Correct ruff failures (#8732)

* fix: Correct ruff problems

* updating DIRECTORY.md

* fix: Fix pre-commit errors

* updating DIRECTORY.md

---------

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
This commit is contained in:
Caeden Perelli-Harris 2023-05-14 22:03:13 +01:00 committed by GitHub
parent 793e564e1d
commit 1faf10b5c2
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
9 changed files with 22 additions and 20 deletions

View File

@ -294,7 +294,6 @@
* [Mergesort](divide_and_conquer/mergesort.py) * [Mergesort](divide_and_conquer/mergesort.py)
* [Peak](divide_and_conquer/peak.py) * [Peak](divide_and_conquer/peak.py)
* [Power](divide_and_conquer/power.py) * [Power](divide_and_conquer/power.py)
* [Strassen Matrix Multiplication](divide_and_conquer/strassen_matrix_multiplication.py)
## Dynamic Programming ## Dynamic Programming
* [Abbreviation](dynamic_programming/abbreviation.py) * [Abbreviation](dynamic_programming/abbreviation.py)
@ -632,6 +631,7 @@
* [Radians](maths/radians.py) * [Radians](maths/radians.py)
* [Radix2 Fft](maths/radix2_fft.py) * [Radix2 Fft](maths/radix2_fft.py)
* [Relu](maths/relu.py) * [Relu](maths/relu.py)
* [Remove Digit](maths/remove_digit.py)
* [Runge Kutta](maths/runge_kutta.py) * [Runge Kutta](maths/runge_kutta.py)
* [Segmented Sieve](maths/segmented_sieve.py) * [Segmented Sieve](maths/segmented_sieve.py)
* Series * Series
@ -694,6 +694,8 @@
## Neural Network ## Neural Network
* [2 Hidden Layers Neural Network](neural_network/2_hidden_layers_neural_network.py) * [2 Hidden Layers Neural Network](neural_network/2_hidden_layers_neural_network.py)
* Activation Functions
* [Exponential Linear Unit](neural_network/activation_functions/exponential_linear_unit.py)
* [Back Propagation Neural Network](neural_network/back_propagation_neural_network.py) * [Back Propagation Neural Network](neural_network/back_propagation_neural_network.py)
* [Convolution Neural Network](neural_network/convolution_neural_network.py) * [Convolution Neural Network](neural_network/convolution_neural_network.py)
* [Input Data](neural_network/input_data.py) * [Input Data](neural_network/input_data.py)
@ -1080,6 +1082,7 @@
## Sorts ## Sorts
* [Bead Sort](sorts/bead_sort.py) * [Bead Sort](sorts/bead_sort.py)
* [Binary Insertion Sort](sorts/binary_insertion_sort.py)
* [Bitonic Sort](sorts/bitonic_sort.py) * [Bitonic Sort](sorts/bitonic_sort.py)
* [Bogo Sort](sorts/bogo_sort.py) * [Bogo Sort](sorts/bogo_sort.py)
* [Bubble Sort](sorts/bubble_sort.py) * [Bubble Sort](sorts/bubble_sort.py)
@ -1170,6 +1173,7 @@
* [Reverse Words](strings/reverse_words.py) * [Reverse Words](strings/reverse_words.py)
* [Snake Case To Camel Pascal Case](strings/snake_case_to_camel_pascal_case.py) * [Snake Case To Camel Pascal Case](strings/snake_case_to_camel_pascal_case.py)
* [Split](strings/split.py) * [Split](strings/split.py)
* [String Switch Case](strings/string_switch_case.py)
* [Text Justification](strings/text_justification.py) * [Text Justification](strings/text_justification.py)
* [Top K Frequent Words](strings/top_k_frequent_words.py) * [Top K Frequent Words](strings/top_k_frequent_words.py)
* [Upper](strings/upper.py) * [Upper](strings/upper.py)

View File

@ -96,7 +96,7 @@ def add_si_prefix(value: float) -> str:
for name_prefix, value_prefix in prefixes.items(): for name_prefix, value_prefix in prefixes.items():
numerical_part = value / (10**value_prefix) numerical_part = value / (10**value_prefix)
if numerical_part > 1: if numerical_part > 1:
return f"{str(numerical_part)} {name_prefix}" return f"{numerical_part!s} {name_prefix}"
return str(value) return str(value)
@ -111,7 +111,7 @@ def add_binary_prefix(value: float) -> str:
for prefix in BinaryUnit: for prefix in BinaryUnit:
numerical_part = value / (2**prefix.value) numerical_part = value / (2**prefix.value)
if numerical_part > 1: if numerical_part > 1:
return f"{str(numerical_part)} {prefix.name}" return f"{numerical_part!s} {prefix.name}"
return str(value) return str(value)

