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"content": "language: python\npython:\n- '2.7'\n- '3.5'\ninstall: \n- pip install --install-option=\"--no-cython-compile\" cython\n- pip install -r requirements.txt\n- nltkd=$(python -c 'from __future__ import print_function; import nltk; print(nltk.data.path[0])')\n- python -m nltk.downloader punkt -d \"$nltkd\"\n- python -m nltk.downloader wordnet -d \"$nltkd\"\n- python -m nltk.downloader averaged_perceptron_tagger -d \"$nltkd\"\nscript:\n- nosetests corpkit/nosetests.py -a '!slow'\ndeploy:\n provider: pypi\n user: mcddjx\n password:\n secure: 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\n on:\n tags: true\n distributions: sdist bdist_wheel\n repo: interrogator/corpkit\ngit:\n submodules: false"
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"content": "## *corpkit*: API readme\n\n> This file is a deprecated introduction to the *corpkit* Python API. It still exists because it contains a lot of useful information and advanced examples that are not found elsewhere. It is deprecated because better documentation is available at [ReadTheDocs](http://corpkit.readthedocs.org/en/latest/).\n\n- [What's in here?](#whats-in-here)\n - [`Corpus()`](#corpus)\n - [Navigating `Corpus` objects](#navigating-corpus-objects)\n - [`interrogate()` method](#interrogate-method)\n - [`concordance()` method](#concordance-method)\n - [`Interrogation`](#interrogation)\n - [`edit()` method](#edit-method)\n - [`visualise()` method](#visualise-method)\n - [Functions, lists, etc.](#functions-lists-etc)\n- [Installation](#installation)\n - [By downloading the repository](#by-downloading-the-repository)\n - [By cloning the repository](#by-cloning-the-repository)\n - [Via `pip`](#via-pip)\n- [Quickstart](#quickstart)\n- [More detailed examples](#more-detailed-examples)\n - [`search`, `exclude` and `show`](#search-exclude-and-show)\n- [Working with coreferences](#working-with-coreferences)\n- [Building corpora](#building-corpora)\n - [Speaker IDs](#speaker-ids)\n - [Navigating parsed corpora](#navigating-parsed-corpora)\n - [Getting general stats](#getting-general-stats)\n- [Concordancing](#concordancing)\n- [Systemic functional stuff](#systemic-functional-stuff)\n- [Keywording](#keywording)\n - [Visualising keywords](#visualising-keywords)\n - [Traditional reference corpora](#traditional-reference-corpora)\n- [Parallel processing](#parallel-processing)\n - [Multiple corpora](#multiple-corpora)\n - [Multiple speakers](#multiple-speakers)\n - [Multiple queries](#multiple-queries)\n- [More complex queries and plots](#more-complex-queries-and-plots)\n - [Visualisation options](#visualisation-options)\n- [Contact](#contact)\n- [Cite](#cite)\n\n\n## What's in here?\n\nEssentially, the module contains classes, methods and functions for building and interrogating corpora, then manipulating or visualising the results. \n\n\n### `Corpus()`\n\nFirst, there's a `Corpus()` class, which models a corpus of CoreNLP XML, lists of tokens, or plaintext files, creating subclasses for subcorpora and corpus files.\n\nTo use it, simple feed it a path to a directory containing `.txt` files, or subfolders containing `.txt` files.\n\n```python\n>>> from corpkit import Corpus\n>>> unparsed = Corpus('path/to/data')\n```\n\nWith the `Corpus()` class, the following attributes are available:\n\n| Attribute | Purpose |\n|-----------|---------|\n| `corpus.subcorpora` | list of subcorpus objects with indexing/slicing methods |\n| `corpus.features` | Corpus features (characters, clauses, words, tokens, process types, passives, etc.) |\n| `corpus.postags` | Distribution of parts of speech |\n| `corpus.wordclasses` | Distribution of word classes |\n\nas well as the following methods:\n\n| Method | Purpose |\n|--------|---------|\n| `corpus.parse()` | Create a parsed version of a plaintext corpus |\n| `corpus.tokenise()` | Create a tokenised version of a plaintext corpus |\n| `corpus.interrogate()` | Interrogate the corpus for lexicogrammatical features |\n| `corpus.concordance()` | Concordance via lexis and/or grammar |\n\n\n#### Navigating `Corpus` objects\n\nOnce you've defined a Corpus, you can move around it very easily:\n\n```python\n### corpus containing annual subcorpora of NYT articles\n>>> corpus = Corpus('data/NYT-parsed')\n\n>>> list(corpus.subcorpora)[:3]\n### [,\n### ,\n### ]\n\n>>> corpus.subcorpora[0].path, corpus.subcorpora[0].datatype\n### ('/Users/daniel/Work/risk/data/NYT-parsed/1987', 'parse')\n\n>>> corpus.subcorpora.c1989.files[10:13]\n### [,\n### ,\n### ]\n\n```\n\nMost attributes, and the `.interrogate()` and `.concordance()` methods, can also be called on `Subcorpus` and `File` objects. `File` objects also have a `.read()` method.\n\n\n#### `interrogate()` method\n\n* Use [Tregex](http://nlp.stanford.edu/~manning/courses/ling289/Tregex.html), regular expressions or wordlists to search parse trees, dependencies, token lists or plain text for complex lexicogrammatical phenomena\n* Search for, exclude and show word, lemma, POS tag, semantic role, governor, dependent, index (etc) of a token\n* N-gramming\n* Two-way UK-US spelling conversion\n* Output Pandas DataFrames that can be easily edited and visualised\n* Use parallel processing to search for a number of patterns, or search for the same pattern in multiple corpora\n* Restrict searches to particular speakers in a corpus\n* Works on collections of corpora, corpora, subcorpora, single files, or slices thereof\n* Quickly save to and load from disk with `save()` and `load()`\n\n\n#### `concordance()` method\n\n* Equivalent to `interrogate()`, but return DataFrame of concordance lines\n* Return any combination and order of words, lemmas, indices, functions, or POS tags\n* Editable and saveable\n* Output to LaTeX, CSV or string with `format()`\n\nThe code below demonstrates the complex kinds of queries that can be handled by the `interrogate()` and `concordance()` methods:\n\n```python\n### import * mostly so that we can access global variables like G, P, V\n### otherwise, use 'w' instead of W, 'p' instead of P, etc. \n>>> from corpkit import *\n\n### select parsed corpus\n>>> corpus = Corpus('data/postcounts-parsed')\n\n### import process type lists and closed class wordlists\n>>> from corpkit.dictionaries import *\n\n### match tokens with governor that is in relational process wordlist, \n### and whose function is `nsubj(pass)` or `csubj(pass)`:\n>>> criteria = {GL: processes.relational.lemmata, F: r'^.subj'}\n\n### exclude tokens whose part-of-speech is verbal, \n### or whose word is in a list of pronouns\n>>> exc = {P: r'^V', W: wordlists.pronouns}\n\n# interrogate, returning slash-delimited function/lemma\n>>> data = corpus.interrogate(criteria, exclude=exc, show=[F,L])\n>>> lines = corpus.concordance(criteria, exclude=exc, show=[F,L])\n\n### show results\n>>> print data, lines.format(n=10, window=40, columns=[L,M,R])\n```\n\nOutput sample:\n\n```\n nsubj/thing nsubj/person nsubj/problem nsubj/way nsubj/son\n01 296 168 134 69 73 \n02 233 147 88 70 70 \n03 250 160 95 80 67 \n04 247 205 88 93 71 \n05 275 193 68 75 61 \n\n0 nk nsubj/it cop/be ccomp/sad advmod/when nsubj/person aux/do neg/not advcl/look ./at prep_at/w\n1 /my dobj/Fluoxetine advmod/now mark/that nsubj/spring ccomp/be advmod/here ./, ./but nsubj/I a\n2 y mark/because expl/there advcl/be det/a nsubj/woman ./across det/the prep_across/hall ./from\n3 num/114 ccomp/pound ./, mark/so det/any nsubj/med nsubj/I rcmod/take aux/can advcl/have de\n4 nsubj/Kat ./, root/be nsubj/you dep/taper ./off ./\n5 /to xcomp/explain prep_from/what det/the nsubj/mark ./on poss/my prep_on/arm ./, conj_and/ne\n6 det/the amod/first ./and conj_and/third nsubj/hospital nsubj/I rcmod/be advmod/at root/have num\n7 e dobj/tv mark/while det/the amod/second nsubj/hospital nsubj/I cop/be rcmod/IP prep/at pcomp/in\n8 nsubj/Ben ./, mark/if nsubj/you cop/be advcl/unhap\n9 h ./of prep_of/sleep advmod/when det/the nsubj/reality advcl/be ./, nsubj/everyone ccomp/need n\n\n```\n\n\n### `Interrogation`\n\nThe `corpus.interrogate()` method returns an `Interrogation` object. These have attributes:\n\n| Attribute | Contains |\n| ---------------|----------|\n| `interrogation.results` | Pandas DataFrame of counts in each subcorpus |\n| `interrogation.totals` | Pandas Series of totals for each subcorpus/result |\n| `interrogation.query` | a `dict` of values used to generate the interrogation |\n\nand methods:\n\n| Method | Purpose |\n|------------|---------|\n| `interrogation.edit()` | Get relative frequencies, merge/remove results/subcorpora, calculate keywords, sort using linear regression, etc. |\n| `interrogation.visualise()` | visualise results via *matplotlib* | \n| `interrogation.save()` | Save data as pickle |\n| `interrogation.quickview()` | Show top results and their absolute/relative frequency |\n\nThese methods have been monkey-patched to Pandas' DataFrame and Series objects, as well, so any slice of a result can be edited or plotted easily.\n\n\n#### `edit()` method\n\n* Remove, keep or merge interrogation results or subcorpora using indices, words or regular expressions (see below)\n* Sort results by name or total frequency\n* Use linear regression to figure out the trajectories of results, and sort by the most increasing, decreasing or static values\n* Show the *p*-value for linear regression slopes, or exclude results above *p*\n* Work with absolute frequency, or determine ratios/percentage of another list: \n * determine the total number of verbs, or total number of verbs that are *be*\n * determine the percentage of verbs that are *be*\n * determine the percentage of *be* verbs that are *was*\n * determine the ratio of *was/were* ...\n * etc.\n* Plot more advanced kinds of relative frequency: for example, find all proper nouns that are subjects of clauses, and plot each word as a percentage of all instances of that word in the corpus (see below)\n\n\n#### `visualise()` method\n\n* Plot using *Matplotlib*\n* Plot anything you like: words, tags, counts for grammatical features ...\n* Create line charts, bar charts, pie charts, etc. with the `kind` argument\n* Use `subplots=True` to produce individual charts for each result\n* Customisable figure titles, axes labels, legends, image size, colormaps, etc.\n* Use `TeX` if you have it\n* Use log scales if you really want\n* Use a number of chart styles, such as `ggplot`, `fivethirtyeight` or `seaborn-talk` (if you've got `seaborn` installed)\n* Save images to file, as `.pdf` or `.png`\n* Experimental interactive plots (hover-over text, interactive legends) using *mpld3*\n\n\n### Functions, lists, etc.\n\nThere are quite a few helper functions for making regular expressions, making new projects, and so on, with more documentation forthcoming. Also included are some lists of words and dependency roles, which can be used to match functional linguistic categories. These are explained in more detail [here](#systemic-functional-stuff).\n\n\n## Installation\n\nYou can get *corpkit* running by downloading or cloning this repository, or via `pip`.\n\n\n### By downloading the repository\n\nHit 'Download ZIP' and unzip the file. Then `cd` into the newly created directory and install:\n\n```shell\ncd corpkit-master\n# might need sudo:\npython setup.py install\n```\n\n\n### By cloning the repository\n\nClone the repo, `cd` into it and run the setup:\n\n```shell\ngit clone https://github.com/interrogator/corpkit.git\ncd corpkit\n# might need sudo:\npython setup.py install\n```\n\n\n### Via `pip`\n\n```shell\n# might need sudo:\npip install corpkit\n# or, for a local install:\n# pip install --user corpkit\n```\n\n*corpkit* should install all the necessary dependencies, including *pandas*, *NLTK*, *matplotlib*, etc, as well as some NLTK data files. \n\n\n## Quickstart\n\nOnce you've got *corpkit*, and a folder containing text files, you're ready to go:\n\n```python\n### import everything\n>>> from corpkit import *\n\n### Make corpus object from path to subcorpora/text files\n>>> unparsed = Corpus('data/nyt/years')\n\n### parse it, return the new parsed corpus object\n>>> corpus = unparsed.parse()\n\n### search corpus for modal auxiliaries and plot the top results\n>>> corpus.interroplot('MD')\n```\n\nOutput: \n\n\n \n\n\n## More detailed examples\n\n`interroplot()` is just a demo method that does three things in order:\n\n1. uses `interrogate()` to search corpus for a (Regex- or Tregex-based) query\n2. uses `edit()` to calculate the relative frequencies of each result\n3. uses `visualise()` to show the top seven results\n \nHere's an example of the three methods at work:\n\n```python\n### make tregex query: head of NP in PP containing 'of' in NP headed by risk word:\n>>> q = r'/NN.?/ >># (NP > (PP <<# /(?i)of/ > (NP <<# (/NN.?/ < /(?i).?\\brisk.?/))))'\n\n### search trees, exclude 'risk of rain', output lemma\n>>> risk_of = corpus.interrogate({T: q}, exclude={W: '^rain$'}, show=L)\n### alternative syntax which may be easier when there's only a single search criterion:\n# >>> risk_of = corpus.interrogate(T, q, exclude={W: '^rain$'}, show=L)\n\n### use edit() to turn absolute into relative frequencies\n>>> to_plot = risk_of.edit('%', risk_of.totals)\n\n### plot the results\n>>> to_plot.visualise('Risk of (noun)', y_label='Percentage of all results',\n... style='fivethirtyeight')\n```\n\nOutput: \n\n\n \n\n\n### `search`, `exclude` and `show`\n\nIn the example above, parse trees are searched, a particular match is excluded, and lemmata are shown. These three arguments (`search`, `exclude` and `show`) are the core of the `interrogate()` and `concordance()` methods.\n\nthe `search` and `exclude` arguments need a `dict`, with the **thing to be searched as keys** and the **search pattern as values**. Here is a list of available keys for plaintext, tokenised and parsed corpora:\n\n| Key | Gloss |\n|-----|-------|\n| `W` | Word |\n| `L` | Lemma |\n| `I` | Index of token in sentence |\n| `N` | N-gram |\n\nFor parsed corpora, there are many other possible keys:\n\n| Key | Gloss |\n|-----|-------|\n| `P` | Part of speech tag |\n| `X` | Word class |\n| `G` | Governor word |\n| `GL` | Governor lemma form |\n| `GP` | Governor POS |\n| `GF` | Governor function |\n| `D` | Dependent word |\n| `DL` | Dependent lemma form |\n| `DP` | Dependent POS |\n| `DF` | Dependent function |\n| `F` | Dependency function |\n| `R` | Distance from 'root' |\n| `T` | Tree |\n| `S` | Predefined general stats |\n\nAllowable combinations are subject to common sense. If you're searching trees, you can't also search governors or dependents. If you're searching an unparsed corpus, you can't search for information provided by the parser. Here are some example `search`/`exclude` values:\n\n| search/exclude | Gloss |\n|--------|-------|\n| `{W: r'^p'}` | Tokens starting with P |\n| `{L: r'any'}` | Any lemma (often equivalent to `r'.*'`) |\n| `{G: r'ing$'}` | Tokens with governor word ending in 'ing' |\n| `{F: funclist}` | Tokens whose dependency function matches a `str` in `funclist` |\n| `{D: r'^br', GL: r'$have$'}` | Tokens with dependent starting with 'br' and 'have' as governor lemma |\n| `{I: '0', F: '^nsubj$'}` | Sentence initial tokens with role of `nsubj` |\n| `{T: r'NP !<<# /NN.?'}` | NPs with non-nominal heads |\n\nIf you'd prefer, you can make a `dict` to handle dependent and governor information, instead of using things like `GL` or `DF`. The following searches produce the same output:\n\n```python\n>>> crit = {W: r'^friend$', \n... D: {F: 'amod', \n... W: 'great'}}\n\n>>> crit = {W: r'^friend$', DF: 'amod', D: 'great'}\n```\n\nBy default, all `search` criteria must match, but any `exclude` criterion is enough to exclude a match. This beahviour can be changed with the `searchmode` and `excludemode` arguments:\n\n```python\n### get words that end in 'ing' OR are nominal:\n>>> out = corpus.interrogate({W: 'ing$', P: r'^N'}, searchmode='any')\n### get any word, but exclude words that end in 'ing' AND are nominal:\n>>> out = corpus.interrogate({W: 'any'}, exclude={W: 'ing$', P: N}, excludemode='all')\n```\n\nThe `show` argument wants a list of keys you'd like to return for each result. The order will be respected. If you only want one thing, a `str` is OK. One additional possibility is `C`, which returns the number of occurrences only.\n\n| `show` | return |\n|--------|--------|\n| `W` | `'champions'` |\n| `[W]` | `'champions'` |\n| `L` | `'champion'` |\n| `P` | `'NNS'` |\n| `X` | `'Noun'` |\n| `T` | `'(np (jj prevailing) (nns champions))'` (depending on Tregex query) |\n| `[P, W]` | `'NNS/champions'` |\n| `[W, P]` | `'champions/NNS'` |\n| `[I, L, R]` | `'2/champion/1'` |\n| `[L, D, F]` | `'champion/prevailing/nsubj'` |\n| `[G, GL, I]` | `'are/be/2'` |\n| `[GL, GF, GP]` | `'be/root/vb'` |\n| `[L, L]` | `'champion/champion'` |\n| `[C]` | `24` |\n\nAgain, common sense dictates what is possible. When searching trees, only trees, words, lemmata, POS and counts can be returned. If showing trees, you can't show anything else. If you use `C`, you can't use anything else.\n\n\n\n## Working with coreferences\n\nOne major challenge in corpus linguistics is the fact that pronouns stand in for other words. Parsing provides coreference resolution, which maps pronouns to the things they denote. You can enable this kind of parsing by specifying the `dcoref` annotator:\n\n```python\n>>> ops = 'tokenize,ssplit,pos,lemma,parse,ner,dcoref'\n>>> parsed = corpus.interrogate(operations=ops)\n```\n\nIf you have done this, you can use `coref=True` while interrogating to allow coreferents to be mapped together:\n\n```python\n>>> corpus.interrogate(query, coref=True)\n```\n\nSo, if you wanted to find all the processes a certain entity is engaged in, you can get a more complete result with:\n\n```python\n>>> from corpkit.dictionaries import roles\n>>> corpus.interrogate({W: 'clinton', GF: roles.process}, coref=True)\n```\n\nThis will count `support` in `Clinton supported the independence of Kosovo`, and also potentially `authorize` in `He authorized the use of force`. You can also toggle the `representative=True` and `non_representative=True` arguments if you want to distinguish between copula and non-copula coreference.\n\n```python\n>>> corpus.interrogate({W: 'clinton', GF: roles.process}, coref=True, representative=False)\n```\n\n\n## Building corpora\n\n*corpkit*'s `Corpus()` class contains `parse()` and `tokenise()`, methods for created parsed and/or tokenised corpora. The main thing you need is **a folder, containing either text files, or subfolders that contain text files**. [Stanford CoreNLP](http://nlp.stanford.edu/software/corenlp.shtml) is required to parse corpora. If you don't have it, *corpkit* can download and install it for you. If you're tokenising, you'll need to make sure you have NLTK's tokeniser data. You can then run:\n\n```python\n>>> unparsed = Corpus('path/to/unparsed/files')\n\n### to parse, you can set a path to corenlp\n>>> corpus = unparsed.parse(corenlppath='Downloads/corenlp')\n\n### to tokenise, point to nltk:\n# >>> corpus = unparsed.tokenise(nltk_data_path='Downloads/nltk_data')\n```\n\nwhich creates the parsed/tokenised corpora, and returns `Corpus()` objects representing them. When parsing, you can also optionally pass in a string of annotators, as per the [CoreNLP documentation](http://nlp.stanford.edu/software/corenlp.shtml):\n\n```python\n>>> ans = 'tokenize,ssplit,pos'\n### you can also set memory and turn off copula head parsing,\n### or multiprocess the parsing job (though you'll want a big machine)\n>>> corpus = unparsed.parse(operations=ans, memory_mb=3000, \n... copula_head=False, multiprocess=4)\n```\n\n\n#### Speaker IDs\n\nSomething novel about *corpkit* is that it can work with corpora containing speaker IDs (scripts, transcripts, logs, etc.), like this:\n\n JOHN: Why did they change the signs above all the bins?\n SPEAKER23: I know why. But I'm not telling.\n\nIf you use:\n\n```python\n>>> corpus = unparsed.parse(speaker_segmentation=True)\n```\n\nThis will:\n\n1. Detect any IDs in any file\n2. Create a duplicate version of the corpus with IDs removed\n3. Parse this 'cleaned' corpus\n4. Add an XML tag to each sentence with the name of the speaker\n5. Return the parsed corpus as a `Corpus()` object\n\nWhen interrogating or concordancing, you can then pass in a keyword argument to restrict searches to one or more speakers:\n\n```python\n>>> s = ['BRISCOE', 'LOGAN']\n>>> npheads = interrogate(T, r'/NN.?/ >># NP', just_speakers=s)\n```\n\nThis makes it possible to not only investigate individual speakers, but to form an understanding of the overall tenor/tone of the text as well: *Who does most of the talking? Who is asking the questions? Who issues commands?*\n\n\n### Navigating parsed corpora\n\nWhen your data is parsed, `Corpus` objects draw on [CoreNLP XML](http://corenlp-xml-library.readthedocs.org/en/latest/) to keep everything seamlessly connected:\n\n```python\n>>> corp = Corpus('data/CHT-parsed')\n>>> corp.subcorpora['2013'].files[1].document.sentences[4235].parse_string\n### '(ROOT (FRAG (CC And) (NP (NP (RB not) (RB just)) (NP (NP (NNP Metrione) ... '\n>>> corp.subcorpora['1997'].files[0].document.sentences[3509].tokens[30].word\n### 'linguistics'\n```\n\n\n### Getting general stats\n\nOnce you have a parsed `Corpus()` object, enter `corpus.features` to interrogate the corpus for some basic frequencies:\n\n```python\n>>> corpus = Corpus('data/sessions-parsed')\n>>> corpus.features\n```\n\nOutput:\n\n```\n Characters Tokens Words Closed class words Open class words Clauses Sentences Unmodalised declarative Mental processes Relational processes Interrogative Passives Verbal processes Modalised declarative Open interrogative Imperative Closed interrogative \n01 26873 8513 7308 4809 3704 2212 577 280 156 98 76 35 39 26 8 2 3 \n02 25844 7933 6920 4313 3620 2270 266 130 195 109 29 19 35 11 5 1 3 \n03 18376 5683 4877 3067 2616 1640 330 174 132 68 30 40 29 8 12 6 1 \n04 20066 6354 5366 3587 2767 1775 319 174 176 83 33 30 20 9 9 4 1 \n05 23461 7627 6217 4400 3227 1978 479 245 154 93 45 51 28 20 5 3 1 \n06 19164 6777 5200 4151 2626 1684 298 111 165 83 43 56 14 10 6 6 2 \n07 22349 7039 5951 4012 3027 1947 343 183 195 82 29 30 38 12 5 5 0 \n08 26494 8760 7124 4960 3800 2379 545 263 170 87 66 36 32 10 6 5 4 \n09 23073 7747 6193 4524 3223 2056 310 149 164 88 21 26 22 10 5 3 0 \n10 20648 6789 5608 3817 2972 1795 437 265 139 101 34 34 39 18 5 3 2 \n11 25366 8533 6899 4925 3608 2207 457 230 203 116 39 48 47 15 10 4 0 \n12 16976 5742 4624 3274 2468 1567 258 135 183 72 23 43 22 4 3 1 6 \n13 25807 8546 6966 4768 3778 2345 477 257 200 124 45 50 36 15 12 3 2 \n```\n\nFeatures such as *relational/mental/verbal* processes are difficult to locate automatically, so these counts are perhaps best seen as approximations. Even so, this data can be very helpful when using `edit()` to generate relative frequencies, for example.\n\n\n## Concordancing\n\nUnlike most concordancers, which are based on plaintext corpora, *corpkit* can concordance grammatically, using the same kind of `search`, `exclude` and `show` values as `interrogate()`.\n\n```python\n>>> subcorpus = corpus.subcorpora.c2005\n### C is added above to make a valid variable name from an int\n### can also be accessed as corpus.subcorpora['2005']\n### or corpus.subcorpora[index]\n>>> query = r'/JJ.?/ > (NP <<# (/NN.?/ < /\\brisk/))'\n### T option for tree searching\n>>> lines = subcorpus.concordance(T, query, window=50, n=10, random=True)\n```\n\nOutput (a `Pandas DataFrame`):\n\n```\n0 hedge funds or high-risk stocks obviously poses a greater risk to the pension program than a portfolio of \n1 contaminated water pose serious health and environmental risks \n2 a cash break-even pace '' was intended to minimize financial risk to the parent company \n3 Other major risks identified within days of the attack \n4 One seeks out stocks ; the other monitors risks \n5 men and women in Colorado Springs who were at high risk for H.I.V. infection , because of \n6 by the marketing consultant Seth Godin , to taking calculated risks , in the opinion of two longtime business \n7 to happen '' in premises '' where there was a high risk of fire \n8 As this was match points , some of them took a slight risk at the second trick by finessing the heart \n9 said that the agency 's continuing review of how Guidant treated patient risks posed by devices like the \n```\n\nYou can also concordance via dependencies:\n\n```python\n### match words starting with 'st' filling function of nsubj\n>>> criteria = {W: r'^st', F: r'nsubj$'}\n### show function, pos and lemma (in that order)\n>>> lines = subcorpus.concordance(criteria, show =[F,P,L])\n>>> lines.format(window=30, n=10, columns=[L,M,R])\n```\n\nOutput:\n\n```\n0 ime ./:/; cc/CC/and det/DT/the nsubj/NN/stock conj:and/VBZ/be advmod/RB/hist\n1 vmod/RB/even compound/NN/sleep nsubj/NNS/study ./,/, appos/NNS/evaluation cas\n2 od:poss/NNS/veteran case/POS/' nsubj/NN/study ccomp/VBZ/suggest mark/IN/that\n3 det/DT/a nsubj/NN/study case/IN/in nmod:poss/NN/today \n4 cc/CC/but det/DT/the nsubj/NN/study root/VBD/find mark/IN/that cas\n5 pound/NN/a amod/JJ/preliminary nsubj/NN/study case/IN/of nmod:of/NNS/woman c\n6 case/IN/for nmod:for/WDT/which nsubj/NNS/statistics acl:relcl/VBD/be xcomp/JJ/avai\n7 amod/JJR/earlier nsubj/NNS/study aux/VBD/have root/VBN/show mar\n8 ay det/DT/the amod/JJR/earlier nsubj/NNS/study aux/VBD/do neg/RB/not ccomp/VB\n9 /there root/VBP/be det/DT/some nsubj/NNS/strategy ./:/- dep/JJS/most case/IN/of \n```\n\nYou can search tokenised corpora or plaintext corpora for regular expressions or lists of words to match. The two queries below will return identical results:\n\n```python\n>>> r_query = r'^fr?iends?$'\n>>> l_query = ['friend', 'friends', 'fiend', 'fiends']\n>>> lines = subcorpus.concordance({W: r_query})\n>>> lines = subcorpus.concordance({W: l_query})\n```\n\nIf you really wanted, you can then go on to use `concordance()` output as a dictionary, or extract keywords and ngrams from it, or keep or remove certain results with `edit()`. If you want to [give the GUI a try](http://interrogator.github.io/corpkit/), you can colour-code and create thematic categories for concordance lines as well.\n\n\n## Systemic functional stuff\n\nBecause I mostly use systemic functional grammar, there is also a simple tool for distinguishing between process types (relational, mental, verbal) when interrogating a corpus. If you add words to the lists in `dictionaries/process_types.py`, corpkit will get their inflections automatically.\n\n```python\n>>> from corpkit.dictionaries import processes\n\n### match nsubj with verbal process as governor\n>>> crit = {F: '^nsubj$', G: processes.verbal}\n### return lemma of the nsubj\n>>> sayers = corpus.interrogate(crit, show=L)\n\n### have a look at the top results\n>>> sayers.quickview(n=20)\n```\n\nOutput:\n\n```\n 0: he (n=24530)\n 1: she (n=5558)\n 2: they (n=5510)\n 3: official (n=4348)\n 4: it (n=3752)\n 5: who (n=2940)\n 6: that (n=2665)\n 7: i (n=2062)\n 8: expert (n=2057)\n 9: analyst (n=1369)\n 10: we (n=1214)\n 11: report (n=1103)\n 12: company (n=1070)\n 13: which (n=1043)\n 14: you (n=987)\n 15: researcher (n=987)\n 16: study (n=901)\n 17: critic (n=826)\n 18: person (n=802)\n 19: agency (n=798)\n 20: doctor (n=770)\n\n```\n\nFirst, let's try removing the pronouns using `edit()`. The quickest way is to use the editable wordlists stored in `dictionaries/wordlists`:\n\n```python\n>>> from corpkit.dictionaries import wordlists\n>>> prps = wordlists.pronouns\n\n# alternative approaches:\n# >>> prps = [0, 1, 2, 4, 5, 6, 7, 10, 13, 14, 24]\n# >>> prps = ['he', 'she', 'you']\n# >>> prps = as_regex(wl.pronouns, boundaries='line')\n# or, by re-interrogating:\n# >>> sayers = corpus.interrogate(crit, show=L, exclude={W: wordlists.pronouns})\n\n### give edit() indices, words, wordlists or regexes to keep remove or merge\n>>> sayers_no_prp = sayers.edit(skip_entries=prps, skip_subcorpora=[1963])\n>>> sayers_no_prp.quickview(n=10)\n```\n\nOutput:\n\n```\n 0: official (n=4342)\n 1: expert (n=2055)\n 2: analyst (n=1369)\n 3: report (n=1098)\n 4: company (n=1066)\n 5: researcher (n=987)\n 6: study (n=900)\n 7: critic (n=825)\n 8: person (n=801)\n 9: agency (n=796)\n```\n\nGreat. Now, let's sort the entries by trajectory, and then plot:\n\n```python\n### sort with edit()\n### use scipy.linregress to sort by 'increase', 'decrease', 'static', 'turbulent' or P\n### other sort_by options: 'name', 'total', 'infreq'\n>>> sayers_no_prp = sayers_no_prp.edit('%', sayers.totals, sort_by='increase')\n\n### make an area chart with custom y label\n>>> sayers_no_prp.visualise('Sayers, increasing', kind='area', \n... y_label='Percentage of all sayers')\n```\n\nOutput:\n\n\n \n\nWe can also merge subcorpora. Let's look for changes in gendered pronouns:\n\n```python\n>>> merges = {'1960s': r'^196', \n... '1980s': r'^198', \n... '1990s': r'^199', \n... '2000s': r'^200',\n... '2010s': r'^201'}\n\n>>> sayers = sayers.edit(merge_subcorpora=merges)\n\n### now, get relative frequencies for he and she\n### SELF calculates percentage after merging/removing etc has been performed,\n### so that he and she will sum to 100%. Pass in `sayers.totals` to calculate \n### he/she as percentage of all sayers\n>>> genders = sayers.edit('%', SELF, just_entries=['he','she'])\n\n### and plot it as a series of pie charts, showing totals on the slices:\n>>> genders.visualise('Pronominal sayers in the NYT', kind='pie',\n... subplots=True, figsize=(15,2.75), show_totals='plot')\n```\n\nOutput:\n\n\n \n\nWoohoo, a decreasing gender divide! \n\n\n## Keywording\n\nAs I see it, there are two main problems with keywording, as typically performed in corpus linguistics. First is the reliance on 'balanced'/'general' reference corpora, which are obviously a fiction. Second is the idea of stopwords. Essentially, when most people calculate keywords, they use stopword lists to automatically filter out words that they think will not be of interest to them. These words are generally closed class words, like determiners, prepositions, or pronouns. This is not a good way to go about things: the relative frequencies of *I*, *you* and *one* can tell us a lot about the kinds of language in a corpus. More seriously, stopwords mean adding subjective judgements about what is interesting language into a process that is useful precisely because it is not subjective or biased.\n\nSo, what to do? Well, first, don't use 'general reference corpora' unless you really really have to. With *corpkit*, you can use your entire corpus as the reference corpus, and look for keywords in subcorpora. Second, rather than using lists of stopwords, simply do not send all words in the corpus to the keyworder for calculation. Instead, try looking for key *predicators* (rightmost verbs in the VP), or key *participants* (heads of arguments of these VPs):\n\n```python\n### just heads of participants' lemma form (no pronouns, though!)\n>>> part = r'/(NN|JJ).?/ >># (/(NP|ADJP)/ $ VP | > VP)'\n>>> p = corpus.interrogate(T, part, show=L)\n```\n\nWhen using `edit()` to calculate keywords, there are a few default parameters that can be easily changed:\n\n| Keyword argument | Function | Default setting | Type\n|---|---|---|---|\n| `threshold` | Remove words occurring fewer than `n` times in reference corpus | `False` | `'high/medium/low'`/ `True/False` / `int`\n| `calc_all` | Calculate keyness for words in both reference and target corpus, rather than just target corpus | `True` | `True/False`\n| `selfdrop` | Attempt to remove target data from reference data when calculating keyness | `True` | `True/False`\n\nLet's have a look at how these options change the output:\n\n```python\n### SELF as reference corpus uses p.results\n>>> options = {'selfdrop': False, \n... 'calc_all': False, \n... 'threshold': False}\n\n>>> for k, v in options.items():\n... key = p.edit('keywords', SELF, k=v)\n... print key.results.ix['2011'].order(ascending=False)\n\n```\nOutput:\n\n| #1: default | | #2: no `selfdrop` | | #3: no `calc_all` | | #4: no `threshold` | |\n|---|---:|---|---:|---|---:|---|---:|\n| risk | 1941.47 | risk | 1909.79 | risk | 1941.47 | bank | 668.19 |\n| bank | 1365.70 | bank | 1247.51 | bank | 1365.70 | crisis | 242.05 |\n| crisis | 431.36 | crisis | 388.01 | crisis | 431.36 | obama | 172.41 |\n| investor | 410.06 | investor | 387.08 | investor | 410.06 | demiraj | 161.90 |\n| rule | 316.77 | rule | 293.33 | rule | 316.77 | regulator | 144.91 |\n| | ... | | ... | | ... | | ... |\n| clinton | -37.80 | tactic | -35.09 | hussein | -25.42 | clinton | -87.33 |\n| vioxx | -38.00 | vioxx | -35.29 | clinton | -37.80 | today | -89.49 |\n| greenspan | -54.35 | greenspan | -51.38 | vioxx | -38.00 | risky | -125.76 |\n| bush | -153.06 | bush | -143.02 | bush | -153.06 | bush | -253.95 |\n| yesterday | -162.30 | yesterday | -151.71 | yesterday | -162.30 | yesterday | -268.29 |\n\nAs you can see, slight variations on keywording give different impressions of the same corpus!\n\nA key strength of *corpkit*'s approach to keywording is that you can generate new keyword lists without re-interrogating the corpus. We can use some Pandas syntax to do this more quickly.\n\n```python\n>>> yrs = ['2011', '2012', '2013', '2014']\n>>> keys = p.results.ix[yrs].sum().edit('keywords', p.results.drop(yrs),\n... threshold=False)\n>>> print keys.results\n```\n\nOutput:\n\n```\nbank 1795.24\nobama 722.36\nromney 560.67\njpmorgan 527.57\nrule 413.94\ndimon 389.86\ndraghi 349.80\nregulator 317.82\nitaly 282.00\ncrisis 243.43\nputin 209.51\ngreece 208.80\nsnowden 208.35\nmf 192.78\nadoboli 161.30\n```\n\n... or track the keyness of a set of words over time:\n\n```python\n>>> twords = ['terror', 'terrorism', 'terrorist']\n>>> terr = p.edit(K, SELF, merge_entries={'terror': twords})\n>>> print terr.results.terror\n```\n\nOutput:\n\n```\n1963 -2.51\n1987 -3.67\n1988 -16.09\n1989 -6.24\n1990 -16.24\n... ...\nName: terror, dtype: float64\n```\n\n\n### Visualising keywords\n\nNaturally, we can use `visualise()` for our keywords too:\n\n```python\n>>> pols.results.terror.visualise('Terror* as Participant in the \\emph{NYT}', \n... kind='area', stacked=False, y_label='L/L Keyness')\n>>> politicians = ['bush', 'obama', 'gore', 'clinton', 'mccain', \n... 'romney', 'dole', 'reagan', 'gorbachev']\n>>> k.results[politicans].visualise('Keyness of politicians in the \\emph{NYT}', \n... num_to_plot='all', y_label='L/L Keyness', kind='area', legend_pos='center left')\n```\nOutput:\n\n\n \n\n\n### Traditional reference corpora\n\nIf you still want to use a standard reference corpus, you can do that (and a dictionary version of the BNC is included). For the reference corpus, `edit()` recognises `dicts`, `DataFrames`, `Series`, files containing `dicts`, or paths to plain text files or trees.\n\n```python\n### arbitrary list of common/boring words\n>>> from corpkit.dictionaries import stopwords\n>>> print p.results.ix['2013'].edit(K, 'bnc.p', skip_entries=stopwords).results\n>>> print p.results.ix['2013'].edit(K, 'bnc.p', calc_all=False).results\n```\n\nOutput (not so useful):\n\n```\n#1 #2\nbank 5568.25 bank 5568.25\nperson 5423.24 person 5423.24\ncompany 3839.14 company 3839.14\nway 3537.16 way 3537.16\nstate 2873.94 state 2873.94\n ... ... \nthree -691.25 ten -199.36\npeople -829.97 bit -205.97\ngoing -877.83 sort -254.71\nerm -2429.29 thought -255.72\nyeah -3179.90 will -679.06\n```\n\n\n## Parallel processing\n\n`interrogate()` can also do parallel-processing. You can generally improve the speed of an interrogation by setting the `multiprocess` argument:\n\n```python\n### set num of parallel processes manually\n>>> data = corpus.interrogate({T: r'/NN.?/ >># NP'}, multiprocess=3)\n### set num of parallel processes automatically\n>>> data = corpus.interrogate({T: r'/NN.?/ >># NP'}, multiprocess=True)\n```\n\nMultiprocessing is particularly useful, however, when you are interested in multiple corpora, speaker IDs, or search queries. The sections below explain how.\n\n\n#### Multiple corpora\n\nTo parallel-process multiple corpora, first, wrap them up as a `Corpora()` object. To do this, you can pass in:\n\n1. a list of paths\n2. a list of `Corpus()` objects\n3. A single path string that contains corpora\n\n```python\n>>> from corpkit.corpus import Corpora\n>>> corpora = Corpora('./data') # path containing corpora\n>>> corpora\n### \n\n### interrogate by parallel processing, 4 at a time\n>>> output = corpora.interrogate(T, r'/NN.?/ < /(?i)^h/', show=L, multiprocess=4)\n\n```\n\nThe output of a multiprocessed interrogation will generally be a `dict` with corpus/speaker/query names as keys. The main exception to this is if you use `show=C`, which will concatenate results from each query into a single `Interrogation` object, using corpus/speaker/query names as column names.\n\n\n#### Multiple speakers\n\nPassing in a list of speaker names will also trigger multiprocessing:\n\n```python\n>>> from dictionary.wordlists import wordlists\n>>> spkrs = ['MEYER', 'JAY']\n>>> each_speaker = corpus.interrogate(W, wordlists.closedclass, just_speakers=spkrs)\n```\n\nThere is also `just_speakers='each'`, which will be automatically expanded to include every speaker name found in the corpus.\n\n\n#### Multiple queries\n\nYou can also run a number of queries over the same corpus in parallel. There are two ways to do this.\n\n```python\n### method one\n>>> query = {'Noun phrases': r'NP', 'Verb phrases': r'VP'}\n>>> phrases = corpus.interrogate(T, query, show=C)\n\n### method two\n>>> query = {'-ing words': {W: r'ing$'}, '-ed verbs': {P: r'^V', W: r'ed$'}}\n>>> patterns = corpus.interrogate(query, show=L)\n```\n\nLet's try multiprocessing with multiple queries, showing count (i.e. returning a single results DataFrame). We can look at different risk processes (e.g. *risk*, *take risk*, *run risk*, *pose risk*, *put at risk*) using constituency parses:\n\n```python\n>>> q = {'risk': r'VP <<# (/VB.?/ < /(?i).?\\brisk.?\\b/)', \n... 'take risk': r'VP <<# (/VB.?/ < /(?i)\\b(take|takes|taking|took|taken)+\\b/) < (NP <<# /(?i).?\\brisk.?\\b/)', \n... 'run risk': r'VP <<# (/VB.?/ < /(?i)\\b(run|runs|running|ran)+\\b/) < (NP <<# /(?i).?\\brisk.?\\b/)', \n... 'put at risk': r'VP <<# /(?i)(put|puts|putting)\\b/ << (PP <<# /(?i)at/ < (NP <<# /(?i).?\\brisk.?/))', \n... 'pose risk': r'VP <<# (/VB.?/ < /(?i)\\b(pose|poses|posed|posing)+\\b/) < (NP <<# /(?i).?\\brisk.?\\b/)'}\n\n# show=C will collapse results from each search into single dataframe\n>>> processes = corpus.interrogate(T, q, show=C)\n>>> proc_rel = processes.edit('%', processes.totals)\n>>> proc_rel.visualise('Risk processes')\n```\n\nOutput:\n\n\n \n\n\n## More complex queries and plots\n\nNext, let's find out what kinds of noun lemmas are subjects of any of these risk processes:\n\n```python\n### a query to find heads of nps that are subjects of risk processes\n>>> query = r'/^NN(S|)$/ !< /(?i).?\\brisk.?/ >># (@NP $ (VP <+(VP) (VP ( <<# (/VB.?/ < /(?i).?\\brisk.?/) ' \\\n... r'| <<# (/VB.?/ < /(?i)\\b(take|taking|takes|taken|took|run|running|runs|ran|put|putting|puts)/) < ' \\\n... r'(NP <<# (/NN.?/ < /(?i).?\\brisk.?/))))))'\n>>> noun_riskers = c.interrogate(T, query, show=L)\n \n>>> noun_riskers.quickview(10)\n```\n\nOutput:\n\n```\n 0: person (n=195)\n 1: company (n=139)\n 2: bank (n=80)\n 3: investor (n=66)\n 4: government (n=63)\n 5: man (n=51)\n 6: leader (n=48)\n 7: woman (n=43)\n 8: official (n=40)\n 9: player (n=39)\n```\n\nWe can use `edit()` to make some thematic categories:\n\n```python\n### get everyday people\n>>> p = ['person', 'man', 'woman', 'child', 'consumer', 'baby', 'student', 'patient']\n### get business, gov, institutions\n>>> i = ['company', 'bank', 'investor', 'government', 'leader', 'president', 'officer', \n... 'politician', 'institution', 'agency', 'candidate', 'firm']\n>>> merges = {'Everyday people': p, Institutions: i}\n\n>>> them_cat = them_cat.edit('%', noun_riskers.totals,\n... merge_entries=merges,\n... sort_by='total',\n... skip_subcorpora=1963,\n... just_entries=merges.keys())\n\n### plot result\n>>> them_cat.visualise('Types of riskers', y_label='Percentage of all riskers')\n```\n\nOutput:\n\n\n \n\nLet's also find out what percentage of the time some nouns appear as riskers:\n\n```python\n### find any head of an np not containing risk\n>>> query = r'/NN.?/ >># NP !< /(?i).?\\brisk.?/'\n>>> noun_lemmata = corpus.interrogate(T, query, show=L)\n\n### get some key terms\n>>> people = ['man', 'woman', 'child', 'baby', 'politician', \n... 'senator', 'obama', 'clinton', 'bush']\n>>> selected = noun_riskers.edit('%', noun_lemmata.results, \n... just_entries=people, just_totals=True, threshold=0, sort_by='total')\n\n### make a bar chart:\n>>> selected.visualise('Risk and power', num_to_plot='all', kind='bar', \n... x_label='Word', y_label='Risker percentage', fontsize=15)\n```\n\nOutput:\n\n\n \n\n\n### Visualisation options\n\nWith a bit of creativity, you can do some pretty awesome data-viz, thanks to *Pandas* and *Matplotlib*. The following plots require only one interrogation:\n\n```python\n>>> modals = corpus.interrogate(T, 'MD < __', show=L)\n### simple stuff: make relative frequencies for individual or total results\n>>> rel_modals = modals.edit('%', modals.totals)\n\n### trickier: make an 'others' result from low-total entries\n>>> low_indices = range(7, modals.results.shape[1])\n>>> each_md = modals.edit('%', modals.totals, merge_entries={'other': low_indices}, \n... sort_by='total', just_totals=True, keep_top=7)\n\n### complex stuff: merge results\n>>> entries_to_merge = [r'(^w|\\'ll|\\'d)', r'^c', r'^m', r'^sh']\n>>> modals = modals.edit(merge_entries=entries_to_merge)\n \n### complex stuff: merge subcorpora\n>>> merges = {'1960s': r'^196', \n... '1980s': r'^198', \n... '1990s': r'^199', \n... '2000s': r'^200',\n... '2010s': r'^201'}\n\n>>> modals = sayers.edit(merge_subcorpora=merges)\n \n### make relative, sort, remove what we don't want\n>>> modals = modals.edit('%', modals.totals, keep_stats=False,\n... just_subcorpora=merges.keys(), sort_by='total', keep_top=4)\n\n### show results\n>>> print rel_modals.results, each_md.results, modals.results\n```\nOutput:\n```\n would will can could ... need shall dare shalt\n1963 22.326833 23.537323 17.955615 6.590451 ... 0.000000 0.537996 0.000000 0\n1987 24.750614 18.505132 15.512505 11.117537 ... 0.072286 0.260228 0.014457 0\n1988 23.138986 19.257117 16.182067 11.219364 ... 0.091338 0.060892 0.000000 0\n... ... ... ... ... ... ... ... ... ...\n2012 23.097345 16.283186 15.132743 15.353982 ... 0.029499 0.029499 0.000000 0\n2013 22.136269 17.286522 16.349301 15.620351 ... 0.029753 0.029753 0.000000 0\n2014 21.618357 17.101449 16.908213 14.347826 ... 0.024155 0.000000 0.000000 0\n[29 rows x 17 columns] \n\nwould 23.235853\nwill 17.484034\ncan 15.844070\ncould 13.243449\nmay 9.581255\nshould 7.292294\nother 7.290155\nName: Combined total, dtype: float64 \n\n would/will/'ll... can/could/ca may/might/must should/shall/shalt\n1960s 47.276395 25.016812 19.569603 7.800941\n1980s 44.756285 28.050776 19.224476 7.566817\n1990s 44.481957 29.142571 19.140310 6.892708\n2000s 42.386571 30.710739 19.182867 7.485681\n2010s 42.581666 32.045745 17.777845 7.397044\n\n```\n\nNow, some intense plotting:\n\n```python\n### exploded pie chart\n>>> each_md.visualise('Pie chart of common modals in the NYT', explode=['other'],\n... num_to_plot='all', kind='pie', colours='Accent', figsize=(11,11))\n\n### bar chart, transposing and reversing the data\n>>> modals.results.iloc[::-1].T.iloc[::-1].visualise('Modals use by decade', kind='barh',\n... x_label='Percentage of all modals', y_label='Modal group')\n\n### stacked area chart\n>>> rel_modals.results.drop('1963').visualise('An ocean of modals', kind='area', \n... stacked=True, colours='summer', figsize =(8,10), num_to_plot='all', \n... legend_pos='lower right', y_label='Percentage of all modals')\n```\n\nOutput:\n
\n\n\n\n
\n\n\n## Contact\n\nTwitter: [@interro_gator](https://twitter.com/interro_gator)\n\n\n## Cite\n\n> `McDonald, D. (2015). corpkit: a toolkit for corpus linguistics. Retrieved from https://www.github.com/interrogator/corpkit. DOI: http://doi.org/10.5281/zenodo.28361`"
},
{
"path": "Dockerfile",
"content": "FROM alpine:latest\nMAINTAINER interro_gator\n\n# set up a workspace so we can cache python stuff\nRUN rm -rf /.src && mkdir /.src\nCOPY requirements.txt /.src/requirements.txt\n\n# add corenlp\n# COPY ~/corenlp /.src\n\n# use the workspace for everything\nWORKDIR /.src\n\n# install the basics\nRUN apk add --update \\\n python3 \\\n python-dev \\\n py-pip \\\n build-base \\\n git \\\n libpng \\\n freetype \\\n pkgconf \\\n libxft-dev \\\n libxml2-dev \\\n readline\n\n# install java for parsing\nRUN apk --update add openjdk8-jre-base\n\n# needed for numpy\nRUN ln -s /usr/include/locale.h /usr/include/xlocale.h\nRUN ln -s /usr/include/libxml2/libxml/xmlversion.h /usr/include/xmlversion.h\nRUN mkdir /usr/include/libxml\nRUN ln -s /usr/include/libxml2/libxml/xmlversion.h /usr/include/libxml/xmlversion.h\nRUN ln -s /usr/include/libxml2/libxml/xmlexports.h /usr/include/xmlexports.h\nRUN ln -s /usr/include/libxml2/libxml/xmlexports.h /usr/include/libxml/xmlexports.h\n\n# stop pip from complaining\nRUN pip install --upgrade pip\n\n# python heavyweight stuff\nRUN pip install cython\nRUN pip install numpy\nRUN pip install colorama\n\n# remove old stuff --- not sure it does much\nRUN rm -rf /var/cache/apk/*\n\n# get matplotlib github version\nRUN git clone git://github.com/matplotlib/matplotlib.git\nRUN cd matplotlib && python setup.py install && cd ..\n\n# install corpkit requirements\nRUN pip install -r requirements.txt\n\nRUN pip install docker-py\n\n# add everything from corpkit to working dir\nCOPY . /.src\n\n# install corpkit itself\nRUN python /.src/setup.py install\n\n# download might be needed for licence issues\n#RUN python -m corpkit.download.corenlp /\n\nCMD python -m corpkit.env docker=corpkit\n\nWORKDIR /projects\n\n"
},
{
"path": "LICENSE",
"content": "The MIT License (MIT)\n\nCopyright (c) 2015 Daniel McDonald\nmcdonaldd, at, unimelb.edu\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\n"
},
{
"path": "Makefile",
"content": "# Makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line.\nSPHINXOPTS =\nSPHINXBUILD = sphinx-build\nPAPER =\nBUILDDIR = _build\n\n# User-friendly check for sphinx-build\nifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)\n$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)\nendif\n\n# Internal variables.\nPAPEROPT_a4 = -D latex_paper_size=a4\nPAPEROPT_letter = -D latex_paper_size=letter\nALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .\n# the i18n builder cannot share the environment and doctrees with the others\nI18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .\n\n.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest coverage gettext\n\nhelp:\n\t@echo \"Please use \\`make ' where is one of\"\n\t@echo \" html to make standalone HTML files\"\n\t@echo \" dirhtml to make HTML files named index.html in directories\"\n\t@echo \" singlehtml to make a single large HTML file\"\n\t@echo \" pickle to make pickle files\"\n\t@echo \" json to make JSON files\"\n\t@echo \" htmlhelp to make HTML files and a HTML help project\"\n\t@echo \" qthelp to make HTML files and a qthelp project\"\n\t@echo \" applehelp to make an Apple Help Book\"\n\t@echo \" devhelp to make HTML files and a Devhelp project\"\n\t@echo \" epub to make an epub\"\n\t@echo \" latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter\"\n\t@echo \" latexpdf to make LaTeX files and run them through pdflatex\"\n\t@echo \" latexpdfja to make LaTeX files and run them through platex/dvipdfmx\"\n\t@echo \" text to make text files\"\n\t@echo \" man to make manual pages\"\n\t@echo \" texinfo to make Texinfo files\"\n\t@echo \" info to make Texinfo files and run them through makeinfo\"\n\t@echo \" gettext to make PO message catalogs\"\n\t@echo \" changes to make an overview of all changed/added/deprecated items\"\n\t@echo \" xml to make Docutils-native XML files\"\n\t@echo \" pseudoxml to make pseudoxml-XML files for display purposes\"\n\t@echo \" linkcheck to check all external links for integrity\"\n\t@echo \" doctest to run all doctests embedded in the documentation (if enabled)\"\n\t@echo \" coverage to run coverage check of the documentation (if enabled)\"\n\nclean:\n\trm -rf $(BUILDDIR)/*\n\nhtml:\n\t$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html\n\t@echo\n\t@echo \"Build finished. The HTML pages are in $(BUILDDIR)/html.\"\n\ndirhtml:\n\t$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml\n\t@echo\n\t@echo \"Build finished. The HTML pages are in $(BUILDDIR)/dirhtml.\"\n\nsinglehtml:\n\t$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml\n\t@echo\n\t@echo \"Build finished. The HTML page is in $(BUILDDIR)/singlehtml.\"\n\npickle:\n\t$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle\n\t@echo\n\t@echo \"Build finished; now you can process the pickle files.\"\n\njson:\n\t$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json\n\t@echo\n\t@echo \"Build finished; now you can process the JSON files.\"\n\nhtmlhelp:\n\t$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp\n\t@echo\n\t@echo \"Build finished; now you can run HTML Help Workshop with the\" \\\n\t \".hhp project file in $(BUILDDIR)/htmlhelp.\"\n\nqthelp:\n\t$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp\n\t@echo\n\t@echo \"Build finished; now you can run \"qcollectiongenerator\" with the\" \\\n\t \".qhcp project file in $(BUILDDIR)/qthelp, like this:\"\n\t@echo \"# qcollectiongenerator $(BUILDDIR)/qthelp/corpkit.qhcp\"\n\t@echo \"To view the help file:\"\n\t@echo \"# assistant -collectionFile $(BUILDDIR)/qthelp/corpkit.qhc\"\n\napplehelp:\n\t$(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp\n\t@echo\n\t@echo \"Build finished. The help book is in $(BUILDDIR)/applehelp.\"\n\t@echo \"N.B. You won't be able to view it unless you put it in\" \\\n\t \"~/Library/Documentation/Help or install it in your application\" \\\n\t \"bundle.\"\n\ndevhelp:\n\t$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp\n\t@echo\n\t@echo \"Build finished.\"\n\t@echo \"To view the help file:\"\n\t@echo \"# mkdir -p $$HOME/.local/share/devhelp/corpkit\"\n\t@echo \"# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/corpkit\"\n\t@echo \"# devhelp\"\n\nepub:\n\t$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub\n\t@echo\n\t@echo \"Build finished. 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The text files are in $(BUILDDIR)/text.\"\n\nman:\n\t$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man\n\t@echo\n\t@echo \"Build finished. The manual pages are in $(BUILDDIR)/man.\"\n\ntexinfo:\n\t$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo\n\t@echo\n\t@echo \"Build finished. The Texinfo files are in $(BUILDDIR)/texinfo.\"\n\t@echo \"Run \\`make' in that directory to run these through makeinfo\" \\\n\t \"(use \\`make info' here to do that automatically).\"\n\ninfo:\n\t$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo\n\t@echo \"Running Texinfo files through makeinfo...\"\n\tmake -C $(BUILDDIR)/texinfo info\n\t@echo \"makeinfo finished; the Info files are in $(BUILDDIR)/texinfo.\"\n\ngettext:\n\t$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale\n\t@echo\n\t@echo \"Build finished. The message catalogs are in $(BUILDDIR)/locale.\"\n\nchanges:\n\t$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes\n\t@echo\n\t@echo \"The overview file is in $(BUILDDIR)/changes.\"\n\nlinkcheck:\n\t$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck\n\t@echo\n\t@echo \"Link check complete; look for any errors in the above output \" \\\n\t \"or in $(BUILDDIR)/linkcheck/output.txt.\"\n\ndoctest:\n\t$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest\n\t@echo \"Testing of doctests in the sources finished, look at the \" \\\n\t \"results in $(BUILDDIR)/doctest/output.txt.\"\n\ncoverage:\n\t$(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage\n\t@echo \"Testing of coverage in the sources finished, look at the \" \\\n\t \"results in $(BUILDDIR)/coverage/python.txt.\"\n\nxml:\n\t$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml\n\t@echo\n\t@echo \"Build finished. The XML files are in $(BUILDDIR)/xml.\"\n\npseudoxml:\n\t$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml\n\t@echo\n\t@echo \"Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml.\"\n"
},
{
"path": "README.md",
"content": "# corpkit: sophisticated corpus linguistics\n\n[](https://gitter.im/interrogator/corpkit?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [](https://zenodo.org/badge/latestdoi/14568/interrogator/corpkit) [](https://travis-ci.org/interrogator/corpkit) [](https://pypi.python.org/pypi/corpkit) [](http://corpkit.readthedocs.org/en/latest/) [](https://hub.docker.com/r/interrogator/corpkit/) [](https://anaconda.org/interro_gator/corpkit)\n\n## **NOTICE: corpkit is now deprecated and unmaintained. It is superceded by [`buzz`](https://github.com/interrogator/buzz), which is better in every way.**\n\n> **corpkit** is a module for doing more sophisticated corpus linguistics. It links state-of-the-art natural language processing technologies to functional linguistic research aims, allowing you to easily build, search and visualise grammatically annotated corpora in novel ways.\n\nThe basic workflow involves making corpora, parsing them, and searching them. The results of searches are [CONLL-U formatted](http://universaldependencies.org/format.html) files, represented as [pandas](http://pandas.pydata.org/) objects, which can be edited, visualised or exported in a lot of ways. The tool has three interfaces, each with its own documentation:\n\n1. [A Python API](http://corpkit.readthedocs.io)\n2. [A natural language interpreter](http://corpkit.readthedocs.io/en/latest/rst_docs/interpreter/corpkit.interpreter.overview.html)\n3. [A graphical interface](http://interrogator.github.io/corpkit/)\n\nA quick demo for each interface is provided in this document.\n\n## Feature summary\n\nFrom all three interfaces, you can do a lot of neat things. In general:\n\n### Parsing\n\n> Corpora are stored as `Corpus` objects, with methods for viewing, parsing, interrogating and concordancing.\n\n* A very simple wrapper around the full Stanford CoreNLP pipeline\n* Automatically add annotations, speaker names and metadata to parser output\n* Detect speaker names and make these into metadata features\n* Multiprocessing\n* Store dependency parsed texts, parse trees and metadata in CONLL-U format\n\n### Interrogating corpora\n\n> Interrogating a corpus produces an `Interrogation` object, with results as Pandas DataFrame attributes.\n\n* Search corpora using regular expressions, wordlists, CQL, Tregex, or a rich, purpose built dependency searching syntax\n* Interrogate any dataset in CONLL-U format (e.g. [the Universal Dependencies Treebanks](https://github.com/UniversalDependencies))\n* Collocation, n-gramming\n* Restrict searches by metadata feature\n* Use metadata as symbolic subcorpora\n* Choose what search results return: show any combination of words, lemmata, POS, indices, distance from root node, syntax tree, etc.\n* Generate concordances alongside interrogations\n* Work with coreference annotation\n\n### Editing results\n\n> `Interrogation` objects have `edit`, `visualise` and `save` methods, to name just a few. Editing creates a new `Interrogation` object.\n\n* Quickly delete, sort, merge entries and subcorpora\n* Make relative frequencies (e.g. calculate results as percentage of all words/clauses/nouns ...)\n* Use linear regression sorting to find increasing, decreasing, turbulent or static trajectories \n* Calculate p values, etc.\n* Keywording\n* Simple multiprocessing available for parsing and interrogating\n* Results are Pandas objects, so you can do fast, good statistical work on them\n\n### Visualising results\n\n> The `visualise` method of `Interrogation` objects uses matplotlib and seaborn if installed to produce high quality figures.\n\n* Many chart types\n* Easily customise titles, axis labels, colours, sizes, number of results to show, etc.\n* Make subplots\n* Save figures in a number of formats\n\n### Concordancing\n\n> When interrogating a corpus, concordances are also produced, which can allow you to check that your query matches what you want it to.\n\n* Colour, sort, delete lines using regular expressions\n* Recalculate results from edited concordance lines (great for removing false positives)\n* Format lines for publication with TeX\n\n### Other stuff\n\n* Language modelling\n* Save and load results, images, concordances\n* Export data to other tools\n* Switch between API, GUI and interpreter whenever you like\n\n## Installation\n\nVia pip:\n\n```shell\npip install corpkit\n```\n\nVia Git:\n\n```shell\ngit clone https://github.com/interrogator/corpkit\ncd corpkit\npython setup.py install\n```\n\nVia Anaconda:\n\n```shell\nconda install -c interro_gator corpkit\n```\n\n## Creating a project\n\nOnce you've got everything installed, you'll want to create a project---this is just a folder hierarchy that stores your corpora, saved results, figures and so on. You can do this in a number of ways:\n\n### Shell\n\n```shell\nnew_project junglebook\ncp -R chapters junglebook/data\n```\n\n### Interpreter\n\n```shell\n> new project named junglebook\n> add ../chapters\n```\n\n### Python\n\n```python\n>>> import shutil\n>>> from corpkit import new_project\n>>> new_project('junglebook')\n>>> shutil.copytree('../chapters', 'junglebook/data')\n```\n\nYou can create projects and add data via the file menu of the graphical interface as well.\n\n## Ways to use *corpkit*\n\nAs explained earlier, there are three ways to use the tool. Each has unique strengths and weaknesses. To summarise them, the Python API is the most powerful, but has the steepest learning curve. The GUI is the least powerful, but easy to learn (though it is still arguably the most powerful linguistics GUI available). The interpreter strikes a happy middle ground, especially for those who are not familiar with Python.\n\n## Interpreter\n\nThe first way to use *corpkit* is by entering its natural language interpreter. To activate it, use the `corpkit` command:\n\n```shell\n$ cd junglebook\n$ corpkit\n```\n\nYou'll get a lovely new prompt into which you can type commands: \n\n```none\ncorpkit@junglebook:no-corpus> \n```\n\nGenerally speaking, it has the comforts of home, such as history, search, backslash line breaking, variable creation and `ls` and `cd` commands. As in `IPython`, any command beginning with an exclamation mark will be executed by the shell. You can also write scripts and execute them with `corpkit script.ck`, or `./script.ck` if you have a shebang.\n\n### Making projects and parsing corpora\n\n```shell\n# make new project\n> new project named junglebook\n# add folder of (subfolders of) text files\n> add '../chapters'\n# specify corpus to work on\n> set chapters as corpus\n# parse the corpus\n> parse corpus with speaker_segmentation and metadata and multiprocess as 2\n```\n\n### Searching and concordancing\n\n```shell\n# search and exclude\n> search corpus for governor-function matching 'root' \\\n... excluding governor-lemma matching 'be'\n\n# show pos, lemma, index, (e.g. 'NNS/thing/3')\n> search corpus for pos matching '^N' showing pos and lemma and index\n\n# further arguments and dynamic structuring\n> search corpus for word matching any \\\n... with subcorpora as pagenum and preserve_case\n\n# show concordance lines\n> show concordance with window as 50 and columns as LMR\n\n# colouring concordances\n> mark m matching 'have' blue\n\n# recalculate results\n> calculate result from concordance\n```\n\n### Variables, editing results\n\n```shell\n\n# variable naming\n> call result root_deps\n# skip some numerical subcorpora\n> edit root_deps by skipping subcorpora matching [1,2,3,4,5]\n# make relative frequencies\n> calculate edited as percentage of self\n# use scipy to calculate trends and sort by them\n> sort edited by decrease\n```\n\n### Visualise edited results\n\n```shell\n> plot edited as line chart \\\n... with x_label as 'Subcorpus' and \\\n... y_label as 'Frequency' and \\\n... colours as 'summer'\n```\n\n### Switching interfaces\n\n```shell\n# open graphical interface\n> gui\n# enter ipython with current namespace\n> ipython\n# use a new/existing jupyter notebook\n> jupyter notebook findings.ipynb\n```\n\n## API\n\nStraight Python is the most powerful way to use *corpkit*, because you can manipulate results with Pandas syntax, construct loops, make recursive queries, and so on. Here are some simple examples of the API syntax:\n\n### Instantiate and search a parsed corpus\n\n```python\n### import everything\n>>> from corpkit import *\n>>> from corpkit.dictionaries import *\n\n### instantiate corpus\n>>> corp = Corpus('chapters-parsed')\n\n### search for anything participant with a governor that\n### is a process, excluding closed class words, and \n### showing lemma forms. also, generate a concordance.\n>>> sch = {GF: roles.process, F: roles.actor}\n>>> part = corp.interrogate(search=sch,\n... exclude={W: wordlists.closedclass},\n... show=[L],\n... conc=True)\n```\n\nYou get an `Interrogation` object back, with a `results` attribute that is a Pandas DataFrame: \n\n```\n daisy gatsby tom wilson eye man jordan voice michaelis \\\nchapter1 13 2 6 0 3 3 0 2 0 \nchapter2 1 0 12 10 1 1 0 0 0 \nchapter3 0 3 0 0 3 8 6 1 0 \nchapter4 6 9 2 0 1 3 1 1 0 \nchapter5 8 14 0 0 3 3 0 2 0 \nchapter6 7 14 9 0 1 2 0 3 0 \nchapter7 26 20 35 10 12 3 16 9 5 \nchapter8 5 4 1 10 2 2 0 1 10 \nchapter9 1 1 1 0 3 3 1 1 0 \n```\n\n### Edit and visualise the result\n\nBelow, we make normalised frequencies and plot:\n\n```python\n### calculate and sort---this sort requires scipy\n>>> part = part.edit('%', SELF)\n\n### make line subplots for the first nine results\n>>> plt = part.visualise('Processes, increasing', subplots=True, layout=(3,3))\n>>> plt.show()\n```\n\n\n\nThere are also some [more detailed API examples over here](https://github.com/interrogator/corpkit/blob/master/API-README.md). This document is fairly thorough, but now deprecated, because the official docs are now over at [ReadTheDocs](http://corpkit.readthedocs.io/en/latest/).\n\n## Example figures\n\n
\n\n Shifting register of scientific English \n
\n\n Participants and processes in online forum talk \n
\n\n Riskers and mood role of risk words in print news journalism \n
\n\n## Graphical interface\n\nScreenshots coming soon! For now, just head [here](http://interrogator.github.io/corpkit/).\n\n## Contact\n\nTwitter: [@interro_gator](https://twitter.com/interro_gator)\n\n## Cite\n\n> `McDonald, D. (2015). corpkit: a toolkit for corpus linguistics. Retrieved from https://www.github.com/interrogator/corpkit. DOI: http://doi.org/10.5281/zenodo.28361`\n"
},
{
"path": "bld.bat",
"content": "\"%PYTHON%\" setup.py install \nif errorlevel 1 exit 1\n\n:: Add more build steps here, if they are necessary.\n\n:: See\n:: http://docs.continuum.io/conda/build.html\n:: for a list of environment variables that are set during the build process.\n"
},
{
"path": "build.sh",
"content": "#!/bin/bash\n\n$PYTHON setup.py install \n\n# Add more build steps here, if they are necessary.\n\n# See\n# http://docs.continuum.io/conda/build.html\n# for a list of environment variables that are set during the build process.\n"
},
{
"path": "conf.py",
"content": "# -*- coding: utf-8 -*-\n#\n# corpkit documentation build configuration file, created by\n# sphinx-quickstart on Thu Nov 5.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this\n# autogenerated file.\n#\n# All configuration values have a default; values that are commented out\n# serve to show the default.\n\nfrom sphinx.highlighting import PygmentsBridge\nfrom pygments.formatters.latex import LatexFormatter\n\nclass CustomLatexFormatter(LatexFormatter):\n def __init__(self, **options):\n super(CustomLatexFormatter, self).__init__(**options)\n self.verboptions = r\"formatcom=\\footnotesize\"\n\nPygmentsBridge.latex_formatter = CustomLatexFormatter\n\nimport sys\nimport os\nimport shlex\n\nfrom recommonmark.parser import CommonMarkParser\n\nsource_parsers = {\n '.md': CommonMarkParser,\n}\n\nsource_suffix = ['.rst', '.md']\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#sys.path.insert(0, os.path.abspath('.'))\n\nsys.path.insert(0,\"/Users/daniel/work/corpkit/corpkit\")\n\n# -- General configuration ------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n#needs_sphinx = '1.0'\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n 'sphinx.ext.autodoc',\n 'sphinx.ext.viewcode',\n 'alabaster'\n]\n\n# Napoleon settings (all default)\n#napoleon_google_docstring = True\n#napoleon_numpy_docstring = True\n#napoleon_include_init_with_doc = False\n#napoleon_include_private_with_doc = False\n#napoleon_include_special_with_doc = False\n#napoleon_use_admonition_for_examples = False\n#napoleon_use_admonition_for_notes = False\n#napoleon_use_admonition_for_references = False\n#napoleon_use_ivar = False\n#napoleon_use_param = True\n#napoleon_use_rtype = True\n#napoleon_use_keyword = True\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n# source_suffix = ['.rst', '.md']\n\n# The encoding of source files.\n#source_encoding = 'utf-8-sig'\n\n# The master toctree document.\nmaster_doc = 'index'\n\n# General information about the project.\nproject = u'corpkit'\ncopyright = u'2016, Daniel McDonald'\nauthor = u'Daniel McDonald'\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The short X.Y version.\nversion = '2.3.8'\n# The full version, including alpha/beta/rc tags.\nrelease = '2.3.8'\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = None\n\n# There are two options for replacing |today|: either, you set today to some\n# non-false value, then it is used:\n#today = ''\n# Else, today_fmt is used as the format for a strftime call.\n#today_fmt = '%B %d, %Y'\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\nexclude_patterns = ['_build', '*/build.py']\n\n# The reST default role (used for this markup: `text`) to use for all\n# documents.\n#default_role = None\n\n# If true, '()' will be appended to :func: etc. cross-reference text.\nadd_function_parentheses = True\n\n# If true, the current module name will be prepended to all description\n# unit titles (such as .. function::).\n#add_module_names = True\n\n# If true, sectionauthor and moduleauthor directives will be shown in the\n# output. They are ignored by default.\n#show_authors = False\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = 'sphinx'\n\n# A list of ignored prefixes for module index sorting.\n#modindex_common_prefix = []\n\n# If true, keep warnings as \"system message\" paragraphs in the built documents.\n#keep_warnings = False\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = False\n\n\n# -- Options for HTML output ----------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n# import alabaster\n# \n# html_theme_path = [alabaster.get_path()]\n# html_theme = 'alabaster'\n# html_sidebars = {\n# '**': [\n# 'about.html',\n# 'navigation.html',\n# 'relations.html',\n# 'searchbox.html',\n# 'donate.html',\n# ]\n# }\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further. For a list of options available for each theme, see the\n# documentation.\n#html_theme_options = {}\n\n# Add any paths that contain custom themes here, relative to this directory.\n#html_theme_path = []\n\n# The name for this set of Sphinx documents. If None, it defaults to\n# \" v documentation\".\n#html_title = None\n\n# A shorter title for the navigation bar. Default is the same as html_title.\n#html_short_title = None\n\n# The name of an image file (relative to this directory) to place at the top\n# of the sidebar.\nhtml_logo = 'images/alpha_gator_small.png'\n\n# The name of an image file (within the static path) to use as favicon of the\n# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32\n# pixels large.\n#html_favicon = None\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n# Add any extra paths that contain custom files (such as robots.txt or\n# .htaccess) here, relative to this directory. These files are copied\n# directly to the root of the documentation.\n#html_extra_path = []\n\n# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,\n# using the given strftime format.\nhtml_last_updated_fmt = '%b %d, %Y'\n\n# If true, SmartyPants will be used to convert quotes and dashes to\n# typographically correct entities.\nhtml_use_smartypants = True\n\n# Custom sidebar templates, maps document names to template names.\n#html_sidebars = {}\n\n# Additional templates that should be rendered to pages, maps page names to\n# template names.\n#html_additional_pages = {}\n\n# If false, no module index is generated.\n#html_domain_indices = True\n\n# If false, no index is generated.\n#html_use_index = True\n\n# If true, the index is split into individual pages for each letter.\n#html_split_index = False\n\n# If true, links to the reST sources are added to the pages.\n#html_show_sourcelink = True\n\n# If true, \"Created using Sphinx\" is shown in the HTML footer. Default is True.\nhtml_show_sphinx = False\n\n# If true, \"(C) Copyright ...\" is shown in the HTML footer. Default is True.\n#html_show_copyright = True\n\n# If true, an OpenSearch description file will be output, and all pages will\n# contain a tag referring to it. The value of this option must be the\n# base URL from which the finished HTML is served.\n#html_use_opensearch = ''\n\n# This is the file name suffix for HTML files (e.g. \".xhtml\").\n#html_file_suffix = None\n\n# Language to be used for generating the HTML full-text search index.\n# Sphinx supports the following languages:\n# 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja'\n# 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr'\n#html_search_language = 'en'\n\n# A dictionary with options for the search language support, empty by default.\n# Now only 'ja' uses this config value\n#html_search_options = {'type': 'default'}\n\n# The name of a javascript file (relative to the configuration directory) that\n# implements a search results scorer. If empty, the default will be used.\n#html_search_scorer = 'scorer.js'\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = 'corpkitdoc'\n\n# -- Options for LaTeX output ---------------------------------------------\n\nlatex_elements = {\n# The paper size ('letterpaper' or 'a4paper').\n'papersize': 'a4paper',\n\n# The font size ('10pt', '11pt' or '12pt').\n#'pointsize': '10pt',\n\n# Additional stuff for the LaTeX preamble.\n# This should help with line breaks in code cells\n'preamble': '\\\\setcounter{tocdepth}{3} \\\\usepackage{pmboxdraw}',\n\n# Latex figure (float) alignment\n'figure_align': 'htbp',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n# author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n (master_doc, 'corpkit.tex', u'corpkit documentation',\n u'Daniel McDonald', 'manual'),\n]\n\n# The name of an image file (relative to this directory) to place at the top of\n# the title page.\nlatex_logo = 'images/alpha_gator_small.png'\n\n# For \"manual\" documents, if this is true, then toplevel headings are parts,\n# not chapters.\n#latex_use_parts = False\n\n# If true, show page references after internal links.\n#latex_show_pagerefs = False\n\n# If true, show URL addresses after external links.\n#latex_show_urls = False\n\n# Documents to append as an appendix to all manuals.\n#latex_appendices = []\n\n# If false, no module index is generated.\n#latex_domain_indices = True\n\n\n# -- Options for manual page output ---------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [\n (master_doc, 'corpkit', u'corpkit documentation',\n [author], 1)\n]\n\n# If true, show URL addresses after external links.\n#man_show_urls = False\n\n\n# -- Options for Texinfo output -------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n# dir menu entry, description, category)\ntexinfo_documents = [\n (master_doc, 'corpkit', u'corpkit documentation',\n author, 'corpkit', 'Corpus linguistic tools.',\n 'Linguistics'),\n]\n\n# Documents to append as an appendix to all manuals.\n#texinfo_appendices = []\n\n# If false, no module index is generated.\n#texinfo_domain_indices = True\n\n# How to display URL addresses: 'footnote', 'no', or 'inline'.\n#texinfo_show_urls = 'footnote'\n\n# If true, do not generate a @detailmenu in the \"Top\" node's menu.\n#texinfo_no_detailmenu = False\n\nautodoc_member_order = 'bysource'\n"
},
{
"path": "corpkit/__init__.py",
"content": "\"\"\"\nA toolkit for corpus linguistics\n\"\"\"\n\nfrom __future__ import print_function\n\n#metadata\n__version__ = \"2.3.8\"\n__author__ = \"Daniel McDonald\"\n__license__ = \"MIT\"\n\n# probably not needed, anymore but adds corpkit to path for tregex.sh\nimport sys\nimport os\nimport inspect\n\nfrom corpkit.constants import LETTERS\n\n# asterisk import\n__all__ = [\n \"load\",\n \"loader\",\n \"load_all_results\",\n \"as_regex\",\n \"new_project\",\n \"Corpus\",\n \"File\",\n \"Corpora\",\n \"gui\"] + LETTERS\n\ncorpath = inspect.getfile(inspect.currentframe())\nbaspat = os.path.dirname(corpath)\n#dicpath = os.path.join(baspat, 'dictionaries')\nfor p in [corpath, baspat]:\n if p not in sys.path:\n sys.path.append(p)\n if p not in os.environ[\"PATH\"].split(':'): \n os.environ[\"PATH\"] += os.pathsep + p\n\n# import classes\nfrom corpkit.corpus import Corpus, File, Corpora\n#from corpkit.model import MultiModel\n\nfrom corpkit.other import (load, loader, load_all_results, \n quickview, as_regex, new_project)\n\nfrom corpkit.lazyprop import lazyprop\n#from corpkit.dictionaries.process_types import Wordlist\nfrom corpkit.process import gui\n\n# monkeypatch editing and plotting to pandas objects\nfrom pandas import DataFrame, Series\n\n# monkey patch functions\ndef _plot(self, *args, **kwargs):\n from corpkit.plotter import plotter\n return plotter(self, *args, **kwargs)\n\ndef _edit(self, *args, **kwargs):\n from corpkit.editor import editor\n return editor(self, *args, **kwargs)\n\ndef _save(self, savename, **kwargs):\n from corpkit.other import save\n save(self, savename, **kwargs)\n\ndef _quickview(self, n=25):\n from corpkit.other import quickview\n quickview(self, n=n)\n\ndef _format(self, *args, **kwargs):\n from corpkit.other import concprinter\n concprinter(self, *args, **kwargs)\n\ndef _texify(self, *args, **kwargs):\n from corpkit.other import texify\n texify(self, *args, **kwargs)\n\ndef _calculate(self, *args, **kwargs):\n from corpkit.process import interrogation_from_conclines\n return interrogation_from_conclines(self)\n\ndef _multiplot(self, leftdict={}, rightdict={}, **kwargs):\n from corpkit.plotter import multiplotter\n return multiplotter(self, leftdict=leftdict, rightdict=rightdict, **kwargs)\n\ndef _perplexity(self):\n \"\"\"\n Pythonification of the formal definition of perplexity.\n\n input: a sequence of chances (any iterable will do)\n output: perplexity value.\n\n from https://github.com/zeffii/NLP_class_notes\n \"\"\"\n def _perplex(chances):\n import math\n chances = [i for i in chances if i] \n N = len(chances)\n product = 1\n for chance in chances:\n product *= chance\n return math.pow(product, -1/N)\n\n return self.apply(_perplex, axis=1)\n\ndef _entropy(self):\n \"\"\"\n entropy(pos.edit(merge_entries=mergetags, sort_by='total').results.T\n \"\"\"\n from scipy.stats import entropy\n import pandas as pd\n escores = entropy(self.edit('/', SELF).results.T)\n ser = pd.Series(escores, index=self.index)\n ser.name = 'Entropy'\n return ser\n\ndef _shannon(self):\n from corpkit.stats import shannon\n return shannon(self)\n\n\ndef _shuffle(self, inplace=False):\n import random\n index = list(self.index)\n random.shuffle(index)\n shuffled = self.ix[index]\n shuffled.reset_index()\n if inplace:\n self = shuffled\n else:\n return shuffled\n\ndef _top(self):\n \"\"\"Show as many rows and cols as possible without truncation\"\"\"\n import pandas as pd\n max_row = pd.options.display.max_rows\n max_col = pd.options.display.max_columns\n return self.iloc[:max_row, :max_col]\n\ndef _tabview(self, **kwargs):\n import pandas as pd\n import tabview\n tabview.view(self, **kwargs)\n\ndef _rel(self, denominator='self', **kwargs):\n from corpkit.editor import editor\n return editor(self, '%', denominator, **kwargs)\n\ndef _keyness(self, measure='ll', denominator='self', **kwargs):\n from corpkit.editor import editor\n return editor(self, 'k', denominator, **kwargs)\n\ndef _plain(df):\n return ' '.join(df['w'])\n\n# monkey patching things\n\nDataFrame.entropy = _entropy\nDataFrame.perplexity = _perplexity\nDataFrame.shannon = _shannon\n\nDataFrame.edit = _edit\nSeries.edit = _edit\n\nDataFrame.rel = _rel\nSeries.rel = _rel\n\nDataFrame.keyness = _keyness\nSeries.keyness = _keyness\n\nDataFrame.visualise = _plot\nSeries.visualise = _plot\n\nDataFrame.tabview = _tabview\n\nDataFrame.multiplot = _multiplot\nSeries.multiplot = _multiplot\n\nDataFrame.save = _save\nSeries.save = _save\n\nDataFrame.quickview = _quickview\nSeries.quickview = _quickview\n\nDataFrame.format = _format\nSeries.format = _format\n\nSeries.texify = _texify\n\nDataFrame.calculate = _calculate\nSeries.calculate = _calculate\n\nDataFrame.shuffle = _shuffle\n\nDataFrame.top = _top\n\nDataFrame.plain = _plain\n\n# Defining letters\nmodule = sys.modules[__name__]\nfor letter in LETTERS:\n if not letter.isalpha():\n trans = letter.replace('A', '-', 1).replace('Z', '+', 1).lower()\n else:\n trans = letter.lower()\n setattr(module, letter, trans)\n # other methods:\n # globals()[letter] = letter.lower()\n # exec('%s = \"%s\"' % (letter, letter.lower()))\n\nANYWORD = r'[A-Za-z0-9:_]'\n"
