here (note that you do not use any prefix, and just specify the name of the column to refer to the current cell value for that column.
When you click Apply, it will perform the calculation, which you can see by clicking on the data table. Note that there is a Calc All Rows button at the bottom -- this will force a manual recalculation of the table should there be any doubt about whether it has done the calculation. There is also an auto_calc flag on the data table -- if you turn this off, then it will ONLY do the calculation when you hit the Calc All Rows button (or call this method from a program).
You can also \"lock in\" the current values by turning OFF the Calc flag on the calc_val column -- the current values will just remain as is. This can be useful for setting values and cleaning up data -- you can create new columns to compute values, lock them in, and then copy them back to the original column, etc.
That's it for calc stuff for now -- next up is matrix data in [[.docs.MatrixDataDoc]].
";
html_text="
Data Calc Loop
The DataCalcLoop set of functions (available in the last section of the Data Proc program toolbox) provide a framework for looping through the rows of a data table and performing any arbitrary kind of calculations or other functions, and then writing either back to the same data table or to a different data table. For those familiar with obscure but powerful linux utilities, it resembles the awk tool.
The DataCalcLoop (DCL) can also be used to do abitrarily complex row selection, by deciding on a row-by-row basis what to copy versus not.
One thing to keep in mind about the DCL -- it is slower than the other data base style functions (select rows, join, etc) because it uses script code to iterate through the data table, instead of having the script call a pre-compiled and very fast hard-coded loop.
As usual, the first step is to create a DataCalcLoopProg in programs, and to drag the MyRandomData into its vars.
Creating a destination data table
First, we create a data table to hold the results of our data calc loop computation. In this case, we're just going to create a plain data table, and then write some program code to create the columns for it, based on our source data table. So, just do NewDataTable and call it something like DataCalcDest.
Next, we add a meth() call (from the Var/Fun program toolbox) on that DataCalcDest into the prog_code, to copy over the columns from the MyRandomData table. After setting the object to DataCalcDest, select Copy_NoData (under the Copy category) for the method to call, and select or type MyRandomData for the argument to this method.
Now, we'll add a new column to the destination data table to hold the results of our calculation. Just duplicate the method call item, and select the method as NewColFloat under the Columns category of functions. For the col_nm argument, enter \"calc_val\" (include the double quotes!).
Creating the Data Calc Loop
We are ready to drag the calc loop item from Data Proc onto the prog_code, and then set the src data as MyRandomData and the dest data as DataCalcDest. You can see that it has thee sub-groups: src_cols, dest_cols, and loop_code -- we just add the names of the columns we want to operate on from both the source and destination tables, and then add some code to operate on them one row at a time in the loop_code.
Do Add Src Column and either type in cycles or just select it in the new item that comes up. Duplicate this one and enter sse -- our computation will involve these two values.
Then do Add Dest Column, and type in calc_val -- it will not be available for selecting because it has yet to be created.
Copying the Source Data
The first thing we want to do in our calculation is to copy over all the data from the source to the destination, and then we'll add the special computed value.
Drag a +dest row item from Data Proc into the loop_code of the DCL (it doesn't require any further configuration), and then add a cpy cols item (which also does not require configuration).
You can now do an incremental test of the program -- Init (you should get a warning about the calc_val not yet existing in the destination data table -- ignore it), and then Run. If you click on the DataCalcDest now, and view the data table, you'll see it is a copy of MyRandomData, with zeros in the new calc_val column.
If you just wanted to select rows, you could have added an if.cont (if -- continue) expression that evaluated the values of the current source row, and if they were not suitable, continued to the next row without doing the remaining code (i.e., adding a dest row and copying the values).
Computing the new calc_val
Now we'll augment our code with the main point of this exercise: computing a new value and writing it to the destination table.
Drag a var= (from Var/Fun toolbox) onto the loop_code (placing it at the end) and you'll see that you can then select d_calc_val for the result var -- this is a new variable automatically computed by the DCL to contain the value of the calc_val column of the destination table. You should also have noticed that s_sse and s_cycles were on the list too -- they are similar new variables for the source columns we specified. A variable is created for each of the src_cols and dest_cols you specify.
For the expr, enter s_sse * s_cycles (you can use Ctrl+L to lookup these variables, or the var_lookup selector).
The last step is to add a =dest row item at the end, which is what actually writes the d_calc_val to the calc_val cell of the destination table. This is a commonly-forgotten step so don't forget it!
Now when you Init and Run, you should see the calc_val contains the product of the sse and cycle columns!
This provides a very brief overview of all the basic elements of the data calc loop system -- clearly there are many many other possibilities -- any arbitrary code can be put in the loop_code, and you can access information from other rows or data tables, etc. Note that there is a special src_row variable created that contains the current row number of the source datatable, so if you want to access other rows of data (e.g., the prior row), you can refer to that row number (be sure to avoid accessing data out of range!).
You can also skip the dest data table entirely, and just modify the source table directly -- in this case, use the =src row item to write the data back to the source data table after you have performed your computations.
Simple Calc Alternative: Calc Columns
For the simple kind of calculation that we just performed, it turns out that there is a simpler alternative to the more powerful and flexible data calc loop: you can just enter an expression in a data column and it will automatically fill in that column with the result of that expression! This is somewhat like entering a formula into a spreadsheet (except it is one formula for the whole column).
To explore a calc column, select in the left browser the calc_val column of your DataCalcDest table. Then click on the Calc button in its edit panel, which will enable the calc_expr field to be edited. Type in sse * cycles here (note that you do not use any prefix, and just specify the name of the column to refer to the current cell value for that column.
When you click Apply, it will perform the calculation, which you can see by clicking on the data table. Note that there is a Calc All Rows button at the bottom -- this will force a manual recalculation of the table should there be any doubt about whether it has done the calculation. There is also an auto_calc flag on the data table -- if you turn this off, then it will ONLY do the calculation when you hit the Calc All Rows button (or call this method from a program).
You can also \"lock in\" the current values by turning OFF the Calc flag on the calc_val column -- the current values will just remain as is. This can be useful for setting values and cleaning up data -- you can create new columns to compute values, lock them in, and then copy them back to the original column, etc.
That's it for calc stuff for now -- next up is matrix data in MatrixDataDoc.
meth() from the Var/Fun toolbox, set the object to ProtoData, and select the AddRows method (under Rows category) -- for the arg set 4 rows.
Duplicate that method call and select the InitValsToRowNo method (under Modify category), and set the arg to \"Name\" -- this will set the name of each prototype to the row number.
Now for the main call for generating the random bit patterns, which is located in the Misc Fun toolbox (note that the functions in this toolbox are also available in the data_proc group in the left tree browser, for interactive use outside of writing a program). Drag the data gen() item onto prog_code (adding it to the end), and select the PermutedBinary_MinDist method under the Random section, which will make random bit patterns with a given number of items turned on (value = 1) with the rest off, and ensuring that all the patterns have a minimum distance from each other (if possible). For the args, set the data to the ProtoData item (use var_lookup or Ctrl+L), enter \"Input\" (incl quotes) for the col_nm, and let's set 6 bits on, with a minimum dist of 10 using the default HAMMING metric specified by the final three args (which you can just leave blank to accept the default values shown in the arg edit dialogs -- note the required flag -- if it is not checked, then you can leave it blank to use the default) -- the HAMMING metric counts a 1 for every cell in the matrix that is different, so with 6 bits on and the rest off, the maximum distance is 12.
You can now Init and Run, and check the ProtoData that was generated. To get a better view of this data, click on View/New Grid View on the ProtoData -- select New Frame. This allows you to see all the patterns at once, as colored squares. If you want a better view of it, click on the tab on the far right of the middle panel to bring up the view control for it, and enter 4 for the Rows (instead of the default of 10), and Apply.
Unless you have special powers, it probably won't be immediately obvious whether these patterns do in fact overlap very little as we specified. To check this, let's compute a distance matrix, which shows how far each pattern is from each other one. Drag a data anal() from Misc Fun to the end. Select the DistMatrixTable method (under the Distance category). We need to create a new data table in objs to hold the results -- call it ProtoDists. This is then the first arg. Set view to true (just enter the word true into the expr), set ProtoData for src_data, and the data col nm = \"Input\", name col nm = \"Name\". For the distance metric to use, type taMath::HAMMING (you can just type taMath:: and then do Ctrl+L to pick from a list of options). That's it!
After you Init and Run, you should see a new right view frame with the distance information, which you'll notice has 8 items instead of the expected 4 -- what happened?? We forgot to reset the ProtoData table prior to adding 4 rows to it. Drag a reset rows from the Data toolbox to the first line of the prog_code, and select ProtoData, and run again.
Now, you should see that the minimum distance in the distance table is 10, exactly as you requested. You can play with the parameters at this point to test things further if you want.
== Generating Random Permutations from the Prototypes ==
Next, we replicate these prototypes into a new data table, and then permute the patterns to create random variants of the prototypical patterns. First, duplicate the ProtoData, and call it ItemData. Also, create a new data table called ItemDists to hold the distance information for these items.
Drag a data proc() from the Misc Fun toolbox to the end of the code, and choose ReplicateRows from the Copy category -- dest is ItemData, src is ProtoData, and n_repl = 6 (6 items per prototype).
Next, add a data gen() to the end, and select FlipBits under Random -- data is ItemData, col_nm is \"Input\", n_off is 1 and n_on is also 1. This will flip one bit at random off from those that are on, and vice-versa for one bit that is currently off.
You can Init and Run at this point, and do a Grid View of the ItemData to see what it looks like -- you should be able to perceive the \"family resemblance\" of all the items within a prototype.
To see this structure in the distance matrix, copy the previous DistMatrixTable call to the end of the program, and change the relevant tables to their Item versions (ItemDists, ItemData), and also set the name_col_nm to \"\" (an empty string) -- this will generate a distance matrix without name labels, which is rendered as a single matrix cell and is more appropriate for larger numbers of items.
When you Run it now, you should see the ItemDists matrix has four distinct squares along the diagonal -- these are the 6 items within each prototype family that are very similar to each other, and distant from all the other items.
