🚨 relax linter exclusions

.pre-commit-config.yaml

@@ -77,7 +77,6 @@ repos:
     hooks:
       - id: mypy
         files: ^(src/mqt|test/python)
-        exclude: "code_construction* | ^data_utils\\.py$"
         args: []
         additional_dependencies:
           - numpy

pyproject.toml

@@ -195,13 +195,6 @@ preview = true
 unsafe-fixes = true
 
 [tool.ruff.lint]
-exclude = [
-    "scripts/*",
-    "*/cc_decoder/plots.py",
-    "*/analog_information_decoding/code_construction/*",
-    "*/analog_information_decoding/utils/data_utils.py",
-
-]
 extend-select = [
     "A",           # flake8-builtins
     "ANN",         # flake8-annotations
@@ -272,6 +265,8 @@ isort.required-imports = ["from __future__ import annotations"]
     "E402", # Allow imports to appear anywhere in Jupyter notebooks
     "I002", # Allow missing `from __future__ import annotations` import
 ]
+"*/cc_decoder/plots.py" = ["T201"]
+"scripts/*" = ["T201"]
 
 [tool.ruff.lint.pydocstyle]
 convention = "google"

scripts/examples/atd_example.py

@@ -1,4 +1,6 @@
-"""example simulation script"""
+"""example simulation script."""
+
+from __future__ import annotations
 
 import numpy as np
 import scipy
@@ -20,7 +22,7 @@
             lers = []
             ebs = []
             for sigma in s:
-                print(sigma)  # noqa: T201
+                print(sigma)
                 sim = AtdSimulator(
                     hx=Hx,
                     lx=Lx,

scripts/examples/decoding-performance-simulations.py

@@ -20,7 +20,7 @@
 nr_failed_runs = 0
 code_k = code.K
 
-with Path(outpath).open("w") as outfile:
+with Path(outpath).open("w", encoding="utf-8") as outfile:
     while curr_p < max_per:
         nr_failed_runs = 0
         for _ in range(runs_per_p):

scripts/examples/runtime-simulations.py

@@ -23,7 +23,7 @@
 nr_runs: int = 1
 per: float = 0.01
 
-with Path(outpath).open("w") as outfile:
+with Path(outpath).open("w", encoding="utf-8") as outfile:
     for code_path in codes:
         sample_sum = 0.0
         for _ in range(nr_samples):

scripts/numerics/visualize/visualizeDecodingRuntime.py

@@ -66,13 +66,13 @@ def runtime() -> None:
     colors = mcolors.BASE_COLORS
 
     for i in range(len(x_data)):
-        col, val = colors.popitem()
+        col, _val = colors.popitem()
         if col in {"w", "k"}:
-            col, val = colors.popitem()
+            col, _val = colors.popitem()
             if col in {"w", "k"}:
-                col, val = colors.popitem()
+                col, _val = colors.popitem()
                 cols.append(col)
-        label = "% 6.3f" % pers[i]
+        label = f"{pers[i]: 6.3f}"
         orders.append(np.argsort(x_data[i]))
         xfinal.append(np.array(x_data[i])[orders[i]])
         yfinal.append(np.array(y_data[i])[orders[i]])
@@ -98,9 +98,9 @@ def runtime_comparison() -> None:
     pers2 = 0.0
     orders = []
 
-    with Path(input_filen).open() as data_file:
+    with Path(input_filen).open(encoding="utf-8") as data_file:
         data = json.load(data_file)
-    with Path(input_filen2).open() as data_file2:
+    with Path(input_filen2).open(encoding="utf-8") as data_file2:
         data2 = json.load(data_file2)
 
     for per in data:
@@ -126,19 +126,19 @@ def runtime_comparison() -> None:
     colors = mcolors.BASE_COLORS
 
     for i in range(len(x_data)):
-        col, val = colors.popitem()
+        col, _val = colors.popitem()
         if col in {"w", "k"}:
-            col, val = colors.popitem()
+            col, _val = colors.popitem()
             if col in {"w", "k"}:
-                col, val = colors.popitem()
+                col, _val = colors.popitem()
                 cols.append(col)
-        label = "%2.2f" % pers[i]
+        label = f"{pers[i]:2.2f}"
         orders.append(np.argsort(x_data[i]))
         xfinal.append(np.array(x_data[i])[orders[i]])
         yfinal.append(np.array(y_data[i])[orders[i]])
         plt.plot(xfinal[i], yfinal[i], "o", label="UFH, p=" + label, color=col)
         start = 0
-        optimized_parameters, pcov = opt.curve_fit(lin_fun, xfinal[i][start:], yfinal[i][start:])
+        optimized_parameters, _pcov = opt.curve_fit(lin_fun, xfinal[i][start:], yfinal[i][start:])
         plt.plot(
             xfinal[i][start:],
             lin_fun(xfinal[i][start:], *optimized_parameters),
@@ -148,13 +148,13 @@ def runtime_comparison() -> None:
         )
 
