GAMS Connect

Table of Contents

Concept

GAMS Connect is a framework inspired by the concept of a so-called ETL (extract, transform, load) procedure that allows to integrate data from various data sources. The GAMS Connect framework consists of the Connect database and the Connect agents that operate on the Connect database. Via the available Connect interfaces the user passes instructions to call Connect agents for reading data from various file types into the Connect database, transforming data in the Connect database, and writing data from the Connect database to various file types. Instructions are passed in YAML syntax. Note that in contrast to a typical ETL procedure, read, transform and write operations do not need to be strictly separated.

The GAMS Connect Framework

Usage

GAMS Connect is available via the GAMS command line parameters ConnectIn and ConnectOut, via embedded code Connect, and as a standalone command line utility gamsconnect.

Instructions processed by the GAMS Connect interfaces need to be passed in YAML syntax as follows: 

- <agent name1>:
    <root option1>: <value>
    <root option2>: <value>
    .
    .
    <root option3>:
      - <option1>: <value>
        <option2>: <value>
        .
        .
      - <option1>: <value>
        <option2>: <value>
        .
        .
      .
      .
- <agent name2>:
    .
    .
.
.

The user lists the tasks to be performed successively. All tasks begin at the same indentation level starting with a - (a dash and a space) followed by the Connect agent name and a : (a colon). Connect agent options are represented in a simple <option>: <value> form. Please check the documentation of Connect Agents for available options. Options at the first indentation level are called root options and typically define general settings, e.g. the file name. While some agents only have root options, others have a more complex options structure, where a root option may be a list of dictionaries containing other options. A common example is the root option symbols (see e.g. GDXReader). Via symbols many agents allow to define symbol specific options, e.g. the name of the symbol. The option tables of agents with a more complex options structure provide a Scope to reflect this structure - options may be allowed at the first indentation level (root) and/or are assigned to other root options (e.g. symbols).

Note that YAML syntax also supports an abbreviated form for lists and dictionary, e.g. <root option3>: [ {<option1>: <value>, <option2>: <value>}, {<option1>: <value>, <option2>: <value>} ].

Here is an example that uses embedded Connect code to process instructions:

$onecho > distance.csv
i;j;distance in miles
seattle;new-york;2,5
seattle;chicago;1,7
seattle;topeka;1,8
san-diego;new-york;2,5
san-diego;chicago;1,8
san-diego;topeka;1,4
$offecho

$onecho > capacity.csv
i,capacity in cases
seattle,350.0
san-diego,600.0
$offecho

Set i 'Suppliers', j 'Markets';
Parameter d(i<,j<) 'Distance', a(i) 'Capacity';
$onEmbeddedCode Connect:
- CSVReader:
    file: distance.csv
    name: distance
    indexColumns: [1, 2]
    valueColumns: [3]
    fieldSeparator: ';'
    decimalSeparator: ','
- CSVReader:
    file: capacity.csv
    name: capacity
    indexColumns: [1]
    valueColumns: [2]
- GAMSWriter:
    symbols:
      - name: distance
        newName: d
      - name: capacity
        newName: a
$offEmbeddedCode

display i, j, d, a;

In this example, we are reading two CSV files distance.csv and capacity.csv using the CSVReader. Then we directly write to symbols in GAMS using the GAMSWriter.

Note that even though GAMS is case insensitive, GAMS Connect is case sensitive, i.e., YAML instructions are treated case sensitive. This also includes, e.g., indices in CSV files. Consider the following example where the index j2 should be substituted by ABC when reading the CSV file y.csv:

$onecho > y.csv
i1,j1,2.5
i1,J2,1.7
i2,j1,1.8
i2,j2,1.4
$offecho

set i,j;
parameter p(i<,j<);

$onEmbeddedCode Connect:
- CSVReader:
    file: y.csv
    name: p
    indexColumns: [1,2]
    valueColumns: [3]
    header: false
    indexSubstitutions: { j2: ABC }
- GAMSWriter:
    writeAll: true
$offEmbeddedCode

display i,j,p;

Since the YAML instructions are treated case sensitive, the index J2 will not be substituted.

            j1          J2         ABC

i1       2.500       1.700
i2       1.800                   1.400

An exception are symbol names in the Connect database. Creating or accessing symbols, e.g. via the name option of many agents, is case insensitive. All instructions provided to the Connect framework are read using UTF-8 encoding (utf-8-sig). This can be customized by adding a comment in the format # coding=<encoding name> or # -*- coding: <encoding name> -*- as first line in the YAML code. Note that UTF-16 encoding is not supported.

Connect Agents Summary

Current Connect agents support the following data source formats: CSV, Excel, GDX and SQL. The following Connect agents are available:

Connect agent Description Supported symbol types
Concatenate Allows concatenating multiple symbols in the Connect database. Sets and parameters
CSVReader Allows reading a symbol from a specified CSV file into the Connect database. Sets and parameters
CSVWriter Allows writing a symbol in the Connect database to a specified CSV file. Sets and parameters
DomainWriter Allows rewriting the domain information of an existing Connect symbol. Sets, parameters, variables, and equations
Filter Allows to reduce symbol data by applying filters on labels and numerical values. Sets, parameters, variables, and equations
GAMSReader Allows reading symbols from the GAMS database into the Connect database. Sets, parameters, variables, and equations
GAMSWriter Allows writing symbols in the Connect database to the GAMS database. Sets, parameters, variables, and equations
GDXReader Allows reading symbols from a specified GDX file into the Connect database. Sets, parameters, variables, and equations
GDXWriter Allows writing symbols in the Connect database to a specified GDX file. Sets, parameters, variables, and equations
LabelManipulator Allows to modify labels of symbols in the Connect database. Sets, parameters, variables, and equations
Options Allows to set more general options that can affect the Connect database and other Connect agents. -
PandasExcelReader Allows reading symbols from a specified Excel file into the Connect database. Sets and parameters
PandasExcelWriter Allows writing symbols in the Connect database to a specified Excel file. Sets and parameters
Projection Allows index reordering and projection onto a reduced index space of a GAMS symbol. Sets, parameters, variables, and equations
PythonCode Allows executing arbitrary Python code. -
RawCSVReader Allows reading unstructured data from a specified CSV file into the Connect database. -
RawExcelReader Allows reading unstructured data from a specified Excel file into the Connect database. -
SQLReader Allows reading symbols from a specified SQL database into the Connect database. Sets and parameters
SQLWriter Allows writing symbols in the Connect database to a specified SQL database. Sets and parameters

Getting Started

We introduce the basic functionalities of GAMS Connect agents on some simple examples. For more examples see section Examples.

Simple Connect Example for CSV

The following example (a modified version of the trnsport model) shows how to read and write CSV files. The full example is part of DataLib as model connect03. Here is a code snippet of the first lines:

$onEcho > distance.csv
i,new-york,chicago,topeka
seattle,2.5,1.7,1.8
san-diego,2.5,1.8,1.4
$offEcho

$onEcho > capacity.csv
i,capacity
seattle,350
san-diego,600
$offEcho

$onEcho > demand.csv
j,demand
new-york,325
chicago,300
topeka,275
$offEcho

Set i 'canning plants', j 'markets';

Parameter d(i<,j<) 'Distance', a(i) 'Capacity', b(j) 'Demand';

$onEmbeddedCode Connect:
- CSVReader:
    file: distance.csv
    name: d
    indexColumns: 1
    valueColumns: "2:lastCol"
- CSVReader:
    file: capacity.csv
    name: a
    indexColumns: 1
    valueColumns: 2
- CSVReader:
    file: demand.csv
    name: b
    indexColumns: 1
    valueColumns: 2
- GAMSWriter:
    writeAll: True
$offEmbeddedCode

[...]

It starts out with the declaration of sets and parameters. With compile-time embedded Connect code, data for the parameters are read from CSV files using the Connect agent CSVReader. The CSVReader agent, for example, reads the CSV file distance.csv and creates the parameter d in the Connect database. The name of the parameter must be given by the option name. Column number 1 is specified as the first domain set using option indexColumns. The valueColumns option is used to specify the column numbers 2, 3 and 4 containing the values. Per default, the first row of the columns specified via valueColumns will be used as the second domain set. The symbolic constant lastCol can be used if the number of index or value columns is unknown. As a last step, all symbols from the Connect database are written to the GAMS database using the Connect agent GAMSWriter. The GAMSWriter agent makes the parameters d, a and b available outside the embedded Connect code. Note that the sets i and j are defined implicitly through parameter d.

Finally, after solving the transport model, Connect can be used to export results to a CSV file:

[...]

Model transport / all /;
solve transport using lp minimizing z;

EmbeddedCode Connect:
- GAMSReader:
    symbols:
        - name: x
- Projection:
    name: x.l(i,j)
    newName: x_level(i,j)
- CSVWriter:
    file: shipment_quantities.csv
    name: x_level
    unstack: True
endEmbeddedCode

This time, we need to use execution-time embedded Connect code. The Connect agent GAMSReader imports variable x into the Connect database. With the Connect agent CSVWriter we write the variable level to the CSV file shipment_quantities.csv:

i_0,new-york,chicago,topeka
seattle,50.0,300.0,0.0
san-diego,275.0,0.0,275.0

Setting the option unstack to True allows to use the last dimension as the header row.

Simple Connect Example for Excel

The following example is part of GAMS Model Library as model cta and shows how to read and write Excel spreadsheets. Here is a code snippet of the first lines:

Set
   i 'rows'
   j 'columns'
   k 'planes';

Parameter
   dat(k<,i<,j<) 'unprotected data table'
   pro(k,i,j)    'information sensitive cells';

* extract data from Excel workbook
$onEmbeddedCode Connect:
- PandasExcelReader:
    file: cox3.xlsx
    symbols:
      - name: dat
        range: Sheet1!A1
        rowDimension: 2
        columnDimension: 1
      - name: pro
        range: Sheet2!A1
        rowDimension: 2
        columnDimension: 1
- GAMSWriter:
    writeAll: True
$offEmbeddedCode

[...]

It starts out with the declaration of sets and parameters. With compile-time embedded Connect code, data for the parameters are read from the Excel file cox3.xlsx using the Connect agent PandasExcelReader. The PandasExcelReader agent allows reading data for multiple symbols that are listed under the keyword symbols, here, parameter dat and pro. For each symbol, the symbol name is given by option name and the Excel range by option range. The option rowDimension defines that the first 2 columns of the data range will be used for the labels. In addition, the option columnDimension defines that the first row of the data range will be used for the labels. As a last step, all symbols from the Connect database are written to the GAMS database using the Connect agent GAMSWriter. The GAMSWriter agent makes the parameters dat and pro available outside the embedded Connect code. Note that the sets i, j and k are defined implicitly through parameter dat.

Finally, after solving the cox3c model with alternative solutions, Connect can be used to export results to Excel:

[...]

loop(l$((obj.l - best)/best <= 0.01),
   ll(l) = yes;
   binrep(s,l)             = round(b.l(s));
   binrep('','','Obj',l)   = obj.l;
   binrep('','','mSec',l)  = cox3c.resUsd*1000;
   binrep('','','nodes',l) = cox3c.nodUsd;
   binrep('Comp','Cells','Adjusted',l) = sum((i,j,k)$(not s(i,j,k)), 1$round(adjn.l(i,j,k) + adjp.l(i,j,k)));
   solve cox3c min obj using mip;
);

embeddedCode Connect:
- GAMSReader:
    symbols:
      - name: binrep
- PandasExcelWriter:
    file: results.xlsx
    symbols:
      - name: binrep
        range: binrep!A1
endEmbeddedCode

This time, we need to use execution-time embedded Connect code. The Connect agent GAMSReader imports the reporting parameter binrep into the Connect database. With the Connect agent PandasExcelWriter we write the parameter into the binrep sheet of the Excel file results.xlsx.

Simple Connect Example for SQL

The following example (a modified version of the whouse model) shows how to read from and write to a SQL database (sqlite). The full example is part of DataLib as model connect04. Here is a code snippet of the first lines:

[...]

Set t 'time in quarters';

Parameter
   price(t)  'selling price ($ per unit)'
   istock(t) 'initial stock      (units)';

Scalar
   storecost 'storage cost  ($ per quarter per unit)'
   storecap  'stocking capacity of warehouse (units)';

$onEmbeddedCode Connect:
- SQLReader:
    connection: {"database": "whouse.db"}
    symbols:
      - name: t
        query: "SELECT * FROM timeTable;"
        type: set
      - name: price
        query: "SELECT * FROM priceTable;"
      - name: istock
        query: "SELECT * FROM iniStockTable;"
      - name: storecost
        query: "SELECT * FROM storeCostTable;"
      - name: storecap
        query: "SELECT * FROM storeCapTable;"
- GAMSWriter:
    writeAll: True
$offEmbeddedCode

[...]

It starts out with the declaration of sets and parameters. With compile-time embedded Connect code, data for all the symbols are read from the sqlite database whouse.db using the Connect agent SQLReader by passing the connection url through the option connection. The SQLReader agent, for example, queries the table priceTable for data and creates the parameter price in the Connect database. The SQLReader allows reading data for multiple symbols that are listed under the keyword symbols and are fetched through the same connection. For each symbol the name must be given by the option name. The SQL query statement is passed through the option query. The symbol type can be specified using the option type. By default, every symbol is treated as a GAMS parameter. As a last step, all symbols from the Connect database are written to the GAMS database using the Connect agent GAMSWriter. The GAMSWriter agent makes all read in symbols available outside the embedded Connect code.

Further, after solving the warehouse model, Connect can be used to export the results to tables in the SQL database.

[...]

Model swp 'simple warehouse problem' / all /;

solve swp minimizing cost using lp;


EmbeddedCode Connect:
- GAMSReader:
    readAll: True
- Projection:
    name: stock.l(t)
    newName: stock_level(t)
- Projection:
    name: sell.l(t)
    newName: sell_level(t)
- Projection:
    name: buy.l(t)
    newName: buy_level(t)
- SQLWriter:
    connection: {"database": "whouse.db"}
    ifExists: replace
    symbols:
      - name: stock_level
        tableName: stock_level
      - name: sell_level
        tableName: sell_level
      - name: buy_level
        tableName: buy_level
endEmbeddedCode

Here, we need to use execution-time embedded Connect code. The Connect agent GAMSReader imports all the variables into the Connect database. The SQLWriter agent then writes each symbol to respective tables in the SQL database whouse.db. For example the stock level:

|t_0     |level     |
|:-------|:---------|
|q-1     |100.0     |
|q-2     |0.0       |
|q-3     |0.0       |
|q-4     |0.0       |

The ifExists option allows to either append to an extending table or replace it with new data. By default, the value for ifExists is set to fails.

Connect Agents

Concatenate

The Concatenate agent allows concatenating multiple symbols (sets or parameters) in the Connect database into a single symbol of the same type. It takes the union of domain sets of all concatenated symbols and uses that as the domain for the output symbol. There are several options to guide this domain finding process which are explained below. The general idea is best explained with an example. Consider three parameters p1(i,j), p2(k,i), and p3(k,l). The union of all domain sets is i, j, k, and l and, hence, the output symbol will be parameterOutput(symbols,i,j,k,l). The very first index of parameterOutput contains the name of the concatenated symbol followed by the domain sets. If a domain set is not used by a concatenated symbol the corresponding records in parameterOutput will feature the emptyUel, a - (dash) by default, as the following figures show:

Parameter p1, p2 and p3
The resulting parameterOutput

The Concatenate agent is especially useful in combination with UI components that provide a pivot table, like GAMS MIRO, to represent many individual output symbols in a single powerful and configurable table format.

