env-able 0.5.2

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.Intersect(input_layer, intersect_layer, output=None)

Computes geometric intersection of input features against a polygon boundary.

• input_layer — point, line, or polygon (file path or GeoDataFrame)
• intersect_layer — polygon layer defining the intersection boundary
• output — file path to save result (.gpkg, .shp, etc.) — omit to return a GeoDataFrame

import env_able as env
env.Intersect("wells.shp", "boundary.shp", "result.gpkg")

---

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

Buffers input features by a given distance and unit.

• input_layer — point, line, or polygon (file path or GeoDataFrame)
• distance — numeric buffer distance
• unit — meters, km, miles, feet, usfeet, nautical miles
• output — file path to save result — omit to return a GeoDataFrame

env.Buffer("wells.shp", 1, "miles", "wells_buffer.gpkg")

---

env.Clip(input_layer, clip_layer, output=None)

Clips input features to the extent of a polygon clip boundary.

• input_layer — point, line, or polygon (file path or GeoDataFrame)
• clip_layer — polygon layer defining the clip boundary
• output — file path to save result — omit to return a GeoDataFrame

env.Clip("roads.shp", "county.shp", "roads_clipped.gpkg")

---

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")

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
remove_layer(name)	Remove a layer by name
clear()	Remove all layers
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
is_running()	Health check

The browser UI includes an ArcGIS Pro-style ribbon with basemap switching and PNG/PDF export, plus a layer panel for recoloring, toggling, and zoom-to.

---

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

v0.5.1 — 2026-07-15

• AtlasClient — env.atlas.connect() returns a programmatic client for Claude to operate Atlas without writing Python files
• client.add_layer, set_viewport, save_layout, load_layout, state and more
• Browser viewport sync — Claude sets view, browser flies to it; user pan/zoom pushed back to server
• Export ribbon tab — Save PNG and Save PDF
• serve(block=False) for non-blocking server startup
• Windows cp1252 fix, env.atlas lazy import fix

v0.5.0 — 2026-07-15

• env.stream — memory-flat streaming pulls into DuckDB; bypasses Databricks row/size caps
• pull_to_file, connector_arrow_frames, rest_external_links_frames, keyset_frames, offset_frames
• pip install env-able[fast] extras group (duckdb + requests)
• Default I/O engine switched to pyogrio

v0.4.0 — 2026-07-15

• env.atlas — interactive MapLibre map server (env.atlas.serve(), add_layer, remove_layer, clear_layers)
• ArcGIS Pro-style ribbon UI with basemap switcher (7 options) and layer panel
• pip install env-able[atlas] extras group

v0.3.0 — 2026-07-14

• pull() — paginated Databricks table fetch with WKT auto-detection and chunked assembly

v0.2.0 — 2026-07-14

• Renamed import alias convention from e to env

v0.1.0 — 2026-07-14

• Initial release: Intersect(), Buffer(), Clip(), morph()
• Smart geometry detection for WKT and lat/lon columns
• Multi-layer GDB/GPKG support