env-able 0.9.2

AI-ready GIS toolkit for energy and subsurface workflows.

env-able

An open source spatial analysis library built for AI-driven GIS workflows. Designed to give AI systems like Claude reliable, hallucination-free tools for spatial operations in energy and subsurface contexts.

Install

pip install env-able

# with Databricks support
pip install env-able[databricks]

# with interactive map server (Atlas)
pip install env-able[atlas]

# with large-scale streaming pulls (DuckDB)
pip install env-able[fast]

Usage

import env_able as env

---

Functions

env.pull(table, output=None, wkt_col=None, chunk_size=None, crs="EPSG:4326")

Pull a Databricks table or query to a local file, chunking around the row/byte limit automatically.

Databricks caps result sets (~4 096 rows for narrow tables, fewer when WKT columns are present). pull() paginates with LIMIT/OFFSET, assembles the complete dataset in memory, and writes it to disk in the requested format.

Parameters

• table — fully-qualified table name (catalog.schema.table) or a complete SELECT query
• output — destination file path; format inferred from extension (.gpkg, .geojson, .shp, .parquet, .csv, .xlsx). Omit to return a GeoDataFrame or DataFrame.
• wkt_col — column containing WKT geometry strings. Auto-detected if omitted.
• chunk_size — rows per Databricks query. Defaults to 512 (WKT) or 4 096 (tabular). Reduce if you hit payload errors on wide tables.
• crs — CRS to assign geometry. Default EPSG:4326.

Environment variables required

DATABRICKS_HOST       # https://adb-<workspace-id>.azuredatabricks.net
DATABRICKS_TOKEN      # personal access token
DATABRICKS_HTTP_PATH  # /sql/1.0/warehouses/<warehouse-id>

import env_able as env

# pull a full table to GeoPackage
env.pull("catalog.schema.wells", "wells.gpkg")

# pull with a filter query
env.pull("SELECT * FROM catalog.schema.wells WHERE state = 'TX'", "wells_tx.gpkg")

# pull tabular (no geometry)
env.pull("catalog.schema.formations", "formations.csv")

# return in-memory without writing
gdf = env.pull("catalog.schema.leases", wkt_col="geom_wkt", crs="EPSG:4269")

---

env.Clip(input_layer, clip_layer, output=None, where=None, preview=False)

Clips input features to the extent of a polygon boundary (ogr2ogr -clipsrc engine — streaming, handles files larger than RAM). Returns a SpatialResult.

• input_layer — point, line, or polygon (file path or GeoDataFrame)
• clip_layer — polygon clip boundary (file path or GeoDataFrame)
• output — output file path (.gpkg, .shp, .geojson, etc.)
• where — optional SQL attribute filter applied before clipping, e.g. "STATE = 'TX'"
• preview — if True, pushes result to a running Atlas server on completion

result = env.Clip("wells.shp", "texas.gpkg", output="wells_tx.gpkg")
result = env.Clip(gdf, "counties.gpkg", where="STATE = 'TX'")
result.preview("Texas Wells", color="#F68D2E")   # push to Atlas
result.to("wells_tx.geojson")                    # write extra format
print(result)                                    # stats: input, output, dropped, CRS, time
gdf = result.gdf                                 # access as GeoDataFrame

---

env.Buffer(input_layer, distance, unit="meters", output=None, where=None, preview=False)

Buffers input features by a given distance (OGR Python API engine). Auto-selects the best UTM zone for accurate metric distances; result is returned in the original CRS. Returns a SpatialResult.

• input_layer — point, line, or polygon (file path or GeoDataFrame)
• distance — numeric buffer distance
• unit — meters, km, miles, feet, usfeet, nautical miles
• output — output file path
• where — optional SQL attribute filter
• preview — if True, pushes result to Atlas on completion

result = env.Buffer("wells.shp", 1, "miles", output="wells_1mi.gpkg")
result = env.Buffer(gdf, 500)                    # 500 m, no file written
print(result)                                    # SpatialResult stats

---

env.Intersect(input_layer, intersect_layer, output=None, where=None, preview=False)

Geometric intersection of two layers (OGR Layer.Intersection() engine). Input geometries are trimmed to their overlap; attributes from both layers appear in the result. Returns a SpatialResult.