Obviously, there are more complex situations with respect to the domain of the resulting parameterOutput. For example, only a subset of domain sets are relevant and the remaining ones should be combined in as few index positions as possible. For this, assume only domain sets i and k from the above example are relevant and j and l can be combined in a single index position - a so-called universal domain. The resulting parameterOutput would look as follows:

The resulting parameterOutput with relevant domains i and k

Moreover, the Concatenate agent needs to deal with universe domain sets * and domain sets that are used multiple times in a concatenated symbol. In addition to the symbols index (always the first index position of the output symbol), by default the union of domain sets of the concatenated symbols determine the domain of the output symbol. If a domain set (including the universe *) appears multiple times in a concatenated symbol domain, these duplicates will be part of the output symbol domain. For example, q1(*,i,j,*) and q2(*,i,i) will result in the output symbol parameterOutput(symbols,*,i,j,*,i,) by default, mapping index positions 1 to 4 of q1 to positions 2 to 5 of parameterOutput and index positions 1 to 3 of q2 to 2, 3, and 6.

All the described situations can be configured with a few options of the agent. The option outputDimensions allows to control the domain of the output symbol. The default behavior (outputDimension: all) gets the domain sets from the concatenated symbols and builds the union with duplicates if required. Alternatively, outputDimensions can be a list of the relevant domain sets (including an empty list). In any case, the agent iterates through the concatenated symbols and maps the index positions of a concatenated symbol into the index positions of the output symbol using the domain set names. Names not present in outputDimensions will be added as universal domains. Per default, the domain set names of a concatenated symbol will be the original domain set names as stored in the Connect database. There are two ways to adjust the domain set names of concatenated symbols: dimensionMap and an explicitly given domain for a symbol in the name option. The dimensionMap which is given once and holds for all symbols allows to map original domain names of concatenated symbols to the desired domain names. The name option provides such a map by symbol and via the index position rather than the domain names of the concatenated symbol. In the above example with p1(i,j), p2(k,i), and p3(k,l), we could put indices i and l as well as j and k together resulting in the following output symbol:

The resulting parameterOutput with i and l as well as j and k combined in a single index position

This can be accomplished in two ways: either we use dimensionMap: {i: il, l: il, j: jk, k: jk} or we use name: p1(il,jk), name: p2(jk,il), and name: p3(jk,il) to explicitly define the domain names for each symbol. Note that it is not required to set outputDimensions: [il,jk] since per default the union of domain sets is built using the mapped domain names. In case a domain set is used more than once in a domain of a concatenated symbol the mapping goes from left to right to find the corresponding output domain. If this is not desired, the Projection agent can be used to reorder index positions in symbols or explicit index naming can be used. In the example with q1(*,i,j,*) and q2(*,i,i), the second index position of q2 will be put together with the second index position of q1. If one wants to map the second i of q2 (in the third index position) together with the i of q1 (in second index position), one can, e.g., do with name: q1(*,i,j,*), and name: q2(*,i2,i).

Note
  1. The Concatenate agent creates result symbols parameterOutput and setOutput for parameters and sets separately. Both have the same output domain. If you want different output domains for parameterOutput and setOutput use two instantiations of the Concatenate agent.
  2. Variables and equations need to be turned into parameters with the Projection agent before they can be concatenated.
  3. If option name is given without an explicit domain for the concatenated symbol, the domain names stored in the Connect container are used and mapped via the dimensionMap option, if provided.
  4. A domain set of a concatenated symbol that cannot be assigned to an index in outputDimensions will be mapped to a so-called universal domain. The Concatenate agent automatically adds as many universal domains as required to the output symbols.

Here is a complete example that uses the Concatenate agent:

Sets
   i(i) / i0*i3 "i_text" /
   j(j) / j0*j3 "j_text" /
   k(k) / k0*k3 "k_text" /;

Parameters
   p1(i) / i1 1 /
   p2(k,j) / k1.j0 2, k1.j1 3, k1.j3 4 /
   p3(j,j) / j1.j2 5, j2.j0 6, j3.j1 7, j3.j2 8 /
   s / 5 /;

Positive Variable x(i,j);
x.l(i,j)$(uniform(0,1)>0.8) = uniformint(0,10);

EmbeddedCode Connect:
- GAMSReader:
    readAll: True
- Projection:
    name: x.l(i,j)
    newName: x_level(i,j)
- Concatenate:
    outputDimensions: [j,i]
- GDXWriter:
    file: concat_output.gdx
    symbols:
      - name: setOutput
      - name: parameterOutput
endEmbeddedCode

The resulting set and parameter outputs look as follows:

The resulting setOutput and parameterOutput

The following options are available for the Concatenate agent.

Option Scope Default Description
concatenateAll root auto Indicate if all sets and parameters in the Connect database will be concatenated.
outputDimensions root all Define the dimensions of the output symbols.
dimensionMap root None Define a mapping for the domain names of concatenated symbols as stored in the Connect database to the desired domain names.
name symbols None Specify the name of the symbol with potentially index space.
newName symbols None Specify a new name for the symbol in the symbols column of the output symbol.
parameterName root parameterOutput Name of the parameter output symbol.
emptyUel root - Define a character to use for empty uels.
setName root setOutput Name of the set output symbol.
symbols root None Specify symbol specific options.
trace root inherited Specify the trace level for debugging output.
universalDimension root uni Specify the base name of universal dimensions.

Detailed description of the options:

concatenateAll = boolean or string (default=auto)

If True all sets and parameters in the given database are concatenated, symbols will be ignored. Default is auto where the Concatenate agent uses symbols if specified, otherwise concatenates all sets and parameters in the Connect database.

outputDimensions = list or string (default=all)

Define the dimensions of the output symbols explicitly using a list, e.g., outputDimensions: [i,j]. The default all gets the domain sets from the concatenated symbols and builds the union with duplicates if required.

dimensionMap = dict (optional)

Define a mapping for domain names of concatenated symbols as stored in the Connect database to the desired domain names. For example, dimensionsMap: {i: ij, j: ij} will map both symbol domains i and j to ij.

name = string (required)

Specify the name of the symbol with potentially index space. Requires the format symname(i1,i2,...,iN). The index space may be specified to establish a mapping for the domain names of the symbol as stored in the Connect database to the desired domain names. If no index space is provided, the domain names stored in the Connect data are used and mapped via the dimensionMap option if provided.

newName = string (optional)

Specify a new name for the symbol in the symbols column of the output symbol.

parameterName = string (default=parameterOutput)

Name of the parameter output symbol.

emptyUel = string (default=-)

Define a character to use for empty uels.

setName = string (default=setOutput)

Name of the set output symbol.

symbols = list (optional)

A list containing symbol specific options. Allows to concatenate a subset of symbols.

trace = integer (optional)

Specify the trace level for debugging output. For trace > 1 some scalar debugging output will be written to the log. For trace > 2 the intermediate data frames will be written abbreviated to the log. For trace > 3 the intermediate data frames will be written entirely to the log (potentially large output). If trace has not been set, the trace value, set by the Options agent, will be used.

universalDimension = string (default=uni)

Specify the base name of universal dimensions.

CSVReader

The CSVReader allows reading a symbol (set or parameter) from a specified CSV file into the Connect database. Its implementation is based on the pandas.DataFrame class and its I/O API method read_csv. See Simple Connect Example for CSV for a simple example that uses the CSVReader.

Option Default Description
autoColumn None Generate automatic column names.
autoRow None Generate automatic row labels.
decimalSeparator . (period) Specify a decimal separator.
fieldSeparator , (comma) Specify a field separator.
file None Specify a CSV file path.
header inferred Specify the header(s) used as the column names.
indexColumns None Specify columns to use as the row labels.
indexSubstitutions None Dictionary used for substitutions in the index columns.
name None Specify a symbol name for the Connect database.
names None List of column names to use.
quoting 0 Control field quoting behavior.
readCSVArguments None Dictionary containing keyword arguments for the pandas.read_csv method.
skipRows None Specify the rows to skip or the number of rows to skip.
stack inferred Stacks the column names to index.
textColumns None Specify columns to get the set element text from.
textSubstitutions None Dictionary used for substitutions in the text columns.
thousandsSeparator None Specify a thousands separator.
trace inherited Specify the trace level for debugging output.
valueColumns None Specify columns to get the values from.
valueSubstitutions None Dictionary used for substitutions in the value columns.

Detailed description of the options:

autoColumn = string (optional)

Generate automatic column names. The autoColumn string is used as the prefix for the column label numbers. This option overrides the use of a header or names. However, if there is a header row, one must skip the row by enabling header or using skipRows.

autoRow = string (optional)

Generate automatic row labels. The autoRow string is used as the prefix for the row label numbers. The generated unique elements will be used in the first index position shifting other elements to the right. Using autoRow can be helpful when there are no labels that can be used as unique elements but also to store entries that would be a duplicate entry without a unique row label.

decimalSeparator = string (default=.)

Specify a decimal separator. [ . (period), , (comma)]

fieldSeparator = string (default=,)

Specify a field separator. [ , (comma), ; (SemiColon), \t (Tab)]

file = string (required)

Specify a CSV file path.

header = boolean, list (optional)

Specify the header(s) used as the column names. Default behavior is to infer the column names: if no names are passed the behavior is identical to header=True and column names are inferred from the first line of data, if column names are passed explicitly then the behavior is identical to header=False. Explicitly pass header=True to be able to replace existing names. Note that missing column names are filled with Unnamed: n (where n is the nth column (zero based) in the DataFrame). Hence, reading the CSV file:

,j1,
i1,1,2
i2,3,4
,5,6

results in the following 2-dimensional parameter: 

            j1  Unnamed: 2

i1       1.000       2.000
i2       3.000       4.000

For a multi-row header, a list of integers can be passed providing the positions of the header rows in the data. Note that reading multi-row header is only supported for parameters. Moreover, the CSVReader can only read all columns and not a subset of columns, wherefore only indexColumns can be specified and all other columns will automatically be read as valueColumns. Note that indexColumns can not be provided as column names together with a multi-row header. autoRow and autoColumn will be ignored in case of a multi-row header. Here is an example how to read data with a multi-row header:

$onecho > multirow_header.csv
j,,j1,j1,j1,j2,j2,j2
k,,k1,k2,k3,k1,k2,k3
h,i,,,,,,
h1,i1,1,2,,4,5,6
h1,i2,,,3,4,5,
$offEcho

$onEmbeddedCode Connect:
- CSVReader:
    file: multirow_header.csv
    name: p
    header: [1,2]
    indexColumns: [1,2]
- PythonCode:
    code: |
        print(connect.container["p"].records)
$offEmbeddedCode

The same can be achieved if the data has no index column names:

j,,j1,j1,j1,j2,j2,j2
k,,k1,k2,k3,k1,k2,k3
h1,i1,1,2,,4,5,6
h1,i2,,,3,4,5,

If the first line of data after the multi-row header has no data in the valueColumns, the CSVReader will interpret this line as index column names.

indexColumns = list or string (optional)

Specify columns to use as the row labels. The columns can either be given as column positions or column names. Column positions can be represented as an integer, a list of integers or a string. For example: indexColumns: 1, indexColumns: [1, 2, 3, 4, 6] or indexColumns: "1:4, 6". The symbolic constant lastCol can be used with the string representation: "2:lastCol". If no header or names is provided, lastCol will be determined by the first line of data. Column names can be represented as a list of strings. For example: indexColumns: ["i1","i2"]. Note that indexColumns and valueColumns/textColumns and must either be given as positions or names not both. Further note that indexColumns as column names are not supported together with a multi-row header.

By default the pandas.read_csv method interprets the following indices as NaN: "", "#N/A", "#N/A N/A", "#NA", "-1.#IND", "-1.#QNAN", "-NaN", "-nan", "1.#IND", "1.#QNAN", "<NA>", "N/A", "NA", "NULL", "NaN", "n/a", "nan", "null". The default can be changed by specifying pandas.read_csv arguments keep_default_na and na_value via readCSVArguments. Rows with indices that are interpreted as NaN will be dropped automatically. The indexSubstitutions option allows to remap NaN entries in the index columns.

indexSubstitutions = dictionary (optional)

Dictionary used for substitutions in the index columns. Each key in indexSubstitutions is replaced by its corresponding value. This option allows arbitrary replacements in the index columns of the DataFrame including stacked column names. Consider the following CSV file:

i1,j1,2.5
i1,,1.7
i2,j1,1.8
i2,,1.4

Reading this data into a 2-dimensional parameter results in a parameter with NaN entries dropped: 

            j1

i1       2.500
i2       1.800

By specifying indexSubstitutions: { .nan: j2 } we can substitue NaN entries by j2: 

            j1          j2

i1       2.500       1.700
i2       1.800       1.400

name = string (required)

Specify a symbol name for the Connect database. Note that each symbol in the Connect database must have a unique name.

names = list (optional)

List of column names to use. If the file contains a header row, then you should explicitly pass header=True to override the column names. Duplicates in this list are not allowed.

quoting = integer (default=0)

Control field quoting behavior. Use QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3). QUOTE_NONNUMERIC (2) instructs the reader to convert all non-quoted fields to type float. QUOTE_NONE (3) instructs reader to perform no special processing of quote characters.

readCSVArguments = dictionary (optional)

Dictionary containing keyword arguments for the pandas.read_csv method. Not all arguments of that method are exposed through the YAML interface of the CSVReader agent. By specifying readCSVArguments, it is possible to pass arguments directly to the pandas.read_csv method that is used by the CSVReader agent. For example, readCSVArguments: {keep_default_na: False, skip_blank_lines: False}.

skipRows = list or integer (optional)

Specify the rows to skip (list) or the number of rows to skip (integer). For example: skipRows: [1, 3] or skipRows: 5.

stack = boolean (optional)

Stacks the column names to index. Defaults to True if there is more than one value/text column, otherwise False. Note that missing column names are filled with Unnamed: n (where n is the nth column (zero based) in the DataFrame).

textColumns = list or string (optional)

Specify columns to get the set element text from. The columns can be given as column positions or column names. Column positions can be represented as a integer, a list of integers or a string. For example: textColumns: 1, textColumns: [1, 2, 3, 4, 6] or textColumns: "1:4, 6". The symbolic constant lastCol can be used with the string representation: "2:lastCol". If no header or names is provided, lastCol will be determined by the first line of data. Column names can be represented as a list of strings. For example: textColumns: ["i1","i2"]. Note that textColumns and indexColumns must either be given as positions or names not both.