},
{
"path": "corpkit/annotate.py",
"content": "\"\"\"\ncorpkit: add annotations to conll-u via concordancing\n\"\"\"\n\ndef process_special_annotation(v, lin):\n \"\"\"\n If the user wants a fancy annotation, like 'add middle column',\n this gets processed here. it's potentially the place where the\n user could add entropy score, or something like that.\n \"\"\"\n if v.lower() not in ['i', 'index', 'm', 'scheme', 't', 'q']:\n return v\n if v == 'index':\n return lin.name\n elif v in ['m', 't']:\n return str(lin[v])\n else:\n return v\n\ndef make_string_to_add(annotation, lin, replace=False):\n \"\"\"\n Make a string representing metadata to add\n \"\"\"\n from corpkit.constants import STRINGTYPE\n if isinstance(annotation, STRINGTYPE):\n if replace:\n return annotation + '\\n'\n else:\n return '# tags=' + annotation + '\\n'\n \n start = str()\n for k, v in annotation.items():\n # these are special names---add more?\n v = process_special_annotation(v, lin) \n if replace:\n start = '%s\\n' % v\n else:\n start += '# %s=%s\\n' % (k, v)\n return start\n\ndef get_line_number_for_entry(data, si, ti, annotation):\n \"\"\"\n Find the place in filename at which to add the string\n \"\"\"\n partstart = '# sent_id %d' % si\n partend = '# sent_id %d' % (si + 1)\n # this way iterates over the lines\n # it could also just find the \n lnum = data.split(partstart)[0].count('\\n') + 2\n sent = data.split(partstart)[1].split(partend)[0]\n field = 'tags' if isinstance(annotation, str) else list(annotation.keys())[0]\n ixx = next((i for i, l in enumerate(sent.splitlines()) \\\n if l.startswith('# %s=' % field)), False)\n \n if ixx is False:\n return lnum, False\n else:\n return lnum + ixx - 2, True\n\n\ndef update_contents(contents, place, text, do_replace=False):\n \"\"\" \n Open file, read lines, add or replace the line with the good one\n \"\"\" \n if do_replace:\n contents[place] = contents[place].rstrip('\\n').replace(text + ';', '') + ';' + text\n else:\n contents.insert(place, text)\n return contents\n\ndef dry_run_text(filepath, contents, place, colours):\n \"\"\"\n Show a dry run of what the annotations would be\n \"\"\"\n import os\n contents[place] = contents[place].rstrip('\\n') + ' <==========\\n'\n try:\n contents[place] = colours['green'] + contents[place] + colours['reset']\n except:\n pass\n\n max_lines = next((i for i, l in enumerate(contents[place:]) if l == '\\n'), 10)\n max_lines = 30 if max_lines > 30 else max_lines\n\n formline = ' Add metadata: %s \\n' % (os.path.basename(filepath))\n bars = '=' * len(formline)\n\n print(bars + '\\n' + formline + bars)\n print(''.join(contents[place-3:max_lines+place]))\n\ndef annotate(open_file, contents):\n \"\"\"\n Add annotation to a single file\n \"\"\"\n from corpkit.constants import PYTHON_VERSION\n contents = ''.join(contents)\n if PYTHON_VERSION == 2:\n contents = contents.encode('utf-8', errors='ignore')\n open_file.seek(0)\n open_file.write(contents)\n open_file.truncate()\n\ndef delete_lines(corpus, annotation, dry_run=True, colour={}):\n \"\"\"\n Show or delete the necessary lines\n \"\"\"\n from corpkit.constants import OPENER, PYTHON_VERSION\n import re\n import os\n tagmode = True\n no_can_do = ['sent_id', 'parse']\n\n if isinstance(annotation, dict):\n tagmode = False\n for k, v in annotation.items():\n if k in no_can_do:\n print(\"You aren't allowed to delete '%s', sorry.\" % k)\n return\n if not v:\n v = r'.*?'\n regex = re.compile(r'(# %s=%s)\\n' % (k, v), re.MULTILINE)\n else:\n if annotation in no_can_do:\n print(\"You aren't allowed to delete '%s', sorry.\" % k)\n return\n regex = re.compile(r'((# tags=.*?)%s;?(.*?))\\n' % annotation, re.MULTILINE)\n\n fs = []\n for (root, dirs, fls) in os.walk(corpus):\n for f in fls:\n fs.append(os.path.join(root, f))\n \n for f in fs:\n \n if PYTHON_VERSION == 2:\n from corpkit.process import saferead\n data = saferead(f)[0]\n else:\n with open(f, 'rb') as fo:\n data = fo.read().decode('utf-8', errors='ignore')\n\n if dry_run:\n if tagmode:\n repl_str = r'\\1 <=======\\n%s\\2\\3 <=======\\n' % colour.get('green', '')\n else:\n repl_str = r'\\1 <=======\\n'\n try:\n repl_str = colour['red'] + repl_str + colour['reset']\n except:\n pass\n data, n = re.subn(regex, repl_str, data)\n nspl = 100 if tagmode else 50\n delim = '<======='\n data = re.split(delim, data, maxsplit=nspl)\n toshow = delim.join(data[:nspl+1])\n toshow = toshow.rsplit('\\n\\n', 1)[0]\n print(toshow)\n if n > 50:\n n = n - 50\n print('\\n... and %d more changes ... ' % n)\n\n else:\n if tagmode:\n repl_str = r'\\2\\3\\n'\n else:\n repl_str = ''\n data = re.sub(regex, repl_str, data)\n with OPENER(f, 'w') as fo:\n from corpkit.constants import PYTHON_VERSION\n if PYTHON_VERSION == 2:\n data = data.encode('utf-8', errors='ignore')\n fo.write(data)\n \n\ndef annotator(df_or_corpus, annotation, dry_run=True, deletemode=False):\n \"\"\"\n Run the annotator pipeline over multiple files\n\n :param corpus: a Corpus object containing the files\n :param annotation: a str or dict containing annotation text\n \"\"\"\n import re\n import os\n from corpkit.constants import OPENER, STRINGTYPE, PYTHON_VERSION\n colour = {}\n try:\n from colorama import Fore, init, Style\n init(autoreset=True)\n colour = {'green': Fore.GREEN, 'reset': Style.RESET_ALL, 'red': Fore.RED}\n except ImportError:\n pass\n if deletemode:\n delete_lines(df_or_corpus.path, annotation, dry_run=dry_run, colour=colour)\n return\n\n file_sent_words = df_or_corpus.reset_index()[['index', 'f', 'i']].values.tolist()\n from collections import defaultdict\n outt = defaultdict(list)\n for index, fn, ix in file_sent_words:\n s, i = ix.split(',', 1)\n outt[fn].append((int(s), int(i), index))\n \n for i, (fname, entries) in enumerate(sorted(outt.items()), start=1): \n with OPENER(fname, 'r+') as fo:\n data = fo.read()\n contents = [i + '\\n' for i in data.split('\\n')]\n for si, ti, index in list(reversed(sorted(set(entries)))):\n line_num, do_replace = get_line_number_for_entry(data, si, ti, annotation)\n anno_text = make_string_to_add(annotation, df_or_corpus.ix[index], replace=do_replace)\n contents = update_contents(contents, line_num, anno_text, do_replace=do_replace)\n if dry_run and i < 50:\n dry_run_text(fname,\n contents,\n line_num,\n colours=colour)\n if not dry_run:\n annotate(fo, contents=contents)\n if not dry_run:\n print('%d annotations made in %s' % (len(entries), fname))\n if dry_run and i > 50:\n break\n\n if dry_run:\n if len(file_sent_words) > 50:\n n = len(file_sent_words) - 50\n print('... and %d more changes ... ' % n)\n\n "
},
{
"path": "corpkit/blanknotebook.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# blanknotebook\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Initialisation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, import `corpkit`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import corpkit\\n\",\n \"from corpkit import (\\n\",\n \" interrogator, plotter, table, quickview, \\n\",\n \" tally, surgeon, merger, conc, keywords, \\n\",\n \" collocates, multiquery, report_display,\\n\",\n \" save_result, load_result\\n\",\n \" )\\n\",\n \"# show figures in browser\\n\",\n \"% matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Next, set a path to your corpus:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"corpus = 'data/corpus'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Define a query to match any word:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# any token containing letters or numbers (i.e. no punctuation):\\n\",\n \"allwords_query = r'/[A-Za-z0-9]/ !< __' \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Interrogate the corpus with the `allwords_query`, and store the results as `allwords`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"allwords = interrogator(annual_trees, '-C', allwords_query) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Check that it worked:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"print allwords.query\\n\",\n \"print allwords.totals\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, plot something:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"plotter('Word count', allwords.total)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, save this result so that you can access it any time:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"save_result(allwords, 'allwords')\\n\",\n \"\\n\",\n \"# load it again with:\\n\",\n \"# allwords = load_result('allwords')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Use the space below to interrogate and plot whatever you like!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
},
{
"path": "corpkit/build.py",
"content": "from __future__ import print_function\nfrom corpkit.constants import STRINGTYPE, PYTHON_VERSION, INPUTFUNC\n\n\"\"\"\nThis file contains a number of functions used in the corpus building process.\nNone of them is intended to be called by the user him/herself.\n\"\"\"\n\ndef download_large_file(proj_path, url, actually_download=True, root=False, **kwargs):\n \"\"\"\n Download something to proj_path, unless it's CoreNLP, which goes to ~/corenlp\n \"\"\"\n import os\n import shutil\n import glob\n import zipfile\n from time import localtime, strftime\n from corpkit.textprogressbar import TextProgressBar\n from corpkit.process import animator\n\n file_name = url.split('/')[-1]\n home = os.path.expanduser(\"~\")\n customdir = kwargs.get('custom_corenlp_dir', False)\n # if it's corenlp, put it in home/corenlp\n # if that dir exists, check if for a zip file\n # if there's a zipfile and it works, move on\n # if there's a zipfile and it's broken, delete it\n if 'stanford' in url:\n if customdir:\n downloaded_dir = customdir\n else:\n downloaded_dir = os.path.join(home, 'corenlp')\n if not os.path.isdir(downloaded_dir):\n os.makedirs(downloaded_dir)\n else:\n poss_zips = glob.glob(os.path.join(downloaded_dir, 'stanford-corenlp-full*.zip'))\n if poss_zips:\n fullfile = poss_zips[-1]\n from zipfile import BadZipfile\n try:\n the_zip_file = zipfile.ZipFile(fullfile) \n ret = the_zip_file.testzip()\n if ret is None:\n return downloaded_dir, fullfile\n else:\n os.remove(fullfile)\n except BadZipfile:\n os.remove(fullfile)\n #else:\n # shutil.rmtree(downloaded_dir)\n else:\n downloaded_dir = os.path.join(proj_path, 'temp')\n try:\n os.makedirs(downloaded_dir)\n except OSError:\n pass\n fullfile = os.path.join(downloaded_dir, file_name)\n\n if actually_download:\n import __main__ as main\n if not root and not hasattr(main, '__file__'):\n txt = 'CoreNLP not found. Download latest version (%s)? (y/n) ' % url\n \n selection = INPUTFUNC(txt)\n\n if 'n' in selection.lower():\n return None, None\n try:\n import requests\n # NOTE the stream=True parameter\n r = requests.get(url, stream=True, verify=False)\n file_size = int(r.headers['content-length'])\n file_size_dl = 0\n block_sz = 8192\n showlength = file_size / block_sz\n thetime = strftime(\"%H:%M:%S\", localtime())\n print('\\n%s: Downloading ... \\n' % thetime)\n par_args = {'printstatus': kwargs.get('printstatus', True),\n 'length': showlength}\n if not root:\n tstr = '%d/%d' % (file_size_dl + 1 / block_sz, showlength)\n p = animator(None, None, init=True, tot_string=tstr, **par_args)\n animator(p, file_size_dl + 1, tstr)\n\n with open(fullfile, 'wb') as f:\n for chunk in r.iter_content(chunk_size=block_sz): \n if chunk: # filter out keep-alive new chunks\n f.write(chunk)\n file_size_dl += len(chunk)\n #print file_size_dl * 100.0 / file_size\n if kwargs.get('note'):\n kwargs['note'].progvar.set(file_size_dl * 100.0 / int(file_size))\n else:\n tstr = '%d/%d' % (file_size_dl / block_sz, showlength)\n animator(p, file_size_dl / block_sz, tstr, **par_args)\n if root:\n root.update()\n except Exception as err:\n import traceback\n print(traceback.format_exc())\n thetime = strftime(\"%H:%M:%S\", localtime())\n print('%s: Download failed' % thetime)\n try:\n f.close()\n except:\n pass\n if root:\n root.update()\n return None, None\n\n if kwargs.get('note'): \n kwargs['note'].progvar.set(100)\n else: \n p.animate(int(file_size))\n thetime = strftime(\"%H:%M:%S\", localtime())\n print('\\n%s: Downloaded successully.' % thetime)\n try:\n f.close()\n except:\n pass\n return downloaded_dir, fullfile\n\ndef extract_cnlp(fullfilepath, corenlppath=False, root=False):\n \"\"\"\n Extract corenlp zip file\n \"\"\"\n import zipfile\n import os\n from time import localtime, strftime\n time = strftime(\"%H:%M:%S\", localtime())\n print('%s: Extracting CoreNLP files ...' % time)\n if root:\n root.update()\n if corenlppath is False:\n home = os.path.expanduser(\"~\")\n corenlppath = os.path.join(home, 'corenlp')\n from zipfile import BadZipfile\n try:\n with zipfile.ZipFile(fullfilepath) as zf:\n zf.extractall(corenlppath)\n except BadZipfile:\n os.remove(corenlppath)\n return False\n time = strftime(\"%H:%M:%S\", localtime())\n print('%s: CoreNLP extracted. ' % time)\n return True\n\ndef get_corpus_filepaths(projpath=False, corpuspath=False,\n restart=False, out_ext='conll'):\n \"\"\"\n get a list of filepaths, a la find . -type f\n restart mode will look in restart dir and remove any existing files\n \"\"\"\n import fnmatch\n import os\n matches = []\n\n # get a list of done files minus their paths and extensions\n # this handles if they have been moved to the right dir or not\n\n already_done = get_filepaths(restart, out_ext) if restart else []\n already_done = [os.path.splitext(os.path.basename(x))[0] for x in already_done]\n\n for root, dirnames, filenames in os.walk(corpuspath):\n for filename in fnmatch.filter(filenames, '*.txt'):\n if filename not in already_done:\n matches.append(os.path.join(root, filename))\n if len(matches) == 0:\n return False, False\n matchstring = '\\n'.join(matches)\n\n # maybe not good:\n if projpath is False:\n projpath = os.path.dirname(os.path.abspath(corpuspath.rstrip('/')))\n\n corpname = os.path.basename(corpuspath)\n\n fp = os.path.join(projpath, 'data', corpname + '-filelist.txt')\n # definitely not good.\n if os.path.join('data', 'data') in fp:\n fp = fp.replace(os.path.join('data', 'data'), 'data')\n with open(fp, \"w\") as f:\n f.write(matchstring + '\\n')\n return fp, matchstring\n\ndef check_jdk():\n \"\"\"\n Check for a Java/OpenJDK\n \"\"\"\n import corpkit\n import subprocess\n from subprocess import PIPE, STDOUT, Popen\n # add any other version string to here\n javastrings = ['java version \"1.8', 'openjdk version \"1.8']\n p = Popen([\"java\", \"-version\"], stdout=PIPE, stderr=PIPE)\n _, stderr = p.communicate()\n encoded = stderr.decode(encoding='utf-8').lower()\n\n return any(j in encoded for j in javastrings)\n\ndef parse_corpus(proj_path=False, \n corpuspath=False, \n filelist=False, \n corenlppath=False, \n operations=False,\n root=False, \n stdout=False, \n memory_mb=2000,\n copula_head=True,\n multiprocessing=False,\n outname=False,\n coref=True,\n **kwargs\n ):\n \"\"\"\n Create a CoreNLP-parsed and/or NLTK tokenised corpus\n \"\"\"\n import subprocess\n from subprocess import PIPE, STDOUT, Popen\n from corpkit.process import get_corenlp_path\n import os\n import sys\n import re\n import chardet\n from time import localtime, strftime\n import time\n\n fileparse = kwargs.get('fileparse', False)\n from corpkit.constants import CORENLP_URL as url\n \n if not check_jdk():\n print('Need latest Java.')\n return\n\n curdir = os.getcwd()\n note = kwargs.get('note', False)\n\n if proj_path is False:\n proj_path = os.path.dirname(os.path.abspath(corpuspath.rstrip('/')))\n\n basecp = os.path.basename(corpuspath)\n\n if fileparse:\n new_corpus_path = os.path.dirname(corpuspath)\n else:\n if outname:\n new_corpus_path = os.path.join(proj_path, 'data', outname)\n else:\n new_corpus_path = os.path.join(proj_path, 'data', '%s-parsed' % basecp)\n new_corpus_path = new_corpus_path.replace('-stripped-', '-')\n\n # todo:\n # this is not stable\n if os.path.join('data', 'data') in new_corpus_path:\n new_corpus_path = new_corpus_path.replace(os.path.join('data', 'data'), 'data')\n\n # this caused errors when multiprocessing\n # it used to be isdir, but supposedly there was a file there\n # i don't see how it's possible ...\n # i think it is a 'race condition', so we'll also put a try/except there\n \n if not os.path.exists(new_corpus_path):\n try:\n os.makedirs(new_corpus_path)\n except OSError:\n pass\n else:\n if not os.path.isfile(new_corpus_path):\n fs = get_filepaths(new_corpus_path, ext=False)\n if not multiprocessing:\n if any([f.endswith('.conll') for f in fs]) or \\\n any([f.endswith('.conllu') for f in fs]):\n print('Folder containing .conll files already exists: %s' % new_corpus_path)\n return False\n \n corenlppath = get_corenlp_path(corenlppath)\n\n success = bool(corenlppath)\n\n if not corenlppath:\n from corpkit.constants import CORENLP_VERSION\n print(\"CoreNLP not found. Auto-installing CoreNLP v%s...\" % CORENLP_VERSION)\n cnlp_dir = os.path.join(os.path.expanduser(\"~\"), 'corenlp')\n corenlppath, fpath = download_large_file(cnlp_dir, url,\n root=root,\n note=note,\n actually_download=True,\n custom_corenlp_dir=corenlppath)\n # cleanup\n if corenlppath is None and fpath is None:\n import shutil\n shutil.rmtree(new_corpus_path)\n shutil.rmtree(new_corpus_path.replace('-parsed', '-stripped'))\n os.remove(new_corpus_path.replace('-parsed', '-filelist.txt'))\n raise ValueError('CoreNLP needed to parse texts.')\n success = extract_cnlp(fpath)\n if not success:\n raise ValueError('CoreNLP installation failed for some reason. Try deleting the ~/corenlp directory and starting over.')\n \n import glob\n globpath = os.path.join(corenlppath, 'stanford-corenlp*')\n corenlppath = [i for i in glob.glob(globpath) if os.path.isdir(i)]\n if corenlppath:\n corenlppath = corenlppath[-1]\n else:\n raise ValueError('CoreNLP installation failed for some reason. Try deleting the ~/corenlp directory and starting over.')\n\n # if not gui, don't mess with stdout\n if stdout is False:\n stdout = sys.stdout\n\n os.chdir(corenlppath)\n if root:\n root.update_idletasks()\n # not sure why reloading sys, but seems needed\n # in order to show files in the gui\n try:\n reload(sys)\n except NameError:\n import importlib\n importlib.reload(sys)\n pass\n if memory_mb is False:\n memory_mb = 2024\n\n # you can pass in 'coref' as kwarg now\n cof = ',dcoref' if coref else ''\n if operations is False:\n operations = 'tokenize,ssplit,pos,lemma,parse,ner' + cof\n\n if isinstance(operations, list):\n operations = ','.join([i.lower() for i in operations])\n\n with open(filelist, 'r') as fo:\n dat = fo.read()\n num_files_to_parse = len([l for l in dat.splitlines() if l])\n\n # get corenlp version number\n reg = re.compile(r'stanford-corenlp-([0-9].[0-9].[0-9])-javadoc.jar')\n fver = next(re.search(reg, s).group(1) for s in os.listdir('.') if re.search(reg, s))\n if fver == '3.6.0':\n extra_jar = 'slf4j-api.jar:slf4j-simple.jar:'\n else:\n extra_jar = ''\n\n out_form = 'xml' if kwargs.get('output_format') == 'xml' else 'json'\n out_ext = 'xml' if kwargs.get('output_format') == 'xml' else 'conll'\n\n arglist = ['java', '-cp', \n 'stanford-corenlp-%s.jar:stanford-corenlp-%s-models.jar:xom.jar:joda-time.jar:%sjollyday.jar:ejml-0.23.jar' % (fver, fver, extra_jar), \n '-Xmx%sm' % str(memory_mb),\n 'edu.stanford.nlp.pipeline.StanfordCoreNLP', \n '-annotators',\n operations, \n '-filelist', filelist,\n '-noClobber',\n '-outputExtension', '.%s' % out_ext,\n '-outputFormat', out_form,\n '-outputDirectory', new_corpus_path]\n if copula_head:\n arglist.append('--parse.flags')\n arglist.append(' -makeCopulaHead')\n print('Java command:')\n print(arglist)\n try:\n proc = subprocess.Popen(arglist, stdout=sys.stdout)\n # maybe a problem with stdout. sacrifice it if need be\n except:\n proc = subprocess.Popen(arglist) \n #p = TextProgressBar(num_files_to_parse)\n while proc.poll() is None:\n sys.stdout = stdout\n thetime = strftime(\"%H:%M:%S\", localtime())\n if not fileparse:\n num_parsed = len([f for f in os.listdir(new_corpus_path) if f.endswith(out_ext)]) \n if num_parsed == 0:\n if root:\n print('%s: Initialising parser ... ' % (thetime))\n if num_parsed > 0 and (num_parsed + 1) <= num_files_to_parse:\n if root:\n print('%s: Parsing file %d/%d ... ' % \\\n (thetime, num_parsed + 1, num_files_to_parse))\n if kwargs.get('note'):\n kwargs['note'].progvar.set((num_parsed) * 100.0 / num_files_to_parse)\n #p.animate(num_parsed - 1, str(num_parsed) + '/' + str(num_files_to_parse))\n time.sleep(1)\n if root:\n root.update()\n \n #p.animate(num_files_to_parse)\n if kwargs.get('note'):\n kwargs['note'].progvar.set(100)\n sys.stdout = stdout\n thetime = strftime(\"%H:%M:%S\", localtime())\n print('%s: Parsing finished. Moving parsed files into place ...' % thetime)\n os.chdir(curdir)\n return new_corpus_path\n\ndef move_parsed_files(proj_path, old_corpus_path, new_corpus_path,\n ext='conll', restart=False):\n \"\"\"\n Make parsed files follow existing corpus structure\n \"\"\"\n import corpkit\n import shutil\n import os\n import fnmatch\n cwd = os.getcwd()\n basecp = os.path.basename(old_corpus_path)\n dir_list = []\n # go through old path, make file list\n for path, dirs, files in os.walk(old_corpus_path):\n for bit in dirs:\n # is the last bit of the line below windows safe?\n dir_list.append(os.path.join(path, bit).replace(old_corpus_path, '')[1:])\n for d in dir_list:\n if not restart:\n os.makedirs(os.path.join(new_corpus_path, d))\n else:\n try:\n os.makedirs(os.path.join(new_corpus_path, d))\n except OSError:\n pass\n\n # make list of parsed filenames that haven't been moved already\n parsed_fs = [f for f in os.listdir(new_corpus_path) if f.endswith('.%s' % ext)]\n\n # make a dictionary of the right paths\n pathdict = {}\n for rootd, dirnames, filenames in os.walk(old_corpus_path):\n for filename in fnmatch.filter(filenames, '*.txt'):\n pathdict[filename] = rootd\n # move each file\n for f in parsed_fs:\n noxml = f.replace('.%s' % ext, '')\n right_dir = pathdict[noxml].replace(old_corpus_path, new_corpus_path)\n frm = os.path.join(new_corpus_path, f)\n tom = os.path.join(right_dir, f)\n # forgive errors on restart mode, because some files \n # might already have been moved into place\n if restart:\n try:\n os.rename(frm, tom)\n except OSError:\n pass\n else:\n os.rename(frm, tom)\n\n return new_corpus_path\n\ndef corenlp_exists(corenlppath=False):\n import corpkit\n import os\n from corpkit.constants import CORENLP_VERSION\n important_files = ['stanford-corenlp-%s-javadoc.jar' % CORENLP_VERSION,\n 'stanford-corenlp-%s-models.jar' % CORENLP_VERSION,\n 'stanford-corenlp-%s-sources.jar' % CORENLP_VERSION,\n 'stanford-corenlp-%s.jar' % CORENLP_VERSION]\n if corenlppath is False:\n home = os.path.expanduser(\"~\")\n corenlppath = os.path.join(home, 'corenlp')\n if os.path.isdir(corenlppath):\n find_install = [d for d in os.listdir(corenlppath) \\\n if os.path.isdir(os.path.join(corenlppath, d)) \\\n and os.path.isfile(os.path.join(corenlppath, d, 'jollyday.jar'))]\n\n if len(find_install) > 0:\n find_install = find_install[0]\n else:\n return False\n javalib = os.path.join(corenlppath, find_install)\n if len(javalib) == 0:\n return False\n if not any([f.endswith('-models.jar') for f in os.listdir(javalib)]):\n return False\n return True\n else:\n return False\n return True\n\ndef get_filepaths(a_path, ext='txt'):\n \"\"\"\n Make list of txt files in a_path and remove non txt files\n \"\"\"\n import os\n files = []\n if os.path.isfile(a_path):\n return [a_path]\n for (root, dirs, fs) in os.walk(a_path):\n for f in fs:\n if ext:\n if not f.endswith('.' + ext):\n continue\n if 'Unidentified' not in f \\\n and 'unknown' not in f \\\n and not f.startswith('.'):\n files.append(os.path.join(root, f))\n #if ext:\n # if not f.endswith('.' + ext):\n # os.remove(os.path.join(root, f))\n return files\n\ndef make_no_id_corpus(pth, newpth, metadata_mode=False, speaker_segmentation=False):\n \"\"\"\n Make version of pth without ids\n \"\"\"\n import os\n import re\n import shutil\n from corpkit.process import saferead\n # define regex broadly enough to accept timestamps, locations if need be\n \n from corpkit.constants import MAX_SPEAKERNAME_SIZE\n idregex = re.compile(r'(^.{,%d}?):\\s+(.*$)' % MAX_SPEAKERNAME_SIZE)\n\n try:\n shutil.copytree(pth, newpth)\n except OSError:\n shutil.rmtree(newpth)\n shutil.copytree(pth, newpth)\n files = get_filepaths(newpth)\n names = []\n metadata = []\n for f in files:\n good_data = []\n fo, enc = saferead(f)\n data = fo.splitlines()\n # for each line in the file, remove speaker and metadata\n for datum in data:\n if speaker_segmentation:\n matched = re.search(idregex, datum)\n if matched:\n names.append(matched.group(1))\n datum = matched.group(2)\n if metadata_mode:\n splitmet = datum.rsplit(' MAX_METADATA_FIELDS:\n break\n return list(fields)\n\ndef get_names(filepath, speakid):\n \"\"\"\n Get a list of speaker names from a file\n \"\"\"\n import re\n from corpkit.process import saferead\n txt, enc = saferead(filepath)\n res = re.findall(speakid, txt)\n if res:\n return sorted(list(set([i.strip() for i in res])))\n\ndef get_speaker_names_from_parsed_corpus(corpus, feature='speaker'):\n \"\"\"\n Use regex to get speaker names from parsed data without parsing it\n \"\"\"\n import os\n import re\n from corpkit.constants import MAX_METADATA_VALUES\n\n path = corpus.path if hasattr(corpus, 'path') else corpus\n \n list_of_files = []\n names = []\n\n # i am not really sure why we need multiline here\n # is it because start of line char is just matching\n speakid = re.compile(r'^# %s=(.*)' % re.escape(feature), re.MULTILINE)\n \n # if passed a dir, do it for every file\n if os.path.isdir(path):\n for (root, dirs, fs) in os.walk(path):\n for f in fs:\n list_of_files.append(os.path.join(root, f))\n elif os.path.isfile(path):\n list_of_files.append(path)\n\n for filepath in list_of_files:\n res = get_names(filepath, speakid)\n if not res:\n continue\n for i in res:\n if i not in names:\n names.append(i)\n if len(names) > MAX_METADATA_VALUES:\n break\n return list(sorted(set(names)))\n\ndef rename_all_files(dirs_to_do):\n \"\"\"\n Get rid of the inserted dirname in filenames after parsing\n \"\"\"\n import os\n if isinstance(dirs_to_do, STRINGTYPE):\n dirs_to_do = [dirs_to_do]\n for d in dirs_to_do:\n if d.endswith('-parsed'):\n ext = 'txt.xml'\n elif d.endswith('-tokenised'):\n ext = '.p'\n else:\n ext = '.txt'\n fs = get_filepaths(d, ext)\n for f in fs:\n fname = os.path.basename(f)\n justdir = os.path.dirname(f)\n subcorpus = os.path.basename(justdir)\n newname = fname.replace('-%s.%s' % (subcorpus, ext), '.%s' % ext)\n os.rename(f, os.path.join(justdir, newname))\n\ndef flatten_treestring(tree):\n \"\"\"\n Turn bracketed tree string into something looking like English\n \"\"\"\n import re\n tree = re.sub(r'\\(.*? ', '', tree).replace(')', '')\n tree = tree.replace('$ ', '$').replace('`` ', '``').replace(' ,', ',').replace(' .', '.').replace(\"'' \", \"''\").replace(\" n't\", \"n't\").replace(\" 're\",\"'re\").replace(\" 'm\",\"'m\").replace(\" 's\",\"'s\").replace(\" 'd\",\"'d\").replace(\" 'll\",\"'ll\").replace(' ', ' ')\n return tree\n\ndef can_folderise(folder):\n \"\"\"\n Check if corpus can be put into folders\n \"\"\"\n import os\n from glob import glob\n if os.path.isfile(folder):\n return False\n fs = glob(os.path.join(folder, '*.txt'))\n if len(fs) > 1:\n if not any(os.path.isdir(x) for x in glob(os.path.join(folder, '*'))):\n return True\n return False\n\ndef folderise(folder):\n \"\"\"\n Move each file into a folder\n \"\"\"\n import os\n import shutil\n from glob import glob\n from corpkit.process import makesafe\n fs = glob(os.path.join(folder, '*.txt'))\n for f in fs:\n newname = makesafe(os.path.splitext(os.path.basename(f))[0])\n newpath = os.path.join(folder, newname)\n if not os.path.exists(newpath):\n os.makedirs(newpath)\n shutil.move(f, os.path.join(newpath))\n"
},
{
"path": "corpkit/completer.py",
"content": "class Completer(object):\n \"\"\"\n Tab completion for interpreter\n \"\"\"\n\n def __init__(self, words):\n self.words = words\n self.prefix = None\n\n def complete(self, prefix, index):\n \"\"\"\n Add paths etc to this\n \"\"\"\n if prefix != self.prefix:\n # we have a new prefix!\n # find all words that start with this prefix\n self.matching_words = [\n w for w in self.words if w.startswith(prefix)\n ]\n self.prefix = prefix\n try:\n return self.matching_words[index]\n except IndexError:\n return None\n"
},
{
"path": "corpkit/configurations.py",
"content": "def configurations(corpus, search, **kwargs):\n \"\"\"\n Get summary of behaviour of a word\n\n see corpkit.corpus.Corpus.configurations() for docs\n \"\"\"\n\n from corpkit.dictionaries.wordlists import wordlists\n from corpkit.dictionaries.roles import roles\n from corpkit.interrogation import Interrodict\n from corpkit.interrogator import interrogator\n from collections import OrderedDict\n\n if search.get('l') and search.get('w'):\n raise ValueError('Search only for a word or a lemma, not both.')\n\n # are we searching words or lemmata?\n if search.get('l'):\n dep_word_or_lemma = 'dl'\n gov_word_or_lemma = 'gl'\n word_or_token = search.get('l')\n else:\n if search.get('w'):\n dep_word_or_lemma = 'd'\n gov_word_or_lemma = 'g'\n word_or_token = search.get('w')\n\n # make nested query dicts for each semantic role\n queries = {'participant': \n\n {'left_participant_in': \n {dep_word_or_lemma: word_or_token,\n 'df': roles.participant1,\n 'f': roles.event},\n\n 'right_participant_in':\n {dep_word_or_lemma: word_or_token,\n 'df': roles.participant2,\n 'f': roles.event},\n\n 'premodified':\n {'f': roles.premodifier, \n gov_word_or_lemma: word_or_token},\n\n 'postmodified':\n {'f': roles.postmodifier, \n gov_word_or_lemma: word_or_token},\n\n 'and_or':\n {'f': 'conj:(?:and|or)',\n 'gf': roles.participant,\n gov_word_or_lemma: word_or_token},\n },\n\n 'process':\n\n {'has_subject':\n {'f': roles.participant1,\n gov_word_or_lemma: word_or_token},\n\n 'has_object':\n {'f': roles.participant2,\n gov_word_or_lemma: word_or_token},\n\n 'modalised_by':\n {'f': r'aux',\n 'w': wordlists.modals,\n gov_word_or_lemma: word_or_token},\n\n 'modulated_by':\n {'f': 'advmod',\n 'gf': roles.event,\n gov_word_or_lemma: word_or_token},\n\n 'and_or':\n {'f': 'conj:(?:and|or)',\n 'gf': roles.event, \n gov_word_or_lemma: word_or_token},\n \n },\n\n 'modifier':\n\n {'modifies':\n {'df': roles.modifier,\n dep_word_or_lemma: word_or_token},\n\n 'modulated_by':\n {'f': 'advmod',\n 'gf': roles.modifier,\n gov_word_or_lemma: word_or_token},\n\n 'and_or':\n {'f': 'conj:(?:and|or)',\n 'gf': roles.modifier,\n gov_word_or_lemma: word_or_token},\n\n }\n }\n\n # allow passing in of single function\n if search.get('f'):\n if search.get('f').lower().startswith('part'):\n queries = queries['participant']\n elif search.get('f').lower().startswith('proc'):\n queries = queries['process']\n elif search.get('f').lower().startswith('mod'):\n queries = queries['modifier']\n else:\n newqueries = {}\n for k, v in queries.items():\n for name, pattern in v.items():\n newqueries[name] = pattern\n queries = newqueries\n queries['and_or'] = {'f': 'conj:(?:and|or)', gov_word_or_lemma: word_or_token}\n\n # count all queries to be done\n # total_queries = 0\n # for k, v in queries.items():\n # total_queries += len(v)\n \n kwargs['search'] = queries\n \n # do interrogation\n data = corpus.interrogate(**kwargs)\n \n # remove result itself\n # not ideal, but it's much more impressive this way.\n if isinstance(data, Interrodict):\n for k, v in data.items():\n v.results = v.results.drop(word_or_token, axis=1, errors='ignore')\n v.totals = v.results.sum(axis=1)\n data[k] = v\n return Interrodict(data)\n else:\n return data\n\n"
},
{
"path": "corpkit/conll.py",
"content": "\"\"\"\ncorpkit: process CONLL formatted data\n\"\"\"\n\ndef parse_conll(f,\n first_time=False,\n just_meta=False,\n usecols=None):\n \"\"\"\n Make a pandas.DataFrame with metadata from a CONLL-U file\n \n Args:\n f (str): Filepath\n first_time (bool, optional): If True, add in sent index\n just_meta (bool, optional): Return only a metadata `dict`\n usecols (None, optional): Which columns must be parsed by pandas.read_csv\n \n Returns:\n pandas.DataFrame: DataFrame containing tokens and a ._metadata attribute\n \"\"\"\n import pandas as pd\n try:\n from StringIO import StringIO\n except ImportError:\n from io import StringIO\n\n from collections import defaultdict\n\n # go to corpkit.constants to modify the order of columns if yours are different\n from corpkit.constants import CONLL_COLUMNS as head\n\n with open(f, 'r') as fo:\n data = fo.read().strip('\\n')\n\n splitdata = []\n metadata = {}\n sents = data.split('\\n\\n') \n for count, sent in enumerate(sents, start=1):\n metadata[count] = defaultdict(set)\n for line in sent.split('\\n'):\n if line and not line.startswith('#') \\\n and not just_meta:\n splitdata.append('\\n%d\\t%s' % (count, line))\n else:\n line = line.lstrip('# ')\n if '=' in line:\n field, val = line.split('=', 1)\n metadata[count][field].add(val)\n metadata[count] = {k: ','.join(v) for k, v in metadata[count].items()}\n if just_meta:\n return metadata\n\n # happens with empty files\n if not splitdata:\n return\n\n # head can only be as long as the list of cols in the df\n num_tabs = splitdata[0].strip('\\t').count('\\t')\n head = head[:num_tabs]\n \n # introduce sentence index for multiindex\n #for i, d in enumerate(splitdata, start=1):\n # d = d.replace('\\n', '\\n%s\\t' % str(i))\n # splitdata[i-1] = d\n\n # turn into something pandas can read \n data = '\\n'.join(splitdata)\n data = data.replace('\\n\\n', '\\n') + '\\n'\n\n # remove slashes as early as possible\n data = data.replace('/', '-slash-')\n\n # open with sent and token as multiindex\n try:\n df = pd.read_csv(StringIO(data), sep='\\t', header=None,\n names=['s'] + head, index_col=['s', 'i'], usecols=usecols)\n #df.index = pd.MultiIndex.from_tuples([(1, i) for i in df.index])\n except ValueError:\n return\n df._metadata = metadata\n return df\n\ndef get_dependents_of_id(idx, df=False, repeat=False, attr=False, coref=False):\n \"\"\"\n Get dependents of a token\n \"\"\"\n sent_id, tok_id = getattr(idx, 'name', idx)\n deps = df.ix[sent_id, tok_id]['d'].split(',')\n out = []\n for govid in deps:\n if attr:\n # might not exist...\n try:\n tok = getattr(df.ix[sent_id,int(govid)], attr, False)\n if tok:\n out.append(tok)\n except (KeyError, IndexError):\n pass\n else:\n out.append((sent_id, int(govid)))\n return out\n\ndef get_governors_of_id(idx, df=False, repeat=False, attr=False, coref=False):\n \"\"\"\n Get governors of a token\n \"\"\"\n \n # it can be a series or a tuple\n sent_id, tok_id = getattr(idx, 'name', idx)\n # get the governor id\n govid = df['g'].loc[sent_id, tok_id]\n if attr:\n return getattr(df.loc[sent_id,govid], attr, 'root')\n return [(sent_id, govid)]\n\ndef get_match(idx, df=False, repeat=False, attr=False, **kwargs):\n \"\"\"\n Dummy function, for the most part\n \"\"\"\n sent_id, tok_id = getattr(idx, 'name', idx)\n if attr:\n return df[attr].ix[sent_id, tok_id]\n return [(sent_id, tok_id)]\n\ndef get_head(idx, df=False, repeat=False, attr=False, **kwargs):\n \"\"\"\n Get the head of a 'constituent'---'\n for 'corpus linguistics', if 'corpus' is searched, return 'linguistics'\n \"\"\"\n\n sent_id, tok_id = getattr(idx, 'name', idx)\n #sent = df.ix[sent_id]\n token = df.ix[sent_id, tok_id]\n\n if not hasattr(token, 'c'):\n # this should error, because the data isn't there at all\n lst_of_ixs = [(sent_id, tok_id)]\n\n elif token['c'] == '_':\n lst_of_ixs = [(sent_id, tok_id)]\n # if it is the head, return it\n elif token['c'].endswith('*'):\n lst_of_ixs = [(sent_id, tok_id)]\n else:\n # should be able to speed this one up!\n just_same_coref = df.loc[sent_id][df.loc[sent_id]['c'] == token['c'] + '*']\n if not just_same_coref.empty:\n lst_of_ixs = [(sent_id, i) for i in just_same_coref.index]\n else:\n lst_of_ixs = [(sent_id, tok_id)]\n if attr:\n lst_of_ixs = [df.loc[i][attr] for i in lst_of_ixs]\n return lst_of_ixs\n\ndef get_representative(idx,\n df=False,\n repeat=False,\n attr=False,\n **kwargs):\n \"\"\"\n Get the representative coref head\n \"\"\"\n sent_id, tok_id = getattr(idx, 'name', idx)\n\n token = df.ix[sent_id, tok_id]\n # if no corefs at all\n if not hasattr(token, 'c'):\n # this should error, because the data isn't there at all\n lst_of_ixs = [(sent_id, tok_id)] \n # if no coref available\n elif token['c'] == '_':\n lst_of_ixs = [(sent_id, tok_id)]\n else:\n just_same_coref = df.loc[df['c'] == token['c'] + '*']\n if not just_same_coref.empty:\n lst_of_ixs = [just_same_coref.iloc[0].name]\n else:\n lst_of_ixs = [(sent_id, tok_id)]\n if attr:\n lst_of_ixs = [df.ix[i][attr] for i in lst_of_ixs]\n return lst_of_ixs\n\ndef get_all_corefs(s, i, df, coref=False):\n # if not in coref mode, skip\n if not coref:\n return [(s, i)]\n # if the word was not a head, forget it\n if not df.ix[s,i]['c'].endswith('*'):\n return [(s, i)]\n try:\n # get any other mention head for this coref chain\n just_same_coref = df.loc[df['c'] == df.ix[s,i]['c']]\n return list(just_same_coref.index)\n except:\n return [(s, i)]\n\ndef search_this(df, obj, attrib, pattern, adjacent=False, coref=False):\n \"\"\"\n Search the dataframe for a single criterion\n \"\"\"\n import re\n out = []\n\n # if searching by head, they need to be heads\n if obj == 'h':\n df = df.loc[df['c'].endswith('*')]\n\n # cut down to just tokens with matching attr\n # but, if the pattern is 'any', don't bother\n if hasattr(pattern, 'pattern') and pattern.pattern == r'.*':\n matches = df\n else:\n matches = df[df[attrib].fillna('').str.contains(pattern)]\n\n # functions for getting the needed object\n revmapping = {'g': get_dependents_of_id,\n 'd': get_governors_of_id,\n 'm': get_match,\n 'h': get_all_corefs,\n 'r': get_representative}\n\n getfunc = revmapping.get(obj)\n\n for idx in list(matches.index):\n\n if adjacent:\n if adjacent[0] == '+':\n tomove = -int(adj[1])\n elif adjacent[0] == '-':\n tomove = int(adj[1])\n idx = (idx[0], idx[1] + tomove)\n \n for mindex in getfunc(idx, df=df, coref=coref):\n\n if mindex:\n out.append(mindex)\n\n return list(set(out))\n\ndef show_fix(show):\n \"\"\"show everything\"\"\"\n objmapping = {'d': get_dependents_of_id,\n 'g': get_governors_of_id,\n 'm': get_match,\n 'h': get_head}\n\n out = []\n for val in show:\n adj, val = determine_adjacent(val)\n obj, attr = val[0], val[-1]\n obj_getter = objmapping.get(obj)\n out.append(adj, val, obj, attr, obj_getter)\n return out\n\ndef dummy(x, *args, **kwargs):\n return x\n\ndef format_toks(to_process, show, df):\n \"\"\"\n Format matches by show values\n \"\"\"\n\n import pandas as pd\n\n objmapping = {'d': get_dependents_of_id,\n 'g': get_governors_of_id,\n 'm': get_match,\n 'h': get_head}\n\n sers = []\n\n dmode = any(x.startswith('d') for x in show)\n if dmode:\n from collections import defaultdict\n dicts = defaultdict(dict)\n\n for val in show:\n adj, val = determine_adjacent(val)\n if adj:\n if adj[0] == '+':\n tomove = int(adj[1])\n elif adj[0] == '-':\n tomove = -int(adj[1])\n\n obj, attr = val[0], val[-1]\n func = objmapping.get(obj, dummy)\n out = defaultdict(dict) if dmode else []\n for ix in list(to_process.index):\n piece = False\n if adj:\n ix = (ix[0], ix[1] + tomove)\n if ix not in df.index:\n piece = 'none'\n if not piece:\n if obj == 'm':\n piece = df.loc[ix][attr.replace('x', 'p')]\n if attr == 'x':\n from corpkit.dictionaries.word_transforms import taglemma\n piece = taglemma.get(piece.lower(), piece.lower())\n piece = [piece]\n else:\n piece = func(ix, df=df, attr=attr)\n if not isinstance(piece, list):\n piece = [piece]\n if dmode:\n dicts[ix][val] = piece\n else:\n out.append(piece[0])\n\n if not dmode:\n ser = pd.Series(out, index=to_process.index)\n ser.name = val\n sers.append(ser)\n\n if not dmode:\n dx = pd.concat(sers, axis=1)\n if len(dx.columns) == 1:\n return dx.iloc[:,0]\n else:\n return dx.apply('/'.join, axis=1)\n else:\n index = []\n data = []\n for ix, dct in dicts.items():\n max_key, max_value = max(dct.items(), key=lambda x: len(x[1]))\n for val, pieces in dct.items():\n if len(pieces) == 1:\n dicts[ix][val] = pieces * len(max_value)\n\n for tup in list(zip(*[i for i in dct.values()])):\n index.append(ix)\n data.append('/'.join(tup))\n return pd.Series(data, index=pd.MultiIndex.from_tuples(index))\n\n\ndef make_series(ser, df=False, obj=False,\n att=False, adj=False):\n \"\"\"\n To apply to a DataFrame to add complex criteria, like 'gf'\n \"\"\"\n # distance mode\n if att == 'a':\n count = 0\n if obj == 'g':\n if ser[obj] == 0:\n return '-1'\n ser = df.loc[ser.name[0], ser['g']]\n while count < 20:\n if ser['mf'].lower() == 'root':\n return str(count)\n ser = df.loc[ser.name[0], ser['g']]\n count += 1\n return '20+'\n\n # h is head of this particular group\n if obj == 'h':\n cohead = ser['c']\n if cohead.endswith('*'):\n return ser['m' + att]\n elif cohead == '_':\n return 'none'\n else:\n sent = df.loc[ser.name[0]]\n just_cof = sent[sent['c'] == cohead + '*']\n if just_cof.empty:\n return ser['m' + att]\n else:\n return just_cof.iloc[0]['m' + att]\n \n # r is the representative mention head\n if obj == 'r':\n cohead = ser['c']\n if cohead == '_':\n return 'none'\n if not cohead.endswith('*'):\n cohead = cohead + '*'\n # iterrows is slow, but we only need the first instance\n just_cof = df[df['c'] == cohead]\n if just_cof.empty:\n return ser['m' + att]\n else:\n return just_cof.iloc[0]['m' + att]\n\n if obj == 'g':\n if ser[obj] == 0:\n return 'root'\n else:\n try:\n return df[att][ser.name[0], ser[obj]]\n # this keyerror can happen if governor is punctuation, for example\n except KeyError:\n return\n\n # if dependent, we need to return a df-like thing instead\n elif obj == 'd':\n #import pandas as pd\n idxs = [(ser.name[0], int(i)) for i in ser[obj].split(',')]\n dat = df[att].ix[idxs]\n return dat\n\n # todo: fix everything below here\n elif obj == 'r': # get the representative\n cohead = ser['c'].rstrip('*')\n refs = df[df['c'] == cohead + '*']\n return refs[att].ix[0]\n\n elif obj == 'h': # get head\n cohead = ser['c']\n if cohead.endswith('*'):\n return ser[att]\n else:\n sent = df[att].loc[ser.name[0]]\n return sent[sent['c'] == cohead + '*']\n\n # potential naming conflict with sent index ...\n elif obj == 's': # get whole phrase\"\n cohead = ser['c']\n sent = df[att].loc[ser.name[0]]\n return sent[sent['c'] == cohead.rstrip('*')].values\n \ndef joiner(ser):\n return ser.str.cat(sep='/') \n\ndef make_new_for_dep(dfmain, dfdep, name):\n \"\"\"\n If showind dependent, we have to make a whole new dataframe\n\n :param dfmain: dataframe with everything in it\n :param dfdep: dataframe with just dependent\n \"\"\"\n import pandas as pd\n import numpy as np\n new = []\n newd = []\n index = []\n for (i, ml), (_, dl) in zip(dfmain.iterrows(), dfdep.iterrows()):\n if all(pd.isnull(i) for i in dl.values):\n index.append(i)\n new.append(ml)\n newd.append('none')\n continue\n else:\n for bit in dl:\n if pd.isnull(bit):\n continue\n index.append(i)\n new.append(ml)\n newd.append(bit)\n \n #todo: account for no matches\n index = pd.MultiIndex.from_tuples(index, names=['s', 'i'])\n newdf = pd.DataFrame(new, index=index)\n newdf[name] = newd\n return newdf\n\ndef turn_pos_to_wc(ser, showval):\n if not showval:\n return ser\n import pandas as pd\n from corpkit.dictionaries.word_transforms import taglemma \n vals = [taglemma.get(piece.lower(), piece.lower())\n for piece in ser.values]\n news = pd.Series(vals, index=ser.index)\n news.name = ser.name[:-1] + 'x'\n return news\n\ndef concline_generator(matches, idxs, df, metadata,\n add_meta, category, fname, preserve_case=False):\n \"\"\"\n Get all conclines\n\n :param matches: a list of formatted matches\n :param idxs: their (sent, word) idx\n \"\"\"\n conc_res = []\n # potential speedup: turn idxs into dict\n from collections import defaultdict\n mdict = defaultdict(list)\n # if remaking idxs here, don't need to do it earlier\n idxs = list(matches.index)\n for mid, (s, i) in zip(matches, idxs):\n #for s, i in matches:\n mdict[s].append((i, mid))\n # shorten df to just relevant sents to save lookup time\n df = df.loc[list(mdict.keys())]\n # don't look up the same sentence multiple times\n for s, tup in sorted(mdict.items()):\n sent = df.loc[s]\n if not preserve_case:\n sent = sent.str.lower()\n meta = metadata[s]\n sname = meta.get('speaker', 'none')\n for i, mid in tup:\n if not preserve_case:\n mid = mid.lower()\n ix = '%d,%d' % (s, i)\n start = ' '.join(sent.loc[:i-1].values)\n end = ' '.join(sent.loc[i+1:].values)\n lin = [ix, category, fname, sname, start, mid, end]\n if add_meta:\n for k, v in sorted(meta.items()):\n if k in ['speaker', 'parse', 'sent_id']:\n continue\n if isinstance(add_meta, list):\n if k in add_meta:\n lin.append(v)\n elif add_meta is True:\n lin.append(v)\n conc_res.append(lin)\n return conc_res\n\ndef p_series_to_x_series(val):\n return taglemma.get(val.lower(), val.lower())\n\ndef fast_simple_conc(dfss, idxs, show,\n metadata=False,\n add_meta=False, \n fname=False,\n category=False,\n only_format_match=True,\n conc=False,\n preserve_case=False,\n gramsize=1,\n window=None):\n \"\"\"\n Fast, simple concordancer, heavily conditional\n to save time.\n \"\"\"\n if dfss.empty:\n return [], []\n \n import pandas as pd\n\n # best case, the user doesn't want any gov-dep stuff\n simple = all(i.startswith('m') and not i.endswith('a') for i in show)\n # worst case, the user wants something from dep\n dmode = any(x.startswith('d') for x in show)\n # make a quick copy if need be because we modify the df\n df = dfss.copy() if not simple else dfss\n # add text to df columns so that it resembles 'show' values\n lst = ['s', 'i', 'w', 'l', 'e', 'p', 'f']\n\n # for ner, change O to 'none'\n if 'e' in df.columns:\n df['e'] = df['e'].str.replace('^O$', 'none')\n\n df.columns = ['m' + i if len(i) == 1 and i in lst \\\n else i for i in list(df.columns)]\n\n # this is the data needed for concordancing\n df_for_lr = df['mw'] if only_format_match else df\n\n just_matches = df.loc[idxs]\n \n # if the showing can't come straight out of the df, \n # we can add columns with the necessary information\n if not simple:\n formatted = []\n import numpy as np\n\n for ind, i in enumerate(show):\n # nothing to do if it's an m feature\n if i.startswith('m') and not i.endswith('a'):\n continue\n # defaults for adjacent work\n\n adj, tomove, adjname = False, False, ''\n adj, i = determine_adjacent(i)\n adjname = ''.join(adj) if hasattr(adj, '__iter__') else ''\n \n # get number of places to shift left or right\n if adj:\n if adj[0] == '+':\n tomove = -int(adj[1])\n elif adj[0] == '-':\n tomove = int(adj[1])\n\n # cut df down to just needed bits for the sake of speed\n # i.e. if we want gov func, get only gov and func cols\n ob, att = i[0], i[-1]\n xmode = att == 'x'\n if xmode:\n att = 'p'\n show[ind] = show[ind][:-1] + 'p'\n # for corefs, we also need the coref data\n if ob in ['h', 'r']:\n dfx = df[['c', 'm' + att]]\n else:\n lst = ['s', 'i', 'w', 'l', 'f', 'p']\n if att in lst and ob != 'm':\n att = 'm' + att\n if ob == 'm' and att != 'a':\n dfx = df[['m' + att]]\n elif att == 'a':\n dfx = df[['mf', 'g']]\n else:\n dfx = df[[ob, att]]\n # decide if we need to format everything\n if (not conc or only_format_match) and not adj:\n to_proc = just_matches\n else:\n to_proc = df\n # now we get or generate the new column\n if ob == 'm' and att != 'a':\n ser = to_proc['m' + att]\n else:\n ser = to_proc.apply(make_series, df=dfx, obj=ob, att=att, axis=1)\n if xmode:\n ser = ser.apply(p_series_to_x_series)\n\n # adjmode simply shifts series and index\n if adj:\n #todo: this shifts next sent into previous sent!\n ser = ser.shift(tomove)\n ser = ser.fillna('none')\n\n # dependent mode produces multiple matches\n # so, we have to make a new dataframe with duplicate indexes\n # todo: what about when there are two dep options?\n ser.name = adjname + i\n if ob != 'd':\n df[ser.name] = ser\n else:\n df = make_new_for_dep(df, ser, i)\n\n df = df.fillna('none')\n\n # x is wordclass. so, we just get pos and translate it\n nshow = [(i.replace('x', 'p'), i.endswith('x')) for i in show]\n\n # generate a series of matches with slash sep if multiple show vals\n if len(nshow) > 1:\n\n if conc and not only_format_match:\n first = turn_pos_to_wc(df[nshow[0][0]], nshow[0][1])\n llist = [turn_pos_to_wc(df[sho], xmode) for sho, xmode in nshow[1:]]\n df = first.str.cat(others=llist, sep='/')\n matches = df[idxs]\n else:\n justm = df.loc[idxs]\n first = turn_pos_to_wc(justm[nshow[0][0]], nshow[0][1])\n llist = [turn_pos_to_wc(justm[sho], xmode) for sho, xmode in nshow[1:]]\n matches = first.str.cat(others=llist, sep='/')\n if conc:\n df = df_for_lr\n else:\n if conc and not only_format_match:\n df = turn_pos_to_wc(df[nshow[0][0]], nshow[0][1])\n matches = df[idxs]\n else:\n matches = turn_pos_to_wc(df[nshow[0][0]][idxs], nshow[0][1])\n if conc:\n df = df_for_lr\n \n # get rid of (e.g.) nan caused by no_punct=True\n matches = matches.dropna(axis=0, how='all')\n\n if not preserve_case:\n matches = matches.str.lower()\n\n if not conc:\n # todo: is matches.values faster?\n return list(matches), []\n else:\n conc_res = concline_generator(matches, idxs, df,\n metadata, add_meta,\n category, fname,\n preserve_case=preserve_case)\n\n return list(matches), conc_res\n\ndef make_collocate_show(show, current):\n \"\"\"\n Turn show into a collocate showing thing\n \"\"\"\n out = []\n for i in show:\n out.append(i)\n for i in show:\n newn = '%s%s' % (str(current), i)\n if not newn.startswith('-'):\n newn = '+' + newn\n out.append(newn)\n return out\n\ndef show_this(df, matches, show, metadata, conc=False,\n coref=False, category=False, show_conc_metadata=False, **kwargs):\n\n only_format_match = kwargs.pop('only_format_match', True)\n ngram_mode = kwargs.get('ngram_mode', True)\n preserve_case = kwargs.get('preserve_case', False)\n gramsize = kwargs.get('gramsize', 1)\n window = kwargs.get('window', None)\n\n matches = sorted(list(matches))\n\n # add index as column if need be\n if any(i.endswith('s') for i in show):\n df['ms'] = [str(i) for i in df.index.labels[0]]\n if any(i.endswith('i') for i in show):\n df['mi'] = [str(i) for i in df.index.labels[1]]\n \n # attempt to leave really fast\n if kwargs.get('countmode'):\n return len(matches), {}\n if len(show) == 1 and not conc and gramsize == 1 and not window:\n if show[0] in ['ms', 'mi', 'mw', 'ml', 'mp', 'mf']:\n get_fast = df.loc[matches][show[0][-1]]\n if not preserve_case:\n get_fast = get_fast.str.lower()\n return list(get_fast), {}\n\n # todo: make work for ngram, collocate and coref\n if all(i[0] in ['m', 'g', '+', '-', 'd', 'h', 'r'] for i in show):\n if gramsize == 1 and not window:\n return fast_simple_conc(df,\n matches,\n show,\n metadata,\n show_conc_metadata,\n kwargs.get('filename', ''),\n category,\n only_format_match,\n conc=conc,\n preserve_case=preserve_case,\n gramsize=gramsize,\n window=window)\n else:\n resbit = []\n concbit = []\n iterab = range(1, gramsize + 1) if gramsize > 1 else range(-window, window+1)\n for i in iterab:\n if i == 0:\n continue\n if window:\n nnshow = make_collocate_show(show, i)\n else:\n nnshow = show\n r, c = fast_simple_conc(df,\n matches,\n nnshow,\n metadata,\n show_conc_metadata,\n kwargs.get('filename', ''),\n category,\n only_format_match,\n conc=conc,\n preserve_case=preserve_case,\n gramsize=gramsize,\n window=window)\n\n resbit.append(r)\n concbit.append(c)\n if not window:\n df = df.shift(1)\n df = df.fillna('none')\n resbit = list(zip(*resbit))\n concbit = list(zip(*concbit))\n out = []\n conc_out = []\n # this is slow but keeps the order\n # remove it esp for resbit where it doesn't matter\n for r in resbit:\n for b in r:\n out.append(b)\n for c in concbit:\n for b in c:\n conc_out.append(b)\n return out, conc_out\n\ndef remove_by_mode(matches, mode, criteria):\n \"\"\"\n If mode is all, remove any entry that occurs < len(criteria)\n \"\"\"\n if mode == 'any':\n return set(matches)\n if mode == 'all':\n from collections import Counter\n counted = Counter(matches)\n return set(k for k, v in counted.items() if v >= len(criteria))\n\ndef determine_adjacent(original):\n \"\"\"\n Figure out if we're doing an adjacent location, get the co-ordinates\n and return them and the stripped original\n \"\"\"\n if original[0] in ['+', '-']:\n adj = (original[0], original[1:-2])\n original = original[-2:]\n else:\n adj = False\n return adj, original\n\ndef cut_df_by_metadata(df, metadata, criteria, coref=False,\n feature='speaker', method='just'):\n \"\"\"\n Keep or remove parts of the DataFrame based on metadata criteria\n \"\"\"\n if not criteria:\n df._metadata = metadata\n return df\n # maybe could be sped up, but let's not for now:\n if coref:\n df._metadata = metadata\n return df\n import re\n good_sents = []\n new_metadata = {}\n from corpkit.constants import STRINGTYPE\n # could make the below more elegant ...\n for sentid, data in sorted(metadata.items()):\n meta_value = data.get(feature, 'none')\n lst_met_vl = meta_value.split(';')\n if isinstance(criteria, (list, set, tuple)):\n criteria = [i.lower() for i in criteria]\n if method == 'just':\n if any(i.lower() in criteria for i in lst_met_vl):\n good_sents.append(sentid)\n new_metadata[sentid] = data\n elif method == 'skip':\n if not any(i in criteria for i in lst_met_vl):\n good_sents.append(sentid)\n new_metadata[sentid] = data\n elif isinstance(criteria, (re._pattern_type, STRINGTYPE)):\n if method == 'just':\n if any(re.search(criteria, i, re.IGNORECASE) for i in lst_met_vl):\n good_sents.append(sentid)\n new_metadata[sentid] = data\n elif method == 'skip':\n if not any(re.search(criteria, i, re.IGNORECASE) for i in lst_met_vl):\n good_sents.append(sentid)\n new_metadata[sentid] = data\n\n df = df.loc[good_sents]\n df = df.fillna('')\n df._metadata = new_metadata\n return df\n\ndef cut_df_by_meta(df, just_metadata, skip_metadata):\n \"\"\"\n Reshape a DataFrame based on filters\n \"\"\"\n if df is not None:\n if just_metadata:\n for k, v in just_metadata.items():\n df = cut_df_by_metadata(df, df._metadata, v, feature=k)\n if skip_metadata:\n for k, v in skip_metadata.items():\n df = cut_df_by_metadata(df, df._metadata, v, feature=k, method='skip')\n return df\n\n\ndef tgrep_searcher(f=False,\n metadata=False,\n from_df=False,\n search=False,\n searchmode=False,\n exclude=False,\n excludemode=False,\n translated_option=False,\n subcorpora=False,\n conc=False,\n root=False,\n preserve_case=False,\n countmode=False,\n show=False,\n lem_instance=False,\n lemtag=False,\n category=False,\n fname=False,\n show_conc_metadata=False,\n only_format_match=True,\n **kwargs):\n\n \"\"\"\n Use tgrep for constituency grammar search\n \"\"\"\n\n from corpkit.process import show_tree_as_per_option, tgrep\n matches = []\n conc_out = []\n # in case search was a dict\n srch = search.get('t') if isinstance(search, dict) else search\n metcat = category if category else ''\n for i, sent in metadata.items():\n results = tgrep(sent['parse'], srch)\n sname = sent.get('speaker')\n metcat = category\n for res in results:\n tok_id, start, middle, end = show_tree_as_per_option(show, res, sent,\n df=from_df, sent_id=i, conc=conc,\n only_format_match=only_format_match)\n #middle, idx = show_tree_as_per_option(show, res, 'conll', sent, df=df, sent_id=i)\n matches.append(middle)\n if conc:\n form_ix = '%d,%d' % (i, tok_id)\n lin = [form_ix, metcat, fname, sname, start, middle, end]\n if show_conc_metadata:\n for k, v in sorted(sent.items()):\n if k in ['speaker', 'parse', 'sent_id']:\n continue\n if isinstance(show_conc_metadata, list):\n if k in show_conc_metadata:\n lin.append(v)\n elif show_conc_metadata is True:\n lin.append(v)\n conc_out.append(lin)\n\n return matches, conc_out\n\ndef slow_tregex(metadata=False,\n search=False,\n searchmode=False,\n exclude=False,\n excludemode=False,\n translated_option=False,\n subcorpora=False,\n conc=False,\n root=False,\n preserve_case=False,\n countmode=False,\n show=False,\n lem_instance=False,\n lemtag=False,\n from_df=False,\n fname=False,\n category=False,\n only_format_match=False,\n **kwargs):\n \"\"\"\n Do the metadata specific version of tregex queries\n \"\"\"\n\n from corpkit.process import tregex_engine, format_tregex, make_conc_lines_from_whole_mid\n \n if isinstance(search, dict):\n search = list(search.values())[0]\n \n speak_tree = [(x.get(subcorpora, 'none'), x['parse']) for x in metadata.values()]\n \n if speak_tree:\n speak, tree = list(zip(*speak_tree))\n else:\n speak, tree = [], []\n \n if all(not x for x in speak):\n speak = False\n\n to_open = '\\n'.join(tree)\n\n concs = []\n\n if not to_open.strip('\\n'):\n if subcorpora:\n return {}, {}\n\n ops = ['-%s' % i for i in translated_option] + ['-o', '-n']\n res = tregex_engine(query=search, \n options=ops, \n corpus=to_open,\n root=root,\n preserve_case=preserve_case,\n speaker_data=False)\n\n res = format_tregex(res, show, exclude=exclude, excludemode=excludemode,\n translated_option=translated_option,\n lem_instance=lem_instance, countmode=countmode, speaker_data=False,\n lemtag=lemtag)\n\n if not res:\n if subcorpora:\n return [], []\n\n if conc:\n ops += ['-w']\n whole_res = tregex_engine(query=search, \n options=ops, \n corpus=to_open,\n root=root,\n preserve_case=preserve_case,\n speaker_data=speak)\n\n # format match too depending on option\n if not only_format_match:\n whole_res = format_tregex(whole_res, show, exclude=exclude, excludemode=excludemode,\n translated_option=translated_option,\n lem_instance=lem_instance, countmode=countmode,\n speaker_data=speak, whole=True,\n lemtag=lemtag)\n\n # make conc lines from conc results\n concs = make_conc_lines_from_whole_mid(whole_res, res, filename=fname, show=show)\n else:\n concs = [False for i in res]\n\n if len(res) > 0 and isinstance(res[0], tuple):\n res = [i[-1] for i in res]\n\n if countmode:\n if isinstance(res, int):\n return res, False\n else:\n return len(res), False\n else:\n return res, concs\n\ndef get_stats(from_df=False, metadata=False, feature=False, root=False, **kwargs):\n \"\"\"\n Get general statistics for a DataFrame\n \"\"\"\n import re\n from corpkit.dictionaries.process_types import processes\n from collections import Counter, defaultdict\n from corpkit.process import tregex_engine\n\n def ispunct(s):\n import string\n return all(c in string.punctuation for c in s)\n\n tree = [x['parse'] for x in metadata.values()]\n \n tregex_qs = {'Imperative': r'ROOT < (/(S|SBAR)/ < (VP !< VBD !< VBG !$ NP !$ SBAR < NP !$-- S '\\\n '!$-- VP !$ VP)) !<< (/\\?/ !< __) !<<- /-R.B-/ !<<, /(?i)^(-l.b-|hi|hey|hello|oh|wow|thank|thankyou|thanks|welcome)$/',\n 'Open interrogative': r'ROOT < SBARQ <<- (/\\?/ !< __)', \n 'Closed interrogative': r'ROOT ( < (SQ < (NP $+ VP)) << (/\\?/ !< __) | < (/(S|SBAR)/ < (VP $+ NP)) <<- (/\\?/ !< __))',\n 'Unmodalised declarative': r'ROOT < (S < (/(NP|SBAR|VP)/ $+ (VP !< MD)))',\n 'Modalised declarative': r'ROOT < (S < (/(NP|SBAR|VP)/ $+ (VP < MD)))',\n 'Clauses': r'/^S/ < __',\n 'Interrogative': r'ROOT << (/\\?/ !< __)',\n 'Processes': r'/VB.?/ >># (VP !< VP >+(VP) /^(S|ROOT)/)'}\n\n result = Counter()\n\n for name in tregex_qs.keys():\n result[name] = 0\n\n result['Sentences'] = len(set(from_df.index.labels[0]))\n result['Passives'] = len(from_df[from_df['f'] == 'nsubjpass'])\n result['Tokens'] = len(from_df)\n # the below has returned a float before. i assume actually a nan?\n result['Words'] = len([w for w in list(from_df['w']) if w and not ispunct(str(w))])\n result['Characters'] = sum([len(str(w)) for w in list(from_df['w']) if w])\n result['Open class'] = sum([1 for x in list(from_df['p']) if x and x[0] in ['N', 'J', 'V', 'R']])\n result['Punctuation'] = result['Tokens'] - result['Words']\n result['Closed class'] = result['Words'] - result['Open class']\n\n to_open = '\\n'.join(tree)\n\n if not to_open.strip('\\n'):\n return {}, {}\n\n for name, q in sorted(tregex_qs.items()):\n options = ['-o', '-t'] if name == 'Processes' else ['-o']\n # c option removed, could cause memory problems\n #ops = ['-%s' % i for i in translated_option] + ['-o', '-n']\n res = tregex_engine(query=q, \n options=options,\n corpus=to_open, \n root=root)\n\n #res = format_tregex(res)\n if not res:\n continue\n\n concs = [False for i in res]\n for (_, met, r), line in zip(res, concs):\n result[name] = len(res)\n if name != 'Processes':\n continue\n non_mat = 0\n for ptype in ['mental', 'relational', 'verbal']:\n reg = getattr(processes, ptype).words.as_regex(boundaries='l')\n count = len([i for i in res if re.search(reg, i[-1])])\n nname = ptype.title() + ' processes'\n result[nname] = count\n\n if root:\n root.update()\n return result, {}\n\ndef get_corefs(df, matches):\n \"\"\"\n Add corefs to a set of matches\n \"\"\"\n out = set()\n df = df['c']\n for s, i in matches:\n # keep original\n out.add((s,i))\n coline = df[(s, i)]\n if coline.endswith('*'):\n same_co = df[df == coline]\n for ix in same_co.index:\n out.add(ix)\n return out\n\ndef pipeline(f=False,\n search=False,\n show=False,\n exclude=False,\n searchmode='all',\n excludemode='any',\n conc=False,\n coref=False,\n from_df=False,\n just_metadata=False,\n skip_metadata=False,\n category=False,\n show_conc_metadata=False,\n statsmode=False,\n search_trees=False,\n lem_instance=False,\n **kwargs):\n \"\"\"\n A basic pipeline for conll querying---some options still to do\n \"\"\"\n\n if isinstance(show, str):\n show = [show]\n\n all_matches = []\n all_exclude = []\n\n if from_df is False or from_df is None:\n df = parse_conll(f, usecols=kwargs.get('usecols'))\n # can fail here if df is none\n if df is None:\n print('Problem reading data from %s.' % f)\n return [], []\n metadata = df._metadata\n else:\n df = from_df\n metadata = kwargs.pop('metadata')\n feature = kwargs.pop('by_metadata', False)\n df = cut_df_by_meta(df, just_metadata, skip_metadata)\n\n searcher = pipeline\n if statsmode:\n searcher = get_stats\n if search_trees == 'tregex':\n searcher = slow_tregex\n elif search_trees == 'tgrep':\n searcher = tgrep_searcher\n\n if feature:\n if df is None:\n print('Problem reading data from %s.' % f)\n return {}, {}\n\n # determine searcher\n resultdict = {}\n concresultdict = {}\n # get all the possible values in the df for the feature of interest\n all_cats = set([i.get(feature, 'none') for i in list(df._metadata.values())])\n for category in all_cats:\n new_df = cut_df_by_metadata(df, df._metadata, category, feature=feature, method='just')\n r, c = searcher(f=False,\n fname=f,\n search=search,\n exclude=exclude,\n show=show,\n searchmode=searchmode,\n excludemode=excludemode,\n conc=conc,\n coref=coref,\n from_df=new_df,\n by_metadata=False,\n category=category,\n show_conc_metadata=show_conc_metadata,\n lem_instance=lem_instance,\n root=kwargs.pop('root', False),\n subcorpora=feature,\n metadata=new_df._metadata,\n **kwargs)\n \n resultdict[category] = r\n concresultdict[category] = c\n return resultdict, concresultdict\n\n if df is None:\n print('Problem reading data from %s.' % f)\n return [], []\n\n kwargs['ngram_mode'] = any(x.startswith('n') for x in show)\n\n #df = cut_df_by_metadata(df, df._metadata, kwargs.get('just_speakers'), coref=coref)\n metadata = df._metadata\n\n try:\n df['w'].str\n except AttributeError:\n raise AttributeError(\"CONLL data doesn't match expectations. \" \\\n \"Try the corpus.conll_conform() method to \" \\\n \"convert the corpus to the latest format.\")\n\n if kwargs.get('no_punct', True):\n df = df[df['w'].fillna('').str.contains(kwargs.get('is_a_word', r'[A-Za-z0-9]'))]\n \n # remove brackets --- could it be done in one regex?\n df = df[~df['w'].str.contains(r'^-.*B-$')]\n\n if kwargs.get('no_closed'):\n from corpkit.dictionaries import wordlists\n crit = wordlists.closedclass.as_regex(boundaries='l', case_sensitive=False)\n df = df[~df['w'].str.contains(crit)]\n\n if statsmode:\n return get_stats(df, metadata, False, root=kwargs.pop('root', False), **kwargs)\n elif search_trees:\n return searcher(from_df=df,\n search=search,\n searchmode=searchmode,\n exclude=exclude,\n excludemode=excludemode,\n conc=conc,\n by_metadata=False,\n metadata=metadata,\n root=kwargs.pop('root', False),\n fname=f,\n show=show,\n **kwargs)\n\n # do no searching if 'any' is requested\n if len(search) == 1 and list(search.keys())[0] == 'w' \\\n and hasattr(list(search.values())[0], 'pattern') \\\n and list(search.values())[0].pattern == r'.*':\n all_matches = list(df.index)\n else:\n for k, v in search.items():\n adj, k = determine_adjacent(k)\n res = search_this(df, k[0], k[-1], v, adjacent=adj, coref=coref)\n for r in res:\n all_matches.append(r)\n all_matches = remove_by_mode(all_matches, searchmode, search)\n if exclude:\n for k, v in exclude.items():\n adj, k = determine_adjacent(k)\n res = search_this(df, k[0], k[-1], v, adjacent=adj, coref=coref)\n for r in res:\n all_exclude.append(r)\n all_exclude = remove_by_mode(all_exclude, excludemode, exclude)\n all_matches = all_matches.difference(all_exclude)\n\n if coref:\n all_matches = get_corefs(df, all_matches)\n\n out, conc_out = show_this(df, all_matches, show, metadata, conc, \n coref=coref, category=category, \n show_conc_metadata=show_conc_metadata,\n **kwargs)\n\n return out, conc_out\n\ndef load_raw_data(f):\n \"\"\"\n Loads the stripped and raw versions of a parsed file\n \"\"\"\n from corpkit.process import saferead\n\n # open the unparsed version of the file, read into memory\n stripped_txtfile = f.replace('.conll', '').replace('-parsed', '-stripped')\n stripped_txtdata, enc = saferead(stripped_txtfile)\n\n # open the unparsed version with speaker ids\n id_txtfile = f.replace('.conll', '').replace('-parsed', '')\n id_txtdata, enc = saferead(id_txtfile)\n\n return stripped_txtdata, id_txtdata\n\ndef get_speaker_from_offsets(stripped, plain, sent_offsets,\n metadata_mode=False,\n speaker_segmentation=False):\n \"\"\"\n Take offsets and get a speaker ID or metadata from them\n \"\"\"\n if not stripped and not plain:\n return {}\n start, end = sent_offsets\n sent = stripped[start:end]\n # find out line number\n # sever at start of match\n cut_old_text = stripped[:start]\n line_index = cut_old_text.count('\\n')\n # lookup this text\n with_id = plain.splitlines()[line_index]\n \n # parse xml tags in original file ...\n meta_dict = {'speaker': 'none'}\n\n if metadata_mode:\n\n metad = with_id.strip().rstrip('>').rsplit(' 1:\n speakerid = split_line[0]\n else:\n speakerid = 'UNIDENTIFIED'\n meta_dict['speaker'] = speakerid\n\n return meta_dict\n\ndef convert_json_to_conll(path,\n speaker_segmentation=False,\n coref=False,\n metadata=False,\n just_files=False):\n \"\"\"\n take json corenlp output and convert to conll, with\n dependents, speaker ids and so on added.\n\n Path is for the parsed corpus, or a list of files within a parsed corpus\n Might need to fix if outname used?\n \"\"\"\n\n import json\n import re\n from corpkit.build import get_filepaths\n from corpkit.constants import CORENLP_VERSION, OPENER\n \n # todo: stabilise this\n #if CORENLP_VERSION == '3.7.0':\n # coldeps = 'enhancedPlusPlusDependencies'\n #else:\n # coldeps = 'collapsed-ccprocessed-dependencies'\n\n print('Converting files to CONLL-U...')\n\n if just_files:\n files = just_files\n else:\n if isinstance(path, list):\n files = path\n else:\n files = get_filepaths(path, ext='conll')\n \n for f in files:\n\n if speaker_segmentation or metadata:\n stripped, raw = load_raw_data(f)\n else:\n stripped, raw = None, None\n\n main_out = ''\n # if the file has already been converted, don't worry about it\n # untested?\n with OPENER(f, 'r') as fo:\n #try:\n\n try:\n data = json.load(fo)\n except ValueError:\n continue\n # todo: differentiate between json errors\n # rsc corpus had one json file with an error\n # outputted by corenlp, and the conversion\n # failed silently here\n #except ValueError:\n # continue\n\n for idx, sent in enumerate(data['sentences'], start=1):\n tree = sent['parse'].replace('\\n', '')\n tree = re.sub(r'\\s+', ' ', tree)\n\n # offsets for speaker_id\n sent_offsets = (sent['tokens'][0]['characterOffsetBegin'], \\\n sent['tokens'][-1]['characterOffsetEnd'])\n \n metad = get_speaker_from_offsets(stripped,\n raw,\n sent_offsets,\n metadata_mode=True,\n speaker_segmentation=speaker_segmentation)\n \n # currently there is no standard for sent_id, so i'm leaving it out, but\n # if https://github.com/UniversalDependencies/docs/issues/273 is updated\n # then i could switch it back\n #output = '# sent_id %d\\n# parse=%s\\n' % (idx, tree)\n output = '# parse=%s\\n' % tree\n for k, v in sorted(metad.items()):\n output += '# %s=%s\\n' % (k, v)\n for token in sent['tokens']:\n index = str(token['index'])\n # this got a stopiteration on rsc data\n governor, func = next(((i['governor'], i['dep']) \\\n for i in sent.get('enhancedPlusPlusDependencies',\n sent.get('collapsed-ccprocessed-dependencies')) \\\n if i['dependent'] == int(index)), ('_', '_'))\n if governor is '_':\n depends = False\n else:\n depends = [str(i['dependent']) for i in sent.get('enhancedPlusPlusDependencies',\n sent.get('collapsed-ccprocessed-dependencies')) if i['governor'] == int(index)]\n if not depends:\n depends = '0'\n #offsets = '%d,%d' % (token['characterOffsetBegin'], token['characterOffsetEnd'])\n line = [index,\n token['word'],\n token['lemma'],\n token['pos'],\n token.get('ner', '_'),\n '_', # this is morphology, which is unannotated always, but here to conform to conll u\n governor,\n func,\n ','.join(depends)]\n # no ints\n line = [str(l) if isinstance(l, int) else l for l in line]\n\n from corpkit.constants import PYTHON_VERSION\n if PYTHON_VERSION == 2:\n try:\n [unicode(l, errors='ignore') for l in line]\n except TypeError:\n pass\n \n output += '\\t'.join(line) + '\\n'\n main_out += output + '\\n'\n\n # post process corefs\n if coref:\n import re\n dct = {}\n idxreg = re.compile('^([0-9]+)\\t([0-9]+)')\n splitmain = main_out.split('\\n')\n # add tab _ to each line, make dict of sent-token: line index\n for i, line in enumerate(splitmain):\n if line and not line.startswith('#'):\n splitmain[i] += '\\t_'\n match = re.search(idxreg, line)\n if match:\n l, t = match.group(1), match.group(2)\n dct[(int(l), int(t))] = i\n \n # for each coref chain, if there are corefs\n for numstring, list_of_dicts in sorted(data.get('corefs', {}).items()):\n # for each mention\n for d in list_of_dicts:\n snum = d['sentNum']\n # get head?\n # this has been fixed in dev corenlp: 'headIndex' --- could simply use that\n # ref : https://github.com/stanfordnlp/CoreNLP/issues/231\n for i in range(d['startIndex'], d['endIndex']):\n \n try:\n ix = dct[(snum, i)]\n fixed_line = splitmain[ix].rstrip('\\t_') + '\\t%s' % numstring\n gv = fixed_line.split('\\t')[6]\n try:\n gov_s = int(gv)\n except ValueError:\n continue\n if gov_s < d['startIndex'] or gov_s > d['endIndex']:\n fixed_line += '*'\n splitmain[ix] = fixed_line\n dct.pop((snum, i))\n except KeyError:\n pass\n\n main_out = '\\n'.join(splitmain)\n\n from corpkit.constants import OPENER \n with OPENER(f, 'w', encoding='utf-8') as fo:\n main_out = main_out.replace(u\"\\u2018\", \"'\").replace(u\"\\u2019\", \"'\")\n fo.write(main_out)\n\n"
},
{
"path": "corpkit/constants.py",
"content": "import sys\nimport codecs\n\n# python 2/3 coompatibility\nPYTHON_VERSION = sys.version_info.major\nSTRINGTYPE = str if PYTHON_VERSION == 3 else basestring\nINPUTFUNC = input if PYTHON_VERSION == 3 else raw_input\nOPENER = open if PYTHON_VERSION == 3 else codecs.open\n\n# quicker access to search, exclude, show types\nfrom itertools import product\n_starts = ['M', 'N', 'B', 'G', 'D', 'H', 'R']\n_ends = ['W', 'L', 'I', 'S', 'P', 'X', 'R', 'F', 'E']\n_others = ['A', 'ANY', 'ANYWORD', 'C', 'SELF', 'V', 'K', 'T']\n_prod = list(product(_starts, _ends))\n_prod = [''.join(i) for i in _prod]\n_letters = sorted(_prod + _starts + _ends + _others)\n\n_adjacent_start = ['A{}'.format(i) for i in range(1, 9)] + \\\n ['Z{}'.format(i) for i in range(1, 9)]\n\n_adjacent = [''.join(i) for i in list(product(_adjacent_start, _prod))]\n\nLETTERS = sorted(_letters + _adjacent)\n\n# translating search values intro words\ntransshow = {'f': 'Function',\n 'l': 'Lemma',\n 'a': 'Distance from root',\n 'w': 'Word',\n 't': 'Trees',\n 'i': 'Index',\n 'n': 'N-grams',\n 'p': 'POS',\n 'e': 'NER',\n 'c': 'Count',\n 'x': 'Word class',\n 's': 'Sentence index'}\n\ntransobjs = {'g': 'Governor',\n 'd': 'Dependent',\n 'm': 'Match',\n 'h': 'Head'}\n\n# below are the column names for the conll-u formatted data\n# corpkit's format is slightly different, but largely compatible.\n\n# Key differences:\n#\n# 1. 'e' is used for NER, rather than lang specific POS\n# 2. 'd' gives a comma-sep list of dependents, rather than head-deprel pairs\n# this is done for processing speed.\n# 3. 'c' is used for corefs, not 'misc comment'. it has an artibrary number \n# representing a dependency chain. head of a mention is marked with an asterisk.\n\n# 'm' does not have anything in it in corpkit, but denotes morphological features\n\n# default: index, word, lem, pos, ner, morph, gov, func, deps, coref\nCONLL_COLUMNS = ['i', 'w', 'l', 'p', 'e', 'v', 'g', 'f', 'd', 'c']\n\n# what the longest possible speaker ID is. this prevents huge lines with colons\n# from getting matched unintentionally\nMAX_SPEAKERNAME_SIZE = 40\n\n# parsing sometimes fails with a java error. if corpus.parse(restart=True), this will try\n# parsing n times before giving up\nREPEAT_PARSE_ATTEMPTS = 3\n\n# location of the current corenlp and its version\n# old, stable\n#CORENLP_URL = 'http://nlp.stanford.edu/software/stanford-corenlp-full-2015-12-09.zip'\n#CORENLP_VERSION = '3.6.0'\n\n# newest, beta\nCORENLP_VERSION = '3.7.0'\nCORENLP_URL = 'http://nlp.stanford.edu/software/stanford-corenlp-full-2016-10-31.zip'\n\n# it can be very slow to load a bunch of unused metadata categories\nMAX_METADATA_FIELDS = 99\nMAX_METADATA_VALUES = 99"
},
{
"path": "corpkit/corpkit",
"content": "#!/usr/bin/env python\n\n\"\"\"\nA script to start the corpkit interpeter with options\n\"\"\"\n\nimport sys\nimport os\n\n# determine if we're running a script\nif len(sys.argv) > 1 and os.path.isfile(sys.argv[-1]):\n fromscript = sys.argv[-1]\nelse:\n fromscript = False\n\ndef install(name, loc):\n \"\"\"\n If we don't have a module, download it\n \"\"\"\n import pip\n import importlib\n try:\n importlib.import_module(name)\n except ImportError:\n pip.main(['install', loc])\n\ntabview = ('tabview', 'git+https://github.com/interrogator/tabview@93644dd1f410de4e47466ea8083bb628b9ccc471#egg=tabview')\ncolorama = ('colorama', 'colorama')\n\n# run a command a la python -c\ncommand = sys.argv[sys.argv.index('-c') + 1] if '-c' in sys.argv else False\n\ndebug = any(i in sys.argv for i in ['--debug', '-d', 'debug'])\nquiet = any(i in sys.argv for i in ['--q', '--quiet'])\nload = any(i in sys.argv for i in ['--load', '-l'])\nprofile = any(i in sys.argv for i in ['--profile', '-p'])\nversion = any(i in sys.argv for i in ['--version', '-v'])\n\nif not any('noinstall' in arg.lower() for arg in sys.argv):\n install(*tabview)\n install(*colorama)\n\nif version:\n import corpkit\n print(corpkit.__version__)\nelif any(i in sys.argv for i in ['--help', '-h']):\n from corpkit.env import help_text\n import pydoc\n pydoc.pipepager(help_text, cmd='less -X -R -S') \nelse:\n from corpkit.env import interpreter\n interpreter(debug=debug, fromscript=fromscript,\n quiet=quiet, python_c_mode=command,\n profile=profile, loadcurrent=load)\n"
