AnniesLasso.parse_label_vector() - Code Metrics - Inspection of "Code status" - andycasey/AnniesLasso - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#1)

by Andy

created 2015-11-21 10:06 UTC

AnniesLasso.parse_label_vector() F

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Metric	Value
dl	0
loc	83
rs	2.0539
cc	18

How to fix Long Method Complexity

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
General utility functions.
"""

__all__ = ["label_vector", "progressbar", "short_hash"]

import logging
import numpy as np
import sys
from time import time
from collections import (Counter, Iterable, OrderedDict)
from hashlib import md5
from itertools import combinations_with_replacement
from six import string_types

logger = logging.getLogger(__name__)


def short_hash(contents):
    """
    Return a short hash string of some iterable content.

    :param contents:
        The contents to calculate a hash for.

    :returns:
        A concatenated string of 10-character length hashes for all items in the
        contents provided.
    """
    if not isinstance(contents, Iterable): contents = [contents]
    return "".join([str(md5(str(item).encode("utf-8")).hexdigest())[:10] \
        for item in contents])


def is_structured_label_vector(label_vector):
    """
    Return whether the provided label vector is structured correctly.
    """

    if not isinstance(label_vector, (list, tuple)):
        return False

    for descriptor in label_vector:
        if not isinstance(descriptor, (list, tuple)):
            return False

        for term in descriptor:
            if not isinstance(term, (list, tuple)) \
            or len(term) != 2 \
            or not isinstance(term[-1], (int, float)):
                return False

    if len(label_vector) == 0 or sum(map(len, label_vector)) == 0:
        return False

    return True


def parse_label_vector(label_vector_description, columns=None, **kwargs):
    """
    Return a structured form of a label vector from unstructured,
    human-readable input.

    :param label_vector_description:
        A human-readable or structured form of a label vector.

    :type label_vector_description:
        str or list

    :param columns: [optional]
        If `columns` are provided, instead of text columns being provided as the
        output parameter, the corresponding index location in `column` will be
        given.

    :returns:
        A structured form of the label vector as a multi-level list.


    :Example:

    >>> parse_label_vector("Teff^4 + logg*Teff^3 + feh + feh^0*Teff")
    [
        [
            ("Teff", 4),
        ],
        [
            ("logg", 1),
            ("Teff", 3)
        ],
        [
            ("feh", 1),
        ],
        [
            ("feh", 0),
            ("Teff", 1)
        ]
    ]
    """

    if is_structured_label_vector(label_vector_description):
        return label_vector_description

    # Allow for custom characters, but don't advertise it.
    # (Astronomers have bad enough habits already.)
    kwds = dict(zip(("sep", "mul", "pow"), "+*^"))
    kwds.update(kwargs)
    sep, mul, pow = (kwds[k] for k in ("sep", "mul", "pow"))

    if isinstance(label_vector_description, string_types):
        label_vector_description = label_vector_description.split(sep)
    label_vector_description = map(str.strip, label_vector_description)

    # Functions to parse the parameter (or index) and order for each term.
    get_power = lambda t: float(t.split(pow)[1].strip()) if pow in t else 1
    if columns is None:
        get_label = lambda d: d.split(pow)[0].strip()
    else:
        get_label = lambda d: list(columns).index(d.split(pow)[0].strip())

    label_vector = []
    for descriptor in (item.split(mul) for item in label_vector_description):

        labels = map(get_label, descriptor)
        orders = map(get_power, descriptor)

        term = OrderedDict()
        for label, order in zip(labels, orders):
            term[label] = term.get(label, 0) + order # Sum repeat term powers.

        # Prevent uses of x^0 etc clogging up the label vector.
        valid_terms = [(l, o) for l, o in term.items() if o != 0]
        if not np.all(np.isfinite([o for l, o in valid_terms])):
            raise ValueError("non-finite power provided")

        if len(valid_terms) > 0:
            label_vector.append(valid_terms)
    
    if sum(map(len, label_vector)) == 0:
        raise ValueError("no valid terms provided")

    return label_vector


def human_readable_label_vector(label_vector, **kwargs):
    """
    Return a human-readable form of the label vector provided.
    """

    if not is_structured_label_vector(label_vector):
        raise TypeError("invalid label vector provided")

    theta = ["1"]
    for descriptor in label_vector:
        cross_terms = []
        for label, order in descriptor:
            if order == 0: continue
            cross_terms.append(
                "".join([str(label), "^{}".format(order) if order > 1 else ""]))
        
        term = " * ".join(cross_terms)
        format = "({0})" if len(cross_terms) > 1 else "{0}"
        theta.append(format.format(term))

    return " + ".join(theta)
        

def progressbar(iterable, message=None, size=100):
    """
    A progressbar.

    :param iterable:
        Some iterable to show progress for.

    :param message: [optional]
        A string message to show as the progressbar header.

