Aspy21

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

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

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 optional caching.

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 aggregate reader types (AVG, MIN, MAX, RNG)
• Smart aggregates: Query aggregates table with or without intervals for flexible period-based statistics
• 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
• Automatic retries of transient failures (connection errors, HTTP 429/502/503/504)
• Fully type-annotated: editors and type checkers know whether a call returns a DataFrame, a list of records or a list of tag names

Use Cases

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

Installation

Install via pip:

pip install aspy21

Requirements

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

Quick Start

Basic Usage with Context Manager

from aspy21 import AspenClient, OutputFormat, ReaderType

# Initialize client using context manager (recommended)
with AspenClient(
    base_url="http://aspen.example.com/ProcessData/AtProcessDataREST.dll",
    auth=("user", "password"),
    datasource="IP21",  # Required for reads and searches
) 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",
        read_type=ReaderType.RAW,
        output=OutputFormat.DATAFRAME,
    )

    print(df)
    # Connection automatically closed

base_url is the address of the ProcessData REST API, including AtProcessDataREST.dll: http://<server>/ProcessData/AtProcessDataREST.dll. datasource is the IP.21 data source name; reads and searches raise ValueError without it.

Usage Examples

Reading Aggregates Data

The aggregates reader types (AVG, MIN, MAX, RNG) query the IP.21 aggregates table for period-based statistics.

Average Values Over Time Intervals

from aspy21 import AspenClient, ReaderType, OutputFormat

with AspenClient(
    base_url="http://aspen.example.com/ProcessData/AtProcessDataREST.dll",
    auth=("user", "password"),
    datasource="IP21"
) as client:
    # Get 10-minute averages for a 1-hour period
    df = client.read(
        ["REACTOR_TEMP", "REACTOR_PRESSURE"],
        start="2025-01-15 08:00:00",
        end="2025-01-15 09:00:00",
        interval=600,  # 10 minutes (600 seconds)
        read_type=ReaderType.AVG,
        output=OutputFormat.DATAFRAME
    )
    # Returns 6 rows (one average per 10-minute interval)
    print(df)

Single Aggregate Value for Entire Period

# Get a single average value over 24 hours (no interval)
with AspenClient(
    base_url="http://aspen.example.com/ProcessData/AtProcessDataREST.dll",
    auth=("user", "password"),
    datasource="IP21"
) as client:
    data = client.read(
        ["REACTOR_TEMP"],
        start="2025-01-15 00:00:00",
        end="2025-01-16 00:00:00",
        read_type=ReaderType.AVG,  # No interval specified
        output=OutputFormat.JSON
    )
    # Returns: [{"timestamp": "...", "tag": "REACTOR_TEMP", "value": 285.4}]
    print(f"24-hour average: {data[0]['value']}")

Minimum and Maximum Values

# Find daily min/max temperatures
with AspenClient(
    base_url="http://aspen.example.com/ProcessData/AtProcessDataREST.dll",
    auth=("user", "password"),
    datasource="IP21"
) as client:
    # Minimum values per hour
    min_df = client.read(
        ["REACTOR_TEMP"],
        start="2025-01-15 00:00:00",
        end="2025-01-16 00:00:00",
        interval=3600,  # 1 hour
        read_type=ReaderType.MIN,
        output=OutputFormat.DATAFRAME
    )

    # Maximum values per hour
    max_df = client.read(
        ["REACTOR_TEMP"],
        start="2025-01-15 00:00:00",
        end="2025-01-16 00:00:00",
        interval=3600,
        read_type=ReaderType.MAX,
        output=OutputFormat.DATAFRAME
    )

    print(f"Hourly minimums:\n{min_df}")
    print(f"Hourly maximums:\n{max_df}")

Range (Max - Min) Values

# Calculate hourly temperature ranges (variability)
with AspenClient(
    base_url="http://aspen.example.com/ProcessData/AtProcessDataREST.dll",
    auth=("user", "password"),
    datasource="IP21"
) as client:
    df = client.read(
        ["REACTOR_TEMP", "COLUMN_TEMP"],
        start="2025-01-15 00:00:00",
        end="2025-01-15 12:00:00",
        interval=3600,  # 1 hour
        read_type=ReaderType.RNG,  # Range = Max - Min
        output=OutputFormat.DATAFRAME
    )
    # Shows temperature variability over each hour
    print(df)

