# Python quick start¶

This section will show a simple example to illustrate various functionalities of the AMPL Python interface. The full example prints the version of the AMPL interpreter used, loads a model from file and the corresponding data file, solves it, gets some of the AMPL entities in Python and uses them to get the results and to assign data programmatically. This section assumes that you are already familiar with the Python language. Full class reference is given in Python API reference.

## Complete listing¶

This is the complete listing of the example. Please note that, for clarity of presentation, all the code in the examples below does not include exception handling.

from amplpy import AMPL
ampl = AMPL()

# Interpret the two files

# Solve
ampl.solve()

# Get objective entity by AMPL name
totalcost = ampl.getObjective('total_cost')
# Print it
print("Objective is:", totalcost.value())

# Reassign data - specific instances
cost = ampl.getParameter('cost')
cost.setValues({'BEEF': 5.01, 'HAM': 4.55})
print("Increased costs of beef and ham.")

# Resolve and display objective
ampl.solve()
print("New objective value:", totalcost.value())

# Reassign data - all instances
cost.setValues([3, 5, 5, 6, 1, 2, 5.01, 4.55])

print("Updated all costs.")

# Resolve and display objective
ampl.solve()
print("New objective value:", totalcost.value())

# Get the values of the variable Buy in a dataframe object
# Print them
print(df)

# Get the values of an expression into a DataFrame object
# Print them
print(df2)


## Needed headers and AMPL environment creation¶

For a simple hello world program, first import the needed classes from the amplpy package.

from amplpy import AMPL, DataFrame


Then copy the following statements to have a hello world application which gets the value of the option version as defined in the underlying AMPL executable and prints the result on the console.

ampl = AMPL()
print(ampl.getOption('version'))


The first line creates a new AMPL object with all default settings, incapsulated in a smart pointer to ensure resource deletion. The second, which is the preferred way to access AMPL options, gets the value of the option version from AMPL as a string and prints the result on the active console.

If the AMPL installation directory is not in the system search path, you should create the AMPL object as follows instead:

from amplpy import AMPL, Environment
ampl = AMPL(Environment('full path to the AMPL installation directory'))


Note that you may need to use raw strings (e.g., r’C:\ampl\ampl.mswin64’) or escape the slashes (e.g., ‘C:\\ampl\\ampl.mswin64’) if the path includes backslashes.

## Load a model from file¶

The following lines use the method amplpy.AMPL.read() to load a model and data stored in external (AMPL) files. If the files are not found, an IOError is thrown.

ampl.read('models/diet/diet.mod')


Once these commands are executed, the AMPL interpreter will have interpreted the content of the two files. No further communication is made between the AMPL interpreter and the Python object, as every entity is created lazily (as needed).

## Solve a problem¶

To solve the currently loaded problem instance, it is sufficient to issue the command:

ampl.solve()


## Get an AMPL entity in the programming environment (get objective value)¶

AMPL API provides Python representations of the AMPL entities. Usually, not all the entities are of interest for the programmer. The generic procedure is:

1. Identify the entities that need interaction (either data read or modification)
2. For each of these entities, get the entity through the AMPL API using one of the following functions: amplpy.AMPL.getVariable(), amplpy.AMPL.getConstraint(), amplpy.AMPL.getObjective(), amplpy.AMPL.getParameter() and amplpy.AMPL.getSet().
totalcost = ampl.getObjective('total_cost')
print("Objective is:", totalcost.get().value())


It can be noted that we access an Objective to interrogate AMPL API about the objective function. It is a collections of objectives. To access the single instance, the function get() should be used in case of the objective, which gets the only instance of the objective. Since objectives are often single instance, the value() function has been implemented in the class amplpy.Objective. So, equivalently to the call above, the following call would return the same value, as it gives direct access to the objective function value:

totalcost.value()


The output of the snippet above is:

Objective is: 118.05940323955669


The same is true for all other entities.

## Modify model data (assign values to parameters)¶

The input data of an optimization model is stored in its parameters; these can be scalar or vectorial entities. Two ways are provided to change the value of vectorial parameter: change specific values or change all values at once. The example shows an example of both ways, reassigning the values of the parameter costs firstly specifically, then altogether. Each time, it then solves the model and get the objective function. The function used to change the values is overloaded, and is in both cases amplpy.Parameter.setValues().

cost = ampl.getParameter('cost')
cost.setValues({'BEEF': 5.01, 'HAM': 4.55})
print("Increased costs of beef and ham.")
ampl.solve();
print("New objective value:", totalcost.value())


The code above assigns the values 5.01 and 4.55 to the parameter cost for the objects beef and ham respectively. If the order of the indexing of an entity is known (i.e. for multiple reassignment), it is not necessary to specify both the index and the value. A collection of values is assigned to each of the parameter values, in the order they are represented in AMPL.

cost.setValues([3, 5, 5, 6, 1, 2, 5.01, 4.55])
print("Updated all costs.")
ampl.solve()
print("New objective value:", totalcost.value())


The statements above produce the following output:

Objective is: 118.05940323955669
Increased costs of beef and ham.
New objective value: 144.41572037510653
Updated all costs
New objective value: 164.54375000000002


## Get numeric values from variables¶

To access all the numeric values contained in a Variable or any other entity, use a amplpy.DataFrame object. Doing so, the data is detached from the entity, and there is a considerable performance gain. To do so, we first get the Variable object from AMPL, then we get its data with the function amplpy.Entity.getValues().

# Get the values of the variable Buy in a dataframe object
# Print them
print(df)


## Get arbitrary values via ampl expressions¶

Often we are interested in very specific values coming out of the optimization session. To make use of the power of AMPL expressions and avoiding cluttering up the environment by creating entities, fetching data through arbitrary AMPL expressions is possible. For this model, we are interested in knowing how close each decision variable is to its upper bound, in percentage. We can obtain this data into a dataframe using the function amplpy.AMPL.getData() with the code :

# Get the values of an expression into a DataFrame object