diff --git a/docs/model.ipynb b/docs/model.ipynb index 3f11f95..56d2a18 100644 --- a/docs/model.ipynb +++ b/docs/model.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -56,8 +56,8 @@ "text/html": [ "
ParameterSet(\n",
        "    name='wflow_rhine_sbm_nc',\n",
-       "    directory=PosixPath('/home/verhoes/git/eWaterCycle/parameter-sets/wflow_rhine_sbm_nc'),\n",
-       "    config=PosixPath('/home/verhoes/git/eWaterCycle/parameter-sets/wflow_rhine_sbm_nc/wflow_sbm_NC.ini'),\n",
+       "    directory=PosixPath('/home/bart/ewatercycle/parameter-sets/wflow_rhine_sbm_nc'),\n",
+       "    config=PosixPath('/home/bart/ewatercycle/parameter-sets/wflow_rhine_sbm_nc/wflow_sbm_NC.ini'),\n",
        "    doi='N/A',\n",
        "    target_model='wflow',\n",
        "    supported_model_versions={'2020.1.1', '2020.1.3', '2020.1.2'},\n",
@@ -73,8 +73,8 @@
       "text/plain": [
        "\u001b[1;35mParameterSet\u001b[0m\u001b[1m(\u001b[0m\n",
        "    \u001b[33mname\u001b[0m=\u001b[32m'wflow_rhine_sbm_nc'\u001b[0m,\n",
-       "    \u001b[33mdirectory\u001b[0m=\u001b[1;35mPosixPath\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'/home/verhoes/git/eWaterCycle/parameter-sets/wflow_rhine_sbm_nc'\u001b[0m\u001b[1m)\u001b[0m,\n",
-       "    \u001b[33mconfig\u001b[0m=\u001b[1;35mPosixPath\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'/home/verhoes/git/eWaterCycle/parameter-sets/wflow_rhine_sbm_nc/wflow_sbm_NC.ini'\u001b[0m\u001b[1m)\u001b[0m,\n",
+       "    \u001b[33mdirectory\u001b[0m=\u001b[1;35mPosixPath\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'/home/bart/ewatercycle/parameter-sets/wflow_rhine_sbm_nc'\u001b[0m\u001b[1m)\u001b[0m,\n",
+       "    \u001b[33mconfig\u001b[0m=\u001b[1;35mPosixPath\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'/home/bart/ewatercycle/parameter-sets/wflow_rhine_sbm_nc/wflow_sbm_NC.ini'\u001b[0m\u001b[1m)\u001b[0m,\n",
        "    \u001b[33mdoi\u001b[0m=\u001b[32m'N/A'\u001b[0m,\n",
        "    \u001b[33mtarget_model\u001b[0m=\u001b[32m'wflow'\u001b[0m,\n",
        "    \u001b[33msupported_model_versions\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'2020.1.1'\u001b[0m, \u001b[32m'2020.1.3'\u001b[0m, \u001b[32m'2020.1.2'\u001b[0m\u001b[1m}\u001b[0m,\n",
@@ -107,10 +107,9 @@
      "data": {
       "text/html": [
        "
WflowForcing(\n",
-       "    model='wflow',\n",
        "    start_time='1991-01-01T00:00:00Z',\n",
        "    end_time='1991-12-31T00:00:00Z',\n",
-       "    directory=PosixPath('/home/verhoes/git/eWaterCycle/parameter-sets/wflow_rhine_sbm_nc'),\n",
+       "    directory=PosixPath('/home/bart/ewatercycle/parameter-sets/wflow_rhine_sbm_nc'),\n",
        "    shape=None,\n",
        "    netcdfinput='inmaps.nc',\n",
        "    Precipitation='/P',\n",
@@ -122,10 +121,9 @@
       ],
       "text/plain": [
        "\u001b[1;35mWflowForcing\u001b[0m\u001b[1m(\u001b[0m\n",
-       "    \u001b[33mmodel\u001b[0m=\u001b[32m'wflow'\u001b[0m,\n",
        "    \u001b[33mstart_time\u001b[0m=\u001b[32m'1991-01-01T00:00:00Z'\u001b[0m,\n",
        "    \u001b[33mend_time\u001b[0m=\u001b[32m'1991-12-31T00:00:00Z'\u001b[0m,\n",
-       "    \u001b[33mdirectory\u001b[0m=\u001b[1;35mPosixPath\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'/home/verhoes/git/eWaterCycle/parameter-sets/wflow_rhine_sbm_nc'\u001b[0m\u001b[1m)\u001b[0m,\n",
