Functional programming extension of Melodie. Using this tool, you can make full use of the advantages of functional programming, making coding more convenient and debugging easier.

• Python 3.8+
• No compulsory dependency. But to use show_progress to show the progress, tqdm is required.

pip install MelodieFuncFlow

To take advantage of functional programming in interactive mode, we strongly suggest to use IPython/Jupyter instead of barely Python.

Also, for script-based programming, MelodieFuncFlow has optimized type hints for the autocompletion of many common editors. But for convenience, this documentation only used interactive environment for demonstration.

The core class is MelodieGenerator. By the code below, we imported MelodieGenerator, and created one instance g.

In [1]: from MelodieFuncFlow import MelodieGenerator

In [2]: g = MelodieGenerator([1, 2, 3, 4, 5])

This generator is also an Iterable:

In [3]: [item for item in MelodieGenerator([1, 2, 3, 4, 5])]
Out[3]: [1, 2, 3, 4, 5]

MelodieGenerator could be converted to list or set.

• to_list put elements to a new list in the order the MelodieGenerator generates
• to_set will put elements to a set, eliminating repetitions of elements.

In [4]: MelodieGenerator([1, 2, 3, 1, 2]).to_list()
Out[4]: [1, 2, 3, 1, 2]

In [5]: MelodieGenerator([1, 2, 3, 1, 2]).to_set()
Out[5]: {1, 2, 3}

Of course, if some elements in generator are not hash-able, to_set method will raise an TypeError:

In [6]: MelodieGenerator([{"a": 123}, {"a": 456}, {"a": 123}]).to_set()

↓↓↓↓↓↓ raised an error ↓↓↓↓↓↓

TypeError: unhashable type: 'dict'

We can access the first element by head or element subscription:

In [2]: g = MelodieGenerator([1, 2, 3, 4, 5])

In [3]: g.head()
Out[3]: 1

In [4]: g.head()
Out[4]: 2

In [5]: g.head()
Out[5]: 3

In [6]: MelodieGenerator([1, 2, 3, 4, 5])[3]
Out[6]: 4

Also, we can slice this generator, and this method returns a new MelodieGenerator.

The slicing rules are:

• If two arguments bound, rbound passed in:

  • bound must satisfy bound >= 0
  • If rbound >= 0, the interval is [bound, rbound)
  • If rbound == -1, the interval is: [bound, +∞). (<0 values except -1 had not been supported yet)

• If only one argument bound passed in, the interval is [0, bound)

• slice has equivalent subscriptions, for example:

  • g.slice(1, 3) <=> g[1:3]
  • g.slice(3) <=> g[:3]
  • g.slice(1, -1) <=> g[1:]
  • Note that the returning value of integer subscriptions like g[3] will not return the MelodieGenerator but the 4th element.

The slicing mechanism is demonstrated below:

In [2]: MelodieGenerator([1, 2, 3, 4, 5]).slice(1, 3).to_list()
Out[2]: [2, 3]

In [2]: MelodieGenerator([1, 2, 3, 4, 5])[1:3].to_list()
Out[2]: [2, 3]

In [3]: MelodieGenerator([1, 2, 3, 4, 5]).slice(1, -1).to_list()
Out[3]: [2, 3, 4, 5]

In [4]: MelodieGenerator([1, 2, 3, 4, 5]).slice(2).to_list()
Out[4]: [3, 4, 5]

This generator supports mapping, which is a common method used in functional programming.

Mapping on the MelodieGenerator will not change the original generator, but create a new generator containing the return values of the operator function in order.

In [7]:  MelodieGenerator([1, 2, 3, 4, 5]).map(lambda x: x * x)
Out[7]: <MelodieFuncFlow.functional.MelodieGenerator at 0x28a5a641640>

In [8]: MelodieGenerator([1, 2, 3, 4, 5]).map(lambda x: x * x).to_list()
Out[8]: [1, 4, 9, 16, 25]

Note: The to_list method called above is intended to let the generator go on. As the values in the MelodieGenerator is evaluated lazily, only calling map method will just create a new generator, which will not execute automatically (demonstrated in Out[7]).

