class Coloncancerpredictfields2s require "pathname" require 'json' include Mongoid::Document include Mongoid::Timestamps Field_relations = {"number_field"=>"Fixnum","text_area"=>"String"} FIELDINFO = {"variable"=>"String","name"=>"String","is_num"=>"Fixnum","hint"=>"String","comment_text"=>"String","choice_fields"=>"Array","range"=>"Array","right"=>"Fixnum","is_float"=>"Fixnum","revert_value"=>"Fixnum","map_values"=>"Array","coloncancer_predict_mapping_file2"=>"String","lpv_impact"=>"Float","active_choice"=>"number_field","disable_condition"=>"text_area"} NonLoclaized = ["variable","is_num","range","right","is_float","revert_value","map_values","coloncancer_predict_mapping_file2","lpv_impact","active_choice","disable_condition"] AdvanceFields = ["revert_value","map_values","coloncancer_predict_mapping_file2"] TherapyFields = ["variable","name","hint","comment_text","choice_fields","lpv_impact","active_choice","disable_condition"] TherapyOnly = ["lpv_impact","active_choice","disable_condition"] ModuleAppPath = Pathname.new(File.expand_path(__dir__)).dirname.dirname.to_s.freeze JS = "colon_cancer_predict2.js" JSFileName = "public/cc2_tool_js_filename.txt".freeze ToolTableMap = I18n.available_locales.map do |locale| [locale, "public/colon_cancer_tool_table_tmp_#{locale}2.txt".freeze] end.to_h field :title ,type:String ,default:"" field :advance_mode, type: Boolean, default: false field :form_show , :type=> Hash ,default: { "0"=>{"variable"=>"age", "name"=>{"zh_tw"=>"年齡
(Age)", "en"=>"Age"}, "is_num"=>1, "hint"=>{"zh_tw"=>"從 20 歲(含)開始至 80 歲 (含)以下", "en"=>"Age must be between 18 and 93"}, "comment_text"=>{"zh_tw"=>"年齡為該病人於確診罹患大 腸癌時之年齡", "en"=>"Age at diagnosis"}, "choice_fields"=>{"zh_tw"=>[], "en"=>[]}, "range"=>[20, 80], "right"=>0, "is_float"=>0, "need_map_values"=>0, "revert_value"=>0, "map_values"=>[]}, "1"=>{"variable"=>"size", "name"=>{"zh_tw"=>"腫瘤 大小(單位:mm)
(Tumor size)", "en"=>"Tumor size"}, "is_num"=>1, "hint"=>{"zh_tw"=>"", "en"=>"The unit of tumor size is millimeter (mm)"}, "comment_text"=>{"zh_tw"=>"若有多 個原發腫瘤,請輸入最大尺寸之原發腫瘤、上限為 100mm", "en"=>"If there was more than one primary tumor, please enter the size of the largest one."}, "choice_fields"=>{"zh_tw"=>[], "en"=>[]}, "range"=>[1, 100], "right"=>1, "is_float"=>0, "need_map_values"=>0, "revert_value"=>0, "map_values"=>[]}, "2"=>{"variable"=>"lymph_nodes_examined", "name"=>{"zh_tw"=>"區域淋巴結檢查數目
(Regional lymph nodes examined)", "en"=>"Regional lymph nodes examined"}, "is_num"=>1, "hint"=>{"zh_tw"=>"", "en"=>""}, "comment_text"=>{"zh_tw"=>"", "en"=>""}, "choice_fields"=>{"zh_tw"=>["未知"], "en"=>["unknown"]}, "range"=>[0, 90], "right"=>0, "is_float"=>0, "need_map_values"=>0, "revert_value"=>0, "map_values"=>[]}, "3"=>{"variable"=>"lymph_nodes_positive", "name"=>{"zh_tw"=>"區域淋巴結侵犯數目
(Regional lymph nodes positive)", "en"=>"Regional lymph nodes positive"}, "is_num"=>1, "hint"=>{"zh_tw"=>"", "en"=>""}, "comment_text"=>{"zh_tw"=>"此變項為預測重要變數,若無此資訊預測容易失真。", "en"=>"Regional lymph nodes positive is a key predictive variable. If this information is omitted, the prediction result would be biased."}, "choice_fields"=>{"zh_tw"=>["未知"], "en"=>["unknown"]}, "range"=>[0, 90], "right"=>1, "is_float"=>0, "need_map_values"=>0, "revert_value"=>0, "map_values"=>[]}, "4"=>{"variable"=>"crm", "name"=>{"zh_tw"=>"病理環切緣
