enm study import fixed

1 files changed, 50 insertions, 30 deletions

diff --git a/lib/nanoparticle.rb b/lib/nanoparticle.rb
index b79981d..6527fa3 100644
--- a/lib/nanoparticle.rb
+++ b/lib/nanoparticle.rb
@@ -8,15 +8,31 @@ module OpenTox
     field :bundles, type: Array, default: []
     field :proteomics, type: Hash, default: {}
 
-    def nanoparticle_neighbors params
-      dataset = Dataset.find(params[:training_dataset_id])
-      Dataset.find(params[:training_dataset_id]).nanoparticles.collect do |np|
-        np["tanimoto"] = 1
-        np unless np.toxicities.empty?
-      end.compact
+    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]})
+          neighbors << {"_id" => np.id, "toxicities" => values, "similarity" => sim} if sim >= min_sim
+        end
+      end
+      neighbors.sort!{|a,b| b["similarity"] <=> a["similarity"]}
+      neighbors
     end
 
     def add_feature feature, value, dataset_id
+      dataset = Dataset.find(dataset_id)
       case feature.category
       when "P-CHEM"
         physchem_descriptors[feature.id.to_s] ||= []
@@ -27,55 +43,59 @@ module OpenTox
         proteomics[feature.id.to_s] << value
         proteomics[feature.id.to_s].uniq!
       when "TOX"
-        toxicities[feature.id.to_s] ||= {}
-        toxicities[feature.id.to_s][dataset_id.to_s] ||= []
         # 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)" 
-          toxicities[feature.id.to_s][dataset_id.to_s] << -Math.log10(value)
+          dataset.add self, feature, -Math.log10(value)
         else
-          toxicities[feature.id.to_s][dataset_id.to_s] << value
+          dataset.add self, feature, value
         end
-        toxicities[feature.id.to_s][dataset_id.to_s].uniq!
+        dataset.save
       else
         warn "Unknown feature type '#{feature.category}'. Value '#{value}' not inserted."
       end
     end
 
     def parse_ambit_value feature, v, dataset_id
+      dataset = Dataset.find(dataset_id)
       v.delete "unit"
       # TODO: ppm instead of weights
       if v.keys == ["textValue"]
-        add_feature feature, v["textValue"], dataset_id
+        add_feature feature, v["textValue"], dataset
       elsif v.keys == ["loValue"]
-        add_feature feature, v["loValue"], dataset_id
+        add_feature feature, v["loValue"], dataset
       elsif v.keys.size == 2 and v["errorValue"]
-        add_feature feature, v["loValue"], dataset_id
-        warn "Ignoring errorValue '#{v["errorValue"]}' for '#{feature.name}'."
+        add_feature feature, v["loValue"], dataset
+        #warn "Ignoring errorValue '#{v["errorValue"]}' for '#{feature.name}'."
       elsif v.keys.size == 2 and v["loQualifier"] == "mean"
-        add_feature feature, v["loValue"], dataset_id
-        warn "'#{feature.name}' is a mean value. Original data is not available."
+        add_feature feature, v["loValue"], dataset
+        #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_id
-        warn "loQualifier and upQualifier are empty."
+        add_feature feature, v["loValue"], dataset
+        #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_id
-        warn "loQualifier and upQualifier are empty."
+        add_feature feature, v["loValue"], dataset
+        #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_id
-        warn "loQualifier and upQualifier are empty."
+        add_feature feature, v["loValue"], dataset
+        #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_id
-        warn "Using mean value of range #{v["loValue"]} - #{v["upValue"]} for '#{feature.name}'. Original data is not available."
+        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."
       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}'."
-        add_feature feature, v["loValue"], dataset_id
+        #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