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The sawyer python package helps users manage workflows and develop processing pipelines for time series data from datalogger and sensor networks. The package is built with environmental observations, like meteorological measurements, in mind. Data management and processing steps are defined using a set of YAML configuration files.

sawyer is useful for small to medium sized projects, and using these tools requires some proficiency with python scripting and using the command line. The package requires pandas, matplotlib, scipy (for gapfilling), and ruamel.yaml.

WARNING: Documentation here is still being built - feel free to send me questions or suggestions.

Assumptions and functionality

Incoming data types

The sawyer package assumes incoming raw data are time series in a tabular format. Data must be parsed into a pandas DataFrame with a datetime row index and one or more observational variables in the column index. Missing observations are ok, but in general sawyer will regularize the time series, and irregular time series may not be handled well.

Project organization

Sawyer projects are organized around data loggers (and their output tables) as the fundamental sources for a data pipeline. There can be many loggers in a sawyer project, but each logger should have a unique name, and sawyer processes and outputs data from each logger in the project separately.

Data processing levels and quality flags

Sawyer data processing pipelines operate using data levels. The incoming data files are designated as raw and will not be altered. As raw data are processed, new files are created at a higher data level, and flag columns will be added alongside each data column. Flag columns indicate the quality checks, data filtering, or gapfilling applied to the data level. There can be multiple data levels in a project, and data levels above raw typically become the source for other, higher levels.

The default data levels are similar to the levels used by Ameriflux. The AmeriFlux levels are a useful conceptual framework for processing environmental data, but users may implement their own levels and naming using the configuration files.


Sawyer can gapfill variables using a few straightforward methods. Some of these methods are sound, and some might not be depending on the data. So, be careful and look closely at the results you get.


The sawyer package aims to collect and preserve metadata during data processing. I'm working on some ways to output structured metadata for data publication, but still in early stages.


You can install sawyer using pip, but its not at PyPI yet. Either clone this repository to your local environment and install with:

pip install path/to/sawyer/

or install direct from GitHub:

pip install git+git://

Note that there is also a dev branch, which might be working great or broken. Ask me if you want to know the status.

Raw data preparation

It is best to try to collect your raw data following a few conventions, and then organize your raw data logger files a little bit, before you start with sawyer.

Time indexes and variable naming

Make sure your data logger is adding an appropriate timestamp, preferably in ISO 8601 format, such as YYYY-MM-DD hh:mm:ss, to every observation collected. Most data loggers will do this already, but using files that have been modified from this standard may be difficult to parse. It is also advisable to either avoid making any timekeeping changes at the data logger level, such as adjusting the clock for daylight savings, or document them thoroughly.

Also make sure that all variables recorded in your raw data files have unique and intelligible names (column headers). Sensor networks can be complex and often have multiple observations of the same variable from different sensor positions or replicates. It is wise to program your datalogger to name variables so that the source of the data stream is clear. AmeriFlux maintains some sensible conventions for flux sites that suggest using the pattern VARTYPE_H_V_R, where

* VARTYPE is the variable/sensor identifier (AirTC, CO2sensor, etc)
* H is the horizontal position (1, 2, 3 in an arbitrary number scheme, or a distance/direction)
* V is the vertical position (depth or height)
* R is the replicate number (optional)

If data logger programming changes, the name of a variable in the resulting data file might change even though the underlying data stream or source hasn't. Be sure to document all such variable name changes.sawyer will allow consistent naming to be used in processed files if configurations are set properly.

Raw data directory

Make a raw data directory with the project name, and then create subdirectories that are named uniquely for each logger. Then, put the raw data files for each logger into these subdirectories. You will point sawyer to this directory using the project configuration file (see below).


Data processing pipelines are defined in a set of YAML configuration files. All configurations should go in a sawyer_config/ directory accesible from your working directory. This directory contains several global configuration files and a subdirectory with configuration files for each data logger in the project. All loggers have a unique name lname. Configurations include:

- `project.yaml`: defines project information, data source location, 
data processing levels, and output locations.
- `loggers.yaml`: defines a set of loggers, each with a unique name
`lname`, that are collecting the data
- `qa_flags.yaml`: defines data flagging and filtering steps for all
- `{lname}/var_rename.yaml`: defines changes in column variable names over
time in case sensors/data streams are renamed at logger `lname`.
- `{lname}/qa_flags.yaml`: defines data flagging and filtering steps for
logger `lname`.
- `{lname}/gafill.yaml`: defines data gapfilling steps for logger `lname`.


MIT license


Manage data workflows and build processing pipelines for data loggers and environmental sensor networks






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