Update to VRE 1.0.8

.github/workflows/main.yml

@@ -7,7 +7,7 @@ on:
       - main
 
 env:
-  DOCKER_IMAGE: 'registry.gitlab.eox.at/esa/vires_vre_ops/vre-swarm-notebook:0.10.11'
+  DOCKER_IMAGE: 'registry.gitlab.eox.at/esa/vires_vre_ops/vre-swarm-notebook:1.0.8'
 
 jobs:
   run:
@@ -18,7 +18,7 @@ jobs:
         run: |
           echo ${{ secrets.EOX_REGISTRY_PASSWORD }} | docker login -u ${{ secrets.EOX_REGISTRY_USER }} --password-stdin registry.gitlab.eox.at
           docker pull ${{ env.DOCKER_IMAGE }}
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
       - name: Execute notebooks and build book
         run: |
           docker run -v $GITHUB_WORKSPACE:/home/jovyan -u root \
@@ -31,5 +31,16 @@ jobs:
               pip install 'jupyter-book==0.15.1' && \
               jupyter-book build --verbose .
               '
-      - name: Deploy preview with netlify
-        run: netlify deploy --dir=_build/html --auth=${{ secrets.NETLIFY_AUTH_TOKEN }} --site=${{ secrets.NETLIFY_SITE_API_ID }}
+      - name: Deploy preview to Netlify
+        if: always()
+        uses: nwtgck/actions-netlify@v3.0
+        with:
+          publish-dir: './_build/html'
+          production-deploy: false
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          enable-pull-request-comment: true
+          enable-commit-comment: false
+          overwrites-pull-request-comment: true
+        env:
+          NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }}
+          NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_API_ID }}

