[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -32,7 +32,7 @@ repos:
       - id: text-unicode-replacement-char
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.1.14"
+    rev: "v0.2.0"
     hooks:
       - id: ruff
         args:

src/stream_mapper/pytorch/_base.py

@@ -75,6 +75,7 @@ def forward(self, data: Data[Array]) -> Array:
         -------
         (N, 3) Array
             fraction, mean, ln-sigma
+
         """
         if self.net is None:
             return self.xp.asarray([])

src/stream_mapper/pytorch/_connect/compat.py

@@ -51,6 +51,7 @@ def _array_at_pytorch(array: Array, idx: Any, /, *, inplace: bool = True) -> Arr
     -------
     ArrayAt[Array]
         Setter.
+
     """
     return ArrayAt(array if inplace else array.clone(), idx, inplace=inplace)
 
@@ -67,6 +68,7 @@ def _get_namespace_pytorch(array: Array, /) -> ArrayNamespace[Array]:
     Returns
     -------
     ArrayNamespace[Array]
+
     """
     return cast("ArrayNamespace[Array]", xp)
 
@@ -83,5 +85,6 @@ def _copy_pytorch(array: Array, /) -> Array:
     Returns
     -------
     Array
+
     """
     return array.clone()

src/stream_mapper/pytorch/_connect/funcs.py

@@ -39,6 +39,7 @@ def _within_bounds_pytorch(
     -------
     ndarray
         Boolean array indicating whether the value is within the bounds.
+
     """
     inbounds = xp.ones_like(value, dtype=xp.bool)
     if lower_bound is not None:

src/stream_mapper/pytorch/_multi.py

@@ -74,6 +74,7 @@ class IndependentModels(ModelsBase, CoreIndependentModels[Array, NNModel]):
         Mapping of parameter names to priors. This is useful for setting priors
         on parameters across models, e.g. the background and stream models in a
         mixture model.
+
     """
 
     def __post_init__(self) -> None:
@@ -92,6 +93,7 @@ def forward(self, data: Data[Array], /) -> Array:
         -------
         Array
             fraction, mean, ln-sigma.
+
         """
         pred = self.xp.concatenate(
             tuple(model(data) for model in self.components.values()), dim=1
@@ -115,6 +117,7 @@ class MixtureModel(ModelsBase, CoreMixtureModel[Array, NNModel]):
         control over the type of the models attribute.
     net : NNModel, optional postional-only
         The neural network that is used to combine the components.
+
     """
 
     net: NNField[NNModel, NNModel] = NNField(default=MISSING)
@@ -138,6 +141,7 @@ def forward(self, data: Data[Array], /) -> Array:
         -------
         Array
             fraction, mean, ln-sigma.
+
         """
         # Predict the weights, except the background weight, which is
         # always 1 - sum(weights).

src/stream_mapper/pytorch/builtin/_isochrone.py

@@ -126,6 +126,7 @@ class IsochroneMVNorm(ModelBase):
             coord_bounds=(...), # photometry mag_names=("g",),
             mag_err_names=("g_err",), color_names=("g-r",),
             color_err_names=("g-r_err",), phot_bounds=(...),
+
     """
 
     net: NNField[NNModel, None] = NNField(default=None)
@@ -293,6 +294,7 @@ def ln_likelihood(
         Returns
         -------
         Array[(N,)]
+
         """
         # 'where' is used to indicate which data points are available. If
         # 'where' is not provided, then all data points are assumed to be

src/stream_mapper/pytorch/builtin/_multinormal.py

@@ -77,6 +77,7 @@ def ln_likelihood(
         Returns
         -------
         Array
+
         """
         # 'where' is used to indicate which data points are available. If
         # 'where' is not provided, then all data points are assumed to be

src/stream_mapper/pytorch/builtin/_skewnorm.py

@@ -64,6 +64,7 @@ def ln_likelihood(
         Returns
         -------
         Array
+
         """
         # 'where' is used to indicate which data points are available. If
         # 'where' is not provided, then all data points are assumed to be

src/stream_mapper/pytorch/builtin/_sloped.py

@@ -37,6 +37,7 @@ class Sloped(ModelBase):
     net : nn.Module, keyword-only
         The network to use. If not provided, a new one will be created. Must be
         a layer with 1 input and ``len(param names)-1`` outputs.
+
     """
 
     _: KW_ONLY
@@ -100,6 +101,7 @@ def ln_likelihood(
         Returns
         -------
         Array
+
         """
         data = self.data_scaler.transform(
             data, names=names_intersect(data.names, self.data_scaler.names), xp=self.xp
@@ -146,6 +148,7 @@ def forward(self, data: Data[Array]) -> Array:
         -------
         Array
             fraction, mean, ln-sigma
+
         """
         # The forward step runs on the normalized coordinates
         data = self.data_scaler.transform(

src/stream_mapper/pytorch/builtin/_truncskewnorm.py

@@ -55,6 +55,7 @@ def ln_likelihood(
         Returns
         -------
         Array
+
         """
         # 'where' is used to indicate which data points are available. If
         # 'where' is not provided, then all data points are assumed to be

src/stream_mapper/pytorch/builtin/compat/_flow.py

@@ -69,6 +69,7 @@ def ln_likelihood(
         Returns
         -------
         Array
+
         """
         # 'where' is used to indicate which data points are available. If
         # 'where' is not provided, then all data points are assumed to be
@@ -103,5 +104,6 @@ def forward(self, data: Data[Array]) -> Array:
         Returns
         -------
         Array
+
         """
         return xp.asarray([])

src/stream_mapper/pytorch/builtin/compat/kde.py

@@ -64,6 +64,7 @@ def ln_likelihood(
         Returns
         -------
         Array
+
         """
         # TODO: support `where` argument.
         with xp.no_grad():
@@ -85,5 +86,6 @@ def forward(self, data: Data[Array]) -> Array:
         Returns
         -------
         Array
+
         """
         return xp.asarray([])

src/stream_mapper/pytorch/nn.py

@@ -39,6 +39,7 @@ def sequential(
     Returns
     -------
     `torch.nn.Sequential`
+
     """
     activation_func = nn.Tanh if activation is None else activation
 

src/stream_mapper/pytorch/params/bounds.py

@@ -66,6 +66,7 @@ def scaled_sigmoid(x: Array, /, lower: Array, upper: Array) -> Array:
     Returns
     -------
     Array
+
     """
     if xp.isneginf(lower) and xp.isposinf(upper):
         return x