Metadata-Version: 2.1
Name: tdmclient
Version: 0.1.2
Summary: Communication with Thymio II robot via the Thymio Device Manager
Home-page: https://github.com/epfl-mobots/tdm-python
Author: Yves Piguet
License: UNKNOWN
Description: # tdmclient
        
        Python package to connect to a [Thymio II robot](https://thymio.org) via the Thymio Device Manager (TDM), a component of the Thymio Suite. The connection between Python and the TDM is done over TCP to the port number advertised by zeroconf.
        
        ## Installation
        
        Make sure that Thymio&nbsp;Suite, Python&nbsp;3, and pip, the package installer for Python, are installed on your computer. You can find instructions at [https://www.thymio.org/program/], [https://www.python.org/downloads/] and [https://pypi.org/project/pip/], respectively.
        
        Then in a terminal window, install tdmclient by typing
        ```
        sudo python3 -m pip install tdmclient
        ```
        on macOS or Linux. On Windows, in Windows PowerShell or Windows Terminal, just type
        ```
        python3 -m pip install tdmclient
        ```
        
        ## Tutorial
        
        Connect a robot to your computer via a USB cable or the RF dongle and launch Thymio Suite. In Thymio Suite, you can click the Aseba Studio icon to check that the Thymio is recognized, and, also optionally, start Aseba Studio (select the robot and click the button "Program with Aseba Studio"). Only one client can control the robot at the same time to change a variable or run a program. If that's what you want to do from Python, either don't start Aseba Studio or unlock the robot by clicking the little lock icon in the tab title near the top left corner of the Aseba Studio window.
        
        Some features of the library can be accessed directly from the command window by typing `python3 -m tdmclient.tools.abc arguments`, where `abc` is the name of the tool.
        
        ### tdmclient.tools.tdmdiscovery
        
        Display the address and port of TDM advertised by zeroconf until control-C is typed:
        ```
        python3 -m tdmclient.tools.tdmdiscovery
        ```
        
        ### tdmclient.tools.run
        
        Run an Aseba program on the first Thymio II robot and store it into the scratchpad so that it's seen in Aseba Studio:
        ```
        python3 -m tdmclient.tools.run --scratchpad examples/blink.aseba
        ```
        
        Stop the program:
        ```
        python3 -m tdmclient.tools.run --stop
        ```
        
        Display other options:
        ```
        python3 -m tdmclient.tools.run --help
        ```
        
        ### tdmclient.tools.watch
        
        Display all node changes (variables, events and program in the scratchpad) until control-C is typed:
        ```
        python3 -m tdmclient.tools.watch
        ```
        
        ### tdmclient.tools.variables
        
        Run the variable browser in a window. The GUI is implemented with TK.
        ```
        python3 -m tdmclient.tools.variables
        ```
        
        At launch, the robot is unlocked, i.e. the variables are just fetched and displayed: _Observe_ is displayed in the status area at the bottom of the window. To be able to change them, activate menu Robot>Control. Then you can click any variable, change its value and type Return to confirm or Esc to cancel.
        
        ### Interactive Python
        
        This section will describe only the use of `ClientAsync`, the highest-level way to interact with a robot, with asynchronous methods which behave nicely in a non-blocking way if you need to perform other tasks such as running a user interface. All the tools described above use `ClientAsync`, except for `tdmclient.tools.tdmdiscovery` which doesn't communicate with the robots.
        
        First we'll type commands interactively by starting Python&nbsp;3 without argument. To start Python&nbsp;3, open a terminal window (Windows Terminal or Command Prompt in Windows, Terminal in macOS or Linux) and type `python3`. TDM replies should arrive quicker than typing at the keyboard. Next section shows how to interact with the TDM from a program where you wait for replies and use them immediately to run as fast as possible.
        
