Updates for VRE 1.0 (#26)

* Minor fixes for pandas & xarray updates * Use VRE image 1.0.0 * Fix notebook cleanup * Use dev version of hapiplot * Add package versions watermarks * Patch dev version of pyswipe; disable running swarmface * Use VRE 1.0.1
Swarm-DISC · Jan 23, 2024 · b79566d · b79566d
1 parent 236ba99
commit b79566d
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diff --git a/.github/workflows/deploy-staging.yml b/.github/workflows/deploy-staging.yml
@@ -5,7 +5,7 @@ on:
   workflow_dispatch:
 
 env:
-  DOCKER_IMAGE: 'registry.gitlab.eox.at/esa/vires_vre_ops/vre-swarm-notebook:0.10.17'
+  DOCKER_IMAGE: 'registry.gitlab.eox.at/esa/vires_vre_ops/vre-swarm-notebook:1.0.1'
 
 jobs:
   pre-commit:

diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml
@@ -5,7 +5,7 @@ on:
   workflow_dispatch:
 
 env:
-  DOCKER_IMAGE: 'registry.gitlab.eox.at/esa/vires_vre_ops/vre-swarm-notebook:0.10.17'
+  DOCKER_IMAGE: 'registry.gitlab.eox.at/esa/vires_vre_ops/vre-swarm-notebook:1.0.1'
 
 jobs:
   pre-commit:

diff --git a/notebooks/01b1_Pandas-and-Plots.ipynb b/notebooks/01b1_Pandas-and-Plots.ipynb
@@ -196,7 +196,7 @@
    "outputs": [],
    "source": [
     "# Generate some sample times at hourly intervals over a month\n",
-    "df[\"time\"] = pd.date_range(\"2020-01-01\", \"2020-02-01\", periods=745, closed=\"left\")\n",
+    "df[\"time\"] = pd.date_range(\"2020-01-01\", \"2020-02-01\", periods=745, inclusive=\"left\")\n",
     "df = df.set_index(\"time\")\n",
     "df"
    ]

diff --git a/notebooks/03e1_Demo-FACxTMS_2F.ipynb b/notebooks/03e1_Demo-FACxTMS_2F.ipynb
@@ -235,7 +235,7 @@
     "    # Extract times from the lower x axis\n",
     "    # Use them to find the nearest Lat values in the dataframe\n",
     "    xtick_times = [dt.datetime.strptime(ts.get_text(), datetime_format) for ts in ax.get_xticklabels()]\n",
-    "    ilocs = [df.index.get_loc(t, method=\"nearest\") for t in xtick_times]\n",
+    "    ilocs = [df.index.get_indexer([t], method=\"nearest\")[0] for t in xtick_times]\n",
     "    lats = df.iloc[ilocs][\"Latitude\"]\n",
     "    lat_labels = [\"{}°\".format(s) for s in np.round(lats.values, decimals=1)]\n",
     "    ax2.set_xticklabels(lat_labels)\n",

