Log

1 1 2 0 0 [] 9 Log Awp_attrib 1 1 Opasnet base connection Interface for uploading data to and downloading from the Opasnet Base. <a href="http://en.opasnet.org/w/Image:Opasnet_base_connection.ANA">Wiki description</a> Jouni Tuomisto 9. maata 2008 10:42 ktluser 9. Febta 2010 22:50 48,24 1,0,0,1,1,1,0,0,0,0 1,18,25,734,523,17 2,102,90,476,316 Arial, 15 0,Model Op_en2676,2,2,0,1,C:\temp\Opasnet base connection.ANA 100,1,1,1,1,9,2970,2100,15,0 2,40,50,640,600 This module saves original data or model results (a study or a variable, respectively) into the Opasnet Base. You need your Opasnet username and password to do that. You must fill in all tables and fields below before the process can be completed. Fill in the data below from top to bottom. If an object with the same Ident already exists in the Opasnet Base, the information will be added to that object. Before you start, make sure that you have created an object page in the Opasnet wiki for each object (study or variable) you want to upload. 260,84,-1 252,76 Username 0 168,244,1 160,12 1,0,0,1,0,0,0,142,0,1 52425,39321,65535 Opasnet_username TabIndex:1 TextAlways Password 0 168,268,1 160,12 1,0,0,1,0,0,0,142,0,1 52425,39321,65535 Opasnet_password TabIndex:2 TextAlways 2 Analytica model ktluser 1. Aprta 2009 9:38 48,24 200,456,1 60,32 1,0,0,1,1,1,0,,0, 1,744,88,353,272,17 N variables 0 140,60,1 116,12 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 N_variables 3 Analytica model: 140,28,-1 124,12 1,0,0,1,0,1,0,,0, 65535,65532,19661 140,140,-1 124,100 1,0,0,1,0,1,0,,0, Object info 0 140,84,1 116,13 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Object_info3 Study or variable info 0 140,108,1 116,12 1,0,0,1,0,0,0,78,0,1 52425,39321,65535 Index_info Replace data? 0 140,132,1 116,12 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Replace_data_ Data source 0 204,380,1 196,12 1,0,0,1,0,0,0,254,0,1 Data_source Platform 0 168,316,1 160,12 1,0,0,1,0,0,0,142,0,1 52425,39321,65535 Platform Writerpsswd 0 168,292,1 160,12 1,0,0,1,0,0,0,142,0,1 52425,39321,65535 Writerpsswd C) For very large variables: Upload only the object and location information. Create csv files of other data to c:\temp\ and upload them separately (you need a direct access to the Opasnet Base). 264,748,-1 256,68 1,0,0,1,0,1,0,,0, Detailed help for Analytica use jtue 8. kesta 2009 14:27 48,24 372,184,1 116,16 1,681,15,586,564,17 Follow these instructions if you have Analytica Enterprise and have an ODBC connection to the Opasnet Base. Read also the simplified help; not everything is repeated here. 284,36,-1 276,28 1,0,0,1,0,1,0,,0, 65535,65532,19661 Platform: You must choose THL computer if you are not using the AWP web interface. 284,92,-1 276,20 1,0,0,1,0,1,0,,0, Writerpsswd: You must know the writer password for the Opasnet Base if you are not using the AWP web interface. 284,149,-1 276,29 1,0,0,1,0,1,0,,0, Object info: - Data source: 1 means that you are copy-pasting data to the 'Observations' field. 2 means that you have a 2D table in an Analytica node. The node must have column index .j (note: it is a local index!) and row index .i. The names of the columns must be in the index .j, and the first row must contain data. 3 means that you have a typical Analytica node with n indices; one of the indices may be Run. The node is transformed into a 2D table using MDArrayToTable. - Analytica identifier is the identifier of the node to be used. The name must be given between 'quotation marks', i.e. as text. - Ident: like in the simplified upload. - Number of indices: like in the simplified upload if data source 2 is used; for 3, the number of indices comes from the node, and this entry is ignored. - Parameter name: like in the simplified upload if data source 2 is used; for 3, the parameter is implicit, and this entry is ignored. - Probabilistic?: like in the simplified upload if data source 2 is used; for 3, if this entry is 1, the sample mode is used and the full distribution is saved, if the entry is not 1, the mid mode is used. - Append to upload: like in the simplified upload. 284,357,-1 276,173 1,0,0,1,0,1,0,,0, B) Upload so that the actual data is not visible without a password. Metadata is visible anyway. 264,624,-1 256,48 1,0,0,1,0,1,0,,0, 2,693,146,476,224 Upload data: 264,516,-1 256,12 1,0,0,1,0,1,0,,0, 65535,65532,19661 A) The default: Upload all data directly to Opasnet Base. 264,548,-1 256,20 1,0,0,1,0,1,0,,0, Basic help and explanations jtue 8. kesta 2009 14:27 48,24 128,184,1 120,16 1,684,5,586,824,17 Object info: * You must give your Opasnet username and password to upload data. The username will be stored together with the upload information. *Object info contains the most important metadata about your data. - Data source must be 1 when using AWP. - Analytica identifier is ignored when using AWP. - Ident is the page identifier in Opasnet. If your study or variable does not already have a page, you must create one. The identifier is found in the metadata box in the top right corner of the Opasnet page. - Number of indices is the number of columns that contain explanatory information (see below). - Parameter name is a common name for all data columns. If omitted, 'Parameter' is used. See below for more details. - If "Probabilistic?" is 1, then each row of the data table is considered a random draw from a data pool. Note that it is assumed that the index values are assumed the same in all rows, and only the index values of the first row are stored. - Append to upload: Typically, each data upload event is given a separate identifier. If you want to continue an existing upload of the same object, you can give the number of that upload, and the new data will be appended. 284,408,-1 276,176 1,0,0,1,0,1,0,,0, Data structure: * Data must be uploaded in the format of a two-dimensional table. The table has rows, one observation in each row, and columns (fields). * There are two kinds of columns. A) Index columns (also called independent variables in statistics) contain determinants of the actual data, such as sex of the observed individuals, or the observation year. B) Parameter columns (also called dependent variables) contain the actual data about the observations, given the index information. * The first row must contain the names of the columns, i.e. the indices and parameters. These names are used when creating indices in the Opasnet Base. 284,128,-1 276,96 1,0,0,1,0,1,0,,0, Observations: * The data are copy-pasted into the field 'Observations'. The source of the data can be any spreadsheet or text processor, as long as each column is separated by a tab, and each row by a line break. Note that the pasted data should be between 'quotation marks'. 284,640,-1 276,48 1,0,0,1,0,1,0,,0, Data info: Fill in the additional information about the data. These are asked for the object, and also for all the indices and the parameter. Note that is an entry with the identical Ident already exists in the Opasnet Base, this information will NOT be uploaded but the existing information will be used instead. All information should be between 'quotation marks' so that they are not mistakenly interpreted as Analytica node identifiers. - Name: a description that may be longer than an identifier. This is typically identical to the respective page in Opasnet. - Unit: unit of measurement. 284,788,-1 276,92 1,0,0,1,0,1,0,,0, Follow these instructions if you are using the Internet interface (AWP). 284,20,-1 276,12 1,0,0,1,0,1,0,,0, 65535,65532,19661 Uploading: * There are two ways of uploading data. A) 'Upload data' is a public format, and all details are openly available. B) 'Upload non-public data' stores the actual data (the values in the parameter columns) into a database that requires a password for reading. However, all other information (including upload metadata and the data in the index fields) are openly available. 284,944,-1 276,56 1,0,0,1,0,1,0,,0, Provide general information: 264,220,-1 256,12 1,0,0,1,0,1,0,,0, 65535,65532,19661 <a href="http://en.opasnet.org/w/Image:Opasnet_base_connection.ANA">Wiki description</a> 592,40,-1 48,28 Choose the format of input data. 264,356,-1 256,12 1,0,0,1,0,1,0,,0, 65535,65532,19661 1 Copy-paste a data table ktluser 4. Febta 2010 7:10 48,24 72,456,1 60,32 1,701,26,554,389,17 Study or variable info 0 164,268,1 112,12 1,0,0,1,0,0,0,78,0,1 52425,39321,65535 Index_info Copy-paste a data table. 304,168,-1 252,120 1,0,0,1,0,1,0,,0, 2,693,146,476,224 Providing upload data: 304,36,-1 252,12 1,0,0,1,0,1,0,,0, 65535,65532,19661 Observations 0 308,152,1 248,80 1,0,0,1,0,0,0,366,0,1 52425,39321,65535 Observations2 TabIndex:3 Data table 1 452,269,1 104,13 1,0,0,1,0,0,0,72,0,1 Data_table Check that your data table looks sensible. 408,244,-1 148,12 Object info 0 160,245,1 112,13 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Object_info1_2 View the uploaded data The largest id values for the selected Opasnet Base tables. The table is updated by pressing the R_cardinals button. get_mean(Object_info_for_lap[Info='Ident']) 288,328,1 88,16 2,440,279,476,332 2,193,270,416,303,0,MIDM 2,43,59,735,421,0,MIDM 39325,65535,39321 [Sys_localindex('IN1'),Sys_localindex('IN2')] 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 [Sys_localindex('IN3'),1,Sys_localindex('IN4'),1,Sys_localindex('IN5'),1,Sys_localindex('IN6'),1,Sys_localindex('IN7'),1,Sys_localindex('IN8'),13,Sys_localindex('IN2'),1,Sys_localindex('IN1'),1] 3 Node to be formatted as data table ktluser 4. Febta 2010 7:10 48,24 328,456,1 60,32 1,729,57,382,341,17 Columns ['Age','Weight'] 224,272,1 48,12 [Formnode Columns1] ['Age','Weight'] N_rows 0 172,125,1 116,13 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 N_rows First row must contain values, not column names! 