},
{
"path": "corpkit/corpkit.1",
"content": ".TH corpkit 1\n.SH NAME\ncorpkit \\- corpus linguistics interface\n.SH SYNOPSIS\n.B corpkit\n[\\fB\\-c\\fR \\fICOMMAND\\fR]\n[\\fB\\-d\\fR]\n[\\fB\\-h\\fR]\n[\\fB\\-l\\fR]\n.IR [file]\n.SH DESCRIPTION\n.B corpkit\nbuilds and searches parsed and/or structured linguistic corpora. It also edits and visualises results, and manages projects.\n.SH OPTIONS\n.TP\n.BR \" \\-\\-c COMMAND\"\\fR\nA quoted command or series of commands to pass to the corpkit interpreter. Use a semicolon to delimit each command. Disables interactivity and exits on completion.\n.TP\n.IR \"file\"\\fR\nPass in a script for the interpeter to run.\n.TP\n.BR \"\\-d, \" \\-\\-debug\\fR\nDebug mode: print info about how command was parsed.\n.TP\n.BR \"\\-l, \" \\-\\-load\\fR\nLoad all saved results into store on startup.\n.TP\n.BR \"\\-h, \" \\-\\-help\\fR\nShow help.\n"
},
{
"path": "corpkit/corpus.py",
"content": "\"\"\"\ncorpkit: Corpus and Corpus-like objects\n\"\"\"\n\nfrom __future__ import print_function\n\nfrom lazyprop import lazyprop\nfrom corpkit.process import classname\nfrom corpkit.constants import STRINGTYPE, PYTHON_VERSION\n\nclass Corpus(object):\n \"\"\"\n A class representing a linguistic text corpus, which contains files,\n optionally within subcorpus folders.\n\n Methods for concordancing, interrogating, getting general stats, getting\n behaviour of particular word, etc.\n\n Unparsed, tokenised and parsed corpora use the same class, though some\n methods are available only to one or the other. Only unparsed corpora \n can be parsed, and only parsed/tokenised corpora can be interrogated.\n \"\"\"\n\n def __init__(self, path, **kwargs):\n import re\n import operator\n import glob\n import os\n from os.path import join, isfile, isdir, abspath, dirname, basename\n from corpkit.process import determine_datatype\n\n # levels are 'c' for corpus, 's' for subcorpus and 'f' for file. Which\n # one is determined automatically below, and processed accordingly. We\n # assume it is a full corpus to begin with.\n\n def get_symbolics(self):\n return {'skip': self.skip,\n 'just': self.just,\n 'symbolic': self.symbolic}\n\n self.data = None\n self._dlist = None\n self.level = kwargs.pop('level', 'c')\n self.datatype = kwargs.pop('datatype', None)\n self.print_info = kwargs.pop('print_info', True)\n self.symbolic = kwargs.get('subcorpora', False)\n self.skip = kwargs.get('skip', False)\n self.just = kwargs.get('just', False)\n self.kwa = get_symbolics(self)\n\n if isinstance(path, (list, Datalist)):\n self.path = abspath(dirname(path[0].path.rstrip('/')))\n self.name = basename(self.path)\n self.data = path\n if self.level == 'd':\n self._dlist = path\n elif isinstance(path, STRINGTYPE):\n self.path = abspath(path)\n self.name = basename(path)\n elif hasattr(path, 'path') and path.path:\n self.path = abspath(path.path)\n self.name = basename(path.path)\n\n # this messy code figures out as quickly as possible what the datatype\n # and singlefile status of the path is. it's messy because it shortcuts\n # full checking where possible some of the shortcutting could maybe be\n # moved into the determine_datatype() funct.\n \n if self.level == 'd':\n self.singlefile = len(self._dlist) > 1\n else:\n self.singlefile = False\n if os.path.isfile(self.path):\n self.singlefile = True\n else:\n if not isdir(self.path):\n if isdir(join('data', path)):\n self.path = abspath(join('data', path))\n \n if self.path.endswith('-parsed') or self.path.endswith('-tokenised'):\n\n for r, d, f in os.walk(self.path):\n if not f:\n continue\n if isinstance(f, str) and f.startswith('.'):\n continue\n if f[0].endswith('conll') or f[0].endswith('conllu'):\n self.datatype = 'conll'\n break\n\n if len([d for d in os.listdir(self.path)\n if isdir(join(self.path, d))]) > 0:\n self.singlefile = False\n if len([d for d in os.listdir(self.path)\n if isdir(join(self.path, d))]) == 0:\n self.level = 's'\n else:\n if self.level == 'c':\n if not self.datatype:\n self.datatype, self.singlefile = determine_datatype(\n self.path)\n if isdir(self.path):\n if len([d for d in os.listdir(self.path)\n if isdir(join(self.path, d))]) == 0:\n self.level = 's'\n\n # if initialised on a file, process as file\n if self.singlefile and self.level == 'c':\n self.level = 'f'\n\n # load each interrogation as an attribute\n if kwargs.get('load_saved', False):\n from corpkit.other import load\n from corpkit.process import makesafe\n if os.path.isdir('saved_interrogations'):\n saved_files = glob.glob(r'saved_interrogations/*')\n for filepath in saved_files:\n filename = os.path.basename(filepath)\n if not filename.startswith(self.name):\n continue\n not_filename = filename.replace(self.name + '-', '')\n not_filename = os.path.splitext(not_filename)[0]\n if not_filename in ['features', 'wordclasses', 'postags']:\n continue\n variable_safe = makesafe(not_filename)\n try:\n setattr(self, variable_safe, load(filename))\n if self.print_info:\n print(\n '\\tLoaded %s as %s attribute.' %\n (filename, variable_safe))\n except AttributeError:\n if self.print_info:\n print(\n '\\tFailed to load %s as %s attribute. Name conflict?' %\n (filename, variable_safe))\n\n if self.print_info:\n print('Corpus: %s' % self.path)\n\n @lazyprop\n def subcorpora(self):\n \"\"\"\n A list-like object containing a corpus' subcorpora.\n \"\"\"\n import re\n import os\n import operator\n from os.path import join, isdir\n if self.level == 'd':\n return\n if self.data.__class__ == Datalist or isinstance(self.data, (Datalist, list)):\n return self.data\n if self.level == 'c':\n variable_safe_r = re.compile(r'[\\W0-9_]+', re.UNICODE)\n sbs = Datalist(sorted([Subcorpus(join(self.path, d), self.datatype, **self.kwa)\n for d in os.listdir(self.path)\n if isdir(join(self.path, d))],\n key=operator.attrgetter('name')), **self.kwa)\n for subcorpus in sbs:\n variable_safe = re.sub(variable_safe_r, '',\n subcorpus.name.lower().split(',')[0])\n setattr(self, variable_safe, subcorpus)\n return sbs\n\n @lazyprop\n def speakerlist(self):\n \"\"\"\n A list of speakers in the corpus\n \"\"\"\n from corpkit.build import get_speaker_names_from_parsed_corpus\n return get_speaker_names_from_parsed_corpus(self)\n\n @lazyprop\n def files(self):\n \"\"\"\n A list-like object containing the files in a folder\n\n >>> corpus.subcorpora[0].files\n \"\"\"\n\n import re\n import os\n import operator\n from os.path import join, isdir\n if self.level == 's':\n fls = [f for f in os.listdir(self.path) if not f.startswith('.')]\n fls = [File(f, self.path, self.datatype, **self.kwa) for f in fls]\n fls = sorted(fls, key=operator.attrgetter('name'))\n return Datalist(fls, **self.kwa)\n elif self.level == 'd':\n return self._dlist\n\n @lazyprop\n def all_filepaths(self):\n \"\"\"\n Lazy-load a list of all filepaths in a corpus\n \"\"\"\n if self.level == 'f':\n return [self.path]\n if self.files:\n return [i.path for i in self.files]\n fs = []\n for sc in self.subcorpora:\n for f in sc.files:\n fs.append(f.path)\n return fs\n\n def conll_conform(self, errors='raise'):\n \"\"\"\n This removes sent index column from old corpkit data\n \"\"\"\n from corpkit.constants import OPENER\n fs = self.all_filepaths\n for i, f in enumerate(fs, start=1):\n badfile = False\n print('Doing %s/%s' % (i, len(fs)))\n fdata = []\n with OPENER(f, 'r', encoding='utf-8') as fo:\n lines = fo.read().splitlines()\n for line in lines:\n if line.startswith('# sent_id'):\n continue\n if line and not line.startswith('#'):\n try:\n splut = line.split('\\t', 1)[1]\n except IndexError:\n raise IndexError('Failed on file %s' % f)\n if not splut[0].isdigit():\n if errors == 'raise':\n raise ValueError('File %s does not appear to be in old format.' % f)\n else:\n if badfile:\n continue\n badfile = True\n continue\n else:\n line = splut\n # add v column with nothing in it\n line = line.split('\\t')\n line.insert(5, '_')\n line = '\\t'.join(line)\n fdata.append(line)\n if badfile and errors != 'raise':\n print('Skipping %s' % f)\n continue\n with OPENER(f, 'w', encoding='utf-8') as fo:\n fo.write('\\n'.join(fdata))\n\n @lazyprop\n def all_files(self):\n \"\"\"\n Lazy-load a list of all filepaths in a corpus\n \"\"\"\n if self.level == 'f':\n return Datalist([self])\n if self.files:\n return self.files\n fs = []\n for sc in self.subcorpora:\n for f in sc.files:\n fs.append(f)\n return Datalist(fs)\n\n def tfidf(self, search={'w': 'any'}, show=['w'], **kwargs):\n \"\"\"\n Generate TF-IDF vector representation of corpus\n using interrogate method. All args and kwargs go to \n :func:`~corpkit.corpus.Corpus.interrogate`.\n\n :returns: Tuple: the vectoriser and matrix\n \"\"\"\n\n from sklearn.feature_extraction.text import TfidfVectorizer\n vectoriser = TfidfVectorizer(input='content',\n tokenizer=lambda x: x.split())\n\n res = self.interrogate(search=search,\n show=show,\n **kwargs).results\n\n # there is also a string repeat method which could be better\n def dupe_string(line):\n \"\"\"Duplicate line name by line count and return string\"\"\"\n return ''.join([(w + ' ') * line[w] for w in line.index])\n\n ser = res.apply(dupe_string, axis=1)\n vec = vectoriser.fit_transform(ser.values)\n #todo: subcorpora names are lost?\n return vectoriser, vec\n\n\n def __str__(self):\n \"\"\"\n String representation of corpus\n \"\"\"\n showing = 'subcorpora'\n if getattr(self, 'subcorpora', False):\n sclen = len(self.subcorpora)\n else:\n showing = 'files'\n sclen = len(self.files)\n\n show = 'Corpus at %s:\\n\\nData type: %s\\nNumber of %s: %d\\n' % (\n self.path, self.datatype, showing, sclen)\n val = self.symbolic if self.symbolic else 'default'\n show += 'Subcorpora: %s\\n' % val\n if self.singlefile:\n show += '\\nCorpus is a single file.\\n'\n #if getattr(self, 'symbolic'):\n # show += 'Symbolic subcorpora: %s\\n' % str(self.symbolic)\n if getattr(self, 'skip'):\n show += 'Skip: %s\\n' % str(self.skip)\n if getattr(self, 'just'):\n show += 'Just: %s\\n' % str(self.just)\n return show\n\n def __repr__(self):\n \"\"\"\n Object representation of corpus\n \"\"\"\n import os\n if not self.subcorpora:\n ssubcorpora = ''\n else:\n ssubcorpora = self.subcorpora\n return \"<%s instance: %s; %d subcorpora>\" % (\n classname(self), os.path.basename(self.path), len(ssubcorpora))\n\n def __getitem__(self, key):\n \"\"\"\n Get attributes from corpus\n todo: symbolic stuff for item selection\n \"\"\"\n from corpkit.constants import STRINGTYPE\n from corpkit.process import makesafe\n\n if getattr(self, 'subcorpora', False):\n get_from = self.subcorpora\n\n elif getattr(self, 'files', False):\n get_from = self.files\n \n else:\n get_from = self.document\n try:\n return get_from.loc[key]\n except:\n return get_from.__getitem__(key)\n\n return get_from.__getitem__(key)\n\n def __delitem__(self, key):\n from corpkit.constants import STRINGTYPE\n from corpkit.process import makesafe\n\n if getattr(self, 'subcorpora', False):\n del_from = self.subcorpora\n\n elif getattr(self, 'files', False):\n del_from = self.files\n \n if isinstance(key, (int, slice)):\n del_from.__delitem__(key)\n\n elif isinstance(key, STRINGTYPE):\n del_from.__delitem__(del_from.index(key))\n\n @lazyprop\n def features(self):\n \"\"\"\n Generate and show basic stats from the corpus, including number of \n sentences, clauses, process types, etc.\n\n :Example:\n\n >>> corpus.features\n SB Characters Tokens Words Closed class words Open class words Clauses\n 01 26873 8513 7308 4809 3704 2212\n 02 25844 7933 6920 4313 3620 2270\n 03 18376 5683 4877 3067 2616 1640\n 04 20066 6354 5366 3587 2767 1775\n\n \"\"\"\n from corpkit.dictionaries.word_transforms import mergetags\n from corpkit.process import get_corpus_metadata, add_df_to_dotfile, make_df_json_name\n\n kwa = {'just_metadata': self.just,\n 'skip_metadata': self.skip,\n 'subcorpora': self.symbolic}\n\n md = get_corpus_metadata(self.path, generate=True)\n name = make_df_json_name('features', self.symbolic)\n\n if name in md:\n import pandas as pd\n try:\n return pd.DataFrame(md[name])\n except ValueError:\n return pd.Series(md[name])\n else:\n feat = self.interrogate('features', **kwa)\n from corpkit.interrogation import Interrodict\n if isinstance(feat, Interrodict):\n feat = feat.multiindex()\n feat = feat.results\n add_df_to_dotfile(self.path, feat, typ='features', subcorpora=self.symbolic) \n return feat\n\n def _get_postags_and_wordclasses(self):\n \"\"\"\n Called by corpus.postags and corpus.wordclasses internally\n \"\"\"\n from corpkit.dictionaries.word_transforms import mergetags\n from corpkit.process import get_corpus_metadata, add_df_to_dotfile, make_df_json_name\n\n kwa = {'just_metadata': self.just,\n 'skip_metadata': self.skip,\n 'subcorpora': self.symbolic}\n\n md = get_corpus_metadata(self.path, generate=True)\n\n pname = make_df_json_name('postags', self.symbolic)\n wname = make_df_json_name('wordclasses', self.symbolic)\n \n if pname in md and wname in md:\n import pandas as pd\n try:\n return pd.DataFrame(md[pname]), pd.DataFrame(md[wname])\n except ValueError:\n return pd.Series(md[pname]), pd.Series(md[wname])\n\n else:\n postags = self.interrogate('postags', **kwa)\n from corpkit.interrogation import Interrodict\n if isinstance(postags, Interrodict):\n postags = postags.multiindex()\n wordclasses = postags.edit(merge_entries=mergetags,\n sort_by='total').results.astype(int)\n postags = postags.results\n add_df_to_dotfile(self.path, postags, typ='postags', subcorpora=self.symbolic)\n add_df_to_dotfile(self.path, wordclasses, typ='wordclasses', subcorpora=self.symbolic)\n return postags, wordclasses\n\n @lazyprop\n def wordclasses(self):\n \"\"\"\n Generate and show basic stats from the corpus, including number of \n sentences, clauses, process types, etc.\n\n :Example:\n\n >>> corpus.wordclasses\n SB Verb Noun Preposition Determiner ...\n 01 26873 8513 7308 5508 ...\n 02 25844 7933 6920 3323 ...\n 03 18376 5683 4877 3137 ...\n 04 20066 6354 5366 4336 ...\n \"\"\"\n postags, wordclasses = self._get_postags_and_wordclasses()\n return wordclasses\n\n @lazyprop\n def postags(self):\n \"\"\"\n Generate and show basic stats from the corpus, including number of \n sentences, clauses, process types, etc.\n\n :Example:\n\n >>> corpus.postags\n SB NN VB JJ IN DT \n 01 26873 8513 7308 4809 3704 ...\n 02 25844 7933 6920 4313 3620 ...\n 03 18376 5683 4877 3067 2616 ...\n 04 20066 6354 5366 3587 2767 ...\n\n \"\"\"\n postags, wordclasses = self._get_postags_and_wordclasses()\n return postags\n\n @lazyprop\n def lexicon(self, **kwargs):\n \"\"\"\n Get a lexicon/frequency distribution from a corpus,\n and save to disk for next time.\n\n :returns: a `DataFrame` of tokens and counts\n \"\"\"\n \n from corpkit.process import get_corpus_metadata, add_df_to_dotfile, make_df_json_name\n\n kwa = {'just_metadata': self.just,\n 'skip_metadata': self.skip,\n 'subcorpora': self.symbolic}\n\n md = get_corpus_metadata(self.path, generate=True)\n name = make_df_json_name('lexicon', self.symbolic)\n \n if name in md:\n import pandas as pd\n return pd.DataFrame(md[name])\n else:\n lexi = self.interrogate('lexicon', **kwa)\n from corpkit.interrogation import Interrodict\n if isinstance(lexi, Interrodict):\n lexi = lexi.multiindex()\n lexi = lexi.results\n add_df_to_dotfile(self.path, lexi, typ='lexicon', subcorpora=self.symbolic)\n return lexi\n\n def configurations(self, search, **kwargs):\n \"\"\"\n Get the overall behaviour of tokens or lemmas matching a regular \n expression. The search below makes DataFrames containing the most \n common subjects, objects, modifiers (etc.) of 'see':\n\n :param search: Similar to `search` in the \n :func:`~corpkit.corpus.Corpus.interrogate` \n method.\n\n Valid keys are:\n\n - `W`/`L` match word or lemma\n - `F`: match a semantic role (`'participant'`, `'process'` or \n `'modifier'`. If `F` not specified, each role will be \n searched for.\n :type search: `dict`\n\n :Example:\n\n >>> see = corpus.configurations({L: 'see', F: 'process'}, show=L)\n >>> see.has_subject.results.sum()\n i 452\n it 227\n you 162\n we 111\n he 94\n\n :returns: :class:`corpkit.interrogation.Interrodict`\n \"\"\"\n if 'subcorpora' not in kwargs:\n kwargs['subcorpora'] = self.symbolic\n if 'just_metadata' not in kwargs:\n kwargs['just_metadata'] = self.just\n if 'skip_metadata' not in kwargs:\n kwargs['skip_metadata'] = self.skip\n from corpkit.configurations import configurations\n return configurations(self, search, **kwargs)\n\n def interrogate(self, search='w', *args, **kwargs):\n \"\"\"\n Interrogate a corpus of texts for a lexicogrammatical phenomenon.\n\n This method iterates over the files/folders in a corpus, searching the\n texts, and returning a :class:`corpkit.interrogation.Interrogation`\n object containing the results. The main options are `search`, where you\n specify search criteria, and `show`, where you specify what you want to\n appear in the output.\n\n :Example:\n\n >>> corpus = Corpus('data/conversations-parsed')\n ### show lemma form of nouns ending in 'ing'\n >>> q = {W: r'ing$', P: r'^N'}\n >>> data = corpus.interrogate(q, show=L)\n >>> data.results\n .. something anything thing feeling everything nothing morning\n 01 14 11 12 1 6 0 1\n 02 10 20 4 4 8 3 0\n 03 14 5 5 3 1 0 0\n ... ...\n\n :param search: What part of the lexicogrammar to search, and what \n criteria to match. The `keys` are the thing to be \n searched, and values are the criteria. To search parse \n trees, use the `T` key, and a Tregex query as the value.\n When searching dependencies, you can use any of:\n\n +--------------------+-------+----------+-----------+-----------+\n | | Match | Governor | Dependent | Head |\n +====================+=======+==========+===========+===========+\n | Word | `W` | `G` | `D` | `H` |\n +--------------------+-------+----------+-----------+-----------+\n | Lemma | `L` | `GL` | `DL` | `HL` |\n +--------------------+-------+----------+-----------+-----------+\n | Function | `F` | `GF` | `DF` | `HF` |\n +--------------------+-------+----------+-----------+-----------+\n | POS tag | `P` | `GP` | `DP` | `HP` |\n +--------------------+-------+----------+-----------+-----------+\n | Word class | `X` | `GX` | `DX` | `HX` |\n +--------------------+-------+----------+-----------+-----------+\n | Distance from root | `A` | `GA` | `DA` | `HA` |\n +--------------------+-------+----------+-----------+-----------+\n | Index | `I` | `GI` | `DI` | `HI` |\n +--------------------+-------+----------+-----------+-----------+\n | Sentence index | `S` | `SI` | `SI` | `SI` |\n +--------------------+-------+----------+-----------+-----------+\n\n Values should be regular expressions or wordlists to \n match.\n\n :type search: `dict`\n\n :Example:\n\n >>> corpus.interrogate({T: r'/NN.?/ < /^t/'}) # T- nouns, via trees\n >>> corpus.interrogate({W: '^t': P: r'^v'}) # T- verbs, via dependencies\n\n :param searchmode: Return results matching any/all criteria\n :type searchmode: `str` -- `'any'`/`'all'`\n\n :param exclude: The inverse of `search`, removing results from search\n :type exclude: `dict` -- `{L: 'be'}`\n\n :param excludemode: Exclude results matching any/all criteria\n :type excludemode: `str` -- `'any'`/`'all'`\n\n :param query: A search query for the interrogation. This is only used\n when `search` is a `str`, or when multiprocessing. When \n `search` If `search` is a str, the search criteria can be \n passed in as `query, in order to allow the simpler syntax:\n\n >>> corpus.interrogate(GL, '(think|want|feel)')\n\n When multiprocessing, the following is possible:\n\n >>> q = {'Nouns': r'/NN.?/', 'Verbs': r'/VB.?/'}\n ### return an :class:`corpkit.interrogation.Interrogation` object with multiindex:\n >>> corpus.interrogate(T, q)\n ### return an :class:`corpkit.interrogation.Interrogation` object without multiindex:\n >>> corpus.interrogate(T, q, show=C)\n\n :type query: `str`, `dict` or `list`\n\n :param show: What to output. If multiple strings are passed in as a `list`, \n results will be colon-separated, in the suppled order. Possible \n values are the same as those for `search`, plus options \n n-gramming and getting collocates:\n\n +------+-----------------------+------------------------+\n | Show | Gloss | Example |\n +======+=======================+========================+\n | N | N-gram word | `The women were` |\n +------+-----------------------+------------------------+\n | NL | N-gram lemma | `The woman be` |\n +------+-----------------------+------------------------+\n | NF | N-gram function | `det nsubj root` |\n +------+-----------------------+------------------------+\n | NP | N-gram POS tag | `DT NNS VBN` |\n +------+-----------------------+------------------------+\n | NX | N-gram word class | `determiner noun verb` |\n +------+-----------------------+------------------------+\n | B | Collocate word | `The_were` |\n +------+-----------------------+------------------------+\n | BL | Collocate lemma | `The_be` |\n +------+-----------------------+------------------------+\n | BF | Collocate function | `det_root` |\n +------+-----------------------+------------------------+\n | BP | Collocate POS tag | `DT_VBN` |\n +------+-----------------------+------------------------+\n | BX | Collocate word class | `determiner_verb` |\n +------+-----------------------+------------------------+\n\n :type show: `str`/`list` of strings\n\n :param lemmatise: Force lemmatisation on results. **Deprecated:\n instead, output a lemma form with the `show` argument**\n :type lemmatise: `bool`\n\n :param lemmatag: When using a Tregex/Tgrep query, the tool will\n attempt to determine the word class of results from the query.\n Passing in a `str` here will tell the lemmatiser the expected\n POS of results to lemmatise. It only has an affect if trees\n are being searched and lemmata are being shown.\n :type lemmatag: `'n'`/`'v'`/`'a'`/`'r'`/`False`\n\n :param save: Save result as pickle to `saved_interrogations/` on \n completion\n :type save: `str`\n\n :param gramsize: Size of n-grams (default 1, i.e. unigrams)\n :type gramsize: `int`\n\n :param multiprocess: How many parallel processes to run\n :type multiprocess: `int`/`bool` (`bool` determines automatically)\n\n :param files_as_subcorpora: (**Deprecated, use subcorpora=files**). Treat each file as a subcorpus, ignoring \n actual subcorpora if present\n :type files_as_subcorpora: `bool`\n\n :param conc: Generate a concordance while interrogating, \n store as `.concordance` attribute\n :type conc: `bool`/`'only'`\n\n :param coref: Also get coreferents for search matches\n :type coref: `bool`\n\n :param tgrep: Use `TGrep` for tree querying. TGrep is less expressive \n than Tregex, and is slower, but can work without Java. This\n option may be turned on internally if Java is not found.\n :type tgrep: `bool`\n\n :param subcorpora: Use a metadata value as subcorpora. \n Passing a list will create a multiindex.\n `'file'` and `'folder'`/`'default'` are also possible values.\n :type subcorpora: `str`/`list`\n\n :param just_metadata: One or more metadata fields and criteria to filter sentences by.\n Only those matching will be kept. Criteria can be a list of words\n or a regular expression. Passing ``{'speaker': 'ENVER'}``\n will search only sentences annotated with ``speaker=ENVER``.\n :type just_metadata: `dict`\n\n :param skip_metadata: A field and regex/list to filter sentences by.\n The inverse of ``just_metadata``.\n :type skip_metadata: `dict`\n\n :param discard: When returning many (i.e. millions) of results, memory can be\n a problem. Setting a discard value will ignore results occurring\n infrequently in a subcorpus. An ``int`` will remove any result\n occurring ``n`` times or fewer. A float will remove this proportion\n of results (i.e. 0.1 will remove 10 per cent)\n :type discard: ``int``/``float``\n\n :returns: A :class:`corpkit.interrogation.Interrogation` object, with \n `.query`, `.results`, `.totals` attributes. If multiprocessing is \n invoked, result may be multiindexed.\n \"\"\"\n from corpkit.interrogator import interrogator\n import pandas as pd\n par = kwargs.pop('multiprocess', None)\n kwargs.pop('corpus', None)\n\n if self.datatype != 'conll':\n raise ValueError('You need to parse or tokenise the corpus before searching.')\n \n # handle symbolic structures\n subcorpora = kwargs.get('subcorpora', False)\n if self.level == 's':\n subcorpora = 'file'\n if self.symbolic:\n subcorpora = self.symbolic\n if 'subcorpora' in kwargs:\n subcorpora = kwargs.pop('subcorpora')\n if subcorpora in ['default', 'folder', 'folders']:\n subcorpora = False\n if subcorpora in ['file', 'files']:\n subcorpora = False\n kwargs['files_as_subcorpora'] = True\n\n if self.skip:\n if kwargs.get('skip_metadata'):\n kwargs['skip_metadata'].update(self.skip)\n else:\n kwargs['skip_metadata'] = self.skip\n\n if self.just:\n if kwargs.get('just_metadata'):\n kwargs['just_metadata'].update(self.just)\n else:\n kwargs['just_metadata'] = self.just\n\n kwargs.pop('subcorpora', False)\n\n if par and self.subcorpora:\n if isinstance(par, int):\n kwargs['multiprocess'] = par\n res = interrogator(self.subcorpora, search,\n subcorpora=subcorpora, *args, **kwargs)\n else:\n kwargs['multiprocess'] = par\n res = interrogator(self, search,\n subcorpora=subcorpora, *args, **kwargs)\n\n if kwargs.get('conc', False) == 'only':\n return res\n\n from corpkit.interrogation import Interrodict\n if isinstance(res, Interrodict) and kwargs.get('use_interrodict'):\n return res\n elif isinstance(res, Interrodict) and not kwargs.get('use_interrodict', False):\n return res.multiindex()\n else:\n if subcorpora:\n res.results.index.name = subcorpora\n\n\n # sort by total\n ind = list(res.results.index)\n if isinstance(res.results, pd.DataFrame):\n if not res.results.empty:\n res.results = res.results[list(res.results.sum().sort_values(ascending=False).index)]\n res.results = res.results.astype(int)\n\n if all(i == 'none' or str(i).isdigit() for i in ind):\n longest = max([len(str(i)) if str(i).isdigit() else 1 for i in ind])\n res.results.index = [str(i).zfill(longest) for i in ind]\n res.results = res.results.sort_index().astype(int)\n else:\n show = res.query.get('show', [])\n outs = []\n from corpkit.constants import transshow, transobjs\n for bit in show:\n name = transobjs.get(bit[0], bit[0]) + '-' + transshow.get(bit[-1], bit[-1])\n name = name.replace('Match-', '').lower()\n outs.append(name)\n name = '/'.join(outs)\n if name:\n res.results.name = name\n return res\n\n def sample(self, n, level='f'):\n \"\"\"\n Get a sample of the corpus\n\n :param n: amount of data in the the sample. If an ``int``, get n files.\n if a ``float``, get float * 100 as a percentage of the corpus\n :type n: ``int``/``float``\n :param level: sample subcorpora (``s``) or files (``f``)\n :type level: ``str``\n :returns: a Corpus object\n \"\"\"\n import random\n\n if isinstance(n, int):\n if level.lower().startswith('s'):\n rs = random.sample(list(self.subcorpora), n)\n rs = sorted(rs, key=lambda x: x.name)\n return Corpus(Datalist(rs),\n print_info=False, datatype='conll')\n else:\n fps = list(self.all_files)\n dl = Datalist(random.sample(fps, n))\n return Corpus(dl, level='d',\n print_info=False, datatype='conll')\n elif isinstance(n, float):\n if level.lower().startswith('s'):\n fps = list(self.subcorpora)\n n = len(fps) / n\n return Corpus(Datalist(random.sample(fps, n)),\n print_info=False, datatype='conll')\n else:\n fps = list(self.all_files)\n n = len(fps) / n\n return Corpus(Datalist(random.sample(fps, n)), level='d',\n print_info=False, datatype='conll')\n\n def delete_metadata(self):\n \"\"\"\n Delete metadata for corpus. May be needed if corpus is changed\n \"\"\"\n import os\n os.remove(os.path.join('data', '.%s.json' % self.name))\n\n @lazyprop\n def metadata(self):\n \"\"\"\n Get metadata for a corpus\n \"\"\"\n from corpkit.process import get_corpus_metadata\n return get_corpus_metadata(self, generate=True)\n\n def parse(self,\n corenlppath=False,\n operations=False,\n copula_head=True,\n speaker_segmentation=False,\n memory_mb=False,\n multiprocess=False,\n split_texts=400,\n outname=False,\n metadata=False,\n coref=True,\n *args,\n **kwargs\n ):\n \"\"\"\n Parse an unparsed corpus, saving to disk\n\n :param corenlppath: Folder containing corenlp jar files (use if *corpkit* can't find\n it automatically)\n :type corenlppath: `str`\n\n :param operations: Which kinds of annotations to do\n :type operations: `str`\n\n :param speaker_segmentation: Add speaker name to parser output if your\n corpus is script-like\n :type speaker_segmentation: `bool`\n\n :param memory_mb: Amount of memory in MB for parser\n :type memory_mb: `int`\n\n :param copula_head: Make copula head in dependency parse\n :type copula_head: `bool`\n\n :param split_texts: Split texts longer than `n` lines for parser memory\n :type split_text: `int`\n\n :param multiprocess: Split parsing across n cores (for high-performance \n computers)\n :type multiprocess: `int`\n\n :param folderise: If corpus is just files, move each into own folder\n :type folderise: `bool`\n\n :param output_format: Save parser output as `xml`, `json`, `conll` \n :type output_format: `str`\n\n :param outname: Specify a name for the parsed corpus\n :type outname: `str`\n\n :param metadata: Use if you have XML tags at the end of lines contaning metadata\n :type metadata: `bool`\n\n :Example:\n\n >>> parsed = corpus.parse(speaker_segmentation=True)\n >>> parsed\n \n\n :returns: The newly created :class:`corpkit.corpus.Corpus`\n \"\"\"\n import os\n if outname:\n outpath = os.path.join('data', outname)\n if os.path.exists(outpath):\n raise ValueError('Path exists: %s' % outpath)\n\n from corpkit.make import make_corpus\n #from corpkit.process import determine_datatype\n #dtype, singlefile = determine_datatype(self.path)\n if self.datatype != 'plaintext':\n raise ValueError(\n 'parse method can only be used on plaintext corpora.')\n kwargs.pop('parse', None)\n kwargs.pop('tokenise', None)\n kwargs['output_format'] = kwargs.pop('output_format', 'conll')\n corp = make_corpus(unparsed_corpus_path=self.path,\n parse=True,\n tokenise=False,\n corenlppath=corenlppath,\n operations=operations,\n copula_head=copula_head,\n speaker_segmentation=speaker_segmentation,\n memory_mb=memory_mb,\n multiprocess=multiprocess,\n split_texts=split_texts,\n outname=outname,\n metadata=metadata,\n coref=coref,\n *args,\n **kwargs)\n if not corp:\n return\n\n if os.path.isfile(corp):\n return File(corp)\n else:\n return Corpus(corp)\n\n def tokenise(self, postag=True, lemmatise=True, *args, **kwargs):\n \"\"\"\n Tokenise a plaintext corpus, saving to disk\n\n :param nltk_data_path: Path to tokeniser if not found automatically\n :type nltk_data_path: `str`\n\n :Example:\n\n >>> tok = corpus.tokenise()\n >>> tok\n \n\n :returns: The newly created :class:`corpkit.corpus.Corpus`\n \"\"\"\n\n from corpkit.make import make_corpus\n #from corpkit.process import determine_datatype\n #dtype, singlefile = determine_datatype(self.path)\n if self.datatype != 'plaintext':\n raise ValueError(\n 'parse method can only be used on plaintext corpora.')\n kwargs.pop('parse', None)\n kwargs.pop('tokenise', None)\n\n c = make_corpus(self.path,\n parse=False,\n tokenise=True,\n postag=postag,\n lemmatise=lemmatise,\n *args,\n **kwargs)\n return Corpus(c)\n\n def concordance(self, *args, **kwargs):\n \"\"\"\n A concordance method for Tregex queries, CoreNLP dependencies,\n tokenised data or plaintext. \n\n :Example:\n\n >>> wv = ['want', 'need', 'feel', 'desire']\n >>> corpus.concordance({L: wv, F: 'root'})\n 0 01 1-01.txt.conll But , so I feel like i do that for w\n 1 01 1-01.txt.conll I felt a little like oh , i\n 2 01 1-01.txt.conll he 's a difficult man I feel like his work ethic\n 3 01 1-01.txt.conll So I felt like i recognized li\n ... ...\n\n Arguments are the same as :func:`~corpkit.corpus.Corpus.interrogate`, \n plus a few extra parameters:\n\n :param only_format_match: If `True`, left and right window will just be\n words, regardless of what is in `show`\n :type only_format_match: `bool`\n\n :param only_unique: Return only unique lines\n :type only_unique: `bool`\n\n :param maxconc: Maximum number of concordance lines\n :type maxconc: `int`\n\n :returns: A :class:`corpkit.interrogation.Concordance` instance, with \n columns showing filename, subcorpus name, speaker name, left \n context, match and right context.\n \"\"\"\n\n kwargs.pop('conc', None)\n kwargs.pop('conc', None)\n kwargs.pop('corpus', None)\n return self.interrogate(conc='only', *args, **kwargs)\n\n def interroplot(self, search, **kwargs):\n \"\"\"\n Interrogate, relativise, then plot, with very little customisability.\n A demo function.\n\n :Example:\n\n >>> corpus.interroplot(r'/NN.?/ >># NP')\n \n\n :param search: Search as per :func:`~corpkit.corpus.Corpus.interrogate`\n :type search: `dict`\n :param kwargs: Extra arguments to pass to :func:`~corpkit.corpus.Corpus.visualise`\n :type kwargs: `keyword arguments`\n\n :returns: `None` (but show a plot)\n \"\"\"\n if isinstance(search, STRINGTYPE):\n search = {'t': search}\n interro = self.interrogate(search=search, show=kwargs.pop('show', 'w'))\n edited = interro.edit('%', 'self', print_info=False)\n edited.visualise(self.name, **kwargs).show()\n\n def save(self, savename=False, **kwargs):\n \"\"\"\n Save corpus instance to file. There's not much reason to do this, really.\n\n >>> corpus.save(filename)\n\n :param savename: Name for the file\n :type savename: `str`\n\n :returns: `None`\n \"\"\"\n from corpkit.other import save\n if not savename:\n savename = self.name\n save(self, savename, savedir=kwargs.pop('savedir', 'data'), **kwargs)\n\n def make_language_model(self,\n name,\n search={'w': 'any'},\n exclude=False,\n show=['w', '+1mw'],\n **kwargs):\n \"\"\"\n Make a language model for the corpus\n\n :param name: a name for the model\n :type name: `str`\n\n :param kwargs: keyword arguments for the interrogate() method\n :type kwargs: `keyword arguments`\n\n :returns: a :class:`corpkit.model.MultiModel`\n \"\"\"\n import os\n from corpkit.other import load\n from corpkit.model import MultiModel\n if not name.endswith('.p'):\n namep = name + '.p'\n else:\n namep = name\n\n # handle symbolic structures\n subcorpora = False\n if self.symbolic:\n subcorpora = self.symbolic\n if kwargs.get('subcorpora', False):\n subcorpora = kwargs.pop('subcorpora')\n kwargs.pop('subcorpora', False)\n\n jst = kwargs.pop('just_metadata') if 'just_metadata' in kwargs else self.just\n skp = kwargs.pop('skip_metadata') if 'skip_metadata' in kwargs else self.skip\n \n pth = os.path.join('models', namep)\n if os.path.isfile(pth):\n print('Returning saved model: %s' % pth)\n return load(name, loaddir='models')\n\n # set some defaults if not passed in as kwargs\n #langmod = not any(i.startswith('n') for i in search.keys())\n\n res = self.interrogate(search,\n exclude,\n show,\n subcorpora=subcorpora,\n just_metadata=jst,\n skip_metadata=skp,\n **kwargs)\n\n return res.language_model(name, search=search, **kwargs)\n\n def annotate(self, conclines, annotation, dry_run=True):\n \"\"\"\n Annotate a corpus\n\n :param conclines: a Concordance or DataFrame containing matches to annotate\n :type annotation: Concordance/DataFrame\n\n :param annotation: a tag or field and value\n :type annotation: ``str``/``dict``\n \n :param dry_run: Show the annotations to be made, but don't do them\n :type dry_run: ``bool``\n\n :returns: ``None``\n \"\"\"\n from corpkit.interrogation import Interrogation\n if isinstance(conclines, Interrogation):\n conclines = getattr(conclines, 'concordance', conclines)\n from corpkit.annotate import annotator\n annotator(conclines, annotation, dry_run=dry_run)\n # regenerate metadata afterward---could be a bit slow?\n if not dry_run:\n self.delete_metadata()\n from corpkit.process import make_dotfile\n make_dotfile(self)\n\n def unannotate(annotation, dry_run=True):\n \"\"\"\n Delete annotation from a corpus\n\n :param annotation: a tag or field and value\n :type annotation: ``str``/``dict``\n\n :returns: ``None``\n \"\"\"\n from corpkit.annotate import annotator\n annotator(self, annotation, dry_run=dry_run, deletemode=True)\n\nclass Subcorpus(Corpus):\n \"\"\"\n Model a subcorpus, containing files but no subdirectories.\n\n Methods for interrogating, concordancing and configurations are the same as\n :class:`corpkit.corpus.Corpus`.\n \"\"\"\n\n def __init__(self, path, datatype, **kwa):\n self.path = path\n kwargs = {'print_info': False, 'level': 's', 'datatype': datatype}\n kwargs.update(kwa)\n self.kwargs = kwargs\n Corpus.__init__(self, self.path, **kwargs)\n\n def __str__(self):\n return self.path\n\n def __repr__(self):\n return \"<%s instance: %s>\" % (classname(self), self.name)\n\n def __getitem__(self, key):\n\n from corpkit.process import makesafe\n\n if isinstance(key, slice):\n # Get the start, stop, and step from the slice\n key = list(key.indices(len(self.files)))\n return Datalist(list(self.files)[slice(*key)])\n #bits = [self[i] for i in range(*key.indices(len(self.files)))]\n #return [self[ii] for ii in range(*key.indices(len(self.files)))])\n elif isinstance(key, int):\n return list(self.files)[key]\n else:\n try:\n return self.files.__getattribute__(key)\n except:\n from corpkit.process import is_number\n if is_number(key):\n return self.__getattribute__('c' + key)\n\nclass File(Corpus):\n \"\"\"\n Models a corpus file for reading, interrogating, concordancing.\n\n Methods for interrogating, concordancing and configurations are the same as\n :class:`corpkit.corpus.Corpus`, plus methods for accessing the file contents \n directly as a `str`, or as a Pandas DataFrame.\n \"\"\"\n\n def __init__(self, path, dirname=False, datatype=False, **kwa):\n import os\n from os.path import join, isfile, isdir\n if dirname:\n self.path = join(dirname, path)\n else:\n self.path = path\n kwargs = {'print_info': False, 'level': 'f', 'datatype': datatype}\n kwargs.update(kwa)\n Corpus.__init__(self, self.path, **kwargs)\n if self.path.endswith('.conll') or self.path.endswith('.conllu'):\n self.datatype = 'conll'\n else:\n self.datatype = 'plaintext'\n\n def __repr__(self):\n return \"<%s instance: %s>\" % (classname(self), self.name)\n\n def __str__(self):\n return self.path\n \n def read(self, **kwargs):\n \"\"\"\n Read file data. If data is pickled, unpickle first\n\n :returns: `str`/unpickled data\n \"\"\"\n from corpkit.constants import OPENER\n with OPENER(self.path, 'r', **kwargs) as fo:\n return fo.read()\n\n @lazyprop\n def document(self):\n \"\"\"\n Return a version of the file that can be manipulated\n\n * For conll, this is a DataFrame\n * For tokens, this is a list of tokens\n * For plaintext, this is a string\n \"\"\"\n if self.datatype == 'conll':\n from corpkit.conll import parse_conll\n return parse_conll(self.path)\n else:\n from corpkit.process import saferead\n return saferead(self.path)[0]\n \n @lazyprop\n def trees(self):\n \"\"\"\n Get an OrderedDict of Tree objects in a File\n \"\"\"\n if self.datatype == 'conll':\n from nltk import Tree\n from collections import OrderedDict\n return OrderedDict({k: Tree.fromstring(v['parse']) \\\n for k, v in sorted(self.document._metadata.items())})\n else:\n raise AttributeError('Data must be parsed to get trees.')\n\n @lazyprop\n def plain(self):\n \"\"\"\n Show the sentences in a File as plaintext\n \"\"\"\n text = []\n if self.datatype == 'conll':\n doc = self.document\n for sent in list(doc.index.levels[0]):\n text.append('%d: ' % sent + ' '.join(list(doc.loc[sent]['w'])))\n else:\n self.read()\n return '\\n'.join(text)\n\nclass Datalist(list):\n\n def __init__(self, data, **kwargs):\n\n self.symbolic = kwargs.get('symbolic', False)\n self.just = kwargs.get('just', False)\n self.skip = kwargs.get('skip', False)\n super(Datalist, self).__init__(data)\n\n def __repr__(self):\n return \"<%s instance: %d items>\" % (classname(self), len(self))\n\n def __getattr__(self, key):\n ix = next((i for i, d in enumerate(self) if d.name == key), None)\n if ix is not None:\n return self[ix]\n\n def __getitem__(self, key):\n from corpkit.constants import STRINGTYPE\n \n if isinstance(key, slice):\n return Datalist([self[i] for i in range(*key.indices(len(self)))])\n \n elif isinstance(key, list):\n if isinstance(key[0], STRINGTYPE):\n dats = [i for i in self if i.name in key]\n else:\n dats = [x for i, x in enumerate(self) if i in key]\n return Datalist(dats)\n\n elif isinstance(key, int):\n return super(Datalist, self).__getitem__(key)\n\n elif isinstance(key, STRINGTYPE):\n ix = next((i for i, x in enumerate(self) if x.name == key), None)\n if ix is not None:\n return super(Datalist, self).__getitem__(ix)\n\n def __delitem__(self, key):\n from corpkit.constants import STRINGTYPE\n if isinstance(key, STRINGTYPE):\n key = next((i for i, d in enumerate(self) if d.name == key), None)\n if key is None:\n return\n super(Datalist, self).__delitem__(key)\n\n\n def interrogate(self, *args, **kwargs):\n \"\"\"\n Interrogate the corpus using :func:`~corpkit.corpus.Corpus.interrogate`\n \"\"\"\n \n kwargs['just'] = self.just\n kwargs['skip'] = self.skip\n kwargs['subcorpora'] = self.symbolic\n\n from corpkit.interrogator import interrogator\n interro = interrogator(self, *args, **kwargs)\n from corpkit.interrogation import Interrodict\n if isinstance(interro, Interrodict):\n interro = interro.multiindex(indexnames=['corpus', 'subcorpus'])\n return interro\n\n def concordance(self, *args, **kwargs):\n \"\"\"\n Concordance the corpus using :func:`~corpkit.corpus.Corpus.concordance`\n \"\"\"\n kwargs['just'] = self.just\n kwargs['skip'] = self.skip\n kwargs['subcorpora'] = self.symbolic\n\n from corpkit.interrogator import interrogator\n return interrogator(self, conc='only', *args, **kwargs)\n\n def configurations(self, search, **kwargs):\n \"\"\"\n Get a configuration using :func:`~corpkit.corpus.Corpus.configurations`\n \"\"\"\n kwargs['just'] = self.just\n kwargs['skip'] = self.skip\n kwargs['subcorpora'] = self.symbolic\n\n from corpkit.configurations import configurations\n return configurations(self, search, **kwargs)\n\nclass Corpora(Datalist):\n \"\"\"\n Models a collection of Corpus objects. Methods are available for \n interrogating and plotting the entire collection. This is the highest level \n of abstraction available.\n\n :param data: Corpora to model. A `str` is interpreted as a path containing \n corpora. A `list` can be a list of corpus paths or \n :class:`corpkit.corpus.Corpus` objects. )\n :type data: `str`/`list`\n \"\"\"\n\n def __init__(self, data=False, **kwargs):\n\n self.name = None\n\n # if no arg, load every corpus in data dir\n if not data:\n data = 'data'\n \n # handle a folder containing corpora\n if isinstance(data, STRINGTYPE):\n import os\n from os.path import join, isfile, isdir\n if not os.path.isdir(data):\n if not os.path.isdir(os.path.join('data', data)):\n raise ValueError('Corpora(str) needs to point to a directory.')\n else:\n data = os.path.join('data', data)\n self.name = os.path.basename(data)\n data = sorted([join(data, d) for d in os.listdir(data)\n if isdir(join(data, d)) and not d.startswith('.')])\n\n # otherwise, make a list of Corpus objects\n\n if not self.name:\n self.name = ','.join([os.path.basename(str(i)) for i in data])\n \n for index, i in enumerate(data):\n if isinstance(i, STRINGTYPE):\n data[index] = Corpus(i, **kwargs)\n\n # now turn it into a Datalist\n Datalist.__init__(self, data, **kwargs)\n\n def __repr__(self):\n return \"<%s instance: %d items>\" % (classname(self), len(self))\n\n def parse(self, **kwargs):\n \"\"\"\n Parse multiple corpora\n\n :param kwargs: Arguments to pass to the\n :func:`~corpkit.corpus.Corpus.parse` method.\n :returns: :class:`corpkit.corpus.Corpora`\n\n \"\"\"\n from corpkit.corpus import Corpora\n objs = []\n for v in list(self):\n objs.append(v.parse(**kwargs))\n return Corpora(objs)\n\n ### the below not working yet\n\n @lazyprop\n def features(self):\n \"\"\"\n Generate features attribute for all corpora\n \"\"\"\n from corpkit.interrogation import Interrodict\n feats = []\n for corpus in self:\n feats.append(corpus.features)\n feats = Interrodict(feats)\n return feats.multiindex()\n\n @lazyprop\n def postags(self):\n \"\"\"\n Generate postags attribute for all corpora\n \"\"\"\n for corpus in self:\n corpus.postags\n\n @lazyprop\n def wordclasses(self):\n \"\"\"\n Generate wordclasses attribute for all corpora\n \"\"\"\n for corpus in self:\n corpus.wordclasses\n\n @lazyprop\n def lexicon(self):\n \"\"\"\n Generate lexicon attribute for all corpora\n \"\"\"\n for corpus in self:\n corpus.lexicon\n"
},
{
"path": "corpkit/cql.py",
"content": "\"\"\"\nTranslating between CQL and corpkit's native \n\"\"\"\n\ndef remake_special(querybit, customs=False, return_list=False, **kwargs):\n \"\"\"\n Expand references to wordlists in queries\n \"\"\"\n import re\n from corpkit.dictionaries import roles, wordlists, processes\n from corpkit.other import as_regex\n \n def convert(d):\n \"\"\"convert dict to named tuple\"\"\"\n from collections import namedtuple\n return namedtuple('outputnames', list(d.keys()))(**d)\n\n if customs:\n customs = convert(customs)\n\n mapped = {'ROLES': roles,\n 'WORDLISTS': wordlists,\n 'PROCESSES': processes,\n 'LIST': customs,\n 'CUSTOM': customs}\n\n if not any(x in querybit.upper() for x in mapped.keys()):\n return querybit\n thereg = r'(?i)(' + '|'.join(mapped.keys()) + r'):([A-Z0-9]+)'\n splitup = re.split(thereg, querybit)\n fixed = []\n skipme = []\n for i, bit in enumerate(splitup):\n if i in skipme:\n continue\n if mapped.get(bit.upper()):\n att = getattr(mapped[bit.upper()], splitup[i+1].lower())\n if hasattr(att, 'words') and att.words:\n att = att.words\n if return_list:\n return att\n att = att.as_regex(**kwargs)\n fixed.append(att)\n skipme.append(i+1)\n else:\n fixed.append(bit)\n return ''.join(fixed) \n\ndef parse_quant(quant):\n \"\"\"\n Normalise quanitifers\n \"\"\"\n if quant.startswith('}{'):\n quant = quant.strip('}{ ')\n if ',' in quant:\n return quant.replace(',', ':')\n else:\n return quant\n return quant\n\ndef process_piece(piece, op='=', quant=False, **kwargs):\n \"\"\"\n Make a single search obj and value\n \"\"\"\n\n from corpkit.process import make_name_to_query_dict\n if op not in piece:\n return False, False\n translator = make_name_to_query_dict()\n target, criteria = piece.split(op, 1)\n criteria = criteria.strip('\"')\n criteria = remake_special(criteria, **kwargs)\n if '-' in target:\n obj, show = target.split('-', 1)\n show = show.lower()\n if show == 'deprel':\n show = 'Function'\n elif show == 'pos':\n show = 'POS'\n form = '{} {}'.format(obj.title(), show.lower())\n else:\n form = target.title()\n if form == 'Deprel':\n form = 'Function'\n elif form == 'Pos':\n form = 'POS'\n return translator.get(form), criteria\n\n\ndef tokenise_cql(query):\n \"\"\"\n Take a cql query and return a list of tuples\n which is token, quantifer\n \"\"\"\n quantstarts = ['+', '{', ',', '}', '?', '*']\n tokens = ''\n count = 0\n for i, t in enumerate(query):\n if t == '[':\n count += 1\n if t == ']':\n count -= 1\n if count != 0:\n tokens += t\n else:\n if count == 0 and t == ']':\n try:\n if not query[i+1].isspace():\n tokens += t\n tokens += '\\n'\n else:\n tokens += t\n except IndexError:\n pass\n # separate on space between tokens\n elif t.isspace():\n tokens += '\\n'\n # if part of quantifier\n if t in quantstarts or t.isdigit():\n tokens += t\n tokens = tokens.split('\\n')\n skips = []\n out = []\n for i, t in enumerate(tokens):\n if i in skips:\n continue\n # get next token\n try:\n nextt = tokens[i+1]\n except IndexError:\n # if it's the last token, if not quantifier, add it\n if not t:\n continue\n if t[0] not in quantstarts:\n out.append([t, False])\n continue\n if any(nextt.startswith(x) for x in quantstarts):\n out.append([t, nextt])\n skips.append(i+1)\n else:\n out.append([t, False])\n return out\n\ndef to_corpkit(cstring, **kwargs):\n \"\"\"\n Turn CQL into corpkit (two dict) format\n \"\"\"\n sdict = {}\n edict = {}\n cstring = tokenise_cql(cstring)\n for i, (c, q) in enumerate(cstring):\n if q:\n i = parse_quant(q)\n c = c.strip('[] ')\n clist = c.split(' & ')\n for piece in clist:\n targ, crit = process_piece(piece, op='!=', quant=q, **kwargs)\n if targ is False and crit is False:\n targ, crit = process_piece(piece, op='=', quant=q, **kwargs)\n # assume it's just a word without operator\n if targ is False and crit is False:\n if i > 0 or isinstance(i, str):\n b = '+{}w'.format(i)\n sdict[b] = piece.strip('\"')\n else:\n sdict['w'] = piece.strip('\"')\n else:\n if i > 0 or isinstance(i, str):\n targ = '+%s%s' % (str(i), targ)\n sdict[targ] = crit\n else:\n if i > 0 or isinstance(i, str):\n targ = '+{}{}'.format(i, targ)\n edict[targ] = crit\n\n return sdict, edict\n\ndef to_cql(dquery, exclude=False, **kwargs):\n \"\"\"\n Turn dictionary query into cql format\n \"\"\"\n qlist = []\n from corpkit.constants import transobjs, transshow\n if exclude:\n operator = '!='\n else:\n operator = '='\n for k, v in dquery.items():\n obj, show = transobjs.get(k[0]), transshow.get(k[1])\n if obj == 'Match':\n form_bit = '{}{}'.format(show.lower(), operator)\n else:\n form_bit = '{}-{}{}'.format(obj.lower(), show.lower(), operator)\n v = getattr(v, 'pattern', v)\n if isinstance(v, list):\n v = as_regex(v, **kwargs)\n form_bit += '\"{}\"'.format(v)\n qlist.append(form_bit)\n return '[' + ' & '.join(qlist) + ']'\n\n\n"
},
{
"path": "corpkit/dictionaries/__init__.py",
"content": "__all__ = [\"wordlists\", \"roles\", \"bnc\", \"processes\", \"verbs\", \n \"uktous\", \"tagtoclass\", \"queries\", \"mergetags\"]\n\nfrom corpkit.dictionaries.bnc import _get_bnc\nfrom corpkit.dictionaries.process_types import processes\nfrom corpkit.dictionaries.process_types import verbs\nfrom corpkit.dictionaries.roles import roles\nfrom corpkit.dictionaries.wordlists import wordlists\nfrom corpkit.dictionaries.queries import queries\nfrom corpkit.dictionaries.word_transforms import taglemma\nfrom corpkit.dictionaries.word_transforms import mergetags\nfrom corpkit.dictionaries.word_transforms import usa_convert\n\nroles = roles\nwordlists = wordlists\nprocesses = processes\nbnc = _get_bnc\nqueries = queries\ntagtoclass = taglemma\nuktous = usa_convert\nmergetags = mergetags\nverbs = verbs"
},
{
"path": "corpkit/dictionaries/bnc.p",
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(to)\np2321\nI156\nsVvirtually\np2322\nI44\nsVcorridor\np2323\nI21\nsVresulting\np2324\nI14\nsVruth\np2325\nI30\nsVdevelopment\np2326\nI324\nsVsophisticated\np2327\nI25\nsVknitting\np2328\nI10\nsVkeys\np2329\nI23\nsVcomprehensive\np2330\nI35\nsVderby\np2331\nI12\nsVyesterday\np2332\nI195\nsVhusbands\np2333\nI10\nsVmoment\np2334\nI221\nsVlevels\np2335\nI121\nsVpurpose\np2336\nI93\nsVnecessity\np2337\nI18\nsVaggregate\np2338\nI11\nsVgenuine\np2339\nI33\nsVimplement\np2340\nI15\nsVrecent\np2341\nI158\nsVrecognized\np2342\nI30\nsVtask\np2343\nI92\nsVbarry\np2344\nI19\nsVspent\np2345\nI116\nsVcounty\np2346\nI113\nsVconcrete\np2347\nI18\nsVwithdraw\np2348\nI16\nsVthoroughly\np2349\nI21\nsVswiss\np2350\nI14\nsVentry\np2351\nI52\nsVchemistry\np2352\nI20\nsVrates\np2353\nI115\nsVspend\np2354\nI74\nsVprevention\np2355\nI15\nsVlads\np2356\nI15\nsVexotic\np2357\nI11\nsVobserved\np2358\nI46\nsVsuffolk\np2359\nI12\nsVshape\np2360\nI62\nsVopenly\np2361\nI12\nsVtennis\np2362\nI28\nsVatomic\np2363\nI11\nsValternative\np2364\nI36\nsV1963\np2365\nI14\nsVinjuries\np2366\nI26\nsVtimber\np2367\nI23\nsVdiscipline\np2368\nI55\nsVcut\np2369\nI29\nsVreaders\np2370\nI47\nsVhated\np2371\nI17\nsVadmission\np2372\nI23\nsVdanger\np2373\nI60\nsVadvertisements\np2374\nI10\nsVchest\np2375\nI38\nsVdc\np2376\nI15\nsVgrim\np2377\nI11\nsVsignals\np2378\nI19\nsVshouting\np2379\nI17\nsVsubjects\np2380\nI77\nsVdeliberately\np2381\nI28\nsVlocation\np2382\nI40\nsVcrying\np2383\nI14\nsVrelevance\np2384\nI17\nsVinput\np2385\nI32\nsVruled\np2386\nI24\nsVeaster\np2387\nI18\nsVexcited\np2388\nI16\nsVsurprised\np2389\nI47\nsVaustralia\np2390\nI50\nsVvictims\np2391\nI29\nsVemergency\np2392\nI40\nsVformat\np2393\nI21\nsVbig\np2394\nI255\nsVbid\np2395\nI31\nsVwives\np2396\nI19\nsVjudgement\np2397\nI25\nsVmatters\np2398\nI80\nsVsuffering\np2399\nI13\nsVbit\np2400\nI11\nsVcarers\np2401\nI12\nsVprojects\np2402\nI55\nsVformal\np2403\nI64\nsVknock\np2404\nI12\nsVimposed\np2405\nI34\nsVcognitive\np2406\nI12\nsVfollows\np2407\nI100\nsVpounds\np2408\nI123\nsVaddressed\np2409\nI27\nsVconsensus\np2410\nI18\nsVcommunications\np2411\nI35\nsVindividuals\np2412\nI80\nsVindication\np2413\nI23\nsVfoolish\np2414\nI12\nsVdisorder\np2415\nI16\nsVreduces\np2416\nI12\nsVprivately\np2417\nI12\nsVoften\np2418\nI376\nsVsenate\np2419\nI13\nsVobliged\np2420\nI17\nsVb.\np2421\nI19\nsVmagnificent\np2422\nI20\nsVback\np2423\nI11\nsVdelegation\np2424\nI16\nsVstrongest\np2425\nI10\nsVpalm\np2426\nI13\nsVexamples\np2427\nI71\nsVsight\np2428\nI66\nsVmirror\np2429\nI37\nsVcurious\np2430\nI22\nsVserver\np2431\nI15\nsVpale\np2432\nI35\nsVourselves\np2433\nI45\nsVscale\np2434\nI74\nsVcontacts\np2435\nI21\nsVpet\np2436\nI13\nsVaffects\np2437\nI14\nsVdecision\np2438\nI168\nsVmeasurements\np2439\nI14\nsVintegration\np2440\nI24\nsVper\np2441\nI135\nsVreligion\np2442\nI44\nsVpen\np2443\nI20\nsVbehave\np2444\nI17\nsVeliminate\np2445\nI11\nsVgood*\np2446\nI25\nsVtemple\np2447\nI14\nsVnose\np2448\nI43\nsVpatient\np2449\nI14\nsV300\np2450\nI24\nsVoffenders\np2451\nI13\nsVcontinuing\np2452\nI19\nsVagreement\np2453\nI133\nsVfeelings\np2454\nI53\nsVnowhere\np2455\nI24\nsVbr\np2456\nI13\nsVrelating\np2457\nI37\nsVby\np2458\nI22\nsVwildlife\np2459\nI20\nsVgoods\np2460\nI101\nsVusa\np2461\nI50\nsVanything\np2462\nI288\nsVtruck\np2463\nI12\nsVmrs.\np2464\nI23\nsVreduced\np2465\nI14\nsVdrama\np2466\nI36\nsVdu~*\np2467\nI20\nsVwhisky\np2468\nI17\nsVbeans\np2469\nI13\nsVstuart\np2470\nI21\nsVtenants\np2471\nI20\nsVpollution\np2472\nI41\nsVrepair\np2473\nI15\nsVjimmy\np2474\nI24\nsVclinton\np2475\nI20\nsVinto\np2476\nI1634\nsVgood\np2477\nI795\nsVintegral\np2478\nI12\nsVministers\np2479\nI68\nsVphase\np2480\nI46\nsVappropriate\np2481\nI113\nsVprimarily\np2482\nI31\nsVsteven\np2483\nI12\nsVlesson\np2484\nI23\nsVcriticisms\np2485\nI12\nsVverbal\np2486\nI15\nsVspending\np2487\nI29\nsVbargaining\np2488\nI12\nsVspecifically\np2489\nI38\nsVcustom\np2490\nI15\nsVoccupy\np2491\nI11\nsVgermans\np2492\nI25\nsVsuit\np2493\nI20\nsVforward\np2494\nI12\nsVbored\np2495\nI14\nsVopens\np2496\nI17\nsVopponent\np2497\nI15\nsVconsiderably\np2498\nI29\nsVinvite\np2499\nI12\nsVinches\np2500\nI23\nsVjewish\np2501\nI22\nsVrelaxed\np2502\nI11\nsVvehicle\np2503\nI42\nsVimmigration\np2504\nI11\nsVhoped\np2505\nI51\nsVboys\np2506\nI80\nsVlink\np2507\nI13\nsVpacific\np2508\nI27\nsVhopes\np2509\nI18\nsVsubsidiary\np2510\nI12\nsVline\np2511\nI221\nsVwedding\np2512\nI33\nsVconsiderable\np2513\nI96\nsVdirected\np2514\nI34\nsVjonathan\np2515\nI16\nsVrejection\np2516\nI15\nsVup\np2517\nI83\nsVus\np2518\nI158\nsVplanet\np2519\nI18\nsVmaturity\np2520\nI14\nsV're\np2521\nI835\nsVexploration\np2522\nI15\nsVmature\np2523\nI15\nsVplanes\np2524\nI11\nsVautonomous\np2525\nI11\nsVwoke\np2526\nI14\nsValarm\np2527\nI21\nsVwell-known\np2528\nI15\nsVsort\np2529\nI19\nsVconstant\np2530\nI47\nsVexpedition\np2531\nI11\nsVdefined\np2532\nI54\nsVlikewise\np2533\nI12\nsVinfluence\np2534\nI25\nsV~ta*\np2535\nI36\nsVchap\np2536\nI16\nsVdiverse\np2537\nI13\nsVrolling\np2538\nI10\nsVnationalism\np2539\nI10\nsVecho\np2540\nI12\nsVcodes\np2541\nI13\nsVedwards\np2542\nI13\nsVthanks\np2543\nI62\nsVde* nop-\np2544\nI107\nsVoccasional\np2545\nI26\nsVpoints\np2546\nI30\nsVactors\np2547\nI16\nsVellen\np2548\nI10\nsVrevision\np2549\nI11\nsVexplaining\np2550\nI18\nsVelements\np2551\nI64\nsVlend\np2552\nI13\nsVfavourite\np2553\nI15\nsVprosecution\np2554\nI21\nsVprepared\np2555\nI52\nsVrestoration\np2556\nI20\nsVutterly\np2557\nI13\nsVdesert\np2558\nI18\nsVago\np2559\nI198\nsVlane\np2560\nI43\nsVland\np2561\nI13\nsVimplies\np2562\nI20\nsVconsumer\np2563\nI44\nsVage\np2564\nI216\nsVvehicles\np2565\nI31\nsVholes\np2566\nI27\nsVwalked\np2567\nI94\nsVappalling\np2568\nI11\nsV2000\np2569\nI16\nsVimplied\np2570\nI16\nsVpresenting\np2571\nI14\nsVcolonial\np2572\nI15\nsVwalker\np2573\nI21\nsVfresh\np2574\nI68\nsVinevitably\np2575\nI31\nsVhello\np2576\nI38\nsVlabelled\np2577\nI12\nsVessay\np2578\nI16\nsVcode\np2579\nI52\nsVjason\np2580\nI11\nsVrubbish\np2581\nI23\nsVillustrates\np2582\nI11\nsVhint\np2583\nI14\nsVresults\np2584\nI147\nsVexisting\np2585\nI94\nsVillustrated\np2586\nI26\nsVmaastricht\np2587\nI12\nsVstops\np2588\nI12\nsVbroader\np2589\nI14\nsVshrugged\np2590\nI24\nsVseemed\np2591\nI238\nsVcontacted\np2592\nI11\nsVturkish\np2593\nI14\nsVconcerned\np2594\nI158\nsVyoung\np2595\nI21\nsVsend\np2596\nI80\nsVintensive\np2597\nI18\nsVhelps\np2598\nI28\nsVvisitor\np2599\nI22\nsVfatal\np2600\nI14\nsVresources\np2601\nI105\nsVbrigade\np2602\nI12\nsVjurisdiction\np2603\nI19\nsVgarden\np2604\nI108\nsVcontinues\np2605\nI40\nsVgrid\np2606\nI11\nsVputting\np2607\nI76\nsVspecific\np2608\nI113\nsVsurgeon\np2609\nI11\nsVcontinued\np2610\nI21\nsVminerals\np2611\nI11\nsVcategories\np2612\nI34\nsVarrange\np2613\nI23\nsVentire\np2614\nI48\nsVmagic\np2615\nI15\nsVtransmission\np2616\nI15\nsVmarry\np2617\nI27\nsVshock\np2618\nI42\nsVfewer\np2619\nI31\nsVanxious\np2620\nI31\nsVthis\np2621\nI4623\nsVrace\np2622\nI74\nsVride\np2623\nI17\nsVclients\np2624\nI49\nsVulster\np2625\nI24\nsVcrop\np2626\nI15\nsVmaintain\np2627\nI54\nsVrecruitment\np2628\nI16\nsVimply\np2629\nI15\nsVvideo\np2630\nI65\nsVuncomfortable\np2631\nI14\nsVsubtle\np2632\nI18\nsVincidentally\np2633\nI11\nsVodd\np2634\nI45\nsVdepression\np2635\nI23\nsVindex\np2636\nI45\nsVsuppliers\np2637\nI18\nsVdirective\np2638\nI17\nsVchicago\np2639\nI11\nsVgiving\np2640\nI125\nsVpipes\np2641\nI12\nsVexpressed\np2642\nI69\nsVpractices\np2643\nI45\nsVaccess\np2644\nI100\nsVconsistently\np2645\nI16\nsVindian\np2646\nI38\nsVtwins\np2647\nI11\nsVfirms\np2648\nI75\nsVbird\np2649\nI36\nsVexercise\np2650\nI24\nsVthin\np2651\nI51\nsVbody\np2652\nI255\nsVjustification\np2653\nI18\nsVled\np2654\nI154\nsVlee\np2655\nI36\nsVleg\np2656\nI53\nsVrespectively\np2657\nI32\nsVgathered\np2658\nI26\nsVjointly\np2659\nI11\nsVhighly\np2660\nI91\nsVdressed\np2661\nI37\nsVobjects\np2662\nI45\nsVpoverty\np2663\nI31\nsVsink\np2664\nI11\nsVlicence\np2665\nI36\nsVothers\np2666\nI282\nsVconsideration\np2667\nI54\nsVinvented\np2668\nI12\nsVfifteen\np2669\nI54\nsVimplicit\np2670\nI12\nsVextreme\np2671\nI33\nsV39\np2672\nI13\nsV38\np2673\nI15\nsVtalent\np2674\nI21\nsV33\np2675\nI17\nsV32\np2676\nI20\nsV31\np2677\nI51\nsV30\np2678\nI114\nsV37\np2679\nI14\nsV36\np2680\nI17\nsV35\np2681\nI25\nsV34\np2682\nI15\nsVsurvey\np2683\nI79\nsVdefeat\np2684\nI30\nsVopinion\np2685\nI75\nsVresidents\np2686\nI35\nsVmakes\np2687\nI166\nsVmaker\np2688\nI10\nsVinvolves\np2689\nI41\nsVcomposed\np2690\nI20\nsVnamed\np2691\nI44\nsVprivate\np2692\nI173\nsVnames\np2693\nI76\nsVscandal\np2694\nI15\nsVtools\np2695\nI32\nsVcausal\np2696\nI12\nsVstanding\np2697\nI28\nsVconfidence\np2698\nI70\nsVexciting\np2699\nI33\nsVcertificate\np2700\nI29\nsVillegal\np2701\nI24\nsVbush\np2702\nI29\nsVnext\np2703\nI30\nsVeleven\np2704\nI38\nsVdoubt\np2705\nI21\nsVbutler\np2706\nI10\nsVthemselves\np2707\nI237\nsVloch\np2708\nI13\nsVdefendant\np2709\nI33\nsVjudged\np2710\nI17\nsVpencil\np2711\nI12\nsVcomparison\np2712\nI33\nsVachieve\np2713\nI68\nsVoccurred\np2714\nI55\nsVtrail\np2715\nI12\nsVcarrying\np2716\nI56\nsVprizes\np2717\nI11\nsVashamed\np2718\nI11\nsVbaby\np2719\nI91\nsVcentral\np2720\nI193\nsViii\np2721\nI50\nsVcharity\np2722\nI35\nsVcustomer\np2723\nI47\nsVaccount\np2724\nI24\nsVballs\np2725\nI15\nsVanimals\np2726\nI86\nsVace\np2727\nI11\nsVtunnel\np2728\nI24\nsVgabriel\np2729\nI13\nsVchallenge\np2730\nI16\nsVstations\np2731\nI37\nsVpour\np2732\nI11\nsVobvious\np2733\nI85\nsVpublications\np2734\nI20\nsVpraise\np2735\nI12\nsVindustrial\np2736\nI116\nsVtrevor\np2737\nI12\nsVclosing\np2738\nI14\nsVgrin\np2739\nI11\nsVcoffin\np2740\nI14\nsVborrowed\np2741\nI10\nsVslid\np2742\nI16\nsVreserved\np2743\nI11\nsVbent\np2744\nI21\nsVe.g.\np2745\nI49\nsVprocess\np2746\nI11\nsVlock\np2747\nI15\nsVproportions\np2748\nI15\nsVslim\np2749\nI12\nsVrode\np2750\nI11\nsVpurposes\np2751\nI58\nsVpieces\np2752\nI56\nsVhigh\np2753\nI11\nsVeffectively\np2754\nI51\nsVreserves\np2755\nI20\nsVslip\np2756\nI14\nsVbones\np2757\nI23\nsVreynolds\np2758\nI11\nsVcontempt\np2759\nI13\nsVnative\np2760\nI24\nsVlamb\np2761\nI10\nsVeducational\np2762\nI59\nsVvaried\np2763\nI13\nsVdemocracy\np2764\nI42\nsVboxing\np2765\nI12\nsVhampshire\np2766\nI12\nsVdelay\np2767\nI23\nsVlamp\np2768\nI14\nsVwinners\np2769\nI20\nsVpair\np2770\nI60\nsVforest\np2771\nI70\nsVanimal\np2772\nI67\nsVcomedy\np2773\nI14\nsVestablishment\np2774\nI40\nsVstock\np2775\nI76\nsVprofile\np2776\nI23\nsVbuildings\np2777\nI66\nsVblocks\np2778\nI21\nsVwaters\np2779\nI22\nsVphilosophy\np2780\nI35\nsVtied\np2781\nI25\nsVbothered\np2782\nI15\nsVpigs\np2783\nI12\nsVpreferred\np2784\nI11\nsVties\np2785\nI13\nsVsolidarity\np2786\nI11\nsVefficiently\np2787\nI11\nsVrealized\np2788\nI31\nsVcounter\np2789\nI17\nsVlines\np2790\nI102\nsVredundant\np2791\nI12\nsVelement\np2792\nI56\nsVchief\np2793\nI38\nsVlose\np2794\nI65\nsVallow\np2795\nI115\nsVfurious\np2796\nI13\nsVsubsequently\np2797\nI37\nsVvolunteers\np2798\nI19\nsVcounted\np2799\nI13\nsVha\np2800\nI30\nsVevolutionary\np2801\nI11\nsVproducer\np2802\nI18\nsVproduces\np2803\nI25\nsVinstitutional\np2804\nI20\nsVmove\np2805\nI68\nsVoutlook\np2806\nI12\nsVdoubtful\np2807\nI13\nsVproduced\np2808\nI129\nsVagricultural\np2809\nI41\nsVarrangement\np2810\nI33\nsVbunch\np2811\nI12\nsVperfect\np2812\nI56\nsVregions\np2813\nI42\nsVle\np2814\nI11\nsVla\np2815\nI19\nsVchosen\np2816\nI11\nsVvaries\np2817\nI13\nsVacres\np2818\nI16\nsVmeantime\np2819\nI14\nsVwilling\np2820\nI39\nsVdegrees\np2821\nI31\nsVinstruments\np2822\nI29\nsVcriminal\np2823\nI44\nsVdad\np2824\nI73\nsVdesigns\np2825\nI28\nsVmechanisms\np2826\nI20\nsVgreater\np2827\nI141\nsVoutlined\np2828\nI20\nsVspell\np2829\nI13\nsVnewman\np2830\nI10\nsVdock\np2831\nI12\nsVcutting\np2832\nI10\nsVkiss\np2833\nI12\nsVcage\np2834\nI10\nsVrealize\np2835\nI22\nsVpresidential\np2836\nI21\nsVapplicants\np2837\nI12\nsVpersonally\np2838\nI27\nsVelite*\np2839\nI12\nsVintelligent\np2840\nI19\nsVtraced\np2841\nI10\nsVidentified\np2842\nI62\nsVsufficiently\np2843\nI26\nsVdelightful\np2844\nI11\nsVbetty\np2845\nI13\nsVtruth\np2846\nI83\nsVshortage\np2847\nI15\nsVcouncillors\np2848\nI20\nsVbeneath\np2849\nI48\nsVglasses\np2850\nI25\nsVshower\np2851\nI15\nsVdoing\np2852\nI279\nsVstrip\np2853\nI16\nsVsociety\np2854\nI238\nsVbooks\np2855\nI131\nsVstatic\np2856\nI12\nsVthirteen\np2857\nI22\nsVprogrammes\np2858\nI65\nsVsexual\np2859\nI69\nsVwitness\np2860\nI18\nsVmatrix\np2861\nI14\nsV'\np2862\nI479\nsVdismissal\np2863\nI15\nsVwholly\np2864\nI22\nsVmate\np2865\nI17\nsVlecture\np2866\nI17\nsVelectronics\np2867\nI15\nsVintervention\np2868\nI32\nsVref\np2869\nI39\nsVred\np2870\nI23\nsVshut\np2871\nI47\nsVapproached\np2872\nI29\nsVinteractions\np2873\nI11\nsVfrank\np2874\nI37\nsVfourteen\np2875\nI27\nsVban\np2876\nI24\nsVapproaches\np2877\nI26\nsVsurely\np2878\nI63\nsVcollection\np2879\nI78\nsVthrust\np2880\nI12\nsVlikelihood\np2881\nI12\nsVdefining\np2882\nI10\nsVbackwards\np2883\nI18\nsVmainland\np2884\nI11\nsVq.v.\np2885\nI23\nsVcould\np2886\nI1683\nsVarea\np2887\nI351\nsVput\np2888\nI596\nsVnew nop-\np2889\nI106\nsVdavid\np2890\nI157\nsVlength\np2891\nI72\nsVallied\np2892\nI14\nsVqualification\np2893\nI13\nsVremoving\np2894\nI14\nsVdavis\np2895\nI14\nsVretain\np2896\nI25\nsVretail\np2897\nI20\nsVfacilitate\np2898\nI10\nsVstimulate\np2899\nI10\nsVsouth\np2900\nI165\nsVfinest\np2901\nI18\nsVblown\np2902\nI12\nsVthird\np2903\nI211\nsV2,000\np2904\nI13\nsVscene\np2905\nI68\nsVowned\np2906\nI36\nsVjesus\np2907\nI55\nsVembarrassed\np2908\nI13\nsVimprovements\np2909\nI24\nsVowner\np2910\nI50\nsVhave*\np2911\nI4735\nsVreliable\np2912\nI22\nsVlegislative\np2913\nI19\nsVexplain\np2914\nI79\nsVancient\np2915\nI50\nsVsadly\np2916\nI19\nsVfascinating\np2917\nI17\nsVstart\np2918\nI85\nsVfestival\np2919\nI31\nsVdemonstration\np2920\nI19\nsVsystem\np2921\nI447\nsVintermediate\np2922\nI13\nsVsergeant\np2923\nI26\nsVtravelled\np2924\nI22\nsVpainful\np2925\nI19\nsVinterests\np2926\nI104\nsVenforcement\np2927\nI13\nsVstomach\np2928\nI30\nsVgear\np2929\nI19\nsVcompleted\np2930\nI55\nsVacquire\np2931\nI20\nsVlamont\np2932\nI12\nsVenvironmental\np2933\nI84\nsVdeeply\np2934\nI37\nsVdecisive\np2935\nI12\nsVsteel\np2936\nI37\nsVloved\np2937\nI46\nsVcolleagues\np2938\nI56\nsVsplit\np2939\nI25\nsVbother\np2940\nI22\nsVroberts\np2941\nI15\nsVsweden\np2942\nI17\nsVreproduction\np2943\nI11\nsVlover\np2944\nI18\nsVvisited\np2945\nI43\nsVabolished\np2946\nI11\nsVsteep\np2947\nI16\nsVcollecting\np2948\nI15\nsVimaginative\np2949\nI10\nsVfalse\np2950\nI36\nsVpartnership\np2951\nI35\nsVgently\np2952\nI40\nsVcomfortable\np2953\nI40\nsVtonight\np2954\nI69\nsVgentle\np2955\nI29\nsVunlikely\np2956\nI56\nsVmiserable\np2957\nI12\nsVthroat\np2958\nI32\nsVapparently\np2959\nI78\nsVclearly\np2960\nI153\nsVviewed\np2961\nI21\nsVdocuments\np2962\nI44\nsVdishes\np2963\nI14\nsVstudying\np2964\nI24\nsVgravity\np2965\nI12\nsVparks\np2966\nI14\nsVmix\np2967\nI12\nsVconcerns\np2968\nI13\nsVprovoked\np2969\nI11\nsVlinda\np2970\nI12\nsVkorean\np2971\nI12\nsVsubject\np2972\nI51\nsVlily\np2973\nI14\nsVcompetence\np2974\nI15\nsVaccuracy\np2975\nI17\nsVworldwide\np2976\nI10\nsVbrazil\np2977\nI17\nsVunless\np2978\nI110\nsVmanor\np2979\nI13\nsVshaped\np2980\nI11\nsVcourtesy\np2981\nI11\nsVsaid\np2982\nI2087\nsVeight\np2983\nI173\nsVpreliminary\np2984\nI18\nsVdevice\np2985\nI29\nsVdraws\np2986\nI11\nsVstriker\np2987\nI13\nsVmedal\np2988\nI11\nsVpayment\np2989\nI54\nsVso-called\np2990\nI27\nsVlined\np2991\nI12\nsVenthusiastic\np2992\nI14\nsVinappropriate\np2993\nI13\nsVclive\np2994\nI10\nsVdisease\np2995\nI89\nsVface\np2996\nI67\nsV1,000\np2997\nI19\nsVmechanical\np2998\nI20\nsVoccasion\np2999\nI53\nsVpainting\np3000\nI38\nsVfact\np3001\nI374\nsVatmosphere\np3002\nI49\nsVselection\np3003\nI60\nsVtext\np3004\nI77\nsVcharles\np3005\nI91\nsVless than*\np3006\nI40\nsVbring\np3007\nI154\nsVmanchester\np3008\nI50\nsVlloyd\np3009\nI23\nsVbedroom\np3010\nI44\nsVportfolio\np3011\nI16\nsVrough\np3012\nI34\nsVmice\np3013\nI10\nsVdecade\np3014\nI37\nsVstaff\np3015\nI226\nsVpause\np3016\nI16\nsVgrabbed\np3017\nI15\nsVknowledge\np3018\nI146\nsVcomfort\np3019\nI26\nsVjaw\np3020\nI11\nsVcontrols\np3021\nI11\nsVshould\np3022\nI1112\nsVbernard\np3023\nI21\nsVplanted\np3024\nI14\nsVtape\np3025\nI46\nsVmolecules\np3026\nI15\nsVriding\np3027\nI17\nsVcommunist\np3028\nI42\nsVhope\np3029\nI55\nsVequilibrium\np3030\nI17\nsVmeant\np3031\nI113\nsVinsight\np3032\nI14\nsVadvisory\np3033\nI16\nsVlistened\np3034\nI24\nsVexceptional\np3035\nI17\nsVbeat\np3036\nI11\nsVfamiliar\np3037\nI57\nsVczechoslovakia\np3038\nI11\nsVlucky\np3039\nI41\nsVbear\np3040\nI12\nsVdivisions\np3041\nI22\nsVbeam\np3042\nI11\nsVexchanges\np3043\nI11\nsVrichards\np3044\nI11\nsVsorts\np3045\nI30\nsVareas\np3046\nI234\nsVsymbols\np3047\nI13\nsVcommittees\np3048\nI27\nsVorgan\np3049\nI13\nsVoffences\np3050\nI24\nsVdenmark\np3051\nI13\nsVrage\np3052\nI12\nsVcomprising\np3053\nI11\nsVtaxes\np3054\nI28\nsVcalling\np3055\nI41\nsVreid\np3056\nI10\nsVstuff\np3057\nI69\nsVpainter\np3058\nI12\nsVfixed\np3059\nI28\nsVgibson\np3060\nI10\nsVstrengthened\np3061\nI10\nsVtemporarily\np3062\nI13\nsVexists\np3063\nI32\nsVframe\np3064\nI32\nsVpacket\np3065\nI12\nsVedition\np3066\nI25\nsVintensity\np3067\nI16\nsVenhanced\np3068\nI11\nsVgreece\np3069\nI17\nsVdiscretion\np3070\nI19\nsVpacked\np3071\nI18\nsVphases\np3072\nI10\nsVwire\np3073\nI22\nsVreform\np3074\nI54\nsVnuclear\np3075\nI81\nsVralph\np3076\nI12\nsVdifficulties\np3077\nI68\nsVroutine\np3078\nI29\nsVprogress\np3079\nI76\nsVboundary\np3080\nI20\nsVemissions\np3081\nI15\nsVindustries\np3082\nI43\nsVends\np3083\nI15\nsVdeliver\np3084\nI22\nsVhistorians\np3085\nI16\nsVconfiguration\np3086\nI10\nsVstaring\np3087\nI29\nsVrestrictions\np3088\nI27\nsVsharply\np3089\nI25\nsVjoke\np3090\nI21\nsVinvited\np3091\nI42\nsVequal\np3092\nI61\nsVdrug\np3093\nI50\nsVtrips\np3094\nI11\nsVetc\np3095\nI50\nsVdoorway\np3096\nI17\nsVfigures\np3097\nI112\nsVbristol\np3098\nI29\nsVpassing\np3099\nI12\nsVheart\np3100\nI137\nsVglorious\np3101\nI11\nsVotherwise\np3102\nI88\nsVcomment\np3103\nI19\nsVadjusted\np3104\nI11\nsVrelevant\np3105\nI79\nsVcm\np3106\nI21\nsVconclude\np3107\nI15\nsVmalcolm\np3108\nI18\nsVcd\np3109\nI10\nsVlaugh\np3110\nI17\nsVcousin\np3111\nI18\nsVallocated\np3112\nI17\nsVwinning\np3113\nI31\nsVearning\np3114\nI12\nsVdebt\np3115\nI54\nsVvideos\np3116\nI11\nsVmidland\np3117\nI15\nsVtremendous\np3118\nI20\nsVcopies\np3119\nI35\nsVgenetic\np3120\nI18\nsVgaze\np3121\nI21\nsViran\np3122\nI20\nsVah\np3123\nI99\nsVindicators\np3124\nI11\nsVcurtain\np3125\nI14\nsVproposal\np3126\nI42\nsVwaste\np3127\nI14\nsVdefine\np3128\nI24\nsVsuggested\np3129\nI106\nsVarises\np3130\nI18\nsVc.\np3131\nI22\nsVessentially\np3132\nI36\nsVneighbour\np3133\nI18\nsVpensioners\np3134\nI14\nsVfinished\np3135\nI79\nsVdeep\np3136\nI27\nsVangles\np3137\nI13\nsVgraphics\np3138\nI20\nsVassessments\np3139\nI11\nsVan\np3140\nI3430\nsVspain\np3141\nI45\nsVhomes\np3142\nI61\nsVholiday\np3143\nI76\nsVcouple\np3144\nI123\nsVimagination\np3145\nI27\nsVplain\np3146\nI11\nsVappearance\np3147\nI54\nsVexamine\np3148\nI37\nsVvalue\np3149\nI175\nsVpromoted\np3150\nI16\nsVuncertainty\np3151\nI22\nsValmost\np3152\nI316\nsVwalks\np3153\nI12\nsVsurprisingly\np3154\nI26\nsVhardware\np3155\nI21\nsVarrangements\np3156\nI58\nsVagainst\np3157\nI562\nsVlifetime\np3158\nI18\nsVclaimed\np3159\nI83\nsVjunction\np3160\nI18\nsVinspector\np3161\nI29\nsVsits\np3162\nI12\nsVproductivity\np3163\nI20\nsVof*\np3164\nI17\nsVadministration\np3165\nI66\nsVhighlighted\np3166\nI11\nsVmatthew\np3167\nI26\nsVfill\np3168\nI39\nsVpaula\np3169\nI11\nsVinjured\np3170\nI11\nsVball\np3171\nI74\nsVnovels\np3172\nI13\nsVend nop-\np3173\nI12\nsVdrink\np3174\nI32\nsVupon\np3175\nI234\nsVv.\np3176\nI54\nsVforehead\np3177\nI13\nsVdust\np3178\nI26\nsVjudgment\np3179\nI33\nsVscholars\np3180\nI11\nsVexpand\np3181\nI18\nsVaudit\np3182\nI22\nsVpriest\np3183\nI21\nsVoff\np3184\nI214\nsVmention\np3185\nI16\nsVnevertheless\np3186\nI72\nsVconfronted\np3187\nI12\nsVcolour\np3188\nI111\nsVthought\np3189\nI95\nsVpatterns\np3190\nI58\nsVcommand\np3191\nI34\nsVsets\np3192\nI22\nsVcomparisons\np3193\nI13\nsVmuscle\np3194\nI18\nsVarising\np3195\nI22\nsVdrawing\np3196\nI25\nsVlatest\np3197\nI65\nsVstores\np3198\nI23\nsVnegligence\np3199\nI13\nsVcoins\np3200\nI14\nsVflesh\np3201\nI25\nsVmoments\np3202\nI32\nsVexecutive\np3203\nI78\nsVdomestic\np3204\nI69\nsVgoing*\np3205\nI658\nsVclinic\np3206\nI15\nsVunderlying\np3207\nI22\nsVrooms\np3208\nI55\nsVseats\np3209\nI47\nsVprotecting\np3210\nI12\nsVpaul\np3211\nI114\nsVgenerous\np3212\nI23\nsVwee\np3213\nI12\nsVfield\np3214\nI143\nsVresidential\np3215\nI29\nsVengage\np3216\nI12\nsVregulatory\np3217\nI12\nsVlake\np3218\nI39\nsVday\np3219\nI12\nsVarrest\np3220\nI16\nsVadd\np3221\nI82\nsVcombine\np3222\nI16\nsVwet\np3223\nI36\nsVpractise\np3224\nI12\nsVought\np3225\nI61\nsVresolved\np3226\nI21\nsVtests\np3227\nI50\nsVincreased\np3228\nI57\nsVarticles\np3229\nI29\nsVchecking\np3230\nI16\nsVchancellor\np3231\nI37\nsVfebruary\np3232\nI84\nsVincreases\np3233\nI20\nsVfive\np3234\nI407\nsVdesk\np3235\nI45\nsVbelgium\np3236\nI14\nsVles*\np3237\nI13\nsVmhm\np3238\nI75\nsVemma\np3239\nI15\nsVlike\np3240\nI16\nsVsuccess\np3241\nI134\nsVchairs\np3242\nI20\nsVsofa\np3243\nI10\nsVtons\np3244\nI12\nsVadmitted\np3245\nI56\nsVozone\np3246\nI13\nsVgarage\np3247\nI21\nsVjournalist\np3248\nI14\nsVwarned\np3249\nI40\nsVbecome\np3250\nI304\nsVworks\np3251\nI63\nsVsoft\np3252\nI61\nsVreplacement\np3253\nI26\nsVamendment\np3254\nI18\nsVgaining\np3255\nI12\nsVclassical\np3256\nI33\nsVauthority\np3257\nI183\nsVhair\np3258\nI144\nsVtony\np3259\nI50\nsVsunderland\np3260\nI14\nsVmiss*\np3261\nI91\nsVconvey\np3262\nI12\nsVrecommendation\np3263\nI14\nsVproper\np3264\nI65\nsVfaint\np3265\nI15\nsVrecognition\np3266\nI58\nsVhappens\np3267\nI58\nsVvocabulary\np3268\nI12\nsVliterally\np3269\nI20\nsVavoid\np3270\nI80\nsVmorris\np3271\nI18\nsVshelter\np3272\nI14\nsVdoes\np3273\nI687\nsVpassion\np3274\nI23\nsVassuming\np3275\nI19\nsVchains\np3276\nI13\nsVmode\np3277\nI27\nsVdeciding\np3278\nI19\nsVbiology\np3279\nI11\nsVblowing\np3280\nI10\nsVproud\np3281\nI32\nsVschedule\np3282\nI24\nsVpressure\np3283\nI119\nsVhost\np3284\nI27\nsValthough\np3285\nI436\nsVaccompanied\np3286\nI33\nsVsnapped\np3287\nI19\nsVloans\np3288\nI27\nsVtrials\np3289\nI21\nsVcounties\np3290\nI16\nsVstage\np3291\nI162\nsVgained\np3292\nI37\nsVsister\np3293\nI75\nsVlifestyle\np3294\nI11\nsVputs\np3295\nI29\nsVshaking\np3296\nI18\nsVwithdrew\np3297\nI10\nsVbasis\np3298\nI145\nsVholdings\np3299\nI12\nsVseeds\np3300\nI16\nsVinsufficient\np3301\nI13\nsVconstraints\np3302\nI19\nsVsoftware\np3303\nI94\nsValliance\np3304\nI31\nsVletters\np3305\nI79\nsVrecognise\np3306\nI36\nsVcatherine\np3307\nI16\nsVcommitment\np3308\nI57\nsVthree\np3309\nI797\nsVassess\np3310\nI27\nsVchronic\np3311\nI17\nsVguard\np3312\nI25\nsVfemale\np3313\nI21\nsVjoyce\np3314\nI13\nsVroads\np3315\nI39\nsVmere\np3316\nI34\nsV1986\np3317\nI79\nsV1987\np3318\nI85\nsV1984\np3319\nI58\nsVprocessor\np3320\nI15\nsVridge\np3321\nI13\nsV1983\np3322\nI57\nsV1980\np3323\nI52\nsV1981\np3324\nI56\nsVhousing\np3325\nI28\nsVoutline\np3326\nI14\nsVspots\np3327\nI12\nsVawarded\np3328\nI25\nsVrecognised\np3329\nI43\nsVfrustration\np3330\nI14\nsVbiggest\np3331\nI46\nsVspecimens\np3332\nI15\nsVnaturally\np3333\nI43\nsVfunction\np3334\nI78\nsVfiscal\np3335\nI13\nsVbuy\np3336\nI124\nsVbus\np3337\nI54\nsVbrand\np3338\nI13\nsVnt\np3339\nI14\nsVpreparations\np3340\nI10\nsVdelivery\np3341\nI36\nsVrepeated\np3342\nI32\nsVconstruction\np3343\nI63\nsVconservative*\np3344\nI64\nsVvaluation\np3345\nI11\nsVentered\np3346\nI59\nsVpartially\np3347\nI13\nsVcount\np3348\nI21\nsVwise\np3349\nI20\nsVglory\np3350\nI17\nsVdangerous\np3351\nI58\nsVofficial\np3352\nI21\nsVsmooth\np3353\nI28\nsVexcitement\np3354\nI26\nsVplaced\np3355\nI86\nsVfrequency\np3356\nI28\nsVonwards\np3357\nI14\nsVfetch\np3358\nI12\nsVdistribution\np3359\nI63\nsVminutes\np3360\nI183\nsVbearing\np3361\nI22\nsVirish\np3362\nI59\nsVrabbits\np3363\nI10\nsVdeaths\np3364\nI25\nsVnurses\np3365\nI25\nsVrecognize\np3366\nI21\nsVcontribute\np3367\nI27\nsVpie\np3368\nI12\nsVpig\np3369\nI13\nsVinn\np3370\nI15\nsVperiods\np3371\nI40\nsVour\np3372\nI950\nsVcommentators\np3373\nI10\nsVshooting\np3374\nI11\nsVpit\np3375\nI17\nsVproceeds\np3376\nI11\nsVinc\np3377\nI57\nsVcompared\np3378\nI88\nsVpicked\np3379\nI63\nsV48\np3380\nI15\nsVvariety\np3381\nI87\nsV46\np3382\nI11\nsVcorporation\np3383\nI35\nsV44\np3384\nI12\nsV45\np3385\nI22\nsVclaiming\np3386\nI22\nsVforests\np3387\nI20\nsV40\np3388\nI57\nsV41\np3389\nI12\nsVdetails\np3390\nI117\nsVnelson\np3391\nI10\nsVrepeat\np3392\nI27\nsVmonday\np3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nop-\np6883\nI44\nsVlived\np6884\nI82\nsVgoodness\np6885\nI15\nsVflight\np6886\nI52\nsVeducation\np6887\nI260\nsVeric\np6888\nI19\nsVmutual\np6889\nI22\nsV'll\np6890\nI726\nsVgay\np6891\nI16\nsVingredients\np6892\nI13\nsVboot\np6893\nI15\nsVpromote\np6894\nI32\nsVenvisaged\np6895\nI12\nsVwestminster\np6896\nI22\nsVnhs\np6897\nI25\nsVbook\np6898\nI243\nsVboom\np6899\nI16\nsVsick\np6900\nI44\nsVyoungsters\np6901\nI17\nsVer\np6902\nI896\nsVconclusion\np6903\nI51\nsVkinds\np6904\nI47\nsVdiagnosis\np6905\nI17\nsVoct.\np6906\nI32\nsVjune\np6907\nI146\nsVultimately\np6908\nI29\nsVstay\np6909\nI11\nsVmargaret\np6910\nI38\nsVtonnes\np6911\nI19\nsVpriced\np6912\nI11\nsVfriends\np6913\nI151\nsVincurred\np6914\nI12\nsVupwards\np6915\nI16\nsVappoint\np6916\nI10\nsVranks\np6917\nI16\nsVpools\np6918\nI11\nsVfactors\np6919\nI87\nsVpersistent\np6920\nI13\nsVcasual\np6921\nI18\nsVportion\np6922\nI11\nsVvolumes\np6923\nI16\nsVideological\np6924\nI17\nsVunderstand\np6925\nI155\nsVindirectly\np6926\nI10\nstp6927\nRp6928\n."
