Class structure

AMPL API library consists of a collection of classes to interact with the underlying AMPL interpreter and to access its inputs and outputs. It uses generic collections to represent the various entities which comprise a mathematical model. The structure of these entities is explained in this section. For a quick introduction to AMPL Entities see Quick Introduction to AMPL.

The main class used to interact with AMPL, instantiate and interrogate the models is amplpy.AMPL. One object of this class represents an execution of an AMPL translator, and is the first class that has to be instantiated when developing a solution based on AMPL API. It allows the interaction with the underlying AMPL translator, issuing commands, getting diagnostics and controlling the process.

AMPL class

For all calculations, AMPL API uses an underlying AMPL execution engine, which is wrapped by the class amplpy.AMPL. Thus, one instance of this class is the first object to be created when writing a program which uses the AMPL API library. The object is quite resource-heavy, therefore it should be explicitly closed as soon as it is not needed anymore, with a call to amplpy.AMPL.close().

All the model creation and structural alteration operations are to be expressed in AMPL language through the AMPL main object; moreover, the class provides access to the current state represented via the classes derived from amplpy.Entity, as shown in section Algebraic entities classes and provides several other functionalities (see Python API Reference).

The functions can be split in three groups: direct AMPL interaction, model interrogation and commands.

Method aliases: camelCase and snake_case

Starting from version 0.8, we provide both camelCase (e.g., readData()) and snake_case (e.g., read_data()) versions of methods for all our classes. Before version 0.8, only camelCase methods were available. The style guide for Python Code PEP8 recommends the use of lowercase with words separated by underscores as necessary to improve readability for variables and method names. However, in our APIs for other languages the predominant style is camelCase (e.g., R, C++, etc.) and in Python snake_case is not always the style used. By providing aliases with both versions of the method names we give you the freedom to choose the style you prefer.

Direct interaction with AMPL

The methods available to input AMPL commands are amplpy.AMPL.eval(), and amplpy.AMPL.read_data(); they send the strings specified (or the specified files) to the AMPL engine for interpretation.

Model interrogation

Evaluating AMPL files or statements creates various kind of entities in the underlying AMPL process. To get the object (or, in general, programmatic) representation of such entities, the programmer can follow two main courses.

Once the desired entities have been created, it is possible to use their properties and methods to manipulate the model and to extract or assign data. Updating the state of the programmatic entities is implemented lazily and uses proper dependency handling. Communication with the underlying engine is therefore executed only when an entity’s properties are being accessed and only when necessary. An entity is invalidated (needs refreshing) if one of the entities it depends from has been manipulated or if a generic AMPL statement evaluation is performed (through amplpy.AMPL.eval() or similar routines). This is one of the reasons why it is generally better to use the embedded functionalities (e.g. fixing a variable through the corresponding API function call) than using AMPL statements: in the latter case, the API invalidates all entities, as the effects of such generic statements cannot be predicted. Refreshing is transparent to the user, but must be taken into account when implementing functions which access data or modify entities frequently.

Alternative method to access entities

For those that prefer a less verbose syntax, there is an alternative and more compact syntax for accessing entities and options:

  • Accessing ampl.var[name] is equivalent to ampl.get_variable(name) (get_variable());

  • Accessing ampl.con[name] is equivalent to ampl.get_constraint(name) (get_constraint());

  • Accessing ampl.obj[name] is equivalent to ampl.get_objective(name) (get_objective());

  • Accessing ampl.set[name] is equivalent to ampl.get_set(name) (get_set());

  • Accessing ampl.param[name] is equivalent to ampl.get_parameter(name) (get_parameter());

  • Accessing ampl.option[name] is equivalent to ampl.get_option(name) (get_option()).

Setting entities and options is also possible:

  • ampl.var[name] = value is equivalent to ampl.get_variable(name).set_value(value) (set_value());

  • ampl.con[name] = value is equivalent to ampl.get_constraint(name).set_dual(value) (set_dual());

  • ampl.set[name] = values is equivalent to ampl.get_set(name).set_values(values) (set_values());

  • ampl.param[name] = value is equivalent to ampl.get_parameter(name).set(value) if the parameter is scalar (set()), ampl.get_parameter(name).set_values(value) otherwise (set_values());

  • ampl.option[name] = value is equivalent to ampl.set_option(name, value) (set_option()).