172,148,-1 124,100 1,0,0,1,0,1,0,,0, Analytica table 0 172,148,1 116,12 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Analytica_table Columns 0 172,101,1 116,13 1,0,0,1,0,0,0,72,0,1 Columns 2 Node formatted as data table: 172,36,-1 124,12 1,0,0,1,0,1,0,,0, 65535,65532,19661 Object info 0 172,172,1 116,13 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Object_info1_2 Study or variable info 0 172,196,1 116,12 1,0,0,1,0,0,0,78,0,1 52425,39321,65535 Index_info 4 Ready-made data-table node ktluser 4. Febta 2010 7:10 48,24 456,456,1 60,32 1,770,118,445,300,17 Data table4 0 148,37,1 100,13 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Data_table4 Model details ktluser 4. Febta 2010 7:10 48,24 576,784,1 48,24 1,0,0,1,1,1,0,,0, 1,419,46,615,547,17 Writer jtue 24. maata 2009 9:36 48,24 184,40,1 48,24 1,559,13,690,439,17 W loc Makes a table to be written to the Loc table. var a:= Locations; var b:= a[.j='obj_id_i']; var c:= cardinals[table1='loc']+a[@.j=1]; a:= array(a.j,[c, c, findid(b,obj,'ident'), a, a, a]); textify(a) 432,240,1 48,16 2,84,125,476,245 2,642,68,606,278,0,MIDM 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 100,1,1,1,1,9,2970,2100,15,0 [Sys_localindex('I'),16,Sys_localindex('I'),1,Sys_localindex('J'),1] W loccell Slices fields that are needed in the Locres table from Inp_locres. var a:= Loccells; var b:= textify(findid(a[.j='id'], obj, 'ident')); var c:= textify(a[.j='loc_id']); b:= findid(b&'+'&c, (if Loc.j='obj_id_i' then Loc&'+'&Loc[.j='location'] else Loc), 'obj_id_i'); a:= array(a.j,[(@a.i+cardinals[table1='loccell']), (a+cardinals[table1='cell']), b]); textify(a) 432,328,1 48,16 2,776,90,476,487 2,178,73,453,537,0,MIDM 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 W cell Slices the fields that are needed in the Res table. Removes duplicate rows. var a:= Cells; a:= array(a.j, [ a[.j='id']+cardinals[table1='cell'], actobj_stat[@actobj_stat1=lap], a, a, a]); textify(a) 432,280,1 48,16 2,759,203,476,379 2,14,241,659,368,0,MIDM 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 W obj Selects relevant information for the Obj table from Objects1 node. var a:= Objects; var b:= if a[.j='ident'] = 0 then -1 else a[.j='ident']; b:= findid(b, obj, 'ident'); b:= if b='0' then cardinals[table1='obj']+a[.j='id'] else b; a:= if a.j='id' then b else a; textify(a) 432,160,1 48,16 2,372,300,476,343 2,429,135,626,444,0,MIDM 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 [] W act Makes a list of objects that contains some additional information to be written into the Objinfo table. var a:= Acts; a:= if a.j='id' {or a.j='series_id'} then a+cardinals[table1='act'] else a; a:= if a=null or a=0 then '' else a&'' 432,200,1 48,16 2,66,82,476,340 2,34,242,690,459,0,MIDM 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 [Sys_localindex('J'),1,Sys_localindex('I'),1,Sys_localindex('J'),1] W res var a:= Results; index i:= subset(if a[.j='result']=null and a[.j='description']=0 then 0 else 1); a:= a[.i=i]; a:= array(a.j, [textify(a.i+Cardinals[table1='res']), textify(a+ Cardinals[table1='cell']), textify(a),a,a]); if a=null then '' else a 432,368,1 48,13 2,629,191,582,297 2,461,218,609,375,0,MIDM 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 [] Object info for lap Additional information for each index and decision node. Description node is the name of a node containing information about the locations of the index. It must be indexed by the index. Object_info[N_vars=Lap] 176,336,1 48,20 2,140,217,476,224 2,653,25,488,226,0,MIDM 2,104,359,460,228,0,MIDM 52425,39321,65535 [N_vars,Info] [N_vars,Info] [1,1,1,0] Index info Table(Ind_info,Indices) 384,32,1 48,16 2,102,90,476,349 2,559,220,666,349,0,MIDM 2,184,194,660,316,0,MIDM [Formnode Study_or_variable_i2, Formnode Study_or_variable_i1, Formnode Study_or_variable_i3] 52425,39321,65535 [Ind_info,Indices] [Ind_info,Indices] [] Ind info ['name','unit'] 384,56,1 48,13 [] Loccells Makes a list of all locations in all results in all variables. The list is as long as is needed for the Loccell table. A subset is taken then for the Cell table. 1) Initialises local variables, and slices variables from Object1. 2)-4) Does the process for each variable one at a time. This happens in function Loccell. 5) Makes i the row index. var a:= Data_table; index h:= a.j[@.j=1..size(a.j)-2]; a:= a[.j=h]; var d:= max(Data_table[.j='obs'],Data_table.i); a:= if 1-proba then (index itemp:= copyindex(a.i); a[.i=itemp]) else ( index grun:= 1..d; index itemp:= 1..size(a.i)/d; a:= a[@.i=(itemp-1)*d+grun]; a:= a[@grun=1]); index j:= ['id', 'cell_id', 'loc_id']; index i:= 1..size(a); a:= array(j,[h, @a.itemp, a]); concatrows(a,h,a.itemp,i) 320,328,1 48,16 2,643,26,526,596 2,70,181,552,488,0,MIDM [Sys_localindex('J'),Sys_localindex('I'),Undefined,Undefined,Undefined,1] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 [] [Sys_localindex('J'),2,Sys_localindex('ITEMP'),1,Sys_localindex('H'),1] Results The usage of local variables: a: the temporary variable that is being edited. e: cardinal of the Cell table. f: cardinal of the Res table. j: output column headings. i: output row numbers. NOTE! ONLY THE DETERMINISTIC VERSION WORKS AT THE MOMENT. 1) Only one piece of information (Observations) is included. 2)-5) The process is done for each variable one at a time (this is indexed by x). 3) Several within-loop local variables are initiated. 4) The variable is given index runn which is equal to run if probabilistic and [0] if not. The array is flattened first to 2-D, the value only is kept. 5) Variables are concatenated to each other. 6) Index i is made the index of the implicit index. NOTE! This node MUST be formatted to Integer, otherwise Res_id will be stored in a wrong format. var e:= 0; var f:= 0; var a:= Data_table[.j='result']; var d:= max(Data_table[.j='obs'],Data_table.i); var b:= if 1-proba then @a.i else ( floor((@a.i-1)/d)+1); index j:= ['id','cell_id','obs','result','restext']; array(j,[0, b, Data_table[.j='obs'], (if istext(a) then '' else a) , (if istext(a) then a else '')]) 320,368,1 48,16 2,634,23,581,615 2,662,12,469,411,0,MIDM [Sys_localindex('J'),Sys_localindex('I')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 [Run,2,Sys_localindex('J'),1,Sys_localindex('I'),1] Locations The format of this node MUST be integer, so that the id and Roww values are stored correctly. var a:= data_table; var b:= [0]; var c:= [0]; var x:= 1; while x<= size(a.j)-2 do ( var h:= a[@.j=x]; var d:= h[.i=unique(h,h.i)]; b:= concat(b,d); c:= concat(c,(if d=0 then slice(a.j,x) else slice(a.j,x))); x:= x+1); index i:= 1..size(b)-1; index j:= ['id', 'std_id', 'obj_id_i', 'location', 'description']; a:= array(j,[i, i, slice(c,i+1), slice(b,i+1), '']) 320,240,1 48,16 2,68,138,476,581 2,92,398,746,259,0,MIDM [Sys_localindex('J'),Sys_localindex('I')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 [Sys_localindex('D'),1,Object_all3,1,Age,1] Data table {var N_indices:= Object_info_for_lap[Info='number of indices']; var b:= } evaluate('Data_table'&selecttext(Data_source,1,1)) {; var a:= if b=1 then Data_table1 else if b=2 then analytica_table else analytica_node; if b>=3 then a else ( index h:= a.columns[@a.columns=1..N_indices]; var c:= Object_info_for_lap[Info='parameter name', @N_vars=1]; c:= if c='' or c=0 then 'parameter' else c; index j:= concat(h,[c,'result','obs']); index parameter:= a.columns[@a.columns=N_indices+(1..size(a.columns)-N_indices)]; index temp:= 1..size(a.rows)*size(parameter); var conv:= if j='result' then @parameter+N_indices else @j; a:= a[@.columns=conv]; a:= if @j=size(j)-2 then parameter else a; a:= if @j=size(j) then @a.rows else a; a:= concatrows(a, parameter, a.rows, temp); a:= if j='result' then (var d:= a[j='result']; if evaluate(d)=null then d else evaluate(d)) else a; index i:= Subset(a[j='result']<>null); a[temp=i]); } 176,208,1 48,16 2,4,175,482,501 2,762,82,521,338,0,MIDM [Formnode Data_table8] [Sys_localindex('J'),Sys_localindex('I')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 [1,1,1,0] [Sys_localindex('I'),1,Sys_localindex('J'),1] Info ['Analytica identifier','ident','name','unit','number of indices','parameter name','probabilistic?'] 240,48,1 48,13 2,102,90,476,379 2,90,166,416,303,0,MIDM [] ['Analytica identifier','ident','name','unit','number of indices','parameter name','probabilistic?'] Objects 19.10.2009 Jouni Tuomisto The Run/Name = Method cell is problematic. It was designed for one-variable uploads, and now it contains both upload-specific and variable-specific information. This should be solved somehow but I don't know how. So, I just make it technically work and don't worry about it now. Some variable-specific info is omitted, some is taken from the first variable. 