change 'num_timestamp' to 'num_timestamps'

ppsci/arch/afno.py

@@ -422,7 +422,7 @@ class AFNONet(base.Arch):
         num_blocks (int, optional): Number of blocks. Defaults to 8.
         sparsity_threshold (float, optional): The value of threshold for softshrink. Defaults to 0.01.
         hard_thresholding_fraction (float, optional): The value of threshold for keep mode. Defaults to 1.0.
-        num_timestamp (int, optional): Number of timestamp. Defaults to 1.
+        num_timestamps (int, optional): Number of timestamp. Defaults to 1.
 
     Examples:
         >>> import ppsci
@@ -445,7 +445,7 @@ def __init__(
         num_blocks: int = 8,
         sparsity_threshold: float = 0.01,
         hard_thresholding_fraction: float = 1.0,
-        num_timestamp: int = 1,
+        num_timestamps: int = 1,
     ):
         super().__init__()
         self.input_keys = input_keys
@@ -457,7 +457,7 @@ def __init__(
         self.out_channels = out_channels
         self.embed_dim = embed_dim
         self.num_blocks = num_blocks
-        self.num_timestamp = num_timestamp
+        self.num_timestamps = num_timestamps
         norm_layer = partial(nn.LayerNorm, epsilon=1e-6)
 
         self.patch_embed = PatchEmbed(
@@ -555,7 +555,7 @@ def forward(self, x):
 
         y = []
         input = x
-        for i in range(self.num_timestamp):
+        for i in range(self.num_timestamps):
             out = self.forward_tensor(input)
             y.append(out)
             input = out

ppsci/data/dataset/era5_dataset.py

@@ -34,7 +34,7 @@ class ERA5Dataset(io.Dataset):
         precip_file_path (Optional[str]): Precipitation data set path. Defaults to None.
         weight_dict (Optional[Dict[str, float]]): Weight dictionary. Defaults to None.
         vars_channel (Optional[Tuple[int, ...]]): The variable channel index in ERA5 dataset. Defaults to None.
-        num_label_timestamp (int, optional): Number of timestamp of label. Defaults to 1.
+        num_label_timestamps (int, optional): Number of timestamp of label. Defaults to 1.
         transforms (Optional[vision.Compose]): Compose object contains sample wise
             transform(s). Defaults to None.
         training (bool, optional): Whether in train mode. Defaults to True.
@@ -56,7 +56,7 @@ def __init__(
         precip_file_path: Optional[str] = None,
         weight_dict: Optional[Dict[str, float]] = None,
         vars_channel: Optional[Tuple[int, ...]] = None,
-        num_label_timestamp: int = 1,
+        num_label_timestamps: int = 1,
         transforms: Optional[vision.Compose] = None,
         training: bool = True,
     ):
@@ -74,7 +74,7 @@ def __init__(
         self.vars_channel = (
             vars_channel if vars_channel is not None else [i for i in range(20)]
         )
-        self.num_label_timestamp = num_label_timestamp
+        self.num_label_timestamps = num_label_timestamps
         self.transforms = transforms
         self.training = training
 
@@ -102,9 +102,9 @@ def __getitem__(self, global_idx):
         local_idx = global_idx % self.n_samples_per_year
         step = 0 if local_idx >= self.n_samples_per_year - 1 else 1
 
-        if self.num_label_timestamp > 1:
-            if local_idx >= self.n_samples_per_year - self.num_label_timestamp:
-                local_idx = self.n_samples_per_year - self.num_label_timestamp - 1
+        if self.num_label_timestamps > 1:
+            if local_idx >= self.n_samples_per_year - self.num_label_timestamps:
+                local_idx = self.n_samples_per_year - self.num_label_timestamps - 1
 
         input_file = self.files[year_idx]
         label_file = (
@@ -124,7 +124,7 @@ def __getitem__(self, global_idx):
 
         input_item = {self.input_keys[0]: input_file[input_idx, self.vars_channel]}
         label_item = {}
-        for i in range(self.num_label_timestamp):
+        for i in range(self.num_label_timestamps):
             if self.precip_file_path is not None:
                 label_item[self.label_keys[i]] = np.expand_dims(
                     label_file[label_idx + i], 0

ppsci/geometry/timedomain.py

@@ -61,9 +61,9 @@ def __init__(
         if time_step is not None:
             if time_step <= 0:
                 raise ValueError(f"time_step({time_step}) must be larger than 0.")
-            self.num_timestamp = int(np.ceil((t1 - t0) / time_step)) + 1
+            self.num_timestamps = int(np.ceil((t1 - t0) / time_step)) + 1
         elif timestamps is not None:
-            self.num_timestamp = len(timestamps)
+            self.num_timestamps = len(timestamps)
 