Combining Multiple Aggregate Types

# Get comprehensive statistics for a process variable
with AspenClient(
    base_url="http://aspen.example.com/ProcessData/AtProcessDataREST.dll",
    auth=("user", "password"),
    datasource="IP21"
) as client:
    tag = "REACTOR_TEMP"
    period_start = "2025-01-15 08:00:00"
    period_end = "2025-01-15 16:00:00"

    # Get all statistics for 1-hour intervals
    avg = client.read([tag], start=period_start, end=period_end,
                      interval=3600, read_type=ReaderType.AVG)
    min_vals = client.read([tag], start=period_start, end=period_end,
                           interval=3600, read_type=ReaderType.MIN)
    max_vals = client.read([tag], start=period_start, end=period_end,
                           interval=3600, read_type=ReaderType.MAX)
    rng = client.read([tag], start=period_start, end=period_end,
                      interval=3600, read_type=ReaderType.RNG)

    # Combine into analysis
    import pandas as pd
    stats = pd.DataFrame({
        'avg': [d['value'] for d in avg],
        'min': [d['value'] for d in min_vals],
        'max': [d['value'] for d in max_vals],
        'range': [d['value'] for d in rng],
    })
    print(stats)

Reading Raw and Interpolated Data

# Raw historical data (as stored in historian)
with AspenClient(
    base_url="http://aspen.example.com/ProcessData/AtProcessDataREST.dll",
    auth=("user", "password"),
    datasource="IP21"
) as client:
    raw_df = client.read(
        ["FLOW_101", "LEVEL_205"],
        start="2025-01-15 08:00:00",
        end="2025-01-15 09:00:00",
        read_type=ReaderType.RAW,
        output=OutputFormat.DATAFRAME
    )
    print(f"Raw data points: {len(raw_df)}")

    # Interpolated data at regular intervals
    int_df = client.read(
        ["FLOW_101", "LEVEL_205"],
        start="2025-01-15 08:00:00",
        end="2025-01-15 09:00:00",
        interval=60,  # 1-minute intervals
        read_type=ReaderType.INT,
        output=OutputFormat.DATAFRAME
    )
    print(f"Interpolated data points: {len(int_df)}")

Reading Several Tags

Values of all tags are merged on one time index, sorted by time. A tag that has no value at a timestamp is NaN there in a DataFrame, and has no record in JSON output:

df = client.read(
    ["FLOW_101", "LEVEL_205"],
    start="2025-01-15 08:00:00",
    end="2025-01-15 09:00:00",
    read_type=ReaderType.RAW,
    include=IncludeFields.STATUS,
    output=OutputFormat.DATAFRAME,
)
#                      FLOW_101  FLOW_101_status  LEVEL_205  LEVEL_205_status
# time
# 2025-01-15 08:00:00      12.5              0.0        NaN               NaN
# 2025-01-15 08:00:30       NaN              NaN       40.1               0.0
# 2025-01-15 08:01:00      12.7              0.0       40.3               0.0

• Status and description columns are named <tag>_status and <tag>_description and follow their tag's column
• Raw history can hold several values of one tag at the same timestamp. Each is kept on its own row, so the index of a RAW read can contain a timestamp more than once
• Tags that returned no data have no column (DataFrame) or no records (JSON)
• limit applies to each tag. All tags are read in one query; if a tag has more than limit values in the range, the tags that query cut short are read again one at a time, so each tag gets up to limit values. Reads below the limit take a single query
• JSON values are plain Python int, float or str. In a DataFrame, a tag column with gaps holds floats, since pandas needs a float column to hold NaN

Snapshot (Current Values)

# Get current values for multiple tags
with AspenClient(
    base_url="http://aspen.example.com/ProcessData/AtProcessDataREST.dll",
    auth=("user", "password"),
    datasource="IP21"
) as client:
    # Method 1: Explicit SNAPSHOT read type
    df = client.read(
        ["TEMP_101", "PRESSURE_102", "FLOW_103"],
        read_type=ReaderType.SNAPSHOT,
        output=OutputFormat.DATAFRAME
    )

    # Method 2: Omit start/end (defaults to SNAPSHOT)
    df = client.read(
        ["TEMP_101", "PRESSURE_102", "FLOW_103"],
        output=OutputFormat.DATAFRAME
    )

    print(df)

Search and Read (Hybrid Mode)

# Search for tags and read their data in one call
with AspenClient(
    base_url="http://aspen.example.com/ProcessData/AtProcessDataREST.dll",
    auth=("user", "password"),
    datasource="IP21"
) as client:
    # Find all reactor temperature tags and get hourly averages
    df = client.search(
        tag="REACTOR_*_TEMP",
        start="2025-01-15 00:00:00",
        end="2025-01-16 00:00:00",
        interval=3600,
        read_type=ReaderType.AVG,
        output=OutputFormat.DATAFRAME
    )
    print(f"Found and read data for {len(df.columns)} tags")
    print(df)

