<?xml version="1.0" ?>
<tool id="qiime_gneiss_gradient-clustering" name="qiime gneiss gradient-clustering" version="2019.4">
	<description> - Hierarchical clustering using gradient information.</description>
	<requirements>
		<requirement type="package" version="2019.4">qiime2</requirement>
	</requirements>
	<command><![CDATA[
qiime gneiss gradient-clustering

--i-table=$itable
--m-gradient-column="$mgradientcolumn"


#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 + ',' + d['additional_input'].__getattr__('file_name')
	#end for
	#return $file_list
#end def

--m-gradient-file=$list_dict_to_string($input_files_mgradientfile)


#if $pnoweighted:
 --p-no-weighted
#end if

--o-clustering=oclustering
;
cp oclustering.qza $oclustering
	]]></command>
	<inputs>
		<param format="qza,no_unzip.zip" label="--i-table: ARTIFACT FeatureTable[Frequency | RelativeFrequency | Composition]       The feature table containing the samples in which the columns will be clustered.                   [required]" name="itable" optional="False" type="data"/>
		<param label="--m-gradient-column: COLUMN  MetadataColumn[Numeric] Contains gradient values to sort the features and samples.                                     [required]" name="mgradientcolumn" optional="False" type="text"/>
		<param label="--p-no-weighted : Specifies if abundance or presence/absence information should be used to perform the clustering. [default: False]" name="pnoweighted" selected="False" type="boolean"/>

		<repeat name="input_files_mgradientfile" optional="False" title="--m-gradient-file">
			<param label="--m-gradient-file: Metadata file or artifact viewable as metadata. This option may be supplied multiple times to merge metadata. [required]" name="additional_input" type="data" format="tabular,qza,no_unzip.zip" />
		</repeat>

	</inputs>
	<outputs>
		<data format="qza" label="${tool.name} on ${on_string}: clustering.qza" name="oclustering"/>
	</outputs>
	<help><![CDATA[
Hierarchical clustering using gradient information.
###################################################

Build a bifurcating tree that represents a hierarchical clustering of
features.  The hiearchical clustering uses Ward hierarchical clustering
based on the mean difference of gradients that each feature is observed in.
This method is primarily used to sort the table to reveal the underlying
block-like structures.

Parameters
----------
table : FeatureTable[Frequency | RelativeFrequency | Composition]
    The feature table containing the samples in which the columns will be
    clustered.
gradient : MetadataColumn[Numeric]
    Contains gradient values to sort the features and samples.
weighted : Bool, optional
    Specifies if abundance or presence/absence information should be used
    to perform the clustering.

Returns
-------
clustering : Hierarchy
    A hierarchy of feature identifiers where each tip corresponds to the
    feature identifiers in the table. This tree can contain tip ids that
    are not present in the table, but all feature ids in the table must be
    present in this tree.
	]]></help>
<macros>
    <import>qiime_citation.xml</import>
</macros>
<expand macro="qiime_citation"/>
</tool>