notebooks/01_quickstart.ipynb

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    "In this notebook we will use Skyrim to get wind speed estimates \n",
    "for a given location for the upcoming week.\n",
    "\n",
    "🛑\n",
    "This notebook would work only if you have a GPU w/ ~24Gb ram.\n",
    "But check it out anyways!\n",
    "\n",
    "🪁\n",
    "Say you are going to Akyaka, Turkey for kitesurfing and you want to know\n",
    "what the wind speed will be like for the upcoming week. \n",
    "\n",
    "Our approach:\n",
    "1. Initialize a pangu model\n",
    "2. Get global predictions\n",
    "3. Visualize global predictions (cause it is super cool!)\n",
    "4. Get local wind speed forecasts and visualize them\n",
    "\n",
    "Want to contribute ❤️? \n",
    "Compare our predictions with IFS predictions using [ecmwf-opendata](https://github.com/ecmwf/ecmwf-opendata) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skyrim.core import Skyrim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pangu', 'fourcastnet', 'fourcastnet_v2', 'dlwp', 'graphcast']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to see all the available models\n",
    "Skyrim.list_available_models()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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     "text": [
      "\u001b[32m2024-05-07 15:15:02.122\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mskyrim.core.models.base\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m19\u001b[0m - \u001b[34m\u001b[1mBuilding pangu model...\u001b[0m\n",
      "\u001b[32m2024-05-07 15:15:53.474\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mskyrim.core.models.base\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m21\u001b[0m - \u001b[34m\u001b[1mBuilding gfs data source...\u001b[0m\n",
      "\u001b[32m2024-05-07 15:15:53.476\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mskyrim.core.models.base\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m23\u001b[0m - \u001b[32m\u001b[1mInitialized pangu in 51.4 seconds\u001b[0m\n",
      "\u001b[32m2024-05-07 15:15:53.477\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mskyrim.core.skyrim\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m38\u001b[0m - \u001b[34m\u001b[1mInitialized PanguModel(model_name=pangu) model with initial conditions from gfs\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# 1\n",
    "# lets initialize a pangu model using Skyrim\n",
    "# this will download the model if not already cached - could take a couple of minutes\n",
    "model = Skyrim(\"pangu\", ic_source=\"gfs\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Today's date: 20240507\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "today = datetime.datetime.now().date().strftime(\"%Y%m%d\")\n",
    "print(f\"Today's date: {today}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# comment out the following two lines to see the warnings and logs\n",
    "import loguru; loguru.logger.disable(\"skyrim\")\n",
    "import warnings; warnings.filterwarnings(\"ignore\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
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   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "100%|██████████| 69/69 [00:11<00:00,  6.26it/s]\n",
      "100%|██████████| 69/69 [00:03<00:00, 20.33it/s]\n"
     ]
    }
   ],
   "source": [
    "# 2\n",
    "# first predict method fetches the initial conditions from gfs\n",
    "# then runs the model and saves the rolled out predictions to disk\n",
    "# final_pred: PanguPrediction: is the final prediction object\n",
    "# pred_paths: list[Path]: is the list of paths to the saved predictions\n",
    "\n",
    "final_pred, pred_paths = model.predict(\n",
