<?xml version="1.0" ?>
<tool id="qiime_longitudinal_volatility" name="qiime longitudinal volatility" version="2019.4">
	<description> - Generate interactive volatility plot</description>
	<requirements>
		<requirement type="package" version="2019.4">qiime2</requirement>
	</requirements>
	<command><![CDATA[
qiime longitudinal volatility

--p-state-column="$pstatecolumn"

#if str($itable) != 'None':
 --i-table=$itable
#end if



#if '__sq__' in str($pindividualidcolumn):
  #set $pindividualidcolumn_temp = $pindividualidcolumn.replace('__sq__', "'")
  #set $pindividualidcolumn = $pindividualidcolumn_temp
#end if

#if str($pindividualidcolumn):
 --p-individual-id-column="$pindividualidcolumn"
#end if



#if str($pdefaultgroupcolumn):
 --p-default-group-column="$pdefaultgroupcolumn"
#end if

#if str($pdefaultmetric):
 --p-default-metric="$pdefaultmetric"
#end if

#if str($pyscale) != 'None':
 --p-yscale=$pyscale
#end if


#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


--o-visualization=ovisualization
;
qiime tools export --input-path 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 label="--p-state-column: TEXT  Metadata column containing state (time) variable information.                               [required]" name="pstatecolumn" optional="False" type="text"/>
		<param format="qza,no_unzip.zip" label="--i-table: ARTIFACT FeatureTable[RelativeFrequency] Feature table containing metrics.          [optional]" name="itable" optional="True" type="data"/>
		<param label="--p-individual-id-column: TEXT Metadata column containing IDs for individual subjects.                                  [optional]" name="pindividualidcolumn" optional="True" type="text"/>
		<param label="--p-default-group-column: TEXT The default metadata column on which to separate groups for comparison (all categorical metadata columns will be available in the visualization). [optional]" name="pdefaultgroupcolumn" optional="True" type="text"/>
		<param label="--p-default-metric: TEXT Numeric metadata or artifact column to test by default (all numeric metadata columns will be available in the visualization).           [optional]" name="pdefaultmetric" optional="True" type="text"/>
		<param label="--p-yscale: " name="pyscale" optional="True" type="select">
			<option selected="True" value="None">Selection is Optional</option>
			<option value="linear">linear</option>
			<option value="pow">pow</option>
			<option value="sqrt">sqrt</option>
			<option value="log">log</option>
		</param>

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

	</inputs>
	<outputs>
		<data format="html" label="${tool.name} on ${on_string}: visualization.qzv" name="ovisualization"/>
	</outputs>
	<help><![CDATA[
Generate interactive volatility plot
####################################

Generate an interactive control chart depicting the longitudinal volatility
of sample metadata and/or feature frequencies across time (as set using the
"state_column" parameter). Any numeric metadata column (and metadata-
transformable artifacts, e.g., alpha diversity results) can be plotted on
the y-axis, and are selectable using the "metric_column" selector. Metric
values are averaged to compare across any categorical metadata column using
the "group_column" selector. Longitudinal volatility for individual
subjects sampled over time is co-plotted as "spaghetti" plots if the
"individual_id_column" parameter is used. state_column will typically be a
measure of time, but any numeric metadata column can be used.

Parameters
----------
metadata : Metadata
    Sample metadata file containing individual_id_column.
state_column : Str
    Metadata column containing state (time) variable information.
individual_id_column : Str, optional
    Metadata column containing IDs for individual subjects.
default_group_column : Str, optional
    The default metadata column on which to separate groups for comparison
    (all categorical metadata columns will be available in the
    visualization).
default_metric : Str, optional
    Numeric metadata or artifact column to test by default (all numeric
    metadata columns will be available in the visualization).
table : FeatureTable[RelativeFrequency], optional
    Feature table containing metrics.
yscale : Str % Choices('linear', 'pow', 'sqrt', 'log'), optional
    y-axis scaling strategy to apply.

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