ptera 1.4.1

Call graph addressing library.

Ptera

Ptera is a powerful way to instrument your code for logging, debugging and testing purposes. With a simple call to ptera.probing(), you can:

• Obtain a stream of the values taken by any variable.
• Probe multiple variables from multiple functions in multiple scopes.
• Apply maps, filters, reductions, and much more to the streams.
• Override the values of variables based on complex conditions.
• Create external asserts or conditional breakpoints.
• Write complex, focused tests.
• Et cetera :)

📖 Read the documentation

Install

pip install ptera

Example

You can use Ptera to observe assignments to any variable in your program:

from ptera import probing

def f(x):
    y = 10
    for i in range(1, x + 1):
        y = y + i
    return y

with probing("f > y").values() as values:
    f(3)

# These are all the values taken by the y variable in f.
assert values == [
    {"y": 10},
    {"y": 11},
    {"y": 13},
    {"y": 16},
]

In the above,

1. We select the variable y of function f using the selector f > y.
2. We use the values() method to obtain a list in which the values of y will be progressively accumulated.
3. When f is called within the probing block, assignments to y are intercepted and appended to the list.
4. When the probing block finishes, the instrumentation is removed and f reverts to its normal behavior.

Creating probes

• ptera.probing: Probe variables inside a with block.
• ptera.global_probe: Activate a global probe.

Using probes

The interface for Ptera's probes is inspired from functional reactive programming and is identical to the interface of giving (itself based on rx). See here for a complete list of operators.

You can always use with probing(...).values() as in the example at the top if you want to keep it simple and just obtain a list of values. You can also use with probing(...).display() to print the values instead.

Beyond that, you can also define complex data processing pipelines. For example:

with probing("f > x") as probe:
    probe["x"].map(abs).max().print()
    f(1234)

The above defines a pipeline that extracts the value of x, applies the abs function on every element, takes the maximum of these absolute values, and then prints it out. Keep in mind that this statement doesn't really do anything at the moment it is executed, it only declares a pipeline that will be activated whenever a probed variable is set afterwards. That is why f is called after and not before.

More examples

Ptera is all about providing new ways to inspect what your programs are doing, so all examples will be based on this simple binary search function:

from ptera import global_probe, probing

def f(arr, key):
    lo = -1
    hi = len(arr)
    while lo < hi - 1:
        mid = lo + (hi - lo) // 2
        if (elem := arr[mid]) > key:
            hi = mid
        else:
            lo = mid
    return lo + 1

##############################
# THE PROBING CODE GOES HERE #
##############################

f(list(range(1, 350, 7)), 136)

To get the output listed in the right column of the table below, the code in the left column should be inserted before the call to f, where the big comment is. Most of the methods on global_probe define the pipeline through which the probed values will be routed (the interface is inspired from functional reactive programming), so it is important to define them before the instrumented functions are called.

Code	Output
The display method provides a simple way to log values. global_probe("f > mid").display()	mid: 24 mid: 11 mid: 17 mid: 20 mid: 18 mid: 19
The print method lets you specify a format string. global_probe("f(mid) > elem").print("arr[{mid}] == {elem}")	arr[24] == 169 arr[11] == 78 arr[17] == 120 arr[20] == 141 arr[18] == 127 arr[19] == 134
Reductions are easy: extract the key and use min, max, etc. global_probe("f > lo")["lo"].max().print("max(lo) = {}") global_probe("f > hi")["hi"].min().print("min(hi) = {}")	max(lo) = 19 min(hi) = 20
Define assertions with fail() (for debugging, also try .breakpoint()!) def unordered(xs): return any(x > y for x, y in zip(xs[:-1], xs[1:])) probe = global_probe("f > arr")["arr"] probe.filter(unordered).fail("List is unordered: {}") f([1, 6, 30, 7], 18)	Traceback (most recent call last): ... File "test.py", line 30, in <module> f([1, 6, 30, 7], 18) File "<string>", line 3, in f__ptera_redirect File "test.py", line 3, in f def f(arr, key): giving.gvn.Failure: List is unordered: [1, 6, 30, 7]
Accumulate into a list: results = global_probe("f > mid")["mid"].accum() f(list(range(1, 350, 7)), 136) print(results) OR with probing("f > mid")["mid"].values() as results: f(list(range(1, 350, 7)), 136) print(results)	[24, 11, 17, 20, 18, 19]

probing

Usage: with ptera.probing(selector) as probe: ...

The selector is a specification of which variables in which functions we want to stream through the probe. One of the variables must be the focus of the selector, meaning that the probe is triggered when that variable is set. The focus may be indicated either as f(!x) or f > x (the focus is x in both cases).

The probe is a wrapper around rx.Observable and supports a large number of operators such as map, filter, min, average, throttle, etc. (the interface is the same as in giving).

Example 1: intermediate variables

Ptera is capable of capturing any variable in a function, not just inputs and return values:

def fact(n):
    curr = 1
    for i in range(n):
        curr = curr * (i + 1)
    return curr

with probing("fact(i, !curr)").print():
    fact(3)
    # {'curr': 1}
    # {'curr': 1, 'i': 0}
    # {'curr': 2, 'i': 1}
    # {'curr': 6, 'i': 2}

The "!" in the selector above means that the focus is curr. This means it is triggered when curr is set. This is why the first result does not have a value for i. You can use the selector fact(!i, curr) to focus on i instead:

with probing("fact(!i, curr)").print():
    fact(3)
    # {'i': 0, 'curr': 1}
    # {'i': 1, 'curr': 1}
    # {'i': 2, 'curr': 2}

You can see that the associations are different (curr is 2 when i is 2, whereas it was 6 with the other selector), but this is simply because they are now triggered when i is set.

Example 2: multiple scopes

A selector may act on several nested scopes in a call graph. For example, the selector f(x) > g(y) > h > z would capture variables x, y and z from the scopes of three different functions, but only when f calls g and g calls h (either directly or indirectly).

def f(x):
    return g(x + 1) * g(-x - 1)

def g(x):
    return x * 2

# Use "as" to rename a variable if there is a name conflict
with probing("f(x) > g > x as gx").print():
    f(5)
    # {'gx': 6, 'x': 5}
    # {'gx': -6, 'x': 5}
    g(10)
    # Prints nothing

Example 3: overriding variables

It is also possible to override the value of a variable with the override (or koverride) methods:

def add_ct(x):
    ct = 1
    return x + ct

with probing("add_ct(x) > ct", overridable=True) as probe:
    # The value of other variables can be used to compute the new value of ct
    probe.override(lambda data: data["x"])

    # You can also use koverride, which calls func(**data)
    # probe.koverride(lambda x: x)

    print(add_ct(3))   # sets ct = x = 3; prints 6
    print(add_ct(10))  # sets ct = x = 20; prints 20

Important: override() only overrides the focus variable. As explained earlier, the focus variable is the one to the right of >, or the one prefixed with !. A Ptera selector is only triggered when the focus variable is set, so realistically it is the only one that it makes sense to override.

This is worth keeping in mind, because otherwise it's not always obvious what override is doing. For example:

with probing("add_ct(x) > ct", overridable=True) as probe:
    # The focus is ct, so override will always set ct
    # Therefore, this sets ct = 10 when x == 3:
    probe.where(x=3).override(10)

    print(add_ct(3))   # sets ct = 10; prints 13
    print(add_ct(10))  # does not override anything; prints 11