Configuration

Introduction

In this section you will learn how to customize GAMS MIRO. As you saw previously when creating your first app, MIRO launches without any further configuration. However, you will find that there are a lot of configuration possibilities to adapt MIRO to your specific model.

The configuration is done via a graphical configuration interface, with which you can create plots and widgets or change certain settings with a few mouse clicks, visually supported by a live preview. In addition, the configuration can also be done manually via a JSON file. In fact, all the graphical Configuration Mode does is create this JSON file.

The Configuration Mode

This chapter shows how to configure MIRO using the graphical interface. For those who feel more comfortable writing JSON, section Configuration via JSON shows how to configure a MIRO app without using the graphical interface. However, we strongly recommend to use the Configuration Mode, especially when you just started using GAMS MIRO.

The Configuration Mode is directly accessible via GAMS Studio:

start the Configuration Mode

In order to launch the Configuration Mode via the command line, the environment variable MIRO_MODE=config needs to be set instead of MIRO_MODE=base. The other steps (see here) remain the same.

After starting, the following screen appears:

start the Configuration Mode
Visually, this mode is very similar to a classic MIRO app. The tabs on the left side lead to the following categories:

Note:

Changes made in the configuration are only effective after a restart of the MIRO application.

General settings

As the name suggests, general options can be set in this section (click on an item for more information).

General

Application title

This option allows you to customize the title of the application that is displayed in the browser window. If a $title has been defined in the GAMS model, this is used by default.

Application title

Color theme to use

Allows you to choose between MIRO light and dark mode as well as the dynamic adaptation of the theme based on the user's system settings.

Light mode:

Light mode

Dark mode:

Dark mode

Show log file in UI

Determine whether the GAMS log file is displayed in the section GAMS interaction or not.

GAMS Log file

Show listing (lst) file in UI

Determine whether the GAMS listing (lst) file is displayed in the section GAMS interaction or not.

GAMS Log file

Use custom MIRO log

You can write a custom MIRO log file and display this in MIRO. Per default, the GAMS log and listing (lst) files are visible in the GAMS interaction section. Besides these files generated by GAMS you can also write your own log (e.g. with the Put Writing Facility). If you already use your own report in your GAMS model, you can easily integrate it into MIRO with this option.

All you have to do is specify the name of this file. The file must be a text file and needs to be located in the MIRO working directory at the end of the model run in order to be included.

Tip:

In the general settings you can choose whether you want to see the GAMS log and/or the listing (lst) file in MIRO at all.

Example:
In this example we extend our transport model with a few lines of code for creating a report transport.dat:

File log /transport.dat/;
put log;
put '--------------------------------'/;
put '            Report'/;
put '--------------------------------'/;
put / / 'Transportation Model parameters' / /
    'Freight cost  ', f,
    @1#6, 'Plant capacity'/;
loop(i, put @3, i.tl, @15, a(i)/);
put /'Market demand'/;
loop(j, put @3, j.tl, @15, b(j)/);

put  / / 'Transportation Model Results' / / ;
loop((i,j), put i.tl, @12, j.tl, @24, x.l(i,j):8:4 /);

Now we want to integrate it into MIRO. In the Configuration Mode we specify the name of the report file in the general settings:

Configure a custom log file in MIRO

The next time the model is solved, this report is displayed in the GAMS interaction section in MIRO:

Configure a custom log file in MIRO
Note:

Be careful with logs that are not written with every GAMS run, but only if, for example, a data test in the model fails. MIRO does not know if a log belongs to the current or an earlier run. If a log file was specified, MIRO looks into the working directory and includes this file after each GAMS run.

Data validation

The MIRO log is particularly suitable for the validation of input data. Checking the consistency of input data and providing reports when inconsistencies are found is a critical factor in avoiding end-user frustration.

If a certain syntax is followed in the MIRO log, data found to be invalid can be marked directly above the respective input data sheet in MIRO:

MIRO log

This works as follows:
In the Pickstock model we create, as in the previous transport example, a reporting file. We want to inform the user when we detect invalid price data. We classify a price as incorrect if it is negative, i.e. < 0. Since the data validation should take place before solving the model, the code must be placed before the solve statement. If we find inconsistent data, we abort the execution with an error message.

* input validation
set error01(date, symbol);

error01(date, symbol) = price(date, symbol) < 0;

file log / miro.log /;
put '------------------------------------'/;
put '        Data validation'/;
put '------------------------------------'/;
if(card(error01),
  put log 'price:: No negative prices allowed!'/;
  loop(error01(date, symbol),
      put log / ' Symbol ' symbol.tl:4 ' has negative price at the date: ' date.tl:0;
    );
  abort "Data errors detected."
);
putclose log;

If the data validation log is integrated in MIRO, the following happens: If you now have a negative price in the input data and click solve, this negative price is detected in the data validation and the model run is aborted. Instead of remaining in the GAMS interaction section, MIRO now switches the view and displays the input table with the first validation error detected. The corresponding message specified in the log file is also displayed (see picture above).

The MIRO log file also displays further information about the incorrect data if defined accordingly:

MIRO log

This way, incorrect data can be quickly found and corrected by the user, before executing an expensive computation.

The syntax that must be used for MIRO to jump directly to the table with the incorrect data is as follows:

'symbolname:: Error message'

In the example above this was:

'price:: No negative prices allowed!'

The name of the symbol whose data is incorrect, followed by two colons, signals MIRO to jump to the table of the corresponding symbol if the model run is aborted. The specified error message that follows the colons is then displayed above the table.

Default scenario comparison mode

This option sets the default comparison method for scenarios (more information about scenario comparison can be found here). Regardless of the selected option, you can always switch between split view, tab view and pivot view within the MIRO application.

Generate graphs for each input sheet automatically (pivot tool)

If this option is activated, a pivot table is created for each input symbol in addition to the table. If an graph is configured for a symbol, this will be used instead.

Number of decimal places used for rounding numerical values

Defines the number of decimal places for numeric symbols in the MIRO application. This is a global setting and has an effect on all output tables, Big Data input tables and the table for loading scenarios in Hypercube Mode.

Note that this only applies to symbols that are treated as numerical values by MIRO. For example, elements of GAMS Sets may contain numbers, but they are not treated as such but as strings. These are therefore not affected by the maximum decimal place set.

Symbol display

Symbols that should not be displayed but can be used in graphs etc.

For some charts, it may be desirable to use additional data that should not be displayed in the user interface. Currently, only custom renderers support using data from more than one symbol (also hidden ones). Note that the data of hidden symbols belongs to a scenario just like visible symbols and is therefore also stored in the database. They are only hidden in the user interface.

Scalars that should not be displayed in the scalars table but can be used in graphs etc.

For some charts it may be desired to use additional (scalar) information. In the following example from the Pickstock model, the scalar output value "end of training phase" was integrated into the chart to mark the end of the training phase:

Scalar value in graph

In this case you may not want to see the value in the scalars table, but it is required for this chart. With this option you can do exactly that, i.e. use scalar values in charts that are otherwise not visible in MIRO.

Should all input widgets (slider, dropdown menu, etc.) be aggregated on a single tab?

If enabled, all scalar input widgets are displayed in the same tab and separated otherwise.

Logo

This option allows you to use your own logo for your MIRO application, which is displayed in the upper left corner.

