# -*- coding: utf-8 -*-
"""
Functions for working with dictionaries.
The :func:`dict_hist` function counts the number of discrete occurrences of hashable
items. Similarly :func:`find_duplicates` looks for indices of items that occur more
than `k=1` times.
The :func:`map_keys` and :func:`map_vals` functions are useful for transforming the keys
and values of a dictionary with less syntax than a dict comprehension.
The :func:`dict_union`, :func:`dict_isect`, and :func:`dict_diff` functions
are similar to the set equivalents.
The :func:`dzip` function zips two iterables and packs them into a dictionary
where the first iterable is used to generate keys and the second generates
values.
The :func:`group_items` function takes two lists and returns a dict mapping
values in the second list to all items in corresponding locations in the first
list.
The :func:`invert_dict` function swaps keys and values. See the function docs
for details on dealing with unique and non-unique values.
The :func:`ddict` and :func:`odict` functions are alias for the commonly used
:func:`collections.defaultdict` and :func:`collections.OrderedDict` classes.
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import sys
import operator as op
import itertools as it
from collections import OrderedDict
from collections import defaultdict
from ubelt import util_const
from ubelt import util_list
from ubelt.util_const import NoParam
__all__ = [
'AutoDict',
'AutoOrderedDict',
'dzip',
'ddict',
'dict_hist',
'dict_subset',
'dict_union',
'dict_isect',
'dict_diff',
'find_duplicates',
'group_items',
'invert_dict',
'map_keys',
'map_vals',
'sorted_keys',
'sorted_vals',
'odict',
'named_product',
'varied_values',
]
PY2 = sys.version_info[0] == 2
if PY2:
import six
from six.moves import zip
iteritems = six.iteritems
else:
def iteritems(d, **kw):
return d.items(**kw)
# Expose for convenience
odict = OrderedDict
ddict = defaultdict
[docs]class AutoDict(dict):
"""
An infinitely nested default dict of dicts.
Implementation of Perl's autovivification feature.
SeeAlso:
:class:`AutoOrderedDict` - the ordered version
References:
.. [1] http://stackoverflow.com/questions/651794/init-dict-of-dicts
Example:
>>> import ubelt as ub
>>> auto = ub.AutoDict()
>>> auto[0][10][100] = None
>>> assert str(auto) == '{0: {10: {100: None}}}'
"""
_base = dict
def __getitem__(self, key):
try:
# value = super(AutoDict, self).__getitem__(key)
value = self._base.__getitem__(self, key)
except KeyError:
value = self[key] = type(self)()
return value
[docs] def to_dict(self):
"""
Recursively casts a AutoDict into a regular dictionary. All nested
AutoDict values are also converted.
Returns:
dict: a copy of this dict without autovivification
Example:
>>> from ubelt.util_dict import AutoDict
>>> auto = AutoDict()
>>> auto[1] = 1
>>> auto['n1'] = AutoDict()
>>> static = auto.to_dict()
>>> assert not isinstance(static, AutoDict)
>>> assert not isinstance(static['n1'], AutoDict)
"""
return self._base(
(key, (value.to_dict() if isinstance(value, AutoDict) else value))
for key, value in self.items())
[docs]class AutoOrderedDict(OrderedDict, AutoDict):
"""
An infinitely nested default dict of dicts that maintains the ordering
of items.
SeeAlso:
:class:`AutoDict` - the unordered version of this class
Example:
>>> import ubelt as ub
>>> auto = ub.AutoOrderedDict()
>>> auto[0][3] = 3
>>> auto[0][2] = 2
>>> auto[0][1] = 1
>>> assert list(auto[0].values()) == [3, 2, 1]
"""
_base = OrderedDict
[docs]def dzip(items1, items2, cls=dict):
"""
Zips elementwise pairs between items1 and items2 into a dictionary.
Values from items2 can be broadcast onto items1.
Args:
items1 (Iterable[A]): full sequence
items2 (Iterable[B]):
can either be a sequence of one item or a sequence of equal length
to ``items1``
cls (Type[dict], default=dict): dictionary type to use.