If the elements inside MelodieGenerator are tuples of same size, star_map method can be used for better organizing the operator function. Here is a demo to convert students' grades from 100 to 5:

In [9]: MelodieGenerator([("Alice", 98), 
                           ("Bob", 95), 
                           ("Carol", 92)]) \
                .star_map(lambda name, grade: grade*5/100) \
                .to_list()
Out[9]: [4.9, 4.75, 4.6]

Code In [9] has the same effect of the code below. As we can see, coding with star_map is easier for understanding the operator.

MelodieGenerator([("Alice", 98), 
                           ("Bob", 95), 
                           ("Carol", 92)]) \
                .map(lambda student: student[1]*5/100) \
                .to_list()

Filtering is another common operation in functional programming. The filter method is used to extract elements from the iterator that meet specific conditions. The filter method does not change the original generator, but returns a new generator with all the elements that satisfy the condition.

In [10]: MelodieGenerator([1, 2, 3, 4, 5]).filter(lambda x: x % 2 == 0).to_list()
Out[10]: [2, 4]

Also, MelodieGenerator supports star_filter, unpacking the tupled elements to match the arguments of condition function.

In [11]: MelodieGenerator([(1, 0), (0, -5), (0, -3)]).star_filter(lambda x, y: y < 0).to_list()
Out[11]: [(0, -5), (0, -3)]

In functional programming, reducing refers to the process of applying a binary operator function (a function that takes two arguments) to all elements of an iterable (such as a list or array), in a cumulative way, so as to reduce the iterable to a single output value.

The binary function is applied to the first two elements of the iterable, then to the result and the next element, and so on, until only one element remains. This final element is the output value of the reduction.

Here is an example written with standard libraries. suppose we have a list of numbers and we want to find their product. the reduce is performed on the list numbers.

from functools import reduce

numbers = [1, 2, 3, 4, 5]
product = reduce((lambda x, y: x * y), numbers)
print(product)  # Output: 120

The calculation steps are listed below. ( lambda x, y: x * y is written as product(x, y)):

1. product(1, 2) = 2, remaining values were [3, 4, 5]
2. product(2, 3) = 6, remaining values were [4, 5]
3. product(6, 4) = 24, remaining values were [5]
4. product(24, 5) = 120, remaining values were []

So finally we got the value 120.

In MelodieGenerator, there are two types of reduction method that we can use, the comparison is shown in the following table.

Note that in the following table:

• Element type of MelodieGenerator are marked as T.

• (A, B) => R indicates a python function with two argument having types A and B accordingly, and returning type R.

An reducing example for MelodieGenerator is as follows.

>>> MelodieGenerator([-1, 1, 3, 5]).reduce(lambda x, y: x + y)
8

>>> MelodieGenerator([[-1], [1, 3, 5]]).reduce(lambda x, y: x + y) 
[-1, 1, 3, 5]

def add_to_dict(dic, elem):
    k, v = elem
    dic[k] = v
    return dic

>>> MelodieGenerator([("Alice", 98), ("Bob", 76)]).fold_left(add_to_dict, {})         
{'Alice': 98, 'Bob': 76}

Extra jobs by extra_job in MelodieGenerator is a kind of operation that will not interfere the computation flow.

For example, in code In [13] and In [14], a print was inserted into the computation flow in two different positions, and middle results were printed. However, the result Out[13] and Out[14] are also the same as the original result Out[12]:

In [12]: MelodieGenerator([-1, 1, 3, 5]).map(lambda value: value * 2).to_list()
Out[12]: [-2, 2, 6, 10]

In [13]: MelodieGenerator([-1, 1, 3, 5]).extra_job(print).map(lambda value: value * 2).to_list()
-1
1
3
5
Out[13]: [-2, 2, 6, 10]

In [14]: MelodieGenerator([-1, 1, 3, 5]).map(lambda value: value * 2).extra_job(print).to_list()
-2
2
6
10
Out[14]: [-2, 2, 6, 10]

Sometimes it is important for the mapping/filtering/extra-job operator to get the index of current element. To do this, we implemented indexed_filter, indexed_map and indexed_extra_job, providing an index together with each element.