+       "    \u001b[33mdirectory\u001b[0m=\u001b[1;35mPosixPath\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'/home/bart/ewatercycle/parameter-sets/wflow_rhine_sbm_nc'\u001b[0m\u001b[1m)\u001b[0m,\n",
        "    \u001b[33mshape\u001b[0m=\u001b[3;35mNone\u001b[0m,\n",
        "    \u001b[33mnetcdfinput\u001b[0m=\u001b[32m'inmaps.nc'\u001b[0m,\n",
        "    \u001b[33mPrecipitation\u001b[0m=\u001b[32m'/P'\u001b[0m,\n",
@@ -157,7 +155,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Pick a version of Wflow model, so the right model code can be executed which understands the parameter set and forcing."
+    "## Set up the model\n",
+    "\n",
+    "To create the model object, we provide it with the forcing and parameter set objects:"
    ]
   },
   {
@@ -165,80 +165,124 @@
    "execution_count": 5,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:ewatercycle_wflow.model:Config file from parameter set is missing API section, adding section\n",
+      "WARNING:ewatercycle_wflow.model:Config file from parameter set is missing RiverRunoff option in API section, added it with value '2, m/s option'\n"
+     ]
+    },
     {
      "data": {
+      "text/html": [
+       "
Wflow(\n",
+       "    parameter_set=ParameterSet(\n",
+       "        name='wflow_rhine_sbm_nc',\n",
+       "        directory=PosixPath('/home/bart/ewatercycle/parameter-sets/wflow_rhine_sbm_nc'),\n",
+       "        config=PosixPath('/home/bart/ewatercycle/parameter-sets/wflow_rhine_sbm_nc/wflow_sbm_NC.ini'),\n",
+       "        doi='N/A',\n",
+       "        target_model='wflow',\n",
+       "        supported_model_versions={'2020.1.1', '2020.1.3', '2020.1.2'},\n",
+       "        downloader=GitHubDownloader(\n",
+       "            org='openstreams',\n",
+       "            repo='wflow',\n",
+       "            branch='master',\n",
+       "            subfolder='examples/wflow_rhine_sbm_nc'\n",
+       "        )\n",
+       "    ),\n",
+       "    forcing=WflowForcing(\n",
+       "        start_time='1991-01-01T00:00:00Z',\n",
+       "        end_time='1991-12-31T00:00:00Z',\n",
+       "        directory=PosixPath('/home/bart/ewatercycle/parameter-sets/wflow_rhine_sbm_nc'),\n",
+       "        shape=None,\n",
+       "        netcdfinput='inmaps.nc',\n",
+       "        Precipitation='/P',\n",
+       "        EvapoTranspiration='/PET',\n",
+       "        Temperature='/TEMP',\n",
+       "        Inflow=None\n",
+       "    )\n",
+       ")\n",
+       "
\n"
+      ],
       "text/plain": [
-       "('2020.1.1', '2020.1.2')"
+       "\u001b[1;35mWflow\u001b[0m\u001b[1m(\u001b[0m\n",
+       "    \u001b[33mparameter_set\u001b[0m=\u001b[1;35mParameterSet\u001b[0m\u001b[1m(\u001b[0m\n",
+       "        \u001b[33mname\u001b[0m=\u001b[32m'wflow_rhine_sbm_nc'\u001b[0m,\n",
+       "        \u001b[33mdirectory\u001b[0m=\u001b[1;35mPosixPath\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'/home/bart/ewatercycle/parameter-sets/wflow_rhine_sbm_nc'\u001b[0m\u001b[1m)\u001b[0m,\n",
+       "        \u001b[33mconfig\u001b[0m=\u001b[1;35mPosixPath\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'/home/bart/ewatercycle/parameter-sets/wflow_rhine_sbm_nc/wflow_sbm_NC.ini'\u001b[0m\u001b[1m)\u001b[0m,\n",
+       "        \u001b[33mdoi\u001b[0m=\u001b[32m'N/A'\u001b[0m,\n",
+       "        \u001b[33mtarget_model\u001b[0m=\u001b[32m'wflow'\u001b[0m,\n",