    :param size: [optional]
        The size of the progressbar. If the size given is zero or negative,
        then no progressbar will be shown.
    """

    # Preparerise.
    t_init = time()
    count = len(iterable)
    def _update(i, t=None):
        if 0 >= size: return
        increment = max(1, int(count / 100))
        if i % increment == 0 or i in (0, count):
            sys.stdout.write("\r[{done}{not_done}] {percent:3.0f}%{t}".format(
                done="=" * int(i/increment),
                not_done=" " * int((count - i)/increment),
                percent=100. * i/count,
                t="" if t is None else " ({0:.0f}s)".format(t-t_init)))
            sys.stdout.flush()

    # Initialise.
    if size > 0:
        logger.info((message or "").rstrip())
        sys.stdout.flush()

    # Updaterise.
    for i, item in enumerate(iterable):
        yield item
        _update(i)

    # Finalise.
    if size > 0:
        _update(count, time())
        sys.stdout.write("\r\n")
        sys.stdout.flush()


def build_label_vector(labels, order, cross_term_order=0, **kwargs):
    """
    Build a label vector description.

    :param labels:
        The labels to use in describing the label vector.

    :param order:
        The maximum order of the terms (e.g., order 3 implies A^3 is a term).

    :param cross_term_order: [optional]
        The maximum order of the cross-terms (e.g., cross_term_order 2 implies
        A^2*B is a term).

    :param mul: [optional]
        The operator to use to represent multiplication in the description of 
        the label vector.

    :param pow: [optional]
        The operator to use to represent exponents in the description of the
        label vector.

    :returns:
        A human-readable form of the label vector.
    """

    sep = kwargs.pop("sep", "+")
    mul = kwargs.pop("mul", "*")
    pow = kwargs.pop("pow", "^")

    #I make no apologies: it's fun to code like this for short complex functions
    items = []
    for o in range(1, 1 + max(order, 1 + cross_term_order)):
        for t in map(Counter, combinations_with_replacement(labels, o)):
            # Python 2 and 3 behave differently here, so generate an ordered
            # dictionary based on sorting the keys.
            t = OrderedDict([(k, t[k]) for k in sorted(t.keys())])
            if len(t) == 1 and order >= max(t.values()) \
            or len(t) > 1 and cross_term_order >= max(t.values()):
                c = [pow.join([[l], [l, str(p)]][p > 1]) for l, p in t.items()]
                if c: items.append(mul.join(map(str, c)))
    return " {} ".format(sep).join(items)