5.1.2010 Jouni Tuomisto Variable-specific parts are simply rejected. Index j:= ['id','ident','name','unit','objtype_id','page','wiki_id']; index i:= concat(N_vars,indices); var Ident:= if @i <= size(N_vars) then Object_info[Info='ident', @N_vars=@i] else i; var a:= Index_info[indices=i, Ind_info=j]; a:= if a=null then '' else a; var e:= Object_info[N_vars=i, Info=j]; a:= if e=null then a else e; var b:= sum(findintext(wikis,ident)*@wikis,wikis); var c:= if b=0 then '' else wikis[@wikis=b]; c:= if b=0 then '2664' else selecttext(ident,1+textlength(c)); a:= array(j,[ @i, ident, a, a, if @i<= size(N_vars) then 1 else 6, if i='run' then '2817' else c&'', if b=0 then '1' else b&'']) 320,160,1 48,16 2,573,29,547,724 2,11,31,656,283,0,MIDM [Sys_localindex('J'),Sys_localindex('I')] [Indices,1,Sys_localindex('J'),1,Sys_localindex('I'),1] Observations 'Test1 Value 1 2nh 2 30' 64,256,1 52,16 2,586,79,476,465 [Formnode Observations3] 52425,39321,65535 Data table1 var a:= splittext(textreplace(Observations2, chr(10),'',true),chr(13)); index i:= 1..size(a); a:= slice(a,i); a:= splittext(a, chr(9)); index j:= 1..size(a)/size(i); for y:= i do (slice(a[i=y],j)) var a:= splittext(textreplace(Observations2, chr(10),'',true),chr(13)); index columns:= splittext(slice(a,1), chr(9)); index rows:= 1..size(a)-1; a:= slice(a,rows+1); a:= splittext(a, chr(9)); a:= for y:= rows do (slice(a[rows=y],@columns)); indexify(a) 64,208,1 48,16 2,7,115,476,362 2,761,412,476,386,0,MIDM [Sys_localindex('J'),Sys_localindex('I')] [1,1,1,0] Testvariable Table(Time,Testindex)( uniform(0,1), uniform(1,2), uniform(2,3) ) 544,440,1 48,24 2,40,50,416,303,0,MIDM [Time,Testindex] [Time,Testindex] [1,0,0,0] Testindex ['item 1'] 544,472,1 48,12 2,102,90,476,224 ['item 1'] W cellsec Slices the fields that are needed in the Res table. Removes duplicate rows. (if w_cell.j = 'mean' then '' else W_cell) 544,280,1 48,16 2,782,213,476,379 2,41,191,618,368,0,MIDM 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 Cells Makes a list of all locations in all results in all variables. The list is as long as is needed for the Loccell table. A subset is taken then for the Cell table. 1) Initialises local variables, and slices variables from Object1. 2)-4) Does the process for each variable one at a time. This happens in function Loccell. 5) Makes i the row index. var a:= Data_table[@.j=size(Data_table.j)-1]; index j:= ['id', 'actobj_id', 'mean', 'sd', 'n']; var d:= max(Data_table[.j='obs'],Data_table.i); index temp:= ['mean','sd']; a:= if 1-proba then array(temp,[a,'']) else ( index grun:= 1..d; index i:= 1..size(a.i)/d; a:= a[@.i=(i-1)*d+grun]; a:= array(temp,[mean(a,grun), sdeviation(a[j='mean'],grun)]) ); a:= array(j,[ @a.i, '', a[temp='mean'], a[temp='sd'], d]) 320,280,1 48,16 2,721,20,526,639 2,304,464,608,328,0,MIDM [Sys_localindex('J'),Sys_localindex('I'),Undefined,Undefined,Undefined,1] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 [] [N_vars,2,Sys_localindex('I'),1,Sys_localindex('J'),1] Data table2 19.10.2009 Jouni Tuomisto If the variable is deterministic, Obs is 0. It is not clear whether it should be or not. This should be checked with other upload methods (1-3) to see that they are consistent. var a:= Object_info_for_lap[Info='Analytica identifier']; index jtemp:= concat(indexnames(getfract(evaluate(a),0.5)),['result','obs']); index j:= textreplace(jtemp,'.',''); a:= if proba then sample(evaluate(a)) else evaluate(a); index temp:= concat(indexnames(a),['result']); index i:= 1..size(a); a:= Mdarraytotable(a, i, temp); a:= if j='obs' and proba then a[temp='Run'] else a[temp=jtemp]; a:= a[@jtemp = @j]; a:= if a=null then 0 else a 64,336,1 48,20 2,53,195,476,444 2,56,66,630,303,0,MIDM [Sys_localindex('I'),Sys_localindex('J')] [1,1,1,0] [N_vars,1,Sys_localindex('J'),1,Sys_localindex('I'),1] Proba var a:= Object_info_for_lap[Info='probabilistic?']; (a=1 or a='Yes' or a='Y' or a='yes' or a='y') 176,392,1 48,16 Data source Choice(Self,4,False) 176,160,1 48,16 2,102,90,476,224 [Formnode Data_source1] 52425,39321,65535 ['1 Copy-paste table','2 Analytica model','3 Node to be formatted as data table','4 Ready-made data table node'] [1,1,0,0] N vars 1.. (if selecttext(Data_source,1,1)='3' then N_variables else 1) 240,72,1 48,12 [] [1] N variables 1 240,96,1 48,12 [Formnode N_variables1] 52425,39321,65535 Lap 1 64,392,1 48,16 [0,1,0,1] Indices copyindex(Find_ind) 384,76,1 48,12 2,140,321,476,409 2,40,50,416,303,0,MIDM [] Test2 Table(Self)( 1,2,3) ['item 1','item 2','item 3'] 544,384,1 48,24 [1,1,0,1] Object info Additional information for each index and decision node. Description node is the name of a node containing information about the locations of the index. It must be indexed by the index. Table(Info,N_vars)( 'Excess_risk_of_iugr_', 'Op_en2693', 'WHO mortality data', '#', 1, 'Weight', 1 ) 240,24,1 48,16 2,140,217,476,224 2,277,418,982,328,0,MIDM 2,590,303,460,228,0,MIDM 52425,39321,65535 [Info,N_vars] [N_vars,Info] [] [1,1,1,0] Analytica table Table(Columns,Rows)( 1,2,3, 4,5,6 ) 64,80,1 48,24 2,248,258,416,303,0,MIDM [Formnode Analytica_table2] 52425,39321,65535 [Columns,Rows] [Columns,Rows] Rows 1..N_rows 64,112,1 48,12 [1,2,3] 3 64,136,1 48,12 1,1,1,1,1,1,0,0,0,0 [Formnode N_rows1] 52425,39321,65535 Acts 5.1.2010 Jouni Tuomisto I separated Acts and Objects. Index j:= ['id', 'acttype_id','who','comments']; index i:= ['create','upload']; var a:= array(j,[ @i, array(i,[1, (if selecttext(replace_data_,1,1)='Y' then 4 else 5)]), opasnet_username, if i ='upload' then 'Analytica '&Analyticaedition&', ('&Analyticaplatform&'), Version: '&textify(Analyticaversion) else '']); a 320,200,1 48,16 2,29,37,525,724 2,88,339,694,283,0,MIDM [Sys_localindex('J'),Sys_localindex('I')] ['',''] [Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1] Upload type 1. 64,224,-1 56,56 1,0,0,1,0,1,0,,0, Upload type 2. 64,84,-1 56,76 1,0,0,1,0,1,0,,0, Upload type 2. 120,356,-1 112,60 1,0,0,1,0,1,0,,0, W actobj Makes a list of objects that contains some additional information to be written into the Objinfo table. var a:= actobj2; if a.j='series_id' and acts[.j= 'acttype_id', .i='upload'] = 5 then Series[.i = a.i] else a; 592,112,1 48,16 2,250,25,476,340 2,44,195,690,459,0,MIDM 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 [] [Sys_localindex('J'),1,Sys_localindex('I'),1,Sys_localindex('J'),1] Find ind var a:= Data_table.j[@.j=1..(size(Data_table.j)-2)]; a:= jointext(a,,','); var b:= Object_info[Info='Analytica identifier']; var c:= ['Run']; var x:= 1; while x<=size(b) do ( c:= concat(c,indexnames(evaluate(slice(b,x)))); x:= x+1); c:= textreplace(c, '.' , ''); index temp:= 1..size(c); c:= slice(c, temp); c:= c[temp= unique(c,temp)]; index i:= 1..(size(c)-1); c:= slice(c, i+1); b:= jointext(c,c.i,','); a:= if selecttext(Data_source,1,1)='3' then b else a; splittext(a,',') 384,104,1 48,12 2,102,90,476,385 [Self,Sys_localindex('I')] ['Run'] Actobj stat Table(Actobj_stat1)( '602','603' ) 544,216,1 48,16 Actobj stat [1,2] 544,240,1 48,12 [1,2] Series for x[]:= actobj2.i do ( var a:= query(' SELECT MAX(act.id) FROM actobj LEFT JOIN act ON actobj.act_id = act.id WHERE obj_id = '&chr(39)&actobj2[.j = 'obj_id', .i = x]&chr(39)&' AND acttype_id = 4 '); a[@.j = 1, @.i=1] ) 480,112,1 48,12 2,258,57,416,303,0,MIDM 39325,65535,39321 [Sys_localindex('I'),Sys_localindex('J')] Actobj2 index j:= ['id', 'act_id', 'obj_id', 'series_id']; var a:= W_obj; var b:= a[.j = 'id']= textify(@a.i+cardinals[table1='obj']); index k1:= subset(b); b:= array(j, ['', w_act[@.j=1, @.i=1], a[.i = k1, .j='id'], '' ]); index k2:= subset(a[.j='objtype_id']='1'); a:= array(j, ['', w_act[@.j=1, @.i=2], a[.i =k2, .j='id'], w_act[@.j=1, @.i=2] ]); index i:= 1..(size(k1)+size(k2)); a:= concat(a, b, k2, k1, i); if j='id' then textify(i+cardinals[table1='actobj']) else a 544,160,1 48,16 2,83,399,416,303,0,MIDM [Sys_localindex('I'),Sys_localindex('J')] [Sys_localindex('I'),1,Sys_localindex('J'),1,Sys_localindex('I'),1] Data table4 Va1 432,408,1 48,16 2,4,175,482,501 2,17,71,736,338,0,MIDM [Formnode Data_table5] 52425,39321,65535 [Sys_localindex('I'),Sys_localindex('J')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 [1,1,1,0] [Sys_localindex('I'),1,Sys_localindex('J'),1] Data table3 {var N_indices:= Object_info_for_lap[Info='number of indices']; var b:= evaluate(selecttext(Data_source,1,1));} var a:= {if b=1 then Data_table1 else if b=2 then} analytica_table {else analytica_node}; indexify(a) {if b>=3 then a else ( index h:= a.columns[@a.columns=1..N_indices]; var c:= Object_info_for_lap[Info='parameter name', @N_vars=1]; c:= if c='' or c=0 then 'parameter' else c; index j:= concat(h,[c,'result','obs']); index parameter:= a.columns[@a.columns=N_indices+(1..size(a.columns)-N_indices)]; index temp:= 1..size(a.rows)*size(parameter); var conv:= if j='result' then @parameter+N_indices else @j; a:= a[@.columns=conv]; a:= if @j=size(j)-2 then parameter else a; a:= if @j=size(j) then @a.rows else a; a:= concatrows(a, parameter, a.rows, temp); a:= if j='result' then (var d:= a[j='result']; if evaluate(d)=null then d else evaluate(d)) else a; index i:= Subset(a[j='result']<>null); a[temp=i]); } 64,40,1 48,16 2,4,175,482,501 2,17,71,736,338,0,MIDM [Sys_localindex('J'),Sys_localindex('I')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 [1,1,1,0] [Sys_localindex('I'),1,Sys_localindex('J'),1] (a) Indexify var N_indices:= Object_info_for_lap[Info='number of indices']; index h:= a.columns[@a.columns=1..N_indices]; var c:= Object_info_for_lap[Info='parameter name', @N_vars=1]; c:= if c='' or c=0 then 'parameter' else c; index j:= concat(h,[c,'result','obs']); index parameter:= a.columns[@a.columns=N_indices+(1..size(a.columns)-N_indices)]; index temp:= 1..size(a.rows)*size(parameter); var conv:= if j='result' then @parameter+N_indices else @j; a:= a[@.columns=conv]; a:= if @j=size(j)-2 then parameter else a; a:= if @j=size(j) then @a.rows else a; a:= concatrows(a, parameter, a.rows, temp); a:= if j='result' then (var d:= a[j='result']; if evaluate(d)=null then d else evaluate(d)) else a; index i:= Subset(a[j='result']<>null); a[temp=i] 536,56,1 48,24 2,485,116,476,423 a index j:= ['Country','Year','Diagnosis','Sex','Age','Deaths']; index i:= 1..10; array(j,i,["Seychelles","2001","AAA","Male","0-365 days","8", "Seychelles","2001","AAA","Female","0-365 days","11", "Seychelles","2001","A00-B99","Male","0-365 days","0", "Seychelles","2001","A00-B99","Female","0-365 days","0", "Seychelles","2001","A00","Male","0-365 days","0", "Seychelles","2001","A00","Female","0-365 days","0", "Seychelles","2001","A09","Male","0-365 days","0", "Seychelles","2001","A09","Female","0-365 days","0", "Seychelles","2001","A01-A08","Male","0-365 days","0", "Seychelles","2001","A01-A08","Female","0-365 days","0"]) 544,328,1 48,24 2,264,274,617,303,0,MIDM [Sys_localindex('J'),Sys_localindex('I')] Reader ktluser 3. Augta 2008 18:31 jtue 9. lokta 2008 14:01 48,24 312,40,1 48,24 1,1,1,1,1,1,0,0,0,0 1,601,119,477,429,17 Arial, 15 (vident:text, seriesid:optional) Read mean Reads the mean data about the vident variable from the Opasnet Base. Uses the run with runid as run.id if specified; otherwise uses the newest run of that variable. PARAMETERS: * Vident: the ident of the variable in the Opasnet Base. * Runid: the id of the run from which the results will be brought. If omitted, the newest result will be brought. Change run to act if isnotspecified(seriesid) or istext(seriesid) then seriesid:= Newest_series(vident); query( ' SELECT obj.ident, obj.name, obj.unit, cell.id as cell_id, mean, sd, n, act_id, comments, time, std.location, ind.ident AS iident, ind.name AS iiname, actobj.series_id FROM obj LEFT JOIN actobj ON obj.id = actobj.obj_id LEFT JOIN act ON act.id = actobj.act_id LEFT JOIN cell ON cell.actobj_id = actobj.id LEFT JOIN loccell ON loccell.cell_id = cell.id LEFT JOIN loc ON loccell.loc_id = loc.id LEFT JOIN loc as std ON loc.std_id = std.id LEFT JOIN obj as ind ON std.obj_id_i = ind.id WHERE obj.ident = '&chr(39)&vident&chr(39)&' AND actobj.series_id = '&chr(39)&seriesid&chr(39) ) 56,80,1 48,12 2,585,25,516,589 39325,65535,39321 vident,seriesid (vident:text) Newest series This function checks for the newest result (according to run_id) of the variable. The function is used if the user does not define the run_id as an optional parameter in functions Read_mean and Read_sample. PARAMETERS: * Vident: the Ident of the variable in the Opasnet Base. var a:= query(' SELECT actobj.series_id, var.ident FROM obj AS var LEFT JOIN actobj ON var.id = actobj.obj_id LEFT JOIN act ON actobj.act_id = act.id WHERE var.ident = "'&vident&'" '); max(a[@.j=1],a.i) 56,22,1 48,22 2,678,59,476,566 39325,65535,39321 vident (vident:text, seriesid:optional) Read sample Reads the sample data about the vident variable from the Opasnet Base. Uses the runident run if specified; otherwise uses the newest run of that variable. PARAMETERS: * Vident: the name of the variable in the Opasnet Base. * Runid: the id of the run from which the results will be brought. If omitted, the newest result will be brought. chenge run to act add restext if isnotspecified(seriesid) or istext(seriesid) then seriesid:= Newest_series(vident); query( ' SELECT obj.id AS obj_id, obj.ident, obj.unit, ind.ident as iident, cell.id AS cell_id, location, mean, n, obs, result, restext FROM obj LEFT JOIN actobj ON actobj.obj_id = obj.id LEFT JOIN act ON actobj.act_id = act.id LEFT JOIN cell ON cell.actobj_id = actobj.id LEFT JOIN loccell ON loccell.cell_id = cell.id LEFT JOIN loc ON loccell.loc_id = loc.id LEFT JOIN obj as ind ON loc.obj_id_i = ind.id LEFT JOIN res ON res.cell_id = cell.id WHERE obj.ident = '&chr(39)&vident&chr(39)&' AND actobj.series_id = '&chr(39)&seriesid&chr(39) ) 56,112,1 48,22 2,55,35,516,612 39325,65535,39321 vident,seriesid Enter variable Ident 'Op_en1912' 168,83,1 48,27 [Formnode Enter_variable1] 52425,39321,65535 Enter variable 0 288,24,1 176,13 1,0,0,1,0,0,0,170,0,1 52425,39321,65535 Enter_variable Newest series Newest_series(Enter_variable) 288,61,1 48,22 2,200,56,416,303,0,MIDM [Sys_localindex('J'),Sys_localindex('I')] Var info read_mean(Enter_variable) 288,116,1 48,12 2,28,65,1144,438,0,MIDM [Sys_localindex('J'),Sys_localindex('I')] (a,b,x) Makeind The input table a must have a structure that is also used as input for MDTable function. The function removes one column with location information and makes a dimension (index) with the locations in the column. Inde is the (local) index that will be added. Note that unlike MDTable function, this can use local indices in the output. index inde / slice(b.m, x) := b[.n = unique(b[@.m = x], b.n), @b.m = x]; a:= if inde = a[@.m=1] then a else 0; index m:= slice(a.m,(2..size(a.m))); a:= a[.m=m] 56,192,1 48,12 2,61,72,476,454 a,b,x (a) Get cell_id Makes a multi-dimensional array with the same structure as the original variable that was stored into the Opasnet Base. The contents of the array are the cell_ids of the variable. The input parameter must be a 2D table with the structure that comes from the Read_mean function. 1) Slices the necessary columns from the input table and converts that to a 2D table that has the same structure as is used for input to the function MDTable. 2) Defines the local indices, and changes a location column to a dimension one at a time until all columns have been changed. NOTE! There is a problem that if there are two or more cells with the exactly same locations, only the one with a largest cell_id will be taken. index k:= ['iident','location','cell_id', 'act_id']; a:= a[.j=k]; a:= if a.k = 'iident' then textreplace(a, ' ', '_', true) else a; index L:= a[@k=1]&'+'&textify(a[@k=3]); index m:= concat(a[.i=unique(a[@k=1],a.i), @k=1],['act_id', 'result']); index n:= a[.i=unique(a[@k=3],a.i), @k=3]; a:= a[@.i=@L]; var c:= a[L=(m)&'+'&textify(n), @k=4, @m=1]; a:= a[L=(m)&'+'&textify(n), @k=2]; a:= if m='result' then n else a; a:= if m='act_id' then c else a; var b:= a; var x:= 1; a:= while x< size(b.m) do ( a:= makeind(a,b,x); x:= x+1; a); a:= max(a[@.m=1], a.n); if size(a.act_id)=1 then a[@.act_id = 1] else a 56,168,1 48,13 2,160,51,476,628 a Var mean get_mean(Enter_variable) 288,140,1 48,12 2,835,77,420,564,0,MIDM [Sys_localindex('ACT_ID'),Sys_localindex('MONTH')] 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 [Sys_localindex('M'),1,Sys_localindex('N'),1,Sys_localindex('ACTION'),1] (vident:text, runident:optional) Get mean Gives the mean result of a (multidimensional) variable stored in the Opasnet Base. The procedure is simple because it utilises the variable structure (with res_ids) derived by the get_res_id function. var a:= read_mean(vident, runident); index o:= a[.j='cell_id']; index j:= ['mean','sd']; var output:= a[@.i=@o, .j = j]; a:= Get_cell_id(a); {output[o=a]} 56,216,1 48,12 2,114,117,476,428 vident,runident (vident:text, runident:optional) Get sample Gives the sample result of a (multidimensional) variable stored in the Opasnet Base. The procedure is simple because it utilises the variable structure (with res_ids) derived by the get_res_id function. Note that if the Analytica samplesize is smaller than the samplesize stored in the Opasnet Base, the extra samples will be discarded. If the samplesize is larger, the remaining rows will be null. 1) Brings the data into the right structure. 2) Chooses whether the actual result is numerical (in the Result column) or text (in the Description column). var a:= read_sample(vident, runident); var b:= textify(Get_cell_id(read_mean(vident,runident))); index k:= textify(a[.j='cell_id'])&'+'&textify(a[.j='obs']); index runn:= min(a[.j='obs'],a.i)..max(a[.j='obs'],a.i); a:= if a[.j='restext'] = '' then a[.j='result'] else a[.j='restext']; a:= a[@.i=@k]; a:= a[k=b&'+'&runn]; a:= if max(runn)=0 then a[@runn=1] else a[@runn=@run]; 56,240,1 48,12 2,613,48,476,556 vident,runident Var sample get_sample(Enter_variable) 288,164,1 48,12 2,86,111,476,224 2,18,155,646,307,0,MEAN [Sys_localindex('OP_EN1899'),Sys_localindex('SALMON')] [Sys_localindex('OP_EN1898'),1,Sys_localindex('YEAR'),1,Sys_localindex('SALMON'),1,Sys_localindex('OP_EN1899'),1] Var run info Describes the runs of the defined variable. This should be made a function. var_run_info(Enter_variable) 288,92,1 48,12 2,41,152,1235,369,0,MIDM [Sys_localindex('J'),Sys_localindex('I')] (vident:text) Var run info This function checks for the newest result (according to run_id) of the variable. The function is used if the user does not define the run_id as an optional parameter in functions Read_mean and Read_sample. PARAMETERS: * Vident: the Ident of the variable in the Opasnet Base. Change: objinfo to act run to act add objact query( ' SELECT obj.id AS ovj_id, obj.ident, obj.name, obj.unit, actobj.series_id, actobj.act_id, comments, act.time, act.who FROM obj LEFT JOIN actobj ON obj.id = actobj.obj_id LEFT JOIN act ON act.id = actobj.act_id WHERE obj.ident = '&chr(39)&vident&chr(39)&' ') 56,56,1 48,13 2,182,31,476,566 39325,65535,39321 vident Use these functions to retireve data from the Opasnet Base: * Newest_series: finds the newest upload of the object. * Obj_act_info: Finds the action information of the object. * Read_mean: Reads the means of each cell. * Get_mean: Makes read_mean table into an array. * Read_sample: Reads the whole sample. * Get_sample: Makes read_sample table into a probabilistic array. 