• input_layer — point, line, or polygon (file path or GeoDataFrame)
• intersect_layer — polygon boundary to intersect against
• output — output file path
• where — optional SQL attribute filter on the input layer
• preview — if True, pushes result to Atlas on completion

result = env.Intersect("wells.shp", "permits.gpkg", output="wells_permits.gpkg")
result.preview("Permitted Wells")

---

env.load(source) — fluent spatial pipeline

Create a chainable SpatialPipeline from a source layer. Add operations with .buffer(), .clip(), .intersect(), set outputs with .to(), push to Atlas with .preview(), then execute with .run() or await .run_async(). Intermediate temp files are cleaned up automatically.

result = (
    env.load("wells.shp")
        .buffer(1, "miles")
        .clip("texas.gpkg")
        .to("wells_1mi_tx.gpkg")
        .preview("Wells in TX", color="#F68D2E")
        .run()
)

# Fan-out multiple pipelines in parallel
import asyncio
results = await asyncio.gather(
    env.load("wells.shp").clip("texas.gpkg").run_async(),
    env.load("wells.shp").clip("new_mexico.gpkg").run_async(),
)

---

Async variants

All three operations have async counterparts that run in a thread-pool executor. GDAL releases the GIL so multiple calls run truly in parallel via asyncio.gather().

import asyncio

# Single async call
result = await env.clip_async("wells.shp", "texas.gpkg")

# Fan-out in parallel
r1, r2, r3 = await asyncio.gather(
    env.clip_async("wells.shp", "texas.gpkg"),
    env.buffer_async("wells.shp", 1, "miles"),
    env.intersect_async("wells.shp", "permits.gpkg"),
)

---

SpatialResult

Returned by Clip, Buffer, Intersect, and the pipeline .run(). Exposes operation stats and chainable output methods.

Attribute / Method	Description
input_count	Feature count of the input layer
output_count	Feature count of the result
dropped	input_count - output_count
slivers	Polygon features with suspiciously small area (intersection artifacts)
crs	CRS of the output (e.g. EPSG:4326)
elapsed_s	Wall-clock seconds for the operation
warnings	List of non-fatal warnings (CRS reprojection, empty result, etc.)
.gdf	Load result as a GeoDataFrame (lazy, cached)
.preview(name, color)	Push to a running Atlas server
.to(*paths)	Write to one or more additional file formats

---

env.morph(input_path, output_path, **kwargs)

Universal format translation. Converts between shp, gpkg, gdb, csv, xlsx, xls, dbf, geojson, json with automatic CRS handling, field name fixes, and multi-layer support.

• input_path — source file or geodatabase
• output_path — destination file. Extension sets the format. Use trailing / for directory output (one file per layer). Use dot notation for named layers: roads.parcels.gpkg
• x_col, y_col — column names for X/Y coordinates (auto-detected if not provided)
• wkt_col — column containing WKT geometry (auto-detected if not provided)
• crs — coordinate reference system e.g. EPSG:4326 (required for tabular → spatial)

env.morph("roads.shp", "roads.gpkg")
env.morph("county.gdb", "county.gpkg")
env.morph("county.gdb", "output_folder/")
env.morph("owners.csv", "owners.geojson", crs="EPSG:4269")
env.morph("owners.csv", "owners.shp", x_col="LONGITUDE", y_col="LATITUDE", crs="EPSG:4269")
env.morph("roads.gpkg", "roads.parcels.gpkg")
env.morph("data.json", "data.gpkg")

# async variant
await env.morph_async("roads.shp", "roads.gpkg")

Smart behavior:

• GDB / GPKG with multiple layers → detects all layers automatically
• CRS mismatch → auto-reprojects
• Shapefile field name limit (10 chars) → auto-truncates with warnings
• Invalid output path → plain English error
• Empty layers → skipped with a warning, not a crash

---

env.atlas — interactive map server

env.atlas launches a browser-based interactive map (MapLibre GL JS) that Claude can load data into and control programmatically. The user opens it in their browser and fine-tunes from there.

