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:36, in UnionDict.__getattr__(self, item)
35 try:
---> 36 return super().__getattr__(item)
37 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 return super().__getattr__(item)
37 except AttributeError:
---> 38 raise AttributeError(f"'{item}' not a key or attribute")
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)
1
f(1, 2)
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 0x7fe4b39159d0>
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:691, in ConstrainedDict.__setitem__(self, key, value)
689 """Sets self[key] to value if value in constraint."""
690 if not self.item_is_valid(key):
--> 691 raise ConstraintError(f"Item '{key}' not in constraint '{self.constraint}'")
692 key, value = self.mask(key), self.value_mask(value)
693 dict.__setitem__(self, key, value)
ConstraintError: Item 'd' not in constraint 'abc'