demo.ipynb

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    "# WALRUS in eWaterCycle\n",
    "\n",
    "This notebook is to showcase/test the containerized model.\n",
    "\n",
    "Note: the [WALRUS BMI](https://raw.githubusercontent.com/eWaterCycle/grpc4bmi-examples/master/walrus/walrus-bmi.r) \n",
    "is barely implemented and relies on \"faking it\"; `.update()` actually doesn't update the model: it ran the entire simulation already on `.initialize()`.\n",
    "\n",
    "*This notebook is only to demonstrate communication between eWaterCycle and an R-based model inside a container.*"
   ]
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   "source": [
    "We import the WALRUS model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ewatercycle_walrus.model import WALRUS\n",
    "from ewatercycle_walrus.forcing import WALRUSForcing\n",
    "\n",
    "import ewatercycle\n",
    "from pathlib import Path\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be able to initialize the model, we need forcing data.\n",
    "\n",
    "This can be generated from ERA5, or loaded if you have pre-generated this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "shape = Path(ewatercycle.__file__).parent / \"testing/data/Rhine/Rhine.shp\"\n",
    "\n",
    "# forcing = WALRUSForcing.generate(\n",
    "#     dataset=\"ERA5\",\n",
    "#     start_time=\"2000-01-01T00:00:00Z\",\n",
    "#     end_time=\"2000-12-31T00:00:00Z\",\n",
    "#     shape=shape.absolute(),\n",
    "# )\n",
    "\n",
    "forcing = WALRUSForcing.load('/home/bart/esmvaltool_output/ewcreps4nriqgh_20241121_092901/work/diagnostic/script')"
   ]
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   "execution_count": 3,
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   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 9kB\n",
       "Dimensions:        (time: 366, date: 366)\n",
       "Coordinates:\n",
       "    time           (date) datetime64[ns] 3kB 2000-01-01T12:00:00 ... 2000-12-...\n",
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       "Data variables:\n",
       "    precipitation  (time) float64 3kB 2.55 0.2932 0.1681 ... 2.524 2.289 0.4903\n",
       "    evspsblpot     (time) float64 3kB 0.612 0.5287 0.4964 ... 0.5296 0.4943\n",
       "Attributes:\n",
       "    title:    WALRUS forcing data\n",
       "    history:  Created by ewatercycle_walrus.forcing.WALRUSForcing.to_xarray()</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f6f8aa18-a6f2-4ad0-b8aa-0d7085bc3635' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f6f8aa18-a6f2-4ad0-b8aa-0d7085bc3635' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>time</span>: 366</li><li><span>date</span>: 366</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1d722078-8bef-4df7-8e57-48fa4217e971' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1d722078-8bef-4df7-8e57-48fa4217e971' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01T12:00:00 ... 2000-12-...</div><input id='attrs-ebb54198-475d-45c1-b6b6-7ba1476dac07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ebb54198-475d-45c1-b6b6-7ba1476dac07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa130e10-e3d4-40bd-bd30-3e63a70da040' class='xr-var-data-in' type='checkbox'><label for='data-aa130e10-e3d4-40bd-bd30-3e63a70da040' 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;2000-01-01T12:00:00.000000000&#x27;, &#x27;2000-01-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;2000-01-03T12:00:00.000000000&#x27;, ..., &#x27;2000-12-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;2000-12-30T12:00:00.000000000&#x27;, &#x27;2000-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-45625f58-7456-4405-9f3c-edf48b5c7818' class='xr-section-summary-in' type='checkbox'  checked><label for='section-45625f58-7456-4405-9f3c-edf48b5c7818' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>precipitation</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.55 0.2932 0.1681 ... 