App logo

README file

Create an individual start page

You have the possibility to integrate a README file into the MIRO application. Its content is displayed in the input section in the first tab and is therefore the first thing the user sees after starting the application. This tab can be used by the developer to introduce and explain the MIRO application or the underlying GAMS model before the user starts working with it.

README file

Usage:
The readme file must be a markdown or HTML document which is located in the model directory. The README option must be activated in the Configuration Mode. Three options are available:

  • README tab title (mandatory)
    With this option you can customize the title of the README tab in the input section. Default: README
  • README file name (mandatory)
    With this option you specify the name of the readme file that you want to display (minimum 4 characters).
  • Enable mathematical typesetting? (optional)
    In case you want to use mathematical notation in your README file, activate this checkbox. You can use inline math by wrapping your equations between $ signs. To render the equation centered on its own line, use two dollar signs: $$.

    Example:
    This integral $\def\f#1{f(#1)} \f{x} = \int_{-\infty}^\infty\hat \f\xi\,e^{2 \pi i \xi x}\,d\xi$ is rendered inline. is rendered as: This integral $\def\f#1{f(#1)} \f{x} = \int_{-\infty}^\infty\hat \f\xi\,e^{2 \pi i \xi x}\,d\xi$ is rendered inline.

    A list of all TeX functions supported by MIRO can be found here.

The readme file can be created or edited directly in Configuration Mode:

Readme editor
Note:

If you want to include images in your readme file, they must be located in the folder static_<modelname> in your model directory. In the markdown/HTML file, the path of the images used must be pre-fixed with "static_<modelname>".

Symbol configuration: Naming, ordering, grouping

Naming

Under the heading Naming, the names of the GAMS symbols displayed in MIRO can be adapted. This includes the labels of the tabs displayed for each symbol as well as the table column headers.

  • Specify symbol alias:
    Specification of the labels of the symbol tabs.
    Symbol alias
  • Specify column headers:
    To adjust a column header, the corresponding element must be deleted and replaced with a new one.
    Column headers
Ordering

Here you can adjust the tab order in which the symbols are to be displayed in MIRO. To do this, simply drag and drop the symbols into the desired order.

Ordering
Note:

If symbols have been grouped using the grouping option, only the individual symbols and not the configured groups are displayed here. If you want to change the position of a group, drag and drop the symbols that belong to this group to the desired position.

Tab grouping

Multiple tabs of input symbols (or output symbols) can be grouped together. All symbols in a group are either displayed in a separate tab or side by side on the same tab.

Input widgets can also be grouped. The order of the widgets can be changed directly when specifying the group members.
Note that adding or removing widgets does not automatically update the dropdown menus for selecting group members. You must restart the Configuration Mode for these changes to be reflected here.

Example:
For the input data of the transport model, one might want to group the symbols with geodata together:

Specify input groups

Which is displayed in the MIRO App as follows:

Input group in MIRO

In some situations you may want to run a model and use the results of the run as input for the next run. Such an iterative process is possible by creating a link between the output and input symbol. By doing so, input data for a symbol ("target") can be populated from an output sheet ("source").

Populating an input table from the linked dataset can be initiated by clicking the Symbol link button button.

The following rules must be observed when configuring symbol links:

  • The source symbol needs to be an output symbol
  • The number of dimensions of "source" and "target" symbol must be identical
  • The header types (string / numeric) of linked symbols need to be identical
  • Neither "source" nor "target" may be a table in which scalar values are grouped. This includes the input scalars table, the output scalars table and the table in which scalar variables and equations are summarized.
  • An output dataset can only link to a single input table
  • An input table can be populated by different output datasets
    Rules for symbol links

Scenario & data

Activate local data upload module?

If enabled, scenario data can be loaded via local files.

Import local data
Use a default scenario that will be loaded on startup?

Allows you to pre-populate tables and widgets with data when starting MIRO. If this option is set and a scenario name is specified, MIRO looks for the corresponding scenario in the database at the application startup and automatically loads all data into the interface. In case always the same input data is used as base data, the step of data import into MIRO can be skipped with this option.

Include a metadata sheet in the Excel file (when exporting a scenario)?

The metatdata sheet contains information about the user name, the scenario name and the time the scenario was created.

Metadata sheet
Include empty sheets in the Excel file?

This option specifies whether empty sheets should be included when exporting a scenario. Sheets can be empty e.g. when a scenario contains only input data (has not been solved).

Should users be allowed to add attachments to scenarios?

Enables to save file attachments for scenarios. Read more about this in the File attachments section here.

Duration the GAMS log and lst files are stored in the database (in days)

If you want to save the log and lst files of individual GAMS jobs, you can use this option to specify how long the files should be saved. 0 means files are not stored at all, 999 means files are stored indefinitely. This setting is ignored when the attachment module is not active. Note that this is currently only supported in the MIRO Base Mode.

Output attachments

Output attachments allow you to automatically attach files after a GAMS run has been successfully completed. This lets you store additional results alongside the ones displayed in the UI. In addition, MIRO gives you the option to choose what should happen when an attachment is not found after a successful GAMS run. Should the user be informed of this by an error message or should it be silently ignored. You can also choose to automatically extract the attachment to the working directory when you start another GAMS run with this scenario. This function adds a state to your scenarios. Per default, scenarios in MIRO are stateless. This means that running two consecutive GAMS runs with the same input data will result in the same output data. By adding an attachment to the scenario that GAMS is allowed to read on consecutive executions, the results may no longer be the same.

Let's have a look at a simple (toy) example:

$if exist report.put $abort "Report was found"

File report;
put report;
put "This is a report."
putclose;

When you run this GAMS model for the first time, it creates a file report.put. When running it a second time, it aborts with a compilation error: "Report was found". If you add the file report.put as an output attachment and select to extract it into the working directory whenever you run this scenario again, your scenario will carry a state. This gives you the possibility to implement processes of any complexity in your MIRO application.

Note:

When this option is activated, additional files will be saved to each solved scenario. You should make sure that the disk space used does not become excessive (e.g. by limiting the output written to the lst file).

Note:

Output attachments are not supported in Hypercube Mode.

Computation

Allow access to the temporary directory?

This option specifies whether all temporarily created files of the model run (like solution reports or the lst and log files) can be accessed by the user. If set to true, the files can be downloaded individually or as a ZIP archive with a click on the Download temporary files button.

Output data screen - scalars
Save trace file with each GAMS run (Hypercube Mode)

The trace file gives performance information about a GAMS run. For performance analysis with the analysis tool PAVER - which is integrated into the MIRO Hypercube Mode - this option needs to be activated. More on the trace file in general can be found here.

Specify extra command line arguments that GAMS will be called with

With this option, command line arguments can be specified that are set for each GAMS run that is started from MIRO. This can be GAMS options like limrow=10 or threads=4 but also custom double-dash parameters. Note, that the these command line arguments are static; they can not be changed by the user from MIRO.
For more information about command line parameters that can be set from MIRO using widgets like sliders, dropdown menus, checkboxes etc. click here.

Analysis scripts

MIRO allows you to run custom analysis scripts in both Base and Hypercube Mode. These scripts have to generate HTML output which will be displayed in the interface once the script finished. Scripts need to be placed in a folder called scripts_<modelname>; this is also where MIRO executes them (important when you use relative paths in your script).

Note:

All software needed to run the script must be installed on the client's machine and added to the PATH, as it is not part of a MIRO application.

