Ekidna

Name	Ekidna JSON
Version	0.0.9 JSON
	download
home_page
Summary	Electrochemistry data analysis tools
upload_time	2024-02-21 21:02:47
maintainer
docs_url	None
author	OzymandiasTheDead
requires_python
license	MIT
keywords	ekidna
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
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            This library contains functions and classes for analysis of, primarily, data collected from 
electrochemistry experiments. Various functions may be useful beyond the scope of electrochemistry.

Examples of tools include: baseline subtraction, pairplots, histograms, standard curve creators, smoothing, etc . . . 

The code is developed by researchers at Ekidna Sensing.



Change Log
===========
0.0.9 (February 14, 2024)
-------------------------
- Ninth Release

Notes:
------
Adjusted existing moving_average_baseline_subtraction function to take a new input, max_iter, which
specifies the maximum number of iterations to be performed by the baseline subtraction algorithm.


Added functions (see "module contents" document for descriptions of these functions):
- running_sd 
- running_mean 
- fitRandlesSevcikModels 
- RS_solution_resistance
- RS_linear_self_blocking
- RS_anomalous_diffusion
- RS_linear_self_blocking_anomalous_diffusion
- RS_basic
- plotRSModels


Added classes (see "module contents" document for descriptions of classes)
- double_power_std_curve
- self_resistance_std_curve

            

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