refactor: 💡 Add sonartypes recommendations (#1138)

* refactor: ♻️ Update regression metrics in PPTXLayoutAPI

* refactor: ♻️ Update model_dump() method calls

* test: ✅ Replace `random.rand` to `random.default_rng`

spectrafit/api/pptx_model.py

@@ -373,9 +373,9 @@ def __init__(self, format: str, data: PPTXDataAPI) -> None:
         self.tmp_fname = self.tmp_plot(pd.DataFrame(data.output.df_fit))
         self.title = data.input.description.project_name
         self.df_gof = pd.DataFrame({k: [v] for k, v in data.solver.goodness_of_fit})
-        self.df_regression = pd.DataFrame(**data.solver.regression_metrics.dict())
+        self.df_regression = pd.DataFrame(**data.solver.regression_metrics.model_dump())
         self.df_variables = pd.DataFrame.from_dict(
-            data.solver.variables, orient="index", columns=None
+            data.solver.variables, orient="index"
         )
 
     def tmp_plot(self, df_fit: pd.DataFrame) -> Path:

spectrafit/api/test/test_cmd_model.py

@@ -76,7 +76,7 @@ def test_global_fit() -> None:
     """Test for global fitting settings of CMD Model."""
     result = CMDModelAPI(infile="")
     result.global_ = 1
-    assert result.dict()["global_"] == 1
+    assert result.model_dump()["global_"] == 1
 
 
 def test_sha256() -> None:

spectrafit/plugins/notebook.py

@@ -369,8 +369,8 @@ def update_layout_axes(
         fig.update_layout(
             title=args_plot.title,
             legend_title=args_plot.legend_title,
-            legend=args_plot.legend.dict(),
-            font=args_plot.font.dict(),
+            legend=args_plot.legend.model_dump(),
+            font=args_plot.font.model_dump(),
             showlegend=args_plot.show_legend,
             width=args_plot.size[0],
             height=height,
@@ -939,7 +939,9 @@ class refers to the `Pydantic`-Definition of the `SpectraFit` class.
     @property
     def pre_process(self) -> None:
         """Pre-processing class."""
-        self.df, _pre_statistic = PreProcessing(df=self.df, args=self.args_pre.dict())()
+        self.df, _pre_statistic = PreProcessing(
+            df=self.df, args=self.args_pre.model_dump()
+        )()
         self.pre_statistic = _pre_statistic["data_statistic"]
         self.df_pre = self.df.copy()
 

spectrafit/test/test_models.py

@@ -30,6 +30,20 @@ def assert_solver_models(mp: Tuple[Minimizer, Any]) -> None:
     assert isinstance(mp, tuple)
 
 
+@pytest.fixture
+def random_df() -> pd.DataFrame:
+    """Fixture for random dataframe."""
+    return pd.DataFrame(
+        {
+            "Energy": np.arange(100).astype(np.float64),
+            "Intensity_1": np.random.default_rng(101).random(100),
+            "Intensity_2": np.random.default_rng(102).random(100),
+            "Intensity_3": np.random.default_rng(103).random(100),
+            "Intensity_4": np.random.default_rng(104).random(100),
+        }
+    )
+
+
 class TestConstants:
     """Test constants."""
 
@@ -250,16 +264,16 @@ class TestModelParametersSolver:
     df = pd.DataFrame(
         {
             "Energy": np.arange(10).astype(np.float64),
-            "Intensity": np.random.rand(10),
+            "Intensity": np.random.default_rng(10).random(10),
         }
     )
     df_global = pd.DataFrame(
         {
             "Energy": np.arange(10).astype(np.float64),
-            "Intensity_1": np.random.rand(10),
-            "Intensity_2": np.random.rand(10),
-            "Intensity_3": np.random.rand(10),
-            "Intensity_4": np.random.rand(10),
+            "Intensity_1": np.random.default_rng(1).random(10),
+            "Intensity_2": np.random.default_rng(2).random(10),
+            "Intensity_3": np.random.default_rng(3).random(10),
+            "Intensity_4": np.random.default_rng(4).random(10),
         }
     )
 
@@ -584,17 +598,11 @@ def args_setting(self) -> Dict[str, Any]:
             },
         }
 
