCWNFh9PxZWkrRyWspsJxFKkiRJE7AHWtJwNXQ5UJK0gIYy2wJa0jAVpKF7ikqS5tFYZltASxquhnozJEkLaCizHQMtSZIkTcAeaEnD1U5nhiRpIQ1ltgW0pMFq6alWkqT5tZTZFtCShquhMJYkLaChzLaAljRMBTQ0o1uSNI/GMttJhJIkSdIE7IGWNEihmhpPJ0maW2uZbQEtabgaCmNJ0gIaymwLaEnD1VAYS5IW0FBmW0BLGqbGJqRIkubRWGb3NokwycFJPp1kS5LNSU7v1j8myRVJbu5+PnpsnzckuSXJ15L85tj6q7t113fL/t36PZN8qNvnmiSH9PV5JGmamdmStHh93oVjO3BmVR0OHAO8NsmTgLOAK6vqMODK7jXdeycBTwaOA/5jkg1jxzu5qp7SLXd2604FvldVhwJvB97a4+eRNGVSteRlCpnZkprWUmb3NoSjqrYB27rf70myBTgQOAF4VrfZ+cDVwOu79X9ZVfcD30xyC3A08Pl5TnMC8Kbu90uAdyZJ1XT+dZO0woyKn2o5s794/FuW8Ik07gVP2LzWTZgKSda6CcPWUGavyhjo7jLdkcA1wAFdUFNV22Yu7TEK6i+M7ba1WzfjvUl2AJcC53SBeyBwa3es7Ul+AOwL3LXL+U8DTute3v/F499y4wp+vD7sxy6foTH7veAJm1tuH6yD7/DL7/79ltsH7X+HAE9c+q7VVBi3pLXMBszs5Wm9fWAbV0Lr7YMpyuzeC+gkj2AUoGdU1Q/n+dfbbG/MfFMnV9VtSfbujvUK4IIF9nlgRdUmYFPXnmur6qjJPsXqar2NrbcP2m9j6+2D9dPGtW7DtDGzJ9d6G1tvH9jGldB6+2C6MrvXJxEm2YNReF5UVR/uVt+RZGP3/kZgZmzcVuDgsd0PAm4HqKrbup/3AB9gdJnwZ/ZJsjuwD3B3X59H0hQpRr0ZS10WkOQvktyZpPXe058ysyU1q7HM7vMuHAHOA7ZU1dvG3roMOKX7/RTgo2PrT+pmaT8eOAz4/5PsnmS/7ph7AC/kgct548d6GXCV458lLdrOZSwLex+jyXXrgpktqXkNZXafQziezuiy3Q1Jru/WvRE4F7g4yanAd4ATAapqc5KLgb9lNBv8tVW1I8nDgU92QbwB+BTwnu545wEXdpNX7mY0I3whm1biw/Ws9Ta23j5ov42ttw8G0MY+76ZRVZ9dZ7dpM7OXrvU2tt4+sI0rofX2wRRldvzHv6Qh2uehG+vXD/mdJe//iZvOvW6h8YZdGP9VVR2x5BNJkprLbJ9EKElLs98uE2I2dZPfJEntWdHMtoCWNEwF7FzWFbi7Wp/xLklTo7HM7vUuHGshyelJbuweRXtGt+5NSW4be6zs88e2n/VRtCvcpgfN7Fzi43GfmuSG7r13ZAXv6D5JG5MckuS/jn2f7+67jXO078Tuf+edSY7aZftWvsNZ29jQd/hvktyU5KtJPpLkUWPvtfIdztrG5X+Hy5jN7dC3FWNm99/GhvLGzF6ZNjaT20PO7KkqoJMcAfxLRrdM+hXghUkO695++9hjZT/ebb/Qo2hXyvt48MzOpTwe912MHi5wWLes5Az/Rbex8/Wx7/PVY+v7auNs7bsR+C3gs+MrG/sOZ21jp4Xv8ArgiKr6p8DfAW+A5r7DWdvYWd532O8tkT7I6Kl8T0yyNaNJeBpjZq9OGzst5I2ZvTJtbCm3F92+ztRk9lQV0MDhwBeq6sdVtR34DPDSebb/6aNoq+qbwMyjaFdUVX2WB9/r9ARGj8Wl+/mS+dqU0f1XH1lVn+9u+3TB2D6r3cZZ9dnG2dpXVVuq6muzbN7MdzhPG2e1Bt/hX3f/rcDoqXIHdb+39B3O1cZZTdTGHsO4ql5eVRurao+qOqiqzltwp+Exs1enjbMys9vO7Hna2ExuDzmzp62AvhF4ZpJ9kzwMeD4P3Oj/dd3lhL/IA5fefvpY2c6uj6Lt0888HhcYfzzubG06sPt91/Vr0UaAxyf5cpLPJPnn3bq1aONsWvoO59Pad/g/AZePtaPF73C8jdDed6jJmNmr00Zo+7+Vlr7D+bT4Hbae21Ob2VM1ibCqtiR5K6PLBz8CvsLo/qTvAv6M0RD0PwP+HaP/URf1WNlVNlebWmrrNuBxVfXdJE8F/t8kT6adNvodTijJ/8rov5WLZlbN0Y41+w5naePyvsNiuRNStExm9qppKm9m4Xe4BK3n9rRn9rT1QFNV51XVr1bVMxldVri5qu6oqh1VtZPRDf0f9FjZzk8fRbsKJn087lZ+9jLIarR11jZ2l4e+2/1+HfB14BfXqI2zaek7nFVL32GSUxg9Le7k7vIZNPYdztbG5X+HBbVz6YtWhJndfxtbyps5tPQdzqq177D13B5CZk9dAZ1k/+7n4xhNBPjgTKB0XsrPPlb2QY+iXaWmTvR43O5y3D1Jjulmp75ybJ9VbWOSx85MTEjyhK6N31ijNs6mpe9wVq18h0mOA14PvLiqfjz2VjPf4VxtXJHvsKEZ3UNlZvffxlbyZh4tfYezauk7bD23h5LZUzWEo3Npkn2BnzB6tOz3klyY5CmMLgB8C/hXADXHo2hXukEZzex8FqObeG8F/oQJH4/bHeo1jGa8PpTRmKLxcUWr1kbgmcCbk2wHdgCvrqqZSQS9tHGO9t0N/HvgscDHklxfVb/Z2Hc4axtp5zt8A7AncMUot/hCVb26se9w1jayBt+hemFm99xG2skbM3tl2thMbg85s32Ut6RB2uchB9Sv/9zLl7z/J2798wUfCytJWhmtZfY09kBL0uLYgSBJ60dDmW0BLWm4GgpjSdICGspsC2hJA+VkQElaP9rK7Km7C4ckSZLUJ3ugJQ1TATu9n7MkrQuNZbYFtKThauhyoCRpAQ1ltgW0pOFqKIwlSQtoKLMdA62pkeTXknw1yV5JHp5kc5Ij1rpdalXBzmUskpbFzNZk2spse6A1Narqi0kuA85h9DSj91fVjQvsJklaA2a21jMLaE2bNwNfBO4Dfm+N26KWFVS1MyFFGigzW4vTWGZbQGvaPAZ4BLAHsBdw79o2R01zKIa01sxsLV5Dme0YaE2bTcDZwEXAW9e4LWpd1dIXSSvBzNbiNZTZ9kBraiR5JbC9qj6QZAPwX5I8u6quWuu2SZJ+lpmt9cwCWlOjqi4ALuh+3wE8bW1bpKZVNXVTfmlozGxNpLHMtoCWNFwOxZCk9aOhzLaAljRY1VBvhiRpfi1ltgW0pIFyMqAkrR9tZbZ34ZAkSZImYA+0pGEqmrqnqCRpHo1ltgW0pOFq6KlWkqQFNJTZFtCSBqmAaqg3Q5I0t9Yy2zHQkoapatSbsdRlEZIcl+RrSW5JclbPn0iSpldjmW0BLUk96J6s9h+A44EnAS9P8qS1bZUkaTaTZrZDOCQNVs+XA48GbqmqbwAk+UvgBOBv+zypJE2rljLbAlrScPU7IeVA4Nax11vxUcWStHQNZbYFtKRBuofvffJTdcl+yzjEXkmuHXu9qao2jb3OLPu0MwNGktaR1jLbAlrSIFXVcT2fYitw8Njrg4Dbez6nJE2l1jLbSYSS1I8vAocleXyShwAnAZetcZskSbObKLPtgZakHlTV9iSvAz4JbAD+oqo2r3GzJEmzmDSzU+WQPEmSJGmxHMIhSZIkTcACWpIkSZqABbQkSZI0AQtoSZIkaQIW0JIkSdIELKAlSZKkCVhAS5IkSROwgJYkSZIm8N8A2S1Hh80IeiIAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -1032,7 +1039,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -1063,7 +1070,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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nA3+7CmWSJE0JK+mSmjR6CGlZ/Ru3V9XRC62U5AHAc4HXLudgktSyFcjswbGSLqlZO1fnFdMnAJ+pqttX42CSNK1WKbN7w0q6pCat4tvrXoRdXSRpWVp842hb9w0kaRUleQjwTOADa10WSdKw2JIuqVGT799YVd8D9p/oQSSpCe31SZ/Y2SY5JMnHk2xOsinJmd38RyW5MsnN3c9HdvOPSXJDN30uyfPH9vXkJDcm2ZLkrUnSzX9gkvd1869JcuikzkfS9NlFljxNGzNbUt+1ltmT/JNkB/CqqjocOBY4I8kRwFnAVVW1Abiq+w5wE3B0VT0ROB74yyQzLf3vAE4HNnTT8d3804BvVtVhwFuAN03wfCRNkZkXYyx1mkJmtqTeajGzJ1ZJr6ptVfWZ7vNdwGbgIOAk4PxutfOB53XrfK+qdnTzH8ToGQGSrAceXlWfqqoCLpjZZta+LgGOm2mxkaSF7Kq9ljxNGzNbUt+1ltmrUuruluZRwDXAo6tqG4x+KQAHjq33lCSbgBuBl3W/AA4Cto7tbms3j+7nLd2+dgDfxv6fkrQsZrYkrb2JPzia5GHA+4FXVtV3dtdoUlXXAE9IcjhwfpLLYc6ORDWz+90sGy/D6YxuvUoSMPNiDBtxZzOzJfVRi5k90Zb0JPswCvuLqmpmCLLbu9uhM7dF75i9XVVtBu4GjmTUCnPw2OKDgdu6z1uBQ7p97Q3sB9w5x/42VtXRi3k7oKR2tPYQ0kLMbEl91lpmT3J0lwDnAZur6s1jiy4FTu0+nwp8qFv/sTMPHSX5SeDxwFe726t3JTm22+cpM9vM2tcLgI91fSAlabdmXoyx1GnamNmS+qzFzJ5kd5enAi8BbkxyQzfvdcA5wMVJTgO+DrywW/aLwFlJfgjsAn67qrZ3y14OvBt4MHB5N8HoF8qFSbYwao05eYLnI0nTzMyWpB6ZWCW9qj7J3P0PAY6bY/0LgQvn2dd1jG6jzp5/D/f9wpCkPTLUJ/4nwcyW1HetZbZvHJXUpgHfApWk5jSY2VbSJTWpYLAPE0lSa1rMbCvpkprVWquMJA1Za5ndVuceSZIkaQBsSZfUpJnhvCRJ/ddiZltJl9Ss1gJfkoastcy2ki6pSS2+YlqShqrFzLZPuqRmtfaKaUkasklndpLjk3wpyZYkZ82xPEne2i3/fJIndfMPSfLxJJuTbEpy5kqcr5V0SZIkNS3JOuDtwAnAEcCLkhwxa7UTgA3ddDrwjm7+DuBVVXU4cCxwxhzb7jG7u0hqU7XXv1GSBmvymX0MsKWqvgKQ5L3AScAXxtY5Cbigqgr4dJJHJFlfVduAbQBVdVeSzcBBs7bdY1bSJTWpxZECJGmoViCzD0hy3dj3jVW1cez7QcAtY9+3Ak+ZtY+51jmIroIOkORQ4CjgmuUUFqykS2qYlXRJGo5lZvb2qjp6N8vn2nntyTpJHga8H3hlVX1nz4t4f/ZJlyRJUuu2AoeMfT8YuG2x6yTZh1EF/aKq+sBKFMhKuqQmzQzntdRJkrR6ViGzrwU2JHlskgcAJwOXzlrnUuCUbpSXY4FvV9W2JAHOAzZX1ZtX6pzt7iKpWWVlW5IGY5KZXVU7krwCuAJYB7yzqjYleVm3/FzgMuDZwBbge8BLu82fCrwEuDHJDd2811XVZcspk5V0Sc1yvHNJGo5JZ3ZXqb5s1rxzxz4XcMYc232SufurL4uVdElNKodglKTBaDGz7ZMuSZIk9Ywt6ZKaNek+6UkeAfw1cCSjYbp+q6o+NdGDStKUau05Iivpkhq1KqO0/AXwkap6QTdawEMmfUBJmk7tjaxlJV1SsybZKpPk4cDTgN8cHat+APxgYgeUpClnS7okNWAVXjH9OOBfgHcl+XngeuDMqrp7OQeVpBatQGYPjg+OStLSbK+qo8emjbOW7w08CXhHVR0F3A2cteqllCQNkpV0SW2q0ZBeS50WYSuwtaqu6b5fwqjSLknaU5PP7N6xu4ukZk3yxRhV9c9Jbkny+Kr6EnAc8IWJHVCSplxrL6Czki6pScWqPIT074GLupFdvsJ9r5CWJO2BVcrsXrGSLkkTUlU3AEevdTkkScNjJV1So9obc1eShqu9zLaSLqlZQ32YSJJa1FpmW0mX1KzW+jdK0pC1ltlW0iU1aTQsV1uBL0lD1WJmO066JEmS1DMTq6QnOSTJx5NsTrIpyZnd/EcluTLJzd3PR3bzn5nk+iQ3dj9/eWxfVyf5UpIbuunAbv4Dk7wvyZYk1yQ5dFLnI2n67KoseZo2ZrakvmstsyfZkr4DeFVVHQ4cC5yR5AhGr8W+qqo2AFdx32uytwO/WlU/C5wKXDhrfy+uqid20x3dvNOAb1bVYcBbgDdN8HwkTZnW3l63ADNbUq+1ltkT65NeVduAbd3nu5JsBg4CTgKe3q12PnA18Jqq+uzY5puAByV5YFV9fzeHOQl4Q/f5EuBtSVI11H8OSauptf6Nu9PnzE78d1our+HK8DqurdYye1UeHO1uaR4FXAM8uvtlQFVtm7kNOsuvA5+dFfbvSrITeD9wdhfqBwG3dPvakeTbwP6MWnjGj386cPrM93Xrb16pU2tS2MtruEzJXnzki+esdTF2K9mLql1rXYyJKdJc4C9W3zJbklrM7IlX0pM8jFFIv7KqvrPQX6FJnsDoFuizxma/uKpuTbJvt6+XABcAc+3sR1pkqmojsLHbfz0jL1jKqajz0bqE43/mrIVX1Lw+8sVzeNoJ/b7T/4nLXzOIMmpl9TGzvTm6dDP/fl7D5fE6Lp93IfbcREd3SbIPo4C+qKo+0M2+Pcn6bvl64I6x9Q8GPgicUlVfnplfVbd2P+8C3gMc0y3aChzSbbs3sB9w5yTPSdL0qGVM08jMltRnrWX2JEd3CXAesLmq3jy26FJGDxnR/fxQt/4jgA8Dr62qfxzbz95JDug+7wOcCNw0x75eAHzMJhdJi9KNubvUadqY2ZJ6rcHMnmR3l6cyusV5Y5IbunmvA84BLk5yGvB14IXdslcAhwGvT/L6bt6zgLuBK7qwXwd8FPirbvl5wIVJtjBqjTl5gucjadpYPRxnZkvqt8Yye5Kju3ySufsfAhw3x/pnA2fPs/6T5znGPdz3C0OStERmtiT1y6qM7iJJfTTUW6CS1KLWMttKuqRm2Rtakoajtcy2ki6pSUV7rTKSNFQtZraVdEltKqCxwJekwWowsyc6TrokSZKkPWdLuqRmtda/UZKGrLXMtpIuqV2NBb4kDVpjmW0lXVKjhvsWOklqT3uZbSVdUrsaa5WRpEFrLLN9cFSSJEnqGVvSJbWp2htzV5IGq8HMtpIuqV2N3TqVpEFrLLOtpEtqWFutMpI0bG1ltpV0SZqQJF8F7gJ2Ajuq6ui1LZEkaSispEtq1+rcOv2lqtq+KkeSpGlmdxdJakRjgS9Jg9ZYZltJl9SmAiY/UkABf5+kgL+sqo2TPqAkTaXVyexesZIuqVm1vFaZA5JcN/Z94xyV8KdW1W1JDgSuTPLFqvrEso4qSY1aZmYPjpV0SVqa7Qs9CFpVt3U/70jyQeAYwEq6JGlBvnFUUrtqGdMCkjw0yb4zn4FnATet9ClIUjMmmNl9ZEu6pHZNtn/jo4EPJoFR1r6nqj4yyQNK0lSzT7oktSETbF2pqq8APz+5I0hSWyaZ2X1kJV1SmwZ8C1SSmtNgZtsnXZIkSeoZW9IlNSrN9W+UpOFqL7OtpEtqV2O3TiVp0BrLbCvpktrVWOBL0qA1ltn2SZckSZJ6xpZ0Se1qrFVGkgatscy2ki6pTUVzDyFJ0mA1mNlW0iU1q7UXY0jSkLWW2RPrk57kkCQfT7I5yaYkZ3bzH5XkyiQ3dz8f2c1/ZpLrk9zY/fzlsX09uZu/Jclb071nO8kDk7yvm39NkkMndT6SplAtY5oyZrak3ptwZic5PsmXuow6a47l6TJtS5LPJ3nSYrddikk+OLoDeFVVHQ4cC5yR5AjgLOCqqtoAXNV9B9gO/GpV/SxwKnDh2L7eAZwObOim47v5pwHfrKrDgLcAb5rg+UjSNDOzJTUryTrg7cAJwBHAi7oMHHcC9+Xa6YyybrHb7rEFK+lJXjHTcrInqmpbVX2m+3wXsBk4CDgJOL9b7Xzged06n62q27r5m4AHda0u64GHV9WnqqqAC2a2mbWvS4DjZlpsJKlVS8ltM1tS444BtlTVV6rqB8B7GWXWuJOAC2rk08AjusxbzLZ7bDEt6T8OXJvk4q4pf48DtbuleRRwDfDoqtoGo18KwIFzbPLrwGer6vuMfklsHVu2tZtH9/OWbl87gG8D++9p+SS1KbX0qeeWldtmtqQ+mnBm35tPnfHsWmidxWy7xxZ8cLSq/o8krweeBbwUeFuSi4HzqurLC22f5GHA+4FXVtV3FvpdkeQJjG6BPmtm1lzFWsSy8X2ezui2hNQbn7j8NWtdhN1bt1f/y7hcUzpSwHJy28yW1FvLy+wDklw39n1jVW0c+76YfJpvnUVl255a1OguVVVJ/hn4Z0b9Fh8JXJLkyqp69XzbJdmHUdhfVFUf6GbfnmR9VW3rbhHcMbb+wcAHgVPGfpFsBQ4e2+3BwG1jyw4BtibZG9gPuHOO8m8ENnbH6H8bmJow6gnQX0n4hctfu9bF2K1rT3jj0jee0gdAZywlt81sSb21/MzeXlVH72b5TD7NGM+uhdZ5wCK23WOL6ZP+O0muB/4v4B+Bn62qlwNPZnSLc77tApwHbK6qN48tupTRQ0Z0Pz/Urf8I4MPAa6vqH2dW7m6v3pXk2G6fp8xsM2tfLwA+Vn2v+UjShC0lt81sSY27FtiQ5LFJHgCczCizxl0KnNKN8nIs8O0u8xaz7R5bTEv6AcCvVdXXxmdW1a4kJ+5mu6cCLwFuTHJDN+91wDnAxUlOA74OvLBb9grgMOD13W1agGdV1R3Ay4F3Aw8GLu8mGP1CuTDJFkatMScv4nwkaWR6q4dLyW0zW1K/TTCzq2pHklcAVwDrgHdW1aYkL+uWnwtcBjwb2AJ8j1F3wnm3XW6ZFtMn/Q93s2zzbpZ9krn76AAcN8f6ZwNnz7Ov64Aj55h/D/f9wpCkPTKtHSmWkttmtqS+m3RmV9VljCri4/POHftcwBmL3Xa5fOOopHZNaSVdkqZSY5k9yZcZSZIkSVoCW9IltauxVhlJGrTGMttKuqQmDeSlRJIk2sxsK+mS2jWlLzOSpKnUWGZbSZfUrsZaZSRp0BrLbB8clSRJknrGlnRJzWqtf6MkDVlrmW0lXVK7Ggt8SRq0xjLbSrqkNq3SSAFJ1gHXAbdW1YmTP6IkTaEGR3exT7okTdaZwOa1LoQkaVispEtqVy1jWoQkBwPPAf56ZQsuSQ2acGb3jd1dJLVr8sH958CrgX0nfiRJmnYDrWwvlS3pkpo18wa7pUzAAUmuG5tOv9++kxOBO6rq+rU4N0maNsvM7MGxJV2SlmZ7VR29m+VPBZ6b5NnAg4CHJ/mbqvqN1SmeJGnIbEmXpAmoqtdW1