Returns:
Dict[A, B]: similar to ``dict(zip(items1, items2))``.
Example:
>>> assert dzip([1, 2, 3], [4]) == {1: 4, 2: 4, 3: 4}
>>> assert dzip([1, 2, 3], [4, 4, 4]) == {1: 4, 2: 4, 3: 4}
>>> assert dzip([], [4]) == {}
"""
try:
len(items1)
except TypeError:
items1 = list(items1)
try:
len(items2)
except TypeError:
items2 = list(items2)
if len(items1) == 0 and len(items2) == 1:
# Corner case:
# allow the first list to be empty and the second list to broadcast a
# value. This means that the equality check wont work for the case
# where items1 and items2 are supposed to correspond, but the length of
# items2 is 1.
items2 = []
if len(items2) == 1 and len(items1) > 1:
items2 = items2 * len(items1)
if len(items1) != len(items2):
raise ValueError('out of alignment len(items1)=%r, len(items2)=%r' % (
len(items1), len(items2)))
return cls(zip(items1, items2))
[docs]def group_items(items, key):
"""
Groups a list of items by group id.
Args:
items (Iterable[A]): a list of items to group
key (Iterable[B] | Callable[[A], B]):
either a corresponding list of group-ids for each item or
a function used to map each item to a group-id.
Returns:
dict[B, List[A]]:
a mapping from each group id to the list of corresponding items
Example:
>>> import ubelt as ub
>>> items = ['ham', 'jam', 'spam', 'eggs', 'cheese', 'banana']
>>> groupids = ['protein', 'fruit', 'protein', 'protein', 'dairy', 'fruit']
>>> id_to_items = ub.group_items(items, groupids)
>>> print(ub.repr2(id_to_items, nl=0))
{'dairy': ['cheese'], 'fruit': ['jam', 'banana'], 'protein': ['ham', 'spam', 'eggs']}
"""
if callable(key):
keyfunc = key
pair_list = ((keyfunc(item), item) for item in items)
else:
pair_list = zip(key, items)
# Initialize a dict of lists
id_to_items = defaultdict(list)
# Insert each item into the correct group
for key, item in pair_list:
id_to_items[key].append(item)
return id_to_items
[docs]def dict_hist(items, weights=None, ordered=False, labels=None):
"""
Builds a histogram of items, counting the number of time each item appears
in the input.
Args:
items (Iterable[T]):
hashable items (usually containing duplicates)
weights (Iterable[float], default=None):
Corresponding weights for each item.
ordered (bool, default=False):
If True the result is ordered by frequency.
labels (Iterable[T], default=None):
Expected labels. Allows this function to pre-initialize the
histogram. If specified the frequency of each label is initialized
to zero and ``items`` can only contain items specified in labels.
Returns:
dict[T, int] :
dictionary where the keys are unique elements from ``items``, and
the values are the number of times the item appears in ``items``.
Example:
>>> import ubelt as ub
>>> items = [1, 2, 39, 900, 1232, 900, 1232, 2, 2, 2, 900]
>>> hist = ub.dict_hist(items)
>>> print(ub.repr2(hist, nl=0))
{1: 1, 2: 4, 39: 1, 900: 3, 1232: 2}
Example:
>>> import ubelt as ub
>>> items = [1, 2, 39, 900, 1232, 900, 1232, 2, 2, 2, 900]
>>> hist1 = ub.dict_hist(items)
>>> hist2 = ub.dict_hist(items, ordered=True)
>>> try:
>>> hist3 = ub.dict_hist(items, labels=[])
>>> except KeyError:
>>> pass
>>> else:
>>> raise AssertionError('expected key error')
>>> weights = [1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1]
>>> hist4 = ub.dict_hist(items, weights=weights)
>>> print(ub.repr2(hist1, nl=0))
{1: 1, 2: 4, 39: 1, 900: 3, 1232: 2}
>>> print(ub.repr2(hist4, nl=0))
{1: 1, 2: 4, 39: 1, 900: 1, 1232: 0}
"""
if labels is None:
hist_ = defaultdict(lambda: 0)
else:
hist_ = {k: 0 for k in labels}
if weights is None:
weights = it.repeat(1)
# Accumulate frequency
for item, weight in zip(items, weights):
hist_[item] += weight
if ordered:
# Order by value
getval = op.itemgetter(1)
hist = OrderedDict([
(key, value)
for (key, value) in sorted(hist_.items(), key=getval)
])
else:
# Cast to a normal dictionary
hist = dict(hist_)
return hist
[docs]def find_duplicates(items, k=2, key=None):
"""
Find all duplicate items in a list.
