| Name | pyf-collection JSON |
| Version |
0.1.4
JSON |
| download |
| home_page | None |
| Summary | None |
| upload_time | 2025-08-19 21:37:17 |
| maintainer | None |
| docs_url | None |
| author | Pablo Picouto Garcia |
| requires_python | >=3.9 |
| license | Copyright (c) 2025 Pablo
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE. |
| keywords |
collection
monads
list
map
functional
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
setuptools
testcontainers
confluent-kafka
OSlash
pykka
pyio-effect
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# PyF Collection
A Python library that brings functional programming collection operations to Python, inspired by monadic operations from languages like Scala and Haskell.
## Installation
```bash
pip install pyf-collection
```
## Overview
PyFCollection is a generic collection wrapper that provides a fluent API for functional programming operations on iterables. It allows you to chain operations like `map`, `filter`, `fold`, and more in a clean, readable way.
## Quick Start
```python
from pyf_collection import PyFCollection
# Create a collection
numbers = PyFCollection([1, 2, 3, 4, 5])
# Chain operations
result = (numbers
.map(lambda x: x * 2)
.filter(lambda x: x > 4)
.to_list())
print(list(result)) # [6, 8, 10]
```
## API Reference
### Constructor
#### `PyFCollection(content: Optional[collections.Iterable[T]])`
Creates a new PyFCollection instance.
```python
# From a list
collection = PyFCollection([1, 2, 3])
# From any iterable
collection = PyFCollection(range(5))
# Empty collection
collection = PyFCollection(None)
```
### Transformation Operations
#### `map(func: Callable[[T], U]) -> PyFCollection[U]`
Transforms each element in the collection using the provided function.
```python
numbers = PyFCollection([1, 2, 3])
doubled = numbers.map(lambda x: x * 2)
print(list(doubled.to_list())) # [2, 4, 6]
# Transform to different type
words = PyFCollection(["hello", "world"])
lengths = words.map(len)
print(list(lengths.to_list())) # [5, 5]
```
#### `flat_map(func: Callable[[T], PyFCollection[U]]) -> PyFCollection[U]`
Maps each element to a PyFCollection and flattens the results.
```python
words = PyFCollection(["hello", "world"])
chars = words.flat_map(lambda word: PyFCollection(list(word)))
print(list(chars.to_list())) # ['h', 'e', 'l', 'l', 'o', 'w', 'o', 'r', 'l', 'd']
# Expand numbers
numbers = PyFCollection([1, 2, 3])
expanded = numbers.flat_map(lambda x: PyFCollection([x, x * 10]))
print(list(expanded.to_list())) # [1, 10, 2, 20, 3, 30]
```
### Filtering Operations
#### `filter(func: Callable[[T], bool]) -> PyFCollection[T]`
Keeps only elements that satisfy the predicate function.
```python
numbers = PyFCollection([1, 2, 3, 4, 5, 6])
evens = numbers.filter(lambda x: x % 2 == 0)
print(list(evens.to_list())) # [2, 4, 6]
```
#### `distinct(dis: T) -> PyFCollection[T]`
Removes all occurrences of the specified element.
```python
numbers = PyFCollection([1, 2, 2, 3, 2, 4])
without_twos = numbers.distinct(2)
print(list(without_twos.to_list())) # [1, 3, 4]
```
### Search Operations
#### `find(func: Callable[[T], bool]) -> Optional[T]`
Returns the first element that satisfies the predicate, or None if not found.
```python
numbers = PyFCollection([1, 2, 3, 4, 5])
first_even = numbers.find(lambda x: x % 2 == 0)
print(first_even) # 2
not_found = numbers.find(lambda x: x > 10)
print(not_found) # None
```
#### `exist(func: Callable[[T], bool]) -> bool`
Returns True if any element satisfies the predicate.
```python
numbers = PyFCollection([1, 2, 3, 4, 5])
has_even = numbers.exist(lambda x: x % 2 == 0)
print(has_even) # True
has_large = numbers.exist(lambda x: x > 10)
print(has_large) # False
```
### Aggregation Operations
#### `fold(acc: U, func: Callable[[U, T], U]) -> PyFCollection[U]`
Reduces the collection to a single value using an accumulator function.
```python
numbers = PyFCollection([1, 2, 3, 4])
sum_result = numbers.fold(0, lambda acc, x: acc + x)
print(list(sum_result.to_list())) # [10]
# String concatenation
words = PyFCollection(["Hello", " ", "World"])
sentence = words.fold("", lambda acc, x: acc + x)
print(list(sentence.to_list())) # ["Hello World"]
```
### Slicing Operations
#### `take(n: int) -> PyFCollection[T]`
Returns a new collection with the first n elements.