    "    date=today, time=\"0000\", lead_time=24 * 7, save=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "28\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['/root/skyrim/outputs/Bpr25Vr1Ym/pangu__gfs__20240507_00:00__20240507_06:00.nc',\n",
       " '/root/skyrim/outputs/Bpr25Vr1Ym/pangu__file__20240507_06:00__20240507_12:00.nc',\n",
       " '/root/skyrim/outputs/Bpr25Vr1Ym/pangu__file__20240507_12:00__20240507_18:00.nc']"
      ]
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see the paths saved snapshots of the predictions\n",
    "print(len(pred_paths))\n",
    "pred_paths[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "Coordinates:\n",
       "  * lat      (lat) float64 90.0 89.75 89.5 89.25 ... -89.25 -89.5 -89.75 -90.0\n",
       "  * lon      (lon) float64 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.2 359.5 359.8\n",
       "  * channel  (channel) <U5 'z1000' 'z925' 'z850' 'z700' ... 'u10m' 'v10m' 't2m'\n",
       "  * time     (time) datetime64[ns] 2024-05-13T18:00:00 2024-05-14"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see the coords of the prediction\n",
    "final_pred.coords"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "array([&#x27;z1000&#x27;, &#x27;z925&#x27;, &#x27;z850&#x27;, &#x27;z700&#x27;, &#x27;z600&#x27;, &#x27;z500&#x27;, &#x27;z400&#x27;, &#x27;z300&#x27;, &#x27;z250&#x27;,\n",
       "       &#x27;z200&#x27;, &#x27;z150&#x27;, &#x27;z100&#x27;, &#x27;z50&#x27;, &#x27;q1000&#x27;, &#x27;q925&#x27;, &#x27;q850&#x27;, &#x27;q700&#x27;, &#x27;q600&#x27;,\n",
       "       &#x27;q500&#x27;, &#x27;q400&#x27;, &#x27;q300&#x27;, &#x27;q250&#x27;, &#x27;q200&#x27;, &#x27;q150&#x27;, &#x27;q100&#x27;, &#x27;q50&#x27;, &#x27;t1000&#x27;,\n",
       "       &#x27;t925&#x27;, &#x27;t850&#x27;, &#x27;t700&#x27;, &#x27;t600&#x27;, &#x27;t500&#x27;, &#x27;t400&#x27;, &#x27;t300&#x27;, &#x27;t250&#x27;, &#x27;t200&#x27;,\n",
       "       &#x27;t150&#x27;, &#x27;t100&#x27;, &#x27;t50&#x27;, &#x27;u1000&#x27;, &#x27;u925&#x27;, &#x27;u850&#x27;, &#x27;u700&#x27;, &#x27;u600&#x27;, &#x27;u500&#x27;,\n",
       "       &#x27;u400&#x27;, &#x27;u300&#x27;, &#x27;u250&#x27;, &#x27;u200&#x27;, &#x27;u150&#x27;, &#x27;u100&#x27;, &#x27;u50&#x27;, &#x27;v1000&#x27;, &#x27;v925&#x27;,\n",
       "       &#x27;v850&#x27;, &#x27;v700&#x27;, &#x27;v600&#x27;, &#x27;v500&#x27;, &#x27;v400&#x27;, &#x27;v300&#x27;, &#x27;v250&#x27;, &#x27;v200&#x27;, &#x27;v150&#x27;,\n",
       "       &#x27;v100&#x27;, &#x27;v50&#x27;, &#x27;msl&#x27;, &#x27;u10m&#x27;, &#x27;v10m&#x27;, &#x27;t2m&#x27;], dtype=&#x27;&lt;U5&#x27;)\n",
       "Coordinates:\n",
       "  * channel  (channel) &lt;U5 &#x27;z1000&#x27; &#x27;z925&#x27; &#x27;z850&#x27; &#x27;z700&#x27; ... &#x27;u10m&#x27; &#x27;v10m&#x27; &#x27;t2m&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'channel'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>channel</span>: 69</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5bffe1ed-12cc-491d-be70-253c5869fd99' class='xr-array-in' type='checkbox' checked><label for='section-5bffe1ed-12cc-491d-be70-253c5869fd99' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>&#x27;z1000&#x27; &#x27;z925&#x27; &#x27;z850&#x27; &#x27;z700&#x27; &#x27;z600&#x27; ... &#x27;msl&#x27; &#x27;u10m&#x27; &#x27;v10m&#x27; &#x27;t2m&#x27;</span></div><div class='xr-array-data'><pre>array([&#x27;z1000&#x27;, &#x27;z925&#x27;, &#x27;z850&#x27;, &#x27;z700&#x27;, &#x27;z600&#x27;, &#x27;z500&#x27;, &#x27;z400&#x27;, &#x27;z300&#x27;, &#x27;z250&#x27;,\n",
       "       &#x27;z200&#x27;, &#x27;z150&#x27;, &#x27;z100&#x27;, &#x27;z50&#x27;, &#x27;q1000&#x27;, &#x27;q925&#x27;, &#x27;q850&#x27;, &#x27;q700&#x27;, &#x27;q600&#x27;,\n",
       "       &#x27;q500&#x27;, &#x27;q400&#x27;, &#x27;q300&#x27;, &#x27;q250&#x27;, &#x27;q200&#x27;, &#x27;q150&#x27;, &#x27;q100&#x27;, &#x27;q50&#x27;, &#x27;t1000&#x27;,\n",
