docs: tighten obstore notebook copy

Refines the intro framing, expected-result notes, and section
explanations across the notebook.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: aboydnw

quickstarts/obstore.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "id": "b02d33be",
    "metadata": {},
-   "source": "# Working with Planetary Computer data using obstore\n\nThis notebook walks through reading Planetary Computer data with [obstore](https://developmentseed.org/obstore/) — a Python library that talks to cloud object stores (Azure Blob, S3, GCS) directly, without going through HTTP wrappers like fsspec. It's the foundation that higher-level libraries (async-geotiff, zarr-python, deck.gl-raster via Lonboard) sit on top of.\n\n**Five reasons to use it over the older `planetary_computer.sign() + fsspec` pattern:**\n\n1. **Reliability** — SAS tokens auto-refresh. No `TokenExpiredError` mid-job, no manual re-signing.\n2. **Cost** — range reads download only the bytes you need (e.g. a 16 KB COG header instead of a 100 MB file).\n3. **Speed** — async surface fires reads in parallel. Roughly N× faster than serial for multi-file workloads.\n4. **Composability** — the same store works with async-geotiff, zarr-python, Lonboard, etc.\n5. **Portability** — `AzureStore`, `S3Store`, `GCSStore` are interchangeable. Cloud-agnostic code.\n\nEach cell below calls out which of these it demonstrates. Speed-relevant cells use `%%time` so you can compare wall-clock numbers.\n\nThe companion [obstore tutorial](../overview/obstore.md) has the full narrative and migration reference."
+   "source": "# Working with Planetary Computer data using obstore\n\nThis notebook walks through reading Planetary Computer data with [obstore](https://developmentseed.org/obstore/). Obstore is a Python library that talks to cloud object stores (Azure Blob, S3, GCS) directly, without going through HTTP wrappers like fsspec. This has a number of key benefits:**\n\n1. **Reliability** - SAS tokens auto-refresh. No `TokenExpiredError` mid-job, no manual re-signing.\n2. **Cost** — range reads download only the bytes you need.\n3. **Speed** — async surface fires reads in parallel. Roughly N× faster than serial for multi-file workloads.\n4. **Composability** — any library that accepts an [obspec](https://github.com/developmentseed/obspec)-compatible store reads through your authenticated connection without re-doing auth.\n5. **Portability** — `AzureStore`, `S3Store`, `GCSStore` are interchangeable. Cloud-agnostic code.\n\nEach cell below calls out which of these it demonstrates. Speed-relevant cells use `%%time` so you can compare wall-clock numbers.\n\nThe companion [obstore tutorial](../overview/obstore.md) has the full narrative and migration reference."
   },
   {
    "cell_type": "markdown",
@@ -33,7 +33,7 @@
    "source": [
     "## Authenticate from a STAC asset\n",
     "\n",
-    "**Demonstrates: reliability.** `PlanetaryComputerCredentialProvider` handles SAS token acquisition and refresh under the hood — no manual `planetary_computer.sign()` calls anywhere in this notebook. If a token expires mid-job, the provider re-acquires it transparently. The old fsspec pattern required you to handle re-signing and retry logic yourself.\n",
+    "`PlanetaryComputerCredentialProvider` handles SAS token acquisition and refresh under the hood — no manual `planetary_computer.sign()` calls anywhere in this notebook. If a token expires mid-job, the provider re-acquires it transparently. The old fsspec pattern required you to handle re-signing and retry logic yourself.\n",
     "\n",
     "**Expected result:** working `provider` object, no output printed."
    ]
@@ -62,7 +62,7 @@
    "id": "b81cc8c9",
    "metadata": {},
    "source": [
-    "Notice the asset href is unsigned — no SAS query string appended. The provider signs it for you at read time."
+    "Notice the asset href is unsigned without a SAS query string appended. The provider signs it for you at read time."
    ]
   },
   {
@@ -82,7 +82,7 @@
    "source": [
     "## Build a store\n",
     "\n",
-    "A *store* is obstore's connection to a specific cloud location. Once built, you hand it to any obstore read/write function — or to any higher-level library that accepts an obstore-compatible store.\n",
+    "A *store* is obstore's connection to a specific cloud location. Once built, you hand it to any obstore read/write function, or to any higher-level library that accepts an obstore-compatible store.\n",
     "\n",
     "**Expected result:** working `store` object, no output printed."
    ]
@@ -106,18 +106,11 @@
    "source": [
     "## Read\n",
     "\n",
-    "`from_asset()` scopes the store to that *specific blob* — the asset URL becomes the store's prefix. So every read uses an empty string as the path; obstore appends the path to the prefix, and you don't want it appending anything. (For multi-object access you'd build a container-scoped store instead — covered further down.)\n",
-    "\n",
-    "Three ways to read, each demonstrating a different value prop:\n",
+    "There are three ways to read data, depending on your needs.:\n",
     "\n",
     "### 1. Read the entire file\n",
     "\n",
-    "**Demonstrates: baseline (the slow path you want to avoid).** Use when you actually want all the bytes. NAIP scenes range 100–500 MB.\n",
-    "\n",
-    "A surprise here: this cell is slow even on a fast connection. Azure Blob caps single-stream downloads at roughly 8–15 MB/s — your home bandwidth doesn't help. To go faster you'd need parallel range reads against the same file (which is what async-geotiff does internally when it reads COG tiles).\n",
-    "\n",
-    "This cell is the foil for everything below. The whole point of range reads and async is to avoid this scenario.\n",
-    "\n",
+    "**This is the slowest path. Use when you actually want all the bytes. For large files, this can take a long time. For example, these NAIP scenes range from 100–500 MB, taking a minute or more depending on your connection.\n",
     "**Expected result:** 100–500 million bytes, 30–90 seconds depending on which NAIP scene came back."
    ]
   },
@@ -142,9 +135,9 @@
    "source": [
     "### 2. Read a byte range (16 KB)\n",
     "\n",
-    "**Demonstrates: cost savings.** A Cloud Optimized GeoTIFF stores its header in the first few KB. Most libraries (async-geotiff, GDAL, rasterio) only need the header to start working — they don't need the pixel data until you ask for a specific window. Range reads make this possible.\n",
+    "A Cloud Optimized GeoTIFF stores its header in the first few KB. Most libraries (async-geotiff, GDAL, rasterio) only need the header to start working. They don't need the pixel data until you ask for a specific window. Range reads make this possible.\n",
     "\n",
-    "**Expected result:** 16,384 bytes, well under a second. Tens of thousands of times less data than the full file above (the exact multiple depends on your scene's size)."
+    "**Expected result:** 16,384 bytes, well under a second. Much less data than the full file above."
    ]
   },
   {
@@ -167,7 +160,7 @@
    "source": [
     "### 3. Read multiple byte ranges in one request\n",
     "\n",
-    "**Demonstrates: latency savings.** When you need several slices of the same file — say, multiple COG tiles — you could issue separate `get_range` calls. Each one is a round-trip to Azure. `get_ranges` batches them into a single HTTP request, cutting round-trip latency.\n",
+    "When you need several slices of the same file you could issue separate `get_range` calls. Each one is a round-trip to Azure. `get_ranges` batches them into a single HTTP request, cutting round-trip latency.\n",
     "\n",
     "**Expected result:** two ranges of 16 KB each, similar wall time to a single `get_range`."
    ]
@@ -193,7 +186,7 @@
    "source": [
     "## Listing requires a container-scoped store\n",
     "\n",
-    "**Demonstrates: reach beyond a single asset.** Up to here we've worked with one blob. To enumerate objects under a prefix (\"show me every NAIP scene in Montana in 2023\"), the store needs to be scoped to the container *and* the credential provider needs container-level `List` permission. The asset-derived provider above only signs the single blob — it can't list — so we build a fresh provider against the container URL.\n",
+    "To enumerate objects under a prefix (\"show me every NAIP scene in Montana in 2023\"), the store needs to be scoped to the container *and* the credential provider needs container-level `List` permission. The asset-derived provider above only signs the single blob so we build a fresh provider against the container URL.\n",
     "\n",
     "**Expected result:** three lines printed, each a blob path and its size in bytes."
    ]
@@ -225,9 +218,9 @@
    "id": "93160f86",
    "metadata": {},
    "source": [
-    "## Concurrent reads (async) — the speed payoff\n",
+    "## Concurrent reads (async)\n",
     "\n",
-    "**Demonstrates: speed via parallelism.** Up to here, reads happen one at a time. For multi-file workloads, running them in parallel is dramatically faster than serial. Below we read the same 4 KB header four times — first serially, then concurrently — and compare wall times.\n",
+    "For multi-file workloads, running them in parallel is dramatically faster than serial. Below we read the same 4 KB header four times - first serially, then concurrently — and compare wall times.\n",
     "\n",
     "Async needs its own credential provider class (`PlanetaryComputerAsyncCredentialProvider`) backed by `aiohttp` instead of `requests`."
