fix errors detected by ruff

Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>

data/raw/shuffle_raw.py

@@ -69,7 +69,8 @@ def _main():
     tmp = np.reshape(tmp, [nframe, -1])
     nframe = tmp.shape[0]
     idx = np.arange(nframe)
-    np.random.shuffle(idx)
+    rng = np.random.default_rng()
+    rng.shuffle(idx)
 
     for ii in raws:
         data = np.loadtxt(inpath + "/" + ii)

deepmd/calculator.py

@@ -6,6 +6,7 @@
 )
 from typing import (
     TYPE_CHECKING,
+    ClassVar,
     Dict,
     List,
     Optional,
@@ -69,7 +70,13 @@ class DP(Calculator):
     """
 
     name = "DP"
-    implemented_properties = ["energy", "free_energy", "forces", "virial", "stress"]
+    implemented_properties: ClassVar[List[str]] = [
+        "energy",
+        "free_energy",
+        "forces",
+        "virial",
+        "stress",
+    ]
 
     def __init__(
         self,

deepmd/descriptor/se_a.py

@@ -144,7 +144,7 @@ class DescrptSeA(DescrptSe):
     .. [1] Linfeng Zhang, Jiequn Han, Han Wang, Wissam A. Saidi, Roberto Car, and E. Weinan. 2018.
        End-to-end symmetry preserving inter-atomic potential energy model for finite and extended
        systems. In Proceedings of the 32nd International Conference on Neural Information Processing
-       Systems (NIPS'18). Curran Associates Inc., Red Hook, NY, USA, 4441–4451.
+       Systems (NIPS'18). Curran Associates Inc., Red Hook, NY, USA, 4441-4451.
     """
 
     def __init__(

deepmd/descriptor/se_a_mask.py

@@ -112,7 +112,7 @@ class DescrptSeAMask(DescrptSeA):
     .. [1] Linfeng Zhang, Jiequn Han, Han Wang, Wissam A. Saidi, Roberto Car, and E. Weinan. 2018.
        End-to-end symmetry preserving inter-atomic potential energy model for finite and extended
        systems. In Proceedings of the 32nd International Conference on Neural Information Processing
-       Systems (NIPS'18). Curran Associates Inc., Red Hook, NY, USA, 4441–4451.
+       Systems (NIPS'18). Curran Associates Inc., Red Hook, NY, USA, 4441-4451.
     """
 
     def __init__(

deepmd/infer/deep_tensor.py

@@ -1,6 +1,8 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 from typing import (
     TYPE_CHECKING,
+    ClassVar,
+    Dict,
     List,
     Optional,
     Tuple,
@@ -39,7 +41,7 @@ class DeepTensor(DeepEval):
         The input map for tf.import_graph_def. Only work with default tf graph
     """
 
-    tensors = {
+    tensors: ClassVar[Dict[str, str]] = {
         # descriptor attrs
         "t_ntypes": "descrpt_attr/ntypes:0",
         "t_rcut": "descrpt_attr/rcut:0",

deepmd/utils/argcheck.py

@@ -177,7 +177,7 @@ def descrpt_se_a_args():
     doc_axis_neuron = "Size of the submatrix of G (embedding matrix)."
     doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version. If you set "None" or "none" here, no activation function will be used.'
     doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection'
-    doc_type_one_side = r"If true, the embedding network parameters vary by types of neighbor atoms only, so there will be $N_\text{types}$ sets of embedding network parameters. Otherwise, the embedding network parameters vary by types of centric atoms and types of neighbor atoms, so there will be $N_\text{types}^2$ sets of embedding network parameters."
+    doc_type_one_side = r"If true, the embedding network parameters vary by types of neighbor atoms only, so there will be $N_\text{types}$ sets of embedding network parameters. Otherwise, the embedding network parameters vary by types of centric atoms and types of neighbor atoms, so there will be $N_\text{types}^2$ sets of embedding network parameters."
     doc_precision = f"The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision."
     doc_trainable = "If the parameters in the embedding net is trainable"
     doc_seed = "Random seed for parameter initialization"
@@ -281,7 +281,7 @@ def descrpt_se_r_args():
     doc_neuron = "Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built."
     doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version. If you set "None" or "none" here, no activation function will be used.'
     doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection'
-    doc_type_one_side = r"If true, the embedding network parameters vary by types of neighbor atoms only, so there will be $N_\text{types}$ sets of embedding network parameters. Otherwise, the embedding network parameters vary by types of centric atoms and types of neighbor atoms, so there will be $N_\text{types}^2$ sets of embedding network parameters."
+    doc_type_one_side = r"If true, the embedding network parameters vary by types of neighbor atoms only, so there will be $N_\text{types}$ sets of embedding network parameters. Otherwise, the embedding network parameters vary by types of centric atoms and types of neighbor atoms, so there will be $N_\text{types}^2$ sets of embedding network parameters."
     doc_precision = f"The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision."
     doc_trainable = "If the parameters in the embedding net are trainable"
     doc_seed = "Random seed for parameter initialization"
@@ -345,7 +345,7 @@ def descrpt_se_atten_common_args():
     doc_axis_neuron = "Size of the submatrix of G (embedding matrix)."
     doc_activation_function = f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version. If you set "None" or "none" here, no activation function will be used.'
     doc_resnet_dt = 'Whether to use a "Timestep" in the skip connection'
-    doc_type_one_side = r"If true, the embedding network parameters vary by types of neighbor atoms only, so there will be $N_\text{types}$ sets of embedding network parameters. Otherwise, the embedding network parameters vary by types of centric atoms and types of neighbor atoms, so there will be $N_\text{types}^2$ sets of embedding network parameters."
+    doc_type_one_side = r"If true, the embedding network parameters vary by types of neighbor atoms only, so there will be $N_\text{types}$ sets of embedding network parameters. Otherwise, the embedding network parameters vary by types of centric atoms and types of neighbor atoms, so there will be $N_\text{types}^2$ sets of embedding network parameters."
     doc_precision = f"The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision."
     doc_trainable = "If the parameters in the embedding net is trainable"
     doc_seed = "Random seed for parameter initialization"