},
{
"path": "corpkit/dictionaries/bnc.py",
"content": "def _get_bnc():\n \"\"\"Load the BNC\"\"\"\n import corpkit\n try:\n import cPickle as pickle\n except ImportError:\n import pickle\n import os\n fullpaths = [os.path.join(os.path.dirname(corpkit.__file__), 'corpkit', 'dictionaries', 'bnc.p'),\n os.path.join(os.path.dirname(corpkit.__file__), 'dictionaries', 'bnc.p'),\n os.path.join(os.path.dirname(corpkit.__file__), 'bnc.p'),\n os.path.join(os.path.dirname(os.path.dirname(corpkit.__file__)), 'bnc.p')]\n fullpath = next(x for x in fullpaths if os.path.isfile(x))\n with open(fullpath, 'rb') as fo:\n data = pickle.load(fo)\n return data\n\nbnc = _get_bnc()"
},
{
"path": "corpkit/dictionaries/eng_verb_lexicon.p",
"content": 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sing'\np12251\naS'revised'\np12252\naS'revised'\np12253\nasS'concretize'\np12254\n(lp12255\nS'concretizes'\np12256\naS'concretizing'\np12257\naS'concretized'\np12258\naS'concretized'\np12259\nasS'rectify'\np12260\n(lp12261\nS'rectifies'\np12262\naS'rectifying'\np12263\naS'rectified'\np12264\naS'rectified'\np12265\nasS'stultify'\np12266\n(lp12267\nS'stultifies'\np12268\naS'stultifying'\np12269\naS'stultified'\np12270\naS'stultified'\np12271\nasS'vituperate'\np12272\n(lp12273\nS'vituperates'\np12274\naS'vituperating'\np12275\naS'vituperated'\np12276\naS'vituperated'\np12277\nasS'assess'\np12278\n(lp12279\nS'assesses'\np12280\naS'assessing'\np12281\naS'assessed'\np12282\naS'assessed'\np12283\nasS'muck'\np12284\n(lp12285\nS'mucks'\np12286\naS'mucking'\np12287\naS'mucked'\np12288\naS'mucked'\np12289\nasS'copolymerize'\np12290\n(lp12291\nS'copolymerizes'\np12292\naS'copolymerizing'\np12293\naS'copolymerized'\np12294\naS'copolymerized'\np12295\nasS'reactivate'\np12296\n(lp12297\nS'reactivates'\np12298\naS'reactivating'\np12299\naS'reactivated'\np12300\naS'reactivated'\np12301\nasS'overestimate'\np12302\n(lp12303\nS'overestimates'\np12304\naS'overestimating'\np12305\naS'overestimated'\np12306\naS'overestimated'\np12307\nasS'imbibe'\np12308\n(lp12309\nS'imbibes'\np12310\naS'imbibing'\np12311\naS'imbibed'\np12312\naS'imbibed'\np12313\nasS'discant'\np12314\n(lp12315\nS'discants'\np12316\naS'discanting'\np12317\naS'discanted'\np12318\naS'discanted'\np12319\nasS'paganize'\np12320\n(lp12321\nS'paganizes'\np12322\naS'paganizing'\np12323\naS'paganized'\np12324\naS'paganized'\np12325\nasS'function'\np12326\n(lp12327\nS'functions'\np12328\naS'functioning'\np12329\naS'functioned'\np12330\naS'functioned'\np12331\nasS'funnel'\np12332\n(lp12333\nS'funnels'\np12334\naS'funnelling'\np12335\naS'funnelled'\np12336\naS'funnelled'\np12337\nasS'frisk'\np12338\n(lp12339\nS'frisks'\np12340\naS'frisking'\np12341\naS'frisked'\np12342\naS'frisked'\np12343\nasS'unbind'\np12344\n(lp12345\nS'unbinds'\np12346\naS'unbinding'\np12347\naS'unbound'\np12348\naS'unbound'\np12349\nasS'unstick'\np12350\n(lp12351\nS'unsticks'\np12352\naS'unsticking'\np12353\naS'unstuck'\np12354\naS'unstuck'\np12355\nasS'foretaste'\np12356\n(lp12357\nS'foretastes'\np12358\naS'foretasting'\np12359\naS'foretasted'\np12360\naS'foretasted'\np12361\nasS'grate'\np12362\n(lp12363\nS'grates'\np12364\naS'grating'\np12365\naS'grated'\np12366\naS'grated'\np12367\nasS'count'\np12368\n(lp12369\nS'counts'\np12370\naS'counting'\np12371\naS'counted'\np12372\naS'counted'\np12373\nasS'compute'\np12374\n(lp12375\nS'computes'\np12376\naS'computing'\np12377\naS'computed'\np12378\naS'computed'\np12379\nasS'shrunk'\np12380\n(lp12381\nS'shrunks'\np12382\naS'shrunking'\np12383\naS'shrunked'\np12384\naS'shrunked'\np12385\nasS'chaperone'\np12386\n(lp12387\nS'chaperons'\np12388\naS'chaperoning'\np12389\naS'chaperoned'\np12390\naS'chaperoned'\np12391\nasS'convince'\np12392\n(lp12393\nS'convinces'\np12394\naS'convincing'\np12395\naS'convinced'\np12396\naS'convinced'\np12397\nasS'freewheel'\np12398\n(lp12399\nS'freewheels'\np12400\naS'freewheeling'\np12401\naS'freewheeled'\np12402\naS'freewheeled'\np12403\nasS'enthrone'\np12404\n(lp12405\nS'enthrones'\np12406\naS'enthroning'\np12407\naS'enthroned'\np12408\naS'enthroned'\np12409\nasS'spreadeagle'\np12410\n(lp12411\nS'spreadeagles'\np12412\naS'spreadeagling'\np12413\naS'spreadeagled'\np12414\naS'spreadeagled'\np12415\nasS'appraise'\np12416\n(lp12417\nS'appraises'\np12418\naS'appraising'\np12419\naS'appraised'\np12420\naS'appraised'\np12421\nasS'kowtow'\np12422\n(lp12423\nS'kowtows'\np12424\naS'kowtowing'\np12425\naS'kowtowed'\np12426\naS'kowtowed'\np12427\nasS'fractionate'\np12428\n(lp12429\nS'fractionates'\np12430\naS'fractionating'\np12431\naS'fractionated'\np12432\naS'fractionated'\np12433\nasS'recognize'\np12434\n(lp12435\nS'recognizes'\np12436\naS'recognizing'\np12437\naS'recognized'\np12438\naS'recognized'\np12439\nasS'gasconade'\np12440\n(lp12441\nS'gasconades'\np12442\naS'gasconading'\np12443\naS'gasconaded'\np12444\naS'gasconaded'\np12445\nasS'contribute'\np12446\n(lp12447\nS'contributes'\np12448\naS'contributing'\np12449\naS'contributed'\np12450\naS'contributed'\np12451\nasS'denote'\np12452\n(lp12453\nS'denotes'\np12454\naS'denoting'\np12455\naS'denoted'\np12456\naS'denoted'\np12457\nasS'withstand'\np12458\n(lp12459\nS'withstands'\np12460\naS'withstanding'\np12461\naS'withstood'\np12462\naS'withstood'\np12463\nasS'ink'\np12464\n(lp12465\nS'inks'\np12466\naS'inking'\np12467\naS'inked'\np12468\naS'inked'\np12469\nasS'letch'\np12470\n(lp12471\nS'letches'\np12472\naS'letching'\np12473\naS'letched'\np12474\naS'letched'\np12475\nasS'engorge'\np12476\n(lp12477\nS'engorges'\np12478\naS'engorging'\np12479\naS'engorged'\np12480\naS'engorged'\np12481\nasS'warsle'\np12482\n(lp12483\nS'warsles'\np12484\naS'warsling'\np12485\naS'warsled'\np12486\naS'warsled'\np12487\nasS'flummox'\np12488\n(lp12489\nS'flummoxes'\np12490\naS'flummoxing'\np12491\naS'flummoxed'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'\np17457\nasS'powerdive'\np17458\n(lp17459\nS'powerdives'\np17460\naS'powerdiving'\np17461\naS'powerdived'\np17462\naS'powerdived'\np17463\nasS'decoke'\np17464\n(lp17465\nS'decokes'\np17466\naS'decoking'\np17467\naS'decoked'\np17468\naS'decoked'\np17469\nasS'hydrate'\np17470\n(lp17471\nS'hydrates'\np17472\naS'hydrating'\np17473\naS'hydrated'\np17474\naS'hydrated'\np17475\nasS'animalize'\np17476\n(lp17477\nS'animalizes'\np17478\naS'animalizing'\np17479\naS'animalized'\np17480\naS'animalized'\np17481\nasS'syllabify'\np17482\n(lp17483\nS'syllabifies'\np17484\naS'syllabifying'\np17485\naS'syllabified'\np17486\naS'syllabified'\np17487\nasS'cleave'\np17488\n(lp17489\nS'cleaves'\np17490\naS'cleaving'\np17491\naS'clove'\np17492\naS'cloven'\np17493\nasS'glide'\np17494\n(lp17495\nS'glides'\np17496\naS'gliding'\np17497\naS'glided'\np17498\naS'glided'\np17499\nasS'guffaw'\np17500\n(lp17501\nS'guffaws'\np17502\naS'guffawing'\np17503\naS'guffawed'\np17504\naS'guffawed'\np17505\nasS'pertain'\np17506\n(lp17507\nS'pertains'\np17508\naS'pertaining'\np17509\naS'pertained'\np17510\naS'pertained'\np17511\nasS'adhibit'\np17512\n(lp17513\nS'adhibits'\np17514\naS'adhibiting'\np17515\naS'adhibited'\np17516\naS'adhibited'\np17517\nasS'retort'\np17518\n(lp17519\nS'retorts'\np17520\naS'retorting'\np17521\naS'retorted'\np17522\naS'retorted'\np17523\nasS'evict'\np17524\n(lp17525\nS'evicts'\np17526\naS'evicting'\np17527\naS'evicted'\np17528\naS'evicted'\np17529\nasS'disentitle'\np17530\n(lp17531\nS'disentitles'\np17532\naS'disentitling'\np17533\naS'disentitled'\np17534\naS'disentitled'\np17535\nasS'despond'\np17536\n(lp17537\nS'desponds'\np17538\naS'desponding'\np17539\naS'desponded'\np17540\naS'desponded'\np17541\nasS'wreathe'\np17542\n(lp17543\nS'wreathes'\np17544\naS'wreathing'\np17545\naS'wreathed'\np17546\naS'wreathed'\np17547\nasS'mummify'\np17548\n(lp17549\nS'mummifies'\np17550\naS'mummifying'\np17551\naS'mummified'\np17552\naS'mummified'\np17553\nasS'dispatch'\np17554\n(lp17555\nS'dispatches'\np17556\naS'dispatching'\np17557\naS'dispatched'\np17558\naS'dispatched'\np17559\nasS'exploit'\np17560\n(lp17561\nS'exploits'\np17562\naS'exploiting'\np17563\naS'exploited'\np17564\naS'exploited'\np17565\nasS'forehand'\np17566\n(lp17567\nS'forehands'\np17568\naS'forehanding'\np17569\naS'forehanded'\np17570\naS'forehanded'\np17571\nasS'labialize'\np17572\n(lp17573\nS'labializes'\np17574\naS'labializing'\np17575\naS'labialized'\np17576\naS'labialized'\np17577\nasS'deregister'\np17578\n(lp17579\nS'deregisters'\np17580\naS'deregistering'\np17581\naS'deregistered'\np17582\naS'deregistered'\np17583\nasS'uglify'\np17584\n(lp17585\nS'uglifies'\np17586\naS'uglifying'\np17587\naS'uglified'\np17588\naS'uglified'\np17589\nasS'resort'\np17590\n(lp17591\nS'resorts'\np17592\naS'resorting'\np17593\naS'resorted'\np17594\naS'resorted'\np17595\nasS'sket'\np17596\n(lp17597\nS'skets'\np17598\naS'sketting'\np17599\naS'sketted'\np17600\naS'sketted'\np17601\nasS'invert'\np17602\n(lp17603\nS'inverts'\np17604\naS'inverting'\np17605\naS'inverted'\np17606\naS'inverted'\np17607\nasS'overjoy'\np17608\n(lp17609\nS'overjoys'\np17610\naS'overjoying'\np17611\naS'overjoyed'\np17612\naS'overjoyed'\np17613\nasS'toss'\np17614\n(lp17615\nS'tosses'\np17616\naS'tossing'\np17617\naS'tossed'\np17618\naS'tossed'\np17619\nasS'asphyxiate'\np17620\n(lp17621\nS'asphyxiates'\np17622\naS'asphyxiating'\np17623\naS'asphyxiated'\np17624\naS'asphyxiated'\np17625\nasS'conflate'\np17626\n(lp17627\nS'conflates'\np17628\naS'conflating'\np17629\naS'conflated'\np17630\naS'conflated'\np17631\nasS'deescalate'\np17632\n(lp17633\nS'deescalates'\np17634\naS'deescalating'\np17635\naS'deescalated'\np17636\naS'deescalated'\np17637\nasS'agglutinate'\np17638\n(lp17639\nS'agglutinates'\np17640\naS'agglutinating'\np17641\naS'agglutinated'\np17642\naS'agglutinated'\np17643\nasS'disport'\np17644\n(lp17645\nS'disports'\np17646\naS'disporting'\np17647\naS'disported'\np17648\naS'disported'\np17649\nasS'encage'\np17650\n(lp17651\nS'encages'\np17652\naS'encaging'\np17653\naS'encaged'\np17654\naS'encaged'\np17655\nasS'crumb'\np17656\n(lp17657\nS'crumbs'\np17658\naS'crumbing'\np17659\naS'crumbed'\np17660\naS'crumbed'\np17661\nasS'compartmentalize'\np17662\n(lp17663\nS'compartmentalizes'\np17664\naS'compartmentalizing'\np17665\naS'compartmentalized'\np17666\naS'compartmentalized'\np17667\nasS'soft-solder'\np17668\n(lp17669\nS'soft-solders'\np17670\naS'soft-soldering'\np17671\naS'soft-soldered'\np17672\naS'soft-soldered'\np17673\nasS'trick'\np17674\n(lp17675\nS'tricks'\np17676\naS'tricking'\np17677\naS'tricked'\np17678\naS'tricked'\np17679\nasS'scud'\np17680\n(lp17681\nS'scuds'\np17682\naS'scudding'\np17683\naS'scudded'\np17684\naS'scudded'\np17685\nasS'crump'\np17686\n(lp17687\nS'crumps'\np17688\naS'crumping'\np17689\naS'crumped'\np17690\naS'crumped'\np17691\nasS'scum'\np17692\n(lp17693\nS'scums'\np17694\naS'scumming'\np17695\naS'scummed'\np17696\naS'scummed'\np17697\nasS'prevue'\np17698\n(lp17699\nS'prevues'\np17700\naS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led'\np29346\nasS'map'\np29347\n(lp29348\nS'maps'\np29349\naS'mapping'\np29350\naS'mapped'\np29351\naS'mapped'\np29352\nasS'mar'\np29353\n(lp29354\nS'mars'\np29355\naS'marring'\np29356\naS'marred'\np29357\naS'marred'\np29358\nasS'mat'\np29359\n(lp29360\nS'mats'\np29361\naS'matting'\np29362\naS'matted'\np29363\naS'matted'\np29364\nasS'legislate'\np29365\n(lp29366\nS'legislates'\np29367\naS'legislating'\np29368\naS'legislated'\np29369\naS'legislated'\np29370\nasS'may'\np29371\n(lp29372\nS'mayest'\np29373\naS\"mayn't\"\np29374\nasS'mad'\np29375\n(lp29376\nS'mads'\np29377\naS'madding'\np29378\naS'madded'\np29379\naS'madded'\np29380\nasS'methylate'\np29381\n(lp29382\nS'methylates'\np29383\naS'methylating'\np29384\naS'methylated'\np29385\naS'methylated'\np29386\nasS'subrogate'\np29387\n(lp29388\nS'subrogates'\np29389\naS'subrogating'\np29390\naS'subrogated'\np29391\naS'subrogated'\np29392\nasS'catcall'\np29393\n(lp29394\nS'catcalls'\np29395\naS'catcalling'\np29396\naS'catcalled'\np29397\naS'catcalled'\np29398\nasS'grow'\np29399\n(lp29400\nS'grows'\np29401\naS'growing'\np29402\naS'grew'\np29403\naS'grown'\np29404\nasS'fossilize'\np29405\n(lp29406\nS'fossilizes'\np29407\naS'fossilizing'\np29408\naS'fossilized'\np29409\naS'fossilized'\np29410\nasS'itinerate'\np29411\n(lp29412\nS'itinerates'\np29413\naS'itinerating'\np29414\naS'itinerated'\np29415\naS'itinerated'\np29416\nasS'omen'\np29417\n(lp29418\nS'omens'\np29419\naS'omening'\np29420\naS'omened'\np29421\naS'omened'\np29422\nasS'perambulate'\np29423\n(lp29424\nS'perambulates'\np29425\naS'perambulating'\np29426\naS'perambulated'\np29427\naS'perambulated'\np29428\nasS'purify'\np29429\n(lp29430\nS'purifies'\np29431\naS'purifying'\np29432\naS'purified'\np29433\naS'purified'\np29434\nasS'disembowel'\np29435\n(lp29436\nS'disembowels'\np29437\naS'disembowelling'\np29438\naS'disembowelled'\np29439\naS'disembowelled'\np29440\nasS'cascade'\np29441\n(lp29442\nS'cascades'\np29443\naS'cascading'\np29444\naS'cascaded'\np29445\naS'cascaded'\np29446\nasS'absquatulate'\np29447\n(lp29448\nS'absquatulates'\np29449\naS'absquatulating'\np29450\naS'absquatulated'\np29451\naS'absquatulated'\np29452\nasS'jail'\np29453\n(lp29454\nS'jails'\np29455\naS'jailing'\np29456\naS'jailed'\np29457\naS'jailed'\np29458\nasS'deposit'\np29459\n(lp29460\nS'deposits'\np29461\naS'depositing'\np29462\naS'deposited'\np29463\naS'deposited'\np29464\nasS'crossfertilize'\np29465\n(lp29466\nS'crossfertilizes'\np29467\naS'crossfertilizing'\np29468\naS'crossfertilized'\np29469\naS'crossfertilized'\np29470\nasS'unleash'\np29471\n(lp29472\nS'unleashes'\np29473\naS'unleashing'\np29474\naS'unleashed'\np29475\naS'unleashed'\np29476\nasS'tawse'\np29477\n(lp29478\nS'tawses'\np29479\naS'tawsing'\np29480\naS'tawsed'\np29481\naS'tawsed'\np29482\nasS'bungle'\np29483\n(lp29484\nS'bungles'\np29485\naS'bungling'\np29486\naS'bungled'\np29487\naS'bungled'\np29488\nasS'gesture'\np29489\n(lp29490\nS'gestures'\np29491\naS'gesturing'\np29492\naS'gestured'\np29493\naS'gestured'\np29494\nasS'uncork'\np29495\n(lp29496\nS'uncorks'\np29497\naS'uncorking'\np29498\naS'uncorked'\np29499\naS'uncorked'\np29500\nasS'shop-lift'\np29501\n(lp29502\nS'shop-lifts'\np29503\naS'shop-lifting'\np29504\naS'shop-lifted'\np29505\naS'shop-lifted'\np29506\nasS'shield'\np29507\n(lp29508\nS'shields'\np29509\naS'shielding'\np29510\naS'shielded'\np29511\naS'shielded'\np29512\nasS're-sign'\np29513\n(lp29514\nS're-signs'\np29515\naS're-signing'\np29516\naS're-signed'\np29517\naS're-signed'\np29518\nasS'clubhaul'\np29519\n(lp29520\nS'clubhauls'\np29521\naS'clubhauling'\np29522\naS'clubhauled'\np29523\naS'clubhauled'\np29524\nasS'recoup'\np29525\n(lp29526\nS'recoups'\np29527\naS'recouping'\np29528\naS'recouped'\np29529\naS'recouped'\np29530\nasS'terrace'\np29531\n(lp29532\nS'terraces'\np29533\naS'terracing'\np29534\naS'terraced'\np29535\naS'terraced'\np29536\nasS'pitch'\np29537\n(lp29538\nS'pitches'\np29539\naS'pitching'\np29540\naS'pitched'\np29541\naS'pitched'\np29542\nasS'incapsulate'\np29543\n(lp29544\nS'incapsulates'\np29545\naS'incapsulating'\np29546\naS'incapsulated'\np29547\naS'incapsulated'\np29548\nasS'equip'\np29549\n(lp29550\nS'equips'\np29551\naS'equipping'\np29552\naS'equipped'\np29553\naS'equipped'\np29554\nasS'grout'\np29555\n(lp29556\nS'grouts'\np29557\naS'grouting'\np29558\naS'grouted'\np29559\naS'grouted'\np29560\nasS'outbrave'\np29561\n(lp29562\nS'outbraves'\np29563\naS'outbraving'\np29564\naS'outbraved'\np29565\naS'outbraved'\np29566\nasS'group'\np29567\n(lp29568\nS'groups'\np29569\naS'grouping'\np29570\naS'grouped'\np29571\naS'grouped'\np29572\nasS'monitor'\np29573\n(lp29574\nS'monitors'\np29575\naS'monitoring'\np29576\naS'monitored'\np29577\naS'monitored'\np29578\nasS'hyphenate'\np29579\n(lp29580\nS'hyphenates'\np29581\naS'hyphening'\np29582\naS'hyphened'\np29583\naS'hyphened'\np29584\nasS'tellurize'\np29585\n(lp29586\nS'tellurizes'\np29587\naS'tellurizing'\np29588\naS'tellurized'\np29589\naS'tellurized'\np29590\nasS'maim'\np29591\n(lp29592\nS'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row'\np31832\n(lp31833\nS'burrows'\np31834\naS'burrowing'\np31835\naS'burrowed'\np31836\naS'burrowed'\np31837\nasS'represent'\np31838\n(lp31839\nS'represents'\np31840\naS'representing'\np31841\nag6736\naS'represented'\np31842\nasS'forget'\np31843\n(lp31844\nS'forgets'\np31845\naS'forgetting'\np31846\naS'forgot'\np31847\naS'forgotten'\np31848\nasS'founder'\np31849\n(lp31850\nsS'paginate'\np31851\n(lp31852\nS'paginates'\np31853\naS'paginating'\np31854\naS'paginated'\np31855\naS'paginated'\np31856\nasS'sunk'\np31857\n(lp31858\nS'sunks'\np31859\naS'sunking'\np31860\naS'sunked'\np31861\naS'sunked'\np31862\nasS'zing'\np31863\n(lp31864\nS'zings'\np31865\naS'zinging'\np31866\naS'zinged'\np31867\naS'zinged'\np31868\nasS'commute'\np31869\n(lp31870\nS'commutes'\np31871\naS'commuting'\np31872\naS'commuted'\np31873\naS'commuted'\np31874\nasS'catheterize'\np31875\n(lp31876\nS'catheterizes'\np31877\naS'catheterizing'\np31878\naS'catheterized'\np31879\naS'catheterized'\np31880\nasS'misadvise'\np31881\n(lp31882\nS'misadvises'\np31883\naS'misadvising'\np31884\naS'misadvised'\np31885\naS'misadvised'\np31886\nasS'crimson'\np31887\n(lp31888\nS'crimsons'\np31889\naS'crimsoning'\np31890\naS'crimsoned'\np31891\naS'crimsoned'\np31892\nasS'bestir'\np31893\n(lp31894\nS'bestirs'\np31895\naS'bestirring'\np31896\naS'bestirred'\np31897\naS'bestirred'\np31898\nasS'parcel'\np31899\n(lp31900\nS'parcels'\np31901\naS'parcelling'\np31902\naS'parcelled'\np31903\naS'parcelled'\np31904\nasS'counterproposal'\np31905\n(lp31906\nS'counterproposals'\np31907\naS'counterproposaling'\np31908\naS'counterproposaled'\np31909\naS'counterproposaled'\np31910\nasS'righten'\np31911\n(lp31912\nS'rightens'\np31913\naS'rightening'\np31914\naS'rightened'\np31915\naS'rightened'\np31916\nasS'hybridize'\np31917\n(lp31918\nS'hybridizes'\np31919\naS'hybridizing'\np31920\naS'hybridized'\np31921\naS'hybridized'\np31922\nasS'malinger'\np31923\n(lp31924\nS'malingers'\np31925\naS'malingering'\np31926\naS'malingered'\np31927\naS'malingered'\np31928\nasS'scour'\np31929\n(lp31930\nS'scours'\np31931\naS'scouring'\np31932\naS'scoured'\np31933\naS'scoured'\np31934\nasS'fall'\np31935\n(lp31936\nS'falls'\np31937\naS'falling'\np31938\naS'fell'\np31939\naS'fallen'\np31940\nasS'bottleneck'\np31941\n(lp31942\nS'bottlenecks'\np31943\naS'bottlenecking'\np31944\naS'bottlenecked'\np31945\naS'bottlenecked'\np31946\nasS'alien'\np31947\n(lp31948\nS'aliens'\np31949\naS'aliening'\np31950\naS'aliened'\np31951\naS'aliened'\np31952\nasS'dampen'\np31953\n(lp31954\nS'dampens'\np31955\naS'dampening'\np31956\naS'dampened'\np31957\naS'dampened'\np31958\nasS'unrip'\np31959\n(lp31960\nS'unrips'\np31961\naS'unripping'\np31962\naS'unripped'\np31963\naS'unripped'\np31964\nasS'subsoil'\np31965\n(lp31966\nS'subsoils'\np31967\naS'subsoiling'\np31968\naS'subsoiled'\np31969\naS'subsoiled'\np31970\nasS'triangulate'\np31971\n(lp31972\nS'triangulates'\np31973\naS'triangulating'\np31974\naS'triangulated'\np31975\naS'triangulated'\np31976\nasS'enrapture'\np31977\n(lp31978\nS'enraptures'\np31979\naS'enrapturing'\np31980\naS'enraptured'\np31981\naS'enraptured'\np31982\nasS'unrig'\np31983\n(lp31984\nS'unrigs'\np31985\naS'unrigging'\np31986\naS'unrigged'\np31987\naS'unrigged'\np31988\nasS'chandelle'\np31989\n(lp31990\nS'chandelles'\np31991\naS'chandelling'\np31992\naS'chandelled'\np31993\naS'chandelled'\np31994\nasS'underwrite'\np31995\n(lp31996\nS'underwrites'\np31997\naS'underwriting'\np31998\naS'underwrote'\np31999\naS'underwritten'\np32000\nasS'abridge'\np32001\n(lp32002\nS'abridges'\np32003\naS'abridging'\np32004\naS'abridged'\np32005\naS'abridged'\np32006\nasS'clinch'\np32007\n(lp32008\nS'clinches'\np32009\naS'clinching'\np32010\naS'clinched'\np32011\naS'clinched'\np32012\nasS'outwork'\np32013\n(lp32014\nS'outworks'\np32015\naS'outworking'\np32016\naS'outworked'\np32017\naS'outworked'\np32018\nasS'gnarl'\np32019\n(lp32020\nS'gnars'\np32021\naS'gnarling'\np32022\naS'gnarled'\np32023\naS'gnarled'\np32024\nasS'zero'\np32025\n(lp32026\nS'zeroes'\np32027\naS'zeroing'\np32028\naS'zeroed'\np32029\naS'zeroed'\np32030\nasS'overlie'\np32031\n(lp32032\nS'overlies'\np32033\naS'overlying'\np32034\naS'overlay'\np32035\naS'overlain'\np32036\nasS'depicture'\np32037\n(lp32038\nS'depictures'\np32039\naS'depicturing'\np32040\naS'depictured'\np32041\naS'depictured'\np32042\nasS'further'\np32043\n(lp32044\nS'furthers'\np32045\naS'furthering'\np32046\naS'furthered'\np32047\naS'furthered'\np32048\nasS'misrepresent'\np32049\n(lp32050\nS'misrepresents'\np32051\naS'misrepresenting'\np32052\naS'misrepresented'\np32053\naS'misrepresented'\np32054\nasS'ribbon'\np32055\n(lp32056\nS'ribbons'\np32057\naS'ribboning'\np32058\naS'ribboned'\np32059\naS'ribboned'\np32060\nasS'dial'\np32061\n(lp32062\nS'dials'\np32063\naS'dialing'\np32064\naS'dialed'\np32065\naS'dialed'\np32066\nasS'ruminate'\np32067\n(lp32068\nS'ruminates'\np32069\naS'ruminating'\np32070\naS'ruminated'\np32071\naS'ruminated'\np32072\nasS'stook'\np32073\n(lp32074\nS'stooks'\np32075\naS'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oured'\np32813\naS'detoured'\np32814\nasS'compound'\np32815\n(lp32816\nS'compounds'\np32817\naS'compounding'\np32818\naS'compounded'\np32819\naS'compounded'\np32820\nasS'wiredraw'\np32821\n(lp32822\nS'wiredraws'\np32823\naS'wiredrawing'\np32824\naS'wiredrew'\np32825\naS'wiredrawn'\np32826\nasS'detach'\np32827\n(lp32828\nS'detaches'\np32829\naS'detaching'\np32830\naS'detached'\np32831\naS'detached'\np32832\nasS'complain'\np32833\n(lp32834\nS'complains'\np32835\naS'complaining'\np32836\naS'complained'\np32837\naS'complained'\np32838\nasS'restrict'\np32839\n(lp32840\nS'restricts'\np32841\naS'restricting'\np32842\naS'restricted'\np32843\naS'restricted'\np32844\nasS'huddle'\np32845\n(lp32846\nS'huddles'\np32847\naS'huddling'\np32848\naS'huddled'\np32849\naS'huddled'\np32850\nasS'auspicate'\np32851\n(lp32852\nS'auspicates'\np32853\naS'auspicating'\np32854\naS'auspicated'\np32855\naS'auspicated'\np32856\nasS'countersign'\np32857\n(lp32858\nS'countersigns'\np32859\naS'countersigning'\np32860\naS'countersigned'\np32861\naS'countersigned'\np32862\nasS'evade'\np32863\n(lp32864\nS'evades'\np32865\naS'evading'\np32866\naS'evaded'\np32867\naS'evaded'\np32868\nasS'token'\np32869\n(lp32870\nS'tokens'\np32871\naS'tokening'\np32872\naS'tokened'\np32873\naS'tokened'\np32874\nasS'safeconduct'\np32875\n(lp32876\nS'safeconducts'\np32877\naS'safeconducting'\np32878\naS'safeconducted'\np32879\naS'safeconducted'\np32880\nasS'reft'\np32881\n(lp32882\nS'reft'\np32883\naS'reft'\np32884\nasS'quintuple'\np32885\n(lp32886\nS'quintuples'\np32887\naS'quintupling'\np32888\naS'quintupled'\np32889\naS'quintupled'\np32890\nasS'politicize'\np32891\n(lp32892\nS'politicizes'\np32893\naS'politicizing'\np32894\naS'politicized'\np32895\naS'politicized'\np32896\nasS'clamp'\np32897\n(lp32898\nS'clamps'\np32899\naS'clamping'\np32900\naS'clamped'\np32901\naS'clamped'\np32902\nasS'harm'\np32903\n(lp32904\nS'harms'\np32905\naS'harming'\np32906\naS'harmed'\np32907\naS'harmed'\np32908\nasS'hark'\np32909\n(lp32910\nS'harks'\np32911\naS'harking'\np32912\naS'harked'\np32913\naS'harked'\np32914\nasS'house'\np32915\n(lp32916\nS'houses'\np32917\naS'housing'\np32918\naS'housed'\np32919\naS'housed'\np32920\nasS'hare'\np32921\n(lp32922\nS'hares'\np32923\naS'haring'\np32924\naS'hared'\np32925\naS'hared'\np32926\nasS'electroplate'\np32927\n(lp32928\nS'electroplates'\np32929\naS'electroplating'\np32930\naS'electroplated'\np32931\naS'electroplated'\np32932\nasS'connect'\np32933\n(lp32934\nS'connects'\np32935\naS'connecting'\np32936\naS'connected'\np32937\naS'connected'\np32938\nasS'fist'\np32939\n(lp32940\nS'fists'\np32941\naS'fisting'\np32942\naS'fisted'\np32943\naS'fisted'\np32944\nasS'ripple'\np32945\n(lp32946\nS'ripples'\np32947\naS'rippling'\np32948\naS'rippled'\np32949\naS'rippled'\np32950\nasS'callus'\np32951\n(lp32952\nS'calluses'\np32953\naS'callusing'\np32954\naS'callused'\np32955\naS'callused'\np32956\nasS'orient'\np32957\n(lp32958\nS'orients'\np32959\naS'orienting'\np32960\naS'oriented'\np32961\naS'oriented'\np32962\nasS'harp'\np32963\n(lp32964\nS'harps'\np32965\naS'harping'\np32966\naS'harped'\np32967\naS'harped'\np32968\nasS'inshrine'\np32969\n(lp32970\nS'inshrines'\np32971\naS'inshrining'\np32972\naS'inshrined'\np32973\naS'inshrined'\np32974\nasS'flower'\np32975\n(lp32976\nS'flowers'\np32977\naS'flowering'\np32978\naS'flowered'\np32979\naS'flowered'\np32980\nasS'prink'\np32981\n(lp32982\nS'prinks'\np32983\naS'prinking'\np32984\naS'prinked'\np32985\naS'prinked'\np32986\nasS'coauthor'\np32987\n(lp32988\nS'coauthors'\np32989\naS'coauthoring'\np32990\naS'coauthored'\np32991\naS'coauthored'\np32992\nasS'vitiate'\np32993\n(lp32994\nS'vitiates'\np32995\naS'vitiating'\np32996\naS'vitiated'\np32997\naS'vitiated'\np32998\nasS'avenge'\np32999\n(lp33000\nS'avenges'\np33001\naS'avenging'\np33002\naS'avenged'\np33003\naS'avenged'\np33004\nasS'print'\np33005\n(lp33006\nS'prints'\np33007\naS'printing'\np33008\naS'printed'\np33009\naS'printed'\np33010\nasS'bully'\np33011\n(lp33012\nS'bullies'\np33013\naS'bullying'\np33014\naS'bullied'\np33015\naS'bullied'\np33016\nasS'kittle'\np33017\n(lp33018\nS'kittles'\np33019\naS'kittling'\np33020\naS'kittled'\np33021\naS'kittled'\np33022\nasS'decompress'\np33023\n(lp33024\nS'decompresses'\np33025\naS'decompressing'\np33026\naS'decompressed'\np33027\naS'decompressed'\np33028\nasS'golly'\np33029\n(lp33030\nS'gollies'\np33031\naS'gollying'\np33032\naS'gollied'\np33033\naS'gollied'\np33034\nasS'recast'\np33035\n(lp33036\nS'recasts'\np33037\naS'recasting'\np33038\naS'recast'\np33039\naS'recast'\np33040\nasS'stratify'\np33041\n(lp33042\nS'stratifies'\np33043\naS'stratifying'\np33044\naS'stratified'\np33045\naS'stratified'\np33046\nasS'omit'\np33047\n(lp33048\nS'omits'\np33049\naS'omitting'\np33050\naS'omitted'\np33051\naS'omitted'\np33052\nasS'desiccate'\np33053\n(lp33054\nS'desiccates'\np33055\naS'desiccating'\np33056\naS'desiccated'\np33057\naS'desiccated'\np33058\nasS'predate'\np33059\n(lp33060\nS'predates'\np33061\naS'predating'\np3306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307\naS'prefiguring'\np33308\naS'prefigured'\np33309\naS'prefigured'\np33310\nasS'marinade'\np33311\n(lp33312\nS'marinades'\np33313\naS'marinading'\np33314\naS'marinaded'\np33315\naS'marinaded'\np33316\nasS'elegize'\np33317\n(lp33318\nS'elegizes'\np33319\naS'elegizing'\np33320\naS'elegized'\np33321\naS'elegized'\np33322\nasS'affray'\np33323\n(lp33324\nS'affrays'\np33325\naS'affraying'\np33326\naS'affrayed'\np33327\naS'affrayed'\np33328\nasS'cwtch'\np33329\n(lp33330\nS'cwtches'\np33331\naS'cwtching'\np33332\naS'cwtched'\np33333\naS'cwtched'\np33334\nasS'protrude'\np33335\n(lp33336\nS'protrudes'\np33337\naS'protruding'\np33338\naS'protruded'\np33339\naS'protruded'\np33340\nasS'dismember'\np33341\n(lp33342\nS'dismembers'\np33343\naS'dismembering'\np33344\naS'dismembered'\np33345\naS'dismembered'\np33346\nasS'blanch'\np33347\n(lp33348\nS'blanches'\np33349\naS'blanching'\np33350\naS'blanched'\np33351\naS'blanched'\np33352\nasS'refute'\np33353\n(lp33354\nS'refutes'\np33355\naS'refuting'\np33356\naS'refuted'\np33357\naS'refuted'\np33358\nasS'trip'\np33359\n(lp33360\nS'trips'\np33361\naS'tripping'\np33362\naS'tripped'\np33363\naS'tripped'\np33364\nasS'feeze'\np33365\n(lp33366\nS'feezes'\np33367\naS'feezing'\np33368\naS'feezed'\np33369\naS'feezed'\np33370\nasS'couch'\np33371\n(lp33372\nS'couches'\np33373\naS'couching'\np33374\naS'couched'\np33375\naS'couched'\np33376\nasS'build'\np33377\n(lp33378\nS'builds'\np33379\naS'building'\np33380\naS'built'\np33381\naS'built'\np33382\nasS'rowel'\np33383\n(lp33384\nS'rowels'\np33385\naS'rowelling'\np33386\naS'rowelled'\np33387\naS'rowelled'\np33388\nasS'pressurize'\np33389\n(lp33390\nS'pressurizes'\np33391\naS'pressurizing'\np33392\naS'pressurized'\np33393\naS'pressurized'\np33394\nasS'flute'\np33395\n(lp33396\nS'flutes'\np33397\naS'fluting'\np33398\naS'fluted'\np33399\naS'fluted'\np33400\nasS'chart'\np33401\n(lp33402\nS'charts'\np33403\naS'charting'\np33404\naS'charted'\np33405\naS'charted'\np33406\nasS'charm'\np33407\n(lp33408\nS'charms'\np33409\naS'charming'\np33410\naS'charmed'\np33411\naS'charmed'\np33412\nasS'outpoint'\np33413\n(lp33414\nS'outpoints'\np33415\naS'outpointing'\np33416\naS'outpointed'\np33417\naS'outpointed'\np33418\nasS'booby-trap'\np33419\n(lp33420\nS'booby-traps'\np33421\naS'booby-trapping'\np33422\naS'booby-trapped'\np33423\naS'booby-trapped'\np33424\nasS'pauperize'\np33425\n(lp33426\nS'pauperizes'\np33427\naS'pauperizing'\np33428\naS'pauperized'\np33429\naS'pauperized'\np33430\nasS'fluctuate'\np33431\n(lp33432\nS'fluctuates'\np33433\naS'fluctuating'\np33434\naS'fluctuated'\np33435\naS'fluctuated'\np33436\nasS'saturate'\np33437\n(lp33438\nS'saturates'\np33439\naS'saturating'\np33440\naS'saturated'\np33441\naS'saturated'\np33442\nasS'ko'\np33443\n(lp33444\nS\"ko's\"\np33445\naS\"ko'ing\"\np33446\naS\"ko'ed\"\np33447\naS\"ko'ed\"\np33448\nasS'squelch'\np33449\n(lp33450\nS'squelches'\np33451\naS'squelching'\np33452\naS'squelched'\np33453\naS'squelched'\np33454\nasS'elasticate'\np33455\n(lp33456\nS'elasticates'\np33457\naS'elasticating'\np33458\naS'elasticated'\np33459\naS'elasticated'\np33460\nasS'impaste'\np33461\n(lp33462\nS'impastes'\np33463\naS'impasting'\np33464\naS'impasted'\np33465\naS'impasted'\np33466\nasS'tampon'\np33467\n(lp33468\nS'tampons'\np33469\naS'tamponing'\np33470\naS'tamponed'\np33471\naS'tamponed'\np33472\nasS'carny'\np33473\n(lp33474\nS'carnies'\np33475\naS'carnying'\np33476\naS'carnied'\np33477\naS'carnied'\np33478\nasS'converge'\np33479\n(lp33480\nS'converges'\np33481\naS'converging'\np33482\naS'converged'\np33483\naS'converged'\np33484\nasS'fink'\np33485\n(lp33486\nS'finks'\np33487\naS'finking'\np33488\naS'finked'\np33489\naS'finked'\np33490\nasS'chock'\np33491\n(lp33492\nS'chocks'\np33493\naS'chocking'\np33494\naS'chocked'\np33495\naS'chocked'\np33496\nasS'fine'\np33497\n(lp33498\nS'fines'\np33499\naS'fining'\np33500\naS'fined'\np33501\naS'fined'\np33502\nasS'find'\np33503\n(lp33504\nS'finds'\np33505\naS'finding'\np33506\naS'found'\np33507\naS'found'\np33508\nasS'scorify'\np33509\n(lp33510\nS'scorifies'\np33511\naS'scorifying'\np33512\naS'scorified'\np33513\naS'scorified'\np33514\nasS'mineralize'\np33515\n(lp33516\nS'mineralizes'\np33517\naS'mineralizing'\np33518\naS'mineralized'\np33519\naS'mineralized'\np33520\nasS'lather'\np33521\n(lp33522\nS'lathers'\np33523\naS'lathering'\np33524\naS'lathered'\np33525\naS'lathered'\np33526\nasS'override'\np33527\n(lp33528\nS'overrides'\np33529\naS'overriding'\np33530\naS'overrode'\np33531\naS'overridden'\np33532\nasS'prearrange'\np33533\n(lp33534\nS'prearranges'\np33535\naS'prearranging'\np33536\naS'prearranged'\np33537\naS'prearranged'\np33538\nasS'devastate'\np33539\n(lp33540\nS'devastates'\np33541\naS'devastating'\np33542\naS'devastated'\np33543\naS'devastated'\np33544\nasS'batten'\np33545\n(lp33546\nS'battens'\np33547\naS'battening'\np33548\naS'battened'\np33549\naS'battened'\np33550\nasS'express'\np33551\n(lp33552\nS'expresses'\np33553\naS'expressing'\np33554\naS'expressed'\np33555\naS'e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'\np40259\naS'wore'\np40260\naS'worn'\np40261\nasS'incross'\np40262\n(lp40263\nS'incrosses'\np40264\naS'incrossing'\np40265\naS'incrossed'\np40266\naS'incrossed'\np40267\nasS'improve'\np40268\n(lp40269\nS'improves'\np40270\naS'improving'\np40271\naS'improved'\np40272\naS'improved'\np40273\nasS'circumvallate'\np40274\n(lp40275\nS'circumvallates'\np40276\naS'circumvallating'\np40277\naS'circumvallated'\np40278\naS'circumvallated'\np40279\nasS'free-wheel'\np40280\n(lp40281\nS'free-wheels'\np40282\nag12401\nag12402\naS'free-wheeled'\np40283\nasS'fault'\np40284\n(lp40285\nS'faults'\np40286\naS'faulting'\np40287\naS'faulted'\np40288\naS'faulted'\np40289\nasS'reconnoitre'\np40290\n(lp40291\nS'reconnoitres'\np40292\naS'reconnoitring'\np40293\naS'reconnoitred'\np40294\naS'reconnoitred'\np40295\nasS'jelly'\np40296\n(lp40297\nS'jellies'\np40298\naS'jellying'\np40299\naS'jellied'\np40300\naS'jellied'\np40301\nasS'disengage'\np40302\n(lp40303\nS'disengages'\np40304\naS'disengaging'\np40305\naS'disengaged'\np40306\naS'disengaged'\np40307\nasS'tattle'\np40308\n(lp40309\nS'tattles'\np40310\naS'tattling'\np40311\naS'tattled'\np40312\naS'tattled'\np40313\nasS'expense'\np40314\n(lp40315\nS'expenses'\np40316\naS'expensing'\np40317\naS'expensed'\np40318\naS'expensed'\np40319\nasS'stipple'\np40320\n(lp40321\nS'stipples'\np40322\naS'stippling'\np40323\naS'stippled'\np40324\naS'stippled'\np40325\nasS'josh'\np40326\n(lp40327\nS'joshes'\np40328\naS'joshing'\np40329\naS'joshed'\np40330\naS'joshed'\np40331\nasS'inactivate'\np40332\n(lp40333\nS'inactivates'\np40334\naS'inactivating'\np40335\naS'inactivated'\np40336\naS'inactivated'\np40337\nasS'trust'\np40338\n(lp40339\nS'trusts'\np40340\naS'trusting'\np40341\naS'trusted'\np40342\naS'trusted'\np40343\nasS'truss'\np40344\n(lp40345\nS'trusses'\np40346\naS'trussing'\np40347\naS'trussed'\np40348\naS'trussed'\np40349\nasS'beef'\np40350\n(lp40351\nS'beefs'\np40352\naS'beefing'\np40353\naS'beefed'\np40354\naS'beefed'\np40355\nasS'tyrannize'\np40356\n(lp40357\nS'tyrannizes'\np40358\naS'tyrannizing'\np40359\naS'tyrannized'\np40360\naS'tyrannized'\np40361\nasS'unsphere'\np40362\n(lp40363\nS'unspheres'\np40364\naS'unsphering'\np40365\naS'unsphered'\np40366\naS'unsphered'\np40367\nasS'beep'\np40368\n(lp40369\nS'beeps'\np40370\naS'beeping'\np40371\naS'beeped'\np40372\naS'beeped'\np40373\nasS'brutify'\np40374\n(lp40375\nS'brutifies'\np40376\naS'brutifying'\np40377\naS'brutified'\np40378\naS'brutified'\np40379\nasS'loft'\np40380\n(lp40381\nS'lofts'\np40382\naS'lofting'\np40383\naS'lofted'\np40384\naS'lofted'\np40385\nasS'steamroller'\np40386\n(lp40387\nsS'craft'\np40388\n(lp40389\nS'crafts'\np40390\naS'crafting'\np40391\naS'crafted'\np40392\naS'crafted'\np40393\nasS'chase'\np40394\n(lp40395\nS'chases'\np40396\naS'chasing'\np40397\naS'chased'\np40398\naS'chased'\np40399\nasS'catch'\np40400\n(lp40401\nS'catches'\np40402\naS'catching'\np40403\naS'caught'\np40404\naS'caught'\np40405\nasS'proselytize'\np40406\n(lp40407\nS'proselytizes'\np40408\naS'proselytizing'\np40409\naS'proselytized'\np40410\naS'proselytized'\np40411\nasS'sallow'\np40412\n(lp40413\nS'sallows'\np40414\naS'sallowing'\np40415\naS'sallowed'\np40416\naS'sallowed'\np40417\nasS'rattoon'\np40418\n(lp40419\nsS'schlep'\np40420\n(lp40421\nS'schleps'\np40422\naS'schlepping'\np40423\naS'schlepped'\np40424\naS'schlepped'\np40425\nasS'lessen'\np40426\n(lp40427\nS'lessens'\np40428\naS'lessening'\np40429\naS'lessened'\np40430\naS'lessened'\np40431\nasS'fallow'\np40432\n(lp40433\nS'fallows'\np40434\naS'fallowing'\np40435\naS'fallowed'\np40436\naS'fallowed'\np40437\nasS'zindabad'\np40438\n(lp40439\nS'zindabads'\np40440\naS'zindabading'\np40441\naS'zindabaded'\np40442\naS'zindabaded'\np40443\nasS'subjugate'\np40444\n(lp40445\nS'subjugates'\np40446\naS'subjugating'\np40447\naS'subjugated'\np40448\naS'subjugated'\np40449\nasS'precede'\np40450\n(lp40451\nS'precedes'\np40452\naS'preceding'\np40453\naS'preceded'\np40454\naS'preceded'\np40455\nasS'pyramid'\np40456\n(lp40457\nS'pyramids'\np40458\naS'pyramiding'\np40459\naS'pyramided'\np40460\naS'pyramided'\np40461\nasS'untuck'\np40462\n(lp40463\nS'untucks'\np40464\naS'untucking'\np40465\naS'untucked'\np40466\naS'untucked'\np40467\nasS'chyack'\np40468\n(lp40469\nS'chyacks'\np40470\naS'chyacking'\np40471\naS'chyacked'\np40472\naS'chyacked'\np40473\nasS'descant'\np40474\n(lp40475\nS'descants'\np40476\naS'descanting'\np40477\naS'descanted'\np40478\naS'descanted'\np40479\nasS'comminute'\np40480\n(lp40481\nS'comminutes'\np40482\naS'comminuting'\np40483\naS'comminuted'\np40484\naS'comminuted'\np40485\nasS'tease'\np40486\n(lp40487\nS'teases'\np40488\naS'teasing'\np40489\naS'teased'\np40490\naS'teased'\np40491\nasS'rough-house'\np40492\n(lp40493\nS'rough-houses'\np40494\naS'rough-housing'\np40495\nag36845\naS'rough-housed'\np40496\nasS'surcharge'\np40497\n(lp40498\nS'surcharges'\np40499\naS'surcharging'\np40500\naS'surcharged'\np40501\naS'surcharged'\np40502\nasS'suggest'\np40503\n(lp40504\nS'suggests'\np40505\naS'sugge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'\np47199\nasS'upright'\np47200\n(lp47201\nS'uprights'\np47202\naS'uprighting'\np47203\naS'uprighted'\np47204\naS'uprighted'\np47205\nasS'crane'\np47206\n(lp47207\nS'cranes'\np47208\naS'craning'\np47209\naS'craned'\np47210\naS'craned'\np47211\nasS'supercool'\np47212\n(lp47213\nS'supercools'\np47214\naS'supercooling'\np47215\naS'supercooled'\np47216\naS'supercooled'\np47217\nasS'showcase'\np47218\n(lp47219\nS'showcases'\np47220\naS'showcasing'\np47221\naS'showcased'\np47222\naS'showcased'\np47223\nasS'mystify'\np47224\n(lp47225\nS'mystifies'\np47226\naS'mystifying'\np47227\naS'mystified'\np47228\naS'mystified'\np47229\nasS'hush'\np47230\n(lp47231\nS'hushes'\np47232\naS'hushing'\np47233\naS'hushed'\np47234\naS'hushed'\np47235\nasS'consist'\np47236\n(lp47237\nS'consists'\np47238\naS'consisting'\np47239\naS'consisted'\np47240\naS'consisted'\np47241\nasS'incase'\np47242\n(lp47243\nS'incases'\np47244\naS'incasing'\np47245\naS'incased'\np47246\naS'incased'\np47247\nasS'elide'\np47248\n(lp47249\nS'elides'\np47250\naS'eliding'\np47251\naS'elided'\np47252\naS'elided'\np47253\nasS'telecasted'\np47254\n(lp47255\nS'telecasts'\np47256\naS'telecasting'\np47257\naS'telecasteded'\np47258\naS'telecasteded'\np47259\nasS'redress'\np47260\n(lp47261\nS'redresses'\np47262\naS'redressing'\np47263\nag14226\naS'redressed'\np47264\nasS'highlight'\np47265\n(lp47266\nS'highlights'\np47267\naS'highlighting'\np47268\naS'highlighted'\np47269\naS'highlighted'\np47270\nasS'estivate'\np47271\n(lp47272\nS'estivates'\np47273\naS'estivating'\np47274\naS'estivated'\np47275\naS'estivated'\np47276\nasS'desorb'\np47277\n(lp47278\nS'desorbs'\np47279\naS'desorbing'\np47280\naS'desorbed'\np47281\naS'desorbed'\np47282\nasS'steepen'\np47283\n(lp47284\nS'steepens'\np47285\naS'steepening'\np47286\naS'steepened'\np47287\naS'steepened'\np47288\nasS'freak'\np47289\n(lp47290\nS'freaks'\np47291\naS'freaking'\np47292\naS'freaked'\np47293\naS'freaked'\np47294\nasS'sublet'\np47295\n(lp47296\nS'sublets'\np47297\naS'subletting'\np47298\naS'sublet'\np47299\naS'sublet'\np47300\nasS'photomap'\np47301\n(lp47302\nS'photomaps'\np47303\naS'photomapping'\np47304\naS'photomapped'\np47305\naS'photomapped'\np47306\nasS'rally'\np47307\n(lp47308\nS'rallies'\np47309\naS'rallying'\np47310\naS'rallied'\np47311\naS'rallied'\np47312\nasS'peach'\np47313\n(lp47314\nS'peaches'\np47315\naS'peaching'\np47316\naS'peached'\np47317\naS'peached'\np47318\nasS'peace'\np47319\n(lp47320\nS'peaces'\np47321\naS'peacing'\np47322\naS'peaced'\np47323\naS'peaced'\np47324\nasS'intwine'\np47325\n(lp47326\nS'intwines'\np47327\naS'intwining'\np47328\naS'intwined'\np47329\naS'intwined'\np47330\nasS'nick'\np47331\n(lp47332\nS'nicks'\np47333\naS'nicking'\np47334\naS'nicked'\np47335\naS'nicked'\np47336\nasS'disenchant'\np47337\n(lp47338\nS'disenchants'\np47339\naS'disenchanting'\np47340\naS'disenchanted'\np47341\naS'disenchanted'\np47342\nasS'caparison'\np47343\n(lp47344\nS'caparisons'\np47345\naS'caparisoning'\np47346\naS'caparisoned'\np47347\naS'caparisoned'\np47348\nasS'ferment'\np47349\n(lp47350\nS'ferments'\np47351\naS'fermenting'\np47352\naS'fermented'\np47353\naS'fermented'\np47354\nasS'buddle'\np47355\n(lp47356\nS'buddles'\np47357\naS'buddling'\np47358\naS'buddled'\np47359\naS'buddled'\np47360\nasS'mock'\np47361\n(lp47362\nS'mocks'\np47363\naS'mocking'\np47364\naS'mocked'\np47365\naS'mocked'\np47366\nasS'teasel'\np47367\n(lp47368\nS'teasels'\np47369\naS'teaselling'\np47370\naS'teaselled'\np47371\naS'teaselled'\np47372\nasS'chirm'\np47373\n(lp47374\nS'chirms'\np47375\naS'chirming'\np47376\naS'chirmed'\np47377\naS'chirmed'\np47378\nasS'inflame'\np47379\n(lp47380\nS'inflames'\np47381\naS'inflaming'\np47382\naS'inflamed'\np47383\naS'inflamed'\np47384\nasS'puncture'\np47385\n(lp47386\nS'punctures'\np47387\naS'puncturing'\np47388\naS'punctured'\np47389\naS'punctured'\np47390\nasS'assibilate'\np47391\n(lp47392\nS'assibilates'\np47393\naS'assibilating'\np47394\naS'assibilated'\np47395\naS'assibilated'\np47396\nasS'wrinkle'\np47397\n(lp47398\nS'wrinkles'\np47399\naS'wrinkling'\np47400\naS'wrinkled'\np47401\naS'wrinkled'\np47402\nasS'sightsee'\np47403\n(lp47404\nS'sightsees'\np47405\naS'sightseeing'\np47406\naS'sightsaw'\np47407\naS'sightseen'\np47408\nasS'relaunch'\np47409\n(lp47410\nS'relaunches'\np47411\naS'relaunching'\np47412\naS'relaunched'\np47413\naS'relaunched'\np47414\nasS'agist'\np47415\n(lp47416\nS'agists'\np47417\naS'agisting'\np47418\naS'agisted'\np47419\naS'agisted'\np47420\nasS'skydive'\np47421\n(lp47422\nS'skydives'\np47423\naS'skydiving'\np47424\naS'skydived'\np47425\naS'skydived'\np47426\nasS'transilluminate'\np47427\n(lp47428\nS'transilluminates'\np47429\naS'transilluminating'\np47430\naS'transilluminated'\np47431\naS'transilluminated'\np47432\nasS'liquefy'\np47433\n(lp47434\nS'liquefies'\np47435\naS'liquefying'\np47436\naS'liquefied'\np47437\naS'liquefied'\np47438\nasS'remit'\np47439\n(lp47440\nS'remits'\np47441\naS'remitting'\np47442\naS'remitted'\np47443\naS'remitted'\np47444\nasS'rehear'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},
{
"path": "corpkit/dictionaries/process_types.py",
"content": "#!/usr/bin/python\n\n# dictionaries: process type wordlists\n# Author: Daniel McDonald\n\n# make regular expressions and lists of inflected words from word lists\ntry:\n from corpkit.lazyprop import lazyprop\nexcept:\n import corpkit\n from lazyprop import lazyprop\n\ndef _verbs():\n import corpkit\n from corpkit.dictionaries.verblist import allverbs\n verblist = [i for i in allverbs if '_' not in i]\n return Wordlist(verblist)\n\ndef load_verb_data():\n \"\"\"load the verb lexicon\"\"\"\n\n def resource_path(relative):\n \"\"\"seemingly not working\"\"\"\n import os\n return os.path.join(os.environ.get(\"_MEIPASS2\", os.path.abspath(\".\")), relative)\n\n import os\n import corpkit\n import pickle\n from corpkit.process import get_gui_resource_dir\n corpath = os.path.dirname(corpkit.__file__)\n baspat = os.path.dirname(corpath)\n dicpath = os.path.join(baspat, 'dictionaries')\n lastpath = os.path.join(baspat, 'corpkit', 'dictionaries')\n \n paths_to_check = [resource_path('eng_verb_lexicon.p'),\n os.path.join(dicpath, 'eng_verb_lexicon.p'),\n os.path.join(get_gui_resource_dir(), 'eng_verb_lexicon.p'),\n os.path.join(lastpath, 'eng_verb_lexicon.p')]\n\n for p in paths_to_check:\n try:\n return pickle.load(open(p, 'rb'))\n except:\n pass\n\n return\n\ndef find_lexeme(verb):\n \"\"\" For a regular verb (base form), returns the forms using a rule-based approach.\n \n taken from pattern.en, because it wouldn't go into py2app properly\n \"\"\"\n vowels = ['a', 'e', 'i', 'o', 'u']\n v = verb.lower()\n if len(v) > 1 and v.endswith(\"e\") and v[-2] not in vowels:\n # Verbs ending in a consonant followed by \"e\": dance, save, devote, evolve.\n return [v, v, v, v+\"s\", v, v[:-1]+\"ing\"] + [v+\"d\"]*6\n if len(v) > 1 and v.endswith(\"y\") and v[-2] not in vowels:\n # Verbs ending in a consonant followed by \"y\": comply, copy, magnify.\n return [v, v, v, v[:-1]+\"ies\", v, v+\"ing\"] + [v[:-1]+\"ied\"]*6\n if v.endswith((\"ss\", \"sh\", \"ch\", \"x\")):\n # Verbs ending in sibilants: kiss, bless, box, polish, preach.\n return [v, v, v, v+\"es\", v, v+\"ing\"] + [v+\"ed\"]*6\n if v.endswith(\"ic\"):\n # Verbs ending in -ic: panic, mimic.\n return [v, v, v, v+\"es\", v, v+\"king\"] + [v+\"ked\"]*6\n if len(v) > 1 and v[-1] not in vowels and v[-2] not in vowels:\n # Verbs ending in a consonant cluster: delight, clamp.\n return [v, v, v, v+\"s\", v, v+\"ing\"] + [v+\"ed\"]*6\n if (len(v) > 1 and v.endswith((\"y\", \"w\")) and v[-2] in vowels) \\\n or (len(v) > 2 and v[-1] not in vowels and v[-2] in vowels and v[-3] in vowels) \\\n or (len(v) > 3 and v[-1] not in vowels and v[-3] in vowels and v[-4] in vowels):\n # Verbs ending in a long vowel or diphthong followed by a consonant: paint, devour, play.\n return [v, v, v, v+\"s\", v, v+\"ing\"] + [v+\"ed\"]*6\n if len(v) > 2 and v[-1] not in vowels and v[-2] in vowels and v[-3] not in vowels:\n # Verbs ending in a short vowel followed by a consonant: chat, chop, or compel.\n return [v, v, v, v+\"s\", v, v+v[-1]+\"ing\"] + [v+v[-1]+\"ed\"]*6\n return [v, v, v, v+\"s\", v, v+\"ing\"] + [v+\"ed\"]*6\n\ndef get_both_spellings(verb_list):\n \"\"\"add alternative spellings to verb_list\"\"\"\n from corpkit.dictionaries.word_transforms import usa_convert\n uk_convert = {v: k for k, v in usa_convert.items()}\n to_add_to_verb_list = []\n for w in verb_list:\n if w in usa_convert.keys():\n to_add_to_verb_list.append(usa_convert[w])\n for w in verb_list:\n if w in uk_convert.keys():\n to_add_to_verb_list.append(uk_convert[w])\n verb_list = sorted(list(set(verb_list + to_add_to_verb_list)))\n return verb_list\n\ndef add_verb_inflections(verb_list):\n \"\"\"add verb inflections to verb_list\"\"\"\n from corpkit.dictionaries.word_transforms import usa_convert\n uk_convert = {v: k for k, v in usa_convert.items()}\n\n # get lexemes\n lexemes = load_verb_data()\n verbforms = []\n \n # for each verb, get or guess the inflections\n # make list of ALL VERBS IN ALL INFLECTIONS\n all_lists = [lst for lst in lexemes.values()]\n allverbs = []\n for lst in all_lists:\n for v in lst:\n if v:\n allverbs.append(v)\n allverbs = list(set(allverbs))\n # use dict first\n for w in verb_list:\n verbforms.append(w)\n try:\n wforms = lexemes[w]\n except KeyError:\n # if not in dict, if it's an inflection, forget it\n if w in allverbs:\n continue\n if \"'\" in w:\n continue\n # if it's a coinage, guess\n else:\n wforms = find_lexeme(w)\n # get list of unique forms\n forms = list(set([form.replace(\"n't\", \"\").replace(\" not\", \"\") for form in wforms if form]))\n \n for f in forms:\n verbforms.append(f)\n \n # deal with contractions\n if w == 'be':\n be_conts = [r\"'m\", r\"'re\", r\"'s\"]\n for cont in be_conts:\n verbforms.append(cont)\n if w == \"have\":\n have_conts = [r\"'d\", r\"'s\", r\"'ve\"]\n for cont in have_conts:\n verbforms.append(cont)\n \n # go over again, and add both possible spellings\n to_add = []\n for w in verbforms:\n if w in usa_convert.keys():\n to_add.append(usa_convert[w])\n for w in verbforms:\n if w in uk_convert.keys():\n to_add.append(uk_convert[w])\n verbforms = sorted(list(set(verbforms + to_add)))\n\n # ensure unicode\n t = []\n for w in verbforms:\n try:\n t.append(unicode(w, errors='ignore'))\n except:\n t.append(w)\n return t\n\n# using 'list' keeps compatibility---change to object with no super call soon\n\nclass Wordlist(list):\n \"\"\"A list of words, containing a `words` attribute and a `lemmata` attribute\"\"\"\n \n def __init__(self, data, **kwargs):\n self.data = data\n self.kwargs = kwargs\n super(Wordlist, self).__init__(self.data)\n\n # make slice also return wordlist\n\n @lazyprop\n def words(self):\n \"\"\"get inflections\"\"\"\n if not self.kwargs.get('single'):\n return Wordlist(add_verb_inflections(get_both_spellings(self.data)), single=True)\n else:\n return\n\n @lazyprop\n def lemmata(self):\n \"\"\"show base forms of verbs\"\"\"\n if not self.kwargs.get('single'):\n return Wordlist(get_both_spellings(self.data), single=True)\n else:\n return\n\n def as_regex(self, boundaries='w', case_sensitive=False, inverse=False, compile=False):\n \"\"\"\n Turn list into regular expression matching any item in list\n \"\"\"\n from corpkit.other import as_regex\n return as_regex(get_both_spellings(self.data),\n boundaries=boundaries,\n case_sensitive=case_sensitive,\n inverse=inverse,\n compile=compile) \n\nclass Processes(object):\n \"\"\"Process types: relational, verbal, mental, material\"\"\"\n def __init__(self):\n relational = [\"become\",\n \"feel\",\n \"be\",\n \"have\",\n \"sound\",\n \"look\",\n \"seem\",\n \"appear\",\n \"smell\"\n ]\n\n verbal = [\"forbid\",\n \"forswear\",\n \"prophesy\",\n \"say\",\n \"swear\",\n \"tell\",\n \"write\",\n \"certify\", \n \"deny\", \n \"imply\", \n \"move\", \n \"notify\", \n \"reply\", \n \"specify\",\n \"accede\",\n \"add\",\n \"admit\",\n \"advise\",\n \"advocate\",\n \"allege\",\n \"announce\",\n \"answer\",\n \"apprise\",\n \"argue\",\n \"ask\",\n \"assert\",\n \"assure\",\n \"attest\",\n \"aver\",\n \"avow\",\n \"bark\",\n \"beg\",\n \"bellow\",\n \"blubber\",\n \"boast\",\n \"brag\",\n \"cable\",\n \"call\",\n \"claim\",\n \"comment\",\n \"complain\",\n \"confess\",\n \"confide\",\n \"confirm\",\n \"contend\",\n \"convey\",\n \"counsel\",\n \"declare\",\n \"demand\",\n \"disclaim\",\n \"disclose\",\n \"divulge\",\n \"emphasise\",\n \"emphasize\",\n \"encourage\",\n \"exclaim\",\n \"explain\",\n \"forecast\",\n \"gesture\",\n \"grizzle\",\n \"guarantee\",\n \"hint\",\n \"holler\",\n \"indicate\",\n \"inform\",\n \"insist\",\n \"intimate\",\n \"mention\",\n \"moan\",\n \"mumble\",\n \"murmur\",\n \"mutter\",\n \"note\",\n \"object\",\n \"offer\",\n \"phone\",\n \"pledge\",\n \"preach\",\n \"predicate\",\n \"preordain\",\n \"prescribe\",\n \"proclaim\",\n \"profess\",\n \"prohibit\",\n \"promise\",\n \"propose\",\n \"protest\",\n \"reaffirm\",\n \"reassure\",\n \"rejoin\",\n \"remark\",\n \"remind\",\n \"repeat\",\n \"report\",\n \"request\",\n \"require\",\n \"respond\",\n \"retort\",\n \"reveal\",\n \"riposte\",\n \"roar\",\n \"scream\",\n \"shout\",\n \"signal\",\n \"state\",\n \"stipulate\",\n \"telegraph\",\n \"telephone\",\n \"testify\",\n \"threaten\",\n \"vow\",\n \"warn\",\n \"wire\",\n \"reemphasise\",\n \"reemphasize\",\n \"rumor\",\n \"rumour\",\n \"yell\",\n # added manually:\n 'tell',\n 'say',\n 'call',\n 'vent',\n 'talk',\n 'ask',\n 'prescribe',\n 'diagnose',\n 'speak',\n 'suggest',\n 'mention',\n 'recommend',\n 'add',\n 'discuss',\n 'agree',\n 'contact',\n 'refer',\n 'explain',\n 'write',\n 'consult',\n 'advise',\n 'insist',\n 'perscribe',\n 'warn',\n 'offer',\n 'inform',\n 'question',\n 'describe',\n 'convince',\n 'order',\n 'report',\n 'lie',\n 'address',\n 'ring',\n 'state',\n \"pray\",\n 'phone',\n 'share',\n 'beg',\n 'blame',\n 'instruct',\n 'chat',\n 'assure',\n 'dx',\n 'recomend',\n 'prescibe',\n 'promise',\n 'communicate',\n 'notify',\n 'claim',\n 'convince',\n 'page',\n 'wish',\n 'post',\n 'complain',\n 'swear']\n\n behavioural = ['laugh', 'cry', 'listen', 'look', 'hear', 'wake', 'awaken', ]\n\n mental = [\"choose\",\n \"feel\",\n \"find\",\n \"forget\",\n \"hear\",\n \"know\",\n \"mean\",\n \"overhear\",\n \"prove\",\n \"read\",\n \"see\",\n \"think\",\n \"understand\",\n \"abide\",\n \"abominate\",\n \"accept\",\n \"acknowledge\",\n \"acquiesce\",\n \"adjudge\",\n \"adore\",\n \"affirm\",\n \"agree\",\n \"allow\",\n \"allure\",\n \"anticipate\",\n \"appreciate\",\n \"ascertain\",\n \"aspire\",\n \"assent\",\n \"assume\",\n \"begrudge\",\n \"believe\",\n \"calculate\",\n \"care\",\n \"conceal\",\n \"concede\",\n \"conceive\",\n \"concern\",\n \"conclude\",\n \"concur\",\n \"condone\",\n \"conjecture\",\n \"consent\",\n \"consider\",\n \"contemplate\",\n \"convince\",\n \"crave\",\n \"decide\",\n \"deduce\",\n \"deem\",\n \"delight\",\n \"desire\",\n \"determine\",\n \"detest\",\n \"discern\",\n \"discover\",\n \"dislike\",\n \"doubt\",\n \"dread\",\n \"enjoy\",\n \"envisage\",\n \"estimate\",\n \"excuse\",\n \"expect\",\n \"exult\",\n \"fear\",\n \"foreknow\",\n \"foresee\",\n \"gather\",\n \"grant\",\n \"grasp\",\n \"hate\",\n \"hope\",\n \"hurt\",\n \"hypothesise\",\n \"hypothesize\",\n \"imagine\",\n \"infer\",\n \"inspire\",\n \"intend\",\n \"intuit\",\n \"judge\",\n \"ken\",\n \"lament\",\n \"like\",\n \"loathe\",\n \"love\",\n \"marvel\",\n \"mind\",\n \"miss\",\n \"need\",\n \"neglect\",\n \"notice\",\n \"observe\",\n \"omit\",\n \"opine\",\n \"perceive\",\n \"plan\",\n \"please\",\n \"posit\",\n \"postulate\",\n \"pray\",\n \"preclude\",\n \"prefer\",\n \"presume\",\n \"presuppose\",\n \"pretend\",\n \"provoke\",\n \"realize\",\n \"realise\",\n \"reason\",\n \"recall\",\n \"reckon\",\n \"recognise\",\n \"recognize\",\n \"recollect\",\n \"reflect\",\n \"regret\",\n \"rejoice\",\n \"relish\",\n \"remember\",\n \"resent\",\n \"resolve\",\n \"rue\",\n \"scent\",\n \"scorn\",\n \"sense\",\n \"settle\",\n \"speculate\",\n \"suffer\",\n \"suppose\",\n \"surmise\",\n \"surprise\",\n \"suspect\",\n \"trust\",\n \"visualise\",\n \"visualize\",\n \"want\",\n \"wish\",\n \"wonder\",\n \"yearn\",\n \"rediscover\",\n \"dream\",\n \"justify\", \n \"figure\", \n \"smell\", \n \"worry\",\n 'know',\n 'think',\n 'feel',\n 'want',\n 'hope',\n 'find',\n 'guess',\n 'love',\n 'wish',\n 'like',\n 'understand',\n 'wonder',\n 'believe',\n 'hate',\n 'remember',\n 'agree',\n 'notice',\n 'learn',\n 'realize',\n 'miss',\n 'appreciate',\n 'decide',\n 'suffer',\n 'deal',\n 'forget',\n 'care',\n 'imagine',\n 'relate',\n 'worry',\n 'figure',\n 'handle',\n 'struggle',\n 'pray',\n 'consider',\n 'enjoy',\n 'expect',\n 'plan',\n 'suppose',\n 'trust',\n 'bother',\n 'blame',\n 'accept',\n 'admit',\n 'assume',\n 'remind',\n 'seek',\n 'bet',\n 'refuse',\n 'cope',\n 'choose',\n 'freak',\n 'fear',\n 'question',\n 'recall',\n 'doubt',\n 'suspect',\n 'focus',\n 'calm'\n ]\n \n can_be_material = ['bother', 'find']\n\n self.relational = Wordlist(relational)\n self.verbal = Wordlist(verbal)\n self.mental = Wordlist(mental)\n self.behavioural = Wordlist(behavioural)\n from corpkit.dictionaries.verblist import allverbs\n nonmat = set(self.relational + self.verbal + self.behavioural + self.mental)\n vbs = [i for i in allverbs if i not in nonmat and '_' not in i]\n self.material = Wordlist(vbs + can_be_material)\n\nprocesses = Processes()\n\nverbs = _verbs()\n"
},
{
"path": "corpkit/dictionaries/queries.py",
"content": "\ntry:\n from corpkit.lazyprop import lazyprop\nexcept:\n import corpkit\n from lazyprop import lazyprop\n\nclass Queries(object):\n\n def __init__(self):\n import corpkit\n from corpkit.dictionaries import roles, processes\n self.sayer = {'f': roles.participant1, 'gl': processes.verbal.lemmata, 'gf': roles.event}\n self.verbiage = {'f': roles.participant2, 'gl': processes.verbal.lemmata, 'gf': roles.event}\n self.senser = {'f': roles.participant1, 'gl': processes.mental.lemmata, 'gf': roles.event}\n self.phenomenon = {'f': roles.participant2, 'gl': processes.mental.lemmata, 'gf': roles.event}\n self.token = {'f': roles.participant1, 'gl': processes.relational.lemmata, 'gf': roles.event}\n self.value = {'f': roles.participant2, 'gl': processes.relational.lemmata, 'gf': roles.event}\n self.agent = {'f': roles.participant1}\n\nqueries = Queries()"
},
{
"path": "corpkit/dictionaries/roles.py",
"content": "# This file translates CoreNLP labels into SFL categories\n\ndef translator():\n from collections import namedtuple\n roledict = {\n 'actor': ['nsubj', 'agent', 'csubj', 'agent'],\n 'adjunct': ['advmod', 'agent', '(prep|nmod)(_|:).*', \n 'advcl', 'tmod'],\n 'auxiliary': ['auxpass', 'aux'],\n 'circumstance': ['advmod', r'(prep|nmod)(_|:).*', 'pobj', 'tmod'],\n 'classifier': ['compound', 'nn'],\n 'complement': ['acomp', 'dobj', 'iobj'],\n 'deictic': ['possessive', 'predet', 'poss', 'det', 'preconj'],\n 'epithet': ['amod'],\n 'event': ['ccomp', 'cop', 'advcl', 'root', 'acl', r'acl(_|:)relcl'],\n 'existential': ['expl'],\n 'finite': ['aux'],\n 'goal': ['nsubjpass', 'acomp', 'dobj', 'csubjpass', 'iobj'],\n 'modifier': ['advmod', 'amod', 'compound', 'nn', r'nmod.*', \n r'acl(_|:)relcl'],\n 'premodifier': ['amod', 'compound', 'nn', r'nmod'],\n 'postmodifier': [r'nmod:.*', r'acl(_|:)relcl'],\n 'modal': ['auxpass', 'aux'],\n 'numerative': ['number', 'quantmod'],\n 'participant': ['xsubj', 'nsubj', 'agent', 'nsubjpass', 'acomp', \n 'csubj', 'dobj', 'appos', 'csubjpass', 'iobj', 'xcomp'],\n 'participant1': ['nsubj', 'agent', 'csubj'],\n 'participant2': ['nsubjpass', 'acomp', 'dobj', 'csubjpass', 'iobj', 'xcomp'],\n 'polarity': ['neg'],\n 'predicator': ['ccomp', 'cop', 'root'],\n 'process': ['ccomp', 'prt', 'cop', 'advcl', 'root', 'aux', \n 'auxpass', 'acl', r'acl(_|:)relcl'],\n 'qualifier': ['rcmod', 'vmod'],\n 'subject': ['nsubj', 'nsubjpass', 'csubj', 'csubjpass'],\n 'textual': ['cc', 'ref', 'mark'],\n 'thing': ['nsubj', 'agent', 'nsubjpass', 'csubj', 'dobj', \n r'(prep|nmod)(_|:).*', 'appos', 'csubjpass', 'pobj', \n 'iobj', 'tmod'],\n 'any': ['acl', r'acl(_|:)relcl', 'advcl', 'advmod', 'amod', \n 'appos', 'aux', 'auxpass', 'case', 'cc', 'cc:preconj', \n 'ccomp', 'compound', 'compound:prt', 'conj', 'cop', \n 'csubj', 'csubjpass', 'dep', 'det', 'det:predet', \n 'discourse', 'dislocated', 'dobj', 'expl', 'foreign', \n 'goeswith', 'iobj', 'list', 'mark', 'mwe', 'name', \n 'neg', 'nmod', 'nmod:npmod', 'nmod:poss', 'nmod:tmod',\n 'nsubj', 'nsubjpass', 'nummod', 'parataxis', 'punct', \n 'remnant', 'reparandum', 'root', 'vocative', 'xcomp']\n\n }\n from corpkit.dictionaries.process_types import Wordlist\n outputnames = namedtuple('roles', sorted([i for i in roledict.keys()]))\n fields = [Wordlist(sorted(roledict[w]), single=True) for w in outputnames._fields]\n return outputnames(*fields)\n\nroles = translator()\n"
},
{
"path": "corpkit/dictionaries/stopwords.py",
"content": "\n# stopwords from spindle\nfrom corpkit.dictionaries.process_types import Wordlist\nstopwords = Wordlist([\"yeah\", \"monday\",\"tuesday\",\"wednesday\",\"thursday\",\"friday\",\n \"saturday\",\"sunday\",\"a\",\"able\",\"about\",\"above\",\"abst\",\"accordance\",\n \"according\",\"accordingly\",\"across\",\"act\",\"actually\",\"added\",\"adj\",\n \"adopted\",\"affected\",\"affecting\",\"affects\",\"after\",\"afterwards\",\n \"again\",\"against\",\"ah\",\"all\",\"almost\",\"alone\",\"along\",\"already\",\n \"also\",\"although\",\"always\",\"am\",\"among\",\"amongst\",\"an\",\"and\",\n \"announce\",\"another\",\"any\",\"anybody\",\"anyhow\",\"anymore\",\"anyone\",\n \"anything\",\"anyway\",\"anyways\",\"anywhere\",\"apparently\",\"approximately\",\n \"are\",\"aren\",\"arent\",\"arise\",\"around\",\"as\",\"aside\",\"ask\",\"asking\",\"at\",\n \"auth\",\"available\",\"away\",\"awfully\",\"b\",\"back\",\"be\",\"became\",\"because\",\n \"become\",\"becomes\",\"becoming\",\"been\",\"before\",\"beforehand\",\"begin\",\n \"beginning\",\"beginnings\",\"begins\",\"behind\",\"being\",\"believe\",\"below\",\n \"beside\",\"besides\",\"between\",\"beyond\",\"biol\",\"both\",\"brief\",\"briefly\",\n \"but\",\"by\",\"c\",\"ca\",\"came\",\"can\",\"cannot\",\"cant\",\"cause\",\"causes\",\n \"certain\",\"certainly\",\"co\",\"com\",\"come\",\"comes\",\"contain\",\"containing\",\n \"contains\",\"could\",\"couldnt\",\"d\",\"date\",\"did\",\"didnt\",\"different\",\"do\",\n \"does\",\"doesnt\",\"doing\",\"done\",\"dont\",\"down\",\"downwards\",\"due\",\"during\",\n \"e\",\"each\",\"ed\",\"edu\",\"effect\",\"eg\",\"eight\",\"eighty\",\"either\",\"else\",\n \"elsewhere\",\"end\",\"ending\",\"enough\",\"especially\",\"et\",\"et-al\",\"etc\",\"even\",\n \"ever\",\"every\",\"everybody\",\"everyone\",\"everything\",\"everywhere\",\"ex\",\n \"except\",\"f\",\"far\",\"few\",\"ff\",\"fifth\",\"first\",\"five\",\"fix\",\"followed\",\n \"following\",\"follows\",\"for\",\"former\",\"formerly\",\"forth\",\"found\",\"four\",\n \"from\",\"further\",\"furthermore\",\"going\",\"g\",\"gave\",\"get\",\"gets\",\"getting\",\n \"give\",\"given\",\"gives\",\"giving\",\"go\",\"goes\",\"gone\",\"got\",\"gotten\",\"h\",\n \"had\",\"happens\",\"hardly\",\"has\",\"hasnt\",\"have\",\"havent\",\"having\",\"he\",\n \"hed\",\"hence\",\"her\",\"here\",\"hereafter\",\"hereby\",\"herein\",\"heres\",\n \"hereupon\",\"hers\",\"herself\",\"hes\",\"hi\",\"hid\",\"him\",\"himself\",\"his\",\n \"hither\",\"home\",\"how\",\"howbeit\",\"however\",\"hundred\",\"i\",\"id\",\"ie\",\n \"if\",\"ill\",\"im\",\"immediate\",\"immediately\",\"importance\",\"important\",\n \"in\",\"inc\",\"indeed\",\"index\",\"information\",\"instead\",\"into\",\"invention\",\n \"inward\",\"is\",\"isnt\",\"it\",\"itd\",\"itll\",\"its\",\"itself\",\"ive\",\"j\",\"just\",\n \"k\",\"keep\",\"keeps\",\"kept\",\"keys\",\"kg\",\"km\",\"know\",\"known\",\"knows\",\n \"l\",\"largely\",\"last\",\"lately\",\"later\",\"latter\",\"latterly\",\"least\",\"less\",\n \"lest\",\"let\",\"lets\",\"like\",\"liked\",\"likely\",\"line\",\"little\",\"ll\",\"look\",\n \"looking\",\"looks\",\"ltd\",\"m\",\"made\",\"mainly\",\"make\",\"makes\",\"many\",\"may\",\n \"maybe\",\"me\",\"mean\",\"means\",\"meantime\",\"meanwhile\",\"merely\",\"mg\",\"might\",\n \"million\",\"miss\",\"ml\",\"more\",\"moreover\",\"most\",\"mostly\",\"mr\",\"mrs\",\"much\",\n \"mug\",\"must\",\"my\",\"myself\",\"n\",\"na\",\"name\",\"namely\",\"nay\",\"nd\",\"near\",\n \"nearly\",\"necessarily\",\"necessary\",\"need\",\"needs\",\"neither\",\"never\",\n \"nevertheless\",\"new\",\"next\",\"nine\",\"ninety\",\"no\",\"nobody\",\"non\",\"none\",\n \"nonetheless\",\"noone\",\"nor\",\"normally\",\"nos\",\"not\",\"noted\",\"nothing\",\n \"now\",\"nowhere\",\"o\",\"obtain\",\"obtained\",\"obviously\",\"of\",\"off\",\"often\",\n \"oh\",\"ok\",\"okay\",\"old\",\"omitted\",\"on\",\"once\",\"one\",\"ones\",\"only\",\"onto\",\n \"or\",\"ord\",\"other\",\"others\",\"otherwise\",\"ought\",\"our\",\"ours\",\"ourselves\",\n \"out\",\"outside\",\"over\",\"overall\",\"owing\",\"own\",\"p\",\"page\",\"pages\",\"part\",\n \"particular\",\"particularly\",\"past\",\"per\",\"perhaps\",\"placed\",\"please\",\n \"plus\",\"poorly\",\"possible\",\"possibly\",\"potentially\",\"pp\",\"predominantly\",\n \"present\",\"previously\",\"primarily\",\"probably\",\"promptly\",\"proud\",\"provides\",\n \"put\",\"q\",\"que\",\"quickly\",\"quite\",\"qv\",\"r\",\"ran\",\"rather\",\"rd\",\"re\",\n \"readily\",\"really\",\"recent\",\"recently\",\"ref\",\"refs\",\"regarding\",\"regardless\",\n \"regards\",\"related\",\"relatively\",\"research\",\"respectively\",\"resulted\",\n \"resulting\",\"results\",\"right\",\"run\",\"s\",\"said\",\"same\",\"saw\",\"say\",\"saying\",\n \"says\",\"sec\",\"section\",\"see\",\"seeing\",\"seem\",\"seemed\",\"seeming\",\"seems\",\n \"seen\",\"self\",\"selves\",\"sent\",\"seven\",\"several\",\"shall\",\"she\",\"shed\",\"shell\",\n \"shes\",\"should\",\"shouldnt\",\"show\",\"showed\",\"shown\",\"showns\",\"shows\",\n \"significant\",\"significantly\",\"similar\",\"similarly\",\"since\",\"six\",\n \"slightly\",\"so\",\"some\",\"somebody\",\"somehow\",\"someone\",\"somethan\",\"something\",\n \"sometime\",\"sometimes\",\"somewhat\",\"somewhere\",\"soon\",\"sorry\",\"specifically\",\n \"specified\",\"specify\",\"specifying\",\"state\",\"states\",\"still\",\"stop\",\n \"strongly\",\"sub\",\"substantially\",\"successfully\",\"such\",\"sufficiently\",\n \"suggest\",\"sup\",\"sure\",\"t\",\"take\",\"taken\",\"taking\",\"tell\",\"tends\",\"th\",\n \"than\",\"thank\",\"thanks\",\"thanx\",\"that\",\"thatll\",\"thats\",\"thatve\",\"the\",\n \"their\",\"theirs\",\"them\",\"themselves\",\"then\",\"thence\",\"there\",\"thereafter\",\n \"thereby\",\"thered\",\"therefore\",\"therein\",\"therell\",\"thereof\",\"therere\",\n \"theres\",\"thereto\",\"thereupon\",\"thereve\",\"these\",\"they\",\"theyd\",\"theyll\",\n \"theyre\",\"theyve\",\"think\",\"this\",\"those\",\"thou\",\"though\",\"thoughh\",\"thousand\",\n \"throug\",\"through\",\"throughout\",\"thru\",\"thus\",\"til\",\"tip\",\"to\",\"together\",\n \"too\",\"took\",\"toward\",\"towards\",\"tried\",\"tries\",\"truly\",\"try\",\"trying\",\n \"ts\",\"twice\",\"two\",\"u\",\"un\",\"under\",\"unfortunately\",\"unless\",\"unlike\",\n \"unlikely\",\"until\",\"unto\",\"up\",\"upon\",\"ups\",\"us\",\"use\",\"used\",\"useful\",\n \"usefully\",\"usefulness\",\"uses\",\"using\",\"usually\",\"v\",\"value\",\"various\",\"ve\",\n \"very\",\"via\",\"viz\",\"vol\",\"vols\",\"vs\",\"w\",\"want\",\"wants\",\"was\",\"wasnt\",\"way\",\n \"we\",\"wed\",\"welcome\",\"well\",\"went\",\"were\",\"werent\",\"weve\",\"what\",\"whatever\",\n \"whatll\",\"whats\",\"when\",\"whence\",\"whenever\",\"where\",\"whereafter\",\"whereas\",\n \"whereby\",\"wherein\",\"wheres\",\"whereupon\",\"wherever\",\"whether\",\"which\",\n \"while\",\"whim\",\"whither\",\"who\",\"whod\",\"whoever\",\"whole\",\"wholl\",\"whom\",\n \"whomever\",\"whos\",\"whose\",\"why\",\"widely\",\"willing\",\"wish\",\"with\",\"within\",\n \"without\",\"wont\",\"words\",\"world\",\"would\",\"wouldnt\",\"www\",\"x\",\"y\",\"yes\",\"yet\",\n \"you\",\"youd\",\"youll\",\"your\",\"youre\",\"yours\",\"yourself\",\"yourselves\",\"youve\",\n \"z\",\"zero\", \"isn\", \"doesn\",\"didn\", \"couldn\", \"mustn\",\"shoudn\",\"wasn\",\"woudn\",\n \"i\", \"me\", \"my\", \"myself\", \"we\", \"our\", \"ours\", \"ourselves\", \"you\", \"your\", \n \"yours\", \"yourself\", \"yourselves\", \"he\", \"him\", \"his\", \"himself\", \"she\", \"her\", \n \"hers\", \"herself\", \"it\", \"its\", \"itself\", \"they\", \"them\", \"their\", \"theirs\", \n \"themselves\", \"what\", \"which\", \"who\", \"whom\", \"this\", \"that\", \"these\", \"those\", \n \"am\", \"is\", \"are\", \"was\", \"were\", \"be\", \"been\", \"being\", \"have\", \"has\", \"had\", \n \"having\", \"do\", \"does\", \"did\", \"doing\", \"a\", \"an\", \"the\", \"and\", \"but\", \"if\", \n \"or\", \"because\", \"as\", \"until\", \"while\", \"of\", \"at\", \"by\", \"for\", \"with\", \n \"about\", \"against\", \"between\", \"into\", \"through\", \"during\", \"before\", \"after\", \n \"above\", \"below\", \"to\", \"from\", \"up\", \"down\", \"in\", \"out\", \"on\", \"off\", \n \"over\", \"under\", \"again\", \"further\", \"then\", \"once\", \"here\", \"there\", \"when\", \n \"where\", \"why\", \"how\", \"all\", \"any\", \"both\", \"each\", \"few\", \"more\", \"most\", \n \"other\", \"some\", \"such\", \"no\", \"nor\", \"not\", \"only\", \"own\", \"same\", \"so\", \n \"than\", \"too\", \"very\", \"s\", \"t\", \"can\", \"will\", \"just\", \"don\", \"should\", \n \"now\", \"gonna\", \"n't\", '-lrb-', '-rrb-', \"'m\", \"'ll\", \"'re\", \"'s\", \"'ve\", \"&\"], single=True)\n\n# add nltk, justext and minilist here ...\n\njusttext_stopwords = Wordlist([\"the\",\"of\",\"and\",\"in\",\"to\",\"a\",\"was\",\"is\",\"The\",\"for\",\"as\",\"on\",\"with\",\"by\",\n \"that\",\"from\",\"at\",\"his\",\"an\",\"he\",\"In\",\"are\",\"were\",\"which\",\"be\",\"has\",\"He\",\"it\",\n \"or\",\"also\",\"had\",\"first\",\"It\",\"their\",\"not\",\"but\",\"have\",\"who\",\"its\",\"one\",\"this\",\n \"been\",\"her\",\"two\",\"they\",\"other\",\"into\",\"after\",\"all\",\"when\",\"more\",\"This\",\"only\",\n \"would\",\"A\",\"she\",\"New\",\"most\",\"can\",\"over\",\"during\",\"where\",\"new\",\"used\",\"such\",\"up\",\n \"between\",\"many\",\"made\",\"some\",\"than\",\"out\",\"United\",\"known\",\"about\",\"time\",\"then\",\n \"became\",\"under\",\"The\",\"being\",\"part\",\"there\",\"him\",\"years\",\"three\",\"through\",\"On\",\n \"including\",\"later\",\"will\",\"American\",\"both\",\"After\",\"until\",\"before\",\"She\",\"well\",\n \"no\",\"against\",\"while\",\"called\",\"second\",\"As\",\"several\",\"University\",\"number\",\"name\",\n \"these\",\"played\",\"early\",\"may\",\"They\",\"World\",\"His\",\"located\",\"National\",\"same\",\"them\",\n \"released\",\"There\",\"de\",\"area\",\"use\",\"work\",\"any\",\"school\",\"since\",\"team\",\"age\",\"so\",\n \"John\",\"won\",\"people\",\"began\",\"each\",\"year\",\"population\",\"now\",\"family\",\"film\",\"found\",\n \"city\",\"British\",\"four\",\"album\",\"could\",\"very\",\"However\",\"South\",\"named\",\"At\",\"around\",\n \"took\",\"former\",\"because\",\"series\",\"For\",\"States\",\"did\",\"within\",\"state\",\"end\",\"based\",\n \"May\",\"I\",\"local\",\"held\",\"September\",\"still\",\"often\",\"those\",\"member\",\"small\",\"town\",\"along\",\n \"back\",\"School\",\"large\",\"January\",\"June\",\"group\",\"served\",\"March\",\"high\",\"own\",\"During\",\"North\",\n \"July\",\"October\",\"if\",\"like\",\"following\",\"built\",\"August\",\"April\",\"music\",\"born\",\"village\",\"game\",\n \"due\",\"last\",\"place\",\"home\",\"State\",\"left\",\"major\",\"set\",\"include\",\"U.S\",\"much\",\"December\",\n \"November\",\"received\",\"When\",\"York\",\"main\",\"War\",\"public\",\"band\",\"born\",\"season\",\"published\",\n \"even\",\"different\",\"original\",\"members\",\"station\",\"single\",\"government\",\"another\",\"near\",\"what\",\n \"died\",\"moved\",\"become\",\"just\",\"February\",\"company\",\"included\",\"song\",\"came\",\"led\",\"late\",\n \"form\",\"national\",\"make\",\"went\",\"These\",\"off\",\"show\",\"French\",\"five\",\"system\",\"few\",\"various\",\n \"given\",\"best\",\"English\",\"City\",\"long\",\"third\",\"among\",\"every\",\"West\",\"German\",\"using\",\"do\",\n \"said\",\"started\",\"currently\",\"having\",\"down\",\"next\",\"order\",\"One\",\"final\",\"take\",\"species\",\n \"established\",\"created\",\"life\",\"play\",\"line\",\"building\",\"History\",\"political\",\"without\",\"support\",\n \"written\",\"district\",\"per\",\"produced\",\"High\",\"popular\",\"League\",\"service\",\"football\",\"considered\",\n \"St\",\"way\",\"returned\",\"International\",\"book\",\"again\",\"although\",\"important\",\"living\",\"role\",\n \"River\",\"students\",\"married\",\"son\",\"top\",\"worked\",\"San\",\"continued\",\"however\",\"founded\",\n \"joined\",\"appeared\",\"total\",\"power\",\"By\",\"record\",\"College\",\"side\",\"William\",\"title\",\"death\",\n \"County\",\"years\",\"career\",\"never\",\"From\",\"north\",\"club\",\"County\",\"military\",\"version\",\n \"European\",\"According\",\"old\",\"While\",\"six\",\"day\",\"average\",\"television\",\"similar\",\"world\",\n \"general\",\"million\",\"With\",\"Some\",\"water\",\"formed\",\"international\",\"usually\",\"current\",\"though\",\n \"south\",\"General\",\"time\",\"community\",\"East\",\"House\",\"land\",\"Although\",\"George\",\"making\",\"player\",\n \"playing\",\"President\",\"development\",\"James\",\"developed\",\"common\",\"should\",\"great\",\"century\",\n \"does\",\"further\",\"run\",\"working\",\"largest\",\"recorded\",\"All\",\"lost\",\"must\",\"elected\",\"history\",\n \"seen\",\"live\",\"opened\",\"short\",\"taken\",\"once\",\"professional\",\"production\",\"point\",\"head\",\n \"children\",\"throughout\",\"games\",\"period\",\"himself\",\"originally\",\"term\",\"control\",\"available\",\n \"less\",\"King\",\"Its\",\"modern\",\"position\",\"eventually\",\"across\",\"Royal\",\"works\",\"site\",\"young\",\n \"wrote\",\"income\",\"house\",\"David\",\"Other\",\"Since\",\"how\",\"able\",\"full\",\"war\",\"Australian\",\n \"Air\",\"London\",\"Army\",\"includes\",\"law\",\"designed\",\"sold\",\"featured\",\"appointed\",\"business\",\"An\",\n \"get\",\"either\",\"Japanese\",\"program\",\"US\",\"Canadian\",\"father\",\"lead\",\"approximately\",\"performed\",\n \"leading\",\"radio\",\"upon\",\"remained\",\"famous\",\"Indian\",\"we\",\"Robert\",\"First\",\"help\",\"west\",\"gave\",\n \"announced\",\"men\",\"result\",\"times\",\"field\",\"you\",\"right\",\"east\",\"almost\",\"country\",\"story\",\n \"Church\",\"followed\",\"good\",\"days\",\"signed\",\"features\",\"together\",\"described\",\"research\",\"sent\",\n \"open\",\"special\",\"close\",\"see\",\"To\",\"character\",\"social\",\"miles\",\"rather\",\"life\",\"Council\",\n \"Western\",\"the\",\"party\",\"official\",\"years\",\"church\"], single=True)"
},
{
"path": "corpkit/dictionaries/verblist.py",
"content": "allverbs = [\"bird's-nest\",\n 'abandon',\n 'abase',\n 'abash',\n 'abate',\n 'abbreviate',\n 'abdicate',\n 'abduct',\n 'abet',\n 'abhor',\n 'abide',\n 'abirritate',\n 'abjure',\n 'ablate',\n 'abnegate',\n 'abolish',\n 'abominate',\n 'abort',\n 'abound',\n 'about-ship',\n 'about-turn',\n 'aboutface',\n 'abrade',\n 'abreact',\n 'abridge',\n 'abrogate',\n 'abscess',\n 'abscise',\n 'abscond',\n 'abseil',\n 'absent',\n 'absolve',\n 'absorb',\n 'absquatulate',\n 'abstain',\n 'abstract',\n 'abuse',\n 'abut',\n 'abye',\n 'accede',\n 'accelerate',\n 'accent',\n 'accentuate',\n 'accept',\n 'access',\n 'accession',\n 'acclaim',\n 'acclimatize',\n 'accommodate',\n 'accompany',\n 'accomplish',\n 'accord',\n 'accost',\n 'account',\n 'accoutre',\n 'accredit',\n 'accrete',\n 'accrue',\n 'acculturate',\n 'accumulate',\n 'accuse',\n 'accustom',\n 'acerbate',\n 'acetify',\n 'acetylate',\n 'ache',\n 'achieve',\n 'achromatize',\n 'acidify',\n 'acidulate',\n 'acierate',\n 'acknowledge',\n 'acquaint',\n 'acquiesce',\n 'acquire',\n 'acquit',\n 'act',\n 'activate',\n 'actualize',\n 'actuate',\n 'acuminate',\n 'adapt',\n 'add',\n 'addict',\n 'addle',\n 'address',\n 'adduce',\n 'adduct',\n 'adhere',\n 'adhibit',\n 'adjoin',\n 'adjourn',\n 'adjudge',\n 'adjudicate',\n 'adjure',\n 'adjust',\n 'adlib',\n 'admeasure',\n 'administer',\n 'administrate',\n 'admire',\n 'admit',\n 'admix',\n 'admonish',\n 'adopt',\n 'adore',\n 'adorn',\n 'adsorb',\n 'adulate',\n 'adulterate',\n 'adumbrate',\n 'advance',\n 'advantage',\n 'adventure',\n 'advert',\n 'advertize',\n 'advise',\n 'advocate',\n 'aerate',\n 'aerify',\n 'aestivate',\n 'affect',\n 'affiance',\n 'affiliate',\n 'affirm',\n 'affix',\n 'afflict',\n 'afford',\n 'afforest',\n 'affranchise',\n 'affray',\n 'affright',\n 'affront',\n 'Africanize',\n 'Afrikanerize',\n 'age',\n 'agglomerate',\n 'agglutinate',\n 'aggrade',\n 'aggrandize',\n 'aggravate',\n 'aggregate',\n 'aggress',\n 'aggrieve',\n 'agist',\n 'agitate',\n 'agonize',\n 'agree',\n 'aid',\n 'ail',\n 'aim',\n 'air',\n 'aircondition',\n 'aircool',\n 'airdrop',\n 'airdry',\n 'airlift',\n 'airmail',\n 'alarm',\n 'albumenize',\n 'alchemize',\n 'alcoholize',\n 'alert',\n 'alibi',\n 'alien',\n 'alienate',\n 'alight',\n 'aliment',\n 'aline',\n 'alit',\n 'alkalify',\n 'alkalize',\n 'allay',\n 'allege',\n 'allegorize',\n 'alleviate',\n 'alliterate',\n 'allocate',\n 'allot',\n 'allow',\n 'allowance',\n 'alloy',\n 'allude',\n 'allure',\n 'ally',\n 'alphabetize',\n 'alter',\n 'altercate',\n 'alternate',\n 'aluminize',\n 'amalgamate',\n 'amass',\n 'amaze',\n 'amble',\n 'ambulate',\n 'ambuscade',\n 'ambush',\n 'ameliorate',\n 'amend',\n 'amerce',\n 'Americanize',\n 'ammoniate',\n 'ammonify',\n 'amnesty',\n 'amortize',\n 'amount',\n 'amplify',\n 'amputate',\n 'amuse',\n 'anagrammatize',\n 'analogize',\n 'analyze',\n 'anastomose',\n 'anathematize',\n 'anatomize',\n 'anchor',\n 'anele',\n 'anesthetize',\n 'anger',\n 'angle',\n 'angle-park',\n 'anglicize',\n 'anglify',\n 'anguish',\n 'angulate',\n 'animadvert',\n 'animalize',\n 'animate',\n 'ankylose',\n 'anneal',\n 'annex',\n 'annihilate',\n 'annotate',\n 'announce',\n 'annoy',\n 'annualize',\n 'annul',\n 'annunciate',\n 'anodize',\n 'anoint',\n 'answer',\n 'antagonize',\n 'ante',\n 'antecede',\n 'antedate',\n 'antevert',\n 'anthologize',\n 'anthropomorphize',\n 'anticipate',\n 'antiquate',\n 'antique',\n 'ape',\n 'aphorize',\n 'apocopate',\n 'apologize',\n 'apostatize',\n 'apostrophize',\n 'apotheosize',\n 'appall',\n 'appeal',\n 'appear',\n 'appease',\n 'append',\n 'apperceive',\n 'appertain',\n 'applaud',\n 'appliqu_e',\n 'apply',\n 'appoint',\n 'apportion',\n 'appose',\n 'appraise',\n 'appreciate',\n 'apprehend',\n 'apprentice',\n 'apprize',\n 'approach',\n 'approbate',\n 'appropriate',\n 'approve',\n 'approximate',\n 'apron',\n 'aquaplane',\n 'aquatint',\n 'arbitrate',\n 'arc',\n 'arch',\n 'archaize',\n 'argue',\n 'argufy',\n 'arise',\n 'arm',\n 'armour',\n 'aromatize',\n 'arouse',\n 'arraign',\n 'arrange',\n 'array',\n 'arrest',\n 'arrive',\n 'arrogate',\n 'arterialize',\n 'article',\n 'articulate',\n 'artificialize',\n 'Aryanize',\n 'ascend',\n 'ascertain',\n 'ascribe',\n 'ask',\n 'asperse',\n 'asphalt',\n 'asphyxiate',\n 'aspirate',\n 'aspire',\n 'assail',\n 'assassinate',\n 'assault',\n 'assay',\n 'assemble',\n 'assent',\n 'assert',\n 'assess',\n 'asseverate',\n 'assibilate',\n 'assign',\n 'assimilate',\n 'assist',\n 'associate',\n 'assoil',\n 'assort',\n 'assuage',\n 'assume',\n 'assure',\n 'asterisk',\n 'astonish',\n 'astound',\n 'astrict',\n 'atomize',\n 'atone',\n 'atrophy',\n 'attach',\n 'attack',\n 'attain',\n 'attaint',\n 'attemper',\n 'attempt',\n 'attend',\n 'attenuate',\n 'attest',\n 'attire',\n 'attitudinize',\n 'attorn',\n 'attract',\n 'attribute',\n 'attune',\n 'auction',\n 'auctioneer',\n 'audit',\n 'audition',\n 'augment',\n 'augur',\n 'auscultate',\n 'auspicate',\n 'Australianize',\n 'authenticate',\n 'authorize',\n 'autoclave',\n 'autograph',\n 'autolyze',\n 'automate',\n 'automatize',\n 'autotomize',\n 'avail',\n 'avalanche',\n 'avenge',\n 'aver',\n 'average',\n 'avert',\n 'aviate',\n 'avoid',\n 'avouch',\n 'avow',\n 'await',\n 'awake',\n 'awaken',\n 'award',\n 'awe',\n 'axe',\n 'azotize',\n 'baa',\n 'babbitt',\n 'babble',\n 'baby',\n 'babysit',\n 'back',\n 'backbite',\n 'backcomb',\n 'backcross',\n 'backdate',\n 'backfill',\n 'backfire',\n 'backhand',\n 'backpedal',\n 'backslide',\n 'backspace',\n 'backstitch',\n 'backstroke',\n 'backtrack',\n 'backwash',\n 'backwater',\n 'badger',\n 'badmouth',\n 'baffle',\n 'bag',\n 'bail',\n 'bait',\n 'baize',\n 'bake',\n 'baksheesh',\n 'balance',\n 'bale',\n 'Balkanize',\n 'ball',\n 'ballast',\n 'balloon',\n 'ballot',\n 'ballyhoo',\n 'ballyrag',\n 'bamboozle',\n 'ban',\n 'band',\n 'bandage',\n 'bandy',\n 'bang',\n 'banish',\n 'bankroll',\n 'bankrupt',\n 'banquet',\n 'bant',\n 'banter',\n 'baptize',\n 'bar',\n 'barbarize',\n 'barber',\n 'barde',\n 'bare',\n 'bargain',\n 'barge',\n 'bark',\n 'barnstorm',\n 'barr_e',\n 'barrack',\n 'barrage',\n 'barrel',\n 'barrel-roll',\n 'barricade',\n 'barter',\n 'base',\n 'bash',\n 'basify',\n 'bask',\n 'basset',\n 'bastardize',\n 'baste',\n 'bastinado',\n 'bat',\n 'batch',\n 'bate',\n 'batfowl',\n 'bath',\n 'bathe',\n 'batten',\n 'batter',\n 'battle',\n 'battledore',\n 'baulk',\n 'bawl',\n 'bay',\n 'bayonet',\n 'be',\n 'beach',\n 'beacon',\n 'bead',\n 'beagle',\n 'beam',\n 'bear',\n 'beard',\n 'beat',\n 'beatify',\n 'beautify',\n 'beaver',\n 'bechance',\n 'beckon',\n 'becloud',\n 'become',\n 'bed',\n 'bedaub',\n 'bedazzle',\n 'bedeck',\n 'bedevil',\n 'bedew',\n 'bedight',\n 'bedim',\n 'bedizen',\n 'bedraggle',\n 'beef',\n 'beep',\n 'beeswax',\n 'beetle',\n 'befall',\n 'befit',\n 'befog',\n 'befool',\n 'befoul',\n 'befriend',\n 'befuddle',\n 'beg',\n 'beget',\n 'beggar',\n 'begin',\n 'begird',\n 'begrime',\n 'begrudge',\n 'beguile',\n 'behave',\n 'behead',\n 'behold',\n 'behove',\n 'bejewel',\n 'belabour',\n 'belay',\n 'belch',\n 'beleaguer',\n 'belie',\n 'believe',\n 'belittle',\n 'bell',\n 'bellow',\n 'belly',\n 'belly-laugh',\n 'bellyache',\n 'bellyland',\n 'belong',\n 'belt',\n 'bemean',\n 'bemire',\n 'bemoan',\n 'bemuse',\n 'bename',\n 'bench',\n 'bend',\n 'benefice',\n 'benefit',\n 'benumb',\n 'bequeath',\n 'berate',\n 'bereave',\n 'bereft',\n 'berry',\n 'berth',\n 'beseech',\n 'beseem',\n 'beset',\n 'beshrew',\n 'besiege',\n 'besmear',\n 'besmirch',\n 'bespangle',\n 'bespatter',\n 'bespeak',\n 'bespread',\n 'besprinkle',\n 'best',\n 'besteaded',\n 'bestialize',\n 'bestir',\n 'bestow',\n 'bestrew',\n 'bestride',\n 'bet',\n 'betake',\n 'bethink',\n 'betide',\n 'betoken',\n 'betray',\n 'betroth',\n 'better',\n 'bewail',\n 'beware',\n 'bewilder',\n 'bewitch',\n 'bewray',\n 'bias',\n 'bicker',\n 'bicycle',\n 'bid',\n 'bide',\n 'biff',\n 'bifurcate',\n 'big-note',\n 'bight',\n 'bike',\n 'bilge',\n 'bilk',\n 'bill',\n 'billet',\n 'bin',\n 'bioassay',\n 'birch',\n 'birddog',\n 'birdlime',\n 'birdy',\n 'birl',\n 'birr',\n 'birth',\n 'bisect',\n 'bit',\n 'bitch',\n 'bite',\n 'bitter',\n 'bituminize',\n 'bivouac',\n 'blab',\n 'blabber',\n 'black-lead',\n 'blackball',\n 'blackbird',\n 'blacken',\n 'blackguard',\n 'blackjack',\n 'blackleg',\n 'blacklist',\n 'blackmail',\n 'blackmarket',\n 'blackout',\n 'blah',\n 'blame',\n 'blanch',\n 'blandish',\n 'blank',\n 'blanket',\n 'blare',\n 'blarney',\n 'blaspheme',\n 'blast',\n 'blat',\n 'blather',\n 'blaze',\n 'blazon',\n 'bleach',\n 'blear',\n 'bleat',\n 'bleed',\n 'bleep',\n 'blemish',\n 'blench',\n 'blend',\n 'blent',\n 'bless',\n 'blest',\n 'blether',\n 'blight',\n 'blind',\n 'blindfold',\n 'blink',\n 'blip',\n 'blister',\n 'blitz',\n 'bloat',\n 'blob',\n 'block',\n 'blockade',\n 'blood',\n 'bloody',\n 'bloom',\n 'blossom',\n 'blot',\n 'blotch',\n 'blouse',\n 'blow',\n 'blow-wave',\n 'blowdry',\n 'blub',\n 'blubber',\n 'bludge',\n 'bludgeon',\n 'blue',\n 'bluepencil',\n 'blueprint',\n 'bluff',\n 'blunder',\n 'blunge',\n 'blunt',\n 'blur',\n 'blurt',\n 'blush',\n 'bluster',\n 'board',\n 'boast',\n 'boat',\n 'bob',\n 'bobol',\n 'bobsleigh',\n 'bode',\n 'bodge',\n 'body',\n 'bodycheck',\n 'boggle',\n 'bogie',\n 'boil',\n 'bolster',\n 'bomb',\n 'bombard',\n 'bond',\n 'bone',\n 'bong',\n 'boo',\n 'boob',\n 'booby-trap',\n 'boodle',\n 'boogie',\n 'boohoo',\n 'book',\n 'boom',\n 'boomerang',\n 'boondoggle',\n 'boost',\n 'boot',\n 'bootleg',\n 'bootlick',\n 'booze',\n 'bop',\n 'borate',\n 'border',\n 'bore',\n 'borrow',\n 'bosom',\n 'boss',\n 'botanize',\n 'botch',\n 'bother',\n 'bottle',\n 'bottlefeed',\n 'bottleneck',\n 'bottom',\n 'boult',\n 'bounce',\n 'bound',\n 'bow',\n 'bowdlerize',\n 'bowl',\n 'bowse',\n 'bowwow',\n 'box',\n 'boxhaul',\n 'boycott',\n 'brabble',\n 'brace',\n 'brachiate',\n 'bracket',\n 'brag',\n 'brail',\n 'Braille',\n 'brain',\n 'brainwash',\n 'braise',\n 'brake',\n 'bramble',\n 'branch',\n 'brand',\n 'brandish',\n 'brattice',\n 'brave',\n 'brawl',\n 'bray',\n 'braze',\n 'brazen',\n 'breach',\n 'bread',\n 'break',\n 'breakaway',\n 'breakfast',\n 'bream',\n 'breast',\n 'breastfeed',\n 'breathalyze',\n 'breathe',\n 'brede',\n 'breech',\n 'breed',\n 'breeze',\n 'brevet',\n 'brew',\n 'brey',\n 'bribe',\n 'brick',\n 'bridge',\n 'bridle',\n 'brief',\n 'brigade',\n 'brighten',\n 'brim',\n 'brine',\n 'bring',\n 'briquette',\n 'bristle',\n 'broach',\n 'broadcast',\n 'broaden',\n 'brocade',\n 'broddle',\n 'broider',\n 'broil',\n 'bromate',\n 'brominate',\n 'bronze',\n 'brood',\n 'brook',\n 'browbeat',\n 'brown',\n 'browse',\n 'bruise',\n 'bruit',\n 'brush',\n 'brutalize',\n 'brutify',\n 'bubble',\n 'buck',\n 'bucket',\n 'buckle',\n 'buckler',\n 'buckram',\n 'bud',\n 'buddle',\n 'budge',\n 'budget',\n 'buffalo',\n 'buffer',\n 'buffet',\n 'bug',\n 'bugger',\n 'bugle',\n 'build',\n 'bulge',\n 'bulk',\n 'bull',\n 'bulldoze',\n 'bulletin',\n 'bulletproof',\n 'bullshit',\n 'bullwhip',\n 'bully',\n 'bullyrag',\n 'bulwark',\n 'bum',\n 'bumble',\n 'bump',\n 'bump-start',\n 'bumper',\n 'bunch',\n 'bundle',\n 'bung',\n 'bungle',\n 'bunk',\n 'bunker',\n 'bunko',\n 'bunt',\n 'buoy',\n 'bur',\n 'burble',\n 'burden',\n 'bureaucratize',\n 'burgeon',\n 'burglarize',\n 'burgle',\n 'burke',\n 'burl',\n 'burlesque',\n 'burn',\n 'burnish',\n 'burp',\n 'burrow',\n 'burst',\n 'burthen',\n 'bury',\n 'bus',\n 'bush',\n 'bushel',\n 'bushwhack',\n 'busk',\n 'bust',\n 'bustle',\n 'busy',\n 'butcher',\n 'butt',\n 'butter',\n 'button',\n 'buttonhole',\n 'buttress',\n 'buy',\n 'buzz',\n 'bypass',\n 'cabal',\n 'cabbage',\n 'cable',\n 'cachinnate',\n 'cackle',\n 'caddy',\n 'cadge',\n 'cage',\n 'cagmag',\n 'cajole',\n 'cake',\n 'calcify',\n 'calcine',\n 'calculate',\n 'calendar',\n 'calender',\n 'calibrate',\n 'calk',\n 'call',\n 'calliper',\n 'callous',\n 'callus',\n 'calm',\n 'calque',\n 'calumniate',\n 'calve',\n 'camber',\n 'camouflage',\n 'camp',\n 'campaign',\n 'camphorate',\n 'can',\n 'canal',\n 'canalize',\n 'cancel',\n 'candle',\n 'candy',\n 'cane',\n 'canker',\n 'cannibalize',\n 'cannon',\n 'cannonade',\n 'cannonball',\n 'canoe',\n 'canonize',\n 'canoodle',\n 'canopy',\n 'canter',\n 'cantilever',\n 'cantillate',\n 'canton',\n 'canulate',\n 'canvass',\n 'cap',\n 'capacitate',\n 'caparison',\n 'caper',\n 'capitalize',\n 'capitulate',\n 'caponize',\n 'capriole',\n 'capsize',\n 'capsulize',\n 'captain',\n 'caption',\n 'captivate',\n 'capture',\n 'caramelize',\n 'caravan',\n 'carbolize',\n 'carbonado',\n 'carbonate',\n 'carbonize',\n 'carburet',\n 'carburize',\n 'card',\n 'care',\n 'careen',\n 'career',\n 'caress',\n 'caricature',\n 'carillon',\n 'cark',\n 'carnify',\n 'carny',\n 'carol',\n 'carouse',\n 'carp',\n 'carpenter',\n 'carpet',\n 'carry',\n 'cartelize',\n 'carve',\n 'cascade',\n 'case',\n 'caseate',\n 'casefy',\n 'caseharden',\n 'cash',\n 'cashier',\n 'casserole',\n 'cast',\n 'castigate',\n 'castle',\n 'castoff',\n 'castrate',\n 'cat',\n 'catalogue',\n 'catalyze',\n 'catapult',\n 'catcall',\n 'catch',\n 'catechize',\n 'categorize',\n 'catenate',\n 'cater',\n 'caterwaul',\n 'catheterize',\n 'catholicize',\n 'catnap',\n 'caucus',\n 'caulk',\n 'cause',\n 'cauterize',\n 'caution',\n 'cave',\n 'cavern',\n 'cavil',\n 'cavort',\n 'caw',\n 'caway',\n 'cease',\n 'cede',\n 'ceil',\n 'celebrate',\n 'cellar',\n 'cement',\n 'cense',\n 'censor',\n 'censure',\n 'centralize',\n 'centre',\n 'centrifuge',\n 'centuplicate',\n 'cere',\n 'cerebrate',\n 'certificate',\n 'certify',\n 'cess',\n 'chafe',\n 'chaff',\n 'chaffer',\n 'chagrin',\n 'chain',\n 'chain-stitch',\n 'chainreact',\n 'chainsmoke',\n 'chair',\n 'chalk',\n 'challenge',\n 'chamber',\n 'chamfer',\n 'chamois',\n 'champ',\n 'champion',\n 'chance',\n 'chandelle',\n 'change',\n 'changeover',\n 'channel',\n 'channelize',\n 'chant',\n 'chap',\n 'chaperone',\n 'chapter',\n 'char',\n 'character',\n 'characterize',\n 'charcoal',\n 'charge',\n 'charm',\n 'chart',\n 'charter',\n 'chase',\n 'chass_e',\n 'chasten',\n 'chastise',\n 'chat',\n 'chatter',\n 'chauffeur',\n 'chaw',\n 'cheapen',\n 'cheat',\n 'check',\n 'checker',\n 'checkmate',\n 'checkrow',\n 'cheek',\n 'cheep',\n 'cheer',\n 'cheese',\n 'chelate',\n 'chelp',\n 'chemosorb',\n 'chequer',\n 'cherish',\n 'chevy',\n 'chew',\n 'chiack',\n 'chicane',\n 'chide',\n 'chill',\n 'chime',\n 'chin',\n 'chine',\n 'chink',\n 'chip',\n 'chirm',\n 'chirp',\n 'chirre',\n 'chirrup',\n 'chisel',\n 'chitchat',\n 'chitter',\n 'chivy',\n 'chlorinate',\n 'chock',\n 'choke',\n 'chomp',\n 'chondrify',\n 'choose',\n 'chop',\n 'chord',\n 'choreograph',\n 'chortle',\n 'chorus',\n 'christen',\n 'Christianize',\n 'chrome',\n 'chronicle',\n 'chuck',\n 'chuckle',\n 'chuff',\n 'chug',\n 'chum',\n 'chump',\n 'chunder',\n 'chunter',\n 'church',\n 'churn',\n 'churr',\n 'chute',\n 'chyack',\n 'cicatrize',\n 'cinch',\n 'cinchonize',\n 'cinder',\n 'cinematograph',\n 'cipher',\n 'circle',\n 'circuit',\n 'circularize',\n 'circulate',\n 'circumambulate',\n 'circumcise',\n 'circumfuse',\n 'circumnavigate',\n 'circumnutate',\n 'circumscribe',\n 'circumstantiate',\n 'circumvallate',\n 'circumvent',\n 'cite',\n 'cityfy',\n 'civilize',\n 'clack',\n 'clad',\n 'claim',\n 'clam',\n 'clamber',\n 'clamour',\n 'clamp',\n 'clang',\n 'clangour',\n 'clank',\n 'clap',\n 'clapboard',\n 'clapperclaw',\n 'clarify',\n 'clarion',\n 'clash',\n 'clasp',\n 'class',\n 'classicize',\n 'classify',\n 'clatter',\n 'claver',\n 'claw',\n 'clay',\n 'clean',\n 'cleanse',\n 'clear',\n 'cleat',\n 'cleave',\n 'cleck',\n 'cleft',\n 'clem',\n 'clench',\n 'clepe',\n 'clerk',\n 'clew',\n 'click',\n 'climax',\n 'climb',\n 'clinch',\n 'cling',\n 'clink',\n 'clinker',\n 'clip',\n 'cloak',\n 'clobber',\n 'clock',\n 'clog',\n 'cloister',\n 'clomb',\n 'clomp',\n 'clonk',\n 'clop',\n 'close',\n 'closet',\n 'closure',\n 'clot',\n 'clothe',\n 'cloture',\n 'cloud',\n 'clout',\n 'clown',\n 'cloy',\n 'club',\n 'clubhaul',\n 'cluck',\n 'clue',\n 'clump',\n 'clunk',\n 'cluster',\n 'clutch',\n 'clutter',\n 'clype',\n 'co-edit',\n 'coach',\n 'coagulate',\n 'coal',\n 'coalesce',\n 'coarsen',\n 'coast',\n 'coat',\n 'coauthor',\n 'coax',\n 'cob',\n 'cobble',\n 'cocainize',\n 'cock',\n 'cocker',\n 'cockle',\n 'cocknify',\n 'cocoon',\n 'cod',\n 'coddle',\n 'code',\n 'codename',\n 'codify',\n 'coedit',\n 'coerce',\n 'coexist',\n 'coextend',\n 'coextrude',\n 'coff',\n 'coffer',\n 'cofound',\n 'cog',\n 'cogitate',\n 'cognize',\n 'cohabit',\n 'cohere',\n 'cohobate',\n 'coif',\n 'coiffure',\n 'coil',\n 'coin',\n 'coincide',\n 'coinsure',\n 'coke',\n 'cold',\n 'coldshoulder',\n 'coldweld',\n 'collaborate',\n 'collapse',\n 'collar',\n 'collate',\n 'collect',\n 'collectivize',\n 'collet',\n 'collide',\n 'colligate',\n 'collimate',\n 'collocate',\n 'collogue',\n 'collude',\n 'colly',\n 'colonize',\n 'colorcode',\n 'colour',\n 'comanage',\n 'comb',\n 'combat',\n 'combine',\n 'combust',\n 'come',\n 'comfort',\n 'command',\n 'commandeer',\n 'commeasure',\n 'commemorate',\n 'commence',\n 'commend',\n 'comment',\n 'commentate',\n 'commercialize',\n 'commingle',\n 'comminute',\n 'commiserate',\n 'commission',\n 'commit',\n 'commix',\n 'commove',\n 'communalize',\n 'commune',\n 'communicate',\n 'communize',\n 'commutate',\n 'commute',\n 'comp`ere',\n 'compact',\n 'companion',\n 'company',\n 'compare',\n 'compartmentalize',\n 'compass',\n 'compel',\n 'compensate',\n 'compere',\n 'compete',\n 'compile',\n 'complain',\n 'complect',\n 'complement',\n 'complete',\n 'complicate',\n 'compliment',\n 'complot',\n 'comply',\n 'comport',\n 'compose',\n 'compost',\n 'compound',\n 'comprehend',\n 'compress',\n 'comprise',\n 'compromise',\n 'compute',\n 'computerize',\n 'concatenate',\n 'concave',\n 'conceal',\n 'concede',\n 'conceive',\n 'concelebrate',\n 'concentrate',\n 'concentre',\n 'conceptualize',\n 'concern',\n 'concertina',\n 'concertize',\n 'conciliate',\n 'conclude',\n 'concoct',\n 'concrete',\n 'concretize',\n 'concur',\n 'concuss',\n 'condemn',\n 'condense',\n 'condescend',\n 'condition',\n 'condole',\n 'condone',\n 'conduce',\n 'conduct',\n 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'contemplate',\n 'contemporize',\n 'contend',\n 'content',\n 'contest',\n 'contextualize',\n 'continue',\n 'contort',\n 'contour',\n 'contract',\n 'contradict',\n 'contradistinguish',\n 'contraindicate',\n 'contrast',\n 'contravene',\n 'contribute',\n 'contrive',\n 'control',\n 'controvert',\n 'contuse',\n 'convalesce',\n 'convene',\n 'conventionalize',\n 'converge',\n 'converse',\n 'convert',\n 'convex',\n 'convey',\n 'convict',\n 'convince',\n 'convoke',\n 'convolve',\n 'convoy',\n 'convulse',\n 'coo',\n 'cooey',\n 'cook',\n 'cool',\n 'coop',\n 'cooper',\n 'cooperate',\n 'coopt',\n 'coordinate',\n 'cop',\n 'cope',\n 'copolymerize',\n 'copper',\n 'copper-bottom',\n 'coproduce',\n 'copulate',\n 'copy',\n 'copyedit',\n 'copyread',\n 'copyright',\n 'coquet',\n 'corbel',\n 'cord',\n 'cordon',\n 'core',\n 'cork',\n 'corkscrew',\n 'corn',\n 'corner',\n 'cornice',\n 'corrade',\n 'corral',\n 'correct',\n 'correlate',\n 'correspond',\n 'corrival',\n 'corroborate',\n 'corrode',\n 'corrugate',\n 'corrupt',\n 'corset',\n 'coruscate',\n 'cosh',\n 'cosher',\n 'cosponsor',\n 'cosset',\n 'cost',\n 'costar',\n 'costume',\n 'cote',\n 'cotter',\n 'couch',\n 'cough',\n 'counsel',\n 'count',\n 'countenance',\n 'counter',\n 'counteract',\n 'counterattack',\n 'counterbalance',\n 'counterchange',\n 'countercharge',\n 'counterclaim',\n 'counterfeit',\n 'countermand',\n 'countermarch',\n 'countermine',\n 'countermove',\n 'counterplot',\n 'counterpoise',\n 'counterproposal',\n 'counterpunch',\n 'countersign',\n 'countersink',\n 'countervail',\n 'counterweigh',\n 'couple',\n 'course',\n 'court',\n 'courtmartial',\n 'cove',\n 'covenant',\n 'cover',\n 'coverup',\n 'covet',\n 'cow',\n 'cower',\n 'cowk',\n 'cowl',\n 'cowp',\n 'cox',\n 'cozen',\n 'crab',\n 'crack',\n 'crackle',\n 'cradle',\n 'craft',\n 'cram',\n 'cramp',\n 'crane',\n 'crank',\n 'crap',\n 'crash',\n 'crash-dive',\n 'crashland',\n 'crate',\n 'crater',\n 'craunch',\n 'crave',\n 'crawl',\n 'crayon',\n 'craze',\n 'creak',\n 'cream',\n 'crease',\n 'create',\n 'credit',\n 'creep',\n 'cremate',\n 'crenellate',\n 'crepe',\n 'crepitate',\n 'crescendo',\n 'crevasse',\n 'crew',\n 'crib',\n 'crick',\n 'criminalize',\n 'criminate',\n 'crimp',\n 'crimple',\n 'crimson',\n 'cringe',\n 'crinkle',\n 'cripple',\n 'crisp',\n 'crisscross',\n 'criticize',\n 'croak',\n 'crochet',\n 'crock',\n 'crook',\n 'croon',\n 'crop',\n 'croquet',\n 'cross',\n 'cross-breed',\n 'cross-fade',\n 'crossbred',\n 'crosscheck',\n 'crosscut',\n 'crossexamine',\n 'crossfertilize',\n 'crosshatch',\n 'crossindex',\n 'crosspollinate',\n 'crossquestion',\n 'crossrefer',\n 'crossreference',\n 'crossruff',\n 'crossstitch',\n 'crouch',\n 'croup',\n 'crow',\n 'crowd',\n 'crown',\n 'crucify',\n 'cruise',\n 'crumb',\n 'crumble',\n 'crump',\n 'crumple',\n 'crunch',\n 'crusade',\n 'crush',\n 'crust',\n 'crutch',\n 'cry',\n 'crystallize',\n 'cub',\n 'cube',\n 'cuckold',\n 'cuckoo',\n 'cuddle',\n 'cudgel',\n 'cue',\n 'cuff',\n 'cuirass',\n 'cull',\n 'culminate',\n 'cultivate',\n 'culture',\n 'cumber',\n 'cumulate',\n 'cup',\n 'cupel',\n 'curarize',\n 'curb',\n 'curd',\n 'curdle',\n 'cure',\n 'curette',\n 'curl',\n 'curry',\n 'curse',\n 'curtail',\n 'curtain',\n 'curtsy',\n 'curve',\n 'curvet',\n 'cushion',\n 'customize',\n 'cut',\n 'cutback',\n 'cutinize',\n 'cwtch',\n 'cybernate',\n 'cycle',\n 'cyclostyle',\n 'cypher',\n 'dab',\n 'dabble',\n 'dado',\n 'daff',\n 'dag',\n 'dagger',\n 'dally',\n 'dam',\n 'damage',\n 'damascene',\n 'damask',\n 'damn',\n 'damnify',\n 'damp',\n 'dampen',\n 'dance',\n 'dander',\n 'dandify',\n 'dandle',\n 'dangle',\n 'dap',\n 'dapple',\n 'dare',\n 'dark',\n 'darken',\n 'darkle',\n 'darn',\n 'dart',\n 'dash',\n 'date',\n 'dateline',\n 'daub',\n 'daunt',\n 'dawdle',\n 'dawn',\n 'day-dream',\n 'daydream',\n 'daze',\n 'dazzle',\n 'de-horn',\n 'deactivate',\n 'deaden',\n 'deadhead',\n 'deadlock',\n 'deafen',\n 'deal',\n 'deaminize',\n 'debag',\n 'debar',\n 'debark',\n 'debase',\n 'debate',\n 'debauch',\n 'debilitate',\n 'debit',\n 'debouch',\n 'debrief',\n 'debug',\n 'debunk',\n 'debus',\n 'debut',\n 'decaffeinate',\n 'decal',\n 'decalcify',\n 'decamp',\n 'decant',\n 'decapitate',\n 'decarbonate',\n 'decarbonize',\n 'decarburize',\n 'decay',\n 'decease',\n 'deceive',\n 'decelerate',\n 'decentralize',\n 'decerebrate',\n 'decern',\n 'decertify',\n 'decide',\n 'decimalize',\n 'decimate',\n 'decipher',\n 'deck',\n 'declaim',\n 'declare',\n 'declass',\n 'declassify',\n 'decline',\n 'declutch',\n 'decoct',\n 'decode',\n 'decoke',\n 'decollate',\n 'decolonize',\n 'decolour',\n 'decompose',\n 'decompound',\n 'decompress',\n 'deconsecrate',\n 'decontaminate',\n 'decontrol',\n 'decorate',\n 'decorticate',\n 'decoy',\n 'decrease',\n 'decree',\n 'decrepitate',\n 'decribe',\n 'decry',\n 'decrypt',\n 'decuple',\n 'decussate',\n 'dedicate',\n 'deduce',\n 'deduct',\n 'deed',\n 'deek',\n 'deem',\n 'deemphasize',\n 'deepen',\n 'deepfreeze',\n 'deepfry',\n 'deepsix',\n 'deescalate',\n 'deface',\n 'defalcate',\n 'defame',\n 'default',\n 'defeat',\n 'defecate',\n 'defect',\n 'defend',\n 'defer',\n 'defilade',\n 'defile',\n 'define',\n 'deflagrate',\n 'deflate',\n 'deflect',\n 'deflocculate',\n 'deflower',\n 'defoliate',\n 'deforce',\n 'deforest',\n 'deform',\n 'defraud',\n 'defray',\n 'defrock',\n 'defrost',\n 'defuze',\n 'defy',\n 'degas',\n 'degauss',\n 'degenerate',\n 'deglutinate',\n 'degrade',\n 'degrease',\n 'degustate',\n 'dehisce',\n 'dehorn',\n 'dehumanize',\n 'dehumidify',\n 'dehydrate',\n 'dehydrogenize',\n 'dehypnotize',\n 'deice',\n 'deify',\n 'deign',\n 'deject',\n 'delaminate',\n 'delate',\n 'delay',\n 'dele',\n 'delete',\n 'deliberate',\n 'delight',\n 'delimitate',\n 'delineate',\n 'deliquesce',\n 'deliver',\n 'delocalize',\n 'delouse',\n 'delude',\n 'deluge',\n 'delve',\n 'demagnetize',\n 'demagogue',\n 'demand',\n 'demarcate',\n 'dematerialize',\n 'demean',\n 'dement',\n 'demilitarize',\n 'demineralize',\n 'demise',\n 'demist',\n 'demit',\n 'demob',\n 'demobilize',\n 'democratize',\n 'demodulate',\n 'demolish',\n 'demonetize',\n 'demonize',\n 'demonstrate',\n 'demoralize',\n 'demote',\n 'demount',\n 'demulsify',\n 'demur',\n 'demystify',\n 'demythologize',\n 'den',\n 'denationalize',\n 'denaturalize',\n 'denaturize',\n 'denazify',\n 'denigrate',\n 'denitrate',\n 'denitrify',\n 'denizen',\n 'denominate',\n 'denote',\n 'denounce',\n 'dent',\n 'denuclearize',\n 'denudate',\n 'denude',\n 'denunciate',\n 'deny',\n 'deodorize',\n 'deoxidize',\n 'deoxygenize',\n 'depart',\n 'departmentalize',\n 'depasture',\n 'depend',\n 'depersonalize',\n 'depict',\n 'depicture',\n 'depilate',\n 'deplane',\n 'deplete',\n 'deplore',\n 'deploy',\n 'deplume',\n 'depolarize',\n 'depoliticize',\n 'depolymerize',\n 'depone',\n 'depopulate',\n 'deport',\n 'depose',\n 'deposit',\n 'deprave',\n 'deprecate',\n 'depreciate',\n 'depredate',\n 'depress',\n 'depressurize',\n 'deprive',\n 'depurate',\n 'depute',\n 'deputize',\n 'deracinate',\n 'deraign',\n 'derail',\n 'derange',\n 'derate',\n 'deration',\n 'deregister',\n 'derequisition',\n 'derestrict',\n 'deride',\n 'derive',\n 'derogate',\n 'derrick',\n 'desalinize',\n 'desalt',\n 'descale',\n 'descant',\n 'descend',\n 'deschool',\n 'describe',\n 'descry',\n 'desecrate',\n 'desegregate',\n 'desensitize',\n 'desert',\n 'deserve',\n 'desexualize',\n 'desiccate',\n 'desiderate',\n 'design',\n 'designate',\n 'desire',\n 'desist',\n 'desolate',\n 'desorb',\n 'despair',\n 'despatch',\n 'despise',\n 'despite',\n 'despoil',\n 'despond',\n 'despumate',\n 'desquamate',\n 'destine',\n 'destroy',\n 'destruct',\n 'desulphurize',\n 'detach',\n 'detail',\n 'detain',\n 'detect',\n 'deter',\n 'deterge',\n 'deteriorate',\n 'determine',\n 'detest',\n 'dethrone',\n 'detonate',\n 'detour',\n 'detoxicate',\n 'detoxify',\n 'detract',\n 'detrain',\n 'detribalize',\n 'detrude',\n 'detruncate',\n 'deuterate',\n 'devalue',\n 'devastate',\n 'develop',\n 'devest',\n 'deviate',\n 'devil',\n 'devise',\n 'devitalize',\n 'devitrify',\n 'devoice',\n 'devolve',\n 'devote',\n 'devour',\n 'dew',\n 'diabolize',\n 'diadem',\n 'diagnose',\n 'diagram',\n 'dial',\n 'dialogize',\n 'dialogue',\n 'dialyze',\n 'diamond',\n 'diaper',\n 'diazotize',\n 'dib',\n 'dibble',\n 'dice',\n 'dichotomize',\n 'dicker',\n 'dictate',\n 'diddle',\n 'die',\n 'die-cast',\n 'diet',\n 'differ',\n 'differentiate',\n 'diffract',\n 'diffuse',\n 'dig',\n 'digest',\n 'dight',\n 'digitalize',\n 'digitize',\n 'dignify',\n 'digress',\n 'dike',\n 'dilapidate',\n 'dilate',\n 'dillydally',\n 'dilute',\n 'dim',\n 'dimension',\n 'dimidiate',\n 'diminish',\n 'dimple',\n 'din',\n 'dine',\n 'ding',\n 'dint',\n 'dip',\n 'diphthongize',\n 'direct',\n 'dirk',\n 'dirty',\n 'disable',\n 'disabuse',\n 'disaccord',\n 'disaccredit',\n 'disaccustom',\n 'disadvantage',\n 'disaffect',\n 'disaffiliate',\n 'disaffirm',\n 'disafforest',\n 'disagree',\n 'disallow',\n 'disambiguate',\n 'disannul',\n 'disappear',\n 'disappoint',\n 'disapprove',\n 'disarm',\n 'disarrange',\n 'disarray',\n 'disarticulate',\n 'disassemble',\n 'disassociate',\n 'disavow',\n 'disband',\n 'disbar',\n 'disbelieve',\n 'disbranch',\n 'disbud',\n 'disburden',\n 'disburse',\n 'disc',\n 'discant',\n 'discard',\n 'discern',\n 'discharge',\n 'discipline',\n 'disclaim',\n 'disclose',\n 'discolour',\n 'discombobulate',\n 'discomfit',\n 'discomfort',\n 'discommend',\n 'discommode',\n 'discommon',\n 'discompose',\n 'disconcert',\n 'disconnect',\n 'disconsider',\n 'discontent',\n 'discontinue',\n 'discord',\n 'discount',\n 'discountenance',\n 'discourage',\n 'discourse',\n 'discover',\n 'discredit',\n 'discriminate',\n 'discuss',\n 'disdain',\n 'disembark',\n 'disembarrass',\n 'disembody',\n 'disembogue',\n 'disembowel',\n 'disembroil',\n 'disenable',\n 'disenchant',\n 'disencumber',\n 'disendow',\n 'disenfranchise',\n 'disengage',\n 'disentail',\n 'disentangle',\n 'disenthrall',\n 'disentitle',\n 'disentomb',\n 'disentwine',\n 'disestablish',\n 'disesteem',\n 'disfavour',\n 'disfeature',\n 'disfigure',\n 'disforest',\n 'disfranchise',\n 'disfrock',\n 'disgorge',\n 'disgrace',\n 'disgruntle',\n 'disguise',\n 'disgust',\n 'dish',\n 'dishearten',\n 'dishevel',\n 'dishonour',\n 'disillusion',\n 'disincline',\n 'disinfect',\n 'disinfest',\n 'disinherit',\n 'disintegrate',\n 'disinter',\n 'disinterest',\n 'disject',\n 'disjoin',\n 'disjoint',\n 'dislike',\n 'dislimn',\n 'dislocate',\n 'dislodge',\n 'dismantle',\n 'dismast',\n 'dismay',\n 'dismember',\n 'dismiss',\n 'dismount',\n 'disobey',\n 'disoblige',\n 'disorder',\n 'disorganize',\n 'disorientate',\n 'disown',\n 'disparage',\n 'dispatch',\n 'dispel',\n 'dispend',\n 'dispense',\n 'disperse',\n 'dispirit',\n 'displace',\n 'displant',\n 'display',\n 'displease',\n 'displeasure',\n 'displode',\n 'disport',\n 'dispose',\n 'dispossess',\n 'dispraise',\n 'disprize',\n 'disproportion',\n 'disproportionate',\n 'disprove',\n 'dispute',\n 'disqualify',\n 'disquiet',\n 'disrate',\n 'disregard',\n 'disrelish',\n 'disremember',\n 'disrespect',\n 'disrobe',\n 'disrupt',\n 'dissatisfy',\n 'dissect',\n 'disseize',\n 'dissemble',\n 'disseminate',\n 'dissent',\n 'dissertate',\n 'disserve',\n 'dissever',\n 'dissimilate',\n 'dissimulate',\n 'dissipate',\n 'dissociate',\n 'dissolve',\n 'dissuade',\n 'distance',\n 'distaste',\n 'distemper',\n 'distend',\n 'distill',\n 'distinguish',\n 'distort',\n 'distract',\n 'distrain',\n 'distress',\n 'distribute',\n 'district',\n 'distrust',\n 'disturb',\n 'disunite',\n 'ditch',\n 'dither',\n 'ditto',\n 'divagate',\n 'divaricate',\n 'dive',\n 'divebomb',\n 'diverge',\n 'diversify',\n 'divert',\n 'divest',\n 'divide',\n 'divine',\n 'divinize',\n 'divorce',\n 'divulgate',\n 'divulge',\n 'divvy',\n 'dizen',\n 'dizzy',\n 'do',\n 'dock',\n 'docket',\n 'doctor',\n 'document',\n 'dodder',\n 'dodge',\n 'doff',\n 'dog',\n 'dog-paddle',\n 'dogear',\n 'dogmatize',\n 'dole',\n 'dolly',\n 'dome',\n 'domesticize',\n 'domicile',\n 'dominate',\n 'domineer',\n 'don',\n 'donate',\n 'dong',\n 'doodle',\n 'doom',\n 'dope',\n 'dose',\n 'doss',\n 'dot',\n 'dote',\n 'double',\n 'double-bank',\n 'double-declutch',\n 'double-fault',\n 'double-stop',\n 'double-time',\n 'doublebogey',\n 'doublecheck',\n 'doublecross',\n 'doublepark',\n 'doublespace',\n 'doubletongue',\n 'doubt',\n 'douche',\n 'douse',\n 'dovetail',\n 'dow',\n 'dower',\n 'down',\n 'downgrade',\n 'downplay',\n 'dowse',\n 'doze',\n 'drab',\n 'drabble',\n 'draft',\n 'draggle',\n 'draghunt',\n 'dragoon',\n 'drain',\n 'dramatize',\n 'drape',\n 'drat',\n 'draw',\n 'drawl',\n 'dread',\n 'dream',\n 'dredge',\n 'dree',\n 'drench',\n 'dress',\n 'dribble',\n 'drift',\n 'drill',\n 'drink',\n 'drip',\n 'drive',\n 'drivel',\n 'drizzle',\n 'drone',\n 'drool',\n 'droop',\n 'drop',\n 'dropkick',\n 'dropout',\n 'drown',\n 'drowse',\n 'drub',\n 'drudge',\n 'drug',\n 'drum',\n 'dry',\n 'dry-salt',\n 'dryclean',\n 'drydock',\n 'dub',\n 'duck',\n 'duel',\n 'duff',\n 'dulcify',\n 'dull',\n 'dumfound',\n 'dummy',\n 'dump',\n 'dun',\n 'dung',\n 'dunk',\n 'dunt',\n 'dup',\n 'dupe',\n 'duplicate',\n 'dusk',\n 'dust',\n 'dwarf',\n 'dwell',\n 'dwindle',\n 'dye',\n 'dyke',\n 'dynamite',\n 'ear',\n 'earbash',\n 'earmark',\n 'earn',\n 'earth',\n 'earwig',\n 'ease',\n 'eat',\n 'eavesdrop',\n 'ebb',\n 'ebonize',\n 'echelon',\n 'echo',\n 'eclipse',\n 'economize',\n 'eddy',\n 'edge',\n 'edify',\n 'edit',\n 'editorialize',\n 'educate',\n 'educe',\n 'edulcorate',\n 'eff',\n 'efface',\n 'effect',\n 'effectuate',\n 'effervesce',\n 'effloresce',\n 'effuse',\n 'egest',\n 'egg',\n 'egotrip',\n 'egress',\n 'ejaculate',\n 'eject',\n 'eke',\n 'elaborate',\n 'elapse',\n 'elasticate',\n 'elasticize',\n 'elate',\n 'elbow',\n 'elect',\n 'electioneer',\n 'electrify',\n 'electrocute',\n 'electrodeposit',\n 'electroform',\n 'electrolyze',\n 'electroplate',\n 'electrotype',\n 'elegize',\n 'elevate',\n 'elicit',\n 'elide',\n 'eliminate',\n 'eloin',\n 'elongate',\n 'elope',\n 'elucidate',\n 'elude',\n 'elute',\n 'elutriate',\n 'emaciate',\n 'emanate',\n 'emancipate',\n 'emasculate',\n 'embalm',\n 'embank',\n 'embargo',\n 'embark',\n 'embarrass',\n 'embattle',\n 'embay',\n 'embellish',\n 'embezzle',\n 'embitter',\n 'emblaze',\n 'emblazon',\n 'emblemize',\n 'embody',\n 'embolden',\n 'embosom',\n 'emboss',\n 'embow',\n 'embowel',\n 'embower',\n 'embrace',\n 'embrangle',\n 'embrocate',\n 'embroider',\n 'embroil',\n 'embus',\n 'emend',\n 'emerge',\n 'emigrate',\n 'emit',\n 'emote',\n 'emotionalize',\n 'empathize',\n 'emphasize',\n 'emplace',\n 'emplane',\n 'employ',\n 'empoison',\n 'empower',\n 'empt',\n 'empty',\n 'emulate',\n 'emulsify',\n 'enable',\n 'enact',\n 'enamel',\n 'enamour',\n 'encage',\n 'encamp',\n 'encarnalize',\n 'encash',\n 'enchain',\n 'enchant',\n 'enchase',\n 'encipher',\n 'encircle',\n 'enclasp',\n 'encode',\n 'encompass',\n 'encore',\n 'encounter',\n 'encourage',\n 'encroach',\n 'encrypt',\n 'encyst',\n 'end',\n 'endamage',\n 'endanger',\n 'endear',\n 'endeavour',\n 'endow',\n 'endure',\n 'energize',\n 'enervate',\n 'enface',\n 'enfeeble',\n 'enfeoff',\n 'enfilade',\n 'enforce',\n 'enfranchise',\n 'engage',\n 'engender',\n 'engineer',\n 'English',\n 'englut',\n 'engorge',\n 'engrail',\n 'engrave',\n 'engross',\n 'enhance',\n 'enigmatize',\n 'enisle',\n 'enjoin',\n 'enjoy',\n 'enkindle',\n 'enlace',\n 'enlarge',\n 'enlighten',\n 'enlist',\n 'enliven',\n 'ennoble',\n 'enounce',\n 'enplane',\n 'enquire',\n 'enrage',\n 'enrapture',\n 'enrich',\n 'enrobe',\n 'enroll',\n 'enroot',\n 'ensanguine',\n 'ensconce',\n 'enshrinshrine',\n 'enshroud',\n 'ensile',\n 'enslave',\n 'ensue',\n 'enswathe',\n 'entail',\n 'entangle',\n 'enter',\n 'entertain',\n 'enthrall',\n 'enthrone',\n 'enthuse',\n 'entice',\n 'entitle',\n 'entoil',\n 'entomb',\n 'entomologize',\n 'entrain',\n 'entrammel',\n 'entrance',\n 'entrap',\n 'entwintwine',\n 'enucleate',\n 'enumerate',\n 'enunciate',\n 'envelop',\n 'envenom',\n 'environ',\n 'envisage',\n 'envision',\n 'envy',\n 'enwind',\n 'enwomb',\n 'enwrap',\n 'enwreath',\n 'epigrammatize',\n 'epilate',\n 'epitomize',\n 'equal',\n 'equalize',\n 'equate',\n 'equilibrate',\n 'equip',\n 'equipoise',\n 'equiponderate',\n 'equivocate',\n 'eradiate',\n 'eradicate',\n 'erase',\n 'erect',\n 'erode',\n 'err',\n 'eructate',\n 'erupt',\n 'escalade',\n 'escalate',\n 'escallop',\n 'escape',\n 'escarp',\n 'escheat',\n 'eschew',\n 'escort',\n 'escribe',\n 'espalier',\n 'espouse',\n 'espy',\n 'esquire',\n 'essay',\n 'establish',\n 'esteem',\n 'esterify',\n 'estimate',\n 'estivate',\n 'estop',\n 'estrange',\n 'estreat',\n 'etch',\n 'eternize',\n 'etherealize',\n 'etherify',\n 'etherize',\n 'ethicize',\n 'etiolate',\n 'etymologize',\n 'euchre',\n 'euhemerize',\n 'eulogize',\n 'euphemize',\n 'euphonize',\n 'Europeanize',\n 'evacuate',\n 'evade',\n 'evaginate',\n 'evaluate',\n 'evanesce',\n 'evangelize',\n 'evanish',\n 'evaporate',\n 'even',\n 'eventuate',\n 'evert',\n 'evict',\n 'evidence',\n 'evince',\n 'eviscerate',\n 'evite',\n 'evoke',\n 'evolve',\n 'exacerbate',\n 'exact',\n 'exaggerate',\n 'exalt',\n 'examine',\n 'exasperate',\n 'excavate',\n 'exceed',\n 'excel',\n 'except',\n 'excerpt',\n 'exchange',\n 'excide',\n 'excise',\n 'excite',\n 'exclaim',\n 'exclude',\n 'excogitate',\n 'excommunicate',\n 'excorciate',\n 'excoriate',\n 'excrete',\n 'excruciate',\n 'exculpate',\n 'excuse',\n 'execrate',\n 'execute',\n 'exemplify',\n 'exempt',\n 'exenterate',\n 'exercise',\n 'exert',\n 'exfoliate',\n 'exhale',\n 'exhaust',\n 'exhibit',\n 'exhilarate',\n 'exhort',\n 'exhume',\n 'exile',\n 'exist',\n 'exit',\n 'exonerate',\n 'exorcize',\n 'expand',\n 'expatiate',\n 'expatriate',\n 'expect',\n 'expectorate',\n 'expedite',\n 'expel',\n 'expend',\n 'expense',\n 'experience',\n 'experiment',\n 'experimentalize',\n 'expertize',\n 'expiate',\n 'expire',\n 'explain',\n 'explant',\n 'explicate',\n 'explode',\n 'exploit',\n 'explore',\n 'export',\n 'expose',\n 'expostulate',\n 'expound',\n 'express',\n 'expropriate',\n 'expunge',\n 'expurgate',\n 'exsanguinate',\n 'exscind',\n 'exsect',\n 'exsert',\n 'exsiccate',\n 'extemporize',\n 'extend',\n 'extenuate',\n 'exterminate',\n 'externalize',\n 'extinguish',\n 'extirpate',\n 'extoll',\n 'extort',\n 'extract',\n 'extradite',\n 'extrapolate',\n 'extravagate',\n 'extravasate',\n 'extricate',\n 'extrude',\n 'exuberate',\n 'exude',\n 'exult',\n 'exuviate',\n 'eye',\n 'eyeball',\n 'eyelet',\n 'f^ete',\n 'fable',\n 'fabricate',\n 'face',\n 'faceharden',\n 'faceoff',\n 'facet',\n 'facilitate',\n 'factor',\n 'factorize',\n 'fade',\n 'fadge',\n 'faff',\n 'fag',\n 'fail',\n 'faint',\n 'fair',\n 'fake',\n 'fall',\n 'fallow',\n 'false-card',\n 'falsify',\n 'falter',\n 'fame',\n 'familiarize',\n 'famish',\n 'fan',\n 'fanaticize',\n 'fancy',\n 'fankle',\n 'fantasize',\n 'faradize',\n 'farce',\n 'fare',\n 'farm',\n 'farrow',\n 'fart',\n 'fascinate',\n 'fash',\n 'fashion',\n 'fast',\n 'fasten',\n 'fat',\n 'fate',\n 'father',\n 'fathom',\n 'fatigue',\n 'fatten',\n 'fault',\n 'favour',\n 'fawn',\n 'fay',\n 'faze',\n 'fear',\n 'feast',\n 'feather',\n 'featherbed',\n 'featherstitch',\n 'feature',\n 'feaze',\n 'fecundate',\n 'fed',\n 'federalize',\n 'federate',\n 'feed',\n 'feel',\n 'feeze',\n 'feign',\n 'feint',\n 'felicitate',\n 'fellow',\n 'feminize',\n 'fence',\n 'fend',\n 'feoff',\n 'ferment',\n 'ferret',\n 'ferrule',\n 'ferry',\n 'fertilize',\n 'ferule',\n 'fester',\n 'festoon',\n 'fetch',\n 'fete',\n 'fetter',\n 'fettle',\n 'feud',\n 'feudalize',\n 'fever',\n 'fib',\n 'fictionalize',\n 'fiddle',\n 'fiddlefaddle',\n 'fidge',\n 'fidget',\n 'field',\n 'fife',\n 'fig',\n 'fight',\n 'figure',\n 'filagree',\n 'filch',\n 'file',\n 'filiate',\n 'filibuster',\n 'fill',\n 'fillagree',\n 'fillet',\n 'fillip',\n 'film',\n 'filmset',\n 'filter',\n 'filtrate',\n 'fin',\n 'finagle',\n 'finalize',\n 'finance',\n 'find',\n 'fine',\n 'fine-draw',\n 'finesse',\n 'finger',\n 'fingerprint',\n 'finish',\n 'fink',\n 'fire',\n 'firecure',\n 'fireproof',\n 'firm',\n 'first-foot',\n 'fishes',\n 'fishtail',\n 'fissure',\n 'fist',\n 'fit',\n 'fix',\n 'fixate',\n 'fizz',\n 'fizzle',\n 'flabbergast',\n 'flag',\n 'flagellate',\n 'flail',\n 'flake',\n 'flam',\n 'flame',\n 'flameout',\n 'flange',\n 'flank',\n 'flannel',\n 'flap',\n 'flare',\n 'flash',\n 'flatten',\n 'flatter',\n 'flaunt',\n 'flavour',\n 'flaw',\n 'fleck',\n 'fledge',\n 'flee',\n 'fleece',\n 'fleer',\n 'fleet',\n 'flense',\n 'flesh',\n 'fletch',\n 'flex',\n 'fley',\n 'flick',\n 'flicker',\n 'flight',\n 'flimflam',\n 'flinch',\n 'fling',\n 'flint',\n 'flip',\n 'flirt',\n 'flit',\n 'flitch',\n 'flite',\n 'flitter',\n 'float',\n 'flocculate',\n 'flock',\n 'flog',\n 'flood',\n 'floodlight',\n 'floor',\n 'flop',\n 'flounce',\n 'flounder',\n 'flour',\n 'flourish',\n 'flout',\n 'flow',\n 'flower',\n 'fluctuate',\n 'flue-cure',\n 'fluff',\n 'fluidize',\n 'fluke',\n 'flume',\n 'flummox',\n 'flunk',\n 'fluoresce',\n 'fluoridate',\n 'fluoridize',\n 'fluorinate',\n 'flurry',\n 'flush',\n 'fluster',\n 'flute',\n 'flutter',\n 'flux',\n 'fly',\n 'flyblow',\n 'flyfish',\n 'flyspeck',\n 'flyte',\n 'foal',\n 'foam',\n 'fob',\n 'focalize',\n 'focus',\n 'fodder',\n 'fog',\n 'foil',\n 'foin',\n 'foist',\n 'fold',\n 'foliate',\n 'folio',\n 'folk',\n 'folk-dance',\n 'follow',\n 'foment',\n 'fondle',\n 'fool',\n 'foot',\n 'foot-slog',\n 'footle',\n 'footnote',\n 'foozle',\n 'forage',\n 'foray',\n 'forbear',\n 'forbid',\n 'force',\n 'force-land',\n 'force-ripe',\n 'forcefeed',\n 'ford',\n 'forearm',\n 'forebode',\n 'forecast',\n 'foreclose',\n 'foredo',\n 'foredoom',\n 'foregather',\n 'forehand',\n 'foreknow',\n 'forelock',\n 'foreordain',\n 'forereach',\n 'forerun',\n 'foresee',\n 'foreshadow',\n 'foreshorten',\n 'foreshow',\n 'forespeak',\n 'forest',\n 'forestall',\n 'foreswear',\n 'foretaste',\n 'foretell',\n 'foretoken',\n 'forewarn',\n 'forfeit',\n 'forfend',\n 'forgat',\n 'forgather',\n 'forge',\n 'forget',\n 'forgive',\n 'forgo',\n 'forjudge',\n 'fork',\n 'form',\n 'formalize',\n 'format',\n 'formicate',\n 'formularize',\n 'formulate',\n 'fornicate',\n 'forsake',\n 'forspeak',\n 'forswear',\n 'fortify',\n 'fortress',\n 'fortune',\n 'forward',\n 'fossick',\n 'fossilize',\n 'foster',\n 'foul',\n 'founder',\n 'fourflush',\n 'fowl',\n 'fox',\n 'foxhunt',\n 'fraction',\n 'fractionate',\n 'fractionize',\n 'fracture',\n 'frag',\n 'fragment',\n 'frame',\n 'franchise',\n 'frank',\n 'frap',\n 'fraternize',\n 'fray',\n 'frazzle',\n 'freak',\n 'freckle',\n 'free',\n 'free-select',\n 'free-wheel',\n 'freeboot',\n 'freelance',\n 'freeload',\n 'freewheel',\n 'freeze',\n 'freezedry',\n 'freight',\n 'French-polish',\n 'Frenchify',\n 'frenzy',\n 'frequent',\n 'fresh',\n 'freshen',\n 'fret',\n 'fribble',\n 'fricassee',\n 'friend',\n 'frig',\n 'frighten',\n 'frill',\n 'fringe',\n 'frisk',\n 'fritt',\n 'fritter',\n 'frivol',\n 'frizz',\n 'frizzle',\n 'frock',\n 'frog',\n 'frogmarch',\n 'frolic',\n 'front',\n 'frost',\n 'froth',\n 'frown',\n 'fructify',\n 'fruit',\n 'frustrate',\n 'fry',\n 'fuck',\n 'fuddle',\n 'fudge',\n 'fuel',\n 'fulfill',\n 'fulgurate',\n 'fuller',\n 'fulminate',\n 'fumble',\n 'fume',\n 'fumigate',\n 'fun',\n 'function',\n 'fund',\n 'funk',\n 'funnel',\n 'fur',\n 'furbelow',\n 'furbish',\n 'furcate',\n 'furl',\n 'furlough',\n 'furnish',\n 'furrow',\n 'further',\n 'fusillade',\n 'fuss',\n 'fustigate',\n 'fuze',\n 'fuzz',\n 'gab',\n 'gabble',\n 'gad',\n 'gaff',\n 'gag',\n 'gaggle',\n 'gain',\n 'gainsay',\n 'gall',\n 'gallant',\n 'Gallicize',\n 'gallivant',\n 'gallop',\n 'galumph',\n 'galvanize',\n 'gam',\n 'gamble',\n 'gambol',\n 'game',\n 'gammon',\n 'gang',\n 'gangrene',\n 'gape',\n 'garage',\n 'garb',\n 'garble',\n 'garden',\n 'gargle',\n 'garland',\n 'garment',\n 'garner',\n 'garnish',\n 'garnishee',\n 'garrison',\n 'garrotte',\n 'garter',\n 'gas',\n 'gasconade',\n 'gash',\n 'gasify',\n 'gasp',\n 'gat',\n 'gate',\n 'gate-crash',\n 'gatecrash',\n 'gather',\n 'gauge',\n 'gawk',\n 'gawp',\n 'gaze',\n 'gazette',\n 'gazump',\n 'gear',\n 'gee',\n 'gelatinize',\n 'geld',\n 'gem',\n 'geminate',\n 'gemmate',\n 'generalize',\n 'generate',\n 'gentle',\n 'genuflect',\n 'geologize',\n 'geometrize',\n 'Germanize',\n 'germinate',\n 'gerrymander',\n 'gestate',\n 'gesticulate',\n 'gesture',\n 'get',\n 'getter',\n 'ghost',\n 'ghostwrite',\n 'gib',\n 'gibber',\n 'gibbet',\n 'gibe',\n 'gie',\n 'gift',\n 'giftwrap',\n 'gig',\n 'giggle',\n 'gild',\n 'gill',\n 'gimlet',\n 'gimme',\n 'gin',\n 'ginger',\n 'gird',\n 'girdle',\n 'girth',\n 'give',\n 'glac_e',\n 'glaciate',\n 'glad',\n 'gladden',\n 'glair',\n 'glamourize',\n 'glance',\n 'glare',\n 'glass',\n 'glaze',\n 'gleam',\n 'glean',\n 'glide',\n 'glimmer',\n 'glimpse',\n 'glint',\n 'glissade',\n 'glisten',\n 'glister',\n 'glitter',\n 'gloat',\n 'globe',\n 'globe-trot',\n 'gloom',\n 'glorify',\n 'glory',\n 'gloss',\n 'glove',\n 'glow',\n 'glower',\n 'gloze',\n 'glue',\n 'glut',\n 'gnarl',\n 'gnash',\n 'gnaw',\n 'Gnosticize',\n 'go',\n 'goad',\n 'gob',\n 'gobble',\n 'goffer',\n 'goggle',\n 'gold-plate',\n 'gollop',\n 'golly',\n 'goof',\n 'goose',\n 'goosestep',\n 'gore',\n 'gorge',\n 'gormandize',\n 'gossip',\n 'goster',\n 'Gothicize',\n 'gotta',\n 'gouge',\n 'govern',\n 'gown',\n 