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pstar

numpy for arbitrary data.

pstar provides easy, expressive, and concise manipulation of arbitrary data.

Examples:

$ pip install pstar

$ python  # Give it a spin! (Or use ipython, if installed.)
from pstar import *


# pdict basics
pd = pdict(foo=1, bar=2.0)
pd.baz = 'three'

pd.qj('Hello, pdict!')
# Logs:
#   qj: <module_level_code> Hello, pdict! <6>: {'bar': 2.0, 'baz': 'three', 'foo': 1}

pd.update({'bin': 44}).qj('Chaining through update!')
# Logs:
#   qj: <module_level_code> Chaining through update! <10>: {'bar': 2.0, 'baz': 'three', 'bin': 44, 'foo': 1}

pd[['foo', 'bar']].qj('Multi-indexing!')
# Logs:
#   qj: <module_level_code> Multi-indexing! <14>: [1, 2.0]

pd[['foo', 'bar', 'bin']] = ['one', 'ii', '44']
pd.qj('Multi-assignment!')
# Logs:
#   qj: <module_level_code> Multi-assignment! <19>: {'bar': 'ii', 'baz': 'three', 'bin': '44', 'foo': 'one'}

pd[pd.peys()] = 'what?! ' + pd.palues()
pd.qj('Easy manipulation of values!')
# Logs:
#   qj: <module_level_code> Easy manipulation of values! <24>: {'bar': 'what?! ii', 'baz': 'what?! three', 'bin': 'what?! 44', 'foo': 'what?! one'}

pd.rekey(foo='floo').qj('Easy manipulation of keys!')
# Logs:
#   qj: <module_level_code> Easy manipulation of keys! <28>: {'bar': 'what?! ii', 'baz': 'what?! three', 'bin': 'what?! 44', 'floo': 'what?! one'}


# defaultpdict basics
dpd = defaultpdict(int)
dpd.bar = dpd.foo + 1

dpd.qj('Hello, defaultpdict!')
# Logs:
#   qj: module_level_code: Hello, defaultpdict! <39>: {'bar': 1, 'foo': 0}

dpd[['foo', 'bar']].qj('The same api as pdict!')
# Logs:
#   qj: module_level_code:   The same api as pdict! <43>: [0, 1]

dpd = defaultpdict(lambda: defaultpdict(list))
dpd.name = 'Thing 1'
dpd.stats.foo.append(1)
dpd.stats.bar.append(22)

dpd.qj('Nested defaultpdicts make great lightweight objects!')
# Logs:
#   qj: module_level_code: Nested defaultpdicts make great lightweight objects! <6>: {'name': 'Thing 1', 'stats': {'bar': [22], 'foo': [1]}}


# plist basics
pl = plist[1, 2, 3]
pl.qj('Hello, plist!')
# Logs:
#   qj: module_level_code: Hello, plist! <33>: [1, 2, 3]

pl *= pl
pl.qj('plists can mostly be used as if they are a single instance of the type they contain!')
# Logs:
#   qj: module_level_code: plists can mostly be used as if they are a single instance of the type they contain! <8>: [1, 4, 9]

pl = plist([pdict(foo=i, bar=i % 2) for i in range(5)])
pl.baz = pl.foo % 3

pl.qj('That includes plists of pdicts, or other arbitrary objects!')
# Logs:
#   qj: module_level_code: That includes plists of pdicts, or other arbitrary objects! <4>: [{'bar': 0, 'baz': 0, 'foo': 0}, {'bar': 1, 'baz': 1, 'foo': 1}, {'bar': 0, 'baz': 2, 'foo': 2}, {'bar': 1, 'baz': 0, 'foo': 3}, {'bar': 0, 'baz': 1, 'foo': 4}]

(pl.bar == 0).qj('Data are meant to be filtered!')[('foo', 'baz')].pstr().replace(', ', ' {bar} ').qj('And processed!').format(bar=(pl.bar == 0).bar).qj('And merged!')
# Logs:
#   qj: module_level_code: Data are meant to be filtered! <1>: [{'bar': 0, 'baz': 0, 'foo': 0}, {'bar': 0, 'baz': 2, 'foo': 2}, {'bar': 0, 'baz': 1, 'foo': 4}]
#   qj: module_level_code:  And processed! <1>: ['(0 {bar} 0)', '(2 {bar} 2)', '(4 {bar} 1)']
#   qj: module_level_code:   And merged! <1>: ['(0 0 0)', '(2 0 2)', '(4 0 1)']

