Delete empty junk file (TheAlgorithms#9062)

* updating DIRECTORY.md

* updating DIRECTORY.md

* Delete empty junk file

* updating DIRECTORY.md

* Fix ruff errors

* Fix more ruff errors

---------

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>

DIRECTORY.md

@@ -5,7 +5,6 @@
   * [In Static Equilibrium](arithmetic_analysis/in_static_equilibrium.py)
   * [Intersection](arithmetic_analysis/intersection.py)
   * [Jacobi Iteration Method](arithmetic_analysis/jacobi_iteration_method.py)
-  * [Junk](arithmetic_analysis/junk.py)
   * [Lu Decomposition](arithmetic_analysis/lu_decomposition.py)
   * [Newton Forward Interpolation](arithmetic_analysis/newton_forward_interpolation.py)
   * [Newton Method](arithmetic_analysis/newton_method.py)

computer_vision/haralick_descriptors.py

@@ -100,7 +100,9 @@ def binarize(image: np.ndarray, threshold: float = 127.0) -> np.ndarray:
     return np.where(image > threshold, 1, 0)
 
 
-def transform(image: np.ndarray, kind: str, kernel: np.ndarray = None) -> np.ndarray:
+def transform(
+    image: np.ndarray, kind: str, kernel: np.ndarray | None = None
+) -> np.ndarray:
     """
     Simple image transformation using one of two available filter functions:
     Erosion and Dilation.
@@ -154,7 +156,7 @@ def transform(image: np.ndarray, kind: str, kernel: np.ndarray = None) -> np.nda
     return transformed
 
 
-def opening_filter(image: np.ndarray, kernel: np.ndarray = None) -> np.ndarray:
+def opening_filter(image: np.ndarray, kernel: np.ndarray | None = None) -> np.ndarray:
     """
     Opening filter, defined as the sequence of
     erosion and then a dilation filter on the same image.
@@ -172,7 +174,7 @@ def opening_filter(image: np.ndarray, kernel: np.ndarray = None) -> np.ndarray:
     return transform(transform(image, "dilation", kernel), "erosion", kernel)
 
 
-def closing_filter(image: np.ndarray, kernel: np.ndarray = None) -> np.ndarray:
+def closing_filter(image: np.ndarray, kernel: np.ndarray | None = None) -> np.ndarray:
     """
     Opening filter, defined as the sequence of
     dilation and then erosion filter on the same image.

conversions/convert_number_to_words.py

@@ -54,7 +54,7 @@ def max_value(cls, system: str) -> int:
 
 
 class NumberWords(Enum):
-    ONES: ClassVar = {
+    ONES: ClassVar[dict[int, str]] = {
         0: "",
         1: "one",
         2: "two",
@@ -67,7 +67,7 @@ class NumberWords(Enum):
         9: "nine",
     }
 
-    TEENS: ClassVar = {
+    TEENS: ClassVar[dict[int, str]] = {
         0: "ten",
         1: "eleven",
         2: "twelve",
@@ -80,7 +80,7 @@ class NumberWords(Enum):
         9: "nineteen",
     }
 
-    TENS: ClassVar = {
+    TENS: ClassVar[dict[int, str]] = {
         2: "twenty",
         3: "thirty",
         4: "forty",

graphs/tarjans_scc.py

@@ -77,7 +77,7 @@ def create_graph(n, edges):
     n_vertices = 7
     source = [0, 0, 1, 2, 3, 3, 4, 4, 6]
     target = [1, 3, 2, 0, 1, 4, 5, 6, 5]
-    edges = [(u, v) for u, v in zip(source, target)]
+    edges = list(zip(source, target))
     g = create_graph(n_vertices, edges)
 
     assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)