Processing of meteorological FODS data.
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.
# Getting Started
### Using a package manager pyfocs can be installed by using:
pip install pyfocs
which installs pyfocs plus all dependencies. This install method has caused problems for Windows OS. If you encounter errors when running pyfocs using this method, we instead recommend following the below method.
### From source Alternatively you can download the source code from this repository (green button with “Clone or Download”), extract the package, navigate to the directory containing it, and run:
python setup.py install
Note that Windows users will need to use anaconda power prompt or a similar python environment.
Both methods should result in the PyFOX.py being callable from the command line.
### Dependency issues Installing the code from source may allow some dependency issues. These can be resolved through
pip install -r requirements.txt
Download the data in the example directory. Within that directory is an example configuration file in yaml format. Adjust the dir_pre and external paths to be those of the example folder. Then, you should be able to run
Alternatively, providing no path to the yaml file will open a file browser for selecting the configuration file.
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](http://xarray.pydata.org) 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](https://github.com/bdestombe/python-geotechnical-profile), 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.
- Archives original .xml files into specified time interval.
- 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.
- 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.
- 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.
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. https://doi.org/10.3390/s111110859
### 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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