by_bar = pl.bar.groupby().qj('Grouping data is also powerful!')
# Logs:
#   qj: module_level_code: Grouping data is also powerful! <1>: [[{'bar': 0, 'baz': 0, 'foo': 0}, {'bar': 0, 'baz': 2, 'foo': 2}, {'bar': 0, 'baz': 1, 'foo': 4}], [{'bar': 1, 'baz': 1, 'foo': 1}, {'bar': 1, 'baz': 0, 'foo': 3}]]

by_bar[('foo', 'baz')].pstr().replace(', ', ' {bar} ').format(bar=by_bar.bar).qj('Now we can get same result for all of the data!')
# Logs:
#   qj: module_level_code: Now we can get same result for all of the data! <1>: [['(0 0 0)', '(2 0 2)', '(4 0 1)'], ['(1 1 1)', '(3 1 0)']]

by_bar.qj('Note that the original data are unchanged!')
# Logs:
#   qj: module_level_code: Note that the original data are unchanged! <1>: [[{'bar': 0, 'baz': 0, 'foo': 0}, {'bar': 0, 'baz': 2, 'foo': 2}, {'bar': 0, 'baz': 1, 'foo': 4}], [{'bar': 1, 'baz': 1, 'foo': 1}, {'bar': 1, 'baz': 0, 'foo': 3}]]

pl.foo.apply(lambda floo, blar, blaz: floo * (blar + blaz), pl.bar, blaz=pl.baz).qj('Want to send your data element-wise to a function? Easy!')
# Logs:
#   qj: module_level_code: Want to send your data element-wise to a function? Easy! <1>: [0, 2, 4, 3, 4]

by_bar.foo.apply(lambda floo, blar, blaz: floo * (blar + blaz), by_bar.bar, blaz=by_bar.baz).qj('The same function just works when using groups!')
# Logs:
#   qj: module_level_code: The same function just works when using groups! <3>: [[0, 4, 4], [2, 3]]

new_pd = pd.qj('Remember pdicts?').palues().qj('Some of their functions return plists').replace('what?! ', 'sweet! ').qj('allowing you to update their values naturally').pdict().qj('and turn it back into a new pdict!')
# Logs:
#   qj: module_level_code: Remember pdicts? <101>: {'bar': 'what?! ii', 'baz': 'what?! three', 'bin': 'what?! 44', 'foo': 'what?! one'}
#   qj: module_level_code:  Some of their functions return plists <101>: ['what?! ii', 'what?! three', 'what?! 44', 'what?! one']
#   qj: module_level_code:   allowing you to update their values naturally <101>: ['sweet! ii', 'sweet! three', 'sweet! 44', 'sweet! one']
#   qj: module_level_code:    and turn it back into a new pdict! <101>: {'bar': 'sweet! ii', 'baz': 'sweet! three', 'bin': 'sweet! 44', 'foo': 'sweet! one'}

# A hint at how easy it can be to do complex data manipulations...
whoa = plist[pd, new_pd]
whoa.palues().replace(whoa.palues()._[:6:1], whoa.palues()._[7::1]).pdict_().qj('whoa!')
# Logs:
#   qj: module_level_code: whoa! <2>: [{'bar': 'ii ii', 'baz': 'three three', 'bin': '44 44', 'foo': 'one one'}, {'bar': 'ii ii', 'baz': 'three three', 'bin': '44 44', 'foo': 'one one'}]

Larger Example -- Documentation Generation:

See build_docs.py for a more extensive example of using pstar. That code generates all of the documentation markdown files for pstar using a simple templating system. It also extracts tests from the code doc strings and adds them to the test suite.

Philosophy:

pstar makes writing and debugging data-processing code easy and concise.

pdict and defaultpdict:

pdict and defaultpdict are drop-in replacements for dict and defaultdict, but provide substantial usability improvements, including dot notation for field access, chaining from calls to update, and easy methods to modify their keys and values.

plist:

plist is close to a drop-in replacement for list. It is also close to a drop-in replacement for whatever values it contains. This is the core trick of plist: write your data processing code like you are working with one datum. The closer your code gets to that ideal, the easier it is to write, debug, and understand.

Chaining:

pstar attempts to always maintain the possibility of chaining. Chaining allows you to write code like a sentence, without needing to break up your thoughts to define intermediate variables or to introduce obvious control flow, such as for loops.

The consequence of this perspective is that code written using pstar can often be written with no explicit looping, and each line of code can be read as a straightforward transformation of data from one relevant state to another.

