Controlling Log Output

EasyScience uses Python's standard logging module for all informational and warning messages. This gives you full control over what output the library produces and where it goes.

Quick Start

The most common operation is suppressing all EasyScience messages. You can do it in one line:

from easyscience import global_object

global_object.log.level = 'ERROR'

Or using the standard library directly:

import logging

logging.getLogger('easyscience').setLevel(logging.ERROR)

Both forms set the package-root logger to ERROR, which suppresses all WARNING, INFO, and DEBUG messages from EasyScience.

Logger Hierarchy

EasyScience loggers are named hierarchically under the root easyscience logger. This lets you suppress the whole package or individual subsystems:

easyscience                        # root — controls everything
├── easyscience.base_classes       # EasyList runtime warnings
├── easyscience.legacy             # ObjBase / CollectionBase deprecations
├── easyscience.deprecated         # @deprecated decorator messages
├── easyscience.fitting            # import-availability warnings
│   ├── easyscience.fitting.bumps  # Bumps fitting runtime messages
│   ├── easyscience.fitting.lmfit  # LMFit fitting runtime messages
│   └── easyscience.fitting.dfo    # DFO fitting runtime messages
├── easyscience.variable           # parameter warnings
└── easyscience.global_object      # undo/redo, debugging diagnostics

Setting the level on a parent logger affects all its children, because child loggers inherit the parent's effective level.

import logging

# Suppress everything from the fitting subsystem (including bumps/lmfit/dfo)
logging.getLogger('easyscience.fitting').setLevel(logging.ERROR)

# Suppress only Bumps runtime messages
logging.getLogger('easyscience.fitting.bumps').setLevel(logging.ERROR)

# Suppress only legacy deprecation notices
logging.getLogger('easyscience.legacy').setLevel(logging.ERROR)

Environment Variable

You can control the log level before importing EasyScience by setting the EASYSCIENCE_LOG_LEVEL environment variable. This is useful when a downstream library cannot configure logging in code before EasyScience is imported.

Accepted values: DEBUG, INFO, WARNING, ERROR, CRITICAL, or a numeric level (e.g. 40).

Example — suppressing all import-time messages:

# Windows PowerShell
$env:EASYSCIENCE_LOG_LEVEL = 'ERROR'
python -c "import easyscience"  # silent

# Linux / macOS
EASYSCIENCE_LOG_LEVEL=ERROR python -c "import easyscience"  # silent

Or from Python, set the environment variable before the import:

import os

os.environ['EASYSCIENCE_LOG_LEVEL'] = 'ERROR'
import easyscience  # now silent

Temporary Suppression (Context Manager)

When you only want to silence messages during a specific operation, use the at_level context manager:

from easyscience import global_object
import logging

# Messages during the fit are suppressed, then the previous level is restored
with global_object.log.at_level(logging.ERROR):
    results = fitter.fit(x, y, weights)

This is the recommended pattern for technique-specific libraries that don't want EasyScience output to leak into their own users' consoles.

Convenience API

The global_object.log object exposes a level property plus convenience methods that mirror logging module-level functions:

Member	Description
`.level`	Get/set the package-root logger level (`'WARNING'` or `logging.WARNING`)
`.debug(msg)`	Log a `DEBUG`-level message
`.info(msg)`	Log an `INFO`-level message
`.warning(msg)`	Log a `WARNING`-level message
`.error(msg)`	Log an `ERROR`-level message
`.critical(msg)`	Log a `CRITICAL`-level message
`.exception(msg)`	Log an `ERROR`-level message with traceback
`.getLogger(name)`	Get a child logger under `easyscience`
`.at_level(level)`	Context manager for temporary level change
`.suspend()`	Suppress all EasyScience output
`.resume()`	Restore the previously set level

Recipes

Recipe 1: Keep your test output clean

When running tests that exercise EasyScience fitting, suppress the library's messages so they don't clutter your test reports:

import logging
from easyscience import global_object

@pytest.fixture(autouse=True)
def _quiet_easyscience():
    # The test body runs *inside* the context manager (yield within `with`)
    with global_object.log.at_level(logging.ERROR):
        yield

Recipe 2: Library author embedding EasyScience

If you maintain a library that uses EasyScience internally, prevent EasyScience from writing to your users' consoles:

# In your library's __init__.py, before any EasyScience import:
import os
os.environ.setdefault('EASYSCIENCE_LOG_LEVEL', 'ERROR')

Recipe 3: Debug mode for development

When developing or debugging, see all EasyScience internal diagnostics:

from easyscience import global_object

global_object.log.level = 'DEBUG'

# Your code here — all EasyScience messages will be visible

Recipe 4: See only error messages

Keep the console clean but still see critical problems:

from easyscience import global_object

global_object.log.level = 'ERROR'

Library-Safe Behaviour

EasyScience follows standard library-logging best practices:

No logging.basicConfig() — EasyScience never reconfigures the global logging setup.
No default stream handlers — EasyScience does not attach handlers that write to stdout or stderr. It only creates log records. Applications and test frameworks decide where those records go.
Child loggers inherit — Child loggers (e.g. easyscience.fitting.bumps) are left at logging.NOTSET by default, so they inherit level and handler configuration from the easyscience package-root logger. Supressing the root suppresses everything.

This means you are in full control. If you don't configure any handlers, Python's built-in logging.lastResort fallback still prints WARNING and above to stderr, so standalone users see important messages out of the box. INFO and DEBUG messages remain hidden until you opt in. If you do configure handlers (as pytest does via its logging plugin, or as an application might via basicConfig), you control what is shown and where.