def test():
'''Test basic workings of `is_iterable`.'''
iterables = [
[1, 2, 3],
(1, 2),
{},
(),
[[1]],
'asdfasdf',
''
]
non_iterables = [
dict,
list,
type,
None,
True,
False,
Exception,
lambda x: x
]
for iterable in iterables:
assert is_iterable(iterable)
for non_iterable in non_iterables:
assert not is_iterable(non_iterable)
def test():
'''Test basic workings of `is_iterable`.'''
iterables = [
[1, 2, 3],
(1, 2),
{},
(),
[[1]],
'asdfasdf',
''
]
non_iterables = [
dict,
list,
type,
None,
True,
False,
Exception,
lambda x: x
]
for iterable in iterables:
assert is_iterable(iterable)
for non_iterable in non_iterables:
assert not is_iterable(non_iterable)
def reverse_with_set_values(d, sort=False):
'''
Reverse the dict, with the values of the new dict being sets.
Example:
reverse_with_set_values({1: 2, 3: 4, 'meow': 2}) == \
{2: {1, 'meow'}, 4: {3}}
Instead of a dict you may also input a tuple in which the first item is an
iterable and the second item is either a key function or an attribute name.
A dict will be constructed from these and used.
If you'd like the result dict to be sorted, pass `sort=True`, and you'll
get a sorted `OrderedDict`. You can also specify the sorting key function
or attribute name as the `sort` argument.
'''
### Pre-processing input: #################################################
# #
if hasattr(d, 'items'): # `d` is a dict
fixed_dict = d
else: # `d` is not a dict
assert cute_iter_tools.is_iterable(d) and len(d) == 2
iterable, key_function_or_attribute_name = d
assert cute_iter_tools.is_iterable(iterable)
key_function = comparison_tools.process_key_function_or_attribute_name(
key_function_or_attribute_name
)
fixed_dict = {key: key_function(key) for key in iterable}
# #
### Finished pre-processing input. ########################################
new_dict = {}
for key, value in fixed_dict.items():
if value not in new_dict:
new_dict[value] = []
new_dict[value].append(key)
# Making into sets:
for key, value in new_dict.copy().items():
new_dict[key] = set(value)
if sort:
from python_toolbox import nifty_collections
ordered_dict = nifty_collections.OrderedDict(new_dict)
if isinstance(sort, (collections.Callable, str)):
key_function = comparison_tools. \
process_key_function_or_attribute_name(sort)
else:
assert sort is True
key_function = None
ordered_dict.sort(key_function)
return ordered_dict
else:
return new_dict
def reverse_with_set_values(d, sort=False):
'''
Reverse the dict, with the values of the new dict being sets.
Example:
reverse_with_set_values({1: 2, 3: 4, 'meow': 2}) == \
{2: {1, 'meow'}, 4: {3}}
Instead of a dict you may also input a tuple in which the first item is an
iterable and the second item is either a key function or an attribute name.
A dict will be constructed from these and used.
If you'd like the result dict to be sorted, pass `sort=True`, and you'll
get a sorted `OrderedDict`. You can also specify the sorting key function
or attribute name as the `sort` argument.
'''
### Pre-processing input: #################################################
# #
if hasattr(d, 'items'): # `d` is a dict
fixed_dict = d
else: # `d` is not a dict
assert cute_iter_tools.is_iterable(d) and len(d) == 2
iterable, key_function_or_attribute_name = d
assert cute_iter_tools.is_iterable(iterable)
key_function = comparison_tools.process_key_function_or_attribute_name(
key_function_or_attribute_name
)
fixed_dict = {key: key_function(key) for key in iterable}
# #
### Finished pre-processing input. ########################################
new_dict = {}
for key, value in fixed_dict.iteritems():
if value not in new_dict:
new_dict[value] = []
new_dict[value].append(key)
# Making into sets:
for key, value in new_dict.copy().iteritems():
new_dict[key] = set(value)
if sort:
from python_toolbox import nifty_collections
ordered_dict = nifty_collections.OrderedDict(new_dict)
if isinstance(sort, (collections.Callable, basestring)):
key_function = comparison_tools. \
process_key_function_or_attribute_name(sort)
else:
assert sort is True
key_function = None
ordered_dict.sort(key_function)
return ordered_dict
else:
return new_dict
def __init__(self, function):
'''
Construct the `TempFunctionCallCounter`.
For `function`, you may pass in either a function object, or a
`(parent_object, function_name)` pair, or a `(getter, setter)` pair.
'''
if cute_iter_tools.is_iterable(function):
first, second = function
if isinstance(second, str):
actual_function = getattr(first, second)
else:
assert callable(first) and callable(second)
actual_function = first(
) # `first` is the getter in this case.
else: # not cute_iter_tools.is_iterable(function)
assert callable(function)
actual_function = function
try:
address = address_tools.object_to_string.get_address(function)
parent_object_address, function_name = address.rsplit('.', 1)
parent_object = address_tools.resolve(parent_object_address)
except Exception:
raise Exception("Couldn't obtain parent/name pair from "
"function; supply one manually or "
"alternatively supply a getter/setter pair.")
first, second = parent_object, function_name
self.call_counting_function = count_calls(actual_function)
TempValueSetter.__init__(self, (first, second),
value=self.call_counting_function)
def __init__(self, function):
"""
Construct the `TempFunctionCallCounter`.
For `function`, you may pass in either a function object, or a
`(parent_object, function_name)` pair, or a `(getter, setter)` pair.
"""
if cute_iter_tools.is_iterable(function):
first, second = function
if isinstance(second, basestring):
actual_function = getattr(first, second)
else:
assert callable(first) and callable(second)
actual_function = first() # `first` is the getter in this case.
else: # not cute_iter_tools.is_iterable(function)
assert callable(function)
actual_function = function
try:
address = address_tools.object_to_string.get_address(function)
parent_object_address, function_name = address.rsplit(".", 1)
parent_object = address_tools.resolve(parent_object_address)
except Exception:
raise Exception(
"Couldn't obtain parent/name pair from "
"function; supply one manually or "
"alternatively supply a getter/setter pair."
)
first, second = parent_object, function_name
self.call_counting_function = count_calls(actual_function)
TempValueSetter.__init__(self, (first, second), value=self.call_counting_function)
def __init__(self, inputs=(), outputs=(), name=None):
'''
Construct the emitter.
`inputs` is a list of inputs, all of them must be emitters.
`outputs` is a list of outputs, they must be either emitters or
callables.
`name` is a string name for the emitter.
'''
assert cute_iter_tools.is_iterable(inputs) and \
cute_iter_tools.is_iterable(outputs)
self._inputs = set()
'''The emitter's inputs.'''
self._outputs = set()
'''The emitter's inputs.'''
for output in outputs:
self.add_output(output)
self.__total_callable_outputs_cache = None
'''
A cache of total callable outputs.
This means the callable outputs of this emitter and any output
emitters.
'''
self._recalculate_total_callable_outputs()
# We made sure to create the callable outputs cache before we add
# inputs, so when we update their cache, it could use ours.
for input in inputs:
self.add_input(input)
self.name = name
'''The emitter's name.'''
def __init__(self, inputs=(), outputs=(), name=None):
'''
Construct the emitter.
`inputs` is a list of inputs, all of them must be emitters.
`outputs` is a list of outputs, they must be either emitters or
callables.
`name` is a string name for the emitter.
'''
assert cute_iter_tools.is_iterable(inputs) and \
cute_iter_tools.is_iterable(outputs)
self._inputs = set()
'''The emitter's inputs.'''
self._outputs = set()
'''The emitter's inputs.'''
for output in outputs:
self.add_output(output)
self.__total_callable_outputs_cache = None
'''
A cache of total callable outputs.
This means the callable outputs of this emitter and any output
emitters.
'''
self._recalculate_total_callable_outputs()
# We made sure to create the callable outputs cache before we add
# inputs, so when we update their cache, it could use ours.
for input in inputs:
self.add_input(input)
self.name = name
'''The emitter's name.'''
def is_subclass(candidate, base_class):
'''
Check if `candidate` is a subclass of `base_class`.
You may pass in a tuple of base classes instead of just one, and it will
check whether `candidate` is a subclass of any of these base classes.
This has the advantage that it doesn't throw an exception if `candidate` is
not a type. (Python issue 10569.)
'''
# todo: disable ability to use nested iterables.
if cute_iter_tools.is_iterable(base_class):
return any(is_subclass(candidate, single_base_class) for
single_base_class in base_class)
elif not isinstance(candidate, (type, types.ClassType)):
return False
else:
return issubclass(candidate, base_class)
def is_subclass(candidate, base_class):
'''
Check if `candidate` is a subclass of `base_class`.
You may pass in a tuple of base classes instead of just one, and it will
check whether `candidate` is a subclass of any of these base classes.
This has the advantage that it doesn't throw an exception if `candidate` is
not a type. (Python issue 10569.)
'''
# todo: disable ability to use nested iterables.
if cute_iter_tools.is_iterable(base_class):
return any(
is_subclass(candidate, single_base_class)
for single_base_class in base_class)
elif not isinstance(candidate, type):
return False
else:
return issubclass(candidate, base_class)
def get_equivalence_classes(iterable, key=None, container=set, *,
use_ordered_dict=False, sort_ordered_dict=False):
'''
Divide items in `iterable` to equivalence classes, using the key function.
Each item will be put in a set with all other items that had the same
result when put through the `key` function.
