module OpenTox class Nanoparticle < Substance include OpenTox field :core_id, type: String, default: nil field :coating_ids, type: Array, default: [] def core Compound.find core_id end def coating coating_ids.collect{|i| Compound.find i } end def fingerprint type=DEFAULT_FINGERPRINT core_fp = core.fingerprint type coating_fp = coating.collect{|c| c.fingerprint type}.flatten.uniq.compact (core_fp.empty? or coating_fp.empty?) ? [] : (core_fp+coating_fp).uniq.compact end def calculate_properties descriptors=PhysChem::OPENBABEL if core.smiles and !coating.collect{|c| c.smiles}.compact.empty? core_prop = core.calculate_properties descriptors coating_prop = coating.collect{|c| c.calculate_properties descriptors if c.smiles} descriptors.collect_with_index{|d,i| [core_prop[i],coating_prop.collect{|c| c[i] if c}]} end end def add_feature feature, value, dataset unless feature.name == "ATOMIC COMPOSITION" or feature.name == "FUNCTIONAL GROUP" # redundand case feature.category when "P-CHEM" properties[feature.id.to_s] ||= [] properties[feature.id.to_s] << value properties[feature.id.to_s].uniq! when "Proteomics" properties[feature.id.to_s] ||= [] properties[feature.id.to_s] << value properties[feature.id.to_s].uniq! when "TOX" if feature.name.match("Cell Viability Assay") and !feature.name.match("SLOPE") # -log10 transformation value = -Math.log10(value) feature.unit = "-log10(#{feature.unit})" unless feature.unit.match "log10" feature.warnings += ["-log10 transformed values"] unless feature.warnings.include? "-log10 transformed values" feature.save end dataset.add self, feature, value else warn "Unknown feature type '#{feature.category}'. Value '#{value}' not inserted." end dataset_ids << dataset.id dataset_ids.uniq! end end def parse_ambit_value feature, v, dataset # TODO add study id to warnings v.delete "unit" # TODO: ppm instead of weights if v.keys == ["textValue"] add_feature feature, v["textValue"], dataset elsif v.keys == ["loValue"] 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}'." 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." elsif v.keys.size == 2 and v["loQualifier"] #== ">=" #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" 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." 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." 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." 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." 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 elsif v == {} # do nothing else warn "Cannot parse Ambit eNanoMapper value '#{v}' for feature '#{feature.name}'." end end end end