By default the pandas.read_csv method interprets the following text as NaN: "", "#N/A", "#N/A N/A", "#NA", "-1.#IND", "-1.#QNAN", "-NaN", "-nan", "1.#IND", "1.#QNAN", "<NA>", "N/A", "NA", "NULL", "NaN", "n/a", "nan", "null". The default can be changed by specifying pandas.read_csv arguments keep_default_na and na_value via readCSVArguments. Rows with texts that are interpreted as NaN will be dropped automatically. The textSubstitutions option allows to remap NaN entries in the text columns.

textSubstitutions = dictionary (optional)

Dictionary used for substitutions in the text columns. Each key in textSubstitutions is replaced by its corresponding value. While it is possible to make arbitrary replacements this is especially useful for controlling sparse/dense reading. The default reading behavior is sparse since rows with text that is interpreted as NaN are dropped automatically. Consider the following CSV file: 

i1,text1
i2,
i3,text3

thousandsSeparator = string (optional)

Specify a thousands separator.

trace = integer (optional)

Specify the trace level for debugging output. For trace > 1 some scalar debugging output will be written to the log. For trace > 2 the intermediate data frames will be written abbreviated to the log. For trace > 3 the intermediate data frames will be written entirely to the log (potentially large output). If trace has not been set, the trace value, set by the Options agent, will be used.

valueColumns = list or string (optional)

Specify columns to get the values from. The columns can be given as column positions or column names. Column positions can be represented as a integer, a list of integers or a string. For example: valueColumns: 1, valueColumns: [1, 2, 3, 4, 6] or valueColumns: "1:4, 6". The symbolic constant lastCol can be used with the string representation: "2:lastCol". If no header or names is provided, lastCol will be determined by the first line of data. Column names can be represented as a list of strings. For example: valueColumns: ["i1","i2"]. Note that valueColumns and indexColumns must either be given as positions or names not both.

By default the pandas.read_csv method interprets the following values as NaN: "", "#N/A", "#N/A N/A", "#NA", "-1.#IND", "-1.#QNAN", "-NaN", "-nan", "1.#IND", "1.#QNAN", "<NA>", "N/A", "NA", "NULL", "NaN", "n/a", "nan", "null". The default can be changed by specifying pandas.read_csv arguments keep_default_na and na_value via readCSVArguments. Rows with values that are interpreted as NaN will be dropped automatically. Changing the default of values that are interpreted as NaN is useful if, e.g., "NA" values should not be dropped but interpreted as GAMS special value NA. Moreover, the valueSubstitutions option allows to remap NaN entries in the value columns.

valueSubstitutions = dictionary (optional)

Dictionary used for substitutions in the value columns. Each key in valueSubstitutions is replaced by its corresponding value. While it is possible to make arbitrary replacements this is especially useful for controlling sparse/dense reading. All NaN entries are removed automatically by default which results in a sparse reading behavior. Consider the following CSV file:

i1,j1,
i1,j2,1.7
i2,j1,
i2,j2,1.4

Reading this data into a 2-dimensional parameter results in a sparse parameter with all NaN entries removed: 

            j2

i1       1.700
i2       1.400

By specifying valueSubstitutions: { .nan: eps } we get a dense representation where all NaN entries are replaced by GAMS special value EPS: 

            j1          j2

i1         EPS       1.700
i2         EPS       1.400

Beside eps there are the following other GAMS special values that can be used by specifying their string representation: inf, -inf, eps, na, and undef. See the GAMS Transfer documentation for more information.

Reading this data into a 1-dimensional set results in a sparse set in which all NaN entries (those that do not have any set element text) are removed: 

'i1' 'text 1',
'i3' 'text 3'

By specifying textSubstitutions: { .nan: '' } we get a dense representation: 

'i1' 'text 1',
'i2',
'i3' 'text 3'

It is also possible to use textSubstitutions in order to interpret the set element text. Let's assume we have the following CSV file: 

,j1,j2,j3
i1,Y,Y,Y
i2,Y,Y,N
i3,0,Y,Y

Reading this data into a 2-dimensional set results in a dense set: 

'i1'.'j1' Y,
'i1'.'j2' Y,
'i1'.'j3' Y,
'i2'.'j1' Y,
'i2'.'j2' Y,
'i2'.'j3' N,
'i3'.'j1' 0,
'i3'.'j2' Y,
'i3'.'j3' Y

By specifying textSubstitutions: { 'N': .nan, '0': .nan } we replace all occurrences of N and 0 by NaN which gets dropped automatically: 

'i1'.'j1' Y,
'i1'.'j2' Y,
'i1'.'j3' Y,
'i2'.'j1' Y,
'i2'.'j2' Y,
'i3'.'j2' Y,
'i3'.'j3' Y

CSVWriter

The CSVWriter allows writing a symbol (set or parameter) in the Connect database to a specified CSV file. Variables and equations need to be turned into parameters with the Projection agent before they can be written. See Simple Connect Example for CSV for a simple example that uses the CSVWriter.

Option Default Description
decimalSeparator . (period) Specify a decimal separator.
file None Specify a CSV file path.
fieldSeparator , (comma) Specify a field separator.
header True Indicate if the header will be written.
name None Specify the name of the symbol in the Connect database.
quoting 0 Control field quoting behavior.
setHeader None Specify a string that will be used as the header.
skipElementText False Indicate if the set element text will be skipped.
toCSVArguments None Dictionary containing keyword arguments for the pandas.to_csv method.
trace inherited Specify the trace level for debugging output.
unstack False Specify the dimensions to be unstacked to the header row(s).

Detailed description of the options:

decimalSeparator = string (default=.)

Specify a decimal separator. [ . (period), , (comma)]

file = string (required)

Specify a CSV file path.

fieldSeparator = string (default=,)

Specify a field separator. [ , (comma), ; (SemiColon), \t (Tab)]

header = boolean (default=True)

Indicate if the header will be written.

name = string (required)

Specify the name of the symbol in the Connect database.

quoting = integer (default=0)

Control field quoting behavior. Use QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3). QUOTE_MINIMAL (0) instructs the writer to only quote those fields which contain special characters such as fieldSeparator. QUOTE_ALL (1) instructs the writer to quote all fields. QUOTE_NONNUMERIC (2) instructs the writer to quote all non-numeric fields. QUOTE_NONE (3) instructs the writer to never quote fields.

setHeader = string (optional)

Specify a string that will be used as the header. If an empty header is desired, the string can be empty.

skipElementText = boolean (default=False)

Indicate if the set element text will be skipped. If False, the set element text will be written in the last column of the CSV file.

toCSVArguments = dictionary (optional)

Dictionary containing keyword arguments for the pandas.to_csv method. Not all arguments of that method are exposed through the YAML interface of the CSVWriter agent. By specifying toCSVArguments, it is possible to pass arguments directly to the pandas.to_csv method that is used by the CSVWriter agent. For example, toExcelArguments: {index_label: ["index1", "index2", "index3"]}.

trace = integer (optional)

Specify the trace level for debugging output. For trace > 1 some scalar debugging output will be written to the log. For trace > 2 the intermediate data frames will be written abbreviated to the log. For trace > 3 the intermediate data frames will be written entirely to the log (potentially large output). If trace has not been set, the trace value, set by the Options agent, will be used.

unstack = boolean, list (default=False)

Specify the dimensions to be unstacked to the header row(s). If False (default) no dimension will be unstacked to the header row. If True the last dimension will be unstacked to the header row. If multiple dimensions should be unstacked to header rows, a list of integers providing the dimension numbers to unstack can be specified.

DomainWriter

The DomainWriter agent allows to rewrite domain information for existing Connect symbols and helps dealing with domain violations.

Option Scope Default Description
dropDomainViolations symbols False Indicate how to deal with domain violations.
name symbols None Specify the name of the symbol in the Connect database.
symbols root None Specify symbol specific options.
trace root inherited Specify the trace level for debugging output.
writeAll root auto Indicate if all symbols in the Connect database will be treated with the root dropDomainViolations setting.

Detailed description of the options:

dropDomainViolations = boolean (root)/boolean or string (symbols) (default=False)

The Connect symbols might have some domain violations. This agent allows to drop these domain violations so a write to GAMS or GDX works properly. Setting the root option dropDomainViolations: True together with writeAll: True will drop domain violations from all symbols in the Connect database. The symbols option also allows to drop domain violations. In the symbols section the dropDomainViolations attribute can be of type boolean (True or False) or of type string (before and after). If the attribute has not been set for the symbol, the attribute is inherited from the root attribute. The value before means that domain violations are dropped before a new domain is applied, see attribute name. The value after means that domain violations are dropped after a new domain is applied. The value True means that domain violations are dropped before and after a new domain is applied. False means to not drop domain violations.

name = string (required)

Specify a symbol name with index space for the Connect database. name requires the format symname(i1,i2,...,iN). The list of indices needs to coincide with the names of the actual GAMS domain sets for a regular domain. A relaxed domain is set if the index is quoted. For example name: x(i,'j') means that for the first index a regular domain with domain set i is established, while for the second index the universal domain * is used and a relaxed domain name j is set.

symbols = list (optional)

A list containing symbol specific options. Allows to concatenate a subset of symbols.

trace = integer (optional)

Specify the trace level for debugging output. For trace > 1 some scalar debugging output will be written to the log. For trace > 2 the intermediate data frames will be written abbreviated to the log. For trace > 3 the intermediate data frames will be written entirely to the log (potentially large output). If trace has not been set, the trace value, set by the Options agent, will be used.

writeAll = boolean or auto (default=auto)

Indicate if all symbols in the Connect database will be treated according to root attribute dropDomainViolations. If True, treat all symbols according to dropDomainViolations and ignore symbols. The default auto becomes True if there are no symbol options specified, otherwise False.

Filter

The Filter agent allows to reduce symbol data by applying filters on labels and numerical values. Here is a complete example that uses the Filter agent:

Set i / seattle, san-diego /
    j / new-york, chicago, topeka /;

Parameter d(i,j) /
seattle.new-york   2.5
seattle.chicago    1.7
seattle.topeka     1.8
san-diego.new-york 2.5
san-diego.chicago  1.8
san-diego.topeka   1.4
/;

$onEmbeddedCode Connect:
- GAMSReader:
    symbols:
      - name: d
- Filter:
    name: d
    newName: d_new
    labelFilters:
      - column: 1
        keep: ['seattle']
      - column: 2
        reject: ['topeka']
    valueFilters:
      - column: value
        rule: x<2.5
- GDXWriter:
    file: report.gdx
    symbols:
      - name: d_new
$offEmbeddedCode

The records of the parameter d are filtered and stored in a new parameter called d_new. Two label filters remove all labels except seattle from the first dimension and remove the label topeka from the second one. The remaining records are filtered by value where only values less than 2.5 are kept in the data. The resulting parameter d_new which is exported into report.gdx has only one record (seattle.chicago 1.7) left.

The following options are available for the Filter agent:

Option Scope Default Description
column labelFilters/valueFilters None Specify the column to which a filter is applied.
eps valueFilters True Used to keep or reject special value EPS.
infinity valueFilters True Used to keep or reject special value +INF.
keep labelFilters None Specify a list of labels to keep.
labelFilters root None Specify filters for index columns of a symbol.
na valueFilters True Used to keep or reject special value NA.
name root None Specify a symbol name for the Connect database.
negativeInfinity valueFilters True Used to keep or reject special value -INF.
newName root None Specify a new name for the symbol in the Connect database.
regex labelFilters None Specify a regular expression to be used for filtering labels.
reject labelFilters None Specify a list of labels to reject.
rule valueFilters None Specify a boolean expression to be used for filtering on numerical columns.
ruleIdentifier valueFilters x The identifier used for the value filter rule.
trace root inherited Specify the trace level for debugging output.
undf valueFilters True Used to keep or reject special value UNDF.
valueFilters root None Specify filters for numerical columns of a symbol.

Detailed description of the options:

column = integer or string (optional)

Used to specify the column on which a label filter or a value filter is applied. For label filters the index position can be specified using an integer. For value filters, the following strings are allowed depending on the symbol type:

  • Set: not allowed
  • Parameter: value, all
  • Variable and Equation: level, marginal, upper, lower, scale, all

Specifying the string all will apply the filter on all columns of the current filter (all index columns for labelFilters and all numerical columns for valueFilters).

eps = boolean (default=True)

Used to keep (True) or reject (False) special value EPS.

infinity = boolean (default=True)

Used to keep (True) or reject (False) special value +INF.

keep = list (optional)

A list of labels to be kept when applying the label filter. For each label filter it is only allowed to specify either keep, reject, or regex at a time.

labelFilters = list (optional)

A list containing label filters.

na = boolean (default=True)

Used to keep (True) or reject (False) special value NA.

name = string (required)

Specify the name of the symbol from the Connect database on whose data the filters will be applied.

negativeInfinity = boolean (default=True)

Used to keep (True) or reject (False) special value -INF.

newName = string (required)

Specify a new name for the symbol in the Connect database which will get the data after all filters have been applied. Each symbol in the Connect database must have a unique name.

regex = string (optional)

A string containing a regular expression that needs to match in order to keep the corresponding label. Uses a full match paradigm which means that the whole label needs to match the specified regular expression. For each label filter it is only allowed to specify either keep, reject, or regex at a time.

reject = string (optional)

A list of labels to be rejected when applying the label filter. For each label filter it is only allowed to specify either keep, reject, or regex at a time.

rule = string (optional)

Used to specify a boolean expression for a value filter. Each numerical value of the specified column is tested and the corresponding record is only kept if the expression evaluates to true. The string needs to contain Python syntax that is valid for pandas.Series. Comparison operators like >, >=, <, <=, ==, or != can be used in combination with boolean operators like & or |, but not and or or. Note that using & or | requires the operands to be enclosed in round brackets in order to form a valid expression. As an example, the expression ((x<=10) & (x>=0)) | (x>20) would keep only those values that are between 0 and 10 (included) or greater than 20.

ruleIdentifier = string (default=x)

Specifies the identifier that is used in the rule of a value filter.

trace = integer (optional)

Specify the trace level for debugging output. For trace > 1 some scalar debugging output will be written to the log. For trace > 2 the intermediate data frames will be written abbreviated to the log. For trace > 3 the intermediate data frames will be written entirely to the log (potentially large output). If trace has not been set, the trace value, set by the Options agent, will be used.

undf = boolean (default=True)

Used to keep (True) or reject (False) special value UNDF.

valueFilters = list (optional)

A list containing value filters.

GAMSReader

The GAMSReader allows reading symbols from the GAMS database into the Connect database. Without GAMS context (e.g. when running the gamsconnect script from the command line) this agent is not available and its execution will result in an exception.

Option Scope Default Description
name symbols None Specify the name of the symbol in the GAMS database.
newName symbols None Specify a new name for the symbol in the Connect database.
readAll root auto Indicate if all symbols in the GAMS database will be read into the Connect database.
symbols root None Specify symbol specific options.
trace root inherited Specify the trace level for debugging output.

Detailed description of the options:

name = string (required)

Specify the name of the symbol in the GAMS database.

newName = string (optional)

Specify a new name for the symbol in the Connect database. Each symbol in the Connect database must have a unique name.

readAll = boolean or auto (default=auto)

Indicate if all symbols in the GAMS database will be read into the Connect database. If True, read all symbols into the Connect database and ignore symbols. The default auto becomes True if there are no symbol options specified, otherwise False.

symbols = list (optional)

A list containing symbol specific options. Allows to read a subset of symbols.

trace = integer (optional)

Specify the trace level for debugging output. For trace > 1 some scalar debugging output will be written to the log. For trace > 2 the intermediate data frames will be written abbreviated to the log. For trace > 3 the intermediate data frames will be written entirely to the log (potentially large output). If trace has not been set, the trace value, set by the Options agent, will be used.

