Update to match modelskill 1.0 beta

DHI · Dec 16, 2023 · e2ba9fd · e2ba9fd
1 parent aa603fa
commit e2ba9fd
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diff --git a/Makefile b/Makefile
@@ -0,0 +1,2 @@
+all:
+	jupyter-book build mini_book
diff --git a/mini_book/exercises/modelskill_assignment.ipynb b/mini_book/exercises/modelskill_assignment.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# FMskill assignment\n",
+    "# ModelSkill assignment\n",
     "\n",
     "You are working on a project modelling waves in the Southern North Sea. You have done 6 different calibration runs and want to choose the \"best\". You would also like to see how your best model is performing compared to a third-party model in NetCDF. \n",
     "\n",
@@ -41,7 +41,7 @@
    "outputs": [],
    "source": [
     "import fmskill\n",
-    "from fmskill import PointObservation, ModelResult, Connector"
+    "from ModelSkill import PointObservation, ModelResult, Connector"
    ]
   },
   {

diff --git a/mini_book/installation.md b/mini_book/installation.md
@@ -6,7 +6,7 @@ This course assumes that you have a working installation Python 3.8+.
 2. (On PCs without MIKE Zero) Install [VC redist](https://aka.ms/vs/16/release/vc_redist.x64.exe)
 3. Install matplotlib (`pip install matplotlib`)
 4. Install netCDF4 (`pip install netCDF4`)
-5. Install/upgrade FMSkill (`pip install --upgrade fmskill`)
+5. Install/upgrade ModelSkill (`pip install --upgrade fmskill`)
 
 You will need to code in either VS Code or Jupyter Lab in this course.
 
@@ -38,4 +38,4 @@ Can you say yes to the following questions?
 * My pandas version is 1.0 or greater
 * My matplotlib version is 3.2 or greater
 * My MIKE IO version is 1.0 or greater
-* My FMSkill version is 0.4 or greater
+* My ModelSkill version is 0.4 or greater
diff --git a/mini_book/model_skill.ipynb b/mini_book/model_skill.ipynb
diff --git a/mini_book/model_skill_visualisation.ipynb b/mini_book/model_skill_visualisation.ipynb
diff --git a/mini_book/multi_model_comparison.ipynb b/mini_book/multi_model_comparison.ipynb
diff --git a/mini_book/point_observations.ipynb b/mini_book/point_observations.ipynb
diff --git a/mini_book/track_observations.ipynb b/mini_book/track_observations.ipynb
@@ -22,18 +22,17 @@
     "* data type (e.g. water level)\n",
     "* unit (e.g. meter)\n",
     "\n",
-    "FMskill is agnostic to the coordinate reference system (CRS) and it is therefore the responsibility of the user to make sure that all data (observations and model) use the same CRS."
+    "ModelSkill is agnostic to the coordinate reference system (CRS) and it is therefore the responsibility of the user to make sure that all data (observations and model) use the same CRS."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "from modelskillill import TrackObservation\n",
-    "from mikeio import eum"
+    "import modelskill as ms"
    ]
   },
   {
@@ -51,9 +50,113 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "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>lon</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>surface_elevation</th>\n",
+       "      <th>significant_wave_height</th>\n",
+       "      <th>wind_speed</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2017-10-26 04:37:37</th>\n",
+       "      <td>8.757272</td>\n",
+       "      <td>53.926136</td>\n",
+       "      <td>1.6449</td>\n",
+       "      <td>0.426</td>\n",
+       "      <td>6.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-10-26 04:37:54</th>\n",
+       "      <td>8.221631</td>\n",
+       "      <td>54.948459</td>\n",
+       "      <td>1.1200</td>\n",
+       "      <td>1.634</td>\n",
+       "      <td>9.030000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-10-26 04:37:55</th>\n",
+       "      <td>8.189390</td>\n",
+       "      <td>55.008547</td>\n",
+       "      <td>1.0882</td>\n",
+       "      <td>1.717</td>\n",
+       "      <td>9.370000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-10-26 04:37:56</th>\n",
+       "      <td>8.157065</td>\n",
+       "      <td>55.068627</td>\n",
+       "      <td>1.0309</td>\n",
+       "      <td>1.869</td>\n",
+       "      <td>9.559999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-10-26 04:37:58</th>\n",
+       "      <td>8.124656</td>\n",
+       "      <td>55.128700</td>\n",
+       "      <td>1.0369</td>\n",
+       "      <td>1.939</td>\n",
+       "      <td>9.980000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          lon        lat  surface_elevation  \\\n",
+       "date                                                          \n",
+       "2017-10-26 04:37:37  8.757272  53.926136             1.6449   \n",
+       "2017-10-26 04:37:54  8.221631  54.948459             1.1200   \n",
+       "2017-10-26 04:37:55  8.189390  55.008547             1.0882   \n",
+       "2017-10-26 04:37:56  8.157065  55.068627             1.0309   \n",
+       "2017-10-26 04:37:58  8.124656  55.128700             1.0369   \n",
+       "\n",
+       "                     significant_wave_height  wind_speed  \n",
+       "date                                                      \n",
+       "2017-10-26 04:37:37                    0.426    6.100000  \n",
+       "2017-10-26 04:37:54                    1.634    9.030000  \n",
+       "2017-10-26 04:37:55                    1.717    9.370000  \n",
+       "2017-10-26 04:37:56                    1.869    9.559999  \n",
+       "2017-10-26 04:37:58                    1.939    9.980000  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "fn = \"data/SW/altimetry_NorthSea_20171027.csv\"\n",
     "df = pd.read_csv(fn, index_col=0, parse_dates=True)  # step 1: create DataFrame\n",
@@ -62,11 +165,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jan/src/book-learn-mikeio-fmskill/.venv/lib/python3.10/site-packages/modelskill/timeseries/_track.py:135: UserWarning: Removed 22 duplicate timestamps with keep=first\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "TrackObservation: surface_elevation, n=1093"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "o1 = TrackObservation(df, item=\"surface_elevation\")  # step 2: create TrackObservation\n",
+    "o1 = ms.TrackObservation(df, item=\"surface_elevation\")  # step 2: create TrackObservation\n",
     "o1"
    ]
   },
@@ -83,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,9 +215,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'DHI_ALTIMETRY_API_KEY'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m api_key \u001b[38;5;241m=\u001b[39m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menviron\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mDHI_ALTIMETRY_API_KEY\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m      2\u001b[0m repo \u001b[38;5;241m=\u001b[39m DHIAltimetryRepository(api_key)\n",
+      "File \u001b[0;32m/usr/lib/python3.10/os.py:680\u001b[0m, in \u001b[0;36m_Environ.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    677\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mencodekey(key)]\n\u001b[1;32m    678\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m    679\u001b[0m     \u001b[38;5;66;03m# raise KeyError with the original key value\u001b[39;00m\n\u001b[0;32m--> 680\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    681\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdecodevalue(value)\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'DHI_ALTIMETRY_API_KEY'"
+     ]
+    }
+   ],
    "source": [
     "api_key = os.environ[\"DHI_ALTIMETRY_API_KEY\"]\n",
     "repo = DHIAltimetryRepository(api_key)"
@@ -132,12 +267,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'data' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m o1 \u001b[38;5;241m=\u001b[39m ms\u001b[38;5;241m.\u001b[39mTrackObservation(\u001b[43mdata\u001b[49m\u001b[38;5;241m.\u001b[39mdf, item\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msignificant_wave_height\u001b[39m\u001b[38;5;124m\"\u001b[39m, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAlti_from_df\u001b[39m\u001b[38;5;124m'\u001b[39m, quantity\u001b[38;5;241m=\u001b[39mms\u001b[38;5;241m.\u001b[39mQuantity(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSignificant wave height\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mm\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m      2\u001b[0m o1\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
+     ]
+    }
+   ],
    "source": [
-    "o1 = TrackObservation(data.df, item=\"significant_wave_height\", name='Alti_from_df')\n",
-    "o1.itemInfo = eum.ItemInfo(eum.EUMType.Significant_wave_height)\n",
+    "o1 = ms.TrackObservation(data.df, item=\"significant_wave_height\", name='Alti_from_df', quantity=ms.Quantity(\"Significant wave height\", \"m\"))\n",
     "o1"
    ]
   },
@@ -163,20 +309,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "o2 = TrackObservation('alti_NS_20191001.dfs0', item=\"Significant Wave Height\")\n",
-    "o2.itemInfo = eum.ItemInfo(eum.EUMType.Significant_wave_height)\n",
+    "o2 = ms.TrackObservation('alti_NS_20191001.dfs0', item=\"Significant Wave Height\", quantity=ms.Quantity(\"Significant wave height\", \"m\")\n",
     "o2"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "o1.df[\"significant_wave_height\"].plot(marker='.')\n",
-    "o2.df[\"Significant Wave Height\"].plot();"
-   ]
   }
  ],
  "metadata": {
@@ -197,7 +332,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.12"
   },
   "orig_nbformat": 4
  },

diff --git a/requirements.txt b/requirements.txt
@@ -6,7 +6,7 @@ netcdf4
 xarray
 mikeio
 shapely
-modelskill
+modelskill>=1.0.b.*
 watobs
 geopandas
 openpyxl