geomag-obs-models/01a_Visualising-Geomagnetic-Observatory-Data.ipynb

@@ -32,22 +32,13 @@
    },
    "outputs": [],
    "source": [
-    "## Uncomment here to autoreload local modules as you edit them\n",
-    "# %load_ext autoreload\n",
-    "# %autoreload 2\n",
-    "\n",
     "import datetime as dt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "import ipywidgets as widgets\n",
     "from viresclient import SwarmRequest\n",
     "\n",
-    "## Use to make interactive matplotlib plots\n",
-    "##  - doesn't work smoothly\n",
-    "##  - investigating using plotly/bokeh instead\n",
-    "# %matplotlib widget\n",
-    "\n",
     "%load_ext watermark\n",
     "%watermark -i -v -p numpy,pandas,xarray,matplotlib,ipywidgets,viresclient"
    ]
@@ -116,11 +107,10 @@
     "        **kwargs\n",
     "    )\n",
     "    if use_xarray:\n",
-    "        ds = data.as_xarray().drop(\"Spacecraft\")\n",
-    "        return ds\n",
+    "        return data.as_xarray()\n",
     "    else:\n",
     "        # Load data in Pandas Dataframe with X, Y, Z columns\n",
-    "        df = data.as_dataframe(expand=True).drop(columns=\"Spacecraft\")\n",
+    "        df = data.as_dataframe(expand=True)\n",
     "        df = df.rename(columns={f\"B_NEC_{i}\": j for i, j in zip(\"NEC\", \"XYZ\")})\n",
     "        return df\n",
     "\n",
@@ -178,20 +168,22 @@
     "    Returns:\n",
     "        Figure\n",
     "    \"\"\"\n",
+    "    observatory = df[\"IAGA_code\"].iloc[0]\n",
+    "    # Restrict to only the XYZ values so that resampling will work\n",
+    "    df = df[[\"X\", \"Y\", \"Z\"]].copy()\n",
     "    # Evaluate annual means to use later,\n",
     "    #   reindexed with the ending index points for each year\n",
-    "    annual_mean = df.resample(\"1y\").mean()\n",
+    "    annual_mean = df.resample(\"Y\").mean()\n",
     "    annual_mean.index = [df.loc[str(year)].index[-1] for year in df.index.year.unique()]\n",
     "    # Subset dataframe to selection\n",
     "    df = df.loc[start_date:end_date]\n",
     "    # Cut the annual mean (and reindex) to match df\n",
     "    #  so we can use it directly in ax.fill_between\n",
     "    annual_mean = annual_mean.loc[str(start_date.year):str(end_date.year)]\n",
     "    annual_mean = annual_mean.reindex(index=df.index, method=\"backfill\")\n",
-    "    observatory = df[\"IAGA_code\"][0]\n",
     "    title = f\"Minute data from {observatory}\"\n",
     "    if hourly_mean:\n",
-    "        df = df.resample(\"1h\").mean()\n",
+    "        df = df.resample(\"h\").mean()\n",
     "        annual_mean = annual_mean.reindex(index=df.index)\n",
     "        title += \": averaged over each hour\"\n",
     "    if show_annual_mean:\n",
@@ -322,7 +314,11 @@
    "outputs": [],
    "source": [
     "def monthly_means(df=obs_hourly):\n",
-    "    \"\"\"Return MultIndex DataFrame of monthly means over each hourly interval\"\"\"\n",
+    "    \"\"\"Return MultiIndex DataFrame of monthly means over each hourly interval\"\"\"\n",
+    "    # Restrict to only the XYZ values so that resampling will work\n",
+    "    df = df[[\"X\", \"Y\", \"Z\"]].copy()\n",
+    "    # Append Declination\n",
+    "    df[\"D\"] = np.rad2deg(np.arctan2(df[\"Y\"], df[\"X\"]))\n",
     "    # Append hour of day, and approx fractional year, to use for plotting\n",
     "    df[\"t_hour\"] = df.index.map(lambda x: x.hour + x.minute/60)\n",
     "    epoch = pd.to_datetime(0, unit='s').to_julian_date()\n",
@@ -337,8 +333,6 @@
     "    monthly[\"D_variation\"] = monthly['D'].values - monthly_all['D'].values.repeat(24)\n",
     "    return monthly\n",
     "\n",
-    "\n",
-    "obs_hourly[\"D\"] = np.rad2deg(np.arctan2(obs_hourly[\"Y\"], obs_hourly[\"X\"]))\n",
     "obs_monthly = monthly_means(obs_hourly)\n",
     "obs_monthly"
    ]
@@ -462,7 +456,7 @@
     "    df = df.copy()\n",
     "\n",
     "    # Evaluate the annual means\n",
-    "    annual_means = df[[\"X\", \"Y\", \"Z\"]].resample(\"1y\").mean()\n",
+    "    annual_means = df[[\"X\", \"Y\", \"Z\"]].resample(\"Y\").mean()\n",
     "    annual_means = annual_means.rename(columns={\"X\": \"X_mean\", \"Y\": \"Y_mean\", \"Z\": \"Z_mean\"})\n",
     "    # Identify the year-end index times\n",
     "    year_ends = [df.loc[str(year)].index[-1] for year in df.index.year.unique()]\n",
@@ -524,7 +518,7 @@
     "            (df[\"time\"].dt.day == 1) & (df[\"time\"].dt.hour == 0)\n",
     "        ).dropna()\n",
     "        if bartrot in month_starts.keys():\n",
-    "            bartrotday = float(month_starts.loc[bartrot].index.values)\n",
+    "            bartrotday = float(month_starts.loc[bartrot].index.values[0])\n",
     "            month = month_starts.loc[bartrot].dt.strftime(\"%b\").values[0]\n",
     "            ax.text(bartrotday, y0.iloc[0] - 85, month, verticalalignment=\"top\")\n",
     "\n",
@@ -592,14 +586,15 @@
     "            asynchronous=False,\n",
     "            show_progress=False,\n",
     "        )\n",
-    "        df = df.resample(\"1y\").mean()\n",
+    "        df = df[[\"X\", \"Y\", \"Z\"]].resample(\"Y\").mean()\n",
     "        df.index = df.index.year\n",
     "        df.index.name = \"Year\"\n",
     "        annual_means[obs] = df\n",
     "    return annual_means\n",
     "\n",
     "\n",
-    "observatories = [\"ESK\", \"NGK\", \"CLF\"]\n",
+    "# observatories = [\"ESK\", \"NGK\", \"CLF\"]  # Why aren't NGK and CLF working?\n",
+    "observatories = [\"ESK\"]\n",
     "annual_means = fetch_annual_means(observatories)"
    ]
   },
@@ -709,7 +704,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.6"
   },
   "toc-autonumbering": true,
   "toc-showcode": false,