cFVdShwMvAxK+iSpMWyJV1SuwZ6C1SSmtRYZltJl9SmVeynWFVXA1evztEkaQoNuG/5UllJl9SuxgJfkgatscy2ki6pXY0FviQNWmOZ7YOjkiRJUs/Yki6pSaG9/o2SNFQtZraVdEntaizwJWnQGstsK+mS2tTgSAGSNFgNZrZ90iVJkqSesSVdUrsaa5WRpEFrLLOtpEtqV2OBL0mD1lhmW0mX1KzW+jdK0pC1ltlW0iW1q7HAl6RBayyzfXBUkiRJ6hlb0iW1qWiuVUaSBqvBzLaSLqlZrfVvlKQhay2zJ9bdJckhST6eZHOSTUnO7OY/KsmVSW7ufj6ym79/t/53k7xt1r6uTvKlJDd004Hd/AcmeV+SLUmuSXLopM5H0hSqZUxTxsyW1HuNZfYk+6TvAF5VVYcDxwJnJDkCOAu4qqo2AFd13wHuAV4P/N48+3txVT2xm+7o5p0GfLOqDgPeArxpQuciaQqllj5NITNbUq+1ltkT6+5SVduAbd3nu5JsBg4CTgKe3q12PnA18Jqquhv4ZJLD9uAwJwFv6D5fArwtSapqoP8ckrQ2+pzZSfbgEJqL13BleB21mlalT3p3S/Mo4Brg0d0vA6pq28xt0EV4V5KdwPuBs7tQPwi4pdvXjiTfBvYHts86/unA6TPfP1qXLO+EGrduHXzki+esdTGGLXsNIuyvPeGNa12EyfLP+Tn1LbMlCWgusydeSU/yMEYh/cqq+s4SKyYvrqpbk+zb7eslwAXAXDv7kX/CqtoIbOzKU79w+WuXUgZ1rj3hjXz4K09Y62IM2nMet4mjXvbmtS7G4H323N9d+sYD7qc4SX3MbG+OLt3Mv5/XcHm8jsu37IapBjN7ouOkJ9mHUUBfVFUf6GbfnmR9t3w9cMd828+oqlu7n3cB7wGO6RZtBQ7p9rU3sB9w50qeg6TplGVO08jMltRXLWb2JEd3CXAesLmqxpsMLwVO7T6fCnxogf3sneSA7vM+wInATXPs6wXAx2xykaQ9Z2ZLUr9MsrvLUxnd4rwxyQ3dvNcB5wAXJzkN+DrwwpkNknwVeDjwgCTPA54FfA24ogv7dcBHgb/qNjkPuDDJFkatMSdP8HwkTRurh+PMbEn91lhmT3J0l08y/x2G4+bZ5tB51n/yPOvfw9gvDEnaE0MdlmsSzGxJfddaZvvGUUntaizwJWnQGstsK+mS2tVY4EvSoDWW2RMd3UWSJEnSnrMlXVKbBvyqaElqToOZbSVdUrsaC3xJGrTGMttKuqRmTbJVJsmDgE8AD2SUtZdU1R9N7oiSNN1sSZekVkw28L8P/HJVfbcbM/yTSS6vqk9P9KiSNK2spEuSlqt7k+Z3u6/7dFNjv2IkSUtlJV1Ss5Z56/SAJNeNfd9YVRvvt/9kHXA9cBjw9qq6ZllHlKSG2d1FklpQLLdde3tVHb3bQ1TtBJ6Y5BHAB5McWVU3LeuoktSi5Wf24DhOuqR21TKmPTlM1beAq4HjV6LYktSkVcrsvrCSLkkTkOTHuhZ0kjwYeAbwxTUtlCRpMOzuIqlJYeL9G9cD53f90vcCLq6qv5voESVpSq1CZveOlXRJ7Zpg4FfV54GjJncESWqMlXRJakOqscSXpAFrLbOtpEtq04AfJpKk5jSY2T44KkmSJM0jyaOSXJnk5u7nI+dZ7/gkX0qyJclZY/P/Q5IvJvl8kg/ODCqwECvpkpqVWvokSVpda5jZZwFXVdUG4Kru+/3LNhok4O3ACcARwIuSHNEtvhI4sqp+DvhvwGsXc1Ar6ZLa1diYu5I0aGuX2ScB53efzweeN8c6xwBbquorVfUD4L3ddlTV31fVjm69TwMHL+ag9kmX1CxbxCVpOJaZ2QckuW7s+8aq2rjIbR9dVdsAqmpbkgPnWOcg4Jax71uBp8yx3m8B71vMQa2kS2qXlXRJGo7lZfb2qjp6voVJPgr8+ByL/mCR+88c8+5X4iR/AOwALlrMDq2kS5IkqWlV9Yz5liW5Pcn6rhV9PXDHHKttBQ4Z+34wcNvYPk4FTgSOq1rcWJL2SZfUpmU8gGQ3GUlaZWub2ZcCp3afTwU+NMc61wIbkjw2yQOAk7vtSHI88BrguVX1vcUe1Eq6pHb54KgkDcfaZfY5wDOT3Aw8s/tOkp9IchlA92DoK4ArgM3AxVW1qdv+bcC+wJVJbkhy7mIOancXSU0KtohL0lCsZWZX1TeA4+aYfxvw7LHvlwGXzbHeYUs5ri3pkiRJUs/Yki6pXYt7dkeS1AeNZbaVdEnNsruLJA1Ha5ltJV1Sm3wAVJKGo8HMtpIuqVnZtdYlkCQtVmuZ7YOjkiRJUs9MrJKe5JAkH0+yOcmmJGd28x+V5MokN3c/H9nN379b/7tJ3jZrX09OcmOSLUnemiTd/AcmeV83/5okh07qfCRNIcdJv5eZLan3GsvsSbak7wBeVVWHA8cCZyQ5AjgLuKqqNgBXdd8B7gFeD/zeHPt6B3A6sKGbju/mnwZ8sxt/8i3AmyZ0LpKmkG8cvR8zW1KvtZbZE6ukV9W2qvpM9/kuRm9fOgg4CTi/W+184HndOndX1ScZBf+9kqwHHl5Vn6qqAi6Y2WbWvi4BjptpsZGk3SpGw3ktdZoyZrakXmsws1flwdHuluZRwDXAo6tqG4x+KSQ5cIHNDwK2jn3f2s2bWXZLt68dSb4N7A9sX7nSS5pWQ21dmTQzW1IftZbZE6+kJ3kY8H7glVX1nSU0msy1QS1i2XgZTmd061WStBtmtiT1w0RHd0myD6Owv6iqPtDNvr27HTpzW/SOBXazFTh47PvBwG1jyw7p9rU3sB9w5+wdVNXGqjq6qo5e6rlImkKNPYS0EDNbUq81ltmTHN0lwHnA5qp689iiS4FTu8+nAh/a3X6626x3JTm22+cpY9uM7+sFwMe6PpCStFuhvYeQdsfMltRnLWb2JLu7PBV4CXBjkhu6ea8DzgEuTnIa8HXghTMbJPkq8HDgAUmeBzyrqr4AvBx4N/Bg4PJugtEvlAuTbGHUGnPyBM9H0jQZ8MNEE2JmS+qvBjN7YpX07qn/+TozHjfPNofOM/864Mg55t/D2C8MSdLSmNmS1C+rMrqLJPXRUG+BSlKLWsvsiT44Kkm9NsGHkOZ7g6ckaYkae3DUlnRJzZpwq8zMGzw/k2Rf4PokV3Z9tiVJe6i1lnQr6ZLaVMCuySV+N8rJzEuA7koy8wZPK+mStKcmnNl9ZCVdkpbmgCTXjX3fWFUb51px1hs8JUlakJV0Se1aXqPM9sW8bGf2GzyXdURJallbDelW0iW1a9L9G+d5g6ckaQnsky5JrZjgizF28wZPSdJS+DIjSWrDhFtl5nyDZ1VdNtGjStKUsiVdkrRsC7zBU5Kk3bKSLqlNA37BhSQ1p8HMtpIuqUkB0lj/RkkaqhYz20q6pHbtWusCSJIWrbHM3mutCyBJkiTp/mxJl9Ss1m6dStKQtZbZVtIltanBh5AkabAazGwr6ZIaVc29GEOShqu9zLaSLqlZrb0YQ5KGrLXM9sFRSZIkqWdsSZfUrsZunUrSoDWW2VbSJbWpII2NuStJg9VgZltJl9SuxlplJGnQGsts+6RLkiRJPWNLuqR2tdUoI0nD1lhmW0mX1KzW3l4nSUPWWmZbSZfUrsYCX5IGrbHMtpIuqU0FNDZSgCQNVoOZ7YOjkiRJUs/Yki6pSaGa698oSUPVYmZbSZfUrsYCX5IGrbHMtpIuqV2NBb4kDVpjmW0lXVKbGnwISZIGq8HMntiDo0kOSfLxJJuTbEpyZjf/UUmuTHJz9/ORY9u8NsmWJF9K8itj86/u5t3QTQd28x+Y5H3dNtckOXRS5yNJ08zMlqR+meToLjuAV1XV4cCxwBlJjgDOAq6qqg3AVd13umUnA08Ajgf+c5J1Y/t7cVU9sZvu6OadBnyzqg4D3gK8aYLnI2nKpGrJ0xQysyX1WmuZPbHuLlW1DdjWfb4ryWbgIOAk4OndaucDVwOv6ea/t6q+D/xTki3AMcCndnOYk4A3dJ8vAd6WJFUD/deQtLqMinv1ObOTLOGMNM5ruDK8jmusscxelT7p3S3No4BrgEd3vwyoqm0zt0EZ/TL49NhmW7t5M96VZCfwfuDsLtQPAm7p9rUjybeB/YHts45/OnB69/X7157wxptW8PQm4QBmnUPPHPCcx23qc/lgANfws+f+bp/LB/2/hgCPX/qm1VzgL1bfMhsws5en7+UDy7gS+l4+MLP3yMQr6UkexiikX1lV39nNX6FzLZj513hxVd2aZN9uXy8BLlhgm/tmVG0ENnblua6qjt6zs1hdfS9j38sH/S9j38sHwynjWpdh2pjZe67vZex7+cAyroS+lw/M7D010TeOJtmHUUBfVFUf6GbfnmR9t3w9MNNXcStwyNjmBwO3AVTVrd3Pu4D3MLqler9tkuwN7AfcOanzkTRFilGrzFKnBSR5Z5I7kvS9FfheZrak3ppwZvfRJEd3CXAesLmq3jy26FLg1O7zqcCHxuaf3D39/1hgA/D/J9k7yQHdPvcBTuS+W5/j+3oB8DH7o0tatF3LmBb2bkYPVA6CmS2p9yab2b0zye4uT2V0i/PGJDd0814HnANcnOQ04OvACwGqalOSi4EvMBpl4Iyq2pnkocAVXdivAz4K/FW3v/OAC7sHlu5kNNLAQjauxMlNWN/L2PfyQf/L2PfyQQNlnOQT/1X1iYENMWhmL13fy9j38oFlXAl9Lx/0OLP7KDZiSGrRfg9eX//q0N9c8vYf+eI51y/U/7OrpP9dVR255ANJklYls/tmon3SJWmKHZDkurHp9IU3kSQNze5e6jZrveO7F7ltSXLWHMt/L0nNdAlcyKoMwShJvVPArmXdSdw+tFYZSRqs5Wf2csy81O2crvJ9FqP3Rdyre5nb24FnMnpI/tokl1bVF7rlh3TLvr7Yg05dS3qSM5PclNFrrV/ZzXtDklvHXlH97LH153yt9QqX6UdGedjdX2XzlSnJk5Pc2C17a1bwrQp7UsYkhyb572PX89xJl3Ge8r2w+3feleToWev35RrOWcYeXcP/kOSLST6f5INJHjG2rC/XcM4yLv8aLmOUALsJrhgze/Jl7FHemNkrU8be5HZDmX0So5e50f183hzrHANsqaqvVNUPgPd22814C/Bq5hh2dj5TVUlPciTwbxldqJ8HTkyyoVv8lrFXVF/Wrb/Qa61Xyrv50VEelvKq7XcwesHHhm5ayZEjFl3GzpfHrufLxuZPqoxzle8m4NeAT4zP7Nk1nLOMnT5cwyuBI6vq54D/BrwWencN5yxjZ3nXcLJDMP4to7dvPj7J1owevNQYM3t1ytjpQ96Y2StTxj7l9qLL11nLzF5OF8X7vdQNOHCOde59WVvn3he8JXkucGtVfW4PjjldlXTgcODTVfW9qtoB/APw/N2sfxLda62r6p+Amddar6iq+gQ/OhbwfH+VzVmmjMYnfnhVfaobsuwC5v5LbjXKOKdJlnGu8lXV5qr60hyr9+Ya7qaMc1qDa/j33f8rMHp75MHd5z5dw/nKOKc9KuMEK+lV9aKqWl9V+1TVwVV13oIbtcfMXp0yzsnM7ndm76aMvcntgWX29qo6emy630gzST6a0V292dNJc5ZljlOZq8RJHgL8AfCHi9zPvaatkn4T8LQk+3cX5dnc97KNV3S3Xt6Z+25TzvtXzyqY76+y+cp0UPd59vy1KCPAY5N8Nsk/JPnX3by1KONc+nQNd6dv1/C3gMvHytHHazheRujfNdSeMbNXp4zQ7/9X+nQNd6eP17DvuT2YzK6qZ1TVkXNMH2L+l7qNm+8Fbz8FPBb4XJKvdvM/k+THFyrTVD04WlWbk7yJ0a2W7wKfYzR+7zuAP2XUD+hPgf+b0X84i3pF9Sqbr0x9Kus24DFV9Y0kTwb+3yRPoD9l9BruoSR/wOj/lYtmZs1TjjW7hnOUcXnXsFjLh5CEmb2KepU3c/AaLkHfc3vKMnvmRWzncP+Xuo27FtiQ0cvdbmXU9ejfVNUmxv5g7irqR1fV9oUOOm0t6VTVeVX1pKp6GqNbMDdX1e1VtbOqdjF6qcaPvKK6c+9rrVfBnr5qeyv3v2W0GmWds4zdrbRvdJ+vB74M/PQalXEufbqGc+rTNUxyKqO3Qr64u9UIPbuGc5Vx+dewoHYtfdKKMLMnX8Y+5c08+nQN59S3a9j33J7CzD4HeGaSmxmN0HJOd54/keSy7px2AK8ArgA2Axd3FfQlm7pKepIDu5+PYfTwx9/OhFbn+dz/FdU/8lrrVSrqHr1qu7t1eVeSY5MEOIW5/5KbeBmT/NjMwyhJHteV8StrVMa59Okazqkv1zDJ8YyGkXpuVX1vbFFvruF8ZVyRa+joLmvOzJ58GfuSN7vRp2s4pz5dw77n9jRmdlV9o6qOq6oN3c87u/m3VdWzx9a7rKp+uqp+qqr+bJ59HbqYVnSYsu4unfcn2R/4IaPXVH8zyYVJnsjoZslXgX8HUPO81nqlC5TRKA9PZ/Rk8Vbgj9jDV213u3o5oyepH8yoj9d4P69VKyPwNOBPkuwAdgIvm/kPdlJlnKd8dwL/Cfgx4MNJbqiqX+nZNZyzjPTnGr4WeCBw5Sgb+XRVvaxn13DOMrIG11ATYWZPuIz0J2/M7JUpY29y28yerJQtQpIatN8DHl3/6sdftOTtP3LLXwzuFdOSNFQtZvY0tqRL0uLYSCFJw9FYZltJl9SuxgJfkgatscy2ki6pUT4AKknD0V5mT93oLpIkSdLQ2ZIuqU0F7HK8c0kahAYz20q6pHY1dutUkgatscy2ki6pXY0FviQNWmOZbZ90TY0kv5Dk80kelOShSTYlOXKty6W+Kti1jEnSspjZ2jPtZbYt6ZoaVXVtkkuBsxm9texvquqmBTaTJK0BM1vaPSvpmjZ/AlwL3AP8zhqXRX1WUNXWQ0hSD5nZWpwGM9tKuqbNo4CHAfsADwLuXtviqNcGegtUmiJmthavscy2T7qmzUbg9cBFwJvWuCzqu6qlT5JWgpmtxWsss21J19RIcgqwo6rek2Qd8F+T/HJVfWytyyZJuj8zW9o9K+maGlV1AXBB93kn8JS1LZF6raq5F2NIfWJma480mNlW0iW1a6C3QCWpSY1ltpV0Sc2qxlplJGnIWstsK+mSGjXch4kkqT3tZbaju0iSJEk9Y0u6pDYVzY25K0mD1WBmW0mX1K7G3l4nSYPWWGZbSZfUpAKqsVYZSRqqFjPbPumS2lQ1apVZ6rQISY5P8qUkW5KcNeEzkqTptQqZ3TdW0iVpAro3KL4dOAE4AnhRkiPWtlSSpKGwu4ukZk341ukxwJaq+gpAkvcCJwFfmORBJWlatdbdxUq6pHZN9hboQcAtY9+34mvPJWnpBtptZamspEtq0l1884qP1iUHLGMXD0py3dj3jVW1cex75timrWYgSVohK5DZ21esMKvESrqkJlXV8RM+xFbgkLHvBwO3TfiYkjSVViGze8cHRyVpMq4FNiR5bJIHACcDl65xmSRJA2FLuiRNQFXtSPIK4ApgHfDOqtq0xsWSJA1EquwiKUmSJPWJ3V0kSZKknrGSLkmSJPWMlXRJkiSpZ6ykS5IkST1jJV2SJEnqGSvpkiRJUs9YSZckSZJ6xkq6JEmS1DP/A+9OSg8h0QVIAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -1096,18 +1103,32 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Structured grid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### nearest"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
-    "struc2d_nan_filled = resample.fillnan_dataarray_structured_grid(struc2d_nan)"
+    "struc2d_nan_filled = resample.fillnan_dataarray_structured_grid(struc2d_nan, method='nearest')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1116,13 +1137,13 @@
        "Text(0.5, 1.0, 'resampled')"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -1144,18 +1165,25 @@
     "axes[1].set_title('resampled')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### linear"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
-    "vertex1_nan_filled = resample.fillnan_dataarray_vertex_grid(vertex1_nan, xyi=xyi, cid=vertex1_nan.cid)"
+    "struc2d_nan_filled_lin = resample.fillnan_dataarray_structured_grid(struc2d_nan, method='linear')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1164,13 +1192,75 @@
        "Text(0.5, 1.0, 'resampled')"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot results\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(12,6))\n",
+    "struc2d_nan.plot(ax=axes[0], edgecolor='k')\n",
+    "axes[0].set_aspect('equal')\n",
+    "axes[0].set_title('original')\n",
+    "struc2d_nan_filled_lin.plot(ax=axes[1], edgecolor='k')\n",
+    "axes[1].set_aspect('equal')\n",
+    "axes[1].set_title('resampled')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## vertex grid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### nearest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vertex1_nan_filled = resample.fillnan_dataarray_vertex_grid(vertex1_nan, xyi=xyi, cid=vertex1_nan.cid, method='nearest')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'resampled')"
