aablocks 0.1.18

A-Alpha Bio SDK for accessing Atlas datasets

aablocks

A-Alpha Bio SDK for accessing Atlas data blocks.

Installation

pip install aablocks

For DataFrame support:

pip install aablocks pandas   # For pandas
pip install aablocks polars   # For polars

Quick Start

Python

import aablocks as aa

# Login (opens browser)
aa.login()

# List data blocks
blocks = aa.list_blocks()

# Get data as pandas DataFrame
df = aa.get_data("ab1001")

CLI

# Login (opens browser)
> aablocks login

# List data blocks
> aablocks list

# Download a data block
> aablocks get ab1614 -o data.csv

# Download structure files
> aablocks structures ab1614

Python API

aa.login()

Authenticate with the Atlas. Opens browser for OAuth. No-op if already logged in.

import aablocks as aa
aa.login()

aa.logout()

Clear cached authentication token.

aa.logout()

aa.list_blocks(all_versions=False, format=None)

List all accessible data blocks.

Parameters:

Name	Type	Description
all_versions	bool	Return all versions (default: latest only)
format	str	"csv", "list", "pandas", or "polars" (default: config)

Returns: list[DataBlock] | str | DataFrame

# As pandas DataFrame
df = aa.list_blocks()

# As DataBlock objects
blocks = aa.list_blocks(format="list")
for d in blocks:
    print(f"{d.id}: {d.name}")

# All versions as polars
df = aa.list_blocks(all_versions=True, format="polars")

aa.get_details(block_id, version=None)

Get metadata for a specific block.

Parameters:

Name	Type	Description
block_id	str	Data block ID (e.g., "ab1001")
version	str	Version (default: latest)

Returns: DataBlock

block = aa.get_details("ab1001")
print(block.name)
print(block.modes)  # ['source', 'ml']

aa.get_datacard(block_id, version=None)

Get the datacard content for a block.

Parameters:

Name	Type	Description
block_id	str	Data block ID
version	str	Version (default: latest)

Returns: dict (see OpenAPI DatacardResponse schema)

datacard = aa.get_datacard("ab1001")
print(datacard["exec"]["scientific_value"])
for uc in datacard["exec"]["use_cases"]:
    print(uc["title"], uc["description"])

aa.get_data(block_id, version=None, mode=None, format=None, output_path=None, output_compressed=False, progress=None, schema=True, load_negatives=False)

Download data for a data block. When schema=True (default), column types are automatically applied when reading as pandas or polars — no manual schema handling needed.

Parameters:

Name	Type	Description
block_id	str	Data block ID (e.g., "ab1001")
version	str	Version (default: latest)
mode	str	Data mode: "source" or "ml"
format	str	"csv", "pandas", or "polars"
output_path	str	Write to file instead of returning
output_compressed	bool	Keep gzip compression
progress	bool	Show progress bar
schema	bool	Apply column types automatically (default: True)
load_negatives	bool	Keep negative control rows (default: False). Set True to include them.

Returns: str | DataFrame | None

# Pandas DataFrame with proper types (Int64, Float64, boolean, string)
df = aa.get_data("ab1001")
print(df.dtypes)

# Polars DataFrame with proper types
df = aa.get_data("ab1001", format="polars")
print(df.schema)

# ML-ready data
df = aa.get_data("ab1001", mode="ml")

# Include negative control rows (ANeg/AlphaNeg)
df = aa.get_data("ab1001", load_negatives=True)

# Skip schema (raw pandas inference)
df = aa.get_data("ab1001", schema=False)

# Download to file
aa.get_data("ab1001", output_path="data.csv")

# Download compressed
aa.get_data("ab1001", output_path="data.csv.gz", output_compressed=True)

aa.download_structures(block_id, version=None, output_path=None, progress=None)

Download all structure files for a data block as a zip archive.

Parameters:

Name	Type	Description
block_id	str	Data block ID (e.g., "ab1001")
version	str	Version (default: latest)
output_path	str	Output file path (default: filename from server)
progress	bool	Show progress bar (default: config)

Returns: str (output file path)

# Download structures (filename from server)
path = aa.download_structures("ab1614")

# Download to specific path
path = aa.download_structures("ab1614", output_path="structures.zip")

aa.download_structure(block_id, filename, version=None, output_path=None)

Download a single structure file for a block.

Parameters:

Name	Type	Description
block_id	str	Data block ID (e.g., "ab1614")
filename	str	Structure filename (e.g., "structure.cif")
version	str	Version (default: latest)
output_path	str	Write to file. If None, returns content as string.

Returns: str (file content or output file path)

# Get structure content as string
content = aa.download_structure("ab1614", "structure.cif")

# Download to file
path = aa.download_structure("ab1614", "structure.cif", output_path="my_file.cif")

aa.set_config(key, value)

Set a configuration value.

Key	Description	Default
api_format	Output format (csv, pandas, polars)	"pandas"
progress	Show download progress	True

aa.set_config("api_format", "polars")
aa.set_config("progress", False)

DataBlock

Data block metadata container returned by list_blocks(format="list") and get_details().

Attribute	Type	Description
id	str	Data block identifier
name	str	Human-readable name
experiment	str	Overview/use case
details	str	Experimental details
modes	list[str]	Data modes (e.g., ["source", "ml"])
version	str	Version number
locked	bool	Locked for current user's tier
url	str | None	Direct URL
structure_count	int | None	Number of structure files
a_size	int | None	A-library size
alpha_size	int | None	Alpha-library size
total_ppi_count	int | None	Total PPI count
unique_ppi_count	int | None	Unique PPI count
product	str | None	Product slug (e.g., atlas-vhh)
product_display_name	str | None	Human-readable product name (e.g., Atlas VHH Consortia)
product_kind	str | None	Product category: consortium, exclusive, or open-source
source	str | None	Product release name (e.g., VHH Q1 2025); None when unrelased

CLI

After installation, the aablocks command is available in your terminal.

Global Options

Option	Description
--version	Show version and exit
--help	Show help and exit

aablocks login

Log in to the Atlas. Opens your browser for authentication. Tokens are cached locally and automatically refreshed.

> aablocks login
Opening browser for authentication...
Logged in successfully.

aablocks logout

Log out and clear cached credentials.

> aablocks logout
Logged out successfully.

aablocks list [OPTIONS]

List all data blocks accessible to the current user.

Option	Description
--all-versions	Include all versions of each data block
-f, --format	Output format: table, csv, or json (default: table)

# List as table (default) — columns: ID, Name, Version, Released, Structures, A Size, Alpha Size, Total PPI, Unique PPI, Modes
> aablocks list
ID           Name                           Version  Released     Structures   A Size     Alpha Size Total PPI    Unique PPI   Modes
--------------------------------------------------------------------------------------------------------------------------------------
ab1001       AlphaBlock 1001                1        2026-01-21                500        200        100000       50000        source, ml
ab1479       AlphaBlock 1479                1        2026-01-21                800        300        240000       95000        source
ab1614       AlphaBlock 1614                1        2026-01-21   34570

# List as JSON
> aablocks list -f json

# List as CSV
> aablocks list -f csv

# Include all versions
> aablocks list --all-versions

aablocks details <block_id> [OPTIONS]

Show detailed metadata for a specific block.

Option	Description
-v, --version	Specific version to retrieve

> aablocks details ab1001
ID:           ab1001
Name:         AlphaBlock 1001
Version:      1
Released:     2026-01-21
Groups:       tier1
Modes:        source, ml
Experiment:   Local affinity landscape on VHH72-SARS-CoV-2 RBD

aablocks datacard <block_id> [OPTIONS]

Show the datacard for a block.

Option	Description
-v, --version	Specific version to retrieve
--raw	Output raw JSON instead of formatted text

# Display formatted datacard text
> aablocks datacard ab1001

# Get raw JSON
> aablocks datacard ab1001 --raw

# Save to file
> aablocks datacard ab1001 --raw > datacard.json

aablocks get <block_id> [OPTIONS]

Download CSV data for a block.

Option	Description
-v, --version	Specific version to retrieve
-m, --mode	Data mode (e.g., source, ml). Default: source
-f, --format	Output format: csv, table, or gz
-o, --output	Output file path
--progress/--no-progress	Show download progress bar
--load-negatives	Include negative control rows (ANeg/AlphaNeg). Excluded by default.

# Print CSV to stdout
> aablocks get ab1001

# Download to file
> aablocks get ab1001 -o data.csv

# Download compressed
> aablocks get ab1001 -o data.csv.gz -f gz

# Get ML-ready variant
> aablocks get ab1001 --mode ml

# Include negative control rows
> aablocks get ab1001 --load-negatives

# Display as table
> aablocks get ab1001 -f table

# Pipe to other tools
> aablocks get ab1001 | head -100 > sample.csv

aablocks structures <block_id> [OPTIONS]

Download structure files for a block. By default, downloads all structures as a zip archive. Use --file to download a single structure file.

Option	Description
-v, --version	Specific version to retrieve
-f, --file	Download a single structure file by name
-o, --output	Write to file at the given path. If no path is given, defaults to the --file filename or the server-provided filename.
--progress/--no-progress	Show download progress bar

# Download all structures as zip (filename from server)
> aablocks structures ab1614
Structures written to ab1614_structures.zip

# Download to specific path
> aablocks structures ab1614 -o my_structures.zip
Structures written to my_structures.zip

# Download a single structure file (prints content to stdout)
> aablocks structures ab1614 --file structure.cif

# Download a single structure file to disk
> aablocks structures ab1614 --file structure.cif -o
Structure written to structure.cif

# Download a single structure file to a specific path
> aablocks structures ab1614 --file structure.cif -o my_file.cif
Structure written to my_file.cif

aablocks config [key] [value]

Get or set configuration options.

# Show all settings
> aablocks config

# Get a specific value
> aablocks config cli_format

# Set a value
> aablocks config cli_format table

Key	Values	Default	Description
api_format	csv, pandas, polars	pandas	Default format for Python API
cli_format	csv, table	csv	Default format for CLI output
progress	true, false	true	Show download progress bars

Examples

Complete Python Workflow

import aablocks as aa

# Authenticate
aa.login()

# Browse data blocks
blocks = aa.list_blocks(format="list")
print(f"Found {len(blocks)} data blocks")

for d in blocks[:3]:
    print(f"{d.id}: {d.name} (v{d.version})")

# Get details
details = aa.get_details("ab1001")
print(f"Modes: {details.modes}")

# Download data (schema applied automatically)
df = aa.get_data("ab1001")
print(df.head())

# ML version
df_ml = aa.get_data("ab1001", mode="ml")

# Read datacard
datacard = aa.get_datacard("ab1001")
print(datacard["exec"]["scientific_value"])

# Download structures
aa.download_structures("ab1614")

Scripting

# List data block IDs only
> aablocks list -f csv | tail -n +2 | cut -d, -f1

# Download all accessible data blocks
> for id in $(aablocks list -f csv | tail -n +2 | cut -d, -f1); do
    aablocks get $id -o "${id}.csv"
done

Schema

Each data block has a column schema that defines proper dtypes for pandas and polars. When using get_data(), schemas are applied automatically (schema=True by default).

Automatic Typing

import aablocks as aa

aa.login()

# Pandas — columns are typed as Int64, Float64, boolean, string
df = aa.get_data("ab1001")
print(df.dtypes)
# mata_description                       string
# alphaseq_affinity                      Float64
# above_background                       boolean
# pos_a                                  Int64

# Nullable integers stay Int64 (not float64) even with missing values
print(df["pos_a"].dtype)  # Int64

# Polars — columns are typed via schema_overrides
df = aa.get_data("ab1001", format="polars")
print(df.schema)
# {'mata_description': Utf8, 'alphaseq_affinity': Float64, 'pos_a': Int64, ...}

# Skip schema (raw inference)
df = aa.get_data("ab1001", schema=False)

Manual Schema Usage

# Fetch the schema separately
schema = aa.get_schema("ab1001")
# {'dtype': {'mata_description': 'string', 'pos_a': 'Int64', ...}, 'parse_dates': []}

# Use directly with pandas
import pandas as pd
df = pd.read_csv("local_data.csv", **schema)

# Convert to polars kwargs
polars_kwargs = aa.pandas_schema_to_polars(schema)
import polars as pl
df = pl.read_csv("local_data.csv", **polars_kwargs)

CLI

# Show schema for default mode
> aablocks schema ab1001
{
  "dtype": {
    "mata_description": "string",
    "alphaseq_affinity": "Float64",
    "above_background": "boolean",
    "pos_a": "Int64"
  }
}

# Show schema for ML mode
> aablocks schema ab1001 -m ml

License

Apache 2.0 — see LICENSE for details.