```python
numbers = PyFCollection([1, 2, 3, 4, 5])
first_three = numbers.take(3)
print(list(first_three.to_list())) # [1, 2, 3]
```
#### `drop(n: int) -> PyFCollection[T]`
Returns a new collection without the first n elements.
```python
numbers = PyFCollection([1, 2, 3, 4, 5])
without_first_two = numbers.drop(2)
print(list(without_first_two.to_list())) # [3, 4, 5]
```
#### `slice(n: int, m: int) -> PyFCollection[T]`
Returns elements from index n to m (exclusive).
```python
numbers = PyFCollection([0, 1, 2, 3, 4, 5])
middle = numbers.slice(2, 4)
print(list(middle.to_list())) # [2, 3]
```
### Output Operations
#### `to_list() -> collections.Iterable[T]`
Converts the collection back to its underlying iterable.
```python
collection = PyFCollection([1, 2, 3])
result = collection.to_list()
print(list(result)) # [1, 2, 3]
```
## Chaining Operations
All operations return a new PyFCollection, allowing for fluent method chaining:
```python
result = (PyFCollection([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
.filter(lambda x: x % 2 == 0) # [2, 4, 6, 8, 10]
.map(lambda x: x * x) # [4, 16, 36, 64, 100]
.take(3) # [4, 16, 36]
.map(lambda x: f"Value: {x}") # ["Value: 4", "Value: 16", "Value: 36"]
.to_list())
print(list(result)) # ["Value: 4", "Value: 16", "Value: 36"]
```
## Type Safety
PyFCollection is fully typed using Python's type hints and generics, providing excellent IDE support and type checking:
```python
from typing import List
# Type inference works correctly
numbers: PyFCollection[int] = PyFCollection([1, 2, 3])
strings: PyFCollection[str] = numbers.map(str) # PyFCollection[str]
```
## Performance
PyFCollection is designed to be performant while maintaining readability. Benchmarks show it can even outperform equivalent vanilla Python implementations in complex pipelines.
### Benchmark Results
A comprehensive benchmark comparing PyFCollection against vanilla Python comprehensions shows impressive performance:
**Test Setup:**
- Dataset: 1,000,000 integers
- Pipeline operations:
1. `map` → double each value
2. `filter` → keep multiples of 3
3. `flat_map` → produce (n, -n) for each element
4. `distinct` → drop a single value (-6)
5. `take` → keep first 100 items
6. `to_list` → materialize the final result
**Results:**
```
PyFCollection → 0.187s (5 runs)
Vanilla Python → 0.235s (5 runs)
Sample output : [6, 12, -12, 18, -18, 24, -24, 30, -30, 36]
```
PyFCollection demonstrates **~20% better performance** than equivalent vanilla Python code, while providing significantly more readable and maintainable code.
### Benchmark Code
```python
from timeit import timeit
from typing import List
from pyf_collection import PyFCollection
def pipeline_pyf() -> List[int]:
data = list(range(1, 1_000_001))
result = (
PyFCollection(data)
.map(lambda x: x * 2)
.filter(lambda x: x % 3 == 0)
.flat_map(lambda x: PyFCollection([x, -x]))
.distinct(-6)
.take(100)
.to_list()
)
return result
def pipeline_vanilla() -> List[int]:
data = list(range(1, 1_000_001))
doubled = (x * 2 for x in data) # map
multiples = (x for x in doubled if x % 3 == 0) # filter
flatmapped = (y for x in multiples for y in (x, -x)) # flat-map
distincted = (x for x in flatmapped if x != -6) # distinct
first_100 = [x for *, x in zip(range(100), distincted)] # take
return first_100
# Run benchmark
vanilla_time = timeit("pipeline_vanilla()", globals=globals(), number=5)
pyf_time = timeit("pipeline_pyf()", globals=globals(), number=5)
print(f"PyFCollection → {pyf_time:.3f}s (5 runs)")
print(f"Vanilla Python → {vanilla_time:.3f}s (5 runs)")
```
## Requirements
- Python >= 3.9
- typing support for generics
## License
MIT License
## Author
Pablo Picouto Garcia
## Contributing
Contributions are welcome! Please visit the [GitHub repository](https://github.com/politrons/Dive-into-Python) for more information.
