EnviroDataQC

This library provides a framework for assessing quality of environmental data.

Data is assessed with respect to:

• Data Range
• Data rate of change
• Data flatlining

Additionally, special methods are provided for assessing wind speed and direction data. Data is classified as either suspicious or bad based on either default or custom user settings.

Installation

pip install envirodataqc

The configuration file is currently a python script in the package, so it may be easier to clone the repository rather than install from PYPI if you want to change the configuration.

Basic Use

Pass data (Pandas series) and measurement type to check_vals(). A Pandas dataframe is returned with three new columns: 'flags_range', 'flags_rate', 'flags_flat'. Measurement types supported are defined in 'QCconfig.py'.

Flags:

• 0 : Good
• 1 : Suspicious
• 2 : Bad

Configuration

Change and/or add dictionaries defined in 'QCconfig.py'. Dictionary entries define "good" ranges and "suspicious" ranges for each flag category. Configuration ranges can be non-continuous and any overlap between "good" and "suspicious" ranges will be flagged as "good".

Wind specific checks

• check_windsp_ratio - Check ratio of mean windspeed to max
• check_windsp_withdir - Check if windspeed is consistent with direction
• check_winddir_withsp - Check if direction is consistent with windspeed

Other Functions

• check_gaps(index) - Given a Pandas datetime index outputs total time where gaps between timestamps are > 1hr.
• daily_quality(data) - Given a Pandas series (index=timestamp, values=data flags), return a pandas series with a consolidated daily quality level (0,1,2)