Commands and options

Some AMPL commands are encapsulated by functions in the amplpy.AMPL class for ease of access. These comprise amplpy.AMPL.solve() and others. To access and set options in AMPL, the functions amplpy.AMPL.get_option() and amplpy.AMPL.set_option() are provided. These functions provide an easier programmatic access to the AMPL options. In general, when an encapsulation is available for an AMPL command, the programmatic access to it is to be preferred to calling the same command using amplpy.AMPL.eval().

Output and errors handling

The output from the AMPL translator is handled implementing the interface amplpy.OutputHandler. The method amplpy.OutputHandler.output() is called at each block of output from the translator. The current output handler can be accessed and set via amplpy.AMPL.get_output_handler() and amplpy.AMPL.set_output_handler(); the default output handler prints each block to the standard console output.

Error handling is two-faced:

The default implementation of the error handler throws exceptions on errors and prints the warnings to stdout.

Modelling entities classes

This group of classes represents the basic entities of an AMPL optimisation model: variables, constraints, objectives, parameters and sets. They are used to access the current state of the AMPL translator (e.g. to find the values of a variable), and to some extent they can be used for data input (e.g. assign values to a parameter, fix a variable).

Objects of these classes cannot be created programmatically by the user: the model creation and structural modification is handled in AMPL (see section AMPL class), through the methods amplpy.AMPL.eval() and The base class is amplpy.Entity.

The classes derived from amplpy.Entity represent algebraic entites (e.g. a variable indexed over a set in AMPL), and are implemented as a map from an object (number, string or tuple) to an instance which allow access to its instances (methods amplpy.Entity.__getitem__() and amplpy.Entity.get()). The case of scalar entities (like the AMPL entity defined by var x;) is handled at Entity level, and will be illustrated in the paragraph regarding instances below. The derived classes are: amplpy.Variable, amplpy.Constraint, amplpy.Parameter, amplpy.Objective and amplpy.Set.

Any instance object represents a single instance of an algebraic entity (e.g. the value of a variable for a specific value of its indexing set), and is treated as a scalar entity. Entities and instances are both handled by the class amplpy.Entity. An entity (algebraic entity in AMPL) can contain various instance objects (instances in AMPL), while each instance has to be part of exactly one entity. The exact methods and properties of the entity depend on the particular kind of entity under consideration (i.e. variable, constraint, parameter).

As an example, for indexed entities, the class amplpy.Variable has functionalities like amplpy.Variable.fix() and amplpy.Variable.unfix(), which would fix or unfix all instances which are part of the algebraic entity, and for instances the class amplpy.Variable has properties like amplpy.Variable.value() and amplpy.Variable.dual() (together with instance level fix and unfix methods).

The class amplpy.Constraint has functionalities like amplpy.Constraint.drop() and amplpy.Constraint.restore() on its entity level, and on its instance level it has properties like amplpy.Constraint.body() and amplpy.Constraint.dual() (and methods like drop and restore for the single instance).

Note that the class amplpy.Parameter, which represent an algebraic parameter, represents its instances by objects (typically double numbers or strings) and therefore does not have special methods on its instance level.

Access to instances and values

The instances can be accessed from the parent entity through functions like amplpy.Entity.get(), available for all entity classes or via the indexing operator. All data corresponding to the entity can be accessed through the instances, but the computational overhead of such kind of access is quite considerable. To avoid this, the user can gain bulk data access through a amplpy.DataFrame object; reference to these object can be obtained using amplpy.Entity.get_values() methods. In case of scalar entities (e.g. the entity declared in AMPL with the statement var x;), all the instance specific methods are replicated at Entity level, to allow the code fragment value = x.value() instead of the more explicit value = x.get().value(). See example below:

from amplpy import AMPL
ampl = AMPL()
ampl.eval('var x;')
x = ampl.get_variable('x')
value = x.value()        # Compact access to scalar entities
value = x.get().value()  # Access through explicit reference to the instance

Indexed entities are central in modelling via AMPL. This is why the amplpy.Entity.get() method and the indexing operator can be used in multiple ways, to adapt to specific use cases. These will be presented below, by mean of some examples.