## Issues
Report issues at: https://github.com/politrons/Dive-into-Python/issues
Raw data
{
"_id": null,
"home_page": null,
"name": "pyf-collection",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": "collection, monads, list, map, functional",
"author": "Pablo Picouto Garcia",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/82/92/f1d1842a360f9a5af26ebb16599de6ddb6bd1736d2bde0056305453064b8/pyf_collection-0.1.4.tar.gz",
"platform": null,
"description": "# PyF Collection\n\nA Python library that brings functional programming collection operations to Python, inspired by monadic operations from languages like Scala and Haskell.\n\n## Installation\n\n```bash\npip install pyf-collection\n```\n\n## Overview\n\nPyFCollection is a generic collection wrapper that provides a fluent API for functional programming operations on iterables. It allows you to chain operations like `map`, `filter`, `fold`, and more in a clean, readable way.\n\n## Quick Start\n\n```python\nfrom pyf_collection import PyFCollection\n\n# Create a collection\nnumbers = PyFCollection([1, 2, 3, 4, 5])\n\n# Chain operations\nresult = (numbers\n .map(lambda x: x * 2)\n .filter(lambda x: x > 4)\n .to_list())\n\nprint(list(result)) # [6, 8, 10]\n```\n## API Reference\n\n### Constructor\n\n#### `PyFCollection(content: Optional[collections.Iterable[T]])`\n\nCreates a new PyFCollection instance.\n\n```python\n# From a list\ncollection = PyFCollection([1, 2, 3])\n\n# From any iterable\ncollection = PyFCollection(range(5))\n\n# Empty collection\ncollection = PyFCollection(None)\n```\n\n### Transformation Operations\n\n#### `map(func: Callable[[T], U]) -> PyFCollection[U]`\n\nTransforms each element in the collection using the provided function.\n\n```python\nnumbers = PyFCollection([1, 2, 3])\ndoubled = numbers.map(lambda x: x * 2)\nprint(list(doubled.to_list())) # [2, 4, 6]\n\n# Transform to different type\nwords = PyFCollection([\"hello\", \"world\"])\nlengths = words.map(len)\nprint(list(lengths.to_list())) # [5, 5]\n```\n\n#### `flat_map(func: Callable[[T], PyFCollection[U]]) -> PyFCollection[U]`\n\nMaps each element to a PyFCollection and flattens the results.\n\n```python\nwords = PyFCollection([\"hello\", \"world\"])\nchars = words.flat_map(lambda word: PyFCollection(list(word)))\nprint(list(chars.to_list())) # ['h', 'e', 'l', 'l', 'o', 'w', 'o', 'r', 'l', 'd']\n\n# Expand numbers\nnumbers = PyFCollection([1, 2, 3])\nexpanded = numbers.flat_map(lambda x: PyFCollection([x, x * 10]))\nprint(list(expanded.to_list())) # [1, 10, 2, 20, 3, 30]\n```\n\n### Filtering Operations\n\n#### `filter(func: Callable[[T], bool]) -> PyFCollection[T]`\n\nKeeps only elements that satisfy the predicate function.\n\n```python\nnumbers = PyFCollection([1, 2, 3, 4, 5, 6])\nevens = numbers.filter(lambda x: x % 2 == 0)\nprint(list(evens.to_list())) # [2, 4, 6]\n```\n\n#### `distinct(dis: T) -> PyFCollection[T]`\n\nRemoves all occurrences of the specified element.\n\n```python\nnumbers = PyFCollection([1, 2, 2, 3, 2, 4])\nwithout_twos = numbers.distinct(2)\nprint(list(without_twos.to_list())) # [1, 3, 4]\n```\n\n### Search Operations\n\n#### `find(func: Callable[[T], bool]) -> Optional[T]`\n\nReturns the first element that satisfies the predicate, or None if not found.\n\n```python\nnumbers = PyFCollection([1, 2, 3, 4, 5])\nfirst_even = numbers.find(lambda x: x % 2 == 0)\nprint(first_even) # 2\n\nnot_found = numbers.find(lambda x: x > 10)\nprint(not_found) # None\n```\n\n#### `exist(func: Callable[[T], bool]) -> bool`\n\nReturns True if any element satisfies the predicate.\n\n```python\nnumbers = PyFCollection([1, 2, 3, 4, 5])\nhas_even = numbers.exist(lambda x: x % 2 == 0)\nprint(has_even) # True\n\nhas_large = numbers.exist(lambda x: x > 10)\nprint(has_large) # False\n```\n\n### Aggregation Operations\n\n#### `fold(acc: U, func: Callable[[U, T], U]) -> PyFCollection[U]`\n\nReduces the collection to a single value using an accumulator function.