     def on_initial(self, t):
         return np.isclose(t, self.t0).flatten()
@@ -117,7 +117,7 @@ def uniform_points(self, n, boundary=True):
             nx = int(np.ceil(n / nt))
         elif self.timedomain.timestamps is not None:
             # exclude start time t0
-            nt = self.timedomain.num_timestamp - 1
+            nt = self.timedomain.num_timestamps - 1
             nx = int(np.ceil(n / nt))
         else:
             nx = int(
@@ -205,7 +205,7 @@ def random_points(self, n, random="pseudo", criteria=None):
                 tx = tx[:n]
             return tx
         elif self.timedomain.timestamps is not None:
-            nt = self.timedomain.num_timestamp - 1
+            nt = self.timedomain.num_timestamps - 1
             t = self.timedomain.timestamps[1:]
             nx = int(np.ceil(n / nt))
 
@@ -402,7 +402,7 @@ def random_boundary_points(self, n, random="pseudo", criteria=None):
                 return t_x
         elif self.timedomain.timestamps is not None:
             # exclude start time t0
-            nt = self.timedomain.num_timestamp - 1
+            nt = self.timedomain.num_timestamps - 1
             t = self.timedomain.timestamps[1:]
             nx = int(np.ceil(n / nt))
 

ppsci/validate/geo_validator.py

@@ -91,19 +91,19 @@ def __init__(
         self.output_keys = list(label_dict.keys())
 
         nx = dataloader_cfg["total_size"]
-        self.num_timestamp = 1
+        self.num_timestamps = 1
         # TODO(sensen): simplify code below
         if isinstance(geom, geometry.TimeXGeometry):
-            if geom.timedomain.num_timestamp is not None:
+            if geom.timedomain.num_timestamps is not None:
                 if with_initial:
                     # include t0
-                    self.num_timestamp = geom.timedomain.num_timestamp
+                    self.num_timestamps = geom.timedomain.num_timestamps
                     assert (
-                        nx % self.num_timestamp == 0
-                    ), f"{nx} % {self.num_timestamp} != 0"
-                    nx //= self.num_timestamp
+                        nx % self.num_timestamps == 0
+                    ), f"{nx} % {self.num_timestamps} != 0"
+                    nx //= self.num_timestamps
                     input = geom.sample_interior(
-                        nx * (geom.timedomain.num_timestamp - 1),
+                        nx * (geom.timedomain.num_timestamps - 1),
                         random,
                         criteria,
                         evenly,
@@ -114,13 +114,13 @@ def __init__(
                     }
                 else:
                     # exclude t0
-                    self.num_timestamp = geom.timedomain.num_timestamp - 1
+                    self.num_timestamps = geom.timedomain.num_timestamps - 1
                     assert (
-                        nx % self.num_timestamp == 0
-                    ), f"{nx} % {self.num_timestamp} != 0"
-                    nx //= self.num_timestamp
+                        nx % self.num_timestamps == 0
+                    ), f"{nx} % {self.num_timestamps} != 0"
+                    nx //= self.num_timestamps
                     input = geom.sample_interior(
-                        nx * (geom.timedomain.num_timestamp - 1),
+                        nx * (geom.timedomain.num_timestamps - 1),
                         random,
                         criteria,
                         evenly,

ppsci/visualize/plot.py

@@ -69,21 +69,21 @@
 ]
 
 
-def _save_plot_from_1d_array(filename, coord, value, value_keys, num_timestamp=1):
+def _save_plot_from_1d_array(filename, coord, value, value_keys, num_timestamps=1):
     """Save plot from given 1D data.
 
     Args:
         filename (str): Filename.
         coord (np.ndarray): Coordinate array.
         value (Dict[str, np.ndarray]): Dict of value array.
         value_keys (Tuple[str, ...]): Value keys.
-        num_timestamp (int, optional): Number of timestamps coord/value contains. Defaults to 1.
+        num_timestamps (int, optional): Number of timestamps coord/value contains. Defaults to 1.
     """
-    fig, a = plt.subplots(len(value_keys), num_timestamp, squeeze=False)
+    fig, a = plt.subplots(len(value_keys), num_timestamps, squeeze=False)
     fig.subplots_adjust(hspace=0.8)
 