'grab',\n 'grabble',\n 'grace',\n 'gradate',\n 'grade',\n 'graduate',\n 'graft',\n 'grain',\n 'grandstand',\n 'grangerize',\n 'grant',\n 'granulate',\n 'graph',\n 'graphitize',\n 'grapple',\n 'grasp',\n 'grass',\n 'grate',\n 'gratify',\n 'gratulate',\n 'grave',\n 'gravel',\n 'gravitate',\n 'graze',\n 'grease',\n 'greaten',\n 'Grecize',\n 'gree',\n 'greet',\n 'grey',\n 'griddle',\n 'gride',\n 'grieve',\n 'grill',\n 'grimace',\n 'grime',\n 'grin',\n 'grind',\n 'grip',\n 'gripe',\n 'grit',\n 'grizzle',\n 'groan',\n 'groin',\n 'groom',\n 'groove',\n 'grope',\n 'gross',\n 'grouch',\n 'ground',\n 'group',\n 'grouse',\n 'grout',\n 'grovel',\n 'grow',\n 'growl',\n 'grub',\n 'grubstake',\n 'grudge',\n 'grumble',\n 'grump',\n 'grunt',\n 'guarantee',\n 'guaranty',\n 'guard',\n 'gudgeon',\n 'guerdon',\n 'guess',\n 'guest',\n 'guffaw',\n 'guide',\n 'guillotine',\n 'guise',\n 'gulf',\n 'gull',\n 'gully',\n 'gulp',\n 'gum',\n 'gumshoe',\n 'gun',\n 'gurgle',\n 'gush',\n 'gusset',\n 'gut',\n 'gutter',\n 'gutturalize',\n 'guy',\n 'guzzle',\n 'gybe',\n 'gyp',\n 'gyrate',\n 'gyve',\n 'habilitate',\n 'habit',\n 'habituate',\n 'hachure',\n 'hack',\n 'hackle',\n 'hackney',\n 'hacksaw',\n 'hade',\n 'haft',\n 'haggle',\n 'hail',\n 'hale',\n 'half-volley',\n 'hallal',\n 'hallmark',\n 'halloo',\n 'hallow',\n 'hallucinate',\n 'halo',\n 'halogenate',\n 'halter',\n 'halve',\n 'ham',\n 'hammer',\n 'hamper',\n 'hamshackle',\n 'hamstring',\n 'hand',\n 'hand-knit',\n 'handcuff',\n 'handfast',\n 'handfeed',\n 'handicap',\n 'handle',\n 'handpick',\n 'hang',\n 'hank',\n 'hanker',\n 'hansel',\n 'hap',\n 'happen',\n 'harangue',\n 'harass',\n 'harbinger',\n 'harbour',\n 'harden',\n 'hare',\n 'hark',\n 'harm',\n 'harmonize',\n 'harness',\n 'harp',\n 'harpoon',\n 'harrow',\n 'harrumph',\n 'harry',\n 'harvest',\n 'hash',\n 'hasp',\n 'hassle',\n 'haste',\n 'hasten',\n 'hat',\n 'hatch',\n 'hatchel',\n 'hate',\n 'haul',\n 'haunt',\n 'have',\n 'haven',\n 'haver',\n 'havoc',\n 'haw',\n 'hawk',\n 'hawse',\n 'hay',\n 'hazard',\n 'haze',\n 'head',\n 'head-load',\n 'headline',\n 'headreach',\n 'heal',\n 'heap',\n 'hear',\n 'hearken',\n 'heart',\n 'hearten',\n 'heat',\n 'heathenize',\n 'heattreat',\n 'heave',\n 'hebetate',\n 'Hebraize',\n 'heckle',\n 'hector',\n 'hedge',\n 'hedge-hop',\n 'hedgehop',\n 'heed',\n 'heel',\n 'heel-and-toe',\n 'heft',\n 'heighten',\n 'heist',\n 'Hellenize',\n 'helm',\n 'help',\n 'helve',\n 'hem',\n 'hemagglutinate',\n 'hemorrhage',\n 'hemstitch',\n 'henpeck',\n 'hent',\n 'herald',\n 'herd',\n 'heroworship',\n 'herringbone',\n 'hesitate',\n 'heterodyne',\n 'hew',\n 'hex',\n 'hibernate',\n 'hiccough',\n 'hiccup',\n 'hide',\n 'hie',\n 'higgle',\n 'highhat',\n 'highlight',\n 'hight',\n 'hightail',\n 'hijack',\n 'hike',\n 'hill',\n 'hilt',\n 'hinder',\n 'hinge',\n 'hinny',\n 'hint',\n 'hire',\n 'Hispanicize',\n 'hiss',\n 'hit',\n 'hitch',\n 'hitchhike',\n 'hive',\n 'hoard',\n 'hoarsen',\n 'hoax',\n 'hob',\n 'hobble',\n 'hobbyhorse',\n 'hobnob',\n 'hock',\n 'hocus',\n 'hocuspocus',\n 'hoe',\n 'hog',\n 'hogtie',\n 'hoick',\n 'hoiden',\n 'hoist',\n 'hoke',\n 'hold',\n 'holden',\n 'hole',\n 'holiday',\n 'holler',\n 'hollow',\n 'holp',\n 'holpen',\n 'holystone',\n 'homage',\n 'home',\n 'homogenize',\n 'homologate',\n 'homologize',\n 'hone',\n 'honey',\n 'honeycomb',\n 'honeymoon',\n 'honor',\n 'honour',\n 'hood',\n 'hoodoo',\n 'hoodwink',\n 'hoof',\n 'hook',\n 'hookup',\n 'hoop',\n 'hooray',\n 'hoot',\n 'Hoover',\n 'hop',\n 'hope',\n 'hopple',\n 'horde',\n 'horn',\n 'hornswoggle',\n 'horrify',\n 'horse',\n 'horseshoe',\n 'horsewhip',\n 'hose',\n 'hospitalize',\n 'host',\n 'hot-press',\n 'hotdog',\n 'hotfoot',\n 'hound',\n 'house',\n 'house-train',\n 'housel',\n 'hovel',\n 'hover',\n 'howl',\n 'huckster',\n 'huddle',\n 'huff',\n 'hug',\n 'huggermugger',\n 'hulk',\n 'hum',\n 'humanize',\n 'humble',\n 'humbug',\n 'humidify',\n 'humiliate',\n 'humour',\n 'hump',\n 'hunch',\n 'hunger',\n 'hunt',\n 'hurdle',\n 'hurl',\n 'hurrah',\n 'hurry',\n 'hurt',\n 'hurtle',\n 'husband',\n 'hush',\n 'hustle',\n 'hutch',\n 'huzzah',\n 'hybridize',\n 'hydrate',\n 'hydrogenize',\n 'hydrolyze',\n 'hydroplane',\n 'hymn',\n 'hyperbolize',\n 'hypersensitize',\n 'hypertrophy',\n 'hyphenate',\n 'hypnotize',\n 'hyposensitize',\n 'hypostasize',\n 'hypostatize',\n 'hypothecate',\n 'hypothesize',\n 'hysterectomize',\n 'ice',\n 'iceskate',\n 'idealize',\n 'ideate',\n 'identify',\n 'idle',\n 'idolatrize',\n 'idolize',\n 'ignite',\n 'ignore',\n 'illegalize',\n 'illtreat',\n 'illude',\n 'illume',\n 'illuminate',\n 'illumine',\n 'illuse',\n 'illustrate',\n 'image',\n 'imagine',\n 'imbed',\n 'imbibe',\n 'imbricate',\n 'imbrue',\n 'imbue',\n 'imitate',\n 'immaterialize',\n 'immerge',\n 'immerse',\n 'immigrate',\n 'immingle',\n 'immix',\n 'immobilize',\n 'immolate',\n 'immortalize',\n 'immunize',\n 'immure',\n 'imp',\n 'impact',\n 'impair',\n 'impale',\n 'impanel',\n 'imparadise',\n 'impart',\n 'impassion',\n 'impaste',\n 'impeach',\n 'impearl',\n 'impede',\n 'impel',\n 'impend',\n 'imperil',\n 'impersonalize',\n 'impersonate',\n 'impetrate',\n 'impinge',\n 'implant',\n 'implead',\n 'implement',\n 'implicate',\n 'implode',\n 'implore',\n 'imply',\n 'impolder',\n 'import',\n 'importune',\n 'impose',\n 'impost',\n 'impound',\n 'impoverish',\n 'impower',\n 'imprecate',\n 'impregnate',\n 'impress',\n 'imprint',\n 'imprison',\n 'impropriate',\n 'improve',\n 'improvise',\n 'impugn',\n 'impulse-buy',\n 'impute',\n 'inactivate',\n 'inarch',\n 'inaugurate',\n 'inbreathe',\n 'inbred',\n 'incandesce',\n 'incapacitate',\n 'incapsulate',\n 'incarcerate',\n 'incardinate',\n 'incarnadine',\n 'incarnate',\n 'incase',\n 'incense',\n 'incept',\n 'incinerate',\n 'incise',\n 'incite',\n 'incline',\n 'inclose',\n 'include',\n 'incommode',\n 'inconvenience',\n 'incorporate',\n 'incrassate',\n 'increase',\n 'incriminate',\n 'incross',\n 'incrust',\n 'incubate',\n 'inculcate',\n 'inculpate',\n 'incumber',\n 'incur',\n 'incurvate',\n 'indemnify',\n 'indent',\n 'indenture',\n 'index',\n 'indicate',\n 'indict',\n 'indispose',\n 'indite',\n 'individualize',\n 'individuate',\n 'indoctrinate',\n 'indorse',\n 'induce',\n 'induct',\n 'indue',\n 'indulge',\n 'indurate',\n 'industrialize',\n 'indwell',\n 'inearth',\n 'inebriate',\n 'infamize',\n 'infatuate',\n 'infect',\n 'infer',\n 'infest',\n 'infibulate',\n 'infiltrate',\n 'infix',\n 'inflame',\n 'inflate',\n 'inflect',\n 'inflict',\n 'influence',\n 'infold',\n 'inform',\n 'infract',\n 'infringe',\n 'infuriate',\n 'infuse',\n 'ingather',\n 'ingeminate',\n 'ingenerate',\n 'ingest',\n 'ingot',\n 'ingraft',\n 'ingrain',\n 'ingratiate',\n 'ingulf',\n 'ingurgitate',\n 'inhabit',\n 'inhale',\n 'inhere',\n 'inherit',\n 'inhibit',\n 'inhume',\n 'initial',\n 'initialize',\n 'initiate',\n 'inject',\n 'injure',\n 'ink',\n 'inlace',\n 'inlay',\n 'inlet',\n 'inmesh',\n 'innervate',\n 'innerve',\n 'innovate',\n 'inoculate',\n 'inosculate',\n 'inquire',\n 'insalivate',\n 'inscribe',\n 'inseminate',\n 'insert',\n 'inset',\n 'inshrine',\n 'insinuate',\n 'insist',\n 'insnare',\n 'insolate',\n 'insoul',\n 'inspan',\n 'inspect',\n 'insphere',\n 'inspire',\n 'inspirit',\n 'inspissate',\n 'install',\n 'instance',\n 'instantiate',\n 'instate',\n 'instigate',\n 'instill',\n 'institute',\n 'institutionalize',\n 'instruct',\n 'insufflate',\n 'insulate',\n 'insult',\n 'insure',\n 'integrate',\n 'intellectualize',\n 'intend',\n 'intenerate',\n 'intensify',\n 'inter',\n 'interact',\n 'interbreed',\n 'intercalate',\n 'intercede',\n 'intercept',\n 'interchange',\n 'intercommunicate',\n 'intercrop',\n 'intercross',\n 'intercut',\n 'interdict',\n 'interdigitate',\n 'interest',\n 'interfere',\n 'interfile',\n 'interflow',\n 'interfuse',\n 'intergrade',\n 'interiorize',\n 'interject',\n 'interlace',\n 'interlaminate',\n 'interlap',\n 'interlard',\n 'interlay',\n 'interleave',\n 'interlineate',\n 'interlink',\n 'interlock',\n 'interlope',\n 'intermarry',\n 'intermingle',\n 'intermit',\n 'intermix',\n 'intern',\n 'internalize',\n 'internationalize',\n 'interosculate',\n 'interpage',\n 'interpellate',\n 'interpenetrate',\n 'interplead',\n 'interpolate',\n 'interpose',\n 'interpret',\n 'interrelate',\n 'interrogate',\n 'interrupt',\n 'intersect',\n 'interspace',\n 'intersperse',\n 'interstratify',\n 'intertwine',\n 'intervene',\n 'interview',\n 'interweave',\n 'intimate',\n 'intimidate',\n 'intitule',\n 'intonate',\n 'intone',\n 'intoxicate',\n 'intreat',\n 'intrench',\n 'intrigue',\n 'introduce',\n 'introject',\n 'intromit',\n 'introspect',\n 'introvert',\n 'intrude',\n 'intrust',\n 'intubate',\n 'intuit',\n 'intumesce',\n 'intussuscept',\n 'intwine',\n 'inundate',\n 'inure',\n 'inurn',\n 'invade',\n 'invaginate',\n 'invalid',\n 'invalidate',\n 'inveigh',\n 'inveigle',\n 'invent',\n 'inventory',\n 'invert',\n 'invest',\n 'investigate',\n 'invigilate',\n 'invigorate',\n 'invite',\n 'invocate',\n 'invoice',\n 'invoke',\n 'involute',\n 'involve',\n 'inweave',\n 'inwrap',\n 'iodate',\n 'iodize',\n 'ionize',\n 'irk',\n 'iron',\n 'ironize',\n 'irradiate',\n 'irrigate',\n 'irritate',\n 'irrupt',\n 'Islamize',\n 'island',\n 'isochronize',\n 'isolate',\n 'isomerize',\n 'issue',\n 'Italianize',\n 'italicize',\n 'itch',\n 'item',\n 'itemize',\n 'iterate',\n 'itinerate',\n 'jab',\n 'jabber',\n 'jack',\n 'jacket',\n 'jackknife',\n 'jade',\n 'jaga',\n 'jagg',\n 'jail',\n 'jam',\n 'jampack',\n 'jangle',\n 'japan',\n 'jape',\n 'jar',\n 'jargon',\n 'jargonize',\n 'jaundice',\n 'jaunt',\n 'jaup',\n 'jaw',\n 'jay-walk',\n 'jaywalk',\n 'jazz',\n 'jeer',\n 'jell',\n 'jellify',\n 'jelly',\n 'jemmy',\n 'jeopardize',\n 'jerk',\n 'jerrybuild',\n 'jess',\n 'jest',\n 'jet',\n 'jettison',\n 'Jew',\n 'jewel',\n 'jib',\n 'jibe',\n 'jig',\n 'jiggle',\n 'jilt',\n 'jimmy',\n 'jingle',\n 'jink',\n 'jinx',\n 'jitter',\n 'job',\n 'jockey',\n 'jog',\n 'jog-trot',\n 'joggle',\n 'join',\n 'joint',\n 'joist',\n 'joke',\n 'jollify',\n 'jolly',\n 'jolt',\n 'jook',\n 'josh',\n 'jot',\n 'jounce',\n 'journalize',\n 'journey',\n 'joust',\n 'joy',\n 'joy-ride',\n 'joypop',\n 'jubilate',\n 'Judaize',\n 'judder',\n 'judge',\n 'jug',\n 'juggle',\n 'jugulate',\n 'jumble',\n 'jump',\n 'jumpstart',\n 'junk',\n 'junket',\n 'justify',\n 'justle',\n 'jut',\n 'juxtapose',\n 'kalsomine',\n 'kangaroo',\n 'kayo',\n 'keck',\n 'kedge',\n 'keek',\n 'keel',\n 'keelhaul',\n 'keen',\n 'keep',\n 'ken',\n 'kennel',\n 'kep',\n 'keratinize',\n 'kerfuffle',\n 'kerne',\n 'kernel',\n 'key',\n 'keyboard',\n 'keynote',\n 'keypunch',\n 'kibble',\n 'kibitz',\n 'kibosh',\n 'kick',\n 'kick-start',\n 'kid',\n 'kidnap',\n 'kill',\n 'kiln',\n 'kilt',\n 'kindle',\n 'kip',\n 'kipper',\n 'kiss',\n 'kite',\n 'kitten',\n 'kittle',\n 'knacker',\n 'knap',\n 'knead',\n 'kneel',\n 'knife',\n 'knight',\n 'knit',\n 'knob',\n 'knock',\n 'knoll',\n 'knot',\n 'know',\n 'knurl',\n 'ko',\n 'kockelsch',\n 'kotow',\n 'kowtow',\n 'kraal',\n 'kyanize',\n 'label',\n 'labialize',\n 'labour',\n 'lace',\n 'lacerate',\n 'lack',\n 'lackey',\n 'lacquer',\n 'lactate',\n 'ladder',\n 'lade',\n 'ladle',\n 'ladyfy',\n 'lag',\n 'laicize',\n 'laik',\n 'lair',\n 'lallygag',\n 'lam',\n 'lamb',\n 'lambaste',\n 'lame',\n 'lament',\n 'laminate',\n 'lampoon',\n 'lance',\n 'land',\n 'landscape',\n 'languish',\n 'lap',\n 'lapidate',\n 'lapidify',\n 'lapse',\n 'lard',\n 'largen',\n 'lark',\n 'larn',\n 'larrup',\n 'lase',\n 'lash',\n 'lasso',\n 'last',\n 'latch',\n 'lath',\n 'lathe',\n 'lather',\n 'Latinize',\n 'lattice',\n 'laud',\n 'laugh',\n 'launch',\n 'launder',\n 'lave',\n 'lavish',\n 'layer',\n 'laze',\n 'leach',\n 'lead',\n 'leaf',\n 'league',\n 'leak',\n 'lean',\n 'leap',\n 'leapfrog',\n 'learn',\n 'lease',\n 'leash',\n 'leather',\n 'leave',\n 'leaven',\n 'lecture',\n 'ledger',\n 'leer',\n 'left',\n 'leg',\n 'legalize',\n 'legislate',\n 'legitimate',\n 'legitimize',\n 'leister',\n 'lend',\n 'lengthen',\n 'lessen',\n 'lesson',\n 'let',\n 'letch',\n 'letter',\n 'levant',\n 'level',\n 'lever',\n 'levigate',\n 'levitate',\n 'levy',\n 'LHlike',\n 'liaise',\n 'libel',\n 'liberalize',\n 'liberate',\n 'librate',\n 'licence',\n 'license',\n 'lick',\n 'lie',\n 'lift',\n 'ligate',\n 'ligature',\n 'light',\n 'lighten',\n 'lignify',\n 'like',\n 'liken',\n 'lilt',\n 'limb',\n 'limber',\n 'lime',\n 'limit',\n 'limn',\n 'limp',\n 'line',\n 'linger',\n 'link',\n 'lionize',\n 'lip',\n 'lipread',\n 'liquate',\n 'liquefy',\n 'liquesce',\n 'liquidate',\n 'liquidize',\n 'liquify',\n 'liquor',\n 'lisp',\n 'list',\n 'listen',\n 'lit',\n 'lithograph',\n 'litigate',\n 'litter',\n 'live',\n 'liven',\n 'lixiviate',\n 'load',\n 'loaf',\n 'loam',\n 'loan',\n 'loathe',\n 'lob',\n 'lobby',\n 'localize',\n 'locate',\n 'lock',\n 'loco',\n 'lodge',\n 'loft',\n 'log',\n 'logroll',\n 'loiter',\n 'loll',\n 'lollop',\n 'long',\n 'look',\n 'loom',\n 'loop',\n 'loophole',\n 'loose',\n 'loosen',\n 'loot',\n 'lop',\n 'lope',\n 'lord',\n 'lose',\n 'lot',\n 'louden',\n 'lounge',\n 'lour',\n 'lout',\n 'love',\n 'lower',\n 'lowercase',\n 'lubricate',\n 'luck',\n 'lucubrate',\n 'luff',\n 'lug',\n 'lull',\n 'lullaby',\n 'lumber',\n 'luminesce',\n 'lump',\n 'lunch',\n 'lunge',\n 'lurch',\n 'lure',\n 'lurk',\n 'lush',\n 'lust',\n 'lustrate',\n 'lustre',\n 'lute',\n 'luxate',\n 'luxuriate',\n 'lynch',\n 'lyophilize',\n 'lyse',\n 'macadamize',\n 'Mace',\n 'macerate',\n 'machicolate',\n 'machinate',\n 'machine',\n 'machinegun',\n 'maculate',\n 'mad',\n 'madden',\n 'maffick',\n 'magnetize',\n 'magnify',\n 'mail',\n 'maim',\n 'maintain',\n 'major',\n 'make',\n 'maladminister',\n 'maledict',\n 'malfunction',\n 'malign',\n 'malinger',\n 'malt',\n 'maltreat',\n 'mamaguy',\n 'mambo',\n 'mammock',\n 'man',\n 'man-handle',\n 'manacle',\n 'manage',\n 'mandate',\n 'manducate',\n 'maneuver',\n 'mangle',\n 'manhandle',\n 'manicure',\n 'manifest',\n 'manifold',\n 'manipulate',\n 'manoeuvre',\n 'mantle',\n 'manufacture',\n 'manumit',\n 'manure',\n 'map',\n 'mar',\n 'maraud',\n 'marble',\n 'marcel',\n 'march',\n 'margin',\n 'marginalize',\n 'marginate',\n 'marinade',\n 'marinate',\n 'mark',\n 'market',\n 'marl',\n 'maroon',\n 'marry',\n 'marshal',\n 'martyr',\n 'marvel',\n 'mash',\n 'mask',\n 'mason',\n 'masquerade',\n 'mass',\n 'massacre',\n 'massage',\n 'massproduce',\n 'mast',\n 'master',\n 'master-mind',\n 'mastermind',\n 'masticate',\n 'masturbate',\n 'mat',\n 'match',\n 'matchmark',\n 'mate',\n 'materialize',\n 'matriculate',\n 'matter',\n 'maturate',\n 'mature',\n 'maul',\n 'maunder',\n 'maximize',\n 'may',\n 'maze',\n 'mean',\n 'meander',\n 'measure',\n 'mechanize',\n 'medal',\n 'meddle',\n 'mediate',\n 'mediatize',\n 'medicate',\n 'meditate',\n 'meet',\n 'meld',\n 'meliorate',\n 'mellow',\n 'melodize',\n 'melodramatize',\n 'melt',\n 'memorialize',\n 'memorize',\n 'menace',\n 'mend',\n 'menstruate',\n 'mention',\n 'mercerize',\n 'merchant',\n 'mercurate',\n 'mercurialize',\n 'merge',\n 'merit',\n 'mesh',\n 'mesmerize',\n 'mess',\n 'metabolize',\n 'metal',\n 'metallize',\n 'metamorphose',\n 'metaphrase',\n 'metaphysicize',\n 'metastasize',\n 'metathesize',\n 'mete',\n 'meter',\n 'methodize',\n 'methought',\n 'methylate',\n 'metricate',\n 'metricize',\n 'metrify',\n 'mew',\n 'mewl',\n 'mezzotint',\n 'miaul',\n 'microfilm',\n 'micturate',\n 'middle',\n 'miff',\n 'might',\n 'migrate',\n 'mike',\n 'milden',\n 'mildew',\n 'militarize',\n 'militate',\n 'milk',\n 'mill',\n 'milt',\n 'mime',\n 'mimeograph',\n 'Mimeograph',\n 'mimic',\n 'mince',\n 'mind',\n 'mine',\n 'mineralize',\n 'mingle',\n 'miniaturize',\n 'minify',\n 'minimize',\n 'minister',\n 'mint',\n 'minute',\n 'mire',\n 'mirror',\n 'misadvise',\n 'misapply',\n 'misapprehend',\n 'misappropriate',\n 'misbecome',\n 'misbehave',\n 'miscalculate',\n 'miscall',\n 'miscarry',\n 'miscast',\n 'misconceive',\n 'misconduct',\n 'misconstrue',\n 'miscount',\n 'miscreate',\n 'miscue',\n 'misdate',\n 'misdeal',\n 'misdemean',\n 'misdirect',\n 'misdoubt',\n 'misfile',\n 'misfire',\n 'misfit',\n 'misgive',\n 'misgovern',\n 'misguide',\n 'mishandle',\n 'mishear',\n 'mishit',\n 'misinform',\n 'misinterpret',\n 'misjudge',\n 'mislay',\n 'mislead',\n 'mislike',\n 'mismanage',\n 'mismatch',\n 'misname',\n 'misplace',\n 'misplay',\n 'mispled',\n 'misprint',\n 'misprize',\n 'mispronounce',\n 'misquote',\n 'misread',\n 'misreport',\n 'misrepresent',\n 'misrule',\n 'miss',\n 'misshape',\n 'mission',\n 'misspell',\n 'misspend',\n 'misstate',\n 'mist',\n 'mistake',\n 'mister',\n 'mistime',\n 'mistranslate',\n 'mistreat',\n 'mistrust',\n 'misunderstand',\n 'misuse',\n 'mitch',\n 'mitigate',\n 'mitre',\n 'mix',\n 'mizzle',\n 'moan',\n 'moat',\n 'mob',\n 'mobilize',\n 'mock',\n 'model',\n 'moderate',\n 'modernize',\n 'modge',\n 'modify',\n 'modulate',\n 'Mohammedanize',\n 'moil',\n 'moisten',\n 'moisturize',\n 'moither',\n 'molest',\n 'mollify',\n 'mollycoddle',\n 'molt',\n 'monetize',\n 'mongrelize',\n 'monitor',\n 'monopolize',\n 'monotonize',\n 'moo',\n 'mooch',\n 'moon',\n 'moonlight',\n 'moor',\n 'moot',\n 'mop',\n 'mope',\n 'moralize',\n 'mordant',\n 'mortar',\n 'mortgage',\n 'mortify',\n 'mortise',\n 'mosey',\n 'mothball',\n 'mother',\n 'mothproof',\n 'motion',\n 'motivate',\n 'motive',\n 'motor',\n 'motorize',\n 'mottle',\n 'mould',\n 'moulder',\n 'moult',\n 'mound',\n 'mount',\n 'mountaineer',\n 'mountebank',\n 'mourn',\n 'mouse',\n 'mouth',\n 'move',\n 'mow',\n 'muck',\n 'muckamuck',\n 'muckrake',\n 'mud',\n 'muddle',\n 'muddy',\n 'muff',\n 'muffle',\n 'mug',\n 'mulch',\n 'mulct',\n 'mull',\n 'multiply',\n 'mumble',\n 'mumm',\n 'mummify',\n 'mump',\n 'munch',\n 'municipalize',\n 'munition',\n 'murdabad',\n 'murder',\n 'mure',\n 'murmur',\n 'murther',\n 'muscle',\n 'muse',\n 'mushroom',\n 'muss',\n 'must',\n 'muster',\n 'mutate',\n 'mutch',\n 'mute',\n 'mutilate',\n 'mutiny',\n 'mutter',\n 'mutualize',\n 'muzz',\n 'muzzle',\n 'mystify',\n 'mythicize',\n 'mythologize',\n 'nab',\n 'nag',\n 'nail',\n 'name',\n 'name-drop',\n 'nap',\n 'napalm',\n 'narcotize',\n 'nark',\n 'narrate',\n 'narrow',\n 'nasalize',\n 'nationalize',\n 'natter',\n 'naturalize',\n 'nauseate',\n 'navigate',\n 'naysay',\n 'Nazify',\n 'near',\n 'neaten',\n 'nebulize',\n 'necessitate',\n 'neck',\n 'necrose',\n 'need',\n 'needle',\n 'negate',\n 'neglect',\n 'negotiate',\n 'neigh',\n 'neighbour',\n 'neologize',\n 'nerve',\n 'nest',\n 'nestle',\n 'net',\n 'nettle',\n 'network',\n 'neuter',\n 'neutralize',\n 'nibble',\n 'nick',\n 'nickel',\n 'nicker',\n 'nickname',\n 'nictitate',\n 'nid-nod',\n 'nidify',\n 'niello',\n 'niggle',\n 'nigrify',\n 'nip',\n 'nitrify',\n 'nitrogenize',\n 'nix',\n 'nobble',\n 'nock',\n 'nod',\n 'noddle',\n 'noise',\n 'nomadize',\n 'nominate',\n 'nonplus',\n 'nonpros',\n 'nonsuit',\n 'normalize',\n 'Normanize',\n 'nose',\n 'nosedive',\n 'nosh',\n 'notarize',\n 'notate',\n 'notch',\n 'note',\n 'notice',\n 'notify',\n 'nourish',\n 'novelize',\n 'nucleate',\n 'nudge',\n 'nullify',\n 'number',\n 'numerate',\n 'nurse',\n 'nurture',\n 'nut',\n 'nuzzle',\n 'oar',\n 'obelize',\n 'obey',\n 'obfuscate',\n 'object',\n 'objectify',\n 'objurgate',\n 'obligate',\n 'oblige',\n 'oblique',\n 'obliterate',\n 'obnubilate',\n 'obscure',\n 'obsecrate',\n 'observe',\n 'obsess',\n 'obsolesce',\n 'obsolete',\n 'obstruct',\n 'obtain',\n 'obtest',\n 'obtrude',\n 'obtund',\n 'obturate',\n 'obvert',\n 'obviate',\n 'occasion',\n 'Occidentalize',\n 'occlude',\n 'occult',\n 'occupy',\n 'occur',\n 'ochre',\n 'octuple',\n 'offend',\n 'offer',\n 'officer',\n 'officiate',\n 'offload',\n 'offprint',\n 'offset',\n 'ogle',\n 'oil',\n 'ok',\n 'okay',\n 'old-talk',\n 'omen',\n 'omit',\n 'ooze',\n 'opalesce',\n 'opaque',\n 'ope',\n 'open',\n 'operate',\n 'operatize',\n 'opiate',\n 'opine',\n 'oppilate',\n 'oppose',\n 'oppress',\n 'oppugn',\n 'opsonize',\n 'opt',\n 'optimize',\n 'orate',\n 'orb',\n 'orbit',\n 'orchestrate',\n 'ordain',\n 'order',\n 'organize',\n 'orient',\n 'Orientalize',\n 'orientate',\n 'originate',\n 'ornament',\n 'orphan',\n 'oscillate',\n 'osculate',\n 'osmose',\n 'ossify',\n 'ostracize',\n 'ought',\n 'oust',\n 'out',\n 'out-herod',\n 'outbalance',\n 'outbid',\n 'outbrave',\n 'outbreed',\n 'outclass',\n 'outcrop',\n 'outcross',\n 'outcry',\n 'outdate',\n 'outdistance',\n 'outdo',\n 'outface',\n 'outfight',\n 'outfit',\n 'outflank',\n 'outfoot',\n 'outfox',\n 'outgain',\n 'outgas',\n 'outgeneral',\n 'outgo',\n 'outgrow',\n 'outgun',\n 'outjockey',\n 'outlast',\n 'outlaw',\n 'outlay',\n 'outleap',\n 'outline',\n 'outlive',\n 'outman',\n 'outmanoeuvre',\n 'outmarch',\n 'outmatch',\n 'outnumber',\n 'outpace',\n 'outperform',\n 'outplay',\n 'outpoint',\n 'outpour',\n 'outrage',\n 'outrange',\n 'outrank',\n 'outreach',\n 'outride',\n 'outrival',\n 'outrun',\n 'outsail',\n 'outsell',\n 'outshine',\n 'outshoot',\n 'outsmart',\n 'outspan',\n 'outspread',\n 'outstand',\n 'outstay',\n 'outstretch',\n 'outstrip',\n 'outthink',\n 'outvie',\n 'outvote',\n 'outwear',\n 'outweigh',\n 'outwit',\n 'outwork',\n 'over-burden',\n 'over-estimate',\n 'over-expose',\n 'over-heat',\n 'over-simplify',\n 'overachieve',\n 'overact',\n 'overarch',\n 'overawe',\n 'overbalance',\n 'overbear',\n 'overbid',\n 'overblow',\n 'overbuild',\n 'overburden',\n 'overcall',\n 'overcapitalize',\n 'overcharge',\n 'overcloud',\n 'overcome',\n 'overcompensate',\n 'overcook',\n 'overcrop',\n 'overcrowd',\n 'overdevelop',\n 'overdo',\n 'overdose',\n 'overdraw',\n 'overdress',\n 'overdrive',\n 'overdye',\n 'overeat',\n 'overemphasize',\n 'overestimate',\n 'overexert',\n 'overexpose',\n 'overfeed',\n 'overflow',\n 'overfly',\n 'overgrow',\n 'overhand',\n 'overhang',\n 'overhaul',\n 'overhear',\n 'overheat',\n 'overindulge',\n 'overissue',\n 'overjoy',\n 'overland',\n 'overlap',\n 'overlay',\n 'overleap',\n 'overlie',\n 'overlive',\n 'overload',\n 'overlook',\n 'overman',\n 'overmaster',\n 'overmatch',\n 'overpass',\n 'overpay',\n 'overpersuade',\n 'overpitch',\n 'overplay',\n 'overpower',\n 'overpraise',\n 'overprint',\n 'overproduce',\n 'overprotect',\n 'overrate',\n 'overreach',\n 'overreact',\n 'overrefine',\n 'override',\n 'overrule',\n 'overrun',\n 'overscore',\n 'oversee',\n 'oversell',\n 'overset',\n 'oversew',\n 'overshadow',\n 'overshoot',\n 'overshot',\n 'oversimplify',\n 'oversleep',\n 'overspend',\n 'overspill',\n 'overstaff',\n 'overstate',\n 'overstay',\n 'oversteer',\n 'overstep',\n 'overstock',\n 'overstrain',\n 'overstuff',\n 'oversubscribe',\n 'overtake',\n 'overtask',\n 'overtax',\n 'overthrow',\n 'overtime',\n 'overtop',\n 'overtrade',\n 'overtrump',\n 'overture',\n 'overturn',\n 'overvalue',\n 'overwatch',\n 'overweigh',\n 'overweight',\n 'overwhelm',\n 'overwind',\n 'overwinter',\n 'overwork',\n 'overwrite',\n 'oviposit',\n 'ovulate',\n 'owe',\n 'own',\n 'oxidate',\n 'oxidize',\n 'oxygenize',\n 'oyster',\n 'ozonize',\n 'pace',\n 'pacify',\n 'pack',\n 'package',\n 'packet',\n 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'pish',\n 'piss',\n 'pistol',\n 'pistolwhip',\n 'pit',\n 'pitapat',\n 'pitch',\n 'pitchfork',\n 'pith',\n 'pitterpatter',\n 'pity',\n 'pivot',\n 'pize',\n 'placard',\n 'placate',\n 'place',\n 'placekick',\n 'plagiarize',\n 'plague',\n 'plain',\n 'plait',\n 'plan',\n 'plane',\n 'plane-table',\n 'planish',\n 'plank',\n 'plant',\n 'plash',\n 'plasmolyze',\n 'plaster',\n 'plasticize',\n 'plate',\n 'platemark',\n 'platinize',\n 'platitudinize',\n 'Platonize',\n 'play',\n 'playact',\n 'playback',\n 'pleach',\n 'plead',\n 'please',\n 'pleasure',\n 'pleat',\n 'pledge',\n 'plenish',\n 'plight',\n 'ploat',\n 'plod',\n 'plodge',\n 'plonk',\n 'plop',\n 'plot',\n 'plough',\n 'plow',\n 'pluck',\n 'plug',\n 'plumb',\n 'plume',\n 'plummet',\n 'plump',\n 'plunder',\n 'plunge',\n 'plunk',\n 'pluralize',\n 'ply',\n 'poach',\n 'pocket',\n 'pockmark',\n 'pod',\n 'podzolize',\n 'poetize',\n 'poind',\n 'point',\n 'poise',\n 'poison',\n 'poke',\n 'polarize',\n 'pole',\n 'poleax',\n 'poleaxe',\n 'polevault',\n 'police',\n 'polish',\n 'politicize',\n 'politick',\n 'polka',\n 'poll',\n 'pollard',\n 'pollinate',\n 'pollute',\n 'polymerize',\n 'pomade',\n 'pommel',\n 'ponce',\n 'ponder',\n 'pong',\n 'poniard',\n 'pontificate',\n 'poohpooh',\n 'pool',\n 'poop',\n 'pop',\n 'popple',\n 'popularize',\n 'populate',\n 'pore',\n 'port',\n 'portage',\n 'portend',\n 'portion',\n 'portray',\n 'pose',\n 'posit',\n 'position',\n 'poss',\n 'posse',\n 'possess',\n 'post',\n 'postdate',\n 'postfix',\n 'postil',\n 'postpone',\n 'postulate',\n 'posture',\n 'posturize',\n 'pot',\n 'potentiate',\n 'pother',\n 'potter',\n 'pouch',\n 'poultice',\n 'pounce',\n 'pound',\n 'pour',\n 'poussette',\n 'pout',\n 'powder',\n 'power',\n 'powerdive',\n 'powwow',\n 'practice',\n 'practise',\n 'praise',\n 'prance',\n 'prank',\n 'prate',\n 'prattle',\n 'pray',\n 'pre-digest',\n 'preach',\n 'preachify',\n 'prearrange',\n 'precancel',\n 'precast',\n 'precede',\n 'precess',\n 'precipitate',\n 'precis',\n 'preclude',\n 'preconceive',\n 'precondition',\n 'preconize',\n 'precontract',\n 'predate',\n 'predecease',\n 'predestinate',\n 'predestine',\n 'predetermine',\n 'predicate',\n 'predict',\n 'predigest',\n 'predispose',\n 'predominate',\n 'preempt',\n 'preen',\n 'preexist',\n 'prefabricate',\n 'preface',\n 'prefer',\n 'prefigure',\n 'prefix',\n 'prejudge',\n 'prejudice',\n 'prelect',\n 'prelude',\n 'premeditate',\n 'premier',\n 'premise',\n 'premiss',\n 'premonish',\n 'preoccupy',\n 'preordain',\n 'prepare',\n 'prepay',\n 'preponderate',\n 'prepossess',\n 'prerecord',\n 'preregister',\n 'presage',\n 'prescind',\n 'prescribe',\n 'present',\n 'preserve',\n 'preside',\n 'presignify',\n 'press',\n 'pressgang',\n 'pressure',\n 'pressure-cook',\n 'pressurize',\n 'prestress',\n 'presume',\n 'presuppose',\n 'pretend',\n 'pretermit',\n 'prettify',\n 'prevail',\n 'prevaricate',\n 'prevent',\n 'previse',\n 'prevue',\n 'prey',\n 'price',\n 'prick',\n 'prickle',\n 'pride',\n 'prig',\n 'prill',\n 'prime',\n 'primp',\n 'prink',\n 'print',\n 'prise',\n 'privateer',\n 'privatize',\n 'privilege',\n 'prize',\n 'probe',\n 'proceed',\n 'process',\n 'procession',\n 'proclaim',\n 'procrastinate',\n 'procreate',\n 'procure',\n 'prod',\n 'produce',\n 'profane',\n 'profess',\n 'proffer',\n 'profit',\n 'profiteer',\n 'prog',\n 'prognosticate',\n 'programme',\n 'programtrade',\n 'progress',\n 'prohibit',\n 'project',\n 'prolapse',\n 'proliferate',\n 'prologue',\n 'prolong',\n 'promenade',\n 'promise',\n 'promote',\n 'prompt',\n 'promulgate',\n 'pronate',\n 'prong',\n 'pronominalize',\n 'pronounce',\n 'proof',\n 'proofread',\n 'prop',\n 'propagandize',\n 'propagate',\n 'propel',\n 'propend',\n 'prophesy',\n 'propitiate',\n 'proportion',\n 'proportionate',\n 'propose',\n 'proposition',\n 'propound',\n 'prorate',\n 'prorogue',\n 'proscribe',\n 'prose',\n 'prosecute',\n 'proselyte',\n 'proselytize',\n 'prospect',\n 'prosper',\n 'prostitute',\n 'prostrate',\n 'protect',\n 'protest',\n 'protract',\n 'protrude',\n 'protuberate',\n 'prove',\n 'provide',\n 'provision',\n 'provoke',\n 'prowl',\n 'prune',\n 'Prussianize',\n 'pry',\n 'psyche',\n 'psycho-analyse',\n 'psychoanalyze',\n 'psychologize',\n 'pubcrawl',\n 'publicize',\n 'publish',\n 'pucker',\n 'puddle',\n 'puff',\n 'pug',\n 'puke',\n 'pule',\n 'pull',\n 'pullulate',\n 'pulp',\n 'pulsate',\n 'pulse',\n 'pulverize',\n 'pummel',\n 'pump',\n 'pun',\n 'punce',\n 'punch',\n 'punctuate',\n 'puncture',\n 'punish',\n 'punt',\n 'pup',\n 'pupate',\n 'pur_ee',\n 'purchase',\n 'purfle',\n 'purge',\n 'purify',\n 'purl',\n 'purloin',\n 'purport',\n 'purpose',\n 'purr',\n 'purse',\n 'pursue',\n 'purvey',\n 'push',\n 'push-start',\n 'pussyfoot',\n 'pustulate',\n 'put',\n 'putput',\n 'putrefy',\n 'putt',\n 'putter',\n 'putty',\n 'puzzle',\n 'pyramid',\n 'quack',\n 'quadrisect',\n 'quadruple',\n 'quadruplicate',\n 'quaff',\n 'quail',\n 'quake',\n 'qualify',\n 'quant',\n 'quantify',\n 'quantize',\n 'quarantine',\n 'quarrel',\n 'quarry',\n 'quarter',\n 'quartersaw',\n 'quash',\n 'quaver',\n 'queen',\n 'queer',\n 'quell',\n 'quench',\n 'query',\n 'quest',\n 'question',\n 'queue',\n 'quibble',\n 'quicken',\n 'quickfreeze',\n 'quickstep',\n 'quiet',\n 'quieten',\n 'quill',\n 'quilt',\n 'quintuple',\n 'quintuplicate',\n 'quip',\n 'quit',\n 'quitclaim',\n 'quiver',\n 'quiz',\n 'quote',\n 'rabbit',\n 'rabble',\n 'race',\n 'racemize',\n 'racketeer',\n 'rackrent',\n 'racquet',\n 'raddle',\n 'radiate',\n 'radio',\n 'radioactivate',\n 'radiotelegraph',\n 'radiotelephone',\n 'raffle',\n 'raft',\n 'rag',\n 'rage',\n 'ragout',\n 'raid',\n 'rail',\n 'railroad',\n 'rain',\n 'rainproof',\n 'raise',\n 'rake',\n 'rally',\n 'ram',\n 'ramble',\n 'ramify',\n 'ramp',\n 'rampage',\n 'rampart',\n 'ranch',\n 'randomize',\n 'range',\n 'rank',\n 'rankle',\n 'ransack',\n 'ransom',\n 'rant',\n 'rap',\n 'rape',\n 'rappel',\n 'rapture',\n 'rarify',\n 'rash',\n 'rasp',\n 'rat',\n 'rate',\n 'ratify',\n 'ratiocinate',\n 'ration',\n 'rationalize',\n 'rattle',\n 'rattoon',\n 'ravage',\n 'rave',\n 'ravel',\n 'ravin',\n 'ravish',\n 'ray',\n 'raze',\n 'razor-cut',\n 'razz',\n 're-act',\n 're-afforest',\n 're-cede',\n 're-count',\n 're-cover',\n 're-create',\n 're-dress',\n 're-echo',\n 're-form',\n 're-fund',\n 're-join',\n 're-present',\n 're-press',\n 're-proof',\n 're-serve',\n 're-sign',\n 're-sort',\n 're-sound',\n 're-trace',\n 're-tread',\n 'reach',\n 'react',\n 'reactivate',\n 'read',\n 'readdress',\n 'readjust',\n 'ready',\n 'reaffirm',\n 'realign',\n 'realize',\n 'ream',\n 'reanimate',\n 'reap',\n 'reappear',\n 'reapportion',\n 'reappraise',\n 'rear',\n 'reard',\n 'rearm',\n 'rearrange',\n 'reason',\n 'reassert',\n 'reassign',\n 'reassure',\n 'reave',\n 'rebate',\n 'rebel',\n 'rebellow',\n 'rebound',\n 'rebuff',\n 'rebuild',\n 'rebuke',\n 'rebut',\n 'recalculate',\n 'recalesce',\n 'recall',\n 'recant',\n 'recap',\n 'recapitulate',\n 'recapture',\n 'recast',\n 'recce',\n 'recede',\n 'receipt',\n 'receive',\n 'recentralize',\n 'recess',\n 'reciprocate',\n 'recite',\n 'reck',\n 'reckon',\n 'reclaim',\n 'reclassify',\n 'recline',\n 'recognize',\n 'recoil',\n 'recollect',\n 'recommend',\n 'recommit',\n 'recompense',\n 'recompose',\n 'reconcile',\n 'recondition',\n 'reconnect',\n 'reconnoitre',\n 'reconsider',\n 'reconstitute',\n 'reconstruct',\n 'reconvert',\n 'record',\n 'recount',\n 'recoup',\n 'recover',\n 'recreate',\n 'recriminate',\n 'recrudesce',\n 'recruit',\n 'recrystallize',\n 'rectify',\n 'recuperate',\n 'recur',\n 'recurve',\n 'recycle',\n 'red',\n 'redact',\n 'redd',\n 'redden',\n 'reddle',\n 'rede',\n 'redeem',\n 'redeploy',\n 'redesign',\n 'redevelop',\n 'redintegrate',\n 'redirect',\n 'redistribute',\n 'redo',\n 'redouble',\n 'redound',\n 'redpencil',\n 'redraft',\n 'redraw',\n 'redress',\n 'reduce',\n 'reduplicate',\n 'reed',\n 'reef',\n 'reek',\n 'reel',\n 'reelect',\n 'reemphasize',\n 'reenact',\n 'reenter',\n 'reest',\n 'reestablish',\n 'reeve',\n 'reexamine',\n 'reexport',\n 'reface',\n 'refer',\n 'referee',\n 'reference',\n 'refile',\n 'refill',\n 'refinance',\n 'refine',\n 'refit',\n 'reflate',\n 'reflect',\n 'refloat',\n 'reflux',\n 'refocuse',\n 'reforest',\n 'reform',\n 'refract',\n 'refrain',\n 'refresh',\n 'refrigerate',\n 'reft',\n 'refuel',\n 'refuge',\n 'refund',\n 'refurbish',\n 'refuse',\n 'refute',\n 'regain',\n 'regale',\n 'regard',\n 'regelate',\n 'regenerate',\n 'regiment',\n 'register',\n 'regorge',\n 'regrate',\n 'regress',\n 'regret',\n 'regroup',\n 'regularize',\n 'regulate',\n 'regurgitate',\n 'rehabilitate',\n 'rehash',\n 'rehear',\n 'rehearse',\n 'reheat',\n 'rehouse',\n 'reify',\n 'reign',\n 'reignite',\n 'reimburse',\n 'reimport',\n 'rein',\n 'reincarnate',\n 'reindict',\n 'reinforce',\n 'reinstate',\n 'reinstitute',\n 'reinsure',\n 'reintroduce',\n 'reinvent',\n 'reinvest',\n 'reinvigorate',\n 'reissue',\n 'reiterate',\n 'reive',\n 'reject',\n 'rejig',\n 'rejoice',\n 'rejoin',\n 'rejuvenate',\n 'rejuvenesce',\n 'rekindle',\n 'relapse',\n 'relate',\n 'relativize',\n 'relaunch',\n 'relax',\n 'relay',\n 'release',\n 'relegate',\n 'relent',\n 'relieve',\n 'reline',\n 'relinquish',\n 'relish',\n 'relive',\n 'relocate',\n 'reluct',\n 'relumine',\n 'rely',\n 'remain',\n 'remainder',\n 'remake',\n 'remand',\n 'remark',\n 'remarry',\n 'rematch',\n 'remedy',\n 'remember',\n 'remilitarize',\n 'remind',\n 'reminisce',\n 'remise',\n 'remit',\n 'remodel',\n 'remonetize',\n 'remonstrate',\n 'remould',\n 'remount',\n 'remove',\n 'remunerate',\n 'rename',\n 'rencounter',\n 'rend',\n 'render',\n 'rendezvous',\n 'renegotiate',\n 'renegue',\n 'renew',\n 'renounce',\n 'renovate',\n 'rent',\n 'reopen',\n 'reorganize',\n 'reorient',\n 'reorientate',\n 'repackage',\n 'repair',\n 'repartition',\n 'repast',\n 'repatriate',\n 'repay',\n 'repeal',\n 'repeat',\n 'repel',\n 'repent',\n 'rephrase',\n 'repine',\n 'replace',\n 'replay',\n 'replenish',\n 'replevin',\n 'replevy',\n 'replicate',\n 'reply',\n 'repoint',\n 'repone',\n 'report',\n 'repose',\n 'reposit',\n 'reposition',\n 'repossess',\n 'repot',\n 'reprehend',\n 'represent',\n 'repress',\n 'reprieve',\n 'reprimand',\n 'reprint',\n 'reprise',\n 'reproach',\n 'reprobate',\n 'reproduce',\n 'reprove',\n 'republicanize',\n 'repudiate',\n 'repugn',\n 'repulse',\n 'repurchase',\n 'repute',\n 'request',\n 'require',\n 'requisition',\n 'requite',\n 'reread',\n 'reroute',\n 'rerun',\n 'reschedule',\n 'rescind',\n 'rescue',\n 'research',\n 'reseat',\n 'resell',\n 'resemble',\n 'resent',\n 'reserve',\n 'reset',\n 'resettle',\n 'reshape',\n 'reshuffle',\n 'reside',\n 'resign',\n 'resile',\n 'resin',\n 'resinate',\n 'resist',\n 'resit',\n 'resole',\n 'resolve',\n 'resonate',\n 'resorb',\n 'resort',\n 'resound',\n 'respect',\n 'respire',\n 'respite',\n 'respond',\n 'rest',\n 'restate',\n 'restock',\n 'restore',\n 'restrain',\n 'restrict',\n 'restring',\n 'restructure',\n 'resubmit',\n 'result',\n 'resume',\n 'resurface',\n 'resurge',\n 'resurrect',\n 'resuscitate',\n 'ret',\n 'retail',\n 'retain',\n 'retake',\n 'retaliate',\n 'retard',\n 'retch',\n 'retell',\n 'rethink',\n 'reticulate',\n 'retire',\n 'retool',\n 'retort',\n 'retouch',\n 'retrace',\n 'retract',\n 'retread',\n 'retreat',\n 'retrench',\n 'retrieve',\n 'retroact',\n 'retrocede',\n 'retrofit',\n 'retrograde',\n 'retrogress',\n 'retroject',\n 'retrospect',\n 'retry',\n 'return',\n 'reunify',\n 'reunite',\n 'rev',\n 'revalorize',\n 'revalue',\n 'revamp',\n 'reveal',\n 'revegetate',\n 'revel',\n 'revenge',\n 'reverberate',\n 'revere',\n 'reverence',\n 'reverse',\n 'revert',\n 'revest',\n 'revet',\n 'review',\n 'revile',\n 'revise',\n 'revisit',\n 'revitalize',\n 'revive',\n 'revivify',\n 'revoice',\n 'revoke',\n 'revolt',\n 'revolutionize',\n 'revolve',\n 'reward',\n 'rewind',\n 'rewire',\n 'reword',\n 'rework',\n 'rewrite',\n 'rhapsodize',\n 'rhubarb',\n 'rib',\n 'ribbon',\n 'rice',\n 'rick',\n 'ricochet',\n 'rid',\n 'riddle',\n 'ride',\n 'ridge',\n 'ridicule',\n 'riff',\n 'riffle',\n 'rifle',\n 'rift',\n 'rig',\n 'right',\n 'righten',\n 'rigidify',\n 'rile',\n 'rim',\n 'rime',\n 'ring',\n 'rinse',\n 'riot',\n 'rip',\n 'ripen',\n 'riposte',\n 'ripple',\n 'rise',\n 'risk',\n 'ritualize',\n 'rival',\n 'rive',\n 'rivet',\n 'roam',\n 'roar',\n 'roast',\n 'rob',\n 'robe',\n 'rock',\n 'rock-and-roll',\n 'rocket',\n 'rodomontade',\n 'roil',\n 'roister',\n 'roll',\n 'rollerskate',\n 'rollick',\n 'romance',\n 'Romanize',\n 'romanticize',\n 'romp',\n 'rone',\n 'Roneo',\n 'rontgenize',\n 'roof',\n 'rook',\n 'roose',\n 'roost',\n 'root',\n 'rootle',\n 'roquet',\n 'rosin',\n 'roster',\n 'rot',\n 'rotate',\n 'rouge',\n 'rough',\n 'rough-house',\n 'roughcast',\n 'roughdry',\n 'roughen',\n 'roughhew',\n 'roughhouse',\n 'round',\n 'roup',\n 'rouse',\n 'roust',\n 'rout',\n 'route',\n 'row',\n 'rowel',\n 'rub',\n 'rubber',\n 'rubberize',\n 'rubberneck',\n 'rubberstamp',\n 'rubbish',\n 'rubefy',\n 'rubricate',\n 'ruck',\n 'ruddle',\n 'rue',\n 'ruff',\n 'ruffle',\n 'ruggedize',\n 'ruin',\n 'rule',\n 'rumble',\n 'ruminate',\n 'rummage',\n 'rumour',\n 'rumple',\n 'run',\n 'rupture',\n 'ruralize',\n 'rush',\n 'Russianize',\n 'rust',\n 'rusticate',\n 'rustle',\n 'rut',\n 'saber',\n 'sabotage',\n 'saccharize',\n 'sack',\n 'sacrifice',\n 'sadden',\n 'saddle',\n 'safeconduct',\n 'safeguard',\n 'safety',\n 'sag',\n 'sail',\n 'sain',\n 'saint',\n 'salify',\n 'salivate',\n 'sallow',\n 'sally',\n 'salt',\n 'salute',\n 'salvage',\n 'salve',\n 'samba',\n 'sample',\n 'sanctify',\n 'sanction',\n 'sand',\n 'sand-blast',\n 'sandbag',\n 'sandblast',\n 'sandcast',\n 'sandpaper',\n 'sandwich',\n 'Sanforize',\n 'sanitize',\n 'sap',\n 'saponify',\n 'sash',\n 'sass',\n 'sate',\n 'satiate',\n 'satirize',\n 'satisfy',\n 'saturate',\n 'sauce',\n 'saunter',\n 'saut_e',\n 'savage',\n 'save',\n 'savour',\n 'savvy',\n 'saw',\n 'say',\n 'scab',\n 'scabble',\n 'scaffold',\n 'scag',\n 'scald',\n 'scale',\n 'scallop',\n 'scalp',\n 'scamp',\n 'scamper',\n 'scan',\n 'scandal',\n 'scandalize',\n 'scant',\n 'scape',\n 'scar',\n 'scare',\n 'scarf',\n 'scarify',\n 'scarp',\n 'scarper',\n 'scat',\n 'scathe',\n 'scatter',\n 'scavenge',\n 'scend',\n 'scent',\n 'sceptre',\n 'schedule',\n 'schematize',\n 'scheme',\n 'schlep',\n 'schmooze',\n 'school',\n 'schuss',\n 'scintillate',\n 'scissor',\n 'sclaff',\n 'scoff',\n 'scold',\n 'scollop',\n 'sconce',\n 'scoop',\n 'scoot',\n 'scorch',\n 'score',\n 'scorify',\n 'scorn',\n 'scotch',\n 'scour',\n 'scourge',\n 'scout',\n 'scowl',\n 'scrabble',\n 'scrag',\n 'scram',\n 'scramb',\n 'scramble',\n 'scrap',\n 'scrape',\n 'scratch',\n 'scrawl',\n 'screak',\n 'scream',\n 'screech',\n 'screen',\n 'screw',\n 'scribble',\n 'scribe',\n 'scrimmage',\n 'scrimp',\n 'scrimshank',\n 'scrimshaw',\n 'script',\n 'scroll',\n 'scroop',\n 'scrouge',\n 'scrounge',\n 'scrub',\n 'scrum',\n 'scrummage',\n 'scrump',\n 'scrunch',\n 'scruple',\n 'scrutinize',\n 'scry',\n 'scud',\n 'scuff',\n 'scuffle',\n 'scull',\n 'sculpt',\n 'sculpture',\n 'scum',\n 'scumble',\n 'scunge',\n 'scunner',\n 'scupper',\n 'scurry',\n 'scutch',\n 'scutter',\n 'scuttle',\n 'scythe',\n 'seal',\n 'seam',\n 'search',\n 'season',\n 'seat',\n 'secede',\n 'secern',\n 'seclude',\n 'second',\n 'secondguess',\n 'secrete',\n 'sectarianize',\n 'section',\n 'sectionalize',\n 'secularize',\n 'secure',\n 'sedate',\n 'seduce',\n 'see',\n 'seed',\n 'seek',\n 'seel',\n 'seem',\n 'seep',\n 'seesaw',\n 'seethe',\n 'segment',\n 'segregate',\n 'seine',\n 'seise',\n 'seize',\n 'select',\n 'sell',\n 'semaphore',\n 'send',\n 'sendoff',\n 'sense',\n 'sensitize',\n 'sentence',\n 'sentimentalize',\n 'sentinel',\n 'separate',\n 'septuple',\n 'sepulchre',\n 'sequester',\n 'sequestrate',\n 'sere',\n 'serenade',\n 'serialize',\n 'sermonize',\n 'serrate',\n 'serve',\n 'service',\n 'set',\n 'settle',\n 'sever',\n 'sew',\n 'sewer',\n 'sex',\n 'sextuplicate',\n 'shackle',\n 'shade',\n 'shadow',\n 'shadowbox',\n 'shaft',\n 'shag',\n 'shake',\n 'shall',\n 'shallow',\n 'shalt',\n 'sham',\n 'shamble',\n 'shame',\n 'shampoo',\n 'shanghai',\n 'shank',\n 'shape',\n 'share',\n 'sharecrop',\n 'shark',\n 'sharp',\n 'sharpen',\n 'shatter',\n 'shave',\n 'sheaf',\n 'shear',\n 'sheath',\n 'sheathe',\n 'sheave',\n 'shed',\n 'sheen',\n 'sheer',\n 'sheet',\n 'shell',\n 'shellac',\n 'shelter',\n 'shelve',\n 'shend',\n 'shepherd',\n 'sherardize',\n 'shew',\n 'shield',\n 'shift',\n 'shikar',\n 'shillyshally',\n 'shim',\n 'shimmer',\n 'shimmy',\n 'shin',\n 'shine',\n 'shingle',\n 'shinty',\n 'ship',\n 'shipwreck',\n 'shire',\n 'shirk',\n 'shirr',\n 'shit',\n 'shiver',\n 'shoal',\n 'shock',\n 'shoe',\n 'shoo',\n 'shoogle',\n 'shoot',\n 'shop',\n 'shop-lift',\n 'shore',\n 'short',\n 'short-list',\n 'shortchange',\n 'shortcircuit',\n 'shorten',\n 'shot',\n 'shotgun',\n 'should',\n 'shoulder',\n 'shouldst',\n 'shout',\n 'shove',\n 'shovel',\n 'show',\n 'showcase',\n 'showd',\n 'shower',\n 'shrank',\n 'shred',\n 'shriek',\n 'shrill',\n 'shrimp',\n 'shrine',\n 'shrink',\n 'shrinkwrap',\n 'shrive',\n 'shrivel',\n 'shroff',\n 'shroud',\n 'shrug',\n 'shrunk',\n 'shuck',\n 'shudder',\n 'shuffle',\n 'shun',\n 'shunt',\n 'shush',\n 'shut',\n 'shutter',\n 'shuttle',\n 'shy',\n 'sibilate',\n 'sic',\n 'sicken',\n 'side',\n 'side-dress',\n 'sideline',\n 'sideslip',\n 'sidestep',\n 'sideswipe',\n 'sidetrack',\n 'sidle',\n 'siege',\n 'sieve',\n 'sift',\n 'sigh',\n 'sight',\n 'sightread',\n 'sightsee',\n 'sign',\n 'signal',\n 'signalize',\n 'signet',\n 'signify',\n 'signpost',\n 'sile',\n 'silence',\n 'silhouette',\n 'silicify',\n 'silk',\n 'silver',\n 'silver-plate',\n 'simmer',\n 'simper',\n 'simplify',\n 'simulate',\n 'simulcast',\n 'sin',\n 'sing',\n 'singe',\n 'single',\n 'single-step',\n 'single-tongue',\n 'singlefoot',\n 'singlespace',\n 'singularize',\n 'sink',\n 'sinter',\n 'sip',\n 'sire',\n 'sit',\n 'site',\n 'situate',\n 'siwash',\n 'size',\n 'sizzle',\n 'sjambok',\n 'skate',\n 'skedaddle',\n 'skeletonize',\n 'skelly',\n 'skelp',\n 'sken',\n 'sket',\n 'sketch',\n 'skewer',\n 'ski',\n 'ski-jump',\n 'skid',\n 'skim',\n 'skimp',\n 'skin',\n 'skinnydip',\n 'skinpop',\n 'skip',\n 'skipper',\n 'skirl',\n 'skirmish',\n 'skirr',\n 'skirt',\n 'skite',\n 'skitter',\n 'skive',\n 'skivvy',\n 'skulk',\n 'skunks',\n 'sky',\n 'sky-rocket',\n 'skydive',\n 'skyjack',\n 'skylark',\n 'skyrocket',\n 'slab',\n 'slack',\n 'slacken',\n 'slake',\n 'slalom',\n 'slam',\n 'slander',\n 'slang',\n 'slant',\n 'slap',\n 'slash',\n 'slat',\n 'slate',\n 'slaughter',\n 'slave',\n 'slaver',\n 'slay',\n 'sleave',\n 'sledge',\n 'sledgehammer',\n 'sleek',\n 'sleep',\n 'sleepwalk',\n 'sleet',\n 'sleeve',\n 'sleigh',\n 'slenderize',\n 'sleuth',\n 'slice',\n 'slick',\n 'slide',\n 'slight',\n 'slim',\n 'slime',\n 'sling',\n 'slink',\n 'slip',\n 'slipper',\n 'slipsheet',\n 'slit',\n 'slither',\n 'sliver',\n 'slobber',\n 'slog',\n 'sloganeer',\n 'slop',\n 'slope',\n 'slosh',\n 'slot',\n 'slouch',\n 'slough',\n 'slow',\n 'slue',\n 'sluff',\n 'slug',\n 'sluice',\n 'slum',\n 'slumber',\n 'slump',\n 'slur',\n 'slurp',\n 'slush',\n 'smack',\n 'smarm',\n 'smart',\n 'smarten',\n 'smash',\n 'smatter',\n 'smear',\n 'smell',\n 'smelt',\n 'smile',\n 'smirch',\n 'smirk',\n 'smite',\n 'smock',\n 'smoke',\n 'smooch',\n 'smoodge',\n 'smooth',\n 'smoothen',\n 'smote',\n 'smother',\n 'smoulder',\n 'smudge',\n 'smuggle',\n 'smut',\n 'smutch',\n 'snack',\n 'snaffle',\n 'snafu',\n 'snake',\n 'snap',\n 'snare',\n 'snarl',\n 'snatch',\n 'sneak',\n 'sneck',\n 'sned',\n 'sneer',\n 'sneeze',\n 'snick',\n 'snicker',\n 'sniff',\n 'sniffle',\n 'snigger',\n 'sniggle',\n 'snip',\n 'snipe',\n 'snitch',\n 'snivel',\n 'snog',\n 'snood',\n 'snooker',\n 'snoop',\n 'snooze',\n 'snore',\n 'snorkel',\n 'snort',\n 'snow',\n 'snowshoe',\n 'snub',\n 'snuff',\n 'snuffle',\n 'snug',\n 'snuggle',\n 'soak',\n 'soap',\n 'soar',\n 'sob',\n 'sober',\n 'socialize',\n 'sock',\n 'socket',\n 'sodden',\n 'soft-solder',\n 'soften',\n 'softland',\n 'softpedal',\n 'softsoap',\n 'soil',\n 'sojourn',\n 'solace',\n 'solarize',\n 'solder',\n 'soldier',\n 'sole',\n 'solemnify',\n 'solemnize',\n 'solfa',\n 'solicit',\n 'solidify',\n 'soliloquize',\n 'solo',\n 'solubilize',\n 'solvate',\n 'solve',\n 'somnambulate',\n 'sonnet',\n 'soot',\n 'soothe',\n 'soothsay',\n 'sop',\n 'sophisticate',\n 'sorn',\n 'sorrow',\n 'sort',\n 'sortie',\n 'sough',\n 'sound',\n 'soundproof',\n 'sour',\n 'souse',\n 'sovietize',\n 'sow',\n 'space',\n 'spacewalk',\n 'spade',\n 'spae',\n 'spag',\n 'spall',\n 'span',\n 'spancel',\n 'spangle',\n 'spank',\n 'spar',\n 'spare',\n 'sparge',\n 'spark',\n 'sparkle',\n 'spatter',\n 'spawn',\n 'spay',\n 'speak',\n 'spear',\n 'spearhead',\n 'specialize',\n 'specify',\n 'speckle',\n 'speculate',\n 'speechify',\n 'speed',\n 'spell',\n 'spellbind',\n 'spelunk',\n 'spend',\n 'spew',\n 'spice',\n 'spiel',\n 'spiflicate',\n 'spike',\n 'spile',\n 'spill',\n 'spin',\n 'spin-dry',\n 'spindle',\n 'spiral',\n 'spire',\n 'spiritualize',\n 'spit',\n 'spite',\n 'splash',\n 'splatter',\n 'splay',\n 'splice',\n 'spline',\n 'splint',\n 'splinter',\n 'split',\n 'splodge',\n 'splosh',\n 'splotch',\n 'splurge',\n 'splutter',\n 'spoil',\n 'spoliate',\n 'sponge',\n 'sponsor',\n 'spoof',\n 'spook',\n 'spool',\n 'spoon',\n 'spoon-feed',\n 'spoor',\n 'spore',\n 'sport',\n 'sporulate',\n 'spot',\n 'spot-weld',\n 'spotlight',\n 'spouse',\n 'spout',\n 'sprain',\n 'sprawl',\n 'spray',\n 'spread',\n 'spreadeagle',\n 'sprig',\n 'spring',\n 'spring-clean',\n 'sprinkle',\n 'sprint',\n 'sprout',\n 'spruce',\n 'spruik',\n 'sprung',\n 'spud',\n 'spue',\n 'spume',\n 'spur',\n 'spurn',\n 'spurt',\n 'sputter',\n 'spy',\n 'squabble',\n 'squall',\n 'squander',\n 'square',\n 'squaredance',\n 'squash',\n 'squat',\n 'squawk',\n 'squeak',\n 'squeal',\n 'squeeze',\n 'squelch',\n 'squiggle',\n 'squilgee',\n 'squint',\n 'squire',\n 'squirm',\n 'squirt',\n 'squish',\n 'stab',\n 'stabilize',\n 'stable',\n 'stablish',\n 'stack',\n 'staff',\n 'stage',\n 'stagemanage',\n 'stagger',\n 'stagnate',\n 'stain',\n 'stake',\n 'stale',\n 'stalemate',\n 'stalk',\n 'stall',\n 'stallfeed',\n 'stammer',\n 'stamp',\n 'stampede',\n 'stanchion',\n 'stand',\n 'standardize',\n 'stang',\n 'staple',\n 'star',\n 'starch',\n 'stare',\n 'stargaze',\n 'start',\n 'startle',\n 'starve',\n 'stash',\n 'state',\n 'station',\n 'staunch',\n 'stave',\n 'stay',\n 'stead',\n 'steady',\n 'steal',\n 'steam',\n 'steam-heat',\n 'steamroller',\n 'steel',\n 'steep',\n 'steepen',\n 'steeplechase',\n 'steer',\n 'steeve',\n 'stellify',\n 'stem',\n 'stencil',\n 'stenograph',\n 'step',\n 'stereochrome',\n 'stereotype',\n 'sterilize',\n 'stet',\n 'stevedore',\n 'stew',\n 'steward',\n 'stick',\n 'stickle',\n 'sticky',\n 'stiffen',\n 'stifle',\n 'stigmatize',\n 'still',\n 'stillhunt',\n 'stilt',\n 'stimulate',\n 'sting',\n 'stint',\n 'stipple',\n 'stipulate',\n 'stir',\n 'stitch',\n 'stithy',\n 'stock',\n 'stockade',\n 'stockpile',\n 'stodge',\n 'stoke',\n 'stomach',\n 'stomp',\n 'stone',\n 'stone-wall',\n 'stonewall',\n 'stonk',\n 'stooge',\n 'stook',\n 'stool',\n 'stoop',\n 'stop',\n 'stope',\n 'stopper',\n 'store',\n 'storm',\n 'story',\n 'stot',\n 'stoush',\n 'stove',\n 'stow',\n 'straddle',\n 'strafe',\n 'straggle',\n 'straightarm',\n 'straighten',\n 'strain',\n 'straiten',\n 'strand',\n 'strangle',\n 'strangulate',\n 'strap',\n 'stratify',\n 'stravaig',\n 'straw',\n 'stray',\n 'streak',\n 'stream',\n 'streamline',\n 'strengthen',\n 'stress',\n 'stretch',\n 'strew',\n 'striate',\n 'strickle',\n 'stride',\n 'stridulate',\n 'strike',\n 'string',\n 'strip',\n 'stripe',\n 'strive',\n 'stroke',\n 'stroll',\n 'strongarm',\n 'strop',\n 'strow',\n 'stroy',\n 'structure',\n 'struggle',\n 'strum',\n 'strut',\n 'stub',\n 'stucco',\n 'stud',\n 'study',\n 'stuff',\n 'stultify',\n 'stum',\n 'stumble',\n 'stump',\n 'stun',\n 'stunk',\n 'stunt',\n 'stupefy',\n 'stutter',\n 'sty',\n 'style',\n 'stylize',\n 'stylopize',\n 'stymy',\n 'sub',\n 'subclass',\n 'subcontract',\n 'subculture',\n 'subdivide',\n 'subduct',\n 'subdue',\n 'subedit',\n 'suberize',\n 'subinfeudate',\n 'subirrigate',\n 'subject',\n 'subjectify',\n 'subjoin',\n 'subjugate',\n 'sublease',\n 'sublet',\n 'sublimate',\n 'sublime',\n 'submerse',\n 'subminiaturize',\n 'submit',\n 'subordinate',\n 'suborn',\n 'subpoena',\n 'subrogate',\n 'subscribe',\n 'subserve',\n 'subside',\n 'subsidize',\n 'subsist',\n 'subsoil',\n 'substantialize',\n 'substantiate',\n 'substantivize',\n 'substitute',\n 'substract',\n 'subsume',\n 'subtend',\n 'subtilize',\n 'subtitle',\n 'subtotal',\n 'subtract',\n 'suburbanize',\n 'subvene',\n 'subvert',\n 'succeed',\n 'succour',\n 'succumb',\n 'succuss',\n 'suck',\n 'sucker',\n 'suckle',\n 'sue',\n 'suffer',\n 'suffice',\n 'suffix',\n 'sufflate',\n 'suffocate',\n 'suffumigate',\n 'suffuse',\n 'sugar',\n 'sugarcoat',\n 'suggest',\n 'suit',\n 'sulk',\n 'sully',\n 'sulphate',\n 'sulphonate',\n 'sulphurate',\n 'sulphuret',\n 'sulphurize',\n 'sum',\n 'summarize',\n 'summer',\n 'summersault',\n 'summons',\n 'sun',\n 'sunbathe',\n 'sunder',\n 'sunk',\n 'sup',\n 'superabound',\n 'superadd',\n 'superannuate',\n 'supercalender',\n 'supercede',\n 'supercharge',\n 'supercool',\n 'supererogate',\n 'superfuse',\n 'superheat',\n 'superimpose',\n 'superinduce',\n 'superintend',\n 'superordinate',\n 'superpose',\n 'superscribe',\n 'supersede',\n 'superstruct',\n 'supervene',\n 'supervise',\n 'supinate',\n 'supper',\n 'supplant',\n 'supplement',\n 'supplicate',\n 'supply',\n 'support',\n 'suppose',\n 'suppress',\n 'suppurate',\n 'surcease',\n 'surcharge',\n 'surcingle',\n 'surf',\n 'surface',\n 'surfeit',\n 'surge',\n 'surmise',\n 'surmount',\n 'surname',\n 'surpass',\n 'surprint',\n 'surprise',\n 'surrender',\n 'surrogate',\n 'surround',\n 'surtax',\n 'survey',\n 'survive',\n 'suspect',\n 'suspend',\n 'suspire',\n 'suss',\n 'sustain',\n 'susurrate',\n 'suture',\n 'swab',\n 'swaddle',\n 'swag',\n 'swage',\n 'swagger',\n 'swallow',\n 'swamp',\n 'swank',\n 'swap',\n 'swarm',\n 'swarth',\n 'swash',\n 'swat',\n 'swathe',\n 'sway',\n 'swear',\n 'sweaway',\n 'sweep',\n 'sweeten',\n 'sweettalk',\n 'swell',\n 'swelter',\n 'swerve',\n 'swig',\n 'swill',\n 'swim',\n 'swindle',\n 'swing',\n 'swinge',\n 'swingle',\n 'swink',\n 'swipe',\n 'swirl',\n 'swish',\n 'switch',\n 'swive',\n 'swivel',\n 'swizzle',\n 'swob',\n 'swoon',\n 'swoop',\n 'swoosh',\n 'swop',\n 'swot',\n 'swound',\n 'syllabify',\n 'syllabize',\n 'syllable',\n 'syllogize',\n 'symbol',\n 'symbolize',\n 'symmetrize',\n 'sympathize',\n 'sync',\n 'synchronize',\n 'syncopate',\n 'syncretize',\n 'syndicate',\n 'synonymize',\n 'synopsize',\n 'synthetize',\n 'sypher',\n 'syphon',\n 'syringe',\n 'syrup',\n 'systemize',\n 'table',\n 'tabu',\n 'tabulate',\n 'tack',\n 'tackle',\n 'tag',\n 'tailor',\n 'taint',\n 'take',\n 'talc',\n 'talk',\n 'tallage',\n 'tallow',\n 'tally',\n 'tallyho',\n 'tambour',\n 'tame',\n 'tamp',\n 'tamper',\n 'tampon',\n 'tan',\n 'tangle',\n 'tango',\n 'tantalize',\n 'tap',\n 'tape',\n 'taper',\n 'tar',\n 'tare',\n 'target',\n 'tariff',\n 'tarmac',\n 'tarnish',\n 'tarry',\n 'tartarize',\n 'task',\n 'tassel',\n 'taste',\n 'tat',\n 'tatter',\n 'tattle',\n 'tattoo',\n 'taunt',\n 'tauten',\n 'tautologize',\n 'taway',\n 'taws',\n 'tawse',\n 'tax',\n 'taxi',\n 'te-hee',\n 'teach',\n 'team',\n 'tear',\n 'tease',\n 'teasel',\n 'ted',\n 'tee',\n 'teem',\n 'teeter',\n 'teethe',\n 'telecasted',\n 'telegraph',\n 'telemeter',\n 'telepathize',\n 'telephone',\n 'telescope',\n 'Teletype',\n 'televise',\n 'telex',\n 'tell',\n 'tellurize',\n 'telpher',\n 'temper',\n 'tempest',\n 'temporize',\n 'tempt',\n 'tenant',\n 'tend',\n 'tender',\n 'tenderize',\n 'tenon',\n 'tense',\n 'tent',\n 'tenter',\n 'tepefy',\n 'tergiversate',\n 'term',\n 'terminate',\n 'terrace',\n 'terrify',\n 'territorialize',\n 'terrorize',\n 'tessellate',\n 'test',\n 'testdrive',\n 'testfire',\n 'testify',\n 'testmarket',\n 'tetanize',\n 'tether',\n 'Teutonize',\n 'thank',\n 'thatch',\n 'thaw',\n 'theologize',\n 'theorize',\n 'thermalize',\n 'thicken',\n 'thieve',\n 'thin',\n 'think',\n 'thirl',\n 'thirst',\n 'thole',\n 'thrall',\n 'thrash',\n 'thread',\n 'threat',\n 'threaten',\n 'threep',\n 'thresh',\n 'thrill',\n 'thrive',\n 'throb',\n 'thrombose',\n 'throne',\n 'throng',\n 'throttle',\n 'throve',\n 'throw',\n 'throwaway',\n 'throwback',\n 'thrum',\n 'thrust',\n 'thud',\n 'thumb',\n 'thumbindex',\n 'thump',\n 'thunder',\n 'thwack',\n 'thwart',\n 'tick',\n 'ticket',\n 'tickle',\n 'ticktock',\n 'tide',\n 'tidy',\n 'tie',\n 'tier',\n 'tiff',\n 'tighten',\n 'tile',\n 'till',\n 'tiller',\n 'tilt',\n 'timber',\n 'time',\n 'tin',\n 'tinct',\n 'tincture',\n 'ting',\n 'tinge',\n 'tingle',\n 'tinker',\n 'tinkle',\n 'tinplate',\n 'tinsel',\n 'tint',\n 'tip',\n 'tipple',\n 'tiptoe',\n 'tire',\n 'tissue',\n 'tithe',\n 'titillate',\n 'titivate',\n 'title',\n 'titrate',\n 'titter',\n 'tittivate',\n 'tittletattle',\n 'tittup',\n 'toady',\n 'toast',\n 'toboggan',\n 'toddle',\n 'toe',\n 'toe-dance',\n 'toenail',\n 'tog',\n 'toggle',\n 'toil',\n 'token',\n 'tolerate',\n 'toll',\n 'tomb',\n 'tone',\n 'tong',\n 'tongue',\n 'tongue-lash',\n 'tonsure',\n 'tool',\n 'toot',\n 'tooth',\n 'tootle',\n 'top',\n 'topdress',\n 'tope',\n 'topple',\n 'topsoil',\n 'torch',\n 'torment',\n 'torpedo',\n 'torrify',\n 'torture',\n 'toss',\n 'tot',\n 'total',\n 'totalize',\n 'tote',\n 'totter',\n 'touch',\n 'touchdown',\n 'touchtype',\n 'toughen',\n 'tour',\n 'tourney',\n 'tousle',\n 'tout',\n 'touzle',\n 'tow',\n 'towel',\n 'trace',\n 'track',\n 'trade',\n 'trademark',\n 'traduce',\n 'traffic',\n 'trail',\n 'train',\n 'traipse',\n 'traject',\n 'tram',\n 'trammel',\n 'tramp',\n 'trample',\n 'trampoline',\n 'trance',\n 'tranquillize',\n 'transact',\n 'transcend',\n 'transcribe',\n 'transect',\n 'transfer',\n 'transfigure',\n 'transfix',\n 'transform',\n 'transfuse',\n 'transgress',\n 'transilluminate',\n 'transistorize',\n 'transit',\n 'translate',\n 'transliterate',\n 'translocate',\n 'transmigrate',\n 'transmit',\n 'transmogrify',\n 'transmute',\n 'transpierce',\n 'transpire',\n 'transplant',\n 'transport',\n 'transpose',\n 'transship',\n 'transubstantiate',\n 'transude',\n 'transvalue',\n 'trap',\n 'trapan',\n 'trapes',\n 'trash',\n 'traumatize',\n 'travail',\n 'travel',\n 'traverse',\n 'travesty',\n 'trawl',\n 'tread',\n 'treadle',\n 'treasure',\n 'treat',\n 'treble',\n 'tree',\n 'trek',\n 'trellis',\n 'tremble',\n 'trench',\n 'trend',\n 'trepan',\n 'trephine',\n 'trespass',\n 'triangulate',\n 'trice',\n 'trichinize',\n 'trick',\n 'trickle',\n 'tricycle',\n 'trifle',\n 'trig',\n 'trigger',\n 'trill',\n 'trim',\n 'trip',\n 'triple',\n 'triple-tongue',\n 'triplicate',\n 'trisect',\n 'tritiate',\n 'triturate',\n 'triumph',\n 'trivialize',\n 'troat',\n 'trode',\n 'trog',\n 'troll',\n 'troop',\n 'tropicalize',\n 'trot',\n 'trouble',\n 'trounce',\n 'troupe',\n 'trow',\n 'trowel',\n 'truant',\n 'truck',\n 'truckle',\n 'trudge',\n 'true',\n 'trump',\n 'trumpet',\n 'truncate',\n 'truncheon',\n 'trundle',\n 'truss',\n 'trust',\n 'try',\n 'tryst',\n 'tub',\n 'tube',\n 'tubulate',\n 'tuck',\n 'tucker',\n 'tuft',\n 'tug',\n 'tumble',\n 'tumefy',\n 'tun',\n 'tune',\n 'tunnel',\n 'tup',\n 'turf',\n 'turmoil',\n 'turn',\n 'turpentine',\n 'turtle',\n 'tusk',\n 'tussle',\n 'tut',\n 'tut-tut',\n 'tutor',\n 'twaddle',\n 'twang',\n 'tweak',\n 'tweet',\n 'tweeze',\n 'twiddle',\n 'twig',\n 'twill',\n 'twin',\n 'twine',\n 'twinge',\n 'twinkle',\n 'twirl',\n 'twist',\n 'twit',\n 'twitch',\n 'twitter',\n 'twotime',\n 'type',\n 'typecast',\n 'typeset',\n 'typewrite',\n 'typify',\n 'tyrannize',\n 'tyre',\n 'uglify',\n 'ulcerate',\n 'ullage',\n 'ululate',\n 'umpire',\n 'unarm',\n 'unbalance',\n 'unbar',\n 'unbelt',\n 'unbend',\n 'unbind',\n 'unbolt',\n 'unbonnet',\n 'unbosom',\n 'unbrace',\n 'unbridle',\n 'unbuckle',\n 'unburden',\n 'unbutton',\n 'uncap',\n 'unchain',\n 'unchurch',\n 'unclasp',\n 'unclog',\n 'unclose',\n 'unclothe',\n 'uncoil',\n 'uncork',\n 'uncouple',\n 'uncover',\n 'uncurl',\n 'undeceive',\n 'underachieve',\n 'underact',\n 'underbid',\n 'underbuy',\n 'undercapitalize',\n 'undercharge',\n 'undercoat',\n 'undercool',\n 'undercut',\n 'underdevelop',\n 'underdrain',\n 'underestimate',\n 'underexpose',\n 'underfeed',\n 'underfund',\n 'undergird',\n 'undergo',\n 'underlay',\n 'underlet',\n 'underlie',\n 'underline',\n 'undermine',\n 'undernourish',\n 'underpay',\n 'underperform',\n 'underpin',\n 'underplay',\n 'underprice',\n 'underprop',\n 'underquote',\n 'underrate',\n 'underscore',\n 'underseal',\n 'undersell',\n 'underset',\n 'undershot',\n 'undersign',\n 'understand',\n 'understate',\n 'understock',\n 'understudy',\n 'undertake',\n 'undertrump',\n 'undervalue',\n 'underwrite',\n 'undo',\n 'undock',\n 'undress',\n 'undulate',\n 'unearth',\n 'unfasten',\n 'unfetter',\n 'unfit',\n 'unfix',\n 'unfold',\n 'unfreeze',\n 'unfrock',\n 'unfurl',\n 'unhair',\n 'unhallow',\n 'unhand',\n 'unharness',\n 'unhelm',\n 'unhinge',\n 'unhook',\n 'unhorse',\n 'uniform',\n 'unify',\n 'unionize',\n 'unite',\n 'universalize',\n 'unkennel',\n 'unknit',\n 'unlace',\n 'unlade',\n 'unlash',\n 'unlatch',\n 'unlay',\n 'unlead',\n 'unlearn',\n 'unleash',\n 'unlimber',\n 'unlive',\n 'unload',\n 'unlock',\n 'unloosen',\n 'unmake',\n 'unman',\n 'unmask',\n 'unmoor',\n 'unmuzzle',\n 'unnerve',\n 'unpack',\n 'unpeg',\n 'unpeople',\n 'unpick',\n 'unpin',\n 'unplug',\n 'unquote',\n 'unravel',\n 'unreason',\n 'unreeve',\n 'unriddle',\n 'unrig',\n 'unrip',\n 'unroll',\n 'unroot',\n 'unsaddle',\n 'unsay',\n 'unscramble',\n 'unscrew',\n 'unseal',\n 'unseam',\n 'unseat',\n 'unsettle',\n 'unsex',\n 'unsheathe',\n 'unship',\n 'unsling',\n 'unsnap',\n 'unsnarl',\n 'unspeak',\n 'unsphere',\n 'unsteady',\n 'unsteel',\n 'unstep',\n 'unstick',\n 'unstop',\n 'unstring',\n 'unswear',\n 'untangle',\n 'unteach',\n 'unthink',\n 'unthread',\n 'unthrone',\n 'untidy',\n 'untie',\n 'untread',\n 'untruss',\n 'untuck',\n 'unveil',\n 'unvoice',\n 'unwind',\n 'unwish',\n 'unwrap',\n 'unyoke',\n 'unzip',\n 'up',\n 'up-anchor',\n 'upbraid',\n 'upbuild',\n 'upcast',\n 'update',\n 'upend',\n 'upgrade',\n 'upheave',\n 'uphold',\n 'upholster',\n 'uplift',\n 'uppercase',\n 'uppercut',\n 'upraise',\n 'uprear',\n 'upright',\n 'uprise',\n 'uproot',\n 'uprouse',\n 'upset',\n 'upspring',\n 'upstage',\n 'upstart',\n 'upsurge',\n 'upsweep',\n 'upswell',\n 'upswing',\n 'uptilt',\n 'upturn',\n 'urbanize',\n 'urge',\n 'urinate',\n 'urticate',\n 'used',\n 'usher',\n 'usurp',\n 'utilize',\n 'utter',\n 'vacate',\n 'vacation',\n 'vaccinate',\n 'vacillate',\n 'vacuum',\n 'vail',\n 'valet',\n 'validate',\n 'valorize',\n 'valuate',\n 'value',\n 'vamoose',\n 'vamp',\n 'vandalize',\n 'vanish',\n 'vanquish',\n 'vaporize',\n 'variegate',\n 'variolate',\n 'varitype',\n 'varnish',\n 'vary',\n 'vassalize',\n 'vat',\n 'vaticinate',\n 'vault',\n 'vaunt',\n 'vector',\n 'veer',\n 'vegetate',\n 'veil',\n 'vein',\n 'velarize',\n 'vellicate',\n 'vend',\n 'veneer',\n 'venerate',\n 'venge',\n 'vent',\n 'ventilate',\n 'ventriloquize',\n 'venture',\n 'verbalize',\n 'verbify',\n 'verge',\n 'verify',\n 'verjuice',\n 'vermiculate',\n 'vernalize',\n 'verse',\n 'versify',\n 'vesicate',\n 'vesiculate',\n 'vest',\n 'vesture',\n 'vet',\n 'veto',\n 'vex',\n 'vibrate',\n 'victimize',\n 'victual',\n 'videotape',\n 'vie',\n 'view',\n 'vignette',\n 'vilify',\n 'vilipend',\n 'vindicate',\n 'vinegar',\n 'vintage',\n 'violate',\n 'visa',\n 'vise',\n 'vision',\n 'visit',\n 'visor',\n 'visualize',\n 'vitalize',\n 'vitiate',\n 'vitrify',\n 'vitriol',\n 'vitriolize',\n 'vittle',\n 'vituperate',\n 'vivify',\n 'vivisect',\n 'vizor',\n 'vocalize',\n 'vociferate',\n 'voice',\n 'void',\n 'volatilize',\n 'volcanize',\n 'volley',\n 'volplane',\n 'volunteer',\n 'vomit',\n 'voodoo',\n 'vote',\n 'vouch',\n 'vouchsafe',\n 'vow',\n 'vowelize',\n 'voyage',\n 'vulcanize',\n 'vulgarize',\n 'wabble',\n 'wad',\n 'waddle',\n 'waddy',\n 'wade',\n 'wadset',\n 'wafer',\n 'waff',\n 'waffle',\n 'waft',\n 'wag',\n 'wage',\n 'wager',\n 'waggle',\n 'waggon',\n 'wagon',\n 'wail',\n 'wainscot',\n 'wait',\n 'waive',\n 'wake',\n 'waken',\n 'wale',\n 'walk',\n 'wall',\n 'wallop',\n 'wallow',\n 'wallpaper',\n 'waltz',\n 'wamble',\n 'wan',\n 'wander',\n 'wane',\n 'wangle',\n 'wank',\n 'wanna',\n 'want',\n 'wanton',\n 'warble',\n 'ward',\n 'ware',\n 'warehouse',\n 'warm',\n 'warn',\n 'warp',\n 'warrant',\n 'warsle',\n 'wash',\n 'wassail',\n 'waste',\n 'watch',\n 'water',\n 'watercool',\n 'watermark',\n 'waterproof',\n 'waterski',\n 'watersoak',\n 'wattle',\n 'waul',\n 'wave',\n 'waver',\n 'wawa',\n 'wawl',\n 'wax',\n 'waxen',\n 'waylay',\n 'weaken',\n 'wean',\n 'wear',\n 'weary',\n 'weather',\n 'weathercock',\n 'weatherproof',\n 'weave',\n 'web',\n 'wed',\n 'wedge',\n 'wee-wee',\n 'weed',\n 'weekend',\n 'ween',\n 'weep',\n 'weigh',\n 'weight',\n 'welch',\n 'welcome',\n 'weld',\n 'well',\n 'welsh',\n 'welt',\n 'welter',\n 'wench',\n 'wend',\n 'wester',\n 'westernize',\n 'wet',\n 'wetnurse',\n 'whack',\n 'whale',\n 'wham',\n 'whang',\n 'whap',\n 'wharf',\n 'wheedle',\n 'wheel',\n 'wheelbarrow',\n 'wheeze',\n 'whelm',\n 'whelp',\n 'wherrit',\n 'whet',\n 'whicker',\n 'whiff',\n 'whiffle',\n 'whimper',\n 'whine',\n 'whinge',\n 'whinny',\n 'whip',\n 'whipsaw',\n 'whipstitch',\n 'whirl',\n 'whirr',\n 'whish',\n 'whisk',\n 'whisper',\n 'whist',\n 'whistle',\n 'whistlestop',\n 'white',\n 'whiten',\n 'whitewash',\n 'whittle',\n 'whizz',\n 'wholesale',\n 'whoop',\n 'whop',\n 'whore',\n 'widen',\n 'widow',\n 'wield',\n 'wig',\n 'wiggle',\n 'wigwag',\n 'wildcat',\n 'wilder',\n 'wile',\n 'will',\n 'wilt',\n 'wimble',\n 'wimple',\n 'win',\n 'wince',\n 'winch',\n 'wind',\n 'windlass',\n 'windmill',\n 'window',\n 'windowshop',\n 'windrow',\n 'wine',\n 'wing',\n 'wink',\n 'winkle',\n 'winnow',\n 'winter',\n 'winterfeed',\n 'winterize',\n 'winterkill',\n 'wipe',\n 'wire',\n 'wiredraw',\n 'wireless',\n 'wis',\n 'wise',\n 'wisecrack',\n 'wish',\n 'wisp',\n 'wist',\n 'witch',\n 'withdraw',\n 'withe',\n 'wither',\n 'withhold',\n 'withstand',\n 'witness',\n 'wive',\n 'wizen',\n 'wobble',\n 'wolf',\n 'wolfwhistle',\n 'woman',\n 'womanize',\n 'wonder',\n 'wont',\n 'woo',\n 'wood',\n 'woof',\n 'woosh',\n 'word',\n 'work',\n 'work-to-rule',\n 'workharden',\n 'worm',\n 'worrit',\n 'worry',\n 'worsen',\n 'worship',\n 'worst',\n 'worth',\n 'would',\n 'wouldst',\n 'wow',\n 'wrack',\n 'wrangle',\n 'wrapped',\n 'wreak',\n 'wreathe',\n 'wreck',\n 'wrench',\n 'wrest',\n 'wrestle',\n 'wrick',\n 'wriggle',\n 'wring',\n 'wrinkle',\n 'writ',\n 'write',\n 'writhe',\n 'writhen',\n 'wrong',\n 'wrongfoot',\n 'wrought',\n 'wry',\n 'X-ray',\n 'Xerox',\n 'xylograph',\n 'yabber',\n 'yacht',\n 'yack',\n 'yammer',\n 'yank',\n 'yap',\n 'yarn',\n 'yaup',\n 'yaw',\n 'yawl',\n 'yawn',\n 'yawp',\n 'yean',\n 'yearn',\n 'yell',\n 'yellow',\n 'yelp',\n 'yield',\n 'yirr',\n 'yoke',\n 'york',\n 'yowl',\n 'zap',\n 'zero',\n 'zest',\n 'zigzag',\n 'zindabad',\n 'zing',\n 'zip',\n 'zone',\n 'zoom',\n 'zugzwang']"
