Source code for astropy.table.groups

# Licensed under a 3-clause BSD style license - see LICENSE.rst

import platform
import warnings

import numpy as np
from .index import get_index_by_names

from astropy.utils.exceptions import AstropyUserWarning


__all__ = ['TableGroups', 'ColumnGroups']


def table_group_by(table, keys):
    # index copies are unnecessary and slow down _table_group_by
    with table.index_mode('discard_on_copy'):
        return _table_group_by(table, keys)


def _table_group_by(table, keys):
    """
    Get groups for ``table`` on specified ``keys``.

    Parameters
    ----------
    table : `Table`
        Table to group
    keys : str, list of str, `Table`, or Numpy array
        Grouping key specifier

    Returns
    -------
    grouped_table : Table object with groups attr set accordingly
    """
    from .table import Table
    from .serialize import represent_mixins_as_columns

    # Pre-convert string to tuple of strings, or Table to the underlying structured array
    if isinstance(keys, str):
        keys = (keys,)

    if isinstance(keys, (list, tuple)):
        for name in keys:
            if name not in table.colnames:
                raise ValueError(f'Table does not have key column {name!r}')
            if table.masked and np.any(table[name].mask):
                raise ValueError(f'Missing values in key column {name!r} are not allowed')

        # Make a column slice of the table without copying
        table_keys = table.__class__([table[key] for key in keys], copy=False)

        # If available get a pre-existing index for these columns
        table_index = get_index_by_names(table, keys)
        grouped_by_table_cols = True

    elif isinstance(keys, (np.ndarray, Table)):
        table_keys = keys
        if len(table_keys) != len(table):
            raise ValueError('Input keys array length {} does not match table length {}'
                             .format(len(table_keys), len(table)))
        table_index = None
        grouped_by_table_cols = False

    else:
        raise TypeError('Keys input must be string, list, tuple, Table or numpy array, but got {}'
                        .format(type(keys)))

    # If there is not already an available index and table_keys is a Table then ensure
    # that all cols (including mixins) are in a form that can sorted with the code below.
    if not table_index and isinstance(table_keys, Table):
        table_keys = represent_mixins_as_columns(table_keys)

    # Get the argsort index `idx_sort`, accounting for particulars
    try:
        # take advantage of index internal sort if possible
        if table_index is not None:
            idx_sort = table_index.sorted_data()
        else:
            idx_sort = table_keys.argsort(kind='mergesort')
        stable_sort = True
    except TypeError:
        # Some versions (likely 1.6 and earlier) of numpy don't support
        # 'mergesort' for all data types.  MacOSX (Darwin) doesn't have a stable
        # sort by default, nor does Windows, while Linux does (or appears to).
        idx_sort = table_keys.argsort()
        stable_sort = platform.system() not in ('Darwin', 'Windows')

    # Finally do the actual sort of table_keys values
    table_keys = table_keys[idx_sort]

    # Get all keys
    diffs = np.concatenate(([True], table_keys[1:] != table_keys[:-1], [True]))
    indices = np.flatnonzero(diffs)

    # If the sort is not stable (preserves original table order) then sort idx_sort in
    # place within each group.
    if not stable_sort:
        for i0, i1 in zip(indices[:-1], indices[1:]):
            idx_sort[i0:i1].sort()

    # Make a new table and set the _groups to the appropriate TableGroups object.
    # Take the subset of the original keys at the indices values (group boundaries).
    out = table.__class__(table[idx_sort])
    out_keys = table_keys[indices[:-1]]
    if isinstance(out_keys, Table):
        out_keys.meta['grouped_by_table_cols'] = grouped_by_table_cols
    out._groups = TableGroups(out, indices=indices, keys=out_keys)

    return out


def column_group_by(column, keys):
    """
    Get groups for ``column`` on specified ``keys``

    Parameters
    ----------
    column : Column object
        Column to group
    keys : Table or Numpy array of same length as col
        Grouping key specifier

    Returns
    -------
    grouped_column : Column object with groups attr set accordingly
    """
    from .table import Table
    from .serialize import represent_mixins_as_columns

    if isinstance(keys, Table):
        keys = represent_mixins_as_columns(keys)
        keys = keys.as_array()

    if not isinstance(keys, np.ndarray):
        raise TypeError('Keys input must be numpy array, but got {}'
                        .format(type(keys)))

    if len(keys) != len(column):
        raise ValueError('Input keys array length {} does not match column length {}'
                         .format(len(keys), len(column)))

    idx_sort = keys.argsort()
    keys = keys[idx_sort]

    # Get all keys
    diffs = np.concatenate(([True], keys[1:] != keys[:-1], [True]))
    indices = np.flatnonzero(diffs)

    # Make a new column and set the _groups to the appropriate ColumnGroups object.
    # Take the subset of the original keys at the indices values (group boundaries).
    out = column.__class__(column[idx_sort])
    out._groups = ColumnGroups(out, indices=indices, keys=keys[indices[:-1]])

    return out


class BaseGroups:
    """
    A class to represent groups within a table of heterogeneous data.

      - ``keys``: key values corresponding to each group
      - ``indices``: index values in parent table or column corresponding to group boundaries
      - ``aggregate()``: method to create new table by aggregating within groups
    """
    @property
    def parent(self):
        return self.parent_column if isinstance(self, ColumnGroups) else self.parent_table

    def __iter__(self):
        self._iter_index = 0
        return self

    def next(self):
        ii = self._iter_index
        if ii < len(self.indices) - 1:
            i0, i1 = self.indices[ii], self.indices[ii + 1]
            self._iter_index += 1
            return self.parent[i0:i1]
        else:
            raise StopIteration
    __next__ = next

    def __getitem__(self, item):
        parent = self.parent

        if isinstance(item, (int, np.integer)):
            i0, i1 = self.indices[item], self.indices[item + 1]
            out = parent[i0:i1]
            out.groups._keys = parent.groups.keys[item]
        else:
            indices0, indices1 = self.indices[:-1], self.indices[1:]
            try:
                i0s, i1s = indices0[item], indices1[item]
            except Exception:
                raise TypeError('Index item for groups attribute must be a slice, '
                                'numpy mask or int array')
            mask = np.zeros(len(parent), dtype=bool)
            # Is there a way to vectorize this in numpy?
            for i0, i1 in zip(i0s, i1s):
                mask[i0:i1] = True
            out = parent[mask]
            out.groups._keys = parent.groups.keys[item]
            out.groups._indices = np.concatenate([[0], np.cumsum(i1s - i0s)])

        return out

    def __repr__(self):
        return f'<{self.__class__.__name__} indices={self.indices}>'

    def __len__(self):
        return len(self.indices) - 1


[docs]class ColumnGroups(BaseGroups): def __init__(self, parent_column, indices=None, keys=None): self.parent_column = parent_column # parent Column self.parent_table = parent_column.parent_table self._indices = indices self._keys = keys @property def indices(self): # If the parent column is in a table then use group indices from table if self.parent_table: return self.parent_table.groups.indices else: if self._indices is None: return np.array([0, len(self.parent_column)]) else: return self._indices @property def keys(self): # If the parent column is in a table then use group indices from table if self.parent_table: return self.parent_table.groups.keys else: return self._keys
[docs] def aggregate(self, func): from .column import MaskedColumn i0s, i1s = self.indices[:-1], self.indices[1:] par_col = self.parent_column masked = isinstance(par_col, MaskedColumn) reduceat = hasattr(func, 'reduceat') sum_case = func is np.sum mean_case = func is np.mean try: if not masked and (reduceat or sum_case or mean_case): if mean_case: vals = np.add.reduceat(par_col, i0s) / np.diff(self.indices) else: if sum_case: func = np.add vals = func.reduceat(par_col, i0s) else: vals = np.array([func(par_col[i0: i1]) for i0, i1 in zip(i0s, i1s)]) except Exception: raise TypeError("Cannot aggregate column '{}' with type '{}'" .format(par_col.info.name, par_col.info.dtype)) out = par_col.__class__(data=vals, name=par_col.info.name, description=par_col.info.description, unit=par_col.info.unit, format=par_col.info.format, meta=par_col.info.meta) return out
[docs] def filter(self, func): """ Filter groups in the Column based on evaluating function ``func`` on each group sub-table. The function which is passed to this method must accept one argument: - ``column`` : `Column` object It must then return either `True` or `False`. As an example, the following will select all column groups with only positive values:: def all_positive(column): if np.any(column < 0): return False return True Parameters ---------- func : function Filter function Returns ------- out : Column New column with the aggregated rows. """ mask = np.empty(len(self), dtype=bool) for i, group_column in enumerate(self): mask[i] = func(group_column) return self[mask]
[docs]class TableGroups(BaseGroups): def __init__(self, parent_table, indices=None, keys=None): self.parent_table = parent_table # parent Table self._indices = indices self._keys = keys @property def key_colnames(self): """ Return the names of columns in the parent table that were used for grouping. """ # If the table was grouped by key columns *in* the table then treat those columns # differently in aggregation. In this case keys will be a Table with # keys.meta['grouped_by_table_cols'] == True. Keys might not be a Table so we # need to handle this. grouped_by_table_cols = getattr(self.keys, 'meta', {}).get('grouped_by_table_cols', False) return self.keys.colnames if grouped_by_table_cols else () @property def indices(self): if self._indices is None: return np.array([0, len(self.parent_table)]) else: return self._indices
[docs] def aggregate(self, func): """ Aggregate each group in the Table into a single row by applying the reduction function ``func`` to group values in each column. Parameters ---------- func : function Function that reduces an array of values to a single value Returns ------- out : Table New table with the aggregated rows. """ i0s, i1s = self.indices[:-1], self.indices[1:] out_cols = [] parent_table = self.parent_table for col in parent_table.columns.values(): # For key columns just pick off first in each group since they are identical if col.info.name in self.key_colnames: new_col = col.take(i0s) else: try: new_col = col.groups.aggregate(func) except TypeError as err: warnings.warn(str(err), AstropyUserWarning) continue out_cols.append(new_col) return parent_table.__class__(out_cols, meta=parent_table.meta)
[docs] def filter(self, func): """ Filter groups in the Table based on evaluating function ``func`` on each group sub-table. The function which is passed to this method must accept two arguments: - ``table`` : `Table` object - ``key_colnames`` : tuple of column names in ``table`` used as keys for grouping It must then return either `True` or `False`. As an example, the following will select all table groups with only positive values in the non-key columns:: def all_positive(table, key_colnames): colnames = [name for name in table.colnames if name not in key_colnames] for colname in colnames: if np.any(table[colname] < 0): return False return True Parameters ---------- func : function Filter function Returns ------- out : Table New table with the aggregated rows. """ mask = np.empty(len(self), dtype=bool) key_colnames = self.key_colnames for i, group_table in enumerate(self): mask[i] = func(group_table, key_colnames) return self[mask]
@property def keys(self): return self._keys