lingpy.sequence package¶
Submodules¶
lingpy.sequence.generate module¶
Module provides simple basic classes for sequence generation using Markov models.

class
lingpy.sequence.generate.
MCBasic
(seqs)¶ Bases:
object
Basic class for creating Markov chains from sequence training data.
Parameters: seq : list
A list of sequences. Sequences are assumed to be tokenized, i.e. they should be either passed as lists or as tuples.

walk
()¶ Create random sequence from the distribution.


class
lingpy.sequence.generate.
MCPhon
(words, tokens=False, prostrings=[], classes=False, class_model=<scamodel "sca">, **keywords)¶ Bases:
lingpy.sequence.generate.MCBasic
Class for the creation of phonetic sequences (“pseudo words”).
Parameters: words : list
List of phonetic sequences. This list can contain tokenized sequences (lists or tuples), or simple untokenized IPA strings.
tokens : bool (default=False)
If set to True, no tokenization of input sequences is carried out.
prostring : list (default=[])
List containing the prosodic profiles of the input sequences. If the list is empty, the profiles are generated automatically.

evaluate_string
(string, tokens=False, **keywords)¶

get_string
(new=True, tokens=False)¶ Generate a string from the Markov chain created from the training data.
Parameters: new : bool (default=True)
Determine whether the string created should be different from the training data or not.
tokens : bool (default=False)
If set to True he full list of tokens that was internally used to represent the sequences as a Markov chain is returned.

lingpy.sequence.ngrams module¶
This modules provides methods for generating and collecting ngrams.
The methods allow to collect different kind of subsequences, such as standard ngrams (preceding context), skip ngrams with both single or multiple gap openings (both preceding and following context), and positional ngrams (both preceding and following context).

class
lingpy.sequence.ngrams.
NgramModel
(pre_order=0, post_order=0, pad_symbol='$$$', sequences=None)¶ Bases:
object
Class for operation upon sequences using ngrams models.
This class allows different operations upon sequences after training ngram models, such as sequence relative likelihood computation (both per state and overall), random sequence generation, computation of a model entropy and of crossentropy/perplexity of a sequence, etc. As model training is computationally and time consuming for large datasets, trained models can be saved and loaded (“serialized”) from disk.

add_sequences
(sequences)¶ Adds sequences to a model, collecting their ngrams.
This method does not return any value, but cleans the internal matrix probability, if one was previously computed, and automatically updates the ngram counters. The actual training, with the computation of smoothed logprobabilities, is not performed automatically, and must be requested by the user by calling the .train() method.
Parameters: sequences: list :
A list of sequences to be added to the model.

entropy
(sequence, base=2.0)¶ Calculates the crossentropy of a sequence.
Parameters: sequence: list :
The sequence whose crossentropy will be calculated.
base: float :
The logarithmic base for the crossentropy calculation. Defaults to 2.0, following the standard approach set by Shannon that allows to consider entropy in terms of bits needed for unique representation.
Returns: ch: float :
The crossentropy calculated for the sequence, a real number.

model_entropy
()¶ Return the model entropy.
This methods collects the P x log(P) for all contexts, returning their sum. This is different from a sequence crossentropy, and should be used to estimate the complexity of a model.
Please note that for very large models the computation of this entropy might run into underflow problems.
Returns: h: float :
The model entropy.

perplexity
(sequence)¶ Calculates the perplexity of a model.
As per definition, this is simply 2.0 to the crossentropy of the given sequence on logarithmic base of 2.0.
Parameters: sequence: list :
The sequence whose perplexity should be calculated.
Returns: perplexity: float :
The calculated perplexity for the sequence.

random_seqs
(k=1, seq_len=None, scale=2, only_longest=False, attempts=10, seed=None)¶ Return a set of random sequences based in the observed transition frequencies.
This function tries to generate a set of k random sequences from the internal model. Given that the random selection and the parameters might lead to a long or infinite search loop, the number of attempts for each word generation is limited, meaning that there is no guarantee that the returned list will be of length k, but only that it will be at most of length k.
Parameters: k: int :
The desired and maximum number of random sequences to be returned. While the algorithm should be robust enough for most cases, there is no guarantee that the desired number or even that a single random sequence will be returned. In case of missing sequences, try increasing the parameter attempts.
seq_len: int or list :
An optional integer with length of the sequences to be generated or a list of lengths to be uniformly drawn for the generated sequences. If the parameter is not specified, the length of the sequences will be drawn by the sequence lengths observed in training according to their frequencies.
scale: numeric :
The exponent used for weighting ngram probabilities according to their length in number of states. The higher this value, the less likely the algorithm will be to drawn shorter ngrams, which contribute to a higher variety in words but can also result in less likely sequences. Defaults to 2.
only_longest: bool :
Whether the algorithm should only collect the longest possible ngrams when computing the search space from which each new random character is obtained. This usually translates into less variation in the generated sequences and a longer searching time, which might need to be increased via the attempts parameters. Defaults to False.
tries: int :
The number of times the algorithm will try to generate a random sequence. If the algorithm is unable to generate a suitable random sequence after the specified number of attempts, the loop will advance to the following sequence (if any). Defaults to 10.
seed: obj :
Any hasheable object, used to feed the random number generator and thus reproduce the generated set of random sequences.
Returns: seqs: list :
A list of size k with random sequences.

score
(sequence, use_length=True)¶ Returns the relative likelihood of a sequence.
The model must have been trained before using this function.
Parameters: sequence: list :
A list of states to be scored.
use_length: bool :
Whether to correct the sequence relative likelihood by using length probability. Defaults to True.
Returns: prob: list :
A list of floats, of the same length of sequence, with the individual logprobability for each state.

state_score
(sequence)¶ Returns the relative likelihood for each state in a sequence.
Please note that this does not perform correction due to sequence length, as optionally and by default performed by the .score() method. The model must have been trained in advance.
Parameters: sequence: list :
A list of states to be scored.
Returns: prob: list :
A list of floats, of the same length of sequence, with the individual logprobability for each state.

train
(method='laplace', normalize=False, bins=None, **kwargs)¶ Train a model after ngrams have been collected.
This method does not return any value, but sets the internal variables with smoothed probabilities (such as self._p and self._p0) and internally marks the model as having been trained.
Parameters: method: str :
The name of the smoothing method to be used, as used by smooth_dist(). Either “uniform”, “random”, “mle”, “lidstone”, “laplace”, “ele”, “wittenbell”, “certaintydegree”, or “sgt”. Defaults to “laplace”.
normalize: boolean :
Whether to normalize the logprobabilities for each ngram in the model after smoothing, i.e., to guarantee that the probabilities (with the probability for unobserved transitions counted a single time) sum to 1.0. This is computationally expansive, and should be only used if the model is intended for later serialization. While experiments with real data demonstrated that this normalization does not improve the results or performance of the methods, the computational cost of normalizing the probabilities might be justified if descriptive statistics of the model, like samples from the matrix of transition probabilities or the entropy/perplexity of a sequence, are needed (such as for publication), as they will be more in line with what is generally expected and will facilitate the comparison of different models.
bins: int :
The number of bins to be assumed when smoothing, for the smoothing methods that use this information. Defaults to the number of unique states observed, as gathered from the count of ngrams with no context.