One flourish you can add to the distance display is to add height information to the grid view -- set the blk hgt field in the view control panel (right-most tab in middle panel) to 1 instead of 0, Apply, and then move the Rotx wheel on the left side of the 3d view -- you should see that the block height now also shows you the distance information.
== Dimensionality Reduction Analysis (Cluster Plot, PCA) ==
The last step in this tutorial is to apply a couple of useful dimensionality reduction techniques to these patterns. First, we do a cluster plot, and then a principal components analysis (PCA).
Make two new data tables in objs called ItemCluster and ItemPCA. Then drag a data anal() to the program and select Cluster under HighDim. The clust_data is ItemCluster, set view to true, the src_data is ItemData, and the columns are \"Input\" and \"Name\" -- leave the metric information blank for the defaults.
When you Run now, you should see a tree-like clustering graph with items from each of the same prototype being clustered together on the right (leaf) side of the graph -- this means that these items are most similar to each other. The primary function of this plot is to obtain a qualitative similarity picture -- only the X axis values in this plot are significant, and then only within relatively tight clusters of items -- the length of these horizontal lines indicates the distance between items in the cluster. At higher levels of clustering, these distances can be hard to reconcile. The vertical axis is ordinal and only used for organizing the items for display purposes.
Next, duplicate this last taDataAnal item and select the PCA2dPrjn method -- most of the args are the same, except you want to set the prjn_data to ItemPCA. When you Run this, you should see a plot with the items clustered together as points within a 2d space -- the X axis represents the projection of the individual patterns onto the first principal component of variance across the item patterns, while the Y axis shows the projection onto the 2nd principal component of variance. This will provide the greatest separation among the points. If you change the final two args to this PCA function to 3 and 4, you'll see that projecting onto the smaller components produces a more mixed distribution of points from the different prototypes -- it is picking up on the \"residual\" variation after factoring out the major differences due to the prototype effects.
To actually see what the eigenvectors for these patterns look like, you can create yet another new data table (ItemEigens) and duplicate the last program item, select PCAEigenTable, and set this new ItemEigens as the output pca_data table. You should recognize blends of the different prototypes in the first few eigenvectors.
There are several functions available in the HighDim toolkit that you are free to explore at this point. Hopefully, if you've made it through this entire tutorial, you have a pretty good sense of some of the kinds of things you can do with data tables!
";
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Matrix Data
In this final section of the tutorial we explore some functions that can only be used on Matrix data (where the cells of a table contain multiple values). We create random bit patterns based on prototypes, and then do various forms of analysis on these patterns.
As usual, create a new MatrixDataProg in programs, and click on Edit Program.
Creating the Prototype Data Table
Do New Data Table in the objs (call it ProtoData), and do columns/NewCol and create a String column called Name (we'll format this data as is typical for input data presented to a network), and then do columns/NewColMatrix, specify a float type, with 2 dimensions, each of size 5 (i.e., a 5x5 matrix) -- call this one Input.
Now we'll write some code to add 4 random bit patterns to this prototype data. Drag a meth() from the Var/Fun toolbox, set the object to ProtoData, and select the AddRows method (under Rows category) -- for the arg set 4 rows.
Duplicate that method call and select the InitValsToRowNo method (under Modify category), and set the arg to \"Name\" -- this will set the name of each prototype to the row number.
Now for the main call for generating the random bit patterns, which is located in the Misc Fun toolbox (note that the functions in this toolbox are also available in the data_proc group in the left tree browser, for interactive use outside of writing a program). Drag the data gen() item onto prog_code (adding it to the end), and select the PermutedBinary_MinDist method under the Random section, which will make random bit patterns with a given number of items turned on (value = 1) with the rest off, and ensuring that all the patterns have a minimum distance from each other (if possible). For the args, set the data to the ProtoData item (use var_lookup or Ctrl+L), enter \"Input\" (incl quotes) for the col_nm, and let's set 6 bits on, with a minimum dist of 10 using the default HAMMING metric specified by the final three args (which you can just leave blank to accept the default values shown in the arg edit dialogs -- note the required flag -- if it is not checked, then you can leave it blank to use the default) -- the HAMMING metric counts a 1 for every cell in the matrix that is different, so with 6 bits on and the rest off, the maximum distance is 12.
You can now Init and Run, and check the ProtoData that was generated. To get a better view of this data, click on View/New Grid View on the ProtoData -- select New Frame. This allows you to see all the patterns at once, as colored squares. If you want a better view of it, click on the tab on the far right of the middle panel to bring up the view control for it, and enter 4 for the Rows (instead of the default of 10), and Apply.
Unless you have special powers, it probably won't be immediately obvious whether these patterns do in fact overlap very little as we specified. To check this, let's compute a distance matrix, which shows how far each pattern is from each other one. Drag a data anal() from Misc Fun to the end. Select the DistMatrixTable method (under the Distance category). We need to create a new data table in objs to hold the results -- call it ProtoDists. This is then the first arg. Set view to true (just enter the word true into the expr), set ProtoData for src_data, and the data col nm = \"Input\", name col nm = \"Name\". For the distance metric to use, type taMath::HAMMING (you can just type taMath:: and then do Ctrl+L to pick from a list of options). That's it!
After you Init and Run, you should see a new right view frame with the distance information, which you'll notice has 8 items instead of the expected 4 -- what happened?? We forgot to reset the ProtoData table prior to adding 4 rows to it. Drag a reset rows from the Data toolbox to the first line of the prog_code, and select ProtoData, and run again.
Now, you should see that the minimum distance in the distance table is 10, exactly as you requested. You can play with the parameters at this point to test things further if you want.
Generating Random Permutations from the Prototypes
Next, we replicate these prototypes into a new data table, and then permute the patterns to create random variants of the prototypical patterns. First, duplicate the ProtoData, and call it ItemData. Also, create a new data table called ItemDists to hold the distance information for these items.
Drag a data proc() from the Misc Fun toolbox to the end of the code, and choose ReplicateRows from the Copy category -- dest is ItemData, src is ProtoData, and n_repl = 6 (6 items per prototype).
Next, add a data gen() to the end, and select FlipBits under Random -- data is ItemData, col_nm is \"Input\", n_off is 1 and n_on is also 1. This will flip one bit at random off from those that are on, and vice-versa for one bit that is currently off.
You can Init and Run at this point, and do a Grid View of the ItemData to see what it looks like -- you should be able to perceive the \"family resemblance\" of all the items within a prototype.
To see this structure in the distance matrix, copy the previous DistMatrixTable call to the end of the program, and change the relevant tables to their Item versions (ItemDists, ItemData), and also set the name_col_nm to \"\" (an empty string) -- this will generate a distance matrix without name labels, which is rendered as a single matrix cell and is more appropriate for larger numbers of items.
When you Run it now, you should see the ItemDists matrix has four distinct squares along the diagonal -- these are the 6 items within each prototype family that are very similar to each other, and distant from all the other items.
One flourish you can add to the distance display is to add height information to the grid view -- set the blk hgt field in the view control panel (right-most tab in middle panel) to 1 instead of 0, Apply, and then move the Rotx wheel on the left side of the 3d view -- you should see that the block height now also shows you the distance information.
Dimensionality Reduction Analysis (Cluster Plot, PCA)
The last step in this tutorial is to apply a couple of useful dimensionality reduction techniques to these patterns. First, we do a cluster plot, and then a principal components analysis (PCA).
Make two new data tables in objs called ItemCluster and ItemPCA. Then drag a data anal() to the program and select Cluster under HighDim. The clust_data is ItemCluster, set view to true, the src_data is ItemData, and the columns are \"Input\" and \"Name\" -- leave the metric information blank for the defaults.
When you Run now, you should see a tree-like clustering graph with items from each of the same prototype being clustered together on the right (leaf) side of the graph -- this means that these items are most similar to each other. The primary function of this plot is to obtain a qualitative similarity picture -- only the X axis values in this plot are significant, and then only within relatively tight clusters of items -- the length of these horizontal lines indicates the distance between items in the cluster. At higher levels of clustering, these distances can be hard to reconcile. The vertical axis is ordinal and only used for organizing the items for display purposes.
Next, duplicate this last taDataAnal item and select the PCA2dPrjn method -- most of the args are the same, except you want to set the prjn_data to ItemPCA. When you Run this, you should see a plot with the items clustered together as points within a 2d space -- the X axis represents the projection of the individual patterns onto the first principal component of variance across the item patterns, while the Y axis shows the projection onto the 2nd principal component of variance. This will provide the greatest separation among the points. If you change the final two args to this PCA function to 3 and 4, you'll see that projecting onto the smaller components produces a more mixed distribution of points from the different prototypes -- it is picking up on the \"residual\" variation after factoring out the major differences due to the prototype effects.
To actually see what the eigenvectors for these patterns look like, you can create yet another new data table (ItemEigens) and duplicate the last program item, select PCAEigenTable, and set this new ItemEigens as the output pca_data table. You should recognize blends of the different prototypes in the first few eigenvectors.
There are several functions available in the HighDim toolkit that you are free to explore at this point. Hopefully, if you've made it through this entire tutorial, you have a pretty good sense of some of the kinds of things you can do with data tables!
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== Enter Title Here ==
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DataProcTutorial - Emergent
DataProcTutorial
From Emergent
This tutorial shows you how to generate data in data tables, and then apply a large number of different manipulation and analysis routines on that data, including graphing. It is available as an interactive Emergent project in demo/data_proc/data_tutorial.proj (just run emergent, open the project and start following the instructions that appear), and in this wiki page. Note: the demo/data_proc/data_tutorial_final.proj project is the version of the project saved after doing the entire tutorial (i.e., the final state of the tutorial project) -- this can be useful to refer to if something is not going right..
The text from here is largely just copied from the tutorial project, and may refer to the presence of the documentation within the project itself, and thus it probably makes the most sense to follow these directions after loading that project in any case, even if you're mostly reading along on the wiki here..
Data Table Tutorial
This tutorial provides an introduction to a variety of techniques for working with DataTables in Emergent. DataTables are one of the most important objects in the system, and can be used for a very wide range of different functions. Essentially, anything that requires storing and manipulating multiple items of data can benefit from a data table. Most of the functionality is exposed through the writing of Programs, so this also serves as a good tutorial for the gui-based programming system in emergent.