Search for all items that appear more than ``k`` times and return a mapping
from each (k)-duplicate item to the positions it appeared in.
Args:
items (Iterable[T]):
hashable items possibly containing duplicates
k (int, default=2):
only return items that appear at least ``k`` times.
key (Callable[[T], Any], default=None):
Returns indices where `key(items[i])` maps to a particular value at
least k times.
Returns:
dict[T: List[int]]:
maps each duplicate item to the indices at which it appears
Example:
>>> import ubelt as ub
>>> items = [0, 0, 1, 2, 3, 3, 0, 12, 2, 9]
>>> duplicates = ub.find_duplicates(items)
>>> # Duplicates are a mapping from each item that occurs 2 or more
>>> # times to the indices at which they occur.
>>> assert duplicates == {0: [0, 1, 6], 2: [3, 8], 3: [4, 5]}
>>> # You can set k=3 if you want to don't mind duplicates but you
>>> # want to find triplicates or quadruplets etc.
>>> assert ub.find_duplicates(items, k=3) == {0: [0, 1, 6]}
Example:
>>> import ubelt as ub
>>> items = [0, 0, 1, 2, 3, 3, 0, 12, 2, 9]
>>> # note: k can less then 2
>>> duplicates = ub.find_duplicates(items, k=0)
>>> print(ub.repr2(duplicates, nl=0))
{0: [0, 1, 6], 1: [2], 2: [3, 8], 3: [4, 5], 9: [9], 12: [7]}
Example:
>>> import ubelt as ub
>>> items = [10, 11, 12, 13, 14, 15, 16]
>>> duplicates = ub.find_duplicates(items, key=lambda x: x // 2)
>>> print(ub.repr2(duplicates, nl=0))
{5: [0, 1], 6: [2, 3], 7: [4, 5]}
"""
# Build mapping from items to the indices at which they appear
duplicates = defaultdict(list)
if key is None:
for count, item in enumerate(items):
duplicates[item].append(count)
else:
for count, item in enumerate(items):
duplicates[key(item)].append(count)
# remove items seen fewer than k times.
for key in list(duplicates.keys()):
if len(duplicates[key]) < k:
del duplicates[key]
duplicates = dict(duplicates)
return duplicates
[docs]def dict_subset(dict_, keys, default=util_const.NoParam, cls=OrderedDict):
"""
Get a subset of a dictionary
Args:
dict_ (Dict[A, B]): superset dictionary
keys (Iterable[A]): keys to take from ``dict_``
default (object, optional): if specified uses default if keys are missing
cls (type, default=OrderedDict): type of the returned dictionary.
Returns:
cls[A, B]: subset dictionary
SeeAlso:
:func:`dict_isect` - similar functionality, but ignores missing keys
Example:
>>> import ubelt as ub
>>> dict_ = {'K': 3, 'dcvs_clip_max': 0.2, 'p': 0.1}
>>> keys = ['K', 'dcvs_clip_max']
>>> subdict_ = ub.dict_subset(dict_, keys)
>>> print(ub.repr2(subdict_, nl=0))
{'K': 3, 'dcvs_clip_max': 0.2}
"""
keys = list(keys)
items = util_list.take(dict_, keys, default)
subdict_ = OrderedDict(list(zip(keys, items)))
return subdict_
[docs]def dict_union(*args):
"""
Dictionary set extension for ``set.union``
Combines the disjoint keys in multiple dictionaries. For intersecting keys,
dictionaries towards the end of the sequence are given precedence.