+      ]
+     },
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 4 Axes>"
       ]
@@ -1191,6 +1281,1224 @@
     "nlmod.visualise.plots.plot_vertex_array(vertex1_nan_filled, vertices, ax=axes[1], gridkwargs={'edgecolor': 'k'})\n",
     "axes[1].set_title('resampled')"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### linear"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vertex1_nan_filled_lin = resample.fillnan_dataarray_vertex_grid(vertex1_nan, xyi=xyi, cid=vertex1_nan.cid, method='linear')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'resampled')"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot results\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(12,6))\n",
+    "nlmod.visualise.plots.plot_vertex_array(vertex1_nan, vertices, ax=axes[0], gridkwargs={'edgecolor': 'k'},\n",
+    "                                        vmin=vertex1_nan.min(), vmax=vertex1_nan.max());\n",
+    "axes[0].set_aspect('equal')\n",
+    "axes[0].set_title('original')\n",
+    "nlmod.visualise.plots.plot_vertex_array(vertex1_nan_filled_lin, vertices, ax=axes[1], gridkwargs={'edgecolor': 'k'})\n",
+    "axes[1].set_title('resampled')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## [6. Vector to grid](#TOC)<a name=\"5\"></a>\n",
+    "\n",
+    "Vector data can be points, lines or polygons often saved as shapefiles and visualised using GIS software. A common operation is to project vector data on a modelgrid. For example to add a surface water line to a grid. Here are some functions in `nlmod` to project vector data on a modelgrid."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vector data:\n",
+    "    - point\n",
+    "    - line\n",
+    "    - polygon\n",
+    "\n",
+    "Vector data -> vector data per cell -> aggregeren per cell -> in het grid zetten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "point_geom = [Point(x,y) for x, y in zip([1000, 1200, 1225, 1300],[20200, 20175, 20175, 20425])]\n",
+    "point_gdf = gpd.GeoDataFrame({'values':[1,52,66,24]}, geometry=point_geom)\n",
+    "line_geom = [LineString([point_geom[0], point_geom[1]]), LineString([point_geom[2], point_geom[3]]), LineString([point_geom[0], point_geom[3]])]\n",
+    "line_gdf = gpd.GeoDataFrame({'values':[1,52,66]}, geometry=line_geom)\n",
+    "pol_geom = [shp_polygon([point_geom[0], point_geom[1], point_geom[2], point_geom[3], point_geom[0]]),\n",
+    "            shp_polygon([point_geom[0], point_geom[1], point_geom[2], Point(1200,20300), point_geom[0]])]\n",
+    "pol_gdf = gpd.GeoDataFrame({'values':[166, 5]}, geometry=pol_geom)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(20050.0, 20500.0)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "struc2d.plot(ax=ax, lw=0.1, edgecolor='k', alpha=0.5)\n",
+    "point_gdf.plot(ax=ax, color='green')\n",
+    "line_gdf.plot(ax=ax, color='purple')\n",
+    "pol_gdf.plot(ax=ax, alpha=0.6)\n",
+    "\n",
+    "ax.set_xlim(ax.get_xlim()[0], 1400)\n",
+    "ax.set_ylim(ax.get_ylim()[0], 20500)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim = flopy.mf6.MFSimulation()\n",
+    "gwf = flopy.mf6.MFModel(sim)\n",
+    "dis = flopy.mf6.ModflowGwfdis(gwf, nlay=1, nrow=3, ncol=3, delr=100, delc=100, xorigin=950,yorigin=20050)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aggregation methods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intersecting with grid: 100%|███████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 573.23it/s]\n",
+      "Aggregate vector data: 100%|████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 250.63it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x1ab9e3f4af0>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "da = nlmod.mdims.gdf2data_array_struc(point_gdf, gwf, field='values', agg_method='max')\n",
+    "da.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intersecting with grid: 100%|███████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 572.76it/s]\n",
+      "Aggregate vector data: 100%|████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 501.23it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x1ab9e48b160>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "da = nlmod.mdims.gdf2data_array_struc(point_gdf, gwf, field='values', agg_method='mean')\n",
+    "da.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intersecting with grid: 100%|███████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 572.99it/s]\n",
+      "Aggregate vector data: 100%|████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 199.30it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x1ab9e521910>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEKCAYAAAAVaT4rAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcG0lEQVR4nO3df5Bd5X3f8fcHSRY2GIIQYFUSlluIzQ83EG2IWhJPYsVEdXGAGBw6FJSpZpTYMIUZPLVMSsedMC04U2hdT5zIlW2hggPhR2GaoUQISIpLZAssLJS1I9nFIFCQBRRkHIh299M/zrPWZbm7e3d1z90rnc/L88y99znnPvc5B/l7nv2ec54j20RERHMcMdMdiIiI3krgj4homAT+iIiGSeCPiGiYBP6IiIZJ4I+IaJjaAr+kxZIekTQoabukq0v9PEkbJe0or8eV+nMkbS3lKUkXtbT1qKTvtSw/sdTPlXSHpJ2SNktaUtf2REQcLlTXdfySFgALbD8p6d3AE8CFwG8DL9u+UdIa4Djbn5H0LuDvbQ+V7z4F/IPy+VHg07a3jPmNTwH/2PbvSroUuMj2b9WyQRERh4naRvy2d9t+srzfBwwCC4ELgPVltfVUBwNs/8T2UKk/EujkiNTa1l3AcknqygZERBymZvfiR0oK5mxgM3CS7d1QHRxG0zZlvV8EvgK8F7i85UAA8FVJw8DdwA2u/lRZCDxX2hqS9CpwPLB3zO+vBlYDHHXUUUs/8IEP1LKdEXF4eeKJJ/baPuFg2vj1Xz3KL7083NnvfefNB22vOJjf60TtgV/S0VTB+hrbr000ILe9GThD0mnAekkP2H4DuMz28yVldDdwOXAr0K6xt/2lYHstsBZgYGDAW7ZseduXIiLGkvTDg23jpZeH+eaDJ3e07qwFO+Yf7O91otareiTNoQrUt9m+p1S/WHL4o+cB9oz9nu1B4HXgzPL5+fK6D7gdOKesugtYXNqaDRwLvFzX9kRETJWBkQ7/1yt1XtUjYB0waPvmlkX3AyvL+5XAfWX995XgjaT3Au8HnpE0W9L8Uj8HOB94uk1bFwMPO7PORUQfMWa/hzsqvVJnqudcqpTMNklbS911wI3AnZJWAc8Cl5RlvwSskbQfGAE+ZXuvpKOAB0vQnwU8BHy5fGcdsEHSTqqR/qU1bk9ExLT0cjTfidoCv+3HaJ+DB1jeZv0NwIY29a8DS8f5jTc4cOCIiOg7xgz3WSKiJ1f1REQ02UhHV6dPTtIzwD5gGBiyPSBpHnAHsAR4BviE7VcmaidTNkRE1MjAMO6odOhXbZ9le6B8XgNssn0qsKl8nlACf0REzUZwR2Wa2t4UO5GkeiIiamRgf+c5/vmSWm80WlvuQ2pt7s8lGfjjsmzcm2LHk8AfEVEjTy2Ns7clhdPOubZfKMF9o6TvTqdPCfwREXUyDHfpoh7bL5TXPZLupbqZ9UVJC8pov+1NsWMlxx8RUaPqzt3OykQkHVWmraHc33Qe1c2sbW+KnUhG/BERtRLD497SNCUnAfeW+c5mA7fb/l+SvkX7m2LHlcAfEVGj6uTuwQd+2z8Afq5N/Uu0uSl2Ign8ERE1qq7j76/HhCTwR0TUbKQLI/5uSuCPiKhRRvwREQ1jxHCfXUCZwB8RUbOkeiIiGsSIv/esme7GWyTwR0TUqLqBK6meiIhGycndiIgGscWwM+KPiGiUkYz4IyKaozq521+htr96ExFxmMnJ3YiIBhrOdfwREc2RO3cjIhpoJFf1REQ0RzVJWwJ/RERjGLE/UzZERDSHTd/dwFVbbyQtlvSIpEFJ2yVdXernSdooaUd5Pa7UnyNpaylPSbqopa2lkrZJ2inpCyoPnZQ0V9IdpX6zpCV1bU9ExPSIkQ5Lr9R5GBoCrrV9GrAMuFLS6cAaYJPtU4FN5TNUT4sfsH0WsAL4Y0mjf5F8CVgNnFrKilK/CnjF9inALcBNNW5PRMSUmWrE30npldp+yfZu20+W9/uAQWAhcAGwvqy2HriwrPMT20Ol/kiq/YWkBcAxth+3beDW0e+MaesuYPnoXwMREf1imCM6Kr3Sk18qKZizgc3ASbZ3Q3VwAE5sWe8XJW0HtgG/Ww4EC4FdLc3tKnWU1+dKW0PAq8DxtW5MRMQUGDHizkqv1H5yV9LRwN3ANbZfm2hAbnszcIak04D1kh6AtokvjzY/wbLWPqymShVx8sknT20DIiIOgoH9fTZXT60jfklzqIL+bbbvKdUvlvTNaBpnz9jv2R4EXgfOpBrhL2pZvAh4obzfBSwubc0GjgVebtPeWtsDtgdOOOGEbmxaRESHxHCHpVfqvKpHwDpg0PbNLYvuB1aW9yuB+8r67xs9mSvpvcD7gWdKOmifpGWlzStGvzOmrYuBh8t5gIiIvmCqO3c7Kb1S598f5wKXA9skbS111wE3AndKWgU8C1xSlv0SsEbSfmAE+JTtvWXZJ4GvAe8EHigFqgPLBkk7qUb6l9a4PRER09KYJ3DZfoz2OXiA5W3W3wBsGKetLVRpn7H1b3DgwBER0XdsZa6eiIgmqU7uZsqGiIgGyTN3IyIapTq525Acf0REVDItc0REg4zeudtPEvgjImqWh61HRDSIDftHuhf4Jc0CtgDP2z5f0jzgDmAJ8AzwCduvTNRGfx2GIiIOM1Wqp6t37l5NNdvxqPGmuh9XAn9ERM26NVePpEXAPwf+W0t126nuJ5JUT0REjbp8Oed/Bv4N8O6WurdMdS/pxHZfbJURf0REraaU6pkvaUtLWf3TVqTzgT22nzjYHmXEHxFRsyk8T3ev7YFxlp0L/Iakj1I9pfAYSf+dMtV9Ge23nep+rIz4IyJqVF3VM6ujMnE7/qztRbaXUM1E/LDtf8k4U91PJCP+iIga9eAGrvGmuh9XAn9ERM2mkOrpiO1HgUfL+5doM9X9RBL4IyJqlEnaIiIaKA9iiYhoEFsMJfBHRDRLUj0REQ2SHH9ERAMl8EdENEgexBIR0UDdvo7/YCXwR0TUyIahLj6IpRsS+CMiapZUT0REgyTHHxHRQE7gj4holn47uVvbGQdJiyU9ImlQ0nZJV5f6eZI2StpRXo8r9R+R9ISkbeX1wy1tPSrpe5K2lnJiqZ8r6Q5JOyVtlrSkru2JiJgOu8rxd1J6pc5TzUPAtbZPA5YBV0o6nfGfCL8X+JjtD1I9TGDDmPYus31WKaNPmFkFvGL7FOAW4KYatyciYhrE8MgRHZVeqS3VUx7+O/oA4H2SBoGFVE+E/5Wy2nqqOaU/Y/vbLV/fDhwpaa7tNyf4mQuAz5X3dwFflCTbHu8Lf/PED/jIEZM+pyAO0saRP53pLhzWRv72Z2e6CzEF/Zbj78khpqRgzgY2M+aJ8EC7J8J/HPj2mKD/1ZLmuV7S6F5cCDxX2hoCXgWOb/P7q0cfXryfiY4jERHdNTpXT1NSPQBIOhq4G7jG9msdrH8GVcrmd1qqLyspoF8u5fLR1ds08bbRvu21tgdsD8xh7lQ3ISJi+lzl+TspvVJr4Jc0hyro32b7nlL9YnkSPGOfCC9pEXAvcIXt74/W236+vO4DbgfOKYt2AYvLd2cDxwIv17lNERFTNYI6Kr1S51U9AtYBg7ZvblnU9onwkn4G+DPgs7a/0dLObEnzy/s5wPnA023aupjqqfM9PG5GREzMTTq5C5xLlZLZJmlrqbuO8Z8IfxVwCnC9pOtL3XnA68CDJejPAh4CvlyWrwM2SNpJNdK/tMbtiYiYln4bjtZ5Vc9jtM/BQ5snwtu+AbhhnPWXjvMbb3DgwBER0Zf67aqe3LkbEVGj6sRtAn9ERKNkkraIiIZpTI4/IiLKtMx5EEtERLP02YA/gT8iolY5uRsR0UB9NuRP4I+IqFlG/BERDWJgZCSBPyKiOQxkxB8R0Sy5jj8iomn6LPD3110FERGHHWF3ViZsRTpS0jclPSVpu6R/X+rnSdooaUd5PW6yHiXwR0TUzR2Wib0JfNj2zwFnASskLQPWAJtsnwpsKp8nlMAfEVEng0fUUZmwmcqPy8c5pRi4AFhf6tcDF07WpQT+iIjaqcPCfElbWsrqt7QizSoPttoDbLS9GTjJ9m6A8nriZL3Jyd2IiLp1fnJ3r+2BcZuxh4GzyqNq75V05nS6kxF/RETdupPjP9Cc/f+AR4EVwIuSFgCU1z2TfT+BPyKiTqM3cHVSJiDphDLSR9I7gV8DvgvcD6wsq60E7pusS0n1RETUrEs3cC0A1kuaRTVov9P2/5T0OHCnpFXAs3TwHPIE/oiIunVhrh7b3wHOblP/ErB8Km0l8EdE1Ex9duduAn9ERJ2meOK2FxL4IyJqNfmJ215L4I+IqFtG/BERDTMy0x14qwT+iIg69eGDWGq7gUvSYkmPSBosU4heXerbTiEq6SOSnpC0rbx+uKWtpaV+p6QvSFKpnyvpjlK/WdKSurYnImK65M5Kr9R55+4QcK3t04BlwJWSTmf8KUT3Ah+z/UGqu882tLT1JWA1cGopK0r9KuAV26cAtwA31bg9ERHT0+UpGw7WpIFf0lWdTOw/lu3dtp8s7/cBg8BCxplC1Pa3bb9Q6rcDR5YR/QLgGNuP2zZwKwemHW1t6y5g+ehfAxER0V4nI/73AN+SdKekFdMJrCUFczbQ6RSiHwe+bftNqoPFrpZlu0od5fW50tYQ8CpwfJvfXz06zel+3pxq9yMiDsohl+qx/W+p0ivrgN8Gdkj6D5L+USc/IOlo4G7gGtuvdbD+GVQpm98ZrWrXrQ6WHaiw19oesD0wh7mddDsiojtMNWVDJ6VHOsrxlxTL35YyBBwH3CXp8xN9T9IcqqB/m+17SvW4U4hKWgTcC1xh+/ulehewqKXZRcALLcsWl+/OBo4FXu5kmyIieuYQzPH/a0lPAJ8HvgF80PYngaVUKZnxvieqvxIGbd/csqjtFKJlutE/Az5r+xujK5d00D5Jy0qbV3Bg2tHWti4GHi4HqYiIvtFvqZ5OruOfD/ym7R+2VtoekXT+BN87F7gc2FYeFQZwHXAj7acQvQo4Bbhe0vWl7jzbe4BPAl8D3gk8UApUB5YNknZSjfQv7WB7IiJ6q8+Go5MGftv/boJlgxMse4z2OXhoM4Wo7RuAG8ZpawvwtkeM2X6DDuaejoiYUYda4I+IiOnrdRqnEwn8ERF16+EVO51I4I+IqFlG/BERTZPAHxHRIMnxR0Q0UAJ/RESzqM8exFLntMwREdGHMuKPiKhbUj0REQ2Sk7sREQ2UwB8R0TAJ/BERzSH676qeBP6IiDolxx8R0UAJ/BERDZPAHxHRLEn1REQ0TQJ/RESDuP+u6slcPRERdXOHZQKSFkt6RNKgpO2Sri718yRtlLSjvB43WXcS+CMiajb63N3JyiSGgGttnwYsA66UdDqwBthk+1RgU/k8oQT+iIi6dWHEb3u37SfL+33AILAQuABYX1ZbD1w4WXeS44+IqFMHQb3FfElbWj6vtb127EqSlgBnA5uBk2zvhurgIOnEyX4kgT8iokZiSpdz7rU9MGF70tHA3cA1tl+TNOU+JdUTEVGzLuX4kTSHKujfZvueUv2ipAVl+QJgz2TtJPBHRNStO1f1CFgHDNq+uWXR/cDK8n4lcN9k3UmqJyKibt25getc4HJgm6Stpe464EbgTkmrgGeBSyZrqLbAL2kxcCvwHmCE6iTFf5E0D7gDWAI8A3zC9iuSjgfuAn4B+Jrtq1raehRYAPxdqTrP9h5Jc8tvLAVeAn7L9jN1bVNExJR1aXZO249RnTJoZ/lU2qoz1TPVa07fAK4HPj1Oe5fZPquU0RzWKuAV26cAtwA31bQtERHT14VUTzfVFvines2p7dfLEe2NKfxMa1t3Acs1nVPcERE10khnpVd6cnJ3omtOgUmvOS2+KmmrpOtbgvtC4LnS1hDwKnB8m99fLWmLpC37efPgNiYiYoq6dVVPt9R+crcb15xSpXmel/Tu0tblVLn9do29bfeVGyDWAgwMDHjjlj+dTh8i+sYR7/mbme5CQ3QhgdDjNE4nah3xd+uaU9vPl9d9wO3AOWXRLmBxaWs2cCzwcje3ISLioDUlx9+ta04lzZY0v7yfA5wPPN2mrYuBh2332bE1Ipps9M7dpqR6pnzNqaRngGOAd0i6EDgP+CHwYAn6s4CHgC+Xr6wDNkjaSTXSv7TG7YmImBaN9Nd4tLbAP51rTm0vGWf9peOs/wYd3KwQETFj+jDHnzt3IyJqlmfuRkQ0TQJ/RESzZMQfEdE0CfwREQ3i3k7H0IkE/oiIGk3xCVw9kcAfEVG3PruvNIE/IqJmGfFHRDRJbuCKiGienNyNiGiYBP6IiCYxObkbEdE0ObkbEdE0CfwREc2RG7giIprGbs6DWCIiouivuJ/AHxFRt6R6IiKaxEBSPRERDdNfcT+BPyKibkn1REQ0TK7qiYhokszOGRHRLNUNXP0V+RP4IyLq1mezcx4x0x2IiDjcye6oTNqO9BVJeyQ93VI3T9JGSTvK63GTtZPAHxFRJ0+hTO5rwIoxdWuATbZPBTaVzxOqLfBLWizpEUmDkrZLurrUtz06STq+rP9jSV8c09ZSSdsk7ZT0BUkq9XMl3VHqN0taUtf2RERMTzVXTydl0pbsvwReHlN9AbC+vF8PXDhZO3WO+IeAa22fBiwDrpR0OuMfnd4Argc+3aatLwGrgVNLGT3irQJesX0KcAtwU03bEhExfXZnBeZL2tJSVnfQ+km2d1c/493AiZN9obaTu6UDo53ZJ2kQWEh1dPqVstp64FHgM7ZfBx6TdEprO5IWAMfYfrx8vpXqiPZAaetzZdW7gC9Kkt1np9Ajork8pUcv7rU9UGNvgB7l+EsK5mxgM1M/Oi0EdrV83lXqRpc9V9oaAl4Fju9axyMiuqHzEf90vFgGyKMD5T2TfaH2wC/paOBu4Brbr02niTZ17mBZax9Wj/7p9KMf/WgaXYiIOAjdO7nbzv3AyvJ+JXDfZF+oNfBLmkMV9G+zfU+pnurRaRewqOXzIuCFlmWLS1uzgWN5+4kPbK+1PWB74IQTTpju5kRETItGRjoqk7YjfR14HHi/pF2SVgE3Ah+RtAP4SPk8odpy/OXKm3XAoO2bWxaNHp1upIOjk+3dkvZJWkaVKroC+K9j2nocuBh4OPn9iOgrpms3cNn+F+MsWj6Vduq8c/dc4HJgm6Stpe46qoB/ZzlSPQtcMvoFSc8AxwDvkHQhcJ7tvwY+SXX96jupTuo+UL6yDtggaSfVSP/SGrcnImLKRGc3Z/VSnVf1PEb7HDyMc3SyvWSc+i3AmW3q36DlwBER0ZeaEvgjIqJI4I+IaJAu5vi7JYE/IqJmnVyx00sJ/BERtTqom7NqkcAfEVEnk8AfEdE4/ZXpSeCPiKhbY67jj4iIIoE/IqJBbBjur1xPAn9ERN0y4o+IaJgE/oiIBjHQwfN0eymBPyKiVgYnxx8R0RwmJ3cjIhonOf6IiIZJ4I+IaJJM0hYR0SwGMi1zRETDZMQfEdEkmbIhIqJZDM51/BERDZM7dyMiGiY5/oiIBrFzVU9ERONkxB8R0STGw8Mz3Ym3SOCPiKhTpmWOiGigPruc84i6Gpa0WNIjkgYlbZd0damfJ2mjpB3l9biW73xW0k5J35P06y31j5a6raWcWOrnSrqjfGezpCV1bU9ExHQY8Ig7Kr1SW+AHhoBrbZ8GLAOulHQ6sAbYZPtUYFP5TFl2KXAGsAL4Q0mzWtq7zPZZpewpdauAV2yfAtwC3FTj9kRETJ3Lg1g6KT1SW+C3vdv2k+X9PmAQWAhcAKwvq60HLizvLwD+xPabtv8vsBM4Z5KfaW3rLmC5JHVtIyIiusDDwx2VXulJjr+kYM4GNgMn2d4N1cFhNG1DdVD4q5av7Sp1o74qaRi4G7jBtsvy50pbQ5JeBY4H9o75/dXA6vLxTUlPd3HzemE+Y7apzx1q/YX0uRcOtf4CvP9gG9jHKw8+5Lvmd7h6T/ZP7YFf0tFUwfoa269NMCBvt2A06XWZ7eclvbu0dTlw6yTfOVBhrwXWlv5ssT0wta2YWYdanw+1/kL63AuHWn+h6vPBtmF7RTf60k115viRNIcqUN9m+55S/aKkBWX5AmA0X78LWNzy9UXACwC2ny+v+4DbOZAC+ul3JM0GjgVermt7IiIOB3Ve1SNgHTBo++aWRfcDK8v7lcB9LfWXlit13gecCnxT0mxJ80ubc4DzgafbtHUx8HBJAUVExDjqTPWcS5WS2SZpa6m7DrgRuFPSKuBZ4BIA29sl3Qn8NdUVQVfaHpZ0FPBgCfqzgIeAL5f21gEbJO2kGulf2kG/1nZj43rsUOvzodZfSJ974VDrLxyafZ6UMkCOiGiWWnP8ERHRfxL4IyIa5rAL/JKulvR0mSbimlL3OUnPt0z58NGW9dtOE1FzH78iaU/r/QTTnMpiqaRtZdkX6rx5bSp9lrRE0t+17O8/6nWfx+nvJeXfxYikgTHr9+s+btvnftjHE/T5DyR9V9J3JN0r6Wdals3ofp5Kf/tlH9fC9mFTgDOprvh5F9WJ64eorg76HPDpNuufDjwFzAXeB3wfmNWDfn4I+Hng6Za6zwNryvs1wE2T9RH4JvBPqO5neAD4Z33S5yWt641ppyd9Hqe/p1HdkPMoMNDJv4M+2Mfj9XnG9/EEfT4PmF3e39RP/5an2N++2Md1lMNtxH8a8Fe2f2J7CPgL4KIJ1p/ONBEHzfZf8vb7DaY0lYWqeyCOsf24q3+Jt7Z8Z6b73FYv+9yuv7YHbX+vzep9u48n6HNbfdLnPy///4PqbvxF5f2M7+cp9retXu/jOhxugf9p4EOSjpf0LuCjHLgp7Kryp9xXWtIoP53yoRg7TUQvvWUqC6B1Kot2fVxY3o+t76Xx+gzwPknflvQXkn651PVDn9vp5308kUNhH/8rqhExHBr7ubW/cGjs4yk7rObjtz0o6SZgI/Bjqj8rh4AvAb9PNZ3D7wP/ieo/cEdTPsyw8frYz33fDZxs+yVJS4H/IekM+rfP2cc1kPR7VP//u220qs1qfbOf2/S37/fxdB1uI35sr7P987Y/RPUn3Q7bL9oetj1CdfPX26Z8KH46TcQMmOpUFrt465+kM9H3tn0uf8q/VN4/QZXL/Vn6o8/t9PM+bqvf97GklVR32V9W0iHQx/u5XX/7fR8fjMMu8OvAQ1pOBn4T+PpocCou4q1TPrxtmohe9rfFlKayKKmVfZKWlSsKrmj5zoz2WdIJKs9SkPQPS59/0Cd9bqef93Fb/byPJa0APgP8hu2ftCzqy/08Xn/7eR8ftJk+u9ztAvxvqmkfngKWl7oNwDbgO1T/+Ba0rP97VEfy79GjM/PA16n+jNxPNXpYRTWd9CZgR3mdN1kfgQGqg9j3gS9S7sSe6T4DHwe2l/8GTwIf63Wfx+nvReX9m8CLwIOHwD5u2+d+2McT9HknVS5/ayl/1C/7eSr97Zd9XEfJlA0REQ1z2KV6IiJiYgn8ERENk8AfEdEwCfwREQ2TwB8R0TAJ/BERDZPAHxHRMAn80ViSfqFM3HekpKNUzXt/5kz3K6JuuYErGk3SDcCRwDuBXbb/4wx3KaJ2CfzRaJLeAXwLeAP4p7aHZ7hLEbVLqieabh5wNPBuqpF/xGEvI/5oNEn3A39C9SjABbavmuEuRdTusHoQS8RUSLoCGLJ9e5l+9/9I+rDth2e6bxF1yog/IqJhkuOPiGiYBP6IiIZJ4I+IaJgE/oiIhkngj4homAT+iIiGSeCPiGiY/w9cKjYujATiCgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "da = nlmod.mdims.gdf2data_array_struc(point_gdf, gwf, field='values', agg_method='nearest')\n",
+    "da.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpolation methods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x1ab9e5b2f70>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "da = nlmod.mdims.gdf2data_array_struc(point_gdf, gwf, field='values', interp_method='nearest')\n",
+    "da.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x1ab9e654670>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "da = nlmod.mdims.gdf2data_array_struc(point_gdf, gwf, field='values', interp_method='linear')\n",
+    "da.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intersecting with grid: 100%|███████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 200.57it/s]\n",
+      "Aggregate vector data: 100%|████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 717.37it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x1ab9e6e79d0>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "da = nlmod.mdims.gdf2data_array_struc(line_gdf, gwf, field='values', agg_method='max_length')\n",
+    "da.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intersecting with grid: 100%|███████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 250.68it/s]\n",
+      "Aggregate vector data:   0%|                                                                     | 0/5 [00:00<?, ?it/s]INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n",
+      "Aggregate vector data: 100%|████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 128.53it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x1ab9e7c4910>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "da = nlmod.mdims.gdf2data_array_struc(line_gdf, gwf, field='values', agg_method='length_weighted')\n",
+    "da.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intersecting with grid: 100%|███████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 214.19it/s]\n",
+      "Aggregate vector data: 100%|████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 280.09it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x1ab9caf82e0>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "da = nlmod.mdims.gdf2data_array_struc(line_gdf, gwf, field='values', agg_method='nearest')\n",
+    "da.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Polygons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intersecting with grid: 100%|███████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 134.20it/s]\n",
+      "Aggregate vector data: 100%|████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 501.39it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x1ab9e75d100>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "da = nlmod.mdims.gdf2data_array_struc(pol_gdf, gwf, field='values', agg_method='max_area')\n",
+    "da.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intersecting with grid: 100%|███████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 157.78it/s]\n",
+      "Aggregate vector data: 100%|████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 357.36it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x1ab9c7fbb20>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "da = nlmod.mdims.gdf2data_array_struc(pol_gdf, gwf, field='values', agg_method='area_weighted')\n",
+    "da.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intersecting with grid: 100%|███████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 125.02it/s]\n",
+      "Aggregate vector data: 100%|████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 263.30it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x1ab9c789190>"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "da = nlmod.mdims.gdf2data_array_struc(pol_gdf, gwf, field='values', agg_method='nearest')\n",
+    "da.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Intersect vector data with grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intersecting with grid: 100%|███████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 399.63it/s]\n",
+      "Intersecting with grid: 100%|███████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 176.91it/s]\n",
+      "Intersecting with grid: 100%|███████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 105.54it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "gdf_point_grid = nlmod.mdims.gdf2grid(point_gdf, gwf)\n",
+    "gdf_line_grid = nlmod.mdims.gdf2grid(line_gdf, gwf)\n",
+    "gdf_pol_grid = nlmod.mdims.gdf2grid(pol_gdf, gwf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(950.0, 1300.0)"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "gdf_point_grid.plot(ax=ax, color='green')\n",