        Start Python&nbsp;3, then import the required class. We also import the helper function `aw`, an alias of the static method `ClientAsync.aw` which is useful when typing commands interactively.
        ```
        from tdmclient import ClientAsync, aw
        ```
        
        Create a client object:
        ```
        client = ClientAsync()
        ```
        
        If the TDM runs on your local computer, its address and port number will be obtained from zeroconf. You can check their value:
        ```
        client.tdm_addr
        ```
        ```
        client.tdm_port
        ```
        
        The client will connect to the TDM which will send messages to us, such as one to announce the existence of a robot. There are two ways to accept and process them:
        - Call explicitly
            ```
            client.process_waiting_messages()
            ```
            If a robot is connected, you should find its description in an array of nodes in the client object:
            ```
            node = client.nodes[0]
            ```
        - Call an asynchronous function in such a way that its result is waited for. This can be done in a coroutine, a special function which is executed at the same time as other tasks your program must perform, with the `await` Python keyword; or handled by the helper function `aw`. Keyword `await` is valid only in a function, hence we cannot call it directly from the Python prompt. In this section, we'll use `aw`. Robots are associated to nodes. To get the first node once it's available (i.e. an object which refers to the first or only robot after having received and processed enough messages from the TDM to have this information), type
            ```
            node = aw(client.wait_for_node())
            ```
            Avoiding calling yourself `process_waiting_messages()` is safer, because other methods like `wait_for_node()` make sure to wait until the expected reply has been received from the TDM.
        
        The value of `node` is an object which contains some properties related to the robot and let you communicate with it. The node id is displayed when you just print the node:
        ```
        node
        ```
        or
        ```
        print(node)
        ```
        
        It's also available as a string:
        ```
        node_id_str = node.id_str
        ```
        
        The node properties are stored as a dict in `node.props`. For example `node.props["name"]` is the robot's name, which you can change:
        ```
        aw(node.rename("my white Thymio"))
        ```
        
        Lock the robot to change variables or run programs (make sure it isn't already used in Thymio Suite):
        ```
        aw(node.lock())
        ```
        
        Compile and load an Aseba program:
        ```
        program = """
        var on = 0  # 0=off, 1=on
        timer.period[0] = 500
        
        onevent timer0
            on = 1 - on  # "on = not on" with a syntax Aseba accepts
            leds.top = [32 * on, 32 * on, 0]
        """
        r = aw(node.compile(program))
        ```
        
        The result `r` is None if the call is successful, or an error number if it has failed. In interactive mode, we won't store anymore the result code if we don't expect and check errors anyway. But it's usually a good thing to be more careful in programs.
        
        No need to store the actual source code for other clients, or anything at all.
        ```
        aw(node.set_scratchpad("Hello, Studio!"))
        ```
        
        Run the program compiled by `compile`:
        ```
        aw(node.run())
        ```
        
        Stop it:
        ```
        aw(node.stop())
        ```
        
        Make the robot move forward by setting both variables `motor.left.target` and `motor.right.target`:
        ```
        v = {
            "motor.left.target": [50],
            "motor.right.target": [50],
        }
        aw(node.set_variables(v))
        ```
        
        Make the robot stop:
        ```
        v = {
            "motor.left.target": [0],
            "motor.right.target": [0],
        }
        aw(node.set_variables(v))
        ```
        
        Unlock the robot:
        ```
        aw(node.unlock())
        ```
        
        Getting variable values is done by observing changes, which requires a function; likewise to receive events. This is easier to do in a Python program file. We'll do it in the next section.
        
        Here is how to send custom events from Python to the robot. The robot must run a program which defines an `onevent` event handler; but in order to accept a custom event name, we have to declare it first to the TDM, outside the program. We'll define an event to send two values for the speed of the wheels, `"speed"`. Method `node.register_events` has one argument, an array of tuples where each tuple contains the event name and the size of its data between 0 for none and a maximum of 32. The robot must be locked if it isn't already to accept `register_events`, `compile`, `run`, and `send_events`.
        ```
        aw(node.lock())
        aw(node.register_events([("speed", 2)]))
        ```
        
        Then we can send and run the program. The event data are obtained from variable `event.args`; in our case only the first two elements are used.
        ```
        program = """
        onevent speed
            motor.left.target = event.args[0]
            motor.right.target = event.args[1]
        """
        aw(node.compile(program))
        aw(node.run())
        ```
        
        Finally, the Python program can send events. Method `node.send_events` has one argument, a dict where keys correspond to event names and values to event data.
        ```
        # turn right
        aw(node.send_events({"speed": [40, 20]}))
        # wait 1 second, or wait yourself before typing the next command
        aw(client.sleep(1))
        # stop the robot
        aw(node.send_events({"speed": [0, 0]}))
        ```
        
        ### Python program
        
        In a program, instead of executing asynchronous methods synchronously with `aw` or `ClientAsync.aw`, we put them in an `async` function and we `await` for their result. The whole async function is executed with method `run_async_program`.
        