lingpy.sequence.ngrams.
bigrams
(sequence, *, order=2, pad_symbol='$$$')¶ Build an iterator for collecting all bigrams of a sequence.
The sequence is padded by default.
Parameters: sequence: list or str :
The sequence from which the bigrams will be collected.
pad_symbol: object :
An optional symbol to be used as startof and endofsequence boundaries. The same symbol is used for both boundaries. Must be a value different from None, defaults to “$$$”.
Returns: out: iterable :
An iterable over the bigrams of the sequence, returned as tuples.
Examples
>>> from lingpy.sequence import * >>> sent = "Insurgents killed in ongoing fighting" >>> for ngram in bigrams(sent): ... print(ngram) ... ('$$$', 'Insurgents') ('Insurgents', 'killed') ('killed', 'in') ('in', 'ongoing') ('ongoing', 'fighting') ('fighting', '$$$')

lingpy.sequence.ngrams.
confirm
(question, *, default=False)¶ Ask a yes/no question interactively.
Parameters: question – The text of the question to ask. Returns: True if the answer was “yes”, False otherwise.

lingpy.sequence.ngrams.
data_path
(*comps)¶

lingpy.sequence.ngrams.
dotjoin
(*args, **kw)¶ Convenience shortcut. Strings to be joined do not have to be passed as list or tuple.
Notes
An implicit conversion of objects to strings is performed as well.

lingpy.sequence.ngrams.
fourgrams
(sequence, *, order=4, pad_symbol='$$$')¶ Build an iterator for collecting all fourgrams of a sequence.
The sequence is padded by default.
Parameters: sequence: list or str :
The sequence from which the fourgrams will be collected.
pad_symbol: object :
An optional symbol to be used as startof and endofsequence boundaries. The same symbol is used for both boundaries. Must be a value different from None, defaults to “$$$”.
Returns: out: iterable :
An iterable over the fourgrams of the sequence, returned as tuples.
Examples
>>> from lingpy.sequence import * >>> sent = "Insurgents killed in ongoing fighting" >>> for ngram in fourgrams(sent): ... print(ngram) ... ('$$$', '$$$', '$$$', 'Insurgents') ('$$$', '$$$', 'Insurgents', 'killed') ('$$$', 'Insurgents', 'killed', 'in') ('Insurgents', 'killed', 'in', 'ongoing') ('killed', 'in', 'ongoing', 'fighting') ('in', 'ongoing', 'fighting', '$$$') ('ongoing', 'fighting', '$$$', '$$$') ('fighting', '$$$', '$$$', '$$$')

lingpy.sequence.ngrams.
get_all_ngrams
(sequence, sort=False)¶ Function returns all possible ngrams of a given sequence.
Parameters: sequence : list or str
The sequence that shall be converted into it’s ngramrepresentation.
Returns: out : list
A list of all ngrams of the input word, sorted in decreasing order of length.
Examples
>>> get_all_ngrams('abcde') ['abcde', 'bcde', 'abcd', 'cde', 'abc', 'bcd', 'ab', 'de', 'cd', 'bc', 'a', 'e', 'b', 'd', 'c']

lingpy.sequence.ngrams.
get_all_ngrams_by_order
(sequence, orders=None, pad_symbol='$$$')¶ Build an iterator for collecting all ngrams of a given set of orders.
If no set of orders (i.e., “lengths”) is provided, this will collect all possible ngrams in the sequence.
Parameters: sequence: list or str :
The sequence from which the ngrams will be collected.
orders: list :
An optional list of the orders of the ngrams to be collected. Can be larger than the length of the sequence, in which case the latter will be padded accordingly if requested. Defaults to the collection of all possible ngrams in the sequence with the minimum padding.
pad_symbol: object :
An optional symbol to be used as startof and endofsequence boundaries. The same symbol is used for both boundaries. Must be a value different from None, defaults to “$$$”.
Returns: out: iterable :
An iterable over the ngrams of the sequence, returned as tuples.
Examples
>>> from lingpy.sequence import * >>> sent = "Insurgents were killed" >>> for ngram in get_all_ngrams_by_order(sent): ... print(ngram) ... ('Insurgents',) ('were',) ('killed',) ('$$$', 'Insurgents') ('Insurgents', 'were') ('were', 'killed') ('killed', '$$$') ('$$$', '$$$', 'Insurgents') ('$$$', 'Insurgents', 'were') ('Insurgents', 'were', 'killed') ('were', 'killed', '$$$') ('killed', '$$$', '$$$')

lingpy.sequence.ngrams.
get_all_posngrams
(sequence, pre_orders, post_orders, pad_symbol='$$$', elm_symbol='###')¶ Build an iterator for collecting all positional ngrams of a sequence.
The elements of the iterator, as returned by “get_posngrams()”, include a tuple of the context, which can be hashed (as any tuple), the transition symbol, and the position of the symbol in the sequence. Such output is primarily intended for statebystate relative likelihood computations with stochastics models, and can be approximated to a collection of “shingles”.
Parameters: sequence: list or str :
The sequence from which the ngrams will be collected.
preorders: int or list :
An integer with the maximum length of the preceding context or a list with all preceding context lengths to be collected. If an integer is passed, all lengths from zero to the informed one will be collected.
postorders: int or list :
An integer with the maximum length of the following context or a list with all following context lengths to be collected. If an integer is passed, all lengths from zero to the informed one will be collected.
pad_symbol: object :
An optional symbol to be used as startof and endofsequence boundaries. The same symbol is used for both boundaries. Must be a value different from None, defaults to “$$$”.
elm_symbol: object :
An optional symbol to be used as transition symbol replacement in the context tuples (the first element in the returned iterator). Defaults to “###”.
Returns: out: iterable :
An iterable over the positional ngrams of the sequence, returned as tuples whose elements are: (1) a tuple representing the context (thus including preceding context, the transition symbol, and the following context), (2) an object with the value of the transition symbol, and (3) the index of the transition symbol in the sequence.
Examples
>>> from lingpy.sequence import * >>> sent = "Insurgents were killed" >>> for ngram in get_all_posngrams(sent, 2, 1): ... print(ngram) ... (('###',), 'Insurgents', 0) (('###',), 'were', 1) (('###',), 'killed', 2) (('###', 'were'), 'Insurgents', 0) (('###', 'killed'), 'were', 1) (('###', '$$$'), 'killed', 2) (('$$$', '###'), 'Insurgents', 0) (('Insurgents', '###'), 'were', 1) (('were', '###'), 'killed', 2) (('$$$', '###', 'were'), 'Insurgents', 0) (('Insurgents', '###', 'killed'), 'were', 1) (('were', '###', '$$$'), 'killed', 2) (('$$$', '$$$', '###'), 'Insurgents', 0) (('$$$', 'Insurgents', '###'), 'were', 1) (('Insurgents', 'were', '###'), 'killed', 2) (('$$$', '$$$', '###', 'were'), 'Insurgents', 0) (('$$$', 'Insurgents', '###', 'killed'), 'were', 1) (('Insurgents', 'were', '###', '$$$'), 'killed', 2)

lingpy.sequence.ngrams.
get_n_ngrams
(sequence, order, pad_symbol='$$$')¶ Build an iterator for collecting all ngrams of a given order.
The sequence can optionally be padded with boundary symbols which are equal for before and and after sequence boundaries.
Parameters: sequence: list or str :
The sequence from which the ngrams will be collected.
order: int :
The order of the ngrams to be collected.
pad_symbol: object :
An optional symbol to be used as startof and endofsequence boundaries. The same symbol is used for both boundaries. Must be a value different from None, defaults to “$$$”.
Returns: out: iterable :
An iterable over the ngrams of the sequence, returned as tuples.
Examples
>>> from lingpy.sequence import * >>> sent = "Insurgents killed in ongoing fighting" >>> for ngram in get_n_ngrams(sent, 2): ... print(ngram) ... ('$$$', 'Insurgents') ('Insurgents', 'killed') ('killed', 'in') ('in', 'ongoing') ('ongoing', 'fighting') ('fighting', '$$$')
>>> for ngram in get_n_ngrams(sent, 1): ... print(ngram) ... ('Insurgents',) ('killed',) ('in',) ('ongoing',) ('fighting',)
>>> for ngram in get_n_ngrams(sent, 0): ... print(ngram) ...

lingpy.sequence.ngrams.
get_posngrams
(sequence, pre_order=0, post_order=0, pad_symbol='$$$', elm_symbol='###')¶ Build an iterator for collecting all positional ngrams of a sequence.
The preceding and a following orders (i.e., “contexts”) must always be informed. The elements of the iterator include a tuple of the context, which can be hashed as any tuple, the transition symbol, and the position of the symbol in the sequence. Such output is primarily intended for statebystate relative likelihood computations with stochastics models.
Parameters: sequence: list or str :
The sequence from which the ngrams will be collected.
pre_order: int :
An optional integer specifying the length of the preceding context. Defaults to zero.
post_order: int :
An optional integer specifying the length of the following context. Defaults to zero.
pad_symbol: object :
An optional symbol to be used as startof and endofsequence boundaries. The same symbol is used for both boundaries. Must be a value different from None, defaults to “$$$”.
elm_symbol: object :
An optional symbol to be used as transition symbol replacement in the context tuples (the first element in the returned iterator). Defaults to “###”.
Returns: out: iterable :
An iterable over the positional ngrams of the sequence, returned as tuples whose elements are: (1) a tuple representing the context (thus including preceding context, the transition symbol, and the following context), (2) an object with the value of the transition symbol, and (3) the index of the transition symbol in the sequence.
Examples
>>> from lingpy.sequence import * >>> sent = "Insurgents killed in ongoing fighting" >>> for ngram in get_posngrams(sent, 2, 1): ... print(ngram) ... (('$$$', '$$$', '###', 'killed'), 'Insurgents', 0) (('$$$', 'Insurgents', '###', 'in'), 'killed', 1) (('Insurgents', 'killed', '###', 'ongoing'), 'in', 2) (('killed', 'in', '###', 'fighting'), 'ongoing', 3) (('in', 'ongoing', '###', '$$$'), 'fighting', 4)

lingpy.sequence.ngrams.
get_skipngrams
(sequence, order, max_gaps, pad_symbol='$$$', single_gap=True)¶ Build an iterator for collecting all skip ngrams of a given length.
The function requires an information of the length of the skip ngrams to be collected, allowing to either collect ngrams with an unlimited number of gap openings (as described and implemented in Guthrie et al. 2006) or with at most one gap opening.
Parameters: sequence: list or str :
The sequence from which the ngrams will be collected. Must not include “None” as an element, as it is used as a sentinel during skip ngram collection following the implementation offered by Bird et al. 2018 (NLTK), which is a de facto standard.
order: int :
The order of the ngrams to be collected (parameter “n” in Guthrie et al. 2006).
max_gaps: int :
The maximum number of gaps in the ngrams to be collected (parameter “k” in Guthrie et al. 2006).
pad_symbol: object :
An optional symbol to be used as startof and endofsequence boundaries. The same symbol is used for both boundaries. Must be a value different from None, defaults to “$$$”.
single_gap: boolean :
An optional logic value indicating if multiple gap openings are to be allowed, as in Guthrie et al. (2006) and Bird et al. (2018), or if at most one gap_opening is to be allowed. Defaults to True.
Returns: out: iterable :
An iterable over the ngrams of the sequence, returned as tuples.
Examples
>>> from lingpy.sequence import * >>> sent = "Insurgents killed in ongoing fighting" >>> for ngram in get_skipngrams(sent, 2, 2): ... print(ngram) ... ('$$$', 'Insurgents') ('Insurgents', 'killed') ('killed', 'in') ('in', 'ongoing') ('ongoing', 'fighting') ('fighting', '$$$') ('$$$', 'killed') ('Insurgents', 'in') ('killed', 'ongoing') ('in', 'fighting') ('ongoing', '$$$') ('$$$', 'in') ('Insurgents', 'ongoing') ('killed', 'fighting') ('in', '$$$') >>> for ngram in get_skipngrams(sent, 2, 2, single_gap=False): ... print(ngram) ... ('$$$', 'Insurgents') ('$$$', 'killed') ('$$$', 'in') ('Insurgents', 'killed') ('Insurgents', 'in') ('Insurgents', 'ongoing') ('killed', 'in') ('killed', 'ongoing') ('killed', 'fighting') ('in', 'ongoing') ('in', 'fighting') ('in', '$$$') ('ongoing', 'fighting') ('ongoing', '$$$') ('fighting', '$$$')

lingpy.sequence.ngrams.
tabjoin
(*args, **kw)¶ Convenience shortcut. Strings to be joined do not have to be passed as list or tuple.
Notes
An implicit conversion of objects to strings is performed as well.