-    len_ts = len(coord) // num_timestamp
-    for t in range(num_timestamp):
+    len_ts = len(coord) // num_timestamps
+    for t in range(num_timestamps):
         st = t * len_ts
         ed = (t + 1) * len_ts
         coord_t = coord[st:ed]
@@ -96,29 +96,29 @@ def _save_plot_from_1d_array(filename, coord, value, value_keys, num_timestamp=1
                 color=cnames[i],
                 label=key,
             )
-            if num_timestamp > 1:
+            if num_timestamps > 1:
                 a[i][t].set_title(f"{key}(t={t})")
             else:
                 a[i][t].set_title(f"{key}")
             a[i][t].grid()
             a[i][t].legend()
 
-        if num_timestamp == 1:
+        if num_timestamps == 1:
             fig.savefig(filename, dpi=300)
         else:
             fig.savefig(f"{filename}_{t}", dpi=300)
 
-    if num_timestamp == 1:
+    if num_timestamps == 1:
         logger.info(f"1D result is saved to {filename}.png")
     else:
         logger.info(
             f"1D result is saved to {filename}_0.png"
-            f" ~ {filename}_{num_timestamp - 1}.png"
+            f" ~ {filename}_{num_timestamps - 1}.png"
         )
 
 
 def save_plot_from_1d_dict(
-    filename, data_dict, coord_keys, value_keys, num_timestamp=1
+    filename, data_dict, coord_keys, value_keys, num_timestamps=1
 ):
     """Plot dict data as file.
 
@@ -127,7 +127,7 @@ def save_plot_from_1d_dict(
         data_dict (Dict[str, Union[np.ndarray, paddle.Tensor]]): Data in dict.
         coord_keys (Tuple[str, ...]): Tuple of coord key. such as ("x", "y").
         value_keys (Tuple[str, ...]): Tuple of value key. such as ("u", "v").
-        num_timestamp (int, optional): Number of timestamp in data_dict. Defaults to 1.
+        num_timestamps (int, optional): Number of timestamp in data_dict. Defaults to 1.
     """
     space_ndim = len(coord_keys) - int("t" in coord_keys)
     if space_ndim not in [1, 2, 3]:
@@ -149,14 +149,14 @@ def save_plot_from_1d_dict(
             value = [x for x in value]
         value = np.concatenate(value, axis=1)
 
-    _save_plot_from_1d_array(filename, coord, value, value_keys, num_timestamp)
+    _save_plot_from_1d_array(filename, coord, value, value_keys, num_timestamps)
 
 
 def _save_plot_from_2d_array(
     filename: str,
     visu_data: Tuple[np.ndarray, ...],
     visu_keys: Tuple[str, ...],
-    num_timestamp: int = 1,
+    num_timestamps: int = 1,
     stride: int = 1,
     xticks: Optional[Tuple[float, ...]] = None,
     yticks: Optional[Tuple[float, ...]] = None,
@@ -167,7 +167,7 @@ def _save_plot_from_2d_array(
         filename (str): Filename.
         visu_data (Tuple[np.ndarray, ...]): Data that requires visualization.
         visu_keys (Tuple[str, ...]]): Keys for visualizing data. such as ("u", "v").
-        num_timestamp (int, optional): Number of timestamps coord/value contains. Defaults to 1.
+        num_timestamps (int, optional): Number of timestamps coord/value contains. Defaults to 1.
         stride (int, optional): The time stride of visualization. Defaults to 1.
         xticks (Optional[Tuple[float, ...]]): Tuple of xtick locations. Defaults to None.
         yticks (Optional[Tuple[float, ...]]): Tuple of ytick locations. Defaults to None.
@@ -179,10 +179,10 @@ def _save_plot_from_2d_array(
 
     fig, ax = plt.subplots(
         len(visu_keys),
-        num_timestamp,
+        num_timestamps,
         squeeze=False,
         sharey=True,
-        figsize=(num_timestamp, len(visu_keys)),
+        figsize=(num_timestamps, len(visu_keys)),
     )
     fig.subplots_adjust(hspace=0.3)
     target_flag = any(["target" in key for key in visu_keys])
@@ -191,7 +191,7 @@ def _save_plot_from_2d_array(
             c_max = np.amax(data)
             c_min = np.amin(data)
 