Base Mode

In the Base Mode, your analysis scripts will appear as a new tab in the output data section. When the user runs your script, MIRO saves the data of the current scenario in a gdx file called data.gdx in: scripts_<modelname>. Your script can then read and analyze this data and produce output in the form of a single HTML file. This file will be loaded and displayed in MIRO once your script finished. Script results are stored in the database as part of a scenario. This means that when you load a scenario (either to compare it with other scenarios or to modify it), the script results are loaded as well.

Please have a look at the following example of an analysis script in Hypercube Mode to learn how to include a script using the Configuration Mode.

Hypercube Mode

In the Hypercube Mode, custom scripts can be used to run analyses on multiple scenarios at once. Let's consider the example that comes with the pickstock model: a Jupyter Notebook that tries to answer high-level questions like "How many training days should I use" and "How many stocks should I choose". You can find this script in scripts_pickstock/hcube_analysis.ipynb. First, we have to tell MIRO that we want to use a custom analysis script. The Configuration Mode allows us to set everything up:

Hypercube analysis script configuration

Each script has to have a unique identifier. The identifier is only used internally and should not be changed. What's displayed in the user interface is the script alias. The command to run is "jupyter", because we want to run a Jupyter Notebook. Note that in order for this to work, Jupyter needs to be installed on the client's machine and added to the PATH. Jupyter allows us to run and convert our notebook to HTML. In order to do so, we specify the relevant arguments. The resulting call would be: jupyter nbconvert --to html --execute hcube_analysis.ipynb.

Now that we set everything up, we can start testing our script. We select a few scenarios that we want to analyze, and click on "Analyze". The following dialog opens:

Dialog to select Hypercube analysis script

We select the script we want to run and click on "Start script". MIRO will now generate GDX containers with the data for each scenario you selected to be analyzed and store them inside: scripts_<modelname>. Additionally, MIRO writes a file: hcube_file_names.txt. This file simply lists the filenames of the gdx containers to be analyzed (separated by a newline). Once the script finished, the results are read by MIRO and displayed:

Hypercube analysis script results

Configure tables

In this section you can customize the general configuration of input and output tables (global settings) as well as the individual settings of multi-dimensional symbol tables.

Input tables (global settings)

Height of input tables (in px)

Sets the height of the table. If the table content is higher than the configured height, a scroll bar is displayed.

Restrict editing of tables? If activated, tables can not be modified by the user.

Sets all input tables to read-only. No manual edits can be made by the user. Database scenarios or local data can still be loaded.

Default column stretching

Sets the general stretch behavior of columns. Available options are no stretch, stretch last column and stretch all columns. Stretched columns mean that the table always fills the entire window width. With large tables, this can have a negative effect on the performance of the application. Columns that are not set to stretch have a default column with of 200px.

Width of a single (non-streched) column.

Here, the default column with of 200px can be adjusted. It is recommended not to choose column widths that are too small, otherwise the table contents may not be displayed correctly.

Enable manual column resizing

Enables/disables the manual setting of the column width by the user.

Manual column resizing
Enable table context menu (accessible via right mouse click)

The context menu (accessible by right-clicking in the table) allows you to access various functions, such as adding or deleting a table row, or undoing the last modifications.

Context menu

Output tables (global settings)

Table style

Output tables can be changed regarding their design. Different designs are available. Example:

Table style: display (default)
Table style: display (default)
Table style: compact
Table style: compact
Include column filters

Table filters can be used to search each table column individually for entries. If there are strings in the column, the filter is a text search. For numeric values, a numeric range can be specified as the filter with the help of a slider. The filters can be displayed above or below the table or can be deactivated completely.

Table filter
Number of items to display per page

Specifies the default value of how many rows should be displayed per table sheet. The value can be changed by the user from the MIRO application.

Numer of items to show
Show row numbers?

Determines whether line numbers should be displayed or not.

Select buttons to use

The export buttons copy, CSV, Excel, PDF and print are available. Additionally, you can hide/show individual columns with the column visibility button.

Note:

Please note that only the currently visible data is exported! If a symbol contains multiple table pages, using a button only affects the currently visible table page! It is recommended to use the native export functionality of MIRO instead.

Export buttons

Symbol tables (symbol specific settings)

By default, every cell in an input table is editable and rows can be inserted and removed. If you only want to allow your users to edit certain tables or even only certain columns within a table, you can customize the visualization for the underlying GAMS parameter. You can also use a different type of table to enter your data. For example, if you have large amounts of data (>100,000 records) in your table, we recommend using either the Big Data Table or the pivot table. In addition, input tables can be annotated with Markdown syntax. This way you can give your users additional information, e.g. what kind of data should be entered here and in which format.

Also global table settings for output symbols can be overwritten for each individual symbol. The available options are almost identical. The only additional settings are the number of decimal places to display and an option to disable graphics for the symbol.

Note that in contrast to the global settings, a symbol-specific configuration needs to be applied by clicking on the corresponding save-button!

Available options for an input symbol table (type: Default):

This is the standard table in MIRO, which works similar to a spreadsheet.

Should table be read-only?

This option prevents the user from making manual changes in the input table of a scenario. If active, the table can only be filled with scenario data from the database/local files.

Select a column to pivot

This option allows you to pivot a table column of a symbol, i.e. to have a new column from each of the elements in this column:

Pivot column

Unlike the table statement in the GAMS model itself (Table symbolname;), this option does not affect the data contract between GAMS and MIRO. Hence, the columns of the symbol remain variable, since the symbol itself is communicated in list format. The pivoting of the configured column then happens "live" in the MIRO application.
Variable columns do not only mean that the column elements can change their names. It is also possible to add or delete whole columns. On the GAMS model side, this is equivalent to adding or deleting a set element of the affected index.

Note:

Instead of pivoting a single column, the MIRO pivot table can be used in case you want to interactively filter, pivot or aggregate columns.

When using the pivot option, note the following:

  • The table columns displayed with the pivot option can no longer be sorted alphabetically or numerically.
  • Furthermore, it is not possible to add or delete columns in the symbol table if it is either read-only or displayed as a heatmap.

Now let's look at a case where we should probably avoid using this pivot functionality: For this we look at the parameter Price of the Pickstock model:

Set date   'date'
    symbol 'stockSymbol';

$onExternalInput
Parameter price(date,symbol) 'Price';
$offExternalInput
If we decide to pivot the set symbol, we would end up with a table where all stock symbols (AAPL, AXP, BA, etc.) are displayed in a separate column:

Visualization of expanded table

A table width quickly increases when the number of set elements of the header index gets large. A total of - for example - 30 stocks to be represented in a table would lead to 30 additional columns. In this case, the list view might be more clear.

Select columns to be readonly

This option prevents the user from making manual changes in selected columns of the input table. Note that this option is only available for table columns that have not been pivoted (see option above).

Heatmap
Hide indexing column?

Option to hide indexing column in input tables.

Turn table into a heatmap?

Turns the table into a heatmap.

Heatmap

Available options for an input symbol table (type: Big Data Table):

This table type is intended for situations in which large amounts of data are to be displayed within a table. Instead of a long, 1-page table, a multi-page table is displayed. Only the data currently visible on the client side is loaded in this table.

Big Data table
Should table be read-only?

This option prevents the user from making manual changes in the input table of a scenario. If active, the table can only be filled with scenario data from the database/local files.

Select a column to pivot

Available options for an input symbol table (type: MIRO Pivot Table):

This table type is also capable of handling large amounts of data (> 1 million records). It allows you to interactively filter, pivot or aggregate data. See here for more information about the MIRO Pivot Table. The configuration of this table can be done directly from the preview on the right side.