Args:
*args : a sequence of dictionaries
Returns:
Dict | OrderedDict :
OrderedDict if the first argument is an OrderedDict, otherwise dict
SeeAlso:
:func:`collections.ChainMap` - a standard python builtin data structure
that provides a view that treats multiple dicts as a single dict.
`https://docs.python.org/3/library/collections.html#chainmap-objects`
Example:
>>> result = dict_union({'a': 1, 'b': 1}, {'b': 2, 'c': 2})
>>> assert result == {'a': 1, 'b': 2, 'c': 2}
>>> dict_union(odict([('a', 1), ('b', 2)]), odict([('c', 3), ('d', 4)]))
OrderedDict([('a', 1), ('b', 2), ('c', 3), ('d', 4)])
>>> dict_union()
{}
"""
if not args:
return {}
else:
dictclass = OrderedDict if isinstance(args[0], OrderedDict) else dict
return dictclass(it.chain.from_iterable(d.items() for d in args))
[docs]def dict_diff(*args):
"""
Dictionary set extension for ``set.difference``
Constructs a dictionary that contains any of the keys in the first arg,
which are not in any of the following args.
Args:
*args : a sequence of dictionaries (or sets of keys)
Returns:
Dict | OrderedDict :
OrderedDict if the first argument is an OrderedDict, otherwise dict
TODO:
- [ ] Add inplace keyword argument, which modifies the first dictionary
inplace.
Example:
>>> dict_diff({'a': 1, 'b': 1}, {'a'}, {'c'})
{'b': 1}
>>> dict_diff(odict([('a', 1), ('b', 2)]), odict([('c', 3)]))
OrderedDict([('a', 1), ('b', 2)])
>>> dict_diff()
{}
>>> dict_diff({'a': 1, 'b': 2}, {'c'})
"""
if not args:
return {}
else:
first_dict = args[0]
if isinstance(first_dict, OrderedDict):
from ubelt import OrderedSet
dictclass = OrderedDict
keys = OrderedSet(first_dict)
else:
dictclass = dict
keys = set(first_dict)
keys.difference_update(*map(set, args[1:]))
return dictclass((k, first_dict[k]) for k in keys)
[docs]def dict_isect(*args):
"""
Dictionary set extension for ``set.intersection``
Constructs a dictionary that contains keys common between all inputs.
The returned values will only belong to the first dictionary.
Args:
*args : a sequence of dictionaries (or sets of keys)
Returns:
Dict | OrderedDict :
OrderedDict if the first argument is an OrderedDict, otherwise dict
Notes:
This function can be used as an alternative to :func:`dict_subset`
where any key not in the dictionary is ignored. See the following
example:
>>> dict_isect({'a': 1, 'b': 2, 'c': 3}, ['a', 'c', 'd'])
{'a': 1, 'c': 3}
Example:
>>> dict_isect({'a': 1, 'b': 1}, {'b': 2, 'c': 2})
{'b': 1}
>>> dict_isect(odict([('a', 1), ('b', 2)]), odict([('c', 3)]))
OrderedDict()
>>> dict_isect()
{}
"""
if not args:
return {}
else:
dictclass = OrderedDict if isinstance(args[0], OrderedDict) else dict
common_keys = set.intersection(*map(set, args))
first_dict = args[0]
return dictclass((k, first_dict[k]) for k in first_dict
if k in common_keys)
[docs]def map_vals(func, dict_):
"""
Apply a function to every value in a dictionary.
Creates a new dictionary with the same keys and modified values.