+       "        \u001b[33msupported_model_versions\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'2020.1.1'\u001b[0m, \u001b[32m'2020.1.3'\u001b[0m, \u001b[32m'2020.1.2'\u001b[0m\u001b[1m}\u001b[0m,\n",
+       "        \u001b[33mdownloader\u001b[0m=\u001b[1;35mGitHubDownloader\u001b[0m\u001b[1m(\u001b[0m\n",
+       "            \u001b[33morg\u001b[0m=\u001b[32m'openstreams'\u001b[0m,\n",
+       "            \u001b[33mrepo\u001b[0m=\u001b[32m'wflow'\u001b[0m,\n",
+       "            \u001b[33mbranch\u001b[0m=\u001b[32m'master'\u001b[0m,\n",
+       "            \u001b[33msubfolder\u001b[0m=\u001b[32m'examples/wflow_rhine_sbm_nc'\u001b[0m\n",
+       "        \u001b[1m)\u001b[0m\n",
+       "    \u001b[1m)\u001b[0m,\n",
+       "    \u001b[33mforcing\u001b[0m=\u001b[1;35mWflowForcing\u001b[0m\u001b[1m(\u001b[0m\n",
+       "        \u001b[33mstart_time\u001b[0m=\u001b[32m'1991-01-01T00:00:00Z'\u001b[0m,\n",
+       "        \u001b[33mend_time\u001b[0m=\u001b[32m'1991-12-31T00:00:00Z'\u001b[0m,\n",
+       "        \u001b[33mdirectory\u001b[0m=\u001b[1;35mPosixPath\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'/home/bart/ewatercycle/parameter-sets/wflow_rhine_sbm_nc'\u001b[0m\u001b[1m)\u001b[0m,\n",
+       "        \u001b[33mshape\u001b[0m=\u001b[3;35mNone\u001b[0m,\n",
+       "        \u001b[33mnetcdfinput\u001b[0m=\u001b[32m'inmaps.nc'\u001b[0m,\n",
+       "        \u001b[33mPrecipitation\u001b[0m=\u001b[32m'/P'\u001b[0m,\n",
+       "        \u001b[33mEvapoTranspiration\u001b[0m=\u001b[32m'/PET'\u001b[0m,\n",
+       "        \u001b[33mTemperature\u001b[0m=\u001b[32m'/TEMP'\u001b[0m,\n",
+       "        \u001b[33mInflow\u001b[0m=\u001b[3;35mNone\u001b[0m\n",
+       "    \u001b[1m)\u001b[0m\n",
+       "\u001b[1m)\u001b[0m\n"
       ]
      },
-     "execution_count": 5,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "ewatercycle.models.Wflow.available_versions"
+    "model = ewatercycle.models.Wflow(\n",
+    "    parameter_set=parameter_set, forcing=forcing\n",
+    ")\n",
+    "print(model)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 6,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:ewatercycle.models.wflow:Config file from parameter set is missing API section, adding section\n",
-      "WARNING:ewatercycle.models.wflow:Config file from parameter set is missing RiverRunoff option in API section, added it with value '2, m/s option'\n"
-     ]
-    }
-   ],
    "source": [
-    "model = ewatercycle.models.Wflow(\n",
-    "    version=\"2020.1.2\", parameter_set=parameter_set, forcing=forcing\n",
-    ")"
+    "Each model also has a version. This version is retrieved from the BMI container image.\n",
+    "\n",
+    "To run a different version of the model, you can specify the container image:\n",
+    "```py\n",
+    "from ewatercycle.container import ContainerImage\n",
+    "\n",
+    "ewatercycle.models.Wflow(\n",
+    "    forcing=forcing,\n",
+    "    bmi_image=ContainerImage(\"ewatercycle/ewatercycle/wflow-grpc4bmi:2020.1.3\")  # Modify the tag (`2020.1.3`) for a different version.\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "To view the model's version, do:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "eWaterCycle Wflow\n",
-      "-------------------\n",
-      "Version = 2020.1.2\n",
-      "Parameter set = \n",
-      "  Parameter set\n",
-      "  -------------\n",