    ]
@@ -251,7 +244,6 @@
    "id": "79c84f80",
    "metadata": {},
    "source": [
-    "We'll time both with `time.perf_counter()` for an apples-to-apples comparison (the `%%time` magic doesn't play well with top-level `await`, so we measure manually).\n",
     "\n",
     "**Warmup the async store.** First call has to acquire a SAS token from Planetary Computer — a separate HTTP round-trip. We do one throwaway read so that overhead doesn't pollute the timing below. (The sync store was already warmed up by cells 1/2/3, which is why we only need to warm the async store.)"
    ]
@@ -336,7 +328,7 @@
    "source": [
     "## Hand the store to async-geotiff\n",
     "\n",
-    "**Demonstrates: composability.** The whole point of obstore is that *other libraries* sit on top of it. Once you have a working `AzureStore`, you can hand it to any library that accepts an [obspec](https://github.com/developmentseed/obspec)-compatible store — async-geotiff, zarr-python, and others — and they'll read through your authenticated connection. No re-auth, no double signing."
+    "Once you have a working `AzureStore`, you can hand it to any library that accepts an [obspec](https://github.com/developmentseed/obspec)-compatible store and they'll read through your authenticated connection."
    ]
   },
   {
@@ -354,9 +346,9 @@
    "id": "1c801295",
    "metadata": {},
    "source": [
-    "Open the NAIP scene as a COG and read its metadata. `geotiff.transform` is the affine that maps pixel coordinates to geographic coordinates. `geotiff.crs` is the coordinate reference system.\n",
+    "Open the NAIP scene as a COG and read its metadata. geotiff.transform tells you the scene's pixel size and corner position on the ground. \n",
     "\n",
-    "**Expected result:** the affine transform and the CRS name (e.g. `NAD83 / UTM zone 11N` — varies by scene)."
+    "**Expected result:** a transform and a CRS name (e.g. NAD83 / UTM zone 11N)."
    ]
   },
   {
@@ -379,7 +371,7 @@
    "id": "f6d207db",
    "metadata": {},
    "source": [
-    "If you want the full CRS details (datum, axis order, area of use), just evaluate `geotiff.crs` on its own — pyproj prints a detailed dump.\n",
+    "If you want the full CRS details (datum, axis order, area of use), just evaluate `geotiff.crs` on its own\n",
     "\n",
     "Notice async-geotiff only fetched ~16 KB to get this metadata, not the full file. The range-read win compounds at every level of the stack."
    ]
@@ -389,25 +381,23 @@
    "id": "9dd739eb",
    "metadata": {},
    "source": [
-    "## Bonus: portability\n",
+    "## Portability\n",
     "\n",
-    "**Demonstrates: cloud-agnostic code.** The same `obstore.get(store, ...)` call works against S3 or GCS — only the store constructor changes. This cell isn't runnable (we don't have S3 creds), but it shows the shape:\n",
+    "The same `obstore.get(store, ...)` call works against S3 or GCS, only the store constructor changes. The example below shows the shape of the request in S3:\n",
     "\n",
     "```python\n",
     "from obstore.store import S3Store\n",
     "\n",
     "s3_store = S3Store(bucket=\"my-bucket\", region=\"us-west-2\")\n",
     "buf = obstore.get(s3_store, \"path/to/object\").bytes()  # same call\n",
-    "```\n",
-    "\n",
-    "Any library that accepts an obspec-compatible store benefits automatically. async-geotiff opening a COG works identically against Azure, S3, or GCS."
+    "```\n"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "b2d0385b",
    "metadata": {},
-   "source": "## You're done\n\nIf you got expected output on every cell above, the obstore stack is wired up end-to-end:\n\n- **Reliability** — authenticated against Planetary Computer with auto-refreshing SAS tokens\n- **Cost** — read 16 KB of a multi-hundred-MB file via range reads (tens of thousands of times less egress)\n- **Latency** — batched multi-range read in a single round-trip\n- **Speed** — parallel reads several times faster than serial\n- **Composability** — handed the same store to async-geotiff, opened a COG with no re-auth\n- **Portability** — same API shape works for S3 and GCS\n\nFrom here, any obspec-compatible library plugs in the same way. Check the companion [Lonboard tutorial](../overview/lonboard.md) for interactive visualization or the [async-geotiff tutorial](../overview/async-geotiff.md) for pixel-level analysis."
+   "source": "## You're done\n\nIf you got expected output on every cell above, the obstore stack is wired up end-to-end.\n\nFrom here, any obspec-compatible library plugs in the same way. Check the companion [Lonboard tutorial](../overview/lonboard.md) for interactive visualization or the [async-geotiff tutorial](../overview/async-geotiff.md) for pixel-level analysis."
   }
  ],
  "metadata": {