Note:

The MIRO Pivot Table has no concept of "order" because the table can be sorted interactively by changing the order of the rows. When you add or change UELs, they are always appended to the underlying data source. So when MIRO passes the data to GAMS, you cannot rely on the order of the elements coming from this table type. If you need UELs in a certain order, you should either use one of the other input table types or declare the sets you need sorted as additional external input (instead of using the implicit set definition).

Available Options for an output symbol table:

Number of decimal places to display

Adjusts the number of decimal places to display for table columns with numeric values.

Only display the table and no graphic for this symbol

If no specific graphic is configured for an output symbol, a pivot table is always displayed alongside the table per default. With this option, you can prevent the display of any graphics, so that the symbol is only visualized as a table.

For all other options please consult the section for global table settings.

Configure widgets

Input widgets can be used instead of tables to enter scalar input data. Examples of such widgets include: sliders, dropdown menus, date selectors or checkboxes. In the configure widgets section of the Configuration Mode you can configure widgets for:

The section Configure widgets looks as follows:

Configuration of input widgets and tables

The symbol type to be configured can be selected at the top. The options are:

  • Symbol
    This option must be selected if we want to configure a GAMS symbol: scalars and 1-dimensional (singleton) sets.
    Furthermore, already configured GAMS options and double dash parameters are listed here, so that the existing configuration can be changed / removed.

    Note:

    After configuring a widget, the corresponding scalar is automatically removed from the Scalars table and rendered as a widget instead.

  • New GAMS option / New double dash parameter
    As the name suggests, GAMS options and double-dash parameters can be configured here as an input widget.
    Since those parameters are specified via the command line in GAMS, they can not be tagged in the GAMS code with $onExternalInput / $offExternalInput . So that they can be set for a GAMS run via MIRO, they must be explicitly specified in the configuration.

    If a GAMS option or a double dash parameter is set via MIRO, your GAMS model will automatically be called with these command line parameters. The values the user selected are therefore available to the GAMS model at compile time.

    Tip:

    The GAMS options and double-dash parameters associated with a scenario are stored in MIRO's internal database. However, the information about them is lost when a scenario is exported as a GDX file. We therefore recommend using singleton sets instead of command line parameters. Both the element label and the element text of a set can be migrated to compile time variables using the Dollar Control Options eval.Set, evalGlobal.Set, and evalLocal.Set.

    Read more here.

    Example:
    If we set the double dash parameter numberPeriods to 11 and for the GAMS option mip we select CPLEX from within MIRO, then the GAMS model is run with:

    gams <modelname>.gms --numberPeriods=11 MIP=CPLEX

    Warning:

    The following GAMS options are reserved by GAMS MIRO and can not be configured: idir1, idir2, curdir, trace, traceopt

The following widgets are available for scalar values:
Click on a widget to get more information

Slider

Slider

Slider range

Slider range

Checkbox

Checkbox

Dropdown menu

Dropdown menu

Multi-dropdown menu

Multi-dropdown menu

Date selector

Date selector

Date range selector

Date range selector

Text input

Text input

Numerix box

Numeric input

Example:
Let's now configure a widget for our transport model. For the scalar value freight a slider is a good choice.
From the list of available symbols we choose the desired scalar, select slider as widget tye and give it a label, which then appears above the slider.
Next, we specify the upper and lower bounds of the slider, the default value as well as the step size.

Configuration of a slider (1)

When we are satisfied with the selected configuration, we click "save". A confirmation that the configuration has been updated successfully will appear:

Successful update of widget configuration

Done!

Configure graphs

Besides the default tabular format, multidimensional GAMS symbols can be visualized as graphs. GAMS MIRO offers comprehensive visualization options. A lot of plotting types are available and only need to be configured, i.e. adapted to your model-specific data.

Advanced: Symbol dimensions

The following section shows some examples for different chart types you can use in MIRO. It is not useful to describe all possible options MIRO supports. Instead, you should just explore it yourself! Start the Configuration Mode and play around with the data until you get a satisfactory result.

Pie chart

Pie chart

Donut chart

Donut chart

Bar chart

Bar chart

Scatter plot

Scatter plot

Line chart

Line chart

Bubble chart

Bubble chart

Histogram

Histogram

Time series diagram

Time series diagram

Map

Map

MIRO Pivot Table

MIRO Pivot Table

Gantt chart

Gantt chart

Valuebox

Valuebox

Custom renderer

Custom renderer

Tip:

To create graphics that are perfectly tailored to your data and not available in MIRO yet, you can also use MIRO's R-API. More information can be found here.

Database management

Create database backups, restore a database, remove database tables.

Warning:

Deleting data from the database cannot be undone!

Database management

Options not available in Configuration Mode

With the Configuration Mode we pursue the goal that a user can completely configure a MIRO application without having to write any JSON code. However, there are a few advanced options that are not yet available in the Configuration Mode and will have to be configured manually when desired. The following section describes these options.

Remote data import and export

A MIRO application can be supplied with data from various sources:

  • Internal database: Import existing scenario data
  • Local files: GDX, Excel
  • Manually entered data

In addition to these default interfaces, the data can also come from or go to external sources.

This MIRO data connector is implemented as a REST service. To set up a new external data source, you have to edit the <modelname>.json file which is located in the directory: conf_<modelname>. The following example shows how to set up an external data source to populate the symbol price:

"remoteImport": [
    {
    "name": "Importer",
      "templates": [
        {
          "symNames": "price",
          "url": "http://127.0.0.1:5000/io",
          "method": "GET",
          "authentication": {
            "username": "@env:API_USER",
            "password": "@env:API_PASS"
          },
          "httpBody": [
            {
              "key": "filename",
              "value": "/Users/miro/Documents/importer/pickstock/price_data.csv"
            }
          ]
        }
      ]
    }
  ]

The configuration of external data sources is template-based. This means that you can specify one and the same template for multiple input symbols. First, we have to specify which input symbols this template should apply to. Then we have to tell MIRO about the endpoint to connect to (in this case a server running on localhost on port 5000). Supported protocols are HTTP and HTTPS.

When the user requests to import data via this data source "Importer", the resulting HTTP request looks as follows: GET /io?filename=%2FUsers%2Fmiro%2FDocuments%2Fimporter%2Fpickstock%2Fprice_data.csv&modelname=pickstock&dataset=price HTTP/1.1. When observing the request, you will notice that in addition to the filename key that you specified, MIRO sends two more key-value pairs: modelname and dataset set to the name of the model and dataset being requested. These will always be appended to the body provided by you.

MIRO waits for the REST endpoint to respond with the requested dataset. Before we talk about the format in which MIRO expects the data, let's look at how the data is exported from MIRO to a remote destination. The format MIRO exports data is the same as data should be sent to MIRO!

One more aspect worth talking about is authentication. When your API is not running on the local machine, but a remote server, you might want to provide credentials with your request. In the example above, we provided the username and password used to authenticate to the API. However, instead of hardcoding these credentials in the configuration file (which is possible), it is recommended to use the special prefix @env: that instructs MIRO to read the credentials from environment variables. Currently, the only supported authentication method is basic access authentication. In case you need another method, send us an email or a pull request!

Warning:

When using basic access authentication, username and password are sent in plain text! Therefore, you have to use HTTPS instead of HTTP to provide confidentiality!!