        Moving forward, waiting for 2 seconds and stopping could be done with the following code. You can store it in a .py file or paste it directly into an interactive Python&nbsp;3 session, as you prefer; but make sure you don't keep the robot locked, you wouldn't be able to lock it a second time. Quitting and restarting Python is a sure way to start from a clean state.
        ```
        from tdmclient import ClientAsync
        
        def motors(left, right):
            return {
                "motor.left.target": [left],
                "motor.right.target": [right],
            }
        
        client = ClientAsync()
        
        async def prog():
            node = await client.wait_for_node()
            await node.lock()
            await node.set_variables(motors(50, 50))
            await client.sleep(2)
            await node.set_variables(motors(0, 0))
            await node.unlock()
        
        client.run_async_program(prog)
        ```
        
        This can be simplified a little bit with the help of `with` constructs:
        ```
        from tdmclient import ClientAsync
        
        def motors(left, right):
            return {
                "motor.left.target": [left],
                "motor.right.target": [right],
            }
        
        with ClientAsync() as client:
            async def prog():
                with await client.lock() as node:
                    await node.set_variables(motors(50, 50))
                    await client.sleep(2)
                    await node.set_variables(motors(0, 0))
            client.run_async_program(prog)
        ```
        
        To read variables, the updates must be observed with a function. The following program calculates a motor speed based on the front proximity sensor to move backward when it detects an obstacle. Instead of calling the async method `set_variables` which expects a result code in a message from the TDM, it just sends a message to change variables with `send_set_variables` without expecting any reply. The TDM will send a reply anyway, but the client will ignore it without trying to associate it with the request message. `sleep()` without argument (or with a negative duration) waits forever, until you interrupt it by typing control-C.
        ```
        from tdmclient import ClientAsync
        
        def motors(left, right):
            return {
                "motor.left.target": [left],
                "motor.right.target": [right],
            }
        
        def on_variables_changed(node, variables):
            try:
                prox = variables["prox.horizontal"]
                prox_front = prox[2]
                speed = -prox_front // 10
                node.send_set_variables(motors(speed, speed))
            except KeyError:
                pass  # prox.horizontal not found
        
        with ClientAsync() as client:
            async def prog():
                with await client.lock() as node:
                    await node.watch(variables=True)
                    node.add_variables_changed_listener(on_variables_changed)
                    await client.sleep()
            client.run_async_program(prog)
        ```
        
        Compare with an equivalent Python program running directly on the Thymio:
        ```
        @onevent
        def prox():
            global prox_horizontal, motor_left_target, motor_right_target
            prox_front = prox_horizontal[2]
            speed = -prox_front // 10
            motor_left_target = speed
            motor_right_target = speed
        ```
        
        You could save it as a .py file and run it with `tdmclient.tools.run` as explained above. If you want to do everything yourself, to understand precisely how tdmclient works or because you want to eventually combine processing on the Thymio and on your computer, here is a Python program running on the PC to convert it to Aseba, compile and load it, and run it.
        ```
        from tdmclient import ClientAsync
        from tdmclient.atranspiler import ATranspiler
        
        thymio_program_python = r"""
        @onevent
        def prox():
            global prox_horizontal, motor_left_target, motor_right_target
            prox_front = prox.horizontal[2]
            speed = -prox_front // 10
            motor_left_target = speed
            motor_right_target = speed
        """
        
        # convert program from Python to Aseba
        transpiler = ATranspiler()
        transpiler.set_source(thymio_program_python)
        transpiler.transpile()
        thymio_program_aseba = transpiler.get_output()
        
        with ClientAsync() as client:
            async def prog():
                with await client.lock() as node:
                    error = await node.compile(thymio_program_aseba)
                    error = await node.run()
            client.run_async_program(prog)
        ```
        
        ### Cached variables
        
        tdmclient offers a simpler way, if slightly slower, to obtain and change Thymio variables. They're accessible as `node["variable_name"]` or `node.v.variable_name`, both for getting and setting values, also when `variable_name` contains dots. Here is an alternative implementation of the remote control version of the program which makes the robot move backward when an obstacle is detected by the front proximity sensor.
        ```
        from tdmclient import ClientAsync
        
        with ClientAsync() as client:
            async def prog():
                with await client.lock() as node:
                    await node.wait_for_variables({"prox.horizontal"})
                    while True:
                        prox_front = node.v.prox.horizontal[2]
                        speed = -prox_front // 10
                        node.v.motor.left.target = speed
                        node.v.motor.right.target = speed
                        node.flush()
                        await client.sleep(0.1)
            client.run_async_program(prog)
        ```
        