GAMSWriter

The GAMSWriter allows writing symbols in the Connect database to the GAMS database. Without GAMS context (e.g. when running the gamsconnect script from the command line) and as part of the connectOut command line option this agent is not available and its execution will result in an exception.

Option Scope Default Description
domainCheckType root/symbols default Specify if domain checking is applied or if records that would cause a domain violation are filtered.
duplicateRecords root/symbols all Specify how to deal with duplicate records.
mergeType root/symbols default Specify if data in a GAMS symbol is merged or replaced.
name symbols None Specify the name of the symbol in the Connect database.
newName symbols None Specify a new name for the symbol in the GAMS database.
symbols root None Specify symbol specific options.
trace root inherited Specify the trace level for debugging output.
writeAll root auto Indicate if all symbols in the Connect database will be written to the GAMS database.

Detailed description of the options:

domainCheckType = string (default=default)

Specify if Domain Checking is applied (checked) or if records that would cause a domain violation are filtered (filtered). If left at default it depends on the setting of $on/offFiltered if GAMS does a filtered load or checks the domains during compile time. During execution time default is the same as filtered.

duplicateRecords = string (default=all)

The Connect container can hold multiple records even for the same indexes. This is only a problem when exchanging the data with GAMS (and GDX). The attribute determines how the agent deals with duplicate records. With the default of all the GAMSWriter will fail in case duplicate records exist. With first the first record will be written to GAMS, with last the last record written to GAMS. With none none of the duplicate records will be written to GAMS. Note that the agent currently deals with duplicate records in a case sensitive way.

mergeType = string (default=default)

Specify if data in a GAMS symbol is merged (merge) or replaced (replace). If left at default it depends on the setting of $on/offMulti[R] if GAMS does a merge, replace, or triggers an error during compile time. During execution time default is the same as merge.

name = string (required)

Specify the name of the symbol in the Connect database.

newName = string (optional)

Specify a new name for the symbol in the GAMS database. Note, each symbol in the GAMS database must have a unique name.

symbols = list (optional)

A list containing symbol specific options. Allows to write a subset of symbols.

trace = integer (optional)

Specify the trace level for debugging output. For trace > 1 some scalar debugging output will be written to the log. For trace > 2 the intermediate data frames will be written abbreviated to the log. For trace > 3 the intermediate data frames will be written entirely to the log (potentially large output). If trace has not been set, the trace value, set by the Options agent, will be used.

writeAll = boolean or auto (default=auto)

Indicate if all symbols in the Connect database will be written to the GAMS database. If True, write all symbols to the GAMS database and ignore symbols. The default auto becomes True if there are no symbol options specified, otherwise False.

GDXReader

The GDXReader allows reading symbols from a specified GDX file into the Connect database.

Option Scope Default Description
file root None Specify a GDX file path.
name symbols None Specify the name of the symbol in the GDX file.
newName symbols None Specify a new name for the symbol in the Connect database.
readAll root auto Indicate if all symbols in the GDX file will be read into the Connect database.
symbols root None Specify symbol specific options.
trace root inherited Specify the trace level for debugging output.

Detailed description of the options:

file = string (required)

Specify a GDX file path.

name = string (required)

Specify the name of the symbol in the GDX file.

newName = string (optional)

Specify a new name for the symbol in the Connect database. Each symbol in the Connect database must have a unique name.

readAll = boolean or auto (default=auto)

Indicate if all symbols in the GDX file will be read into the Connect database. If True, read all symbols into the Connect database and ignore symbols. The default auto becomes True if there are no symbol options specified, otherwise False.

symbols = list (optional)

A list containing symbol specific options. Allows to read a subset of symbols.

trace = integer (optional)

Specify the trace level for debugging output. For trace > 1 some scalar debugging output will be written to the log. For trace > 2 the intermediate data frames will be written abbreviated to the log. For trace > 3 the intermediate data frames will be written entirely to the log (potentially large output). If trace has not been set, the trace value, set by the Options agent, will be used.

GDXWriter

The GDXWriter allows writing symbols in the Connect database to a specified GDX file.

Option Scope Default Description
duplicateRecords root/symbols all Specify how to deal with duplicate records.
file root None Specify a GDX file path.
name symbols None Specify the name of the symbol in the Connect database.
newName symbols None Specify a new name for the symbol in the GDX file.
symbols root None Specify symbol specific options.
trace root inherited Specify the trace level for debugging output.
writeAll root auto Indicate if all symbols in the Connect database will be written to the GDX file.

Detailed description of the options:

duplicateRecords = string (default=all)

The Connect container can hold multiple records even for the same indexes. This is only a problem when exchanging the data with GDX (and GAMS). The attribute determines how the agent deals with duplicate records. With the default of all the GDXWriter will fail in case duplicate records exist. With first the first record will be written to GDX, with last the last record written to GDX. With none none of the duplicate records will be written to GDX. Note that the agent currently deals with duplicate records in a case sensitive way.

file = string (required)

Specify a GDX file path.

name = string (required)

Specify the name of the symbol in the Connect database.

newName = string (optional)

Specify a new name for the symbol in the GDX file. Note, each symbol in the GDX file must have a unique name.

symbols = list (optional)

A list containing symbol specific options. Allows to write a subset of symbols.

trace = integer (optional)

Specify the trace level for debugging output. For trace > 1 some scalar debugging output will be written to the log. For trace > 2 the intermediate data frames will be written abbreviated to the log. For trace > 3 the intermediate data frames will be written entirely to the log (potentially large output). If trace has not been set, the trace value, set by the Options agent, will be used.

writeAll = boolean or auto (default=auto)

Indicate if all symbols in the Connect database will be written to the GDX file. If True, write all symbols to the GDX file and ignore symbols. The default auto becomes True if there are no symbol options specified, otherwise False.

LabelManipulator

The LabelManipulator agent allows to modify labels of symbols in the Connect database. Four different modes are available:

  • case: Applies either upper, lower, or capitalize casing to labels.
  • code: Replaces labels using a Python code.
  • map: Uses a 1-dimensional GAMS set to perform an explicit mapping of labels.
  • regex: Performs a replacement based on a regular expression.

Here is a complete example that uses the LabelManipulator agent in all four modes:

Set i / seattle, san-diego /
    j / new-york, chicago, topeka /
    map / chicago 'Berlin', san-diego 'Oslo' /;

Parameter d(i,j) /
seattle.new-york   2.5
seattle.chicago    1.7
seattle.topeka     1.8
san-diego.new-york 2.5
san-diego.chicago  1.8
san-diego.topeka   1.4
/;

$onEmbeddedCode Connect:
- GAMSReader:
    readAll: true
- LabelManipulator:
    map:
        setName: map
- LabelManipulator:
    case:
        rule: upper
- LabelManipulator:
    symbols:
      - name: d
    code:
        rule: x.split('-')[-1]
- LabelManipulator:
    symbols:
      - name: d
    regex:
        pattern: '[^O]$'
        replace: '\g<0>X'
- PythonCode:
    code: |
        print("Set i:\n", connect.container["i"].records)
        print("Set j:\n",connect.container["j"].records)
        print("Parameter d:\n",connect.container["d"].records)
$offEmbeddedCode

The first LabelManipulator applies a mapping provided by the set map to the labels of all symbols in the Connect database. This maps chicago to Berlin and san-diego to Oslo. The second LabelManipulator changes the labels of all symbols to upper case. The third LabelManipulator is only applied on symbol d and splits labels at - into a list and keeps the last entry. This changes NEW-YORK to YORK. The last LabelManipulator is also only applied on symbol d and adds an X to the end of all labels that do not end with an O. The resulting symbols look as follows:

Set i:
        uni element_text
0  SEATTLE             
1     OSLO 

Set j:
         uni element_text
0  NEW-YORK             
1    BERLIN             
2    TOPEKA 

Parameter d:
           i        j  value
0  SEATTLEX    YORKX    2.5
1  SEATTLEX  BERLINX    1.7
2  SEATTLEX  TOPEKAX    1.8
3      OSLO    YORKX    2.5
4      OSLO  BERLINX    1.8
5      OSLO  TOPEKAX    1.4

The following options are available for the LabelManipulator agent:

Option Scope Default Description
case root None Apply specified casing to labels.
code root None Replace labels using Python code.
invert map False Used to invert the mapping direction.
map root None Replace labels using a 1-dimensional GAMS set containing an explicit key-value mapping.
name symbols None Specify a symbol name for the Connect database.
newName symbols None Specify a new name for the symbol in the Connect database.
outputSet regex/case/code None Name of the output set that contains the applied mapping.
pattern regex None The regular expression that needs to match.
regex root None Replace labels using a regular expression.
replace regex None The rule used for replacing labels that match the given pattern.
rule case/code None case: The type of casing to be applied. code: Python function that defines the mapping behavior.
ruleIdentifier code x The identifier used for labels in the rule.
setName map None The name of the GAMS set used in map mode.
symbols root None Specify symbol specific options.
trace root inherited Specify the trace level for debugging output.
writeAll root auto Indicate if all symbols in the Connect database will be affected.

Detailed description of the options:

case = dictionary (optional)

Used for executing the LabelManipulator in case mode to apply a specified casing to labels. It is only allowed to specify either case, code, map or regex.

code = dictionary (optional)

Used for executing the LabelManipulator in code mode to replace labels using a Python function. The given Python function is executed with each label and its return value is used as the replacement. It is only allowed to specify either case, code, map or regex.

invert = boolean (default=False)

Used for controlling the mapping direction in map mode. If set to False (default), the labels that match a label of the provided 1-dimensional GAMS set are replaced by the corresponding set element text. If set to True the direction is inverted, meaning all labels that match a set element text of the GAMS set are replaced by the corresponding label.

map = dictionary (optional)

Used for executing the LabelManipulator in map mode to replace labels using a 1-dimensional GAMS set containing an explicit key-value mapping. It is only allowed to specify either case, code, map or regex.

name = string (optional)

Specify the name of the symbol in the GAMS database. Data of the symbol gets replaced if no newName is specified.

newName = string (optional)

Specify a new name for the symbol in the Connect database. The original symbol specified under name remains unchanged. Each symbol in the Connect database must have a unique name.

outputSet = string (optional)

Name of the output set that contains mappings that were actually applied on the symbol labels. Per default no output set is written. Providing a name for the output set indicates that an output set should be written to the Connect database. Note that each symbol in the Connect database must have a unique name. Supported by case, code and regex mode.

pattern = string (optional)

The regular expression that needs to match for a label to be replaced.

regex = dictionary (optional)

Used for executing the LabelManipulator in regex mode to replace labels using a regular expression. It is only allowed to specify either case, code, map or regex.

replace = string (optional)

A string that specifies the replacement for all labels for which a given pattern in regex mode matches.

rule = string (optional)

Can be specified in case and code mode.

case: The type of casing to be applied. Allowed values are:

  • lower: Change all labels to lower case.
  • upper: Change all labels to upper case.
  • capitalize: Change all labels to a capitalized casing - first letter becomes upper case, all others become lower case.

code: A Python function that defines the mapping behavior.

ruleIdentifier = string (default=x)

Specifies the identifier that is used in the rule of the code mode.

setName = string

Replace labels using a 1-dimensional GAMS set containing an explicit key-value mapping. A 1-dimensional GAMS set that contains an explicit key-value mapping to replace labels. If invert: False (default) all labels that match a label of the GAMS set will be replaced by the corresponding set element text.

symbols = dictionary (optional)

A list containing symbol specific options. Allows to execute the LabelManipulator on a subset of GAMS symbols in the Connect database.

trace = integer (optional)

Specify the trace level for debugging output. For trace > 1 some scalar debugging output will be written to the log. For trace > 2 the intermediate data frames will be written abbreviated to the log. For trace > 3 the intermediate data frames will be written entirely to the log (potentially large output). If trace has not been set, the trace value, set by the Options agent, will be used.

writeAll = boolean or auto (default=auto)

Indicate if the LabelManipulator is applied to all symbols of the database or only a subset of GAMS symbols in the Connect database. The default auto becomes True if there are no symbol options specified, otherwise False.

Options

The Options agent allows to set more general options that can affect the Connect database and other Connect agents. More specifically, the value of an option set via the Options agent can be inherited as a default value to Connect agents that utilize the considered option.

Option Default Description
trace 0 Specify the trace level for debugging output.

Detailed description of the options:

trace = integer (default=0)

Specify the trace level for debugging output. A trace level of 0 (default) means no debugging output. For trace > 0 the Connect database will write some scalar debugging output to the log. The debugging output of Connect agents depends on their implementation of trace, please refer to the corresponding documentation.

PandasExcelReader

The PandasExcelReader agent allows to read symbols (sets and parameters) from an Excel file into the Connect database. Its implementation is based on the pandas.DataFrame class and its I/O API method read_excel. The PandasExcelReader primarily aims to read Excel files that have been written by its counterpart - the PandasExcelWriter. See Simple Connect Example for Excel for a simple example that uses the PandasExcelReader.

Note
The PandasExcelReader supports .xlsx and .xlsm files. For other file formats, such as .xls or .ods files, it might be required to install additional Python packages. Please check the pandas.read_excel documentation for more information about supported file formats and required Python packages.
Option Scope Default Description
columnDimension root/symbols 1 Column dimension of the symbol.
drop symbols None Specify a string for dropping each row containing it in one of its labels.
excelFileArguments root None Dictionary containing keyword arguments for the pandas.ExcelFile constructor.
file root None Specify an Excel file path.
multiColumnBlankRow root/symbols True Indicator for existence of blank row after the column indexes (for columnDimension>1).
name symbols None Specify the name of the symbol in the Connect database.
range symbols None Specify the Excel range of a symbol.
readExcelArguments symbols None Dictionary containing keyword arguments for the pandas.read_excel method.
rowDimension root/symbols 1 Row dimension of the symbol.
symbols root None Specify symbol specific options.
trace root inherited Specify the trace level for debugging output.
type root/symbols par Control the symbol type.
valueSubstitutions symbols None Dictionary used for mapping in the value column of the DataFrame.

