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def ngrams(string, n=3, punctuation=PUNCTUATION, continuous=False):
""" Returns a list of n-grams (tuples of n successive words) from the given string.
Alternatively, you can supply a Text or Sentence object.
With continuous=False, n-grams will not run over sentence markers (i.e., .!?).
Punc... |
def deflood(s, n=3):
""" Returns the string with no more than n repeated characters, e.g.,
deflood("NIIIICE!!", n=1) => "Nice!"
deflood("nice.....", n=3) => "nice..."
"""
if n == 0:
return s[0:0]
return re.sub(r"((.)\2{%s,})" % (n-1), lambda m: m.group(1)[0] * n, s) |
def pprint(string, token=[WORD, POS, CHUNK, PNP], column=4):
""" Pretty-prints the output of Parser.parse() as a table with outlined columns.
Alternatively, you can supply a tree.Text or tree.Sentence object.
"""
if isinstance(string, basestring):
print("\n\n".join([table(sentence, fill=colu... |
def _read(path, encoding="utf-8", comment=";;;"):
""" Returns an iterator over the lines in the file at the given path,
strippping comments and decoding each line to Unicode.
"""
if path:
if isinstance(path, basestring) and os.path.exists(path):
# From file path.
if P... |
def penntreebank2universal(token, tag):
""" Returns a (token, tag)-tuple with a simplified universal part-of-speech tag.
"""
if tag.startswith(("NNP-", "NNPS-")):
return (token, "%s-%s" % (NOUN, tag.split("-")[-1]))
if tag in ("NN", "NNS", "NNP", "NNPS", "NP"):
return (token, NOUN)
i... |
def find_tokens(string, punctuation=PUNCTUATION, abbreviations=ABBREVIATIONS, replace=replacements, linebreak=r"\n{2,}"):
""" Returns a list of sentences. Each sentence is a space-separated string of tokens (words).
Handles common cases of abbreviations (e.g., etc., ...).
Punctuation marks are split... |
def _suffix_rules(token, tag="NN"):
""" Default morphological tagging rules for English, based on word suffixes.
"""
if isinstance(token, (list, tuple)):
token, tag = token
if token.endswith("ing"):
tag = "VBG"
if token.endswith("ly"):
tag = "RB"
if token.endswith("s") an... |
def find_tags(tokens, lexicon={}, model=None, morphology=None, context=None, entities=None, default=("NN", "NNP", "CD"), language="en", map=None, **kwargs):
""" Returns a list of [token, tag]-items for the given list of tokens:
["The", "cat", "purs"] => [["The", "DT"], ["cat", "NN"], ["purs", "VB"]]
... |
def find_chunks(tagged, language="en"):
""" The input is a list of [token, tag]-items.
The output is a list of [token, tag, chunk]-items:
The/DT nice/JJ fish/NN is/VBZ dead/JJ ./. =>
The/DT/B-NP nice/JJ/I-NP fish/NN/I-NP is/VBZ/B-VP dead/JJ/B-ADJP ././O
"""
chunked = [x for x in tagg... |
def find_prepositions(chunked):
""" The input is a list of [token, tag, chunk]-items.
The output is a list of [token, tag, chunk, preposition]-items.
PP-chunks followed by NP-chunks make up a PNP-chunk.
"""
# Tokens that are not part of a preposition just get the O-tag.
for ch in chunked... |
def find_relations(chunked):
""" The input is a list of [token, tag, chunk]-items.
The output is a list of [token, tag, chunk, relation]-items.
A noun phrase preceding a verb phrase is perceived as sentence subject.
A noun phrase following a verb phrase is perceived as sentence object.
"... |
def find_keywords(string, parser, top=10, frequency={}, **kwargs):
""" Returns a sorted list of keywords in the given string.
The given parser (e.g., pattern.en.parser) is used to identify noun phrases.
The given frequency dictionary can be a reference corpus,
with relative document frequenc... |
def tense_id(*args, **kwargs):
""" Returns the tense id for a given (tense, person, number, mood, aspect, negated).
Aliases and compound forms (e.g., IMPERFECT) are disambiguated.
"""
# Unpack tense given as a tuple, e.g., tense((PRESENT, 1, SG)):
if len(args) == 1 and isinstance(args[0], (list,... |
def _multilingual(function, *args, **kwargs):
""" Returns the value from the function with the given name in the given language module.
By default, language="en".
"""
return getattr(_module(kwargs.pop("language", "en")), function)(*args, **kwargs) |
def language(s):
""" Returns a (language, confidence)-tuple for the given string.
"""
s = decode_utf8(s)
s = set(w.strip(PUNCTUATION) for w in s.replace("'", "' ").split())
n = float(len(s) or 1)
p = {}
for xx in LANGUAGES:
lexicon = _module(xx).__dict__["lexicon"]
p[xx] = su... |
def _lazy(self, method, *args):
""" If the list is empty, calls lazylist.load().
Replaces lazylist.method() with list.method() and calls it.
"""
if list.__len__(self) == 0:
self.load()
setattr(self, method, types.MethodType(getattr(list, method), self))
... |
def train(self, token, tag, previous=None, next=None):
""" Trains the model to predict the given tag for the given token,
in context of the given previous and next (token, tag)-tuples.
"""
self._classifier.train(self._v(token, previous, next), type=tag) |
def classify(self, token, previous=None, next=None, **kwargs):
""" Returns the predicted tag for the given token,
in context of the given previous and next (token, tag)-tuples.
"""
return self._classifier.classify(self._v(token, previous, next), **kwargs) |
def apply(self, token, previous=(None, None), next=(None, None)):
""" Returns a (token, tag)-tuple for the given token,
in context of the given previous and next (token, tag)-tuples.
"""
return [token[0], self._classifier.classify(self._v(token[0], previous, next))] |
def _v(self, token, previous=None, next=None):
""" Returns a training vector for the given (word, tag)-tuple and its context.
"""
def f(v, s1, s2):
if s2:
v[s1 + " " + s2] = 1
p, n = previous, next
p = ("", "") if not p else (p[0] or "", p[1] or "")
... |
def apply(self, token, previous=(None, None), next=(None, None)):
""" Applies lexical rules to the given token, which is a [word, tag] list.
"""
w = token[0]
for r in self:
if r[1] in self._cmd: # Rule = ly hassuf 2 RB x
f, x, pos, cmd = bool(0), r[0], r[-2], ... |
def insert(self, i, tag, affix, cmd="hassuf", tagged=None):
""" Inserts a new rule that assigns the given tag to words with the given affix,
e.g., Morphology.append("RB", "-ly").
"""
if affix.startswith("-") and affix.endswith("-"):
affix, cmd = affix[+1:-1], "char"
... |
def apply(self, tokens):
""" Applies contextual rules to the given list of tokens,
where each token is a [word, tag] list.
"""
o = [("STAART", "STAART")] * 3 # Empty delimiters for look ahead/back.
t = o + tokens + o
for i, token in enumerate(t):
for r in ... |
def insert(self, i, tag1, tag2, cmd="prevtag", x=None, y=None):
""" Inserts a new rule that updates words with tag1 to tag2,
given constraints x and y, e.g., Context.append("TO < NN", "VB")
"""
if " < " in tag1 and not x and not y:
tag1, x = tag1.split(" < "); cmd="prevta... |
def apply(self, tokens):
""" Applies the named entity recognizer to the given list of tokens,
where each token is a [word, tag] list.
"""
# Note: we could also scan for patterns, e.g.,
# "my|his|her name is|was *" => NNP-PERS.
i = 0
while i < len(tokens):
... |
def append(self, entity, name="pers"):
""" Appends a named entity to the lexicon,
e.g., Entities.append("Hooloovoo", "PERS")
"""
e = map(lambda s: s.lower(), entity.split(" ") + [name])
self.setdefault(e[0], []).append(e) |
def find_keywords(self, string, **kwargs):
""" Returns a sorted list of keywords in the given string.
"""
return find_keywords(string,
parser = self,
top = kwargs.pop("top", 10),
frequency = kwargs.pop("frequency", {}), **kwargs
... |
def find_tokens(self, string, **kwargs):
""" Returns a list of sentences from the given string.
Punctuation marks are separated from each word by a space.
"""
# "The cat purs." => ["The cat purs ."]
return find_tokens(string,
punctuation = kwargs.get( "punctu... |
def find_tags(self, tokens, **kwargs):
""" Annotates the given list of tokens with part-of-speech tags.
Returns a list of tokens, where each token is now a [word, tag]-list.
"""
# ["The", "cat", "purs"] => [["The", "DT"], ["cat", "NN"], ["purs", "VB"]]
return find_tags(tokens... |
def find_chunks(self, tokens, **kwargs):
""" Annotates the given list of tokens with chunk tags.
Several tags can be added, for example chunk + preposition tags.
"""
# [["The", "DT"], ["cat", "NN"], ["purs", "VB"]] =>
# [["The", "DT", "B-NP"], ["cat", "NN", "I-NP"], ["purs", ... |
def parse(self, s, tokenize=True, tags=True, chunks=True, relations=False, lemmata=False, encoding="utf-8", **kwargs):
""" Takes a string (sentences) and returns a tagged Unicode string (TaggedString).
Sentences in the output are separated by newlines.
With tokenize=True, punctuation is ... |
def split(self, sep=TOKENS):
""" Returns a list of sentences, where each sentence is a list of tokens,
where each token is a list of word + tags.
"""
if sep != TOKENS:
return unicode.split(self, sep)
if len(self) == 0:
return []
return [[[x.rep... |
def TENSES(self):
""" Yields a list of tenses for this language, excluding negations.
Each tense is a (tense, person, number, mood, aspect)-tuple.
"""
a = set(TENSES[id] for id in self._format)
a = a.union(set(TENSES[id] for id in self._default.keys()))
a = a.union(se... |
def lemma(self, verb, parse=True):
""" Returns the infinitive form of the given verb, or None.
"""
if dict.__len__(self) == 0:
self.load()
if verb.lower() in self._inverse:
return self._inverse[verb.lower()]
if verb in self._inverse:
return sel... |
def lexeme(self, verb, parse=True):
""" Returns a list of all possible inflections of the given verb.
"""
a = []
b = self.lemma(verb, parse=parse)
if b in self:
a = [x for x in self[b] if x != ""]
elif parse is True: # rule-based
a = self.find_lexe... |
def conjugate(self, verb, *args, **kwargs):
""" Inflects the verb and returns the given tense (or None).
For example: be
- Verbs.conjugate("is", INFINITVE) => be
- Verbs.conjugate("be", PRESENT, 1, SINGULAR) => I am
- Verbs.conjugate("be", PRESENT, 1, PLURAL) => w... |
def tenses(self, verb, parse=True):
""" Returns a list of possible tenses for the given inflected verb.
"""
verb = verb.lower()
a = set()
b = self.lemma(verb, parse=parse)
v = []
if b in self:
v = self[b]
elif parse is True: # rule-based
... |
def load(self, path=None):
""" Loads the XML-file (with sentiment annotations) from the given path.
By default, Sentiment.path is lazily loaded.
"""
# <word form="great" wordnet_id="a-01123879" pos="JJ" polarity="1.0" subjectivity="1.0" intensity="1.0" />
# <word form="damnmi... |
def synset(self, id, pos=ADJECTIVE):
""" Returns a (polarity, subjectivity)-tuple for the given synset id.
For example, the adjective "horrible" has id 193480 in WordNet:
Sentiment.synset(193480, pos="JJ") => (-0.6, 1.0, 1.0).
"""
id = str(id).zfill(8)
if not id.s... |
def assessments(self, words=[], negation=True):
""" Returns a list of (chunk, polarity, subjectivity, label)-tuples for the given list of words:
where chunk is a list of successive words: a known word optionally
preceded by a modifier ("very good") or a negation ("not good").
"""... |
def annotate(self, word, pos=None, polarity=0.0, subjectivity=0.0, intensity=1.0, label=None):
""" Annotates the given word with polarity, subjectivity and intensity scores,
and optionally a semantic label (e.g., MOOD for emoticons, IRONY for "(!)").
"""
w = self.setdefault(word, {})... |
def train(self, s, path="spelling.txt"):
""" Counts the words in the given string and saves the probabilities at the given path.
This can be used to generate a new model for the Spelling() constructor.