-      "  name=wflow_rhine_sbm_nc\n",
-      "  directory=/home/verhoes/git/eWaterCycle/ewatercycle/docs/examples/parameter-sets/wflow_rhine_sbm_nc\n",
-      "  config=/home/verhoes/git/eWaterCycle/ewatercycle/docs/examples/parameter-sets/wflow_rhine_sbm_nc/wflow_sbm_NC.ini\n",
-      "  doi=N/A\n",
-      "  target_model=wflow\n",
-      "  supported_model_versions={'2020.1.2', '2020.1.1'}\n",
-      "Forcing = \n",
-      "  Forcing data for Wflow\n",
-      "  ----------------------\n",
-      "  Directory: /home/verhoes/git/eWaterCycle/ewatercycle/docs/examples/parameter-sets/wflow_rhine_sbm_nc\n",
-      "  Start time: 1991-01-01T00:00:00Z\n",
-      "  End time: 1991-12-31T00:00:00Z\n",
-      "  Shapefile: None\n",
-      "  Additional information for model config:\n",
-      "    - netcdfinput: inmaps.nc\n",
-      "    - Precipitation: /P\n",
-      "    - Temperature: /TEMP\n",
-      "    - EvapoTranspiration: /PET\n",
-      "    - Inflow: None\n"
-     ]
+     "data": {
+      "text/plain": [
+       "'2020.1.3'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "print(model)"
+    "model.version"
    ]
   },
   {
@@ -250,16 +294,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[('start_time', '1991-01-01T00:00:00Z'), ('end_time', '1991-12-31T00:00:00Z')]"
+       "dict_items([('start_time', '1991-01-01T00:00:00Z'), ('end_time', '1991-12-31T00:00:00Z')])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -268,9 +312,16 @@
     "model.parameters"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we can run the model, we have to set it up. For this you do:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "tags": []
    },
@@ -285,12 +336,30 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/home/verhoes/git/eWaterCycle/ewatercycle/docs/examples/output/wflow_20211008_084304/wflow_ewatercycle.ini\n",
-      "/home/verhoes/git/eWaterCycle/ewatercycle/docs/examples/output/wflow_20211008_084304\n"
-     ]
+     "data": {
+      "text/html": [
+       "
/home/bart/ewatercycle/output/wflow_20231129_134611/wflow_ewatercycle.ini\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[35m/home/bart/ewatercycle/output/wflow_20231129_134611/\u001b[0m\u001b[95mwflow_ewatercycle.ini\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
/home/bart/ewatercycle/output/wflow_20231129_134611\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[35m/home/bart/ewatercycle/output/\u001b[0m\u001b[95mwflow_20231129_134611\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -323,75 +392,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1991-01-01T00:00:00Z\n",
-      "1991-01-02T00:00:00Z\n",
-      "1991-01-03T00:00:00Z\n",
-      "1991-01-04T00:00:00Z\n",
-      "1991-01-05T00:00:00Z\n",
-      "1991-01-06T00:00:00Z\n",
-      "1991-01-07T00:00:00Z\n",
-      "1991-01-08T00:00:00Z\n",
-      "1991-01-09T00:00:00Z\n",
-      "1991-01-10T00:00:00Z\n",
-      "1991-01-11T00:00:00Z\n",
-      "1991-01-12T00:00:00Z\n",
-      "1991-01-13T00:00:00Z\n",
-      "1991-01-14T00:00:00Z\n",
-      "1991-01-15T00:00:00Z\n",
-      "1991-01-16T00:00:00Z\n",
-      "1991-01-17T00:00:00Z\n",
-      "1991-01-18T00:00:00Z\n",
-      "1991-01-19T00:00:00Z\n",
-      "1991-01-20T00:00:00Z\n",
-      "1991-01-21T00:00:00Z\n",
-      "1991-01-22T00:00:00Z\n",
-      "1991-01-23T00:00:00Z\n",
-      "1991-01-24T00:00:00Z\n",
-      "1991-01-25T00:00:00Z\n",