lingpy.sequence.ngrams.
trigrams
(sequence, *, order=3, pad_symbol='$$$')¶ Build an iterator for collecting all trigrams of a sequence.
The sequence is padded by default.
Parameters: sequence: list or str :
The sequence from which the trigrams will be collected.
pad_symbol: object :
An optional symbol to be used as startof and endofsequence boundaries. The same symbol is used for both boundaries. Must be a value different from None, defaults to “$$$”.
Returns: out: iterable :
An iterable over the trigrams of the sequence, returned as tuples.
Examples
>>> from lingpy.sequence import * >>> sent = "Insurgents killed in ongoing fighting" >>> for ngram in trigrams(sent): ... print(ngram) ... ('$$$', '$$$', 'Insurgents') ('$$$', 'Insurgents', 'killed') ('Insurgents', 'killed', 'in') ('killed', 'in', 'ongoing') ('in', 'ongoing', 'fighting') ('ongoing', 'fighting', '$$$') ('fighting', '$$$', '$$$')
lingpy.sequence.profile module¶
Module provides methods for the handling of orthography profiles.

lingpy.sequence.profile.
context_profile
(wordlist, ref='ipa', col='doculect', semi_diacritics='hsʃ̢ɕʂʐʑʒw', merge_vowels=False, brackets=None, splitters='/, ;~', merge_geminates=True, clts=False, bad_word='<???>', bad_sound='<?>', unknown_sound='!{0}', examples=2)¶ Create an advanced Orthography Profile with context and doculect information.
Parameters: wordlist : ~lingpy.basic.wordlist.Wordlist
A wordlist from which you want to derive an initial orthography profile.
ref : str (default=”ipa”)
The name of the reference column in which the words are stored.
col : str (default=”doculect”)
Indicate in which column the information on the language variety is stored.
semi_diacritics : str
Indicate characters which can occur both as “diacritics” (second part in a sound) or alone.
merge_vowels : bool (default=True)
Indicate whether consecutive vowels should be merged.
brackets : dict
A dictionary with opening brackets as key and closing brackets as values. Defaults to a predefined set of frequently occurring brackets.
splitters : str
The characters which force the automatic splitting of an entry.
clts : dict (default=None)
A dictionary(like) object that converts a given source sound into a potential target sound, using the get()method of the dictionary. Normally, we think of a CLTS instance here (that is: a crosslinguistic transcription system as defined in the pyclts package).
bad_word : str (default=”«???»”)
Indicate how words that could not be parsed should be handled. Note that both “bad_word” and “bad_sound” are formatstrings, so you can add formatting information here.
bad_sound : str (default=”«?»”)
Indicate how sounds that could not be converted to a sound class be handled. Note that both “bad_word” and “bad_sound” are formatstrings, so you can add formatting information here.
unknown_sound : str (default=”!{0}”)
If with_clts is set to True, use this string to indicate that sounds are classified as “unknown sound” in the CLTS framework.
examples : int(default=2)
Indicate the number of examples that should be printed out.
Returns: profile : generator
A generator of tuples (three items), indicating the segment, its frequency, the conversion to sound classes in the Dolgopolsky soundclass model, and the unicodecodepoints.

lingpy.sequence.profile.
simple_profile
(wordlist, ref='ipa', semi_diacritics='hsʃ̢ɕʂʐʑʒw', merge_vowels=False, brackets=None, splitters='/, ;~', merge_geminates=True, bad_word='<???>', bad_sound='<?>', clts=None, unknown_sound='!{0}')¶ Create an initial Orthography Profile using Lingpy’s clean_string procedure.
Parameters: wordlist : ~lingpy.basic.wordlist.Wordlist
A wordlist from which you want to derive an initial orthography profile.
ref : str (default=”ipa”)
The name of the reference column in which the words are stored.
semi_diacritics : str
Indicate characters which can occur both as “diacritics” (second part in a sound) or alone.
merge_vowels : bool (default=True)
Indicate whether consecutive vowels should be merged.
brackets : dict
A dictionary with opening brackets as key and closing brackets as values. Defaults to a predefined set of frequently occurring brackets.
splitters : str
The characters which force the automatic splitting of an entry.
clts : dict (default=None)
A dictionary(like) object that converts a given source sound into a potential target sound, using the get()method of the dictionary. Normally, we think of a CLTS instance here (that is: a crosslinguistic transcription system as defined in the pyclts package).
bad_word : str (default=”«???»”)
Indicate how words that could not be parsed should be handled. Note that both “bad_word” and “bad_sound” are formatstrings, so you can add formatting information here.
bad_sound : str (default=”«?»”)
Indicate how sounds that could not be converted to a sound class be handled. Note that both “bad_word” and “bad_sound” are formatstrings, so you can add formatting information here.
unknown_sound : str (default=”!{0}”)
If with_clts is set to True, use this string to indicate that sounds are classified as “unknown sound” in the CLTS framework.
Returns: profile : generator
A generator of tuples (three items), indicating the segment, its frequency, the conversion to sound classes in the Dolgopolsky soundclass model, and the unicodecodepoints.
lingpy.sequence.smoothing module¶
Module providing various methods for using Ngram models.
The smoothing methods are implemented to be as compatible as possible with those offered by NLTK. In fact, both implementation and comments try to follow Bird at al. as close as possible.

lingpy.sequence.smoothing.
certaintydegree_dist
(freqdist, **kwargs)¶ Returns a logprobability distribution based on the degree of certainty.
In this distribution a mass probability is reserved for unobserved samples from a computation of the degree of certainty that the are no unobserved samples.
Under development and test by Tiago Tresoldi, this is an experimental probability distribution that should not be used as the sole or main distribution for the time being.
Parameters: freqdist : dict
Frequency distribution of samples (keys) and counts (values) from which the probability distribution will be calculated.
bins: int :
The optional number of sample bins that can be generated by the experiment that is described by the probability distribution. If not specified, it will default to the number of samples in the frequency distribution.
unobs_prob : float
An optional mass probability to be reserved for unobserved states, from 0.0 to 1.0.
Returns: state_prob: dict :
A dictionary of sample to logprobabilities for all the samples in the frequency distribution.
unobserved_prob: float :
The logprobability for samples not found in the frequency distribution.

lingpy.sequence.smoothing.
ele_dist
(freqdist, **kwargs)¶ Returns an ExpectedLikelihood estimate logprobability distribution.
In an ExpectedLikelihood estimate logprobability the frequency distribution of observed samples is used to estimate the probability distribution of the experiment that generated such observation, following a parameter given by a real number gamma set by definition to 0.5. As such, it is a generalization of the Lidstone estimate.
Parameters: freqdist : dict
Frequency distribution of samples (keys) and counts (values) from which the probability distribution will be calculated.
bins: int :
The optional number of sample bins that can be generated by the experiment that is described by the probability distribution. If not specified, it will default to the number of samples in the frequency distribution.
Returns: state_prob: dict :
A dictionary of sample to logprobabilities for all the samples in the frequency distribution.
unobserved_prob: float :
The logprobability for samples not found in the frequency distribution.