},
{
"path": "corpkit/dictionaries/word_transforms.py",
"content": "\"\"\"\ncorpkit: manual word cludging\n\"\"\"\n\n# WordNet/CoreNLP lemmatiser are used for lemmatisation, but they can both \n# struggle with a few things. During lemmatisation, words will be corrected via \n# this list. So, if you are asking for lemmas and getting a result you don't like,\n# you can insert the word and its correction below and it will be transformed\n# automatically.\n\nwordlist = {u\"felt\": u\"feel\",\n u\"'s\": u\"be\",\n u\"'re\": u\"be\",\n u\"'m\": u\"be\",\n u\"'d\": u\"have\",\n u\"'ve\": u\"have\",\n u\"n't\": u\"not\",\n u\"smelt\": u\"smell\",\n u\"saw\": u\"see\",\n u\"hated\": u\"hate\",\n u\"people\": u\"person\",\n u\"peoples\": u\"person\",\n u\"persons\": u\"person\",\n u\"women\": u\"woman\",\n u\"men\": u\"man\",\n u\"teen-ager\": u\"teenager\",\n u\"teen-agers\": u\"teenager\",\n u\"republicans\": u\"republican\",\n u\"them\": u\"they\",\n u\"us\": u\"we\",\n u\"me\": u\"i\",\n u\"her\": u\"she\",\n u\"him\": u\"he\",\n u\"pdocs\": u\"pdoc\",\n u'can': u'can',\n u'will': u'will',\n u'would': u'will',\n u'could': u'can',\n u'ca': u'can',\n u'may': u'may',\n u'should': u'shall',\n u'shalt': u'shall',\n u\"'ll\": u'will',\n u'might': u'may',\n u'wo': u'will',\n u'shall': u'shall',\n u'mighta': u'may'}\n\n# Turn POS tags into word classes\n\ntaglemma = {u\"cc\": u\"Coordinating conjunction\",\n u\"cd\": u\"Cardinal number\",\n u\"dt\": u\"Determiner\",\n u\"ex\": u\"Ex. there\",\n u\"fw\": u\"Foreign word\",\n u\"in\": u\"Preposition\",\n u\"ls\": u\"List item marker\",\n u\"md\": u\"Modal\",\n u\"pdt\": u\"Predeterminer\",\n u\"pos\": u\"Possessive ending\",\n u\"rp\": u\"Particle\",\n u\"sym\": u\"Symbol\",\n u\"to\": u\"to\",\n u\"uh\": u\"Interjection\",\n u\"wdt\": u\"Wh-determiner\",\n u\"wp\": u\"Wh-pronoun\",\n u\"wp$\": u\"Possessive wh-pronoun\",\n u\"wrb\": u\"Wh-adverb\",\n u\"vbp\": u\"Verb\",\n u\"vbz\": u\"Verb\",\n u\"vbn\": u\"Verb\",\n u\"vbd\": u\"Verb\",\n u\"vbg\": u\"Verb\",\n u\"vb\": u\"Verb\",\n u\"nnp\": u\"Noun\",\n u\"nns\": u\"Noun\",\n u\"nnps\": u\"Noun\",\n u\"nn\": u\"Noun\",\n u\"prp\": u\"Pronoun\",\n u\"prp$\": u\"Pronoun\",\n u\"jj\": u\"Adjective\",\n u\"jjr\": u\"Adjective\",\n u\"jjs\": u\"Adjective\",\n u\"rb\": u\"Adverb\",\n u\"rbr\": u\"Adverb\",\n u\"rbs\": u\"Adverb\",\n u\"wp\": u\"Wh- pronoun\",\n u\"wp$\": u\"Wh- pronoun\",\n u\"-rrb-\": u\"Bracket\",\n u\"-lrb-\": u\"Bracket\",\n u\"-rsb-\": u\"Bracket\",\n u\"-lsb-\": u\"Bracket\",\n u\"to\": u\"To\",\n u'$': u'Symbol',\n u'x': u'Other',\n u\"!\": u'Punctuation',\n u'\"': u'Punctuation',\n u\"#\": u'Punctuation',\n u\"%\": u'Punctuation',\n u\"&\": u'Punctuation',\n u\"\\\\\": u'Punctuation',\n u\"'\": u'Punctuation',\n u\"(\": u'Punctuation',\n u\")\": u'Punctuation',\n u\"*\": u'Punctuation',\n u\"+\": u'Punctuation',\n u\",\": u'Punctuation',\n u\"-\": u'Punctuation',\n u\".\": u'Punctuation',\n u\"/\": u'Punctuation',\n u\":\": u'Punctuation',\n u\";\": u'Punctuation',\n u\"<\": u'Punctuation',\n u\"=\": u'Punctuation',\n u\">\": u'Punctuation',\n u\"?\": u'Punctuation',\n u\"@\": u'Punctuation',\n u\"[\": u'Punctuation',\n u\"]\": u'Punctuation',\n u\"^\": u'Punctuation',\n u\"_\": u'Punctuation',\n u\"`\": u'Punctuation',\n u\"{\": u'Punctuation',\n u\"|\": u'Punctuation',\n u\"}\": u'Punctuation',\n u\"~\": u'Punctuation'}\n\n# the below produced with:\n#\n# from corpkit import *\n# from corpkit.dictionaries import *\n# out = {}\n# for wordclass in tagtoclass.values():\n# criteria = [tag for tag, clas in tagtoclass.items() if clas == wordclass]\n# reg = as_regex(criteria, boundaries='l')\n# out[wordclass] = reg\n\nmergetags = {u'Adjective': r'(?i)^(?:jj|jjr|jjs)$',\n u'Adverb': r'(?i)^(?:rb|rbr|rbs)$',\n u'Bracket': r'(?i)^(?:\\-lrb\\-|\\-lsb\\-|\\-rrb\\-|\\-rsb\\-)$',\n u'Cardinal number': r'(?i)^(?:cd)$',\n u'Coordinating conjunction': r'(?i)^(?:cc)$',\n u'Determiner': r'(?i)^(?:dt)$',\n u'Ex. there': r'(?i)^(?:ex)$',\n u'Foreign word': r'(?i)^(?:fw)$',\n u'Interjection': r'(?i)^(?:uh)$',\n u'List item marker': r'(?i)^(?:ls)$',\n u'Modal': r'(?i)^(?:md)$',\n u'Noun': r'(?i)^(?:nn|nnp|nnps|nns)$',\n U'Other': r'(?i)^(?:x)$',\n u'Particle': r'(?i)^(?:rp)$',\n u'Possessive ending': r'(?i)^(?:pos)$',\n u'Predeterminer': r'(?i)^(?:pdt)$',\n u'Preposition': r'(?i)^(?:in)$',\n u'Pronoun': r'(?i)^(?:prp|prp\\$)$',\n u'Symbol': r'(?i)^(?:\\$|sym)$',\n u'To': r'(?i)^(?:to)$',\n u'Verb': r'(?i)^(?:vb|vbd|vbg|vbn|vbp|vbz)$',\n u'Wh- pronoun': r'(?i)^(?:wp|wp\\$)$',\n u'Wh-adverb': r'(?i)^(?:wrb)$',\n u'Wh-determiner': r'(?i)^(?:wdt)$'}\n\n# A simple spelling converter\nusa_convert = {u\"accessorise\": u\"accessorize\",\n u\"accessorised\": u\"accessorized\",\n u\"accessorises\": u\"accessorizes\",\n u\"accessorising\": u\"accessorizing\",\n u\"acclimatisation\": u\"acclimatization\",\n u\"acclimatise\": u\"acclimatize\",\n u\"acclimatised\": u\"acclimatized\",\n u\"acclimatises\": u\"acclimatizes\",\n u\"acclimatising\": u\"acclimatizing\",\n u\"accoutrements\": u\"accouterments\",\n u\"aeon\": u\"eon\",\n u\"aeons\": u\"eons\",\n u\"aerogramme\": u\"aerogram\",\n u\"aerogrammes\": u\"aerograms\",\n u\"aeroplane\": u\"airplane\",\n u\"aeroplanes\": u\"airplanes\",\n u\"aesthete\": u\"esthete\",\n u\"aesthetes\": u\"esthetes\",\n u\"aesthetic\": u\"esthetic\",\n u\"aesthetically\": u\"esthetically\",\n u\"aesthetics\": u\"esthetics\",\n u\"aetiology\": u\"etiology\",\n u\"ageing\": u\"aging\",\n u\"aggrandisement\": u\"aggrandizement\",\n u\"agonise\": u\"agonize\",\n u\"agonised\": u\"agonized\",\n u\"agonises\": u\"agonizes\",\n u\"agonising\": u\"agonizing\",\n u\"agonisingly\": u\"agonizingly\",\n u\"almanack\": u\"almanac\",\n u\"almanacks\": u\"almanacs\",\n u\"aluminium\": u\"aluminum\",\n u\"amortisable\": u\"amortizable\",\n u\"amortisation\": u\"amortization\",\n u\"amortisations\": u\"amortizations\",\n u\"amortise\": u\"amortize\",\n u\"amortised\": u\"amortized\",\n u\"amortises\": u\"amortizes\",\n u\"amortising\": u\"amortizing\",\n u\"amphitheatre\": u\"amphitheater\",\n u\"amphitheatres\": u\"amphitheaters\",\n u\"anaemia\": u\"anemia\",\n u\"anaemic\": u\"anemic\",\n u\"anaesthesia\": u\"anesthesia\",\n u\"anaesthetic\": u\"anesthetic\",\n u\"anaesthetics\": u\"anesthetics\",\n u\"anaesthetise\": u\"anesthetize\",\n u\"anaesthetised\": u\"anesthetized\",\n u\"anaesthetises\": u\"anesthetizes\",\n u\"anaesthetising\": u\"anesthetizing\",\n u\"anaesthetist\": u\"anesthetist\",\n u\"anaesthetists\": u\"anesthetists\",\n u\"anaesthetize\": u\"anesthetize\",\n u\"anaesthetized\": u\"anesthetized\",\n u\"anaesthetizes\": u\"anesthetizes\",\n u\"anaesthetizing\": u\"anesthetizing\",\n u\"analogue\": u\"analog\",\n u\"analogues\": u\"analogs\",\n u\"analyse\": u\"analyze\",\n u\"analysed\": u\"analyzed\",\n u\"analyses\": u\"analyzes\",\n u\"analysing\": u\"analyzing\",\n u\"anglicise\": u\"anglicize\",\n u\"anglicised\": u\"anglicized\",\n u\"anglicises\": u\"anglicizes\",\n u\"anglicising\": u\"anglicizing\",\n u\"annualised\": u\"annualized\",\n u\"antagonise\": u\"antagonize\",\n u\"antagonised\": u\"antagonized\",\n u\"antagonises\": u\"antagonizes\",\n u\"antagonising\": u\"antagonizing\",\n u\"apologise\": u\"apologize\",\n u\"apologised\": u\"apologized\",\n u\"apologises\": u\"apologizes\",\n u\"apologising\": u\"apologizing\",\n u\"appal\": u\"appall\",\n u\"appals\": u\"appalls\",\n u\"appetiser\": u\"appetizer\",\n u\"appetisers\": u\"appetizers\",\n u\"appetising\": u\"appetizing\",\n u\"appetisingly\": u\"appetizingly\",\n u\"arbour\": u\"arbor\",\n u\"arbours\": u\"arbors\",\n u\"archaeological\": u\"archeological\",\n u\"archaeologically\": u\"archeologically\",\n u\"archaeologist\": u\"archeologist\",\n u\"archaeologists\": u\"archeologists\",\n u\"archaeology\": u\"archeology\",\n u\"ardour\": u\"ardor\",\n u\"armour\": u\"armor\",\n u\"armoured\": u\"armored\",\n u\"armourer\": u\"armorer\",\n u\"armourers\": u\"armorers\",\n u\"armouries\": u\"armories\",\n u\"armoury\": u\"armory\",\n u\"artefact\": u\"artifact\",\n u\"artefacts\": u\"artifacts\",\n u\"authorise\": u\"authorize\",\n u\"authorised\": u\"authorized\",\n u\"authorises\": u\"authorizes\",\n u\"authorising\": u\"authorizing\",\n u\"axe\": u\"ax\",\n u\"backpedalled\": u\"backpedaled\",\n u\"backpedalling\": u\"backpedaling\",\n u\"bannister\": u\"banister\",\n u\"bannisters\": u\"banisters\",\n u\"baptise\": u\"baptize\",\n u\"baptised\": u\"baptized\",\n u\"baptises\": u\"baptizes\",\n u\"baptising\": u\"baptizing\",\n u\"bastardise\": u\"bastardize\",\n u\"bastardised\": u\"bastardized\",\n u\"bastardises\": u\"bastardizes\",\n u\"bastardising\": u\"bastardizing\",\n u\"battleaxe\": u\"battleax\",\n u\"baulk\": u\"balk\",\n u\"baulked\": u\"balked\",\n u\"baulking\": u\"balking\",\n u\"baulks\": u\"balks\",\n u\"bedevilled\": u\"bedeviled\",\n u\"bedevilling\": u\"bedeviling\",\n u\"behaviour\": u\"behavior\",\n u\"behavioural\": u\"behavioral\",\n u\"behaviourism\": u\"behaviorism\",\n u\"behaviourist\": u\"behaviorist\",\n u\"behaviourists\": u\"behaviorists\",\n u\"behaviours\": u\"behaviors\",\n u\"behove\": u\"behoove\",\n u\"behoved\": u\"behooved\",\n u\"behoves\": u\"behooves\",\n u\"bejewelled\": u\"bejeweled\",\n u\"belabour\": u\"belabor\",\n u\"belaboured\": u\"belabored\",\n u\"belabouring\": u\"belaboring\",\n u\"belabours\": u\"belabors\",\n u\"bevelled\": u\"beveled\",\n u\"bevvies\": u\"bevies\",\n u\"bevvy\": u\"bevy\",\n u\"biassed\": u\"biased\",\n u\"biassing\": u\"biasing\",\n u\"bingeing\": u\"binging\",\n u\"bougainvillaea\": u\"bougainvillea\",\n u\"bougainvillaeas\": u\"bougainvilleas\",\n u\"bowdlerise\": u\"bowdlerize\",\n u\"bowdlerised\": u\"bowdlerized\",\n u\"bowdlerises\": u\"bowdlerizes\",\n u\"bowdlerising\": u\"bowdlerizing\",\n u\"breathalyse\": u\"breathalyze\",\n u\"breathalysed\": u\"breathalyzed\",\n u\"breathalyser\": u\"breathalyzer\",\n u\"breathalysers\": u\"breathalyzers\",\n u\"breathalyses\": u\"breathalyzes\",\n u\"breathalysing\": u\"breathalyzing\",\n u\"brutalise\": u\"brutalize\",\n u\"brutalised\": u\"brutalized\",\n u\"brutalises\": u\"brutalizes\",\n u\"brutalising\": u\"brutalizing\",\n u\"buses\": u\"busses\",\n u\"busing\": u\"bussing\",\n u\"caesarean\": u\"cesarean\",\n u\"caesareans\": u\"cesareans\",\n u\"calibre\": u\"caliber\",\n u\"calibres\": u\"calibers\",\n u\"calliper\": u\"caliper\",\n u\"callipers\": u\"calipers\",\n u\"callisthenics\": u\"calisthenics\",\n u\"canalise\": u\"canalize\",\n u\"canalised\": u\"canalized\",\n u\"canalises\": u\"canalizes\",\n u\"canalising\": u\"canalizing\",\n u\"cancellation\": u\"cancelation\",\n u\"cancellations\": u\"cancelations\",\n u\"cancelled\": u\"canceled\",\n u\"cancelling\": u\"canceling\",\n u\"candour\": u\"candor\",\n u\"cannibalise\": u\"cannibalize\",\n u\"cannibalised\": u\"cannibalized\",\n u\"cannibalises\": u\"cannibalizes\",\n u\"cannibalising\": u\"cannibalizing\",\n u\"canonise\": u\"canonize\",\n u\"canonised\": u\"canonized\",\n u\"canonises\": u\"canonizes\",\n u\"canonising\": u\"canonizing\",\n u\"capitalise\": u\"capitalize\",\n u\"capitalised\": u\"capitalized\",\n u\"capitalises\": u\"capitalizes\",\n u\"capitalising\": u\"capitalizing\",\n u\"caramelise\": u\"caramelize\",\n u\"caramelised\": u\"caramelized\",\n u\"caramelises\": u\"caramelizes\",\n u\"caramelising\": u\"caramelizing\",\n u\"carbonise\": u\"carbonize\",\n u\"carbonised\": u\"carbonized\",\n u\"carbonises\": u\"carbonizes\",\n u\"carbonising\": u\"carbonizing\",\n u\"carolled\": u\"caroled\",\n u\"carolling\": u\"caroling\",\n u\"catalogue\": u\"catalog\",\n u\"catalogued\": u\"cataloged\",\n u\"catalogues\": u\"catalogs\",\n u\"cataloguing\": u\"cataloging\",\n u\"catalyse\": u\"catalyze\",\n u\"catalysed\": u\"catalyzed\",\n u\"catalyses\": u\"catalyzes\",\n u\"catalysing\": u\"catalyzing\",\n u\"categorise\": u\"categorize\",\n u\"categorised\": u\"categorized\",\n u\"categorises\": u\"categorizes\",\n u\"categorising\": u\"categorizing\",\n u\"cauterise\": u\"cauterize\",\n u\"cauterised\": u\"cauterized\",\n u\"cauterises\": u\"cauterizes\",\n u\"cauterising\": u\"cauterizing\",\n u\"cavilled\": u\"caviled\",\n u\"cavilling\": u\"caviling\",\n u\"centigramme\": u\"centigram\",\n u\"centigrammes\": u\"centigrams\",\n u\"centilitre\": u\"centiliter\",\n u\"centilitres\": u\"centiliters\",\n u\"centimetre\": u\"centimeter\",\n u\"centimetres\": u\"centimeters\",\n u\"centralise\": u\"centralize\",\n u\"centralised\": u\"centralized\",\n u\"centralises\": u\"centralizes\",\n u\"centralising\": u\"centralizing\",\n u\"centre\": u\"center\",\n u\"centred\": u\"centered\",\n u\"centrefold\": u\"centerfold\",\n u\"centrefolds\": u\"centerfolds\",\n u\"centrepiece\": u\"centerpiece\",\n u\"centrepieces\": u\"centerpieces\",\n u\"centres\": u\"centers\",\n u\"channelled\": u\"channeled\",\n u\"channelling\": u\"channeling\",\n u\"characterise\": u\"characterize\",\n u\"characterised\": u\"characterized\",\n u\"characterises\": u\"characterizes\",\n u\"characterising\": u\"characterizing\",\n u\"cheque\": u\"check\",\n u\"chequebook\": u\"checkbook\",\n u\"chequebooks\": u\"checkbooks\",\n u\"chequered\": u\"checkered\",\n u\"cheques\": u\"checks\",\n u\"chilli\": u\"chili\",\n u\"chimaera\": u\"chimera\",\n u\"chimaeras\": u\"chimeras\",\n u\"chiselled\": u\"chiseled\",\n u\"chiselling\": u\"chiseling\",\n u\"circularise\": u\"circularize\",\n u\"circularised\": u\"circularized\",\n u\"circularises\": u\"circularizes\",\n u\"circularising\": u\"circularizing\",\n u\"civilise\": u\"civilize\",\n u\"civilised\": u\"civilized\",\n u\"civilises\": u\"civilizes\",\n u\"civilising\": u\"civilizing\",\n u\"clamour\": u\"clamor\",\n u\"clamoured\": u\"clamored\",\n u\"clamouring\": u\"clamoring\",\n u\"clamours\": u\"clamors\",\n u\"clangour\": u\"clangor\",\n u\"clarinettist\": u\"clarinetist\",\n u\"clarinettists\": u\"clarinetists\",\n u\"collectivise\": u\"collectivize\",\n u\"collectivised\": u\"collectivized\",\n u\"collectivises\": u\"collectivizes\",\n u\"collectivising\": u\"collectivizing\",\n u\"colonisation\": u\"colonization\",\n u\"colonise\": u\"colonize\",\n u\"colonised\": u\"colonized\",\n u\"coloniser\": u\"colonizer\",\n u\"colonisers\": u\"colonizers\",\n u\"colonises\": u\"colonizes\",\n u\"colonising\": u\"colonizing\",\n u\"colour\": u\"color\",\n u\"colourant\": u\"colorant\",\n u\"colourants\": u\"colorants\",\n u\"coloured\": u\"colored\",\n u\"coloureds\": u\"coloreds\",\n u\"colourful\": u\"colorful\",\n u\"colourfully\": u\"colorfully\",\n u\"colouring\": u\"coloring\",\n u\"colourize\": u\"colorize\",\n u\"colourized\": u\"colorized\",\n u\"colourizes\": u\"colorizes\",\n u\"colourizing\": u\"colorizing\",\n u\"colourless\": u\"colorless\",\n u\"colours\": u\"colors\",\n u\"commercialise\": u\"commercialize\",\n u\"commercialised\": u\"commercialized\",\n u\"commercialises\": u\"commercializes\",\n u\"commercialising\": u\"commercializing\",\n u\"compartmentalise\": u\"compartmentalize\",\n u\"compartmentalised\": u\"compartmentalized\",\n u\"compartmentalises\": u\"compartmentalizes\",\n u\"compartmentalising\": u\"compartmentalizing\",\n u\"computerise\": u\"computerize\",\n u\"computerised\": u\"computerized\",\n u\"computerises\": u\"computerizes\",\n u\"computerising\": u\"computerizing\",\n u\"conceptualise\": u\"conceptualize\",\n u\"conceptualised\": u\"conceptualized\",\n u\"conceptualises\": u\"conceptualizes\",\n u\"conceptualising\": u\"conceptualizing\",\n u\"connexion\": u\"connection\",\n u\"connexions\": u\"connections\",\n u\"contextualise\": u\"contextualize\",\n u\"contextualised\": u\"contextualized\",\n u\"contextualises\": u\"contextualizes\",\n u\"contextualising\": u\"contextualizing\",\n u\"cosier\": u\"cozier\",\n u\"cosies\": u\"cozies\",\n u\"cosiest\": u\"coziest\",\n u\"cosily\": u\"cozily\",\n u\"cosiness\": u\"coziness\",\n u\"cosy\": u\"cozy\",\n u\"councillor\": u\"councilor\",\n u\"councillors\": u\"councilors\",\n u\"counselled\": u\"counseled\",\n u\"counselling\": u\"counseling\",\n u\"counsellor\": u\"counselor\",\n u\"counsellors\": u\"counselors\",\n u\"crenellated\": u\"crenelated\",\n u\"criminalise\": u\"criminalize\",\n u\"criminalised\": u\"criminalized\",\n u\"criminalises\": u\"criminalizes\",\n u\"criminalising\": u\"criminalizing\",\n u\"criticise\": u\"criticize\",\n u\"criticised\": u\"criticized\",\n u\"criticises\": u\"criticizes\",\n u\"criticising\": u\"criticizing\",\n u\"crueller\": u\"crueler\",\n u\"cruellest\": u\"cruelest\",\n u\"crystallisation\": u\"crystallization\",\n u\"crystallise\": u\"crystallize\",\n u\"crystallised\": u\"crystallized\",\n u\"crystallises\": u\"crystallizes\",\n u\"crystallising\": u\"crystallizing\",\n u\"cudgelled\": u\"cudgeled\",\n u\"cudgelling\": u\"cudgeling\",\n u\"customise\": u\"customize\",\n u\"customised\": u\"customized\",\n u\"customises\": u\"customizes\",\n u\"customising\": u\"customizing\",\n u\"cypher\": u\"cipher\",\n u\"cyphers\": u\"ciphers\",\n u\"decentralisation\": u\"decentralization\",\n u\"decentralise\": u\"decentralize\",\n u\"decentralised\": u\"decentralized\",\n u\"decentralises\": u\"decentralizes\",\n u\"decentralising\": u\"decentralizing\",\n u\"decriminalisation\": u\"decriminalization\",\n u\"decriminalise\": u\"decriminalize\",\n u\"decriminalised\": u\"decriminalized\",\n u\"decriminalises\": u\"decriminalizes\",\n u\"decriminalising\": u\"decriminalizing\",\n u\"defence\": u\"defense\",\n u\"defenceless\": u\"defenseless\",\n u\"defences\": u\"defenses\",\n u\"dehumanisation\": u\"dehumanization\",\n u\"dehumanise\": u\"dehumanize\",\n u\"dehumanised\": u\"dehumanized\",\n u\"dehumanises\": u\"dehumanizes\",\n u\"dehumanising\": u\"dehumanizing\",\n u\"demeanour\": u\"demeanor\",\n u\"demilitarisation\": u\"demilitarization\",\n u\"demilitarise\": u\"demilitarize\",\n u\"demilitarised\": u\"demilitarized\",\n u\"demilitarises\": u\"demilitarizes\",\n u\"demilitarising\": u\"demilitarizing\",\n u\"demobilisation\": u\"demobilization\",\n u\"demobilise\": u\"demobilize\",\n u\"demobilised\": u\"demobilized\",\n u\"demobilises\": u\"demobilizes\",\n u\"demobilising\": u\"demobilizing\",\n u\"democratisation\": u\"democratization\",\n u\"democratise\": u\"democratize\",\n u\"democratised\": u\"democratized\",\n u\"democratises\": u\"democratizes\",\n u\"democratising\": u\"democratizing\",\n u\"demonise\": u\"demonize\",\n u\"demonised\": u\"demonized\",\n u\"demonises\": u\"demonizes\",\n u\"demonising\": u\"demonizing\",\n u\"demoralisation\": u\"demoralization\",\n u\"demoralise\": u\"demoralize\",\n u\"demoralised\": u\"demoralized\",\n u\"demoralises\": u\"demoralizes\",\n u\"demoralising\": u\"demoralizing\",\n u\"denationalisation\": u\"denationalization\",\n u\"denationalise\": u\"denationalize\",\n u\"denationalised\": u\"denationalized\",\n u\"denationalises\": u\"denationalizes\",\n u\"denationalising\": u\"denationalizing\",\n u\"deodorise\": u\"deodorize\",\n u\"deodorised\": u\"deodorized\",\n u\"deodorises\": u\"deodorizes\",\n u\"deodorising\": u\"deodorizing\",\n u\"depersonalise\": u\"depersonalize\",\n u\"depersonalised\": u\"depersonalized\",\n u\"depersonalises\": u\"depersonalizes\",\n u\"depersonalising\": u\"depersonalizing\",\n u\"deputise\": u\"deputize\",\n u\"deputised\": u\"deputized\",\n u\"deputises\": u\"deputizes\",\n u\"deputising\": u\"deputizing\",\n u\"desensitisation\": u\"desensitization\",\n u\"desensitise\": u\"desensitize\",\n u\"desensitised\": u\"desensitized\",\n u\"desensitises\": u\"desensitizes\",\n u\"desensitising\": u\"desensitizing\",\n u\"destabilisation\": u\"destabilization\",\n u\"destabilise\": u\"destabilize\",\n u\"destabilised\": u\"destabilized\",\n u\"destabilises\": u\"destabilizes\",\n u\"destabilising\": u\"destabilizing\",\n u\"dialled\": u\"dialed\",\n u\"dialling\": u\"dialing\",\n u\"dialogue\": u\"dialog\",\n u\"dialogues\": u\"dialogs\",\n u\"diarrhoea\": u\"diarrhea\",\n u\"digitise\": u\"digitize\",\n u\"digitised\": u\"digitized\",\n u\"digitises\": u\"digitizes\",\n u\"digitising\": u\"digitizing\",\n u\"disc\": u\"disk\",\n u\"discolour\": u\"discolor\",\n u\"discoloured\": u\"discolored\",\n u\"discolouring\": u\"discoloring\",\n u\"discolours\": u\"discolors\",\n u\"discs\": u\"disks\",\n u\"disembowelled\": u\"disemboweled\",\n u\"disembowelling\": u\"disemboweling\",\n u\"disfavour\": u\"disfavor\",\n u\"dishevelled\": u\"disheveled\",\n u\"dishonour\": u\"dishonor\",\n u\"dishonourable\": u\"dishonorable\",\n u\"dishonourably\": u\"dishonorably\",\n u\"dishonoured\": u\"dishonored\",\n u\"dishonouring\": u\"dishonoring\",\n u\"dishonours\": u\"dishonors\",\n u\"disorganisation\": u\"disorganization\",\n u\"disorganised\": u\"disorganized\",\n u\"distil\": u\"distill\",\n u\"distils\": u\"distills\",\n u\"dramatisation\": u\"dramatization\",\n u\"dramatisations\": u\"dramatizations\",\n u\"dramatise\": u\"dramatize\",\n u\"dramatised\": u\"dramatized\",\n u\"dramatises\": u\"dramatizes\",\n u\"dramatising\": u\"dramatizing\",\n u\"draught\": u\"draft\",\n u\"draughtboard\": u\"draftboard\",\n u\"draughtboards\": u\"draftboards\",\n u\"draughtier\": u\"draftier\",\n u\"draughtiest\": u\"draftiest\",\n u\"draughts\": u\"drafts\",\n u\"draughtsman\": u\"draftsman\",\n u\"draughtsmanship\": u\"draftsmanship\",\n u\"draughtsmen\": u\"draftsmen\",\n u\"draughtswoman\": u\"draftswoman\",\n u\"draughtswomen\": u\"draftswomen\",\n u\"draughty\": u\"drafty\",\n u\"drivelled\": u\"driveled\",\n u\"drivelling\": u\"driveling\",\n u\"duelled\": u\"dueled\",\n u\"duelling\": u\"dueling\",\n u\"economise\": u\"economize\",\n u\"economised\": u\"economized\",\n u\"economises\": u\"economizes\",\n u\"economising\": u\"economizing\",\n u\"edoema\": u\"edema\",\n u\"editorialise\": u\"editorialize\",\n u\"editorialised\": u\"editorialized\",\n u\"editorialises\": u\"editorializes\",\n u\"editorialising\": u\"editorializing\",\n u\"empathise\": u\"empathize\",\n u\"empathised\": u\"empathized\",\n u\"empathises\": u\"empathizes\",\n u\"empathising\": u\"empathizing\",\n u\"emphasise\": u\"emphasize\",\n u\"emphasised\": u\"emphasized\",\n u\"emphasises\": u\"emphasizes\",\n u\"emphasising\": u\"emphasizing\",\n u\"enamelled\": u\"enameled\",\n u\"enamelling\": u\"enameling\",\n u\"enamoured\": u\"enamored\",\n u\"encyclopaedia\": u\"encyclopedia\",\n u\"encyclopaedias\": u\"encyclopedias\",\n u\"encyclopaedic\": u\"encyclopedic\",\n u\"endeavour\": u\"endeavor\",\n u\"endeavoured\": u\"endeavored\",\n u\"endeavouring\": u\"endeavoring\",\n u\"endeavours\": u\"endeavors\",\n u\"energise\": u\"energize\",\n u\"energised\": u\"energized\",\n u\"energises\": u\"energizes\",\n u\"energising\": u\"energizing\",\n u\"enrol\": u\"enroll\",\n u\"enrols\": u\"enrolls\",\n u\"enthral\": u\"enthrall\",\n u\"enthrals\": u\"enthralls\",\n u\"epaulette\": u\"epaulet\",\n u\"epaulettes\": u\"epaulets\",\n u\"epicentre\": u\"epicenter\",\n u\"epicentres\": u\"epicenters\",\n u\"epilogue\": u\"epilog\",\n u\"epilogues\": u\"epilogs\",\n u\"epitomise\": u\"epitomize\",\n u\"epitomised\": u\"epitomized\",\n u\"epitomises\": u\"epitomizes\",\n u\"epitomising\": u\"epitomizing\",\n u\"equalisation\": u\"equalization\",\n u\"equalise\": u\"equalize\",\n u\"equalised\": u\"equalized\",\n u\"equaliser\": u\"equalizer\",\n u\"equalisers\": u\"equalizers\",\n u\"equalises\": u\"equalizes\",\n u\"equalising\": u\"equalizing\",\n u\"eulogise\": u\"eulogize\",\n u\"eulogised\": u\"eulogized\",\n u\"eulogises\": u\"eulogizes\",\n u\"eulogising\": u\"eulogizing\",\n u\"evangelise\": u\"evangelize\",\n u\"evangelised\": u\"evangelized\",\n u\"evangelises\": u\"evangelizes\",\n u\"evangelising\": u\"evangelizing\",\n u\"exorcise\": u\"exorcize\",\n u\"exorcised\": u\"exorcized\",\n u\"exorcises\": u\"exorcizes\",\n u\"exorcising\": u\"exorcizing\",\n u\"extemporisation\": u\"extemporization\",\n u\"extemporise\": u\"extemporize\",\n u\"extemporised\": u\"extemporized\",\n u\"extemporises\": u\"extemporizes\",\n u\"extemporising\": u\"extemporizing\",\n u\"externalisation\": u\"externalization\",\n u\"externalisations\": u\"externalizations\",\n u\"externalise\": u\"externalize\",\n u\"externalised\": u\"externalized\",\n u\"externalises\": u\"externalizes\",\n u\"externalising\": u\"externalizing\",\n u\"factorise\": u\"factorize\",\n u\"factorised\": u\"factorized\",\n u\"factorises\": u\"factorizes\",\n u\"factorising\": u\"factorizing\",\n u\"faecal\": u\"fecal\",\n u\"faeces\": u\"feces\",\n u\"familiarisation\": u\"familiarization\",\n u\"familiarise\": u\"familiarize\",\n u\"familiarised\": u\"familiarized\",\n u\"familiarises\": u\"familiarizes\",\n u\"familiarising\": u\"familiarizing\",\n u\"fantasise\": u\"fantasize\",\n u\"fantasised\": u\"fantasized\",\n u\"fantasises\": u\"fantasizes\",\n u\"fantasising\": u\"fantasizing\",\n u\"favour\": u\"favor\",\n u\"favourable\": u\"favorable\",\n u\"favourably\": u\"favorably\",\n u\"favoured\": u\"favored\",\n u\"favouring\": u\"favoring\",\n u\"favourite\": u\"favorite\",\n u\"favourites\": u\"favorites\",\n u\"favouritism\": u\"favoritism\",\n u\"favours\": u\"favors\",\n u\"feminise\": u\"feminize\",\n u\"feminised\": u\"feminized\",\n u\"feminises\": u\"feminizes\",\n u\"feminising\": u\"feminizing\",\n u\"fertilisation\": u\"fertilization\",\n u\"fertilise\": u\"fertilize\",\n u\"fertilised\": u\"fertilized\",\n u\"fertiliser\": u\"fertilizer\",\n u\"fertilisers\": u\"fertilizers\",\n u\"fertilises\": u\"fertilizes\",\n u\"fertilising\": u\"fertilizing\",\n u\"fervour\": u\"fervor\",\n u\"fibre\": u\"fiber\",\n u\"fibreglass\": u\"fiberglass\",\n u\"fibres\": u\"fibers\",\n u\"fictionalisation\": u\"fictionalization\",\n u\"fictionalisations\": u\"fictionalizations\",\n u\"fictionalise\": u\"fictionalize\",\n u\"fictionalised\": u\"fictionalized\",\n u\"fictionalises\": u\"fictionalizes\",\n u\"fictionalising\": u\"fictionalizing\",\n u\"fillet\": u\"filet\",\n u\"filleted\": u\"fileted\",\n u\"filleting\": u\"fileting\",\n u\"fillets\": u\"filets\",\n u\"finalisation\": u\"finalization\",\n u\"finalise\": u\"finalize\",\n u\"finalised\": u\"finalized\",\n u\"finalises\": u\"finalizes\",\n u\"finalising\": u\"finalizing\",\n u\"flautist\": u\"flutist\",\n u\"flautists\": u\"flutists\",\n u\"flavour\": u\"flavor\",\n u\"flavoured\": u\"flavored\",\n u\"flavouring\": u\"flavoring\",\n u\"flavourings\": u\"flavorings\",\n u\"flavourless\": u\"flavorless\",\n u\"flavours\": u\"flavors\",\n u\"flavoursome\": u\"flavorsome\",\n u\"flyer / flier\": u\"flier / flyer\",\n u\"foetal\": u\"fetal\",\n u\"foetid\": u\"fetid\",\n u\"foetus\": u\"fetus\",\n u\"foetuses\": u\"fetuses\",\n u\"formalisation\": u\"formalization\",\n u\"formalise\": u\"formalize\",\n u\"formalised\": u\"formalized\",\n u\"formalises\": u\"formalizes\",\n u\"formalising\": u\"formalizing\",\n u\"fossilisation\": u\"fossilization\",\n u\"fossilise\": u\"fossilize\",\n u\"fossilised\": u\"fossilized\",\n u\"fossilises\": u\"fossilizes\",\n u\"fossilising\": u\"fossilizing\",\n u\"fraternisation\": u\"fraternization\",\n u\"fraternise\": u\"fraternize\",\n u\"fraternised\": u\"fraternized\",\n u\"fraternises\": u\"fraternizes\",\n u\"fraternising\": u\"fraternizing\",\n u\"fulfil\": u\"fulfill\",\n u\"fulfilment\": u\"fulfillment\",\n u\"fulfils\": u\"fulfills\",\n u\"funnelled\": u\"funneled\",\n u\"funnelling\": u\"funneling\",\n u\"galvanise\": u\"galvanize\",\n u\"galvanised\": u\"galvanized\",\n u\"galvanises\": u\"galvanizes\",\n u\"galvanising\": u\"galvanizing\",\n u\"gambolled\": u\"gamboled\",\n u\"gambolling\": u\"gamboling\",\n u\"gaol\": u\"jail\",\n u\"gaolbird\": u\"jailbird\",\n u\"gaolbirds\": u\"jailbirds\",\n u\"gaolbreak\": u\"jailbreak\",\n u\"gaolbreaks\": u\"jailbreaks\",\n u\"gaoled\": u\"jailed\",\n u\"gaoler\": u\"jailer\",\n u\"gaolers\": u\"jailers\",\n u\"gaoling\": u\"jailing\",\n u\"gaols\": u\"jails\",\n u\"gases\": u\"gasses\",\n u\"gauge\": u\"gage\",\n u\"gauged\": u\"gaged\",\n u\"gauges\": u\"gages\",\n u\"gauging\": u\"gaging\",\n u\"generalisation\": u\"generalization\",\n u\"generalisations\": u\"generalizations\",\n u\"generalise\": u\"generalize\",\n u\"generalised\": u\"generalized\",\n u\"generalises\": u\"generalizes\",\n u\"generalising\": u\"generalizing\",\n u\"ghettoise\": u\"ghettoize\",\n u\"ghettoised\": u\"ghettoized\",\n u\"ghettoises\": u\"ghettoizes\",\n u\"ghettoising\": u\"ghettoizing\",\n u\"gipsies\": u\"gypsies\",\n u\"glamorise\": u\"glamorize\",\n u\"glamorised\": u\"glamorized\",\n u\"glamorises\": u\"glamorizes\",\n u\"glamorising\": u\"glamorizing\",\n u\"glamour\": u\"glamor\",\n u\"globalisation\": u\"globalization\",\n u\"globalise\": u\"globalize\",\n u\"globalised\": u\"globalized\",\n u\"globalises\": u\"globalizes\",\n u\"globalising\": u\"globalizing\",\n u\"glueing\": u\"gluing\",\n u\"goitre\": u\"goiter\",\n u\"goitres\": u\"goiters\",\n u\"gonorrhoea\": u\"gonorrhea\",\n u\"gramme\": u\"gram\",\n u\"grammes\": u\"grams\",\n u\"gravelled\": u\"graveled\",\n u\"grey\": u\"gray\",\n u\"greyed\": u\"grayed\",\n u\"greying\": u\"graying\",\n u\"greyish\": u\"grayish\",\n u\"greyness\": u\"grayness\",\n u\"greys\": u\"grays\",\n u\"grovelled\": u\"groveled\",\n u\"grovelling\": u\"groveling\",\n u\"groyne\": u\"groin\",\n u\"groynes\": u\"groins\",\n u\"gruelling\": u\"grueling\",\n u\"gruellingly\": u\"gruelingly\",\n u\"gryphon\": u\"griffin\",\n u\"gryphons\": u\"griffins\",\n u\"gynaecological\": u\"gynecological\",\n u\"gynaecologist\": u\"gynecologist\",\n u\"gynaecologists\": u\"gynecologists\",\n u\"gynaecology\": u\"gynecology\",\n u\"haematological\": u\"hematological\",\n u\"haematologist\": u\"hematologist\",\n u\"haematologists\": u\"hematologists\",\n u\"haematology\": u\"hematology\",\n u\"haemoglobin\": u\"hemoglobin\",\n u\"haemophilia\": u\"hemophilia\",\n u\"haemophiliac\": u\"hemophiliac\",\n u\"haemophiliacs\": u\"hemophiliacs\",\n u\"haemorrhage\": u\"hemorrhage\",\n u\"haemorrhaged\": u\"hemorrhaged\",\n u\"haemorrhages\": u\"hemorrhages\",\n u\"haemorrhaging\": u\"hemorrhaging\",\n u\"haemorrhoids\": u\"hemorrhoids\",\n u\"harbour\": u\"harbor\",\n u\"harboured\": u\"harbored\",\n u\"harbouring\": u\"harboring\",\n u\"harbours\": u\"harbors\",\n u\"harmonisation\": u\"harmonization\",\n u\"harmonise\": u\"harmonize\",\n u\"harmonised\": u\"harmonized\",\n u\"harmonises\": u\"harmonizes\",\n u\"harmonising\": u\"harmonizing\",\n u\"homoeopath\": u\"homeopath\",\n u\"homoeopathic\": u\"homeopathic\",\n u\"homoeopaths\": u\"homeopaths\",\n u\"homoeopathy\": u\"homeopathy\",\n u\"homogenise\": u\"homogenize\",\n u\"homogenised\": u\"homogenized\",\n u\"homogenises\": u\"homogenizes\",\n u\"homogenising\": u\"homogenizing\",\n u\"honour\": u\"honor\",\n u\"honourable\": u\"honorable\",\n u\"honourably\": u\"honorably\",\n u\"honoured\": u\"honored\",\n u\"honouring\": u\"honoring\",\n u\"honours\": u\"honors\",\n u\"hospitalisation\": u\"hospitalization\",\n u\"hospitalise\": u\"hospitalize\",\n u\"hospitalised\": u\"hospitalized\",\n u\"hospitalises\": u\"hospitalizes\",\n u\"hospitalising\": u\"hospitalizing\",\n u\"humanise\": u\"humanize\",\n u\"humanised\": u\"humanized\",\n u\"humanises\": u\"humanizes\",\n u\"humanising\": u\"humanizing\",\n u\"humour\": u\"humor\",\n u\"humoured\": u\"humored\",\n u\"humouring\": u\"humoring\",\n u\"humourless\": u\"humorless\",\n u\"humours\": u\"humors\",\n u\"hybridise\": u\"hybridize\",\n u\"hybridised\": u\"hybridized\",\n u\"hybridises\": u\"hybridizes\",\n u\"hybridising\": u\"hybridizing\",\n u\"hypnotise\": u\"hypnotize\",\n u\"hypnotised\": u\"hypnotized\",\n u\"hypnotises\": u\"hypnotizes\",\n u\"hypnotising\": u\"hypnotizing\",\n u\"hypothesise\": u\"hypothesize\",\n u\"hypothesised\": u\"hypothesized\",\n u\"hypothesises\": u\"hypothesizes\",\n u\"hypothesising\": u\"hypothesizing\",\n u\"idealisation\": u\"idealization\",\n u\"idealise\": u\"idealize\",\n u\"idealised\": u\"idealized\",\n u\"idealises\": u\"idealizes\",\n u\"idealising\": u\"idealizing\",\n u\"idolise\": u\"idolize\",\n u\"idolised\": u\"idolized\",\n u\"idolises\": u\"idolizes\",\n u\"idolising\": u\"idolizing\",\n u\"immobilisation\": u\"immobilization\",\n u\"immobilise\": u\"immobilize\",\n u\"immobilised\": u\"immobilized\",\n u\"immobiliser\": u\"immobilizer\",\n u\"immobilisers\": u\"immobilizers\",\n u\"immobilises\": u\"immobilizes\",\n u\"immobilising\": u\"immobilizing\",\n u\"immortalise\": u\"immortalize\",\n u\"immortalised\": u\"immortalized\",\n u\"immortalises\": u\"immortalizes\",\n u\"immortalising\": u\"immortalizing\",\n u\"immunisation\": u\"immunization\",\n u\"immunise\": u\"immunize\",\n u\"immunised\": u\"immunized\",\n u\"immunises\": u\"immunizes\",\n u\"immunising\": u\"immunizing\",\n u\"impanelled\": u\"impaneled\",\n u\"impanelling\": u\"impaneling\",\n u\"imperilled\": u\"imperiled\",\n u\"imperilling\": u\"imperiling\",\n u\"individualise\": u\"individualize\",\n u\"individualised\": u\"individualized\",\n u\"individualises\": u\"individualizes\",\n u\"individualising\": u\"individualizing\",\n u\"industrialise\": u\"industrialize\",\n u\"industrialised\": u\"industrialized\",\n u\"industrialises\": u\"industrializes\",\n u\"industrialising\": u\"industrializing\",\n u\"inflexion\": u\"inflection\",\n u\"inflexions\": u\"inflections\",\n u\"initialise\": u\"initialize\",\n u\"initialised\": u\"initialized\",\n u\"initialises\": u\"initializes\",\n u\"initialising\": u\"initializing\",\n u\"initialled\": u\"initialed\",\n u\"initialling\": u\"initialing\",\n u\"instal\": u\"install\",\n u\"instalment\": u\"installment\",\n u\"instalments\": u\"installments\",\n u\"instals\": u\"installs\",\n u\"instil\": u\"instill\",\n u\"instils\": u\"instills\",\n u\"institutionalisation\": u\"institutionalization\",\n u\"institutionalise\": u\"institutionalize\",\n u\"institutionalised\": u\"institutionalized\",\n u\"institutionalises\": u\"institutionalizes\",\n u\"institutionalising\": u\"institutionalizing\",\n u\"intellectualise\": u\"intellectualize\",\n u\"intellectualised\": u\"intellectualized\",\n u\"intellectualises\": u\"intellectualizes\",\n u\"intellectualising\": u\"intellectualizing\",\n u\"internalisation\": u\"internalization\",\n u\"internalise\": u\"internalize\",\n u\"internalised\": u\"internalized\",\n u\"internalises\": u\"internalizes\",\n u\"internalising\": u\"internalizing\",\n u\"internationalisation\": u\"internationalization\",\n u\"internationalise\": u\"internationalize\",\n u\"internationalised\": u\"internationalized\",\n u\"internationalises\": u\"internationalizes\",\n u\"internationalising\": u\"internationalizing\",\n u\"ionisation\": u\"ionization\",\n u\"ionise\": u\"ionize\",\n u\"ionised\": u\"ionized\",\n u\"ioniser\": u\"ionizer\",\n u\"ionisers\": u\"ionizers\",\n u\"ionises\": u\"ionizes\",\n u\"ionising\": u\"ionizing\",\n u\"italicise\": u\"italicize\",\n u\"italicised\": u\"italicized\",\n u\"italicises\": u\"italicizes\",\n u\"italicising\": u\"italicizing\",\n u\"itemise\": u\"itemize\",\n u\"itemised\": u\"itemized\",\n u\"itemises\": u\"itemizes\",\n u\"itemising\": u\"itemizing\",\n u\"jeopardise\": u\"jeopardize\",\n u\"jeopardised\": u\"jeopardized\",\n u\"jeopardises\": u\"jeopardizes\",\n u\"jeopardising\": u\"jeopardizing\",\n u\"jewelled\": u\"jeweled\",\n u\"jeweller\": u\"jeweler\",\n u\"jewellers\": u\"jewelers\",\n u\"jewellery\": u\"jewelry\",\n u\"judgement\": u\"judgment\",\n u\"kilogramme\": u\"kilogram\",\n u\"kilogrammes\": u\"kilograms\",\n u\"kilometre\": u\"kilometer\",\n u\"kilometres\": u\"kilometers\",\n u\"labelled\": u\"labeled\",\n u\"labelling\": u\"labeling\",\n u\"labour\": u\"labor\",\n u\"laboured\": u\"labored\",\n u\"labourer\": u\"laborer\",\n u\"labourers\": u\"laborers\",\n u\"labouring\": u\"laboring\",\n u\"labours\": u\"labors\",\n u\"lacklustre\": u\"lackluster\",\n u\"legalisation\": u\"legalization\",\n u\"legalise\": u\"legalize\",\n u\"legalised\": u\"legalized\",\n u\"legalises\": u\"legalizes\",\n u\"legalising\": u\"legalizing\",\n u\"legitimise\": u\"legitimize\",\n u\"legitimised\": u\"legitimized\",\n u\"legitimises\": u\"legitimizes\",\n u\"legitimising\": u\"legitimizing\",\n u\"leukaemia\": u\"leukemia\",\n u\"levelled\": u\"leveled\",\n u\"leveller\": u\"leveler\",\n u\"levellers\": u\"levelers\",\n u\"levelling\": u\"leveling\",\n u\"libelled\": u\"libeled\",\n u\"libelling\": u\"libeling\",\n u\"libellous\": u\"libelous\",\n u\"liberalisation\": u\"liberalization\",\n u\"liberalise\": u\"liberalize\",\n u\"liberalised\": u\"liberalized\",\n u\"liberalises\": u\"liberalizes\",\n u\"liberalising\": u\"liberalizing\",\n u\"licence\": u\"license\",\n u\"licenced\": u\"licensed\",\n u\"licences\": u\"licenses\",\n u\"licencing\": u\"licensing\",\n u\"likeable\": u\"likable\",\n u\"lionisation\": u\"lionization\",\n u\"lionise\": u\"lionize\",\n u\"lionised\": u\"lionized\",\n u\"lionises\": u\"lionizes\",\n u\"lionising\": u\"lionizing\",\n u\"liquidise\": u\"liquidize\",\n u\"liquidised\": u\"liquidized\",\n u\"liquidiser\": u\"liquidizer\",\n u\"liquidisers\": u\"liquidizers\",\n u\"liquidises\": u\"liquidizes\",\n u\"liquidising\": u\"liquidizing\",\n u\"litre\": u\"liter\",\n u\"litres\": u\"liters\",\n u\"localise\": u\"localize\",\n u\"localised\": u\"localized\",\n u\"localises\": u\"localizes\",\n u\"localising\": u\"localizing\",\n u\"louvre\": u\"louver\",\n u\"louvred\": u\"louvered\",\n u\"louvres\": u\"louvers\",\n u\"lustre\": u\"luster\",\n u\"magnetise\": u\"magnetize\",\n u\"magnetised\": u\"magnetized\",\n u\"magnetises\": u\"magnetizes\",\n u\"magnetising\": u\"magnetizing\",\n u\"manoeuvrability\": u\"maneuverability\",\n u\"manoeuvrable\": u\"maneuverable\",\n u\"manoeuvre\": u\"maneuver\",\n u\"manoeuvred\": u\"maneuvered\",\n u\"manoeuvres\": u\"maneuvers\",\n u\"manoeuvring\": u\"maneuvering\",\n u\"manoeuvrings\": u\"maneuverings\",\n u\"marginalisation\": u\"marginalization\",\n u\"marginalise\": u\"marginalize\",\n u\"marginalised\": u\"marginalized\",\n u\"marginalises\": u\"marginalizes\",\n u\"marginalising\": u\"marginalizing\",\n u\"marshalled\": u\"marshaled\",\n u\"marshalling\": u\"marshaling\",\n u\"marvelled\": u\"marveled\",\n u\"marvelling\": u\"marveling\",\n u\"marvellous\": u\"marvelous\",\n u\"marvellously\": u\"marvelously\",\n u\"materialisation\": u\"materialization\",\n u\"materialise\": u\"materialize\",\n u\"materialised\": u\"materialized\",\n u\"materialises\": u\"materializes\",\n u\"materialising\": u\"materializing\",\n u\"maximisation\": u\"maximization\",\n u\"maximise\": u\"maximize\",\n u\"maximised\": u\"maximized\",\n u\"maximises\": u\"maximizes\",\n u\"maximising\": u\"maximizing\",\n u\"meagre\": u\"meager\",\n u\"mechanisation\": u\"mechanization\",\n u\"mechanise\": u\"mechanize\",\n u\"mechanised\": u\"mechanized\",\n u\"mechanises\": u\"mechanizes\",\n u\"mechanising\": u\"mechanizing\",\n u\"mediaeval\": u\"medieval\",\n u\"memorialise\": u\"memorialize\",\n u\"memorialised\": u\"memorialized\",\n u\"memorialises\": u\"memorializes\",\n u\"memorialising\": u\"memorializing\",\n u\"memorise\": u\"memorize\",\n u\"memorised\": u\"memorized\",\n u\"memorises\": u\"memorizes\",\n u\"memorising\": u\"memorizing\",\n u\"mesmerise\": u\"mesmerize\",\n u\"mesmerised\": u\"mesmerized\",\n u\"mesmerises\": u\"mesmerizes\",\n u\"mesmerising\": u\"mesmerizing\",\n u\"metabolise\": u\"metabolize\",\n u\"metabolised\": u\"metabolized\",\n u\"metabolises\": u\"metabolizes\",\n u\"metabolising\": u\"metabolizing\",\n u\"metre\": u\"meter\",\n u\"metres\": u\"meters\",\n u\"micrometre\": u\"micrometer\",\n u\"micrometres\": u\"micrometers\",\n u\"militarise\": u\"militarize\",\n u\"militarised\": u\"militarized\",\n u\"militarises\": u\"militarizes\",\n u\"militarising\": u\"militarizing\",\n u\"milligramme\": u\"milligram\",\n u\"milligrammes\": u\"milligrams\",\n u\"millilitre\": u\"milliliter\",\n u\"millilitres\": u\"milliliters\",\n u\"millimetre\": u\"millimeter\",\n u\"millimetres\": u\"millimeters\",\n u\"miniaturisation\": u\"miniaturization\",\n u\"miniaturise\": u\"miniaturize\",\n u\"miniaturised\": u\"miniaturized\",\n u\"miniaturises\": u\"miniaturizes\",\n u\"miniaturising\": u\"miniaturizing\",\n u\"minibuses\": u\"minibusses\",\n u\"minimise\": u\"minimize\",\n u\"minimised\": u\"minimized\",\n u\"minimises\": u\"minimizes\",\n u\"minimising\": u\"minimizing\",\n u\"misbehaviour\": u\"misbehavior\",\n u\"misdemeanour\": u\"misdemeanor\",\n u\"misdemeanours\": u\"misdemeanors\",\n u\"misspelt\": u\"misspelled\",\n u\"mitre\": u\"miter\",\n u\"mitres\": u\"miters\",\n u\"mobilisation\": u\"mobilization\",\n u\"mobilise\": u\"mobilize\",\n u\"mobilised\": u\"mobilized\",\n u\"mobilises\": u\"mobilizes\",\n u\"mobilising\": u\"mobilizing\",\n u\"modelled\": u\"modeled\",\n u\"modeller\": u\"modeler\",\n u\"modellers\": u\"modelers\",\n u\"modelling\": u\"modeling\",\n u\"modernise\": u\"modernize\",\n u\"modernised\": u\"modernized\",\n u\"modernises\": u\"modernizes\",\n u\"modernising\": u\"modernizing\",\n u\"moisturise\": u\"moisturize\",\n u\"moisturised\": u\"moisturized\",\n u\"moisturiser\": u\"moisturizer\",\n u\"moisturisers\": u\"moisturizers\",\n u\"moisturises\": u\"moisturizes\",\n u\"moisturising\": u\"moisturizing\",\n u\"monologue\": u\"monolog\",\n u\"monologues\": u\"monologs\",\n u\"monopolisation\": u\"monopolization\",\n u\"monopolise\": u\"monopolize\",\n u\"monopolised\": u\"monopolized\",\n u\"monopolises\": u\"monopolizes\",\n u\"monopolising\": u\"monopolizing\",\n u\"moralise\": u\"moralize\",\n u\"moralised\": u\"moralized\",\n u\"moralises\": u\"moralizes\",\n u\"moralising\": u\"moralizing\",\n u\"motorised\": u\"motorized\",\n u\"mould\": u\"mold\",\n u\"moulded\": u\"molded\",\n u\"moulder\": u\"molder\",\n u\"mouldered\": u\"moldered\",\n u\"mouldering\": u\"moldering\",\n u\"moulders\": u\"molders\",\n u\"mouldier\": u\"moldier\",\n u\"mouldiest\": u\"moldiest\",\n u\"moulding\": u\"molding\",\n u\"mouldings\": u\"moldings\",\n u\"moulds\": u\"molds\",\n u\"mouldy\": u\"moldy\",\n u\"moult\": u\"molt\",\n u\"moulted\": u\"molted\",\n u\"moulting\": u\"molting\",\n u\"moults\": u\"molts\",\n u\"moustache\": u\"mustache\",\n u\"moustached\": u\"mustached\",\n u\"moustaches\": u\"mustaches\",\n u\"moustachioed\": u\"mustachioed\",\n u\"multicoloured\": u\"multicolored\",\n u\"nationalisation\": u\"nationalization\",\n u\"nationalisations\": u\"nationalizations\",\n u\"nationalise\": u\"nationalize\",\n u\"nationalised\": u\"nationalized\",\n u\"nationalises\": u\"nationalizes\",\n u\"nationalising\": u\"nationalizing\",\n u\"naturalisation\": u\"naturalization\",\n u\"naturalise\": u\"naturalize\",\n u\"naturalised\": u\"naturalized\",\n u\"naturalises\": u\"naturalizes\",\n u\"naturalising\": u\"naturalizing\",\n u\"neighbour\": u\"neighbor\",\n u\"neighbourhood\": u\"neighborhood\",\n u\"neighbourhoods\": u\"neighborhoods\",\n u\"neighbouring\": u\"neighboring\",\n u\"neighbourliness\": u\"neighborliness\",\n u\"neighbourly\": u\"neighborly\",\n u\"neighbours\": u\"neighbors\",\n u\"neutralisation\": u\"neutralization\",\n u\"neutralise\": u\"neutralize\",\n u\"neutralised\": u\"neutralized\",\n u\"neutralises\": u\"neutralizes\",\n u\"neutralising\": u\"neutralizing\",\n u\"normalisation\": u\"normalization\",\n u\"normalise\": u\"normalize\",\n u\"normalised\": u\"normalized\",\n u\"normalises\": u\"normalizes\",\n u\"normalising\": u\"normalizing\",\n u\"odour\": u\"odor\",\n u\"odourless\": u\"odorless\",\n u\"odours\": u\"odors\",\n u\"oesophagus\": u\"esophagus\",\n u\"oesophaguses\": u\"esophaguses\",\n u\"oestrogen\": u\"estrogen\",\n u\"offence\": u\"offense\",\n u\"offences\": u\"offenses\",\n u\"omelette\": u\"omelet\",\n u\"omelettes\": u\"omelets\",\n u\"optimise\": u\"optimize\",\n u\"optimised\": u\"optimized\",\n u\"optimises\": u\"optimizes\",\n u\"optimising\": u\"optimizing\",\n u\"organisation\": u\"organization\",\n u\"organisational\": u\"organizational\",\n u\"organisations\": u\"organizations\",\n u\"organise\": u\"organize\",\n u\"organised\": u\"organized\",\n u\"organiser\": u\"organizer\",\n u\"organisers\": u\"organizers\",\n u\"organises\": u\"organizes\",\n u\"organising\": u\"organizing\",\n u\"orthopaedic\": u\"orthopedic\",\n u\"orthopaedics\": u\"orthopedics\",\n u\"ostracise\": u\"ostracize\",\n u\"ostracised\": u\"ostracized\",\n u\"ostracises\": u\"ostracizes\",\n u\"ostracising\": u\"ostracizing\",\n u\"outmanoeuvre\": u\"outmaneuver\",\n u\"outmanoeuvred\": u\"outmaneuvered\",\n u\"outmanoeuvres\": u\"outmaneuvers\",\n u\"outmanoeuvring\": u\"outmaneuvering\",\n u\"overemphasise\": u\"overemphasize\",\n u\"overemphasised\": u\"overemphasized\",\n u\"overemphasises\": u\"overemphasizes\",\n u\"overemphasising\": u\"overemphasizing\",\n u\"oxidisation\": u\"oxidization\",\n u\"oxidise\": u\"oxidize\",\n u\"oxidised\": u\"oxidized\",\n u\"oxidises\": u\"oxidizes\",\n u\"oxidising\": u\"oxidizing\",\n u\"paederast\": u\"pederast\",\n u\"paederasts\": u\"pederasts\",\n u\"paediatric\": u\"pediatric\",\n u\"paediatrician\": u\"pediatrician\",\n u\"paediatricians\": u\"pediatricians\",\n u\"paediatrics\": u\"pediatrics\",\n u\"paedophile\": u\"pedophile\",\n u\"paedophiles\": u\"pedophiles\",\n u\"paedophilia\": u\"pedophilia\",\n u\"palaeolithic\": u\"paleolithic\",\n u\"palaeontologist\": u\"paleontologist\",\n u\"palaeontologists\": u\"paleontologists\",\n u\"palaeontology\": u\"paleontology\",\n u\"panelled\": u\"paneled\",\n u\"panelling\": u\"paneling\",\n u\"panellist\": u\"panelist\",\n u\"panellists\": u\"panelists\",\n u\"paralyse\": u\"paralyze\",\n u\"paralysed\": u\"paralyzed\",\n u\"paralyses\": u\"paralyzes\",\n u\"paralysing\": u\"paralyzing\",\n u\"parcelled\": u\"parceled\",\n u\"parcelling\": u\"parceling\",\n u\"parlour\": u\"parlor\",\n u\"parlours\": u\"parlors\",\n u\"particularise\": u\"particularize\",\n u\"particularised\": u\"particularized\",\n u\"particularises\": u\"particularizes\",\n u\"particularising\": u\"particularizing\",\n u\"passivisation\": u\"passivization\",\n u\"passivise\": u\"passivize\",\n u\"passivised\": u\"passivized\",\n u\"passivises\": u\"passivizes\",\n u\"passivising\": u\"passivizing\",\n u\"pasteurisation\": u\"pasteurization\",\n u\"pasteurise\": u\"pasteurize\",\n u\"pasteurised\": u\"pasteurized\",\n u\"pasteurises\": u\"pasteurizes\",\n u\"pasteurising\": u\"pasteurizing\",\n u\"patronise\": u\"patronize\",\n u\"patronised\": u\"patronized\",\n u\"patronises\": u\"patronizes\",\n u\"patronising\": u\"patronizing\",\n u\"patronisingly\": u\"patronizingly\",\n u\"pedalled\": u\"pedaled\",\n u\"pedalling\": u\"pedaling\",\n u\"pedestrianisation\": u\"pedestrianization\",\n u\"pedestrianise\": u\"pedestrianize\",\n u\"pedestrianised\": u\"pedestrianized\",\n u\"pedestrianises\": u\"pedestrianizes\",\n u\"pedestrianising\": u\"pedestrianizing\",\n u\"penalise\": u\"penalize\",\n u\"penalised\": u\"penalized\",\n u\"penalises\": u\"penalizes\",\n u\"penalising\": u\"penalizing\",\n u\"pencilled\": u\"penciled\",\n u\"pencilling\": u\"penciling\",\n u\"personalise\": u\"personalize\",\n u\"personalised\": u\"personalized\",\n u\"personalises\": u\"personalizes\",\n u\"personalising\": u\"personalizing\",\n u\"pharmacopoeia\": u\"pharmacopeia\",\n u\"pharmacopoeias\": u\"pharmacopeias\",\n u\"philosophise\": u\"philosophize\",\n u\"philosophised\": u\"philosophized\",\n u\"philosophises\": u\"philosophizes\",\n u\"philosophising\": u\"philosophizing\",\n u\"philtre\": u\"filter\",\n u\"philtres\": u\"filters\",\n u\"phoney\": u\"phony\",\n u\"plagiarise\": u\"plagiarize\",\n u\"plagiarised\": u\"plagiarized\",\n u\"plagiarises\": u\"plagiarizes\",\n u\"plagiarising\": u\"plagiarizing\",\n u\"plough\": u\"plow\",\n u\"ploughed\": u\"plowed\",\n u\"ploughing\": u\"plowing\",\n u\"ploughman\": u\"plowman\",\n u\"ploughmen\": u\"plowmen\",\n u\"ploughs\": u\"plows\",\n u\"ploughshare\": u\"plowshare\",\n u\"ploughshares\": u\"plowshares\",\n u\"polarisation\": u\"polarization\",\n u\"polarise\": u\"polarize\",\n u\"polarised\": u\"polarized\",\n u\"polarises\": u\"polarizes\",\n u\"polarising\": u\"polarizing\",\n u\"politicisation\": u\"politicization\",\n u\"politicise\": u\"politicize\",\n u\"politicised\": u\"politicized\",\n u\"politicises\": u\"politicizes\",\n u\"politicising\": u\"politicizing\",\n u\"popularisation\": u\"popularization\",\n u\"popularise\": u\"popularize\",\n u\"popularised\": u\"popularized\",\n u\"popularises\": u\"popularizes\",\n u\"popularising\": u\"popularizing\",\n u\"pouffe\": u\"pouf\",\n u\"pouffes\": u\"poufs\",\n u\"practise\": u\"practice\",\n u\"practised\": u\"practiced\",\n u\"practises\": u\"practices\",\n u\"practising\": u\"practicing\",\n u\"praesidium\": u\"presidium\",\n u\"praesidiums\": u\"presidiums\",\n u\"pressurisation\": u\"pressurization\",\n u\"pressurise\": u\"pressurize\",\n u\"pressurised\": u\"pressurized\",\n u\"pressurises\": u\"pressurizes\",\n u\"pressurising\": u\"pressurizing\",\n u\"pretence\": u\"pretense\",\n u\"pretences\": u\"pretenses\",\n u\"primaeval\": u\"primeval\",\n u\"prioritisation\": u\"prioritization\",\n u\"prioritise\": u\"prioritize\",\n u\"prioritised\": u\"prioritized\",\n u\"prioritises\": u\"prioritizes\",\n u\"prioritising\": u\"prioritizing\",\n u\"privatisation\": u\"privatization\",\n u\"privatisations\": u\"privatizations\",\n u\"privatise\": u\"privatize\",\n u\"privatised\": u\"privatized\",\n u\"privatises\": u\"privatizes\",\n u\"privatising\": u\"privatizing\",\n u\"professionalisation\": u\"professionalization\",\n u\"professionalise\": u\"professionalize\",\n u\"professionalised\": u\"professionalized\",\n u\"professionalises\": u\"professionalizes\",\n u\"professionalising\": u\"professionalizing\",\n u\"programme\": u\"program\",\n u\"programmes\": u\"programs\",\n u\"prologue\": u\"prolog\",\n u\"prologues\": u\"prologs\",\n u\"propagandise\": u\"propagandize\",\n u\"propagandised\": u\"propagandized\",\n u\"propagandises\": u\"propagandizes\",\n u\"propagandising\": u\"propagandizing\",\n u\"proselytise\": u\"proselytize\",\n u\"proselytised\": u\"proselytized\",\n u\"proselytiser\": u\"proselytizer\",\n u\"proselytisers\": u\"proselytizers\",\n u\"proselytises\": u\"proselytizes\",\n u\"proselytising\": u\"proselytizing\",\n u\"psychoanalyse\": u\"psychoanalyze\",\n u\"psychoanalysed\": u\"psychoanalyzed\",\n u\"psychoanalyses\": u\"psychoanalyzes\",\n u\"psychoanalysing\": u\"psychoanalyzing\",\n u\"publicise\": u\"publicize\",\n u\"publicised\": u\"publicized\",\n u\"publicises\": u\"publicizes\",\n u\"publicising\": u\"publicizing\",\n u\"pulverisation\": u\"pulverization\",\n u\"pulverise\": u\"pulverize\",\n u\"pulverised\": u\"pulverized\",\n u\"pulverises\": u\"pulverizes\",\n u\"pulverising\": u\"pulverizing\",\n u\"pummelled\": u\"pummel\",\n u\"pummelling\": u\"pummeled\",\n u\"pyjama\": u\"pajama\",\n u\"pyjamas\": u\"pajamas\",\n u\"pzazz\": u\"pizzazz\",\n u\"quarrelled\": u\"quarreled\",\n u\"quarrelling\": u\"quarreling\",\n u\"radicalise\": u\"radicalize\",\n u\"radicalised\": u\"radicalized\",\n u\"radicalises\": u\"radicalizes\",\n u\"radicalising\": u\"radicalizing\",\n u\"rancour\": u\"rancor\",\n u\"randomise\": u\"randomize\",\n u\"randomised\": u\"randomized\",\n u\"randomises\": u\"randomizes\",\n u\"randomising\": u\"randomizing\",\n u\"rationalisation\": u\"rationalization\",\n u\"rationalisations\": u\"rationalizations\",\n u\"rationalise\": u\"rationalize\",\n u\"rationalised\": u\"rationalized\",\n u\"rationalises\": u\"rationalizes\",\n u\"rationalising\": u\"rationalizing\",\n u\"ravelled\": u\"raveled\",\n u\"ravelling\": u\"raveling\",\n u\"realisable\": u\"realizable\",\n u\"realisation\": u\"realization\",\n u\"realisations\": u\"realizations\",\n u\"realise\": u\"realize\",\n u\"realised\": u\"realized\",\n u\"realises\": u\"realizes\",\n u\"realising\": u\"realizing\",\n u\"recognisable\": u\"recognizable\",\n u\"recognisably\": u\"recognizably\",\n u\"recognisance\": u\"recognizance\",\n u\"recognise\": u\"recognize\",\n u\"recognised\": u\"recognized\",\n u\"recognises\": u\"recognizes\",\n u\"recognising\": u\"recognizing\",\n u\"reconnoitre\": u\"reconnoiter\",\n u\"reconnoitred\": u\"reconnoitered\",\n u\"reconnoitres\": u\"reconnoiters\",\n u\"reconnoitring\": u\"reconnoitering\",\n u\"refuelled\": u\"refueled\",\n u\"refuelling\": u\"refueling\",\n u\"regularisation\": u\"regularization\",\n u\"regularise\": u\"regularize\",\n