Update NB09 for w_40.

Author: jeffcarlin

09_Single_Star_Lightcurve_with_Butler.ipynb

@@ -8,7 +8,7 @@
     "<img align=\"left\" src = https://project.lsst.org/sites/default/files/Rubin-O-Logo_0.png width=250 style=\"padding: 10px\"> \n",
     "<br>\n",
     "<b>Single Star Lightcurve with the Butler</b> <br>\n",
-    "Last verified to run on 2021-08-16 with LSST Science Pipelines release w_2021_33 <br>\n",
+    "Last verified to run on 2021-10-13 with LSST Science Pipelines release w_2021_40 <br>\n",
     "Contact authors: Melissa Graham, Jeff Carlin <br>\n",
     "Target audience: All DP0 delegates. <br>\n",
     "Container Size: medium <br>\n",
@@ -72,7 +72,7 @@
     "import lsst.daf.base as dafBase\n",
     "\n",
     "### If you want to use the TAP Service in Section 2, uncomment.\n",
-    "# from rubin_jupyter_utils.lab.notebook import get_tap_service\n",
+    "# from lsst.rsp import get_tap_service\n",
     "# service = get_tap_service()\n",
     "\n",
     "### General python / astronomy packages\n",
@@ -87,6 +87,7 @@
     "from astropy.io import fits\n",
     "from astropy.timeseries import LombScargle\n",
     "\n",
+    "from tqdm.notebook import tqdm\n",
     "import time"
    ]
   },
@@ -384,8 +385,8 @@
     "\n",
     "# # To retrieve data for all `refs`, set N_refs to be equal to totalNrefs (or to a large value like 1000).\n",
     "# # Alternatively to test the retrieval of a few `refs`, set smaller (e.g., 10).\n",
-    "# # Nrefs = totalNrefs\n",
-    "# Nrefs = 5\n",
+    "# Nrefs = totalNrefs\n",
+    "# Nrefs = 15\n",
     "\n",
     "# # Instantiate empty lists\n",
     "# ra_arr = []\n",
@@ -397,7 +398,7 @@
     "# mjd_arr = []\n",
     "\n",
     "# # Loop over all refs\n",
-    "# for i, d in enumerate(refs):\n",
+    "# for i, d in enumerate(tqdm(refs)):\n",
     "#     t1 = time.time()\n",
     "\n",
     "#     if i <= Nrefs:\n",
@@ -762,6 +763,14 @@
     "#### Summary \n",
     "You have now seen how to extract and work with time series data for a variable star. As you've seen, this is rather clunky with the DP0.1 dataset and tools. Extracting individual measurements from visits will be much easier in planned data products (e.g., in DP0.2), where the forced photometry on each visit will be tabulated in its own type of table."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07cbca6e-e0d3-41a5-8dd1-543c2650d2b5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {