[pre-commit.ci] pre-commit autoupdate (#2752)

<!--pre-commit.ci start-->
updates:
- [github.com/astral-sh/ruff-pre-commit: v0.0.284 →
v0.0.285](astral-sh/ruff-pre-commit@v0.0.284...v0.0.285)
- [github.com/asottile/blacken-docs: 1.15.0 →
1.16.0](adamchainz/blacken-docs@1.15.0...1.16.0)
<!--pre-commit.ci end-->

---------

Signed-off-by: Jinzhe Zeng <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jinzhe Zeng <[email protected]>

Author: pre-commit-ci[bot]

.pre-commit-config.yaml

@@ -33,7 +33,7 @@ repos:
       files: \.py$
 -   repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: v0.0.284
+    rev: v0.0.285
     hooks:
     - id: ruff
       args: ["--fix"]
@@ -45,7 +45,7 @@ repos:
       args: ["--write"]
 # Python inside docs
 -   repo: https://github.com/asottile/blacken-docs
-    rev: 1.15.0
+    rev: 1.16.0
     hooks:
     -   id: blacken-docs
 # C++

deepmd/fit/polar.py

@@ -107,15 +107,15 @@ def __init__(
             self.scale = [1.0 for ii in range(self.ntypes)]
         # if self.diag_shift is None:
         #    self.diag_shift = [0.0 for ii in range(self.ntypes)]
-        if type(self.sel_type) is not list:
+        if not isinstance(self.sel_type, list):
             self.sel_type = [self.sel_type]
         self.sel_type = sorted(self.sel_type)
         self.constant_matrix = np.zeros(
             self.ntypes
         )  # self.ntypes x 1, store the average diagonal value
         # if type(self.diag_shift) is not list:
         #    self.diag_shift = [self.diag_shift]
-        if type(self.scale) is not list:
+        if not isinstance(self.scale, list):
             self.scale = [self.scale for ii in range(self.ntypes)]
         self.scale = np.array(self.scale)
         self.dim_rot_mat_1 = descrpt.get_dim_rot_mat_1()

doc/development/create-a-model.md

@@ -16,6 +16,7 @@ After implementation, you need to register the component with a key:
 ```py
 from deepmd.descriptor import Descriptor
 
+
 @Descriptor.register("some_descrpt")
 class SomeDescript(Descriptor):
     def __init__(self, arg1: bool, arg2: float) -> None:
@@ -32,6 +33,7 @@ from typing import List
 from dargs import Argument
 from deepmd.utils.argcheck import descrpt_args_plugin
 
+
 @descrpt_args_plugin.register("some_descrpt")
 def descrpt_some_args() -> List[Argument]:
     return [
@@ -57,11 +59,13 @@ The arguments here should be consistent with the class arguments of your new com
 You may use `setuptools` to package new codes into a new Python package. It's crucial to add your new component to `entry_points['deepmd']` in `setup.py`:
 
 ```py
-    entry_points={
-        'deepmd': [
-            'some_descrpt=deepmd_some_descrtpt:SomeDescript',
+entry_points = (
+    {
+        "deepmd": [
+            "some_descrpt=deepmd_some_descrtpt:SomeDescript",
         ],
     },
+)
 ```
 
 where `deepmd_some_descrtpt` is the module of your codes. It is equivalent to `from deepmd_some_descrtpt import SomeDescript`.

doc/model/dprc.md

@@ -57,10 +57,23 @@ As described in the paper, the DPRc model only corrects $E_\text{QM}$ and $E_\te
 {ref}`exclude_types <model/descriptor[se_e2_a]/exclude_types>` can be generated by the following Python script:
 ```py
 from itertools import combinations_with_replacement, product
+
 qm = (0, 1, 3, 5)
 mm = (2, 4)
-print("QM/QM:", list(map(list, list(combinations_with_replacement(mm, 2)) + list(product(qm, mm)))))
-print("QM/MM:", list(map(list, list(combinations_with_replacement(qm, 2)) + list(combinations_with_replacement(mm, 2)))))
+print(
+    "QM/QM:",
+    list(map(list, list(combinations_with_replacement(mm, 2)) + list(product(qm, mm)))),
+)
+print(
+    "QM/MM:",
+    list(
+        map(
+            list,
+            list(combinations_with_replacement(qm, 2))
+            + list(combinations_with_replacement(mm, 2)),
+        )
+    ),
+)
 ```
 
 Also, DPRc assumes MM atom energies ({ref}`atom_ener <model/fitting_net[ener]/atom_ener>`) are zero:

doc/third-party/out-of-deepmd-kit.md

@@ -1,35 +1,36 @@
-# Interfaces out of DeePMD-kit
-
-The codes of the following interfaces are not a part of the DeePMD-kit package and maintained by other repositories. We list these interfaces here for user convenience.
-
-## dpdata
-
-[dpdata](https://github.com/deepmodeling/dpdata) provides the `predict` method for `System` class:
-
-```py
-import dpdata
-dsys = dpdata.LabeledSystem('OUTCAR')
-dp_sys = dsys.predict("frozen_model_compressed.pb")
-```
-
-By inferring with the DP model `frozen_model_compressed.pb`, dpdata will generate a new labeled system `dp_sys` with inferred energies, forces, and virials.
-
-## OpenMM plugin for DeePMD-kit
-
-An [OpenMM](https://github.com/openmm/openmm) plugin is provided from [JingHuangLab/openmm_deepmd_plugin](https://github.com/JingHuangLab/openmm_deepmd_plugin), written by the [Huang Lab](http://www.compbiophysics.org/) at Westlake University.
-
-## AMBER interface to DeePMD-kit
-
-An [AMBER](https://ambermd.org/) interface to DeePMD-kit is written by the [York [Lab](https://theory.rutgers.edu/) from Rutgers University. It is open-source at [GitLab RutgersLBSR/AmberDPRc](https://gitlab.com/RutgersLBSR/AmberDPRc/). Details can be found in [this paper](https://doi.org/10.1021/acs.jctc.1c00201).
-
-## DP-GEN
-
-[DP-GEN](https://github.com/deepmodeling/dpgen) provides a workflow to generate accurate DP models by calling DeePMD-kit's command line interface (CLI) in the local or remote server. Details can be found in [this paper](https://doi.org/10.1016/j.cpc.2020.107206).
-
-## MLatom
-
-[Mlatom](http://mlatom.com/) provides an interface to the DeePMD-kit within MLatom's workflow by calling DeePMD-kit's CLI. Details can be found in [this paper](https://doi.org/10.1007/s41061-021-00339-5).
-
-## ABACUS
-
-[ABACUS](https://github.com/deepmodeling/abacus-develop/) can run molecular dynamics with a DP model. User is required to [build ABACUS with DeePMD-kit](https://abacus.deepmodeling.com/en/latest/advanced/install.html#build-with-deepmd-kit).
+# Interfaces out of DeePMD-kit
+
+The codes of the following interfaces are not a part of the DeePMD-kit package and maintained by other repositories. We list these interfaces here for user convenience.
+
+## dpdata
+
+[dpdata](https://github.com/deepmodeling/dpdata) provides the `predict` method for `System` class:
+
+```py
+import dpdata
+
+dsys = dpdata.LabeledSystem("OUTCAR")
+dp_sys = dsys.predict("frozen_model_compressed.pb")
+```
+
+By inferring with the DP model `frozen_model_compressed.pb`, dpdata will generate a new labeled system `dp_sys` with inferred energies, forces, and virials.
+
+## OpenMM plugin for DeePMD-kit
+
+An [OpenMM](https://github.com/openmm/openmm) plugin is provided from [JingHuangLab/openmm_deepmd_plugin](https://github.com/JingHuangLab/openmm_deepmd_plugin), written by the [Huang Lab](http://www.compbiophysics.org/) at Westlake University.
+
+## AMBER interface to DeePMD-kit
+
+An [AMBER](https://ambermd.org/) interface to DeePMD-kit is written by the [York [Lab](https://theory.rutgers.edu/) from Rutgers University. It is open-source at [GitLab RutgersLBSR/AmberDPRc](https://gitlab.com/RutgersLBSR/AmberDPRc/). Details can be found in [this paper](https://doi.org/10.1021/acs.jctc.1c00201).
+
+## DP-GEN
+
+[DP-GEN](https://github.com/deepmodeling/dpgen) provides a workflow to generate accurate DP models by calling DeePMD-kit's command line interface (CLI) in the local or remote server. Details can be found in [this paper](https://doi.org/10.1016/j.cpc.2020.107206).
+
+## MLatom
+
+[Mlatom](http://mlatom.com/) provides an interface to the DeePMD-kit within MLatom's workflow by calling DeePMD-kit's CLI. Details can be found in [this paper](https://doi.org/10.1007/s41061-021-00339-5).
+
+## ABACUS
+
+[ABACUS](https://github.com/deepmodeling/abacus-develop/) can run molecular dynamics with a DP model. User is required to [build ABACUS with DeePMD-kit](https://abacus.deepmodeling.com/en/latest/advanced/install.html#build-with-deepmd-kit).

doc/train/training.md

@@ -46,12 +46,12 @@ import matplotlib.pyplot as plt
 
 data = np.genfromtxt("lcurve.out", names=True)
 for name in data.dtype.names[1:-1]:
-    plt.plot(data['step'], data[name], label=name)
+    plt.plot(data["step"], data[name], label=name)
 plt.legend()
-plt.xlabel('Step')
-plt.ylabel('Loss')
-plt.xscale('symlog')
-plt.yscale('log')
+plt.xlabel("Step")
+plt.ylabel("Loss")
+plt.xscale("symlog")
+plt.yscale("log")
 plt.grid()
 plt.show()
 ```