Including Status and Descriptions

from aspy21 import IncludeFields

with AspenClient(
    base_url="http://aspen.example.com/ProcessData/AtProcessDataREST.dll",
    auth=("user", "password"),
    datasource="IP21"
) as client:
    # Include quality status codes
    df = client.read(
        ["REACTOR_TEMP"],
        start="2025-01-15 08:00:00",
        end="2025-01-15 09:00:00",
        interval=600,
        read_type=ReaderType.AVG,
        include=IncludeFields.STATUS,
        output=OutputFormat.DATAFRAME
    )
    # DataFrame includes REACTOR_TEMP and REACTOR_TEMP_status columns

    # Include descriptions
    df = client.read(
        ["REACTOR_TEMP"],
        start="2025-01-15 08:00:00",
        end="2025-01-15 09:00:00",
        interval=600,
        read_type=ReaderType.AVG,
        include=IncludeFields.DESCRIPTION,
        output=OutputFormat.DATAFRAME
    )

    # Include both status and descriptions
    df = client.read(
        ["REACTOR_TEMP"],
        start="2025-01-15 08:00:00",
        end="2025-01-15 09:00:00",
        interval=600,
        read_type=ReaderType.AVG,
        include=IncludeFields.ALL,
        output=OutputFormat.DATAFRAME
    )

Authentication

HTTP Basic Authentication

with AspenClient(
    base_url="http://aspen.example.com/ProcessData/AtProcessDataREST.dll",
    auth=("user", "password"),
    datasource="IP21"  # Required for historical reads
) as client:
    # Your code here
    pass

No Authentication

For public or internal endpoints:

with AspenClient(
    base_url="http://aspen.example.com/ProcessData/AtProcessDataREST.dll",
    datasource="IP21"  # Required for historical reads
) as client:
    # Your code here
    pass

Caching

Overview

An optional in-memory cache returns repeated reads of past data without a server round trip. It uses TTL (Time-To-Live) expiry with eviction of the oldest entries, and is thread-safe. It is off by default.

What Is Cached

• Historical reads (RAW, INT) whose end time is more than a minute in the past: 24 hours (ttl_historical) - this data doesn't change
• Aggregate reads (AVG, MIN, MAX, RNG) of past periods: 24 hours (ttl_aggregates)
• Search results: 1 hour (ttl_search) - tag lists change infrequently
• Not cached: snapshot reads, reads ending in the last minute or in the future, empty results, and failed requests

Enabling Cache

Basic Usage (Default Configuration)

from aspy21 import AspenClient

# Enable caching with defaults
with AspenClient(
    base_url="http://aspen.example.com/ProcessData/AtProcessDataREST.dll",
    auth=("user", "password"),
    datasource="IP21",
    cache=True,  # Enable with default settings
) as client:
    # First read hits API
    data1 = client.read(["TAG1"], start="2025-01-01 08:00:00", end="2025-01-01 09:00:00")

    # Second identical read hits cache (10-100x faster!)
    data2 = client.read(["TAG1"], start="2025-01-01 08:00:00", end="2025-01-01 09:00:00")

    # Check cache statistics
    stats = client.get_cache_stats()
    print(f"Cache hit rate: {stats['hit_rate_percent']}%")

Custom Configuration

from aspy21 import AspenClient, CacheConfig

# Configure cache with custom TTLs and size
config = CacheConfig(
    enabled=True,
    max_size=500,           # Limit to 500 cached entries
    ttl_historical=7200,    # 2 hours for RAW and INT reads
    ttl_search=1800,        # 30 minutes for search results
    ttl_aggregates=7200,    # 2 hours for aggregates
)

with AspenClient(
    base_url="http://aspen.example.com/ProcessData/AtProcessDataREST.dll",
    auth=("user", "password"),
    datasource="IP21",
    cache=config,  # Use custom configuration
) as client:
    # Your code here
    pass

Share Cache Across Clients

from aspy21 import AspenClient, AspenCache

# Create a shared cache instance
shared_cache = AspenCache()

# Use same cache for multiple clients
with AspenClient(base_url=url1, datasource="IP21", cache=shared_cache) as client1:
    with AspenClient(base_url=url2, datasource="IP21", cache=shared_cache) as client2:
        # Both clients share the same cache; their entries are kept apart by server and
        # datasource
        client1.read([...])
        client2.read([...])

