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Processing of meteorological FODS data.

Project description

# pyfocs

pyfocs has been known by btmm_process (obscure non-pythonic name) and pyfox (an unmaintained package on PyPi) resulting in the new name for the library.

# Installation and running the example

## Installation pyfocs can be installed by using:

pip install pyfocs

which installs v0.1.1b or by downloading the source code, navigating to the directory containing it, and running

python install

Both methods should result in the being executable from the command line.

## Example

Download the data in the example directory. Within that directory is an example configuraiton file in yaml format. Adjust the dir_pre and external paths to be those in the example folder. Then, you should be able to run pyfocs using: path/to/example_configuration.yml

Alternatively, providing no path to the yaml file will open a file browser for selecting the configuration file.

# Introduction

The Bayreuth Micrometeorology python library for processing Fiber Optic Distributed Sensing (FODS) data. The library consists of a family of simple functions and a master script (PyFOX) that can be used to process output from a Silixa Distribute Temperature Sensing (DTS) device, such as an Ultima or XT, from the original *.xml files to calibrated temperatures with physical labels. This library is built around the [xarray]( package for handling n-dimensional data, especially in a netcdf format.

## Other libraries

Other similar libraries exist, such as the [one developed at Delft University](, which can be more useful for some applications, especially those with double-ended configurations.

# PyFOX Steps

Data and the surrounding directory structure is assumed to follow ![this outline.](data_structure_scheme.jpg). Each Subdirectory corresponds to a particular step in the processing.

  1. Archives original .xml files into specified time interval.
  2. Creates netcdfs of the raw data, including the instrument reported temperature, stokes intensity, and anti-stokes intensity. Dimensions of Length Along the Fiber, LAF, and time.
  3. Labels the data, integrates external data streams and other reference data, performs step-loss corrections, performs single ended calibration based on Hausner et al., (2011). Splits multicore data into individual cores. Reports instrument reported temperature, calibrated temperature, log-power ratio of stoke and anti-stokes intensities, stokes intensity, anti-stokes intensities, and all data labels. Dimensions are LAF and time. New coordinates specified by location type in the location library can be used to label the data along with a number of labels by number of LAF coordinate.
  4. Converts data labels with physical coordinates. Drops the LAF label and only includes the physical location (xyz) and time. Each core dimension is saved as a separate netcdf. Cores do not share the xyz dimension and must be aligned with each other. They do share the time dimension.

## Example jupyter notebook

For space reasons we only include the data for following steps 2-4 in the example notebook. The example notebook walks through the iterative approach for processing FODS data.

### References

Hausner, M. B., Suárez, F., Glander, K. E., & Giesen, N. Van De. (2011). Calibrating Single-Ended Fiber-Optic Raman Spectra Distributed Temperature Sensing Data. Sensors, 11, 10859–10879.

### Muppet Archiver

Batch script for scheduled archiving of .xml files on the Silixa DTS devices. Why muppet? Unviersity of Bayreuth Micrometeorology names their Silixa devices after muppet characters. Requires an anaconda 3.* distribution of python. Task scheduler must point to the .bat script and not the python script.

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