[pre-commit.ci] auto fixes from pre-commit.com hooks

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zincware · Jan 29, 2024 · b6c385f · b6c385f
1 parent 3edff60
commit b6c385f
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Showing 6 changed files with 76 additions and 106 deletions.
diff --git a/ipsuite/analysis/bin_property.py b/ipsuite/analysis/bin_property.py
@@ -205,11 +205,9 @@ def run(self):
         self.get_plots(counts[0], bin_edges[0], hydrostatic=True)
         self.get_plots(counts[1], bin_edges[1], hydrostatic=False)
 
-        self.labels_df = pd.DataFrame(
-            {
-                "hydro_bin_edges": bin_edges[0][1:],
-                "hydro_counts": counts[0],
-                "deviatoric_bin_edges": bin_edges[1][1:],
-                "deviatoric_counts": counts[1],
-            }
-        )
+        self.labels_df = pd.DataFrame({
+            "hydro_bin_edges": bin_edges[0][1:],
+            "hydro_counts": counts[0],
+            "deviatoric_bin_edges": bin_edges[1][1:],
+            "deviatoric_counts": counts[1],
+        })
diff --git a/ipsuite/analysis/ensemble.py b/ipsuite/analysis/ensemble.py
@@ -94,12 +94,9 @@ def run(self):
         figures[2].savefig(self.histogram)
 
     def get_plots(self):
-        energy = np.stack(
-            [
-                np.stack([x.get_potential_energy() for x in p])
-                for p in self.prediction_list
-            ]
-        )
+        energy = np.stack([
+            np.stack([x.get_potential_energy() for x in p]) for p in self.prediction_list
+        ])
 
         figures = []
         # Plot the energy

diff --git a/ipsuite/analysis/model/predict.py b/ipsuite/analysis/model/predict.py
@@ -105,18 +105,14 @@ def get_dataframes(self):
         """Create a pandas dataframe from the given data."""
         true_data, pred_data = self.get_data()
 
-        self.energy_df = pd.DataFrame(
-            {
-                "true": (
-                    np.array([x.get_potential_energy() / len(x) for x in true_data])
-                    * 1000
-                ),
-                "prediction": (
-                    np.array([x.get_potential_energy() / len(x) for x in pred_data])
-                    * 1000
-                ),
-            }
-        )
+        self.energy_df = pd.DataFrame({
+            "true": (
+                np.array([x.get_potential_energy() / len(x) for x in true_data]) * 1000
+            ),
+            "prediction": (
+                np.array([x.get_potential_energy() / len(x) for x in pred_data]) * 1000
+            ),
+        })
 
         try:
             true_forces = [np.reshape(x.get_forces(), (-1, 3)) for x in true_data]
@@ -125,18 +121,16 @@ def get_dataframes(self):
             pred_forces = [np.reshape(x.get_forces(), (-1, 3)) for x in pred_data]
             pred_forces = np.concatenate(pred_forces, axis=0) * 1000
 
-            self.forces_df = pd.DataFrame(
-                {
-                    "true": np.linalg.norm(true_forces, axis=-1),
-                    "true_x": true_forces[:, 0],
-                    "true_y": true_forces[:, 1],
-                    "true_z": true_forces[:, 2],
-                    "prediction": np.linalg.norm(pred_forces, axis=-1),
-                    "prediction_x": pred_forces[:, 0],
-                    "prediction_y": pred_forces[:, 1],
-                    "prediction_z": pred_forces[:, 2],
-                }
-            )
+            self.forces_df = pd.DataFrame({
+                "true": np.linalg.norm(true_forces, axis=-1),
+                "true_x": true_forces[:, 0],
+                "true_y": true_forces[:, 1],
+                "true_z": true_forces[:, 2],
+                "prediction": np.linalg.norm(pred_forces, axis=-1),
+                "prediction_x": pred_forces[:, 0],
+                "prediction_y": pred_forces[:, 1],
+                "prediction_z": pred_forces[:, 2],
+            })
         except PropertyNotImplementedError:
             self.forces_df = pd.DataFrame({})
 
@@ -153,24 +147,18 @@ def get_dataframes(self):
             hydro_pred = np.reshape(hydro_pred, -1)
             deviat_pred = np.reshape(deviat_pred, -1)
 