Detailed description of the options:

columnDimension = integer (default=1)

Column dimension: the number of rows in the data range that will be used to define the labels for columns. The first columnDimension rows of the data range will be used for labels.

drop = string (optional)

Specify a string for dropping each row containing it in one of its labels. The specified string is interpreted as a regular expression.

excelFileArguments = dictionary (optional)

Dictionary containing keyword arguments for the pandas.ExcelFile constructor.

file = string (required)

Specify an Excel file path.

multiColumnBlankRow = boolean (default=True)

For symbols where more than one dimension is in the columns, i.e. columnDimension>1 the PandasExcelReader expects a blank row before the data starts. This is also the shape the PandasExcelWriter writes:

Blank row between column headers and data

If multiColumnBlankRow is set to False, the PandasExcelReader expects for table with columnDimension>1 that this blank line is missing. This works properly with the exception of the following corner case:

Blank row between column headers and data missing and first data row is entirely blank

together with the following Connect instructions 

- PandasExcelReader:
    file: myfile.xlsx
    symbols:
      - name: s
        rowDimension: 1
        columnDimension: 2
        range: B2:E6
        type: set
        multiColumnBlankRow: False
        valueSubstitutions: { .nan: '' } # read dense

one would expect that the Connect database contains a set with the following elements k1*k3.(i1.j1,i2.j2,i3.j3) but Pandas interprets the row 4 (because it is entirely blank) as the row with the index name. Hence the data starts in row 5 and the Connect database is missing the k1 records: k2*k3.(i1.j1,i2.j2,i3.j3). In such a case one either needs the blank row between column indexes and data or manages to have this row not entirely empty (which can even be done outside the specified Excel range).

name = string (required)

Specify a symbol name for the Connect database. Note that each symbol in the Connect database must have a unique name.

range = string (required)

Specify the Excel range of a symbol using the format sheet!range. range can be either a single cell also known as open range (north-west corner like B2) or a full range (north-west and south-east corner like B2:D4). For symbols with columnDimension=0 and/or rowDimension=0, the ending row and/or ending column or the open range can be deduced and is used to restrict the data area.

readExcelArguments = dictionary (optional)

Dictionary containing keyword arguments for the pandas.read_excel method. Not all arguments of that method are exposed through the YAML interface of the PandasExcelReader agent. By specifying readExcelArguments, it is possible to pass arguments directly to the pandas.read_excel method that is used by the PandasExcelReader agent. For example, readExcelArguments: {keep_default_na: False}.

rowDimension = integer (default=1)

Row dimension: the number of columns in the data range that will be used to define the labels for the rows. The first rowDimension columns of the data range will be used for the labels.

symbols = list (required)

A list containing symbol specific options.

trace = integer (optional)

Specify the trace level for debugging output. For trace > 1 some scalar debugging output will be written to the log. For trace > 2 the intermediate data frames will be written abbreviated to the log. For trace > 3 the intermediate data frames will be written entirely to the log (potentially large output). If trace has not been set, the trace value, set by the Options agent, will be used.

type = string (default=par)

Control the symbol type. Supported symbol types are par for parameters and set for sets.

valueSubstitutions = dictionary (optional)

Dictionary used for mapping in the value column of the DataFrame. Each key in valueSubstitutions is replaced by its corresponding value. The replacement is only performed on the value column of the DataFrame which is the numerical value in case of a GAMS parameter and the set element text in case of a GAMS set. While it is possible to make arbitrary replacements this is especially useful for controlling sparse/dense reading. All NaN entries are removed automatically by default which results in a sparse reading behavior. Let's assume we have the following spreadsheet:

Two dimensional data containing NaN entries

Reading this data into a 2-dimensional parameter results in a sparse parameter in which all NaN entries are removed: 

'i1'.'j1' 2.5,
'i1'.'j2' 1.7,
'i2'.'j2' 1.8,
'i2'.'j3' 1.4

By specifying valueSubstitutions: { .nan: eps } we get a dense representation in which all NaN entries are replaced by GAMS special value EPS: 

'i1'.'j1' 2.5,
'i1'.'j2' 1.7,
'i1'.'j3' Eps,
'i2'.'j1' Eps,
'i2'.'j2' 1.8,
'i2'.'j3' 1.4

Beside eps there are the following other GAMS special values that can be used by specifying their string representation: inf, -inf, eps, na, and undef. See the GAMS Transfer documentation for more information.

Let's assume we have data representing a GAMS set:

Data representing a GAMS set

Reading this data into a 1-dimensional set results in a sparse set in which all NaN entries (those that do not have any set element text) are removed: 

'i1' 'text 1',
'i3' 'text 3'

By specifying valueSubstitutions: { .nan: '' } we get a dense representation: 

'i1' 'text 1',
'i2',
'i3' 'text 3'

It is also possible to use valueSubstitutions in order to interpret the set element text. Let's assume we have the following Excel data:

Data representing a GAMS set

Reading this data into a 2-dimensional set results in a dense set: 

'i1'.'j1' No,
'i1'.'j2' Y,
'i1'.'j3' Y,
'i2'.'j1' Y,
'i2'.'j2' Y,
'i2'.'j3' Y,
'i3'.'j1' Y,
'i3'.'j2' Y,
'i3'.'j3' N

By specifying valueSubstitutions: { 'N': .nan, 'No': .nan } we replace all occurrences of N and No by NaN which gets dropped automatically. Note that No has to be quotes in order to not be interpreted as False by the YAML parser: 

'i1'.'j2' Y,
'i1'.'j3' Y,
'i2'.'j1' Y,
'i2'.'j2' Y,
'i2'.'j3' Y,
'i3'.'j1' Y,
'i3'.'j2' Y

Fundamentals of Reading Data with PandasExcelReader

As mentioned at the start of this section, the PandasExcelReader works best with tables written by the PandasExcelWriter with a full range (north-west and south-east corner) specification. Nevertheless, the PandasExcelReader can also process tables not precisely in the format and shape given by PandasExcelWriter and also works with an open range (north-west corner only) specification. While the PandasExcelReader shares some functionality with the tool gdxxrw there are also significant differences and this section explains some of the perhaps unexpected behavior of this Connect agent.

  • Symbols with rowDimension=0 and/or columnDimension=0 have an artificial index in 0-dim index and the range specification need to include this artificial index. For example, if one wants to read a scalar, there are two artificial indexes rval and cval and the north-west corner starts in the artificial index row and column, i.e. range is B2 or B2:C3 in the following example:
Artificial indexes rval and cval when reading a scalar

The names of the artificial indexes are irrelevant, they can even be blank. But as a consequence of the artificial indexes, PandasExcelReader cannot read a scalar that is located in row 1 or column A. Similarly, symbols with dim>0 but either rowDimension or columnDimension equal to 0 need an artificial index:

Artificial index value when reading a symbol with rowDimension=0
Artificial index value when reading a symbol with columnDimension=0

Again, the name of the artificial index is irrelevant, it can even be blank. In both examples, the range starts in north-west corner B2. For symbols with rowDimension>0 and columnDimension>0 there is no artificial index.

  • Tables with more than one index in the columns can be read best if the column headers and the data are separated by a blank line (this is the way PandasExcelWriter writes such tables). The PandasExcelReader attribute multiColumnBlankRow allows some control. See the option descriptions for details.

  • Blank data is read as nan (not a number) and such records are dropped before the data is written to the Connect database. The valueSubstitutions attribute of PandasExcelReader allows to remap nan to other values. Blank index positions are treated very differently. The behavior depends on multiple factors.

    If PandasExcelReader encounters a blank index at the beginning of the rows it fills the index with nan. With a blank row index in the middle of the table (i.e. if there was a good label before in this column), PandasExcelReader repeats the previous index of this column. In case of columnDimension=1 blank column indexes are filled with Unnamed: n (where n in the nth column (zero based) in the sheet). Hence the following table

Table with blank row and column index cells (columnDimension=1)

is transformed into the following pandas.DataFrame (use PandasExcelReader attribute trace>2 to print intermediate data frames): 

               j1  Unnamed: 4    j3
     NaN NaN  NaN         6.0   NaN
     i2  i2   2.0         7.0  12.0
     i2  i3   3.0         NaN  13.0
     i2  i3   NaN         NaN   NaN
     i5  i3   5.0        10.0  15.0
     i6  i6   6.0        11.0  16.0

which arrives after dropping the nan values and indexes in the Connect database (here a display as a GAMS parameter) as: 

                    j1  Unnamed: 4          j3
     i2.i2           2           7          12
     i2.i3           3                      13
     i5.i3           5          10          15
     i6.i6           6          11          16

In case of columnDimension>1 blank column index at the beginning of the column index are filled with Unnamed: n_level_k (where n in the nth column (zero based) in the sheet and k is the column dimension). With a blank column index in the middle of the index columns (i.e. if there was a good label before in this row), PandasExcelReader repeats the previous index but might add a suffix .1, .2, ... to disambiguate the column names. Hence the following table

Table with blank row and column index cells (columnDimension=2)

is transformed into the following pandas.DataFrame (use PandasExcelReader attribute trace>2 to print intermediate data frames): 

             Unnamed: 3_level_0   k1    k1    k1    k4
             Unnamed: 3_level_1   j1    j2  j2.1    j4
     NaN NaN                NaN  NaN   6.0   NaN  17.0
     i2  i2                 NaN  2.0   7.0  12.0  18.0
     i2  i3                 NaN  3.0   NaN  13.0  19.0
     i2  i3                 NaN  NaN   NaN   NaN   NaN
     i5  i3                 NaN  5.0  10.0  15.0  20.0
     i6  i6                 NaN  6.0  11.0  16.0  21.0

which arrives after dropping the nan values and indexes in the Connect database (here a display as a GAMS parameter) as: 

                 k1.j1       k1.j2     k1.j2.1       k4.j4
     i2.i2           2           7          12          18
     i2.i3           3                      13          19
     i5.i3           5          10          15          20
     i6.i6           6          11          16          21

The PandasExcelReader attribute drop helps to get rid of unwanted indexes (e.g. drop: Unnamed or drop: "\."). Removing row indexes that result from the continuation of previous indexes is significantly harder. Hence such rows should be entirely empty (then the row is dropped because it has only nan values). It is best to avoid empty index rows and columns altogether.

  • When one specifies an open range (north-west corner only) the PandasExcelReader will read from this north-west corner all the way to the end of the sheet (press Ctrl-End in Excel to locate the cursor into the last cell of a sheet). It does not stop at blank row or column indexes as the tool gdxxrw does. This aggravates the situation with empty index cells. Hence the following table
Table with a non-blank cell at the end of the sheet

will result in the following pandas.DataFrame (use PandasExcelReader attribute trace>2 to print intermediate data frames): 

          j1   j2   j3  Unnamed: 5  Unnamed: 6  Unnamed: 7  Unnamed: 8
     i1  1.0  2.0  NaN         NaN         NaN         NaN         NaN
     i2  NaN  1.0  NaN         NaN         NaN         NaN         NaN
     i3  NaN  NaN  1.0         NaN         NaN         NaN         NaN
     i3  NaN  NaN  NaN         NaN         NaN         NaN         NaN
     i3  NaN  NaN  NaN         NaN         NaN         NaN         NaN
     i3  NaN  NaN  NaN         NaN         NaN         NaN         NaN
     i3  NaN  NaN  NaN         NaN         NaN         NaN       999.0

which arrives after dropping the nan values and indexes in the Connect database (here a display as a GAMS parameter) as: 

                 j1          j2          j3  Unnamed: 8
     i1           1           2
     i2                       1
     i3                                   1         999

PandasExcelWriter

The PandasExcelWriter agent allows to write symbols (sets and parameters) from the Connect database to an Excel file. Variables and equations need to be turned into parameters with the Projection agent before they can be written. Its implementation is based on the pandas.DataFrame class and its I/O API method to_excel. If the Excel file exists, the PandasExcelWriter appends to the existing file and if sheets exist the PandasExcelWriter overlays, i.e. writes contents to the existing sheets without removing the old contents. This behavior can be changed via the option excelWriterArguments. See Simple Connect Example for Excel for a simple example that uses the PandasExcelWriter.

Note
The PandasExcelWriter supports .xlsx and .xlsm files. .xls files are not supported. For other file formats, such as .ods files, it might be required to install additional Python packages and adjust the writers behavior, e.g. change the mode from append to write excelWriterArguments: {mode: w}. Please check the pandas.ExcelWriter documentation for more information about supported file formats and required Python packages.
The PandasExcelWriter per default writes merged cells. To deactivate writing merged cells, use the option toExcelArguments as follows toExcelArguments: {merge_cells: False}. Please be aware that writing merged cells may cause problems in append mode and with sheets overlaying. The user may change the writers behavior, e.g. from overlaying to replacing the sheet if it exits excelWriterArguments: {if_sheet_exists: replace}.
Attention
Please be aware of the following limitation when appending to an Excel file with formulas using the PandasExcelWriter: Whereas Excel stores formulas and the corresponding values, both Pandas I/O API methods read_excel and to_excel read/store either formulas or values, not both. As a consequence, when appending to an Excel file with formulas using the PandasExcelWriter, all the cells with the formulas will not have values anymore and a subsequent read by the PandasExcelReader results into NaN for cells with formulas. To avoid this, one way is to have a "read-only" input Excel file and write to a separate output Excel file. On Windows one can merge both Excel files at the end using tool win32.ExcelMerge (see Connect Example for Excel (executeTool win32.ExcelMerge)).
Option Scope Default Description
excelWriterArguments root None Dictionary containing keyword arguments for the pandas.ExcelWriter constructor.
file root None Specify an Excel file path.
name symbols None Specify the name of the symbol in the Connect database.
range symbols None Specify the Excel range of a symbol.
rowDimension root/symbols None Row dimension of the symbol.
symbols root None Specify symbol specific options.
toExcelArguments symbols None Dictionary containing keyword arguments for the pandas.to_excel method.
trace root inherited Specify the trace level for debugging output.
valueSubstitutions symbols None Dictionary used for mapping in the value column of the DataFrame.

Detailed description of the options:

excelWriterArguments = dictionary (optional)

Dictionary containing keyword arguments for the pandas.ExcelWriter constructor. For example, excelWriterArguments: {if_sheet_exists: replace} for deleting contents of an existing sheet before writing to it. To change the mode from append to write: excelWriterArguments: {mode: w}.

file = string (required)

Specify an Excel file path.

name = string (required)

Specify a symbol name for the Connect database.

range = string (required)

Specify the Excel range of a symbol using the format sheet!range. range can be either a single cell (north-west corner like B2) or a full range (north-west and south-east corner like B2:D4). For writing purposes, the south-east corner is ignored. Please note, that PandasExcelWriter always writes an index in the row and column. So even for a scalar or an indexed symbol with rowDimension=0 or dim-rowDimension=0 there will be some index information in the 0 index. For example, the following Connect script

parameter x0 / 3.14 /, x1 / i1 1, i2 2, i3 3 /;
$onEmbeddedCode Connect:
- GAMSReader:
    readAll: True
- PandasExcelWriter:
    file: x.xlsx
    symbols:
      - name: x0
        range: Sheet1!a1
      - name: x1
        rowDimension: 1
        range: Sheet1!d1
      - name: x1
        rowDimension: 0
        range: Sheet1!g1
$offEmbeddedCode

creates the following Excel output:

Value index for 0 index dimension

rowDimension = integer (optional)

Row dimension: The first rowDimension index positions of the symbol to be written will written to the rows. dim-rowDimension index positions will be written into the column headers.

symbols = list (required)

A list containing symbol specific options.

toExcelArguments = dictionary (optional)

Dictionary containing keyword arguments for the pandas.DataFrame.to_excel method. Not all arguments of that method are exposed through the YAML interface of the PandasExcelWriter agent. By specifying toExcelArguments, it is possible to pass arguments directly to the pandas.to_excel method that is used by the PandasExcelWriter agent. For example, toExcelArguments: {merge_cells: True}.

trace = integer (optional)

Specify the trace level for debugging output. For trace > 1 some scalar debugging output will be written to the log. For trace > 2 the intermediate data frames will be written abbreviated to the log. For trace > 3 the intermediate data frames will be written entirely to the log (potentially large output). If trace has not been set, the trace value, set by the Options agent, will be used.

valueSubstitutions = dictionary (optional)

Dictionary used for mapping in the value column of the DataFrame. Each key in valueSubstitutions is replaced by its corresponding value. The replacement is only performed on the value column of the DataFrame which is the numerical value in case of a GAMS parameter, variable or equation and the set element text in case of a GAMS set.

