run ruff

Author: njzjz

deepmd/calculator.py

@@ -103,7 +103,7 @@ def __init__(
             self.type_dict = type_dict
         else:
             self.type_dict = dict(
-                zip(self.dp.get_type_map(), range(self.dp.get_ntypes()))
+                zip(self.dp.get_type_map(), range(self.dp.get_ntypes()), strict=False)
             )
 
     def calculate(

deepmd/dpmodel/atomic_model/linear_atomic_model.py

@@ -155,7 +155,7 @@ def get_model_sels(self) -> list[int | list[int]]:
     def _sort_rcuts_sels(self) -> tuple[tuple[Array, Array], list[int]]:
         # sort the pair of rcut and sels in ascending order, first based on sel, then on rcut.
         zipped = sorted(
-            zip(self.get_model_rcuts(), self.get_model_nsels()),
+            zip(self.get_model_rcuts(), self.get_model_nsels(), strict=False),
             key=lambda x: (x[1], x[0]),
         )
         return [p[0] for p in zipped], [p[1] for p in zipped]
@@ -235,12 +235,12 @@ def forward_atomic(
         )
         raw_nlists = [
             nlists[get_multiple_nlist_key(rcut, sel)]
-            for rcut, sel in zip(self.get_model_rcuts(), self.get_model_nsels())
+            for rcut, sel in zip(self.get_model_rcuts(), self.get_model_nsels(), strict=False)
         ]
         nlists_ = [
             nl if mt else nlist_distinguish_types(nl, extended_atype, sel)
             for mt, nl, sel in zip(
-                self.mixed_types_list, raw_nlists, self.get_model_sels()
+                self.mixed_types_list, raw_nlists, self.get_model_sels(), strict=False
             )
         ]
         ener_list = []

deepmd/dpmodel/descriptor/hybrid.py

@@ -101,7 +101,7 @@ def __init__(
             start_idx = np.cumsum(np.pad(hybrid_sel, (1, 0), "constant"))[:-1]
             end_idx = start_idx + np.array(sub_sel)
             cut_idx = np.concatenate(
-                [range(ss, ee) for ss, ee in zip(start_idx, end_idx)]
+                [range(ss, ee) for ss, ee in zip(start_idx, end_idx, strict=False)]
             )
             nlist_cut_idx.append(cut_idx)
         self.nlist_cut_idx = nlist_cut_idx
@@ -310,7 +310,7 @@ def call(
             )
         else:
             nl_distinguish_types = None
-        for descrpt, nci in zip(self.descrpt_list, self.nlist_cut_idx):
+        for descrpt, nci in zip(self.descrpt_list, self.nlist_cut_idx, strict=False):
             # cut the nlist to the correct length
             if self.mixed_types() == descrpt.mixed_types():
                 nl = xp.take(nlist, nci, axis=2)

deepmd/dpmodel/infer/deep_eval.py

@@ -228,7 +228,7 @@ def eval(
         return dict(
             zip(
                 [x.name for x in request_defs],
-                out,
+                out, strict=False,
             )
         )
 

deepmd/dpmodel/utils/nlist.py

@@ -234,7 +234,7 @@ def build_multiple_neighbor_list(
     rr = xp.where(nlist_mask, xp.full_like(rr, float("inf")), rr)
     nlist0 = nlist
     ret = {}
-    for rc, ns in zip(rcuts[::-1], nsels[::-1]):
+    for rc, ns in zip(rcuts[::-1], nsels[::-1], strict=False):
         tnlist_1 = nlist0[:, :, :ns]
         tnlist_1 = xp.where(rr[:, :, :ns] > rc, xp.full_like(tnlist_1, -1), tnlist_1)
         ret[get_multiple_nlist_key(rc, ns)] = tnlist_1

deepmd/jax/infer/deep_eval.py

@@ -248,7 +248,7 @@ def eval(
         return dict(
             zip(
                 [x.name for x in request_defs],
-                out,
+                out, strict=False,
             )
         )
 

deepmd/pd/infer/deep_eval.py

@@ -384,7 +384,7 @@ def eval(
         return dict(
             zip(
                 [x.name for x in request_defs],
-                out,
+                out, strict=False,
             )
         )
 

deepmd/pd/model/descriptor/se_a.py

@@ -763,7 +763,7 @@ def forward(
         # nfnl x nnei
         exclude_mask = self.emask(nlist, extended_atype).reshape([nfnl, self.nnei])
         for embedding_idx, (ll, compress_data_ii, compress_info_ii) in enumerate(
-            zip(self.filter_layers.networks, self.compress_data, self.compress_info)
+            zip(self.filter_layers.networks, self.compress_data, self.compress_info, strict=False)
         ):
             if self.type_one_side:
                 ii = embedding_idx

deepmd/pd/model/model/transform_output.py

@@ -122,7 +122,7 @@ def take_deriv(
     split_vv1 = paddle.split(vv1, [1] * size, axis=-1)
     split_svv1 = paddle.split(svv1, [1] * size, axis=-1)
     split_ff, split_avir = [], []
-    for vvi, svvi in zip(split_vv1, split_svv1):
+    for vvi, svvi in zip(split_vv1, split_svv1, strict=False):
         # nf x nloc x 3, nf x nloc x 9
         ffi, aviri = task_deriv_one(
             vvi,

deepmd/pd/utils/dataloader.py

@@ -167,7 +167,7 @@ def construct_dataset(system):
         else:
             self.batch_sizes = batch_size * np.ones(len(systems), dtype=int)
         assert len(self.systems) == len(self.batch_sizes)
-        for system, batch_size in zip(self.systems, self.batch_sizes):
+        for system, batch_size in zip(self.systems, self.batch_sizes, strict=False):
             if dist.is_available() and dist.is_initialized():
                 system_batch_sampler = DistributedBatchSampler(
                     system,