Instead of importing data from a remote data source, you can also export data to a remote destination. In this example the data of the symbol stock_weight should be exported to a CSV file: export_stock_weight.csv. As we now push data to the API, we use the POST HTTP method. The structure of the JSON configuration is identical to that of remote importers you have seen before:

"remoteExport": [
    {
      "name": "Exporter",
      "templates": [
        {
          "symNames": "stock_weight",
          "url": "http://127.0.0.1:5000/io",
          "method": "POST",
          "authentication": {
            "username": "@env:API_USER",
            "password": "@env:API_PASS"
          },
          "httpBody": [
            {
              "key": "filename",
              "value": "/Users/miro/Documents/exporter/pickstock/export_stock_weight.csv"
            }
          ]
        }
      ]
    }
  ]

What's more interesting is the request body that MIRO sends to the remote server:

{"data":[{"symbol":"AXP","value":0.472295069483016},{"symbol":"MMM","value":0.316292266161662},{"symbol":"MSFT","value":0.406195141778428}],"modelname":"pickstock","dataset":"stock_weight","options":{"filename":"/Users/miro/Documents/importer/pickstock/price_data.csv"}}

MIRO sends data serialized as JSON. The modelname and dataset keys are sent along just like in the GET request we saw previously when importing data. In addition, we get the custom key-value pairs we specified in a special object called options as well as the actual data of the symbol in data. Note how the table is serialized. In case you need MIRO to serialize tables in a different format, send us an email or a pull request.

Custom input widgets

MIRO has an API that allows you to use custom input widgets such as charts to produce input data. This means that input data to your GAMS model can be generated by interactively modifying a chart or any other type of renderer.

Before reading this section, you should first study the chapter about custom renderers. Custom widgets are an extension of custom renderers that allow you to return data back to MIRO.

To understand how this works, we will look at an example app that allows you to solve Sudokus. We would like to visualize the Sudoku in a 9x9 grid that is divided into 9 subgrids - 3x3 cells each. We will use the same tool that we use to display input tables in MIRO, but we could have used any other R package or even a combination of those. Let's first look at the boilerplate code required for any custom input widget:


<rendererName>Output <- function(id, height = NULL, options = NULL, path = NULL){
  ns <- NS(id)
}

render<rendererName> <- function(input, output, session, data, options = NULL, path = NULL, rendererEnv = NULL, ...){
  force(data)
  return(reactive(data))
}
                                        
You will notice that the boilerplate code for custom widgets is almost identical to that of custom renderers. Let's focus on the differences: force(data) forces the data argument to evaluate. This is necessary because otherwise your function would not be updated when new data is pushed into your widget (for example, by the user loading a scenario from the database). If you are interested in the details, search for lazy evaluation in R. The main difference to a custom renderer is that we now have to return the input data to be passed to GAMS. Note that we return the data wrapped inside a reactive expression. This will ensure that you always return the current state of your data. When the user interacts with your widget, the data is updated.

Let's get back to the Sudoku example we mentioned earlier. We place a file sudoku.R within the custom renderer directory of our app: <modeldirectory>renderer_sudoku. Renderer files can have any valid file name with the extension .R as long as they are located within the <modeldirectory>/renderer_<modelname> directory.

To tell MIRO about which input symbol(s) should use our new custom widget, we have to edit the sudoku.json file located in the <modeldirectory>/conf_<modelname> directory. To use our custom widget sudoku for an input symbol named initial_state in our model, the following needs to be added to the configuration file:

{
"inputWidgets": {
    "initial_state": {
      "widgetType": "custom",
      "rendererName": "sudoku",
      "alias": "Initial state",
      "options": {
        "isInput": true
      }
    }
  }
}

We specified that we want an input widget of type custom for our GAMS symbol initial_state. Furthermore, we declared an alias for this symbol which defines the tab title. We also provided a list of options to our renderer functions. In our Sudoku example, we want to use the same renderer for both input data and output data. Thus, when using our new renderer for the input symbol initial_state, we pass an option isInput with the value true to our renderer function.

Let's concentrate again on the renderer functions and extend the boilerplate code from before:


sudokuOutput <- function(id, height = NULL, options = NULL, path = NULL){
  ns <- NS(id)
  rHandsontableOutput(ns('sudoku'))
}

renderSudoku <- function(input, output, session, data, options = NULL, path = NULL, rendererEnv = NULL, ...){
  force(data)
  output$sudoku <- renderRHandsontable(rhandsontable(data, 
                                                     readOnly = !isTRUE(options$isInput), 
                                                     rowHeaders = FALSE))
  if(isTRUE(options$isInput)){
    return(reactive(hot_to_r(input$sudoku)))
  }
}
                                        

Let's disect what we just did: First, we defined our two renderer functions sudokuOutput and renderSudoku. Since we want to use the R package rhandsontable to display our Sudoku grid, we have to use the placeholder function rHandsontableOutput as well as the corresponding renderer function renderRHandsontable. If you are wondering what the hell placeholder and renderer functions are, read the section on custom renderers.

Note that we use the option isInput we specified previously to determine whether our table should be read-only or not. Furthermore, we only return a reactive expression when we use the renderer function to return data - in the case of a custom input widget. A detail you might stumble upon is the expression hot_to_r(input$sudoku). This is simply a way to deserialize the data coming from the UI that the rhandsontable tool provides. What's important is that we return an R data frame that has exactly the number of columns MIRO expects our input symbol to have (in this example initial_state).

That's all there is to it! We configured our first custom widget. To use the same renderer for the the results that are stored in a GAMS symbol called result, simply add the following lines to the sudoku.json file. Note that we do not set the option isInput here.

"dataRendering": {
    "results": {
      "outType": "sudoku"
    }
  }

The full version of the custom renderer described here as well as the corresponding GAMS model Sudoku can be found in the MIRO model library.

If you read until this point, you might have noticed that there is a parameter in the renderer function that we did not talk about yet: rendererEnv. This parameter is a static R environment that is persistent across different calls to the renderer function. Each time the input data of your widget is updated (e.g. due to the user loading a new scenario from the database), your renderer function is called. One case where this can become problematic is when you make use of observers in your renderer functions. Every time your renderer function is called, all observers are re-registered, which leads to duplicated observers. To avoid this problem, you must ensure that observers are cleaned up when they are no longer needed. You do this by assigning them to the rendererEnv environment. An example where this is extensively used is the model tsp, which can be found in the MIRO model library.

Configuration via JSON files

The entire configuration of MIRO is done via JSON files. After the first start of a MIRO app, the following files are located in <modeldirectory>/conf_<modelname>:

  1. <modelname>_io.json
    This file describes the data contract between MIRO and GAMS and is generated automatically each time MIRO is run in development mode. All GAMS symbols that are marked in your GAMS model with the $onExternalInput / $offExternalInput and $onExternalOutput / $offExternalOutput tags are listed here. You won't ever have to modify this file!
  2. <modelname>.json
    All the configuration of MIRO happens in the other JSON file <modelname>.json, e.g. transport.json. When you use the Configuration Mode to configure your app, this file is generated and modified. Here you can customize the general appearance of MIRO, set symbol-specific options, adjust the functionality and much more. Furthermore, all the graphics and widgets are specified here. If you're using a version control system like Git, you should check this file in to keep track of any changes made to the configuration of your app.
Note:

If you don't start MIRO via GAMS Studio but via the command line, the JSON files are not created automatically. Read more about this here.