        # Combined statistics
        stats = shared_cache.get_stats()

Cache Management

Get Statistics

stats = client.get_cache_stats()
print(f"Cache size: {stats['size']} entries")
print(f"Hit rate: {stats['hit_rate_percent']}%")
print(f"Hits: {stats['hits']}, Misses: {stats['misses']}")

Clear Cache

# Clear all cached entries
count = client.clear_cache()
print(f"Cleared {count} entries")

Invalidate Specific Entries

# Invalidate cached reads for specific tags/time range
count = client.invalidate_cache(
    tags=["TAG1", "TAG2"],
    start="2025-01-01 00:00:00",
    end="2025-01-01 01:00:00"
)

# Any argument can be omitted: this removes every cached read that includes TAG1
count = client.invalidate_cache(tags=["TAG1"])

A cached read is removed when it includes any of the given tags and its start and end match. Only reads from this client's server and datasource are affected; search results are kept.

Performance

A cache hit skips the request to IP.21, so it takes milliseconds however long the query took on the server. How much load it saves depends on how often the same reads repeat.

Example Timing:

import time

with AspenClient(base_url=url, auth=auth, datasource="IP21", cache=True) as client:
    # First call - hits API
    start = time.time()
    df1 = client.read(["TAG1"], start="2025-01-01 08:00:00", end="2025-01-01 09:00:00")
    api_time = time.time() - start
    print(f"API call: {api_time:.3f}s")

    # Second call - from cache
    start = time.time()
    df2 = client.read(["TAG1"], start="2025-01-01 08:00:00", end="2025-01-01 09:00:00")
    cache_time = time.time() - start
    print(f"Cache hit: {cache_time:.3f}s")

Cache Behavior Details

Cache Keys:

• A cached result is only returned for a request with the same tags (in the same order), time range, interval, read type, fields, limit and output format, from the same server, datasource and server_timezone
• Results are copied in and out of the cache, so changing a returned DataFrame or list does not change later cache hits

Thread Safety:

• All cache operations are thread-safe
• Can be safely used in multi-threaded applications

Memory Management:

• The oldest entry is evicted when max_size is reached
• Automatic cleanup of expired entries
• Configurable size limits prevent memory issues

API Reference

AspenClient

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

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]: ...

Type checkers and editors infer the exact type from output: pd.DataFrame for OutputFormat.DATAFRAME, list[dict] for OutputFormat.JSON (the default).

Parameters:

Returns:

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

Raises: ValueError (no tags, or no datasource), AspenSqlError (IP.21 rejected the query), httpx.HTTPStatusError and httpx.RequestError (after any retries). See Error Handling.

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]: ...

Type checkers and editors infer the exact type:

• Search without start: list[str], or list[dict] with include=IncludeFields.DESCRIPTION or ALL
• Hybrid mode (with start): pd.DataFrame for output=OutputFormat.DATAFRAME, otherwise list[dict]

Parameters:

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, interval seconds apart (default)
• ReaderType.SNAPSHOT - Current snapshot of tag values
• ReaderType.AVG - Average values from aggregates table (with/without interval)
• ReaderType.MIN - Minimum value over the period (from aggregates table)
• ReaderType.MAX - Maximum value over the period (from aggregates table)
• ReaderType.RNG - Range (max-min) over the period (from aggregates table)

Aggregates types (AVG, MIN, MAX, RNG) query the aggregates table with the period automatically calculated in tenths of seconds:

• Without interval: period = (end - start) * 10 → returns 1 aggregate value for entire range
• With interval: period = interval * 10 → returns multiple values (one per interval period)

Example: A 24-hour period becomes 864000 tenths of seconds (24 × 60 × 60 × 10), while a 10-minute interval becomes 6000 (600 × 10).

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

Examples:

from aspy21 import AspenClient, IncludeFields, OutputFormat, ReaderType

# AVG with interval: returns multiple average values (one per interval)
df = client.read(
    ["TAG1", "TAG2"],
    start="2025-01-01 00:00:00",
    end="2025-01-01 01:00:00",
    read_type=ReaderType.AVG,
    interval=600,  # 10-minute averages - returns 6 values (60min / 10min)
    include=IncludeFields.ALL,
    output=OutputFormat.DATAFRAME
)

# AVG without interval: returns single aggregate value for entire period
avg_value = client.read(
    ["TAG1"],
    start="2025-01-01 00:00:00",
    end="2025-01-02 00:00:00",
    read_type=ReaderType.AVG,  # Single average over 24 hours
    output=OutputFormat.JSON
)
# Returns: [{"timestamp": "...", "tag": "TAG1", "value": 42.5}]