\n\n```python\nnumbers = PyFCollection([1, 2, 3, 4])\nsum_result = numbers.fold(0, lambda acc, x: acc + x)\nprint(list(sum_result.to_list())) # [10]\n\n# String concatenation\nwords = PyFCollection([\"Hello\", \" \", \"World\"])\nsentence = words.fold(\"\", lambda acc, x: acc + x)\nprint(list(sentence.to_list())) # [\"Hello World\"]\n```\n\n### Slicing Operations\n\n#### `take(n: int) -> PyFCollection[T]`\n\nReturns a new collection with the first n elements.\n\n```python\nnumbers = PyFCollection([1, 2, 3, 4, 5])\nfirst_three = numbers.take(3)\nprint(list(first_three.to_list())) # [1, 2, 3]\n```\n\n#### `drop(n: int) -> PyFCollection[T]`\n\nReturns a new collection without the first n elements.\n\n```python\nnumbers = PyFCollection([1, 2, 3, 4, 5])\nwithout_first_two = numbers.drop(2)\nprint(list(without_first_two.to_list())) # [3, 4, 5]\n```\n\n#### `slice(n: int, m: int) -> PyFCollection[T]`\n\nReturns elements from index n to m (exclusive).\n\n```python\nnumbers = PyFCollection([0, 1, 2, 3, 4, 5])\nmiddle = numbers.slice(2, 4)\nprint(list(middle.to_list())) # [2, 3]\n```\n\n### Output Operations\n\n#### `to_list() -> collections.Iterable[T]`\n\nConverts the collection back to its underlying iterable.\n\n```python\ncollection = PyFCollection([1, 2, 3])\nresult = collection.to_list()\nprint(list(result)) # [1, 2, 3]\n```\n\n## Chaining Operations\n\nAll operations return a new PyFCollection, allowing for fluent method chaining:\n\n```python\nresult = (PyFCollection([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n .filter(lambda x: x % 2 == 0) # [2, 4, 6, 8, 10]\n .map(lambda x: x * x) # [4, 16, 36, 64, 100]\n .take(3) # [4, 16, 36]\n .map(lambda x: f\"Value: {x}\") # [\"Value: 4\", \"Value: 16\", \"Value: 36\"]\n .to_list())\n\nprint(list(result)) # [\"Value: 4\", \"Value: 16\", \"Value: 36\"]\n```\n\n## Type Safety\n\nPyFCollection is fully typed using Python's type hints and generics, providing excellent IDE support and type checking:\n\n```python\nfrom typing import List\n\n# Type inference works correctly\nnumbers: PyFCollection[int] = PyFCollection([1, 2, 3])\nstrings: PyFCollection[str] = numbers.map(str) # PyFCollection[str]\n```\n\n\n## Performance\n\nPyFCollection is designed to be performant while maintaining readability. Benchmarks show it can even outperform equivalent vanilla Python implementations in complex pipelines.\n\n### Benchmark Results\n\nA comprehensive benchmark comparing PyFCollection against vanilla Python comprehensions shows impressive performance:\n\n**Test Setup:**\n- Dataset: 1,000,000 integers\n- Pipeline operations:\n 1. `map` \u2192 double each value\n 2. `filter` \u2192 keep multiples of 3\n 3. `flat_map` \u2192 produce (n, -n) for each element\n 4. `distinct` \u2192 drop a single value (-6)\n 5. `take` \u2192 keep first 100 items\n 6. `to_list` \u2192 materialize the final result\n\n**Results:**\n```\nPyFCollection \u2192 0.187s (5 runs)\nVanilla Python \u2192 0.235s (5 runs)\nSample output : [6, 12, -12, 18, -18, 24, -24, 30, -30, 36]\n```\n\nPyFCollection demonstrates **~20% better performance** than equivalent vanilla Python code, while providing significantly more readable and maintainable code.