Metadata-Version: 1.1
Name: pyfocs
Version: 0.1.1
Summary: Processing of meteorological FODS data.
Home-page: UNKNOWN
Author: Karl Lapo and Anita Freundorfer
Author-email: karl.lapo@uni-bayreuth.de
License: MIT
Description: # btmm_process
        
        ## Installation
        
        ## 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](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.
        
        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.
        
        ### 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. https://doi.org/10.3390/s111110859
        
        ### Muppet Archiver
        
        Batch script for scheduled archiving of `.xml` files on the Silixa DTS devices. Why muppet? BTMM 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.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