-    def test_all_model_local(self, args_setting: Dict[str, Any]) -> None:
+    def test_all_model_local(
+        self, random_df: pd.DataFrame, args_setting: Dict[str, Any]
+    ) -> None:
         """Test of the AllModel class for local fitting."""
-        df = pd.DataFrame(
-            {
-                "Energy": np.arange(100).astype(np.float64),
-                "Intensity_1": np.random.rand(100),
-                "Intensity_2": np.random.rand(100),
-                "Intensity_3": np.random.rand(100),
-                "Intensity_4": np.random.rand(100),
-            }
-        )
+        df = random_df
         args = {
             "autopeak": False,
             "global_": 0,
@@ -604,17 +612,11 @@ def test_all_model_local(self, args_setting: Dict[str, Any]) -> None:
         mp = SolverModels(df=df, args=args)()
         assert_solver_models(mp)
 
-    def test_all_model_global(self, args_setting: Dict[str, Any]) -> None:
+    def test_all_model_global(
+        self, random_df: pd.DataFrame, args_setting: Dict[str, Any]
+    ) -> None:
         """Test of the AllModel class for global fitting."""
-        df = pd.DataFrame(
-            {
-                "Energy": np.arange(100).astype(np.float64),
-                "Intensity_1": np.random.rand(100),
-                "Intensity_2": np.random.rand(100),
-                "Intensity_3": np.random.rand(100),
-                "Intensity_4": np.random.rand(100),
-            }
-        )
+        df = random_df
         args = {
             "autopeak": False,
             "global_": 1,
@@ -625,17 +627,9 @@ def test_all_model_global(self, args_setting: Dict[str, Any]) -> None:
         mp = SolverModels(df=df, args=args)()
         assert_solver_models(mp)
 
-    def test_all_model_global_fail(self) -> None:
+    def test_all_model_global_fail(self, random_df: pd.DataFrame) -> None:
         """Test of the AllModel class for global fitting."""
-        df = pd.DataFrame(
-            {
-                "Energy": np.arange(100).astype(np.float64),
-                "Intensity_1": np.random.rand(100),
-                "Intensity_2": np.random.rand(100),
-                "Intensity_3": np.random.rand(100),
-                "Intensity_4": np.random.rand(100),
-            }
-        )
+        df = random_df
         args = {
             "autopeak": True,
             "global_": 1,

spectrafit/test/test_report.py

@@ -27,9 +27,9 @@ def test_raise_error(self) -> None:
             _ = RegressionMetrics(
                 pd.DataFrame(
                     {
-                        "intensity_0": np.random.rand(10),
-                        "intensity_1": np.random.rand(10),
-                        "fit_0": np.random.rand(10),
+                        "intensity_0": np.random.default_rng(0).normal(size=10),
+                        "intensity_1": np.random.default_rng(1).normal(size=10),
+                        "fit_0": np.random.default_rng(2).normal(size=10),
                     }
                 )
             )

spectrafit/test/test_tools.py

@@ -29,7 +29,7 @@ def df() -> pd.DataFrame:
     return pd.DataFrame(
         {
             "energy": np.linspace(0, 10, 100),
-            "intensity": np.random.rand(100),
+            "intensity": np.random.default_rng(0).normal(size=100),
         }
     )
 
@@ -40,10 +40,10 @@ def df_large() -> pd.DataFrame:
     return pd.DataFrame(
         {
             "Energy": np.arange(10).astype(np.float64),
-            "Intensity_1": np.random.rand(10),
-            "Intensity_2": np.random.rand(10),
-            "Intensity_3": np.random.rand(10),
-            "Intensity_4": np.random.rand(10),
+            "Intensity_1": np.random.default_rng(5).normal(size=10),
+            "Intensity_2": np.random.default_rng(6).normal(size=10),
+            "Intensity_3": np.random.default_rng(7).normal(size=10),
+            "Intensity_4": np.random.default_rng(8).normal(size=10),
         }
     )
 

tools/spectra_generator.py

@@ -22,13 +22,13 @@ def double_peak(fname: str) -> None:
             "energy": x,
             "y_1": stats.norm.pdf(x, 1, sigma) + 0.75 * stats.norm.pdf(x, 5, sigma),
             "y_2": stats.norm.pdf(x, 1, sigma)
-            + np.random.rand(100) / 10.0
+            + np.random.default_rng(11).normal(size=100) / 10.0
             + 0.75 * stats.norm.pdf(x, 5, sigma)
-            + np.random.rand(100) / 10.0,
+            + np.random.default_rng(22).normal(size=100) / 10.0,
             "y_3": stats.norm.pdf(x, 1, sigma)
-            + np.random.rand(100) / 100.0
+            + np.random.default_rng(33).normal(size=100) / 100.0
             + 0.75 * stats.norm.pdf(x, 5, sigma)
-            + np.random.rand(100) / 100.0,
+            + np.random.default_rng(44).normal(size=100) / 100.0,
         }
     ).to_csv(Path(fname), index=False)