split out #172 to make this PR smaller

_quarto.yml

@@ -69,7 +69,7 @@ website:
                   text: "SOMA slice query"
                 - href: "apis/python/examples/normalizing.md"
                   text: "Normalizing a collection"
-                - href: "apis/python/examples/soco-reconnaissance.md"
+                - href: "apis/python/examples/soma-collection-reconnaissance.md"
                   text: "SOMA-collection reconnaissance"
                 - href: "apis/python/examples/soco-slice-query.md"
                   text: "SOMA-collection slice query"

apis/python/examples/ingesting-data-files.md

@@ -8,15 +8,9 @@ tools/ingestor -o /mini-corpus/tiledb-data -n /mini-corpus/anndata/10x_pbmc68k_r
 ...
 ```
 
-Note this can take several hours total. The benefit of using an optimized storage solution (with
-admittedly non-negligible ingest time) is that all subsequent queries benefit from that optimized
-storage. In particular, various cross-corpus data queries shown in these examples take just seconds
-or minutes.
-
-A key point is **write once, read from multiple tools** -- in particular, using `tiledbsc-py` (this
-package) or [`tiledbsc-r`](https://github.com/TileDB-Inc/tiledbsc) you can read SOMAs in either
-language, regardless of which language was used to store them. This lets you use
-best-in-class/state-of-the-art analysis algorithms, whichever language they're implemented in.
+Note this takes many hours. The benefit of using an optimized storage solution (with admittedly
+non-negligible ingest time) is that all subsequent queries benefit from that optimized storage. In
+particular, various cross-corpus data queries shown in these examples take just seconds or minutes.
 
 ## Populate a SOMA collection
 
@@ -36,14 +30,6 @@ populate-soco -o /mini-corpus/soco -a /mini-corpus/tiledb-data/*
 
 Note this is quite quick.
 
-As a keystroke-saver, use the `tools/ingestor` script's `--soco` option which will populate the SOMA
-collection at ingest time, so you don't even have to run `populate-soco` as an afterstep.
-
-```
-tools/ingestor -o /mini-corpus/tiledb-data --soco -n /mini-corpus/anndata/0cfab2d4-1b79-444e-8cbe-2ca9671ca85e.h5ad
-tools/ingestor -o /mini-corpus/tiledb-data --soco -n /mini-corpus/anndata/10x_pbmc68k_reduced.h5ad
-```
-
 ## Names and URIs
 
 Next let's start taking a look across the collection.

apis/python/examples/inspecting-schema.md

@@ -73,20 +73,20 @@ dtype('uint8'), 'feature_name': dtype('S'), 'feature_reference': dtype('<U')}
 ```
 print("OBS NAMES")
 for soma in soco:
-    print(soma.name)
+    print(soma.uri)
     for attr_name in soma.obs.keys():
         print("  obs", attr_name)
 
 print("VAR NAMES")
 for soma in soco:
-    print(soma.name)
+    print(soma.uri)
     for attr_name in soma.var.keys():
         print("  var", attr_name)
 ```
 
 ```
 OBS NAMES
-tabula-sapiens-immune
+file:///mini-corpus/tiledb-data/tabula-sapiens-immune
   obs tissue_in_publication
   obs assay_ontology_term_id
   obs donor
@@ -113,7 +113,7 @@ tabula-sapiens-immune
   obs tissue
   obs ethnicity
   obs development_stage
-integrated-human-lung-cell-atlas
+file:///mini-corpus/tiledb-data/integrated-human-lung-cell-atlas
   obs is_primary_data
   obs assay_ontology_term_id
   obs cell_type_ontology_term_id
@@ -186,7 +186,7 @@ integrated-human-lung-cell-atlas
 ...
 
 VAR NAMES
-tabula-sapiens-immune
+file:///mini-corpus/tiledb-data/tabula-sapiens-immune
   var feature_type
   var ensemblid
   var highly_variable
@@ -199,7 +199,7 @@ tabula-sapiens-immune
   var feature_is_filtered
   var feature_name
   var feature_reference
-integrated-human-lung-cell-atlas
+file:///mini-corpus/tiledb-data/integrated-human-lung-cell-atlas
   var n_cells
   var highly_variable
   var means