comparison qiime2/qiime_sample-classifier_metatable.xml @ 0:370e0b6e9826 draft

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0:370e0b6e9826

<?xml version="1.0" ?>
<tool id="qiime_sample-classifier_metatable" name="qiime sample-classifier metatable" version="2019.4">
	<description> - Convert (and merge) positive numeric metadata (in)to feature table.</description>
	<requirements>
		<requirement type="package" version="2019.4">qiime2</requirement>
	</requirements>
	<command><![CDATA[
qiime sample-classifier metatable


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

#if str($pmissingsamples) != 'None':
 --p-missing-samples=$pmissingsamples
#end if

#if str($pmissingvalues) != 'None':
 --p-missing-values=$pmissingvalues
#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-converted-table=oconvertedtable
;
cp oconvertedtable.qza $oconvertedtable
	]]></command>
	<inputs>
		<param format="qza,no_unzip.zip" label="--i-table: ARTIFACT FeatureTable[Frequency] Feature table containing all features that should be used for target prediction.                  [optional]" name="itable" optional="True" type="data"/>
		<param label="--p-missing-samples: " name="pmissingsamples" optional="True" type="select">
			<option selected="True" value="None">Selection is Optional</option>
			<option value="error">error</option>
			<option value="ignore">ignore</option>
		</param>
		<param label="--p-missing-values: " name="pmissingvalues" optional="True" type="select">
			<option selected="True" value="None">Selection is Optional</option>
			<option value="drop_samples">drop_samples</option>
			<option value="drop_features">drop_features</option>
			<option value="error">error</option>
			<option value="fill">fill</option>
		</param>

		<repeat name="input_files_mmetadatafile" optional="True" title="--m-metadata-file">
			<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="qza" label="${tool.name} on ${on_string}: convertedtable.qza" name="oconvertedtable"/>
	</outputs>
	<help><![CDATA[
Convert (and merge) positive numeric metadata (in)to feature table.
###################################################################

Convert numeric sample metadata from TSV file into a feature table.
Optionally merge with an existing feature table. Only numeric metadata will
be converted; categorical columns will be silently dropped. By default, if
a table is used as input only samples found in both the table and metadata
(intersection) are merged, and others are silently dropped. Set
missing_samples="error" to raise an error if samples found in the table are
missing from the metadata file. The metadata file can always contain a
superset of samples. Note that columns will be dropped if they are non-
numeric, contain only unique values, contain no unique values (zero
variance), contain only empty cells, or contain negative values. This
method currently only converts postive numeric metadata into feature data.
Tip: convert categorical columns to dummy variables to include them in the
output feature table.

Parameters
----------
metadata : Metadata
    Metadata file to convert to feature table.
table : FeatureTable[Frequency], optional
    Feature table containing all features that should be used for target
    prediction.
missing_samples : Str % Choices('error', 'ignore'), optional
    How to handle missing samples in metadata. "error" will fail if missing
    samples are detected. "ignore" will cause the feature table and
    metadata to be filtered, so that only samples found in both files are
    retained.
missing_values : Str % Choices('drop_samples', 'drop_features', 'error', 'fill'), optional
    How to handle missing values (nans) in metadata. Either "drop_samples"
    with missing values, "drop_features" with missing values, "fill"
    missing values with zeros, or "error" if any missing values are found.

Returns
-------
converted_table : FeatureTable[Frequency]
    Converted feature table
	]]></help>
<macros>
    <import>qiime_citation.xml</import>
</macros>
<expand macro="qiime_citation"/>
</tool>