ruff checks

Author: imann128

examples/ogdc_leakage_case/compare_leakage.py

@@ -5,11 +5,11 @@
 run: python compare_leakage.py
 """
 
-import sys, os
+import sys
+import os
 sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
 
 import pandas as pd
-import numpy as np
 from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
 from sklearn.metrics import accuracy_score, roc_auc_score
 

examples/sensor-example/sensor_example.ipynb

@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "67c5aea4",
    "metadata": {
     "execution": {
@@ -232,9 +232,10 @@
     }
    ],
    "source": [
-    "ax = clean.plot(title=\"Clean synthetic sensor stream (no faults)\")\n",
+    "ax = clean.plot(title=\"Clean synthetic sensor stream\")\n",
     "ax.set_ylabel(\"reading\")\n",
-    "plt.tight_layout(); plt.show()"
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
@@ -317,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "b5afeb69",
    "metadata": {
     "execution": {
@@ -345,15 +346,18 @@
     "a1.plot(df.index, df[\"temperature\"], color=\"tab:red\")\n",
     "a1.axvspan(df.index[stuck_start], df.index[stuck_start + 5], color=\"orange\", alpha=0.3,\n",
     "           label=\"stuck sensor (ANO001)\")\n",
-    "a1.set_ylabel(\"temperature\"); a1.legend(loc=\"upper right\")\n",
+    "a1.set_ylabel(\"temperature\")\n",
+    "a1.legend(loc=\"upper right\")\n",
     "\n",
     "a2.plot(df.index, df[\"humidity\"], color=\"tab:blue\")\n",
     "a2.axvspan(df.index[gap_start], df.index[gap_start + 5], color=\"purple\", alpha=0.25,\n",
     "           label=\"outage gap (PRF002)\")\n",
-    "a2.set_ylabel(\"humidity\"); a2.legend(loc=\"upper right\")\n",
+    "a2.set_ylabel(\"humidity\")\n",
+    "a2.legend(loc=\"upper right\")\n",
     "\n",
     "fig.suptitle(\"Faulted sensor stream: frozen segment + outage gap\")\n",
-    "plt.tight_layout(); plt.show()"
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {

tests/test_contextual.py

@@ -1,8 +1,7 @@
 import pytest
 import pandas as pd
-import numpy as np
 from tsauditor.anomaly.contextual import audit_contextual_anomalies
-from tsauditor.report.summary import Issue, WARNING
+from tsauditor.report.summary import WARNING
 
 def test_clean_df_no_anomalies(clean_financial_df):
     """Case 1 — Clean df -> no issues on continuous market data columns."""

tests/test_missing.py

@@ -1,8 +1,7 @@
-import pytest
 import pandas as pd
 import numpy as np
 from tsauditor.profiler.missing import audit_missing
-from tsauditor.report.summary import Issue, WARNING
+from tsauditor.report.summary import WARNING
 
 def test_clean_df_no_missing_values(clean_financial_df):
     # Case 1 — Clean df with no missing values -> no issues.

tests/test_point.py

@@ -2,7 +2,6 @@
 import pandas as pd
 import numpy as np
 from tsauditor.anomaly.point import audit_point_anomalies
-from tsauditor.report.summary import WARNING
 
 @pytest.fixture
 def base_date_index():

tests/test_profiler.py

@@ -1,8 +1,7 @@
 import pytest
 import pandas as pd
-import numpy as np
 from tsauditor.profiler.frequency import audit_frequency
-from tsauditor.report.summary import Issue, CRITICAL, WARNING
+from tsauditor.report.summary import CRITICAL, WARNING
 
 def test_clean_financial_df_no_issues(clean_financial_df):
     # Case 1 — Clean daily financial df -> no issues returned.

tests/test_remediation.py

@@ -1,4 +1,3 @@
-import pytest
 
 from tsauditor.report.summary import Issue, GuardReport, CRITICAL, WARNING, INFO
 

tests/test_scaffold.py

@@ -11,7 +11,7 @@
 import pytest
 
 import tsauditor as tsa
-from tsauditor.report.summary import GuardReport, Issue, CRITICAL, WARNING, INFO
+from tsauditor.report.summary import GuardReport, Issue, CRITICAL, WARNING
 from tsauditor.utils.validation import validate_dataframe, infer_frequency
 
 

tests/test_stationarity.py

@@ -2,7 +2,6 @@
 import pandas as pd
 import numpy as np
 from tsauditor.profiler.stationarity import audit_stationarity
-from tsauditor.report.summary import INFO
 
 @pytest.fixture
 def base_date_index():

tsauditor/anomaly/point.py

@@ -1,5 +1,4 @@
 import pandas as pd
-import numpy as np
 from tsauditor.report.summary import Issue, WARNING
 
 def audit_point_anomalies(
@@ -36,7 +35,8 @@ def audit_point_anomalies(
 
         # 3. Z-score Method
         mean, std = series.mean(), series.std()
-        if std == 0: continue
+        if std == 0: 
+            continue
         z_scores = (series - mean) / std
         z_mask = abs(z_scores) > z_thresh