In [15]: MelodieGenerator([-1, 1, 3, 5]).indexed_map(lambda index, value: (index, value)).to_list()
Out[15]: [(0, -1), (1, 1), (2, 3), (3, 5)]

In [16]: MelodieGenerator([1, 2, 3, 4]).indexed_filter(lambda index, value: index > 1 and value % 2 == 0).to_list()
Out[16]: [4]

In [17]: MelodieGenerator([-1, 1, 3, 5]).indexed_extra_job(print).to_list()
0 -1
1 1
2 3
3 5
Out[17]: [-1, 1, 3, 5]

Note: Currently, indexed map/filter with tuple unpacks have not been implemented.

You might have noticed that in the examples above, MelodieGenerator was evaluated right after the creation. That is because this generator is irreversible, we cannot evaluate it for the second time (e.g., by head, to_list, to_set or slice) :

>>> g =  MelodieGenerator([-1, 1, 3, 5])
>>> g.to_list()
[-1, 1, 3, 5]
>>> g.to_list()
[]

If you want to evaluate it repeatedly, you could freeze it. freeze will generate a MelodieFrozenGenerator object, which is a subclass of MelodieGenerator.

Most of the method implementations in MelodieFrozenGenerator are identical to MelodieGenerator, and it can be evaluated multiple times.

>>> g = MelodieGenerator([-1, 1, 3, 5]).freeze()
>>> g.to_list()
[-1, 1, 3, 5]
>>> g.to_list()
[-1, 1, 3, 5]

Besides, frozen generator can be sorted. Method sort will return a new MelodieFrozenGenerator with elements sorted.

>>> g = MelodieGenerator([1, 3, 4, 2, 5]).freeze()
>>> g.sort(lambda x: x).to_list() # ascending by default
[1, 2, 3, 4, 5]
>>> g.sort(lambda x: x, reverse=True).to_list() # descending 
[5, 4, 3, 2, 1]

To sort by a custom comparator function:

def cmp(a, b):
    if a == b:
        return 0
    elif a > b:
        return 1
    else:   
        return -1

>>> g = MelodieGenerator([1, 3, 4, 2, 5]).freeze()
>>> g.relsort(cmp).to_list() # ascending by default
[1, 2, 3, 4, 5]
>>> g.relsort(cmp, reverse=True).to_list() # descending
[5, 4, 3, 2, 1]

Note 1: head method on frozen generator will always return the first value

>>> g = MelodieGenerator([1,2,3,4,5]).freeze()
>>> g.head()
1
>>> g.head()
1

Note 2: Operations like filter and map on MelodieFrozenGenerator will return a general MelodieGenerator. To freeze the filtered/mapped result, please use .freeze()/.f method on the new generator.

In [1]: from MelodieFuncFlow import *

In [2]: g_freeze = MelodieGenerator([1, 2, 3, 4, 5]).f

In [3]: g_mapped = g_freeze.map(lambda x: x*x)

In [4]: g_mapped.head()
Out[4]: 1

In [5]: g_mapped.head()
Out[5]: 4

In [6]: g_mapped.head()
Out[6]: 9

In [7]: g_mapped_frozen = g_freeze.map(lambda x: x*x).f

In [8]: g_mapped_frozen.head()
Out[8]: 1

In [9]: g_mapped_frozen.head()
Out[9]: 1

Note: tqdm must be installed. If you are using IPython, ipywidgets is required as well. Just execute:

pip install tqdm
pip install ipywidgets # Used when using IPython/Jupyter.

To show progress, just use show_progress method.

In [19]: import time
In [20]: MelodieGenerator([-1, 1, 3, 5]).extra_job(lambda x: time.sleep(1)).show_progress().to_list()

4it [00:04,  1.01s/it]

Out[20]: [-1, 1, 3, 5]

For frozen generator, as the total value is known, show_progress will show the progress bar.

>>> MelodieGenerator([-1, 1, 3, 5]).freeze().show_progress().extra_job(lambda x: time.sleep(1)).to_list() 
100% ██████████████████████████████████████████████| 4/4 [00:04<00:00,  1.01s/it]

• 0.3.0

  • Fixed the type hinting problem of MelodieFrozenGenerator;
  • Fixed typos in this README.

• 0.2.0

  • Distinguished reduce and fold_left methods;
  • Added slicing methods for element accessing.

• 0.1.0

  • Initial Release.

This project is licensed under the MIT License - See the LICENSE.md file for details