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wxGUI Graphical Modeler

DESCRIPTION

       The  Graphical  Modeler  is  a  wxGUI component which allows the user to create, edit, and
       manage simple  and  complex  models  using  an  easy-to-use  interface.   When  performing
       analytical  operations  in GRASS GIS, the operations are not isolated, but part of a chain
       of operations. Using the Graphical Modeler, a chain of processes (i.e. GRASS GIS  modules)
       can  be  wrapped  into  one process (i.e. model). Subsequently it is easier to execute the
       model later on even with slightly different inputs or parameters.
       Models represent a programming technique used in GRASS GIS  to  concatenate  single  steps
       together  to  accomplish a task. It is advantageous when the user see boxes and ovals that
       are connected by lines and represent some tasks rather than seeing lines  of  coded  text.
       The  Graphical  Modeler  can  be  used  as a custom tool that automates a process. Created
       models can simplify or shorten a task which can be run many  times  and  it  can  also  be
       easily shared with others. Important to note is that models cannot perform specified tasks
       that one cannot also manually perform with GRASS  GIS.  It  is  recommended  to  first  to
       develop the process manually, note down the steps (e.g. by using the Copy button in module
       dialogs) and later replicate them in model.

       The Graphical Modeler allows you to:

           ·   define data items (raster, vector, 3D raster maps)

           ·   define actions (GRASS commands)

           ·   define relations between data and action items

           ·   define loops (e.g. map series) and conditions (if-else statements)

           ·   define model variables

           ·   parameterize GRASS commands

           ·   define intermediate data

           ·   validate and run model

           ·   save model properties to a file (GRASS Model File|*.gxm)

           ·   export model to Python script

           ·   export model to image file

   Main dialog
       The Graphical Modeler can be launched from  the  Layer  Manager  menu  File  ->  Graphical
       modeler   or   from  the  main  toolbar  .  It’s  also  available  as  stand-alone  module
       g.gui.gmodeler.

       The main Graphical Modeler menu contains options which enable the user  to  fully  control
       the  model.  Directly  under  the  main menu one can find toolbar with buttons (see figure
       below). There are options including (1) Create new model, (2) Load model  from  file,  (3)
       Save  current model to file, (4) Export model to image, (5) Export model to Python script,
       (6) Add command (GRASS modul) to model,  (7)  Add  data  to  model,  (8)  Manually  define
       relation  between  data  and  commands,  (9) Add loop/series to model, (10) Add comment to
       model, (11) Redraw model canvas, (12) Validate model, (13) Run model,  (14)  Manage  model
       variables, (15) Model settings, (16) Show manual, (17) Quit Graphical Modeler.

       Figure: Components of Graphical Modeler menu toolbar.

       There  is also a lower menu bar in the Graphical modeler dialog where one can manage model
       items, visualize commands, add or  manage  model  variables,  define  default  values  and
       descriptions.  The  Python  editor dialog window allows seeing workflows written in Python
       code. The rightmost tab of the bottom menu is automatically triggered when  the  model  is
       activated  and  shows all the steps of running GRASS modeler modules. In case of errors in
       the calculation process, it is written at that place.
       Figure: Lower Graphical Modeler menu toolbar.

   Components of models
       The workflow is usually established from four types of diagrams. Input and  derived  model
       data  are  usually represented with oval diagrams. This type of model elements stores path
       to specific data on the user’s disk. It is possible to insert vector  data,  raster  data,
       database  tables,  etc.   The  type of data is clearly distinguishable in the model by its
       color.  Different model elements are shown in the figures below.

           ·   (A) raster data:

           ·   (B) relation:

           ·   (C) GRASS module:

           ·   (D) loop:

           ·   (E) database table:

           ·   (F) 3D raster data:

           ·   (G) vector data:

           ·   (H) disabled GRASS module:

           ·   (I) comment:
       Figure: A model to perform unsupervised  classification  using  MLC  (i.maxlik)  and  SMAP
       (i.smap).