(Circumferential resection margin(per 0.1 mm))", "en"=>"Circumferential resection margin (per 0.1 mm)"}, "is_num"=>1, "hint"=>{"zh_tw"=>"", "en"=>""}, "comment_text"=>{"zh_tw"=>"", "en"=>""}, "choice_fields"=>{"zh_tw"=>[], "en"=>[]}, "range"=>[0, 980], "right"=>0, "is_float"=>1, "need_map_values"=>0, "revert_value"=>0, "map_values"=>[]}, "5"=>{"variable"=>"grade", "name"=>{"zh_tw"=>"腫瘤級數
(Tumor grade)", "en"=>"Tumor grade"}, "is_num"=>0, "hint"=>{"zh_tw"=>"", "en"=>""}, "comment_text"=>{"zh_tw"=>"腫瘤級數代表腫瘤組織與正常組織間的分化程度,若無分化級數資訊,請選擇“未知”選項,將以級數 1 進行預測。", "en"=>"The grade refers to how different the cancer cells are from normal cells. Please select “unknown” if there is no information about grade. The prediction model would use “grade 2” as the alternative variable."}, "choice_fields"=>{"zh_tw"=>["1", "2", "3", "未知"], "en"=>["1", "2", "3", "unknown"]}, "range"=>[], "right"=>1, "is_float"=>0, "need_map_values"=>0, "revert_value"=>0, "map_values"=>[]}, "6"=>{"variable"=>"pstage", "name"=>{"zh_tw"=>"病理分期
(Pathological stage)", "en"=>"Pathological stage"}, "is_num"=>0, "hint"=>{"zh_tw"=>"", "en"=>""}, "comment_text"=>{"zh_tw"=>"若無分期資訊,請選擇“未知”選項,將以病理分期第 1 期進行預測。", "en"=>"Please select “unknown” if there is no information about “Pathological stage”. The prediction model would use “1th stage” as the alternative variable."}, "choice_fields"=>{"zh_tw"=>["1", "2", "3", "4", "未知"], "en"=>["1", "2", "3", "4", "unknown"]}, "range"=>[], "right"=>0, "is_float"=>0, "need_map_values"=>0, "revert_value"=>0, "map_values"=>[]}, "7"=>{"variable"=>"pi", "name"=>{"zh_tw"=>"神經侵襲
(Perineural Invasion)", "en"=>"Perineural Invasion"}, "hint"=>{"zh_tw"=>"", "en"=>""}, "comment_text"=>{"zh_tw"=>"若無神經侵襲資訊,請選擇“未知”選項,將以未神經侵襲進行預測。", "en"=>""}, "choice_fields"=>{"zh_tw"=>["是", "否", "未知"], "en"=>["Yes", "No", "unknown"]}, "is_num"=>0, "range"=>[], "right"=>1, "is_float"=>0, "revert_value"=>0, "map_values"=>[], "need_map_values"=>0}, "8"=>{"variable"=>"obstruction", "name"=>{"zh_tw"=>"腸阻塞
(Obstruction)", "en"=>"Obstruction"}, "hint"=>{"zh_tw"=>"", "en"=>""}, "comment_text"=>{"zh_tw"=>"若無腸阻塞資訊,請 選擇“未知”選項,將以無腸阻塞進行預測。", "en"=>""}, "choice_fields"=>{"zh_tw"=>["是", "否", "未知"], "en"=>["Yes", "No", "unknown"]}, "is_num"=>0, "range"=>[], "right"=>0, "is_float"=>0, "revert_value"=>0, "map_values"=>[], "need_map_values"=>0}, "9"=>{"variable"=>"perforation", "name"=>{"zh_tw"=>"腸穿孔
(Perforation)", "en"=>"Perforation"}, "hint"=>{"zh_tw"=>"", "en"=>""}, "comment_text"=>{"zh_tw"=>"若無腸阻塞資訊,請選擇“未知”選項,將以無腸穿孔進行預測。", "en"=>""}, "choice_fields"=>{"zh_tw"=>["是", "否", " 未知"], "en"=>["Yes", "No", "unknown"]}, "is_num"=>0, "range"=>[], "right"=>1, "is_float"=>0, "revert_value"=>0, "map_values"=>[], "need_map_values"=>0} } field :form_show_in_result , :type=> Hash ,default: { "0"=>{"variable"=>"Chemotherapy", "name"=>{"zh_tw"=>"化學治療", "en"=>"Chemotherapy"}, "hint"=>{"zh_tw"=>"", "en"=>""}, "comment_text"=>{"zh_tw"=>"", "en"=>""}, "choice_fields"=>{"zh_tw"=>["否", "是"], "en"=>["No", "Yes"]}, "lpv_impact"=>-0.6693, "active_choice"=>2, "disable_condition"=>""} } field :form_result_is_right , :type=> Integer ,default: 0 field :text_descibe ,type:Hash ,default: { "zh_tw"=>"歡迎使用台灣準備大腸癌預後系統!
