Merge branch 'develop' into fix_doctest

docs/zh/examples/chip_heat.md

@@ -48,7 +48,7 @@ $$
 $$
 
 <figure markdown>
-  ![domain_chip.pdf](https://paddle-org.bj.bcebos.com/paddlescience/docs/ChipHeat/domain_chip.pdf){ loading=lazy style="height:80%;width:80%" align="center" }
+  ![domain_chip.pdf](https://paddle-org.bj.bcebos.com/paddlescience/docs/ChipHeat/domain_chip.PNG){ loading=lazy style="height:80%;width:80%" align="center" }
   <figcaption> 内部具有随机热源分布的 2D 芯片模拟区域，边界上可以为任意的边界条件。</figcaption>
 </figure>
 
@@ -94,7 +94,7 @@ PI-DeepONet模型，将 DeepONet 和 PINN 方法相结合，是一种结合了
 对于芯片热仿真问题，PI-DeepONet 模型可以表示为如图所示的模型结构：
 
 <figure markdown>
-  ![pi_deeponet.pdf](https://paddle-org.bj.bcebos.com/paddlescience/docs/ChipHeat/pi_deeponet.pdf){ loading=lazy style="height:80%;width:80%" align="center" }
+  ![pi_deeponet.pdf](https://paddle-org.bj.bcebos.com/paddlescience/docs/ChipHeat/pi_deeponet.PNG){ loading=lazy style="height:80%;width:80%" align="center" }
 </figure>
 
 如图所示，我们一共使用了 3 个分支网络和一个主干网络，分支网络分别输入边界类型指标、随机热源分布 $S(x, y)$ 和边界函数 $Q(x, y)$，主干网络输入二维坐标点坐标信息。每个分支网和主干网均输出 $q$ 维特征向量，通过 Hadamard（逐元素）乘积组合所有这些输出特征，然后将所得向量相加为预测温度场的标量输出。

ppsci/data/dataset/era5_dataset.py

@@ -178,7 +178,7 @@ class ERA5SampledDataset(io.Dataset):
         >>> dataset_size = len(dataset)  # doctest: +SKIP
         >>> # get the first sample of the data
         >>> first_sample = dataset[0]  # doctest: +SKIP
-        >>> print("First sample:", first_sample)
+        >>> print("First sample:", first_sample)  # doctest: +SKIP
     """
 
     def __init__(

ppsci/geometry/mesh.py

@@ -72,6 +72,22 @@ def from_pymesh(cls, mesh: "pymesh.Mesh") -> "Mesh":
 
         Returns:
             Mesh: Instantiated ppsci.geometry.Mesh object.
+
+        Examples:
+            >>> import ppsci
+            >>> import pymesh
+            >>> import numpy as np
+            >>> box = pymesh.generate_box_mesh(np.array([0, 0, 0]), np.array([1, 1, 1]))  # doctest: +SKIP
+            >>> mesh = ppsci.geometry.Mesh.from_pymesh(box)  # doctest: +SKIP
+            >>> print(mesh.vertices)  # doctest: +SKIP
+            [[0. 0. 0.]
+             [1. 0. 0.]
+             [1. 1. 0.]
+             [0. 1. 0.]
+             [0. 0. 1.]
+             [1. 0. 1.]
+             [1. 1. 1.]
+             [0. 1. 1.]]
         """
         # check if pymesh is installed when using Mesh Class
         if not checker.dynamic_import_to_globals(["pymesh"]):
@@ -182,6 +198,40 @@ def translate(self, translation: np.ndarray, relative: bool = True) -> "Mesh":
 
         Returns:
             Mesh: Translated Mesh object.
+
+        Examples:
+            >>> import ppsci
+            >>> import pymesh
+            >>> import numpy as np
+            >>> box = pymesh.generate_box_mesh(np.array([0, 0, 0]), np.array([1, 1, 1]))  # doctest: +SKIP
+            >>> mesh = ppsci.geometry.Mesh(box)  # doctest: +SKIP
+            >>> print(mesh.vertices)  # doctest: +SKIP
+            [[0. 0. 0.]
+             [1. 0. 0.]
+             [1. 1. 0.]
+             [0. 1. 0.]
+             [0. 0. 1.]
+             [1. 0. 1.]
+             [1. 1. 1.]
+             [0. 1. 1.]]
+            >>> print(mesh.translate((-0.5, 0, 0.5), False).vertices) # the center is moved to the translation vector.  # doctest: +SKIP
+            [[-1.  -0.5  0. ]
+             [ 0.  -0.5  0. ]
+             [ 0.   0.5  0. ]
+             [-1.   0.5  0. ]
+             [-1.  -0.5  1. ]
+             [ 0.  -0.5  1. ]
+             [ 0.   0.5  1. ]
+             [-1.   0.5  1. ]]
+            >>> print(mesh.translate((-0.5, 0, 0.5), True).vertices) # the translation vector is directly added to the geometry coordinates  # doctest: +SKIP
+            [[-0.5  0.   0.5]
+             [ 0.5  0.   0.5]
+             [ 0.5  1.   0.5]
+             [-0.5  1.   0.5]
+             [-0.5  0.   1.5]
+             [ 0.5  0.   1.5]
+             [ 0.5  1.   1.5]
+             [-0.5  1.   1.5]]
         """
         vertices = np.array(self.vertices, dtype=paddle.get_default_dtype())
         faces = np.array(self.faces)
@@ -221,6 +271,32 @@ def scale(
 
         Returns:
             Mesh: Scaled Mesh object.
+
+        Examples:
+            >>> import ppsci
+            >>> import pymesh
+            >>> import numpy as np
+            >>> box = pymesh.generate_box_mesh(np.array([0, 0, 0]), np.array([1, 1, 1]))  # doctest: +SKIP
+            >>> mesh = ppsci.geometry.Mesh(box)  # doctest: +SKIP
+            >>> print(mesh.vertices)  # doctest: +SKIP
+            [[0. 0. 0.]
+             [1. 0. 0.]
+             [1. 1. 0.]
+             [0. 1. 0.]
+             [0. 0. 1.]
+             [1. 0. 1.]
+             [1. 1. 1.]
+             [0. 1. 1.]]
+            >>> mesh = mesh.scale(2, (0.25, 0.5, 0.75))  # doctest: +SKIP
+            >>> print(mesh.vertices)  # doctest: +SKIP
+            [[-0.25 -0.5  -0.75]
+             [ 1.75 -0.5  -0.75]
+             [ 1.75  1.5  -0.75]
+             [-0.25  1.5  -0.75]
+             [-0.25 -0.5   1.25]
+             [ 1.75 -0.5   1.25]
+             [ 1.75  1.5   1.25]
+             [-0.25  1.5   1.25]]
         """
         vertices = np.array(self.vertices, dtype=paddle.get_default_dtype())
         faces = np.array(self.faces, dtype=paddle.get_default_dtype())

ppsci/utils/writer.py

@@ -67,7 +67,7 @@ def save_csv_file(
         ...     use_header=True,
         ...     delimiter=",",
         ...     encoding="utf-8",
-        ... )
+        ... )  # doctest: +SKIP
 
         >>> # == test.csv ==
         >>> # A,B
@@ -150,7 +150,7 @@ def save_tecplot_file(
         ...    num_y=3,
         ...    alias_dict={"X": "x", "Y": "y"},
         ...    num_timestamps=2,
-        ... )
+        ... )  # doctest: +SKIP
         >>> # == test_t-0.dat ==
         >>> # title = "./test_t-0.dat"
         >>> # variables = "X", "Y"