Compare pre-night simulations with completed visits

Author: ehneilsen

notebooks/custom_plot.json

@@ -3,7 +3,7 @@
         "name": "Seeing",
         "settings": {
             "kind": "scatter",
-            "x": "start_date",
+            "x": "start_timestamp",
             "y": [
                 "seeingFwhmEff",
                 "seeingFwhm500",

notebooks/introduction.ipynb

@@ -486,7 +486,7 @@
     "The `visit` table is `pandas.DataFrame` with the following properties:\n",
     "\n",
     "- A unique index with the name `observationId` that increases monatonically with time.\n",
-    "- A `start_date` column with a `dtype` of `datetime64[ns, UTC]`. This allows the `bokeh` and other tools to recognize the column as a date and time, and treat it accordingly.\n",
+    "- A `start_timestamp` column with a `dtype` of `datetime64[ns, UTC]`. This allows the `bokeh` and other tools to recognize the column as a date and time, and treat it accordingly.\n",
     "- Columns as defined in the [FBS scheduler output schema](https://rubin-scheduler.lsst.io/fbs-output-schema.html). \n",
     "- Any number of additional columns, for example others from the FBS output schema, added by `maf` stackers, or from ConsDB.\n",
     "\n",
@@ -530,7 +530,7 @@
     "    ],\n",
     ")\n",
     "visits = pd.DataFrame(visits_recarray).set_index(\"observationId\")\n",
-    "visits[\"start_date\"] = pd.to_datetime(\n",
+    "visits[\"start_timestamp\"] = pd.to_datetime(\n",
     "    visits.observationStartMJD + 2400000.5, origin=\"julian\", unit=\"D\", utc=True\n",
     ")"
    ]

notebooks/prenight.ipynb

@@ -680,7 +680,10 @@
    "cell_type": "raw",
    "id": "73ea70fc-3b1a-4ce5-86c0-89150442983a",
    "metadata": {
-    "tags": []
+    "tags": [],
+    "vscode": {
+     "languageId": "raw"
+    }
    },
    "source": [
     "import pandas as pd\n",
@@ -691,7 +694,7 @@
     "schema_converter = SchemaConverter()\n",
     "visits = schema_converter.obs2opsim(observations)\n",
     "\n",
-    "visits[\"start_date\"] = pd.to_datetime(\n",
+    "visits[\"start_timestamp\"] = pd.to_datetime(\n",
     "    visits[\"observationStartMJD\"] + 2400000.5, origin=\"julian\", unit=\"D\", utc=True\n",
     ")"
    ]
@@ -700,10 +703,13 @@
    "cell_type": "raw",
    "id": "8518be8b-6aa7-4bc6-ab24-017c691d6c79",
    "metadata": {
-    "tags": []
+    "tags": [],
+    "vscode": {
+     "languageId": "raw"
+    }
    },
    "source": [
-    "ex = hvplot.explorer(visits, kind=\"scatter\", x=\"start_date\", y=\"airmass\", by=[\"note\"])\n",
+    "ex = hvplot.explorer(visits, kind=\"scatter\", x=\"start_timestamp\", y=\"airmass\", by=[\"note\"])\n",
     "ex"
    ]
   },

notebooks/prenight_matplotlib_extension.ipynb

@@ -156,11 +156,11 @@
    "source": [
     "def create_cumm_az_plot(visits):\n",
     "    fig, axes = plt.subplots(3, sharex=True, gridspec_kw={\"hspace\": 0})\n",
-    "    axes[0].plot(visits.start_date, visits.cummTelAz)\n",
+    "    axes[0].plot(visits.start_timestamp, visits.cummTelAz)\n",
     "    axes[0].set_ylabel(\"Cumulative az rot.\")\n",
-    "    axes[1].plot(visits.start_date, visits.azimuth)\n",
+    "    axes[1].plot(visits.start_timestamp, visits.azimuth)\n",
     "    axes[1].set_ylabel(\"Field Az\")\n",
-    "    axes[2].plot(visits.start_date, visits.slewTime)\n",
+    "    axes[2].plot(visits.start_timestamp, visits.slewTime)\n",
     "    axes[2].set_ylabel(\"Slew time\")\n",
     "    axes[2].xaxis.set_major_formatter(mpl.dates.DateFormatter(\"%H:%M\"))\n",
     "    axes[2].set_xlabel(\"Time (UTC)\")\n",

notebooks/prenight_multielement_extension.ipynb

@@ -195,7 +195,7 @@
     "        .groupby(note_block)\n",
     "        .agg(\n",
     "            {\n",
-    "                \"start_date\": \"min\",\n",
+    "                \"start_timestamp\": \"min\",\n",
     "                \"note\": \"first\",\n",
     "                \"observationId\": \"count\",\n",
     "                \"visitTime\": \"sum\",\n",
@@ -212,7 +212,7 @@
     "        \"observationId\": \"# visits\",\n",
     "        \"visitTime\": \"duration (s)\",\n",
     "        \"filter\": \"filters\",\n",
-    "        \"start_date\": \"start time (UTC)\",\n",
+    "        \"start_timestamp\": \"start time (UTC)\",\n",
     "        \"airmass\": \"max airmass\",\n",
     "        \"moonDistance\": \"min moon sep.\",\n",
     "        \"sunAlt\": \"max sun alt\",\n",