# Other aggregates (MIN/MAX/RNG) work the same way
min_value = client.read(
    ["TAG1"],
    start="2025-01-01 00:00:00",
    end="2025-01-02 00:00:00",
    read_type=ReaderType.MIN,  # Minimum value over 24 hours
    output=OutputFormat.JSON
)

Configuration

Connection Settings

with AspenClient(
    base_url="http://aspen.example.com/ProcessData/AtProcessDataREST.dll",
    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

Timestamps and Time Zones

IP.21 interprets query timestamps in the server's local time and returns server local times (the REST API labels them Z, but they are not UTC). aspy21 therefore works in server local time:

• Default (server_timezone=None): returned timestamps are naive server-local times. Naive start/end values are sent as they are; timezone-aware values are converted to this machine's local zone first.
• server_timezone="Europe/Berlin": returned timestamps are timezone-aware in that zone, and timezone-aware start/end values are converted to it.
• For a server that really returns UTC, use server_timezone="UTC".

with AspenClient(
    base_url="http://aspen.example.com/ProcessData/AtProcessDataREST.dll",
    auth=("user", "password"),
    datasource="IP21",
    server_timezone="Europe/Berlin",
) as client:
    data = client.read(["ATI111"], start="2025-06-20T06:00:00Z", end="2025-06-20T07:00:00Z")
    # Queries 08:00-09:00 server time; timestamps come back as "...+02:00"

Aggregate values (AVG, MIN, MAX, RNG) are stamped at the end of their period.

Logging

aspy21 logs through the standard logging module under the aspy21 logger. To print its messages with a timestamped format:

from aspy21 import configure_logging

configure_logging("INFO")  # DEBUG, INFO, WARNING (default), ERROR or CRITICAL
configure_logging()        # level from the ASPEN_LOG_LEVEL environment variable

At DEBUG, every request is logged with its SQL query and the start of the response. Applications that configure logging themselves can set the level of the aspy21 logger instead.

Retries

Requests that fail for a reason likely to pass are repeated, up to retries times (default: 2, so at most 3 attempts):

• Retried: connection errors (refused, reset, dropped), connect timeouts, and HTTP 429, 502, 503 and 504
• Not retried: read timeouts (a query slow enough to time out usually times out again, and repeating it adds load on the historian), other HTTP errors such as 401 or 500, and SQL errors (AspenSqlError)

Between attempts the client waits 0.5s, then 1s, 2s and so on up to 8s. If the server sends a Retry-After header (in seconds), that wait is used instead, up to 30s. Each retry is logged as a warning. When all attempts fail, the error from the last one is raised.

# More attempts for an unreliable network
client = AspenClient(base_url=..., datasource="IP21", retries=5)

# Fail fast, no retries
client = AspenClient(base_url=..., datasource="IP21", retries=0)

Retries apply to every request, including those sent through a custom http_client.

Error Handling

IP.21 reports most errors with HTTP 200 and the error in the response body. These are raised as AspenError:

• AspenSqlError - the SQL endpoint rejected a query (reads, search by description) or returned a response that could not be read
• AspenBrowseError - the Browse endpoint (search by tag name) reported an error or returned a response that could not be read

AspenError is a subclass of ValueError. Invalid arguments, such as an empty tag list or a client without datasource, raise a plain ValueError.

from aspy21 import AspenClient, AspenError, OutputFormat
import httpx

try:
    with AspenClient(
        base_url="http://aspen.example.com/ProcessData/AtProcessDataREST.dll",
        auth=("user", "password"),
        datasource="IP21"
    ) as client:
        df = client.read(
            ["ATI111"],
            start="2025-06-20 08:00:00",
            end="2025-06-20 09:00:00",
            output=OutputFormat.DATAFRAME,
        )
except AspenError as e:
    print(f"IP.21 reported an error: {e}")  # Returned by the server with HTTP 200
except ValueError as e:
    print(f"Invalid request: {e}")  # e.g. no datasource
except httpx.HTTPStatusError as e:
    print(f"HTTP error: {e.response.status_code}")
except httpx.RequestError as e:
    print(f"Connection error: {e}")

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

Dependencies

Core Dependencies

• httpx (>= 0.27) - HTTP client with async support
• pandas (>= 2.0) - DataFrame output and date parsing
• tenacity (>= 9.0) - Retries of transient failures

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

License

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

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 85%

Support

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

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.