Debugging:

During data processing, it is easy to spend a great deal of time debugging while getting the data into the desired shape or format. Most debugging starts with log statements. pstar incorporates in-chain logging with qj so that code can be minimally modified to add and remove logging.

qj is a logger built for debugging, and has many useful features that are available directly in pstar, including dropping into the debugger at any point in your code:

pl = plist['abc', 'def', '123']
pl = pl.qj().replace(pl._[0].qj(d=1), pl._[-1].qj()).qj()
# Logs:
#   qj: module_level_code: <empty log> <2>: ['abc', 'def', '123']
#   qj: module_level_code:  <empty log> <2>: ['a', 'd', '1']
# Then drops into the debugger. After debugging completes, logs:
#   qj: module_level_code:   <empty log> <2>: ['c', 'f', '3']
#   qj: module_level_code:    <empty log> <2>: ['cbc', 'fef', '323']

See qj for documentation.

Testing and Examples:

pstar is extensively tested. Additionally, almost all of the example code found in the documentation is automatically added to the test suite when documentation is built. Therefore, every block of code in a page of documentation is a self-contained, runnable example that you can copy into a python terminal and run immediately.

Note that because many of the tests check the contents of plists, and plists do filtering when compared, many of the tests look like: assert (foos.aslist() == [...]) in order to bypass the filtering and just run an equality check on two lists. Under normal use, you do not need to call plist.aslist() very often.

Concision:

In the very simple example below, pstar does in six lines with no explicit control flow, what takes 10 lines and three levels of indentation in regular python. The extra lines are from the explicit control flow and the inability to chain the output to a print statement.

# Trivial pstar data processing:
pl = plist([pdict(foo=i, bar=i % 2) for i in range(5)])
pl.baz = (pl.foo + pl.bar) % (len(pl) // 2 + 1)
by_bar = pl.bar.groupby()
(by_bar.bar == 0).bin = '{floo} {blaz} {other}'
(by_bar.bar != 0).bin = '{floo} {blar} {blaz}'
output = by_bar.bin.format(floo=by_bar.foo, blar=by_bar.bar, blaz=by_bar.baz, other=(by_bar.baz + by_bar.foo) * by_bar.bar).qj('output')

# Non-pstar equivalent:
l = [dict(foo=i, bar=i % 2) for i in range(5)]
output = [[], []]
for d in l:
  d['baz'] = (d['foo'] + d['bar']) % (len(l) // 2 + 1)
  if d['bar'] == 0:
    d['bin'] = '{floo} {blaz} {other}'
  else:
    d['bin'] = '{floo} {blar} {blaz}'
  output[d['bar']].append(d['bin'].format(floo=d['foo'], blar=d['bar'], blaz=d['baz'], other=(d['baz'] + d['foo']) * d['bar']))
print('output: ', output)

Worse than the extra length and complexity, the non-plist code has a bug: if the values for bar are ever something other than 0 or 1, the output list will fail. The pstar version of the code is completely robust to that kind of bug. The only assumptions about the data are that it is provided with two fields, 'foo' and 'bar', and that both of the fields are numeric.

See build_docs.py for a more extensive example of using pstar.

Basic Usage:

Install with pip:

$ pip install pstar

Add the following import:

from pstar import *

Equivalently:

from pstar import defaultpdict, frozenpset, pdict, plist, pset, ptuple, pstar

Basic defaultpdict use:

defaultdict subclass where everything is automatically a property.

Examples:

Use with dot notation or subscript notation:

pd = defaultpdict()
pd.foo = 1
assert (pd['foo'] == pd.foo == 1)

Set the desired default constructor as normal to avoid having to construct individual values:

pd = defaultpdict(int)
assert (pd.foo == 0)

list subscripts also work and return a plist of the corresponding keys:

pd = defaultpdict(foo=1, bar=2)
assert (pd[['foo', 'bar']].aslist() == [1, 2])

Setting with a list subscript also works, using a single element or a matching list for the values:

pd = defaultpdict()
pd[['foo', 'bar']] = 1
assert (pd[['foo', 'bar']].aslist() == [1, 1])
pd[['foo', 'bar']] = [1, 2]
assert (pd[['foo', 'bar']].aslist() == [1, 2])

update returns self, rather than None, to support chaining:

pd = defaultpdict(foo=1, bar=2)
pd.update(bar=3).baz = 4
assert (pd.bar == 3)
assert ('baz' in pd.keys())

Nested defaultpdicts make nice lightweight objects:

pd = defaultpdict(lambda: defaultpdict(list))
pd.foo = 1
pd.stats.bar.append(2)
assert (pd['foo'] == 1)
assert (pd.stats.bar == [2])

Conversion:

You can convert from defaultpdict to defaultdict and back using arithmetic operations on the defaultpdict class itself, for convenience:

d1 = defaultdict(int, {'foo': 1, 'bar': 2})
pd = defaultpdict * d1
assert (type(d1) == defaultdict)
assert (type(pd) == defaultpdict)
assert (pd == d1)

d2 = pd / defaultpdict
assert (type(d2) == defaultdict)
assert (d2 == d1)

See pstar for more details on conversion.