lingpy.sequence.smoothing.
laplace_dist
(freqdist, **kwargs)¶ Returns a Laplace estimate logprobability distribution.
In a Laplace estimate logprobability the frequency distribution of observed samples is used to estimate the probability distribution of the experiment that generated such observation, following a parameter given by a real number gamma set by definition to 1. As such, it is a generalization of the Lidstone estimate.
Parameters: freqdist : dict
Frequency distribution of samples (keys) and counts (values) from which the probability distribution will be calculated.
bins: int :
The optional number of sample bins that can be generated by the experiment that is described by the probability distribution. If not specified, it will default to the number of samples in the frequency distribution.
Returns: state_prob: dict :
A dictionary of sample to logprobabilities for all the samples in the frequency distribution.
unobserved_prob: float :
The logprobability for samples not found in the frequency distribution.

lingpy.sequence.smoothing.
lidstone_dist
(freqdist, **kwargs)¶ Returns a Lidstone estimate logprobability distribution.
In a Lidstone estimate logprobability the frequency distribution of observed samples is used to estimate the probability distribution of the experiment that generated such observation, following a parameter given by a real number gamma typycally randing from 0.0 to 1.0. The Lidstone estimate approximates the probability of a sample with count c from an experiment with N outcomes and B bins as (c+gamma)/(N+B*gamma). This is equivalent to adding gamma to the count of each bin and taking the MaximumLikelihood estimate of the resulting frequency distribution, with the corrected space of observation; the probability for an unobserved sample is given by frequency of a sample with gamma observations.
Also called “additive smoothing”, this estimation method is frequently used with a gamma of 1.0 (the socalled “Laplace smoothing”) or of 0.5 (the socalled “Expected likelihood estimate”, or ELE).
Parameters: freqdist : dict
Frequency distribution of samples (keys) and counts (values) from which the probability distribution will be calculated.
gamma : float
A real number used to parameterize the estimate.
bins: int :
The optional number of sample bins that can be generated by the experiment that is described by the probability distribution. If not specified, it will default to the number of samples in the frequency distribution.
Returns: state_prob: dict :
A dictionary of sample to logprobabilities for all the samples in the frequency distribution.
unobserved_prob: float :
The logprobability for samples not found in the frequency distribution.

lingpy.sequence.smoothing.
mle_dist
(freqdist, **kwargs)¶ Returns a MaximumLikelihood Estimation logprobability distribution.
In an MLE logprobability distribution the probability of each sample is approximated as the frequency of the same sample in the frequency distribution of observed samples. It is the distribution people intuitively adopt when thinking of probability distributions. A mass probability can optionally be reserved for unobserved samples.
Parameters: freqdist : dict
Frequency distribution of samples (keys) and counts (values) from which the probability distribution will be calculated.
unobs_prob : float
An optional mass probability to be reserved for unobserved states, from 0.0 to 1.0.
Returns: state_prob: dict :
A dictionary of sample to logprobabilities for all the samples in the frequency distribution.
unobserved_prob: float :
The logprobability for samples not found in the frequency distribution.

lingpy.sequence.smoothing.
random_dist
(freqdist, **kwargs)¶ Returns a random logprobability distribution.
In a random logprobability distribution all samples, no matter the observed counts, will have a random logprobability computed from a set of randomly drawn floating point values. A mass probability can optionally be reserved for unobserved samples.
Parameters: freqdist : dict
Frequency distribution of samples (keys) and counts (values) from which the probability distribution will be calculated.
unobs_prob : float
An optional mass probability to be reserved for unobserved states, from 0.0 to 1.0.
seed : any hasheable value
An optional seed for the random number generator, defaulting to None.
Returns: state_prob: dict :
A dictionary of sample to logprobabilities for all the samples in the frequency distribution.
unobserved_prob: float :
The logprobability for samples not found in the frequency distribution.

lingpy.sequence.smoothing.
sgt_dist
(freqdist, **kwargs)¶ Returns a Simple GoodTuring logprobability distribution.
The returned logprobability distribution is based on the GoodTuring frequency estimation, as first developed by Alan Turing and I. J. Good and implemented in a more easily computable way by Gale and Sampson’s (1995/2001 reprint) in the socalled “Simple GoodTuring”.
This implementation is based mostly in the one by “maxbane” (2011) (https://github.com/maxbane/simplegoodturing/blob/master/sgt.py), as well as in the original one in C by Geoffrey Sampson (1995; 2000; 2005; 2008) (https://www.grsampson.net/Resources.html), and in the one by Loper, Bird et al. (20012018, NLTK Project) (http://www.nltk.org/_modules/nltk/probability.html). Please note that due to minor differences in implementation intended to guarantee nonzero probabilities even in cases of expected underflow, as well as our relience on scipy’s libraries for speed and our way of handling probabilities that are not computable when the assumptions of SGT are not met, most results will not exactly match those of the ‘gold standard’ of Gale and Sampson, even though the differences are never expected to be significative and are equally distributed across the samples.
Parameters: freqdist : dict
Frequency distribution of samples (keys) and counts (values) from which the probability distribution will be calculated.
p_value : float
The pvalue for calculating the confidence interval of the empirical Turing estimate, which guides the decision of using either the Turing estimate “x” or the loglinear smoothed “y”. Defaults to 0.05, as per the reference implementation by Sampson, but consider that the authors, both in their paper and in the code following suggestions credited to private communication with Fan Yang, consider using a value of 0.1.
allow_fail : bool
A logic value informing if the function is allowed to fail, throwing RuntimeWarning exceptions, if the essential assumptions on the frequency distribution are not met, i.e., if the slope of the loglinear regression is > 1.0 or if an unobserved count is reached before we are able to cross the smoothing threshold. If set to False, the estimation might result in an unreliable probability distribution; defaults to True.
default_p0 : float
An optional value indicating the probability for unobserved samples (“p0”) in cases where no samples with a single count are observed; if this value is not specified, “p0” will default to a Laplace estimation for the current frequency distribution. Please note that this is intended change from the reference implementation by Gale and Sampson.
Returns: state_prob: dict :
A dictionary of sample to logprobabilities for all the samples in the frequency distribution.
unobserved_prob: float :
The logprobability for samples not found in the frequency distribution.

lingpy.sequence.smoothing.
smooth_dist
(freqdist, method, **kwargs)¶ Returns a smoothed logprobability distribution from a named method.
This method is used to generalize over all implemented smoothing methods, especially in terms of serialization. The method argument informs which smoothing mehtod to use and passes all the arguments to the appropriate function.
Parameters: freqdist : dict
Frequency distribution of samples (keys) and counts (values) from which the logprobability distribution will be calculated.
method: str :
The name of the probability smoothing method to use. Either “uniform”, “random”, “mle”, “lidstone”, “laplace”, “ele”, “wittenbell”, “certaintydegree”, or “sgt”.
kwargs: additional arguments :
Additional arguments passed to the appropriate smoothing method function.
Returns: state_prob: dict :
A dictionary of sample to logprobabilities for all the samples in the frequency distribution.
unobserved_prob: float :
The logprobability for samples not found in the frequency distribution.