"""
model = {}
for w in re.findall("[a-z]+", s.lower()):
model... |
def _edit1(self, w):
""" Returns a set of words with edit distance 1 from the given word.
"""
# Of all spelling errors, 80% is covered by edit distance 1.
# Edit distance 1 = one character deleted, swapped, replaced or inserted.
split = [(w[:i], w[i:]) for i in range(len(w) + 1)]... |
def _edit2(self, w):
""" Returns a set of words with edit distance 2 from the given word
"""
# Of all spelling errors, 99% is covered by edit distance 2.
# Only keep candidates that are actually known words (20% speedup).
return set(e2 for e1 in self._edit1(w) for e2 in self._edi... |
def suggest(self, w):
""" Return a list of (word, confidence) spelling corrections for the given word,
based on the probability of known words with edit distance 1-2 from the given word.
"""
if len(self) == 0:
self.load()
if len(w) == 1:
return [(w, 1.... |
def zip(*args, **kwargs):
""" Returns a list of tuples, where the i-th tuple contains the i-th element
from each of the argument sequences or iterables (or default if too short).
"""
args = [list(iterable) for iterable in args]
n = max(map(len, args))
v = kwargs.get("default", None)
ret... |
def chunked(sentence):
""" Returns a list of Chunk and Chink objects from the given sentence.
Chink is a subclass of Chunk used for words that have Word.chunk == None
(e.g., punctuation marks, conjunctions).
"""
# For example, to construct a training vector with the head of previous chunks a... |
def tree(string, token=[WORD, POS, CHUNK, PNP, REL, ANCHOR, LEMMA]):
""" Transforms the output of parse() into a Text object.
The token parameter lists the order of tags in each token in the input string.
"""
return Text(string, token) |
def xml_encode(string):
""" Returns the string with XML-safe special characters.
"""
string = string.replace("&", "&")
string = string.replace("<", "<")
string = string.replace(">", ">")
string = string.replace("\"",""")
string = string.replace(SLASH, "/")
return string |
def xml_decode(string):
""" Returns the string with special characters decoded.
"""
string = string.replace("&", "&")
string = string.replace("<", "<")
string = string.replace(">", ">")
string = string.replace(""","\"")
string = string.replace("/", SLASH)
return string |
def parse_xml(sentence, tab="\t", id=""):
""" Returns the given Sentence object as an XML-string (plain bytestring, UTF-8 encoded).
The tab delimiter is used as indendation for nested elements.
The id can be used as a unique identifier per sentence for chunk id's and anchors.
For example: "I... |
def parse_string(xml):
""" Returns a slash-formatted string from the given XML representation.
The return value is a TokenString (for MBSP) or TaggedString (for Pattern).
"""
string = ""
# Traverse all the <sentence> elements in the XML.
dom = XML(xml)
for sentence in dom(XML_SENTENCE):
... |
def _parse_tokens(chunk, format=[WORD, POS, CHUNK, PNP, REL, ANCHOR, LEMMA]):
""" Parses tokens from <word> elements in the given XML <chunk> element.
Returns a flat list of tokens, in which each token is [WORD, POS, CHUNK, PNP, RELATION, ANCHOR, LEMMA].
If a <chunk type="PNP"> is encountered, trave... |
def _parse_relation(chunk, type="O"):
""" Returns a string of the roles and relations parsed from the given <chunk> element.
The chunk type (which is part of the relation string) can be given as parameter.
"""
r1 = chunk.get(XML_RELATION)
r2 = chunk.get(XML_ID, chunk.get(XML_OF))
r1 = [x != ... |
def _parse_token(word, chunk="O", pnp="O", relation="O", anchor="O",
format=[WORD, POS, CHUNK, PNP, REL, ANCHOR, LEMMA]):
""" Returns a list of token tags parsed from the given <word> element.
Tags that are not attributes in a <word> (e.g., relation) can be given as parameters.
"""
... |
def nltk_tree(sentence):
""" Returns an NLTK nltk.tree.Tree object from the given Sentence.
The NLTK module should be on the search path somewhere.
"""
from nltk import tree
def do_pnp(pnp):
# Returns the PNPChunk (and the contained Chunk objects) in NLTK bracket format.
s = ' '.... |
def graphviz_dot(sentence, font="Arial", colors=BLUE):
""" Returns a dot-formatted string that can be visualized as a graph in GraphViz.