-            self.stress_df = pd.DataFrame(
-                {
-                    "true": true_stress,
-                    "prediction": pred_stress,
-                }
-            )
-            self.stress_hydro_df = pd.DataFrame(
-                {
-                    "true": hydro_true,
-                    "prediction": hydro_pred,
-                }
-            )
-            self.stress_deviat_df = pd.DataFrame(
-                {
-                    "true": deviat_true,
-                    "prediction": deviat_pred,
-                }
-            )
+            self.stress_df = pd.DataFrame({
+                "true": true_stress,
+                "prediction": pred_stress,
+            })
+            self.stress_hydro_df = pd.DataFrame({
+                "true": hydro_true,
+                "prediction": hydro_pred,
+            })
+            self.stress_deviat_df = pd.DataFrame({
+                "true": deviat_true,
+                "prediction": deviat_pred,
+            })
         except PropertyNotImplementedError:
             self.stress_df = pd.DataFrame({})
             self.stress_hydro_df = pd.DataFrame({})

diff --git a/ipsuite/calculators/ase_md.py b/ipsuite/calculators/ase_md.py
@@ -102,13 +102,11 @@ def modify(self, thermostat, step, total_steps):
                     [[self.end_cell, 0, 0], [0, self.end_cell, 0], [0, 0, self.end_cell]]
                 )
             elif isinstance(self.end_cell, list):
-                self.end_cell = np.array(
-                    [
-                        [self.end_cell[0], 0, 0],
-                        [0, self.end_cell[1], 0],
-                        [0, 0, self.end_cell[2]],
-                    ]
-                )
+                self.end_cell = np.array([
+                    [self.end_cell[0], 0, 0],
+                    [0, self.end_cell[1], 0],
+                    [0, 0, self.end_cell[2]],
+                ])
 
         percentage = step / (total_steps - 1)
         # if num_ramp_oscillations is set, the cell size is ramped to end_cell within
@@ -312,13 +310,11 @@ class NPTThermostat(base.IPSNode):
 
     def get_thermostat(self, atoms):
         if self.tetragonal_strain:
-            mask = np.array(
-                [
-                    [True, False, False],
-                    [False, True, False],
-                    [False, False, True],
-                ]
-            )
+            mask = np.array([
+                [True, False, False],
+                [False, True, False],
+                [False, False, True],
+            ])
         else:
             mask = None
         self.time_step *= units.fs

diff --git a/ipsuite/models/nequip.py b/ipsuite/models/nequip.py
@@ -137,15 +137,13 @@ def get_metrics_from_plots(self):
 
     def deploy_model(self):
         """Deploy the model using nequip-deploy."""
-        subprocess.check_call(
-            [
-                "nequip-deploy",
-                "build",
-                "--train-dir",
-                (self.model_directory / "dvc-run").as_posix(),
-                self.deployed_model.as_posix(),
-            ]
-        )
+        subprocess.check_call([
+            "nequip-deploy",
+            "build",
+            "--train-dir",
+            (self.model_directory / "dvc-run").as_posix(),
+            self.deployed_model.as_posix(),
+        ])
 
     def run(self):
         """Primary method to run which executes all steps of the model training."""

diff --git a/tests/conftest.py b/tests/conftest.py
@@ -3,6 +3,7 @@
 References
 ----------
 https://docs.pytest.org/en/6.2.x/fixture.html#scope-sharing-fixtures-across-classes-modules-packages-or-session
+
 """
 
 import logging
@@ -131,35 +132,27 @@ def data_repo(tmp_path, request) -> pathlib.Path:
 def atoms_with_composed_forces():
     atoms = Atoms(
         "OH2",
-        positions=np.array(
-            [
-                [0.0, 0.0, 0.0],
-                [1.0, 0.0, 0.0],
-                [0.0, 1.0, 0.0],
-            ]
-        ),
-    )
-    ft = np.array(
-        [
-            [0.0, 0.0, 1.0 * 15.999],
-            [0.0, 0.0, 1.008],
-            [0.0, 0.0, 1.008],
-        ]
-    )
-    fr = np.array(
-        [
+        positions=np.array([
             [0.0, 0.0, 0.0],
-            [0.0, 0.0, 1.008],
-            [0.0, 0.0, -1.008],
-        ]
-    )
-    fv = np.array(
-        [
-            [1.0, 1.0, 0.0],
-            [-1.0, 0.0, 0.0],
-            [0.0, -1.0, 0.0],
-        ]
+            [1.0, 0.0, 0.0],
+            [0.0, 1.0, 0.0],
+        ]),
     )
+    ft = np.array([
+        [0.0, 0.0, 1.0 * 15.999],
+        [0.0, 0.0, 1.008],
+        [0.0, 0.0, 1.008],
+    ])
+    fr = np.array([
+        [0.0, 0.0, 0.0],
+        [0.0, 0.0, 1.008],
+        [0.0, 0.0, -1.008],
+    ])
+    fv = np.array([
+        [1.0, 1.0, 0.0],
+        [-1.0, 0.0, 0.0],
+        [0.0, -1.0, 0.0],
+    ])
     atoms.calc = ase.calculators.singlepoint.SinglePointCalculator(
         atoms, forces=ft + fr + fv
     )