To return to this document at any time, just hit the ProjectDocs tab at the top of this middle panel where you are now reading.
Also, as you work with each documentation section, it is usually a good idea to do Object/Edit Dialog in the menu just above this text, which will open the documentation in a separate window that you can more easily come back to.
Some basic terminology:
- Left browser panel is the left portion of the window with a \"tree\" of objects in the simulation (inlcuding the network, and the input/output data, etc).
- Middle edit panel is where you are currently reading -- it can display different things depending on the selected tabs at the top, and what is currently selected in the left browser panel. The left-most tab usually shows what is selected in the browser, and the other tabs with \"pins\" down are locked in place and contain this document and the Wizard, which we will be making heavy use of. The right-most tab represents the configuration information for the 3D display shown in the right-most view panel (which is now called \"Frame1\" and is empty).
- Right view panel shows 3d displays of various simulation objects, incuding the network, input/output patterns, and graphs of results, etc.
Note: this tutorial is designed for version 4.0.17 or greater of Emergent! Some features may not work in earlier versions.
Overview of Tutorial
We begin by creating some data, which we then process in a variety of ways. Each step is described in a separate document object located in the docs section of the left browser panel, and linked here:
- DataTut DataGenDoc -- generating data, including basic datatable gui usage (required first step)
- DataTut DataAnalysisDoc -- analyzing data in various ways (grouping, stats, dimensionality reduction, etc)
- DataTut DataBaseDoc -- data-base style operations for modifying data in various ways
- DataTut DataCalcLoopDoc -- a very powerful mechanism for iterating through data tables row-by-row and performing various computations
- DataTut MatrixDataDoc -- matrix data cells: creating random bit patterns, and high-dimensional analysis tools.
Each document involves creating a separate Program with its own data tables, so after the first step of generating the data, each one can be independently explored in any order.
Personal tools
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- This page was last modified on 14 August 2008, at 06:23.
- This page has been accessed 763 times.
- About Emergent
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value 1 0=1;
val_type_fixed=0;
};
};
name="cond2";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 0;0;0;0;0;0;0;0;0;0;
1;1;1;1;1;1;1;1;1;1;
0;0;0;0;0;0;0;0;0;0;
1;1;1;1;1;1;1;1;1;1;
};
};
int_Data @[4] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="cycles";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 19;16;16;18;11;20;12;21;20;25;
18;35;27;28;23;31;36;26;25;30;
29;18;26;17;19;25;17;30;29;24;
33;30;33;28;21;34;34;19;31;29;
};
};
String_Data @[5] {
name="Cond1_lbl";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] "LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";
"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";
"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";
"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";
};
};
String_Data @[6] {
name="Cond2_lbl";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] "SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";
"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";
"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";
"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";
};
};
String_Data @[7] {
name="Cond_lbl";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] "LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";
"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";
"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";
"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";
};
};
};
data_flags=SAVE_ROWS|AUTO_CALC;
auto_load=NO_AUTO_LOAD;
auto_load_file=;
keygen 4 0=0;
};
};
DataTable_Group @.gp[1] {
name="OutputData";
el_typ=DataTable;
el_def=0;
};
DataTable_Group @.gp[2] {
name="AnalysisData";
el_typ=DataTable;
el_def=0;
};
};
data_proc {
name=;
el_typ=taDataProc;
el_def=0;
taDataProc @[0] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItemBase;
el_def=0;
UserDataItem @[0] {
name="NO_CLIP";
value 1 0=1;
val_type_fixed=0;
};
};
name="data_base";
};
taDataAnal @[1] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItemBase;
el_def=0;
UserDataItem @[0] {
name="NO_CLIP";
value 1 0=1;
val_type_fixed=0;
};
};
name="data_anal";
};
taDataGen @[2] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItemBase;
el_def=0;
UserDataItem @[0] {
name="NO_CLIP";
value 1 0=1;
val_type_fixed=0;
};
};
name="data_gen";
};
taImageProc @[3] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItemBase;
el_def=0;
UserDataItem @[0] {
name="NO_CLIP";
value 1 0=1;
val_type_fixed=0;
};
};
name="image_proc";
};
};
programs {
name=;
el_typ=Program;
el_def=0;
tags=;
desc=;
Program @[0] {
name="DataGenProg_";
short_nm="DGPrg_";
tags=;
desc=;
flags=;
objs {
name=;
el_typ=taNBase;
el_def=0;
};
types {
name=;
el_typ=DynEnumType;
el_def=0;
DynEnumType @[0] {
name="Cond1";
desc=;
enums {
name=;
el_typ=DynEnumItem;
el_def=0;
DynEnumItem @[0] {
name="LOC";
value=0;
desc="LOC";
};
DynEnumItem @[1] {
name="OBJ";
value=1;
desc="OBJ";
};
};
bits=0;
};
DynEnumType @[1] {
name="Cond2";
desc=;
enums {
name=;
el_typ=DynEnumItem;
el_def=0;
DynEnumItem @[0] {
name="SRC";
value=0;
desc="SRC";
};
DynEnumItem @[1] {
name="TRG";
value=1;
desc="TRG";
};
};
bits=0;
};
};
args {
name=;
el_typ=ProgVar;
el_def=0;
};
vars {
name=;
el_typ=ProgVar;
el_def=0;
ProgVar @[0] {
name="sse";
var_type=T_Real;
real_val=17.17617738004168;
objs_ptr=0;
flags=CTRL_PANEL|NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[1] {
name="batch";
var_type=T_Int;
int_val=10;
objs_ptr=0;
flags=CTRL_PANEL|NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[2] {
name="cond1";
var_type=T_Int;
int_val=2;
objs_ptr=0;
flags=CTRL_PANEL|NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[3] {
name="cond2";
var_type=T_Int;
int_val=2;
objs_ptr=0;
flags=CTRL_PANEL|NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[4] {
name="cycles";
var_type=T_Int;
int_val=29;
objs_ptr=0;
flags=CTRL_PANEL|NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[5] {
name="DataTable_0";
var_type=T_Object;
object_type=DataTable;
object_val=.projects[0].data.gp[0][0]$$;
objs_ptr=0;
flags=CTRL_PANEL|NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[6] {
name="Cond1_lbl";
var_type=T_DynEnum;
dyn_enum_val {
enum_type=.projects[0].programs[0].types[0]$$;
value=1;
};
objs_ptr=0;
flags=CTRL_PANEL|NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[7] {
name="Cond2_lbl";
var_type=T_DynEnum;
dyn_enum_val {
enum_type=.projects[0].programs[0].types[1]$$;
value=1;
};
objs_ptr=0;
flags=CTRL_PANEL|NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[8] {
name="Cond_lbl";
var_type=T_String;
string_val="OBJ_TRG";
objs_ptr=0;
flags=CTRL_PANEL|NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
};
functions {
name=;
el_typ=Function;
el_def=0;
};
load_code {
name=;
el_typ=ProgEl;
el_def=0;
};
init_code {
name=;
el_typ=ProgEl;
el_def=0;
};
prog_code {
name=;
el_typ=ProgEl;
el_def=0;
ResetDataRows @[0] {
desc=;
flags=;
data_var=.projects[0].programs[0].vars[5]$$;
};
ForLoop @[1] {
desc=;
flags=;
loop_code {
name=;
el_typ=ProgEl;
el_def=0;
ForLoop @[0] {
desc=;
flags=;
loop_code {
name=;
el_typ=ProgEl;
el_def=0;
ForLoop @[0] {
desc=;
flags=;
loop_code {
name=;
el_typ=ProgEl;
el_def=0;
AssignExpr @[0] {
desc=;
flags=;
result_var=.projects[0].programs[0].vars[6]$$;
expr {
expr="cond1";
};
};
AssignExpr @[1] {
desc=;
flags=;
result_var=.projects[0].programs[0].vars[7]$$;
expr {
expr="cond2";
};
};
AssignExpr @[2] {
desc=;
flags=;
result_var=.projects[0].programs[0].vars[8]$$;
expr {
expr="(String)Cond1_lbl + \"_\" + Cond2_lbl";
};
};
AddNewDataRow @[3] {
desc=;
flags=;
data_var=$.projects[0].programs[0].vars[5]$;
};
DataVarProg @[4] {
desc=;
flags=;
data_var=$.projects[0].programs[0].vars[5]$;
set_data=1;
row_spec=CUR_ROW;
row_var=NULL;
quiet=0;
var_1=.projects[0].programs[0].vars[2]$$;
var_2=.projects[0].programs[0].vars[3]$$;
var_3=.projects[0].programs[0].vars[1]$$;
var_4=NULL;
};
DataVarProg @[5] {
desc=;
flags=;
data_var=$.projects[0].programs[0].vars[5]$;
set_data=1;
row_spec=CUR_ROW;
row_var=NULL;
quiet=0;
var_1=$.projects[0].programs[0].vars[6]$;
var_2=$.projects[0].programs[0].vars[7]$;
var_3=$.projects[0].programs[0].vars[8]$;
var_4=NULL;
};
RandomCall @[6] {