lingpy.sequence.smoothing.
uniform_dist
(freqdist, **kwargs)¶ Returns a uniform logprobability distribution.
In a uniform logprobability distribution all samples, no matter the observed counts, will have the same logprobability. A mass probability can optionally be reserved for unobserved samples.
Parameters: freqdist : dict
Frequency distribution of samples (keys) and counts (values) from which the logprobability distribution will be calculated.
unobs_prob : float
An optional mass probability to be reserved for unobserved states, from 0.0 to 1.0.
Returns: state_prob: dict :
A dictionary of sample to logprobabilities for all the samples in the frequency distribution.
unobserved_prob: float :
The logprobability for samples not found in the frequency distribution.

lingpy.sequence.smoothing.
wittenbell_dist
(freqdist, **kwargs)¶ Returns a WittenBell estimate logprobability distribution.
In a WittenBell estimate logprobability a uniform probability mass is allocated to yet unobserved samples by using the number of samples that have only been observed once. The probability mass reserved for unobserved samples is equal to T / (N +T), where T is the number of observed samples and N the number of total observations. This equates to the MaximumLikelihood Estimate of a new type of sample occurring. The remaining probability mass is discounted such that all probability estimates sum to one, yielding:
 p = T / Z (N + T), if count == 0
 p = c / (N + T), otherwise
Parameters: freqdist : dict
Frequency distribution of samples (keys) and counts (values) from which the probability distribution will be calculated.
bins: int :
The optional number of sample bins that can be generated by the experiment that is described by the probability distribution. If not specified, it will default to the number of samples in the frequency distribution.
Returns: state_prob: dict :
A dictionary of sample to logprobabilities for all the samples in the frequency distribution.
unobserved_prob: float :
The logprobability for samples not found in the frequency distribution.
lingpy.sequence.sound_classes module¶
Module provides various methods for the handling of sound classes.

lingpy.sequence.sound_classes.
asjp2tokens
(seq, merge_vowels=True)¶

lingpy.sequence.sound_classes.
check_tokens
(tokens, **keywords)¶ Function checks whether tokens are given in a consistent input format.

lingpy.sequence.sound_classes.
class2tokens
(tokens, classes, gap_char='', local=False)¶ Turn aligned soundclass sequences into an aligned sequences of IPA tokens.
Parameters: tokens : list
The list of tokens corresponding to the unaligned IPA string.
classes : string or list
The aligned class string.
gap_char : string (default=”“)
The character which indicates gaps in the output string.
local : bool (default=False)
If set to True a local alignment with prefix and suffix can be converted.
Returns: alignment : list
A list of tokens with gaps at the positions where they occured in the alignment of the class string.
See also
Examples
>>> from lingpy import * >>> tokens = ipa2tokens('t͡sɔyɡə') >>> aligned_sequence = 'CUKE' >>> print ', '.join(class2tokens(tokens,aligned_sequence)) t͡s, ɔy, , ɡ, ə

lingpy.sequence.sound_classes.
clean_string
(sequence, semi_diacritics='hsʃ̢ɕʂʐʑʒw', merge_vowels=False, segmentized=False, rules=None, ignore_brackets=True, brackets=None, split_entries=True, splitters='/, ;~', preparse=None, merge_geminates=True, normalization_form='NFC')¶ Function exhaustively checks how well a sequence is understood by LingPy.
Parameters: semi_diacritics : str
Indicate characters which can occur both as “diacritics” (second part in a sound) or alone.
merge_vowels : bool (default=True)
Indicate whether consecutive vowels should be merged.
segmentized : False
Indicate whether the input string is already segmentized or not. If set to True, items in brackets can no longer be ignored.
rules : dict
Replacement rules to be applied to a segmentized string.
ignore_brackets : bool
If set to True, ignore all content within a given bracket.
brackets : dict
A dictionary with opening brackets as key and closing brackets as values. Defaults to a predefined set of frequently occurring brackets.
split_entries : bool (default=True)
Indicate whether multiple entries (with a comma etc.) should be split into separate entries.
splitters : str
The characters which force the automatic splitting of an entry.
preparse : list
List of tuples, giving simple replacement patterns (source and target), which are applied before every processing starts.
Returns: cleaned_strings : list
A list of cleaned strings which are segmented by space characters. If splitters are encountered, indicating that the entry contains two variants, the list will contain one for each element in a separate entry. If there are no splitters, the list has only size one.

lingpy.sequence.sound_classes.
codepoint
(s)¶ Return unicode codepoint(s) for a character set.

lingpy.sequence.sound_classes.
get_all_ngrams
(sequence, sort=False)¶ Function returns all possible ngrams of a given sequence.
Parameters: sequence : list or str
The sequence that shall be converted into it’s ngramrepresentation.
Returns: out : list
A list of all ngrams of the input word, sorted in decreasing order of length.
Examples
>>> get_all_ngrams('abcde') ['abcde', 'bcde', 'abcd', 'cde', 'abc', 'bcd', 'ab', 'de', 'cd', 'bc', 'a', 'e', 'b', 'd', 'c']

lingpy.sequence.sound_classes.
ipa2tokens
(istring, **keywords)¶ Tokenize IPAencoded strings.
Parameters: seq : str
The input sequence that shall be tokenized.
diacritics : {str, None} (default=None)
A string containing all diacritics which shall be considered in the respective analysis. When set to None, the default diacritic string will be used.
vowels : {str, None} (default=None)
A string containing all vowel symbols which shall be considered in the respective analysis. When set to None, the default vowel string will be used.
tones : {str, None} (default=None)
A string indicating all tone letter symbals which shall be considered in the respective analysis. When set to None, the default tone string will be used.
combiners : str (default=”͜͡”)
A string with characters that are used to combine two separate characters (compare affricates such as t͡s).
breaks : str (default=”.”)
A string containing the characters that indicate that a new token starts right after them. These can be used to indicate that two consecutive vowels should not be treated as diphtongs or for diacritics that are put before the following letter.
merge_vowels : bool (default=False)
Indicate, whether vowels should be merged into diphtongs (default=True), or whether each vowel symbol should be considered separately.
merge_geminates : bool (default=False)
Indicate, whether identical symbols should be merged into one token, or rather be kept separate.
expand_nasals : bool (default=False)
semi_diacritics: str (default=’‘) :
Indicate which symbols shall be treated as “semidiacritics”, that is, as symbols which can occur on their own, but which eventually, when preceded by a consonant, will form clusters with it. If you want to disable this features, just set the keyword to an empty string.
clean_string : bool (default=False)
Conduct a rough stringcleaning strategy by which all items between brackets are removed along with the brackets, and
Returns: tokens : list
A list of IPA tokens.
See also
Examples
>>> from lingpy import * >>> myseq = 't͡sɔyɡə' >>> ipa2tokens(myseq) ['t͡s', 'ɔy', 'ɡ', 'ə']

lingpy.sequence.sound_classes.
ono_parse
(word, output='', **keywords)¶ Carry out a rough onsetnucleusoffset parse of a word in IPA.
Notes
Method is an approximation and not supposed to do without flaws. It is, however, rather helpful in most instances. It defines a so far simple model in which 7 different contexts for each word are distinguished:
 “#”: onset cluster in a word’s initial
 “C”: onset cluster in a word’s noninitial
 “V”: nucleus vowel in a word’s initial syllable
 “v”: nucleus vowel in a word’s noninitial and nonfinal syllable
 “>”: nucleus vowel in a word’s final syllable
 “c”: offset cluster in a word’s nonfinal syllable
 “$”: offset cluster in a word’s final syllable

lingpy.sequence.sound_classes.
pgrams
(sequence, **keywords)¶ Convert a given sequence into bigrams consisting of prosodic string symbols and the tokens of the original sequence.