2.289 0.4903</div><input id='attrs-5bf23ea6-85fb-4de6-a1d6-735bb3d33c04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bf23ea6-85fb-4de6-a1d6-735bb3d33c04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5aef453e-d2a9-4437-9461-eabf2aa29629' class='xr-var-data-in' type='checkbox'><label for='data-5aef453e-d2a9-4437-9461-eabf2aa29629' 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>mm/day</dd></dl></div><div class='xr-var-data'><pre>array([2.55036736e+00, 2.93165118e-01, 1.68072626e-01, 3.62627602e+00,\n",
       "       1.06889844e+00, 5.64533174e-01, 6.87862158e-01, 8.84739637e-01,\n",
       "       5.08689499e+00, 2.51186776e+00, 1.22414529e-01, 2.69524660e-02,\n",
       "       7.91053995e-02, 1.04904450e-01, 1.63190350e-01, 4.85075325e-01,\n",
       "       1.74949408e+00, 3.87633872e+00, 7.36631274e-01, 9.74386275e-01,\n",
       "       3.68027449e+00, 6.93395090e+00, 5.03401852e+00, 3.47298563e-01,\n",
       "       2.00504381e-02, 9.74734053e-02, 2.37453002e-02, 2.35568717e-01,\n",
       "       1.32922630e+01, 1.19023008e+01, 1.46745360e+00, 1.55957267e-01,\n",
       "       4.66035128e+00, 1.36483479e+00, 5.48703551e-01, 2.97101021e-01,\n",
       "       6.29395619e-02, 3.68505883e+00, 1.25638762e+01, 4.89718485e+00,\n",
       "       4.66320944e+00, 3.17492914e+00, 1.23048210e+00, 8.26379108e+00,\n",
       "       1.99829233e+00, 5.85849571e+00, 9.88970566e+00, 5.35794020e+00,\n",
       "       8.61606407e+00, 9.61550522e+00, 4.10568857e+00, 2.98374981e-01,\n",
       "       4.59915221e-01, 2.83105075e-01, 4.51965857e+00, 6.83560944e+00,\n",
       "       1.46241322e-01, 1.05016455e-02, 3.93050551e+00, 9.60843658e+00,\n",
       "       9.92119217e+00, 2.06314707e+00, 1.02594032e+01, 5.62446451e+00,\n",
       "       7.18138039e-01, 1.42455176e-02, 8.41915309e-01, 6.02624512e+00,\n",
       "       3.40384293e+00, 2.48870707e+00, 2.32534671e+00, 2.79887646e-01,\n",
       "       1.91741049e-01, 8.00657654e+00, 2.85450029e+00, 4.47708178e+00,\n",
       "       4.52730846e+00, 5.55023432e+00, 7.22838044e-01, 9.25093424e-03,\n",
       "...\n",
       "       2.16248202e+00, 1.91484940e+00, 1.80063367e+00, 8.19065094e-01,\n",
       "       4.09364319e+00, 8.64508972e-02, 8.80802050e-03, 2.62397807e-02,\n",
       "       9.13135231e-01, 1.23740292e+00, 1.11611700e+00, 3.90150309e+00,\n",
       "       2.59284210e+00, 7.54465759e-01, 1.13730097e+00, 5.44275045e+00,\n",
       "       9.23326015e+00, 3.74962878e+00, 2.61222315e+00, 8.40825081e+00,\n",
       "       2.21777081e+00, 2.83501029e-01, 9.21031094e+00, 2.79759169e+00,\n",
       "       2.88445830e+00, 1.81624317e+00, 1.01320791e+00, 6.78128004e-02,\n",
       "       4.37429619e+00, 9.83117771e+00, 8.25993061e+00, 1.05026662e+00,\n",
       "       1.36958492e+00, 1.00604229e+01, 6.82436407e-01, 2.38019800e+00,\n",
       "       2.67174149e+00, 3.66582608e+00, 4.15520811e+00, 5.03267288e+00,\n",
       "       3.15510488e+00, 2.51112747e+00, 6.42869234e+00, 8.13353479e-01,\n",
       "       4.25170898e+00, 3.44613157e-02, 1.12741642e-01, 1.44835186e+00,\n",
       "       1.79695344e+00, 7.22967923e-01, 1.71789026e+00, 1.49734974e-01,\n",
       "       2.88058972e+00, 2.27817011e+00, 4.62714958e+00, 1.13649166e+00,\n",
       "       5.08798361e+00, 1.32726800e+00, 1.73233354e+00, 1.70346105e+00,\n",
       "       7.65070581e+00, 5.98302412e+00, 2.51292753e+00, 1.92482233e+00,\n",
