[pre-commit.ci] pre-commit autoupdate (#11275)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.1.14 → v0.2.0](astral-sh/ruff-pre-commit@v0.1.14...v0.2.0)

* Upgrade pyproject.toml

* Revert sudoku_solver.py RUF017 Avoid quadratic list summation

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Christian Clauss <cclauss@me.com>

Author: pre-commit-ci[bot]

.pre-commit-config.yaml

@@ -16,7 +16,7 @@ repos:
       - id: auto-walrus
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.14
+    rev: v0.2.0
     hooks:
       - id: ruff
       - id: ruff-format

ciphers/mixed_keyword_cypher.py

@@ -67,7 +67,7 @@ def mixed_keyword(
     if verbose:
         print(mapping)
     # create the encrypted text by mapping the plaintext to the modified alphabet
-    return "".join(mapping[char] if char in mapping else char for char in plaintext)
+    return "".join(mapping.get(char, char) for char in plaintext)
 
 
 if __name__ == "__main__":

data_structures/arrays/sudoku_solver.py

@@ -22,7 +22,7 @@ def cross(items_a, items_b):
     + [cross(rs, cs) for rs in ("ABC", "DEF", "GHI") for cs in ("123", "456", "789")]
 )
 units = {s: [u for u in unitlist if s in u] for s in squares}
-peers = {s: set(sum(units[s], [])) - {s} for s in squares}
+peers = {s: set(sum(units[s], [])) - {s} for s in squares}  # noqa: RUF017
 
 
 def test():

data_structures/linked_list/is_palindrome.py

@@ -171,11 +171,9 @@ def is_palindrome_dict(head: ListNode | None) -> bool:
         if len(v) % 2 != 0:
             middle += 1
         else:
-            step = 0
-            for i in range(len(v)):
+            for step, i in enumerate(range(len(v))):
                 if v[i] + v[len(v) - 1 - step] != checksum:
                     return False
-                step += 1
         if middle > 1:
             return False
     return True

digital_image_processing/filters/gaussian_filter.py

@@ -22,11 +22,9 @@ def gaussian_filter(image, k_size, sigma):
 
     # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
     image_array = zeros((dst_height * dst_width, k_size * k_size))
-    row = 0
-    for i, j in product(range(dst_height), range(dst_width)):
+    for row, (i, j) in enumerate(product(range(dst_height), range(dst_width))):
         window = ravel(image[i : i + k_size, j : j + k_size])
         image_array[row, :] = window
-        row += 1
 
     #  turn the kernel into shape(k*k, 1)
     gaussian_kernel = gen_gaussian_kernel(k_size, sigma)

electronics/resistor_equivalence.py

@@ -20,13 +20,11 @@ def resistor_parallel(resistors: list[float]) -> float:
     """
 
     first_sum = 0.00
-    index = 0
-    for resistor in resistors:
+    for index, resistor in enumerate(resistors):
         if resistor <= 0:
             msg = f"Resistor at index {index} has a negative or zero value!"
             raise ValueError(msg)
         first_sum += 1 / float(resistor)
-        index += 1
     return 1 / first_sum
 
 
@@ -44,13 +42,11 @@ def resistor_series(resistors: list[float]) -> float:
     ValueError: Resistor at index 2 has a negative value!
     """
     sum_r = 0.00
-    index = 0
-    for resistor in resistors:
+    for index, resistor in enumerate(resistors):
         sum_r += resistor
         if resistor < 0:
             msg = f"Resistor at index {index} has a negative value!"
             raise ValueError(msg)
-        index += 1
     return sum_r
 
