Source code for hipscat.catalog.partition_info

"""Container class to hold per-partition metadata"""

from __future__ import annotations

import warnings
from typing import List

import numpy as np
import pandas as pd
import pyarrow as pa

from hipscat.io import FilePointer, file_io, paths
from hipscat.io.parquet_metadata import (
    read_row_group_fragments,
    row_group_stat_single_value,
    write_parquet_metadata_for_batches,
)
from hipscat.pixel_math import HealpixPixel




class PartitionInfo:
    """Container class for per-partition info."""



    METADATA_ORDER_COLUMN_NAME = "Norder"



    METADATA_DIR_COLUMN_NAME = "Dir"



    METADATA_PIXEL_COLUMN_NAME = "Npix"


    def __init__(self, pixel_list: List[HealpixPixel], catalog_base_dir: str = None) -> None:
        self.pixel_list = pixel_list
        self.catalog_base_dir = catalog_base_dir



    def get_healpix_pixels(self) -> List[HealpixPixel]:
        """Get healpix pixel objects for all pixels represented as partitions.

        Returns:
            List of HealpixPixel
        """
        return self.pixel_list




    def get_highest_order(self) -> int:
        """Get the highest healpix order for the dataset.

        Returns:
            int representing highest order.
        """
        max_pixel = np.max(self.pixel_list)
        return max_pixel.order




    def write_to_file(
        self,
        partition_info_file: FilePointer = None,
        catalog_path: FilePointer = None,
        storage_options: dict = None,
    ):
        """Write all partition data to CSV file.

        If no paths are provided, the catalog base directory from the `read_from_dir` call is used.

        Args:
            partition_info_file: FilePointer to where the `partition_info.csv`
                file will be written.
            catalog_path: base directory for a catalog where the `partition_info.csv`
                file will be written.
            storage_options (dict): dictionary that contains abstract filesystem credentials

        Raises:
            ValueError: if no path is provided, and could not be inferred.
        """
        if partition_info_file is None:
            if catalog_path is not None:
                partition_info_file = paths.get_partition_info_pointer(catalog_path)
            elif self.catalog_base_dir is not None:
                partition_info_file = paths.get_partition_info_pointer(self.catalog_base_dir)
            else:
                raise ValueError("partition_info_file is required if info was not loaded from a directory")

        file_io.write_dataframe_to_csv(
            self.as_dataframe(), partition_info_file, index=False, storage_options=storage_options
        )




    def write_to_metadata_files(self, catalog_path: FilePointer = None, storage_options: dict = None):
        """Generate parquet metadata, using the known partitions.

        If no catalog_path is provided, the catalog base directory from the `read_from_dir` call is used.

        Args:
            catalog_path (FilePointer): base path for the catalog
            storage_options (dict): dictionary that contains abstract filesystem credentials

        Raises:
            ValueError: if no path is provided, and could not be inferred.
        """
        if catalog_path is None:
            if self.catalog_base_dir is None:
                raise ValueError("catalog_path is required if info was not loaded from a directory")
            catalog_path = self.catalog_base_dir

        batches = [
            [
                pa.RecordBatch.from_arrays(
                    [[pixel.order], [pixel.dir], [pixel.pixel]],
                    names=[
                        self.METADATA_ORDER_COLUMN_NAME,
                        self.METADATA_DIR_COLUMN_NAME,
                        self.METADATA_PIXEL_COLUMN_NAME,
                    ],
                )
            ]
            for pixel in self.get_healpix_pixels()
        ]

        write_parquet_metadata_for_batches(batches, catalog_path, storage_options)


    @classmethod


    def read_from_dir(cls, catalog_base_dir: FilePointer, storage_options: dict = None) -> PartitionInfo:
        """Read partition info from a file within a hipscat directory.

        This will look for a `partition_info.csv` file, and if not found, will look for
        a `_metadata` file. The second approach is typically slower for large catalogs
        therefore a warning is issued to the user. In internal testing with large catalogs,
        the first approach takes less than a second, while the second can take 10-20 seconds.

        Args:
            catalog_base_dir: path to the root directory of the catalog
            storage_options (dict): dictionary that contains abstract filesystem credentials

        Returns:
            A `PartitionInfo` object with the data from the file

        Raises:
            FileNotFoundError: if neither desired file is found in the catalog_base_dir
        """
        metadata_file = paths.get_parquet_metadata_pointer(catalog_base_dir)
        partition_info_file = paths.get_partition_info_pointer(catalog_base_dir)
        if file_io.does_file_or_directory_exist(partition_info_file, storage_options=storage_options):
            pixel_list = PartitionInfo._read_from_csv(partition_info_file, storage_options=storage_options)
        elif file_io.does_file_or_directory_exist(metadata_file, storage_options=storage_options):
            warnings.warn("Reading partitions from parquet metadata. This is typically slow.")
            pixel_list = PartitionInfo._read_from_metadata_file(
                metadata_file, storage_options=storage_options
            )
        else:
            raise FileNotFoundError(
                f"_metadata or partition info file is required in catalog directory {catalog_base_dir}"
            )
        return cls(pixel_list, catalog_base_dir)


    @classmethod


    def read_from_file(
        cls, metadata_file: FilePointer, strict: bool = False, storage_options: dict = None
    ) -> PartitionInfo:
        """Read partition info from a `_metadata` file to create an object

        Args:
            metadata_file (FilePointer): FilePointer to the `_metadata` file
            storage_options (dict): dictionary that contains abstract filesystem credentials
            strict (bool): use strict parsing of _metadata file. this is slower, but
                gives more helpful error messages in the case of invalid data.

        Returns:
            A `PartitionInfo` object with the data from the file
        """
        return cls(cls._read_from_metadata_file(metadata_file, strict, storage_options))


    @classmethod


    def _read_from_metadata_file(
        cls, metadata_file: FilePointer, strict: bool = False, storage_options: dict = None
    ) -> List[HealpixPixel]:
        """Read partition info list from a `_metadata` file.

        Args:
            metadata_file (FilePointer): FilePointer to the `_metadata` file
            storage_options (dict): dictionary that contains abstract filesystem credentials
            strict (bool): use strict parsing of _metadata file. this is slower, but
                gives more helpful error messages in the case of invalid data.

        Returns:
            A `PartitionInfo` object with the data from the file
        """
        if strict:
            pixel_list = [
                HealpixPixel(
                    row_group_stat_single_value(row_group, cls.METADATA_ORDER_COLUMN_NAME),
                    row_group_stat_single_value(row_group, cls.METADATA_PIXEL_COLUMN_NAME),
                )
                for row_group in read_row_group_fragments(metadata_file, storage_options)
            ]
        else:
            total_metadata = file_io.read_parquet_metadata(metadata_file, storage_options)
            num_row_groups = total_metadata.num_row_groups

            first_row_group = total_metadata.row_group(0)
            norder_column = -1
            npix_column = -1

            for i in range(0, first_row_group.num_columns):
                column = first_row_group.column(i)
                if column.path_in_schema == cls.METADATA_ORDER_COLUMN_NAME:
                    norder_column = i
                elif column.path_in_schema == cls.METADATA_PIXEL_COLUMN_NAME:
                    npix_column = i

            if norder_column == -1 or npix_column == -1:
                raise ValueError("Metadata missing Norder or Npix column")

            row_group_index = np.arange(0, num_row_groups)

            pixel_list = [
                HealpixPixel(
                    total_metadata.row_group(index).column(norder_column).statistics.min,
                    total_metadata.row_group(index).column(npix_column).statistics.min,
                )
                for index in row_group_index
            ]
        ## Remove duplicates, preserving order.
        ## In the case of association partition join info, we may have multiple entries
        ## for the primary order/pixels.
        return list(dict.fromkeys(pixel_list))


    @classmethod


    def read_from_csv(cls, partition_info_file: FilePointer, storage_options: dict = None) -> PartitionInfo:
        """Read partition info from a `partition_info.csv` file to create an object

        Args:
            partition_info_file (FilePointer): FilePointer to the `partition_info.csv` file
            storage_options (dict): dictionary that contains abstract filesystem credentials

        Returns:
            A `PartitionInfo` object with the data from the file
        """
        return cls(cls._read_from_csv(partition_info_file, storage_options))


    @classmethod


    def _read_from_csv(cls, partition_info_file: FilePointer, storage_options: dict = None) -> PartitionInfo:
        """Read partition info from a `partition_info.csv` file to create an object

        Args:
            partition_info_file (FilePointer): FilePointer to the `partition_info.csv` file
            storage_options (dict): dictionary that contains abstract filesystem credentials

        Returns:
            A `PartitionInfo` object with the data from the file
        """
        if not file_io.does_file_or_directory_exist(partition_info_file, storage_options=storage_options):
            raise FileNotFoundError(f"No partition info found where expected: {str(partition_info_file)}")

        data_frame = file_io.load_csv_to_pandas(partition_info_file, storage_options=storage_options)

        return [
            HealpixPixel(order, pixel)
            for order, pixel in zip(
                data_frame[cls.METADATA_ORDER_COLUMN_NAME],
                data_frame[cls.METADATA_PIXEL_COLUMN_NAME],
            )
        ]




    def as_dataframe(self):
        """Construct a pandas dataframe for the partition info pixels.

        Returns:
            Dataframe with order, directory, and pixel info.
        """
        partition_info_dict = {
            PartitionInfo.METADATA_ORDER_COLUMN_NAME: [],
            PartitionInfo.METADATA_PIXEL_COLUMN_NAME: [],
            PartitionInfo.METADATA_DIR_COLUMN_NAME: [],
        }
        for pixel in self.pixel_list:
            partition_info_dict[PartitionInfo.METADATA_ORDER_COLUMN_NAME].append(pixel.order)
            partition_info_dict[PartitionInfo.METADATA_PIXEL_COLUMN_NAME].append(pixel.pixel)
            partition_info_dict[PartitionInfo.METADATA_DIR_COLUMN_NAME].append(pixel.dir)
        return pd.DataFrame.from_dict(partition_info_dict)


    @classmethod


    def from_healpix(cls, healpix_pixels: List[HealpixPixel]) -> PartitionInfo:
        """Create a partition info object from a list of constituent healpix pixels.

        Args:
            healpix_pixels: list of healpix pixels
        Returns:
            A `PartitionInfo` object with the same healpix pixels
        """
        return cls(healpix_pixels)