From c90644211e214a50f6fdb3a936bf247f45f1f4be Mon Sep 17 00:00:00 2001
From: Christoph Helma <helma@in-silico.ch>
Date: Fri, 13 May 2016 13:38:24 +0200
Subject: compound tests fixed

---
 lib/nanoparticle.rb | 40 +++++++++++++++-------------------------
 1 file changed, 15 insertions(+), 25 deletions(-)

(limited to 'lib/nanoparticle.rb')

diff --git a/lib/nanoparticle.rb b/lib/nanoparticle.rb
index 6527fa3..7890a19 100644
--- a/lib/nanoparticle.rb
+++ b/lib/nanoparticle.rb
@@ -11,19 +11,14 @@ module OpenTox
     def nanoparticle_neighbors min_sim: 0.1, type:, dataset_id:, prediction_feature_id:
       dataset = Dataset.find(dataset_id)
       neighbors = []
-      p dataset.data_entries.size
-      p dataset.substance_ids.size
-      p dataset.substance_ids.collect{|i| i.to_s} == dataset.data_entries.keys
-      p dataset.substance_ids.collect{|i| i.to_s} 
-      p dataset.data_entries.keys
       dataset.nanoparticles.each do |np|
-        prediction_feature_id
-        p dataset.data_entries[np.id.to_s]
         values = dataset.values(np,prediction_feature_id)
-        p values
         if values
           common_descriptors = physchem_descriptors.keys & np.physchem_descriptors.keys
-          sim = Algorithm::Similarity.cosine(common_descriptors.collect{|d| physchem_descriptors[d]}, common_descriptors.collect{|d| np.physchem_descriptors[d]})
+          common_descriptors.select!{|id| NumericFeature.find(id) }
+          query_descriptors = common_descriptors.collect{|d| physchem_descriptors[d].first}
+          neighbor_descriptors = common_descriptors.collect{|d| np.physchem_descriptors[d].first}
+          sim = Algorithm::Similarity.cosine(query_descriptors,neighbor_descriptors)
           neighbors << {"_id" => np.id, "toxicities" => values, "similarity" => sim} if sim >= min_sim
         end
       end
@@ -44,12 +39,7 @@ module OpenTox
         proteomics[feature.id.to_s].uniq!
       when "TOX"
         # TODO generic way of parsing TOX values
-        p dataset.name
-        p self.name
-        p feature.name
-        p feature.unit
-        p value
-        if feature.name == "7.99 Toxicity (other) ICP-AES" and feature.unit == "mL/ug(Mg)" 
+        if feature.name == "Net cell association" and feature.unit == "mL/ug(Mg)" 
           dataset.add self, feature, -Math.log10(value)
         else
           dataset.add self, feature, value
@@ -70,32 +60,32 @@ module OpenTox
         add_feature feature, v["loValue"], dataset
       elsif v.keys.size == 2 and v["errorValue"]
         add_feature feature, v["loValue"], dataset
-        #warn "Ignoring errorValue '#{v["errorValue"]}' for '#{feature.name}'."
+        warn "Ignoring errorValue '#{v["errorValue"]}' for '#{feature.name}'."
       elsif v.keys.size == 2 and v["loQualifier"] == "mean"
         add_feature feature, v["loValue"], dataset
-        #warn "'#{feature.name}' is a mean value. Original data is not available."
+        warn "'#{feature.name}' is a mean value. Original data is not available."
       elsif v.keys.size == 2 and v["loQualifier"] #== ">="
-        #warn "Only min value available for '#{feature.name}', entry ignored"
+        warn "Only min value available for '#{feature.name}', entry ignored"
       elsif v.keys.size == 2 and v["upQualifier"] #== ">="
-        #warn "Only max value available for '#{feature.name}', entry ignored"
+        warn "Only max value available for '#{feature.name}', entry ignored"
       elsif v.keys.size == 3 and v["loValue"] and v["loQualifier"].nil? and v["upQualifier"].nil?
         add_feature feature, v["loValue"], dataset
-        #warn "loQualifier and upQualifier are empty."
+        warn "loQualifier and upQualifier are empty."
       elsif v.keys.size == 3 and v["loValue"] and v["loQualifier"] == "" and v["upQualifier"] == ""
         add_feature feature, v["loValue"], dataset
-        #warn "loQualifier and upQualifier are empty."
+        warn "loQualifier and upQualifier are empty."
       elsif v.keys.size == 4 and v["loValue"] and v["loQualifier"].nil? and v["upQualifier"].nil?
         add_feature feature, v["loValue"], dataset
-        #warn "loQualifier and upQualifier are empty."
+        warn "loQualifier and upQualifier are empty."
       elsif v.size == 4 and v["loQualifier"] and v["upQualifier"] and v["loValue"] and v["upValue"]
         add_feature feature, [v["loValue"],v["upValue"]].mean, dataset
-        #warn "Using mean value of range #{v["loValue"]} - #{v["upValue"]} for '#{feature.name}'. Original data is not available."
+        warn "Using mean value of range #{v["loValue"]} - #{v["upValue"]} for '#{feature.name}'. Original data is not available."
       elsif v.size == 4 and v["loQualifier"] == "mean" and v["errorValue"]
-        #warn "'#{feature.name}' is a mean value. Original data is not available. Ignoring errorValue '#{v["errorValue"]}' for '#{feature.name}'."
+        warn "'#{feature.name}' is a mean value. Original data is not available. Ignoring errorValue '#{v["errorValue"]}' for '#{feature.name}'."
         add_feature feature, v["loValue"], dataset
       elsif v == {} # do nothing
       else
-        #warn "Cannot parse Ambit eNanoMapper value '#{v}' for feature '#{feature.name}'."
+        warn "Cannot parse Ambit eNanoMapper value '#{v}' for feature '#{feature.name}'."
       end
     end
 
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