Args:
func (Callable[[B], C] | Mapping[B, C]): a function or indexable object
dict_ (Dict[A, B]): a dictionary
Returns:
Dict[A, C]: transformed dictionary
Example:
>>> dict_ = {'a': [1, 2, 3], 'b': []}
>>> newdict = map_vals(len, dict_)
>>> assert newdict == {'a': 3, 'b': 0}
Example:
>>> # Can also use an indexable as ``func``
>>> dict_ = {'a': 0, 'b': 1}
>>> func = [42, 21]
>>> newdict = map_vals(func, dict_)
>>> assert newdict == {'a': 42, 'b': 21}
>>> print(newdict)
"""
if not hasattr(func, '__call__'):
func = func.__getitem__
keyval_list = [(key, func(val)) for key, val in iteritems(dict_)]
dictclass = OrderedDict if isinstance(dict_, OrderedDict) else dict
newdict = dictclass(keyval_list)
return newdict
[docs]def map_keys(func, dict_):
"""
Apply a function to every key in a dictionary.
Creates a new dictionary with the same values and modified keys. An error
is raised if the new keys are not unique.
Args:
func (Callable[[A], C] | Mapping[A, C]): a function or indexable object
dict_ (Dict[A, B]): a dictionary
Returns:
Dict[C, B]: transformed dictionary
Raises:
Exception : if multiple keys map to the same value
Example:
>>> dict_ = {'a': [1, 2, 3], 'b': []}
>>> func = ord
>>> newdict = map_keys(func, dict_)
>>> print(newdict)
>>> assert newdict == {97: [1, 2, 3], 98: []}
>>> dict_ = {0: [1, 2, 3], 1: []}
>>> func = ['a', 'b']
>>> newdict = map_keys(func, dict_)
>>> print(newdict)
>>> assert newdict == {'a': [1, 2, 3], 'b': []}
"""
if not hasattr(func, '__call__'):
func = func.__getitem__
keyval_list = [(func(key), val) for key, val in iteritems(dict_)]
dictclass = OrderedDict if isinstance(dict_, OrderedDict) else dict
newdict = dictclass(keyval_list)
if len(newdict) != len(dict_):
raise Exception('multiple input keys mapped to the same output key')
return newdict
[docs]def sorted_vals(dict_, key=None, reverse=False):
"""
Return an ordered dictionary sorted by its values
Args:
dict_ (Dict[A, B]):
dictionary to sort. The values must be of comparable types.
key (Callable[[B], Any], optional):
customizes the sorting by ordering using transformed values
reverse (bool, default=False):
if True returns in descending order
Returns:
OrderedDict[A, B]: new dictionary where the values are ordered
Example:
>>> import ubelt as ub
>>> dict_ = {'spam': 2.62, 'eggs': 1.20, 'jam': 2.92}
>>> newdict = sorted_vals(dict_)
>>> print(ub.repr2(newdict, nl=0))
{'eggs': 1.2, 'spam': 2.62, 'jam': 2.92}
>>> newdict = sorted_vals(dict_, reverse=True)
>>> print(ub.repr2(newdict, nl=0))
{'jam': 2.92, 'spam': 2.62, 'eggs': 1.2}
>>> newdict = sorted_vals(dict_, key=lambda x: x % 1.6)
>>> print(ub.repr2(newdict, nl=0))
{'spam': 2.62, 'eggs': 1.2, 'jam': 2.92}
"""
if key is None:
newdict = OrderedDict(sorted(dict_.items(), key=lambda kv: kv[1],
reverse=reverse))
else:
newdict = OrderedDict(sorted(dict_.items(), key=lambda kv: key(kv[1]),
reverse=reverse))
return newdict
sorted_values = sorted_vals # ? Is this a better name?