1			#!/usr/bin/env python
2			# -- coding: utf-8 --
3
4			"""
5			General utility functions.
6			"""
7
8			__all__ = ["label_vector", "progressbar", "short_hash"]
9
10			import logging
11			import numpy as np
12			import sys
13			from time import time
14			from collections import (Counter, Iterable, OrderedDict)
15			from hashlib import md5
16			from itertools import combinations_with_replacement
17			from six import string_types
18
19			logger = logging.getLogger(__name__)
20
21
22			def short_hash(contents):
23			"""
24			Return a short hash string of some iterable content.
25
26			:param contents:
27			The contents to calculate a hash for.
28
29			:returns:
30			A concatenated string of 10-character length hashes for all items in the
31			contents provided.
32			"""
33			if not isinstance(contents, Iterable): contents = [contents]
34			return "".join([str(md5(str(item).encode("utf-8")).hexdigest())[:10] \
35			for item in contents])
36
37
38			def is_structured_label_vector(label_vector):
39			"""
40			Return whether the provided label vector is structured correctly.
41			"""
42
43			if not isinstance(label_vector, (list, tuple)):
44			return False
45
46			for descriptor in label_vector:
47			if not isinstance(descriptor, (list, tuple)):
48			return False
49
50			for term in descriptor:
51			if not isinstance(term, (list, tuple)) \
52			or len(term) != 2 \
53			or not isinstance(term[-1], (int, float)):
54			return False
55
56			if len(label_vector) == 0 or sum(map(len, label_vector)) == 0:
57			return False
58
59			return True
60
61
62			def parse_label_vector(label_vector_description, columns=None, **kwargs):
63			"""
64			Return a structured form of a label vector from unstructured,
65			human-readable input.
66
67			:param label_vector_description:
68			A human-readable or structured form of a label vector.
69
70			:type label_vector_description:
71			str or list
72
73			:param columns: [optional]
74			If `columns` are provided, instead of text columns being provided as the
75			output parameter, the corresponding index location in `column` will be
76			given.
77
78			:returns:
79			A structured form of the label vector as a multi-level list.
80
81
82			:Example:
83
84			>>> parse_label_vector("Teff^4 + loggTeff^3 + feh + feh^0Teff")
85			[
86			[
87			("Teff", 4),
88			],
89			[
90			("logg", 1),
91			("Teff", 3)
92			],
93			[
94			("feh", 1),
95			],
96			[
97			("feh", 0),
98			("Teff", 1)
99			]
100			]
101			"""
102
103			if is_structured_label_vector(label_vector_description):
104			return label_vector_description
105
106			# Allow for custom characters, but don't advertise it.
107			# (Astronomers have bad enough habits already.)
108			kwds = dict(zip(("sep", "mul", "pow"), "+*^"))
109			kwds.update(kwargs)
110			sep, mul, pow = (kwds[k] for k in ("sep", "mul", "pow"))
111
112			if isinstance(label_vector_description, string_types):
113			label_vector_description = label_vector_description.split(sep)
114			label_vector_description = map(str.strip, label_vector_description)
115
116			# Functions to parse the parameter (or index) and order for each term.
117			get_power = lambda t: float(t.split(pow)[1].strip()) if pow in t else 1
118			if columns is None:
119			get_label = lambda d: d.split(pow)[0].strip()
120			else:
121			get_label = lambda d: list(columns).index(d.split(pow)[0].strip())
122
123			label_vector = []
124			for descriptor in (item.split(mul) for item in label_vector_description):
125
126			labels = map(get_label, descriptor)
127			orders = map(get_power, descriptor)
128
129			term = OrderedDict()
130			for label, order in zip(labels, orders):
131			term[label] = term.get(label, 0) + order # Sum repeat term powers.
132
133			# Prevent uses of x^0 etc clogging up the label vector.
134			valid_terms = [(l, o) for l, o in term.items() if o != 0]
135			if not np.all(np.isfinite([o for l, o in valid_terms])):
136			raise ValueError("non-finite power provided")
137
138			if len(valid_terms) > 0:
139			label_vector.append(valid_terms)
140
141			if sum(map(len, label_vector)) == 0:
142			raise ValueError("no valid terms provided")
143
144			return label_vector
145
146
147			def human_readable_label_vector(label_vector, **kwargs):
148			"""
149			Return a human-readable form of the label vector provided.
150			"""
151
152			if not is_structured_label_vector(label_vector):
153			raise TypeError("invalid label vector provided")
154
155			theta = ["1"]
156			for descriptor in label_vector:
157			cross_terms = []
158			for label, order in descriptor:
159			if order == 0: continue
160			cross_terms.append(
161			"".join([str(label), "^{}".format(order) if order > 1 else ""]))
162
163			term = " * ".join(cross_terms)
164			format = "({0})" if len(cross_terms) > 1 else "{0}"
165			theta.append(format.format(term))
166
167			return " + ".join(theta)
168
169
170			def progressbar(iterable, message=None, size=100):
171			"""
172			A progressbar.
173
174			:param iterable:
175			Some iterable to show progress for.
176
177			:param message: [optional]
178			A string message to show as the progressbar header.
179
180			:param size: [optional]
181			The size of the progressbar. If the size given is zero or negative,
182			then no progressbar will be shown.
183			"""
184
185			# Preparerise.
186			t_init = time()
187			count = len(iterable)
188			def _update(i, t=None):
189			if 0 >= size: return
190			increment = max(1, int(count / 100))
191			if i % increment == 0 or i in (0, count):
192			sys.stdout.write("\r[{done}{not_done}] {percent:3.0f}%{t}".format(
193			done="=" * int(i/increment),
194			not_done=" " * int((count - i)/increment),
195			percent=100. * i/count,
196			t="" if t is None else " ({0:.0f}s)".format(t-t_init)))
197			sys.stdout.flush()
198
199			# Initialise.
200			if size > 0:
201			logger.info((message or "").rstrip())
202			sys.stdout.flush()
203
204			# Updaterise.
205			for i, item in enumerate(iterable):
206			yield item
207			_update(i)
208
209			# Finalise.
210			if size > 0:
211			_update(count, time())
212			sys.stdout.write("\r\n")
213			sys.stdout.flush()
214
215
216			def build_label_vector(labels, order, cross_term_order=0, **kwargs):
217			"""
218			Build a label vector description.
219
220			:param labels:
221			The labels to use in describing the label vector.
222
223			:param order:
224			The maximum order of the terms (e.g., order 3 implies A^3 is a term).
225
226			:param cross_term_order: [optional]
227			The maximum order of the cross-terms (e.g., cross_term_order 2 implies
228			A^2*B is a term).
229
230			:param mul: [optional]
231			The operator to use to represent multiplication in the description of
232			the label vector.
233
234			:param pow: [optional]
235			The operator to use to represent exponents in the description of the
236			label vector.
237
238			:returns:
239			A human-readable form of the label vector.
240			"""
241
242			sep = kwargs.pop("sep", "+")
243			mul = kwargs.pop("mul", "*")
244			pow = kwargs.pop("pow", "^")
245
246			#I make no apologies: it's fun to code like this for short complex functions
247			items = []
248			for o in range(1, 1 + max(order, 1 + cross_term_order)):
249			for t in map(Counter, combinations_with_replacement(labels, o)):
250			# Python 2 and 3 behave differently here, so generate an ordered
251			# dictionary based on sorting the keys.
252			t = OrderedDict([(k, t[k]) for k in sorted(t.keys())])
253			if len(t) == 1 and order >= max(t.values()) \
254			or len(t) > 1 and cross_term_order >= max(t.values()):
255			c = [pow.join([[l], [l, str(p)]][p > 1]) for l, p in t.items()]
256			if c: items.append(mul.join(map(str, c)))
257			return " {} ".format(sep).join(items)
258

andycasey / AnniesLasso

Pull Request — master (#1)

AnniesLasso.parse_label_vector() F

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