280,294,-1 168,110 (a:text) Query Performs a query and results the standard table with columns .j and rows .i. Lap; index i:= DBquery(Odbc,a); index j:= dblabels(i); dbtable(i,j) 56,144,1 48,13 2,100,154,476,566 39325,65535,39321 a Wikis Names of different wikis used. Table(Self)( 'Op_en','Op_fi','Heande','En','Fit','Erac','Beneris','Intarese','Piltti','Kantiva','Bioher','Heimtsa') [1,2,3,4,5,8,9,10,11,13,14,15] 184,168,1 48,16 65535,52427,65534 [Self] [] (a) Textify Changes a number to a text value with up to 15 significant numbers. This bypasses the number formatting problem that tends to convert e.g. 93341 to '93.34K'. If the input is null, the result is ''. if a = null then '' else a&'' 72,376,1 48,12 2,309,205,559,372 a 2,F,4,14,0,0,4,0,$,0,"ABBREV",0 (a; file:texttype) Tablefy a:= '"'&a&'"'; a:= jointext(a,a.j,';'); Writetextfile('c:\temp\'&file, a) 72,352,1 48,13 2,44,303,476,224 65535,45873,39321 a,file 2,F,4,14,0,0,4,0,$,0,"ABBREV",0 Concatenation UDFs This library contains functions to make various instances of concatenation more convenient. Concat3 thru Concat10 are generalizations of the built-in Concat function which concatenate from 3 to 10 arrays in a single call (while the built-in Concat concatenates two arrays). ConcatRows concatenates all the rows of a single array. David Kendall & Lonnie Chrisman Mon, Jan 26, 2004 8:49 AM Lonnie Wed, Sep 05, 2007 3:23 PM 48,24 488,40,1 68,20 1,0,0,1,1,1,0,0,0,0 1,50,200,488,454,23 (A1, A2, A3: ArrayType; I1, I2, I3, J: IndexType ) Concat3 Concatenates three arrays, A1, A2, and A3. I1, I2, and I3 are the indexes that are joined; J is the index of the new array; J usually is the concatenation of I1, I2, and I3 Index I12 := Concat(I1,I2); Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, J ) 88,64,1 48,26 2,56,56,986,596 A1,A2,A3,I1,I2,I3,J (A1, A2, A3, A4: ArrayType; I1, I2, I3, I4, J: IndexType ) Concat4 Concatenates four arrays, A1, A2, A3, and A4. I1, I2, I3, and I4 are the indexes that are joined; J is the index of the new array; J usually is the concatenation of I1, I2, I3, and I4. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, J); 192,64,1 48,24 2,30,30,986,596 A1,A2,A3,A4,I1,I2,I3,I4,J 0 (A1, A2, A3, A4, A5, A6, A7, A8, A9: ArrayType; I1, I2, I3, I4, I5, I6, I7, I8, I9, J: IndexType) Concat9 Concatenates nine arrays, A1, ..., A9. I1, ..., I9 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I9. Index I12 := Concat(I1,I2); Index I123 := Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Index I123456 := Concat(I12345, I6); Index I1234567 := Concat(I123456, I7); Index I12345678 := Concat(I1234567, I8); Concat( Concat( Concat( Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, I123456), A7, I123456, I7, I1234567), A8, I1234567, I8, I12345678), A9, I12345678, I9, J); 88,232,1 48,24 2,27,120,469,638 A1,A2,A3,A4,A5,A6,A7,A8,A9,I1,I2,I3,I4,I5,I6,I7,I8,I9,J 0 (A1, A2, A3, A4, A5: ArrayType; I1, I2, I3, I4, I5, J: IndexType ) Concat5 Concatenates five arrays, A1, ..., A5. I1, ..., I5 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I5. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Index I1234 := Concat(I123, I4); Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, J); 88,120,1 48,24 2,160,160,986,596 A1,A2,A3,A4,A5,I1,I2,I3,I4,I5,J (A1, A2, A3, A4, A5, A6: ArrayType; I1, I2, I3, I4, I5, I6, J: IndexType ) Concat6 Concatenates six arrays, A1, ..., A6. I1, ..., I6 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I6. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, J); 192,120,1 48,24 2,644,94,602,712 A1,A2,A3,A4,A5,A6,I1,I2,I3,I4,I5,I6,J 0 (A1, A2, A3, A4, A5, A6, A7: ArrayType; I1, I2, I3, I4, I5, I6, I7, J: IndexType ) Concat7 Concatenates seven arrays, A1, ..., A7. I1, ..., I7 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I7. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Index I123456 := Concat(I12345, I6); Concat( Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, I123456), A7, I123456, I7, J); 88,176,1 48,24 2,580,98,551,565 A1,A2,A3,A4,A5,A6,A7,I1,I2,I3,I4,I5,I6,I7,J (A1, A2, A3, A4, A5, A6, A7, A8: ArrayType; I1, I2, I3, I4, I5, I6, I7, I8, J: IndexType ) Concat8 Concatenates eight arrays, A1, ..., A8. I1, ..., I8 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I8. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Index I123456 := Concat(I12345, I6); Index I1234567 := Concat(I123456, I7); Concat( Concat( Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, I123456), A7, I123456, I7, I1234567), A8, I1234567, I8, J); 192,176,1 48,24 2,12,98,561,737 A1,A2,A3,A4,A5,A6,A7,A8,I1,I2,I3,I4,I5,I6,I7,I8,J 0 (A1, A2, A3, A4, A5, A6, A7, A8, A9, A10: ArrayType; I1, I2, I3, I4, I5, I6, I7, I8, I9, I10, J: IndexType) Concat10 Concatenates ten arrays, A1, ..., A10. I1, ..., I10 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I10. Index I12 := Concat(I1,I2); Index I123 := Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Index I123456 := Concat(I12345, I6); Index I1234567 := Concat(I123456, I7); Index I12345678 := Concat(I1234567, I8); Index I123456789 := Concat(I12345678, I9); Concat( Concat( Concat( Concat( Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, I123456), A7, I123456, I7, I1234567), A8, I1234567, I8, I12345678), A9, I12345678, I9, I123456789), A10, I123456789, I10, J); 192,232,1 48,24 2,542,93,632,744 A1,A2,A3,A4,A5,A6,A7,A8,A9,A10,I1,I2,I3,I4,I5,I6,I7,I8,I9,I10,J 0 (A : ArrayType ; RowIndex,ColIndex,ResultIndex : IndexType) ConcatRows (A,I,J,K) Takes an array, A indexed by RowIndex & ColIndex, and concatenates each row, henceforth flattening the array by one dimension. The result is indexed by ResultIndex, which must be an index with size(RowIndex) * size(ColIndex) elements. index L := [ identifier of RowIndex, identifier of ColIndex, "val"]; slice(Mdarraytotable(A,ResultIndex,L),L,3) 320,64,1 64,24 2,499,85,478,348 A,RowIndex,ColIndex,ResultIndex ODBC Library Lonnie Thu, Sep 11, 1997 2:15 PM Lonnie Tue, Feb 05, 2008 10:03 AM 48,24 496,96,1 52,20 1,1,1,1,1,1,0,0,0,0 1,20,272,499,462,17 Arial, 13 (A:ArrayType;I:IndexType;L:IndexType;row:IndexType;dbTableName) InsertRecSql Generates the SQL "INSERT INTO" statement for one line of table A. A is a 2-D table indexed by rows I and columns L. L's domain serves as the column names in the database table. dbTableName is the name of the table in the database. The result begins with two semi-colons, since it will be used with an SQL statement preceeding it. 29.8.2008 Jouni Tuomisto I added the parameter IGNORE because it ignores rows that would cause duplicate-key violations. This way, there is no need to check for e.g. existing locations of new indices. 6.1.2009 Jouni Tuomisto I changed the A[I=row] to A[@I=@row] because the original function does not work correctly, if there are non-unique rows in the index. (';;INSERT IGNORE INTO ' & dbTableName & '(' & JoinText(L,L,',') & ') VALUES (' & Vallist(A[@I=@row],L)) & ') ' 184,32,1 52,24 2,591,203,487,469 A,I,L,row,dbTableName (V:ArrayType;I:IndexType) ValList Takes a list of values, and returns a string which the concatenation of each value, separated by commas, and with each value quoted. JoinText( '''' & V & '''', I, ',') 72,32,0 52,24 2,642,360,476,224 V,I 1,F,4,14,0,0 (Tabl:ArrayType;RowIndex:IndexType;LabelIndex:IndexType;dbTableName) WriteTableSql(Table,Rows,Labels,dbTableName) Returns the SQL that will write the table to the database table. This can be used as the second argument to DBWrite. This SQL statement replaces the entire contents of an existing table with the new data. 