       "       2.19691563e+00, 2.66298151e+00, 5.72951976e-03, 5.46118384e-03,\n",
       "       1.08938001e-03, 1.24402810e-02, 2.41637659e+00, 3.57577801e+00,\n",
       "       3.55053520e+00, 2.56336403e+00, 1.71210682e+00, 2.52409029e+00,\n",
       "       2.28906965e+00, 4.90292728e-01])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>evspsblpot</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.612 0.5287 ... 0.5296 0.4943</div><input id='attrs-aa3c48ba-7e83-423d-9e02-01f2703bfabe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa3c48ba-7e83-423d-9e02-01f2703bfabe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e87632b-d5aa-4ddd-a6eb-9366f28dcbbf' class='xr-var-data-in' type='checkbox'><label for='data-2e87632b-d5aa-4ddd-a6eb-9366f28dcbbf' 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>mm/day</dd></dl></div><div class='xr-var-data'><pre>array([0.61197209, 0.52868533, 0.49643391, 0.57141078, 0.48898908,\n",
       "       0.48052913, 0.57301295, 0.57149422, 0.61401844, 0.56200022,\n",
       "       0.53976828, 0.53104073, 0.53722185, 0.53592455, 0.55601478,\n",
       "       0.57447982, 0.62792712, 0.62535006, 0.5679059 , 0.61116964,\n",
       "       0.62851679, 0.63413894, 0.62393862, 0.59494728, 0.61093211,\n",
       "       0.63612318, 0.62115502, 0.62691301, 0.68319833, 0.66203213,\n",
       "       0.69503367, 0.68552995, 0.68859875, 0.69606525, 0.7298705 ,\n",
       "       0.73617107, 0.7489996 , 0.73891926, 0.71783078, 0.78177613,\n",
       "       0.83998305, 0.77987063, 0.82959694, 0.74770933, 0.84141749,\n",
       "       0.79937273, 0.88877213, 0.86219215, 0.78367984, 0.87635666,\n",
       "       0.89699322, 0.94914728, 0.98838544, 0.97044992, 0.78913462,\n",
       "       0.9087947 , 1.06171584, 1.2235868 , 1.25837111, 0.9782505 ,\n",
       "       0.96327573, 1.1363529 , 0.96736848, 1.23493421, 1.21546268,\n",
       "       1.4064883 , 1.20109963, 0.98805577, 1.15196538, 1.15793312,\n",
       "       1.17036998, 1.25707448, 1.54302871, 1.13762403, 1.48301363,\n",
       "       1.04285455, 1.00691867, 1.03497064, 1.23646021, 1.76748514,\n",
       "       1.96432471, 2.14285493, 2.02201605, 1.66930985, 1.8701427 ,\n",
       "       1.54503191, 1.5197438 , 1.5095582 , 1.2612077 , 1.0681107 ,\n",
       "       1.70749629, 1.70831728, 2.20327878, 2.35089397, 2.26138783,\n",
       "       1.46105564, 2.05437088, 2.39445639, 2.43859482, 2.40570116,\n",
       "...\n",
       "       1.5636996 , 1.62496042, 1.89589179, 1.17707813, 1.07932329,\n",
       "       1.32871747, 1.1585865 , 1.25025618, 1.08407545, 1.18566203,\n",
       "       1.16488028, 1.11343396, 1.16690171, 1.12904954, 0.95834655,\n",
       "       1.13265669, 0.99075133, 1.12577784, 1.0915581 , 1.19529223,\n",
       "       0.95722324, 0.9565056 , 0.80718678, 1.016361  , 1.07000804,\n",
       "       1.06561947, 1.03313768, 0.88091576, 0.88395959, 0.81268102,\n",
       "       0.81293821, 0.86674684, 0.81497526, 0.82568979, 0.73679239,\n",
       "       0.79852182, 0.78841448, 0.73494852, 0.73523295, 0.73601937,\n",
       "       0.69904429, 0.70619565, 0.69680649, 0.68201637, 0.6627515 ,\n",
       "       0.6586197 , 0.64055485, 0.65908247, 0.648278  , 0.61218333,\n",
       "       0.58907908, 0.64035547, 0.61346644, 0.62932593, 0.59426332,\n",
       "       0.57709044, 0.57137483, 0.6219092 , 0.58548367, 0.5337835 ,\n",
       "       0.55641317, 0.54892427, 0.56337553, 0.41386214, 0.46378958,\n",
       "       0.44505224, 0.50836945, 0.53913951, 0.43089056, 0.356473  ,\n",
       "       0.53071994, 0.51678342, 0.50351501, 0.39456266, 0.58614796,\n",
