GitHub Issue #5 Addressed

Author: MelissaGraham

02_Intermediate_TAP_Query.ipynb

@@ -99,7 +99,7 @@
     "\n",
     "The TAP service uses a query language similar to SQL (Structured Query Langage) called ADQL (Astronomical Data Query Language). The <a href=\"http://www.ivoa.net/documents/latest/ADQL.html\">documentation for ADQL</a> includes more information about syntax and keywords.\n",
     "\n",
-    "**Hazard Warning:** Not all ADQL functionality are supported yet in the DP0 RSP."
+    "**Hazard Warning:** Not all ADQL functionality is supported yet in the DP0 RSP."
    ]
   },
   {
@@ -111,7 +111,7 @@
     "# Import the Rubin TAP service utilities \n",
     "from rubin_jupyter_utils.lab.notebook import get_tap_service, retrieve_query    \n",
     "\n",
-    "# Get an instance the TAP service \n",
+    "# Get an instance of the TAP service \n",
     "service = get_tap_service()\n",
     "assert service is not None"
    ]
@@ -217,7 +217,7 @@
     "\n",
     "The Object catalog (dp01_dc2_catalogs.object) contains sources detected in the coadded images (also called stacked or combined images). The Object catalog is likely to be the catalog that is of the most interest to DP0 delgates. \n",
     "\n",
-    "The `object` catalog is described in the <a href=\"https://arxiv.org/abs/2101.04855\">DESC's DC2 data release note</a>, and more information about the simulated data in the <a href=\"https://ui.adsabs.harvard.edu/abs/2021ApJS..253...31L/abstract\">DESC's DC2 paper</a>. "
+    "The `object` catalog is described in the <a href=\"https://arxiv.org/abs/2101.04855\">DESC's DC2 data release note</a>, and more information about the simulated data can be found in the <a href=\"https://ui.adsabs.harvard.edu/abs/2021ApJS..253...31L/abstract\">DESC's DC2 paper</a>. "
    ]
   },
   {
@@ -286,7 +286,7 @@
    "source": [
     "#### 2.2 Cone search around a point with specified radius\n",
     "\n",
-    "We will execute a cone search centered on (RA, Dec) = (61.863, -35.79) with a radius of 20 arcseconds and applying a cut on magnitude. The following illustrates a selection from the object table in a circular region of 0.1 degree radius  degrees and join the object table with the truth table. We expect to get 15,670 results."
+    "We will execute a cone search centered on (RA, Dec) = (61.863, -35.79) with a radius of 20 arcseconds and applying a cut on magnitude. The following illustrates a selection from the object table in a circular region of 0.1 degrees. In the next section additional constraints are imposed on the selection by joining the object table with the truth table. We expect to get 15,670 results."
    ]
   },
   {
@@ -337,7 +337,7 @@
    "source": [
     "#### 2.3. Cone search joining the results with the truth infomation \n",
     "\n",
-    "We will now join the results from the above query with the truth information.  We will aslo add in some quality filters on the match. Objects in the truth-match table that do not have matches in the object table have “match_objectId = -1,” while those with legitimate matches contain the ‘objectId’ of the corresponding object from the object table in “match_objectId.” By requiring this to be greater than or equal to zero, we extract only objects with matches. We also keep only sources satisfying the “is_good_match” flag, which is described in the schema as being “True if this object–truth matching pair satisfies all matching criteria.” (Note that “1” and “TRUE” are equivalent in ADQL.)\n",
+    "We will now join the results from the above query with the truth information.  We will also add in some quality filters on the match. Objects in the truth-match table that do not have matches in the object table have “match_objectId = -1,” while those with legitimate matches contain the ‘objectId’ of the corresponding object from the object table in “match_objectId.” By requiring this to be greater than or equal to zero, we extract only objects with matches. We also keep only sources satisfying the “is_good_match” flag, which is described in the schema as being “True if this object–truth matching pair satisfies all matching criteria.” (Note that “1” and “TRUE” are equivalent in ADQL.)\n",
     "\n",
     "With these additional quality filters applied to the matching with the truth information, we only get 14424 results."
    ]
@@ -393,7 +393,7 @@
    "metadata": {},
    "source": [
     "#### 3.1 Data preparation\n",
-    "The basis for any data visualization is the underlying data. We will prepare ColumnDataSource (CDS) from the data returned by the query above that can be passed directly to bokeh. The CDS is the core of bokeh plots. Bokeh automatically creates a CDS from data passed as python lists ot numpy arrays.  CDS are useful as they allow data to be shared between multiple plots and renderers, enabling brushing and linking.  A CDS is essentially a collection of sequences of data that have their own unique column name. \n",
+    "The basis for any data visualization is the underlying data. We will prepare ColumnDataSource (CDS) from the data returned by the query above that can be passed directly to bokeh. The CDS is the core of bokeh plots. Bokeh automatically creates a CDS from data passed as python lists or numpy arrays.  CDS are useful as they allow data to be shared between multiple plots and renderers, enabling brushing and linking.  A CDS is essentially a collection of sequences of data that have their own unique column name. \n",
     "\n",
     "Getting the data preparation phase right is key to creating powerful visualizations. "
    ]
@@ -478,7 +478,7 @@
    "metadata": {},
    "source": [
     "#### 3.3 Color-color (r-i vs. g-r) diagram. \n",
-    "We will add a color-color (r-i vs. g-r) diagram and make use the advanced linking features of bokeh to enable brushing and linking between the the color-magnitude diagram and this color-color plot. The CMD in 4.2 is very crowed as it contains 14424 data points. We will now filter on the truth-type to plot stars ony"
+    "We will add a color-color (r-i vs. g-r) diagram and make use of the advanced linking features of bokeh to enable brushing and linking between the the color-magnitude diagram and this color-color plot. The CMD in 4.2 is very crowded as it contains 14424 data points. We will now filter on the truth-type to plot stars only."
    ]
   },
   {
@@ -615,7 +615,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Another way to add data can be added to the CDS\n",
+    "# Another way that data can be added to the CDS\n",
     "source.add(results['cModelFlux_i'] / results['flux_i'], name='flux_ratio')\n",
     "source.add(results['truth_mag_r'], name='truth_mag_r')"
    ]