Add Dutch National Flag Algorithm for Sorting Arrays of 0s, 1s, and 2s

ciphers/base64_cipher.py

@@ -105,13 +105,13 @@ def base64_decode(encoded_data: str) -> bytes:
 
     # Check if the encoded string contains non base64 characters
     if padding:
-        assert all(char in B64_CHARSET for char in encoded_data[:-padding]), (
-            "Invalid base64 character(s) found."
-        )
+        assert all(
+            char in B64_CHARSET for char in encoded_data[:-padding]
+        ), "Invalid base64 character(s) found."
     else:
-        assert all(char in B64_CHARSET for char in encoded_data), (
-            "Invalid base64 character(s) found."
-        )
+        assert all(
+            char in B64_CHARSET for char in encoded_data
+        ), "Invalid base64 character(s) found."
 
     # Check the padding
     assert len(encoded_data) % 4 == 0 and padding < 3, "Incorrect padding"

data_structures/arrays/dutch_national_flag.py

@@ -0,0 +1,29 @@
+def dutch_national_flag(arr):
+    """
+    Sorts an array containing only 0s, 1s and 2s
+
+    Args:
+        arr(list): The input array (containing only 0s, 1s and 2s)
+
+    Returns:
+        list: Sorted array
+    """
+
+    low, mid, high = 0, 0, len(arr) - 1
+
+    while mid <= high:
+        if arr[mid] == 0:
+            arr[low], arr[mid] = arr[mid], arr[low]
+            low += 1
+            mid += 1
+        elif arr[mid] == 1:
+            mid += 1
+        else:
+            arr[mid], arr[high] = arr[high], arr[mid]
+            high -= 1
+    return arr
+
+
+if __name__ == "__main__":
+    arr = [2, 0, 2, 1, 2, 0, 1]
+    print("Sorted array: ", dutch_national_flag(arr))

data_structures/hashing/number_theory/prime_numbers.py

@@ -32,9 +32,9 @@ def is_prime(number: int) -> bool:
     """
 
     # precondition
-    assert isinstance(number, int) and (number >= 0), (
-        "'number' must been an int and positive"
-    )
+    assert isinstance(number, int) and (
+        number >= 0
+    ), "'number' must been an int and positive"
 
     if 1 < number < 4:
         # 2 and 3 are primes

data_structures/heap/min_heap.py

@@ -124,9 +124,9 @@ def is_empty(self):
         return len(self.heap) == 0
 
     def decrease_key(self, node, new_value):
-        assert self.heap[self.idx_of_element[node]].val > new_value, (
-            "newValue must be less that current value"
-        )
+        assert (
+            self.heap[self.idx_of_element[node]].val > new_value
+        ), "newValue must be less that current value"
         node.val = new_value
         self.heap_dict[node.name] = new_value
         self.sift_up(self.idx_of_element[node])

data_structures/kd_tree/tests/test_kdtree.py

@@ -48,14 +48,14 @@ def test_build_kdtree(num_points, cube_size, num_dimensions, depth, expected_res
         assert kdtree is not None, "Expected a KDNode, got None"
 
         # Check if root has correct dimensions
-        assert len(kdtree.point) == num_dimensions, (
-            f"Expected point dimension {num_dimensions}, got {len(kdtree.point)}"
-        )
+        assert (
+            len(kdtree.point) == num_dimensions
+        ), f"Expected point dimension {num_dimensions}, got {len(kdtree.point)}"
 
         # Check that the tree is balanced to some extent (simplistic check)
-        assert isinstance(kdtree, KDNode), (
-            f"Expected KDNode instance, got {type(kdtree)}"
-        )
+        assert isinstance(
+            kdtree, KDNode
+        ), f"Expected KDNode instance, got {type(kdtree)}"
 
 
 def test_nearest_neighbour_search():

data_structures/suffix_tree/tests/test_suffix_tree.py

@@ -22,37 +22,37 @@ def test_search_existing_patterns(self) -> None:
         patterns = ["ana", "ban", "na"]
         for pattern in patterns:
             with self.subTest(pattern=pattern):
-                assert self.suffix_tree.search(pattern), (
-                    f"Pattern '{pattern}' should be found."
-                )
+                assert self.suffix_tree.search(
+                    pattern
+                ), f"Pattern '{pattern}' should be found."
 
     def test_search_non_existing_patterns(self) -> None:
         """Test searching for patterns that do not exist in the suffix tree."""
         patterns = ["xyz", "apple", "cat"]
         for pattern in patterns:
             with self.subTest(pattern=pattern):
-                assert not self.suffix_tree.search(pattern), (
-                    f"Pattern '{pattern}' should not be found."
-                )
+                assert not self.suffix_tree.search(
+                    pattern
+                ), f"Pattern '{pattern}' should not be found."
 
     def test_search_empty_pattern(self) -> None:
         """Test searching for an empty pattern."""
         assert self.suffix_tree.search(""), "An empty pattern should be found."
 