View File

@ -121,8 +121,8 @@ def rgb_to_hsv(red: int, green: int, blue: int) -> list[float]:
float_red = red / 255 float_red = red / 255
float_green = green / 255 float_green = green / 255
float_blue = blue / 255 float_blue = blue / 255
value = max(max(float_red, float_green), float_blue) value = max(float_red, float_green, float_blue)
chroma = value - min(min(float_red, float_green), float_blue) chroma = value - min(float_red, float_green, float_blue)
saturation = 0 if value == 0 else chroma / value saturation = 0 if value == 0 else chroma / value
if chroma == 0: if chroma == 0:

View File

@ -96,7 +96,7 @@ def test_nearest_neighbour(
def test_local_binary_pattern(): def test_local_binary_pattern():
file_path: str = "digital_image_processing/image_data/lena.jpg" file_path = "digital_image_processing/image_data/lena.jpg"
# Reading the image and converting it to grayscale. # Reading the image and converting it to grayscale.
image = imread(file_path, 0) image = imread(file_path, 0)

View File

@ -122,7 +122,7 @@ def strassen(matrix1: list, matrix2: list) -> list:
if dimension1[0] == dimension1[1] and dimension2[0] == dimension2[1]: if dimension1[0] == dimension1[1] and dimension2[0] == dimension2[1]:
return [matrix1, matrix2] return [matrix1, matrix2]
maximum = max(max(dimension1), max(dimension2)) maximum = max(dimension1, dimension2)
maxim = int(math.pow(2, math.ceil(math.log2(maximum)))) maxim = int(math.pow(2, math.ceil(math.log2(maximum))))
new_matrix1 = matrix1 new_matrix1 = matrix1
new_matrix2 = matrix2 new_matrix2 = matrix2

View File

@ -24,7 +24,7 @@ class Fibonacci:
return self.sequence[:index] return self.sequence[:index]
def main(): def main() -> None:
print( print(
"Fibonacci Series Using Dynamic Programming\n", "Fibonacci Series Using Dynamic Programming\n",
"Enter the index of the Fibonacci number you want to calculate ", "Enter the index of the Fibonacci number you want to calculate ",

View File

@ -1,12 +1,12 @@
from __future__ import annotations from __future__ import annotations
import typing
from collections.abc import Iterable from collections.abc import Iterable
from typing import Union
import numpy as np import numpy as np
Vector = Union[Iterable[float], Iterable[int], np.ndarray] Vector = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
VectorOut = Union[np.float64, int, float] VectorOut = typing.Union[np.float64, int, float] # noqa: UP007
def euclidean_distance(vector_1: Vector, vector_2: Vector) -> VectorOut: def euclidean_distance(vector_1: Vector, vector_2: Vector) -> VectorOut:

View File

@ -147,6 +147,6 @@ if __name__ == "__main__":
# Print results # Print results
print() print()
print("Results: ") print("Results: ")
print(f"Horizontal Distance: {str(horizontal_distance(init_vel, angle))} [m]") print(f"Horizontal Distance: {horizontal_distance(init_vel, angle)!s} [m]")
print(f"Maximum Height: {str(max_height(init_vel, angle))} [m]") print(f"Maximum Height: {max_height(init_vel, angle)!s} [m]")
print(f"Total Time: {str(total_time(init_vel, angle))} [s]") print(f"Total Time: {total_time(init_vel, angle)!s} [s]")

View File

@ -13,11 +13,9 @@ class TreeNode:
self.left = None self.left = None
def build_tree(): def build_tree() -> TreeNode:
print("\n********Press N to stop entering at any point of time********\n") print("\n********Press N to stop entering at any point of time********\n")
check = input("Enter the value of the root node: ").strip().lower() or "n" check = input("Enter the value of the root node: ").strip().lower()
if check == "n":
return None
q: queue.Queue = queue.Queue() q: queue.Queue = queue.Queue()
tree_node = TreeNode(int(check)) tree_node = TreeNode(int(check))
q.put(tree_node) q.put(tree_node)
@ -37,7 +35,7 @@ def build_tree():
right_node = TreeNode(int(check)) right_node = TreeNode(int(check))
node_found.right = right_node node_found.right = right_node
q.put(right_node) q.put(right_node)
return None raise
def pre_order(node: TreeNode) -> None: def pre_order(node: TreeNode) -> None:
@ -272,7 +270,7 @@ if __name__ == "__main__":
doctest.testmod() doctest.testmod()
print(prompt("Binary Tree Traversals")) print(prompt("Binary Tree Traversals"))
node = build_tree() node: TreeNode = build_tree()
print(prompt("Pre Order Traversal")) print(prompt("Pre Order Traversal"))
pre_order(node) pre_order(node)
print(prompt() + "\n") print(prompt() + "\n")