geomag-obs-models/02a_K-Index-Calculation.ipynb

@@ -152,9 +152,7 @@
     "        dt.datetime(2003, 1, 1),\n",
     "        dt.datetime(2004, 1, 1),\n",
     "    )\n",
-    "    df = data.as_dataframe(expand=True).drop(\n",
-    "        columns=[\"Spacecraft\"]\n",
-    "    )\n",
+    "    df = data.as_dataframe(expand=True)\n",
     "    df = df.rename(columns={f\"B_NEC_{i}\": j for i, j in zip(\"NEC\", \"XYZ\")})\n",
     "    return df"
    ]
@@ -552,7 +550,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,

geomag-obs-models/04b_SHA2.ipynb

@@ -16,7 +16,6 @@
     "# Import notebook dependencies\n",
     "import sys\n",
     "import matplotlib.pyplot as plt\n",
-    "plt.style.use('seaborn-white')\n",
     "import numpy as np\n",
     "import pandas as pd \n",
     "\n",
@@ -602,7 +601,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,

geomag-obs-models/04c_GroundObs-to-Model.ipynb

@@ -109,7 +109,7 @@
     "    _ds = df.to_xarray()\n",
     "    _ds[\"Latitude\"] = 90 - _ds[\"Colat\"]\n",
     "    _ds[\"Longitude\"] = (_ds[\"Long\"] % 360 + 540) % 360 - 180\n",
-    "    _ds = _ds.drop(\"index\")\n",
+    "    _ds = _ds.drop_vars(\"index\")\n",
     "    return _ds\n",
     "\n",
     "input_data = load_data(year=2000.5)\n",
@@ -393,7 +393,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,

geomag-obs-models/05a_Swarm-Measurements.ipynb

@@ -82,7 +82,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "_ds = ds.drop(\"Timestamp\").sel(NEC=\"C\")\n",
+    "_ds = ds.drop_vars(\"Timestamp\").sel(NEC=\"C\")\n",
     "_ds.hvplot.scatter(\n",
     "    x=\"Longitude\", y=\"Latitude\", color=\"B_NEC\",\n",
     "    rasterize=True, colorbar=True, cmap=\"coolwarm\", clabel=\"Vertical (downwards) magnetic field (nT)\"\n",
@@ -229,7 +229,7 @@
     "ds = append_model_evaluations(ds, model_coeffs)\n",
     "\n",
     "# Plot the \n",
-    "_ds = ds.drop(\"Timestamp\").sel(NEC=\"C\")\n",
+    "_ds = ds.drop_vars(\"Timestamp\").sel(NEC=\"C\")\n",
     "_ds.hvplot.scatter(\n",
     "    x=\"Longitude\", y=\"Latitude\", color=\"B_NEC_res_model\",\n",
     "    rasterize=True, colorbar=True, cmap=\"coolwarm\", clim=(-40, 40), clabel=\"Vertical (B_C) data-model residuals (nT)\",\n",
@@ -251,7 +251,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "_ds = ds.drop(\"Timestamp\").sel(NEC=\"E\")\n",
+    "_ds = ds.drop_vars(\"Timestamp\").sel(NEC=\"E\")\n",
     "_ds.hvplot.scatter(\n",
     "    x=\"Longitude\", y=\"Latitude\", color=\"B_NEC_res_model\",\n",
     "    rasterize=True, colorbar=True, cmap=\"coolwarm\", clim=(-40, 40), clabel=\"Eastward (B_E) data-model residuals (nT)\",\n",
@@ -275,7 +275,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "_ds = ds.drop(\"Timestamp\").sel(NEC=\"E\")\n",
+    "_ds = ds.drop_vars(\"Timestamp\").sel(NEC=\"E\")\n",
     "_ds.hvplot.scatter(\n",
     "    x=\"MLT\", y=\"Latitude\", color=\"B_NEC_res_model\",\n",
     "    rasterize=True, colorbar=True, cmap=\"coolwarm\", clim=(-40, 40), clabel=\"Eastward (B_E) data-model residuals (nT)\",\n",
@@ -289,7 +289,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds.drop(\"Timestamp\").hvplot.scatter(x=\"Latitude\", y=\"B_NEC_res_model\", col=\"NEC\", rasterize=True, colorbar=False, cmap=\"bwr\")"
+    "ds.drop_vars(\"Timestamp\").hvplot.scatter(x=\"Latitude\", y=\"B_NEC_res_model\", col=\"NEC\", rasterize=True, colorbar=False, cmap=\"bwr\")"
    ]
   },
   {
@@ -350,7 +350,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,