Comments (3)
Here's an example of a potential API. Indexing accepts either an interval, a key, or a tuple (interval, key). Internally, the structure would be a dict of IntervalDict (performance might be an issue, but this new IntervalMultiDict would be mostly proposed for convenience).
>>> m = P.IntervalMultiDict()
>>> m[P.closed(8, 12), 'teacher'] = 't1'
>>> m[P.closed(8, 10), 'room'] = 'r1'
>>> m[P.closed(11, 12), 'room'] = 'r2'
>>> m['room'] # Providing a key returns a (new?) IntervalDict instance
IntervalDict({P.closed(8, 10): 'r1', P.closed(11, 12): 'r2'})
>>> m[P.closed(8, 10] # Returns a (likely new) IntervalMultiDict
{'teacher': IntervalDict({P.closed(8,10): 't1'}), 'room': IntervalDict(....)}
>>> m[P.singleton(9)] # If interval is a singleton, returns a classical dict
{'teacher': 't1', 'room': 'r1'}
>>> m[P.singleton(9), 'room'] # Singleton + key return a value
'r1'
>>> m[9, 'room'] # For convenience, if a tuple is passed and the interval is a singleton, its value can be provided directly
'r1'
>>> m[P.singleton(9), ['room', 'teacher']] # A list of keys can be passed
{'room': 'r1', 'teacher': 't1'}
>>> m[P.closed(10, 12)] = { 'room': 'r3', 'course': 'c1'} # Dict can be used for assignment
>>> m[P.singleton(11)]
{'teacher': 't1', 'room': 'r3', 'course': 'c1'}
Mutability would only we allowed through indexing (i.e., m[x]
always return a new instance, either of an IntervalDict if x is a key or a list of keys, or an IntervalMultiDict if x is an Interval. Changes on these returned values has no impact on the initial structure. That means that m[p,k] = x
mutates m
while m[p][k] = x
does not).
Since this API is quite distinct from the one of a traditional dictionary, perhaps this structure shouldn't be called "IntervalMultiDict". Using "Dict" in the name implicitly suggests we mimick the API of a dict
(as was the case for IntervalDict
) but this means we need to implement many more operations (such as keys(), values(), items() returning views), and this can be tricky at first
from portion.
While convenient, these two examples are "incorrect" in the sense that one would expect the output type to be consistent for all intervals, including singletons:
>>> m[P.singleton(9)] # If interval is a singleton, returns a classical dict
{'teacher': 't1', 'room': 'r1'}
>>> m[P.singleton(9), 'room'] # Singleton + key return a value
'r1'
In the first example, P.singleton(9)
cannot be replaced by 9
since there's no way to detect in m[x]
whether x
aims to refer to a value from the interval domain, or a key from the dictionary.
from portion.
I'll keep this for later...
from portion.
Related Issues (20)
- Get number of times multiple intervals overlap HOT 3
- Iteration of empty intervals is inconsistent. in general "empty := (+inf,-inf)" is problematic HOT 15
- Is this library performant enough to work with (non-atomic) intervals which span integers between 1 and 1 billion? HOT 1
- Using an external comparator? HOT 3
- iterate is broken with subclasses of Interval HOT 12
- Add __format__ method to Interval (improvement) HOT 16
- Thoughts about text-annotation use case and Pandas Ext. API HOT 5
- interval diameter (length, width, measure, range, or size) HOT 8
- Add join / merge method HOT 2
- "compatible version" specifier in setup.py confuses poetry HOT 2
- AttributeError: module 'portion.interval' has no attribute 'empty' HOT 3
- Add example for pandas in README HOT 5
- importlib error with create_api HOT 2
- Error to import interval, inf, imath from interval HOT 1
- importlib.machinery error with create_api HOT 6
- Enclosure Calculation Bug HOT 1
- Empty Calculation Bug HOT 3
- The performance issues of interval calculations in large quantities. HOT 1
- mass/Lebesgue measure? HOT 7
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from portion.