Basic frozenpset use:

Placeholder frozenset subclass. Mostly unimplemented.

You can construct frozenpsets in the normal manners for frozensets:

ps = frozenpset([1, 2.0, 'three'])
ps = frozenpset({1, 2.0, 'three'})

frozenpset also supports a convenience constructor from a list literal:

ps = frozenpset[1, 2.0, 'three']

Conversion:

You can convert from frozenpset to frozenset and back using arithmetic operations on the frozenpset class itself, for convenience:

s1 = frozenset([1, 2.0, 'three'])
ps = frozenpset * s1
assert (type(s1) == frozenset)
assert (type(ps) == frozenpset)
assert (ps == s1)

s2 = ps / frozenpset
assert (type(s2) == frozenset)
assert (s2 == s1)

See pstar for more details on conversion.

Basic pdict use:

dict subclass where everything is automatically a property.

Examples:

Use with dot notation or subscript notation:

pd = pdict()
pd.foo = 1
assert (pd['foo'] == pd.foo == 1)

list subscripts also work and return a plist of the corresponding keys:

pd = pdict(foo=1, bar=2)
assert (pd[['foo', 'bar']].aslist() == [1, 2])

Setting with a list subscript also works, using a single element or a matching list for the values:

pd = pdict()
pd[['foo', 'bar']] = 1
assert (pd[['foo', 'bar']].aslist() == [1, 1])
pd[['foo', 'bar']] = [1, 2]
assert (pd[['foo', 'bar']].aslist() == [1, 2])

update returns self, rather than None, to support chaining:

pd = pdict(foo=1, bar=2)
pd.update(bar=3).baz = 4
assert (pd.bar == 3)
assert ('baz' in pd.keys())
assert (pd.baz == 4)

Conversion:

You can convert from pdict to dict and back using arithmetic operations on the pdict class itself, for convenience:

d1 = {'foo': 1, 'bar': 2}
pd = pdict * d1
assert (type(d1) == dict)
assert (type(pd) == pdict)
assert (pd == d1)

d2 = pd / pdict
assert (type(d2) == dict)
assert (d2 == d1)

See pstar for more details on conversion.

Basic plist use:

list subclass for powerful, concise data processing.

Homogeneous access:

plist is the natural extension of object-orientation to homogeneous lists of arbitrary objects. With plist, you can treat a list of objects of the same type as if they are a single object of that type, in many (but not all) circumstances.

pl = plist['abc', 'def', 'ghi']
assert ((pl + ' -> ' + pl.upper()).aslist() ==
        ['abc -> ABC', 'def -> DEF', 'ghi -> GHI'])

Indexing:

Indexing plists is meant to be both powerful and natural, while accounting the fact that the elements of the plist may need to be indexed as well.

See __getitem__, __setitem__, and __delitem__ for more details.

Indexing into the plist itself:

foos = plist([pdict(foo=0, bar=0), pdict(foo=1, bar=1), pdict(foo=2, bar=0)])

# Basic scalar indexing:
assert (foos[0] ==
        dict(foo=0, bar=0))

# plist slice indexing:
assert (foos[:2].aslist() ==
        [dict(foo=0, bar=0), dict(foo=1, bar=1)])

# plist int list indexing:
assert (foos[[0, 2]].aslist() ==
        [dict(foo=0, bar=0), dict(foo=2, bar=0)])

Indexing into the elements of the plist:

# Basic scalar indexing:
assert (foos['foo'].aslist() ==
        [0, 1, 2])

# tuple indexing
assert (foos[('foo', 'bar')].aslist() ==
        [(0, 0), (1, 1), (2, 0)])

# list indexing
assert (foos[['foo', 'bar', 'bar']].aslist() ==
        [0, 1, 0])

Indexing into the elementes of the plist when the elements are indexed by ints, slices, or other means that confict with plist indexing:

pl = plist[[1, 2, 3], [4, 5, 6], [7, 8, 9]]

# Basic scalar indexing:
assert (pl._[0].aslist() ==
        [1, 4, 7])

# slice indexing (note the use of the 3-argument version of slicing):
assert (pl._[:2:1].aslist() ==
        [[1, 2], [4, 5], [7, 8]])