-      "1991-01-26T00:00:00Z\n",
-      "1991-01-27T00:00:00Z\n",
-      "1991-01-28T00:00:00Z\n",
-      "1991-01-29T00:00:00Z\n",
-      "1991-01-30T00:00:00Z\n",
-      "1991-01-31T00:00:00Z\n",
-      "1991-02-01T00:00:00Z\n",
-      "1991-02-02T00:00:00Z\n",
-      "1991-02-03T00:00:00Z\n",
-      "1991-02-04T00:00:00Z\n",
-      "1991-02-05T00:00:00Z\n",
-      "1991-02-06T00:00:00Z\n",
-      "1991-02-07T00:00:00Z\n",
-      "1991-02-08T00:00:00Z\n",
-      "1991-02-09T00:00:00Z\n",
-      "1991-02-10T00:00:00Z\n",
-      "1991-02-11T00:00:00Z\n",
-      "1991-02-12T00:00:00Z\n",
-      "1991-02-13T00:00:00Z\n",
-      "1991-02-14T00:00:00Z\n",
-      "1991-02-15T00:00:00Z\n",
-      "1991-02-16T00:00:00Z\n",
-      "1991-02-17T00:00:00Z\n",
-      "1991-02-18T00:00:00Z\n",
-      "1991-02-19T00:00:00Z\n",
-      "1991-02-20T00:00:00Z\n",
-      "1991-02-21T00:00:00Z\n",
-      "1991-02-22T00:00:00Z\n",
-      "1991-02-23T00:00:00Z\n",
-      "1991-02-24T00:00:00Z\n",
-      "1991-02-25T00:00:00Z\n",
-      "1991-02-26T00:00:00Z\n",
-      "1991-02-27T00:00:00Z\n",
-      "1991-02-28T00:00:00Z\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "while model.time < model.end_time:\n",
     "    model.update()\n",
@@ -446,6 +449,7 @@
        "}\n",
        "\n",
        "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
        "body.vscode-dark {\n",
        "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
        "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
@@ -458,7 +462,7 @@
        "}\n",
        "\n",
        ".xr-wrap {\n",
-       "  display: block;\n",
+       "  display: block !important;\n",
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
        "}\n",
@@ -675,6 +679,11 @@
        "  grid-column: 4;\n",
        "}\n",
        "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
        ".xr-var-name,\n",
        ".xr-var-dims,\n",
        ".xr-var-dtype,\n",
@@ -696,14 +705,16 @@
        "}\n",
        "\n",
        ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
        "  display: none;\n",
        "  background-color: var(--xr-background-color) !important;\n",
        "  padding-bottom: 5px !important;\n",
        "}\n",
        "\n",
        ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
        "  display: block;\n",
        "}\n",
        "\n",
@@ -713,13 +724,16 @@
        "\n",
        ".xr-var-name span,\n",
        ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
        ".xr-attrs {\n",
        "  padding-left: 25px !important;\n",
        "}\n",
        "\n",
        ".xr-attrs,\n",
        ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
        "  grid-column: 1 / -1;\n",
        "}\n",
        "\n",
@@ -757,7 +771,8 @@
        "}\n",
        "\n",
        ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
        "  display: inline-block;\n",
        "  vertical-align: middle;\n",
        "  width: 1em;\n",
@@ -766,26 +781,26 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "
<xarray.DataArray 'RiverRunoff' (latitude: 169, longitude: 187)>\n",
-       "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.]])\n",
+       "
<xarray.DataArray 'RiverRunoff' (time: 1, latitude: 169, longitude: 187)>\n",
+       "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.]]], dtype=float32)\n",
        "Coordinates:\n",
        "  * longitude  (longitude) float64 5.227 5.264 5.3 5.337 ... 11.97 12.01 12.05\n",