lingpy.sequence.sound_classes.
pid
(almA, almB, mode=2)¶ Calculate the Percentage Identity (PID) score for aligned sequence pairs.
Parameters: almA, almB : string or list
The aligned sequences which can be either a string or a list.
mode : { 1, 2, 3, 4, 5 }
Indicate which of the four possible PID scores described in
Raghava2006
should be calculated, the fifth possibility is added for linguistic purposes: identical positions / (aligned positions + internal gap positions),
 identical positions / aligned positions,
 identical positions / shortest sequence, or
 identical positions / shortest sequence (including internal gap pos.)
 identical positions / (aligned positions + 2 * number of gaps)
Returns: score : float
The PID score of the given alignment as a floating point number between 0 and 1.
See also
lingpy.compare.Multiple.get_pid
,Notes
The PID score is a common measure for the diversity of a given alignment. The implementation employed by LingPy follows the description of
Raghava2006
where four different variants of PID scores are distinguished. Essentially, the PID score is based on the comparison of identical residue pairs with the total number of residue pairs in a given alignment.Examples
Load an alignment from the test suite.
>>> from lingpy import * >>> pairs = PSA(get_file('test.psa'))
Extract the alignments of the first aligned sequence pair.
>>> almA,almB,score = pairs.alignments[0]
Calculate the PID score of the alignment.
>>> pid(almA,almB) 0.44444444444444442

lingpy.sequence.sound_classes.
prosodic_string
(string, _output=True, **keywords)¶ Create a prosodic string of the sonority profile of a sequence.
Parameters: seq : list
A list of integers indicating the sonority of the tokens of the underlying sequence.
stress : str (default=rcParams[‘stress’])
A string containing the stress symbols used in the analysis. Defaults to the stress as defined in ~lingpy.settings.rcParams.
diacritics : str (default=rcParams[‘diacritics’])
A string containing diacritic symbols used in the analysis. Defaults to the diacritic symbolds defined in ~lingpy.settings.rcParams.
cldf : bool (default=False)
If set to True, this will allow for a specific treatment of phonetic symbols which cannot be completely resolved (e.g., laryngeal h₂ in IndoEuropean). Following the CLDF specifications (in particular the specifications for writing transcriptions in segmented strings, as employed by the CLTS initiative), in cases of insecurity of pronunciation, users can adopt a
`source/target`
style, where the source is the symbol used, e.g., in a reconstruction system, and the target is a proposed phonetic interpretation. This practice is also accepted by the EDICTOR tool.Returns: prostring : string
A prosodic string corresponding to the sonority profile of the underlying sequence.
See also
prosodic
Notes
A prosodic string is a sequence of specific characters which indicating their resprective prosodic context (see
List2012
orList2012a
for a detailed description). In contrast to the previous model, the current implementation allows for a more finegraded distinction between different prosodic segments. The current scheme distinguishes 9 prosodic positions:A
: sequenceinitial consonantB
: syllableinitial, nonsequence initial consonant in a context of ascending sonorityC
: nonsyllable, noninitial consonant in ascending sonority contextL
: nonsyllablefinal consonant in descending environmentM
: syllablefinal consonant in descending environmentN
: wordfinal consonantX
: first vowel in a wordY
: nonfinal vowel in a wordZ
: vowel occuring in the last position of a wordT
: tone_
: word break
Examples
>>> prosodic_string(ipa2tokens('t͡sɔyɡə') 'AXBZ'

lingpy.sequence.sound_classes.
prosodic_weights
(prostring, _transform={})¶ Calculate prosodic weights for each position of a sequence.
Parameters: prostring : string
A prosodic string as it is returned by
prosodic_string()
._transform : dict
A dictionary that determines how prosodic strings should be transformed into prosodic weights. Use this dictionary to adjust the prosodic strings to your own userdefined prosodic weight schema.
Returns: weights : list
A list of floats reflecting the modification of the weight for each position.
See also
Notes
Prosodic weights are specific scaling factors which decrease or increase the gap score of a given segment in alignment analyses (see
List2012
orList2012a
for a detailed description).Examples
>>> from lingpy import * >>> prostring = '#vC>' >>> prosodic_weights(prostring) [2.0, 1.3, 1.5, 0.7]

lingpy.sequence.sound_classes.
sampa2uni
(seq)¶ Convert sequence in IPAsampaformat to IPAunicode.
Notes
This function is based on code taken from Peter Kleiweg (http://www.let.rug.nl/~kleiweg/L04/devel/python/xsampa.html).

lingpy.sequence.sound_classes.
syllabify
(seq, output='flat', **keywords)¶ Carry out a simple syllabification of a sequence, using sonority as a proxy.
Parameters: output: {“flat”, “breakpoints”, “nested”} (default=”flat”) :
Define how to output the syllabification. Select between: * “flat”: A syllable separator is introduced to mark the syllable boundaries * “breakpoins”: A tuple consisting of indices that slice the original sequence into syllables is returned. * “nested”: A nested list reflecting the syllable structure is returned.
sep : str (default=”◦”)
Select your preferred syllable separator.
Returns: syllable : list
Either a flat list containing a morpheme separator, or a nested list, reflecting the syllable structure, or a list of tuples containing the indices indicating where the input sequence should be sliced in order to split it into syllables.
Notes
When analyzing the sequence, we start a new syllable in all cases where we reach a deepest point in the sonority hierarchy of the sonority profile of the sequence. When passing an aligned string to this function, the gaps will be ignored when computing boundaries, but later on reintroduced, if the alignment is passed in segmented form.

