Python Mapping for Parameters and Return Values

In Parameters

All parameters are passed by reference in the Python mapping; it is guaranteed that the value of a parameter will not be changed by the invocation.

Here is an interface with operations that pass parameters of various types from client to server:

SLICE

struct NumberAndString
{
    int x;
    string str;
}

sequence<string> StringSeq;

dictionary<long, StringSeq> StringTable;

interface ClientToServer
{
    void op1(int i, float f, bool b, string s);
    void op2(NumberAndString ns, StringSeq ss, StringTable st);
    void op3(ClientToServer* proxy);
}

The Slice compiler generates the following proxy for this definition:

PY

class ClientToServerPrx(Ice.ObjectPrx):
    def op1(self, i, f, b, s, context=None):
        # ...

    def op2(self, ns, ss, st, context=None):
        # ...

    def op3(self, proxy, context=None):
        # ...

Given a proxy to a ClientToServer interface, the client code can pass parameters as in the following example:

Python

PY

p = ...                                 # Get proxy...

p.op1(42, 3.14f, True, "Hello world!")  # Pass simple literals

i = 42
f = 3.14f
b = True
s = "Hello world!"
p.op1(i, f, b, s)                       # Pass simple variables

ns = NumberAndString()
ns.x = 42
ns.str = "The Answer"
ss = [ "Hello world!" ]
st = {}
st[0] = ns
p.op2(ns, ss, st)                       # Pass complex variables

p.op3(p)                                # Pass proxy

Out Parameters

As in Java, Python functions do not support reference arguments. That is, it is not possible to pass an uninitialized variable to a Python function in order to have its value initialized by the function. The Java mapping overcomes this limitation with the use of holder classes that represent each out parameter. The Python mapping takes a different approach, one that is more natural for Python users.

The semantics of out parameters in the Python mapping depend on whether the operation returns one value or multiple values. An operation returns multiple values when it has declared multiple out parameters, or when it has declared a non-void return type and at least one out parameter.

If an operation returns multiple values, the client receives them in the form of a result tuple. A non-void return value, if any, is always the first element in the result tuple, followed by the out parameters in the order of declaration.

If an operation returns only one value, the client receives the value itself.

Here again are the same Slice definitions we saw earlier, but this time with all parameters being passed in the out direction:

SLICE

struct NumberAndString
{
    int x;
    string str;
}

sequence<string> StringSeq;

dictionary<long, StringSeq> StringTable;

interface ServerToClient
{
    int op1(out float f, out bool b, out string s);
    void op2(out NumberAndString ns,
             out StringSeq ss,
             out StringTable st);
    void op3(out ServerToClient* proxy);
}

The Python mapping generates the following code for this definition:

PY

class ServerToClientPrx(Ice.ObjectPrx):
    def op1(self, context=None):
        # ...

    def op2(self, context=None):
        # ...

    def op3(self, context=None):
        # ...

Given a proxy to a ServerToClient interface, the client code can receive the results as in the following example:

PY

p = ...              # Get proxy...
i, f, b, s = p.op1()
ns, ss, st = p.op2()
stcp = p.op3()

The operations have no in parameters, therefore no arguments are passed to the proxy methods. Since op1 and op2 return multiple values, their result tuples are unpacked into separate values, whereas the return value of op3 requires no unpacking.

Parameter Type Mismatches

Although the Python compiler cannot check the types of arguments passed to a function, the Ice run time does perform validation on the arguments to a proxy invocation and reports any type mismatches as a ValueError exception.

Null Parameters

Some Slice types naturally have "empty" or "not there" semantics. Specifically, sequences, dictionaries, and strings all can be None, but the corresponding Slice types do not have the concept of a null value. To make life with these types easier, whenever you pass None as a parameter or return value of type sequence, dictionary, or string, the Ice run time automatically sends an empty sequence, dictionary, or string to the receiver.

This behavior is useful as a convenience feature: especially for deeply-nested data types, members that are sequences, dictionaries, or strings automatically arrive as an empty value at the receiving end. This saves you having to explicitly initialize, for example, every string element in a large sequence before sending the sequence in order to avoid a run-time error. Note that using null parameters in this way does not create null semantics for Slice sequences, dictionaries, or strings. As far as the object model is concerned, these do not exist (only empty sequences, dictionaries, and strings do). For example, it makes no difference to the receiver whether you send a string as None or as an empty string: either way, the receiver sees an empty string.

Optional Parameters in Python

Optional parameters use the same mapping as required parameters. The only difference is that None can be passed as the value of an optional parameter or return value. Consider the following operation:

SLICE

optional(1) int execute(optional(2) string params, out optional(3) float value);

The corresponding Python proxy method is:

PY

def execute(
    self,
    params: str | None = None,
    context: dict[str, str] | None = None) -> tuple[int | None, float | None]:
    ...

def executeAsync(
    self,
    params: str | None = None,
    context: dict[str, str] | None = None) -> Awaitable[
        tuple[int | None, float | None]]:
    ...

and the corresponding Python skeleton method is:

PY

@abstractmethod
def execute(
    self,
    params: str | None,
    current: Current) -> tuple[
        int | None, float | None] | Awaitable[tuple[int | None, float | None]]:
    ...