sia with dpsubtype

Author: MelissaGraham

DP1/100_How_to_Use_RSP_Tools/103_Image_access_and_display/103_2_Simple_Image_Access_SIA.ipynb

@@ -331,7 +331,7 @@
    "source": [
     "# coord = SkyCoord(target_ra, target_dec, unit=\"deg\")\n",
     "# coord_circle = (coord, 0.05)\n",
-    "# results = sia_service.search(pos=coord_circle, maxrec=3)\n",
+    "# results = sia_service.search(pos=coord_circle, maxrec=use_maxrec)\n",
     "# results.to_table()"
    ]
   },
@@ -361,7 +361,7 @@
    "outputs": [],
    "source": [
     "# bounds = (target_ra-0.05, target_ra+0.05, target_dec-0.05, target_dec+0.05)\n",
-    "# results = sia_service.search(pos=bounds, maxrec=3)\n",
+    "# results = sia_service.search(pos=bounds, maxrec=use_maxrec)\n",
     "# results.to_table()"
    ]
   },
@@ -394,7 +394,7 @@
     "#            target_ra+0.05, target_dec-0.05,\n",
     "#            target_ra+0.05, target_dec+0.05,\n",
     "#            target_ra-0.05, target_dec+0.05)\n",
-    "# results = sia_service.search(pos=polygon, maxrec=3)\n",
+    "# results = sia_service.search(pos=polygon, maxrec=use_maxrec)\n",
     "# results.to_table()"
    ]
   },
@@ -421,9 +421,10 @@
    "id": "d04e2397-b075-46d9-bdff-80e0ec0a7321",
    "metadata": {},
    "source": [
-    "Query by image type, which in the SIA2 service is the calibration level (`calib_level`).\n",
+    "Query by image type, which in the SIA2 service is specified by the calibration level (`calib_level`)\n",
+    "and by the data product subtype (`dpsubtype`).\n",
     "\n",
-    "Calibration levels are:\n",
+    "Calibration levels and data product subtypes names are:\n",
     " - 1 : `raw`\n",
     " - 2 : `visit_image`\n",
     " - 3 : `deep_coadd`, `template_coadd`, `difference_image`"
@@ -442,163 +443,146 @@
    "id": "4361b7b0-cee1-44ba-96dd-1829f4b10fa3",
    "metadata": {},
    "source": [
-    "Query for `visit_images` by setting `calib_level=2`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "11fe7623-125e-4afc-94f6-4246e2844662",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = sia_service.search(pos=circle, calib_level=2, maxrec=5)\n",
-    "results.to_table()"
+    "Query for `visit_images` by setting `calib_level=2` *OR* `dpsubtype='lsst.visit_image'`,\n",
+    "since there is only one dataproduct subtype (`visit_image`) for calibration level 2.\n",
+    "\n",
+    "Return the results as Astropy tables using `.to_table()`.\n",
+    "\n",
+    "Set `calib_level`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "769206dd-3fcb-475c-98bc-190258d9901d",
+   "id": "00b558f2-90e8-4266-aa64-65237e462002",
    "metadata": {},
    "outputs": [],
    "source": [
-    "del results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7e872f8f-0251-4a6d-ac0b-6952c98deaf1",
-   "metadata": {},
-   "source": [
-    "#### 3.2.2. Deep coadd images"
+    "results = sia_service.search(pos=circle, calib_level=2).to_table()\n",
+    "print(len(results))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "08b71293-d3ab-49ca-9a95-2566488eec33",
+   "id": "76e5918b-0321-4782-a36f-2a5de4ee03fb",
    "metadata": {},
    "source": [
-    "Query for `deep_coadd` images by setting `calib_level=3`.\n",
-    "\n",
-    "This will also return the `template_coadd` images and `difference_images`."
+    "Option to redo the query by setting `dpsubtype` to confirm they return the same number of visit images."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f597b4e1-f377-46fd-83cd-2ee6ae5e45bc",
+   "id": "11fe7623-125e-4afc-94f6-4246e2844662",
    "metadata": {},
    "outputs": [],
    "source": [
-    "results = sia_service.search(pos=circle, calib_level=3)\n",
-    "print(len(results))"
+    "# results = sia_service.search(pos=circle, dpsubtype='lsst.visit_image').to_table()\n",
+    "# print(len(results))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8f62bb47-7a7c-49cd-969a-6d60b5488abe",
+   "id": "4c126fe2-8bdb-4888-97e6-12ee4ae5dbaa",
    "metadata": {},
    "source": [
-    "Convert the `results` to an `astropy` `table`."
+    "Option to show the results table, which will be displayed as truncated."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ef4601bd-0936-4a32-b3d9-14a26fe99218",
+   "id": "2475d930-0896-4751-b38a-cb8e552f4776",
    "metadata": {},
    "outputs": [],
    "source": [
-    "table = results.to_table()"
+    "# results"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "9239fea9-0dc7-4586-b1bb-54b2952f4f30",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "769206dd-3fcb-475c-98bc-190258d9901d",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Option to show the full table of results."
+    "del results"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cc57c45c-2c02-447b-bd64-19979959bff2",
+   "cell_type": "markdown",
+   "id": "7e872f8f-0251-4a6d-ac0b-6952c98deaf1",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# table"
+    "#### 3.2.2. Deep coadd images"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fde6bcb0-48c2-4c6b-a476-30ec6be2adde",
+   "id": "08b71293-d3ab-49ca-9a95-2566488eec33",
    "metadata": {},
    "source": [
-    "Print the number of `deep_coadd`, `template_coadd`, and `difference_images` that were returned."
+    "Query for `deep_coadd` images by setting `calib_level=3` and `dpsubtype='lsst.deep_coadd'`.\n",
+    "\n",