Custom visualization options

In case you want to change the language of MIRO or display a scalar input symbol as a slider, this information must be stored in the <modelname>.json file in JSON syntax. When you first launch MIRO for a new model, the <modelname>.json file is almost empty:

{}

Each adjustment you want to make must be captured within the curly braces of this file.

To ensure that the configuration is correct in terms of syntax and content, all JSON files are validated against schema in the background. Only if the validation is successful the application starts. If a schema is violated, an error message is displayed.
The schemas are located in <MIRORoot>/miro/conf. For the <modelname>_io.json this is the schema <MIRORoot>/miro/conf/io_config_schema.json and for the <modelname>.json the schema <MIRORoot>/miro/conf/config_schema.json is used. Since the latter file is used for the configuration, only the corresponding schema is of interest. You can have a look at it here.

Tip:

In the sections on configuring widgets and charts, you can also find examples of what the configuration looks like in JSON.

Example: tables

Let's get back and extend our demo model transport. We will do this by adjusting the file transport.json in order to to adapt MIRO to our needs.

GAMS MIRO comes with several options on how you can feed your model with input data. By default, GAMS input parameters, sets and scalars are displayed in an editable table:

editable table

The corresponding GAMS declaration statement looks like this: Parameter d(i,j) 'distance in thousands of miles';. By default, every cell in the table is editable and rows can be inserted and removed. If you only want to allow your users to edit certain tables or even only certain columns within a table, you can customize the visualization for the underlying GAMS parameter. You do so by adding this kind of information to the transport.json file. For example, if we decide that the table for our parameter d should not be editable, this information needs to be added to our JSON file as follows:

{
  "inputWidgets": {
    "d": {
      "widgetType": "table",
      "readonly": true
    }
  }
}

Example: scalars

As with all MIRO customizations, the information about the type of visualization of a GAMS scalar is configured via the file <modelname>.json.
Let's get back to our transport app. In the previous section we configured the table of parameter d so that it is no longer editable. We achieved this by writing the corresponding information into the transport.json file. Now we extend the configuration of our app further by adding information about how to display the scalar value f ('freight in dollars per case per thousand miles'). Instead of displaying it in a table, we want to use a slider:

{
  "inputWidgets": {
    "d": {
      "widgetType": "table",
      "readonly": true
    },
    "f": {
      "widgetType": "slider",
      "alias": "Freight in dollars per case per thousand miles",
      "label": "Select the freight costs",
      "min": 1,
      "max": 500,
      "default": 90,
      "step": 1
    }
  }
}

When we restart our app, the scalar is now automatically removed from the Scalars table and instead rendered as a separate widget:

Input widgets for scalar values

In the sections on widgets and charts you will find a JSON example for each widget or chart type.

Command line parameters

We have already mentioned the possibility of setting GAMS command line parameters via MIRO. Since GAMS Options and Double Dash Parameters are specified via the command line in GAMS, they can not be tagged in the GAMS code with $onExternalInput / $offExternalInput . To be able to set them from MIRO, they can be specified in the <modelname>.json file. In order for MIRO to recognize whether your symbol is a double dash parameter or a GAMS option, you must prefix the symbol name with either _gmspar_ for double dash parameters or _gmsopt_ for GAMS options.

Note:

Command line parameters can not be displayed as a table, but only as a widget. Both, GAMS options and double-dash parameters can be displayed as a slider, dropdown menu, checkbox, text input or numeric input. For double-dash parameters date(-range) selector and slider range are also available.

Let's assume you want MIRO to call your GAMS model with the double dash parameter --numberPeriods, which can be set to a value between 1 and 14. You also want to be able to select the MIP solver to use. As you have licenses for CPLEX, Gurobi and XPRESS, you can only use either of these three solvers. Thus, your <modelname>.json file could look as follows:

{
  "inputWidgets": {
    "_gmspar_numberPeriods": {
      "widgetType": "slider",
      "alias": "Number of time periods",
      "label": "Select the number of time periods to solve",
      "min": 1,
      "max": 14,
      "default": 11,
      "step": 1
    },
    "_gmsopt_mip": {
      "widgetType": "dropdown",
      "alias": "MIP Solver",
      "label": "Solver to use for MIP models",
      "choices": [
        "CPLEX",
        "GUROBI",
        "XPRESS"
      ],
      "multiple": false,
      "selected": "CPLEX"
    }
  }
}

The resulting interface in MIRO now looks as follows:

Input widgets for command line parameters

Advanced topics

Widgets with ranges

Tip:

In the MIRO Base Mode, widgets with ranges are only available for double-dash parameters!

We covered the basics of how to use widgets for your scalar inputs as well as command line parameters. Let's now take a look at how widgets that return two scalars instead of one are treated. Those widgets include a slider with two handles (also referred to as a slider range) and a date range selector:

The lower value (or starting date of the date range) is postfixed with _lo and the upper value (or end date of the date range) is postfixed with _up. This means that if you specified a slider range for your double dash parameter --RANGE, you can access the lower value of the range with %RANGE$LO% and the upper value with %RANGE$UP% while %RANGE% will not be set at all. Let's look at the following example: We would like to specify a time window that we want to fetch stock data for. Thus, we define a new double dash parameter --TW that we want to be visualized in the form of a date range selector. We add the following JSON snippet to our <modelname>.json configuration file:


{
  "inputWidgets": {
    "_gmspar_TW": {
       "alias": "time window",
       "widgetType": "daterange",
       "label": "Time window",
       "start": "2018-02-20",
       "end": "2019-02-20",
       "min": "2017-01-01",
       "max": "2019-12-31",
       "autoclose": true,
       "noHcube": true
    },
    [...] 
                                    

In our model, we can access the lower and upper value of this slider via the compile time variables %TW$LO% and %TW$UP%.

Dependencies among widgets

One special feature of GAMS MIRO is that you can define interdependencies between different parameters. Scalars can take two forms of dependencies to other parameters: They can feed their attributes from them; and they can manipulate the content of those parameters. Let's look at an example: we have a set of scenarios that can be selected as well as a singleton set with the currently selected/active scenario. A singleton set is suited to be represented as a dropdown menu, hence we choose that type of widget here. We would like to filter our parameters based on the currently selected scenario so that we only see data that is relevant for this scenario (this type of dependency is also referred to as a backward dependency). This means that in case scenario 1 is selected via our dropdown menu, we only want to display data relevant to scenario 1 to the user. Additionally, we want the choices of our dropdown menu to be the elements of all available scenarios (also referred to as a forward dependency).

Dropdown menu filters table

Similar dependencies can also be considered for sliders. An example is the pickstock model. Here, the number of stocks you can select for your portfolio can not be larger than the number of stocks in your dataset. Similarly, the number of days you can use to train your model must not be larger than the number of days you have in the stock price dataset. The slider attributes - namely the maximum value that can be selected - should change according to the price data the user uploads. Thus, we also have forward dependencies here.

A table that shows the currently supported forms of dependencies for the different input widgets is shown below.

Input widget type Forward dependency Backward dependency Example model
Dropdown menu -
Slider pickstock
Checkbox -
Date selector -
Daterange selector () -
Note:

You need dependency support for a type of widget which is currently not available? Please feel free to contact us at: miro@gams.com. You added dependency support for another type of widget yourself? Send us a pull request on GitHub!

