updating nb 9

Author: christinawilliams

DP1/100_How_to_Use_RSP_Tools/103_Image_access_and_display/103_9_Image_stamps.ipynb

@@ -365,8 +365,6 @@
     "            \"hspace\": 0})\n",
     "\n",
     "    axes = axes.flatten()\n",
-    "    #fig, axes = plt.subplots(n_rows, n_cols, figsize=figsize)\n",
-    "    #axes = axes.flatten() if n_subplots > 1 else [axes]\n",
     "\n",
     "    for ax in axes:\n",
     "        ax.axis(\"off\")\n",
@@ -456,12 +454,44 @@
     "print(ra, dec)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "823d68a9-210e-4201-99b0-b520be6fe80f",
+   "metadata": {},
+   "source": [
+    "The cell below will perform a query to make sure the images retrieved span the time during which the DIAObject was changing and thus detectable as a DIASource. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15af811d-db19-4592-ad04-cde33486b11f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dia_query = f\"\"\"\n",
+    "SELECT MIN(midpointMjdTai) as first_detect_time,\n",
+    "       MAX(midpointMjdTai) as last_detect_time\n",
+    "FROM dp1.DiaSource\n",
+    "WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec), \n",
+    "               CIRCLE('ICRS', {ra}, {dec}, 0.001)) = 1\n",
+    "\"\"\"\n",
+    "\n",
+    "dia_job = service.submit_job(dia_query)\n",
+    "dia_job.run()\n",
+    "dia_job.wait(phases=['COMPLETED', 'ERROR'])\n",
+    "dia_results = dia_job.fetch_result().to_table()\n",
+    "\n",
+    "first_detect = dia_results['first_detect_time'][0]\n",
+    "last_detect = dia_results['last_detect_time'][0]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "e49e571a-c652-4e7b-9a06-81fd8a17e6d1",
    "metadata": {},
    "source": [
-    "Use the SIA service to search for calibration level 2 images (`visit_images`) that overlap the search `circle`. Filter the results for r-band `visit_images` and sort them by observation time."
+    "Use the SIA service to search for calibration level 2 images (`visit_images`) that overlap the search `circle`. "
    ]
   },
   {
@@ -473,10 +503,40 @@
    "source": [
     "results_sci = sia_service.search(pos=circle, calib_level=2)\n",
     "\n",
-    "lvl2_table = results_sci.to_table()\n",
+    "lvl2_table = results_sci.to_table()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e95d41db-dc67-4d75-b1cd-911fef74c112",
+   "metadata": {},
+   "source": [
+    "Filter the results for r-band `visit_images`, and to only return the visit images during which there was a DIA detection. The `if` statement ensures that a DIA detection was identified for that object. Lastly, sort the query results by observation time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47c923a8-8001-4ad6-9474-6dd31d2b683a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if (not np.ma.is_masked(first_detect) and first_detect is not None and\n",
+    "    not np.ma.is_masked(last_detect) and last_detect is not None):\n",
+    "\n",
+    "    tx = np.where(\n",
+    "        (lvl2_table['dataproduct_subtype'] == 'lsst.visit_image')\n",
+    "        & (lvl2_table['lsst_band'] == 'r')\n",
+    "        & (lvl2_table['t_min'] >= first_detect)\n",
+    "        & (lvl2_table['t_min'] <= last_detect)\n",
+    "    )[0]\n",
+    "\n",
+    "    print(f\"Found {len(tx)} visits taken between MJD {first_detect: .2f} and {last_detect: .2f}.\")\n",
+    "\n",
+    "else:\n",
+    "    print(\"No DIA sources were ever detected in this region. Returning empty table.\")\n",
+    "    tx = []\n",
     "\n",
-    "tx = np.where((lvl2_table['dataproduct_subtype'] == 'lsst.visit_image')\n",
-    "              & (lvl2_table['lsst_band'] == 'r'))[0]\n",
     "scitab = lvl2_table[tx]\n",
     "scitab.sort('t_max')"
    ]
@@ -527,7 +587,7 @@
    "source": [
     "print('begin date:', scitab['t_max'][0], difftab['t_max'][0])\n",
     "print('end date:', scitab['t_max'][len(scitab['t_max'])-1])\n",
-    "print('number of visits:', len(scitab['t_max']))"
+    "print('number of visits:', len(scitab['t_max']))\n"
    ]
   },
   {
@@ -724,7 +784,7 @@
    "source": [
     "## 3. Bulk image cutouts\n",
     "\n",
-    "Bulk image cutouts will be a key visualization tool for DIA objects. Below, demonstrate how to retrieve many image cutouts (and save as png files)."
+    "Bulk image cutouts will be a key visualization tool for DIA objects. Below, demonstrate how to retrieve many image cutouts (and save as png files). Since many visits span the time during which the target was a DIA source, limit the number to visualize to the first 20 (to save time). The full set of cutouts can be visualized by replacing the first line with `num_visits = len(scitab['t_max'])`"
    ]
   },
   {
@@ -734,7 +794,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "num_visits = 20 #len(scitab['t_max'])\n",
+    "num_visits = 20\n",
     "print('number of visits:', num_visits)\n",
     "for i in range(num_visits):\n",
     "    plt.title(f\"MJD {np.round(scitab['t_max'][i], 4)}\")\n",
@@ -803,7 +863,7 @@
    "source": [
     "print(tempdir)\n",
     "make_gif(tempdir)\n",
-    "display(dimg(data=open(tempdir+'/animation.gif', 'rb').read()))"
+    "dimg(data=open(tempdir+'/animation.gif', 'rb').read())"
    ]
   },
   {
@@ -813,32 +873,6 @@
    "source": [
     "> **Figure 4**: A gif animation generated from the reproject visit images in Figure 3."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "172dc7bb-67ee-49f9-bee1-a6f50ad96c89",
-   "metadata": {},
-   "source": [
-    "## 4. Future functionality\n",
-    "\n",
-    "when rotate, don't do grid in final version, then get to a releasable version if you explain that the wcs rotate and just produce a rotated image (png, no wcs info) rotated visit image stamp that you can make a png image out of. Say a few things about how we're working on how to overplot grid lines and wcs functionality. this gets us to a publishable thing "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a8eb043b-5afe-4990-9050-bd059aaf5abf",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "\n",
-    "only for epochs where it was detected in visit images. users can extrapolate to get all images. \n",
-    "\n",
-    "show how to do smaller more constrained part (i.e timeline over which visits are retrieved; where a dia ). \n",
-    "\n",
-    "\n",
-    "start with an ra/dec and date range where you already know. don't even need to refer to the other notebook\n"
-   ]
   }
  ],
  "metadata": {