Requires pip install env-able[atlas]

import env_able as env

# Start the server (non-blocking — runs in background thread)
env.atlas.serve(block=False)

# Connect and operate
client = env.atlas.connect()
client.add_layer(gdf, "Wells", color="#f5a623")   # push a GeoDataFrame
client.set_viewport([-103.0, 32.0], zoom=7)       # frame the view
print(client.state())                              # check what's on the map
client.save_layout("wells_map.atlas.json")         # save for later

AtlasClient methods:

Method	Description
add_layer(data, name, color)	Push GeoDataFrame or file path; serializes inline, no temp file
upload_layer(path, name, color)	Load any format (gpkg, geojson, csv, xlsx, zip/shp); converts via morph
remove_layer(name)	Remove a layer by name
clear()	Remove all layers
set_layer_color(name, color)	Change a layer's color; browser updates on next poll
reorder_layers(names)	Set rendering order — first name = top of map
validate_join(input_layer, input_field, join_source, join_field)	Preview join match stats without modifying any layer
join_field(input_layer, input_field, join_source, join_field, fields=None)	Left-join attributes from a loaded layer or table into another layer; fields limits which columns are added
set_viewport(center, zoom)	Set map view — browser flies there within ~2 s
get_viewport()	Read current viewport (reflects user pan/zoom)
state()	Layer count, names, colors, feature counts, current viewport
save_layout(path)	Write full map state to .atlas.json
load_layout(path)	Restore a saved layout
export_svg(path)	Export all data layers as a vector SVG (no basemap)
is_running()	Health check

The browser UI includes an ArcGIS Pro-style ribbon with basemap switching, file upload, and PNG/PDF export, plus a layer panel with drag-and-drop reorder, color picker (server-synced), visibility toggle, zoom-to, and remove.

---

env.stream — large-scale Databricks pulls

env.stream pages arbitrarily large tables through DuckDB without holding them in RAM, writing directly to GPKG, GeoJSON, Parquet, or CSV. Bypasses the ~4096-row / ~2 MB Databricks response cap.

Requires pip install env-able[fast]

from env_able.stream import pull_to_file, connector_arrow_frames

# Stream a full table to GeoPackage via Arrow (no row cap)
frames = connector_arrow_frames(
    "SELECT * FROM catalog.schema.wells",
    host="https://adb-xxxx.azuredatabricks.net",
    http_path="/sql/1.0/warehouses/xxxx",
    token="dapixxxx"
)
rows = pull_to_file(frames, "wells.gpkg", wkt_col="geom_wkt")
print(f"{rows:,} rows written")

Transports:

Function	Method	Cap
connector_arrow_frames	Databricks SQL connector Arrow batches	None
rest_external_links_frames	Statement Execution API, Cloud Fetch	None
keyset_frames	Seek/keyset pagination	Configurable page size
offset_frames	LIMIT/OFFSET pagination	Configurable page size

---

Changelog

v0.9.0 — 2026-07-21

Breaking: Clip, Buffer, Intersect now return SpatialResult instead of GeoDataFrame. Use .gdf to get the underlying GeoDataFrame.

• GDAL/OGR spatial engine — operations rewritten as true GDAL calls, not geopandas wrappers
  • Clip — ogr2ogr -clipsrc; streaming, no full memory load
  • Buffer — OGR Python API with auto-UTM zone selection; result in original CRS
  • Intersect — OGR Layer.Intersection(); attributes from both layers in result
• SpatialResult — rich return type: counts, CRS, timing, warnings, .gdf, .preview(), .to()
• Async variants — clip_async(), buffer_async(), intersect_async() via thread-pool; GDAL releases GIL for true parallelism
• Fluent pipeline — env.load(source).buffer(...).clip(...).to(...).preview(...).run() and .run_async()
• where= filter — SQL attribute filter on all three operations
• morph spatial→spatial via ogr2ogr — file-to-file conversions no longer load full dataset into memory
• morph_async() — async variant of env.morph

v0.8.4 — 2026-07-21

• client.join_field() — programmatic left-join: carry attributes from any loaded layer or table into another layer in-place; optional fields list to limit what's added
• client.validate_join() — preview match stats without modifying any layer
• client.export_svg(path) — export all data layers as a vector SVG; browser Export ribbon SVG button
• Join Field UI — field checkboxes with Select All / Deselect All; ArcGIS Pro-style two-section validation stats; many-to-one join handling

v0.8.0 — 2026-07-17

• Multi-file shapefile upload — select .shp + companions together; missing .shx regenerated automatically
• Layer right-click menu — Rename, Zoom To, Attribute Table, Remove
• Folder drag-and-drop — drag a shapefile folder from Explorer onto the map
• CRS prompt modal — manual EPSG/WKT override when CRS cannot be detected
• ESRI WKT fallback — handles non-standard projection names via pyproj → GDAL → regex parameter extraction

Full history in CHANGELOG.md