Scalar Entities In general, as seen above, access to an instance of a scalar entity is not needed, as all functionalities of the instance are replicated at entity level in this case. Anyway, to gain explicit access to an instance, the function amplpy.Entity.get() can be used without parameters, as shown below.

ampl.eval('var x;')
x = ampl.get_variable('x').get()

Indexed Entities Instances of indexed entities can be accessed as shown below:

from amplpy import AMPL
ampl = AMPL()
ampl.eval('var x{1..2, 4..5, 7..8};')
x = ampl.get_variable('x')

# Option 1:
instance = x[1, 4, 7]
# Option 2:
instance = x.get(1, 4, 7)

index = (1, 4, 7)
# Option 3:
instance = x[index]
# Option 4:
instance = x.get(index)

AMPL API allows access to the instances through iterators. See the examples below which use the same declarations of the example above to illustrate how to:

  • Find if an instance exists or not

  • Enumerate all the instances

# Find using iterator
instance = x.find(t)
if instance is None:
    print("Instance not found")

# Access all instances using an iterator
for index, instance in x:

# Create a dictionary mapping each index to the corresponding instance
xdict = dict(x)

The currently defined entities are obtained from the various get methods of the amplpy.AMPL object (see section AMPL class). Once a reference to an entity is created, the entity is automatically kept up-to-date with the corresponding entity in the AMPL interpreter. That is, if a reference to a newly created AMPL variable is obtained by means of amplpy.AMPL.get_variable(), and the model the variable is part of is then solved by means of amplpy.AMPL.solve(), the values of the instances of the variable will automatically be updated. The following code snippet should demonstrate the concept.

ampl.eval('var x;')
ampl.eval('maximize z: x;')
ampl.eval('subject to c: x<=10;')
x = ampl.get_variable('x')

# At this point x.value() evaluates to 0
print(x.value())  # prints 0


# At this point x.value() evaluates to 10
print(x.value())  # prints 10

Relation between entities and data

The entities and instances in AMPL store data (numbers or strings) and can be indexed, hence the instances available depend on the values in the indexing set(s). The order in which these indexing sets is handled in the AMPL entities is not always consistent with the ordering in which the data for such sets is defined, so it is often desirable, even when interested in only data (decoupled from the AMPL entities) to keep track of the indexing values which corresponds to each value.

Moreover, when dealing with AMPL entities (like amplpy.Variable), consistency is guaranteed for every instance. This means that, if a reference to an instance is kept and in the underlying AMPL interpreter the value of the instance is changed, the value read from the instance object will be always consistent with the AMPL value and, if an instance is deleted in AMPL, an exception will be thrown when accessing it. This has the obvious benefit of allowing the user to rely on the values of the instances, but has a price in terms of computational overhead. For example, accessing in this way the value of 1000 instances:

from amplpy import AMPL
ampl = AMPL()
ampl.eval('set A := 1..1000; param c{i in A} default 0; var x{i in 1..1000} := c[i];')

# Enumerate through all the instances of c and set their values
c = ampl.get_parameter("c");
for i in range(1, c.num_instances()+1):
    c[i] = i*1.1

# Enumerate through all the instances and print their values
x = ampl.get_variable("x")
for index, xi in x:

will check at each access if the referenced instance is valid or not, resulting in a computational overhead.

To ease data communication and handling, the class amplpy.DataFrame is provided. Its usage is two-fold:

  • It allows definition of data for multiple parameters in one single call to the underlying interpterer

  • It decouples data and entities, reducing the computational overhead and risks related to concurrency

amplpy.DataFrame objects should therefore be used in these circumnstances, together with the methods amplpy.AMPL.set_data() and amplpy.Entity.get_values().

# Create a new dataframe with one indexing column (A) and another column (c)
from amplpy import AMPL, DataFrame
df = DataFrame(index='A', columns='c')
for i in range(1, 1000+1):
    df.add_row(i, i*1.1)

ampl = AMPL()
ampl.eval('set A; param c{i in A} default 0; var x{i in A} := c[i];')
# Assign data to the set A and the parameter c in one line
ampl.set_data(df, 'A')

x = ampl.get_variable('x')
# From the following line onwards, df is uncoupled from the
# modelling system,
df = x.get_values()

# Prints all the values
for row in df:

# Retrieve all rows
rows = [tuple(row) for row in df]

# Prints all the values in the DataFrame

The underlying AMPL interpreter does not need to be open when using the dataframe object, but it maintains all the correspondence between indexing set and actual value of the instances.

Access to scalar values

Simplified access to scalar values, like the value of a scalar variable or parameter or, in general, any AMPL expression that can be evaluated to a single string or number, is possible using the convenience method amplpy.AMPL.get_value(). This method will fail if called on an AMPL expression which does not evaluate to a single value. See below for an example:

from amplpy import AMPL
ampl = AMPL()
ampl.eval('var x{i in 1..3} := i;')
ampl.eval('param p symbolic := "test";')
ampl.eval('param pp := 4;')
# x2 will have the value 2
# p will have the value "test"
# pp will have the value 4