       "       &#x27;t925&#x27;, &#x27;t850&#x27;, &#x27;t700&#x27;, &#x27;t600&#x27;, &#x27;t500&#x27;, &#x27;t400&#x27;, &#x27;t300&#x27;, &#x27;t250&#x27;, &#x27;t200&#x27;,\n",
       "       &#x27;t150&#x27;, &#x27;t100&#x27;, &#x27;t50&#x27;, &#x27;u1000&#x27;, &#x27;u925&#x27;, &#x27;u850&#x27;, &#x27;u700&#x27;, &#x27;u600&#x27;, &#x27;u500&#x27;,\n",
       "       &#x27;u400&#x27;, &#x27;u300&#x27;, &#x27;u250&#x27;, &#x27;u200&#x27;, &#x27;u150&#x27;, &#x27;u100&#x27;, &#x27;u50&#x27;, &#x27;v1000&#x27;, &#x27;v925&#x27;,\n",
       "       &#x27;v850&#x27;, &#x27;v700&#x27;, &#x27;v600&#x27;, &#x27;v500&#x27;, &#x27;v400&#x27;, &#x27;v300&#x27;, &#x27;v250&#x27;, &#x27;v200&#x27;, &#x27;v150&#x27;,\n",
       "       &#x27;v100&#x27;, &#x27;v50&#x27;, &#x27;msl&#x27;, &#x27;u10m&#x27;, &#x27;v10m&#x27;, &#x27;t2m&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-7f4a3f65-66e5-422f-bea3-559aab0f352b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7f4a3f65-66e5-422f-bea3-559aab0f352b' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>channel</span></div><div class='xr-var-dims'>(channel)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;z1000&#x27; &#x27;z925&#x27; ... &#x27;v10m&#x27; &#x27;t2m&#x27;</div><input id='attrs-eeb7e28f-7d2e-4f45-8830-514eb7655330' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eeb7e28f-7d2e-4f45-8830-514eb7655330' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79114018-aab1-435d-a755-55a4565f464b' class='xr-var-data-in' type='checkbox'><label for='data-79114018-aab1-435d-a755-55a4565f464b' 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;z1000&#x27;, &#x27;z925&#x27;, &#x27;z850&#x27;, &#x27;z700&#x27;, &#x27;z600&#x27;, &#x27;z500&#x27;, &#x27;z400&#x27;, &#x27;z300&#x27;, &#x27;z250&#x27;,\n",
       "       &#x27;z200&#x27;, &#x27;z150&#x27;, &#x27;z100&#x27;, &#x27;z50&#x27;, &#x27;q1000&#x27;, &#x27;q925&#x27;, &#x27;q850&#x27;, &#x27;q700&#x27;, &#x27;q600&#x27;,\n",
       "       &#x27;q500&#x27;, &#x27;q400&#x27;, &#x27;q300&#x27;, &#x27;q250&#x27;, &#x27;q200&#x27;, &#x27;q150&#x27;, &#x27;q100&#x27;, &#x27;q50&#x27;, &#x27;t1000&#x27;,\n",
       "       &#x27;t925&#x27;, &#x27;t850&#x27;, &#x27;t700&#x27;, &#x27;t600&#x27;, &#x27;t500&#x27;, &#x27;t400&#x27;, &#x27;t300&#x27;, &#x27;t250&#x27;, &#x27;t200&#x27;,\n",
       "       &#x27;t150&#x27;, &#x27;t100&#x27;, &#x27;t50&#x27;, &#x27;u1000&#x27;, &#x27;u925&#x27;, &#x27;u850&#x27;, &#x27;u700&#x27;, &#x27;u600&#x27;, &#x27;u500&#x27;,\n",
       "       &#x27;u400&#x27;, &#x27;u300&#x27;, &#x27;u250&#x27;, &#x27;u200&#x27;, &#x27;u150&#x27;, &#x27;u100&#x27;, &#x27;u50&#x27;, &#x27;v1000&#x27;, &#x27;v925&#x27;,\n",
       "       &#x27;v850&#x27;, &#x27;v700&#x27;, &#x27;v600&#x27;, &#x27;v500&#x27;, &#x27;v400&#x27;, &#x27;v300&#x27;, &#x27;v250&#x27;, &#x27;v200&#x27;, &#x27;v150&#x27;,\n",
       "       &#x27;v100&#x27;, &#x27;v50&#x27;, &#x27;msl&#x27;, &#x27;u10m&#x27;, &#x27;v10m&#x27;, &#x27;t2m&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ff9ed651-6f10-4b7d-8b16-4a19d57669f4' class='xr-section-summary-in' type='checkbox'  ><label for='section-ff9ed651-6f10-4b7d-8b16-4a19d57669f4' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>channel</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-331e260f-bb63-43f9-b0d6-f0cf8b2fc3f5' class='xr-index-data-in' type='checkbox'/><label for='index-331e260f-bb63-43f9-b0d6-f0cf8b2fc3f5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;z1000&#x27;, &#x27;z925&#x27;, &#x27;z850&#x27;, &#x27;z700&#x27;, &#x27;z600&#x27;, &#x27;z500&#x27;, &#x27;z400&#x27;, &#x27;z300&#x27;, &#x27;z250&#x27;,\n",
       "       &#x27;z200&#x27;, &#x27;z150&#x27;, &#x27;z100&#x27;, &#x27;z50&#x27;, &#x27;q1000&#x27;, &#x27;q925&#x27;, &#x27;q850&#x27;, &#x27;q700&#x27;, &#x27;q600&#x27;,\n",