+    "gdf_line_grid['ind'] = range(gdf_line_grid.shape[0])\n",
+    "gdf_line_grid.plot('ind', ax=ax, cmap='jet')\n",
+    "gdf_pol_grid['ind'] = range(gdf_pol_grid.shape[0])\n",
+    "gdf_pol_grid.plot('ind',ax=ax, alpha=0.6)\n",
+    "\n",
+    "struc2d.plot(ax=ax, facecolor='none', edgecolor='k')\n",
+    "ax.set_xlim(ax.get_xlim()[0], 1300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Aggregate parameters per model cell\n",
+    "\n",
+    "Aggregatie options:\n",
+    "- point: max, min, mean\n",
+    "- line: max, min, length_weighted, max_length\n",
+    "- polygon: max, min, area_weighted, area_max\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>cellid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>POINT (1000.000 20200.000)</td>\n",
+       "      <td>(1, 0)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>52</td>\n",
+       "      <td>POINT (1200.000 20175.000)</td>\n",
+       "      <td>(1, 2)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>66</td>\n",
+       "      <td>POINT (1225.000 20175.000)</td>\n",
+       "      <td>(1, 2)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   values                    geometry  cellid\n",
+       "0       1  POINT (1000.000 20200.000)  (1, 0)\n",
+       "1      52  POINT (1200.000 20175.000)  (1, 2)\n",
+       "2      66  POINT (1225.000 20175.000)  (1, 2)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Aggregate vector data: 100%|████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 668.47it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>66.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     values\n",
+       "1 0     1.0\n",
+       "  2    66.0"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# point\n",
+    "display(gdf_point_grid)\n",
+    "nlmod.mdims.aggregate_vector_per_cell(gdf_point_grid,{'values':'max'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>cellid</th>\n",
+       "      <th>ind</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>LINESTRING (1000.000 20200.000, 1050.000 20193...</td>\n",
+       "      <td>(1, 0)</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>LINESTRING (1050.000 20193.750, 1150.000 20181...</td>\n",
+       "      <td>(1, 1)</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>LINESTRING (1150.000 20181.250, 1200.000 20175...</td>\n",
+       "      <td>(1, 2)</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>52</td>\n",
+       "      <td>LINESTRING (1225.000 20175.000, 1247.500 20250...</td>\n",
+       "      <td>(1, 2)</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>52</td>\n",
+       "      <td>LINESTRING (1247.500 20250.000, 1250.000 20258...</td>\n",
+       "      <td>(0, 2)</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>66</td>\n",
+       "      <td>LINESTRING (1000.000 20200.000, 1050.000 20237...</td>\n",
+       "      <td>(1, 0)</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>66</td>\n",
+       "      <td>LINESTRING (1050.000 20237.500, 1066.667 20250...</td>\n",
+       "      <td>(1, 1)</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>66</td>\n",
+       "      <td>LINESTRING (1066.667 20250.000, 1150.000 20312...</td>\n",
+       "      <td>(0, 1)</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>66</td>\n",
+       "      <td>LINESTRING (1150.000 20312.500, 1200.000 20350...</td>\n",
+       "      <td>(0, 2)</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   values                                           geometry  cellid  ind\n",
+       "0       1  LINESTRING (1000.000 20200.000, 1050.000 20193...  (1, 0)    0\n",
+       "0       1  LINESTRING (1050.000 20193.750, 1150.000 20181...  (1, 1)    1\n",
+       "0       1  LINESTRING (1150.000 20181.250, 1200.000 20175...  (1, 2)    2\n",
+       "1      52  LINESTRING (1225.000 20175.000, 1247.500 20250...  (1, 2)    3\n",
+       "1      52  LINESTRING (1247.500 20250.000, 1250.000 20258...  (0, 2)    4\n",
+       "2      66  LINESTRING (1000.000 20200.000, 1050.000 20237...  (1, 0)    5\n",
+       "2      66  LINESTRING (1050.000 20237.500, 1066.667 20250...  (1, 1)    6\n",
+       "2      66  LINESTRING (1066.667 20250.000, 1150.000 20312...  (0, 1)    7\n",
+       "2      66  LINESTRING (1150.000 20312.500, 1200.000 20350...  (0, 2)    8"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Aggregate vector data: 100%|████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 334.24it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
+       "      <th>1</th>\n",
+       "      <td>66.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>64.289282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
+       "      <th>0</th>\n",
+       "      <td>36.986642</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12.135181</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>32.030954</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        values\n",
+       "0 1  66.000000\n",
+       "  2  64.289282\n",
+       "1 0  36.986642\n",
+       "  1  12.135181\n",
+       "  2  32.030954"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# line\n",
+    "display(gdf_line_grid)\n",
+    "nlmod.mdims.aggregate_vector_per_cell(gdf_line_grid,{'values':'length_weighted'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>cellid</th>\n",
+       "      <th>ind</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>166</td>\n",
+       "      <td>POLYGON ((1066.667 20250.000, 1150.000 20312.5...</td>\n",
+       "      <td>(0, 1)</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>166</td>\n",
+       "      <td>POLYGON ((1250.000 20258.333, 1247.500 20250.0...</td>\n",
+       "      <td>(0, 2)</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>166</td>\n",
+       "      <td>POLYGON ((1050.000 20193.750, 1000.000 20200.0...</td>\n",
+       "      <td>(1, 0)</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>166</td>\n",
+       "      <td>POLYGON ((1150.000 20181.250, 1050.000 20193.7...</td>\n",
+       "      <td>(1, 1)</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>166</td>\n",
+       "      <td>POLYGON ((1247.500 20250.000, 1225.000 20175.0...</td>\n",
+       "      <td>(1, 2)</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>POLYGON ((1100.000 20250.000, 1150.000 20275.0...</td>\n",
+       "      <td>(0, 1)</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>POLYGON ((1150.000 20275.000, 1200.000 20300.0...</td>\n",
+       "      <td>(0, 2)</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>POLYGON ((1050.000 20193.750, 1000.000 20200.0...</td>\n",
+       "      <td>(1, 0)</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>POLYGON ((1150.000 20181.250, 1050.000 20193.7...</td>\n",
+       "      <td>(1, 1)</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>POLYGON ((1210.000 20250.000, 1225.000 20175.0...</td>\n",
+       "      <td>(1, 2)</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   values                                           geometry  cellid  ind\n",
+       "0     166  POLYGON ((1066.667 20250.000, 1150.000 20312.5...  (0, 1)    0\n",
+       "0     166  POLYGON ((1250.000 20258.333, 1247.500 20250.0...  (0, 2)    1\n",
+       "0     166  POLYGON ((1050.000 20193.750, 1000.000 20200.0...  (1, 0)    2\n",
+       "0     166  POLYGON ((1150.000 20181.250, 1050.000 20193.7...  (1, 1)    3\n",
+       "0     166  POLYGON ((1247.500 20250.000, 1225.000 20175.0...  (1, 2)    4\n",
+       "1       5  POLYGON ((1100.000 20250.000, 1150.000 20275.0...  (0, 1)    5\n",
+       "1       5  POLYGON ((1150.000 20275.000, 1200.000 20300.0...  (0, 2)    6\n",
+       "1       5  POLYGON ((1050.000 20193.750, 1000.000 20200.0...  (1, 0)    7\n",
+       "1       5  POLYGON ((1150.000 20181.250, 1050.000 20193.7...  (1, 1)    8\n",
+       "1       5  POLYGON ((1210.000 20250.000, 1225.000 20175.0...  (1, 2)    9"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Aggregate vector data: 100%|████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 313.34it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
+       "      <th>1</th>\n",
+       "      <td>134.838710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>135.390494</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
+       "      <th>0</th>\n",
+       "      <td>98.916667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>89.061947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>95.590529</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         values\n",
+       "0 1  134.838710\n",
+       "  2  135.390494\n",
+       "1 0   98.916667\n",
+       "  1   89.061947\n",
+       "  2   95.590529"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# polygon\n",
+    "display(gdf_pol_grid)\n",
+    "nlmod.mdims.aggregate_vector_per_cell(gdf_pol_grid,{'values':'area_weighted'})"
+   ]
   }
  ],
  "metadata": {
diff --git a/examples/08_gis.ipynb b/examples/08_gis.ipynb
index dc7dee42..b273c49c 100644
--- a/examples/08_gis.ipynb
+++ b/examples/08_gis.ipynb
@@ -2591,7 +2591,7 @@
   },
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/nlmod/cache.py b/nlmod/cache.py
index c473b1be..d16db09d 100644
--- a/nlmod/cache.py
+++ b/nlmod/cache.py
@@ -205,7 +205,7 @@ def decorator(*args, cachedir=None, cachename=None, **kwargs):
         if not cachename.endswith('.pklz'):
             cachename += '.pklz'
 
