Useful Utilities

UnionDict – a dict with set like operations and keys as attributes

This object combines the key-element storage of a dict with the union operation of a set() object. It is used in the cogent3.draw module, primarily for the figure and layout attributes.

Accessing elements of a UnionDict

Keys in a UnionDict can be accessed like attributes

from cogent3.util.union_dict import UnionDict

data = UnionDict(a=2, b={"c": 24, "d": [25]})
data.a

2

data["a"]

2

data.b.d

[25]

Updating a UnionDict

If you use the | bitwise operator to compare two dicts and the left one is a UnionDict, a union operation is performed.

from cogent3.util.union_dict import UnionDict

data = UnionDict(a=2, b={"c": 24, "d": [25]})
data.b |= {"d": 25}
data.b

{'c': 24, 'd': 25}

This can also be done using the union method.

data.b.union({"d": [25]})

data.b
{"c": 24, "d": [25]}

{'c': 24, 'd': [25]}

Accessing a non-existent UnionDict key

from cogent3.util.union_dict import UnionDict

data = UnionDict(a=2, b={"c": 24, "d": [25]})
data["k"]

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Cell In[7], line 4
      1 from cogent3.util.union_dict import UnionDict
      3 data = UnionDict(a=2, b={"c": 24, "d": [25]})
----> 4 data["k"]

KeyError: 'k'

But if accessing as an attribute, you get an attribute error.

data.k

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
File ~/work/cogent3.github.io/cogent3.github.io/.venv/lib/python3.12/site-packages/cogent3/util/union_dict.py:35, in UnionDict.__getattr__(self, item)
     34 try:
---> 35     return super().__getattr__(item)
     36 except AttributeError:

AttributeError: 'super' object has no attribute '__getattr__'

During handling of the above exception, another exception occurred:

AttributeError                            Traceback (most recent call last)
Cell In[8], line 1
----> 1 data.k

File ~/work/cogent3.github.io/cogent3.github.io/.venv/lib/python3.12/site-packages/cogent3/util/union_dict.py:38, in UnionDict.__getattr__(self, item)
     36 except AttributeError:
     37     msg = f"'{item}' not a key or attribute"
---> 38     raise AttributeError(msg)

AttributeError: 'k' not a key or attribute

Using Cogent3’s optimisers for your own functions

You have a function that you want to maximise/minimise. The parameters in your function may be bounded (must lie in a specific interval) or not. The cogent3 optimisers can be applied to these cases. The Powell (a local optimiser) and SimulatedAnnealing (a global optimiser) classes in particular have had their interfaces standardised for such use cases. We demonstrate for a very simple function below.

We write a simple factory function that uses a provided value for omega to compute the squared deviation from an estimate, then use it to create our optimisable function.

import numpy

def DiffOmega(omega):
    def omega_from_S(S):
        omega_est = S / (1 - numpy.e ** (-1 * S))
        return abs(omega - omega_est) ** 2

    return omega_from_S

omega = 0.1
f = DiffOmega(omega)

We then import the minimise function and use it to minimise the function, obtaining the fit statistic and the associated estimate of S. Note that we provide lower and upper bounds (which are optional) and an initial guess for our parameter of interest (S).

from cogent3.maths.optimisers import maximise, minimise

S = minimise(
    f,  # the function
    xinit=1.0,  # the initial value
    bounds=(-100, 100),  # [lower,upper] bounds for the parameter
    local=True,  # just local optimisation, not Simulated Annealing
    show_progress=False,
)
assert 0.0 <= f(S) < 1e-6
print("S=%.4f" % S)

S=-3.6150

The minimise and maximise functions can also handle multidimensional optimisations, just make xinit (and the bounds) lists rather than scalar values.

Miscellaneous functions

Force a variable to be iterable

This support method will force a variable to be an iterable, allowing you to guarantee that the variable will be safe for use in, say, a for loop.

from cogent3.util.misc import iterable

my_var = 10
for i in my_var:
    print("will not work")

for i in iterable(my_var):
    print(i)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[11], line 4
      1 from cogent3.util.misc import iterable
      3 my_var = 10
----> 4 for i in my_var:
      5     print("will not work")
      7 for i in iterable(my_var):

TypeError: 'int' object is not iterable

Curry a function

curry(f,x)(y) = f(x,y) or = lambda y: f(x,y). This was modified from the Python Cookbook. Docstrings are also carried over.