-        for t_idx in range(num_timestamp):
+        for t_idx in range(num_timestamps):
             t = t_idx * stride
             ax[i, t_idx].imshow(
                 data[t, :, :],
@@ -226,7 +226,7 @@ def save_plot_from_2d_dict(
     filename: str,
     data_dict: Dict[str, Union[np.ndarray, paddle.Tensor]],
     visu_keys: Tuple[str, ...],
-    num_timestamp: int = 1,
+    num_timestamps: int = 1,
     stride: int = 1,
     xticks: Optional[Tuple[float, ...]] = None,
     yticks: Optional[Tuple[float, ...]] = None,
@@ -237,7 +237,7 @@ def save_plot_from_2d_dict(
         filename (str): Output filename.
         data_dict (Dict[str, Union[np.ndarray, paddle.Tensor]]): Data in dict.
         visu_keys (Tuple[str, ...]): Keys for visualizing data. such as ("u", "v").
-        num_timestamp (int, optional): Number of timestamp in data_dict. Defaults to 1.
+        num_timestamps (int, optional): Number of timestamp in data_dict. Defaults to 1.
         stride (int, optional): The time stride of visualization. Defaults to 1.
         xticks (Optional[Tuple[float,...]]): The list of xtick locations. Defaults to None.
         yticks (Optional[Tuple[float,...]]): The list of ytick locations. Defaults to None.
@@ -246,7 +246,7 @@ def save_plot_from_2d_dict(
     if isinstance(visu_data[0], paddle.Tensor):
         visu_data = [x.numpy() for x in visu_data]
     _save_plot_from_2d_array(
-        filename, visu_data, visu_keys, num_timestamp, stride, xticks, yticks
+        filename, visu_data, visu_keys, num_timestamps, stride, xticks, yticks
     )
 
 
@@ -308,21 +308,21 @@ def _save_plot_from_3d_array(
     filename: str,
     visu_data: Tuple[np.ndarray, ...],
     visu_keys: Tuple[str, ...],
-    num_timestamp: int = 1,
+    num_timestamps: int = 1,
 ):
     """Save plot from given 3D data.
 
     Args:
         filename (str): Filename.
         visu_data (Tuple[np.ndarray, ...]): Data that requires visualization.
         visu_keys (Tuple[str, ...]]): Keys for visualizing data. such as ("u", "v").
-        num_timestamp (int, optional): Number of timestamps coord/value contains. Defaults to 1.
+        num_timestamps (int, optional): Number of timestamps coord/value contains. Defaults to 1.
     """
 
     fig = plt.figure(figsize=(10, 10))
-    len_ts = len(visu_data[0]) // num_timestamp
-    for t in range(num_timestamp):
-        ax = fig.add_subplot(1, num_timestamp, t + 1, projection="3d")
+    len_ts = len(visu_data[0]) // num_timestamps
+    for t in range(num_timestamps):
+        ax = fig.add_subplot(1, num_timestamps, t + 1, projection="3d")
         st = t * len_ts
         ed = (t + 1) * len_ts
         visu_data_t = [data[st:ed] for data in visu_data]
@@ -343,40 +343,40 @@ def _save_plot_from_3d_array(
             loc="upper right",
             framealpha=0.95,
         )
-        if num_timestamp == 1:
+        if num_timestamps == 1:
             fig.savefig(filename, dpi=300)
         else:
             fig.savefig(f"{filename}_{t}", dpi=300)
 
-    if num_timestamp == 1:
+    if num_timestamps == 1:
         logger.info(f"3D result is saved to {filename}.png")
     else:
         logger.info(
             f"3D result is saved to {filename}_0.png"
-            f" ~ {filename}_{num_timestamp - 1}.png"
+            f" ~ {filename}_{num_timestamps - 1}.png"
         )
 
 
 def save_plot_from_3d_dict(
     filename: str,
     data_dict: Dict[str, Union[np.ndarray, paddle.Tensor]],
     visu_keys: Tuple[str, ...],
-    num_timestamp: int = 1,
+    num_timestamps: int = 1,
 ):
     """Plot dict data as file.
 
     Args:
         filename (str): Output filename.
         data_dict (Dict[str, Union[np.ndarray, paddle.Tensor]]): Data in dict.
         visu_keys (Tuple[str, ...]): Keys for visualizing data. such as ("u", "v").
-        num_timestamp (int, optional): Number of timestamp in data_dict. Defaults to 1.
+        num_timestamps (int, optional): Number of timestamp in data_dict. Defaults to 1.
     """
 
     visu_data = [data_dict[k] for k in visu_keys]
     if isinstance(visu_data[0], paddle.Tensor):
         visu_data = [x.numpy() for x in visu_data]
 
-    _save_plot_from_3d_array(filename, visu_data, visu_keys, num_timestamp)
+    _save_plot_from_3d_array(filename, visu_data, visu_keys, num_timestamps)
 
 
 def _save_plot_weather_from_array(