****************
Useful Utilities
****************

.. authors, Daniel McDonald, Gavin Huttley, Antonio Gonzalez Pena, Rob Knight

.. include:: union_dict.rst

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.

.. jupyter-execute::

    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``).

.. jupyter-execute::

    from cogent3.maths.optimisers import minimise, maximise

    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)

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

Miscellaneous functions
=======================

.. index:: cogent3.util.misc

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.

.. jupyter-execute::
    :raises: TypeError

    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)

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.

.. jupyter-execute::

    from cogent3.util.misc import curry

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

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

Test to see if an object is iterable
------------------------------------

Perform a simple test to see if an object supports iteration

.. jupyter-execute::

    from cogent3.util.misc import is_iterable

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

.. jupyter-execute::

    is_iterable(cannot_iter)

Test to see if an object is a single char
-----------------------------------------

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

.. jupyter-execute::

    from cogent3.util.misc import is_char

    class foo:
        pass

    is_char("a")

.. jupyter-execute::

    is_char("ab")

.. jupyter-execute::

    is_char(foo())

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

.. jupyter-execute::

    from cogent3.util.misc import recursive_flatten

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

.. jupyter-execute::

    recursive_flatten(l)

Test to determine if ``list`` of ``tuple``
------------------------------------------

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

.. jupyter-execute::

    from cogent3.util.misc import not_list_tuple

    not_list_tuple(1)

.. jupyter-execute::

    not_list_tuple([1])

.. jupyter-execute::

    not_list_tuple("ab")

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

.. jupyter-execute::

    from cogent3.util.misc import add_lowercase

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

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

.. jupyter-execute::

    from cogent3.util.misc import DistanceFromMatrix
    from numpy import array

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

.. jupyter-execute::

    f(1, 2)

Check class types
-----------------

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

.. jupyter-execute::

    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

.. jupyter-execute::

    no in cc

.. jupyter-execute::

    5 in cc

.. jupyter-execute::

    {"a": 5} in cc

.. jupyter-execute::

    "asasas" in cc

.. jupyter-execute::

    md in cc

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.

.. jupyter-execute::

    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)

.. jupyter-execute::

    ls[0]

.. jupyter-execute::

    ls.upper()

.. jupyter-execute::

    ls.split("_")

Wrap a function to hide from a class
------------------------------------

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

.. jupyter-execute::

    from cogent3.util.misc import FunctionWrapper

    f = FunctionWrapper(str)
    f

.. jupyter-execute::

    f(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``

.. jupyter-execute::

    from cogent3.util.misc import ConstrainedDict

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

.. jupyter-execute::
    :raises: ConstraintError

    d["d"] = 5