'DELETE FROM '& Dbtablename & JoinText(Insertrecsql(Tabl, Rowindex, Labelindex, Rowindex, Dbtablename),RowIndex) 328,32,1 88,24 2,728,341,510,476 Tabl,RowIndex,LabelIndex,dbTableName (Tabl:ArrayType;RowIndex:IndexType;LabelIndex:IndexType;dbTableName) AppendTableSql(Table,Rows,Labels,dbTableName) Returns the SQL that will write the table to the database table. This can be used as the second argument to DBWrite. This SQL statement replaces the entire contents of an existing table with the new data. JoinText(Insertrecsql(Tabl, Rowindex, Labelindex, Rowindex, Dbtablename),RowIndex) 328,88,1 88,24 2,559,127,510,476 Tabl,RowIndex,LabelIndex,dbTableName Tables List of such tables in Opasnet Base that are being written to by this module. ['act','actobj','cell','obj','loc','loccell','obj','res'] 296,216,1 48,12 2,396,363,377,227,0,MIDM Cardinals The largest id values for the selected Opasnet Base tables. The table is updated by pressing the R_cardinals button. for x[]:= table1 do ( var a:= query('SELECT MAX(id) AS id FROM '&x&' '); max(max(a,a.i),a.j)) 296,192,1 48,12 2,634,394,476,332 2,193,270,416,303,0,MIDM 2,659,299,416,303,0,MIDM 39325,65535,39321 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 (in, table; cond:texttype) Findid This function gets an id from a table. in: the property for which the id is needed. In MUST be unique in cond and it must contain index i. table: the table from where the id is brought. The table MUST have .j as the column index, .i as the row index, and a column named 'id'. cond: the name of the field that is compared with in. Cond must be text. index L:= in[.i=unique(in, in.i)]; var a:= if (L&' ') = (table[.j=cond]&' ') then table[.j='id'] else 0; a:= textify(sum(a, table.i)); a[.L=in] 72,280,1 48,12 2,636,101,494,519 in,table,cond (var, table) Write if size(var)>0 then dbwrite((if platform = 'Lumina AWP' then 'Driver={MySQL ODBC 3.51 Driver};Server=193.167.179.97' else 'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102')&';Database=opasnet_base;User=resultwriter; Password='&writerpsswd&';Option=3' , appendtablesql(var,var.i, var.j, table&' ')) if size(var)>0 then dbwrite((if platform = 'Lumina AWP' then 'Driver={MySQL ODBC 3.51 Driver};Server=193.167.179.97' else 'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102')&';Database=opasnet_base;User=result_writer; Password='&writerpsswd&';Option=3' , appendtablesql(var,var.i, var.j, table&' ')) 72,304,1 48,12 2,751,65,501,457 65535,45873,39321 var,table Opasnet username The username for Opasnet wiki 'Add username' 72,176,1 48,22 1,1,1,1,1,1,0,0,0,0 2,102,90,476,398 [Formnode Username1] 52425,39321,65535 [] Opasnet password The user's password for Opasnet wiki. 'Add password' 72,232,1 48,22 1,1,1,1,1,1,0,0,0,0 2,102,90,476,520 [Formnode Password1] 52425,39321,65535 ODBC Contains the parameters for the open database connectivity (ODBC). For Lumina AWP use the following should be used: 'Driver={MySQL ODBC 3.51 Driver};Server=193.167.179.97;Database=opasnet_base;User=result_reader; Password=ora4ever;Option=3' For THL internal use the following should be used: 'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102;Database=opasnet_base;User=result_reader; Password=ora4ever;Option=3' var a:= if platform='Lumina AWP' then 'Driver={MySQL ODBC 3.51 Driver};Server=193.167.179.97' else 'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102'; a&';Database=opasnet_base;User=result_reader; Password=ora4ever;Option=3' 184,136,1 48,12 1,1,0,1,1,1,0,,0, 2,180,61,508,420 2,56,66,918,303,0,MIDM Loc Lap; query(' SELECT loc.*, ind.* FROM loc, obj as ind WHERE loc.obj_id_i = ind.id ') 296,160,1 48,13 1,1,0,1,1,1,0,0,0,0 2,370,45,476,445 2,43,42,1147,516,0,MIDM 39325,65535,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 Obj This node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information. Lap; query('SELECT * FROM obj ') 296,136,1 48,13 1,1,0,1,1,1,0,0,0,0 2,378,21,493,501 2,218,87,977,421,0,MIDM 39325,65535,39321 [Sys_localindex('J'),Sys_localindex('I')] [Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1] (var, table) Write1 if size(var)>0 then appendtablesql(var,var.i, var.j, table&' ') 72,328,1 48,13 2,284,58,476,224 var,table '' 184,200,0 52,12 1,1,1,1,1,1,0,0,0,0 2,163,375,476,224 [Formnode Writerpsswd1] 52425,39321,65535 Platform Choice(Self,2,False,1) 72,136,1 48,12 [Formnode Platform1] 52425,39321,65535 ['Lumina AWP','THL computer'] Object info1-2 subtable(Object_info[Info=Info1_2, @n_vars=1]) 184,248,1 48,24 2,102,90,476,373 2,39,314,416,303,0,MIDM 2,599,363,416,303,0,MIDM [Formnode Object_info8, Formnode Object_info7] 52425,39321,65535 [N_vars,Info1_2] [Self,Ind_info] ['','',''] Info1-2 ['ident','name','unit','number of indices','parameter name','probabilistic?'] 184,280,1 48,13 1,1,1,1,1,1,0,0,0,0 Object info3 Additional information for each index and decision node. Description node is the name of a node containing information about the locations of the index. It must be indexed by the index. subtable(Object_info[Info=Info3]) 184,320,1 48,16 2,140,217,476,224 2,417,216,703,359,0,MIDM 2,752,344,460,228,0,MIDM [Formnode Object_info6] 52425,39321,65535 [Info3,N_vars] [N_vars,Info] [0,1,1,0] Info3 ['Analytica identifier','ident','name','unit','probabilistic?'] 184,352,1 48,13 1,1,1,1,1,1,0,0,0,0 Do next This is a temporary node that is ovewritten when Upload_data and Upload_results are being run. '' 296,352,1 48,16 Replace data? Choice(Self,1,False) 296,256,1 48,22 [Formnode Replace_data_1] 52425,39321,65535 ['Yes, replace previous data','No, append to previous data'] [] Enter anacode "index vehicle_type:= ['Bus','Minibus','Car d','Car g']; var Car_maintenance:= Triangular( 0.03, 0.058, 0.086 ); var Fuel_price:= (var a:= 0.95*triangular(0.8,1,1.2); var b:= 1.22*triangular(0.8,1,1.2); array(Vehicle_type,[a,a,a,b])); var Fuel_consumption:= (var a:= (8.7/100)*Triangular(0.75,1,1.25); var b:= (5.7/100)*Triangular(0.75,1,1.25); var c:= (8/100)*Triangular(0.5,1,1.5); a:= array(Vehicle_type,[a,a,b,c]); ); fuel_price*fuel_consumption+car_maintenance" 496,280,1 48,24 [Formnode Enter_anacode1] 52425,39321,65535 Enter anacode 0 432,448,1 160,56 1,0,0,1,0,0,0,182,0,1 52425,39321,65535 Enter_anacode Example code index vehicle_type:= ['Bus','Minibus','Car d','Car g']; var Car_maintenance:= Triangular( 0.03, 0.058, 0.086 ); var Fuel_price:= (var a:= 0.95*triangular(0.8,1,1.2); var b:= 1.22*triangular(0.8,1,1.2); array(Vehicle_type,[a,a,a,b])); var Fuel_consumption:= (var a:= (8.7/100)*Triangular(0.75,1,1.25); var b:= (5.7/100)*Triangular(0.75,1,1.25); var c:= (8/100)*Triangular(0.5,1,1.5); a:= array(Vehicle_type,[a,a,b,c]); ); fuel_price*fuel_consumption+car_maintenance 496,216,1 48,24 Code node evaluate(Enter_anacode) 496,336,1 48,16 2,104,114,416,303,0,SAMP [Undefined,Sys_localindex('VEHICLE_TYPE'),Undefined,Undefined,Undefined,1] [1,0,0,0] Provide data in the format you selected. 264,412,-1 256,12 1,0,0,1,0,1,0,,0, 65535,65532,19661 jgrellie 22 May 2008 17:19 pmea 3. helta 2010 15:42 48,24 592,280,0 48,32 1,1,1,1,1,1,0,0,0,0 1,3,8,651,425,17 Arial, 15 Notes jgrellie 29 Jul 2008 18:06 48,24 512,320,1 64,64 1,1,1,1,1,1,1,,0, 1,711,2,569,666,17 52427,65535,62258 Arial, 13 100,1,1,1,1,9,2970,2100,15,0 Sex is irrelevant for this model, since it only looks at exposure of mothers 104,51,-1 76,23 1,0,0,1,0,1,1,,0, Arial, 10 The model does not address bottled water scenarios at present. It is purely a diagnostic assessment and uses zero TTHM as a counterfactual. 104,128,-1 76,44 1,0,0,1,0,1,1,,0, Arial, 10 One of the problems with this model is that all water ingested is used (amount) and this is multiplied by a proportion of tap water instead of an amount 272,344,-1 80,52 1,0,0,1,0,1,1,,0, 2,102,90,476,224 Arial, 10 The inclusion of "proportion of population" is also not useful. It would be better to represent these proportions using distributions. How is this possible? Could I not just incorporate this dimension into another variable in this part of the model? For example, in duration? 104,288,-1 76,104 1,0,0,1,0,1,1,,0, Arial, 10 It is not seemingly necessary to keep the concentration variable. Although it could remain just as a factor. E.g. instead of "20", it would be "2". 392,272,-1 36,121 1,0,0,1,0,1,1,,0, Arial, 10 It would make sense to define a baseline where there is no variability (i.e. inputting only midpoints) and then one where we account for some uncertainty. In addition there would be scenarios about bottled water etc. Otherwise we end up without any variability expressed in the outputs for the baseline (except for that in the monitoring data) 272,168,-1 80,116 1,0,0,1,0,1,1,,0, Arial, 10 Te5 Is the split of tap water variable really necessary? Or is this dealt with by another variable? 64,536,-1 36,72 1,0,0,1,0,1,1,,0, Arial, 10 Te5 The model does not currently have the functionality to produce an estimate of the health impact in DALYs. The attributable burden in terms of cases of disease is the endpoint used for this first pass assessment. 472,416,-1 44,156 1,0,0,1,0,1,1,,0, Arial, 10 Te5 Uptake factors used to calculate exposure through ingestion, inhalation and absorption were derived from those used in Whitaker et al. 2003. It should be noted that these were developed for chloroform (not TTHMs) so should be interpreted with caution. 