aspy21 0.2.0b15

Modern Python client for Aspen InfoPlus.21 (IP.21) with pandas DataFrame output, tag search, and automatic retry logic

Aspy21

Python Client for Aspen InfoPlus.21 (IP.21)

Note: This is an independent, unofficial client library. Not affiliated with AspenTech.

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Overview

aspy21 is a modern, high-performance Python client for Aspen InfoPlus.21 (IP.21) built on the AspenOne ProcessData REST API. It provides unified access to process historian data with pandas DataFrame output, automatic batching, and intelligent retry logic.

Key Capabilities

• REST-based communication with Aspen IP.21 historian
• Basic HTTP authentication (cross-platform compatible)
• Hybrid search mode: Search for tags and read their data in one call
• Tag search with wildcards and description filtering
• Unified interface for analog, discrete, and text tags
• Support for RAW, INT, SNAPSHOT, and AVG reader types
• Pandas DataFrame or JSON output with optional status and description fields
• Enum-based parameters for cleaner, type-safe API
• Configurable row limits and query parameters
• Built-in retry logic with exponential backoff
• Type-annotated and fully tested (85% coverage)

Use Cases

• Industrial data analysis and reporting
• Integration with data analytics pipelines
• Process monitoring and dashboard development
• Time-series data extraction and transformation

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Installation

Install via pip:

pip install aspy21

Requirements

• Python 3.9+
• httpx >= 0.27
• pandas >= 2.0
• tenacity >= 9.0

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Quick Start

Basic Usage with Context Manager

from aspy21 import AspenClient, OutputFormat, ReaderType

# Initialize client using context manager (recommended)
with AspenClient(
    base_url="https://aspen.myplant.local/ProcessData",
    auth=("user", "password")
) as client:
    # Read historical data
    df = client.read(
        ["ATI111", "AP101.PV"],
        start="2025-06-20 08:00:00",
        end="2025-06-20 09:00:00",
        interval=600,
        read_type=ReaderType.RAW,
        output=OutputFormat.DATAFRAME,
    )

    print(df)
    # Connection automatically closed

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Authentication

HTTP Basic Authentication

with AspenClient(
    base_url="https://aspen.example.com/ProcessData",
    auth=("user", "password")
) as client:
    # Your code here
    pass

No Authentication

For public or internal endpoints:

with AspenClient(
    base_url="http://aspen.example.com/ProcessData"
) as client:
    # Your code here
    pass

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API Reference

AspenClient

Constructor Parameters (all except base_url are keyword-only):

Parameter	Type	Default	Description
base_url	str	required	Base URL of Aspen ProcessData REST API
auth	Auth|tuple|None	None	Authentication as (username, password) tuple or httpx Auth object
timeout	float	30.0	Request timeout in seconds
verify_ssl	bool	True	Whether to verify SSL certificates
datasource	str|None	None	Aspen datasource name (required for search)

read() Method

Read historical or snapshot data for multiple tags.

Signature:

def read(
    tags: list[str],
    *,
    start: str | None = None,
    end: str | None = None,
    interval: int | None = None,
    read_type: ReaderType = ReaderType.INT,
    include: IncludeFields = IncludeFields.NONE,
    limit: int = 100_000,
    output: OutputFormat = OutputFormat.JSON,
) -> pd.DataFrame | list[dict]:

Parameters:

Parameter	Type	Default	Description
tags	list[str]	required	List of tag names to retrieve (positional-only)
start	str|None	None	Start timestamp (ISO format). When omitted, defaults to SNAPSHOT read.
end	str|None	None	End timestamp (ISO format). When omitted, defaults to SNAPSHOT read.
interval	int|None	None	Interval in seconds for aggregated data (AVG reads)
read_type	ReaderType	INT	Data retrieval mode (RAW, INT, SNAPSHOT, AVG)
include	IncludeFields	NONE	Which optional fields to include (NONE, STATUS, DESCRIPTION, ALL)
limit	int	100000	Maximum rows to return per tag
output	OutputFormat	JSON	Output format (JSON or DATAFRAME)

Returns:

• If output=OutputFormat.DATAFRAME: pandas.DataFrame with time index and columns for each tag.
• If output=OutputFormat.JSON: List of dictionaries with timestamp, tag, value, and optional description/status fields.

Examples:

# JSON output (default)
data = client.read(
    ["ATI111"],
    start="2025-01-01 00:00:00",
    end="2025-01-01 01:00:00"
)

# DataFrame output with status and descriptions
df = client.read(
    ["ATI111", "AP101.PV"],
    start="2025-01-01 00:00:00",
    end="2025-01-01 01:00:00",
    output=OutputFormat.DATAFRAME,
    include=IncludeFields.ALL
)

Snapshot reads:

• Supplying no start/end (or explicitly choosing ReaderType.SNAPSHOT) returns the latest values.
• When include=IncludeFields.STATUS or IncludeFields.ALL, includes quality/status codes.

search() Method

Search for tags by name pattern and/or description. Optionally read their data in hybrid mode.