       "       &#x27;q500&#x27;, &#x27;q400&#x27;, &#x27;q300&#x27;, &#x27;q250&#x27;, &#x27;q200&#x27;, &#x27;q150&#x27;, &#x27;q100&#x27;, &#x27;q50&#x27;, &#x27;t1000&#x27;,\n",
       "       &#x27;t925&#x27;, &#x27;t850&#x27;, &#x27;t700&#x27;, &#x27;t600&#x27;, &#x27;t500&#x27;, &#x27;t400&#x27;, &#x27;t300&#x27;, &#x27;t250&#x27;, &#x27;t200&#x27;,\n",
       "       &#x27;t150&#x27;, &#x27;t100&#x27;, &#x27;t50&#x27;, &#x27;u1000&#x27;, &#x27;u925&#x27;, &#x27;u850&#x27;, &#x27;u700&#x27;, &#x27;u600&#x27;, &#x27;u500&#x27;,\n",
       "       &#x27;u400&#x27;, &#x27;u300&#x27;, &#x27;u250&#x27;, &#x27;u200&#x27;, &#x27;u150&#x27;, &#x27;u100&#x27;, &#x27;u50&#x27;, &#x27;v1000&#x27;, &#x27;v925&#x27;,\n",
       "       &#x27;v850&#x27;, &#x27;v700&#x27;, &#x27;v600&#x27;, &#x27;v500&#x27;, &#x27;v400&#x27;, &#x27;v300&#x27;, &#x27;v250&#x27;, &#x27;v200&#x27;, &#x27;v150&#x27;,\n",
       "       &#x27;v100&#x27;, &#x27;v50&#x27;, &#x27;msl&#x27;, &#x27;u10m&#x27;, &#x27;v10m&#x27;, &#x27;t2m&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;channel&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ca7210fe-d676-41de-bedd-b77422277c92' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ca7210fe-d676-41de-bedd-b77422277c92' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'channel' (channel: 69)>\n",
       "array(['z1000', 'z925', 'z850', 'z700', 'z600', 'z500', 'z400', 'z300', 'z250',\n",
       "       'z200', 'z150', 'z100', 'z50', 'q1000', 'q925', 'q850', 'q700', 'q600',\n",
       "       'q500', 'q400', 'q300', 'q250', 'q200', 'q150', 'q100', 'q50', 't1000',\n",
       "       't925', 't850', 't700', 't600', 't500', 't400', 't300', 't250', 't200',\n",
       "       't150', 't100', 't50', 'u1000', 'u925', 'u850', 'u700', 'u600', 'u500',\n",
       "       'u400', 'u300', 'u250', 'u200', 'u150', 'u100', 'u50', 'v1000', 'v925',\n",
       "       'v850', 'v700', 'v600', 'v500', 'v400', 'v300', 'v250', 'v200', 'v150',\n",
       "       'v100', 'v50', 'msl', 'u10m', 'v10m', 't2m'], dtype='<U5')\n",
       "Coordinates:\n",
       "  * channel  (channel) <U5 'z1000' 'z925' 'z850' 'z700' ... 'u10m' 'v10m' 't2m'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see the variables/channels in the prediction\n",
    "final_pred.channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NOTE: pred object just holds the final prediction at the lead time\n",
    "# to get the raw prediction data uncomment the following line\n",
    "# final_pred.prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GIF already exists: pangu_20240507_00:00_to_20240514_00:00_u10m.gif\n",
      "GIF already exists: pangu_20240507_00:00_to_20240514_00:00_v10m.gif\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div style=\"display:flex; justify-content:space-between;\">\n",
       "        <img src=\"pangu_20240507_00:00_to_20240514_00:00_u10m.gif\" style=\"width: 50.0%; margin-right: 2px;\"><img src=\"pangu_20240507_00:00_to_20240514_00:00_v10m.gif\" style=\"width: 50.0%;\">\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3\n",
    "# lets visualize the weekly global prediction\n",
    "from skyrim.libs.plotting import visualize_rollout\n",
    "\n",
    "visualize_rollout(output_paths=pred_paths, channels=[\"u10m\", \"v10m\"], output_dir=\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# say we want to check if we can kite surf at Akyaka, Turkey\n",
    "# we have a nice plotting module to plot wind speed at a location\n",
    "\n",
    "from skyrim.libs.plotting import plot_wind_speed\n",
    "\n",
    "akyaka_coords = {\"lat\": 37.0557, \"lon\": 28.3242}\n",
    "ax = plot_wind_speed(**akyaka_coords, rollout_snapshots=pred_paths)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "earth2mip",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}