Fix typos (#1150)

Author: co63oc

docs/zh/examples/drivaernetplusplus.md

@@ -345,7 +345,7 @@ export ep2=-----------------ID-----------------
 
 17. 下载数据，将 PubDAS 中的 License.txt 从 ep1 数据源传送到 ep2（自己路径下，第 8，9 步设置）。
 ``` sh
-# here is defaut path (your home path)
+# here is default path (your home path)
 globus transfer $ep1:License.txt $ep2:/~/License.txt
 Message: The transfer has been accepted and a task has been created and queued for execution
 Task ID: -----------------传送任务ID-----------------

docs/zh/examples/phygeonet.md

@@ -214,7 +214,7 @@ T=3
 ![image](https://paddle-org.bj.bcebos.com/paddlescience/docs/PhyGeoNet/heat_equation_bc_2.png)
 
 T=6
-![iamge](https://paddle-org.bj.bcebos.com/paddlescience/docs/PhyGeoNet/heat_equation_bc_3.png)
+![image](https://paddle-org.bj.bcebos.com/paddlescience/docs/PhyGeoNet/heat_equation_bc_3.png)
 
 ## 6. 总结
 

docs/zh/examples/tempoGAN.md

@@ -146,7 +146,7 @@ examples/tempoGAN/conf/tempogan.yaml:27:37
 --8<--
 ```
 
-注意到其中包含 3 个 bool 类型的变量 `use_amp`、`use_spatialdisc` 和 `use_tempodisc`，它们分别表示是否使用混合精度训练(AMP)、是否使用 Discriminator 和是否使用 Discriminator_tempo，当 `use_spatialdisc` 和 `use_tempodisc` 都被设置为 `False` 时，本问题的网络结构将会变为一个单纯的 Genrator 模型，不再是 GAN 网络了。
+注意到其中包含 3 个 bool 类型的变量 `use_amp`、`use_spatialdisc` 和 `use_tempodisc`，它们分别表示是否使用混合精度训练(AMP)、是否使用 Discriminator 和是否使用 Discriminator_tempo，当 `use_spatialdisc` 和 `use_tempodisc` 都被设置为 `False` 时，本问题的网络结构将会变为一个单纯的 Generator 模型，不再是 GAN 网络了。
 
 同时需要指定训练轮数和学习率等超参数，注意由于 GAN 网络训练流程与一般单个模型的网络不同，`EPOCHS` 的设置也有所不同。
 

examples/adr/README.md

@@ -1 +1 @@
-souce code will be available after code already orgnized in the near future.
+source code will be available after code already orgnized in the near future.

examples/moflow/optimize_moflow.py

@@ -72,7 +72,7 @@ def load_property_csv(filepath, normalize=True):
 
 
 def smiles_to_adj(mol_smiles, data_name="qm9"):
-    """Use simles to adj, atoms
+    """Use smiles to adj, atoms
 
     Args:
         mol_smiles: eg. CC(C)(C)c1ccc2occ(CC(=O)Nc3ccccc3F)c2c1

jointContribution/CFDGCN/mesh_utils.py

@@ -268,7 +268,7 @@ def delauney(x):
         face = np.array([[0, 1, 2]])
     else:
         raise ValueError(
-            f"Not enough points to contruct Delaunay triangulation, got {pos.shape[0]} "
+            f"Not enough points to construct Delaunay triangulation, got {pos.shape[0]} "
             + "but expected at least 3"
         )
 

jointContribution/PIRBN/README.md

@@ -13,13 +13,13 @@ Inspired by findings, we proposed the PIRBN, which can exhibit the local propert
 
 Numerical examples include:
 
- - 1D sine funtion (**Eq. 13** in the manuscript)
+ - 1D sine function (**Eq. 13** in the manuscript)
 
       **PDE**: $\frac{\partial^2 }{\partial x^2}u(x)-4\mu^2\pi^2 sin(2\mu\pi(x))=0, x\in[0,1]$
 
       **BC**:  $u(0)=u(1)=0.$
 
- - 1D sine funtion (**Eq. 15** in the manuscript)
+ - 1D sine function (**Eq. 15** in the manuscript)
       **PDE**: $\frac{\partial^2 }{\partial x^2}u(x-100)-4\mu^2\pi^2 sin(2\mu\pi(x-100))=0, x\in[100,101]$
 
       **BC**:  $u(100)=u(101)=0.$
@@ -32,7 +32,7 @@ For more details in terms of mathematical proofs and numerical examples, please
 
 <https://github.com/JinshuaiBai/PIRBN>
 
-## Enviornmental settings
+## Environmental settings
 
 ``` shell
 pip install -r requirements.txt

jointContribution/PIRBN/jacobian_function.py

@@ -24,9 +24,9 @@ def jacobian(y, x):
     J = paddle.zeros(J_shape)
     J_flat = flat(
         J, start_axis=0, stop_axis=len(y.shape) - 1
-    )  # partialy flatten as y_flat
+    )  # partially flatten as y_flat
     for i, y_i in enumerate(y):
-        assert y_i.size == 1, f"y_i.size shoule be 1, but got {y_i.size}"
+        assert y_i.size == 1, f"y_i.size should be 1, but got {y_i.size}"
         grad = paddle.grad(y_i, x, allow_unused=True)[
             0
         ]  # grad[i] == sum by j (dy[j] / dx[i])

jointContribution/PIRBN/rbn_net.py

@@ -133,7 +133,7 @@ class Activation(paddle.nn.Layer):
     def __init__(self, n_neu, c, n_in, activation_function="gaussian"):
         super(Activation, self).__init__()
         self.activation_function = activation_function
-        # PINN      y = w2 * (tanh(w1 * x + b1)) + b2         w,b are trainable parameters, b is bais
+        # PINN      y = w2 * (tanh(w1 * x + b1)) + b2         w,b are trainable parameters, b is bias
         # PIRBN     y = w * exp(b^2 * |x-c|^2)          w,b are trainable parameters, c is constant, b is not bias
 
         if self.activation_function == "gaussian":

jointContribution/graphcast/datasets.py

@@ -50,7 +50,7 @@ def get_day_progress(
         point at which UNIX time starts).
       longitude: 1D array of longitudes at which day progress is computed.
     Returns:
-      2D array of day progress values normalized to be in the [0, 1) inverval
+      2D array of day progress values normalized to be in the [0, 1) interval
         for each time point at each longitude.
     """
     # [0.0, 1.0) Interval.