class Cancerpredictfields
  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","cancer_predict_mapping_file"=>"String","lpv_impact"=>"Float","active_choice"=>"number_field","disable_condition"=>"text_area"}
  NonLoclaized = ["variable","is_num","range","right","is_float","revert_value","map_values","cancer_predict_mapping_file","lpv_impact","active_choice","disable_condition"]
  AdvanceFields = ["revert_value","map_values","cancer_predict_mapping_file"]
  TherapyFields = ["variable","name","hint","comment_text","choice_fields","lpv_impact","active_choice","disable_condition"]
  TherapyOnly = ["lpv_impact","active_choice","disable_condition"]
  JSFileName = "public/cancer_tool_js_filename.txt".freeze
  ModuleAppPath = Pathname.new(File.expand_path(__dir__)).dirname.dirname.to_s.freeze
  JS = "cancer_predict.js"
  ToolTableMap = I18n.available_locales.map do |locale|
    [locale, "public/cancer_tool_table_tmp_#{locale}.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<br/>(Age)", "en"=>"Age"}, "is_num"=>1, "hint"=>{"zh_tw"=>"從 18 歲(含)開始至 93 歲", "en"=>"Age must be between 18 and 93"}, "comment_text"=>{"zh_tw"=>"年齡為該婦女於確診罹患乳癌時之年齡", "en"=>"Age at diagnosis"}, "choice_fields"=>{"zh_tw"=>[], "en"=>[]}, "range"=>[18, 93], "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)<br/>(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", "en"=>"If there was more than one primary tumor, please enter the size of the largest one."}, "choice_fields"=>{"zh_tw"=>[], "en"=>[]}, "range"=>[1, 300], "right"=>0, "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<br/>(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<br/>(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"=>0, "is_float"=>0, "need_map_values"=>0, "revert_value"=>0, "map_values"=>[]},
                            "4"=>{"variable"=>"grade", "name"=>{"zh_tw"=>"腫瘤分化程度<br/>(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 2 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"=>0, "is_float"=>0, "need_map_values"=>0, "revert_value"=>0, "map_values"=>[]},
                            "5"=>{"variable"=>"ER_status", "name"=>{"zh_tw"=>"ER 狀態<br/>(ER status)", "en"=>"ER status"}, "is_num"=>0, "hint"=>{"zh_tw"=>"", "en"=>""}, "comment_text"=>{"zh_tw"=>"ER:雌激素受體，若無 ER 資訊請選擇未知，將以佔多數比例陽 性作為後續預測。", "en"=>"ER status describes the status of estrogen receptor. Please select “unknown” if there is no information about ER status. The prediction model would use “Positive” (the majority class) as the alternative variable."}, "choice_fields"=>{"zh_tw"=>["陽性", "陰性", "未知"], "en"=>["positive", "negative", "unknown"]}, "range"=>[], "right"=>0, "is_float"=>0, "need_map_values"=>0, "revert_value"=>0, "map_values"=>[]},
                            "6"=>{"variable"=>"PR_status", "name"=>{"zh_tw"=>"PR 狀態<br/>(PR status)", "en"=>"PR status"}, "is_num"=>0, "hint"=>{"zh_tw"=>"", "en"=>""}, "comment_text"=>{"zh_tw"=>"PR:黃體素受體， 若無 PR 資訊請選擇未知，將以佔多數比例陽性作為後續預測。", "en"=>"PR status describes the status of progesterone receptor. Please select  “unknown” if there is no information about PR status. The prediction model would use “Positive” (the majority class) as the alternative variable."}, "choice_fields"=>{"zh_tw"=>["陽性", "陰性", "未知"], "en"=>["positive", "negative", "unknown"]}, "range"=>[], "right"=>1, "is_float"=>0, "need_map_values"=>0, "revert_value"=>0, "map_values"=>[]},
                            "7"=>{"variable"=>"HER2_status", "name"=>{"zh_tw"=>"HER2 狀態<br/>(HER2 status)", "en"=>"HER2 status"}, "is_num"=>0, "hint"=>{"zh_tw"=>"", "en"=>""}, "comment_text"=>{"zh_tw"=>"HER2:第二型人類上皮成長因子接受器蛋白，若無 HER2 資訊請選擇未知，將以佔多數比例陰性作為後續預測。", "en"=>"HER2 status describes the status of human epidermal growth factor receptor 2. Please select “unknown” if there is no information about HER2 status. The prediction model would use “Negative” (the majority class) as the alternative variable."}, "choice_fields"=>{"zh_tw"=>["陽性", "陰性", "未知"], "en"=>["positive", "negative", "unknown"]}, "range"=>[], "right"=>1, "is_float"=>0, "need_map_values"=>0, "revert_value"=>0, "map_values"=>[]},
                            "8"=>{"variable"=>"Distant_Metastasis", "name"=>{"zh_tw"=>"遠端轉移<br/>(Distant Metastasis)", "en"=>"Distant Metastasis"}, "is_num"=>0, "hint"=>{"zh_tw"=>"", "en"=>""}, "comment_text"=>{"zh_tw"=>"若無遠端轉移資訊請選擇未知，將以佔多數比例未遠端轉移作為後續預測。", "en"=>"Please select “unknown” if there is no information about distant Metastasis. The prediction model would use “No” (the majority class) as the alternative variable."}, "choice_fields"=>{"zh_tw"=>["是", "否", "未知"], "en"=>["yes", "no", "unknown"]}, "range"=>[], "right"=>1, "is_float"=>0, "need_map_values"=>0, "revert_value"=>0, "map_values"=>[]},
                            "9"=>{"variable"=>"micrometastasis", "name"=>{"zh_tw"=>"淋巴結顯微轉移<br/>(Lymph nodes micrometastasis)", "en"=>"Micrometastasis"}, "is_num"=>0, "hint"=>{"zh_tw"=>"", "en"=>""}, "comment_text"=>{"zh_tw"=>"若無顯微移資訊請選擇未知，將以無顯微轉移作為後續預測。", "en"=>"Please select “unknown” if there is no information about lymph nodes micrometastasis. The prediction model would use “No” as the alternative variable."}, "choice_fields"=>{"zh_tw"=>["是", "否", "未知"], "en"=>["yes", "no", "unknown"]}, "range"=>[], "right"=>1, "is_float"=>0, "need_map_values"=>0, "revert_value"=>0, "map_values"=>[]},
                            "10"=>{"variable"=>"tumor_direct_extension", "name"=>{"zh_tw"=>"腫瘤浸潤至胸壁和/或皮膚<br/>(Tumor direct extension to the chest wall and/or to the skin)", "en"=>"Tumor direct extension to the chest wall and/or to the skin"}, "is_num"=>0, "hint"=>{"zh_tw"=>"", "en"=>""}, "comment_text"=>{"zh_tw"=>"若無腫瘤浸潤至胸壁或皮膚資訊請選擇未知，將以佔多數比例無腫瘤浸潤至胸壁作為後續預測。", "en"=>"Please select “unknown” if there is no information about tumor direct extension to the chest wall and/or to the skin. The prediction model would use “No” (the majority class) as the alternative variable"}, "choice_fields"=>{"zh_tw"=>["是", "否", "未知"], "en"=>["yes", "no", "unknown"]}, "range"=>[], "right"=>1, "is_float"=>0, "need_map_values"=>0, "revert_value"=>0, "map_values"=>[]},
                            "11"=>{"variable"=>"lvi", "name"=>{"zh_tw"=>"淋巴管或血管侵犯<br/>(Lymph vessel or vascular invasion, LVI)", "en"=>"Lymph vessel or vascular invasion, LVI"}, "is_num"=>0, "hint"=>{"zh_tw"=>"", "en"=>"Lymph vessel or vascular invasion"}, "comment_text"=>{"zh_tw"=>"若無淋巴管或血管侵犯資訊，請選擇“未知”選項，將以佔多數比例的淋巴管或血管未侵犯進行預測", "en"=>"Please select “unknown” if there is no information about Lymph vessel or vascular invasion. The prediction model would use “No” (the majority class) as the alternative variable."}, "choice_fields"=>{"zh_tw"=>["是", "否", "未知"], "en"=>["yes", "no", "unknown"]}, "range"=>[], "right"=>1, "is_float"=>0, "need_map_values"=>0, "revert_value"=>0, "map_values"=>[]}
                          }
  field :form_show_in_result ,  :type=> Hash ,default: {
                            "0"=>{"variable"=>"hormone_therapy", "name"=>{"zh_tw"=>"賀爾蒙治療", "en"=>"Hormone/Steroid therapy"}, "is_num"=>0, "hint"=>{"zh_tw"=>"適用賀爾蒙受體陽性病人", "en"=>"Hormone/ steroid therapy is available when ER status is positive"}, "comment_text"=>{"zh_tw"=>"", "en"=>""}, "choice_fields"=>{"zh_tw"=>["否", "是"], "en"=>["No", "Yes"]}, "range"=>[], "right"=>0, "is_float"=>0, "revert_value"=>0, "map_values"=>"", "active_choice"=>2, "disable_condition"=>"ER_status == 2 && PR_status == 2", "lpv_impact"=>-0.8397},
                            "1"=>{"variable"=>"Chemotherapy", "name"=>{"zh_tw"=>"化學治療", "en"=>"Chemotherapy"}, "is_num"=>0, "hint"=>{"zh_tw"=>"", "en"=>""}, "comment_text"=>{"zh_tw"=>"", "en"=>""}, "choice_fields"=>{"zh_tw"=>["否", "是"], "en"=>["No", "Yes"]}, "range"=>[], "right"=>0, "is_float"=>0, "revert_value"=>0, "map_values"=>"", "active_choice"=>2, "disable_condition"=>"", "lpv_impact"=>-0.4147},
                            "2"=>{"variable"=>"Radiotherapy", "name"=>{"zh_tw"=>"放射治療", "en"=>"Radiotherapy"}, "is_num"=>0, "hint"=>{"zh_tw"=>"", "en"=>""}, "comment_text"=>{"zh_tw"=>"", "en"=>""}, "choice_fields"=>{"zh_tw"=>["否", "是"], "en"=>["No", "Yes"]}, "range"=>[], "right"=>0, "is_float"=>0, "revert_value"=>0, "map_values"=>"", "active_choice"=>2, "disable_condition"=>"", "lpv_impact"=>-0.3203},
                            "3"=>{"variable"=>"Targeted_therapy", "name"=>{"zh_tw"=>"標靶治療", "en"=>"Targeted therapy"}, "is_num"=>0, "hint"=>{"zh_tw"=>"抗HER2治療", "en"=>""}, "comment_text"=>{"zh_tw"=>"", "en"=>""}, "choice_fields"=>{"zh_tw"=>["否", "是"], "en"=>["No", "Yes"]}, "range"=>[], "right"=>0, "is_float"=>0, "revert_value"=>0, "map_values"=>"", "active_choice"=>2, "disable_condition"=>"HER2_status != 1", "lpv_impact"=>-0.4687}
                          }
  field :form_result_is_right , :type=> Integer ,default: 0
  field :text_descibe ,type:Hash ,default: {
                            "zh_tw"=>"歡迎使用台灣準備乳癌預後系統！<br />\r\n本預測系統由台灣癌症登記資料庫2011至2015年間共20,997位乳癌病人所建立<br />\r\n並驗證美國流行病學癌症資料庫59,271位病人所建立 。<br />\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!<br />\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.<br />\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: [BSON::ObjectId('5df62cfc8cd8924e79000009'), BSON::ObjectId('5df745a58cd8924491000006'), BSON::ObjectId('5e7848c98cd8924f8d00004a'), BSON::ObjectId('5ea8f3e48cd892760b000011')]
  field :title_images_id ,type:Array , default: [BSON::ObjectId('5df87cd88cd8924491000036')]
  field :title_texts ,type:Hash ,default: {"zh_tw"=>"華人癌症存活預測", "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<br/>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<br/>{{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:<br/>{{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 = ((age1-0.7276655)*(-10.87)+(age2+0.4540707)*8.968+(size1-0.643632)*0.7678+(nposit-1.346932)*0.5339+
      grade_2*0.4795+grade_3*0.818+subtype_HER2*0.1806+subtype_triple*0.6457+pstage_2*0.5311+
      pstage_3*1.134+pstage_4*2.172+lvi_yes*0.3321-0.04+chemo*(-0.4147)+radio*(-0.3203)+hormone*(-0.8397)+target*(0.3321)
)"
  field :years_settings , type: Array , default: ["exp(-0.001476145)^( exp(lpv) )","exp(-0.01261639)^( exp(lpv) )","exp(-0.02519608)^( exp(lpv) )"]
  field :tmp_years_settings , type: Array , default: []
  field :tmp_years_settings_for_ruby , type: Array , default: []
  field :hidden_variables, type: String, default: "age1 = (age / 100.0) ^ (0.5)
  age2 = age1 * log(age / 100.0)
  size1 = log(size / 10.0)
  ratio = (lymph_nodes_examined == 0 ? 0 : (1.0 * lymph_nodes_positive / lymph_nodes_examined))
  ratio = (ratio > 1 ? 1 : ratio)
  T4 = (tumor_direct_extension == 1)