Projection

The Projection agent allows index reordering and projection onto a reduced index space of a GAMS symbol. For variables and equations a suffix (.l, .m, .lo, .up, or .scale) can be extracted and is written to a parameter. Otherwise, the type of the source symbol determines the type of the new symbol, unless asSet is set to True. Variables and equations can be turned into parameters with an extra index containing the labels level, marginal, lower, upper, and scale if the attribute asParameter is set to True. Moreover, if name is a list of scalar symbols of the same type (parameters, variables, or equations), they can be stored in a one-dimensional symbols of the same type with the index label being the name of the scalar symbol.

Option Default Description
aggregationMethod first Specify the aggregation method for the projection.
asParameter False Indicate that variable or equation symbols will be turned into a parameter with an extra index that contains the suffix.
asSet False Indicate that the new symbol is a set independent of the type of the source symbol.
name None Specify a symbol name with index space and potentially suffix for the Connect database.
newName None Specify a new name with index space for the symbol in the Connect database.
text None Element text for resulting sets.
trace inherited Specify the trace level for debugging output.

Detailed description of the options:

aggregationMethod = string (default=first)

Specify the method to aggregate when at least one index position is projected out. The default is first, meaning that the first record will be stored in the new symbol. For sets, variables, and equations (without a suffix specified) only first and last are meaningful. For parameters, variables, and equations with suffix many other aggregation methods are available and meaningful: max, mean, median, min, prod, sem (unbiased standard error of the mean), sum, std (standard deviation), nunique (number of distinct elements), first, last. The projection agent is based on pandas DataFrames and more detailed explanations of the aggregation method can be found at the pandas website.

asParameter = boolean (default=False)

If the symbol specified by name is a variable or equation and asParameter is set to True, the new symbol (after potential aggregationMethod is applied) will be a parameter with an additional index (at the end of the index list) that contains the suffix labels level, marginal, lower, upper, and scale and the corresponding suffix value.

asSet = boolean (default=False)

Usually the type of the source symbol and the use of a suffix with variables and equations determine the type of the target symbol. With asSet set to True the target symbol will be a set.

name = string or list of strings(required)

One either specifies a single symbol name with index space and potentially suffix for the Connect database or a list of symbol names of scalar symbols. In the prior case, name requires the format symname[.suffix](i1,i2,...,iN). The suffix is only allowed on variable and equation symbols and need to be either l, m, lo, up, or scale. The list of indices does not need to coincide with the names of the actual GAMS domain sets. This index list together with the index list specified for newName is solely intended to establish the index order in the symbol specified by newName. In the latter case (a list of symbol names) the symbols need to be scalar symbols of the same type (parameter, variable, or equation) and a new one-dimensional symbol (of the same type) is created (using newName) that holds the symbol names as labels.

newName = string (required)

Specify a new name with index space for the projected or reordered symbol in the Connect database. Note that each symbol in the Connect database must have a unique name. newName is given as symname(i1,i2,...,iN). The list of indices does not need to coincide with the names of the actual GAMS domain sets. This index list together with the index list specified for name is solely intended to establish the index order. Hence, the names in the index list need to be unique and only names that are part of the index list specified for name can be used. For example: name: p(I,j,k) and newName: q(k,i).

text = string (default=None)

Control the handling of element text if the resulting symbol is a set. If set to "", the text will be dropped. When left at default (None) and the projected symbol is a set, the element text of the original set will be used. For other symbols types, the text will be dropped. If text is a string, this string will be assigned to all elements. The string can contain place holders {i1} that will be replaced with the content of the matching index position. For example, text: "{i2} - {i1}: {element_text}", where {i1} and {i2} should be index space names in the symbol name with index space (attribute name). {element_text} refers to original element text (set) or string representation of a numerical value (parameter or variable/equation with a given suffix) of the source symbol.

trace = integer (optional)

Specify the trace level for debugging output. For trace > 1 some scalar debugging output will be written to the log. For trace > 2 the intermediate arrays and data frames will be written abbreviated to the log. For trace > 3 the intermediate arrays and data frames will be written entirely to the log (potentially large output). If trace has not been set, the trace value, set by the Options agent, will be used.

PythonCode

The PythonCode agent allows to execute arbitrary Python code. From within the code, it is possible to access the GAMS database via gams.db (if the PythonCode agent is running in a GAMS context) and the Connect database via connect.container. The GAMS database is an instance of GamsDatabase, whereas the Connect database is a GAMS Transfer Container object. Furthermore there is a predefined instructions list that can be filled with tasks that are automatically executed.

Option Default Description
code None Python code to be executed.

Detailed description of the options:

code = string (required)

Python code to be executed. The YAML syntax offers the pipe character (|) for specifying multi-line strings: 

- PythonCode:
    code: |
      print("Print from Python")
      # insert more Python code here

It is possible to generate instructions by appending tasks to the Python instructions list. A task is specified by using Python data structures that match the schema of a specific Connect agent. At the end of the Python code, all tasks in the instructions list are automatically generated and executed. The following example shows how to fill the instructions list with three PandasExcelWriter tasks that write different parameters (p0, p1, p2) into separate Excel workbooks (data_p0.xlsx, data_p1.xlsx, data_p2.xlsx). 

- GAMSReader:
    readAll: True
- PythonCode:
    code: |
      symbols = [ 'p0', 'p1', 'p2' ]
      for s in symbols:
        instructions.append(
        {
          'PandasExcelWriter':
          {
            'file': 'data_{}.xlsx'.format(s),
            'symbols': [{'name': s, 'rowDimension': connect.container.data[s].dimension, 'range': s+'!A1'}]
          }
        })

Using connect.container allows to access the Connect database directly in the Python code. The connect.container is a GAMS Transfer Container object and data within the container is stored as Pandas DataFrames. Please refer to the documentation of GAMS Transfer to learn more about GAMS Transfer and its functionalities.

The following complete example shows how to access and modify Connect container data and manually add a new symbol with the modified data to the Connect container:

Set i /1, 2, 3/
    i_mod;

$onembeddedCode Connect:
- GAMSReader:
    readAll: True
- PythonCode:
    code: |
        i_mod_records = [ 'i'+n for n in connect.container.data["i"].records.iloc[:,0] ]
        connect.container.addSet("i_mod", ["*"], records=i_mod_records)
- GAMSWriter:
    symbols:
      - name: i_mod
$offembeddedCode

display i, i_mod;

The first line takes the data of set i and adds an i at the beginning of each uel in the first column of the dataframe. The last line writes the modified dataframe as set i_mod to the Connect database.

Here is another complete example modifying Connect container data and adding a new symbol with the modified data:

Parameter p;

$onEcho > p_raw.csv
i,j,2000,2001
i1,j1,1,2
i2,j2,3,4
i3,j3,5,6
$offEcho

$onEmbeddedCode Connect:
- CSVReader:
    file: p_raw.csv
    name: p_raw
    header: True
    indexColumns: "1,2"
    valueColumns: "2:lastCol"
- PythonCode:
    code: |
        p_records = [ [r[0] + '_' + r[1]] + list(r) for i,r in connect.container.data["p_raw"].records.iterrows() ]
        connect.container.addParameter("p", ["*"]*4, records=p_records)
- GAMSWriter:
    symbols:
      - name: p
$offEmbeddedCode

display p;

In this example, we take the data of parameter p_raw and insert a column of the concatenated row dimensions into the first column of the dataframe. The modified dataframe is then added to the Connect container as records for the new symbol p. Here is a display of GAMS parameter p:

INDEX 1 = i1_j1

             2000        2001

i1.j1       1.000       2.000

INDEX 1 = i2_j2

             2000        2001

i2.j2       3.000       4.000

INDEX 1 = i3_j3

             2000        2001

i3.j3       5.000       6.000

RawCSVReader

The RawCSVReader allows reading of unstructured data from a specified CSV file into the Connect database. Due to performance issues this agent is recommended for small to medium sized unstructured CSV only. This reader works similarly compared to the RawExcelReader agent. It reads the entire CSV file and represents its content in a couple of GAMS sets:

  • r / r1, r2, ... / (rows)
  • c / c1, c2, ... / (columns)
  • vs(r,c) / s1.r1.c2 "cell text", ... / (cells with explanatory text)
  • vu(r,c,*) / s1.r1.c1."cell text" "cell text", ... (cells with potential GAMS label)

and a parameter vf(r,c) / r2.c2 3.14, ... / (cells with numerical values). Unlike RawExcelReader cells with a date will be not interpreted and stored in vs and vu. Cells with a string value will be stored in vs. If the string length exceeds the maximum length allowed for elements text, it will be truncated. RawCSVReader will try to represent the cell value as a number and if this succeeds stores the number in vf. Strings of GAMS special values names INF, EPS, NA, and UNDEF as well as TRUE and FALSE will be also converted to its numerical counterpart. It will also try to represent the cell value as a string and stores this as a label in the third position in vu. GAMS labels have a length limitation and hence RawCSVReader automatically shortens the label to fit this limit. RawCSVReader will provide a unique label (ending in ~n where n is an integer for strings exceeding the label length limit) for each string in the CSV file. The full string (if it fits) will be available as the element text of the vu record.

Option Default Description
cName c Symbol name for columns.
columnLabel C Label for columns.
file None Specify a CSV file path.
readAsString True Control the automatic pandas type conversion of cells.
readCSVArguments None Dictionary containing keyword arguments for the pandas.read_csv method.
rName r Symbol name for rows.
rowLabel R Label for rows.
trace inherited Specify the trace level for debugging output.
vfName vf Symbol name for cells with a numerical value.
vsName vs Symbol name for cells with an explanatory text.
vuName vu Symbol name for cells with a potential GAMS label.

Detailed description of the options:

cName = string (default=c)

Control the name of the set of columns.

columnLabel = string (default=C)

Control the labels for the set of columns (c).

file = string (required)

Specify a CSV file path.

readAsString = boolean (optional)

Control the type of cells returned by pandas.read_csv. If this is set to True (default) all cells are returned as string and the agent tries to interpret the string itself. If this is set to False pandas will try to infer the type. In such a case the agent can't distinguish between cells with 1 and 1.00 because pandas turned the integer 1 already into a float and the agent has no way for recovering the original cell.

readCSVArguments = dictionary (optional)

Dictionary containing keyword arguments for the pandas.read_csv method. By specifying readCSVArguments, it is possible to pass arguments directly to the pandas.read_csv method. For example, readCSVArguments: {delimiter: ';'}.

rName = string (default=r)

Control the name of the set of rows.

rowLabel = string (default=R)

Control the labels for the set of rows (r).

trace = integer (optional)

Specify the trace level for debugging output. For trace > 1 some scalar debugging output will be written to the log. For trace > 2 the cell values and it's processing will be written entirely to the log (potentially large output). If trace has not been set, the trace value, set by the Options agent, will be used.

vfName = string (default=vf)

Control the name of the parameter for cells with a numerical value.

vsName = string (default=vs)

Control the name of the set for cells with an explanatory text.

vuName = string (default=vu)

Control the name of the set for cells with a potential GAMS label.

RawExcelReader

The RawExcelReader allows reading of unstructured data from a specified Excel file into the Connect database. Due to performance issues this agent is recommended for small to medium sized unstructured Excel files only. This reader works similarly compared to the xlsdump tool. It reads the entire spreadsheet and represents its content in a couple of GAMS sets:

  • s /s1, s2,.../ (workbook sheets)
  • w / Sheet1, Sheet2, ... / (workbook sheets by name)
  • ws(s,w) / s1.Sheet1, s2.Sheet2, ... / (workbook map)
  • r / r1, r2, ... / (rows)
  • c / c1, c2, ... / (columns)
  • vs(s,r,c) / s1.r1.c2 "cell text", ... / (cells with explanatory text)
  • vu(s,r,c,*) / s1.r1.c1."cell text" "cell text", ... (cells with potential GAMS label)

and a parameter vf(s,r,c) / s1.r2.c2 3.14, ... / (cells with numerical values). Cells with a date will be stored in it's string representation in vu and as a Julian date in vf. Cells with a string value will be stored in vs. If the string length exceeds the maximum length allowed for elements text, it will be truncated. Excel offers many other cell value types. RawExcelReader will try to represent the cell value as a number and if this succeeds stores the number in vf. Strings of GAMS special values names INF, EPS, NA, and UNDEF will be also converted to its numerical counterpart. It will also try to represent the cell value as a string and stores this as a label in the fourth position in vu. GAMS labels have a length limitation and hence RawExcelReader automatically shortens the label to fit this limit. RawExcelReader will provide a unique label (ending in ~n where n is an integer for strings exceeding the label length limit) for each string in the workbook. The full string (if it fits) will be available as the element text of the vu record.

Option Default Description
cName c Symbol name for columns.
columnLabel C Label for columns.
file None Specify an Excel file path.
mergedCells False Control the handling of empty cells that are part of a merged Excel range.
rName r Symbol name for rows.
rowLabel R Label for rows.
sheetLabel S Label for workbook sheets.
sName s Symbol name for workbook sheets.
trace inherited Specify the trace level for debugging output.
vfName vf Symbol name for cells with a numerical value.
vsName vs Symbol name for cells with an explanatory text.
vuName vu Symbol name for cells with a potential GAMS label.
wName w Symbol name for workbook sheets by name.
wsName ws Symbol name for workbook map.

Detailed description of the options:

cName = string (default=c)

Control the name of the set of columns.

columnLabel = string (default=C)

Control the labels for the set of columns (c).

file = string (required)

Specify an Excel file path.

mergedCells = boolean (default=False)

Control the handling of empty cells that are part of a merged Excel range. If False, the cells are left empty. If True, the merged value is used in all cells. Note that setting this option to True has an impact on performance since the Excel workbook has to be opened in a non-read-only mode that results in non-constant memory consumption (no lazy loading).

rName = string (default=r)

Control the name of the set of rows.

rowLabel = string (default=R)

Control the labels for the set of rows (r).

sheetLabel = string (default=S)

Control the labels for the set of workbook sheet (s).

sName = string (default=s)

Control the name of the set of workbook sheets.

trace = integer (optional)

Specify the trace level for debugging output. For trace > 1 some scalar debugging output will be written to the log. For trace > 2 the cell values and it's processing will be written entirely to the log (potentially large output). If trace has not been set, the trace value, set by the Options agent, will be used.

vfName = string (default=vf)

Control the name of the parameter for cells with a numerical value.

vsName = string (default=vs)

Control the name of the set for cells with an explanatory text.

vuName = string (default=vu)

Control the name of the set for cells with a potential GAMS label.

wName = string (default=w)

Control the name of the set of workbook sheets by name.

wsName = string (default=ws)

Control the name of the set of the workbook map.

SQLReader

The SQLReader agent allows to read symbols (sets and parameters) from a specified database management system into the Connect database. It connects to MySQL, Postgres, MS-SQL (SQL-Server), SQLite and PyODBC through their respective python packages to provide native SQL query support. Further, it also utilizes pandas.DataFrame class' I/O API method read_sql to connect to any other database provided the relevant drivers are present on the system. See Simple Connect Example for SQL for a simple example that uses the SQLReader.

