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Persistent vector implementation. Meant as a replacement for the cases where you would normally
use a Python list.
Do not instantiate directly, instead use the factory functions :py:func:`v` and :py:func:`pvector` to
create an instance.
Heavily influenced by the persistent vector available in Clojure. Initially this was more or
less just a port of the Java code for the Clojure vector. It has since been modified and to
some extent optimized for usage in Python.
The vector is organized as a trie, any mutating method will return a new vector that contains the changes. No
updates are done to the original vector. Structural sharing between vectors are applied where possible to save
space and to avoid making complete copies.
This structure corresponds most closely to the built in list type and is intended as a replacement. Where the
semantics are the same (more or less) the same function names have been used but for some cases it is not possible,
for example assignments.
The PVector implements the Sequence protocol and is Hashable.
Inserts are amortized O(1). Random access is log32(n) where n is the size of the vector.
The following are examples of some common operations on persistent vectors:
>>> p = v(1, 2, 3)
>>> p2 = p.append(4)
>>> p3 = p2.extend([5, 6, 7])
>>> p
pvector([1, 2, 3])
>>> p2
pvector([1, 2, 3, 4])
>>> p3
pvector([1, 2, 3, 4, 5, 6, 7])
>>> p3[5]
6
>>> p.set(1, 99)
pvector([1, 99, 3])
>>>
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>>> len(v(1, 2, 3))
3
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Get value at index. Full slicing support.
>>> v1 = v(5, 6, 7, 8)
>>> v1[2]
7
>>> v1[1:3]
pvector([6, 7])
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>>> v1 = v(1, 2)
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pvector([1, 2, 3, 4])
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>>> 3 * v1
pvector([1, 2, 1, 2, 1, 2])
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>>> v1 = v(1, 2, 3)
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True
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Create a new evolver for this pvector. The evolver acts as a mutable view of the vector
with "transaction like" semantics. No part of the underlying vector i updated, it is still
fully immutable. Furthermore multiple evolvers created from the same pvector do not
interfere with each other.
You may want to use an evolver instead of working directly with the pvector in the
following cases:
* Multiple updates are done to the same vector and the intermediate results are of no
interest. In this case using an evolver may be a more efficient and easier to work with.
* You need to pass a vector into a legacy function or a function that you have no control
over which performs in place mutations of lists. In this case pass an evolver instance
instead and then create a new pvector from the evolver once the function returns.
The following example illustrates a typical workflow when working with evolvers. It also
displays most of the API (which i kept small by design, you should not be tempted to
use evolvers in excess ;-)).
Create the evolver and perform various mutating updates to it:
>>> v1 = v(1, 2, 3, 4, 5)
>>> e = v1.evolver()
>>> e[1] = 22
>>> _ = e.append(6)
>>> _ = e.extend([7, 8, 9])
>>> e[8] += 1
>>> len(e)
9
The underlying pvector remains the same:
>>> v1
pvector([1, 2, 3, 4, 5])
The changes are kept in the evolver. An updated pvector can be created using the
persistent() function on the evolver.
>>> v2 = e.persistent()
>>> v2
pvector([1, 22, 3, 4, 5, 6, 7, 8, 10])
The new pvector will share data with the original pvector in the same way that would have
been done if only using operations on the pvector.
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Return a new vector with elements in specified positions replaced by values (multi set).
Elements on even positions in the argument list are interpreted as indexes while
elements on odd positions are considered values.
>>> v1 = v(1, 2, 3)
>>> v1.mset(0, 11, 2, 33)
pvector([11, 2, 33])
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Return a new vector with element at position i replaced with val. The original vector remains unchanged.
Setting a value one step beyond the end of the vector is equal to appending. Setting beyond that will
result in an IndexError.
>>> v1 = v(1, 2, 3)
>>> v1.set(1, 4)
pvector([1, 4, 3])
>>> v1.set(3, 4)
pvector([1, 2, 3, 4])
>>> v1.set(-1, 4)
pvector([1, 2, 4])
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Return a new vector with val appended.
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>>> v1.append(3)
pvector([1, 2, 3])
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Return a new vector with all values in obj appended to it. Obj may be another
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>>> v1.extend([4, 5])
pvector([1, 2, 3, 4, 5])
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Return first index of value. Additional indexes may be supplied to limit the search to a
sub range of the vector.
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>>> v1.index(3)
2
>>> v1.index(3, 3, 5)
4
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Return the number of times that value appears in the vector.
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>>> v1.count(4)
2
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Transform arbitrarily complex combinations of PVectors and PMaps. A transformation
consists of two parts. One match expression that specifies which elements to transform
and one transformation function that performs the actual transformation.
>>> from pyrsistent import freeze, ny
>>> news_paper = freeze({'articles': [{'author': 'Sara', 'content': 'A short article'},
... {'author': 'Steve', 'content': 'A slightly longer article'}],
... 'weather': {'temperature': '11C', 'wind': '5m/s'}})
>>> short_news = news_paper.transform(['articles', ny, 'content'], lambda c: c[:25] + '...' if len(c) > 25 else c)
>>> very_short_news = news_paper.transform(['articles', ny, 'content'], lambda c: c[:15] + '...' if len(c) > 15 else c)
>>> very_short_news.articles[0].content
'A short article'
>>> very_short_news.articles[1].content
'A slightly long...'
When nothing has been transformed the original data structure is kept
>>> short_news is news_paper
True
>>> very_short_news is news_paper
False
>>> very_short_news.articles[0] is news_paper.articles[0]
True
Nr
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Delete a portion of the vector by index or range.
>>> v1 = v(1, 2, 3, 4, 5)
>>> v1.delete(1)
pvector([1, 3, 4, 5])
>>> v1.delete(1, 3)
pvector([1, 4, 5])
Nr
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Remove the first occurrence of a value from the vector.
>>> v1 = v(1, 2, 3, 2, 1)
>>> v2 = v1.remove(1)
>>> v2
pvector([2, 3, 2, 1])
>>> v2.remove(1)
pvector([2, 3, 2])
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Create a new persistent vector containing the elements in iterable.
>>> v1 = pvector([1, 2, 3])
>>> v1
pvector([1, 2, 3])
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>>> v1
pvector([1, 2, 3])
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