deepmd/utils/network.py

@@ -183,7 +183,7 @@ def embedding_net(
     References
     ----------
     .. [1] Kaiming  He,  Xiangyu  Zhang,  Shaoqing  Ren,  and  Jian  Sun. Identitymappings
-       in deep residual networks. InComputer Vision – ECCV 2016,pages 630–645. Springer
+       in deep residual networks. InComputer Vision - ECCV 2016,pages 630-645. Springer
        International Publishing, 2016.
     """
     input_shape = xx.get_shape().as_list()

deepmd_cli/main.py

@@ -312,7 +312,7 @@ def main_parser() -> argparse.ArgumentParser:
     # The table is composed of fifth-order polynomial coefficients and is assembled
     # from two sub-tables. The first table takes the step(parameter) as it's uniform
     # step, while the second table takes 10 * step as it\s uniform step
-    #  The range of the first table is automatically detected by deepmd-kit, while the
+    #  The range of the first table is automatically detected by deepmd-kit, while the
     # second table ranges from the first table's upper boundary(upper) to the
     # extrapolate(parameter) * upper.
     parser_compress = subparsers.add_parser(

source/tests/test_descrpt_sea_ef_rot.py

@@ -108,7 +108,7 @@ def make_test_data(self, nframes):
         one_type = []
         for ii in range(2, 2 + self.ntypes):
             one_type = one_type + [ii - 2 for jj in range(self.natoms[ii])]
-        np.random.shuffle(one_type)
+        np.random.shuffle(one_type)  # noqa: NPY002
         one_type = np.array(one_type, dtype=int).reshape([1, -1])
         dtype = np.tile(one_type, [nframes, 1])
         defield = np.random.random(dcoord.shape)
@@ -162,7 +162,7 @@ def test_rot_axis(self, suffix=""):
         )
         self.sess.run(tf.global_variables_initializer())
 
-        np.random.seed(0)
+        np.random.seed(0)  # noqa: NPY002
         # make test data
         nframes = 2
         dcoord, dbox, dtype, defield = self.make_test_data(nframes)
@@ -308,7 +308,7 @@ def test_rot_diff_axis(self, suffix=""):
         )
         self.sess.run(tf.global_variables_initializer())
 
-        np.random.seed(0)
+        np.random.seed(0)  # noqa: NPY002
         # make test data
         nframes = 2
         dcoord, dbox, dtype, defield = self.make_test_data(nframes)
@@ -423,7 +423,7 @@ def test_rot_field_corot(self, suffix=""):
         )
         self.sess.run(tf.global_variables_initializer())
 
-        np.random.seed(0)
+        np.random.seed(0)  # noqa: NPY002
         # make test data
         nframes = 2
         dcoord, dbox, dtype, defield = self.make_test_data(nframes)

source/tests/test_fitting_stat.py

@@ -28,12 +28,12 @@ def _make_fake_data(sys_natoms, sys_nframes, avgs, stds):
         tmp_data_a = []
         for jj in range(ndof):
             tmp_data_f.append(
-                np.random.normal(
+                np.random.normal(  # noqa: NPY002
                     loc=avgs[jj], scale=stds[jj], size=(sys_nframes[ii], 1)
                 )
             )
             tmp_data_a.append(
-                np.random.normal(
+                np.random.normal(  # noqa: NPY002
                     loc=avgs[jj], scale=stds[jj], size=(sys_nframes[ii], sys_natoms[ii])
                 )
             )