  T1 = !T4 && (size <= 20)
  T2 = !T4 && !T1 && (size > 20 && size <= 50)
  T = (T4 ? 'T4' : (T1 ? 'T1' : (T2 ? 'T2' : 'T3')))
  N0 = (lymph_nodes_positive == 0)
  N1_or_N1mi = !N0 && (lymph_nodes_positive >= 1 && lymph_nodes_positive <= 3)
  N1 = N1_or_N1mi && micrometastasis != 1
  N1mi = N1_or_N1mi && micrometastasis == 1
  N2 = !N0 && !N1_or_N1mi && (lymph_nodes_positive <= 9)
  N = (N0 ? 'N0' : (N1 ? 'N1' : (N1mi ? 'N1mi' : (N2 ? 'N2' : 'N3'))))
  M = (Distant_Metastasis != 1) ? 'M0' : 'M1'
  pstage = (M == 'M1' ? 4 : ((T == 'T1' && (N == 'N0' || N == 'N1mi')) ? 1 : (((T == 'T2' || T == 'T3') && (N == 'N0')) || ((T == 'T1' || T == 'T2') && (N == 'N1')) ? 2 : 3)) )
  nposit = ((ratio + 0.1) / 0.1) ^ 0.5
  grade_2 = (grade == 2 || grade == 4) ? 1 : 0
  grade_3 = (grade == 3) ? 1 : 0
  subtype_1 = (ER_status != 2 || PR_status != 2) && (HER2_status != 1)
  subtype_2 = !subtype_1 && (HER2_status == 1)
  subtype_3 = !subtype_1 && !subtype_2 && (ER_status == 2 && PR_status == 2 && HER2_status != 1)
  subtype_HER2 = subtype_2 ? 1 : 0
  subtype_triple = subtype_3 ? 1 : 0
  pstage_2 = (pstage == 2) ? 1 : 0
  pstage_3 = (pstage == 3) ? 1 : 0
  pstage_4 = (pstage == 4) ? 1 : 0
  lvi_yes = (lvi == 1) ? 1 : 0
  chemo = (Chemotherapy == 2) ? 1 : 0
  radio = (Radiotherapy == 2) ? 1 : 0
  hormone = (hormone_therapy == 2) ? 1 : 0
  target = (Targeted_therapy == 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('cancerpredict.table.'+name)}
        @therapy_choices = [I18n.t('cancerpredict.table.Surgeryonly')]
        self.form_show_in_result.values.each{|choice| @therapy_choices.push choice["name"][locale].to_s}
        tmp_table = "<span class=\"result_title print_only\">#{I18n.t("cancerpredict.table.table")}</span><div style=\"clear: both\"></div>"
        tmp_table += '<input id="current_year" type="hidden" value="'+@years[-1].to_s+'" index="0"/><p id="cancer_table_texts">'+self.table_above_texts[locale].to_s+'</p>'
        tmp_table += ('<a style="display: inline-block;">'+(locale.to_s == 'zh_tw' ? '第' : '')+'</a><a style="display: inline-block;">')
        @years.each{|year| tmp_table += ('<button class="cancer_years cancer_table_btn  btn btn-default btn-sm">'+year.to_s+'</button>')}
        tmp_table += ('</a><a style="display: inline-block;">'+(locale == 'zh_tw' ? '年' : '')+'</a>')
        tmp_table += '<table><thead><tr>'
        @table_head.each_with_index{|head,index| tmp_table += ('<th class="cancer_th '+@head_name[index]+'">' + head + '</th>')}
        tmp_table += '</tr></thead><tbody>'
        @therapy_choices.each_with_index do |choice,i|
          tmp_table += '<tr class="'+@therapy_names[i].to_s+'">'
          @table_head.each_with_index do |head,index|
            tmp_table += ('<td class="cancer_td '+ @head_name[index]+'">' + ((index == 0) ? (((i==0)? '' : '+') + choice) : '-') + '</td>')
          end
          tmp_table += '</tr>'
        end
        tmp_table_translations[locale] = tmp_table
        @texts = self.text_above_texts[locale].to_s.gsub('<br/>','</span><br/><span>').gsub('{{Surgery_only}}','<span class="'+@therapy_names[0]+' Overall_Survival"></span>')
        @texts = @texts.split('{{years}}')
        @texts.delete('')
        tmp_text = "<span class=\"result_title print_only\">#{I18n.t("cancerpredict.table.text")}</span><div style=\"clear: both\"></div>"
        tmp_text += ('<span>'+@texts[0].to_s)
        @years.each{|year| tmp_text += ('<button class="cancer_years cancer_table_btn  btn btn-default btn-sm" style="float:none;">'+year.to_s+'</button>')}
