[Doc] Add more contributors and refine several docstrings (#836)

* auto generate contributors instead of adding manually

* refine return type of docstrings

Author: HydrogenSulfate

docs/index.md

@@ -200,7 +200,7 @@
 ./README.md:thanks
 --8<--
 
-- PaddleScience 的部分代码由以下优秀社区开发者贡献（按 [Contributors](https://github.com/PaddlePaddle/PaddleScience/graphs/contributors) 排序）：
+- PaddleScience 的部分代码由以下优秀开发者贡献（按 [Contributors](https://github.com/PaddlePaddle/PaddleScience/graphs/contributors) 排序）：
 
     <style>
         .avatar {
@@ -216,32 +216,7 @@
             transform:translateY(-10px);
         }
     </style>
-    <a href="https://github.com/Asthestarsfalll"><img class="avatar" src="https://avatars.githubusercontent.com/Asthestarsfalll" alt="avatar" /></a>
-    <a href="https://github.com/co63oc"><img class="avatar" src="https://avatars.githubusercontent.com/co63oc" alt="avatar" /></a>
-    <a href="https://github.com/MayYouBeProsperous"><img class="avatar" src="https://avatars.githubusercontent.com/MayYouBeProsperous" alt="avatar" /></a>
-    <a href="https://github.com/AndPuQing"><img class="avatar" src="https://avatars.githubusercontent.com/AndPuQing" alt="avatar" /></a>
-    <a href="https://github.com/lknt"><img class="avatar" src="https://avatars.githubusercontent.com/lknt" alt="avatar" /></a>
-    <a href="https://github.com/mrcangye"><img class="avatar" src="https://avatars.githubusercontent.com/mrcangye" alt="avatar" /></a>
-    <a href="https://github.com/yangguohao"><img class="avatar" src="https://avatars.githubusercontent.com/yangguohao" alt="avatar" /></a>
-    <a href="https://github.com/ooooo-create"><img class="avatar" src="https://avatars.githubusercontent.com/ooooo-create" alt="avatar" /></a>
-    <a href="https://github.com/megemini"><img class="avatar" src="https://avatars.githubusercontent.com/megemini" alt="avatar" /></a>
-    <a href="https://github.com/DUCH714"><img class="avatar" src="https://avatars.githubusercontent.com/DUCH714" alt="avatar" /></a>
-    <a href="https://github.com/zlynna"><img class="avatar" src="https://avatars.githubusercontent.com/zlynna" alt="avatar" /></a>
-    <a href="https://github.com/jjyaoao"><img class="avatar" src="https://avatars.githubusercontent.com/jjyaoao" alt="avatar" /></a>
-    <a href="https://github.com/jiamingkong"><img class="avatar" src="https://avatars.githubusercontent.com/jiamingkong" alt="avatar" /></a>
-    <a href="https://github.com/Liyulingyue"><img class="avatar" src="https://avatars.githubusercontent.com/Liyulingyue" alt="avatar" /></a>
-    <a href="https://github.com/DrRyanHuang"><img class="avatar" src="https://avatars.githubusercontent.com/DrRyanHuang" alt="avatar" /></a>
-    <a href="https://github.com/zbt78"><img class="avatar" src="https://avatars.githubusercontent.com/zbt78" alt="avatar" /></a>
-    <a href="https://github.com/Gxinhu"><img class="avatar" src="https://avatars.githubusercontent.com/Gxinhu" alt="avatar" /></a>
-    <a href="https://github.com/XYM-1"><img class="avatar" src="https://avatars.githubusercontent.com/XYM-1" alt="avatar" /></a>
-    <a href="https://github.com/xusuyong"><img class="avatar" src="https://avatars.githubusercontent.com/xusuyong" alt="avatar" /></a>
-    <a href="https://github.com/DrownFish19"><img class="avatar" src="https://avatars.githubusercontent.com/DrownFish19" alt="avatar" /></a>
-    <a href="https://github.com/NKNaN"><img class="avatar" src="https://avatars.githubusercontent.com/NKNaN" alt="avatar" /></a>
-    <a href="https://github.com/ruoyunbai"><img class="avatar" src="https://avatars.githubusercontent.com/ruoyunbai" alt="avatar" /></a>
-    <a href="https://github.com/sanbuphy"><img class="avatar" src="https://avatars.githubusercontent.com/sanbuphy" alt="avatar" /></a>
-    <a href="https://github.com/ccsuzzh"><img class="avatar" src="https://avatars.githubusercontent.com/ccsuzzh" alt="avatar" /></a>
-    <a href="https://github.com/enkilee"><img class="avatar" src="https://avatars.githubusercontent.com/enkilee" alt="avatar" /></a>
-    <a href="https://github.com/GreatV"><img class="avatar" src="https://avatars.githubusercontent.com/GreatV" alt="avatar" /></a>
+    <div id="contributors"></div>
 
 ## 🤝合作单位
 

docs/javascripts/contributors.js

@@ -0,0 +1,26 @@
+const owner = 'PaddlePaddle';
+const repo = 'PaddleScience';
+
+fetch(`https://api.github.com/repos/${owner}/${repo}/contributors?per_page=65&page=1`, {
+    headers: {
+        "Accept": "application/vnd.github.v3+json"
+    }
+})
+.then(response => {
+    if (!response.ok) {
+        throw new Error(`HTTP error! status: ${response.status}`);
+    }
+    return response.json();
+})
+.then(data => {
+    const contributorsDiv = document.getElementById('contributors');
+    data.forEach(contributor => {
+        const a = document.createElement('a');
+        a.href = `https://github.com/${contributor.login}`;
+        a.innerHTML = `<img class="avatar" src="${contributor.avatar_url}" alt="avatar" />`;
+        contributorsDiv.appendChild(a);
+    });
+})
+.catch((error) => {
+    console.error('Fetching contributors failed:', error);
+});

mkdocs.yml

@@ -211,6 +211,7 @@ markdown_extensions:
 extra_javascript:
   - javascripts/bd_statistics.js
   - javascripts/mathjax.js
+  - javascripts/contributors.js
   - https://polyfill.io/v3/polyfill.min.js?features=es6
   - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
 

ppsci/equation/pde/base.py

@@ -85,9 +85,6 @@ def add_equation(self, name: str, equation: Callable):
     def parameters(self) -> List[paddle.Tensor]:
         """Return learnable parameters contained in PDE.
 
-        Args:
-            None
-
         Returns:
             List[Tensor]: A list of learnable parameters.
 
@@ -106,9 +103,6 @@ def parameters(self) -> List[paddle.Tensor]:
     def state_dict(self) -> Dict[str, paddle.Tensor]:
         """Return named learnable parameters in dict.
 
-        Args:
-            None
-
         Returns:
             Dict[str, Tensor]: A dict of states(str) and learnable parameters(Tensor).
 
@@ -125,14 +119,16 @@ def state_dict(self) -> Dict[str, paddle.Tensor]:
 
         return self.learnable_parameters.state_dict()
 
-    def set_state_dict(self, state_dict: Dict[str, paddle.Tensor]):
+    def set_state_dict(
+        self, state_dict: Dict[str, paddle.Tensor]
+    ) -> Tuple[List[str], List[str]]:
         """Set state dict from dict.
 
         Args:
             state_dict (Dict[str, paddle.Tensor]): The state dict to be set.
 
         Returns:
-            None
+            Tuple[List[str], List[str]]: List of missing_keys and unexpected_keys.
 
         Examples:
             >>> import paddle
@@ -148,7 +144,7 @@ def set_state_dict(self, state_dict: Dict[str, paddle.Tensor]):
             Tensor(shape=[], dtype=float64, place=Place(gpu:0), stop_gradient=False,
                    0.))])
         """
-        self.learnable_parameters.set_state_dict(state_dict)
+        return self.learnable_parameters.set_state_dict(state_dict)
 
     def __str__(self):
         return ", ".join(

ppsci/geometry/geometry.py

@@ -659,11 +659,8 @@ def __and__(self, other: "Geometry") -> "Geometry":
     def __str__(self) -> str:
         """Return the name of class.
 
-        Args:
-            None.
-
         Returns:
-            str: The name of class.
+            str: Meta information of geometry.
 
         Examples:
             >>> import ppsci

ppsci/geometry/timedomain.py

@@ -75,14 +75,14 @@ def __init__(
         elif timestamps is not None:
             self.num_timestamps = len(timestamps)
 
-    def on_initial(self, t: np.ndarray):
+    def on_initial(self, t: np.ndarray) -> np.ndarray:
         """Check if a specific time is on the initial time point.
 
         Args:
             t (np.ndarray): The time to be checked.
 
         Returns:
-            bool: True or False for whether the specific time is on the initial time point.
+            np.ndarray: Bool numpy array of whether the specific time is on the initial time point.
 
         Examples:
             >>> import paddle
@@ -132,7 +132,7 @@ def boundary_normal(self, x):
         normal = self.geometry.boundary_normal(x[:, 1:])
         return np.hstack((x[:, :1], normal))
 
-    def uniform_points(self, n: int, boundary: bool = True):
+    def uniform_points(self, n: int, boundary: bool = True) -> np.ndarray:
         """Uniform points on the spatial-temporal domain.
         Geometry volume ~ bbox.
         Time volume ~ diam.
@@ -202,7 +202,7 @@ def uniform_points(self, n: int, boundary: bool = True):
 
     def random_points(
         self, n: int, random: str = "pseudo", criteria: Optional[Callable] = None
-    ):
+    ) -> np.ndarray:
         """Generate random points on the spatial-temporal domain.
 
         Args:
@@ -347,7 +347,9 @@ def random_points(
         t = np.random.permutation(t)
         return np.hstack((t, x))
 
-    def uniform_boundary_points(self, n: int, criteria: Optional[Callable] = None):
+    def uniform_boundary_points(
+        self, n: int, criteria: Optional[Callable] = None
+    ) -> np.ndarray:
         """Uniform boundary points on the spatial-temporal domain.
         Geometry surface area ~ bbox.
         Time surface area ~ diam.
@@ -427,7 +429,7 @@ def uniform_boundary_points(self, n: int, criteria: Optional[Callable] = None):
 
     def random_boundary_points(
         self, n: int, random: str = "pseudo", criteria: Optional[Callable] = None
-    ):
+    ) -> np.ndarray:
         """Random boundary points on the spatial-temporal domain.
 
         Args:
@@ -621,7 +623,7 @@ def random_boundary_points(
             else:
                 return t_x
 
-    def uniform_initial_points(self, n: int):
+    def uniform_initial_points(self, n: int) -> np.ndarray:
         """Generate evenly distributed point coordinates on the spatial-temporal domain at the initial moment.
 
         Args:
@@ -645,7 +647,7 @@ def uniform_initial_points(self, n: int):
             x = x[:n]
         return np.hstack((np.full([n, 1], t, dtype=paddle.get_default_dtype()), x))
 
-    def random_initial_points(self, n: int, random: str = "pseudo"):
+    def random_initial_points(self, n: int, random: str = "pseudo") -> np.ndarray:
         """Generate randomly distributed point coordinates on the spatial-temporal domain at the initial moment.
 
         Args:
@@ -668,7 +670,9 @@ def random_initial_points(self, n: int, random: str = "pseudo"):
         t = self.timedomain.t0
         return np.hstack((np.full([n, 1], t, dtype=paddle.get_default_dtype()), x))
 
-    def periodic_point(self, x: Dict[str, np.ndarray], component: int):
+    def periodic_point(
+        self, x: Dict[str, np.ndarray], component: int
+    ) -> Dict[str, np.ndarray]:
         """process given point coordinates to satisfy the periodic boundary conditions of the geometry.
 
         Args:
@@ -704,7 +708,7 @@ def sample_initial_interior(
         criteria: Optional[Callable] = None,
         evenly: bool = False,
         compute_sdf_derivatives: bool = False,
-    ):
+    ) -> Dict[str, np.ndarray]:
         """Sample random points in the time-geometry and return those meet criteria.
 
         Args: