Cleans up 02_Data_Visualization_Tools.md, 03_Using_PySTAC.md

book/02_Software_Tools/02_Data_Visualization_Tools.md

@@ -98,9 +98,9 @@ print(type(tokyo_point))
 <!-- #endregion -->
 
 ```python jupyter={"source_hidden": true}
-# simple utility to make a rectangle of width dx & height dy & centre pt
-def make_rect(pt,dx,dy):
-    '''Returns rectangle represented as tuple (x_lo, y_lo, x_hi, y_hi)
+# simple utility to make a rectangle centered at pt of width dx & height dy
+def make_bbox(pt,dx,dy):
+    '''Returns bounding-box represented as tuple (x_lo, y_lo, x_hi, y_hi)
     given inputs pt=(x, y), width & height dx & dy respectively,
     where x_lo = x-dx/2, x_hi=x+dx/2, y_lo = y-dy/2, y_hi = y+dy/2.
     '''
@@ -115,19 +115,19 @@ We can test the preceding function using the longitude-latitude coordinates of M
 # Verify that the function bounds works as intended
 marrakesh_lonlat = (-7.93, 31.67)
 dlon, dlat = 0.5, 0.25
-marrakesh_rect = make_rect(marrakesh_lonlat, dlon, dlat)
-print(marrakesh_rect)
+marrakesh_bbox = make_bbox(marrakesh_lonlat, dlon, dlat)
+print(marrakesh_bbox)
 ```
 
 <!-- #region jupyter={"source_hidden": true} -->
 The utility `geoviews.Rectangles` accepts a list of bounding boxes (each described by a tuple of the form `(x_min, y_min, x_max, y_max)`) to plot. We can again use `geoviews.opts` to tailor the rectangle as needed.
 <!-- #endregion -->
 
 ```python jupyter={"source_hidden": true}
-rectangle = gv.Rectangles([marrakesh_rect])
+rectangle = gv.Rectangles([marrakesh_bbox])
 rect_opts = opts.Rectangles(
                                 line_width=0,
-                                alpha=0.25,
+                                alpha=0.1,
                                 color='red'
                             )
 ```

book/03_Using_NASA_EarthData/03_Using_PySTAC.md

@@ -68,7 +68,12 @@ Next, let's define search parameters so we can retrieve data pertinent to that f
 # Define AOI as left, bottom, ri/ght and top lat/lon extent
 aoi = (-59.63818, -35.02927, -58.15723, -33.77271)
 # We will search data for the month of March 2024
-date_range = '2024-03-01/2024-03-31'
+date_range = '2024-03-08/2024-03-15'
+# Define a dictionary with appropriate keys: 'bbox' and 'datetime'
+search_params = {
+                  'bbox' : aoi, 
+                  'datetime' : date_range,
+                }
 ```
 
 <!-- #region jupyter={"source_hidden": true} -->
@@ -77,37 +82,34 @@ Make a quick visual check that the tuple `aoi` actually describes the geographic
 
 ```python jupyter={"source_hidden": true}
 basemap = gv.tile_sources.OSM
-rect = gv.Rectangles(aoi).opts(opts.Rectangles(alpha=0.25, color='cyan'))
+rect = gv.Rectangles([aoi]).opts(opts.Rectangles(alpha=0.25, color='cyan'))
 
 rect*basemap
 ```
 
 ## Executing a search with the PySTAC API
 
 ```python jupyter={"source_hidden": true}
-# We open a client instance to search for data, and retrieve relevant data records
+# Define the base URL for the STAC to search
 STAC_URL = 'https://cmr.earthdata.nasa.gov/stac'
-
-# Setup PySTAC client
-catalog = Client.open(f'{STAC_URL}/POCLOUD/')
-collections = ["OPERA_L3_DSWX-HLS_V1"]
-
-# We would like to search data using the search parameters defined above.
-opts = {
-    'bbox' : aoi, 
-    'collections': collections,
-    'datetime' : date_range,
-}
-
-search = catalog.search(**opts)
+# Update the dictionary opts with list of collections to search
+collections = ["OPERA_L3_DSWX-HLS_V1_1.0"]
+search_params.update(collections=collections)
+print(search_params)
 ```
 
-```python jupyter={"source_hidden": true}
-print(f'{type(search)=}')
-```
+<!-- #region jupyter={"source_hidden": true} -->
+Having defined the search parameters, we can instantiate a `Client` and search the spatio-temporal asset catalog.
+<!-- #endregion -->
 
 ```python jupyter={"source_hidden": true}
-results = list(search.items_as_dicts())
+# We open a client instance to search for data, and retrieve relevant data records
+catalog = Client.open(f'{STAC_URL}/POCLOUD/')
+search_results = catalog.search(**search_params)
+
+print(f'{type(search_results)=}')
+
+results = list(search_results.items_as_dicts())
 print(f"Number of tiles found intersecting given bounding box: {len(results)}")
 ```
 
@@ -180,7 +182,7 @@ granules
 Examining the index reveals that the timestamps of the granules returned are not unique, i.e., granules correspond to distinct data products deriveded during a single aerial acquisition by a satellite.
 <!-- #endregion -->
 
-```python jupyter={"source_hidden": true}
+```python
 len(granules.index.unique()) / len(granules) # Notice the timestamps are not all unique, i.e., some are repeated
 ```