desc=;
flags=;
result_var=.projects[0].programs[0].vars[4]$$;
object_type=Random;
method=Random::Gauss;
meth_args {
name=;
el_typ=ProgArg;
el_def=0;
ProgArg @[0] {
arg_type=double;
type="double";
name="stdev";
required=1;
def_val=;
expr {
expr="5";
};
};
};
};
RandomCall @[7] {
desc=;
flags=;
result_var=.projects[0].programs[0].vars[0]$$;
object_type=Random;
method=Random::Gauss;
meth_args {
name=;
el_typ=ProgArg;
el_def=0;
ProgArg @[0] {
arg_type=double;
type="double";
name="stdev";
required=1;
def_val=;
expr {
expr="2";
};
};
};
};
VarIncr @[8] {
desc=;
flags=;
var=$.projects[0].programs[0].vars[4]$;
expr {
expr="20 + cond1 * 4 + cond2 * 6 + cond1 * cond2 * 1";
};
};
VarIncr @[9] {
desc=;
flags=;
var=$.projects[0].programs[0].vars[0]$;
expr {
expr="10 + cond1 * 2 + cond2 * 3 + cond1 * cond2 * 1";
};
};
DataVarProg @[10] {
desc=;
flags=;
data_var=$.projects[0].programs[0].vars[5]$;
set_data=1;
row_spec=CUR_ROW;
row_var=NULL;
quiet=0;
var_1=$.projects[0].programs[0].vars[4]$;
var_2=$.projects[0].programs[0].vars[0]$;
var_3=NULL;
var_4=NULL;
};
};
init {
expr="batch = 0";
};
test {
expr="batch < 10";
};
iter {
expr="batch++";
};
};
};
init {
expr="cond2 = 0";
};
test {
expr="cond2 < 2";
};
iter {
expr="cond2++";
};
};
};
init {
expr="cond1 = 0";
};
test {
expr="cond1 < 2";
};
iter {
expr="cond1++";
};
};
};
step_prog=NULL;
step_n=1;
};
Program @[1] {
name="DataAnalProg";
short_nm="DtnPrg";
tags=;
desc=;
flags=;
objs {
name=;
el_typ=DataTable;
el_def=0;
DataTable @[0] {
name="grouped_data";
desc=;
data {
name="data";
el_typ=DataColTp;
el_def=0;
int_Data @[0] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="cond1_group";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[4] 0;0;1;1;};
};
int_Data @[1] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="cond2_group";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[4] 0;1;0;1;};
};
float_Data @[2] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="cycles_mean";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[4] 17.799999;27.9;23.4;29.200001;};
};
float_Data @[3] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="cycles_sem";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[4] 1.3316655;1.7156146;1.654623;1.6719913;};
};
float_Data @[4] {
name="sse_mean";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[4] 10.072459;12.76901;12.325922;15.545204;};
};
float_Data @[5] {
name="sse_sem";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[4] 0.39280418;0.37325189;0.80624425;0.53217852;};
};
String_Data @[6] {
name="Cond_lbl_first";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[4] "LOC_SRC";"LOC_TRG";"OBJ_SRC";"OBJ_TRG";};
};
};
data_flags=SAVE_ROWS|AUTO_CALC;
auto_load=NO_AUTO_LOAD;
auto_load_file=;
keygen 4 0=0;
};
DataTable @[1] {
name="float_cycles";
desc=;
data {
name="data";
el_typ=float_Data;
el_def=0;
float_Data @[0] {
name="sse";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 10.295033;8.8212519;11.638759;9.3775711;9.1099291;9.2620935;12.363664;9.7212934;8.9768114;11.158186;
11.761817;14.748217;14.096684;12.947308;11.987118;12.416912;11.329432;12.482451;11.766458;14.153706;
9.70755;13.669331;10.485395;15.666759;12.95701;7.3281188;11.931825;15.142352;13.604895;12.76599;
15.258935;11.164324;17.267067;15.677229;15.594991;16.044418;15.412886;16.000809;15.855208;17.176178;
};
};
int_Data @[1] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="batch";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 0;1;2;3;4;5;6;7;8;9;
0;1;2;3;4;5;6;7;8;9;
0;1;2;3;4;5;6;7;8;9;
0;1;2;3;4;5;6;7;8;9;
};
};
int_Data @[2] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="cond1";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 0;0;0;0;0;0;0;0;0;0;
0;0;0;0;0;0;0;0;0;0;
1;1;1;1;1;1;1;1;1;1;
1;1;1;1;1;1;1;1;1;1;
};
};
int_Data @[3] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="cond2";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 0;0;0;0;0;0;0;0;0;0;
1;1;1;1;1;1;1;1;1;1;
0;0;0;0;0;0;0;0;0;0;
1;1;1;1;1;1;1;1;1;1;
};
};
int_Data @[4] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="cycles";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 19;16;16;18;11;20;12;21;20;25;
18;35;27;28;23;31;36;26;25;30;
29;18;26;17;19;25;17;30;29;24;
33;30;33;28;21;34;34;19;31;29;
};
};
String_Data @[5] {
name="Cond1_lbl";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] "LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";
"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";
"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";
"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";
};
};
String_Data @[6] {
name="Cond2_lbl";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] "SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";
"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";
"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";
"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";
};
};
String_Data @[7] {
name="Cond_lbl";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] "LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";
"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";
"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";
"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";
};
};
float_Data @[8] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="cycles_flt";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 19;16;16;18;11;20;12;21;20;25;
18;35;27;28;23;31;36;26;25;30;
29;18;26;17;19;25;17;30;29;24;
33;30;33;28;21;34;34;19;31;29;
};
};
};
data_flags=SAVE_ROWS|AUTO_CALC;
auto_load=NO_AUTO_LOAD;
auto_load_file=;
keygen 4 0=0;
};
};
types {
name=;
el_typ=ProgType;
el_def=0;
};
args {
name=;
el_typ=ProgVar;
el_def=0;
};
vars {
name=;
el_typ=ProgVar;
el_def=0;
ProgVar @[0] {
name="DataTable_0";
var_type=T_Object;
object_type=DataTable;
object_val=$.projects[0].data.gp[0][0]$;
objs_ptr=0;
flags=CTRL_PANEL|NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[1] {
name="grouped_data";
var_type=T_Object;
object_type=DataTable;
object_val=.projects[0].programs[1].objs[0]$$;
objs_ptr=1;
flags=NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[2] {
name="float_cycles";
var_type=T_Object;
object_type=DataTable;
object_val=.projects[0].programs[1].objs[1]$$;
objs_ptr=1;
flags=NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[3] {
name="r";
var_type=T_Real;
real_val=0.4475246965885162;
objs_ptr=0;
flags=CTRL_PANEL|NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
};
functions {
name=;
el_typ=Function;
el_def=0;
};
load_code {
name=;
el_typ=ProgEl;
el_def=0;
};
init_code {
name=;
el_typ=ProgEl;
el_def=0;
};
prog_code {
name=;
el_typ=ProgEl;
el_def=0;
DataGroupProg @[0] {
desc=;
flags=;
src_data_var=.projects[0].programs[1].vars[0]$$;
dest_data_var=.projects[0].programs[1].vars[1]$$;
group_spec {
name="group_spec";
ops {
name=;
el_typ=DataGroupEl;
el_def=0;
DataGroupEl @[0] {
col_name="cond1";
agg {name="AggregateSpec_0": op=GROUP: rel={name="Relation_0": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: };
};
DataGroupEl @[1] {
col_name="cond2";
agg {name="AggregateSpec_0": op=GROUP: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: };
};
DataGroupEl @[2] {
col_name="cycles";
agg {name="AggregateSpec_0": op=MEAN: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: };
};
DataGroupEl @[3] {
col_name="cycles";
agg {name="AggregateSpec_0": op=SEM: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: };
};
DataGroupEl @[4] {
col_name="sse";
agg {name="AggregateSpec_0": op=MEAN: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: };
};
DataGroupEl @[5] {
col_name="sse";
agg {name="AggregateSpec_0": op=SEM: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: };
};
DataGroupEl @[6] {
col_name="Cond_lbl";
agg {name="AggregateSpec_0": op=FIRST: rel={name="": rel=LESSTHANOREQUAL: val=0: use_var=0: var=NULL: }: };
};
};
append_agg_name=1;
};
};
MethodCall @[1] {
desc=;
flags=;
result_var=NULL;
obj=.projects[0].programs[1].vars[2]$$;
method=taBase::CopyFrom;
meth_args {
name=;
el_typ=ProgArg;
el_def=0;
ProgArg @[0] {
arg_type=taBase_ptr;
type="taBase*";
name="cpy_from";
required=1;
def_val=;
expr {
expr="DataTable_0";
};
};
};
};
MethodCall @[2] {
desc=;
flags=;
result_var=NULL;
obj=$.projects[0].programs[1].vars[2]$;
method=DataTable::NewColFloat;
meth_args {
name=;
el_typ=ProgArg;
el_def=0;
ProgArg @[0] {
arg_type=const_taString_ref;
type="const taString&";
name="col_nm";
required=1;
def_val=;
expr {
expr="\"cycles_flt\"";
};
};
};
};
UserScript @[3] {
desc=;
flags=;
script {
expr="float_cycles[\"cycles_flt\"].CopyFromCol_Robust(float_cycles[\"cycles\"]);";
};
};
MathCall @[4] {
desc=;
flags=;
result_var=.projects[0].programs[1].vars[3]$$;
object_type=taMath_float;
method=taMath_float::vec_correl;
meth_args {
name=;
el_typ=ProgArg;
el_def=0;
ProgArg @[0] {
arg_type=const_float_Matrix_ptr;
type="const float_Matrix*";
name="vec";
required=1;
def_val=;
expr {
expr="DataTable_0[\"sse\"].ar";
};
};
ProgArg @[1] {
arg_type=const_float_Matrix_ptr;
type="const float_Matrix*";
name="oth_vec";
required=1;
def_val=;
expr {
expr="float_cycles[\"cycles_flt\"].ar";
};
};
};
};
};
step_prog=NULL;
step_n=1;
};
Program @[2] {
name="DataBaseProg";
short_nm="DBPrg";
tags=;
desc=;
flags=;
objs {
name=;
el_typ=DataTable;
el_def=0;
DataTable @[0] {
name="SelectedData";
desc=;
data {
name="data";
el_typ=DataColTp;
el_def=0;
float_Data @[0] {
name="sse";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[20] 10.295033;8.8212519;11.638759;9.3775711;9.1099291;9.2620935;12.363664;9.7212934;8.9768114;11.158186;
11.761817;14.748217;14.096684;12.947308;11.987118;12.416912;11.329432;12.482451;11.766458;14.153706;
};
};
int_Data @[1] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="batch";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[20] 0;1;2;3;4;5;6;7;8;9;
0;1;2;3;4;5;6;7;8;9;
};
};
int_Data @[2] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="cond1";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[20] 0;0;0;0;0;0;0;0;0;0;
0;0;0;0;0;0;0;0;0;0;
};
};
int_Data @[3] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="cond2";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[20] 0;0;0;0;0;0;0;0;0;0;
1;1;1;1;1;1;1;1;1;1;
};
};
int_Data @[4] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="cycles";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[20] 19;16;16;18;11;20;12;21;20;25;
18;35;27;28;23;31;36;26;25;30;
};
};
String_Data @[5] {
name="Cond1_lbl";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[20] "LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";
"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";
};
};
String_Data @[6] {
name="Cond2_lbl";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[20] "SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";
"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";
};
};
String_Data @[7] {
name="Cond_lbl";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[20] "LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";
"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";
};
};
};
data_flags=SAVE_ROWS|AUTO_CALC;
auto_load=NO_AUTO_LOAD;
auto_load_file=;
keygen 4 0=0;
};
DataTable @[1] {
name="CondData";
desc=;
data {
name="data";
el_typ=String_Data;
el_def=0;
String_Data @[0] {
name="Cond_lbl";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[4] "LOC_SRC";"LOC_TRG";"OBJ_SRC";"OBJ_TRG";};
};
float_Data @[1] {
name="cycle_off";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[4] 1;2;3;4;};
};
float_Data @[2] {
name="n_objs";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[4] 5;6;7;8;};
};
};
data_flags=SAVE_ROWS|AUTO_CALC;
auto_load=NO_AUTO_LOAD;
auto_load_file=;
keygen 4 0=0;
};
DataTable @[2] {
name="OnlySomeConds";
desc=;
data {
name="data";
el_typ=String_Data;
el_def=0;
String_Data @[0] {
name="Cond_lbl";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[3] "LOC_SRC";"LOC_TRG";"OBJ_SRC";};
};
float_Data @[1] {
name="cycle_off";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[3] 1;2;3;};
};
float_Data @[2] {
name="n_objs";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[3] 5;6;7;};
};
};
data_flags=SAVE_ROWS|AUTO_CALC;
auto_load=NO_AUTO_LOAD;
auto_load_file=;
keygen 4 0=0;
};
DataTable @[3] {
name="joinedDataTable";
desc=;
data {
name="data";
el_typ=DataColTp;
el_def=0;
float_Data @[0] {
name="sse";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 11.158186;9.3775711;8.9768114;8.8212519;9.2620935;9.7212934;11.638759;10.295033;12.363664;9.1099291;
12.947308;14.748217;14.096684;11.761817;11.987118;14.153706;11.766458;12.482451;11.329432;12.416912;
12.76599;13.604895;15.142352;11.931825;7.3281188;12.95701;15.666759;10.485395;13.669331;9.70755;
15.258935;17.176178;16.000809;15.412886;16.044418;15.594991;15.677229;15.855208;17.267067;11.164324;
};
};
int_Data @[1] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="batch";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 9;3;8;1;5;7;2;0;6;4;
3;1;2;0;4;9;8;7;6;5;
9;8;7;6;5;4;3;2;1;0;
0;9;7;6;5;4;3;8;2;1;
};
};
int_Data @[2] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="cond1";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 0;0;0;0;0;0;0;0;0;0;
0;0;0;0;0;0;0;0;0;0;
1;1;1;1;1;1;1;1;1;1;
1;1;1;1;1;1;1;1;1;1;
};
};
int_Data @[3] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="cond2";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 0;0;0;0;0;0;0;0;0;0;
1;1;1;1;1;1;1;1;1;1;
0;0;0;0;0;0;0;0;0;0;
1;1;1;1;1;1;1;1;1;1;
};
};
int_Data @[4] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="cycles";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 25;18;20;16;20;21;16;19;12;11;
28;35;27;18;23;30;25;26;36;31;
24;29;30;17;25;19;17;26;18;29;
33;29;19;34;34;21;28;31;33;30;
};
};
String_Data @[5] {
name="Cond1_lbl";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] "LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";
"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";
"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";
"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";
};
};
String_Data @[6] {
name="Cond2_lbl";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] "SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";
"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";
"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";
"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";
};
};
String_Data @[7] {
name="Cond_lbl";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] "LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";
"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";
"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";
"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";
};
};
float_Data @[8] {
name="cycle_off";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 1;1;1;1;1;1;1;1;1;1;
2;2;2;2;2;2;2;2;2;2;
3;3;3;3;3;3;3;3;3;3;
0;0;0;0;0;0;0;0;0;0;
};
};
float_Data @[9] {
name="n_objs";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 5;5;5;5;5;5;5;5;5;5;
6;6;6;6;6;6;6;6;6;6;
7;7;7;7;7;7;7;7;7;7;
0;0;0;0;0;0;0;0;0;0;
};
};
};
data_flags=SAVE_ROWS|AUTO_CALC;
auto_load=NO_AUTO_LOAD;
auto_load_file=;
keygen 4 0=0;
};
};
types {
name=;
el_typ=ProgType;
el_def=0;
};
args {
name=;
el_typ=ProgVar;
el_def=0;
};
vars {
name=;
el_typ=ProgVar;
el_def=0;
ProgVar @[0] {
name="DataTable_0";
var_type=T_Object;
object_type=DataTable;
object_val=$.projects[0].data.gp[0][0]$;
objs_ptr=0;
flags=CTRL_PANEL|NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[1] {
name="SelectedData";
var_type=T_Object;
object_type=DataTable;
object_val=.projects[0].programs[2].objs[0]$$;
objs_ptr=1;
flags=NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[2] {
name="CondData";
var_type=T_Object;
object_type=DataTable;
object_val=.projects[0].programs[2].objs[1]$$;
objs_ptr=1;
flags=NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[3] {
name="joinedDataTable";
var_type=T_Object;
object_type=DataTable;
object_val=.projects[0].programs[2].objs[3]$$;
objs_ptr=1;
flags=NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[4] {
name="OnlySomeConds";
var_type=T_Object;
object_type=DataTable;
object_val=.projects[0].programs[2].objs[2]$$;
objs_ptr=1;
flags=NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
};
functions {
name=;
el_typ=Function;
el_def=0;
};
load_code {
name=;
el_typ=ProgEl;
el_def=0;
};
init_code {
name=;
el_typ=ProgEl;
el_def=0;
};
prog_code {
name=;
el_typ=ProgEl;
el_def=0;
DataSelectRowsProg @[0] {
desc=;
flags=;
src_data_var=.projects[0].programs[2].vars[0]$$;
dest_data_var=.projects[0].programs[2].vars[1]$$;
select_spec {
name="select_spec";
ops {
name=;
el_typ=DataSelectEl;
el_def=0;
DataSelectEl @[0] {
col_name="Cond1_lbl";
on=1;
rel=EQUAL;
use_var=0;
cmp 9 0="LOC";
var=NULL;
enable_var=NULL;
};
};
comb_op=AND;
};
};
DataJoinProg @[1] {
desc=;
flags=;
src_data_var=$.projects[0].programs[2].vars[0]$;
dest_data_var=.projects[0].programs[2].vars[3]$$;
src_b_data_var=.projects[0].programs[2].vars[4]$$;
join_spec {
name="join_spec";
col_a {
col_name="Cond_lbl";
};
col_b {
col_name="Cond_lbl";
};
type=LEFT;
nomatch_warn=1;
};
};
};
step_prog=NULL;
step_n=1;
};
Program @[3] {
name="DataCalcLoopProg";
short_nm=;
tags=;
desc=;
flags=;
objs {
name=;
el_typ=DataTable;
el_def=0;
DataTable @[0] {
name="DataCalcDest";
desc=;
data {
name="data";
el_typ=DataColTp;
el_def=0;
float_Data @[0] {