        "  * latitude   (latitude) float64 45.89 45.93 45.97 46.0 ... 51.98 52.02 52.05\n",
-       "    time       object 1991-02-28 00:00:00\n",
+       "  * time       (time) datetime64[ns] 1991-02-28\n",
        "Attributes:\n",
-       "    units:     m/s
  • time
    (time)
    datetime64[ns]
    1991-02-28
    array(['1991-02-28T00:00:00.000000000'], dtype='datetime64[ns]')
    • longitude
      PandasIndex
      PandasIndex(Index([ 5.227163314819336,  5.263830184936523,  5.300496578216553,\n",
+       "         5.33716344833374, 5.3738298416137695,  5.410496711730957,\n",
+       "        5.447163105010986,  5.483829975128174,  5.520496845245361,\n",
+       "        5.557163238525391,\n",
+       "       ...\n",
+       "         11.7171630859375, 11.753829956054688, 11.790496826171875,\n",
+       "       11.827163696289062, 11.863829612731934, 11.900496482849121,\n",
+       "       11.937163352966309, 11.973830223083496, 12.010497093200684,\n",
+       "       12.047163009643555],\n",
+       "      dtype='float64', name='longitude', length=187))
    • latitude
      PandasIndex
      PandasIndex(Index([ 45.89426803588867,  45.93093490600586,  45.96760177612305,\n",
+       "        46.00426483154297, 46.040931701660156, 46.077598571777344,\n",
+       "        46.11426544189453,  46.15093231201172, 46.187599182128906,\n",
+       "       46.224266052246094,\n",
+       "       ...\n",
+       "       51.724266052246094,  51.76093292236328,  51.79759979248047,\n",
+       "       51.834266662597656, 51.870933532714844,  51.90760040283203,\n",
+       "        51.94426727294922, 51.980934143066406, 52.017601013183594,\n",
+       "        52.05426788330078],\n",
+       "      dtype='float64', name='latitude', length=169))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['1991-02-28'], dtype='datetime64[ns]', name='time', freq=None))
  • units :
     m/s
    • "
       ],
       "text/plain": [
-       "\n",
-       "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.]])\n",
+       "\n",
+       "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.]]], dtype=float32)\n",
        "Coordinates:\n",
        "  * longitude  (longitude) float64 5.227 5.264 5.3 5.337 ... 11.97 12.01 12.05\n",
        "  * latitude   (latitude) float64 45.89 45.93 45.97 46.0 ... 51.98 52.02 52.05\n",
-       "    time       object 1991-02-28 00:00:00\n",
+       "  * time       (time) datetime64[ns] 1991-02-28\n",
        "Attributes:\n",
        "    units:     m/s"
       ]
@@ -879,24 +912,43 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "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.]])\n",
-      "Coordinates:\n",
-      "  * longitude  (longitude) float64 5.227 5.264 5.3 5.337 ... 11.97 12.01 12.05\n",
-      "  * latitude   (latitude) float64 45.89 45.93 45.97 46.0 ... 51.98 52.02 52.05\n",
-      "    time       object 1991-02-28 00:00:00\n",
-      "Attributes:\n",
-      "    units:     m/s\n"
-     ]
+     "data": {
+      "text/html": [
+       "
    <xarray.DataArray 'RiverRunoff' (time: 1, latitude: 169, longitude: 187)>\n",
+       "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.]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float64 5.227 5.264 5.3 5.337 ... 11.97 12.01 12.05\n",
+       "  * latitude   (latitude) float64 45.89 45.93 45.97 46.0 ... 51.98 52.02 52.05\n",
