enable docstring code format (#3267)

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

deepmd/infer/deep_eval.py

@@ -467,7 +467,7 @@ def eval_typeebd(self) -> np.ndarray:
         Get the output of type embedding network of `graph.pb`:
 
         >>> from deepmd.infer import DeepPotential
-        >>> dp = DeepPotential('graph.pb')
+        >>> dp = DeepPotential("graph.pb")
         >>> dp.eval_typeebd()
         """
         return self.deep_eval.eval_typeebd()

deepmd/infer/deep_pot.py

@@ -43,10 +43,10 @@ class DeepPot(DeepEval):
     --------
     >>> from deepmd.infer import DeepPot
     >>> import numpy as np
-    >>> dp = DeepPot('graph.pb')
-    >>> coord = np.array([[1,0,0], [0,0,1.5], [1,0,3]]).reshape([1, -1])
+    >>> dp = DeepPot("graph.pb")
+    >>> coord = np.array([[1, 0, 0], [0, 0, 1.5], [1, 0, 3]]).reshape([1, -1])
     >>> cell = np.diag(10 * np.ones(3)).reshape([1, -1])
-    >>> atype = [1,0,1]
+    >>> atype = [1, 0, 1]
     >>> e, f, v = dp.eval(coord, cell, atype)
 
     where `e`, `f` and `v` are predicted energy, force and virial of the system, respectively.

deepmd/infer/model_devi.py

@@ -296,9 +296,9 @@ def calc_model_devi(
     >>> from deepmd.tf.infer import calc_model_devi
     >>> from deepmd.tf.infer import DeepPot as DP
     >>> import numpy as np
-    >>> coord = np.array([[1,0,0], [0,0,1.5], [1,0,3]]).reshape([1, -1])
+    >>> coord = np.array([[1, 0, 0], [0, 0, 1.5], [1, 0, 3]]).reshape([1, -1])
     >>> cell = np.diag(10 * np.ones(3)).reshape([1, -1])
-    >>> atype = [1,0,1]
+    >>> atype = [1, 0, 1]
     >>> graphs = [DP("graph.000.pb"), DP("graph.001.pb")]
     >>> model_devi = calc_model_devi(coord, cell, atype, graphs)
     """

deepmd/tf/common.py

@@ -243,13 +243,13 @@ def cast_precision(func: Callable) -> Callable:
     Examples
     --------
     >>> class A:
-    ...   @property
-    ...   def precision(self):
-    ...     return tf.float32
+    ...     @property
+    ...     def precision(self):
+    ...         return tf.float32
     ...
-    ...   @cast_precision
-    ...   def f(x: tf.Tensor, y: tf.Tensor) -> tf.Tensor:
-    ...     return x ** 2 + y
+    ...     @cast_precision
+    ...     def f(x: tf.Tensor, y: tf.Tensor) -> tf.Tensor:
+    ...         return x**2 + y
     """
 
     @wraps(func)

deepmd/tf/descriptor/descriptor.py

@@ -32,7 +32,7 @@ class Descriptor(PluginVariant):
 
     Examples
     --------
-    >>> descript = Descriptor(type="se_e2_a", rcut=6., rcut_smth=0.5, sel=[50])
+    >>> descript = Descriptor(type="se_e2_a", rcut=6.0, rcut_smth=0.5, sel=[50])
     >>> type(descript)
     <class 'deepmd.tf.descriptor.se_a.DescrptSeA'>
 

deepmd/tf/entrypoints/neighbor_stat.py

@@ -42,7 +42,30 @@ def neighbor_stat(
 
     Examples
     --------
-    >>> neighbor_stat(system='.', rcut=6., type_map=["C", "H", "O", "N", "P", "S", "Mg", "Na", "HW", "OW", "mNa", "mCl", "mC", "mH", "mMg", "mN", "mO", "mP"])
+    >>> neighbor_stat(
+    ...     system=".",
+    ...     rcut=6.0,
+    ...     type_map=[
+    ...         "C",
+    ...         "H",
+    ...         "O",
+    ...         "N",
+    ...         "P",
+    ...         "S",
+    ...         "Mg",
+    ...         "Na",
+    ...         "HW",
+    ...         "OW",
+    ...         "mNa",
+    ...         "mCl",
+    ...         "mC",
+    ...         "mH",
+    ...         "mMg",
+    ...         "mN",
+    ...         "mO",
+    ...         "mP",
+    ...     ],
+    ... )
     min_nbor_dist: 0.6599510670195264
     max_nbor_size: [23, 26, 19, 16, 2, 2, 1, 1, 72, 37, 5, 0, 31, 29, 1, 21, 20, 5]
     """

deepmd/tf/infer/deep_eval.py

@@ -514,7 +514,7 @@ def eval_typeebd(self) -> np.ndarray:
         Get the output of type embedding network of `graph.pb`:
 
         >>> from deepmd.tf.infer import DeepPotential
-        >>> dp = DeepPotential('graph.pb')
+        >>> dp = DeepPotential("graph.pb")
         >>> dp.eval_typeebd()
         """
         t_typeebd = self._get_tensor("t_typeebd:0")
@@ -1429,7 +1429,7 @@ def eval_typeebd(self) -> np.ndarray:
         Get the output of type embedding network of `graph.pb`:
 
         >>> from deepmd.tf.infer import DeepPotential
-        >>> dp = DeepPotential('graph.pb')
+        >>> dp = DeepPotential("graph.pb")
         >>> dp.eval_typeebd()
         """
         t_typeebd = self._get_tensor("t_typeebd:0")

deepmd/tf/utils/parallel_op.py

@@ -35,12 +35,10 @@ class ParallelOp:
     >>> def builder():
     ...     x = tf.placeholder(tf.int32, [1])
     ...     return {"x": x}, (x + 1)
-    ...
     >>> p = ParallelOp(builder, nthreads=4)
     >>> def feed():
     ...     for ii in range(10):
     ...         yield {"x": [ii]}
-    ...
     >>> print(*p.generate(tf.Session(), feed()))
     [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]
     """

deepmd/utils/plugin.py

@@ -24,7 +24,7 @@ class Plugin:
     >>> @plugin.register("xx")
         def xxx():
             pass
-    >>> print(plugin.plugins['xx'])
+    >>> print(plugin.plugins["xx"])
     """
 
     def __init__(self):

pyproject.toml

@@ -223,6 +223,9 @@ ignore = "D413, D416, D203, D107, D213"
 profile = "black"
 force_grid_wrap = 1
 
+[tool.ruff.format]
+docstring-code-format = true
+
 [tool.ruff.lint]
 select = [
     "E", # errors

source/install/build_tf.py

@@ -294,7 +294,7 @@ def set_directory(path: Path):
     Examples
     --------
     >>> with set_directory("some_path"):
-    ...    do_something()
+    ...     do_something()
     """
     cwd = Path().absolute()
     path.mkdir(exist_ok=True, parents=True)