Example:
>>> get_equivalence_classes(range(10), lambda x: x % 3)
{0: {0, 9, 3, 6}, 1: {1, 4, 7}, 2: {8, 2, 5}}
Returns a `dict` with keys being the results of the function, and the
values being the sets of items with those values.
Alternate usages:
Instead of a key function you may pass in an attribute name as a
string, and that attribute will be taken from each item as the key.
Instead of an iterable and a key function you may pass in a `dict` (or
similar mapping) into `iterable`, without specifying a `key`, and the
value of each item in the `dict` will be used as the key.
Example:
>>> get_equivalence_classes({1: 2, 3: 4, 'meow': 2})
{2: {1, 'meow'}, 4: {3}}
If you'd like the result to be in an `OrderedDict`, specify
`use_ordered_dict=True`, and the items will be ordered according to
insertion order. If you'd like that `OrderedDict` to be sorted, pass in
`sort_ordered_dict=True`. (It automatically implies
`use_ordered_dict=True`.) You can also pass in a sorting key function or
attribute name as the `sort_ordered_dict` argument.
'''
from python_toolbox import comparison_tools
### Pre-processing input: #################################################
# #
if key is None:
if isinstance(iterable, collections.abc.Mapping):
d = iterable
else:
try:
d = dict(iterable)
except ValueError:
raise Exception(
"You can't put in a non-dict without also supplying a "
"`key` function. We need to know which key to use."
)
else: # key is not None
assert cute_iter_tools.is_iterable(iterable)
key_function = comparison_tools.process_key_function_or_attribute_name(
key
)
d = {key: key_function(key) for key in iterable}
# #
### Finished pre-processing input. ########################################
if use_ordered_dict or sort_ordered_dict:
from python_toolbox import nifty_collections
new_dict = nifty_collections.OrderedDict()
else:
new_dict = {}
for key, value in d.items():
new_dict.setdefault(value, []).append(key)
# Making into desired container:
for key, value in new_dict.copy().items():
new_dict[key] = container(value)
if sort_ordered_dict:
if isinstance(sort_ordered_dict, (collections.abc.Callable, str)):
key_function = comparison_tools. \
process_key_function_or_attribute_name(sort_ordered_dict)
new_dict.sort(key_function)
elif sort_ordered_dict is True:
new_dict.sort()
return new_dict
else:
return new_dict
def get_equivalence_classes(iterable, key=None, container=set,
use_ordered_dict=False, sort_ordered_dict=False):
'''
Divide items in `iterable` to equivalence classes, using the key function.
Each item will be put in a set with all other items that had the same
result when put through the `key` function.
Example:
>>> get_equivalence_classes(range(10), lambda x: x % 3)
{0: {0, 9, 3, 6}, 1: {1, 4, 7}, 2: {8, 2, 5}}
Returns a `dict` with keys being the results of the function, and the
values being the sets of items with those values.
Alternate usages:
Instead of a key function you may pass in an attribute name as a
string, and that attribute will be taken from each item as the key.
Instead of an iterable and a key function you may pass in a `dict` (or
similar mapping) into `iterable`, without specifying a `key`, and the
value of each item in the `dict` will be used as the key.
Example:
>>> get_equivalence_classes({1: 2, 3: 4, 'meow': 2})
{2: {1, 'meow'}, 4: {3}}
If you'd like the result to be in an `OrderedDict`, specify
`use_ordered_dict=True`, and the items will be ordered according to
insertion order. If you'd like that `OrderedDict` to be sorted, pass in
`sort_ordered_dict=True`. (It automatically implies
`use_ordered_dict=True`.) You can also pass in a sorting key function or
attribute name as the `sort_ordered_dict` argument.
'''
from python_toolbox import comparison_tools
### Pre-processing input: #################################################
# #
if key is None:
if isinstance(iterable, collections.Mapping):
d = iterable
else:
try:
d = dict(iterable)
except ValueError:
raise Exception(
"You can't put in a non-dict without also supplying a "
"`key` function. We need to know which key to use."
)
else: # key is not None
assert cute_iter_tools.is_iterable(iterable)
key_function = comparison_tools.process_key_function_or_attribute_name(
key
)
d = dict((key, key_function(key)) for key in iterable)
# #
### Finished pre-processing input. ########################################
if use_ordered_dict or sort_ordered_dict:
from python_toolbox import nifty_collections
new_dict = nifty_collections.OrderedDict()
else:
new_dict = {}
for key, value in d.items():
new_dict.setdefault(value, []).append(key)
# Making into desired container:
for key, value in new_dict.copy().items():
new_dict[key] = container(value)
if sort_ordered_dict:
if isinstance(sort_ordered_dict, (collections.Callable, str)):
key_function = comparison_tools. \
process_key_function_or_attribute_name(sort_ordered_dict)
new_dict.sort(key_function)
elif sort_ordered_dict is True:
new_dict.sort()
return new_dict
else:
return new_dict
