Colon_Cancer_Predict1/app/models/coloncancerpredictfields1s.rb

423 lines
26 KiB
Ruby
Raw Normal View History

2022-05-23 10:32:51 +00:00
class Coloncancerpredictfields1s
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_file1"=>"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_file1","lpv_impact","active_choice","disable_condition"]
AdvanceFields = ["revert_value","map_values","coloncancer_predict_mapping_file1"]
TherapyFields = ["variable","name","hint","comment_text","choice_fields","lpv_impact","active_choice","disable_condition"]
TherapyOnly = ["lpv_impact","active_choice","disable_condition"]
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 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)<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、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<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"=>1, "is_float"=>0, "need_map_values"=>0, "revert_value"=>0, "map_values"=>[]},
"4"=>{"variable"=>"grade", "name"=>{"zh_tw"=>"腫瘤級數<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 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"=>0, "is_float"=>0, "need_map_values"=>0, "revert_value"=>0, "map_values"=>[]},
"5"=>{"variable"=>"pstage", "name"=>{"zh_tw"=>"病理分期<br/>(pathologic stage)", "en"=>"Pathologic 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"=>"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"=>["1", "2", "3", "4", "未知"], "en"=>["positive", "negative", "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"=>"Chemotherapy", "name"=>{"zh_tw"=>"化學治療", "en"=>"Chemotherapy"}, "hint"=>{"zh_tw"=>"", "en"=>""}, "comment_text"=>{"zh_tw"=>"", "en"=>""}, "choice_fields"=>{"zh_tw"=>["", ""], "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!<br />\r\n本預測系統由台灣癌症登記資料庫2007至2015年間共20,218位大腸癌病 人與經<br />\r\n驗證美國流行病學癌症資料庫22,670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: []
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<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 = ((age - 62.31261) * (0.01385)+ (size - 45.85882)* (0.00786) + (nposit - 0.109264)* (1.83191) +
grade_2*0.29793+grade_3*0.67389+pstage_2*1.1152+
pstage_3*2.2501+pstage_4*3.6206+chemo*(-0.6693)
)"
field :years_settings , type: Array , default: ["exp(-0.005433642)^exp(lpv)", "exp(-0.02357545)^exp(lpv)", "exp(-0.03637668)^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
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('coloncancerpredict1.table.'+name)}
@therapy_choices = [I18n.t('coloncancerpredict1.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("coloncancerpredict1.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("coloncancerpredict1.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 generate_eval_formula
eval_hidden_variables = "def eval_hidden_variables(result); #{self.tmp_hidden_variables_for_ruby}; end"
Coloncancerpredict1sController.module_eval(eval_hidden_variables)
eval_formula = "def eval_formula(result); #{self.tmp_lpv_ruby_code}; end"
Coloncancerpredict1sController.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_file1].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
js_codes = generate_jscode
module_app_path = Pathname.new(File.expand_path(__dir__)).dirname.dirname.to_s
save_path = module_app_path + '/app/assets/javascripts/colon_cancer_predict1.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)
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