     # general qlpd decoder data
-    label = "%2.2f" % pers2
+    label = f"{pers2:2.2f}"
     orders2 = np.argsort(x_data2)
     xfinal2 = np.array(x_data2)[orders2]
     yfinal2 = np.array(y_data2)[orders2]
     plt.plot(xfinal2, yfinal2, "d", label="GD, p=" + label, color="green")
     start = 0
-    optimized_parameters2, pcov = opt.curve_fit(quad_fun, xfinal2[start:], yfinal2[start:])
+    optimized_parameters2, _pcov = opt.curve_fit(quad_fun, xfinal2[start:], yfinal2[start:])
     plt.plot(
         xfinal2[start:],
         quad_fun(xfinal2[start:], *optimized_parameters2),

scripts/numerics/visualize/visualizeWordErrRate.py

@@ -15,9 +15,9 @@ def wer() -> None:
     input_filename = ""
     input_filename2 = ""
     input_filename3 = ""
-    fig, ax = plt.subplots(1)
+    _fig, ax = plt.subplots(1)
 
-    with Path(input_filename).open() as f:
+    with Path(input_filename).open(encoding="utf-8") as f:
         data = json.load(f)
     x_data = []
     y_data = []
@@ -29,7 +29,7 @@ def wer() -> None:
     x_dataf = np.array(x_data)[order]
     y_dataf = np.array(y_data)[order]
 
-    with Path(input_filename2).open() as f:
+    with Path(input_filename2).open(encoding="utf-8") as f:
         data2 = json.load(f)
     x_data2 = []
     y_data2 = []
@@ -41,7 +41,7 @@ def wer() -> None:
     x_data2f = np.array(x_data2)[order2]
     y_data2f = np.array(y_data2)[order2]
 
-    with Path(input_filename3).open() as f:
+    with Path(input_filename3).open(encoding="utf-8") as f:
         data3 = json.load(f)
     x_data3 = []
     y_data3 = []
@@ -77,17 +77,17 @@ def wer_comp() -> None:
     plt.rcParams.update({"font.size": 15})
     input_filename = ""
     input_filename2 = ""
-    fig, ax = plt.subplots(1)
+    _fig, ax = plt.subplots(1)
 
-    with Path(input_filename).open() as f:
+    with Path(input_filename).open(encoding="utf-8") as f:
         data = json.load(f)
     x_data = []
     y_data = []
     for key in data:
         x_data.append(float(key))
         y_data.append(data[key])
 
-    with Path(input_filename2).open() as f:
+    with Path(input_filename2).open(encoding="utf-8") as f:
         data2 = json.load(f)
     x_data2 = []
     y_data2 = []

src/mqt/qecc/analog_information_decoding/code_construction/code_constructor.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import json
-import subprocess
+import subprocess  # noqa: S404
 from pathlib import Path
 from typing import TYPE_CHECKING, Any
 
@@ -145,7 +145,7 @@ def _compute_distances(hx: NDArray[np.int32], hz: NDArray[np.int32], codename: s
     code_dict: Any = {}
     _m, n = hx.shape
     code_k = n - mod2.rank(hx) - mod2.rank(hz)
-    with Path(f"generated_codes/{codename}/info.txt").open() as f:
+    with Path(f"generated_codes/{codename}/info.txt").open(encoding="utf-8") as f:
         code_dict = dict(
             line[: line.rfind("#")].split(" = ") for line in f if not line.startswith("#") and line.strip()
         )
@@ -157,7 +157,7 @@ def _compute_distances(hx: NDArray[np.int32], hz: NDArray[np.int32], codename: s
     code_dict["dMX"] = int(code_dict["dMX"])
     code_dict["dMZ"] = int(code_dict["dMZ"])
 
-    with Path(f"generated_codes/{codename}/code_params.txt").open("w") as file:
+    with Path(f"generated_codes/{codename}/code_params.txt").open("w", encoding="utf-8") as file:
         file.write(json.dumps(code_dict))
 
 

src/mqt/qecc/analog_information_decoding/code_construction/sparse_code_constructor.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import json
-import subprocess
+import subprocess  # noqa: S404
 from pathlib import Path
 from typing import TYPE_CHECKING, Any
 
@@ -153,7 +153,7 @@ def _compute_distances(hx: NDArray[np.int32], hz: NDArray[np.int32], codename: s
     run_compute_distances(codename)
     _, n = hx.shape
     code_k = n - mod2.rank(hx) - mod2.rank(hz)
-    with Path(f"/codes/generated_codes/{codename}/info.txt").open() as f:
+    with Path(f"/codes/generated_codes/{codename}/info.txt").open(encoding="utf-8") as f:
         code_dict: dict[str, Any] = dict(
             line[: line.rfind("#")].split(" = ") for line in f if not line.startswith("#") and line.strip()
         )
@@ -163,7 +163,7 @@ def _compute_distances(hx: NDArray[np.int32], hz: NDArray[np.int32], codename: s
     code_dict["dX"] = int(code_dict["dX"])
     code_dict["dZ"] = int(code_dict["dZ"])
 
-    with Path(f"/codes/generated_codes/{codename}/code_params.txt").open("w") as file:
+    with Path(f"/codes/generated_codes/{codename}/code_params.txt").open("w", encoding="utf-8") as file:
         file.write(json.dumps(code_dict))
 
 
@@ -175,7 +175,7 @@ def _store_code_params(hx: csr_matrix, hz: csr_matrix, codename: str) -> None:
     code_k = n - mod2.rank(hx) - mod2.rank(hz)
     code_dict["n"] = n
     code_dict["k"] = code_k
-    with Path(f"/codes/generated_codes/{codename}/code_params.txt").open("w") as file:
+    with Path(f"/codes/generated_codes/{codename}/code_params.txt").open("w", encoding="utf-8") as file:
         file.write(json.dumps(code_dict))