lingpy.sequence.sound_classes.
token2class
(token, model, stress=None, diacritics=None, cldf=None)¶ Convert a single token into a soundclass.
 tokens : str
 A token (phonetic segment).
 model :
Model
 A
Model
object.  stress : str (default=rcParams[‘stress’])
 A string containing the stress symbols used in the analysis. Defaults to the stress as defined in ~lingpy.settings.rcParams.
 diacritics : str (default=rcParams[‘diacritics’])
 A string containing diacritic symbols used in the analysis. Defaults to the diacritic symbolds defined in ~lingpy.settings.rcParams.
 cldf : bool (default=False)
 If set to True, this will allow for a specific treatment of phonetic
symbols which cannot be completely resolved (e.g., laryngeal h₂ in
IndoEuropean). Following the CLDF
specifications (in particular the specifications for writing
transcriptions in segmented strings, as employed by the CLTS initiative), in cases of insecurity
of pronunciation, users can adopt a
`source/target`
style, where the source is the symbol used, e.g., in a reconstruction system, and the target is a proposed phonetic interpretation. This practice is also accepted by the EDICTOR tool.
Returns: sound_class : str
A soundclass representation of the phonetic segment. If the segment cannot be resolved, the respective string will be rendered as “0” (zero).
See also

lingpy.sequence.sound_classes.
tokens2class
(tokens, model, stress=None, diacritics=None, cldf=True)¶ Convert tokenized IPA strings into their respective class strings.
Parameters: tokens : list
A list of tokens as they are returned from
ipa2tokens()
.model :
Model
A
Model
object.stress : str (default=rcParams[‘stress’])
A string containing the stress symbols used in the analysis. Defaults to the stress as defined in ~lingpy.settings.rcParams.
diacritics : str (default=rcParams[‘diacritics’])
A string containing diacritic symbols used in the analysis. Defaults to the diacritic symbolds defined in ~lingpy.settings.rcParams.
cldf : bool (default=True)
If set to True, as by default, this will allow for a specific treatment of phonetic symbols which cannot be completely resolved (e.g., laryngeal h₂ in IndoEuropean). Following the CLDF specifications (in particular the specifications for writing transcriptions in segmented strings, as employed by the CLTS initiative), in cases of insecurity of pronunciation, users can adopt a
`source/target`
style, where the source is the symbol used, e.g., in a reconstruction system, and the target is a proposed phonetic interpretation. This practice is also accepted by the EDICTOR tool.Returns: classes : list
A soundclass representation of the tokenized IPA string in form of a list. If sound classes cannot be resolved, the respective string will be rendered as “0” (zero).
See also
Notes
The function ~lingpy.sequence.sound_classes.token2class returns a “0” (zero) if the sound is not recognized by LingPy’s sound class models. While an unknown sound in a longer sequence is no problem for alignment algorithms, we have some unwanted and often even unforeseeable behavior, if the sequence is completely unknown. For this reason, this function raises a ValueError, if a resulting sequence only contains unknown sounds.
Examples
>>> from lingpy import * >>> tokens = ipa2tokens('t͡sɔyɡə') >>> classes = tokens2class(tokens,'sca') >>> print(classes) CUKE

lingpy.sequence.sound_classes.
tokens2morphemes
(tokens, **keywords)¶ Split a string into morphemes if it contains separators.
Parameters: sep : str (default=”◦”)
Select your morpheme separator.
word_sep: str (default=”_”) :
Select your word separator.
Returns: morphemes : list
A nested list of the original segments split into morphemes.
Notes
Function splits a list of tokens into subsequent lists of morphemes if the list contains morpheme separators. If no separators are found, but tonemarkers, it will still split the string according to the tones. If you want to avoid this behavior, set the keyword split_on_tones to False.
Module contents¶
Module provides methods and functions for dealing with linguistic sequences.

lingpy.sequence.
bigrams
(sequence, *, order=2, pad_symbol='$$$')¶ Build an iterator for collecting all bigrams of a sequence.
The sequence is padded by default.
Parameters: sequence: list or str :
The sequence from which the bigrams will be collected.
pad_symbol: object :
An optional symbol to be used as startof and endofsequence boundaries. The same symbol is used for both boundaries. Must be a value different from None, defaults to “$$$”.
Returns: out: iterable :
An iterable over the bigrams of the sequence, returned as tuples.
Examples
>>> from lingpy.sequence import * >>> sent = "Insurgents killed in ongoing fighting" >>> for ngram in bigrams(sent): ... print(ngram) ... ('$$$', 'Insurgents') ('Insurgents', 'killed') ('killed', 'in') ('in', 'ongoing') ('ongoing', 'fighting') ('fighting', '$$$')

lingpy.sequence.
confirm
(question, *, default=False)¶ Ask a yes/no question interactively.
Parameters: question – The text of the question to ask. Returns: True if the answer was “yes”, False otherwise.

lingpy.sequence.
data_path
(*comps)¶

lingpy.sequence.
dotjoin
(*args, **kw)¶ Convenience shortcut. Strings to be joined do not have to be passed as list or tuple.
Notes
An implicit conversion of objects to strings is performed as well.

lingpy.sequence.
fourgrams
(sequence, *, order=4, pad_symbol='$$$')¶ Build an iterator for collecting all fourgrams of a sequence.
The sequence is padded by default.
Parameters: sequence: list or str :
The sequence from which the fourgrams will be collected.
pad_symbol: object :
An optional symbol to be used as startof and endofsequence boundaries. The same symbol is used for both boundaries. Must be a value different from None, defaults to “$$$”.
Returns: out: iterable :
An iterable over the fourgrams of the sequence, returned as tuples.
Examples
>>> from lingpy.sequence import * >>> sent = "Insurgents killed in ongoing fighting" >>> for ngram in fourgrams(sent): ... print(ngram) ... ('$$$', '$$$', '$$$', 'Insurgents') ('$$$', '$$$', 'Insurgents', 'killed') ('$$$', 'Insurgents', 'killed', 'in') ('Insurgents', 'killed', 'in', 'ongoing') ('killed', 'in', 'ongoing', 'fighting') ('in', 'ongoing', 'fighting', '$$$') ('ongoing', 'fighting', '$$$', '$$$') ('fighting', '$$$', '$$$', '$$$')

lingpy.sequence.
tabjoin
(*args, **kw)¶ Convenience shortcut. Strings to be joined do not have to be passed as list or tuple.
Notes
An implicit conversion of objects to strings is performed as well.

lingpy.sequence.
trigrams
(sequence, *, order=3, pad_symbol='$$$')¶ Build an iterator for collecting all trigrams of a sequence.
The sequence is padded by default.
Parameters: sequence: list or str :
The sequence from which the trigrams will be collected.
pad_symbol: object :
An optional symbol to be used as startof and endofsequence boundaries. The same symbol is used for both boundaries. Must be a value different from None, defaults to “$$$”.
Returns: out: iterable :
An iterable over the trigrams of the sequence, returned as tuples.
Examples
>>> from lingpy.sequence import * >>> sent = "Insurgents killed in ongoing fighting" >>> for ngram in trigrams(sent): ... print(ngram) ... ('$$$', '$$$', 'Insurgents') ('$$$', 'Insurgents', 'killed') ('Insurgents', 'killed', 'in') ('killed', 'in', 'ongoing') ('in', 'ongoing', 'fighting') ('ongoing', 'fighting', '$$$') ('fighting', '$$$', '$$$')