        Scalar variables have an `int` value. Array variables are iterable, i.e. they can be used in `for` loops, converted to lists with function `list`, and used by functions such as `max` and `sum`. They can be stored as a whole and retain their link with the robot: getting an element retieves the most current value, and setting an element caches the value so that it will be sent to the robot by the next call to `node.flush()`.
        Here is an interactive session which illustrates what can be done.
        ```
        >>> from tdmclient import ClientAsync
        >>> client = ClientAsync()
        >>> node = client.aw(client.wait_for_node())
        >>> client.aw(node.wait_for_variables({"leds.top"}))
        >>> rgb = node.v.leds.top
        >>> rgb
        Node array variable leds.top[3]
        >>> list(rgb)
        [0, 0, 0]
        >>> client.aw(node.lock_node())
        >>> rgb[0] = 32  # red
        >>> node.var_to_send
        {'leds.top': [32, 0, 0]}
        >>> node.flush()  # robot turns red
        ```
        ## Python-to-Aseba transpiler
        
        The official programming language of the Thymio is Aseba, a rudimentary event-driven text language. In the current official software environment, it's compiled by the TDM to machine code for a virtual processor, which is itself a program which runs on the Thymio. Virtual processors are common on many platforms; they're often referred as _VM_ (Virtual Machine), and their machine code as _bytecode_.
        
        Most programming languages for the Thymio eventually involve bytecode running on its VM. They can be divided into two main categories:
        - Programs compiled to bytecode running autonomously in the VM on the microcontroller of the Thymio. In Thymio Suite and its predecessor Aseba Studio, this includes the Aseba language; and VPL, VPL&nbsp;3, and Blockly, where programs are made of graphical representations of programming constructs and converted to bytecode in two steps, first to Aseba, then from Aseba to bytecode with the standard TDM Aseba compiler.
        - Programs running on the computer and controlling the Thymio remotely. In Thymio Suite and Aseba Studio, this includes Scratch. Python programs which use `thymiodirect` or `tdmclient` can also belong to this category. A small Aseba program runs on the Thymio, receives commands and sends back data via events.
        
        Exceptions would include programs which control remotely the Thymio exclusively by fetching and changing variables; and changing the Thymio firmware, the low-level program compiled directly for its microcontroller.
        
        Remote programs can rely on much greater computing power and virtually unlimited data storage capacity. On the other hand, communication is slow, especially with the USB radio dongle. It restricts what can be achieved in feedback loops when commands to actuators are based on sensor measurements.
        
        Alternative compilers belonging to the first category, not needing the approval of Mobsya, are possible and have actually been implemented. While the Thymio VM is tightly coupled to the requirements of the Aseba language, some of the most glaring Aseba language limitations can be circumvented. Ultimately, the principal restrictions are the amount of code and data memory and the execution speed.
        
        Sending arbitrary bytecode to the Thymio cannot be done by the TDM. The TDM accepts only Aseba source code, compiles it itself, and sends the resulting bytecode to the Thymio. So with the TDM, to support alternative languages, we must convert them to Aseba programs. To send bytecode, or assembly code which is a bytecode text representation easier to understand for humans, an alternative would be the Python package `thymiodirect`.
        
        Converting source code (the original text program representation) from a language to another one is known as _transpilation_ (or _transcompilation_). This document describes a transpiler which converts programs from Python to Aseba. Its goal is to run Python programs locally on the Thymio, be it for autonomous programs or for control and data acquisition in cooperation with the computer via events. Only a small subset of Python is supported. Most limitations of Aseba are still present.
        
        ### Features
        
        The transpiler is implemented in class `ATranspiler`, completely independently of other `tdmclient` functionality. The input is a complete program in Python; the output is an Aseba program.
        