# list indexing:
pl = pl.np()
assert (pl._[[True, False, True]].apply(list).aslist() ==
        [[1, 3], [4, 6], [7, 9]])

root and uproot:

plists all have a root object. For newly created plists, the root is self, but as computations are performed on the plist, the root of the resulting plists almost always remain the original plist:

pl = plist[1, 2, 3]
# plist operations don't modify the original (except where natural)!
assert ((pl + 5) is not pl)
assert ((pl + 5).root() is pl)

In some cases, you don't want to maintain the original root. To reset the root to self, simply call uproot:

pl2 = pl + 5
assert (pl2.root() is not pl2)
assert (pl2.uproot().root() is pl2)
assert (pl2.root() is pl2)

See root and uproot for more details.

Filtering:

plist overrides comparison operations to provide filtering. This is reasonable, since an empty plist is a False value, just like an empty list, so a filter that filters everything is equivalent to the comparison failing.

Filtering always returns the root of the plist, which allows you to filter a plist on arbitrary values computed from the root, and then proceed with your computation on the (filtered) original data.

See comparator and filter for more details.

foos = plist([pdict(foo=0, bar=0), pdict(foo=1, bar=1), pdict(foo=2, bar=0)])
# Filtering on a property:
zero_bars = foos.bar == 0
# The result is a plist of the original pdicts, correctly filtered:
assert (zero_bars.aslist() ==
        [{'foo': 0, 'bar': 0},
         {'foo': 2, 'bar': 0}])

# filter can take any function to filter by, but it defaults to bool():
nonzero_bars = foos.bar.filter()
assert (nonzero_bars.aslist() ==
        [{'foo': 1, 'bar': 1}])

Grouping and Sorting:

Just as with filtering, you can group and sort a plist on any arbitrary value computed from the plist.

This shows a basic grouping by a property of the data. Note that groupby returns the root, just like filtering:

foos = plist([pdict(foo=0, bar=1), pdict(foo=1, bar=0), pdict(foo=2, bar=1)])
# Note that the `bar == 1` group comes before the `bar == 0` group. The ordering
# is determined by the sort order of the plist.
assert (foos.bar.groupby().aslist() ==
        [[{'bar': 1, 'foo': 0}, {'bar': 1, 'foo': 2}], [{'bar': 0, 'foo': 1}]])
# Note that foos is unchanged:
assert (foos.aslist() ==
        [{'bar': 1, 'foo': 0}, {'bar': 0, 'foo': 1}, {'bar': 1, 'foo': 2}])

In contrast, sorting a plist modifies the order of both the current plist and its root, but returns the current plist instead of the root:

assert (foos.bar.sortby().aslist() ==
        [0, 1, 1])
assert (foos.aslist() ==
        [{'bar': 0, 'foo': 1}, {'bar': 1, 'foo': 0}, {'bar': 1, 'foo': 2}])

This distinction between the behavios of groupby and sortby permits natural chaining of the two when sorted groups are desired. It also ensures that plists computed from the same root will be ordered in the same way.

foos = plist([pdict(foo=0, bar=1), pdict(foo=1, bar=0), pdict(foo=2, bar=1)])
assert (foos.bar.sortby().groupby().aslist() ==
        [[{'bar': 0, 'foo': 1}], [{'bar': 1, 'foo': 0}, {'bar': 1, 'foo': 2}]])

See groupby and sortby for more details.

Function Application and Multiple Arguments:

The most prominent case where you can't treat a plist as a single object is when you need to pass a single object to some function that isn't a property of the elements of the plist. In this case, just use apply:

pl = plist['abc', 'def', 'ghi']
assert (pl.apply('foo: {}'.format).aslist() ==
        ['foo: abc', 'foo: def', 'foo: ghi'])

Where apply shines (and all calls to plist element functions) is when dealing with multi-argument functions. In this case, you will often find that you want to call the function with parallel values from parallel plists. That is easy and natural to do, just like calling the function with corresponding non-plist values:

foos = plist([pdict(foo=0, bar=0), pdict(foo=1, bar=1), pdict(foo=2, bar=0)])
foos.baz = 'abc' * foos.foo
# Do a multi-argument string format with plist.apply:
assert (foos.foo.apply('foo: {} bar: {} baz: {baz}'.format, foos.bar, baz=foos.baz).aslist() ==
        ['foo: 0 bar: 0 baz: ', 'foo: 1 bar: 1 baz: abc', 'foo: 2 bar: 0 baz: abcabc'])
# Do the same string format directly using the plist as the format string:
assert (('foo: ' + foos.foo.pstr() + ' bar: {} baz: {baz}').format(foos.bar, baz=foos.baz).aslist() ==
        ['foo: 0 bar: 0 baz: ', 'foo: 1 bar: 1 baz: abc', 'foo: 2 bar: 0 baz: abcabc'])

See __call__, apply, and reduce for more details.