"""
s = 'digraph sentence {\n'
s += '\tranksep=0.75;\n'
s += '\tnodesep=0.15;\n'
s += '\tnode [penwidth=1, fontname="%s", shape=record, margin=0.1, height=0.3... |
def table(sentence, fill=1, placeholder="-"):
""" Returns a string where the tags of tokens in the sentence are organized in outlined columns.
"""
tags = [WORD, POS, IOB, CHUNK, ROLE, REL, PNP, ANCHOR, LEMMA]
tags += [tag for tag in sentence.token if tag not in tags]
def format(token, tag):
... |
def tags(self):
""" Yields a list of all the token tags as they appeared when the word was parsed.
For example: ["was", "VBD", "B-VP", "O", "VP-1", "A1", "be"]
"""
# See also. Sentence.__repr__().
ch, I,O,B = self.chunk, INSIDE+"-", OUTSIDE, BEGIN+"-"
tags = [OUTSIDE ... |
def next(self, type=None):
""" Returns the next word in the sentence with the given type.
"""
i = self.index + 1
s = self.sentence
while i < len(s):
if type in (s[i].type, None):
return s[i]
i += 1 |
def previous(self, type=None):
""" Returns the next previous word in the sentence with the given type.
"""
i = self.index - 1
s = self.sentence
while i > 0:
if type in (s[i].type, None):
return s[i]
i -= 1 |
def head(self):
""" Yields the head of the chunk (usually, the last word in the chunk).
"""
if self.type == "NP" and any(w.type.startswith("NNP") for w in self):
w = find(lambda w: w.type.startswith("NNP"), reversed(self))
elif self.type == "NP": # "the cat" => "cat"
... |
def related(self):
""" Yields a list of all chunks in the sentence with the same relation id.
"""
return [ch for ch in self.sentence.chunks
if ch != self and intersects(unzip(0, ch.relations), unzip(0, self.relations))] |
def anchor_id(self):
""" Yields the anchor tag as parsed from the original token.
Chunks that are anchors have a tag with an "A" prefix (e.g., "A1").
Chunks that are PNP attachmens (or chunks inside a PNP) have "P" (e.g., "P1").
Chunks inside a PNP can be both anchor and atta... |
def modifiers(self):
""" For verb phrases (VP), yields a list of the nearest adjectives and adverbs.
"""
if self._modifiers is None:
# Iterate over all the chunks and attach modifiers to their VP-anchor.
is_modifier = lambda ch: ch.type in ("ADJP", "ADVP") and ch.relation... |
def nearest(self, type="VP"):
""" Returns the nearest chunk in the sentence with the given type.
This can be used (for example) to find adverbs and adjectives related to verbs,
as in: "the cat is ravenous" => is what? => "ravenous".
"""
candidate, d = None, len(self.sente... |
def next(self, type=None):
""" Returns the next chunk in the sentence with the given type.
"""
i = self.stop
s = self.sentence
while i < len(s):
if s[i].chunk is not None and type in (s[i].chunk.type, None):
return s[i].chunk
i += 1 |
def previous(self, type=None):
""" Returns the next previous chunk in the sentence with the given type.
"""
i = self.start - 1
s = self.sentence
while i > 0:
if s[i].chunk is not None and type in (s[i].chunk.type, None):
return s[i].chunk
i... |
def append(self, word, lemma=None, type=None, chunk=None, role=None, relation=None, pnp=None, anchor=None, iob=None, custom={}):
""" Appends the next word to the sentence / chunk / preposition.
For example: Sentence.append("clawed", "claw", "VB", "VP", role=None, relation=1)
- word :... |
def parse_token(self, token, tags=[WORD, POS, CHUNK, PNP, REL, ANCHOR, LEMMA]):
""" Returns the arguments for Sentence.append() from a tagged token representation.
The order in which token tags appear can be specified.
The default order is (separated by slashes):
- word,
... |
def _parse_relation(self, tag):
""" Parses the chunk tag, role and relation id from the token relation tag.