        Here are the implemented features:
        - Python syntax. The official Python parser is used, hence no surprise should be expected, including with spaces, tabs, parentheses, and comments.
        - Integer and boolean base types. Both are stored as signed 16-bit numbers, without error on overflow.
        - Global variables. Variables are collected from the left-hand side part of plain assignments (assignment to variables without indexing). For arrays, there must exist at least one assignment of a list, directly or indirectly (i.e. `a=[1,2];b=a` is valid). Size conflicts are flagged as errors.
        - Expressions with scalar arithmetic, comparisons (including chained comparisons), and boolean logic and conditional expressions with short-circuit evaluation. Numbers and booleans can be mixed freely. The following Python operators and functions are supported: infix operators `+`, `-`, `*`, `//` (integer division), `%` (converted to modulo instead of remainder, whose sign can differ with negative operands), `&`, `|`, `^`, `<<`, `>>`, `==`, `!=`, `>`, `>=`, `<`, `<=`, `and`, `or`; prefix operators `+`, `-`, `~`, `not`; and functions `abs` and `len`.
        - Constants `False` and `True`.
        - Assignments of scalars to scalar variables or array elements; or lists to whole array variables.
        - Augmented assignments `+=`, `-=`, `*=`, `//=`, `%=`, `&=`, `|=`, `^=`, `<<=`, `>>=`.
        - Programming constructs `if` `elif` `else`, `while` `else`, `for` `in range` `else`, `pass`, `return`. The `for` loop must use a `range` generator with 1, 2 or 3 arguments.
        - Functions with scalar arguments, with or without return value (either a scalar value in all `return` statement; or no `return` statement or only without value, and call from the top level of expression statements, i.e. not at a place where a value is expected). Variable-length arguments `*args` and `**kwargs`, default values and multiple arguments with the same name are forbidden. Variables are local unless declared as global or not assigned to. Thymio predefined variables must also be declared explicitly as global when used in functions. In Python, dots are replaced by underscores; e.g. `leds_top` in Python corresponds to `leds.top` in Aseba.
        - Function definitions for event handlers with the `@onevent` decorator. The function name must match the event name (such as `def timer0():` for the first timer event). Arguments are not supported; otherwise variables in event handlers behave like in plain function definitions.
        - Function call `emit("name")` or `emit("name", param1, param2, ...)` to emit an event without or with parameters. The first argument must be a literal string, delimited with single or double quotes. Raw strings (prefixed with `r`) are allowed, f-strings or byte strings are not. Remaining arguments, if any, must be scalar expressions and are passed as event data.
        - In expression statements, in addition to function calls, the ellipsis `...` can be used as a synonym of `pass`.
        
        Perhaps the most noticeable missing features are the non-integer division operator `/` (Python has operator `//` for the integer division), and the `break` and `continue` statements, also missing in Aseba and difficult to transpile to sane code without `goto`. High on our to-do list: functions with arguments and return value.
        
        The transpilation is mostly straightforward. Mixing numeric and boolean expressions often requires splitting them into multiple statements and using temporary variables. The `for` loop is transpiled to an Aseba `while` loop because in Aseba, `for` is limited to constant ranges. Comments are lost because the official Python parser used for the first phase ignores them. Since functions are transpiled to subroutines, recursive functions are forbidden.
        
        ### Example
        
        Blinking top RGB led:
        ```
        on = False
        timer_period[0] = 500
        
        @onevent
        def timer0():
            global on, leds_top
            on = not on
            if on:
                leds_top = [32, 32, 0]
            else:
                leds_top = [0, 0, 0]
        ```
        
        To transpile this program, assuming it's stored in `examples/blink.py`:
        ```
        python3 -m tdmclient.atranspiler examples/blink.py
        ```
        
        The result is
        ```
        var on
        var tmp[1]
        
        on = 0
        timer.period[0] = 500
        
        onevent timer0
            if on == 0 then
                tmp[0] = 1
            else
                tmp[0] = 0
            end
            on = tmp[0]
            if on != 0 then
                leds.top = [32, 32, 0]
            else
                leds.top = [0, 0, 0]
            end
        ```
        
        To run this program:
        ```
        python3 -m tdmclient.tools.run examples/blink.py
        ```
        
        ### Feature comparison
        
        The table below shows a mapping between Aseba and Python features. Empty cells stand for lack of a direct equivalent. Prefixes `const_`, `numeric_` or `bool_` indicate restrictions on what's permitted. Standard Python features which are missing are not transpiled; they cause an error.
        