     def test_search_full_text(self) -> None:
         """Test searching for the full text."""
-        assert self.suffix_tree.search(self.text), (
-            "The full text should be found in the suffix tree."
-        )
+        assert self.suffix_tree.search(
+            self.text
+        ), "The full text should be found in the suffix tree."
 
     def test_search_substrings(self) -> None:
         """Test searching for substrings of the full text."""
         substrings = ["ban", "ana", "a", "na"]
         for substring in substrings:
             with self.subTest(substring=substring):
-                assert self.suffix_tree.search(substring), (
-                    f"Substring '{substring}' should be found."
-                )
+                assert self.suffix_tree.search(
+                    substring
+                ), f"Substring '{substring}' should be found."
 
 
 if __name__ == "__main__":

digital_image_processing/filters/gabor_filter.py

@@ -48,9 +48,9 @@ def gabor_filter_kernel(
             _y = -sin_theta * px + cos_theta * py
 
             # fill kernel
-            gabor[y, x] = np.exp(-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)) * np.cos(
-                2 * np.pi * _x / lambd + psi
-            )
+            gabor[y, x] = np.exp(
+                -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)
+            ) * np.cos(2 * np.pi * _x / lambd + psi)
 
     return gabor
 

dynamic_programming/climbing_stairs.py

@@ -25,9 +25,9 @@ def climb_stairs(number_of_steps: int) -> int:
         ...
     AssertionError: number_of_steps needs to be positive integer, your input -7
     """
-    assert isinstance(number_of_steps, int) and number_of_steps > 0, (
-        f"number_of_steps needs to be positive integer, your input {number_of_steps}"
-    )
+    assert (
+        isinstance(number_of_steps, int) and number_of_steps > 0
+    ), f"number_of_steps needs to be positive integer, your input {number_of_steps}"
     if number_of_steps == 1:
         return 1
     previous, current = 1, 1

dynamic_programming/iterating_through_submasks.py

@@ -37,9 +37,9 @@ def list_of_submasks(mask: int) -> list[int]:
 
     """
 
-    assert isinstance(mask, int) and mask > 0, (
-        f"mask needs to be positive integer, your input {mask}"
-    )
+    assert (
+        isinstance(mask, int) and mask > 0
+    ), f"mask needs to be positive integer, your input {mask}"
 
     """
     first submask iterated will be mask itself then operation will be performed

financial/time_and_half_pay.py

@@ -17,15 +17,15 @@ def pay(hours_worked: float, pay_rate: float, hours: float = 40) -> float:
     10.0
     """
     # Check that all input parameters are float or integer
-    assert isinstance(hours_worked, (float, int)), (
-        "Parameter 'hours_worked' must be of type 'int' or 'float'"
-    )
-    assert isinstance(pay_rate, (float, int)), (
-        "Parameter 'pay_rate' must be of type 'int' or 'float'"
-    )
-    assert isinstance(hours, (float, int)), (
-        "Parameter 'hours' must be of type 'int' or 'float'"
-    )
+    assert isinstance(
+        hours_worked, (float, int)
+    ), "Parameter 'hours_worked' must be of type 'int' or 'float'"
+    assert isinstance(
+        pay_rate, (float, int)
+    ), "Parameter 'pay_rate' must be of type 'int' or 'float'"
+    assert isinstance(
+        hours, (float, int)
+    ), "Parameter 'hours' must be of type 'int' or 'float'"
 
     normal_pay = hours_worked * pay_rate
     over_time = max(0, hours_worked - hours)

greedy_methods/fractional_knapsack.py

@@ -39,9 +39,11 @@ def frac_knapsack(vl, wt, w, n):
     return (
         0
         if k == 0
-        else sum(vl[:k]) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
-        if k != n
-        else sum(vl[:k])
+        else (
+            sum(vl[:k]) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
+            if k != n
+            else sum(vl[:k])
+        )
     )
 
 

machine_learning/frequent_pattern_growth.py

@@ -240,7 +240,9 @@
         ascend_tree(leaf_node.parent, prefix_path)
 
 
-def find_prefix_path(base_pat: frozenset, tree_node: TreeNode | None) -> dict:  # noqa: ARG001
+def find_prefix_path(
+    base_pat: frozenset, tree_node: TreeNode | None
+) -> dict:  # noqa: ARG001
     """
     Find the conditional pattern base for a given base pattern.
 

maths/numerical_analysis/integration_by_simpson_approx.py

@@ -88,18 +88,18 @@ def simpson_integration(function, a: float, b: float, precision: int = 4) -> flo
     AssertionError: precision should be positive integer your input : -1
 