[docs]def sorted_keys(dict_, key=None, reverse=False):
"""
Return an ordered dictionary sorted by its keys
Args:
dict_ (Dict[A, B]):
dictionary to sort. The keys must be of comparable types.
key (Callable[[A], Any], optional):
customizes the sorting by ordering using transformed keys
reverse (bool, default=False):
if True returns in descending order
Returns:
OrderedDict[A, B]: new dictionary where the keys are ordered
Example:
>>> import ubelt as ub
>>> dict_ = {'spam': 2.62, 'eggs': 1.20, 'jam': 2.92}
>>> newdict = sorted_keys(dict_)
>>> print(ub.repr2(newdict, nl=0))
{'eggs': 1.2, 'jam': 2.92, 'spam': 2.62}
>>> newdict = sorted_keys(dict_, reverse=True)
>>> print(ub.repr2(newdict, nl=0))
{'spam': 2.62, 'jam': 2.92, 'eggs': 1.2}
>>> newdict = sorted_keys(dict_, key=lambda x: sum(map(ord, x)))
>>> print(ub.repr2(newdict, nl=0))
{'jam': 2.92, 'eggs': 1.2, 'spam': 2.62}
"""
if key is None:
newdict = OrderedDict(sorted(dict_.items(), key=lambda kv: kv[0],
reverse=reverse))
else:
newdict = OrderedDict(sorted(dict_.items(), key=lambda kv: key(kv[0]),
reverse=reverse))
return newdict
[docs]def invert_dict(dict_, unique_vals=True):
"""
Swaps the keys and values in a dictionary.
Args:
dict_ (Dict[A, B]): dictionary to invert
unique_vals (bool, default=True): if False, the values of the new
dictionary are sets of the original keys.
Returns:
Dict[B, A] | Dict[B, Set[A]]:
the inverted dictionary
Notes:
The must values be hashable.
If the original dictionary contains duplicate values, then only one of
the corresponding keys will be returned and the others will be
discarded. This can be prevented by setting ``unique_vals=False``,
causing the inverted keys to be returned in a set.
Example:
>>> import ubelt as ub
>>> dict_ = {'a': 1, 'b': 2}
>>> inverted = ub.invert_dict(dict_)
>>> assert inverted == {1: 'a', 2: 'b'}
Example:
>>> import ubelt as ub
>>> dict_ = ub.odict([(2, 'a'), (1, 'b'), (0, 'c'), (None, 'd')])
>>> inverted = ub.invert_dict(dict_)
>>> assert list(inverted.keys())[0] == 'a'
Example:
>>> import ubelt as ub
>>> dict_ = {'a': 1, 'b': 0, 'c': 0, 'd': 0, 'f': 2}
>>> inverted = ub.invert_dict(dict_, unique_vals=False)
>>> assert inverted == {0: {'b', 'c', 'd'}, 1: {'a'}, 2: {'f'}}
"""
if unique_vals:
if isinstance(dict_, OrderedDict):
inverted = OrderedDict((val, key) for key, val in dict_.items())
else:
inverted = {val: key for key, val in dict_.items()}
else:
# Handle non-unique keys using groups
inverted = defaultdict(set)
for key, value in dict_.items():
inverted[value].add(key)
inverted = dict(inverted)
return inverted
[docs]def named_product(_=None, **basis):
"""
Generates the Cartesian product of the ``basis.values()``, where each
generated item labeled by ``basis.keys()``.
In other words, given a dictionary that maps each "axes" (i.e. some
variable) to its "basis" (i.e. the possible values that it can take),
generate all possible points in that grid (i.e. unique assignments of
variables to values).
Args:
_ (dict | None, default=None):
Use of this positional argument is not recommend. Instead specify
all arguments as keyword args.
If specified, this should be a dictionary is unioned with the
keyword args. This exists to support ordered dictionaries before
Python 3.6, and may eventually be removed.
basis (Dict[K, List[T]]):
A dictionary where the keys correspond to "columns" and the values
are a list of possible values that "column" can take.
I.E. each key corresponds to an "axes", the values are the list of
possible values for that "axes".
Yields:
Dict[K, T] - a "row" in the "longform" data containing a point in the
Cartesian product.
Notes:
This function is similar to :func:`itertools.product`, the only
difference is that the generated items are a dictionary that retains
the input keys instead of an tuple.
This function used to be called "basis_product", but "named_product"
might be more appropriate. This function exists in other places ([1],
[2], and [3]).