name="sse";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 10.295033;8.8212519;11.638759;9.3775711;9.1099291;9.2620935;12.363664;9.7212934;8.9768114;11.158186;
11.761817;14.748217;14.096684;12.947308;11.987118;12.416912;11.329432;12.482451;11.766458;14.153706;
9.70755;13.669331;10.485395;15.666759;12.95701;7.3281188;11.931825;15.142352;13.604895;12.76599;
15.258935;11.164324;17.267067;15.677229;15.594991;16.044418;15.412886;16.000809;15.855208;17.176178;
};
};
int_Data @[1] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="batch";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 0;1;2;3;4;5;6;7;8;9;
0;1;2;3;4;5;6;7;8;9;
0;1;2;3;4;5;6;7;8;9;
0;1;2;3;4;5;6;7;8;9;
};
};
int_Data @[2] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="cond1";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 0;0;0;0;0;0;0;0;0;0;
0;0;0;0;0;0;0;0;0;0;
1;1;1;1;1;1;1;1;1;1;
1;1;1;1;1;1;1;1;1;1;
};
};
int_Data @[3] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="cond2";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 0;0;0;0;0;0;0;0;0;0;
1;1;1;1;1;1;1;1;1;1;
0;0;0;0;0;0;0;0;0;0;
1;1;1;1;1;1;1;1;1;1;
};
};
int_Data @[4] {
UserDataItem_List @*(.user_data_) {
name=;
el_typ=UserDataItem;
el_def=0;
UserDataItem @[0] {
name="NARROW";
value 1 0=1;
val_type_fixed=0;
};
};
name="cycles";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 19;16;16;18;11;20;12;21;20;25;
18;35;27;28;23;31;36;26;25;30;
29;18;26;17;19;25;17;30;29;24;
33;30;33;28;21;34;34;19;31;29;
};
};
String_Data @[5] {
name="Cond1_lbl";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] "LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";
"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";"LOC";
"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";
"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";"OBJ";
};
};
String_Data @[6] {
name="Cond2_lbl";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] "SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";
"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";
"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";"SRC";
"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";"TRG";
};
};
String_Data @[7] {
name="Cond_lbl";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] "LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";"LOC_SRC";
"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";"LOC_TRG";
"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";"OBJ_SRC";
"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";"OBJ_TRG";
};
};
float_Data @[8] {
name="calc_val";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[40] 195.60564;141.14003;186.22014;168.79628;100.20922;185.24187;148.36397;204.14716;179.53622;278.95465;
211.71271;516.18756;380.61047;362.5246;275.7037;384.92429;407.85953;324.54373;294.16144;424.61118;
281.51895;246.04794;272.62027;266.3349;246.1832;183.20297;202.84102;454.27057;394.54193;306.38376;
503.54486;334.92972;569.81323;438.9624;327.49481;545.51025;524.03809;304.01538;491.51147;498.10916;
};
};
};
data_flags=SAVE_ROWS|AUTO_CALC;
auto_load=NO_AUTO_LOAD;
auto_load_file=;
keygen 4 0=0;
};
};
types {
name=;
el_typ=ProgType;
el_def=0;
};
args {
name=;
el_typ=ProgVar;
el_def=0;
};
vars {
name=;
el_typ=ProgVar;
el_def=0;
ProgVar @[0] {
name="DataTable_0";
var_type=T_Object;
object_type=DataTable;
object_val=$.projects[0].data.gp[0][0]$;
objs_ptr=0;
flags=CTRL_PANEL|NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[1] {
name="DataCalcDest";
var_type=T_Object;
object_type=DataTable;
object_val=.projects[0].programs[3].objs[0]$$;
objs_ptr=1;
flags=NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
};
functions {
name=;
el_typ=Function;
el_def=0;
};
load_code {
name=;
el_typ=ProgEl;
el_def=0;
};
init_code {
name=;
el_typ=ProgEl;
el_def=0;
};
prog_code {
name=;
el_typ=ProgEl;
el_def=0;
MethodCall @[0] {
desc=;
flags=;
result_var=NULL;
obj=.projects[0].programs[3].vars[1]$$;
method=DataTable::Copy_NoData;
meth_args {
name=;
el_typ=ProgArg;
el_def=0;
ProgArg @[0] {
arg_type=const_DataTable_ref;
type="const DataTable&";
name="cp";
required=1;
def_val=;
expr {
expr="DataTable_0";
};
};
};
};
MethodCall @[1] {
desc=;
flags=;
result_var=NULL;
obj=$.projects[0].programs[3].vars[1]$;
method=DataTable::NewColFloat;
meth_args {
name=;
el_typ=ProgArg;
el_def=0;
ProgArg @[0] {
arg_type=const_taString_ref;
type="const taString&";
name="col_nm";
required=1;
def_val=;
expr {
expr="\"calc_val\"";
};
};
};
};
DataCalcLoop @[2] {
desc=;
flags=;
src_data_var=.projects[0].programs[3].vars[0]$$;
dest_data_var=$.projects[0].programs[3].vars[1]$;
src_cols {
name="src_cols";
el_typ=DataOpEl;
el_def=0;
DataOpEl @[0] {
col_name="cycles";
};
DataOpEl @[1] {
col_name="sse";
};
};
dest_cols {
name="dest_cols";
el_typ=DataOpEl;
el_def=0;
DataOpEl @[0] {
col_name="calc_val";
};
};
loop_code {
name=;
el_typ=ProgEl;
el_def=0;
DataCalcAddDestRow @[0] {
desc=;
flags=;
src_data_var=$.projects[0].programs[3].vars[0]$;
dest_data_var=$.projects[0].programs[3].vars[1]$;
};
DataCalcCopyCommonCols @[1] {
desc=;
flags=;
src_data_var=$.projects[0].programs[3].vars[0]$;
dest_data_var=$.projects[0].programs[3].vars[1]$;
only_named_cols=0;
};
AssignExpr @[2] {
desc=;
flags=;
result_var=.projects[0].programs[3].prog_code[2].dest_col_vars[0]$$;
expr {
expr="s_sse * s_cycles";
};
};
DataCalcSetDestRow @[3] {
desc=;
flags=;
src_data_var=$.projects[0].programs[3].vars[0]$;
dest_data_var=$.projects[0].programs[3].vars[1]$;
};
};
use_col_numbers=0;
src_col_vars {
name=;
el_typ=ProgVar;
el_def=0;
ProgVar @[0] {
name="s_cycles";
var_type=T_Int;
int_val=0;
objs_ptr=0;
flags=NULL_CHECK|LOCAL_VAR;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[1] {
name="s_sse";
var_type=T_Real;
real_val=0;
objs_ptr=0;
flags=NULL_CHECK|LOCAL_VAR;
reference=0;
desc=;
init_from=NULL;
};
};
dest_col_vars {
name=;
el_typ=ProgVar;
el_def=0;
ProgVar @[0] {
name="d_calc_val";
var_type=T_Real;
real_val=0;
objs_ptr=0;
flags=NULL_CHECK|LOCAL_VAR;
reference=0;
desc=;
init_from=NULL;
};
};
};
};
step_prog=NULL;
step_n=1;
};
Program @[4] {
name="MatrixDataProg";
short_nm=;
tags=;
desc=;
flags=;
objs {
name=;
el_typ=DataTable;
el_def=0;
DataTable @[0] {
name="ProtoData";
desc=;
data {
name="data";
el_typ=String_Data;
el_def=0;
String_Data @[0] {
name="Name";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[4] "0";"1";"2";"3";};
};
float_Data @[1] {
name="Input";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=1;
cell_geom{ 5;5;};
calc_expr {
expr=;
};
dim_names {
name=;
[2] ;;};
ar {
name=;
[5 5 4] 0;0;0;0;0;0;0;0;0;0;
1;0;1;0;1;0;0;1;0;0;
0;0;1;1;0;0;0;0;0;0;
0;1;0;0;0;0;0;0;1;0;
1;0;0;1;0;1;0;0;0;1;
0;0;0;0;0;1;0;0;1;1;
0;1;0;0;0;0;0;0;0;0;
0;0;1;0;1;0;0;1;0;1;
0;0;0;1;0;0;0;0;0;0;
1;0;0;0;1;0;1;0;0;0;
};
};
};
data_flags=SAVE_ROWS|AUTO_CALC;
auto_load=NO_AUTO_LOAD;
auto_load_file=;
keygen 4 0=0;
};
DataTable @[1] {
name="ItemData";
desc=;
data {
name="data";
el_typ=String_Data;
el_def=0;
String_Data @[0] {
name="Name";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[0] };
};
float_Data @[1] {
name="Input";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=1;
cell_geom{ 5;5;};
calc_expr {
expr=;
};
dim_names {
name=;
[2] ;;};
ar {
name=;
[5 5 0] };
};
};
data_flags=SAVE_ROWS|AUTO_CALC;
auto_load=NO_AUTO_LOAD;
auto_load_file=;
keygen 4 0=0;
};
DataTable @[2] {
name="ProtoDists";
desc=;
data {
name="data";
el_typ=String_Data;
el_def=0;
String_Data @[0] {
name="Name";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[4] "0";"1";"2";"3";};
};
float_Data @[1] {
name="0";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[4] 0;12;10;12;};
};
float_Data @[2] {
name="1";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[4] 12;0;10;10;};
};
float_Data @[3] {
name="2";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[4] 10;10;0;10;};
};
float_Data @[4] {
name="3";
col_flags=SAVE_ROWS|SAVE_DATA;
is_matrix=0;
cell_geom{ 1;};
calc_expr {
expr=;
};
dim_names {
name=;
[0] };
ar {
name=;
[4] 12;10;10;0;};
};
};
data_flags=SAVE_ROWS|AUTO_CALC;
auto_load=NO_AUTO_LOAD;
auto_load_file=;
keygen 4 0=0;
};
DataTable @[3] {
name="ItemDists";
desc=;
data {
name="data";
el_typ=DataColTp;
el_def=0;
};
data_flags=SAVE_ROWS|AUTO_CALC;
auto_load=NO_AUTO_LOAD;
auto_load_file=;
keygen 4 0=0;
};
};
types {
name=;
el_typ=ProgType;
el_def=0;
};
args {
name=;
el_typ=ProgVar;
el_def=0;
};
vars {
name=;
el_typ=ProgVar;
el_def=0;
ProgVar @[0] {
name="ProtoData";
var_type=T_Object;
object_type=DataTable;
object_val=.projects[0].programs[4].objs[0]$$;
objs_ptr=1;
flags=NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[1] {
name="ProtoDists";
var_type=T_Object;
object_type=DataTable;
object_val=NULL;