+    "Do not pass `use_maxrec` here, retrieve all the `deep_coadd` images that match the search constraints."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "da7aa11c-93f2-43bd-9014-b3edc07b34de",
+   "id": "f597b4e1-f377-46fd-83cd-2ee6ae5e45bc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "values, counts = np.unique(table['dataproduct_subtype'],\n",
-    "                           return_counts=True)\n",
-    "for value, count in zip(values, counts):\n",
-    "    print(value, count)"
+    "results = sia_service.search(pos=circle, calib_level=3, dpsubtype='lsst.deep_coadd').to_table()\n",
+    "print(len(results))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5a3bc533-3865-4d63-8593-9f588b19c810",
+   "id": "9239fea9-0dc7-4586-b1bb-54b2952f4f30",
    "metadata": {},
    "source": [
-    "Subset the resulting images that are deep coadds in the r-band."
+    "Option to show the full table of results."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "62654699-3b7d-4864-9f0e-a854d969d2e3",
+   "id": "cc57c45c-2c02-447b-bd64-19979959bff2",
    "metadata": {},
    "outputs": [],
    "source": [
-    "tx = np.where((table['dataproduct_subtype'] == 'lsst.deep_coadd')\n",
-    "              & (table['lsst_band'] == 'r'))[0]\n",
-    "print(len(tx))"
+    "# results"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d34c068c-2191-488c-9ef3-deb6d5de043c",
+   "id": "fde6bcb0-48c2-4c6b-a476-30ec6be2adde",
    "metadata": {},
    "source": [
-    "All `deep_coadd` images overlap at the edges.\n",
-    "The fact that there are two r-band `deep_coadd` images means the target coordinates used are near the image edges and in the overlap region.\n",
-    "\n",
-    "Print the table of the r-band `deep_coadd` images only."
+    "Print the number of unique values of `lsst_band` (filters)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "44c100ac-3296-4846-ae0c-8b39051679b6",
+   "id": "da7aa11c-93f2-43bd-9014-b3edc07b34de",
    "metadata": {},
    "outputs": [],
    "source": [
-    "table[tx]"
+    "values, counts = np.unique(results['lsst_band'],\n",
+    "                           return_counts=True)\n",
+    "for value, count in zip(values, counts):\n",
+    "    print(value, count)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "197a2b2a-cf18-4b40-ba7f-ec9300116a18",
+   "id": "5a3bc533-3865-4d63-8593-9f588b19c810",
    "metadata": {},
    "source": [
-    "Keep only the `table` to use in the next sections."
+    "There are 2 overlapping patches for the search coordinates, and thus 2 `deep_coadd` images for each of the six filters."
    ]
   },
   {
@@ -608,7 +592,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "del results, tx"
+    "del results, values, counts"
    ]
   },
   {
@@ -618,7 +602,9 @@
    "source": [
     "#### 3.2.3. Template coadd images\n",
     "\n",
-    "Use the same initial query as in Section 3.2.2, but then subset only to the results with `dataproduct_subtype` equal to `lsst.template_coadd`."
+    "Query for `template_coadd` images by setting `calib_level=3` and `dpsubtype='lsst.template_coadd'`.\n",
+    "\n",
+    "Since the `template_coadd` images are also stored by patch, there are also 12 that match the query constraints."
    ]
   },
   {
@@ -628,27 +614,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tx = np.where((table['dataproduct_subtype'] == 'lsst.template_coadd')\n",
-    "              & (table['lsst_band'] == 'r'))[0]\n",
-    "print(len(tx))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "653f6e5e-af41-4207-a5e2-4dce8ea98ade",
-   "metadata": {},
-   "source": [
-    "Option to display the subsetted table."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ef5f1c7a-c8af-44a1-b352-5399ff92a111",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# table[tx]"
+    "results = sia_service.search(pos=circle, calib_level=3, dpsubtype='lsst.template_coadd')\n",
+    "print(len(results))"
    ]
   },
   {
@@ -658,7 +625,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "del tx"
+    "del results"
    ]
   },
   {
@@ -668,7 +635,9 @@
    "source": [
     "#### 3.2.4. Difference images\n",
     "\n",
-    "Use the same initial query as in Section 3.2.2, but then subset only to the results with `dataproduct_subtype` equal to `lsst.difference_image`."
+    "Query for `difference_images` by setting `calib_level=3` and `dpsubtype='lsst.difference_image'`.\n",
+    "\n",
+    "There will be as many difference images returned as there are `visit_images`."
    ]
   },
   {
@@ -678,27 +647,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tx = np.where((table['dataproduct_subtype'] == 'lsst.difference_image')\n",
-    "              & (table['lsst_band'] == 'r'))[0]\n",
-    "print(len(tx))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "160d484b-b1be-4303-9ef7-e5d89f265b25",
-   "metadata": {},
-   "source": [
-    "Option to display the subsetted table."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "81e0ac68-f7b4-4155-8386-74c5ffdee8cd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# table[tx]"
+    "results = sia_service.search(pos=circle, calib_level=3, dpsubtype='lsst.difference_image')\n",
+    "print(len(results))"
    ]
   },
   {
@@ -708,7 +658,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "del table, tx"
+    "del results"
    ]
   },
   {