--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/qiime2-2020.8/qiime_sample-classifier_metatable.xml	Thu Sep 03 09:33:04 2020 +0000
@@ -0,0 +1,120 @@
+<?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>
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