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learningmatter-mit · Apr 28, 2024 · 11cbde1 · 11cbde1
1 parent 93ef2f4
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Showing 6 changed files with 13 additions and 13 deletions.
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -8,10 +8,10 @@ repos:
       - id: end-of-file-fixer
       - id: trailing-whitespace
   - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.4.2
-  hooks:
-    - id: ruff
-      types_or: [ python, pyi, jupyter ]
-      args: [ --fix ]
-    - id: ruff-format
-      types_or: [ python, pyi, jupyter ]
+    rev: v0.4.2
+    hooks:
+      - id: ruff
+        types_or: [ python, pyi, jupyter ]
+        args: [ --fix ]
+      - id: ruff-format
+        types_or: [ python, pyi, jupyter ]
diff --git a/LICENSE b/LICENSE
@@ -18,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+SOFTWARE.
diff --git a/alchemical_mace/optimize.py b/alchemical_mace/optimize.py
@@ -7,11 +7,12 @@ class ExponentiatedGradientDescent(torch.optim.Optimizer):
     Implements Exponentiated Gradient Descent.
 
     Args:
-        params (iterable of torch.Tensor or dict): iterable of parameters to optimize or 
+        params (iterable of torch.Tensor or dict): iterable of parameters to optimize or
             dicts defining parameter groups.
         lr (float, optional): learning rate. Defaults to 1e-3.
         eps (float, optional): small constant for numerical stability. Defaults to 1e-8.
     """
+
     def __init__(
         self,
         params: Union[Iterable[torch.Tensor], Iterable[Dict[str, Any]]],

diff --git a/notebooks/2_compositional_optimization.ipynb b/notebooks/2_compositional_optimization.ipynb
@@ -21,7 +21,6 @@
     "    alchemical_mace_mp,\n",
     "    get_z_table_and_r_max,\n",
     ")\n",
-    "from alchemical_mace.optimize import ExponentiatedGradientDescent\n",
     "from alchemical_mace.utils import suppress_print\n",
     "\n",
     "plt.style.use(\"default\")"

diff --git a/requirements.txt b/requirements.txt
@@ -9,4 +9,4 @@ pandas==2.2.2
 matplotlib==3.8.0
 mpltern==1.0.2
 tqdm==4.66.1
-ipykernel==6.25.2
+ipykernel==6.25.2
diff --git a/scripts/perovskite_alchemy.py b/scripts/perovskite_alchemy.py
@@ -131,7 +131,7 @@
 
 # Define alchemical path
 t = np.linspace(0.0, 1.0, args.alchemy_switch_steps)
-lambda_steps = t ** 5 * (70 * t ** 4 - 315 * t ** 3 + 540 * t ** 2 - 420 * t + 126)
+lambda_steps = t**5 * (70 * t**4 - 315 * t**3 + 540 * t**2 - 420 * t + 126)
 lambda_values = [
     np.zeros(args.alchemy_equil_steps),
     lambda_steps,
@@ -167,7 +167,7 @@ def get_observables(dynamics, time, lambda_value):
 total_steps = 2 * args.alchemy_equil_steps + 2 * args.alchemy_switch_steps
 
 observables = []
-for step in (tqdm(range(total_steps), desc="Alchemical switching")):
+for step in tqdm(range(total_steps), desc="Alchemical switching"):
     lambda_value = lambda_values[step]
     grad_enabled = calculate_gradients[step]