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ArcGIS Toolbox for use with ArcGISpro that allows for import and trend processing of water-level transducer data

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utah-geological-survey/loggerloader

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loggerloader

A datalogger processing app

Installation

The loggerloader application has a Tkinter-based graphical user interface that has been compiled as a portable Windows exe file.

loggerloader can also be installed as a python library. loggerloader should be compatible with Python 3.7 or above.
It has been tested most rigously on Python 3.7.
It should work on both 32 and 64-bit platforms. I have used it on Linux and Windows machines.

Requirements for this python toolbox to work:

  • Pandas v. 1.5.0 or higher
  • Numpy v. 1.0.0 or higher
  • xlrd v. 0.5.4 or higher

Description

The Utah Geological Survey uses transducers to monitor groundwater levels over time. Many of these transducers are either Solinst or Global Water (not a endorsement). This is a set of scripts and gui created to importing and interpreting data logger files. This tool can currently handle the following file types:

  • .xle
  • .lev
  • .csv

Loggerloader:

  • allows a user to upload data from an .xle file common with some water well transducers.

  • matches well and barometric data to same sample intervals

  • adjust data using manual measurements

  • removes skips and jumps from data

This class has functions used to import transducer data and condition it for analysis.

The most important class in this library is NewTransImp, which uses the path and filename of an xle file, commonly produced by Solinst pressure transducers, to convert that file into a Pandas DataFrame.

A Jupyter Notebook using some of the transport functions can be found here.

Usage

See the documentation for a more complete description on how to use this library and its executables.

Credits

Much of the GUI depends on the amazing library pandastable. The dataexplore application is worth examining.

Farrell, D 2016 DataExplore: An Application for General Data Analysis in Research and Education. Journal of Open Research Software, 4: e9, DOI: http://dx.doi.org/10.5334/jors.94