file for loading data with HF datasets load_dataset() module

long_term_precipitation_forecast/dataset.py

@@ -0,0 +1,99 @@
+import os
+import datasets
+
+class LongTermPrecipitationDataset(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        """
+        Defines the dataset metadata and feature structure.
+        """
+        return datasets.DatasetInfo(
+            description="Dataset containing .nc files per year for variables.",
+            features=datasets.Features({
+                "file_path": datasets.Value("string"),  # Store file paths
+                "year": datasets.Value("string"),       # Track year
+                "subfolder": datasets.Value("string")   # Track subfolder (sf1, sf2)
+            }),
+            supervised_keys=None,  # Update if supervised task is defined
+            homepage="https://huggingface.co/datasets/nasa-impact/WINDSET/tree/main/long_term_precipitation_forecast",
+            license="MIT",
+        )
+
+    def _split_generators(self, dl_manager):
+        """
+        Define the dataset splits for train, validation, and test.
+        """
+        # Define the directory containing the dataset
+        data_dir = os.path.join(os.getcwd(), "long_term_precipitation_forecast")
+
+        # Get the directories for each split
+        train_dir = os.path.join(data_dir, "training_data")
+        validation_dir = os.path.join(data_dir, "validation_data")
+        test_dir = os.path.join(data_dir, "test_data")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"split_dir": train_dir},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"split_dir": validation_dir},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"split_dir": test_dir},
+            ),
+        ]
+
+    def _get_subfolders(self, base_dir):
+        """
+        Get all subfolders from the base directory.
+        """
+        return [os.path.join(base_dir, subfolder) for subfolder in os.listdir(base_dir) if os.path.isdir(os.path.join(base_dir, subfolder))]
+
+    def _get_year_folders(self, subfolder_dir):
+        """
+        Get all year folders inside a subfolder.
+        """
+        return [os.path.join(subfolder_dir, year_folder) for year_folder in os.listdir(subfolder_dir) if os.path.isdir(os.path.join(subfolder_dir, year_folder))]
+
+    def _generate_data_from_files(self, data_dir):
+        """
+        Generate file paths for each subfolder, year, and daily file.
+        """
+        example_id = 0
+
+        # Loop through subfolders
+        for subfolder in os.listdir(data_dir):
+            subfolder_path = os.path.join(data_dir, subfolder)
+
+            if os.path.isdir(subfolder_path):
+                # Loop through year folders inside the subfolder
+                for year_folder in os.listdir(subfolder_path):
+                    year_folder_path = os.path.join(subfolder_path, year_folder)
+
+                    if os.path.isdir(year_folder_path):
+                        # Loop through daily files inside the year folder
+                        for daily_file in os.listdir(year_folder_path):
+                            daily_file_path = os.path.join(year_folder_path, daily_file)
+
+                            if daily_file.endswith(".nc"):  # Only select NetCDF files
+                                # Yield file information for each data point
+                                yield example_id, {
+                                    "file_path": daily_file_path,
+                                    "year": year_folder,
+                                    "subfolder": subfolder,
+                                }
+                                example_id += 1
+                            else:
+                                raise FileNotFoundError(f"{daily_file_path} not found")
+
+    def _generate_examples(self, split_dir):
+        """
+        Generates examples for the dataset from the split directory.
+        """
+        # Call the data generator to get the file paths
+        for example_id, example in self._generate_data_from_files(split_dir):
+            yield example_id, example