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# ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# ----------------------------------------------------------------------------
from abc import ABCMeta, abstractmethod
import numpy as np
import pandas as pd
class _Indexing(metaclass=ABCMeta):
def __init__(self, instance, axis=None):
self._obj = instance
self._axis = axis
def __call__(self, axis=None):
"""Set the axis to index on."""
# verify axis param, discard value
self._obj._is_sequence_axis(axis)
return self.__class__(self._obj, axis=axis)
def __getitem__(self, indexable):
if self._axis is not None:
if self._obj._is_sequence_axis(self._axis):
return self._slice_on_first_axis(self._obj, indexable)
else:
return self._slice_on_second_axis(self._obj, indexable)
if type(indexable) is tuple:
if len(indexable) > 2:
raise ValueError("Can only slice on two axes. Tuple is length:"
" %r" % len(indexable))
elif len(indexable) > 1:
return self._handle_both_axes(*indexable)
else:
indexable, = indexable
return self._slice_on_first_axis(self._obj, indexable)
def _handle_both_axes(self, seq_index, pos_index):
seq_index = self._convert_ellipsis(seq_index)
pos_index = self._convert_ellipsis(pos_index)
if not hasattr(seq_index, '__iter__') and seq_index == slice(None):
# Only slice second axis
return self._slice_on_second_axis(self._obj, pos_index)
else:
r = self._slice_on_first_axis(self._obj, seq_index)
if type(r) is self._obj.dtype:
# [1, 1] [1, *]
return r[pos_index]
else:
# [*, 1] [*, *]
return self._slice_on_second_axis(r, pos_index)
def _slice_on_second_axis(self, obj, indexable):
indexable = self._convert_ellipsis(indexable)
if self.is_scalar(indexable, axis=1):
# [..., 1]
return self._get_position(obj, indexable)
else:
# [..., *]
return self._slice_positions(obj, indexable)
def _slice_on_first_axis(self, obj, indexable):
indexable = self._convert_ellipsis(indexable)
if self.is_scalar(indexable, axis=0):
# [1]
return self._get_sequence(obj, indexable)
else:
# [*]
return self._slice_sequences(obj, indexable)
def _convert_ellipsis(self, indexable):
if indexable is Ellipsis:
return slice(None)
return indexable
@abstractmethod
def is_scalar(self, indexable, axis):
raise NotImplementedError
@abstractmethod
def _get_sequence(self, obj, indexable):
raise NotImplementedError
@abstractmethod
def _slice_sequences(self, obj, indexable):
raise NotImplementedError
def _get_position(self, obj, indexable):
return obj._get_position_(indexable)
def _slice_positions(self, obj, indexable):
indexable = self._assert_bool_vector_right_size(indexable, axis=1)
indexable = self._convert_iterable_of_slices(indexable)
return obj._slice_positions_(indexable)
def _convert_iterable_of_slices(self, indexable):
# _assert_bool_vector_right_size will have converted the iterable to
# an ndarray if it wasn't yet.
if isinstance(indexable, np.ndarray) and indexable.dtype == object:
indexable = np.r_[tuple(indexable)]
return indexable
def _assert_bool_vector_right_size(self, indexable, axis):
if isinstance(indexable, np.ndarray):
pass
elif hasattr(indexable, '__iter__'):
indexable = np.asarray(list(indexable))
else:
return indexable
if indexable.dtype == bool and len(indexable) != self._obj.shape[axis]:
raise IndexError("Boolean index's length (%r) does not match the"
" axis length (%r)" % (len(indexable),
self._obj.shape[axis]))
return indexable
class TabularMSAILoc(_Indexing):
def is_scalar(self, indexable, axis):
return np.isscalar(indexable)
def _get_sequence(self, obj, indexable):
return obj._get_sequence_iloc_(indexable)
def _slice_sequences(self, obj, indexable):
indexable = self._assert_bool_vector_right_size(indexable, axis=0)
indexable = self._convert_iterable_of_slices(indexable)
return obj._slice_sequences_iloc_(indexable)
class TabularMSALoc(_Indexing):
def is_scalar(self, indexable, axis):
"""
Sometimes (MultiIndex!) something that looks like a scalar, isn't
and vice-versa.
Consider:
A 0
1
2
B 0
1
2
'A' looks like a scalar, but isn't.
('A', 0) doesn't look like a scalar, but it is.
"""
index = self._obj.index
complete_key = False
partial_key = False
duplicated_key = False
if axis == 0 and self._has_fancy_index():
try:
if type(indexable) is tuple:
complete_key = (len(indexable) == len(index.levshape) and
indexable in index)
partial_key = not complete_key and indexable in index
except TypeError: # Unhashable type, no biggie
pass
if index.has_duplicates:
duplicated_key = indexable in index.get_duplicates()
return (not duplicated_key and
((np.isscalar(indexable) and not partial_key) or complete_key))
def _get_sequence(self, obj, indexable):
self._assert_tuple_rules(indexable)
return obj._get_sequence_loc_(indexable)
def _slice_sequences(self, obj, indexable):
self._assert_tuple_rules(indexable)
if (self._has_fancy_index() and
type(indexable) is not tuple and
pd.core.common.is_list_like(indexable) and
len(indexable) > 0):
if not self.is_scalar(indexable[0], axis=0):
raise TypeError("A list is used with complete labels, try"
" using a tuple to indicate independent"
" selections of a `pd.MultiIndex`.")
# prevents
# pd.Series.loc[['x', 'b', 'b', 'a']] from being interepereted as
# pd.Series.loc[[('a', 0), ('b', 1)]] who knows why it does this.
elif indexable[0] not in self._obj.index:
raise KeyError(repr(indexable[0]))
# pandas acts normal if the first element is actually a scalar
self._assert_bool_vector_right_size(indexable, axis=0)
return obj._slice_sequences_loc_(indexable)
def _assert_tuple_rules(self, indexable):
# pandas is scary in what it will accept sometimes...
if type(indexable) is tuple:
if not self._has_fancy_index():
# prevents unfriendly errors
raise TypeError("Cannot provide a tuple to the first axis of"
" `loc` unless the MSA's `index` is a"
" `pd.MultiIndex`.")
elif self.is_scalar(indexable[0], axis=0):
# prevents unreasonable results
# pd.Series.loc[('a', 0), ('b', 1)] would be interpreted as
# pd.Series.loc[('a', 1)] which is horrifying.
raise TypeError("A tuple provided to the first axis of `loc`"
" represents a selection for each index of a"
" `pd.MultiIndex`; it should not contain a"
" complete label.")
def _has_fancy_index(self):
return hasattr(self._obj.index, 'levshape')
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