refine docs

Author: pranav-walimbe

README.md

@@ -45,8 +45,6 @@ result = sf.lazy().filter(pl.col("population") > 100_000).range_query(-10.0, 35.
 
 ## Why PyCanopy
 
-During my undergrad research, I saw firsthand how spatial dataframe tooling could use performance improvements.
-
 The driving motivator behind creating this library was to provide the optimizations of relational DBs (query planning, indexing, etc) in a fast, Polars-like interface meant for in-memory spatial work.
 
 Edit [June 19 2026]: Apache SedonaDB released a cool Python DataFrame API. There are similarities between their API and this tool but some key differences are that this library uses (1) a Polars-native query engine and (2) a cost model that decides whether and how to index.

docs/api/distance-operations.md

@@ -27,19 +27,11 @@ For a join (`within_distance_join`, `polygon_within_distance_join`), reproject b
 
 ## Recommendation by input type
 
-PyCanopy doesn't have a CRS system currently so it's recommended to use the following conversion prior to interacting with the engine.
-
 ```mermaid
 flowchart TD
-    A[geopandas GeoSeries] -->|carries .crs| D[Reprojected GeoSeries]
-    B[WKB point or polygon column] -->|CRS lived at file level, not in the bytes| E[Decode, reproject, re-feed]
-    C[numpy array, GeoArrow, shapely list, xy tuples, coordinate sequences] -->|no CRS anywhere| F[pyproj or geopandas on raw arrays]
-    D --> G[SpatialFrame or from_polygons]
-    E --> G
-    F --> G
-    G --> H[distance now means real-world units]
+    A[geopandas GeoSeries] -->|has .crs| R[Reproject]
+    B[WKB point or polygon column] -->|decode first| R
+    C[numpy, GeoArrow, shapely list, tuples] -->|raw coordinates| R
+    R --> S[SpatialFrame]
+    S --> D[distance in real-world units]
 ```
-
-- **geopandas `GeoSeries`**: reproject in place with `gs.to_crs(gs.estimate_utm_crs())`.
-- **WKB point or polygon columns**: decode with `wkb_points_to_xy` (points) or `Engine.from_wkb_polygons` (polygons), reproject the decoded coordinates, then feed the result to PyCanopy.
-- **numpy arrays, GeoArrow, shapely lists, tuples, coordinate sequences**: no CRS ever attached, reproject the raw arrays directly with `pyproj` or `geopandas`.

docs/contributing.md

@@ -52,20 +52,11 @@ ruff format && ruff check
 cargo fmt && cargo clippy
 ```
 
-The key rules:
+To avoid a slopocolypse, I recommend using these guidelines:
 
 - No em dashes, no semicolons in comments or docstrings.
 - All Python imports at module level.
 - Public Python functions use Google-style docstrings (`Args:`, `Returns:`).
 - Private Python functions use a `#` comment as the first line in the body — no docstring.
 - Rust `pub` items require `///` doc comments; every module file requires `//!`.
 - Single-line comments have no trailing period; multi-line comment blocks end each sentence with a period.
-
-## Docs
-
-```bash
-uv sync --group docs
-mkdocs serve
-```
-
-The asset copy hook (`hooks.py`) runs automatically on `mkdocs serve` and `mkdocs gh-deploy`.