\r\n本預測系統由台灣癌症登記資料庫2011至2015年間共4,982位大腸癌病人所建立。
\r\n若要開始 請在下方選擇相關資訊", "en"=>"Welcome to the Taiwan Breast Cancer Prediction System!
\r\nThe prediction system is constructed using clinical data from 90,841 breast cancer patients in the Taiwan Cancer Registry database between 2011 to 2015, and validated using clinical data from 49,374 breast cancer patients in the U.S.-based Surveillance, Epidemiology and End Results (SEER) database.
\r\nTo start, please select the information below." } field :small ,type:Hash ,default:{'font_size'=>"0.825em",'active'=>0} field :medium ,type:Hash ,default:{'font_size'=>"1em",'active'=>1} field :large ,type:Hash ,default:{'font_size'=>"1.25em",'active'=>0} field :head_images_id ,type:Array , default: [] field :title_images_id ,type:Array , default: [] field :title_texts ,type:Hash ,default: {"zh_tw"=>"大腸癌線上預測工具", "en"=>"Asian breast cancer prediction"} field :table_above_texts ,type:Hash ,default: {"zh_tw"=>"下表之分析為針對手術後病人,根據選定的術後治療,分別估計在第1年、3 及5年的存活率。", "en"=>"The analysis is for women who had undergone surgery.The table shows the 1-, 3- and 5-year survival rates,based on the treatment you have selected."} field :text_above_texts ,type:Hash ,default: {"zh_tw"=>"此研究分析來自已接受根除性手術後之婦女所得之結果,根據您所輸入的資訊以及治療方式,在術後
第{{years}}年,", "en"=>"The analysis is for women who had undergone surgery. Base on the information and the treatment you have selected, the predictions of survival status
{{years}}"} field :surgery_only_texts ,type:Hash ,default: {"zh_tw"=>"100 位只接受根除性手術的婦女中,有{{Surgery_only}}位婦女,術後{{surgery_year}}年仍為存活", "en"=>"after surgery are as follows:
{{Surgery_only}} out of 100 women treated with surgery only are alive at {{surgery_year}} years."} field :extra_texts ,type:Hash ,default: {"zh_tw"=>",此外", "en"=>""} field :extra_therapy_texts ,type:Hash ,default: {"zh_tw"=>"100 位在術後有接受{{extra_therapy}}的婦女中,有{{survival_num}}位婦女,術後{{surgery_year}}年仍為存活(多了{{Additional_Benefit}}位)", "en"=>"{{survival_num}} out of 100 women treated with {{extra_therapy}} are alive (an extra {{Additional_Benefit}})"} field :danger_texts ,type:Hash ,default: {"zh_tw"=>"請注意紅框的輸入資料是否符合要求!", "en"=>"Please check whether input data in red blocks are correct!"} field :years ,type:Array ,default:[1,3,5] field :texts_between_Result_and_result_block ,type:Hash ,default: {"zh_tw"=>"如果欲將預測結果應用於臨床上,請務必與您的主治醫師討論後再做最後決定。", "en"=>"Please note that the patients need to consult with their medical doctors before making any decision."} #field :image_uploader ,type:Object field :prediction_formula , type: String ,default: "lpv = ((age-62.35503)* (0.01620647)+ (size-45.78922)* (0.004637678) + (nposit-0.1034535)* (1.768812)+ grade_2* (1.02731848) + grade_3 * (1.460761) + pstage_2* (1.536694) +pstage_3* (2.618133) + pstage_4* (3.7651795)+(crm-278.6691)*(-0.000376257)+ pi_yes*(0.560681)+ ob_yes*(0.332975) +per_yes*(0.7378405) + chemo * (-0.7937134))" field :years_settings , type: Array , default: ["exp(-0.00131011)^exp(lpv)", "exp(-0.006207639)^exp(lpv)", "exp(-0.009758056)^exp(lpv)"] field :tmp_years_settings , type: Array , default: [] field :tmp_years_settings_for_ruby , type: Array , default: [] field :hidden_variables, type: String, default: "ratio = (lymph_nodes_examined == 0 ? 0 : (1.0 * lymph_nodes_positive / lymph_nodes_examined)) ratio = (ratio > 1 ? 1 : ratio) nposit = ((ratio + 0.1) / 0.1) ^ 0.5 grade_1 = (grade == 1 || grade == 4) ? 1 : 0 grade_2 = (grade == 2) ? 1 : 0 grade_3 = (grade == 3) ? 