Wording changes & content reduction as per @jkrick feedback

Author: jaladh-singhal

tutorials/parquet-catalog-demos/irsa-hats-with-lsdb.md

@@ -11,7 +11,7 @@ kernelspec:
   name: python3
 ---
 
-# Access IRSA HATS collections using lsdb
+# Access HATS Collections Using LSDB: Euclid Q1 and ZTF DR23
 
 +++
 
@@ -246,13 +246,9 @@ These can be useful for our **column filters** — the columns we want to SELECT
 We can also filter the schema DataFrame by name, unit, type, etc., to identify columns most relevant for our **row filters** — WHERE rows satisfy conditions on column values for our query.
 For example, let's explore the columns that are part of the PHZ (photometric redshift) catalog to identify photometric redshifts and source types:
 
-```{code-cell} ipython3
-euclid_schema_df[euclid_schema_df["name"].str.startswith("phz_")] # phz_ prefix is for PHZ catalog columns in this merged catalog
-```
-
 ```{code-cell} ipython3
 euclid_schema_df[
-    euclid_schema_df["name"].str.startswith("phz_")
+    euclid_schema_df["name"].str.startswith("phz_") # phz_ prefix is for PHZ catalog columns in this merged catalog
     & euclid_schema_df["type"].str.contains("int") # to see flag type columns
 ]
 ```
@@ -322,21 +318,13 @@ ztf_schema_df = pq_schema_to_df(ztf_schema)
 ztf_schema_df
 ```
 
-```{code-cell} ipython3
-ztf_schema_df[ztf_schema_df["unit"].str.contains("mag")] # to identify magnitude quantities
-```
-
-You can explore the schema further to identify other columns of interest.
+You can filter the schema further by units, type, etc. to identify other columns of interest.
 It's also useful to go through the [ZTF DR23 release notes](https://irsa.ipac.caltech.edu/data/ZTF/docs/releases/ztf_release_notes_latest) and [explanatory supplement](https://irsa.ipac.caltech.edu/data/ZTF/docs/ztf_explanatory_supplement.pdf) at IRSA for more details on column selections and caveats.
 
 For this tutorial, the following columns are most relevant to us:
 
 ```{code-cell} ipython3
 ztf_columns = ztf_schema_df["name"].tolist()[:6]
-ztf_columns
-```
-
-```{code-cell} ipython3
 ztf_columns.extend([
     'fid', 'filtercode', 
     'ngoodobsrel',  
@@ -436,7 +424,9 @@ with Client(n_workers=get_nworkers(euclid_x_ztf),
 euclid_x_ztf_df
 ```
 
-[Optional] Let's purify the crossmatched catalog by analyzing the distance between matched sources and removing the matches that don't meet a quality cut on percentile.
+### 5.3 [Optional] Filter the crossmatched catalog
+
+Let's purify the crossmatched catalog by analyzing the distance between matched sources and removing the matches that don't meet a quality cut on percentile.
 We also keep the matches that are outside this cutoff but are still within the same 19th order HEALPix tile.
 
 ```{code-cell} ipython3
@@ -467,7 +457,7 @@ Going forward, we will use this purified crossmatched catalog `euclid_x_ztf_filt
 
 +++
 
-### 5.3 Identify objects of interest from the crossmatch
+### 5.4 Identify objects of interest from the crossmatch
 
 +++
 
@@ -573,32 +563,8 @@ magrms_threshold
 variable_galaxies = euclid_x_ztf_filtered_df.query(
     f"chisq_ztf >= {chisq_threshold} & magrms_ztf >= {magrms_threshold}"
     ).sort_values("chisq_ztf", ascending=False) # sort by significant variability
-variable_galaxies
-```
-
-```{code-cell} ipython3
-plt.figure(figsize=(10, 6))
-all_redshifts = euclid_x_ztf_filtered_df["phz_phz_median_euclid"]
-bins = np.histogram_bin_edges(all_redshifts, bins=100)
-
-plt.hist(all_redshifts, bins=bins, label='Galaxies', histtype='step', alpha=0.8)
-plt.hist(
-        variable_galaxies["phz_phz_median_euclid"],
-        bins=bins,
-        label=f'Variable Galaxies \nwith RMS mag > 95% ({magrms_threshold:.6f})\nand Chi-sq > 95% ({chisq_threshold:.6f})',
-        histtype='step',
-        alpha=0.8
-    )
-
-plt.xlabel("Euclid redshift")
-plt.ylabel("Galaxy Count")
-plt.legend()
-plt.yscale("log")
-plt.xlim(0, 2.5)
-plt.show()
 ```
 
-Most of the variable galaxies histogram is concentrated in the low redshift (z < 1) range, as expected from the hexbin density plots above.
 Let's inspect the top variable galaxies that we can use for plotting their light curves:
 
 ```{code-cell} ipython3
@@ -645,7 +611,7 @@ ztf_lcs
 ```
 
 As earlier, this creates a lazy catalog object with the partition(s) that contains our IDs.
-We can load the light curves data into a DataFrame by using the `compute()` method: 
+We can load the light curves data into a DataFrame by using the `compute()` method:
 
 ```{code-cell} ipython3
 # Uncomment following if ztf_lcs contain more than 1 partition