objs_ptr=1;
flags=NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[2] {
name="ItemData";
var_type=T_Object;
object_type=DataTable;
object_val=.projects[0].programs[4].objs[1]$$;
objs_ptr=1;
flags=NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
ProgVar @[3] {
name="ItemDists";
var_type=T_Object;
object_type=DataTable;
object_val=.projects[0].programs[4].objs[3]$$;
objs_ptr=1;
flags=NULL_CHECK|USED|EDIT_VAL;
reference=0;
desc=;
init_from=NULL;
};
};
functions {
name=;
el_typ=Function;
el_def=0;
};
load_code {
name=;
el_typ=ProgEl;
el_def=0;
};
init_code {
name=;
el_typ=ProgEl;
el_def=0;
};
prog_code {
name=;
el_typ=ProgEl;
el_def=0;
ResetDataRows @[0] {
desc=;
flags=;
data_var=.projects[0].programs[4].vars[0]$$;
};
ResetDataRows @[1] {
desc=;
flags=;
data_var=.projects[0].programs[4].vars[2]$$;
};
ResetDataRows @[2] {
desc=;
flags=;
data_var=.projects[0].programs[4].vars[1]$$;
};
MethodCall @[3] {
desc=;
flags=;
result_var=NULL;
obj=$.projects[0].programs[4].vars[0]$;
method=DataTable::AddRows;
meth_args {
name=;
el_typ=ProgArg;
el_def=0;
ProgArg @[0] {
arg_type=int;
type="int";
name="n";
required=0;
def_val="1";
expr {
expr="4";
};
};
};
};
MethodCall @[4] {
desc=;
flags=;
result_var=NULL;
obj=$.projects[0].programs[4].vars[0]$;
method=DataTable::InitValsToRowNo;
meth_args {
name=;
el_typ=ProgArg;
el_def=0;
ProgArg @[0] {
arg_type=Variant;
type="Variant";
name="col";
required=1;
def_val=;
expr {
expr="\"Name\"";
};
};
};
};
DataGenCall @[5] {
desc=;
flags=;
result_var=NULL;
object_type=taDataGen;
method=taDataGen::PermutedBinary_MinDist;
meth_args {
name=;
el_typ=ProgArg;
el_def=0;
ProgArg @[0] {
arg_type=DataTable_ptr;
type="DataTable*";
name="data";
required=1;
def_val=;
expr {
expr="ProtoData";
};
};
ProgArg @[1] {
arg_type=const_taString_ref;
type="const taString&";
name="col_nm";
required=1;
def_val=;
expr {
expr="\"Input\"";
};
};
ProgArg @[2] {
arg_type=int;
type="int";
name="n_on";
required=1;
def_val=;
expr {
expr="6";
};
};
ProgArg @[3] {
arg_type=float;
type="float";
name="dist";
required=1;
def_val=;
expr {
expr="10";
};
};
ProgArg @[4] {
arg_type=taMath::DistMetric;
type="taMath::DistMetric";
name="metric";
required=0;
def_val="taMath::HAMMING";
expr {
expr=;
};
};
ProgArg @[5] {
arg_type=bool;
type="bool";
name="norm";
required=0;
def_val="false";
expr {
expr=;
};
};
ProgArg @[6] {
arg_type=float;
type="float";
name="tol";
required=0;
def_val="0.0f";
expr {
expr=;
};
};
};
};
DataAnalCall @[6] {
desc=;
flags=;
result_var=$.projects[0].programs[4].vars[1]$;
object_type=taDataAnal;
method=taDataAnal::DistMatrixTable;
meth_args {
name=;
el_typ=ProgArg;
el_def=0;
ProgArg @[0] {
arg_type=DataTable_ptr;
type="DataTable*";
name="dist_mat";
required=1;
def_val=;
expr {
expr="ProtoDists";
};
};
ProgArg @[1] {
arg_type=bool;
type="bool";
name="view";
required=1;
def_val=;
expr {
expr="true";
};
};
ProgArg @[2] {
arg_type=DataTable_ptr;
type="DataTable*";
name="src_data";
required=1;
def_val=;
expr {
expr="ProtoData";
};
};
ProgArg @[3] {
arg_type=const_taString_ref;
type="const taString&";
name="data_col_nm";
required=1;
def_val=;
expr {
expr="\"Input\"";
};
};
ProgArg @[4] {
arg_type=const_taString_ref;
type="const taString&";
name="name_col_nm";
required=1;
def_val=;
expr {
expr="\"Name\"";
};
};
ProgArg @[5] {
arg_type=taMath::DistMetric;
type="taMath::DistMetric";
name="metric";
required=1;
def_val=;
expr {
expr="taMath::HAMMING";
};
};
ProgArg @[6] {
arg_type=bool;
type="bool";
name="norm";
required=0;
def_val="false";
expr {
expr=;
};
};
ProgArg @[7] {
arg_type=float;
type="float";
name="tol";
required=0;
def_val="0.0f";
expr {
expr=;
};
};
ProgArg @[8] {
arg_type=bool;
type="bool";
name="incl_scalars";
required=0;
def_val="false";
expr {
expr=;
};
};
};
};
DataProcCall @[7] {
desc=;
flags=;
result_var=NULL;
object_type=taDataProc;
method=taDataProc::ReplicateRows;
meth_args {
name=;
el_typ=ProgArg;
el_def=0;
ProgArg @[0] {
arg_type=DataTable_ptr;
type="DataTable*";
name="dest";
required=1;
def_val=;
expr {
expr="ItemData";
};
};
ProgArg @[1] {
arg_type=DataTable_ptr;
type="DataTable*";
name="src";
required=1;
def_val=;
expr {
expr="ProtoData";
};
};
ProgArg @[2] {
arg_type=int;
type="int";
name="n_repl";
required=1;
def_val=;
expr {
expr="6";
};
};
};
};
DataGenCall @[8] {
desc=;
flags=;
result_var=NULL;
object_type=taDataGen;
method=taDataGen::FlipBits;
meth_args {
name=;
el_typ=ProgArg;
el_def=0;
ProgArg @[0] {
arg_type=DataTable_ptr;
type="DataTable*";
name="data";
required=1;
def_val=;
expr {
expr="ItemData";
};
};
ProgArg @[1] {
arg_type=const_taString_ref;
type="const taString&";
name="col_nm";
required=1;
def_val=;
expr {
expr="\"Input\"";
};
};
ProgArg @[2] {
arg_type=int;
type="int";
name="n_off";
required=1;
def_val=;
expr {
expr="1";
};
};
ProgArg @[3] {
arg_type=int;
type="int";
name="n_on";
required=1;
def_val=;
expr {
expr="1";
};
};
};
};
};
step_prog=NULL;
step_n=1;
};
};
viewers {
name=;
el_typ=TopLevelViewer;
el_def=0;
MainWindowViewer @[0] {
name="Browser3";
m_data=.projects[0]$$;
visible=1;
m_is_root=0;
m_is_viewer_xor_browser=0;
m_is_proj_viewer=1;
m_is_dialog=0;
toolbars {
name=;
el_typ=ToolBar;
el_def=0;
ToolBar @[0] {
name="Application";
m_data=NULL;
visible=0;
lft=0;
top=0;
o=Horizontal;
};
};
frames {
name=;
el_typ=FrameViewer;
el_def=0;
tabBrowseViewer @[0] {
name="Tree";
m_data=NULL;
visible=1;
root_typ=LeabraProject;
root_md=NULL;
m_root=$.projects[0]$;
};
PanelViewer @[1] {
name="Panels";
m_data=NULL;
visible=1;
};
T3DataViewer @[2] {
name="T3Frames";
m_data=NULL;
visible=1;
frames {
name=;
el_typ=T3DataViewFrame;
el_def=0;
T3DataViewFrame @[0] {
name="DataTable_0";
m_data=NULL;
visible=1;
root_view {
name=;
m_data=NULL;
m_transform=NULL;
children {
name=;
el_typ=T3DataViewMain;
el_def=0;
GraphTableView @[0] {
name="GraphTableView_0";
m_data=$.projects[0].data.gp[0][0]$;
FloatTransform @*(.m_transform) {scale={x=1: y=1: z=1: }: rotate={x=0: y=0: z=1: rot=0: }: translate={x=1: y=0: z=0: }: };
children {
name=;
el_typ=GraphColView;
el_def=0;
GraphColView @[0] {
name="sse";
m_data=.projects[0].data.gp[0][0].data[0]$$;
m_transform=NULL;
visible=1;
fixed_range {fix_min=0: min=0: fix_max=0: max=19.0259: };
data_range {min=0: max=0: };
};
GraphColView @[1] {
name="batch";
m_data=.projects[0].data.gp[0][0].data[1]$$;
m_transform=NULL;
visible=1;
fixed_range {fix_min=0: min=0: fix_max=0: max=9: };
data_range {min=0: max=0: };
};
GraphColView @[2] {
name="cond1";
m_data=.projects[0].data.gp[0][0].data[2]$$;
m_transform=NULL;
visible=1;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
data_range {min=0: max=0: };
};
GraphColView @[3] {
name="cond2";
m_data=.projects[0].data.gp[0][0].data[3]$$;
m_transform=NULL;
visible=1;
fixed_range {fix_min=0: min=0: fix_max=0: max=1: };
data_range {min=0: max=0: };
};
GraphColView @[4] {
name="cycles";
m_data=.projects[0].data.gp[0][0].data[4]$$;
m_transform=NULL;
visible=1;
fixed_range {fix_min=0: min=12: fix_max=0: max=39: };
data_range {min=0: max=0: };
};
GraphColView @[5] {
name="Cond1_lbl";
m_data=.projects[0].data.gp[0][0].data[5]$$;
m_transform=NULL;
visible=1;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
data_range {min=0: max=0: };
};
GraphColView @[6] {
name="Cond2_lbl";
m_data=.projects[0].data.gp[0][0].data[6]$$;
m_transform=NULL;
visible=1;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
data_range {min=0: max=0: };
};
GraphColView @[7] {
name="Cond_lbl";
m_data=.projects[0].data.gp[0][0].data[7]$$;
m_transform=NULL;
visible=1;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
data_range {min=0: max=0: };
};
};
main_xform {scale={x=1: y=1: z=1: }: rotate={x=0: y=0: z=1: rot=0: }: translate={x=1: y=0: z=0: }: };
view_rows=10000;
view_range {min=0: max=39: };
display_on=1;
manip_ctrl_on=1;
graph_type=XY;
plot_style=POINTS;
negative_draw=0;
negative_draw_z=1;
line_width=2;
point_size=MEDIUM;
point_spacing=1;
bar_space=0.2;
label_spacing=-1;
width=1;
depth=1;
axis_font_size=0.05;
label_font_size=0.04;
x_axis {
name="GraphAxisView_0";
m_data=NULL;
m_transform=NULL;
on=1;
axis=X;
col_name="cycles";
fixed_range {fix_min=0: min=11: fix_max=0: max=36: };
color {name="black": r=0: g=0: b=0: a=1: desc="": };
data_range {min=11: max=36: };
range {min=11: max=36: };
n_ticks=10;
axis_length=1;
row_num=0;
};
z_axis {
name="GraphAxisView_1";
m_data=NULL;
m_transform=NULL;
on=0;
axis=Z;
col_name="cycles";
fixed_range {fix_min=0: min=12: fix_max=0: max=39: };
color {name="black": r=0: g=0: b=0: a=1: desc="": };
data_range {min=12: max=39: };
range {min=12: max=39: };
n_ticks=10;
axis_length=1;
row_num=0;
};
plot_1 {
name="GraphPlotView_0";
m_data=NULL;
m_transform=NULL;
on=1;
axis=Y;
col_name="sse";
