comparison qiime2/qiime_sample-classifier_metatable.xml @ 29:3ba9833030c1 draft

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29:3ba9833030c1

<?xml version="1.0" ?>
<tool id="qiime_sample-classifier_metatable" name="qiime sample-classifier metatable"
      version="2020.8">
  <description>Convert (and merge) positive numeric metadata (in)to feature table.</description>
  <requirements>
    <requirement type="package" version="2020.8">qiime2</requirement>
  </requirements>
  <command><![CDATA[
qiime sample-classifier metatable

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

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

#if str($pmissingvalues) != 'None':
--p-missing-values=$pmissingvalues
#end if

#if $pdropallunique:
 --p-drop-all-unique
#end if

--o-converted-table=oconvertedtable

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

;
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="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          Metadata file to convert to feature table. arguments will be merged)                                                     [required]" name="additional_input" optional="False" type="data" />
    </repeat>
    <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>
    <param label="--p-drop-all-unique: --p-drop-all-unique: / --p-no-drop-all-unique If True, columns that contain a unique value for every ID will be dropped.                    [default: False]" name="pdropallunique" selected="False" type="boolean" />
    <param label="--examples: Show usage examples and exit." name="examples" optional="False" type="data" />
    
  </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 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.
drop_all_unique : Bool, optional
    If True, columns that contain a unique value for every ID will be
    dropped.

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