+       "  * time       (time) datetime64[ns] 1991-02-28\n",
+       "Attributes:\n",
+       "    units:     m/s\n",
+       "
    \n"
+      ],
+      "text/plain": [
+       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.DataArray\u001b[0m\u001b[39m \u001b[0m\u001b[32m'RiverRunoff'\u001b[0m\u001b[39m \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mtime: \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m, latitude: \u001b[0m\u001b[1;36m169\u001b[0m\u001b[39m, longitude: \u001b[0m\u001b[1;36m187\u001b[0m\u001b[1;39m)\u001b[0m\u001b[1m>\u001b[0m\n",
+       "\u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1m[\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m., \u001b[33m...\u001b[0m, \u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m.\u001b[1m]\u001b[0m,\n",
+       "        \u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m., \u001b[33m...\u001b[0m, \u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m.\u001b[1m]\u001b[0m,\n",
+       "        \u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m., \u001b[33m...\u001b[0m, \u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m.\u001b[1m]\u001b[0m,\n",
+       "        \u001b[33m...\u001b[0m,\n",
+       "        \u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m., \u001b[33m...\u001b[0m, \u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m.\u001b[1m]\u001b[0m,\n",
+       "        \u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m., \u001b[33m...\u001b[0m, \u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m.\u001b[1m]\u001b[0m,\n",
+       "        \u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m., \u001b[33m...\u001b[0m, \u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m., \u001b[1;36m0\u001b[0m.\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m, \u001b[33mdtype\u001b[0m=\u001b[35mfloat32\u001b[0m\u001b[1m)\u001b[0m\n",
+       "Coordinates:\n",
+       "  * longitude  \u001b[1m(\u001b[0mlongitude\u001b[1m)\u001b[0m float64 \u001b[1;36m5.227\u001b[0m \u001b[1;36m5.264\u001b[0m \u001b[1;36m5.3\u001b[0m \u001b[1;36m5.337\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m11.97\u001b[0m \u001b[1;36m12.01\u001b[0m \u001b[1;36m12.05\u001b[0m\n",
+       "  * latitude   \u001b[1m(\u001b[0mlatitude\u001b[1m)\u001b[0m float64 \u001b[1;36m45.89\u001b[0m \u001b[1;36m45.93\u001b[0m \u001b[1;36m45.97\u001b[0m \u001b[1;36m46.0\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m51.98\u001b[0m \u001b[1;36m52.02\u001b[0m \u001b[1;36m52.05\u001b[0m\n",
+       "  * time       \u001b[1m(\u001b[0mtime\u001b[1m)\u001b[0m datetime64\u001b[1m[\u001b[0mns\u001b[1m]\u001b[0m \u001b[1;36m1991\u001b[0m-\u001b[1;36m02\u001b[0m-\u001b[1;36m28\u001b[0m\n",
+       "Attributes:\n",
+       "    units:     m/s\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -911,7 +963,7 @@
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 15,
@@ -920,14 +972,12 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "
    "
+       "
    "
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -956,7 +1006,7 @@
     {
      "data": {
       "text/plain": [
-       "array([200.49531555,  44.60787582,   0.        ])"
+       "array([200.49532 ,  44.607876,   0.      ], dtype=float32)"
       ]
      },
      "execution_count": 16,
@@ -1010,7 +1060,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.10.0"
   }
  },
  "nbformat": 4,