Delphi Python Utilities
This package provides various utilities used by the Delphi group at Carnegie Mellon University for its data pipelines and analyses.
Submodules:
archive: Diffing and archiving CSV files.export: DataFrame to CSV export.geomap: Mappings between geographic resolutions.logger: Structured JSON logger.nancodes: Enum constants encoding not-a-number cases.runner: Orchestrator for running an indicator pipeline.signal: Indicator (signal) naming.slack_notifier: Slack notification integration.smooth: Data smoothing functions.utils: JSON parameter interactions.validator: Data sanity checks and anomaly detection.
Source code can be found here: https://github.com/cmu-delphi/covidcast-indicators/
Logger Usage
To make our structured logging as useful as it can be, particularly within the context of how we use logs in Elastic, the event argument (typically the first unnamed arg) should be a static string (to make filtering easier), and each dynamic/varying value should be specified in an individual meaningfully- and consistently-named argument to the logger call (for use in filtering, thresholding, grouping, visualization, etc).
Commonly used argument names:
- data_source
- geo_type
- signal
- issue_date
- filename
Single-thread usage.
from delphi_utils.logger import get_structured_logger
logger = get_structured_logger('my_logger')
logger.info('Hello', name='World')
Multi-thread usage.
from delphi_utils.logger import get_structured_logger, pool_and_threadedlogger
def f(x, threaded_logger):
threaded_logger.info(f'x={x}')
return x*x
logger = get_structured_logger('my_logger')
logger.info('Hello, world!')
with pool_and_threadedlogger(logger, n_cpu) as (pool, threaded_logger):
for i in range(10):
pool.apply_async(f, args=(i, threaded_logger))
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Release files for delphi-utils 0.3.27
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