        if @texts.count > 1
          tmp_text +=  (@texts[1]+'</span>') if @texts.count > 1
        else
          tmp_text += '</span>'
        end
        if !self.surgery_only_texts[locale].blank?
          @surgery_only_texts = self.surgery_only_texts[locale]
          @surgery_only_texts.insert(0,'<p class="show"><span>')
          @surgery_only_texts = @surgery_only_texts.gsub('{{Surgery_only}}','<span class="'+@therapy_names[0]+' Overall_Survival"></span><span>')
          @surgery_only_texts = @surgery_only_texts.gsub('{{surgery_year}}','</span><span class="surgery_year">'+@years[-1].to_s+'</span><span>')
          @surgery_only_texts += '</span>'
        else
          @surgery_only_texts = ''
        end
        tmp_text += @surgery_only_texts
        tmp_text += '<span class="addition">'+(self.extra_texts[locale].to_s rescue '')+'</span><div class="extra-text" style="display:none;"><div class="texts_show" style="clear:both;"></div></div></p>'
        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"
    CancerpredictsController.module_eval(eval_hidden_variables)
    eval_formula = "def eval_formula(result); #{self.tmp_lpv_ruby_code}; end"
    CancerpredictsController.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[:cancer_predict_mapping_file].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<obj.active_treatment.length;i++){
          var servive_ratio = round(calculate_servive_ratio(obj.year,obj.lpv_real[i])*100,2);
          var benefit = servive_ratio - obj.servive_ratio_arr[obj.servive_ratio_arr.length-1];
          obj.servive_ratio_arr.push(servive_ratio);
          $('tr.'+obj.active_treatment[i]+' td.Overall_Survival').html(servive_ratio+'%');
          $('.'+obj.active_treatment[i]+'.Overall_Survival').html(Math.round(servive_ratio));
          if(i != 0){
            $('tr.'+obj.active_treatment[i]+' td.Additional_Benefit').html(round(benefit,2)+'%');
            $('.'+obj.active_treatment[i]+'.Additional_Benefit').html(Math.round(benefit));
          }
        }
        //$('.'+obj.active_treatment[0]+'.Overall_Survival').html(Math.round(obj.servive_ratio_arr[0]));
      };"
    @years = self.years
    switch_texts = "
      #{formula_variables.map{|v| "var #{v} = obj['#{v}'];"}.join("\n      ")}
      switch(year) {"
    @years.each do |year|
      year_index = @years.index(year)
      switch_texts +=
      "
        case  '#{year}':
          servive_ratio = #{text_to_math(self.years_settings[year_index])};
          break;"
    end
    switch_texts += "
        default:
          console.log('not found year.');
      }"
    js_code = js_code +"

    function calculate_servive_ratio(year,obj){
      var servive_ratio;#{switch_texts}
      return servive_ratio;
    }
    "
    return js_code
  end
  def text_to_math(text)
    text.gsub("\r\n","\n").gsub('^','**').gsub('exp','Math.exp').gsub('log','Math.log')
  end
  def replace_str_with_idx(org_str,st, ed, replace_str)
    org_str.slice!(st, ed - st + 1)
    org_str.insert(st, replace_str)
    org_str
  end
  def auto_write_predict_js(force_reload=true)
    js_codes = generate_jscode
    save_path = "#{ModuleAppPath}/app/assets/javascripts/#{JS}"
    file_texts = File.read(save_path)
    need_write = false
    str1 = "/* auto add start */"
    index1 = file_texts.index(str1)
    str2 = "/* auto add end */"
    index2 = file_texts.index(str2)
    if (!index1.nil? && !index2.nil?)