- VP => VP, [], []
- VP-1 => VP, [1], [None]
- ADJP-PRD => ADJP, [None], [PRD]
- NP-SBJ-1 => NP, [1], [SBJ]
... |
def _do_word(self, word, lemma=None, type=None):
""" Adds a new Word to the sentence.
Other Sentence._do_[tag] functions assume a new word has just been appended.
"""
# Improve 3rd person singular "'s" lemma to "be", e.g., as in "he's fine".
if lemma == "'s" and type in ("VB"... |
def _do_chunk(self, type, role=None, relation=None, iob=None):
""" Adds a new Chunk to the sentence, or adds the last word to the previous chunk.
The word is attached to the previous chunk if both type and relation match,
and if the word's chunk tag does not start with "B-" (i.e., iob !=... |
def _do_relation(self):
""" Attaches subjects, objects and verbs.
If the previous chunk is a subject/object/verb, it is stored in Sentence.relations{}.
"""
if self.chunks:
ch = self.chunks[-1]
for relation, role in ch.relations:
if role == "SBJ... |
def _do_pnp(self, pnp, anchor=None):
""" Attaches prepositional noun phrases.
Identifies PNP's from either the PNP tag or the P-attachment tag.
This does not determine the PP-anchor, it only groups words in a PNP chunk.
"""
if anchor or pnp and pnp.endswith("PNP"):
... |
def _do_anchor(self, anchor):
""" Collects preposition anchors and attachments in a dictionary.
Once the dictionary has an entry for both the anchor and the attachment, they are linked.
"""
if anchor:
for x in anchor.split("-"):
A, P = None, None
... |
def _do_conjunction(self, _and=("and", "e", "en", "et", "und", "y")):
""" Attach conjunctions.
CC-words like "and" and "or" between two chunks indicate a conjunction.
"""
w = self.words
if len(w) > 2 and w[-2].type == "CC" and w[-2].chunk is None:
cc = w[-2].stri... |
def get(self, index, tag=LEMMA):
""" Returns a tag for the word at the given index.
The tag can be WORD, LEMMA, POS, CHUNK, PNP, RELATION, ROLE, ANCHOR or a custom word tag.
"""
if tag == WORD:
return self.words[index]
if tag == LEMMA:
return self.word... |
def loop(self, *tags):
""" Iterates over the tags in the entire Sentence,
For example, Sentence.loop(POS, LEMMA) yields tuples of the part-of-speech tags and lemmata.
Possible tags: WORD, LEMMA, POS, CHUNK, PNP, RELATION, ROLE, ANCHOR or a custom word tag.
Any order or combi... |
def indexof(self, value, tag=WORD):
""" Returns the indices of tokens in the sentence where the given token tag equals the string.
The string can contain a wildcard "*" at the end (this way "NN*" will match "NN" and "NNS").
The tag can be WORD, LEMMA, POS, CHUNK, PNP, RELATION, ROLE, ANC... |
def slice(self, start, stop):
""" Returns a portion of the sentence from word start index to word stop index.
The returned slice is a subclass of Sentence and a deep copy.
"""
s = Slice(token=self.token, language=self.language)
for i, word in enumerate(self.words[start:stop])... |
def constituents(self, pnp=False):
""" Returns an in-order list of mixed Chunk and Word objects.
With pnp=True, also contains PNPChunk objects whenever possible.
"""
a = []
for word in self.words:
if pnp and word.pnp is not None:
if len(a) == 0 or ... |
def from_xml(cls, xml):
""" Returns a new Text from the given XML string.
"""
s = parse_string(xml)
return Sentence(s.split("\n")[0], token=s.tags, language=s.language) |
def xml(self):
""" Yields the sentence as an XML-formatted string (plain bytestring, UTF-8 encoded).
All the sentences in the XML are wrapped in a <text> element.