       Another example:
       Figure: A model to perform estimation of average annual soil loss caused by sheet and rill
       erosion using The Universal Soil Loss Equation.

       Example as part of landslide prediction process:
       Figure: A model to perform creation of parametric  maps  used  by  geologists  to  predict
       landslides in the area of interest.

EXAMPLE

       In this example the zipcodes_wake vector data and the elev_state_500m raster data from the
       North Carolina sample dataset (original raster and vector  data)  are  used  to  calculate
       average  elevation  for  every  zone.  The  important part of the process is the Graphical
       Modeler, namely its possibilities of process automation.

   The workflow shown as a series of commands
       In the command console the procedure looks as follows:
       # input data import
       r.import input=elev_state_500m.tif output=elevation
       v.import input=zipcodes_wake.shp output=zipcodes_wake
       # computation region settings
       g.region vector=zipcodes_wake
       # raster statistics (average values), upload to vector map table calculation
       v.rast.stats -c map=zipcodes_wake raster=elevation column_prefix=rst method=average
       # univariate statistics on selected table column for zipcode map calculation
       v.db.univar map=zipcodes_wake column=rst_average
       # conversion from vector to raster layer (due to result presentation)
       v.to.rast input=zipcodes_wake output=zipcodes_avg use=attr attribute_column=rst_average
       # display settings
       r.colors -e map=zipcodes_avg color=bgyr
       d.mon start=wx0 bgcolor=white
       d.barscale style=arrow_ends color=black bgcolor=white fontsize=10
       d.rast map=zipcodes_avg bgcolor=white
       d.vect map=zipcodes_wake type=boundary color=black
       d.northarrow style=1a at=85.0,15.0 color=black fill_color=black width=0 fontsize=10
       d.legend raster=zipcodes_avg lines=50 thin=5 labelnum=5 color=black fontsize=10

   Defining the workflow in the Graphical Modeler
       To start performing above steps as an automatic process with the Graphical  Modeler  press
       the   icon or type g.gui.gmodeler. The simplest way of inserting elements is by adding the
       complete GRASS command to the Command field  in  the  GRASS  command  dialog  (see  figure
       below).   With  full text search one can do faster module hunting. Next, the label and the
       command can be added. In case that only a module name  is  inserted,  after  pressing  the
       Enter  button,  the module dialog window is displayed and it is possible to set all of the
       usual module options (parameters and flags).

       Figure: Dialog for adding GRASS commands to model.

   Managing model parameters
       All used modules can be parameterized in the model. That causes launching the dialog  with
       input  options  for  model  after  the  model  is  run.  In  this  example,  input  layers
       (zipcodes_wake vector map and elev_state_500m raster map) are parameterized. Parameterized
       elements  show  their  diagram  border  slightly  thicker  than  those  of unparameterized
       elements.
       Figure: Model parameter settings.

       The final model, the list of all model items, and the Python code window with Save and Run
       option are shown in the figures below.
       Figure: A model to perform average statistics for zipcode zones.

       Figure: Items with Python editor window.

       For convenience, this model for the Graphical Modeler is also available for download here.

       The  model is run by clicking the Run button . When all inputs are set, the results can be
       displayed as shown in the next Figure:
       Figure: Average elevation for  ZIP  codes  using  North  Carolina  sample  dataset  as  an
       automatic calculation performed by Graphical Modeler.

   Managing model properties
       When one wants to run model again with the same data or the same names, it is necessary to
       use --overwrite option. It will cause maps with identical names to be overwritten. Instead
       of  setting  it  for  every  module  separately  it  is handy to change the Model Property
       settings globally.  This dialog includes also metadata settings, where model  name,  model
       description and author(s) of the model can be specified.
       Figure: Model properties.

