# HG changeset patch
# User galaxyp
# Date 1586875982 14400
# Node ID 8b30c8ffa687724e6be74e12ebd8a3a494738bd4
# Parent  00816d9855fc9d9421830750e080f7bcb5a1d9cb
"planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/pyprophet commit d1c34e31a93761cf7cfd4068bcdb70495d4d90bb"

diff -r 00816d9855fc -r 8b30c8ffa687 pyprophet_score.xml
--- a/pyprophet_score.xml	Thu Apr 02 01:34:49 2020 -0400
+++ b/pyprophet_score.xml	Tue Apr 14 10:53:02 2020 -0400
@@ -1,4 +1,4 @@
-<tool id="pyprophet_score" name="PyProphet score" version="@VERSION@.1">
+<tool id="pyprophet_score" name="PyProphet score" version="@VERSION@.2">
     <description>
         Error-rate estimation for MS1, MS2 and transition-level data
     </description>
@@ -10,13 +10,13 @@
     <![CDATA[
         pyprophet score
         --in='$input'
+        --classifier=$conditional_classifier.classifier
+
         #if str($conditional_classifier.classifier)=='XGBoost':
-            --classifier=$conditional_classifier.classifier
             $conditional_classifier.xgb_autotune
-        #elif str($conditional_classifier.classifier)=='LDA':
-            --classifier=$conditional_classifier.classifier
-        #elif str($conditional_classifier.classifier)=='prev_weights':
-            --apply_weights=$conditional_classifier.apply_weights
+        #end if
+        #if $apply_weights: 
+            --apply_weights='$apply_weights'
         #end if
         --xeval_fraction=$xeval_fraction
         --xeval_num_iter=$xeval_num_iter
@@ -50,19 +50,16 @@
     <inputs>
         <param name="input" type="data" format="osw" label="Input file" help="This file needs to be in OSW format (--in)" />
         <conditional name="conditional_classifier">
-            <param name="classifier" type="select" label="Either a 'LDA' or 'XGBoost' classifier is used for semi-supervised learning or previously calculated Pyprophet score weights can be loaded" help="(--classifier)">
+            <param argument="--classifier" type="select" label="Either a 'LDA' or 'XGBoost' classifier is used for semi-supervised learning" >
               <option value="LDA" selected="True" >LDA</option>
               <option value="XGBoost">XGBoost</option>
-              <option value="prev_weights">Apply previously calculated weights</option>
             </param>
             <when value="LDA"/>
             <when value="XGBoost">
                 <param name="xgb_autotune" type="boolean" truevalue="--xgb_autotune" falsevalue="--no-xgb_autotune" label="XGBoost: Autotune hyperparameters" help="(--xgb_autotune / --no-xgb_autotune)"/>
             </when>
-            <when value="prev_weights">
-                <param name="apply_weights" type="data" format="osw" label="Apply PyProphet score weights file instead of semi-supervised learning." help="(--apply_weights)" />
-            </when>
         </conditional>
+        <param argument="apply_weights" type="data" format="osw" optional="True" label="Apply PyProphet score weights file (osw format) instead of semi-supervised learning." />
         <param argument="--level" type="select" display="radio" label="The data level selected for scoring. 'ms1ms2' integrates both MS1- and MS2-level scores and can be used instead of 'ms2'-level results" >
           <option value="ms1" >MS1</option>
           <option value="ms2" >MS2</option>
@@ -121,10 +118,7 @@
         </test>
         <test>
             <param name="input" value="merged.osw" ftype="osw"/>
-            <conditional name="conditional_classifier">
-                <param name="classifier" value="prev_weights"/>
-                <param name="apply_weights" value="score.osw" ftype="osw"/>
-            </conditional>
+            <param name="apply_weights" value="score.osw" ftype="osw"/>
             <param name="level" value="ms2"/>
             <param name="xeval_num_iter" value="2" />
             <param name="ss_num_iter" value="2" />
@@ -152,3 +146,4 @@
     </help>
     <expand macro="citations"/>
 </tool>
+