Mercurial > repos > imgteam > curve_fitting
view curve_fitting.xml @ 3:00feb1ecd040 draft
"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/curve_fitting/ commit db4c2a87a21f32e5d12d11e68f32773bfc06fcfd"
author | imgteam |
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date | Sat, 24 Jul 2021 17:38:45 +0000 |
parents | dd5fede16ee4 |
children | 004c57179c61 |
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<tool id="ip_curve_fitting" name="Curve Fitting" version="0.0.1" profile="20.05"> <description>to data points using (1st- or 2nd-degree) polynomial function</description> <requirements> <requirement type="package" version="1.20.2">numpy</requirement> <requirement type="package" version="3.0.7">openpyxl</requirement> <requirement type="package" version="1.2.4">pandas</requirement> <requirement type="package" version="1.6.2">scipy</requirement> </requirements> <command> <![CDATA[ python '$__tool_directory__/curve_fitting.py' '$fn_in' ./output.xlsx '$degree' '$penalty' '$alpha' ]]> </command> <inputs> <param name="fn_in" type="data" format="xlsx" label="File name of input data points (xlsx)" /> <param name="degree" type="select" label="Degree of the polynomial function"> <option value="2" selected="True">2nd degree</option> <option value="1">1st degree</option> </param> <param name="penalty" type="select" label="Optimization objective for fitting"> <option value="abs" selected="True">Least absolute deviations (LAD)</option> <option value="quadratic">Least squares (LS)</option> <option value="student-t">Robust non-convex penalty</option> </param> <param name="alpha" type="float" value="0.01" label="Significance level for generating assistive curves" /> </inputs> <outputs> <data format="xlsx" name="fn_out" from_work_dir="output.xlsx"/> </outputs> <tests> <test> <param name="fn_in" value="spots_linked.xlsx"/> <param name="degree" value="2"/> <param name="penalty" value="abs"/> <param name="alpha" value="0.01"/> <output name="fn_out" value="curves_fitted.xlsx" ftype="xlsx" compare="sim_size"/> </test> </tests> <help> **What it does** This tool fits (1st- or 2nd-degree) polynomial curves to data points. Optional: Given a significance level, assistive curves will also be generated. </help> </tool>