        | Aseba | Python
        | --- | ---
        | infix `+` `-` `*` `/` | infix `+` `-` `*` `//` `%`
        | infix `%` (remainder) | infix `%` (modulo)
        | infix `<<` `>>` `|` `&` `^` | infix `<<` `>>` `|` `&` `^`
        | prefix `-` `~` `not` | prefix `-` `~` `not`
        | | prefix +
        | `==` `!=` `<` `<=` `>` `>=` | `==` `!=` `<` `<=` `>` `>=`
        | | `a < b < c` (chained comparisons)
        | `and` `or` (without shortcut) | `and` `or` (with shortcut)
        | | `val1 if test else val2`
        | `var v` | no declarations
        | `var a[size]` |
        | `var a[] = [...]` | `a = [...]`
        | `v = numeric_expr` | `v = any_expr`
        | `v[index_expr]` | `v[index_expr]`
        | `v[constant_range]` |
        | `if bool_expr then` | `if any_expr:`
        | `elseif bool_expr then` | `elif any_expr:`
        | `else` | `else:`
        | `end` | indenting
        | `when bool_expr do` |
        | `while bool_expr do` | `while any_expr:`
        | `for v in 0 : const_b - 1 do` | `for v in range(expr_a, expr_b):`
        | `for v in const_a : const_b - 1 do` | `for v in range(expr_a, expr_b):`
        | `for v in const_a : const_b -/+ 1 step const_s do` | `for v in range(expr_a, expr_b, expr_s):`
        | `sub fun` | `def fun():`
        | all variables are global | `global g`
        | | assigned variables are local by default
        | | `def fun(arg1, arg2, ...):`
        | `return` | `return`
        | | `return expr`
        | `callsub fun` | `fun()`
        | | `fun(expr1, expr2, ...)`
        | | `fun(...)` in expression
        | `onevent name` | `@onevent` `def name():`
        | all variables are global | `global g`
        | | assigned variables are local by default
        | `emit name` | `emit("name")`
        | `emit name [expr1, expr2, ...]` | `emit("name", expr1, expr2, ...)`
        | `call natfun expr1, expr2, ...` | `nf_natfun(expr1, expr2, ...)` (see below)
        | | `natfun(expr1, ...)` in expressions
        
        In Python, the names of native functions have underscores instead of dots. Many native functions can be called with the syntax of a plain function call, with a name prefixed with `nf_` and the same arguments as in Aseba. In the table below, uppercase letters stand for arrays, lowercase letters for scalar values, `A`, `B`, `a` and `b` for inputs, `R` and `r` for result, and `P` for both input and result.
        
        | Aseba | Python
        | --- | ---
        | `call math.copy(R, A)` | `nf_math_copy(R, A)`
        | `call math.fill(R, a)` | `nf_math_fill(R, a)`
        | `call math.addscalar(R, A, b)` | `nf_math_addscalar(R, A, b)`
        | `call math.add(R, A, B)` | `nf_math_add(R, A, B)`
        | `call math.sub(R, A, B)` | `nf_math_sub(R, A, B)`
        | `call math.mul(R, A, B)` | `nf_math_mul(R, A, B)`
        | `call math.div(R, A, B)` | `nf_math_div(R, A, B)`
        | `call math.min(R, A, B)` | `nf_math_min(R, A, B)`
        | `call math.max(R, A, B)` | `nf_math_max(R, A, B)`
        | `call math.clamp(R, A, B, C)` | `nf_math_clamp(R, A, B, C)`
        | `call math.rand(R)` | `nf_math_rand(R)`
        | `call math.sort(P)` | `nf_math_sort(P)`
        | `call math.muldiv(R, A, B, C)` | `nf_math_muldiv(R, A, B, C)`
        | `call math.atan2(R, A, B)` | `nf_math_atan2(R, A, B)`
        | `call math.sin(R, A)` | `nf_math_sin(R, A)`
        | `call math.cos(R, A)` | `nf_math_cos(R, A)`
        | `call math.rot2(R, A, b)` | `nf_math_rot2(R, A, b)`
        | `call math.sqrt(R, A)` | `nf_math_sqrt(R, A)`
        
        A few of them have a name without the `nf_` prefix, scalar arguments and a single scalar result. They can be used in an assignment or in expressions.
        
        | Aseba native function | Python function call
        | --- | ---
        | `math.min` | `math_min(a, b)`
        | `math.max` | `math_max(a, b)`
        | `math.clamp` | `math_clamp(a, b, c)`
        | `math.rand` | `math_rand()`
        | `math.muldiv` | `math_muldiv(a, b, c)`
        | `math.atan2` | `math_atan2(a, b)`
        | `math.sin` | `math_sin(a)`
        | `math.cos` | `math_cos(a)`
        | `math.sqrt` | `math_sqrt(a)`
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Intended Audience :: Education
Requires-Python: >=3.6
Description-Content-Type: text/markdown