 

hashes/hamming_code.py

@@ -123,16 +123,14 @@ def emitter_converter(size_par, data):
         # Bit counter one for a given parity
         cont_bo = 0
         # counter to control the loop reading
-        cont_loop = 0
-        for x in data_ord:
+        for cont_loop, x in enumerate(data_ord):
             if x is not None:
                 try:
                     aux = (bin_pos[cont_loop])[-1 * (bp)]
                 except IndexError:
                     aux = "0"
                 if aux == "1" and x == "1":
                     cont_bo += 1
-            cont_loop += 1
         parity.append(cont_bo % 2)
 
         qtd_bp += 1
@@ -164,21 +162,20 @@ def receptor_converter(size_par, data):
     parity_received = []
     data_output = []
 
-    for x in range(1, len(data) + 1):
+    for i, item in enumerate(data, 1):
         # Performs a template of bit positions - who should be given,
         #  and who should be parity
-        if qtd_bp < size_par and (np.log(x) / np.log(2)).is_integer():
+        if qtd_bp < size_par and (np.log(i) / np.log(2)).is_integer():
             data_out_gab.append("P")
             qtd_bp = qtd_bp + 1
         else:
             data_out_gab.append("D")
 
         # Sorts the data to the new output size
         if data_out_gab[-1] == "D":
-            data_output.append(data[cont_data])
+            data_output.append(item)
         else:
-            parity_received.append(data[cont_data])
-        cont_data += 1
+            parity_received.append(item)
 
     # -----------calculates the parity with the data
     data_out = []
@@ -215,17 +212,14 @@ def receptor_converter(size_par, data):
     for bp in range(1, size_par + 1):
         # Bit counter one for a certain parity
         cont_bo = 0
-        # Counter to control loop reading
-        cont_loop = 0
-        for x in data_ord:
+        for cont_loop, x in enumerate(data_ord):
             if x is not None:
                 try:
                     aux = (bin_pos[cont_loop])[-1 * (bp)]
                 except IndexError:
                     aux = "0"
                 if aux == "1" and x == "1":
                     cont_bo += 1
-            cont_loop += 1
         parity.append(str(cont_bo % 2))
 
         qtd_bp += 1

machine_learning/k_means_clust.py

@@ -237,7 +237,7 @@ def report_generator(
             [
                 ("sum", "sum"),
                 ("mean_with_zeros", lambda x: np.mean(np.nan_to_num(x))),
-                ("mean_without_zeros", lambda x: x.replace(0, np.NaN).mean()),
+                ("mean_without_zeros", lambda x: x.replace(0, np.nan).mean()),
                 (
                     "mean_25-75",
                     lambda x: np.mean(

machine_learning/sequential_minimum_optimization.py

@@ -589,7 +589,7 @@ def plot_partition_boundary(
     ax.contour(
         xrange,
         yrange,
-        np.mat(grid).T,
+        np.asmatrix(grid).T,
         levels=(-1, 0, 1),
         linestyles=("--", "-", "--"),
         linewidths=(1, 1, 1),

neural_network/convolution_neural_network.py

@@ -41,11 +41,11 @@ def __init__(
         self.rate_weight = rate_w
         self.rate_thre = rate_t
         self.w_conv1 = [
-            np.mat(-1 * np.random.rand(self.conv1[0], self.conv1[0]) + 0.5)
+            np.asmatrix(-1 * np.random.rand(self.conv1[0], self.conv1[0]) + 0.5)
             for i in range(self.conv1[1])
         ]
-        self.wkj = np.mat(-1 * np.random.rand(self.num_bp3, self.num_bp2) + 0.5)
-        self.vji = np.mat(-1 * np.random.rand(self.num_bp2, self.num_bp1) + 0.5)
+        self.wkj = np.asmatrix(-1 * np.random.rand(self.num_bp3, self.num_bp2) + 0.5)
+        self.vji = np.asmatrix(-1 * np.random.rand(self.num_bp2, self.num_bp1) + 0.5)
         self.thre_conv1 = -2 * np.random.rand(self.conv1[1]) + 1
         self.thre_bp2 = -2 * np.random.rand(self.num_bp2) + 1
         self.thre_bp3 = -2 * np.random.rand(self.num_bp3) + 1