Basic pset use:

Placeholder set subclass. Mostly unimplemented.

You can construct psets in the normal manners for sets:

ps = pset([1, 2.0, 'three'])
ps = pset({1, 2.0, 'three'})

pset also supports a convenience constructor from a list literal:

ps = pset[1, 2.0, 'three']

Conversion:

You can convert from pset to set and back using arithmetic operations on the pset class itself, for convenience:

s1 = set([1, 2.0, 'three'])
ps = pset * s1
assert (type(s1) == set)
assert (type(ps) == pset)
assert (ps == s1)

s2 = ps / pset
assert (type(s2) == set)
assert (s2 == s1)

See pstar for more details on conversion.

Basic ptuple use:

Placeholder tuple subclass. Mostly unimplemented.

You can construct ptuples in the normal manner for tuples:

pt = ptuple((1, 2.0, 'three'))

ptuple also supports a convenience constructor from a list literal:

pt = ptuple[1, 2.0, 'three']

Conversion:

You can convert from ptuple to tuple and back using arithmetic operations on the ptuple class itself, for convenience:

t1 = tuple([1, 2.0, 'three'])
pt = ptuple * t1
assert (type(t1) == tuple)
assert (type(pt) == ptuple)
assert (pt == t1)

t2 = pt / ptuple
assert (type(t2) == tuple)
assert (t2 == t1)

See pstar for more details on conversion.

Converting to and from pstar:

pstar makes it easy to convert between python and pstar types:

data = [dict(foo=[0, 1, 2], bar=dict(bin=0), baz=defaultdict(int, a=1, b=2, c=3)),
        dict(foo=[1, 2, 3], bar=dict(bin=1), baz=frozenset([3, 4, 5])),
        dict(foo=[2, 3, 4], bar=dict(bin=0), baz=set([7, 8, 9]))]

# Recursively convert to pstar types:
pl = pstar(data)
assert (isinstance(pl, plist))
assert (pl.apply(type).aslist() == [pdict, pdict, pdict])
assert (pl.foo.apply(type).aslist() == [plist, plist, plist])
assert (pl.bar.apply(type).aslist() == [pdict, pdict, pdict])
assert (pl.baz.apply(type).aslist() == [defaultpdict, frozenpset, pset])

# Recursively convert back from pstar types:
data2 = pl / pstar
assert (data2 == data)

See pstar for more details and fine-grained control over the conversion process.

API Overview:

Links for detailed documentation are below.


pstar.defaultpdict(defaultdict)

defaultdict subclass where everything is automatically a property.

pstar.defaultpdict.__init__(self, *a, **kw)

Initialize defaultpdict.

pstar.defaultpdict.__getattr__(self, name)

Override getattr. If name starts with '_', attempts to find that attribute on self. Otherwise, looks for a field of that name in self.

pstar.defaultpdict.__getitem__(self, key)

Subscript operation. Keys can be any normal dict keys or lists of such keys.

pstar.defaultpdict.__setattr__(self, name, value)

Attribute assignment operation. Forwards to subscript assignment.

pstar.defaultpdict.__setitem__(self, key, value)

Subscript assignment operation. Keys and values can be scalars or lists.

pstar.defaultpdict.__str__(self)

Readable string representation of self.

pstar.defaultpdict.copy(self)

Copy self to new defaultpdict. Performs a shallow copy.

pstar.defaultpdict.palues(self)

Equivalent to self.values(), but returns a plist with values sorted as in self.peys().

pstar.defaultpdict.peys(self)

Get self.keys() as a sorted plist.

pstar.defaultpdict.pitems(self)

Equivalent to self.items(), but returns a plist with items sorted as in self.peys().

pstar.defaultpdict.qj(self, *a, **kw)

Call the qj logging function with self as the value to be logged. All other arguments are passed through to qj.

pstar.defaultpdict.rekey(self, map_or_fn=None, inplace=False, **kw)

Change the keys of self or a copy while keeping the same values.

pstar.defaultpdict.update(self, *a, **kw)

Update self. Returns self to allow chaining.


pstar.frozenpset(frozenset)

Placeholder frozenset subclass. Mostly unimplemented.

pstar.frozenpset.qj(self, *a, **kw)

Call the qj logging function with self as the value to be logged. All other arguments are passed through to qj.


pstar.pdict(dict)

dict subclass where everything is automatically a property.