References:
.. [1] https://gist.github.com/minstrel271/d51654af3fa4e6411267
.. [2] https://py-toolbox.readthedocs.io/en/latest/modules/itertools.html#
.. [3] https://twitter.com/raymondh/status/970380630822305792
Example:
>>> # An example use case is looping over all possible settings in a
>>> # configuration dictionary for a grid search over parameters.
>>> import ubelt as ub
>>> basis = {
>>> 'arg1': [1, 2, 3],
>>> 'arg2': ['A1', 'B1'],
>>> 'arg3': [9999, 'Z2'],
>>> 'arg4': ['always'],
>>> }
>>> import ubelt as ub
>>> # sort input data for older python versions
>>> basis = ub.odict(sorted(basis.items()))
>>> got = list(ub.named_product(basis))
>>> print(ub.repr2(got, nl=-1))
[
{'arg1': 1, 'arg2': 'A1', 'arg3': 9999, 'arg4': 'always'},
{'arg1': 1, 'arg2': 'A1', 'arg3': 'Z2', 'arg4': 'always'},
{'arg1': 1, 'arg2': 'B1', 'arg3': 9999, 'arg4': 'always'},
{'arg1': 1, 'arg2': 'B1', 'arg3': 'Z2', 'arg4': 'always'},
{'arg1': 2, 'arg2': 'A1', 'arg3': 9999, 'arg4': 'always'},
{'arg1': 2, 'arg2': 'A1', 'arg3': 'Z2', 'arg4': 'always'},
{'arg1': 2, 'arg2': 'B1', 'arg3': 9999, 'arg4': 'always'},
{'arg1': 2, 'arg2': 'B1', 'arg3': 'Z2', 'arg4': 'always'},
{'arg1': 3, 'arg2': 'A1', 'arg3': 9999, 'arg4': 'always'},
{'arg1': 3, 'arg2': 'A1', 'arg3': 'Z2', 'arg4': 'always'},
{'arg1': 3, 'arg2': 'B1', 'arg3': 9999, 'arg4': 'always'},
{'arg1': 3, 'arg2': 'B1', 'arg3': 'Z2', 'arg4': 'always'}
]
Example:
>>> import ubelt as ub
>>> list(ub.named_product(a=[1, 2, 3]))
[{'a': 1}, {'a': 2}, {'a': 3}]
>>> # xdoctest: +IGNORE_WANT
>>> list(ub.named_product(a=[1, 2, 3], b=[4, 5]))
[{'a': 1, 'b': 4},
{'a': 1, 'b': 5},
{'a': 2, 'b': 4},
{'a': 2, 'b': 5},
{'a': 3, 'b': 4},
{'a': 3, 'b': 5}]
"""
# Handle one positional argument.
if _ is not None:
_basis = _
_basis.update(basis)
basis = _basis
keys = list(basis.keys())
for vals in it.product(*basis.values()):
kw = dict(zip(keys, vals))
yield kw
[docs]def varied_values(longform, min_variations=0, default=NoParam):
"""
Given a list of dictionaries, find the values that differ between them.
Args:
longform (List[Dict]):
This is longform data, as described in [1]_. It is a list of
dictionaries.
Each item in the list - or row - is a dictionary and can be thought
of as an observation. The keys in each dictionary are the columns.
The values of the dictionary must be hashable. Lists will be
converted into tuples.
min_variations (int, default=0):
"columns" with fewer than ``min_variations`` unique values are
removed from the result.
default (object, default=NoParam):
if specified, unspecified columns are given this value.
Returns:
dict : a mapping from each "column" to the set of unique values it took
over each "row". If a column is not specified for each row, it is
assumed to take a `default` value, if it is specified.
Raises:
KeyError: If ``default`` is unspecified and all the rows
do not contain the same columns.
References:
.. [1] https://seaborn.pydata.org/tutorial/data_structure.html#long-form-data
Example:
>>> # An example use case is to determine what values of a
>>> # configuration dictionary were tried in a random search
>>> # over a parameter grid.