Custom Renderers

GAMS MIRO allows you to use your own custom renderers for your datasets. In this section we will show you how to create really sophisticated graphics that can not be configured with the Configuration Mode of GAMS MIRO.

Note:

The following example shows how a map can be created using a custom renderer. Note that maps can also be configured in the Configuration Mode. However, the following example shows how the R API can be used for custom graphics in general.

Result of custom renderer - Map with markers
How to use a custom renderer

In order to visualize a dataset, you will need to write two functions: a placeholder where your graphs etc. will later be rendered as well as the rendering function itself. To learn more about the concepts of this dual component approach, visit the website for the R Shiny framework that GAMS MIRO is based upon: https://shiny.rstudio.com/. In particular, we are using Shiny Modules to realize the interface between MIRO and your custom renderer functions. The template for the two components of every custom renderer is as follows:


<rendererName>Output <- function(id, height = NULL, options = NULL, path = NULL){
  ns <- NS(id)
}

render<rendererName> <- function(input, output, session, data, options = NULL, path = NULL, views = NULL, ...){
}
                                        

Note that you need to replace <rendererName> with your preferred name. Let's go through this code step by step. As mentioned, for each custom renderer we need to specify two functions: one that generates the the placeholder and one that fills this placeholder with data. The name of the placeholder function must be postfixed with "Output" and the name of the function that specifies the actual rendering must be prefixed with the keyword "render". Let's get back to our transport example. We would like to see the flow of goods visualized on a map. We now write our own custom renderer which we name: transportMap. Thus, our initial template looks like this:

transportMapOutput <- function(id, height = NULL, options = NULL, path = NULL){
  ns <- NS(id)
}
renderTransportMap <- function(input, output, session, data, options = NULL, path = NULL, views = NULL, ...){
}
Note:

R is case sensitive and the first letter after the render keyword needs to be uppercase! Thus, if the name of your renderer starts with a lowercase letter, you need to convert it to uppercase in the render* function.

Note:

Custom renderer scripts must be located in a folder named: renderer_<modelname> in your model directory. You can name these .r files as you like.

Both functions take a number of parameters. Let's start with the placeholder function: Each custom renderer has its own ID. In order to avoid name collisions with other custom renderers or functions of GAMS MIRO, we need to prefix our input and output elements with this ID. How should we prefix our custom input and output functions, though? Fortunately, Shiny provides us with the function: NS(). This function takes the ID of our custom renderer as its input and returns a function (functions that return functions are often called closures in R) that does the prefixing for us. Thus, whenever we want to specify a new input or output element, we simply hand the ID we would like to use for this element over to this prefixing function (which in our case is bound to the ns variable). We can also specify a height for our renderer as well as the path where the renderer files are located. We can also pass additional options to our renderer functions.

Let's get back to our example. As we would like to visualize our optimized schedule on an interactive map, we choose the popular Leaflet library. Fortunately, there is already an R/Shiny interface for this library: Leaflet for R. This R package comes with the two functions: leafletOutput() that generates the placeholder and renderLeaflet() that renders a Leaflet map object created by the leaflet() function which takes our dataframe as its first argument. So let's put the pieces together and extend our code:

transportMapOutput <- function(id, height = NULL, options = NULL, path = NULL){
  ns <- NS(id)
  leaflet::leafletOutput(ns("map"), height = height)
}
renderTransportMap <- function(input, output, session, data, options = NULL, path = NULL, views = NULL, outputScalarsFull = NULL, ...){
  output$map <- leaflet::renderLeaflet(leaflet::leaflet(data))
}

Note that we used the aforementioned ns() function to prefix the ID ("map") that we chose for our Leaflet element. Just like any other placeholder element, the function leafletOutput() generates an element that can be accessed via the list-like output object.

Inside our rendering function we assign this object the Leaflet map that is created by the renderLeaflet() function. In case you find the whole concept of having an output function, a rendering function, an output object etc. still very confusing, you should take a look at the official tutorial series for the Shiny framework.
To summarize: elements that generate data can be accessed by the input object; elements that transform data to some form of visualization via the output object and any user-specific information via the session object.

Note:

If you use functions from packages other than shiny, DT, rhandsontable, dplyr, readr or the R base packages, make sure you specify the package/namespace that exports this function. In our example, the functions leafletOutput, renderLeaflet and leaflet are exported from the package leaflet. So we need to use the double colon operator to access them (e.g. leaflet::leafletOutput).

The data that you want to visualize is supplied to your rendering function by the data argument - an R tibble, a data structure that is very similar to a Data Frame. In case you specified additional datasets to be communicated with your custom renderer, data will be a named list of tibbles where the names are the GAMS symbol names. In addition, the values of all output scalars can be accessed via the argument outputScalarsFull (a tibble with the columns scalar, description, value). The function argument path is a string (a one-dimensional character vector) that specifies the absolute path to your renderer_<modelname> directory. This is useful if you want to include external files in your custom renderer functions. Optional parameters that you want to pass to the renderer can be accessed via the argument options - a (nested) list.

Tip:

The list options always has a nested list with the key _metdata_, which gives you access to the metadata of the symbol to render, such as the symbol type (set, parameter, ...) and header information (header name, alias, type). For example, the symbol type can be accessed as follows: options[["_metadata_"]][["symtype"]].

Now that we are familiar with the template that every custom renderer builds upon, we are still missing one fundamental concept so that we can use our custom renderer: binding the renderer to dataset(s) we wish to visualize.

This binding of GAMS parameter to renderer function is specified - just like any other renderer binding - in the <modelname>.json file; more precisely the dataRendering section. Let's assume that in our transportation example the GAMS parameter that specifies the optimal schedule is defined as optSched(lngp, latp, lngm, latm, plant, market) where (lngP,latP) and (lngm, latm) are the coordinates of the plants and markets respectively. Our transport.json file should then look like this:

{
   "dataRendering":{
      "optSched":{
         "outType":"transportMap",
         "height":"700",
         "options":{
            "title":"Optimal transportation schedule"
         }
      }
   }
}

As you can see we bound the GAMS parameter optSched to our new custom renderer transportMap. Furthermore, we specified a parameter: title that can be accessed by our custom renderer via the options list.

If we decided to run our MIRO app now, we still would not be able to see anything other than a blank area. Thus, we will need to fill our renderer with some life:

transportMapOutput <- function(id, height = NULL, options = NULL, path = NULL){
  ns <- NS(id)
  tagList(
    textOutput(ns("title")),
    leaflet::leafletOutput(ns("map"))
  )
}
renderTransportMap <- function(input, output, session, data, options = NULL, path = NULL, views = NULL, ...){
  output$title <- renderText(options$title)
  output$map   <- leaflet::renderLeaflet(leaflet::leaflet(data) %>%
                                           leaflet::addTiles() %>%
                                           leaflet::addMarkers(~lngp, ~latp, label = ~plant)
  )
}

We have added a new placeholder for the title. Note the use of the tagList() function. Since every R function has a single return value which is either the last evaluated expression of the function or the argument to the first return() function that is encountered in the function body, we need to return a list object. A tagList() is simply a list with an additional attribute to identify that the elements are html tags.

Within our rendering function, we set the title, add the default OpenStreetMap tiles as well as some markers for our plants.