from cogent3.util.misc import curry

def foo(x, y):
    """Some function"""
    return x + y

bar = curry(foo, 5)
print(bar.__doc__)
bar(10)

 curry(foo,5)
== curried from foo ==
 Some function

15

Test to see if an object is iterable

Perform a simple test to see if an object supports iteration

from cogent3.util.misc import is_iterable

can_iter = [1, 2, 3, 4]
cannot_iter = 1.234
is_iterable(can_iter)

True

is_iterable(cannot_iter)

False

Test to see if an object is a single char

Perform a simple test to see if an object is a single character

from cogent3.util.misc import is_char

class foo:
    pass

is_char("a")

True

is_char("ab")

False

is_char(foo())

False

Flatten a deeply nested iterable

To flatten a deeply nested iterable, use recursive_flatten. This method supports multiple levels of nesting, and multiple iterable types

from cogent3.util.misc import recursive_flatten

l = [[[[1, 2], "abcde"], [5, 6]], [7, 8], [9, 10]]

recursive_flatten(l)

[1, 2, 'a', 'b', 'c', 'd', 'e', 5, 6, 7, 8, 9, 10]

Test to determine if list of tuple

Perform a simple check to see if an object is not a list or a tuple

from cogent3.util.misc import not_list_tuple

not_list_tuple(1)

True

not_list_tuple([1])

False

not_list_tuple("ab")

True

Create a case-insensitive iterable

Create a case-insensitive object, for instance, if you want the key ‘a’ and ‘A’ to point to the same item in a dict

from cogent3.util.misc import add_lowercase

d = {"A": 5, "B": 6, "C": 7, "foo": 8, 42: "life"}
add_lowercase(d)

{'A': 5, 'B': 6, 'C': 7, 'foo': 8, 42: 'life', 'a': 5, 'b': 6, 'c': 7}

Construct a distance matrix lookup function

Automatically construct a distance matrix lookup function. This is useful for maintaining flexibility about whether a function is being computed or if a lookup is being used

from numpy import array

from cogent3.util.misc import DistanceFromMatrix

m = array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
f = DistanceFromMatrix(m)
f(0, 0)

np.int64(1)

f(1, 2)

np.int64(6)

Check class types

Check an object against base classes or derived classes to see if it is acceptable

from cogent3.util.misc import ClassChecker

class not_okay(object):
    pass

no = not_okay()

class okay(object):
    pass

o = okay()

class my_dict(dict):
    pass

md = my_dict()
cc = ClassChecker(str, okay, dict)
o in cc

True

no in cc

False

5 in cc

False

{"a": 5} in cc

True

"asasas" in cc

True

md in cc

True

Delegate to a separate object

Delegate object method calls, properties and variables to the appropriate object. Useful to combine multiple objects together while assuring that the calls will go to the correct object.

from cogent3.util.misc import Delegator

class ListAndString(list, Delegator):
    def __init__(self, items, string):
        Delegator.__init__(self, string)
        for i in items:
            self.append(i)

ls = ListAndString([1, 2, 3], "ab_cd")
len(ls)

3

ls[0]

1

ls.upper()

'AB_CD'

ls.split("_")

['ab', 'cd']

Wrap a function to hide from a class

Wrap a function to hide it from a class so that it isn’t a method.

from cogent3.util.misc import FunctionWrapper

f = FunctionWrapper(str)
f

<cogent3.util.misc.FunctionWrapper at 0x7f4b2830abd0>

f(123)

'123'

Construct a constrained container

Wrap a container with a constraint. This is useful for enforcing that the data contained is valid within a defined context. Cogent3 provides a base ConstrainedContainer which can be used to construct user-defined constrained objects. Cogent3 also provides ConstrainedString, ConstrainedList, and ConstrainedDict. These provided types fully cover the builtin types while staying integrated with the ConstrainedContainer.

Here is a light example of the ConstrainedDict

from cogent3.util.misc import ConstrainedDict

d = ConstrainedDict({"a": 1, "b": 2, "c": 3}, constraint="abc")
d

{'a': 1, 'b': 2, 'c': 3}

d["d"] = 5

---------------------------------------------------------------------------
ConstraintError                           Traceback (most recent call last)
Cell In[40], line 1
----> 1 d["d"] = 5

File ~/work/cogent3.github.io/cogent3.github.io/.venv/lib/python3.12/site-packages/cogent3/util/misc.py:687, in ConstrainedDict.__setitem__(self, key, value)
    685 if not self.item_is_valid(key):
    686     msg = f"Item '{key}' not in constraint '{self.constraint}'"
--> 687     raise ConstraintError(msg)
    688 key, value = self.mask(key), self.value_mask(value)
    689 dict.__setitem__(self, key, value)

ConstraintError: Item 'd' not in constraint 'abc'