[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ default_language_version:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.6.0
     hooks:
       # list of supported hooks: https://pre-commit.com/hooks.html
       - id: trailing-whitespace
@@ -12,20 +12,20 @@ repos:
       - id: detect-private-key
 
   # python code formatting/linting
-  - repo: https://github.com/charliermarsh/ruff-pre-commit
+  - repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: "v0.0.263"
+    rev: "v0.6.8"
     hooks:
       - id: ruff
         args: [--fix]
   - repo: https://github.com/psf/black
-    rev: 23.3.0
+    rev: 24.8.0
     hooks:
       - id: black
         args: [--line-length, "100"]
   # yaml formatting
   - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v3.0.0-alpha.9-for-vscode
+    rev: v4.0.0-alpha.8
     hooks:
       - id: prettier
         types: [yaml]

pseudo_labeller/model/__init__.py

@@ -1,2 +1,3 @@
 """Models"""
+
 from pseudo_labeller.model.idam import PsuedoIrradienceForecastor

pseudo_labeller/model/idam.py

@@ -1,4 +1,5 @@
 """Psuedo-irradience forecastor/labeller"""
+
 import einops
 import torch
 import torch.nn as nn
@@ -68,7 +69,7 @@ def __init__(
                 in_channels=input_channels,
                 out_channels=conv3d_channels,
                 kernel_size=(kernel_size, kernel_size, kernel_size),
-                padding=(1,0,0),
+                padding=(1, 0, 0),
             )
         )
         for i in range(0, num_layers):
@@ -77,7 +78,7 @@ def __init__(
                     in_channels=conv3d_channels,
                     out_channels=conv3d_channels,
                     kernel_size=(kernel_size, kernel_size, kernel_size),
-                    padding=(1,0,0),
+                    padding=(1, 0, 0),
                 )
             )
 
@@ -95,9 +96,7 @@ def __init__(
         # Small head model to convert from latent space to PV generation for training
         # Input is per-pixel input data, this will be
         # reshaped to the same output steps as the latent head
-        self.pv_meta_input = nn.Linear(
-            pv_meta_input_channels, out_features=hidden_dim
-        )
+        self.pv_meta_input = nn.Linear(pv_meta_input_channels, out_features=hidden_dim)
 
         # Output is forecast steps channels, each channel is a timestep
         # For labelling, this should be 1, forecasting the middle
@@ -142,7 +141,5 @@ def forward(self, x: torch.Tensor, pv_meta: torch.Tensor = None, output_latents:
         x = torch.cat([x, pv_meta], dim=1)
         # Get pv_meta_output
         x = self.pv_meta_output(x)
-        x = F.relu(
-            self.pv_meta_output2(x)
-        )  # Generation can only be positive or 0, so ReLU
+        x = F.relu(self.pv_meta_output2(x))  # Generation can only be positive or 0, so ReLU
         return x

setup.py

@@ -1,4 +1,5 @@
 """ Usual setup file for package """
+
 # read the contents of your README file
 from pathlib import Path