fixed_range {fix_min=0: min=7.328119: fix_max=0: max=17.26707: };
color {name="black": r=0: g=0: b=0: a=1: desc="": };
data_range {min=7.328119: max=17.26707: };
range {min=7.328119: max=17.26707: };
n_ticks=10;
axis_length=1;
line_style=SOLID;
point_style=CIRCLE;
alt_y=0;
};
plot_2 {
name="GraphPlotView_1";
m_data=NULL;
m_transform=NULL;
on=0;
axis=Y;
col_name=;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
color {name="red": r=1: g=0: b=0: a=1: desc="": };
data_range {min=0: max=0: };
range {min=0: max=0: };
n_ticks=10;
axis_length=1;
line_style=SOLID;
point_style=SQUARE;
alt_y=0;
};
plot_3 {
name="GraphPlotView_2";
m_data=NULL;
m_transform=NULL;
on=0;
axis=Y;
col_name=;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
color {name="blue": r=0: g=0: b=1: a=1: desc="": };
data_range {min=0: max=0: };
range {min=0: max=0: };
n_ticks=10;
axis_length=1;
line_style=SOLID;
point_style=DIAMOND;
alt_y=0;
};
plot_4 {
name="GraphPlotView_3";
m_data=NULL;
m_transform=NULL;
on=0;
axis=Y;
col_name=;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
color {name="green3": r=0: g=0.8039216: b=0: a=1: desc="": };
data_range {min=0: max=0: };
range {min=0: max=0: };
n_ticks=10;
axis_length=1;
line_style=SOLID;
point_style=TRIANGLE;
alt_y=0;
};
plot_5 {
name="GraphPlotView_4";
m_data=NULL;
m_transform=NULL;
on=0;
axis=Y;
col_name=;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
color {name="purple": r=0.627451: g=0.1254902: b=0.9411765: a=1: desc="": };
data_range {min=0: max=0: };
range {min=0: max=0: };
n_ticks=10;
axis_length=1;
line_style=SOLID;
point_style=PLUS;
alt_y=0;
};
plot_6 {
name="GraphPlotView_5";
m_data=NULL;
m_transform=NULL;
on=0;
axis=Y;
col_name=;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
color {name="orange": r=1: g=0.6470588: b=0: a=1: desc="": };
data_range {min=0: max=0: };
range {min=0: max=0: };
n_ticks=10;
axis_length=1;
line_style=SOLID;
point_style=CROSS;
alt_y=0;
};
plot_7 {
name="GraphPlotView_6";
m_data=NULL;
m_transform=NULL;
on=0;
axis=Y;
col_name=;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
color {name="brown": r=0.6470588: g=0.1647059: b=0.1647059: a=1: desc="": };
data_range {min=0: max=0: };
range {min=0: max=0: };
n_ticks=10;
axis_length=1;
line_style=SOLID;
point_style=STAR;
alt_y=0;
};
plot_8 {
name="GraphPlotView_7";
m_data=NULL;
m_transform=NULL;
on=0;
axis=Y;
col_name=;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
color {name="chartreuse": r=0.4980392: g=1: b=0: a=1: desc="": };
data_range {min=0: max=0: };
range {min=0: max=0: };
n_ticks=10;
axis_length=1;
line_style=SOLID;
point_style=MINUS;
alt_y=0;
};
alt_y_1=0;
alt_y_2=0;
alt_y_3=0;
alt_y_4=0;
alt_y_5=0;
err_1 {
name="GraphPlotView_8";
m_data=NULL;
m_transform=NULL;
on=0;
axis=Y;
col_name=;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
color {name="black": r=0: g=0: b=0: a=1: desc="": };
data_range {min=0: max=0: };
range {min=0: max=0: };
n_ticks=10;
axis_length=1;
line_style=SOLID;
point_style=CIRCLE;
alt_y=0;
};
err_2 {
name="GraphPlotView_9";
m_data=NULL;
m_transform=NULL;
on=0;
axis=Y;
col_name=;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
color {name="black": r=0: g=0: b=0: a=1: desc="": };
data_range {min=0: max=0: };
range {min=0: max=0: };
n_ticks=10;
axis_length=1;
line_style=SOLID;
point_style=CIRCLE;
alt_y=0;
};
err_3 {
name="GraphPlotView_10";
m_data=NULL;
m_transform=NULL;
on=0;
axis=Y;
col_name=;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
color {name="black": r=0: g=0: b=0: a=1: desc="": };
data_range {min=0: max=0: };
range {min=0: max=0: };
n_ticks=10;
axis_length=1;
line_style=SOLID;
point_style=CIRCLE;
alt_y=0;
};
err_4 {
name="GraphPlotView_11";
m_data=NULL;
m_transform=NULL;
on=0;
axis=Y;
col_name=;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
color {name="black": r=0: g=0: b=0: a=1: desc="": };
data_range {min=0: max=0: };
range {min=0: max=0: };
n_ticks=10;
axis_length=1;
line_style=SOLID;
point_style=CIRCLE;
alt_y=0;
};
err_5 {
name="GraphPlotView_12";
m_data=NULL;
m_transform=NULL;
on=0;
axis=Y;
col_name=;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
color {name="black": r=0: g=0: b=0: a=1: desc="": };
data_range {min=0: max=0: };
range {min=0: max=0: };
n_ticks=10;
axis_length=1;
line_style=SOLID;
point_style=CIRCLE;
alt_y=0;
};
err_6 {
name="GraphPlotView_13";
m_data=NULL;
m_transform=NULL;
on=0;
axis=Y;
col_name=;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
color {name="black": r=0: g=0: b=0: a=1: desc="": };
data_range {min=0: max=0: };
range {min=0: max=0: };
n_ticks=10;
axis_length=1;
line_style=SOLID;
point_style=CIRCLE;
alt_y=0;
};
err_7 {
name="GraphPlotView_14";
m_data=NULL;
m_transform=NULL;
on=0;
axis=Y;
col_name=;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
color {name="black": r=0: g=0: b=0: a=1: desc="": };
data_range {min=0: max=0: };
range {min=0: max=0: };
n_ticks=10;
axis_length=1;
line_style=SOLID;
point_style=CIRCLE;
alt_y=0;
};
err_8 {
name="GraphPlotView_15";
m_data=NULL;
m_transform=NULL;
on=0;
axis=Y;
col_name=;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
color {name="black": r=0: g=0: b=0: a=1: desc="": };
data_range {min=0: max=0: };
range {min=0: max=0: };
n_ticks=10;
axis_length=1;
line_style=SOLID;
point_style=CIRCLE;
alt_y=0;
};
err_spacing=1;
err_bar_width=0.02;
color_mode=VALUE_COLOR;
color_axis {
name="GraphAxisView_2";
m_data=NULL;
m_transform=NULL;
on=0;
axis=Y;
col_name=;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
color {name="black": r=0: g=0: b=0: a=1: desc="": };
data_range {min=0: max=0: };
range {min=0: max=0: };
n_ticks=10;
axis_length=1;
row_num=0;
};
colorscale {
name="ColorScale_0";
chunks=133;
min=-1;
max=1;
range=0;
zero=0;
spec=.colorspecs[0]$$;
auto_scale=0;
};
raster_axis {
name="GraphAxisView_3";
m_data=NULL;
m_transform=NULL;
on=0;
axis=Y;
col_name=;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
color {name="black": r=0: g=0: b=0: a=1: desc="": };
data_range {min=0: max=0: };
range {min=0: max=0: };
n_ticks=10;
axis_length=1;
row_num=0;
};
thresh=0.5;
thr_line_len=0.48;
matrix_mode=SEP_GRAPHS;
mat_layout=BOT_ZERO;
mat_odd_vert=1;
two_d_font=0;
two_d_font_scale=350;
};
};
};
bg_color {r=0.8: g=0.8: b=0.8: a=1: };
text_color {r=0: g=0: b=0: a=1: };
headlight_on=1;
stereo_view=STEREO_NONE;
saved_views {
name=;
el_typ=T3SavedView;
el_def=0;
T3SavedView @[0] {
name="View 0";
view_saved=1;
pos {x=1.4275: y=0.4449999: z=1.376935: };
orient {x=0: y=0: z=1: rot=0: };
focal_dist=1.866935;
};
T3SavedView @[1] {
name="View 1";
view_saved=0;
pos {x=0: y=0: z=0: };
orient {x=0: y=0: z=1: rot=0: };
focal_dist=0;
};
T3SavedView @[2] {
name="View 2";
view_saved=0;
pos {x=0: y=0: z=0: };
orient {x=0: y=0: z=1: rot=0: };
focal_dist=0;
};
T3SavedView @[3] {
name="View 3";
view_saved=0;
pos {x=0: y=0: z=0: };
orient {x=0: y=0: z=1: rot=0: };
focal_dist=0;
};
T3SavedView @[4] {
name="View 4";
view_saved=0;
pos {x=0: y=0: z=0: };
orient {x=0: y=0: z=1: rot=0: };
focal_dist=0;
};
T3SavedView @[5] {
name="View 5";
view_saved=0;
pos {x=0: y=0: z=0: };
orient {x=0: y=0: z=1: rot=0: };
focal_dist=0;
};
};
};
T3DataViewFrame @[1] {
name="grouped_data";
m_data=NULL;
visible=1;
root_view {
name="T3DataViewRoot_0";
m_data=NULL;
m_transform=NULL;
children {
name=;
el_typ=T3DataViewMain;
el_def=0;
GraphTableView @[0] {
name="GraphTableView_1";
m_data=$.projects[0].programs[1].objs[0]$;
FloatTransform @*(.m_transform) {scale={x=1: y=1: z=1: }: rotate={x=0: y=0: z=1: rot=0: }: translate={x=1: y=0: z=0: }: };
children {
name=;
el_typ=GraphColView;
el_def=0;
GraphColView @[0] {
name="cond1_group";
m_data=.projects[0].programs[1].objs[0].data[0]$$;
m_transform=NULL;
visible=1;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
data_range {min=0: max=0: };
};
GraphColView @[1] {
name="cond2_group";
m_data=.projects[0].programs[1].objs[0].data[1]$$;
m_transform=NULL;
visible=1;
fixed_range {fix_min=0: min=0: fix_max=0: max=0: };
data_range {min=0: max=0: };
};
GraphColView @[2] {
name="cycles_mean";
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view_saved=0;
pos {x=0: y=0: z=0: };
orient {x=0: y=0: z=1: rot=0: };
focal_dist=0;
};
T3SavedView @[4] {
name="View 4";
view_saved=0;
pos {x=0: y=0: z=0: };
orient {x=0: y=0: z=1: rot=0: };
focal_dist=0;
};
T3SavedView @[5] {
name="View 5";
view_saved=0;
pos {x=0: y=0: z=0: };
orient {x=0: y=0: z=1: rot=0: };
focal_dist=0;
};
};
};
};
};
};
docks {
name=;
el_typ=DockViewer;
el_def=0;
ToolBoxDockViewer @[0] {
name="Tools";
m_data=NULL;
visible=1;
dock_flags=DV_MOVABLE|DV_FLOATABLE;
dock_area=1;
};
};
};
};
last_change_desc=;
networks {
name=;
el_typ=LeabraNetwork;
el_def=0;
};
};