update deblender nb Fred comments

Author: christinawilliams

DP1/200_Data_Products/206_Deblender_Products/206_2_Deblender_Footprints.ipynb

@@ -829,11 +829,9 @@
    "id": "e5d82724-fbd0-47f7-8be0-057d806f09ab",
    "metadata": {},
    "source": [
-    "## 5. Analyzing data for our source\n",
+    "## 5. Analyzing a single object\n",
     "\n",
-    "### 5.1 Matching the source\n",
-    "\n",
-    "Recall that we began with an object located at a given RA and DEC and stored it's source ID. We now iterate over the blend models until we find the source with the same **objectId**."
+    "Individual objects can be inspected. Here, select the second brightest object in the blend (listed as Source 1 in the output in Section 4.1) and access its objectId."
    ]
   },
   {
@@ -843,10 +841,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for sidx, source in enumerate(blend.sources):\n",
-    "    if source.record_id == sid:\n",
-    "        break\n",
-    "print(f\"Selected source is at index {sidx}\")"
+    "object1 = blend.sources[1]\n",
+    "print(object1.record_id)\n"
    ]
   },
   {
@@ -856,7 +852,7 @@
    "source": [
     "### 5.2 Creating the flux re-distributed models\n",
     "\n",
-    "In the science pipelines measurements are not made on the scarlet models directly. Imperfections in the PSF measurement and deviations of bright galaxies from a simple two-component monotonic morphology solution (see Melchior paper cited above) result in improper measurements. Instead we perform a deblending algorithm based on the SDSS deblender, where we use the scarlet models as templates and re-distribute the flux from the input image based on the ratio of values for overlapping templates in an image."
+    "In the LSST science pipelines, measurements are not made on the scarlet models directly. Imperfections in the PSF measurement and deviations of bright galaxies from a simple two-component monotonic morphology solution (see Melchior paper cited above) result in improper measurements. THIS STATEMENT TBD: Instead, a deblending algorithm based on the SDSS deblender is employed, using the scarlet models as templates, and flux is re-distributed from the input image based on the ratio of values for overlapping templates in an image."
    ]
   },
   {
@@ -876,10 +872,47 @@
    "source": [
     "### 5.3 Display the source model\n",
     "\n",
-    "We now view the three different models for the source:\n",
-    "- The deconvolved scarlet model\n",
-    "- The convolution of the scarlet model with the difference kernel\n",
-    "- The flux-redistributed model that the science pipelines use for measurement"
+    "Three different models for the object are available for inspection. 1) The deconvolved scarlet model; 2) the convolution of the scarlet model with the difference kernel; and 3) the flux-redistributed model that the science pipelines use for measurement. The section demonstrates how to plot these three different models for source 1 in the blend.\n",
+    "\n",
+    "First, extract the deconvolved model from the object. The output is a Scarlet Lite image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da188ade-30ce-4f85-a55a-e001dabb67e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source_model = object1.get_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04ecbf7d-6039-49e2-ad8c-848f911d4817",
+   "metadata": {},
+   "source": [
+    "Next, project the model into a box large enough to fit the convolved model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0be3f3d-c6f8-4238-8e19-0a0b16aebf74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "psf_radius = int((np.max(blend.observation.psfs.shape)+1)//2)\n",
+    "enlarged_box = source_model.bbox.grow(psf_radius)\n",
+    "convolved_model = source_model.copy().project(bbox=enlarged_box)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "747da137-5b5d-499d-b828-4e102b81c6ac",
+   "metadata": {},
+   "source": [
+    "Finally, plot the three models for the object for inspection."
    ]
   },
   {
@@ -889,33 +922,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Extract the deconvovled model from the source (a scarlet lite Image)\n",
-    "source_model = source.get_model()\n",
-    "\n",
     "fig, ax = plt.subplots(1, 3, figsize=(15, 5))\n",
     "\n",
     "# Display the deconvolved model\n",
-    "extent = scl.display.get_extent(source.bbox)\n",
-    "rgb = scl.display.img_to_rgb(source_model[display_bands], norm=norm)\n",
+    "extent = sl.display.get_extent(object1.bbox)\n",
+    "rgb = sl.display.img_to_rgb(source_model[display_bands], norm=norm)\n",
     "ax[0].imshow(rgb, origin='lower', extent=extent)\n",
     "ax[0].set_title(\"Deconvolved Model\")\n",
     "\n",
-    "# Project the model into a box large enough to fit the convolved model\n",
-    "psf_radius = int((np.max(blend.observation.psfs.shape)+1)//2)\n",
-    "enlarged_box = source_model.bbox.grow(psf_radius)\n",
-    "convolved_model = source_model.copy().project(bbox=enlarged_box)\n",
-    "\n",
     "# Convolve the model with the difference kernel\n",
     "convolved_model = blend.observation.convolve(convolved_model)\n",
-    "extent = scl.display.get_extent(enlarged_box)\n",
-    "rgb = scl.display.img_to_rgb(convolved_model[display_bands], norm=norm)\n",
+    "extent = sl.display.get_extent(enlarged_box)\n",
+    "rgb = sl.display.img_to_rgb(convolved_model[display_bands], norm=norm)\n",
     "ax[1].imshow(rgb, origin='lower', extent=extent)\n",
     "ax[1].set_title(\"Convolved Model\")\n",
     "\n",
     "# Display the flux-weighted image\n",
-    "flux_model = source.flux_weighted_image\n",
-    "extent = scl.display.get_extent(flux_model.bbox)\n",
-    "rgb = scl.display.img_to_rgb(flux_model[display_bands], norm=norm)\n",
+    "flux_model = object1.flux_weighted_image\n",
+    "extent = sl.display.get_extent(flux_model.bbox)\n",
+    "rgb = sl.display.img_to_rgb(flux_model[display_bands], norm=norm)\n",
     "ax[2].imshow(rgb, origin='lower', extent=extent)\n",
     "ax[2].set_title(\"Flux Re-distributed Model\")\n",
     "\n",