diff --git a/notebooks/03i1_Demo-VOBS.ipynb b/notebooks/03i1_Demo-VOBS.ipynb
@@ -336,9 +336,8 @@
     "            _ds[var] = _ds[var].roll({\"NEC\": 1}, roll_coords=False)\n",
     "            _ds[var].attrs = ds[var].attrs\n",
     "    # Rename NEC dims & coords to RTP\n",
-    "    _ds = _ds.assign_coords({\"NEC\": [\"Radial\", \"Theta\", \"Phi\"]})\n",
-    "    _ds = _ds.rename_dims({\"NEC\": \"RTP\"}).rename({\"NEC\": \"RTP\"})\n",
-    "    _ds = _ds.set_index({\"RTP\": \"RTP\"}).set_coords(\"RTP\")\n",
+    "    _ds = _ds.rename({\"NEC\": \"RTP\"})\n",
+    "    _ds = _ds.assign_coords({\"RTP\": [\"Radial\", \"Theta\", \"Phi\"]})\n",
     "    _ds[\"RTP\"].attrs = {\n",
     "        \"units\": \"\",\n",
     "        \"description\": \"RTP frame - Radial, Theta, Phi [R,T,P] = [-C,-N,E]\"\n",
@@ -359,7 +358,7 @@
     "    )\n",
     "    data = request.get_between(\n",
     "        dt.datetime(1999, 1, 1),\n",
-    "        dt.datetime.now(),\n",
+    "        dt.datetime(2024, 1, 1),\n",
     "        asynchronous=False, show_progress=False\n",
     "    )\n",
     "    ds = data.as_xarray(reshape=reshape)\n",
@@ -423,7 +422,7 @@
    "source": [
     "rad = {}\n",
     "for mission in (\"Orsted\", \"CHAMP\", \"Cryosat\", \"Swarm\", \"Composite\"):\n",
-    "    rad[mission] = int(ALL_VOBS[f\"{mission}_4M\"][\"Radius\"].values[0]/1e3)\n",
+    "    rad[mission] = int(ALL_VOBS[f\"{mission}_4M\"][\"Radius\"][0]/1e3)\n",
     "\n",
     "plt.figure(figsize=(10,3))\n",
     "for mission in (\"Orsted\", \"CHAMP\", \"Cryosat\", \"Swarm\", \"Composite\"):\n",
@@ -463,34 +462,34 @@
     "            _ds_1m = ds_1m.sel(RTP=rtp)\n",
     "            # Observed field\n",
     "            axes[i, 0].errorbar(\n",
-    "                _ds_1m[\"Timestamp\"].values, _ds_1m[\"B_OB\"].values, _ds_1m[\"sigma_OB\"].values,\n",
+    "                _ds_1m[\"Timestamp\"].values, _ds_1m[\"B_OB\"].values, np.abs(_ds_1m[\"sigma_OB\"].values),\n",
     "                fmt=\".\", label=\"1M\"\n",
     "            )\n",
     "            # Core field\n",
     "            axes[i, 1].errorbar(\n",
-    "                _ds_1m[\"Timestamp\"].values, _ds_1m[\"B_CF\"].values, _ds_1m[\"sigma_CF\"].values,\n",
+    "                _ds_1m[\"Timestamp\"].values, _ds_1m[\"B_CF\"].values, np.abs(_ds_1m[\"sigma_CF\"].values),\n",
     "                fmt=\".\",\n",
     "            )\n",
     "            # Secular variation (of core field)\n",
     "            axes[i, 2].errorbar(\n",
-    "                _ds_1m[\"Timestamp_SV\"].values, _ds_1m[\"B_SV\"].values, _ds_1m[\"sigma_SV\"].values,\n",
+    "                _ds_1m[\"Timestamp_SV\"].values, _ds_1m[\"B_SV\"].values, np.abs(_ds_1m[\"sigma_SV\"].values),\n",
     "                fmt=\".\",\n",
     "            )\n",
     "        _ds_4m = ds_4m.sel(RTP=rtp)\n",
     "        # Observed field\n",
     "        axes[i, 0].errorbar(\n",
-    "            _ds_4m[\"Timestamp\"].values, _ds_4m[\"B_OB\"].values, _ds_4m[\"sigma_OB\"].values,\n",
+    "            _ds_4m[\"Timestamp\"].values, _ds_4m[\"B_OB\"].values, np.abs(_ds_4m[\"sigma_OB\"].values),\n",
     "            fmt=\".\", label=\"4M\"\n",
     "        )\n",
     "        # Core field\n",
     "        axes[i, 1].errorbar(\n",
-    "            _ds_4m[\"Timestamp\"].values, _ds_4m[\"B_CF\"].values, _ds_4m[\"sigma_CF\"].values,\n",
+    "            _ds_4m[\"Timestamp\"].values, _ds_4m[\"B_CF\"].values, np.abs(_ds_4m[\"sigma_CF\"].values),\n",
     "            fmt=\".\",\n",
     "        )\n",
     "        axes[i, 1].set_ylim(axes[i, 0].get_ylim())\n",
     "        # Secular variation (of core field)\n",
     "        axes[i, 2].errorbar(\n",
-    "            _ds_4m[\"Timestamp_SV\"].values, _ds_4m[\"B_SV\"].values, _ds_4m[\"sigma_SV\"].values,\n",
+    "            _ds_4m[\"Timestamp_SV\"].values, _ds_4m[\"B_SV\"].values, np.abs(_ds_4m[\"sigma_SV\"].values),\n",
     "            fmt=\".\",\n",
     "        )\n",
     "    axes[0, 0].set_ylabel(\"Radial\\n[nT]\")\n",