   Defining variables
       Another  useful  trick is the possibility to set variables. Their content can be used as a
       substitute for other items. Value of variables can be values  such  as  raster  or  vector
       data,  integer,  float,  string  value or they may constitute some region, mapset, file or
       direction data type.  Then it is not necessary to set any parameters for input  data.  The
       dialog  with  variable settings is automatically displayed after model is run. So, instead
       of model parameters (e.g. r.import a v.import, see the  Figure  Run  model  dialog  above)
       there are Variables.
       Figure: Model with variable inputs.

       The key point is the usage of % before the substituting variable and settings in Variables
       dialog. For example, in case of a model variable raster that points to an input file  path
       and  which value is required to be used as one of inputs for a particular model, it should
       be specified in the Variables dialog with its respective name (raster), data type, default
       value and description. Then it should be set in the module dialog as input called %raster.
       Figure: Example of raster file variable settings.
       Figure: Example of raster file variable usage.

   Saving the model file
       Finally,  the model settings can be stored as a GRASS GIS Model file with *.gxm extension.
       The advantage is that it can be shared as a reusable workflow that  may  be  run  also  by
       other users with different data.

       For example, this model can later be used to calculate the average precipitation for every
       administrative region in Slovakia using the precip raster data from Slovakia precipitation
       dataset  and  administration boundaries of Slovakia from Slovak Geoportal (only with a few
       clicks).

   Handling intermediate data
       There can be some data in a model that did not exist before the process and that it is not
       worth  it  to  maintain  after  the  process  executes.  They  can  be  described as being
       Intermediate by single clicking using the right mouse button, see figure below.  All  such
       data  should be deleted following model completion. The boundary of intermediate component
       is dotted line.
       Figure: Usage and definition of intermediate data in model.

   Using the Python editor
       By using the Python editor in the Graphical Modeler one can add Python code and  then  run
       it  with  Run  button or just save it as a Python script *.py.  The result is shown in the
       Figure below:
       Figure: Python editor in the wxGUI Graphical Modeler.

   Defining loops
       In the example below the MODIS MOD13Q1 (NDVI) satellite data products are used in a  loop.
       The  original  data  are  stored as coded integer values that need to be multiplied by the
       value 0.0001 to represent real ndvi values. Moreover,  GRASS  GIS  provides  a  predefined
       color  table called ndvi to represent ndvi data.  In this case it is not necessary to work
       with every image separately.
       The Graphical Modeler is an appropriate tool to process data in  an  effective  way  using
       loop  and  variables  (%map  for a particular MODIS image in mapset and %ndvi for original
       data name suffix).  After the loop component is added to model, it is necessary to  define
       series of maps with required settings of map type, mapset, etc.
       Figure: MODIS data representation in GRASS GIS after Graphical Modeler usage.

       When  the  model  is supplemented by all of modules, these modules should be ticked in the
       boxes of loop dialog. The final model and its results are shown below.
       Figure: Model with loop.

       Figure: MODIS data representation in GRASS GIS after Graphical Modeler usage.

       The steps to enter in the command console of the Graphical Modeler would be as follows:
       # note that the white space usage differs from the standard command line usage
       # rename original image with preselected suffix
       g.rename raster = %map,%map.%ndvi
       # convert integer values
       r.mapcalc expression = %map = %map.%ndvi * 0.0001
       # set color table appropriate for nvdi data
       r.colors = map = %map color = ndvi

SEE ALSO

        wxGUI
       wxGUI components

       See also selected user models available from this SVN repository.

       See also the wiki page (especially various video tutorials).

AUTHORS

       Martin Landa, OSGeoREL, Czech Technical University in Prague, Czech Republic
       Various manual improvements by Ludmila Furkevicova, Slovak  University  of  Technology  in
       Bratislava, Slovak Republic

       $Date: 2017-11-11 19:59:24 +0100 (Sat, 11 Nov 2017) $

SOURCE CODE

       Available at: wxGUI Graphical Modeler source code (history)

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       © 2003-2019 GRASS Development Team, GRASS GIS 7.6.1 Reference Manual