Note:

The syntax ~lngp that you see here is simply a shorthand for data$lngp - the pipe operator a(x) %>% b(y) a shorthand for tmp <- a(x); b(tmp, y)

If you run your app now, you will be able to see a map with markers at the coordinates of the plants as you specified them in your GAMS sets: lngp and latp. When you hover over the markers, you will be able to see the names of the plants as defined by the set: plants. You can see a screenshot of the result below:
Result of custom renderer - Map with markers

You now know everything you need in order to get started writing your own custom renderers! Congratulations! In case you create a new renderer that you would like to share so that others can benefit from your work as well, please contact us!

Additional datasets

A custom renderer for an output symbol can use the data of other GAMS symbols. For example - let's stay with the above example of a map - the geographical information can come from other symbols than the transport data. For a custom renderer to access this data, the corresponding symbols must be specified in the <modelname>.json file under "additionalData":

{
   "dataRendering":{
      "optSched":{
         "outType":"transportMap",
         "height":"700",
         "options":{
            "title":"Optimal transportation schedule"
         },
         "additionalData": ["symbol_a", "symbol_b"]
      }
   }
}
Note:

All symbols that a custom renderer should use must be included in the GAMS/MIRO data contract.

Additional R packages

In a custom renderer you can include any R packages that are not part of MIRO. This allows you to use all data visualization and data processing capabilities of R. In order for such packages to be installed when starting a MIRO application, they must be specified in the <modelname>.json file at the configuration of the corresponding symbol under "packages":

{
   "dataRendering":{
      "optSched":{
         "outType":"transportMap",
         "height":"700",
         "options":{
            "title":"Optimal transportation schedule"
         },
         "additionalData": ["symbol_a", "symbol_b"],
         "packages": ["package_a", "package_b"]
      }
   }
}

Note that these additional packages are installed but not loaded! This means that when you call the function, you must explicitly specify the package name using the Double Colon Operator: pkg::name(...). See also the info box in the previous section.

Tip:

Additional R packages are installed in the workspace of GAMS MIRO the first time a MIRO application is started.

Views

In cases where your renderers are interactive, you might want to allow users to store the current state of your renderer and load it later with a single click. This is what MIRO views were designed for. A MIRO view can be any (nested) list that is JSON serializable. Views are bound to a particular GAMS symbol and each view has a unique id. One example of a renderer where the MIRO views API is used is the pivot table renderer.

You might have already seen that there is another argument in the renderer function that we have not yet talked about: the views argument. This is a reference to an instance of the views R6 class. You can get, add and remove views as well as register callbacks via this object. The following section explains how to use the API.

Get view data

views$get(session, id = NULL)
                                        
Arguments
session The session object passed to the custom renderer
id id of the view to load
Value

In case id is NULL: a named list where the names are the ids of the views and the values are the view data.

In case id is not NULL: a list with the data of the view. Will throw an error in case the id provided does not exist.

Description

This method allows you to retrieve the data of views registered for the symbol. You can retrieve either the data of all views (id is NULL) or the data of a specific view (id not NULL).

Example

# myViews <- list(filter1 = list(filter = list(element1 = "Value 1", element2 = c("Value 2", "Value 4"))),
#                 filter2 = list(filter = list(element3 = "Value 7")))

views$get(session, "filter1")
#> $filter
#> $filter$element1
#> [1] "Value 1"
#> 
#> $filter$element2
#> [1] "Value 2" "Value 4"

views$get(session)
#> $filter1
#> $filter1$filter
#> $filter1$filter$element1
#> [1] "Value 1"
#> 
#> $filter1$filter$element2
#> [1] "Value 2" "Value 4"
#> 
#> 
#> 
#> $filter2
#> $filter2$filter
#> $filter2$filter$element3
#> [1] "Value 7"

views$get(session, "filter3")
#> Error: View with id: filter3 could not be found.
                                        
Get view ids

views$getIds(session)
                                        
Arguments
session The session object passed to the custom renderer
Value

Character vector with the view ids registered for the symbol.

Description

Retrieves the view ids that are currently registered for the symbol. It is equivalent to names(views$get(session)).

Example

# myViews <- list(filter1 = list(filter = list(element1 = "Value 1", element2 = c("Value 2", "Value 4"))),
#                 filter2 = list(filter = list(element3 = "Value 7")))

views$getIds(session)
#> [1] "filter1" "filter2"
                                        
Add view

views$add(session, id, viewConf)
                                        
Arguments
session The session object passed to the custom renderer
id id of the view to add
viewConf The view configuration
Description

Adds/registers a new view. The view configuration can be any JSON serializable list. If a view with the same id already exists for the symbol, the previous view is replaced. In case the current renderer is read-only (not a sandbox scenario), an error is thrown. You can test whether the renderer is read-only with the method views$isReadonly(session).

Example

# myViews <- list(filter1 = list(filter = list(element1 = "Value 1", element2 = c("Value 2", "Value 4"))),
#                 filter2 = list(filter = list(element3 = "Value 7")))

views$add(session, "filter3", list(filter = list(element4 = "Value 10")))
                                        
Remove view

views$remove(session, id)
                                        
Arguments
session The session object passed to the custom renderer
id id of the view to remove
Description

Removes a view with the specified id. If no view with this id exists, an error is thrown. In case the current renderer is read-only (not a sandbox scenario), an error is thrown. You can test whether the renderer is read-only with the method views$isReadonly(session).

Example

# myViews <- list(filter1 = list(filter = list(element1 = "Value 1", element2 = c("Value 2", "Value 4"))),
#                 filter2 = list(filter = list(element3 = "Value 7")))

views$get(session)
#> $filter1
#> $filter1$filter
#> $filter1$filter$element1
#> [1] "Value 1"
#> 
#> $filter1$filter$element2
#> [1] "Value 2" "Value 4"
#> 
#> 
#> 
#> $filter2
#> $filter2$filter
#> $filter2$filter$element3
#> [1] "Value 7"

views$remove(session, "filter2")

views$get(session, "filter1")
#> $filter
#> $filter$element1
#> [1] "Value 1"
#> 
#> $filter$element2
#> [1] "Value 2" "Value 4"

views$remove(session, "filter2")
#> Error: View with id: filter2 does not exist, so it could not be removed.
                                        
Register update callback

views$registerUpdateCallback(session, callback)
                                        
Arguments
session The session object passed to the custom renderer
callback a callback function
Description

You can register a callback function that is triggered whenever the view data of a symbol is modified outside the renderer. This happens when a user modifies the view data via the metadata dialog. Note that the callback function is not triggered by the methods views$add or views$remove described above.

Example

updateCallback <- function(){
  print(sprintf("View data changed from outside! New view ids: %s.",
                views$getIds(session)))
}

views$registerUpdateCallback(session, updateCallback)
                                        

Language Files

GAMS MIRO comes with the ability to change the preferred language. Setting your preferred language results in MIRO embedding the corresponding language file where all the language specific components are specified via JSON syntax. MIRO currently ships with three of those language files: English, German and Chinese. If you would like to add another language to this list, you are more than welcome to translate one of the existing language files. The language files are located at: <MIRORoot>/miro/conf/en.json and are validated against the JSON Schema file: <MIRORoot>/miro/conf/language_schema.json. Note that there are sometimes placeholders in the language files. Let's take the error message: "The value '%s' for the slider: '%s' is not of numeric type." for example. %s will then be replaced with the value and name of the slider by MIRO. Thus, if you create a new language file, be sure to include these placeholders there!

If you would like to contribute your language file, so that other people can profit from your work as well, send us an email: miro@gams.com.