1 : 0 pstage_2 = (pstage == 2) ? 1 : 0 pstage_3 = (pstage == 3) ? 1 : 0 pstage_4 = (pstage == 4) ? 1 : 0 pi_yes = (pi == 1) ? 1 : 0 ob_yes = (obstruction == 1) ? 1 : 0 per_yes = (perforation == 1) ? 1 : 0 chemo = (Chemotherapy == 2) ? 1 : 0 " field :fix_hidden_variables, type: Array, default: [] field :tmp_hidden_variables_for_ruby, type: String, default: "" field :tmp_hidden_variables_for_js, type: String, default: "" field :lpv_calc, type: Hash, default: {} #for js code field :tmp_lpv_ruby_code, type: String, default: "" field :tmp_lpv_variables, type: Array, default: [] field :mapping_data_from_csv , type: String ,default: "" field :all_variables, type: Array, default: [] field :treatment_method, type: Array, default: ['Surgery_only'] field :treatment_method_active_indices, type: Array, default: [1] field :result_table, type: String, default: "", localize: true field :result_text, type: String, default: "", localize: true field :therapy_lpv, type: Array, default: [0] #before_create :set_expire before_save do self.form_show.each do |num,property| property[:need_map_values] = (property[:map_values].class == Array && property[:choice_fields].class == Array && property[:map_values].length == property[:choice_fields].length) ? 1 : 0 end result_keys = [] self.form_show.each do |num,property| variable_name = property[:variable] if variable_name.present? result_keys << variable_name end end self.form_show_in_result.each do |num,property| variable_name = property[:variable] if variable_name.present? result_keys << variable_name end end mapping_data = JSON.parse(self.mapping_data_from_csv) rescue {} if self.advance_mode && mapping_data.present? mapping_data.each do |k,v| result_keys += (v.keys rescue []) end end result_keys = result_keys.uniq self.all_variables = result_keys formula = text_to_math(self.prediction_formula) tmp_hidden_variables = text_to_math(self.hidden_variables) result_keys.each do |k| formula = formula.gsub(/(\A|[^\w])#{k}($|[^\w])/){|f| "#{$1}result[\"#{k.strip}\"]#{$2}" } tmp_hidden_variables = tmp_hidden_variables.gsub(/(\A|[^\w])#{k}($|[^\w])/){|f| "#{$1}result[\"#{k.strip}\"]#{$2}" } end self.tmp_hidden_variables_for_js = tmp_hidden_variables.rstrip.gsub(/\n\s+/,"\n ").gsub("\n",";\n") + ";" self.fix_hidden_variables = [] self.tmp_hidden_variables_for_ruby = tmp_hidden_variables.split(/^([^=!]+)=([^=!])/).select{|s| s.present?}.each_slice(2).map do |a,b| a = a.strip self.fix_hidden_variables << a if b ("result[\"#{a}\"]=" + b.gsub("\n","")) else a end end.join("\n") self.fix_hidden_variables = self.fix_hidden_variables.uniq formula = formula.split(/^([^=!]+)=([^=!])/).select{|s| s.present?}.each_slice(2).map do |a,b| a = a.strip if b ("result[\"#{a}\"]=" + b.gsub("\n","")) else a end end.join("\n") self.fix_hidden_variables.each do |v| formula = formula.gsub(/(\A|[^\w\"])#{v}($|[^\w])/){|f| "#{$1}result[\"#{v.strip}\"]#{$2}"} self.tmp_hidden_variables_for_ruby = self.tmp_hidden_variables_for_ruby.gsub(/(\A|[^\w\"])#{v}($|[^\w])/){|f| "#{$1}result[\"#{v.strip}\"]#{$2}"} end self.tmp_lpv_ruby_code = formula formula_variables = formula.enum_for(:scan,/([^\=\(\)]+)?=[^=]/).map {|x| x[-1] }.compact.map{|s| s.strip[8..