\n\n### Benchmark Code\n\n```python\nfrom timeit import timeit\nfrom typing import List\nfrom pyf_collection import PyFCollection\n\ndef pipeline_pyf() -> List[int]:\n data = list(range(1, 1_000_001))\n \n result = (\n PyFCollection(data)\n .map(lambda x: x * 2)\n .filter(lambda x: x % 3 == 0)\n .flat_map(lambda x: PyFCollection([x, -x]))\n .distinct(-6)\n .take(100)\n .to_list()\n )\n return result\n\ndef pipeline_vanilla() -> List[int]:\n data = list(range(1, 1_000_001))\n \n doubled = (x * 2 for x in data) # map\n multiples = (x for x in doubled if x % 3 == 0) # filter\n flatmapped = (y for x in multiples for y in (x, -x)) # flat-map\n distincted = (x for x in flatmapped if x != -6) # distinct\n first_100 = [x for *, x in zip(range(100), distincted)] # take\n return first_100\n\n# Run benchmark\nvanilla_time = timeit(\"pipeline_vanilla()\", globals=globals(), number=5)\npyf_time = timeit(\"pipeline_pyf()\", globals=globals(), number=5)\n\nprint(f\"PyFCollection \u2192 {pyf_time:.3f}s (5 runs)\")\nprint(f\"Vanilla Python \u2192 {vanilla_time:.3f}s (5 runs)\")\n```\n\n## Requirements\n\n- Python >= 3.9\n- typing support for generics\n\n## License\n\nMIT License\n\n## Author\n\nPablo Picouto Garcia\n\n## Contributing\n\nContributions are welcome! Please visit the [GitHub repository](https://github.com/politrons/Dive-into-Python) for more information.\n\n## Issues\n\nReport issues at: https://github.com/politrons/Dive-into-Python/issues\n",
"bugtrack_url": null,
"license": "Copyright (c) 2025 Pablo\n Permission is hereby granted, free of charge, to any person obtaining a copy\n of this software and associated documentation files (the \u201cSoftware\u201d), to deal\n in the Software without restriction, including without limitation the rights\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n copies of the Software, and to permit persons to whom the Software is\n furnished to do so, subject to the following conditions:\n The above copyright notice and this permission notice shall be included in\n all copies or substantial portions of the Software.\n THE SOFTWARE IS PROVIDED \u201cAS IS\u201d, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n THE SOFTWARE.",
"summary": null,
"version": "0.1.4",
"project_urls": {
"Homepage": "https://github.com/politrons/Dive-into-Python",
"Issues": "https://github.com/politrons/Dive-into-Python"
},
"split_keywords": [
"collection",
" monads",
" list",
" map",
" functional"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "eed5afe1af5c1259c0ddbd145fe83f514ed84cd0d7002014473f53b13683af35",
"md5": "3dfb4b1aa27644cb90466984428f37f1",
"sha256": "d42dbc496e5419182b1876132e85ab96cb90b450b7dc42b40a4b2fce859da1ca"
},
"downloads": -1,
"filename": "pyf_collection-0.1.4-py3-none-any.whl",
"has_sig": false,
"md5_digest": "3dfb4b1aa27644cb90466984428f37f1",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 6354,
"upload_time": "2025-08-19T21:37:16",
"upload_time_iso_8601": "2025-08-19T21:37:16.755082Z",
"url": "https://files.pythonhosted.org/packages/ee/d5/afe1af5c1259c0ddbd145fe83f514ed84cd0d7002014473f53b13683af35/pyf_collection-0.1.4-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "8292f1d1842a360f9a5af26ebb16599de6ddb6bd1736d2bde0056305453064b8",
"md5": "4bb1edd71661d601ec9da0e0b33a856a",
"sha256": "0bdc144c2fa7c17ec73303b23bd4d43ad39e0408fede210ce14790db7a21633d"
},
"downloads": -1,
"filename": "pyf_collection-0.1.4.tar.gz",
"has_sig": false,
"md5_digest": "4bb1edd71661d601ec9da0e0b33a856a",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 6569,
"upload_time": "2025-08-19T21:37:17",
"upload_time_iso_8601": "2025-08-19T21:37:17.745652Z",
"url": "https://files.pythonhosted.org/packages/82/92/f1d1842a360f9a5af26ebb16599de6ddb6bd1736d2bde0056305453064b8/pyf_collection-0.1.4.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-08-19 21:37:17",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "politrons",
"github_project": "Dive-into-Python",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [
{
"name": "setuptools",
"specs": [
[
"~=",
"80.3.1"
]
]
},
{
"name": "testcontainers",
"specs": [
[
"~=",
"4.12.0"
]
]
},
{
"name": "confluent-kafka",
"specs": [
[
"~=",
"2.11.0"
]
]
},
{
"name": "OSlash",
"specs": [
[
"~=",
"0.6.3"
]
]
},
{
"name": "pykka",
"specs": [
[
"~=",
"4.2.0"
]
]
},
{
"name": "pyio-effect",
"specs": [
[
"==",
"0.1.3"
]
]
}
],
"lcname": "pyf-collection"
}