pstar.pdict.__init__(self, *a, **kw)

Initialize pdict.

pstar.pdict.__getitem__(self, key)

Subscript operation. Keys can be any normal dict keys or lists of such keys.

pstar.pdict.__setitem__(self, key, value)

Subscript assignment operation. Keys and values can be scalars or lists.

pstar.pdict.__str__(self)

Readable string representation of self.

pstar.pdict.copy(self)

Copy self to new defaultpdict. Performs a shallow copy.

pstar.pdict.palues(self)

Equivalent to self.values(), but returns a plist with values sorted as in self.peys().

pstar.pdict.peys(self)

Get self.keys() as a sorted plist.

pstar.pdict.pitems(self)

Equivalent to self.items(), but returns a plist with items sorted as in self.peys().

pstar.pdict.qj(self, *a, **kw)

Call the qj logging function with self as the value to be logged. All other arguments are passed through to qj.

pstar.pdict.rekey(self, map_or_fn=None, inplace=False, **kw)

Change the keys of self or a copy while keeping the same values.

pstar.pdict.update(self, *a, **kw)

Update self. Returns self to allow chaining.


pstar.plist(list)

list subclass for powerful, concise data processing.

pstar.plist.__init__(self, *args, **kwargs)

Constructs plist.

pstar.plist._(self)

Causes the next call to self to be performed as deep as possible in the plist.

pstar.plist.__(self)

Causes the next call to self to be performed on the innermost plist.

pstar.plist.__call__(self, *args, **kwargs)

Call each element of self, possibly recusively.

pstar.plist.__contains__(self, other)

Implements the in operator to avoid inappropriate use of plist comparators.

pstar.plist.__delattr__(self, name)

Deletes an attribute on elements of self.

pstar.plist.__delitem__(self, key)

Deletes items of self using a variety of indexing styles.

pstar.plist.__delslice__(self, i, j)

Delegates to __delitem__ whenever possible. For compatibility with python 2.7.

pstar.plist.__dir__(self)

Allow natural tab-completion on self and its contents.

pstar.plist.__enter__(self)

Allow the use of plists in with statements.

pstar.plist.__exit__(self, exc_type, exc_value, traceback)

Allow the use of plists in with statements.

pstar.plist.__getattr__(self, name, _pepth=0)

Recursively attempt to get the attribute name.

pstar.plist.__getattribute__(self, name)

Returns a plist of the attribute for self, or for each element.

pstar.plist.__getitem__(self, key)

Returns a new plist using a variety of indexing styles.

pstar.plist.__getslice__(self, i, j)

Delegates to __getitem__ whenever possible. For compatibility with python 2.7.

pstar.plist.__setattr__(self, name, val)

Sets an attribute on a plist or its elements to val.

pstar.plist.__setitem__(self, key, val)

Sets items of self using a variety of indexing styles.

pstar.plist.__setslice__(self, i, j, sequence)

Delegates to __setitem__ whenever possible. For compatibility with python 2.7.

pstar.plist.all(self, *args, **kwargs)

Returns self if args[0] evaluates to True for all elements of self.

pstar.plist.any(self, *args, **kwargs)

Returns self if args[0] evaluates to True for any elements of self.

pstar.plist.apply(self, func, *args, **kwargs)

Apply an arbitrary function to elements of self, forwarding arguments.

pstar.plist.aslist(self)

Recursively convert all nested plists from self to lists, inclusive.

pstar.plist.aspdict(self)

Convert self to a pdict if there is a natural mapping of keys to values in self.

pstar.plist.aspset(self)

Recursively convert all nested plists from self to psets, inclusive.

pstar.plist.astuple(self)

Recursively convert all nested plists from self to tuples, inclusive.

pstar.plist.binary_op(self, other)

plist binary operation; applied element-wise to self.

pstar.plist.comparator(self, other, return_inds=False)

plist comparison operator. Comparisons filter plists.

pstar.plist.copy(self)

Copy self to new plist. Performs a shallow copy.

pstar.plist.enum(self)

Wrap the current plist values in tuples where the first item is the index.

pstar.plist.filter(self, func=<type 'bool'>, *args, **kwargs)

Filter self by an arbitrary function on elements of self, forwarding arguments.

pstar.plist.groupby(self)

Group self.root() by the values in self and return self.root().

pstar.plist.lfill(self, v=0, s=None)

Returns a list with the structure of self filled in order from v.

pstar.plist.logical_op(self, other)

plist logical operation. Logical operations perform set operations on plists.

pstar.plist.me(self, name_or_plist='me', call_pepth=0)