200,432,-1 176,30 1,0,0,1,0,1,1,,0, Arial, 10 Use figures from Whitaker et al (2003) only!! 160,528,-1 36,37 1,0,0,1,0,1,1,,0, 65535,65535,65535 Arial, 10 Te11 Try to use method from arsenic diagram to deal with the baseline vs. scenario uncertainty problem 256,536,-1 44,65 1,0,0,1,0,1,1,,0, 65535,65535,65535 Arial, 10 WP3.4 TTHM and IUGR model jgrellie 06 Aug 2008 09:21 48,24 320,128,1 64,64 1,0,0,1,1,1,0,,0, 1,143,49,831,527,17 65535,45873,39321 52428,52428,52428 78,1,1,0,2,9,2970,2100,15,0 Mean concentrations of TTHM for each area µg TTHM per litre Table(Area1)( 0,1,0,0,0,0,0,3,0,0,1,0,3,0,26,0,2,5,23,7,9,0,13,6,0,0,17,0,0,19,19,43,0,8,3,91,0,25,0,13,0,0,0,0,0,7.13,0) 152,248,1 56,32 1,1,1,1,1,1,1,,0, 2,258,103,476,224 2,509,95,546,493,0,MIDM 2,40,50,1073,278,1,MIDM [Formnode Mean_concentrations1] Arial, 12 Standard deviations of TTHM concentrations for each area µg TTHM per litre Table(Area1)( 0,2.449489743,0,0,0,0,0,3.287784027,0,0,0.5,0,3.959797975,0,5.196152423,0,3.059738551,2.516611478,21.07921567,8.544003745,7.848885271,0,9.699942869000001,1.354314095,0,0,4.949747468,0,0,11.0338872,1.527525232,5.354126135,0,6.740072064,4.242640687,8.485281374,0,3.535533906,0,6.582805886,0,0,0,0,0,0,0) 152,440,1 64,32 1,1,1,1,1,1,1,,0, 2,102,92,472,222 [Formnode Standard_deviations1] Arial, 12 Monitoring data distributions of TTHM concentrations for each area µg of TTHM per litre Lognormal(,, Mean_concentrations_, Standard_deviations_) 152,344,1 72,44 1,1,1,1,1,1,1,,0, 2,0,-23,1024,657 2,0,0,1024,657,1,MIDM Arial, 12 [Area1,Area1,Undefined,Undefined,2] Area ['061','111','098','165','398','224','092','927','091','049','078','257','106','286','285','153','758','418','536','837','908','408','598','743','905','050','529','537','577','609','680','684','783','853','886','895','491','593','740','297','208','564','567','765','240','698','851'] 136,48,1 48,24 1,1,1,1,1,1,1,,0, 2,591,173,561,452 [Formnode England_and_wales_w1] Arial, 12 ['061','111','098','165','398','224','092','927','091','049','078','257','106','286','285','153','758','418','536','837','908','408','598','743','905','050','529','537','577','609','680','684','783','853','886','895','491','593','740','297','208','564','567','765','240','698','851'] Exposure-response function for TTHM in tap water and IUGR Note - this is an approximation - i need to work out what the correct way of dealing with this is - maybe follow arsenic model Normal( .01311364, 6.495597m ) 464,248,1 80,40 1,1,1,1,1,1,1,,0, 2,0,-23,1024,657 2,486,357,780,339,0,CONF [Formnode Exposure_response_f1] Distresol:8 Diststeps:1 {!40000|Att_contlinestyle Graph_pdf_valdim:6} {!40000|Att_graphvaluerange Exposure_response_fu:1,,,,0} Arial, 12 [] [Undefined,Undefined,2] Total live births for each area Number of births Table(Area1,Sex_of_neonates)( 92,74, 83,88, 125,120, 99,86, 505,478, 43,55, 1334,1277, 172,153, 3154,2925, 1832,1713, 40,47, 248,262, 262,255, 388,428, 260,240, 129,123, 855,815, 142,146, 221,213, 1229,1156, 96,91, 83,84, 114,120, 379,316, 340,341, 59,63, 100,92, 43,47, 47,59, 380,354, 124,132, 218,201, 16,21, 951,915, 62,47, 61,72, 264,213, 73,74, 125,117, 499,471, 76,67, 993,950, 91,103, 46,45, 126,121, 368,327, 56,43 ) 640,440,1 48,36 1,1,1,1,1,1,1,0,0,0 2,0,0,1024,657 2,74,9,1024,657,0,MIDM 2,57,-2,363,885,0,MIDM [Formnode Total_live_births_i1] {!40000|Att_graphindexrange Sex_of_neonates:1,,,,,,10} {!40000|Att_graphvaluerange Total_live_births_fo:1,,,,,,10} Baroverlap:0 {!40000|Flip:0} {!40000|Att_catlinestyle Graph_primary_valdim:9} {!40000|Att_stackedbar Graph_primary_valdim:0} {!40000|Graph_pagebrush: } Arial, 12 [Sex_of_neonates,Area1] [Sex_of_neonates,Area1] [Index Area1, Index Sex_of_neonates] ['item 1'] [Sex,2,Water_companies,1,Age_categories,1] Background prevalence of IUGR Cases per 100,000 births Background prevalence of SGA in Filand is nominally 4.3% of all births. Given that prevalence is typically reported as a rate in 100,000, 4,3% equates to 4,3/100 or 4,300 in 100,000 i.e. 4,300 IUGR births per 100,000 live births Ref: http://www.stakes.fi/tilastot/tilastotiedotteet/2009/tr22_09.pdf THL: Births and newborns 2008. THL Statistical report 2009. Lognormal( , , 4300 , ) 640,248,1 64,32 1,1,1,1,1,1,1,,0, 2,0,0,1152,753 2,472,482,416,303,0,STAT [Formnode Background_prevalen1] {!40000|Att_contlinestyle Graph_pdf_valdim:6} {!40000|Att_graphvaluerange Background_prevalenc:1,,,,0} Arial, 12 [] Excess risk of IUGR attributable to TTHM ((exp(Exposure_response_fu/10*Modelled_exposure_of)-1)/exp(Exposure_response_fu/10*Modelled_exposure_of)) 464,344,1 48,36 1,1,1,1,1,1,1,,0, 2,268,461,926,273 2,30,23,453,705,0,MIDM Probindex:[0.5 ] Arial, 12 [Exposure_scenarios,Area1,0,Undefined,1] [Sys_localindex('PROBABILITY'),1,Area1,1,Exposure_scenarios,1] Excess cases of IUGR attributable to TTHM Cases Excess_risk_of_iugr_*Background_prevalenc*Total_live_births_fo/100000 640,344,1 48,40 1,1,1,1,1,1,1,,0, 2,0,2,1152,751 2,0,-23,1440,799,0,MEAN [Formnode Attributable_burden1] Probindex:[0.05, 0.5, 0.95 ] Statsselect:[0, 1, 0, 0, 0, 0, 0, 0 ] {!40000|Att_contlinestyle Run:1} Distresol:10 {!40000|Att_graphvaluerange Graph_pdf_valdim:1,0,1,1,0} {!40000|Att_contlinestyle Graph_pdf_valdim:6} Diststeps:0 {!40000|Att_graphvaluerange Excess_cases_of_iugr:1,,1,,,,,-10K,30K} {!40000|Att_catlinestyle Graph_primary_valdim:9} Arial, 12 [Sex_of_neonates,Area1,1,1,1] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 [Index Area1, Index Sex_of_neonates] [Exposure_scenarios,1,Area1,23,Sex_of_neonates,1] Modelled exposure of population to TTHM for each area µg TTHM per litre Monitoring_data_dist*Exposure_adjustment_ 320,344,1 64,28 1,1,1,1,1,1,1,,0, 2,0,0,1152,752 2,0,0,1152,753,0,MIDM Arial, 12 [Exposure_scenarios,Area1,Undefined,Undefined,1] [Exposure_scenarios,3,Area1,1,Sys_localindex('STEP'),1] Type of drink ['cold','hot'] 624,48,1 48,24 1,1,1,1,1,1,1,,0, Arial, 12 ['cold','hot'] Bathing type ['shower','bath'] 256,48,1 48,24 1,1,1,1,1,1,1,,0, Arial, 12 ['shower','bath'] Sex of neonates ['Male (M)','Female (F)'] 504,48,1 48,24 1,1,1,1,1,1,1,,0, Arial, 12 ['Male (M)','Female (F)'] Indices (by which data within variables in the model are ordered) 384,56,-1 304,48 1,0,0,1,1,1,1,,1, Arial, 12 Exposure scenarios ['baseline','baseline with variability','user defined'] 384,48,1 48,24 1,1,1,1,1,1,1,,0, 2,0,-23,1281,953 Arial, 12 ['baseline','baseline with variability','user defined'] Total daily TTHM uptake slope from ingestion, inhalation and absorption Indexless_daily_tthm+Indexless_daily_tth1 320,164,1 60,44 1,1,1,1,1,1,1,,0, 2,0,-23,1280,952 2,0,0,952,669,1,MIDM Arial, 12 [Undefined,Exposure_scenarios,Undefined,Undefined,1] [Bathing_scenarios,3,Bottled_water_scenar,1,Sys_localindex('STEP'),1] Exposure adjustment factor Unitless factor Table(Exposure_scenarios)( Total_daily_tthm_upt[Exposure_scenarios="baseline"]/Total_daily_tthm_upt[Exposure_scenarios="baseline"],Total_daily_tthm_upt[Exposure_scenarios="baseline with variability"]/Total_daily_tthm_upt[Exposure_scenarios="baseline"],Total_daily_tthm_upt[Exposure_scenarios="user defined"]/Total_daily_tthm_upt[Exposure_scenarios="baseline"]) 320,268,1 48,31 1,1,1,1,1,1,1,,0, 2,102,90,476,224 2,188,272,702,303,0,MIDM 2,-34,394,1281,350,0,CONF {!40000|Att_graphvaluerange Exposure_adjustment_:1,0} {!40000|Att_contlinestyle Graph_pdf_valdim:6} {!40000|Att_catlinestyle Graph_pdf_valdim:9} Arial, 12 [Undefined,Self] [Exposure_scenarios,Exposure_scenarios,1,1,1] ['item 1'] [Bottled_water_scenar,3,Bathing_scenarios,2,Sys_localindex('STEP'),1] TTHM ingestion module jgrellie 29 Jul 2008 18:06 48,24 152,164,1 48,28 1,1,1,1,1,1,1,,0, 1,-9,98,611,486,17 52425,39321,65535 65535,65535,65535 Arial, 12 100,1,1,0,2,9,2970,2100,15,0 Daily TTHM uptake slope from ingestion Unitless Average_daily_tap_wa*Percentage_tap_water*Proportion_of_tap_wa*Proportion_of_tthm_r*Uptake_of_tthm_per_u 256,264,1 60,36 1,1,1,1,1,1,1,,0, 2,512,486,476,224 2,0,-23,1281,953,1,PDFP {!40000|Att_graphvaluerange Daily_tthm_uptake_s2:1,0} {!40000|Att_contlinestyle Graph_pdf_valdim:6} {!40000|Att_catlinestyle Graph_pdf_valdim:9} Diststeps:0 Arial, 12 [Undefined,Exposure_scenarios,Undefined,Undefined,1] [Index Type_of_drink, Index Exposure_scenarios] [Type_of_drink,1,Exposure_scenarios,-1,Sys_localindex('STEP'),1] Indexless daily TTHM uptake slope from ingestion Unitless Sum(Daily_tthm_uptake_s2, Type_of_drink) 416,264,1 60,36 1,1,1,1,1,1,1,,0, 2,725,253,476,224 2,0,-23,1281,953,0,MIDM Arial, 12 [Undefined,Exposure_scenarios,Undefined,1] [Exposure_scenarios,3] Percentage tap water consumed out of total water % Table(Exposure_scenarios)( 1,1,User_defined_percent) 256,160,1 60,32 1,1,1,1,1,1,1,,0, 2,0,-23,952,669 2,344,354,416,303,0,MIDM Arial, 12 2,%,4,2,0,0,4,0,$,0,"ABBREV",0 ['item 1'] Uptake of TTHM per ug/L TTHM per litre of tap water ingested ug per ug/L of CHCl3 /L Uptake of TTHM in ug per ug/L chloroform in water per litre of water consumed. Note that this is derived from the Whitaker paper, where chloroform was calculated - not TTHM. 0.003676 96,264,1 52,36 1,1,1,1,1,1,1,,0, 2,0,-23,952,669 Arial, 12 Proportion of TTHM retained after heating and pouring Unitless The proportion of TTHM lost from drinks due to heating and pouring of the water Table(Type_of_drink)( 0.15,1) 416,384,1 68,44 1,1,1,1,1,1,1,,0, 2,0,-23,952,669 2,454,16,416,303,0,MIDM 2,216,226,416,303,0,MIDM Arial, 12 [Type_of_drink,Exposure_scenarios] [Undefined,Type_of_drink,Undefined,Undefined,1] [Type_of_drink,2,Sys_localindex('STATISTICS'),1] Average daily tap water consumption L/day This is not the exact distribution used by Whitaker et al 2003. They seem to have used a distribution with a slightly higher mean value. Table(Exposure_scenarios)( Lognormal( ,,0.758, 0.567),Lognormal( ,,0.758, 0.567),User_defined_average) 96,160,1 64,32 1,1,1,1,1,1,1,,0, 2,102,90,476,224 2,40,50,416,303,0,MIDM 2,159,127,416,303,1,PDFP Arial, 12 [Undefined,Exposure_scenarios] [Undefined,Exposure_scenarios,Undefined,Undefined,1] User-defined percentage of tap water consumed out of total water % .7 416,160,1 48,44 1,1,1,1,1,1,1,,0, 2,310,157,476,224 Arial, 12 User-defined average daily tap water consumption Lognormal( 1.2, , , 