Note
The connectivity to MS-Access databases is available on Windows only and requires a 64-bit MS-Access ODBC driver. See connection for more information.
Option Scope Default Description
connection root None Connection dictionary to specify credentials for the database.
connectionArguments root None Dictionary containing keyword arguments for the connect constructor of the respective SQL library or for the sqlalchemy.create_engine constructor.
connectionType root sqlite Specify the connection type to be used in order to connect to that database.
dTypeMap root/symbols None Dictionary used to specify the dtype of columns.
indexSubstitutions symbols True Dictionary used for substitutions in the index columns.
name symbols None Specify the name of the symbol in the Connect database.
query symbols None Specify the SQL query.
readSQLArguments symbols None Dictionary containing keyword arguments for the pandas.read_sql method.
symbols root None Specify symbol specific options.
trace root inherited Specify the trace level for debugging output.
type root/symbols par Control the symbol type.
valueColumns symbols inferred Specify columns to get the values from.
valueSubstitutions symbols None Dictionary used for mapping in the value column of the DataFrame.

Detailed description of the options:

connection = dict (required)

Allows to specify the credentials to access the database. Below are examples for connection dictionaries based on the selected connectionType.

SQLite: 

connection: {'database': 'absolute//path//to//datafile.db'}

Postgres/MySQL/SQLServer: 

connection: {'user': <username of the database server>, 'password': <password>, 'host': <hostname or ip adress>, 'port': <port number of remote machine>, 'database': <database name which you want to connect to>}

MS-Access: 

connection: {'DRIVER': 'Microsoft Access Driver (*.mdb, *.accdb)', 'DBQ': 'absolute//path//to//datafile.db'}
Note
The connectivity to MS-Access databases is available on Windows only and requires a 64-bit MS-Access ODBC driver. If no MS-Access is installed or a 32-bit version of MS-Access, download and install a 64-bit MS-Access ODBC Driver as a redistributable package from MS (e.g. MS-Access 2013 Runtime_x64).

SQLAlchemy:

Use connectionType: sqlalchemy to connect to various databases. Note that the argument drivername: <dialect+driver> is required.

For dialect SQLite: 

connection: {'drivername': 'sqlite', 'database': 'absolute//path//to//datafile.db'}

For dialect Postgres: 

connection: {'drivername': 'postgresql+psycopg2', 'username': <username of the database server>, 'password': <password>, 'host': <hostname or ip adress>, 'port': <port number of remote machine>, 'database': <database name which you want to connect to>}

For dialect MySQL: 

connection: {'drivername': 'mysql+pymysql', 'username': <username of the database server>, 'password': <password>, 'host': <hostname or ip adress>, 'port': <port number of remote machine>, 'database': <database name which you want to connect to>}

For dialect MS-SQL(SQLServer): 

connection: {'drivername': 'mssql+pymssql', 'username': <username of the database server>, 'password': <password>, 'host': <hostname or ip adress>, 'port': <port number of remote machine>, 'database': <database name which you want to connect to>}

For dialect MS-Access: 

connection: {'drivername': 'access+pyodbc', 'query': {'odbc_connect': 'DRIVER={Microsoft Access Driver (*.mdb, *.accdb)};DBQ=absolute//path//to//datafile.db;'}}
Note
Connecting to databases other than SQLite, Postgres, MySQL and MS-SQL through SQLAlchemy would require the respective driver of that database to be present on the system. For example, in the above example sqlalchemy-access must be available in order to connect to MS-Access through SQLAlchemy.

PyODBC:

Use connectionType: pyodbc to connect to various databases. Therefore, either specify the DSN (Data Source Name): 

connection: {'DSN': <Data Source Name>}

or use: 

connection: {'user': <username of the database server>, 'password': <password>, 'host': <hostname or ip adress>, 'port': <port number of remote machine>, 'database': <database name which you want to connect to>, 'driver': <name of the ODBC driver>}

connectionArguments = dict (optional)

Dictionary containing keyword arguments for the connect constructor of the respective SQL library or for the sqlalchemy.create_engine constructor.

connectionType = string (default=sqlite)

Following is the list of valid options for connection type.

  1. sqlite (default)
  2. postgres
  3. mysql
  4. sqlserver
  5. access
  6. sqlalchemy
  7. pyodbc

A connection using pyodbc can be established to any database for which a DSN (Data Source Name) is available on the system. The pyodbc connection works readily on windows platform where DSN is already setup. Further, for non-windows platforms, the user is responsible for installing the pyodbc package and handling any other dependencies for the local python installation.

dTypeMap = dict (optional)

Allows to specify the dtype of columns in a dictionary as key: value pairs, i.e. <column>: <dtype>.

indexSubstitutions = dict (optional)

Dictionary used for substitutions in the index columns. Each key in indexSubstitutions is replaced by its corresponding value. This option allows arbitrary replacements in the index columns.

name = string (required)

Name of the symbol in the Connect database. The name must be unique for each symbol.

query = string (required)

Specify the SQL query which will fetch the desired table from the database system.

readSQLArguments = dict (optional)

Dictionary containing keyword arguments for the pandas.read_sql method. Not all arguments of that method are exposed through the YAML interface of the SQLReader agent. By specifying readSQLArguments, it is possible to pass arguments directly to the pandas.read_sql method that is used by the SQLReader agent if connectionType is sqlalchemy. If connectionType is not sqlalchemy, this option can be used to parameterize the SQL query. A small example showing how this option can be used is given below. 

query: "SELECT i,j FROM stock_table WHERE value_col > %(value)s"
readSQLArguments: {'value': 10}
Note
Different Database management systems use different parameter markers and some do not support named parameter markers, e.g., PyODBC.

symbols = list (required)

A list containing symbol specific options.

trace = integer (optional)

Specify the trace level for debugging output. For trace > 1 some scalar debugging output will be written to the log. For trace > 2 the intermediate arrays and data frames will be written abbreviated to the log. For trace > 3 the intermediate arrays and data frames will be written entirely to the log (potentially large output). If trace has not been set, the trace value, set by the Options agent, will be used.

type = string (default=par)

Control the symbol type. Supported symbol types are par for GAMS parameters and set for GAMS sets.

valueColumns = list or string (optional)

Specify columns to get the values from. The value columns contain numerical values in case of a GAMS parameter and set element text in case of a GAMS set. The columns are given as column names represented as a list of strings. For example: valueColumns: ["i1", "i2"]. If there is more than one value column specified, the column names are stacked to index automatically. As string one can specify the symbolic constant lastCol (i.e. valueColumns: "lastCol") or an empty string (i.e. valueColumns: ""). When lastCol is passed the last column will be treated as a value column and all the other columns will be treated as index columns. When an empty list is passed all columns will be treated as index columns. Specifying an empty list is only valid for symbol type set since symbol type par requires at least one value column. The default for symbol type par is lastCol and the default for symbol type set is an empty string.

valueSubstitutions = dict (optional)

Dictionary used for mapping in the value column of the DataFrame. Each key in valueSubstitutions is replaced by its corresponding value. The replacement is only performed on the value column of the DataFrame which is the numerical value in case of a GAMS parameter and the set element text in case of a GAMS set. While it is possible to make arbitrary replacements this is especially useful for controlling sparse/dense reading.

SQLWriter

The SQLWriter agent allows to write symbols (sets and parameters) from the Connect database to a specified database management system. Variables and equations need to be turned into parameters with the Projection agent before they can be written. It connects to MySQL, Postgres, MS-SQL (SQL-Server), SQLite and PyODBC through their respective python packages to provide faster write operations. Further, it also utilizes pandas.DataFrame class' I/O API method to_sql to connect to any other database provided the relevant drivers are present on the system. See Simple Connect Example for SQL for a simple example that uses the SQLWriter.

Note
The connectivity to MS-Access databases is available on Windows only and requires a 64-bit MS-Access ODBC driver. See connection for more information.
Option Scope Default Description
connection root None Connection dictionary to specify credentials for the database.
connectionArguments root None Dictionary containing keyword arguments for the connect constructor of the respective SQL library or for the sqlalchemy.create_engine constructor.
connectionType root None Specify the connection type to be used in order to connect to that database.
ifExists root/symbols fail Specify the behavior when a table with the same name exists in the database/schema.
insertMethod root/symbols default Specify the insertion method to be used to write the table in the database.
name symbols None Specify the name of the symbol in the Connect database.
schemaName root/symbols None Specify the schema name.
symbols root None Specify symbol specific options.
tableName symbols None Specify the SQL table/relation in the provided database/schema.
toSQLArguments root/symbols None Dictionary containing keyword arguments for the pandas.to_sql method.
trace root inherited Specify the trace level for debugging output.
unstack root/symbols False Indicate if the last index column will be used as a header row.
valueSubstitutions root/symbols None Dictionary used for mapping in the value column of the DataFrame.
writeAll root auto Indicate if all set and parameter type symbols in the Connect database will be written to the specified DBMS.

Detailed description of the options:

connection = dict (required)

Allows to specify the credentials to access the database. See SQLReader connection for further details and some examples.

Note
The connectivity to MS-Access databases is available on Windows only and requires a 64-bit MS-Access ODBC driver. If no MS-Access is installed or a 32-bit version of MS-Access, download and install a 64-bit MS-Access ODBC Driver as a redistributable package from MS (e.g. MS-Access 2013 Runtime_x64).

connectionArguments = dict (optional)

Dictionary containing keyword arguments for the connect constructor of the respective SQL library or for the sqlalchemy.create_engine constructor.

connectionType = string (default=sqlite)

Following is the list of valid options for connection type.

  1. sqlite (default)
  2. postgres
  3. mysql
  4. sqlserver
  5. access
  6. sqlalchemy
  7. pyodbc

A connection using pyodbc can be established to any database for which a DSN (Data Source Name) is available on the system. The pyodbc connection works readily on windows platform where DSN is already setup. Further, for non-windows platforms, the user is responsible for installing the pyodbc package and handling any other dependencies for the local python installation.

Note
pyodbc provides a simple and consistent API to connect to many different databases using the Open Database Connectivity (ODBC) interface. This introduces the challenge that database specific properties are not taken into consideration. For example, different databases have different escaping syntax in order to escape special characters in column names of a table. Thus, the implementation for connection type pyodbc is kept as a general purpose connector to different databases while not escaping the special characters.

ifExists = string (default=fail)

Specify the behavior when a table with the same name exists in the database/schema. Valid values are fail, replace and append.

insertMethod = string (default=default)

Specify the insertion method to be used to write the table in the database. Valid values are bcp, bulkInsert, and default.

Note
The options bulkInsert and bcp is not available with connection type sqlalchemy. Further, the option bcp is reserved for connection type sqlserver. This is useful when the SQL-Server database is available remotely. The bcp method uses MS-SQL's command line utility of the same name to insert huge amount of data. In order to use this insertion method, the user must download and install the utility as per the system requirements. The utility is available for Windows as well as for Linux and macOS. On the other hand, if the SQL-Server database is available locally, then the bulkInsert method can be used and the bcp utility is not required.
The option bulkInsert uses special SQL queries for connection type mysql, postgres, sqlserver and access. It creates a temporary csv file and then imports the same in the database. It is to be noted that for MySQL the option LOCAL_INFILE must be enabled at the server side for this method to work successfully.

name = string (required)

Specify the name of the symbol in the Connect database.

schemaName = string (optional)

Specify the schema name for writing the table to the correct location. In Postgres, by default, it writes to a public schema already present in every database.

symbols = list (required)

A list containing symbol specific options.

tableName = string (required)

Name of the SQL table/relation in the provided database/schema.

toSQLArguments = dict (optional)

Dictionary containing keyword arguments for the pandas.to_sql method. Not all arguments of that method are exposed through the YAML interface of the SQLWriter agent. By specifying toSQLArguments, it is possible to pass arguments directly to the pandas.to_sql method that is used by the SQLWriter agent if connectionType is sqlalchemy.

trace = integer (optional)

Specify the trace level for debugging output. For trace > 1 some scalar debugging output will be written to the log. For trace > 2 the intermediate arrays and data frames will be written abbreviated to the log. For trace > 3 the intermediate arrays and data frames will be written entirely to the log (potentially large output). If trace has not been set, the trace value, set by the Options agent, will be used.

unstack = boolean (default=False)

Indicate if the last index column will be used as a header row.

valueSubstitutions = dict (optional)

Dictionary used for mapping in the value column of the DataFrame. Each key in valueSubstitutions is replaced by its corresponding value. The replacement is only performed on the value column of the DataFrame which is the numerical value in case of a GAMS parameter, variable or equation and the set element text in case of a GAMS set.

writeAll = string (default=auto)

Indicate if all set and parameter type symbols in the Connect database will be written to the specified DBMS. If True, the table name in the database is defined by the name of the symbol that is written to that table.

Examples

This section provides a collection of more complex examples. For simple examples see section Getting Started.

Connect Example for Excel (executeTool win32.ExcelMerge)

The following example shows how to read and write Excel files in Connect. On Windows with Excel installed, the output sheets are merged back into the input workbook using tool win32.ExcelMerge. The entire code is listed at the end of the example. This model is part of DataLib as model connect05. First, the original matrix a is read using the GAMSReader and is then written to input.xlsx using the PandasExcelWriter. After clearing symbols i and a in the GAMS database, the PandasExcelReader is used to read file input.xlsx back in and create parameter a in the Connect database. The Projection agent extracts set i from parameter a. With the GAMSWriter, symbols i and a are written to the GAMS database. The tool linalg.invert calculates the inverse inva of a which is then written to output.xlsx using Connect's GAMSReader and PandasExcelWriter at execution time. The following lines then check if the code is not executed on a UNIX system and if Excel is available. If both is true, output.xlsx is merged into input.xlsx using tool win32.ExcelMerge and both symbols inva and a can be read from input.xslx with a single instance of the PandasExcelReader. If the code is executed on a UNIX system and/or Excel is not available, output.xlsx can not be merged into input.xlsx, and both files need to be read to create the symbols inva and a. The last part makes sure that inva is the inverse of a.

set i / i1*i3 /; alias (i,j,k);
table a(i,j) 'original matrix'
      i1     i2     i3
i1    1      2      3
i2    1      3      4
i3    1      4      3
;
$onEmbeddedCode Connect:
- GAMSReader:
    symbols:
      - name: a
- PandasExcelWriter:
    file: input.xlsx
    symbols:
      - name: a
$offEmbeddedCode

$onMultiR
$clear i a

$onEmbeddedCode Connect:
- PandasExcelReader:
    file: input.xlsx
    symbols:
      - name: a
        range: a!a1
- Projection:
    name: a(i,j)
    newName: i(i)
    asSet: True
- GAMSWriter:
    writeAll: True
$offEmbeddedCode i a

parameter
  inva(i,j) 'inverse of a'
  chk(i,j)  'check the product a * inva'
  ;

executeTool.checkErrorLevel 'linalg.invert i a inva';

EmbeddedCode Connect:
- GAMSReader:
    symbols:
      - name: inva
- PandasExcelWriter:
    file: output.xlsx
    symbols:
      - name: inva
endEmbeddedCode

Scalar mergedRead /0/;
executeTool 'win32.msappavail Excel';
mergedRead$(errorLevel=0) = 1;

if (mergedRead,
    executeTool.checkErrorLevel 'win32.excelMerge output.xlsx input.xlsx';
    EmbeddedCode Connect:
    - PandasExcelReader:
        file: input.xlsx
        symbols:
          - name: a
            range: a!a1
          - name: inva
            range: inva!a1
    - GAMSWriter:
        writeAll: True
    endEmbeddedCode a inva
else
    EmbeddedCode Connect:
    - PandasExcelReader:
        file: input.xlsx
        symbols:
          - name: a
            range: a!a1
    - PandasExcelReader:
        file: output.xlsx
        symbols:
          - name: inva
            range: inva!a1
    - GAMSWriter:
        writeAll: True
    endEmbeddedCode a inva
);

chk(i,j) = sum{k, a(i,k)*inva(k,j)};
chk(i,j) = round(chk(i,j),15);
display a,inva,chk;
chk(i,i) = chk(i,i) - 1;
abort$[card(chk)] 'a * ainv <> identity';