      file_texts = replace_str_with_idx(file_texts, index1 + str1.length, index2 - 1, js_codes)
      need_write = true
    end
    str3 = "/*lpv_calc_formula_start*/"
    index3 = file_texts.index(str3)
    str4 = "/*lpv_calc_formula_end*/"
    index4 = file_texts.index(str4)
    if (!index3.nil? && !index4.nil?)
      file_texts = replace_str_with_idx(file_texts, index3 + str3.length, index4 - 1, self.lpv_calc.to_json.gsub("@",".") + ';')
      need_write = true
    end
    tmp_disable_jscodes = ""
    str5 = "/*disable_condition start*/"
    index5 = file_texts.index(str5)
    str6 = "/*disable_condition end*/"
    index6 = file_texts.index(str6)
    self.form_show_in_result.each do |num,property|
      if property[:disable_condition].present?
        tmp = property[:disable_condition].clone
        self.all_variables.each do |k|
          tmp = tmp.gsub(/(\A|[^\w])#{k}($|[^\w])/){|f| "#{$1}post_json[\"#{k}\"]#{$2}" }
        end
        variable = property[:variable]
        tmp_disable_jscodes += "\n      if(#{tmp}){
        $('##{variable} .cancer_table_btn').attr('disabled','disabled');
        $('[for=\"#{variable}\"]').css('color','rgb(204, 204, 204)');
      }else{
        $('##{variable} .cancer_table_btn').removeAttr('disabled');
        $('[for=\"#{variable}\"]').css('color','');
      }"
      end
    end
    if (!index5.nil? && !index6.nil?)
      tmp_disable_jscodes += "\n\t\t\t"
      file_texts = replace_str_with_idx(file_texts, index5 + str5.length, index6 - 1, tmp_disable_jscodes)
      need_write = true
    end
    str7 = "/*therapy_lpv start*/"
    index7 = file_texts.index(str7)
    str8 = "/*therapy_lpv end*/"
    index8 = file_texts.index(str8)
    self.therapy_lpv = [0]
    self.form_show_in_result.each do |num,property|
      if property[:lpv_impact].present?
        self.therapy_lpv << property[:lpv_impact]
      else
        self.therapy_lpv << 0
      end
    end
    if (!index7.nil? && !index8.nil?)
      file_texts = replace_str_with_idx(file_texts, index7 + str7.length, index8 - 1, self.therapy_lpv.to_s + ';')
      need_write = true
    end
    if need_write
      File.write(save_path,file_texts)
      reload_js_asset(save_path, force_reload)
    elsif !(File.exist?(JSFileName))
      File.open(JSFileName, 'w+'){|f| f.write(save_path)}
    end
  end
  def get_years_settings_dict
    lpv_variable_name = (prediction_formula.include?("=") ? prediction_formula.split("=")[0].strip : "" rescue "")
    res = self.years.map.with_index do |y, i|
      tmp_formula = self.tmp_years_settings[i]
      if lpv_variable_name.present?
        tmp_formula = tmp_formula.gsub(lpv_variable_name, "lpv_current")
      else
        tmp_formula = tmp_formula.gsub("lpv", "lpv_current")
      end
      [y.to_s.sub(".","@"), tmp_formula]
    end
    res.to_h
  end
end