     """
-    assert callable(function), (
-        f"the function(object) passed should be callable your input : {function}"
-    )
+    assert callable(
+        function
+    ), f"the function(object) passed should be callable your input : {function}"
     assert isinstance(a, (float, int)), f"a should be float or integer your input : {a}"
     assert isinstance(function(a), (float, int)), (
         "the function should return integer or float return type of your function, "
         f"{type(a)}"
     )
     assert isinstance(b, (float, int)), f"b should be float or integer your input : {b}"
-    assert isinstance(precision, int) and precision > 0, (
-        f"precision should be positive integer your input : {precision}"
-    )
+    assert (
+        isinstance(precision, int) and precision > 0
+    ), f"precision should be positive integer your input : {precision}"
 
     # just applying the formula of simpson for approximate integration written in
     # mentioned article in first comment of this file and above this function

maths/prime_check.py

@@ -73,12 +73,12 @@ def test_primes(self):
     def test_not_primes(self):
         with pytest.raises(ValueError):
             is_prime(-19)
-        assert not is_prime(0), (
-            "Zero doesn't have any positive factors, primes must have exactly two."
-        )
-        assert not is_prime(1), (
-            "One only has 1 positive factor, primes must have exactly two."
-        )
+        assert not is_prime(
+            0
+        ), "Zero doesn't have any positive factors, primes must have exactly two."
+        assert not is_prime(
+            1
+        ), "One only has 1 positive factor, primes must have exactly two."
         assert not is_prime(2 * 2)
         assert not is_prime(2 * 3)
         assert not is_prime(3 * 3)

maths/primelib.py

@@ -66,9 +66,9 @@ def is_prime(number: int) -> bool:
     """
 
     # precondition
-    assert isinstance(number, int) and (number >= 0), (
-        "'number' must been an int and positive"
-    )
+    assert isinstance(number, int) and (
+        number >= 0
+    ), "'number' must been an int and positive"
 
     status = True
 
@@ -254,9 +254,9 @@ def greatest_prime_factor(number):
     """
 
     # precondition
-    assert isinstance(number, int) and (number >= 0), (
-        "'number' must been an int and >= 0"
-    )
+    assert isinstance(number, int) and (
+        number >= 0
+    ), "'number' must been an int and >= 0"
 
     ans = 0
 
@@ -296,9 +296,9 @@ def smallest_prime_factor(number):
     """
 
     # precondition
-    assert isinstance(number, int) and (number >= 0), (
-        "'number' must been an int and >= 0"
-    )
+    assert isinstance(number, int) and (
+        number >= 0
+    ), "'number' must been an int and >= 0"
 
     ans = 0
 
@@ -399,9 +399,9 @@ def goldbach(number):
     """
 
     # precondition
-    assert isinstance(number, int) and (number > 2) and is_even(number), (
-        "'number' must been an int, even and > 2"
-    )
+    assert (
+        isinstance(number, int) and (number > 2) and is_even(number)
+    ), "'number' must been an int, even and > 2"
 
     ans = []  # this list will returned
 
@@ -525,9 +525,9 @@ def kg_v(number1, number2):
             done.append(n)
 
     # precondition
-    assert isinstance(ans, int) and (ans >= 0), (
-        "'ans' must been from type int and positive"
-    )
+    assert isinstance(ans, int) and (
+        ans >= 0
+    ), "'ans' must been from type int and positive"
 
     return ans
 
@@ -574,9 +574,9 @@ def get_prime(n):
             ans += 1
 
     # precondition
-    assert isinstance(ans, int) and is_prime(ans), (
-        "'ans' must been a prime number and from type int"
-    )
+    assert isinstance(ans, int) and is_prime(
+        ans
+    ), "'ans' must been a prime number and from type int"
 
     return ans
 
@@ -705,9 +705,9 @@ def is_perfect_number(number):
     """
 
     # precondition
-    assert isinstance(number, int) and (number > 1), (
-        "'number' must been an int and >= 1"
-    )
+    assert isinstance(number, int) and (
+        number > 1
+    ), "'number' must been an int and >= 1"
 
     divisors = get_divisors(number)
 

matrix/matrix_class.py

@@ -204,9 +204,11 @@ def cofactors(self) -> Matrix:
         return Matrix(
             [
                 [
-                    self.minors().rows[row][column]
-                    if (row + column) % 2 == 0
-                    else self.minors().rows[row][column] * -1
+                    (
+                        self.minors().rows[row][column]
+                        if (row + column) % 2 == 0
+                        else self.minors().rows[row][column] * -1
+                    )
                     for column in range(self.minors().num_columns)
                 ]
                 for row in range(self.minors().num_rows)

neural_network/input_data.py

@@ -160,9 +160,9 @@ def __init__(
             self._num_examples = 10000
             self.one_hot = one_hot
         else:
-            assert images.shape[0] == labels.shape[0], (
-                f"images.shape: {images.shape} labels.shape: {labels.shape}"
-            )
+            assert (
+                images.shape[0] == labels.shape[0]
+            ), f"images.shape: {images.shape} labels.shape: {labels.shape}"
             self._num_examples = images.shape[0]
 
             # Convert shape from [num examples, rows, columns, depth]