<?xml version="1.0" ?>
<tool id="qiime_diversity_adonis" name="qiime diversity adonis"
      version="2020.8">
  <description>adonis PERMANOVA test for beta group significance</description>
  <requirements>
    <requirement type="package" version="2020.8">qiime2</requirement>
  </requirements>
  <command><![CDATA[
qiime diversity adonis

--i-distance-matrix=$idistancematrix
# if $input_files_mmetadatafile:
  # def list_dict_to_string(list_dict):
    # set $file_list = list_dict[0]['additional_input'].__getattr__('file_name')
    # for d in list_dict[1:]:
      # set $file_list = $file_list + ' --m-metadata-file=' + d['additional_input'].__getattr__('file_name')
    # end for
    # return $file_list
  # end def
--m-metadata-file=$list_dict_to_string($input_files_mmetadatafile)
# end if

--p-formula=$pformula

--p-permutations=$ppermutations

--p-n-jobs=$pnjobs

--o-visualization=ovisualization

#if str($examples) != 'None':
--examples=$examples
#end if

;
cp osubsampledsequences.qza $osubsampledsequences

;
qiime tools export  ovisualization.qzv --output-path out
&& mkdir -p '$ovisualization.files_path'
&& cp -r out/* '$ovisualization.files_path'
&& mv '$ovisualization.files_path/index.html' '$ovisualization'

;
qiime tools export  ovisualization.qzv --output-path out
&& mkdir -p '$ovisualization.files_path'
&& cp -r out/* '$ovisualization.files_path'
&& mv '$ovisualization.files_path/index.html' '$ovisualization'

  ]]></command>
  <inputs>
    <param format="qza,no_unzip.zip" label="--i-distance-matrix: ARTIFACT DistanceMatrix     Matrix of distances between pairs of samples. [required]" name="idistancematrix" optional="False" type="data" />
    <repeat name="input_files_mmetadatafile" optional="False" title="--m-metadata-file">
      <param format="tabular,qza,no_unzip.zip" label="--m-metadata-file: METADATA... (multiple          Sample metadata containing formula terms. arguments will be merged)                                                     [required]" name="additional_input" optional="False" type="data" />
    </repeat>
    <param label="--p-formula: TEXT     Model formula containing only independent terms contained in the sample metadata. These can be continuous variables or factors, and they can have interactions as in a typical R formula. E.g., the formula \'treatment+block\' would test whether the input distance matrix partitions based on \'treatment\' and \'block\' sample metadata. The formula \'treatment*block\' would test both of those effects as well as their interaction. Enclose formulae in quotes to avoid unpleasant surprises.                        [required]" name="pformula" optional="False" type="text" />
    <param label="--p-permutations: INTEGER Range(1, None)     The number of permutations to be run when computing p-values.                                [default: 999]" min="1" name="ppermutations" optional="True" type="integer" value="999" />
    <param label="--examples: Show usage examples and exit." name="examples" optional="False" type="data" />
    
  </inputs>

  <outputs>
    <data format="html" label="${tool.name} on ${on_string}: visualization.html" name="ovisualization" />
    
  </outputs>

  <help><![CDATA[
adonis PERMANOVA test for beta group significance
###############################################################

Determine whether groups of samples are significantly different from one
another using the ADONIS permutation-based statistical test in vegan-R. The
function partitions sums of squares of a multivariate data set, and is
directly analogous to MANOVA (multivariate analysis of variance). This
action differs from beta_group_significance in that it accepts R formulae
to perform multi-way ADONIS tests; beta_group_signficance only performs
one-way tests. For more details see
http://cc.oulu.fi/~jarioksa/softhelp/vegan/html/adonis.html

Parameters
----------
distance_matrix : DistanceMatrix
    Matrix of distances between pairs of samples.
metadata : Metadata
    Sample metadata containing formula terms.
formula : Str
    Model formula containing only independent terms contained in the sample
    metadata. These can be continuous variables or factors, and they can
    have interactions as in a typical R formula. E.g., the formula
    "treatment+block" would test whether the input distance matrix
    partitions based on "treatment" and "block" sample metadata. The
    formula "treatment*block" would test both of those effects as well as
    their interaction. Enclose formulae in quotes to avoid unpleasant
    surprises.
permutations : Int % Range(1, None), optional
    The number of permutations to be run when computing p-values.
n_jobs : Int % Range(1, None), optional
    Number of parallel processes to run.

Returns
-------
visualization : Visualization
  ]]></help>
  <macros>
    <import>qiime_citation.xml</import>
  </macros>
  <expand macro="qiime_citation"/>
</tool>