Connect Example for Excel

The following example shows how to read and write Excel files in Connect. The entire code is listed at the end of the example. This model is part of DataLib as model connect01. The example (inspired by the model herves) reads a 3-dimensional parameter from a spreadsheet that has one row index (code) at the left side of the table and the other row index (labId) at the right of the table. A column index (cut) is at the top of the table. The column index consists of floating-point numbers. The goal it to read the data into GAMS but modify the labels of some sets: Only the first two decimal digits of the elements in cut are significant. Moreover, the labId should be prefixed with an L. A new spreadsheet with the new labels should be written. The layout of the table should remain with the exception of moving the labId column also to the left. Here is a screenshot of the original table:

Original spreadsheet data of table raw

The following GAMS code uses a separate GAMS program (getdata.gms) to get the raw data from the original spreadsheet. Connect runs inside a compile-time embedded code section and uses the Connect agent RawExcelReader to get the raw Excel data. In some subsequent GAMS code the sets rr and cut[Id] as well as the parameter raw are filled knowing the layout of the table (the code is written in a way that the table can grow). This GAMS program gets executed and instructed to create a GDX file. In a compile-time embedded Connect section the relevant symbols (rr, cutId, and raw) are read from this GDX file. The Projection agent extracts the domain labid from the set rr and some Python code using Connect agent PythonCode makes the label adjustments and sorts the data nicely. The Python code uses the connect.container methods to read from and write to the Connect database. Finally, the GAMSWriter agent sends the data to GAMS. In the main program at execution-time an embedded Connect code section exports the labdata parameter in the required form (after reading it from GAMS with the GAMSReader agent). Here is a screenshot of the resulting table:

Table in newly created spreadsheet with new labels and layout

In the remainder of the GAMS code another execution-time embedded Connect code is used to read the data back from the newly created spreadsheet using Connect agent PandasExcelReader. The set rr is created from parameter labdata using the Projection agent and everything is written back to GAMS with Connect agent GAMSWriter. The original data and the data from the newly created spreadsheet are exported to GDX (using execute_unload) and compared to verify that the data is identical by calling gdxdiff.

Set code, labId, cut, rr(code<,labId);
parameter labdata(code,labid,cut);
$onEcho > getdata.gms
* Symbols for RawExcelReader
alias (u,*); Set s,w,r,c,ws(s,w),vs(s,r,c),vu(s,r,c,u); Parameter vf(s,r,c);
$onEmbeddedCode Connect:
- RawExcelReader:
    file: labdata.xlsx
- GAMSWriter:
    writeAll: True
$offEmbeddedCode

* Symbols to be filled
alias (*,code,labId,cut); Parameter raw(code,labId,cut); Set cutId, rr(code,labId)
Set cX(c,cut) 'column index', rX(r,code,labId) 'row index';
Singleton set cLast(c); Scalar lastPos;
loop(ws(s,'ZAg'),
  lastPos = smax(vu(s,r,c,u), c.pos); cLast(c) = c.pos=lastPos;
  loop(r$(ord(r)>4),
    rX(r,code,labId) $= vu(s,r,'C1',code) and vu(s,r,cLast,labId));
  loop(c$(ord(c)>1 and not cLast(c)),
    cX(c,cut) $= vu(s,'R4',c,cut));
  loop((rX(r,code,labId),cX(c,cut)),
    raw(code,labId,cut) = vf(s,r,c))
  loop(cX(c,cut),
    cutId(cut) = yes)
);
option rr<rX;
$offEcho
$call.checkErrorLevel gams getdata.gms lo=%gams.lo% gdx=getdata.gdx
$onEmbeddedCode Connect:
- GDXReader:
    file: getdata.gdx
    symbols: [ {name: rr}, {name: raw}, {name: cutId, newName: cut} ]
- Projection:
    name: rr(code,labid)
    newName: labid(labid)
- PythonCode:
    code: |
      labid_records = sorted([ 'L'+t[0] for t in connect.container.data['labid'].records.values ], key=lambda t: int(t[1:]))
      rr_records = sorted([ (t[0],
                               'L'+t[1]) for t in connect.container.data['rr'].records.values ], key=lambda t: int(t[0]))
      # Trim elements of set cut to two decimal places
      cut_records = sorted([ '{:.2f}'.format(float(t[0])) for t in connect.container.data['cut'].records.values ], key=float)
      labdata_records = [ (t[0],
                             'L'+t[1],
                             '{:.2f}'.format(float(t[2])),
                             t[-1]) for t in connect.container.data['raw'].records.values ]

      connect.container.addSet('labid_mod', ['*'], records=labid_records)
      connect.container.addSet('rr_mod', ['*']*2, records=rr_records)
      connect.container.addSet('cut_mod', ['*'], records=cut_records)
      connect.container.addParameter('labdata', ['*']*3, records=labdata_records)
- GAMSWriter:
    symbols: [ {name: labid_mod, newName: labid}, {name: rr_mod, newName: rr}, {name: cut_mod, newName: cut}, {name: labdata} ]
$offEmbeddedCode
execute_unload 'labdata.gdx', labdata, cut, rr;

* Reintroduce 0 (zeros)
labdata(rr,cut) = labdata(rr,cut) + eps;

execute 'rm -f labdatanew.xlsx';
* Write new workbook with good table
EmbeddedCode Connect:
- GAMSReader:
    symbols: [ {name: labdata} ]
- PandasExcelWriter:
    file: labdatanew.xlsx
    symbols:
      - name: labdata
        rowDimension: 2
        range: ZAg!A4
endEmbeddedCode
option clear=rr, clear=labdata;

EmbeddedCode Connect:
- PandasExcelReader:
    file: labdatanew.xlsx
    symbols:
      - name: labdata
        rowDimension: 2
        columnDimension: 1
        range: ZAg!A4
- Projection:
    name: labdata(code,labid,cut)
    newName: rr(code,labid)
    asSet: True
- GAMSWriter:
    writeAll: True
endEmbeddedCode
execute_unload 'labdatanew.gdx', labdata, cut, rr;
execute.checkErrorLevel 'gdxdiff labdata.gdx labdatanew.gdx > %system.NullFile%';

Connect Example for CSV

The following example shows how to read and write CSV files in Connect. The entire code is listed at the end of the example. This model is part of DataLib as model connect02. It starts out with defining some data (stockprice) in a table statement in GAMS. With compile-time embedded Connect code utilizing the GAMSReader agent to bring this data into Connect and exporting it as a CSV file with agent CSVWriter. The GDXWriter agent also creates a GDX file with the data which is then used in a subsequent call to feed gdxdump that produces the same CSV file as CSVWriter. The text comparison tool diff is used to compare the two CSV files. The CSV file look as follows:

"date_0","AAPL","GOOG","MMM","MSFT","WMT"
"2012-20-11",12.124061,314.008026,60.966354,21.068886,46.991535
"2112-20-11",12.139372,311.741516,60.731037,20.850344,47.150307
"2212-20-11",12.203673,313.674286,61.467381,20.890808,46.991535
"2312-20-11",12.350039,315.387848,62.401108,21.068886,47.626663
"2712-20-11",12.448025,318.929565,62.461876,21.076981,47.499634
"2812-20-11",12.328911,318.655609,61.604042,20.898905,47.420238
"2912-20-11",12.404848,320.000549,62.332813,21.060795,47.626663
"3012-20-11",12.401172,321.744019,62.044331,21.012224,47.444057

In remainder of the example this CSV file is read back via the Connect agent CSVReader. The code also utilizes the tool csv2gdx to read the CSV file into a GDX file. The code compares the results of both methods. Csv2gdx also creates sets with the index elements as Dim1, Dim2, ... Therefore, Connect utilizes the Projection agent to extract the index sets date and symbol from the parameter stockprice as sets Dim1 and Dim2. The Connect agent GDXWriter creates a GDX file of the Connect database which then can be compared with the GDX file created by csv2gdx. The GDX comparison tool gdxdiff is used to compare the two GDX files.

Set date,symbol;
Table stockprice(date<,symbol<)
                AAPL       GOOG       MMM      MSFT       WMT
2012-20-11 12.124061 314.008026 60.966354 21.068886 46.991535
2112-20-11 12.139372 311.741516 60.731037 20.850344 47.150307
2212-20-11 12.203673 313.674286 61.467381 20.890808 46.991535
2312-20-11 12.350039 315.387848 62.401108 21.068886 47.626663
2712-20-11 12.448025 318.929565 62.461876 21.076981 47.499634
2812-20-11 12.328911 318.655609 61.604042 20.898905 47.420238
2912-20-11 12.404848 320.000549 62.332813 21.060795 47.626663
3012-20-11 12.401172 321.744019 62.044331 21.012224 47.444057
;

* Use Connect CSVWriter to write GAMS data in CSV format moving the symbol index into the column (unstack: True)
$onEmbeddedCode Connect:
- GAMSReader:
    symbols: [ {name: stockprice} ]
- GDXWriter:
    file: sp_connect.gdx
    writeAll: True
- CSVWriter:
    file: sp_connect.csv
    name: stockprice
    header: True
    unstack: True
    quoting: 2
$offEmbeddedCode

* Use gdxdump to create a CSV file and text compare the Connect and gdxdump CSV files
$call.checkErrorLevel  gdxdump sp_connect.gdx output=sp_gdxdump.csv symb=stockprice format=csv columnDimension=Y > %system.NullFile%
$call.checkErrorLevel  diff -q sp_connect.csv sp_gdxdump.csv > %system.nullFile%

* Use Connect CSVReader to read the newly created CSV file and deposit the result in a csv2gdx compatible format
$onEmbeddedCode Connect:
- CSVReader:
    file: sp_connect.csv
    name: stockprice
    indexColumns: 1
    valueColumns: "2:lastCol"
- Projection:
    name: stockprice(date,symbol)
    newName: Dim1(date)
    asSet: True
- Projection:
    name: stockprice(date,symbol)
    newName: Dim2(symbol)
    asSet: True
- GDXWriter:
    file: sp_connect.gdx
    writeAll: True
$offEmbeddedCode

* Use csv2gdx to create a GDX file and compare the Connect and csv2gdx GDX files
$call.checkErrorLevel csv2gdx sp_connect.csv output=sp_csv2gdx.gdx id=stockprice index=1 value=2..lastCol useHeader=y > %system.NullFile%
$call.checkErrorLevel gdxdiff sp_connect.gdx sp_csv2gdx.gdx > %system.NullFile%

Text Substitutions in YAML File

In many cases one would like to parameterize the text in the Connect instruction file. For example, some of the Connect agents require a file name. Instead of hard coding the file name into the YAML instructions, text substitutions allow to have a place holder for the attribute that is substituted out before giving the instructions to Connect. The place holder in the YAML file uses the syntax %SOMETEXT%, similar to the GAMS compile-time variables. For example:

- CSVReader:
    file: %MYFILENAME%
    name: distance
    indexColumns: [1, 2]
    valueColumns: [3]

Depending on how Connect runs, the substitution is done in various ways. The section Substitutions in Embedded Connect Code described the substitution mechanisms for embedded Connect code. When Connect is initiated via the command line parameters connectIn or connectOut, the user defined parameter specified by double-dash command line parameters and the given GAMS command line parameters, e.g. %gams.input% will be substituted in the YAML file. The list of parameters available for substitution is printed to the GAMS log at the beginning of the job in the section GAMS Parameters defined.

When Connect is initiated via the shell command gamsconnect all substitutions need to be specified on the command line: 

gamsconnect myci.yaml key1=val1 key2=val2 ...

key can be just MYFILENAME or be composed like gams.Input or system.dirSep.

Use Connect Agents in Custom Python Code

Instead of passing instructions via one of the Connect interfaces, users can execute tasks directly in their Python code by creating an instance of ConnectDatabase and calling method .exec_task(task). The task argument is expected to be a Python dictionary of form: 

{
   '<agent name>':
   {
      '<root option1>': <value>,
      '<root option2>': <value>,
      ... ,
      '<root option3>':
      [
         {
             '<option1>': <value>,
             '<option2>': <value>,
             ...
         },
         {
             '<option1>': <value>,
             '<option2>': <value>,
             ...
         },
         ...
      ]
   }
}

Users can either construct the Python dictionary themselves or let YAML create the dictionary from a YAML script. The following example creates an instance of ConnectDatabase and executes two tasks: First, the CSV file stockprice.csv is read into the Connect database and second, the symbol stockprice is written to the GAMS database. In this example, the tasks are directly specified as Python dictionaries.

Set dates, stocks;
Parameter stockprice(dates<,stocks<);

$onEcho > stockprice.csv
date;symbol;price
2016/01/04;AAPL;105,35
2016/01/04;AXP;67,59
2016/01/04;BA;140,50
$offEcho

$onEmbeddedCode Python:
from gams.connect import ConnectDatabase
cdb = ConnectDatabase(gams._system_directory, gams)
cdb.exec_task({'CSVReader': {'file': 'stockprice.csv', 'name': 'stockprice', 'indexColumns': [1, 2],
                            'valueColumns': [3], 'fieldSeparator': ';', 'decimalSeparator': ','}})
cdb.exec_task({'GAMSWriter': {'symbols': [{'name': 'stockprice'}]}})
$offEmbeddedCode

display stockprice;

We can also construct the Python dictionaries by using YAML:

Set dates, stocks;
Parameter stockprice(dates<,stocks<);

$onEcho > stockprice.csv
date;symbol;price
2016/01/04;AAPL;105,35
2016/01/04;AXP;67,59
2016/01/04;BA;140,50
$offEcho

$onEmbeddedCode Python:
import yaml
from gams.connect import ConnectDatabase
cdb = ConnectDatabase(gams._system_directory, gams)

inst = yaml.safe_load('''
- CSVReader:
    file: stockprice.csv
    name: stockprice
    indexColumns: [1, 2]
    valueColumns: [3]
    fieldSeparator: ';'
    decimalSeparator: ','
- GAMSWriter:
    symbols:
      - name: stockprice
''')
for task in inst:
  cdb.exec_Task(task)
$offEmbeddedCode

display stockprice;

Here YAML creates a list of dictionaries (i.e. a list of tasks) from the given YAML script.

Command Line Utility gamsconnect

The GAMS system directory contains the utility gamsconnect to run Connect instructions directly from the command line. On Windows the utility has the callable extension .cmd. This script wraps the Python script connectdriver.py by calling the Python interpreter that ships with GAMS. gamsconnect operates as the other Connect drivers on a YAML instruction file. The agents GAMSReader and GAMSWriter are not available from gamsconnect and will trigger an exception. Substitutions can be passed to gamsconnect via command line arguments as key=value, e.g. filename=myfile.csv and even gams.scrdir=/tmp/. gamsconnect is called like this:

gamsconnect <YAMLFile> [key1=value1 [key2=value2 [key3=value3 [...]]]]