Release branch for 0.5.0

pyproject.toml

@@ -47,3 +47,12 @@ name = "testpypi"
 url = "https://test.pypi.org/simple/"
 publish-url = "https://test.pypi.org/legacy/"
 explicit = true
+
+[tool.mypy]
+# Docs: https://mypy.readthedocs.io/en/stable/config_file.html#using-a-pyproject-toml-file
+python_version = "3.10"
+warn_unused_configs = true
+warn_return_any = false
+namespace_packages = false
+files = ["src/tsdata"]
+exclude = "test"

src/tsdata/_loader/__init__.py

@@ -0,0 +1 @@
+"""Implementing loading for datasets."""

src/tsdata/_loader/as_pandas.py

@@ -0,0 +1,26 @@
+"""Load as a pandas dataframe."""
+
+from pathlib import Path
+
+import pandas as pd
+
+from .base import FileDatasetCollection
+
+
+class PandasRawDatasetCollection(FileDatasetCollection[pd.DataFrame]):
+    """Load files from CSV or Parquet files, without post-processing."""
+
+    @classmethod
+    def supported_file_types(cls) -> set[str]:
+        """File types supported by this Pandas type."""
+        return {"csv", "parquet"}
+
+    def load_file(self, path: Path) -> pd.DataFrame:
+        """Load a file."""
+        match path.suffix:
+            case ".csv":
+                return pd.read_csv(path, index_col=False, header="infer")
+            case ".parquet":
+                return pd.read_parquet(path)
+            case _:
+                raise ValueError(f"Unsupported file type: {path.suffix}")

src/tsdata/_loader/base.py

@@ -0,0 +1,77 @@
+"""Time series data loader."""
+
+from abc import abstractmethod
+from collections.abc import Iterator, Mapping
+from itertools import chain
+from pathlib import Path
+from typing import Generic, TypeVar
+
+T = TypeVar("T")
+
+
+class DatasetCollection(Mapping[str, T]):
+    """Lazily-loading dataset collection.
+
+    Mapping between dataset name and the eventual return type.
+    """
+
+    @abstractmethod
+    def __getitem__(self, key: str) -> T:
+        """Load the dataset with the given name."""
+
+    @abstractmethod
+    def __len__(self) -> int:
+        """The number of datasets available."""
+
+    @abstractmethod
+    def __iter__(self) -> Iterator[str]:
+        """Iterator over keys."""
+
+
+class FileDatasetCollection(DatasetCollection[T], Generic[T]):
+    """File-based dataset collection."""
+
+    # To override
+
+    @classmethod
+    @abstractmethod
+    def supported_file_types(self) -> set[str]:
+        """File types supported by this dataset type."""
+
+    @abstractmethod
+    def load_file(self, path: Path) -> T:
+        """Load the file given the path as the given return type."""
+
+    # Ready parts
+
+    def __init__(self, dir: Path | str) -> None:
+        """Create the dataset based on the given directory."""
+        self.dir = Path(dir).resolve().absolute()
+
+    def __repr__(self) -> str:
+        """Recreative string representation."""
+        cn = type(self).__qualname__
+        d = str(self.dir)
+        return f"{cn}({d!r})"
+
+    @property
+    def files(self) -> dict[str, Path]:
+        """Iterator over all files within the dataset's directory."""
+        globs = [self.dir.glob(f"*.{ft}") for ft in self.supported_file_types()]
+        files = {x.stem: x for x in chain(*globs)}
+        return files
+
+    # Implement mapping interface
+
+    def __iter__(self) -> Iterator[str]:
+        """Iterator over keys."""
+        return iter(self.files.keys())
+
+    def __len__(self) -> int:
+        """Number of avilable files."""
+        return len(self.files)
+
+    def __getitem__(self, key: str) -> T:
+        """Load file based on key."""
+        filepath = self.files[key]
+        return self.load_file(filepath)

src/tsdata/fpp3/__init__.py

@@ -0,0 +1,25 @@
+"""Loads data exported from Forecasting: Principles and Practice, 3rd Edition.
+
+This module is automatically generated from available CSV files.
+
+See Also
+--------
+https://otexts.com/fpp3/
+"""
+
+from pathlib import Path
+
+import pandas as pd
+
+from tsdata._loader.as_pandas import PandasRawDatasetCollection
+
+raw = PandasRawDatasetCollection(dir=Path(__file__).parent)
+
+
+def __getattr__(name: str) -> pd.DataFrame:
+    if name in raw.keys():
+        return raw[name]
+    try:
+        return globals()[name]
+    except KeyError:
+        raise AttributeError(name)

src/tsdata/fpppy/AirPassengers.csv

@@ -0,0 +1,145 @@
+ds,y
+1949-01-01,112
+1949-02-01,118
+1949-03-01,132
+1949-04-01,129
+1949-05-01,121
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