-3]} self.tmp_lpv_variables = formula_variables self.tmp_years_settings = self.years_settings.map do |s| text_to_math(s) end self.tmp_years_settings_for_ruby = self.tmp_years_settings.clone formula_variables.each do |variable_name| self.tmp_years_settings_for_ruby = self.tmp_years_settings_for_ruby.map do |y| y.gsub(variable_name,"result[\"#{variable_name}\"]") end end self.treatment_method = ['Surgery_only'] self.form_show_in_result.values.each do |choice| variable = choice["variable"] if variable.present? self.treatment_method << variable end end tmp_table_translations = {} tmp_text_translations = {} @years = self.years @head_name = ['Treatment','Additional_Benefit','Overall_Survival'] # @head_name = ['Treatment','Overall_Survival'] @therapy_names = self.treatment_method I18n.available_locales.each do |locale| I18n.with_locale(locale) do @table_head = @head_name.map{|name| I18n.t('coloncancerpredict2.table.'+name)} @therapy_choices = [I18n.t('coloncancerpredict2.table.Surgeryonly')] self.form_show_in_result.values.each{|choice| @therapy_choices.push choice["name"][locale].to_s} tmp_table = "#{I18n.t("coloncancerpredict2.table.table")}
" tmp_table += '

'+self.table_above_texts[locale].to_s+'

' tmp_table += (''+(locale.to_s == 'zh_tw' ? '第' : '')+'') @years.each{|year| tmp_table += ('')} tmp_table += (''+(locale == 'zh_tw' ? '年' : '')+'') tmp_table += '' @table_head.each_with_index{|head,index| tmp_table += ('')} tmp_table += '' @therapy_choices.each_with_index do |choice,i| tmp_table += '' @table_head.each_with_index do |head,index| tmp_table += ('') end tmp_table += '' end tmp_table_translations[locale] = tmp_table @texts = self.text_above_texts[locale].to_s.gsub('
','
').gsub('{{Surgery_only}}','') @texts = @texts.split('{{years}}') @texts.delete('') tmp_text = "#{I18n.t("coloncancerpredict2.table.text")}
" tmp_text += (''+@texts[0].to_s) @years.each{|year| tmp_text += ('')} if @texts.count > 1 tmp_text += (@texts[1]+'') if @texts.count > 1 else tmp_text += '
' end if !self.surgery_only_texts[locale].blank? @surgery_only_texts = self.surgery_only_texts[locale] @surgery_only_texts.insert(0,'

') @surgery_only_texts = @surgery_only_texts.gsub('{{Surgery_only}}','') @surgery_only_texts = @surgery_only_texts.gsub('{{surgery_year}}',''+@years[-1].to_s+'') @surgery_only_texts += '' else @surgery_only_texts = '' end tmp_text += @surgery_only_texts tmp_text += ''+(self.extra_texts[locale].to_s rescue '')+'

' tmp_text_translations[locale] = tmp_text end end self.result_table_translations = tmp_table_translations self.result_text_translations = tmp_text_translations self.treatment_method_active_indices = [1] self.form_show_in_result.each do |num, property| v = property[:active_choice] if v.present? self.treatment_method_active_indices << (v - 1) else self.treatment_method_active_indices << 1 end end self.lpv_calc = get_years_settings_dict self.generate_eval_formula end def reload_any_asset(path, type=nil, force_reload=false) env = Rails.application.assets new_env = Sprockets::Environment.new(Rails.root.to_s) do |env| env.version = ::Rails.env tmp_path = "#{Rails.application.config.root}/tmp/cache/assets/#{::Rails.env}" env.cache = ::Sprockets::Cache::FileStore.new(tmp_path) env.context_class.class_eval do include ::Sprockets::Rails::Helper end end new_env.config = env.config stats = env.cached.instance_variable_get(:@stats) new_path = path.sub(/\.erb$/,'') if force_reload || (stats && stats[path]) #Need reload asset if type.nil? ext = File.extname(new_path) env.mime_types.each do |t, h| if h[:extensions].include?(ext) type = t break end end end if type full_path = 'file://'+path+'?type='+type uris = env.cached.instance_variable_get(:@uris) keys = uris.keys.select{|k| k.include?(full_path)} asset = new_env.load(full_path) if keys.count != 0 keys.each do |k| uris[k] = asset end end new_path = new_path.sub(/\.[^.]+$/){|ext| '-' + asset.digest + ext} File.binwrite(new_path, asset.to_s) yield new_path if block_given? end end end def reload_js_asset(path, force_reload=false) reload_any_asset(path, 'application/javascript', force_reload) do |new_path| File.open(JSFileName, 'w+'){|f| f.write(new_path)} end end def generate_eval_formula eval_hidden_variables = "def eval_hidden_variables(result); #{self.tmp_hidden_variables_for_ruby}; end" Coloncancerpredict2sController.module_eval(eval_hidden_variables) eval_formula = "def eval_formula(result); #{self.tmp_lpv_ruby_code}; end" Coloncancerpredict2sController.module_eval(eval_formula) end def generate_jscode js_code = "var map_values , mapping_hash , temp_index ,temp_value , index , closest_value;\r\n" mapping_data_from_csv = JSON.parse(self.mapping_data_from_csv) rescue {} tmp_hash = self.form_show.values + self.form_show_in_result.values tmp_hash.each do |property| @variable = property[:variable] if @variable.present? if property[:is_num] == 1 js_code += " result['#{@variable}'] = Number(result_json['#{@variable}']);\r\n" elsif property[:choice_fields].present? if !(self.advance_mode) js_code += " result['#{@variable}'] = Number(result_json['#{@variable}']);\r\n" else if property[:need_map_values] == 1 js_code += " map_values = #{property[:map_values]};\r\n" js_code += " result['#{@variable}'] = map_values[Number(result_json['#{@variable}'']) - 1];\r\n" else if property[:revert_value] != 1 js_code += " result['#{@variable}'] = Number(result_json['#{@variable}']) - 1;\r\n" else js_code += " result['#{@variable}'] = (#{property[:choice_fields].length} - Number(result_json['#{@variable}']));\r\n" end end end end if self.advance_mode && property[:coloncancer_predict_mapping_file2].present? if (mapping_data_from_csv != {} && !mapping_data_from_csv[@variable].blank?) js_code += " mapping_hash = mapping_data_from_csv['#{@variable}'];\r\n" js_code += " temp_index = 0;\r\n" js_code += " temp_value = result['#{@variable}'];\r\n" js_code += " index = 0; $.each(mapping_hash,function(k,v){ if( index == 0 ){ var index_val = v.indexOf(temp_value); if( index_val != -1 ){ temp_index = index_val; }else{ closest_value = v.get_nearest_value(temp_value); temp_index = v.indexOf(closest_value) } } result[k] = v[temp_index]; index++; });\r\n" end end end end js_code += "\n Object.keys(result).forEach(function(k){ if(Number.isNaN(result[k])){ result[k] = 0; } })" js_code += "\n #{self.tmp_hidden_variables_for_js}" formula = text_to_math(self.prediction_formula) self.all_variables.each do |k| formula = formula.gsub(/(\A|[^\w])#{k}($|[^\w])/){|f| "#{$1}result[\"#{k.strip}\"]#{$2}" } end formula_variables = self.tmp_lpv_variables.map{|v| v} js_code = "\n function calculate_first_lpv(result_json){ result = {}; #{js_code} try{ #{formula.gsub(/\s{2,10}/," ").gsub("\n","\n ")} }catch(e){console.log(e)}; result['lpv_variable'] = {}; #{formula_variables.map{|v| "result['lpv_variable']['#{v}'] = #{v};"}.join("\n ") } result['lpv'] = #{formula_variables.count == 0 ? 0 : formula_variables.last}; result['lpv_variable']['lpv'] = result['lpv']; return result; }; function calculate_and_change_result_value(obj){ obj.servive_ratio_arr = []; for(var i = 0;i
' + head + '
' + ((index == 0) ? (((i==0)? '' : '+') + choice) : '-') + '