       "       0.31350416, 0.45863885, 0.38140243, 0.54143298, 0.48541829,\n",
       "       0.48643589, 0.51405174, 0.53557849, 0.5433079 , 0.42060131,\n",
       "       0.41362271, 0.410319  , 0.41184425, 0.51009232, 0.58454084,\n",
       "       0.51067895, 0.48925492, 0.52822053, 0.53135389, 0.52964294,\n",
       "       0.49428284])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-29bb6bc1-e337-4403-9298-069808bbcbef' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-29bb6bc1-e337-4403-9298-069808bbcbef' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-475cbb09-e2d7-431f-af1e-cac09eb2ea61' class='xr-section-summary-in' type='checkbox'  checked><label for='section-475cbb09-e2d7-431f-af1e-cac09eb2ea61' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>WALRUS forcing data</dd><dt><span>history :</span></dt><dd>Created by ewatercycle_walrus.forcing.WALRUSForcing.to_xarray()</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 9kB\n",
       "Dimensions:        (time: 366, date: 366)\n",
       "Coordinates:\n",
       "    time           (date) datetime64[ns] 3kB 2000-01-01T12:00:00 ... 2000-12-...\n",
       "Dimensions without coordinates: date\n",
       "Data variables:\n",
       "    precipitation  (time) float64 3kB 2.55 0.2932 0.1681 ... 2.524 2.289 0.4903\n",
       "    evspsblpot     (time) float64 3kB 0.612 0.5287 0.4964 ... 0.5296 0.4943\n",
       "Attributes:\n",
       "    title:    WALRUS forcing data\n",
       "    history:  Created by ewatercycle_walrus.forcing.WALRUSForcing.to_xarray()"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forcing.to_xarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can start the model. The `WALRUS` class uses a container hosted on the Github container registry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = WALRUS(forcing=forcing) \n",
    "cfg_file, _ = model.setup()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the config file generated by eWaterCycle, we can intialize the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.initialize(cfg_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can directly ask the BMI what the name is of the component:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'WALRUS'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.bmi.get_component_name()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model can be updated to the next time step with `.update()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.update()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WALRUS has a few output variable names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('ETact', 'Q', 'fGS', 'fQS', 'dV', 'dVeq', 'dG', 'hQ', 'hS', 'w')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.output_var_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can request the discarge value at the current time step:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_value(\"Q\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test the model, we'll make a hydrograph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "discharges = []\n",
    "for _ in range(365):\n",
    "    discharges.append(model.get_value(\"Q\"))\n",
    "    model.update()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fac54272e70>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(np.array(discharges))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make sure that the container running the model is nicely shut down and doesn't keep running in the background (taking up resources) we need to 'finalize' it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.finalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "lock2",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}