-        fname_cache = os.path.join(cachedir, cachename)  # netcdf file
+        fname_cache = os.path.join(cachedir, cachename)  # pklz file
         fname_args_cache = fname_cache.replace(
             '.pklz', '_cache.pklz')  # pickle with function arguments
 
@@ -330,7 +330,7 @@ def _same_function_arguments(func_args_dic, func_args_dic_cache):
 
         # check if cache and function call have same argument values
         if item is None:
-            # Value of None type is always None so the check happened in previous if statement
+            # Value of None type is always None so the check happens in previous if statement
             pass
         elif isinstance(item, (numbers.Number, bool, str, bytes, list, tuple)):
             if item != func_args_dic_cache[key]:
@@ -338,7 +338,7 @@ def _same_function_arguments(func_args_dic, func_args_dic_cache):
                     'cache was created using different function argument values, do not use cached data')
                 return False
         elif isinstance(item, np.ndarray):
-            if not np.array_equal(item, func_args_dic_cache[key]):
+            if not np.allclose(item, func_args_dic_cache[key]):
                 logger.info(
                     'cache was created using different numpy array values, do not use cached data')
                 return False
@@ -353,7 +353,7 @@ def _same_function_arguments(func_args_dic, func_args_dic_cache):
                 logger.info(
                     'cache was created using different dictionaries, do not use cached data')
                 return False
-        elif isinstance(item, flopy.mf6.ModflowGwf):
+        elif isinstance(item, (flopy.mf6.ModflowGwf, flopy.modflow.mf.Modflow)):
             if str(item) != str(func_args_dic_cache[key]):
                 logger.info(
                     'cache was created using different groundwater flow model, do not use cached data')
diff --git a/nlmod/mdims/mgrid.py b/nlmod/mdims/mgrid.py
index 9c259b16..f8838b29 100644
--- a/nlmod/mdims/mgrid.py
+++ b/nlmod/mdims/mgrid.py
@@ -12,6 +12,7 @@
 import sys
 
 import flopy
+import pandas as pd
 import geopandas as gpd
 import numpy as np
 import shapely
@@ -21,7 +22,9 @@
 from flopy.utils.gridintersect import GridIntersect
 from shapely.prepared import prep
 from tqdm import tqdm
+from scipy.interpolate import griddata
 
+from shapely.geometry import Point
 from .. import cache, mfpackages, util
 
 logger = logging.getLogger(__name__)
@@ -50,8 +53,8 @@ def modelgrid_from_model_ds(model_ds, gridprops=None):
                 f'extent should be a list, tuple or numpy array, not {type(model_ds.extent)}')
 
         modelgrid = StructuredGrid(delc=np.array([model_ds.delc] * model_ds.dims['y']),
-                                   delr=np.array([model_ds.delr]
-                                                 * model_ds.dims['x']),
+                                   delr=np.array([model_ds.delr] *
+                                                 model_ds.dims['x']),
                                    xoff=model_ds.extent[0], yoff=model_ds.extent[2])
     elif model_ds.gridtype == 'vertex':
         _, gwf = mfpackages.sim_tdis_gwf_ims_from_model_ds(model_ds)
@@ -790,6 +793,225 @@ def polygon_to_area(modelgrid, polygon, da,
     return area_array
 
 
+def gdf2data_array_struc(gdf, gwf,
+                         field='VALUE',
+                         agg_method=None,
+                         interp_method=None):
+    """ Project vector data on a structured grid. Aggregate data if multiple
+    geometries are in a single cell
+
+    Parameters
+    ----------
+    gdf : geopandas.GeoDataframe
+        vector data can only contain a single geometry type.
+    gwf : flopy groundwater flow model
+        model with a structured grid.
+    field : str, optional
+        column name in the geodataframe. The default is 'VALUE'.
+    interp_method : str or None, optional
+        method to obtain values in cells without geometry by interpolating
+        between cells with values. Options are 'nearest' and 'linear'.
+    agg_method : str, optional
+        aggregation method to handle multiple geometries in one cell, options
+        are:
+        - max, min, mean,
+        - length_weighted (lines), max_length (lines),
+        - area_weighted (polygon), area_max (polygon).
+        The default is 'max'.
+
+    Returns
+    -------
+    da : xarray DataArray
+        DESCRIPTION.
+
+    """
+    xmid = gwf.modelgrid.get_xcellcenters_for_layer(0)[0]
+    ymid = gwf.modelgrid.get_ycellcenters_for_layer(0)[:, 0]
+    da = xr.DataArray(np.nan, dims=('y', 'x'),
+                      coords={'y': ymid, 'x': xmid})
+
+    # interpolate data
+    if interp_method is not None:
+        arr = interpolate_gdf_to_array(gdf, gwf, field=field,
+                                       method=interp_method)
+        da.values = arr
+
+        return da
+
+    gdf_cellid = gdf2grid(gdf, gwf, 'vertex')
+
+    if gdf_cellid.cellid.duplicated().any():
+        # aggregate data
+        if agg_method is None:
+            raise ValueError('multiple geometries in one cell please define aggregation method')
+        gdf_agg = aggregate_vector_per_cell(gdf_cellid, {field: agg_method},
+                                            gwf)
+    else:
+        # aggregation not neccesary
+        gdf_agg = gdf_cellid[[field]]
+        gdf_agg.set_index(pd.MultiIndex.from_tuples(gdf_cellid.cellid.values),
+                          inplace=True)
+
+    for ind, row in gdf_agg.iterrows():
+        da.values[ind[0], ind[1]] = row[field]
+
+    return da
+
+
+def interpolate_gdf_to_array(gdf, gwf, field='values', method='nearest'):
+    """ interpolate data from a point gdf
+
+
+    Parameters
+    ----------
+    gdf : geopandas.GeoDataframe
+        vector data can only contain a single geometry type.
+    gwf : flopy groundwater flow model
+        model with a structured grid.
+    field : str, optional
+        column name in the geodataframe. The default is 'values'.
+    method : str or None, optional
+        method to obtain values in cells without geometry by interpolating
+        between cells with values. Options are 'nearest' and 'linear'.
+
+    Returns
+    -------
+    arr : np.array
+        numpy array with interpolated data.
+
+    """
+    # check geometry
+    geom_types = gdf.geometry.type.unique()
+    if geom_types[0] != 'Point':
+        raise NotImplementedError('can only use interpolation with point geometries')
+
+    # check field
+    if field not in gdf.columns:
+        raise ValueError(f"Missing column in DataFrame: {field}")
+
+    points = np.array([[g.x, g.y] for g in gdf.geometry])
+    values = gdf[field].values
+    xi = np.vstack((gwf.modelgrid.xcellcenters.flatten(),
+                    gwf.modelgrid.ycellcenters.flatten())).T
+    vals = griddata(points, values, xi, method=method)
+    arr = np.reshape(vals, (gwf.modelgrid.nrow, gwf.modelgrid.ncol))
+
+    return arr
+
+
+def _agg_max_area(gdf, col):
+    return gdf.loc[gdf.area.idxmax(), col]
+
+
+def _agg_area_weighted(gdf, col):
+    nanmask = gdf[col].isna()
+    aw = ((gdf.area * gdf[col]).sum(skipna=True)
+           / gdf.loc[~nanmask].area.sum())
+    return aw
+
+
+def _agg_max_length(gdf, col):
+    return gdf.loc[gdf.length.idxmax(), col]
+
+
+def _agg_length_weighted(gdf, col):
+    nanmask = gdf[col].isna()
+    aw = ((gdf.length * gdf[col]).sum(skipna=True)
+           / gdf.loc[~nanmask].length.sum())
+    return aw
+
+
+def _agg_nearest(gdf, col, gwf):
+    cid = gdf['cellid'].values[0]
+    cellcenter = Point(gwf.modelgrid.xcellcenters[0][cid[1]],
+                       gwf.modelgrid.ycellcenters[:, 0][cid[0]])
+    val = gdf.iloc[gdf.distance(cellcenter).argmin()].loc[col]
+    return val
+
+
+def _get_aggregates_values(group, fields_methods, gwf=None):
+
+    agg_dic = {}
+    for field, method in fields_methods.items():
+        # aggregation is only necesary if group shape is greater than 1
+        if group.shape[0] == 1:
+            agg_dic[field] = group[field].values[0]
+        if method == 'max':
+            agg_dic[field] = group[field].max()
+        elif method == 'min':
+            agg_dic[field] = group[field].min()
+        elif method == 'mean':
+            agg_dic[field] = group[field].mean()
+        elif method == 'nearest':
+            agg_dic[field] = _agg_nearest(group, field, gwf)
+        elif method == 'length_weighted':  # only for lines
+            agg_dic[field] = _agg_length_weighted(group, field)
+        elif method == 'max_length':  # only for lines
+            agg_dic[field] = _agg_max_length(group, field)
+        elif method == "area_weighted":  # only for polygons
+            agg_dic[field] = _agg_area_weighted(group, field)
+        elif method == "max_area":  # only for polygons
+            agg_dic[field] = _agg_max_area(group, field)
+        elif method == 'center_grid':  # only for polygons
+            raise NotImplementedError
+        else:
+            raise ValueError(f"Method '{method}' not recognized!")
+
+    return agg_dic
+
+
+def aggregate_vector_per_cell(gdf, fields_methods, gwf=None):
+    """Aggregate vector features per cell.
+
+    Parameters
+    ----------
+    gdf : geopandas.GeoDataFrame
+        GeoDataFrame containing points, lines or polygons per grid cell.
+    fields_methods: dict
+        fields (keys) in the Geodataframe with their aggregation method (items)
+        aggregation methods can be:
+        max, min, mean, length_weighted (lines), max_length (lines),
+        area_weighted (polygon), area_max (polygon).
+    gwf : flopy Groundwater flow model
+        only necesary if one of the field methods is 'nearest'
+
+    Returns
+    -------
+    celldata : pd.DataFrame
+        DataFrame with aggregated surface water parameters per grid cell
+    """
+    # check geometry types
+    geom_types = gdf.geometry.type.unique()
+    if len(geom_types) > 1:
+        if len(geom_types) == 2 and ('Polygon' in geom_types) and ('MultiPolygon' in geom_types):
+            pass
+        else:
+            raise TypeError('cannot aggregate geometries of different types')
+    if bool({'length_weighted', 'max_length'} & set(fields_methods.values())):
+        assert geom_types[0] == 'LineString', 'can only use length methods with line geometries'
+    if bool({'area_weighted', 'max_area'} & set(fields_methods.values())):
+        if ('Polygon' in geom_types) or ('MultiPolygon' in geom_types):
+            pass
+        else:
+            raise TypeError('can only use area methods with polygon geometries')
+
+    # check fields
+    missing_cols = set(fields_methods.keys()).difference(gdf.columns)
+    if len(missing_cols) > 0:
+        raise ValueError(f"Missing columns in DataFrame: {missing_cols}")
+
+    # aggregate data
+    gr = gdf.groupby(by="cellid")
+    celldata = pd.DataFrame(index=gr.groups.keys())
+    for cid, group in tqdm(gr, desc="Aggregate vector data"):
+        agg_dic = _get_aggregates_values(group, fields_methods,
+                                         gwf)
+        for key, item in agg_dic.items():
+            celldata.loc[cid, key] = item
+
+    return celldata
+
+
 def gdf_to_bool_data_array(gdf, mfgrid, model_ds):
     """convert a GeoDataFrame with polygon geometries into a data array
     corresponding to the modelgrid in which each cell is 1 (True) if one or
@@ -1034,7 +1256,7 @@ def get_vertices(model_ds, modelgrid=None,
             vertices_arr = all_vertices
 
     else:
-        return ValueError('Specify gridprops or modelgrid')
+        raise ValueError('Specify gridprops or modelgrid')
 
     vertices_da = xr.DataArray(vertices_arr,
                                dims=('cid', 'vert_per_cid', 'xy'),
diff --git a/nlmod/mdims/resample.py b/nlmod/mdims/resample.py
index 77c9afb9..b98a09e5 100644
--- a/nlmod/mdims/resample.py
+++ b/nlmod/mdims/resample.py
@@ -44,7 +44,8 @@ def resample_dataarray2d_to_vertex_grid(da_in, gridprops=None,
         data array with dimension (cid).
     """
     if (xyi is None) or (cid is None):
-        xyi, cid = mgrid.get_xyi_cid(gridprops)
+        xyi, cid = mgrid.get_xyi_cid(gridprops=gridprops,
+                                     model_ds=kwargs.pop("model_ds", None))
 
     # get x and y values of all cells in dataarray
     mg = np.meshgrid(da_in.x.data, da_in.y.data)
@@ -159,7 +160,7 @@ def resample_dataset_to_vertex_grid(ds_in, gridprops,
                                                            xyi=xyi, cid=cid,
                                                            method=method)
 
-        elif ds_in[data_var].dims == ('layer'):
+        elif ds_in[data_var].dims == ('layer') or ds_in[data_var].dims == ('layer',):
             data_arr = ds_in[data_var]
 
         else:
@@ -470,7 +471,7 @@ def get_resampled_ml_layer_ds_vertex(raw_ds=None,
     return ml_layer_ds
 
 
-def fillnan_dataarray_structured_grid(xar_in):
+def fillnan_dataarray_structured_grid(xar_in, method='nearest'):
     """fill not-a-number values in a structured grid, DataArray.
 
     The fill values are determined using the 'nearest' method of the
@@ -482,6 +483,9 @@ def fillnan_dataarray_structured_grid(xar_in):
     xar_in : xarray DataArray
         DataArray with nan values. DataArray should have 2 dimensions
         (y and x).
+    method : str, optional
+        method used in scipy.interpolate.griddata to resample, default is
+        nearest.
 
     Returns
     -------
@@ -510,8 +514,8 @@ def fillnan_dataarray_structured_grid(xar_in):
     points_in = points_all[np.where(mask1)[0]]
     values_in = values_all[np.where(mask1)[0]]
 
-    # get nearest value for all nan values
-    values_out = griddata(points_in, values_in, points_all, method='nearest')
+    # get value for all nan values
+    values_out = griddata(points_in, values_in, points_all, method=method)
     arr_out = values_out.reshape(xar_in.shape)
 
     # create DataArray without nan values
@@ -523,7 +527,8 @@ def fillnan_dataarray_structured_grid(xar_in):
 
 
 def fillnan_dataarray_vertex_grid(xar_in, gridprops=None,
-                                  xyi=None, cid=None):
+                                  xyi=None, cid=None,
+                                  method='nearest'):
     """fill not-a-number values in a vertex grid, DataArray.
 
     The fill values are determined using the 'nearest' method of the
@@ -539,6 +544,9 @@ def fillnan_dataarray_vertex_grid(xar_in, gridprops=None,
         array with x and y coördinates of cell centers, shape(len(cid), 2).
     cid : list
         list with cellids.
+    method : str, optional
+        method used in scipy.interpolate.griddata to resample, default is
+        nearest.
 
     Returns
     -------
@@ -562,8 +570,8 @@ def fillnan_dataarray_vertex_grid(xar_in, gridprops=None,
     xyi_in = xyi[mask1]
     values_in = values_all[mask1]
 
-    # get nearest value for all nan values
-    values_out = griddata(xyi_in, values_in, xyi, method='nearest')
+    # get value for all nan values
+    values_out = griddata(xyi_in, values_in, xyi, method=method)
 
     # create DataArray without nan values
     xar_out = xr.DataArray(values_out, dims=('cid'),
diff --git a/nlmod/mfpackages/__init__.py b/nlmod/mfpackages/__init__.py
index 48f91968..3dbcabd3 100644
--- a/nlmod/mfpackages/__init__.py
+++ b/nlmod/mfpackages/__init__.py
@@ -1,2 +1,2 @@
-from . import constant_head, recharge, surface_water
+from . import constant_head, recharge, surface_water, horizontal_flow_barrier
 from .mfpackages import *
diff --git a/nlmod/mfpackages/horizontal_flow_barrier.py b/nlmod/mfpackages/horizontal_flow_barrier.py
new file mode 100644
index 00000000..95aaf534
--- /dev/null
+++ b/nlmod/mfpackages/horizontal_flow_barrier.py
@@ -0,0 +1,158 @@
+import flopy
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from shapely.geometry import Polygon, Point
+
+from .. import mdims
+
+
+def line2hfb(gdf, gwf, prevent_rings=True, plot=False):
+    """ Obtain the cells with a horizontal flow barrier between them from a
+    geodataframe with line elements.
+
+
+    Parameters
+    ----------
+    gdf : gpd.GeoDataframe
+        geodataframe with line elements.
+    gwf : flopy.mf6.modflow.mfgwf.ModflowGwf
+        grondwater flow model.
+    prevent_rings : bool, optional
+        DESCRIPTION. The default is True.
+    plot : bool, optional
+        If True create a simple plot of the grid cells and shapefile. For a
+        more complex plot you can use plot_hfb. The default is False.
+
+    Returns
+    -------
+    cellids : 2d list of ints
+        a list with pairs of cells that have a hfb between them.
+
+    """
+    # for the idea, sea:
+    # https://gis.stackexchange.com/questions/188755/how-to-snap-a-road-network-to-a-hexagonal-grid-in-qgis
+
+    gdfg = mdims.gdf2grid(gdf, gwf)
+
+    cell2d = pd.DataFrame(gwf.disv.cell2d.array).set_index('icell2d')
+    vertices = pd.DataFrame(gwf.disv.vertices.array).set_index('iv')
+
+    # for every cell determine which cell-edge could form the line
+    # by testing for an intersection with a triangle to the cell-center
+    icvert = cell2d.loc[:, cell2d.columns.str.startswith('icvert')].values
+
+    hfb_seg = []
+    for index in gdfg.index.unique():
+        # Get the nearest hexagon sides where routes cross
+        for icell2d in gdfg.loc[index, 'cellid']:
+            for i in range(cell2d.at[icell2d, 'ncvert'] - 1):
+                iv1 = icvert[icell2d, i]
+                iv2 = icvert[icell2d, i + 1]
+                # make sure vert1 is lower than vert2
+                if iv1 > iv2:
+                    iv1, iv2 = iv2, iv1
+                coords = [(cell2d.at[icell2d, 'xc'], cell2d.at[icell2d, 'yc']),
+                          (vertices.at[iv1, 'xv'], vertices.at[iv1, 'yv']),
+                          (vertices.at[iv2, 'xv'], vertices.at[iv2, 'yv'])]
+                triangle = Polygon(coords)
+                if triangle.intersects(gdf.loc[index, 'geometry']):
+                    hfb_seg.append((icell2d, iv1, iv2))
+    hfb_seg = np.array(hfb_seg)
+
+    if prevent_rings:
+        # find out if there are cells with segments on each side
+        # remove the segments whose centroid is farthest from the line
+        for icell2d in np.unique(hfb_seg[:, 0]):
+            mask = hfb_seg[:, 0] == icell2d
+            if mask.sum() >= cell2d.at[icell2d, 'ncvert'] - 1:
+                segs = hfb_seg[mask]
+                dist = []
+                for seg in segs:
+                    p = Point(vertices.loc[seg[1:3], 'xv'].mean(),
+                              vertices.loc[seg[1:3], 'yv'].mean())
+                    dist.append(gdf.distance(p).min())
+                iv1, iv2 = segs[np.argmax(dist), [1, 2]]
+                mask = (hfb_seg[:, 1] == iv1) & (hfb_seg[:, 2] == iv2)
+                hfb_seg = hfb_seg[~mask]
+
+    # get unique segments
+    hfb_seg = np.unique(hfb_seg[:, 1:], axis=0)
+
+    # Get rid of disconnected (or 'open') segments
+    # Let's remove disconnected/open segments
+    iv = np.unique(hfb_seg)
+    segments_per_iv = pd.Series([np.sum(hfb_seg == x) for x in iv], index=iv)
+    mask = np.full(hfb_seg.shape[0], True)
+    for i, segment in enumerate(hfb_seg):
+        # one vertex is not connected and the other one at least to two other segments
+        if ((segments_per_iv[segment[0]] == 1 and
+             segments_per_iv[segment[1]] >= 3) or
+            (segments_per_iv[segment[1]] == 1 and
+             segments_per_iv[segment[0]] >= 3)):
+            mask[i] = False
+    hfb_seg = hfb_seg[mask]
+
+    if plot:
+        # test by plotting
+        ax = gdfg.plot()
+        for i, seg in enumerate(hfb_seg):
+            x = [vertices.at[seg[0], 'xv'], vertices.at[seg[1], 'xv']]
+            y = [vertices.at[seg[0], 'yv'], vertices.at[seg[1], 'yv']]
+            ax.plot(x, y)
+
+    # find out between which cellid's these segments are
+    segments = []
+    for icell2d in cell2d.index:
+        for i in range(cell2d.at[icell2d, 'ncvert'] - 1):
+            iv1 = icvert[icell2d, i]
+            iv2 = icvert[icell2d, i + 1]
+            # make sure vert1 is lower than vert2
+            if iv1 > iv2:
+                iv1, iv2 = iv2, iv1
+            segments.append((icell2d, (iv1, iv2)))
+    segments = pd.DataFrame(segments, columns=['icell2d', 'verts'])
+    segments = segments.set_index(['verts'])
+
+    cellids = []
+    for seg in hfb_seg:
+        cellids.append(list(segments.loc[[tuple(seg)]].values[:, 0]))
+    return cellids
+
+
+def plot_hfb(cellids, gwf, ax=None):
+    """ plots a horizontal flow barrier
+
+
+    Parameters
+    ----------
+    cellids : list of lists of integers or flopy.mf6.ModflowGwfhfb
+        list with the ids of adjacent cells that should get a horizontal
+        flow barrier, hfb is the output of line2hfb.
+    gwf : flopy groundwater flow model
+        DESCRIPTION.
+    ax : matplotlib axes
+
+
+    Returns
+    -------
+    fig : TYPE
+        DESCRIPTION.
+    ax : TYPE
+        DESCRIPTION.
+
+    """
+    if ax is None:
+        _, ax = plt.subplots()
+
+    if isinstance(cellids, flopy.mf6.ModflowGwfhfb):
+        spd = cellids.stress_period_data.data[0]
+        cellids = [[line[0][1], line[1][1]] for line in spd]
+
+    for line in cellids:
+        pc1 = Polygon(gwf.modelgrid.get_cell_vertices(line[0]))
+        pc2 = Polygon(gwf.modelgrid.get_cell_vertices(line[1]))
+        x, y = pc1.intersection(pc2).xy
+        ax.plot(x, y, color='red')
+
+    return ax
diff --git a/nlmod/mfpackages/mfpackages.py b/nlmod/mfpackages/mfpackages.py
index 96ca4230..439a0780 100644
--- a/nlmod/mfpackages/mfpackages.py
+++ b/nlmod/mfpackages/mfpackages.py
@@ -495,8 +495,8 @@ def get_tdis_perioddata(model_ds):
         - tsmult (double) is the multiplier for the length of successive time
           steps. The length of a time step is calculated by multiplying the
           length of the previous time step by TSMULT. The length of the first
-          time step, :math:`\Delta t_1`, is related to PERLEN, NSTP, and
-          TSMULT by the relation :math:`\Delta t_1= perlen \frac{tsmult -
+          time step, :math:`\\Delta t_1`, is related to PERLEN, NSTP, and
+          TSMULT by the relation :math:`\\Delta t_1= perlen \frac{tsmult -
           1}{tsmult^{nstp}-1}`.
     """
     perlen = model_ds.perlen
diff --git a/nlmod/read/geotop.py b/nlmod/read/geotop.py
index c1d62b4b..f1f1da73 100644
--- a/nlmod/read/geotop.py
+++ b/nlmod/read/geotop.py
@@ -14,6 +14,13 @@
 logger = logging.getLogger(__name__)
 
 
+def get_default_lithoklasse_translation_table():
+    return pd.read_csv(os.path.join(nlmod.NLMOD_DATADIR,
+                                    'geotop',
+                                    'litho_eenheden.csv'),
+                       index_col=0)
+
+
 @cache.cache_netcdf
 def get_geotop(extent, delr, delc,
                regis_ds, regis_layer='HLc'):
@@ -33,7 +40,7 @@ def get_geotop(extent, delr, delc,
     delc : int or float,
         cell size along columns, equal to dy
     regis_ds: xarray.DataSet
-        regis dataset used to cut geotop to the same x and y coördinates
+        regis dataset used to cut geotop to the same x and y coordinates
     regis_layer: str, optional
         layer of regis dataset that will be filled with geotop. The default is
         'HLc'.
@@ -240,7 +247,8 @@ def get_top_bot_from_geo_eenheid(geotop_ds_raw, geo_eenheid_translate_df):
     return geotop_ds_mod
 
 
-def add_stroombanen_and_get_kh(geotop_ds_raw, top, bot, geo_names):
+def add_stroombanen_and_get_kh(geotop_ds_raw, top, bot, geo_names,
+                               f_anisotropy=0.25):
     """add stroombanen to tops and bots of geo_eenheden, also computes kh per
     geo_eenheid. Kh is computed by taking the average of all kh's of a
     geo_eenheid within a cell (e.g. if one geo_eenheid has a thickness of 1,5m
@@ -257,6 +265,10 @@ def add_stroombanen_and_get_kh(geotop_ds_raw, top, bot, geo_names):
         raster with bottom of each geo_eenheid, shape(nlay,nrow,ncol)
     geo_names: list of str
         names of each geo_eenheid
+    f_anisotropy: float, optional
+        anisotropy factor kv/kh, ratio between vertical and horizontal
+        hydraulic conductivities, by default 0.25.
+
 
     Returns
     -------
@@ -308,7 +320,7 @@ def add_stroombanen_and_get_kh(geotop_ds_raw, top, bot, geo_names):
     geotop_ds_mod['top'] = da_top
     geotop_ds_mod['bot'] = da_bot
     geotop_ds_mod['kh'] = da_kh
-    geotop_ds_mod['kv'] = geotop_ds_mod['kh'] * .25
+    geotop_ds_mod['kv'] = geotop_ds_mod['kh'] * f_anisotropy
     geotop_ds_mod['thickness'] = da_thick
 
     return geotop_ds_mod
diff --git a/nlmod/read/jarkus.py b/nlmod/read/jarkus.py
index c621e14f..32ee4f7b 100644
--- a/nlmod/read/jarkus.py
+++ b/nlmod/read/jarkus.py
@@ -55,7 +55,7 @@ def get_bathymetry(model_ds, northsea,
 
     # try to get bathymetry via opendap
     try:
-        url = 'http://opendap.deltares.nl/thredds/dodsC/opendap/rijkswaterstaat/jarkus/grids/catalog.nc'
+        url = 'https://opendap.deltares.nl/thredds/dodsC/opendap/rijkswaterstaat/jarkus/grids/catalog.nc'
         jarkus_ds = get_dataset_jarkus(model_ds.extent, url)
     except OSError:
         import gdown
diff --git a/nlmod/read/knmi.py b/nlmod/read/knmi.py
index 9db9a222..9d21baca 100644
--- a/nlmod/read/knmi.py
+++ b/nlmod/read/knmi.py
@@ -124,7 +124,7 @@ def get_recharge(model_ds,
             locations['evap_point'] == prec_evap[1])]
 
         # calculate recharge time series
-        prec = oc_knmi_prec.loc[prec_evap[0], 'obs']['RH']
+        prec = oc_knmi_prec.loc[prec_evap[0], 'obs']['RD']
         evap = oc_knmi_evap.loc[prec_evap[1], 'obs']['EV24']
         recharge_ts = (prec - evap).dropna()
 
diff --git a/nlmod/util.py b/nlmod/util.py
index c305fcc6..0aac8c8c 100644
--- a/nlmod/util.py
+++ b/nlmod/util.py
@@ -50,10 +50,10 @@ def write_and_run_model(gwf, model_ds, write_model_ds=True,
 
     if write_model_ds:
         logger.info('write model dataset to cache')
-        model_ds.to_netcdf(os.path.join(model_ds.attrs['cachedir'],
-                                        'full_model_ds.nc'))
         model_ds.attrs['model_dataset_written_to_disk_on'] = dt.datetime.now(
         ).strftime("%Y%m%d_%H:%M:%S")
+        model_ds.to_netcdf(os.path.join(model_ds.attrs['cachedir'],
+                                        'full_model_ds.nc'))
 
     logger.info('write modflow files to model workspace')
     gwf.simulation.write_simulation()
@@ -135,6 +135,9 @@ def get_da_from_da_ds(da_ds, dims=('y', 'x'), data=None):
     da : xr.DataArray
         DataArray with coordinates from model_ds
     """
+    if not isinstance(dims, tuple):
+        raise TypeError('keyword argument dims should be of type tuple not {type(dims)}')
+
     coords = {dim: da_ds[dim] for dim in dims}
     da = xr.DataArray(data, dims=dims,
                       coords=coords)
@@ -479,7 +482,7 @@ def getmfexes(pth='.', version='', pltfrm=None):
     pymake.download_and_unzip(download_url, pth)
 
 
-def add_heads_to_model_ds(model_ds, fill_nans=False, fname_hds=None):
+def get_heads_dataarray(model_ds, fill_nans=False, fname_hds=None):
     """reads the heads from a modflow .hds file and returns an xarray
     DataArray.
 
@@ -547,6 +550,7 @@ def get_heads_array(fname_hds, gridtype='structured',
     """
     hdobj = flopy.utils.HeadFile(fname_hds)
     head = hdobj.get_alldata()
+    # TODO: this will sometimes set largest head to NaN...
     head[head == head.max()] = np.nan
 
     if gridtype == 'vertex':
diff --git a/nlmod/version.py b/nlmod/version.py
index fc79d63d..9dd16a34 100644
--- a/nlmod/version.py
+++ b/nlmod/version.py
@@ -1 +1 @@
-__version__ = '0.2.1'
+__version__ = '0.2.2'
\ No newline at end of file
diff --git a/nlmod/visualise/gis.py b/nlmod/visualise/gis.py
index 6ce45c1c..0b75068b 100644
--- a/nlmod/visualise/gis.py
+++ b/nlmod/visualise/gis.py
@@ -275,6 +275,11 @@ def model_dataset_to_vector_file(model_ds,
     # get all data variables in the model dataset
     da_names = set(model_ds)
 
+    # add data variables with only time variant data to exclude list
+    for da_name in da_names:
+        if ('time',) == model_ds[da_name].dims:
+            exclude += (da_name,)
+
     # exclude some names from export
     da_names -= set(exclude)
 
diff --git a/requirements.txt b/requirements.txt
index c91f1d9a..f3a7d450 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,7 +1,6 @@
 rasterio>=1.2.6
-git+git://github.com/modflowpy/pymake@1.2.1#egg=pymake
-requests>=2.25.1
 numpy>=1.20.1
+requests>=2.25.1
 tqdm>=4.59.0
 flopy>=3.3.3
 pandas>=1.1.5
@@ -9,8 +8,8 @@ pytest>=6.2.3
 Shapely>=1.7.1
 xarray>=0.19.0
 geopandas==0.9.0
-gdown==3.13.0
-hydropandas>=0.3.7
+gdown>=4.4.0
+hydropandas>=0.5.1
 scipy>=1.6.2
 OWSLib==0.24.1
 nbformat==5.1.3