"""
xml = []
xml.append('<?xml version="1.0" encoding="%s"?>' % XML_ENCODING.get(self.encoding, self.encoding))
... |
def get_credits():
"""Extract credits from `AUTHORS.rst`"""
credits = read(os.path.join(_HERE, "AUTHORS.rst")).split("\n")
from_index = credits.index("Active Contributors")
credits = "\n".join(credits[from_index + 2:])
return credits |
def rst2markdown_github(path_to_rst, path_to_md, pandoc="pandoc"):
"""
Converts ``rst`` to **markdown_github**, using :program:`pandoc`
**Input**
* ``FILE.rst``
**Output**
* ``FILE.md``
"""
_proc = subprocess.Popen([pandoc, "-f", "rst",
"-t", "m... |
def console_help2rst(cwd, help_cmd, path_to_rst, rst_title,
format_as_code=False):
"""
Extract HELP information from ``<program> -h | --help`` message
**Input**
* ``$ <program> -h | --help``
* ``$ cd <cwd> && make help``
**Output**
* ``docs/src/console_he... |
def update_docs(readme=True, makefiles=True):
"""Update documentation (ready for publishing new release)
Usually called by ``make docs``
:param bool make_doc: generate DOC page from Makefile help messages
"""
if readme:
_pandoc = get_external_executable("pandoc")
rst2markdown_gith... |
def rms(self, x, params=()):
""" Returns root mean square value of f(x, params) """
internal_x, internal_params = self.pre_process(np.asarray(x),
np.asarray(params))
if internal_params.ndim > 1:
raise NotImplementedError("Paramet... |
def solve_series(self, x0, params, varied_data, varied_idx,
internal_x0=None, solver=None, propagate=True, **kwargs):
""" Solve system for a set of parameters in which one is varied
Parameters
----------
x0 : array_like
Guess (subject to ``self.post_proc... |
def plot_series(self, xres, varied_data, varied_idx, **kwargs):
""" Plots the results from :meth:`solve_series`.
Parameters
----------
xres : array
Of shape ``(varied_data.size, self.nx)``.
varied_data : array
See :meth:`solve_series`.
varied_idx ... |
def plot_series_residuals(self, xres, varied_data, varied_idx, params, **kwargs):
""" Analogous to :meth:`plot_series` but will plot residuals. """
nf = len(self.f_cb(*self.pre_process(xres[0], params)))
xerr = np.empty((xres.shape[0], nf))
new_params = np.array(params)
for idx,... |
def plot_series_residuals_internal(self, varied_data, varied_idx, **kwargs):
""" Analogous to :meth:`plot_series` but for internal residuals from last run. """
nf = len(self.f_cb(*self.pre_process(
self.internal_xout[0], self.internal_params_out[0])))
xerr = np.empty((self.internal_x... |
def solve_and_plot_series(self, x0, params, varied_data, varied_idx, solver=None, plot_kwargs=None,
plot_residuals_kwargs=None, **kwargs):
""" Solve and plot for a series of a varied parameter.
Convenience method, see :meth:`solve_series`, :meth:`plot_series` &
:me... |
def pre_process(self, x0, params=()):
""" Used internally for transformation of variables. """
# Should be used by all methods matching "solve_*"
if self.x_by_name and isinstance(x0, dict):
x0 = [x0[k] for k in self.names]
if self.par_by_name and isinstance(params, dict):
... |
def post_process(self, xout, params_out):
""" Used internally for transformation of variables. """
# Should be used by all methods matching "solve_*"
for post_processor in self.post_processors:
xout, params_out = post_processor(xout, params_out)
return xout, params_out |
def solve(self, x0, params=(), internal_x0=None, solver=None, attached_solver=None, **kwargs):
""" Solve with user specified ``solver`` choice.
Parameters
----------
x0: 1D array of floats
Guess (subject to ``self.post_processors``)
params: 1D array_like of floats
... |
def _solve_scipy(self, intern_x0, tol=1e-8, method=None, **kwargs):
""" Uses ``scipy.optimize.root``
See: http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root.html
Parameters
----------
intern_x0: array_like
initial guess
tol: float
... |
def solve(self, x0, params=(), internal_x0=None, solver=None,
conditional_maxiter=20, initial_conditions=None, **kwargs):
""" Solve the problem (systems of equations)
Parameters
----------
x0 : array
Guess.
params : array
See :meth:`NeqSys.s... |
def solve(guess_a, guess_b, power, solver='scipy'):
""" Constructs a pyneqsys.symbolic.SymbolicSys instance and returns from its ``solve`` method. """
# The problem is 2 dimensional so we need 2 symbols
x = sp.symbols('x:2', real=True)
# There is a user specified parameter ``p`` in this problem:
p =... |
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