diff --git a/notebooks/03z1_External-Data.ipynb b/notebooks/03z1_External-Data.ipynb
@@ -100,43 +100,28 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install \"hapiplot==0.2.0\" --quiet\n",
+    "!pip install \"git+https://github.com/hapi-server/plot-python@f95ce860d456dd9278a62897d6f83d416c988273\" --quiet\n",
     "\n",
     "from hapiplot import hapiplot"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4aeea673",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hapiplot(data, meta);"
-   ]
-  },
   {
    "cell_type": "markdown",
-   "id": "0e2e6ec5",
+   "id": "1119a42c-1733-49be-beb4-2370637fe0b0",
    "metadata": {},
    "source": [
-    "## Other packages (TODO)\n",
-    "\n",
-    "- pysat\n",
-    "- spacepy\n",
-    "- mangopy\n",
-    "- madrigalweb\n",
-    "- pyaurorax\n",
-    "- ??? (any suggestions? contact [email protected])"
+    "(NB: We are using a more recent development version of hapiplot, rather than what is on PyPI)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e60071a3",
+   "id": "4aeea673",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "hapiplot(data, meta);"
+   ]
   }
  ],
  "metadata": {

diff --git a/notebooks/07a1_SW-chaosmagpy.ipynb b/notebooks/07a1_SW-chaosmagpy.ipynb
@@ -24,6 +24,16 @@
     "Finlay, C.C., Kloss, C., Olsen, N., Hammer, M. Toeffner-Clausen, L., Grayver, A and Kuvshinov, A. (2020), The CHAOS-7 geomagnetic field model and observed changes in the South Atlantic Anomaly, Earth Planets and Space 72, doi:10.1186/s40623-020-01252-9"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext watermark\n",
+    "%watermark -i -v -p chaosmagpy,numpy,matplotlib"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -149,5 +159,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/notebooks/07b1_SW-pyamps.ipynb b/notebooks/07b1_SW-pyamps.ipynb
@@ -24,6 +24,16 @@
     "Laundal, K. M., Finlay, C. C., Olsen, N. & Reistad, J. P. (2018), Solar wind and seasonal influence on ionospheric currents from Swarm and CHAMP measurements, Journal of Geophysical Research - Space Physics. doi:10.1029/2018JA025387"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext watermark\n",
+    "%watermark -i -v -p pyamps,numpy,matplotlib"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -77,5 +87,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/notebooks/07c1_SW-pyswipe.ipynb b/notebooks/07c1_SW-pyswipe.ipynb
@@ -21,6 +21,26 @@
     "[Code](https://github.com/Dartspacephysiker/pyswipe) "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Temporarily patch in fixed version\n",
+    "!pip install --quiet git+https://github.com/smithara/pyswipe/@hotfix-numpy-1.25"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext watermark\n",
+    "%watermark -i -v -p pyswipe,numpy,matplotlib"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -537,5 +557,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/notebooks/07e1_SW-swarmface.ipynb b/notebooks/07e1_SW-swarmface.ipynb
@@ -27,6 +27,16 @@
     "Blagau A and Vogt J (2023) SwarmFACE: A Python package for field-aligned currents exploration with Swarm. Front. Astron. Space Sci. 9:1077845. doi: 10.3389/fspas.2022.1077845"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext watermark\n",
+    "%watermark -i -v -p SwarmFACE,viresclient,numpy,pandas,matplotlib"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -48,79 +58,30 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
+    "The package is currently awaiting updates to make it work here.\n",
+    "\n",
+    "Example usage:\n",
+    "\n",
+    "```python\n",
     "# Settings\n",
     "dtime_beg = '2014-05-04T17:48:00'\n",
     "dtime_end = '2014-05-04T17:54:00'\n",
     "sat = ['C']\n",
     "res = 'LR'\n",
     "N3d=[0.2276, 0.6924, 0.6847]\n",
-    "tincl = ['2014-05-04 17:49:50', '2014-05-04 17:52:44']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "tincl = ['2014-05-04 17:49:50', '2014-05-04 17:52:44']\n",
+    "\n",
     "# Fetch data and apply algorithm using above settings\n",
     "j_df, input_df, param = j1sat(\n",
     "    dtime_beg, dtime_end, sat, res=res,\n",
     "    N3d=N3d, tincl=tincl\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The inputs are stored within `input_df`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "input_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "... and the newly calculated FAC current in `j_df`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "j_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "... with some extra metadata in `param`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "param"
+    ")\n",
+    "```\n",
+    "\n",
+    "The inputs are stored within `input_df`, and the newly calculated FAC current in `j_df` with some extra metadata in `param`."
    ]
   }
  ],
@@ -148,5 +109,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }