[Fix] Fix doctest

ppsci/arch/amgnet.py

@@ -579,7 +579,9 @@ class AMGNet(nn.Layer):
 
     Examples:
         >>> import ppsci
-        >>> model = ppsci.arch.AMGNet(("input", ), ("pred", ), 5, 3, 64, 2)
+        >>> model = ppsci.arch.AMGNet(
+        ...     ("input", ), ("pred", ), 5, 3, 64, 2, "sum", 6, "norm",
+        ... )
     """
 
     def __init__(

ppsci/arch/chip_deeponets.py

@@ -60,7 +60,7 @@ class ChipDeepONets(base.Arch):
 
     Examples:
         >>> import ppsci
-        >>> model = ppsci.arch.ChipDeepONet(
+        >>> model = ppsci.arch.ChipDeepONets(
         ...     ('u',),
         ...     ('bc',),
         ...     ('bc_data',),

ppsci/arch/epnn.py

@@ -65,12 +65,12 @@ class Epnn(base.Arch):
 
     Examples:
         >>> import ppsci
-        >>> ann_node_sizes_state = [1]
+        >>> ann_node_sizes_state = [1, 20]
         >>> model = ppsci.arch.Epnn(
         ...     ("x",),
         ...     ("y",),
         ...     node_sizes=ann_node_sizes_state,
-        ...     activations=("leaky_relu"),
+        ...     activations=("leaky_relu",),
         ...     drop_p=0.0,
         ... )
     """
@@ -84,7 +84,9 @@ def __init__(
         drop_p: float,
     ):
         super().__init__()
-        self.active_func = [act_mod.get_activation(i) for i in activations]
+        self.active_func = [
+            act_mod.get_activation(act_name) for act_name in activations
+        ]
         self.node_sizes = node_sizes
         self.drop_p = drop_p
         self.layers = []

ppsci/arch/he_deeponets.py

@@ -52,7 +52,7 @@ class HEDeepONets(base.Arch):
 
     Examples:
         >>> import ppsci
-        >>> model = ppsci.arch.HEDeepONet(
+        >>> model = ppsci.arch.HEDeepONets(
         ...     ('qm_h',),
         ...     ('qm_c',),
         ...     ("x",'t'),

ppsci/constraint/boundary_constraint.py

@@ -69,7 +69,7 @@ class BoundaryConstraint(base.Constraint):
         ...     },
         ...     ppsci.loss.MSELoss("mean"),
         ...     name="BC",
-        ... )
+        ... ) # doctest: +SKIP
     """
 
     def __init__(

ppsci/constraint/initial_constraint.py

@@ -74,7 +74,7 @@ class InitialConstraint(base.Constraint):
         ...     },
         ...     ppsci.loss.MSELoss("mean"),
         ...     name="IC",
-        ... )
+        ... ) # doctest: +SKIP
     """
 
     def __init__(

ppsci/constraint/integral_constraint.py

@@ -71,7 +71,7 @@ class IntegralConstraint(base.Constraint):
         ...     },
         ...     ppsci.loss.MSELoss("mean"),
         ...     name="IgC",
-        ... )
+        ... ) # doctest: +SKIP
     """
 
     def __init__(

ppsci/constraint/interior_constraint.py

@@ -71,7 +71,7 @@ class InteriorConstraint(base.Constraint):
         ...     },
         ...     ppsci.loss.MSELoss("mean"),
         ...     name="EQ",
-        ... )
+        ... ) # doctest: +SKIP
     """
 
     def __init__(

ppsci/data/dataset/era5_dataset.py

@@ -175,10 +175,10 @@ class ERA5SampledDataset(io.Dataset):
         ...     "label_keys": ("output",),
         ... )  # doctest: +SKIP
         >>> # get the length of the dataset
-        >>> dataset_size = len(dataset)
+        >>> dataset_size = len(dataset)  # doctest: +SKIP
         >>> # get the first sample of the data
-        >>> first_sample = dataset[0]
-        >>> print("First sample:", first_sample)
+        >>> first_sample = dataset[0]  # doctest: +SKIP
+        >>> print("First sample:", first_sample)  # doctest: +SKIP
     """
 
     def __init__(

ppsci/data/dataset/mrms_dataset.py

@@ -187,10 +187,10 @@ class MRMSSampledDataset(io.Dataset):
         ...     "num_total_timestamps": 29,
         ... )  # doctest: +SKIP
         >>> # get the length of the dataset
-        >>> dataset_size = len(dataset)
+        >>> dataset_size = len(dataset)  # doctest: +SKIP
         >>> # get the first sample of the data
-        >>> first_sample = dataset[0]
-        >>> print("First sample:", first_sample)
+        >>> first_sample = dataset[0]  # doctest: +SKIP
+        >>> print("First sample:", first_sample)  # doctest: +SKIP
     """
 
     def __init__(

ppsci/data/dataset/vtu_dataset.py

@@ -39,15 +39,15 @@ class VtuDataset(io.Dataset):
             transform(s).
 
     Examples:
-        >>> from ppsci.dataset import VtuDataset
+        >>> from ppsci.data.dataset import VtuDataset
 
         >>> dataset = VtuDataset(file_path='example.vtu') # doctest: +SKIP
 
         >>> # get the length of the 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
     """
 
     # Whether support batch indexing for speeding up fetching process.

ppsci/data/process/transform/preprocess.py

@@ -32,18 +32,20 @@ class Translate:
     Examples:
         >>> import ppsci
         >>> import numpy as np
-        >>> translate = ppsci.data.transform.Translate({"x": 1.0, "y": -1.0})
-        >>> input_data = np.array([[[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0], [7.0, 8.0]]])
-        >>> input_dict = {"x": input_data[:,:,0], "y": input_data[:,:,1]}
+
+        >>> input_dict = {"x": np.array([5.0, 10.0]), "y": np.array([20.0, 40.0])}
         >>> label_dict = {"x": np.array([1.0, 2.0]), "y": np.array([3.0, 4.0])}
         >>> weight_dict = {"x": np.array([10.0, 20.0]), "y": np.array([30.0, 40.0])}
+
+        >>> translate = ppsci.data.transform.Translate({"x": 1.0, "y": -1.0})
         >>> translated_input_dict, translated_label_dict, translated_weight_dict = translate(input_dict, label_dict, weight_dict)
+
         >>> print(translated_input_dict)
-        {"x": array([[2., 3.], [4., 5.]]), "y": array([[0., 1.], [2., 3.]])}
+        {'x': array([ 6., 11.]), 'y': array([19., 39.])}
         >>> print(translated_label_dict)
-        {"x": array([2., 3.]), "y": array([3., 4.])}
+        {'x': array([1., 2.]), 'y': array([3., 4.])}
         >>> print(translated_weight_dict)
-        {"x": array([10., 20.]), "y": array([30., 40.])}
+        {'x': array([10., 20.]), 'y': array([30., 40.])}
     """
 
     def __init__(self, offset: Dict[str, float]):
@@ -293,23 +295,24 @@ class FunctionalTransform:
         >>> # The function will perform some transformations on the data in data_dict, convert all labels in label_dict to uppercase,
         >>> # and modify the weights in weight_dict by dividing each weight by 10.
         >>> # Finally, it returns the transformed data, labels, and weights as a tuple.
+        >>> import ppsci
         >>> def transform_func(data_dict, label_dict, weight_dict):
         ...     for key in data_dict:
         ...         data_dict[key] = data_dict[key] * 2
         ...     for key in label_dict:
-        ...         label_dict[key] = label_dict[key].upper()
+        ...         label_dict[key] = label_dict[key] + 1.0
         ...     for key in weight_dict:
         ...         weight_dict[key] = weight_dict[key] / 10
         ...     return data_dict, label_dict, weight_dict
         >>> transform = ppsci.data.transform.FunctionalTransform(transform_func)
         >>> # Define some sample data, labels, and weights
         >>> data = {'feature1': np.array([1, 2, 3]), 'feature2': np.array([4, 5, 6])}
-        >>> label = {'class': 'class1', 'instance': 'instance1'}
+        >>> label = {'class': 0.0, 'instance': 0.1}
         >>> weight = {'weight1': 0.5, 'weight2': 0.5}
         >>> # Apply the transform function to the data, labels, and weights using the FunctionalTransform instance
         >>> transformed_data = transform(data, label, weight)
         >>> print(transformed_data)
-        ({'feature1': [2, 4, 6], 'feature2': [8, 10, 12]}, {'class': 'CLASS1', 'instance': 'INSTANCE1'}, {'weight1': 0.5, 'weight2': 0.5})
+        ({'feature1': array([2, 4, 6]), 'feature2': array([ 8, 10, 12])}, {'class': 1.0, 'instance': 1.1}, {'weight1': 0.05, 'weight2': 0.05})
     """
 
     def __init__(

ppsci/equation/fpde/fractional_poisson.py

@@ -38,7 +38,9 @@ class FractionalPoisson(PDE):
 
     Examples:
         >>> import ppsci
-        >>> fpde = ppsci.equation.FractionalPoisson(ALPHA, geom["disk"], [8, 100])
+        >>> geom_disk = ppsci.geometry.Disk([0, 0], 1)
+        >>> ALPHA = 0.5
+        >>> fpde = ppsci.equation.FractionalPoisson(ALPHA, geom_disk, [8, 100])
     """
     dtype = paddle.get_default_dtype()
 

ppsci/experimental/math_module.py

@@ -427,34 +427,34 @@ def trapezoid_integrate(
         >>> res = ppsci.experimental.trapezoid_integrate(y)
         >>> print(res)
         Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
-            [2., 8.])
+               [2., 8.])
         >>> res = ppsci.experimental.trapezoid_integrate(y, mode="cumsum")
         >>> print(res)
         Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
-            [[0.50000000, 2.        ],
-            [3.50000000, 8.        ]])
+               [[0.50000000, 2.        ],
+                [3.50000000, 8.        ]])
         >>> res = ppsci.experimental.trapezoid_integrate(
-            y, x=paddle.to_tensor([[0, 1, 2], [3, 4, 5]], dtype="float32")
-        )
+        ...     y, x=paddle.to_tensor([[0, 1, 2], [3, 4, 5]], dtype="float32")
+        ... )
         >>> print(res)
         Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
-            [2., 8.])
+               [2., 8.])
         >>> res = ppsci.experimental.trapezoid_integrate(
-            y, x=paddle.to_tensor([0, 1], dtype="float32"), axis=0
-        )
+        ...     y, x=paddle.to_tensor([0, 1], dtype="float32"), axis=0
+        ... )
         >>> print(res)
         Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
-            [1.50000000, 2.50000000, 3.50000000])
+               [1.50000000, 2.50000000, 3.50000000])
         >>> res = ppsci.experimental.trapezoid_integrate(
-            y, x=paddle.to_tensor([0, 1, 2], dtype="float32"), axis=1
-        )
+        ...     y, x=paddle.to_tensor([0, 1, 2], dtype="float32"), axis=1
+        ... )
         >>> print(res)
         Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
-            [2., 8.])
+               [2., 8.])
         >>> res = ppsci.experimental.trapezoid_integrate(y, dx=2)
         >>> print(res)
         Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
-            [4. , 16.])
+               [4. , 16.])
     """
     if mode == "sum":
         return paddle.trapezoid(y, x, dx, axis)

ppsci/geometry/mesh.py

@@ -75,11 +75,11 @@ def from_pymesh(cls, mesh: "pymesh.Mesh") -> "Mesh":
 
         Examples:
             >>> import ppsci
-            >>> import pymesh
-            >>> import numpy as np
-            >>> box = pymesh.generate_box_mesh(np.array([0, 0, 0]), np.array([1, 1, 1]))
-            >>> mesh = ppsci.geometry.Mesh.from_pymesh(box)
-            >>> print(mesh.vertices)
+            >>> import pymesh  # doctest: +SKIP
+            >>> import numpy as np  # doctest: +SKIP
+            >>> 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.]
@@ -201,11 +201,11 @@ def translate(self, translation: np.ndarray, relative: bool = True) -> "Mesh":
 
         Examples:
             >>> import ppsci
-            >>> import pymesh
+            >>> import pymesh  # doctest: +SKIP
             >>> import numpy as np
-            >>> box = pymesh.generate_box_mesh(np.array([0, 0, 0]), np.array([1, 1, 1]))
-            >>> mesh = ppsci.geometry.Mesh(box)
-            >>> print(mesh.vertices)
+            >>> 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.]
@@ -214,7 +214,7 @@ def translate(self, translation: np.ndarray, relative: bool = True) -> "Mesh":
              [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.
+            >>> 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. ]
@@ -223,7 +223,7 @@ def translate(self, translation: np.ndarray, relative: bool = True) -> "Mesh":
              [ 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
+            >>> 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]
@@ -274,11 +274,11 @@ def scale(
 
         Examples:
             >>> import ppsci
-            >>> import pymesh
+            >>> import pymesh  # doctest: +SKIP
             >>> import numpy as np
-            >>> box = pymesh.generate_box_mesh(np.array([0, 0, 0]), np.array([1, 1, 1]))
-            >>> mesh = ppsci.geometry.Mesh(box)
-            >>> print(mesh.vertices)
+            >>> 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.]
@@ -287,8 +287,8 @@ def scale(
              [1. 0. 1.]
              [1. 1. 1.]
              [0. 1. 1.]]
-            >>> mesh = mesh.scale(2, (0.25, 0.5, 0.75))
-            >>> print(mesh.vertices)
+            >>> 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]

ppsci/solver/solver.py

@@ -103,7 +103,7 @@ class Solver:
         ...     },
         ...     ppsci.loss.MSELoss("mean"),
         ...     name="EQ",
-        ... )
+        ... )  # doctest: +SKIP
         >>> solver = ppsci.solver.Solver(
         ...     model,
         ...     {"EQ": pde_constraint},
@@ -598,8 +598,8 @@ def predict(
             >>> input_dict = {'x': paddle.rand([32, 1]),
             ...               'y': paddle.rand([32, 1])}
             >>> pred = solver.predict(input_dict) # doctest: +SKIP
-            >>> for k, v in pred.items():
-            ...     print(k, v.shape)
+            >>> for k, v in pred.items(): # doctest: +SKIP
+            ...     print(k, v.shape) # doctest: +SKIP
             u [32, 1]
             v [32, 1]
         """

ppsci/utils/misc.py

@@ -212,9 +212,12 @@ class Timer(ContextDecorator):
 
         >>> timer = misc.Timer("cost_of_func", auto_print=False)
         >>> timer.start()
+        >>> def func():
+        ...     w = sum(range(0, 10))
         >>> func()
         >>> timer.end()
         >>> print(f"time cost of 'cost_of_func' is {timer.interval:.2f}")
+        time cost of 'cost_of_func' is 0.00
     """
 
     interval: float  # Time cost for code within Timer context

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"

ppsci/visualize/radar.py

@@ -38,6 +38,7 @@ class VisualizerRadar(base.Visualizer):
 
     Examples:
         >>> import ppsci
+        >>> import paddle
         >>> frames_tensor = paddle.randn([1, 29, 512, 512, 2])
         >>> visualizer =  ppsci.visualize.VisualizerRadar(
         ...     {"input": frames_tensor},