Signature:

def search(
    tag: str = "*",
    *,
    description: str | None = None,
    case_sensitive: bool = False,
    limit: int = 10_000,
    # Optional read parameters for hybrid mode
    start: str | None = None,
    end: str | None = None,
    interval: int | None = None,
    read_type: ReaderType = ReaderType.INT,
    include: IncludeFields = IncludeFields.NONE,
    output: OutputFormat = OutputFormat.JSON,
) -> pd.DataFrame | list[dict] | list[str]:

Parameters:

Parameter	Type	Default	Description
tag	str	"*"	Tag name pattern with wildcards (*, ?)
description	str|None	None	Description filter (case-insensitive). Uses SQL endpoint when provided.
case_sensitive	bool	False	Case-sensitive tag matching (Browse endpoint only)
limit	int	10000	Max results (search mode) or max rows per tag (hybrid mode)
start	str|None	None	Triggers hybrid mode: Start timestamp for data retrieval
end	str|None	None	End timestamp (defaults to current time if omitted)
interval	int|None	None	Interval in seconds for aggregated data
read_type	ReaderType	INT	Data retrieval mode for hybrid mode
include	IncludeFields	NONE	Fields to include (NONE, STATUS, DESCRIPTION, ALL)
output	OutputFormat	JSON	Output format (JSON or DATAFRAME)

Returns:

• Search-only mode (no start):
  • If include=NONE or STATUS: List of tag name strings
  • If include=DESCRIPTION or ALL: List of dicts with 'name' and 'description'
• Hybrid mode (with start):
  • If output=JSON: List of dicts with timestamp, tag, value, and optional fields
  • If output=DATAFRAME: pandas DataFrame

Requirements:

• datasource must be configured in AspenClient

Modes:

1. Search-only (no start): Find tags matching criteria
2. Hybrid mode (with start): Search for tags AND read their data in one call

Wildcards:

• * - Matches any number of characters
• ? - Matches exactly one character

Examples:

# Search-only: Get tag names (default)
tag_names = client.search("TEMP*")
# Returns: ["TEMP_101", "TEMP_102", ...]

# Search-only: Get tags with descriptions
tags = client.search("TEMP*", include=IncludeFields.DESCRIPTION)
# Returns: [{"name": "TEMP_101", "description": "Reactor temp"}, ...]

# Hybrid mode: Search and read data in one call
df = client.search(
    "TEMP*",
    start="2025-01-01 00:00:00",
    end="2025-01-01 01:00:00",
    output=OutputFormat.DATAFRAME
)
# Returns: DataFrame with data for all TEMP* tags

# Search by description (SQL endpoint)
tags = client.search(description="reactor", include=IncludeFields.DESCRIPTION)

# Combine pattern and description
tags = client.search("AI_1*", description="pressure", include=IncludeFields.DESCRIPTION)

Enums

ReaderType

Data retrieval modes:

• ReaderType.RAW - Raw data points as stored in historian
• ReaderType.INT - Interpolated values at specified intervals (default)
• ReaderType.SNAPSHOT - Current snapshot of tag values
• ReaderType.AVG - Average values over specified intervals

IncludeFields

Controls which optional fields to include in responses:

• IncludeFields.NONE - Include only timestamp and value (default)
• IncludeFields.STATUS - Include status/quality codes
• IncludeFields.DESCRIPTION - Include tag descriptions
• IncludeFields.ALL - Include both status and description

OutputFormat

Controls output format:

• OutputFormat.JSON - Return as list of dictionaries (default)
• OutputFormat.DATAFRAME - Return as pandas DataFrame

Example:

from aspy21 import AspenClient, IncludeFields, OutputFormat, ReaderType

# Use all three enums together
df = client.read(
    ["TAG1", "TAG2"],
    start="2025-01-01 00:00:00",
    end="2025-01-01 01:00:00",
    read_type=ReaderType.AVG,
    include=IncludeFields.ALL,
    output=OutputFormat.DATAFRAME
)

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Configuration

Connection Settings

with AspenClient(
    base_url="https://aspen.example.com/ProcessData",
    auth=("user", "password"),
    timeout=60.0,           # Request timeout (seconds)
    verify_ssl=True,        # SSL certificate verification
    datasource="IP21"       # Required for search operations
) as client:
    # Your code here
    pass

Retry Behavior

The client automatically retries failed requests with exponential backoff:

• Maximum attempts: 3
• Initial delay: 0.5 seconds
• Maximum delay: 8 seconds

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Error Handling

from aspy21 import AspenClient, OutputFormat
import httpx

try:
    with AspenClient(
        base_url="https://aspen.example.com/ProcessData",
        auth=("user", "password")
    ) as client:
        df = client.read(
            ["ATI111"],
            start="2025-06-20 08:00:00",
            end="2025-06-20 09:00:00",
            output=OutputFormat.DATAFRAME,
        )
except httpx.HTTPStatusError as e:
    print(f"HTTP error: {e.response.status_code}")
except httpx.RequestError as e:
    print(f"Connection error: {e}")

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Development

Running Tests

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run with coverage
pytest --cov=src/aspy21 --cov-report=html

Type Checking

pyright

Code Formatting

ruff format

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Dependencies

Core Dependencies

• httpx (>= 0.27) - HTTP client with async support
• pandas (>= 2.0) - DataFrame output and date parsing
• tenacity (>= 9.0) - Retry logic with exponential backoff

Development Dependencies

• pytest, pytest-cov - Testing framework
• respx - HTTP mocking for tests
• ruff - Code formatting and linting
• pyright - Static type checking
• pre-commit - Git hooks

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License

This project is licensed under the MIT License. See LICENSE file for details.

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Contributing

Contributions are welcome. Please ensure:

1. All tests pass (pytest)
2. Code is formatted (ruff format)
3. Type checking passes (pyright)
4. Coverage remains above 75%

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Support

For issues, questions, or feature requests, please open an issue on GitHub.

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Disclaimer

This project is an independent open-source client library and is not affiliated with, endorsed by, or sponsored by AspenTech. "Aspen InfoPlus.21", "IP.21", and "AspenTech" are trademarks or registered trademarks of Aspen Technology, Inc.

This software interacts with Aspen InfoPlus.21 systems through their documented REST API endpoints. Users must have appropriate licenses and authorization to access AspenTech systems.

Users are responsible for compliance with their AspenTech license agreements and applicable terms of service. This library merely provides a technical interface and does not grant any rights to AspenTech software or services.