Sets the current plist as a variable available in the caller's context.

pstar.plist.none(self, *args, **kwargs)

Returns self if args[0] evaluates to False for all elements of self.

pstar.plist.nonempty(self, r=0)

Returns a new plist with empty sublists removed.

pstar.plist.np(self, *args, **kwargs)

Converts the elements of self to numpy.arrays, forwarding passed args.

pstar.plist.pand(self, name='__plist_and_var__', call_pepth=0)

Stores self into a plist of tuples that gets extended with each call.

pstar.plist.pd(self, *args, **kwargs)

Converts self into a pandas.DataFrame, forwarding passed args.

pstar.plist.pdepth(self, s=False)

Returns a plist of the recursive depth of each leaf element, from 0.

pstar.plist.pdict(self, *args, **kwargs)

Convert self to a pdict if there is a natural mapping of keys to values in self.

pstar.plist.pequal(self, other)

Shortcutting recursive equality function.

pstar.plist.pfill(self, v=0, s=None)

Returns a plist with the structure of self filled in order from v.

pstar.plist.pleft(self)

Returns a plist with the structure of self filled plen(-1) to 0.

pstar.plist.plen(self, r=0, s=False)

Returns a plist of the length of a recursively-selected layer of self.

pstar.plist.plt(self, **kwargs)

Convenience method for managing matplotlib.pyplot state within a plist chain.

pstar.plist.pset(self)

Converts the elements of self into pset objects.

pstar.plist.pshape(self)

Returns a plist of the same structure as self, filled with leaf lengths.

pstar.plist.pstr(self)

Returns a plist with leaf elements converted to strings.

pstar.plist.pstructure(self)

Returns a list of the number of elements in each layer of self.

pstar.plist.puniq(self)

Returns a new plist with only a single element of each value in self.

pstar.plist.qj(self, *args, **kwargs)

Applies logging function qj to self for easy in-chain logging.

pstar.plist.reduce(self, func, *args, **kwargs)

Apply a function repeatedly to its own result, returning a plist of length at most 1.

pstar.plist.remix(self, *args, **kwargs)

Returns a new plist of pdicts based on selected data from self.

pstar.plist.root(self)

Returns the root of the plist.

pstar.plist.sortby(self, key=None, reverse=False)

Sorts self and self.root() in-place and returns self.

pstar.plist.unary_op(self)

plist unary operation; applied element-wise to self.

pstar.plist.ungroup(self, r=1, s=None)

Inverts the last grouping operation applied and returns a new plist.

pstar.plist.uproot(self)

Sets the root to self so future root() calls return this plist.

pstar.plist.values_like(self, value=0)

Returns a plist with the structure of self filled with value.

pstar.plist.wrap(self)

Adds and returns an outer plist around self.

pstar.plist.zip(self, *others)

Zips self with others, recursively.


pstar.pset(set)

Placeholder set subclass. Mostly unimplemented.

pstar.pset.qj(self, *a, **kw)

Call the qj logging function with self as the value to be logged. All other arguments are passed through to qj.


pstar.pstar(object)

Recursively converts between standard python types and pstar types.


pstar.ptuple(tuple)

Placeholder tuple subclass. Mostly unimplemented.

pstar.ptuple.qj(self, *a, **kw)

Call the qj logging function with self as the value to be logged. All other arguments are passed through to qj.

Testing:

pstar has extensive tests that all pass on python 2.7 and 3.6. You can run them with nosetests:

$ nosetests
..........................................................................................................................................................................................................................SS
----------------------------------------------------------------------
Ran 220 tests in 1.453s

OK (skipped=2)

Or you can run them directly:

$ python3 pstar/tests/pstar_test.py
..........................................................................................................................................................................................................................ss
----------------------------------------------------------------------
Ran 220 tests in 0.693s

OK (skipped=2)

If you are adding a test or modifying an existing test, be sure to do so in pstar_test.py.template, rather than pstar/tests/pstar_test.py. After modification, you can rebuild pstar_test.py and rerun all of the tests with the following command (assuming that nosetests is installed for python 2.7 and python3 is your binary for python 3.6+:

python build_docs.py; nosetests --nologcapture --nocapture; python3 pstar/tests/pstar_test.py

--nologcapture and --nocapture are useful for writing or debugging tests that involve logging, such as the various tests of qj integration. Otherwise they are not needed.

Disclaimer:

This project is not an official Google project. It is not supported by Google and Google specifically disclaims all warranties as to its quality, merchantability, or fitness for a particular purpose.

Contributing:

See how to contribute.

License:

Apache 2.0.