>>> import ubelt as ub
>>> longform = [
>>> {'col1': 1, 'col2': 'foo', 'col3': None},
>>> {'col1': 1, 'col2': 'foo', 'col3': None},
>>> {'col1': 2, 'col2': 'bar', 'col3': None},
>>> {'col1': 3, 'col2': 'bar', 'col3': None},
>>> {'col1': 9, 'col2': 'bar', 'col3': None},
>>> {'col1': 1, 'col2': 'bar', 'col3': None},
>>> ]
>>> varied = ub.varied_values(longform)
>>> print('varied = {}'.format(ub.repr2(varied, nl=1)))
varied = {
'col1': {1, 2, 3, 9},
'col2': {'bar', 'foo'},
'col3': {None},
}
Example:
>>> import ubelt as ub
>>> import random
>>> longform = [
>>> {'col1': 1, 'col2': 'foo', 'col3': None},
>>> {'col1': 1, 'col2': [1, 2], 'col3': None},
>>> {'col1': 2, 'col2': 'bar', 'col3': None},
>>> {'col1': 3, 'col2': 'bar', 'col3': None},
>>> {'col1': 9, 'col2': 'bar', 'col3': None},
>>> {'col1': 1, 'col2': 'bar', 'col3': None, 'extra_col': 3},
>>> ]
>>> # Operation fails without a default
>>> import pytest
>>> with pytest.raises(KeyError):
>>> varied = ub.varied_values(longform)
>>> #
>>> # Operation works with a default
>>> varied = ub.varied_values(longform, default='<unset>')
>>> expected = {
>>> 'col1': {1, 2, 3, 9},
>>> 'col2': {'bar', 'foo', (1, 2)},
>>> 'col3': set([None]),
>>> 'extra_col': {'<unset>', 3},
>>> }
>>> print('varied = {!r}'.format(varied))
>>> assert varied == expected
Example:
>>> # xdoctest: +REQUIRES(PY3)
>>> # Random numbers are different in Python2, so skip in that case
>>> import ubelt as ub
>>> import random
>>> num_cols = 11
>>> num_rows = 17
>>> rng = random.Random(0)
>>> # Generate a set of columns
>>> columns = sorted(ub.hash_data(i)[0:8] for i in range(num_cols))
>>> # Generate rows for each column
>>> longform = [
>>> {key: ub.hash_data(key)[0:8] for key in columns}
>>> for _ in range(num_rows)
>>> ]
>>> # Add in some varied values in random positions
>>> for row in longform:
>>> if rng.random() > 0.5:
>>> for key in sorted(row.keys()):
>>> if rng.random() > 0.95:
>>> row[key] = 'special-' + str(rng.randint(1, 32))
>>> varied = ub.varied_values(longform, min_variations=1)
>>> print('varied = {}'.format(ub.repr2(varied, nl=1, sort=True)))
varied = {
'095f3e44': {'8fb4d4c9', 'special-23'},
'365d11a1': {'daa409da', 'special-31', 'special-32'},
'5815087d': {'1b823610', 'special-3'},
'7b54b668': {'349a782c', 'special-10'},
'b8244d02': {'d57bca90', 'special-8'},
'f27b5bf8': {'fa0f90d1', 'special-19'},
}
"""
# Enumerate all defined columns
columns = set()
for row in longform:
if default is NoParam and len(row) != len(columns) and len(columns):
missing = set(columns).symmetric_difference(set(row))
raise KeyError((
'No default specified and not every '
'row contains columns {}').format(missing))
columns.update(row.keys())
# Build up the set of unique values for each column
varied = ddict(set)
for row in longform:
for key in columns:
value = row.get(key, default)
if isinstance(value, list):
value = tuple(value)
varied[key].add(value)
# Remove any column that does not have enough variation
if min_variations > 0:
for key, values in list(varied.items()):
if len(values) <= min_variations:
varied.pop(key)
return varied