0.5) 96,56,1 48,36 1,1,1,1,1,1,1,,0, 2,102,90,476,224 Arial, 12 Proportion of tap water consumed after boiling Table(Type_of_drink)( Uniform(0,1),1) 256,384,1 64,36 1,1,1,1,1,1,1,,0, 2,0,-23,1280,952 2,0,-23,1280,952,0,MIDM 2,0,-23,1281,953,1,MIDM Arial, 12 [Type_of_drink,Undefined] [Type_of_drink,Exposure_scenarios] TTHM inhalation and absorption module jgrellie 29 Jul 2008 18:06 48,24 464,164,1 56,28 1,1,1,1,1,1,1,,0, 1,1,2,1009,793,17 52425,39321,65535 65535,65535,65535 Arial, 12 73,1,1,0,2,9,2970,2100,15,0 Daily TTHM uptake slope from inhalation and absorption ug (Uptake_factors_for_t*Total_time_spent_sho*Overall_percentages_)/90 320,320,1 60,67 1,1,1,1,1,1,1,,0, 2,720,346,416,303,1,PDFP Diststeps:0 {!40000|Att_contlinestyle Graph_pdf_valdim:6} {!40000|Att_graphvaluerange Graph_pdf_valdim:1,0,1,1,0} {!40000|Att_graphvaluerange Daily_tthm_uptake_sl:1,,,,1} Arial, 12 [Undefined,Bathing_type,Undefined,Undefined,1] [Index Exposure_scenarios] [Exposure_scenarios,1,Bathing_type,1,Sys_localindex('STEP'),1] Indexless daily TTHM uptake slope from inhalation and absorption sum(Daily_tthm_uptake_sl, Bathing_type) 512,320,1 64,67 1,1,1,1,1,1,1,,0, 2,443,256,476,224 2,40,50,416,303,1,PDFP Arial, 12 [Undefined,Exposure_scenarios,Undefined,1,1] [Exposure_scenarios,1,Sys_localindex('STEP'),1] Total time spent showering or bathing over 90 days Minutes (min) No__of_showers_or_ba*Bathing_and_showerin 320,456,1 60,44 1,1,1,1,1,1,1,,0, 2,0,-23,1280,952,1,PDFP Arial, 12 [Undefined,Exposure_scenarios,Undefined,Undefined,1] [Bathing_type,1,Exposure_scenarios,3,Sys_localindex('STEP'),1] Bathing and showering duration Minutes (min) Lognormal( Median_of_bathing_an, Geometric_standard_d ) 512,568,1 60,40 1,1,1,1,1,1,1,,0, 2,148,66,460,243 2,85,271,468,223,0,MIDM 2,0,-23,1281,953,1,PDFP {!40000|Att_contlinestyle Graph_pdf_valdim:6} {!40000|Att_graphvaluerange Bathing_and_showerin:1,,0,,0,,,0,150} Diststeps:1 Arial, 12 [Undefined,Bathing_type,Undefined,Undefined,1] [Exposure_scenarios,1,Bathing_type,1,Sys_localindex('STEP'),1] Median of bathing and showering duration MInutes (min) Table(Bathing_type,Exposure_scenarios)( 7.174262723074754,7.174262723074754,User_defined_median_, 16.44464677109705,16.44464677109705,User_defined_median_ ) 512,448,1 48,40 1,1,1,1,1,1,1,,0, 2,0,-23,1280,952 2,716,524,416,303,1,MIDM Arial, 12 [Bathing_type,Exposure_scenarios] [Exposure_scenarios,Bathing_type] Geometric standard deviation of bathing and showering duration Minutes (min) Table(Bathing_type,Exposure_scenarios)( 1.472409242986545,1.472409242986545,User_defined_geometr, 1.915540829013896,1.915540829013896,User_defined_geometr ) 672,568,1 48,58 1,1,1,1,1,1,1,,0, 2,0,-23,1280,952 2,247,235,416,303,0,MIDM 2,561,254,488,289,1,MIDM Arial, 12 [Bathing_type,Exposure_scenarios] [Exposure_scenarios,Bathing_type] Showering/bathing rate for 90-day period No. of baths or showers Total number of showers/baths for the 90-day period Table(Exposure_scenarios)( Uniform( 60, 100 ),Uniform( 60, 100 ),User_defined_showeri) 320,688,1 80,44 1,1,1,1,1,1,1,,0, 2,0,-23,1280,952 2,136,146,416,303,0,MIDM Arial, 12 No. of showers or baths for the 90-day period No. of showers or baths Poisson(Showering_bathing_ra) 320,576,1 80,44 1,1,1,1,1,1,1,,0, 2,0,-23,1281,953 2,0,-23,1280,952,1,PDFP Grid:0 {!40000|Att_catlinestyle Graph_prob_valdim:6} Distresol:100 Diststeps:0 Arial, 12 [Undefined,Exposure_scenarios,Undefined,Undefined,1] [Index Exposure_scenarios] [Exposure_scenarios,0,Sys_localindex('POSSIBLE_VALUES'),1] Uptake factors for TTHM by bathing type ug per ug/L per min Table(Bathing_type)( Uniform(0.001114,0.001524),1.384m) 160,320,1 76,48 1,1,1,1,1,1,1,,0, 2,445,104,570,798 2,104,118,410,299,0,MIDM 2,328,338,416,303,1,PDFP Arial, 12 [Undefined,Bathing_type,Undefined,Undefined,1] Proportion of baths taken by those who bathe and shower Uniform(0,1) 512,192,1 64,52 1,1,1,1,1,1,1,,0, 2,0,-23,1281,953 Arial, 12 Percentage of women only using one type of bathing activity % Table(Bathing_type,Exposure_scenarios)( 0.61,0.61,User_defined_percen1, 0.18,0.18,User_defined_percen1 ) 160,64,1 68,44 1,1,1,1,1,1,1,,0, 2,0,-23,1280,952 2,0,-23,1280,952,0,MIDM 2,0,0,1281,953,1,MIDM Arial, 12 [Bathing_type,Exposure_scenarios] [Bathing_type,Exposure_scenarios] 2,%,4,2,0,0,4,0,$,0,"ABBREV",0 Overall percentages of bathing activity % Table(Bathing_type)( Percentage_of_women_+(Percentage_of_women1*(1-Proportion_of_baths_)),Percentage_of_women_+(Percentage_of_women1*Proportion_of_baths_)) 320,192,1 52,40 1,1,1,1,1,1,1,,0, 2,512,85,590,303,0,MIDM 2,0,0,1281,953,1,MIDM Arial, 12 [Bathing_type,Exposure_scenarios,Undefined,Undefined,1] Percentage of women both showering and bathing % 1-(sum(Percentage_of_women_,Bathing_type)) 160,192,1 48,49 1,1,1,1,1,1,1,,0, 2,0,-23,1281,953 2,392,402,416,303,1,MIDM Arial, 12 User-defined percentage of women only using one type of bathing activity % Table(Bathing_type)( 0.8100000000000001,0.05) 800,64,1 48,52 1,1,1,1,1,1,1,,0, 2,0,-23,1280,952 Arial, 12 [Bathing_type,Undefined] 2,%,4,2,0,0,4,0,$,0,"ABBREV",0 User-defined geometric standard deviation of bathing and showering duration Table(Bathing_type)( 1.6,1.9) 800,569,1 48,60 1,1,1,1,1,1,1,,0, 2,0,-23,1280,952 2,633,203,416,303,0,MIDM Arial, 12 [Bathing_type,Undefined] User-defined median of bathing and showering duration Table(Bathing_type)( 10,20) 800,448,1 48,44 1,1,1,1,1,1,1,,0, 2,0,-23,1280,952 Arial, 12 [Bathing_type,Undefined] User-defined showering/bathing rate for 90-day period Uniform( 50, 120 ) 800,688,1 48,36 1,1,1,1,1,1,1,,0, 2,0,-23,1281,953 2,72,82,416,303,1,PDFP Arial, 12 WP3.4 TTHM and IUGR user interface jgrellie 06 Aug 2008 09:21 48,24 128,128,1 64,64 1,352,0,931,826,17 65535,52427,57888 Mean concentrations of TTHM for each water company (England & Wales, 2007) 0 576,64,1 332,16 0,0,0,0,0,0,0,72 Mean_concentrations_ Standard deviations of TTHM concentration for each water company (England & Wales, 2007) 0 536,96,1 372,16 1,0,0,1,0,0,0,72,0,1 Standard_deviations_ England and Wales water companies (2007) 0 696,744,1 208,13 1,0,0,1,0,0,0,72,0,1 Area1 Exposure-response function for TTHM and IUGR (derived from meta-analysis) 0 584,528,1 320,16 1,0,0,1,0,0,0,72,0,1 Exposure_response_fu Total live births in England & Wales (2005) 0 696,616,1 208,16 1,0,0,1,0,0,0,72,0,1 Total_live_births_fo Background prevalence of IUGR per 100,000 in England and Wales 0 616,560,1 288,16 1,0,0,1,0,0,0,72,0,1 Background_prevalenc Ingestion 464,224,2 412,56 1,0,0,1,0,1,1,,0, 52427,65535,65535 Arial, 19 Te10 Inhalation and absorption 464,376,3 412,84 1,0,0,1,0,1,1,,0, 52427,65535,65535 Arial, 19 Monitoring data 464,72,-1 432,44 1,0,0,1,0,1,1,,0, Arial, 19 Te10 Calculating attributable risk 464,536,-1 432,44 1,0,0,1,0,1,1,,0, Arial, 19 Te10 Calculating attributable burden 464,632,-1 432,36 1,0,0,1,0,1,1,,0, Arial, 19 Te10 Indices 464,744,-1 432,60 1,0,0,1,0,1,1,,0, Arial, 19 Te14 Calculating the risk adjustment factor - altering the user-defined exposure scenario 464,304,-1 432,176 1,0,0,1,0,1,1,,0, Arial, 19 Attributable burden (excess cases of IUGR in England & Wales) 1 632,648,1 272,13 1,0,0,1,0,0,0,72,0,1 Excess_cases_of_iugr 1 128,48,-1 16,12 1,0,0,1,1,1,0,,1, 2 320,48,-1 16,12 1,0,0,1,1,1,0,,1, WP3.4 TTHM and IUGR analysis of uncertainty jgrellie 09 Aug 2008 13:45 48,24 512,128,1 64,64 1,1,2,565,533,17 This variable simply describes a list of all the uncertain variables in the model. 472,168,-1 68,148 1,0,0,1,0,1,0,,0, Te23 The result of this variable returns a rank correlation of importance for all the uncertain variables in the model. The number of samples should be reduced in "Result", "Uncertainty options", "Sample size" if computational times are too long. Note also that since some variables considered as probabilistic may contain some deterministic data (for one element in an array), it may be necessary to ignore warning messages that appear while carrying out the analysis. 208,232,-1 156,212 1,0,0,1,0,1,0,,0, Excess cases of IUGR attributable to TTHM Importance Abs( RankCorrel( Excess_cases_of_iug2, Excess_cases_of_iugr ) ) 208,96,1 48,58 1,0,0,1,1,1,0,0,0,0 2,0,0,1280,952,1,MEAN {!40000|Flip:8} [Excess_cases_of_iug2,Undefined] [Index Area1, Index Type_of_drink, Index Bathing_type, Index Sex_of_neonates] [Type_of_drink,-1,Bathing_type,-1,Sex_of_neonates,-1,Exposure_scenarios,3,Area1,-1,Excess_cases_of_iug2,1] Excess cases of IUGR attributable to TTHM Inputs [ Monitoring_data_dist, Exposure_response_fu, Background_prevalenc, Proportion_of_tthm_r, Average_daily_tap_wa, Proportion_of_tap_wa, Bathing_and_showerin, Showering_bathing_ra, No__of_showers_or_ba, Uptake_factors_for_t, Proportion_of_baths_] ['Monitoring data distributions of TTHM concentrations for each area','Exposure-response function for TTHM in tap water and IUGR','Background prevalence of IUGR','Proportion of TTHM retained after heating and pouring','Average daily tap water consumption','Proportion of tap water consumed after boiling','Bathing and showering duration','Showering/bathing rate for 90-day period','No. of showers or baths for the 90-day period','Uptake factors for TTHM by bathing type','Proportion of baths taken by those who bathe and shower'] 472,96,1 48,58 1,0,0,1,1,1,0,0,0,0 Te20 3 512,48,-1 16,12 1,0,0,1,1,1,0,,1,