u\"regularised\": u\"regularized\",\n u\"regularises\": u\"regularizes\",\n u\"regularising\": u\"regularizing\",\n u\"remodelled\": u\"remodeled\",\n u\"remodelling\": u\"remodeling\",\n u\"remould\": u\"remold\",\n u\"remoulded\": u\"remolded\",\n u\"remoulding\": u\"remolding\",\n u\"remoulds\": u\"remolds\",\n u\"reorganisation\": u\"reorganization\",\n u\"reorganisations\": u\"reorganizations\",\n u\"reorganise\": u\"reorganize\",\n u\"reorganised\": u\"reorganized\",\n u\"reorganises\": u\"reorganizes\",\n u\"reorganising\": u\"reorganizing\",\n u\"revelled\": u\"reveled\",\n u\"reveller\": u\"reveler\",\n u\"revellers\": u\"revelers\",\n u\"revelling\": u\"reveling\",\n u\"revitalise\": u\"revitalize\",\n u\"revitalised\": u\"revitalized\",\n u\"revitalises\": u\"revitalizes\",\n u\"revitalising\": u\"revitalizing\",\n u\"revolutionise\": u\"revolutionize\",\n u\"revolutionised\": u\"revolutionized\",\n u\"revolutionises\": u\"revolutionizes\",\n u\"revolutionising\": u\"revolutionizing\",\n u\"rhapsodise\": u\"rhapsodize\",\n u\"rhapsodised\": u\"rhapsodized\",\n u\"rhapsodises\": u\"rhapsodizes\",\n u\"rhapsodising\": u\"rhapsodizing\",\n u\"rigour\": u\"rigor\",\n u\"rigours\": u\"rigors\",\n u\"ritualised\": u\"ritualized\",\n u\"rivalled\": u\"rivaled\",\n u\"rivalling\": u\"rivaling\",\n u\"romanticise\": u\"romanticize\",\n u\"romanticised\": u\"romanticized\",\n u\"romanticises\": u\"romanticizes\",\n u\"romanticising\": u\"romanticizing\",\n u\"rumour\": u\"rumor\",\n u\"rumoured\": u\"rumored\",\n u\"rumours\": u\"rumors\",\n u\"sabre\": u\"saber\",\n u\"sabres\": u\"sabers\",\n u\"saltpetre\": u\"saltpeter\",\n u\"sanitise\": u\"sanitize\",\n u\"sanitised\": u\"sanitized\",\n u\"sanitises\": u\"sanitizes\",\n u\"sanitising\": u\"sanitizing\",\n u\"satirise\": u\"satirize\",\n u\"satirised\": u\"satirized\",\n u\"satirises\": u\"satirizes\",\n u\"satirising\": u\"satirizing\",\n u\"saviour\": u\"savior\",\n u\"saviours\": u\"saviors\",\n u\"savour\": u\"savor\",\n u\"savoured\": u\"savored\",\n u\"savouries\": u\"savories\",\n u\"savouring\": u\"savoring\",\n u\"savours\": u\"savors\",\n u\"savoury\": u\"savory\",\n u\"scandalise\": u\"scandalize\",\n u\"scandalised\": u\"scandalized\",\n u\"scandalises\": u\"scandalizes\",\n u\"scandalising\": u\"scandalizing\",\n u\"sceptic\": u\"skeptic\",\n u\"sceptical\": u\"skeptical\",\n u\"sceptically\": u\"skeptically\",\n u\"scepticism\": u\"skepticism\",\n u\"sceptics\": u\"skeptics\",\n u\"sceptre\": u\"scepter\",\n u\"sceptres\": u\"scepters\",\n u\"scrutinise\": u\"scrutinize\",\n u\"scrutinised\": u\"scrutinized\",\n u\"scrutinises\": u\"scrutinizes\",\n u\"scrutinising\": u\"scrutinizing\",\n u\"secularisation\": u\"secularization\",\n u\"secularise\": u\"secularize\",\n u\"secularised\": u\"secularized\",\n u\"secularises\": u\"secularizes\",\n u\"secularising\": u\"secularizing\",\n u\"sensationalise\": u\"sensationalize\",\n u\"sensationalised\": u\"sensationalized\",\n u\"sensationalises\": u\"sensationalizes\",\n u\"sensationalising\": u\"sensationalizing\",\n u\"sensitise\": u\"sensitize\",\n u\"sensitised\": u\"sensitized\",\n u\"sensitises\": u\"sensitizes\",\n u\"sensitising\": u\"sensitizing\",\n u\"sentimentalise\": u\"sentimentalize\",\n u\"sentimentalised\": u\"sentimentalized\",\n u\"sentimentalises\": u\"sentimentalizes\",\n u\"sentimentalising\": u\"sentimentalizing\",\n u\"sepulchre\": u\"sepulcher\",\n u\"sepulchres\": u\"sepulchers\",\n u\"serialisation\": u\"serialization\",\n u\"serialisations\": u\"serializations\",\n u\"serialise\": u\"serialize\",\n u\"serialised\": u\"serialized\",\n u\"serialises\": u\"serializes\",\n u\"serialising\": u\"serializing\",\n u\"sermonise\": u\"sermonize\",\n u\"sermonised\": u\"sermonized\",\n u\"sermonises\": u\"sermonizes\",\n u\"sermonising\": u\"sermonizing\",\n u\"sheikh\": u\"sheik\",\n u\"shovelled\": u\"shoveled\",\n u\"shovelling\": u\"shoveling\",\n u\"shrivelled\": u\"shriveled\",\n u\"shrivelling\": u\"shriveling\",\n u\"signalise\": u\"signalize\",\n u\"signalised\": u\"signalized\",\n u\"signalises\": u\"signalizes\",\n u\"signalising\": u\"signalizing\",\n u\"signalled\": u\"signaled\",\n u\"signalling\": u\"signaling\",\n u\"smoulder\": u\"smolder\",\n u\"smouldered\": u\"smoldered\",\n u\"smouldering\": u\"smoldering\",\n u\"smoulders\": u\"smolders\",\n u\"snivelled\": u\"sniveled\",\n u\"snivelling\": u\"sniveling\",\n u\"snorkelled\": u\"snorkeled\",\n u\"snorkelling\": u\"snorkeling\",\n u\"snowplough\": u\"snowplow\",\n u\"snowploughs\": u\"snowplow\",\n u\"socialisation\": u\"socialization\",\n u\"socialise\": u\"socialize\",\n u\"socialised\": u\"socialized\",\n u\"socialises\": u\"socializes\",\n u\"socialising\": u\"socializing\",\n u\"sodomise\": u\"sodomize\",\n u\"sodomised\": u\"sodomized\",\n u\"sodomises\": u\"sodomizes\",\n u\"sodomising\": u\"sodomizing\",\n u\"solemnise\": u\"solemnize\",\n u\"solemnised\": u\"solemnized\",\n u\"solemnises\": u\"solemnizes\",\n u\"solemnising\": u\"solemnizing\",\n u\"sombre\": u\"somber\",\n u\"specialisation\": u\"specialization\",\n u\"specialisations\": u\"specializations\",\n u\"specialise\": u\"specialize\",\n u\"specialised\": u\"specialized\",\n u\"specialises\": u\"specializes\",\n u\"specialising\": u\"specializing\",\n u\"spectre\": u\"specter\",\n u\"spectres\": u\"specters\",\n u\"spiralled\": u\"spiraled\",\n u\"spiralling\": u\"spiraling\",\n u\"splendour\": u\"splendor\",\n u\"splendours\": u\"splendors\",\n u\"squirrelled\": u\"squirreled\",\n u\"squirrelling\": u\"squirreling\",\n u\"stabilisation\": u\"stabilization\",\n u\"stabilise\": u\"stabilize\",\n u\"stabilised\": u\"stabilized\",\n u\"stabiliser\": u\"stabilizer\",\n u\"stabilisers\": u\"stabilizers\",\n u\"stabilises\": u\"stabilizes\",\n u\"stabilising\": u\"stabilizing\",\n u\"standardisation\": u\"standardization\",\n u\"standardise\": u\"standardize\",\n u\"standardised\": u\"standardized\",\n u\"standardises\": u\"standardizes\",\n u\"standardising\": u\"standardizing\",\n u\"stencilled\": u\"stenciled\",\n u\"stencilling\": u\"stenciling\",\n u\"sterilisation\": u\"sterilization\",\n u\"sterilisations\": u\"sterilizations\",\n u\"sterilise\": u\"sterilize\",\n u\"sterilised\": u\"sterilized\",\n u\"steriliser\": u\"sterilizer\",\n u\"sterilisers\": u\"sterilizers\",\n u\"sterilises\": u\"sterilizes\",\n u\"sterilising\": u\"sterilizing\",\n u\"stigmatisation\": u\"stigmatization\",\n u\"stigmatise\": u\"stigmatize\",\n u\"stigmatised\": u\"stigmatized\",\n u\"stigmatises\": u\"stigmatizes\",\n u\"stigmatising\": u\"stigmatizing\",\n u\"storey\": u\"story\",\n u\"storeys\": u\"stories\",\n u\"subsidisation\": u\"subsidization\",\n u\"subsidise\": u\"subsidize\",\n u\"subsidised\": u\"subsidized\",\n u\"subsidiser\": u\"subsidizer\",\n u\"subsidisers\": u\"subsidizers\",\n u\"subsidises\": u\"subsidizes\",\n u\"subsidising\": u\"subsidizing\",\n u\"succour\": u\"succor\",\n u\"succoured\": u\"succored\",\n u\"succouring\": u\"succoring\",\n u\"succours\": u\"succors\",\n u\"sulphate\": u\"sulfate\",\n u\"sulphates\": u\"sulfates\",\n u\"sulphide\": u\"sulfide\",\n u\"sulphides\": u\"sulfides\",\n u\"sulphur\": u\"sulfur\",\n u\"sulphurous\": u\"sulfurous\",\n u\"summarise\": u\"summarize\",\n u\"summarised\": u\"summarized\",\n u\"summarises\": u\"summarizes\",\n u\"summarising\": u\"summarizing\",\n u\"swivelled\": u\"swiveled\",\n u\"swivelling\": u\"swiveling\",\n u\"symbolise\": u\"symbolize\",\n u\"symbolised\": u\"symbolized\",\n u\"symbolises\": u\"symbolizes\",\n u\"symbolising\": u\"symbolizing\",\n u\"sympathise\": u\"sympathize\",\n u\"sympathised\": u\"sympathized\",\n u\"sympathiser\": u\"sympathizer\",\n u\"sympathisers\": u\"sympathizers\",\n u\"sympathises\": u\"sympathizes\",\n u\"sympathising\": u\"sympathizing\",\n u\"synchronisation\": u\"synchronization\",\n u\"synchronise\": u\"synchronize\",\n u\"synchronised\": u\"synchronized\",\n u\"synchronises\": u\"synchronizes\",\n u\"synchronising\": u\"synchronizing\",\n u\"synthesise\": u\"synthesize\",\n u\"synthesised\": u\"synthesized\",\n u\"synthesiser\": u\"synthesizer\",\n u\"synthesisers\": u\"synthesizers\",\n u\"synthesises\": u\"synthesizes\",\n u\"synthesising\": u\"synthesizing\",\n u\"syphon\": u\"siphon\",\n u\"syphoned\": u\"siphoned\",\n u\"syphoning\": u\"siphoning\",\n u\"syphons\": u\"siphons\",\n u\"systematisation\": u\"systematization\",\n u\"systematise\": u\"systematize\",\n u\"systematised\": u\"systematized\",\n u\"systematises\": u\"systematizes\",\n u\"systematising\": u\"systematizing\",\n u\"tantalise\": u\"tantalize\",\n u\"tantalised\": u\"tantalized\",\n u\"tantalises\": u\"tantalizes\",\n u\"tantalising\": u\"tantalizing\",\n u\"tantalisingly\": u\"tantalizingly\",\n u\"tasselled\": u\"tasseled\",\n u\"technicolour\": u\"technicolor\",\n u\"temporise\": u\"temporize\",\n u\"temporised\": u\"temporized\",\n u\"temporises\": u\"temporizes\",\n u\"temporising\": u\"temporizing\",\n u\"tenderise\": u\"tenderize\",\n u\"tenderised\": u\"tenderized\",\n u\"tenderises\": u\"tenderizes\",\n u\"tenderising\": u\"tenderizing\",\n u\"terrorise\": u\"terrorize\",\n u\"terrorised\": u\"terrorized\",\n u\"terrorises\": u\"terrorizes\",\n u\"terrorising\": u\"terrorizing\",\n u\"theatre\": u\"theater\",\n u\"theatregoer\": u\"theatergoer\",\n u\"theatregoers\": u\"theatergoers\",\n u\"theatres\": u\"theaters\",\n u\"theorise\": u\"theorize\",\n u\"theorised\": u\"theorized\",\n u\"theorises\": u\"theorizes\",\n u\"theorising\": u\"theorizing\",\n u\"tonne\": u\"ton\",\n u\"tonnes\": u\"tons\",\n u\"towelled\": u\"toweled\",\n u\"towelling\": u\"toweling\",\n u\"toxaemia\": u\"toxemia\",\n u\"tranquillise\": u\"tranquilize\",\n u\"tranquillised\": u\"tranquilized\",\n u\"tranquilliser\": u\"tranquilizer\",\n u\"tranquillisers\": u\"tranquilizers\",\n u\"tranquillises\": u\"tranquilizes\",\n u\"tranquillising\": u\"tranquilizing\",\n u\"tranquillity\": u\"tranquility\",\n u\"tranquillize\": u\"tranquilize\",\n u\"tranquillized\": u\"tranquilized\",\n u\"tranquillizer\": u\"tranquilizer\",\n u\"tranquillizers\": u\"tranquilizers\",\n u\"tranquillizes\": u\"tranquilizes\",\n u\"tranquillizing\": u\"tranquilizing\",\n u\"tranquilly\": u\"tranquility\",\n u\"transistorised\": u\"transistorized\",\n u\"traumatise\": u\"traumatize\",\n u\"traumatised\": u\"traumatized\",\n u\"traumatises\": u\"traumatizes\",\n u\"traumatising\": u\"traumatizing\",\n u\"travelled\": u\"traveled\",\n u\"traveller\": u\"traveler\",\n u\"travellers\": u\"travelers\",\n u\"travelling\": u\"traveling\",\n u\"travelogue\": u\"travelog\",\n u\"travelogues\": u\"travelogs\",\n u\"trialled\": u\"trialed\",\n u\"trialling\": u\"trialing\",\n u\"tricolour\": u\"tricolor\",\n u\"tricolours\": u\"tricolors\",\n u\"trivialise\": u\"trivialize\",\n u\"trivialised\": u\"trivialized\",\n u\"trivialises\": u\"trivializes\",\n u\"trivialising\": u\"trivializing\",\n u\"tumour\": u\"tumor\",\n u\"tumours\": u\"tumors\",\n u\"tunnelled\": u\"tunneled\",\n u\"tunnelling\": u\"tunneling\",\n u\"tyrannise\": u\"tyrannize\",\n u\"tyrannised\": u\"tyrannized\",\n u\"tyrannises\": u\"tyrannizes\",\n u\"tyrannising\": u\"tyrannizing\",\n u\"tyre\": u\"tire\",\n u\"tyres\": u\"tires\",\n u\"unauthorised\": u\"unauthorized\",\n u\"uncivilised\": u\"uncivilized\",\n u\"underutilised\": u\"underutilized\",\n u\"unequalled\": u\"unequaled\",\n u\"unfavourable\": u\"unfavorable\",\n u\"unfavourably\": u\"unfavorably\",\n u\"unionisation\": u\"unionization\",\n u\"unionise\": u\"unionize\",\n u\"unionised\": u\"unionized\",\n u\"unionises\": u\"unionizes\",\n u\"unionising\": u\"unionizing\",\n u\"unorganised\": u\"unorganized\",\n u\"unravelled\": u\"unraveled\",\n u\"unravelling\": u\"unraveling\",\n u\"unrecognisable\": u\"unrecognizable\",\n u\"unrecognised\": u\"unrecognized\",\n u\"unrivalled\": u\"unrivaled\",\n u\"unsavoury\": u\"unsavory\",\n u\"untrammelled\": u\"untrammeled\",\n u\"urbanisation\": u\"urbanization\",\n u\"urbanise\": u\"urbanize\",\n u\"urbanised\": u\"urbanized\",\n u\"urbanises\": u\"urbanizes\",\n u\"urbanising\": u\"urbanizing\",\n u\"utilisable\": u\"utilizable\",\n u\"utilisation\": u\"utilization\",\n u\"utilise\": u\"utilize\",\n u\"utilised\": u\"utilized\",\n u\"utilises\": u\"utilizes\",\n u\"utilising\": u\"utilizing\",\n u\"valour\": u\"valor\",\n u\"vandalise\": u\"vandalize\",\n u\"vandalised\": u\"vandalized\",\n u\"vandalises\": u\"vandalizes\",\n u\"vandalising\": u\"vandalizing\",\n u\"vaporisation\": u\"vaporization\",\n u\"vaporise\": u\"vaporize\",\n u\"vaporised\": u\"vaporized\",\n u\"vaporises\": u\"vaporizes\",\n u\"vaporising\": u\"vaporizing\",\n u\"vapour\": u\"vapor\",\n u\"vapours\": u\"vapors\",\n u\"verbalise\": u\"verbalize\",\n u\"verbalised\": u\"verbalized\",\n u\"verbalises\": u\"verbalizes\",\n u\"verbalising\": u\"verbalizing\",\n u\"victimisation\": u\"victimization\",\n u\"victimise\": u\"victimize\",\n u\"victimised\": u\"victimized\",\n u\"victimises\": u\"victimizes\",\n u\"victimising\": u\"victimizing\",\n u\"videodisc\": u\"videodisk\",\n u\"videodiscs\": u\"videodisks\",\n u\"vigour\": u\"vigor\",\n u\"visualisation\": u\"visualization\",\n u\"visualisations\": u\"visualizations\",\n u\"visualise\": u\"visualize\",\n u\"visualised\": u\"visualized\",\n u\"visualises\": u\"visualizes\",\n u\"visualising\": u\"visualizing\",\n u\"vocalisation\": u\"vocalization\",\n u\"vocalisations\": u\"vocalizations\",\n u\"vocalise\": u\"vocalize\",\n u\"vocalised\": u\"vocalized\",\n u\"vocalises\": u\"vocalizes\",\n u\"vocalising\": u\"vocalizing\",\n u\"vulcanised\": u\"vulcanized\",\n u\"vulgarisation\": u\"vulgarization\",\n u\"vulgarise\": u\"vulgarize\",\n u\"vulgarised\": u\"vulgarized\",\n u\"vulgarises\": u\"vulgarizes\",\n u\"vulgarising\": u\"vulgarizing\",\n u\"waggon\": u\"wagon\",\n u\"waggons\": u\"wagons\",\n u\"watercolour\": u\"watercolor\",\n u\"watercolours\": u\"watercolors\",\n u\"weaselled\": u\"weaseled\",\n u\"weaselling\": u\"weaseling\",\n u\"westernisation\": u\"westernization\",\n u\"westernise\": u\"westernize\",\n u\"westernised\": u\"westernized\",\n u\"westernises\": u\"westernizes\",\n u\"westernising\": u\"westernizing\",\n u\"womanise\": u\"womanize\",\n u\"womanised\": u\"womanized\",\n u\"womaniser\": u\"womanizer\",\n u\"womanisers\": u\"womanizers\",\n u\"womanises\": u\"womanizes\",\n u\"womanising\": u\"womanizing\",\n u\"woollen\": u\"woolen\",\n u\"woollens\": u\"woolens\",\n u\"woollies\": u\"woolies\",\n u\"woolly\": u\"wooly\",\n u\"worshipped\": u\"worshiped\",\n u\"worshipping\": u\"worshiping\",\n u\"worshipper\": u\"worshiper\",\n u\"yodelled\": u\"yodeled\",\n u\"yodelling\": u\"yodeling\",\n u\"yoghourt\": u\"yogurt\",\n u\"yoghourts\": u\"yogurts\",\n u\"yoghurt\": u\"yogurt\",\n u\"yoghurts\": u\"yogurts\"}\n\n"
},
{
"path": "corpkit/dictionaries/wordlists.py",
"content": "\"\"\"\nlists of closed class words\n\"\"\"\n\n# feel free to correct/add things---this was just a quick grab from the web\n\ndef closed_class_wordlists():\n \"\"\"add words here if need be\"\"\"\n from collections import namedtuple\n\n pronouns = [u\"all\",\n u\"another\",\n u\"any\",\n u\"anybody\",\n u\"anyone\",\n u\"anything\",\n u\"both\",\n u\"each\",\n u\"each\",\n u\"other\",\n u\"either\",\n u\"everybody\",\n u\"everyone\",\n u\"everything\",\n u\"few\",\n u\"he\",\n u\"her\",\n u\"hers\",\n u\"herself\",\n u\"him\",\n u\"himself\",\n u\"his\",\n u\"it\",\n u\"i\",\n u\"its\",\n u\"itself\",\n u\"many\",\n u\"me\",\n u\"mine\",\n u\"more\",\n u\"most\",\n u\"much\",\n u\"myself\",\n u\"neither\",\n u\"no\",\n u\"one\",\n u\"nobody\",\n u\"none\",\n u\"nothing\",\n u\"one\",\n u\"another\",\n u\"other\",\n u\"others\",\n u\"ours\",\n u\"ourselves\",\n u\"several\",\n u\"she\",\n u\"some\",\n u\"somebody\",\n u\"someone\",\n u\"something\",\n u\"that\",\n u\"their\",\n u\"theirs\",\n u\"them\",\n u\"there\",\n u\"themselves\",\n u\"these\",\n u\"they\",\n u\"this\",\n u\"those\",\n u\"us\",\n u\"we\",\n u\"what\",\n u\"whatever\",\n u\"which\",\n u\"whichever\",\n u\"who\",\n u\"whoever\",\n u\"whom\",\n u\"whomever\",\n u\"whose\",\n u\"you\",\n u\"your\",\n u\"yours\",\n u\"yourself\",\n u\"yourselves\"]\n\n articles = [u\"a\",\n u\"an\",\n u\"the\",\n u\"teh\"]\n\n determiners = [u\"all\",\n u\"anotha\",\n u\"another\",\n u\"any\",\n u\"any-and-all\",\n u\"atta\",\n u\"both\",\n u\"certain\",\n u\"couple\",\n u\"dat\",\n u\"dem\",\n u\"dis\",\n u\"each\",\n u\"either\",\n u\"enough\",\n u\"enuf\",\n u\"enuff\",\n u\"every\",\n u\"few\",\n u\"fewer\",\n u\"fewest\",\n u\"her\",\n u\"hes\",\n u\"his\",\n u\"its\",\n u\"last\",\n u\"least\",\n #u\"little\",\n u\"many\",\n u\"more\",\n u\"most\",\n u\"much\",\n u\"muchee\",\n u\"my\",\n u\"neither\",\n #u\"next\",\n u\"nil\",\n u\"no\",\n u\"none\",\n u\"other\",\n u\"our\",\n u\"overmuch\",\n u\"owne\",\n u\"plenty\",\n u\"quodque\",\n #u\"said\",\n u\"several\",\n u\"some\",\n u\"such\",\n u\"sufficient\",\n u\"that\",\n u\"their\",\n u\"them\",\n u\"these\",\n u\"they\",\n u\"thilk\",\n u\"thine\",\n u\"this\",\n u\"those\",\n u\"thy\",\n u\"umpteen\",\n u\"us\",\n u\"various\",\n u\"wat\",\n u\"we\",\n u\"what\",\n u\"whatever\",\n u\"which\",\n u\"whichever\",\n u\"yonder\",\n u\"you\",\n u\"your\"]\n\n prepositions = [u\"about\",\n u\"above\",\n u\"across\",\n u\"after\",\n u\"against\",\n u\"along\",\n u\"among\",\n u\"around\",\n u\"at\",\n u\"before\",\n u\"behind\",\n u\"below\",\n u\"beneath\",\n u\"beside\",\n u\"between\",\n u\"by\",\n u\"down\",\n u\"during\",\n u\"except\",\n u\"for\",\n u\"from\",\n u\"front\",\n u\"in\",\n u\"inside\",\n u\"instead\",\n u\"into\",\n u\"like\",\n u\"near\",\n u\"of\",\n u\"off\",\n u\"on\",\n u\"onto\",\n u\"out\",\n u\"outside\",\n u\"over\",\n u\"past\",\n u\"since\",\n u\"through\",\n u\"to\",\n u\"top\",\n u\"toward\",\n u\"under\",\n u\"underneath\",\n u\"until\",\n u\"up\",\n u\"upon\",\n u\"with\",\n u\"within\",\n u\"without\"]\n\n connectors = [u\"about\",\n u\"above\",\n u\"across\",\n u\"after\",\n u\"against\",\n u\"along\",\n u\"among\",\n u\"around\",\n u\"at\",\n u\"before\",\n u\"behind\",\n u\"below\",\n u\"beneath\",\n u\"beside\",\n u\"between\",\n u\"by\",\n u\"down\",\n u\"during\",\n u\"except\",\n u\"for\",\n u\"from\",\n u\"front\",\n u\"in\",\n u\"inside\",\n u\"instead\",\n u\"into\",\n u\"like\",\n u\"near\",\n u\"of\",\n u\"off\",\n u\"on\",\n u\"onto\",\n u\"out\",\n u\"outside\",\n u\"over\",\n u\"past\",\n u\"since\",\n u\"through\",\n u\"to\",\n u\"top\",\n u\"toward\",\n u\"under\",\n u\"underneath\",\n u\"until\",\n u\"up\",\n u\"upon\",\n u\"with\",\n u\"within\",\n u\"without\"]\n\n modals = [u\"would\", \n u\"will\", \n u\"can\", \n u\"could\", \n u\"may\", \n u\"should\", \n u\"might\", \n u\"must\", \n u\"ca\", \n u\"'ll\", \n u\"'d\", \n u\"wo\", \n u\"ought\", \n u\"need\", \n u\"shall\", \n u\"dare\", \n u\"shalt\"]\n\n conjunctions = [u\"though\",\n u\"although\",\n u\"even though\",\n u\"while\",\n u\"if\",\n u\"only if\",\n u\"unless\",\n u\"until\",\n u\"provided that\",\n u\"assuming that\",\n u\"even if\",\n u\"in case\",\n u\"lest\",\n u\"than\",\n u\"rather than\",\n u\"whether\",\n u\"as much as\",\n u\"whereas\",\n u\"after\",\n u\"as long as\",\n u\"as soon as\",\n u\"before\",\n u\"by the time\",\n u\"now that\",\n u\"once\",\n u\"since\",\n u\"till\",\n u\"until\",\n u\"when\",\n u\"whenever\",\n u\"while\",\n u\"because\",\n u\"since\",\n u\"so that\",\n u\"why\",\n u\"that\",\n u\"what\",\n u\"whatever\",\n u\"which\",\n u\"whichever\",\n u\"who\",\n u\"whoever\",\n u\"whom\",\n u\"whomever\",\n u\"whose\",\n u\"how\",\n u\"as though\",\n u\"as if\",\n u\"where\",\n u\"wherever\",\n u\"for\",\n u\"and\",\n u\"nor\",\n u\"but\",\n u\"or\",\n u\"yet\",\n u\"so\",\n u\"however\"]\n\n stopwords = [\"yeah\", \"monday\",\"tuesday\",\"wednesday\",\"thursday\",\"friday\",\n \"saturday\",\"sunday\",\"a\",\"able\",\"about\",\"above\",\"abst\",\"accordance\",\n \"according\",\"accordingly\",\"across\",\"act\",\"actually\",\"added\",\"adj\",\n \"adopted\",\"affected\",\"affecting\",\"affects\",\"after\",\"afterwards\",\n \"again\",\"against\",\"ah\",\"all\",\"almost\",\"alone\",\"along\",\"already\",\n \"also\",\"although\",\"always\",\"am\",\"among\",\"amongst\",\"an\",\"and\",\n \"announce\",\"another\",\"any\",\"anybody\",\"anyhow\",\"anymore\",\"anyone\",\n \"anything\",\"anyway\",\"anyways\",\"anywhere\",\"apparently\",\"approximately\",\n \"are\",\"aren\",\"arent\",\"arise\",\"around\",\"as\",\"aside\",\"ask\",\"asking\",\"at\",\n \"auth\",\"available\",\"away\",\"awfully\",\"b\",\"back\",\"be\",\"became\",\"because\",\n \"become\",\"becomes\",\"becoming\",\"been\",\"before\",\"beforehand\",\"begin\",\n \"beginning\",\"beginnings\",\"begins\",\"behind\",\"being\",\"believe\",\"below\",\n \"beside\",\"besides\",\"between\",\"beyond\",\"biol\",\"both\",\"brief\",\"briefly\",\n \"but\",\"by\",\"c\",\"ca\",\"came\",\"can\",\"cannot\",\"cant\",\"cause\",\"causes\",\n \"certain\",\"certainly\",\"co\",\"com\",\"come\",\"comes\",\"contain\",\"containing\",\n \"contains\",\"could\",\"couldnt\",\"d\",\"date\",\"did\",\"didnt\",\"different\",\"do\",\n \"does\",\"doesnt\",\"doing\",\"done\",\"dont\",\"down\",\"downwards\",\"due\",\"during\",\n \"e\",\"each\",\"ed\",\"edu\",\"effect\",\"eg\",\"eight\",\"eighty\",\"either\",\"else\",\n \"elsewhere\",\"end\",\"ending\",\"enough\",\"especially\",\"et\",\"et-al\",\"etc\",\"even\",\n \"ever\",\"every\",\"everybody\",\"everyone\",\"everything\",\"everywhere\",\"ex\",\n \"except\",\"f\",\"far\",\"few\",\"ff\",\"fifth\",\"first\",\"five\",\"fix\",\"followed\",\n \"following\",\"follows\",\"for\",\"former\",\"formerly\",\"forth\",\"found\",\"four\",\n \"from\",\"further\",\"furthermore\",\"going\",\"g\",\"gave\",\"get\",\"gets\",\"getting\",\n \"give\",\"given\",\"gives\",\"giving\",\"go\",\"goes\",\"gone\",\"got\",\"gotten\",\"h\",\n \"had\",\"happens\",\"hardly\",\"has\",\"hasnt\",\"have\",\"havent\",\"having\",\"he\",\n \"hed\",\"hence\",\"her\",\"here\",\"hereafter\",\"hereby\",\"herein\",\"heres\",\n \"hereupon\",\"hers\",\"herself\",\"hes\",\"hi\",\"hid\",\"him\",\"himself\",\"his\",\n \"hither\",\"home\",\"how\",\"howbeit\",\"however\",\"hundred\",\"i\",\"id\",\"ie\",\n \"if\",\"ill\",\"im\",\"immediate\",\"immediately\",\"importance\",\"important\",\n \"in\",\"inc\",\"indeed\",\"index\",\"information\",\"instead\",\"into\",\"invention\",\n \"inward\",\"is\",\"isnt\",\"it\",\"itd\",\"itll\",\"its\",\"itself\",\"ive\",\"j\",\"just\",\n \"k\",\"keep\",\"keeps\",\"kept\",\"keys\",\"kg\",\"km\",\"know\",\"known\",\"knows\",\n \"l\",\"largely\",\"last\",\"lately\",\"later\",\"latter\",\"latterly\",\"least\",\"less\",\n \"lest\",\"let\",\"lets\",\"like\",\"liked\",\"likely\",\"line\",\"little\",\"ll\",\"look\",\n \"looking\",\"looks\",\"ltd\",\"m\",\"made\",\"mainly\",\"make\",\"makes\",\"many\",\"may\",\n \"maybe\",\"me\",\"mean\",\"means\",\"meantime\",\"meanwhile\",\"merely\",\"mg\",\"might\",\n \"million\",\"miss\",\"ml\",\"more\",\"moreover\",\"most\",\"mostly\",\"mr\",\"mrs\",\"much\",\n \"mug\",\"must\",\"my\",\"myself\",\"n\",\"na\",\"name\",\"namely\",\"nay\",\"nd\",\"near\",\n \"nearly\",\"necessarily\",\"necessary\",\"need\",\"needs\",\"neither\",\"never\",\n \"nevertheless\",\"new\",\"next\",\"nine\",\"ninety\",\"no\",\"nobody\",\"non\",\"none\",\n \"nonetheless\",\"noone\",\"nor\",\"normally\",\"nos\",\"not\",\"noted\",\"nothing\",\n \"now\",\"nowhere\",\"o\",\"obtain\",\"obtained\",\"obviously\",\"of\",\"off\",\"often\",\n \"oh\",\"ok\",\"okay\",\"old\",\"omitted\",\"on\",\"once\",\"one\",\"ones\",\"only\",\"onto\",\n \"or\",\"ord\",\"other\",\"others\",\"otherwise\",\"ought\",\"our\",\"ours\",\"ourselves\",\n \"out\",\"outside\",\"over\",\"overall\",\"owing\",\"own\",\"p\",\"page\",\"pages\",\"part\",\n \"particular\",\"particularly\",\"past\",\"per\",\"perhaps\",\"placed\",\"please\",\n \"plus\",\"poorly\",\"possible\",\"possibly\",\"potentially\",\"pp\",\"predominantly\",\n \"present\",\"previously\",\"primarily\",\"probably\",\"promptly\",\"proud\",\"provides\",\n \"put\",\"q\",\"que\",\"quickly\",\"quite\",\"qv\",\"r\",\"ran\",\"rather\",\"rd\",\"re\",\n \"readily\",\"really\",\"recent\",\"recently\",\"ref\",\"refs\",\"regarding\",\"regardless\",\n \"regards\",\"related\",\"relatively\",\"research\",\"respectively\",\"resulted\",\n \"resulting\",\"results\",\"right\",\"run\",\"s\",\"said\",\"same\",\"saw\",\"say\",\"saying\",\n \"says\",\"sec\",\"section\",\"see\",\"seeing\",\"seem\",\"seemed\",\"seeming\",\"seems\",\n \"seen\",\"self\",\"selves\",\"sent\",\"seven\",\"several\",\"shall\",\"she\",\"shed\",\"shell\",\n \"shes\",\"should\",\"shouldnt\",\"show\",\"showed\",\"shown\",\"showns\",\"shows\",\n \"significant\",\"significantly\",\"similar\",\"similarly\",\"since\",\"six\",\n \"slightly\",\"so\",\"some\",\"somebody\",\"somehow\",\"someone\",\"somethan\",\"something\",\n \"sometime\",\"sometimes\",\"somewhat\",\"somewhere\",\"soon\",\"sorry\",\"specifically\",\n \"specified\",\"specify\",\"specifying\",\"state\",\"states\",\"still\",\"stop\",\n \"strongly\",\"sub\",\"substantially\",\"successfully\",\"such\",\"sufficiently\",\n \"suggest\",\"sup\",\"sure\",\"t\",\"take\",\"taken\",\"taking\",\"tell\",\"tends\",\"th\",\n \"than\",\"thank\",\"thanks\",\"thanx\",\"that\",\"thatll\",\"thats\",\"thatve\",\"the\",\n \"their\",\"theirs\",\"them\",\"themselves\",\"then\",\"thence\",\"there\",\"thereafter\",\n \"thereby\",\"thered\",\"therefore\",\"therein\",\"therell\",\"thereof\",\"therere\",\n \"theres\",\"thereto\",\"thereupon\",\"thereve\",\"these\",\"they\",\"theyd\",\"theyll\",\n \"theyre\",\"theyve\",\"think\",\"this\",\"those\",\"thou\",\"though\",\"thoughh\",\"thousand\",\n \"throug\",\"through\",\"throughout\",\"thru\",\"thus\",\"til\",\"tip\",\"to\",\"together\",\n \"too\",\"took\",\"toward\",\"towards\",\"tried\",\"tries\",\"truly\",\"try\",\"trying\",\n \"ts\",\"twice\",\"two\",\"u\",\"un\",\"under\",\"unfortunately\",\"unless\",\"unlike\",\n \"unlikely\",\"until\",\"unto\",\"up\",\"upon\",\"ups\",\"us\",\"use\",\"used\",\"useful\",\n \"usefully\",\"usefulness\",\"uses\",\"using\",\"usually\",\"v\",\"value\",\"various\",\"ve\",\n \"very\",\"via\",\"viz\",\"vol\",\"vols\",\"vs\",\"w\",\"want\",\"wants\",\"was\",\"wasnt\",\"way\",\n \"we\",\"wed\",\"welcome\",\"well\",\"went\",\"were\",\"werent\",\"weve\",\"what\",\"whatever\",\n \"whatll\",\"whats\",\"when\",\"whence\",\"whenever\",\"where\",\"whereafter\",\"whereas\",\n \"whereby\",\"wherein\",\"wheres\",\"whereupon\",\"wherever\",\"whether\",\"which\",\n \"while\",\"whim\",\"whither\",\"who\",\"whod\",\"whoever\",\"whole\",\"wholl\",\"whom\",\n \"whomever\",\"whos\",\"whose\",\"why\",\"widely\",\"willing\",\"wish\",\"with\",\"within\",\n \"without\",\"wont\",\"words\",\"world\",\"would\",\"wouldnt\",\"www\",\"x\",\"y\",\"yes\",\"yet\",\n \"you\",\"youd\",\"youll\",\"your\",\"youre\",\"yours\",\"yourself\",\"yourselves\",\"youve\",\n \"z\",\"zero\", \"isn\", \"doesn\",\"didn\", \"couldn\", \"mustn\",\"shoudn\",\"wasn\",\"woudn\",\n \"i\", \"me\", \"my\", \"myself\", \"we\", \"our\", \"ours\", \"ourselves\", \"you\", \"your\", \n \"yours\", \"yourself\", \"yourselves\", \"he\", \"him\", \"his\", \"himself\", \"she\", \"her\", \n \"hers\", \"herself\", \"it\", \"its\", \"itself\", \"they\", \"them\", \"their\", \"theirs\", \n \"themselves\", \"what\", \"which\", \"who\", \"whom\", \"this\", \"that\", \"these\", \"those\", \n \"am\", \"is\", \"are\", \"was\", \"were\", \"be\", \"been\", \"being\", \"have\", \"has\", \"had\", \n \"having\", \"do\", \"does\", \"did\", \"doing\", \"a\", \"an\", \"the\", \"and\", \"but\", \"if\", \n \"or\", \"because\", \"as\", \"until\", \"while\", \"of\", \"at\", \"by\", \"for\", \"with\", \n \"about\", \"against\", \"between\", \"into\", \"through\", \"during\", \"before\", \"after\", \n \"above\", \"below\", \"to\", \"from\", \"up\", \"down\", \"in\", \"out\", \"on\", \"off\", \n \"over\", \"under\", \"again\", \"further\", \"then\", \"once\", \"here\", \"there\", \"when\", \n \"where\", \"why\", \"how\", \"all\", \"any\", \"both\", \"each\", \"few\", \"more\", \"most\", \n \"other\", \"some\", \"such\", \"no\", \"nor\", \"not\", \"only\", \"own\", \"same\", \"so\", \n \"than\", \"too\", \"very\", \"s\", \"t\", \"can\", \"will\", \"just\", \"don\", \"should\", \n \"now\", \"gonna\", \"n't\", '-lrb-', '-rrb-', \"'m\", \"'ll\", \"'re\", \"'s\", \"'ve\", \"&\"]\n\n titlewords = [u'admiral', u'archbishop', u'alan', u'merrill', u'sarah', \n 'queen', u'king', u'sen', u'chancellor', u'prime minister', \n 'cardinal', u'bishop', u'father', u'hon', u'rev', u'reverend', \n 'pope', u'sir', u'doctor', u'professor', u'president', \n 'senator', u'congressman', u'congresswoman', u'mr', u'ms', \n 'mrs', u'miss', u'dr', u'bill', u'hillary', u'hillary rodham', \n 'saddam', u'osama', u'ayatollah', u'george', u'george w', \n 'mitt', u'malcolm', u'barack', u'ronald', u'john', u'john f', \n 'william', u'al', u'bob']\n\n whpro = [u'who', u'what',u'why', u'where',u'when', u'how']\n\n other = ['not']\n\n #all_lists = [pronouns, articles, determiners, prepositions, connectors, modals]\n from corpkit.dictionaries.process_types import Wordlist\n closedclass = sorted(list(set(pronouns + articles + conjunctions + determiners + prepositions + connectors + modals + other)))\n outputnames = namedtuple('wordlists', ['pronouns', 'conjunctions', 'articles', 'determiners', 'prepositions', 'connectors', 'modals', 'closedclass', 'stopwords', 'titles', 'whpro'])\n eachlist = [pronouns, conjunctions, articles, determiners, prepositions, connectors, modals, closedclass, stopwords, titlewords, whpro]\n eachlist = [Wordlist(l, single=True) for l in eachlist]\n output = outputnames(*eachlist)\n return output\n\nwordlists = closed_class_wordlists()"
},
{
"path": "corpkit/download/__init__.py",
"content": ""
},
{
"path": "corpkit/download/corenlp.py",
"content": "def corenlp_downloader(custompath=False):\n \"\"\"\n Very simple CoreNLP downloader\n\n :param custompath: A path where you want to put CoreNLP\n :type custompath: ``str``\n\n :Usage:\n python -m corpkit.download.corenlp\n \"\"\"\n\n import os\n from corpkit.build import download_large_file\n from corpkit.process import get_corenlp_path\n from corpkit.constants import CORENLP_URL as url\n\n cnlp_dir = os.path.join(os.path.expanduser(\"~\"), 'corenlp')\n \n corenlppath, fpath = download_large_file(cnlp_dir, url,\n actually_download=True,\n custom_corenlp_dir=custompath)\n\nif __name__ == '__main__':\n import sys\n custompath = False if len(sys.argv) == 1 else sys.argv[-1]\n corenlp_downloader(custompath=custompath)\n "
},
{
"path": "corpkit/editor.py",
"content": "\"\"\"\ncorpkit: edit Interrogation, Concordance and Interrodict objects\n\"\"\"\nfrom __future__ import print_function\nfrom corpkit.constants import STRINGTYPE, PYTHON_VERSION\n\ndef editor(interrogation, \n operation=None,\n denominator=False,\n sort_by=False,\n keep_stats=False,\n keep_top=False,\n just_totals=False,\n threshold='medium',\n just_entries=False,\n skip_entries=False,\n span_entries=False,\n merge_entries=False,\n just_subcorpora=False,\n skip_subcorpora=False,\n span_subcorpora=False,\n merge_subcorpora=False,\n replace_names=False,\n replace_subcorpus_names=False,\n projection=False,\n remove_above_p=False,\n p=0.05, \n print_info=False,\n spelling=False,\n selfdrop=True,\n calc_all=True,\n keyword_measure='ll',\n **kwargs\n ):\n \"\"\"\n See corpkit.interrogation.Interrogation.edit() for docstring\n \"\"\"\n\n # grab arguments, in case we get dict input and have to iterate\n locs = locals()\n\n import corpkit\n\n import re\n import collections\n import pandas as pd\n import numpy as np\n\n from pandas import DataFrame, Series\n from time import localtime, strftime\n \n try:\n get_ipython().getoutput()\n except TypeError:\n have_ipython = True\n except NameError:\n have_ipython = False\n try:\n from IPython.display import display, clear_output\n except ImportError:\n pass\n # new ipython error\n except AttributeError:\n have_ipython = False\n pass\n\n # to use if we also need to worry about concordance lines\n return_conc = False\n\n from corpkit.interrogation import Interrodict, Interrogation, Concordance\n if interrogation.__class__ == Interrodict:\n locs.pop('interrogation', None)\n from collections import OrderedDict\n outdict = OrderedDict()\n for i, (k, v) in enumerate(interrogation.items()):\n # only print the first time around\n if i != 0:\n locs['print_info'] = False\n\n if isinstance(denominator, STRINGTYPE) and denominator.lower() == 'self':\n denominator = interrogation\n\n # if df2 is also a dict, get the relevant entry\n\n if isinstance(denominator, (dict, Interrodict)):\n #if sorted(set([i.lower() for i in list(dataframe1.keys())])) == \\\n # sorted(set([i.lower() for i in list(denominator.keys())])):\n # locs['denominator'] = denominator[k]\n\n # fix: this repeats itself for every key, when it doesn't need to\n # denominator_sum: \n if kwargs.get('denominator_sum'):\n locs['denominator'] = denominator.collapse(axis='key')\n\n if kwargs.get('denominator_totals'):\n locs['denominator'] = denominator[k].totals\n else:\n locs['denominator'] = denominator[k].results\n\n\n outdict[k] = v.results.edit(**locs)\n if print_info:\n \n thetime = strftime(\"%H:%M:%S\", localtime())\n print(\"\\n%s: Finished! Output is a dictionary with keys:\\n\\n '%s'\\n\" % (thetime, \"'\\n '\".join(sorted(outdict.keys()))))\n return Interrodict(outdict)\n\n elif isinstance(interrogation, (DataFrame, Series)):\n dataframe1 = interrogation\n elif isinstance(interrogation, Interrogation):\n #if interrogation.__dict__.get('concordance', None) is not None:\n # concordances = interrogation.concordance\n branch = kwargs.pop('branch', 'results')\n if branch.lower().startswith('r') :\n dataframe1 = interrogation.results\n elif branch.lower().startswith('t'):\n dataframe1 = interrogation.totals\n elif branch.lower().startswith('c'):\n dataframe1 = interrogation.concordance\n return_conc = True\n else:\n dataframe1 = interrogation.results\n \n elif isinstance(interrogation, Concordance) or \\\n all(x in list(dataframe1.columns) for x in [ 'l', 'm', 'r']):\n return_conc = True\n print('heree')\n dataframe1 = interrogation\n # hope for the best\n else:\n dataframe1 = interrogation\n\n the_time_started = strftime(\"%Y-%m-%d %H:%M:%S\")\n\n pd.options.mode.chained_assignment = None\n\n try:\n from process import checkstack\n except ImportError:\n from corpkit.process import checkstack\n \n if checkstack('pythontex'):\n print_info=False\n\n def combiney(df, df2, operation='%', threshold='medium', prinf=True):\n \"\"\"\n Mash df and df2 together in appropriate way\n \"\"\"\n totals = False\n # delete under threshold\n if just_totals:\n if using_totals:\n if not single_totals:\n to_drop = list(df2[df2['Combined total'] < threshold].index)\n df = df.drop([e for e in to_drop if e in list(df.index)])\n if prinf:\n to_show = []\n [to_show.append(w) for w in to_drop[:5]]\n if len(to_drop) > 10:\n to_show.append('...')\n [to_show.append(w) for w in to_drop[-5:]]\n if len(to_drop) > 0:\n print('Removing %d entries below threshold:\\n %s' % (len(to_drop), '\\n '.join(to_show)))\n if len(to_drop) > 10:\n print('... and %d more ... \\n' % (len(to_drop) - len(to_show) + 1))\n else:\n print('')\n else:\n denom = df2\n else:\n denom = list(df2)\n if single_totals:\n if operation == '%':\n totals = df.sum() * 100.0 / float(df.sum().sum())\n df = df * 100.0\n try:\n df = df.div(denom, axis=0)\n except ValueError:\n \n thetime = strftime(\"%H:%M:%S\", localtime())\n print('%s: cannot combine DataFrame 1 and 2: different shapes' % thetime)\n elif operation == '+':\n try:\n df = df.add(denom, axis=0)\n except ValueError:\n \n thetime = strftime(\"%H:%M:%S\", localtime())\n print('%s: cannot combine DataFrame 1 and 2: different shapes' % thetime)\n elif operation == '-':\n try:\n df = df.sub(denom, axis=0)\n except ValueError:\n \n thetime = strftime(\"%H:%M:%S\", localtime())\n print('%s: cannot combine DataFrame 1 and 2: different shapes' % thetime)\n elif operation == '*':\n totals = df.sum() * float(df.sum().sum())\n try:\n df = df.mul(denom, axis=0)\n except ValueError:\n \n thetime = strftime(\"%H:%M:%S\", localtime())\n print('%s: cannot combine DataFrame 1 and 2: different shapes' % thetime)\n elif operation == '/':\n try:\n totals = df.sum() / float(df.sum().sum())\n df = df.div(denom, axis=0)\n except ValueError:\n \n thetime = strftime(\"%H:%M:%S\", localtime())\n print('%s: cannot combine DataFrame 1 and 2: different shapes' % thetime)\n\n elif operation == 'a':\n for c in [c for c in list(df.columns) if int(c) > 1]:\n df[c] = df[c] * (1.0 / int(c))\n df = df.sum(axis=1) / df2\n \n elif operation.startswith('c'):\n import warnings\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n df = pandas.concat([df, df2], axis=1)\n return df, totals\n\n elif not single_totals:\n if not operation.startswith('a'):\n # generate totals\n if operation == '%':\n totals = df.sum() * 100.0 / float(df2.sum().sum())\n if operation == '*':\n totals = df.sum() * float(df2.sum().sum())\n if operation == '/':\n totals = df.sum() / float(df2.sum().sum())\n if operation.startswith('c'):\n # add here the info that merging will not work \n # with identical colnames\n import warnings\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n d = pd.concat([df.T, df2.T])\n # make index nums\n d = d.reset_index()\n # sum and remove duplicates\n d = d.groupby('index').sum()\n dx = d.reset_index('index')\n dx.index = list(dx['index'])\n df = dx.drop('index', axis=1).T\n\n def editf(datum):\n meth = {'%': datum.div,\n '*': datum.mul,\n '/': datum.div,\n '+': datum.add,\n '-': datum.sub}\n\n if datum.name in list(df2.columns):\n\n method = meth[operation]\n mathed = method(df2[datum.name], fill_value=0.0)\n if operation == '%':\n return mathed * 100.0\n else:\n return mathed\n else:\n return datum * 0.0\n\n df = df.apply(editf)\n\n else:\n for c in [c for c in list(df.columns) if int(c) > 1]:\n df[c] = df[c] * (1.0 / int(c))\n df = df.sum(axis=1) / df2.T.sum()\n\n return df, totals\n\n def skip_keep_merge_span(df):\n \"\"\"\n Do all skipping, keeping, merging and spanning\n \"\"\"\n from corpkit.dictionaries.process_types import Wordlist\n if skip_entries:\n if isinstance(skip_entries, (list, Wordlist)):\n df = df.drop(list(skip_entries), axis=1, errors='ignore')\n else:\n df = df.loc[:,~df.columns.str.contains(skip_entries)]\n if just_entries:\n if isinstance(just_entries, (list, Wordlist)):\n je = [i for i in list(just_entries) if i in list(df.columns)]\n df = df[je]\n else:\n df = df.loc[:,df.columns.str.contains(just_entries)]\n if merge_entries:\n for newname, crit in merge_entries.items():\n if isinstance(crit, (list, Wordlist)):\n crit = [i for i in list(crit) if i in list(df.columns)]\n cr = [i for i in list(crit) if i in list(df.columns)]\n summed = df[cr].sum(axis=1)\n df = df.drop(list(cr), axis=1, errors='ignore')\n else:\n summed = df.loc[:,df.columns.str.contains(crit)].sum(axis=1)\n df = df.loc[:,~df.columns.str.contains(crit)]\n df.insert(0, newname, summed, allow_duplicates=True)\n if span_entries:\n df = df.iloc[:,span_entries[0]:span_entries[1]]\n if skip_subcorpora:\n if isinstance(skip_subcorpora, (list, Wordlist)):\n df = df.drop(list(skip_subcorpora), axis=0, errors='ignore')\n else:\n df = df[~df.index.str.contains(skip_subcorpora)]\n if just_subcorpora:\n if isinstance(just_subcorpora, (list, Wordlist)):\n js = [i for i in list(just_subcorpora) if i in list(df.index)]\n df = df.loc[js]\n else:\n df = df[df.index.str.contains(just_subcorpora)]\n if merge_subcorpora:\n df = df.T\n for newname, crit in merge_subcorpora.items():\n if isinstance(crit, (list, Wordlist)):\n crit = [i for i in list(crit) if i in list(df.columns)]\n summed = df[list(crit)].sum(axis=1)\n df = df.drop(list(crit), axis=1, errors='ignore')\n else:\n summed = df.loc[:,df.columns.str.contains(crit)].sum(axis=1)\n df = df.loc[:,~df.columns.str.contains(crit)] \n df.insert(0, newname, summed, allow_duplicates=True)\n df = df.T\n if span_subcorpora:\n df = df.iloc[span_subcorpora[0]:span_subcorpora[1],:]\n return df\n\n def parse_input(df, the_input):\n \"\"\"turn whatever has been passed in into list of words that can \n be used as pandas indices---maybe a bad way to go about it\"\"\"\n parsed_input = False\n import re\n if the_input == 'all':\n the_input = r'.*'\n if isinstance(the_input, int):\n try:\n the_input = str(the_input)\n except:\n pass\n the_input = [the_input]\n elif isinstance(the_input, STRINGTYPE):\n regex = re.compile(the_input)\n parsed_input = [w for w in list(df) if re.search(regex, w)]\n return parsed_input\n from corpkit.dictionaries.process_types import Wordlist\n if isinstance(the_input, Wordlist) or the_input.__class__ == Wordlist:\n the_input = list(the_input)\n if isinstance(the_input, list):\n if isinstance(the_input[0], int):\n parsed_input = [word for index, word in enumerate(list(df)) if index in the_input]\n elif isinstance(the_input[0], STRINGTYPE):\n try:\n parsed_input = [word for word in the_input if word in df.columns]\n except AttributeError: # if series\n parsed_input = [word for word in the_input if word in df.index]\n return parsed_input\n\n def synonymise(df, pos='n'):\n \"\"\"pass a df and a pos and convert df columns to most common synonyms\"\"\"\n from nltk.corpus import wordnet as wn\n #from dictionaries.taxonomies import taxonomies\n from collections import Counter\n fixed = []\n for w in list(df.columns):\n try:\n syns = []\n for syns in wn.synsets(w, pos=pos):\n for w in syns:\n synonyms.append(w)\n top_syn = Counter(syns).most_common(1)[0][0]\n fixed.append(top_syn)\n except:\n fixed.append(w)\n df.columns = fixed\n return df\n\n def convert_spell(df, convert_to='US', print_info=print_info):\n \"\"\"turn dataframes into us/uk spelling\"\"\"\n from dictionaries.word_transforms import usa_convert\n if print_info:\n print('Converting spelling ... \\n')\n if convert_to == 'UK':\n usa_convert = {v: k for k, v in list(usa_convert.items())}\n fixed = []\n for val in list(df.columns):\n try:\n fixed.append(usa_convert[val])\n except:\n fixed.append(val)\n df.columns = fixed\n return df\n\n def merge_duplicates(df, print_info=print_info):\n if print_info:\n print('Merging duplicate entries ... \\n')\n # now we have to merge all duplicates\n for dup in df.columns.get_duplicates():\n #num_dupes = len(list(df[dup].columns))\n temp = df[dup].sum(axis=1)\n #df = df.drop([dup for d in range(num_dupes)], axis=1)\n df = df.drop(dup, axis=1)\n df[dup] = temp\n return df\n\n def name_replacer(df, replace_names, print_info=print_info):\n \"\"\"replace entry names and merge\"\"\"\n import re\n # get input into list of tuples\n # if it's a string, we want to delete it\n if isinstance(replace_names, STRINGTYPE):\n replace_names = [(replace_names, '')]\n # this is for some malformed list\n if not isinstance(replace_names, dict):\n if isinstance(replace_names[0], STRINGTYPE):\n replace_names = [replace_names]\n # if dict, make into list of tupes\n if isinstance(replace_names, dict):\n replace_names = [(v, k) for k, v in replace_names.items()]\n for to_find, replacement in replace_names:\n if print_info:\n if replacement:\n print('Replacing \"%s\" with \"%s\" ...\\n' % (to_find, replacement))\n else:\n print('Deleting \"%s\" from entry names ...\\n' % to_find)\n to_find = re.compile(to_find)\n if not replacement:\n replacement = ''\n df.columns = [re.sub(to_find, replacement, l) for l in list(df.columns)]\n df = merge_duplicates(df, print_info=False)\n return df\n\n def newname_getter(df, parsed_input, newname='combine', prinf=True, merging_subcorpora=False):\n \"\"\"makes appropriate name for merged entries\"\"\"\n if merging_subcorpora:\n if newname is False:\n newname = 'combine'\n if isinstance(newname, int):\n the_newname = list(df.columns)[newname]\n elif isinstance(newname, STRINGTYPE):\n if newname == 'combine':\n if len(parsed_input) <= 3:\n the_newname = '/'.join(parsed_input)\n elif len(parsed_input) > 3:\n the_newname = '/'.join(parsed_input[:3]) + '...'\n else:\n the_newname = newname\n if not newname:\n # revise this code\n import operator\n sumdict = {}\n for item in parsed_input:\n summed = sum(list(df[item]))\n sumdict[item] = summed\n the_newname = max(iter(sumdict.items()), key=operator.itemgetter(1))[0]\n if not isinstance(the_newname, STRINGTYPE):\n the_newname = str(the_newname, errors='ignore')\n return the_newname\n\n def projector(df, list_of_tuples, prinf=True):\n \"\"\"project abs values\"\"\"\n if isinstance(list_of_tuples, list):\n tdict = {}\n for a, b in list_of_tuples:\n tdict[a] = b\n list_of_tuples = tdict\n for subcorpus, projection_value in list(list_of_tuples.items()):\n if isinstance(subcorpus, int):\n subcorpus = str(subcorpus)\n df.ix[subcorpus] = df.ix[subcorpus] * projection_value\n if prinf:\n if isinstance(projection_value, float):\n print('Projection: %s * %s' % (subcorpus, projection_value))\n if isinstance(projection_value, int):\n print('Projection: %s * %d' % (subcorpus, projection_value))\n if prinf:\n print('')\n return df\n\n def lingres(ser, index):\n from scipy.stats import linregress\n from pandas import Series\n ix = ['slope', 'intercept', 'r', 'p', 'stderr']\n return Series(linregress(index, ser.values), index=ix)\n\n def do_stats(df):\n \"\"\"do linregress and add to df\"\"\"\n\n try: \n from scipy.stats import linregress\n except ImportError:\n thetime = strftime(\"%H:%M:%S\", localtime())\n print('%s: sort type not available in this version of corpkit.' % thetime)\n return False\n\n indices = list(df.index)\n first_year = list(df.index)[0]\n try:\n x = [int(y) - int(first_year) for y in indices]\n except ValueError:\n x = list(range(len(indices)))\n\n stats = df.apply(lingres, axis=0, index=x)\n df = df.append(stats)\n df = df.replace([np.inf, -np.inf], 0.0)\n\n return df\n\n def resort(df, sort_by = False, keep_stats = False):\n \"\"\"\n Sort results, potentially using scipy's linregress\n \"\"\"\n \n # translate options and make sure they are parseable\n stat_field = ['slope', 'intercept', 'r', 'p', 'stderr']\n easy_sorts = ['total', 'infreq', 'name', 'most', 'least', 'reverse']\n stat_sorts = ['increase', 'decrease', 'static', 'turbulent']\n options = stat_field + easy_sorts + stat_sorts\n sort_by_convert = {'most': 'total', True: 'total', 'least': 'infreq'}\n sort_by = sort_by_convert.get(sort_by, sort_by)\n\n # probably broken :(\n if just_totals:\n if sort_by == 'name':\n return df.sort_index()\n else:\n return df.sort_values(by='Combined total', ascending=sort_by != 'total', axis=1)\n\n stats_done = False\n if keep_stats or sort_by in stat_field + stat_sorts:\n df = do_stats(df)\n stats_done = True\n if isinstance(df, bool):\n if df is False:\n return False\n \n if isinstance(df, Series):\n if stats_done:\n stats = df.ix[range(-5, 0)]\n df = df.drop(list(stats.index))\n if sort_by == 'name':\n df = df.sort_index()\n elif sort_by == 'reverse':\n df = df[::-1]\n else:\n df = df.sort_values(ascending=sort_by != 'total')\n if stats_done:\n df = df.append(stats)\n return df\n\n if sort_by == 'name':\n # currently case sensitive\n df = df.reindex_axis(sorted(df.columns), axis=1)\n elif sort_by in ['total', 'infreq']:\n if df1_istotals:\n df = df.T\n df = df[list(df.sum().sort_values(ascending=sort_by != 'total').index)]\n \n elif sort_by == 'reverse':\n df = df.T[::-1].T\n # sort by slope etc., or search by subcorpus name\n if sort_by in stat_field or sort_by not in options:\n asc = kwargs.get('reverse', False)\n df = df.T.sort_values(by=sort_by, ascending=asc).T\n \n if sort_by in ['increase', 'decrease', 'static', 'turbulent']:\n slopes = df.ix['slope']\n if sort_by == 'increase':\n df = df[slopes.argsort()[::-1]]\n elif sort_by == 'decrease':\n df = df[slopes.argsort()]\n elif sort_by == 'static':\n df = df[slopes.abs().argsort()]\n elif sort_by == 'turbulent':\n df = df[slopes.abs().argsort()[::-1]]\n if remove_above_p:\n df = df.T\n df = df[df['p'] <= p]\n df = df.T\n\n # remove stats field by default\n if not keep_stats:\n df = df.drop(stat_field, axis=0, errors='ignore')\n return df\n\n def set_threshold(big_list, threshold, prinf=True):\n if isinstance(threshold, STRINGTYPE):\n if threshold.startswith('l'):\n denominator = 10000\n if threshold.startswith('m'):\n denominator = 5000\n if threshold.startswith('h'):\n denominator = 2500\n if isinstance(big_list, DataFrame):\n tot = big_list.sum().sum()\n\n if isinstance(big_list, Series):\n tot = big_list.sum()\n tshld = float(tot) / float(denominator)\n else:\n tshld = threshold\n if prinf:\n print('Threshold: %d\\n' % tshld)\n return tshld\n\n # copy dataframe to be very safe\n df = dataframe1.copy()\n # make cols into strings\n try:\n df.columns = [str(c) for c in list(df.columns)]\n except:\n pass\n\n if operation is None:\n operation = 'None'\n\n if isinstance(interrogation, Concordance):\n return_conc = True\n # do concordance work\n if return_conc:\n if just_entries:\n if isinstance(just_entries, int):\n just_entries = [just_entries]\n if isinstance(just_entries, STRINGTYPE):\n df = df[df['m'].str.contains(just_entries)]\n if isinstance(just_entries, list):\n if all(isinstance(e, STRINGTYPE) for e in just_entries):\n mp = df['m'].map(lambda x: x in just_entries)\n df = df[mp]\n else:\n df = df.ix[just_entries]\n\n if skip_entries:\n if isinstance(skip_entries, int):\n skip_entries = [skip_entries]\n if isinstance(skip_entries, STRINGTYPE):\n df = df[~df['m'].str.contains(skip_entries)]\n if isinstance(skip_entries, list):\n if all(isinstance(e, STRINGTYPE) for e in skip_entries):\n mp = df['m'].map(lambda x: x not in skip_entries)\n df = df[mp]\n else:\n df = df.drop(skip_entries, axis=0)\n\n if just_subcorpora:\n if isinstance(just_subcorpora, int):\n just_subcorpora = [just_subcorpora]\n if isinstance(just_subcorpora, STRINGTYPE):\n df = df[df['c'].str.contains(just_subcorpora)]\n if isinstance(just_subcorpora, list):\n if all(isinstance(e, STRINGTYPE) for e in just_subcorpora):\n mp = df['c'].map(lambda x: x in just_subcorpora)\n df = df[mp]\n else:\n df = df.ix[just_subcorpora]\n\n if skip_subcorpora:\n if isinstance(skip_subcorpora, int):\n skip_subcorpora = [skip_subcorpora]\n if isinstance(skip_subcorpora, STRINGTYPE):\n df = df[~df['c'].str.contains(skip_subcorpora)]\n if isinstance(skip_subcorpora, list):\n if all(isinstance(e, STRINGTYPE) for e in skip_subcorpora):\n mp = df['c'].map(lambda x: x not in skip_subcorpora)\n df = df[mp]\n else:\n df = df.drop(skip_subcorpora, axis=0)\n\n return Concordance(df)\n\n if print_info:\n print('\\n***Processing results***\\n========================\\n')\n\n df1_istotals = False\n if isinstance(df, Series):\n df1_istotals = True\n df = DataFrame(df)\n # if just a single result\n else:\n df = DataFrame(df)\n if operation.startswith('k'):\n if sort_by is False:\n if not df1_istotals:\n sort_by = 'turbulent'\n if df1_istotals:\n df = df.T\n \n # figure out if there's a second list\n # copy and remove totals if there is\n single_totals = True\n using_totals = False\n outputmode = False\n\n if denominator.__class__ == Interrogation:\n try:\n denominator = denominator.results\n except AttributeError:\n denominator = denominator.totals\n\n if denominator is not False and not isinstance(denominator, STRINGTYPE):\n df2 = denominator.copy()\n using_totals = True\n if isinstance(df2, DataFrame):\n if len(df2.columns) > 1:\n single_totals = False\n else:\n df2 = Series(df2.iloc[:,0])\n elif isinstance(df2, Series):\n single_totals = True\n #if operation == 'k':\n #raise ValueError('Keywording requires a DataFrame for denominator. Use \"self\"?')\n else:\n if operation in ['k', 'a', '%', '/', '*', '-', '+']:\n denominator = 'self' \n if denominator == 'self':\n outputmode = True\n\n if operation.startswith('a') or operation.startswith('A'):\n if list(df.columns)[0] != '0' and list(df.columns)[0] != 0:\n df = df.T\n if using_totals:\n if not single_totals:\n df2 = df2.T\n\n if projection:\n # projection shouldn't do anything when working with '%', remember.\n df = projector(df, projection)\n if using_totals:\n df2 = projector(df2, projection)\n\n if spelling:\n df = convert_spell(df, convert_to=spelling)\n df = merge_duplicates(df, print_info=False)\n\n if not single_totals:\n df2 = convert_spell(df2, convert_to=spelling, print_info=False)\n df2 = merge_duplicates(df2, print_info=False)\n if not df1_istotals:\n sort_by = 'total'\n\n if replace_names:\n df = name_replacer(df, replace_names)\n df = merge_duplicates(df)\n if not single_totals:\n df2 = name_replacer(df2, replace_names, print_info=False)\n df2 = merge_duplicates(df2, print_info=False)\n if not sort_by:\n sort_by = 'total'\n\n if replace_subcorpus_names:\n df = name_replacer(df.T, replace_subcorpus_names)\n df = merge_duplicates(df).T\n df = df.sort_index()\n if not single_totals:\n if isinstance(df2, DataFrame):\n df2 = df2.T\n df2 = name_replacer(df2, replace_subcorpus_names, print_info=False)\n df2 = merge_duplicates(df2, print_info=False)\n if isinstance(df2, DataFrame):\n df2 = df2.T\n df2 = df2.sort_index()\n if not sort_by:\n sort_by = 'total'\n\n # remove old stats if they're there:\n statfields = ['slope', 'intercept', 'r', 'p', 'stderr']\n try:\n df = df.drop(statfields, axis=0)\n except:\n pass\n if using_totals:\n try:\n df2 = df2.drop(statfields, axis=0)\n except:\n pass\n\n # remove totals and tkinter order\n for name, ax in zip(['Total'] * 2 + ['tkintertable-order'] * 2, [0, 1, 0, 1]):\n if name == 'Total' and df1_istotals:\n continue\n try:\n df = df.drop(name, axis=ax, errors='ignore')\n except:\n pass\n for name, ax in zip(['Total'] * 2 + ['tkintertable-order'] * 2, [0, 1, 0, 1]):\n if name == 'Total' and single_totals:\n continue\n\n try:\n df2 = df2.drop(name, axis=ax, errors='ignore')\n except:\n pass\n\n df = skip_keep_merge_span(df)\n try:\n df2 = skip_keep_merge_span(df2)\n except:\n pass\n\n # drop infinites and nans\n df = df.replace([np.inf, -np.inf], np.nan)\n df = df.fillna(0.0)\n\n if just_totals:\n df = DataFrame(df.sum(), columns=['Combined total'])\n if using_totals:\n if not single_totals:\n df2 = DataFrame(df2.sum(), columns=['Combined total'])\n else:\n df2 = df2.sum()\n\n tots = df.sum(axis=1)\n\n if using_totals or outputmode:\n if not operation.startswith('k'):\n tshld = 0\n # set a threshold if just_totals\n if outputmode is True:\n df2 = df.T.sum()\n if not just_totals:\n df2.name = 'Total'\n else:\n df2.name = 'Combined total'\n using_totals = True\n single_totals = True\n if just_totals:\n if not single_totals:\n tshld = set_threshold(df2, threshold, prinf=print_info)\n df, tots = combiney(df, df2, operation=operation, threshold=tshld, prinf=print_info)\n \n # if doing keywording...\n if operation.startswith('k'):\n\n if isinstance(denominator, STRINGTYPE):\n if denominator == 'self':\n df2 = df.copy()\n else:\n df2 = denominator\n\n from corpkit.keys import keywords\n df = keywords(df, df2, \n selfdrop=selfdrop, \n threshold=threshold, \n print_info=print_info,\n editing=True,\n calc_all=calc_all,\n sort_by=sort_by,\n measure=keyword_measure,\n **kwargs)\n \n # drop infinites and nans\n df = df.replace([np.inf, -np.inf], np.nan)\n df = df.fillna(0.0)\n\n # resort data\n if sort_by or keep_stats:\n df = resort(df, keep_stats=keep_stats, sort_by=sort_by)\n if isinstance(df, bool):\n if df is False:\n return 'linregress'\n\n if keep_top:\n if not just_totals:\n df = df[list(df.columns)[:keep_top]]\n else:\n df = df.head(keep_top)\n\n if just_totals:\n # turn just_totals into series:\n df = Series(df['Combined total'], name='Combined total')\n\n if df1_istotals:\n if operation.startswith('k'):\n try:\n df = Series(df.ix[dataframe1.name])\n df.name = '%s: keyness' % df.name\n except:\n df = df.iloc[0, :]\n df.name = 'keyness' % df.name\n\n # generate totals branch if not percentage results:\n # fix me\n if df1_istotals or operation.startswith('k'):\n if not just_totals:\n try:\n total = Series(df['Total'], name='Total')\n except:\n total = 'none'\n pass\n\n #total = df.copy()\n else:\n total = 'none'\n else:\n # might be wrong if using division or something...\n try:\n total = df.T.sum(axis=1)\n except:\n total = 'none'\n \n if not isinstance(tots, DataFrame) and not isinstance(tots, Series):\n total = df.sum(axis=1)\n else:\n total = tots\n\n if isinstance(df, DataFrame):\n if df.empty:\n datatype = 'object'\n else:\n datatype = df.iloc[0].dtype\n else:\n datatype = df.dtype\n locs['datatype'] = datatype\n\n # TURN INT COL NAMES INTO STR\n try:\n df.results.columns = [str(d) for d in list(df.results.columns)]\n except:\n pass\n\n def add_tkt_index(df):\n \"\"\"add an order for tkintertable if using gui\"\"\"\n if isinstance(df, Series):\n df = df.T\n df = df.drop('tkintertable-order', errors='ignore', axis=0)\n df = df.drop('tkintertable-order', errors='ignore', axis=1)\n dat = [i for i in range(len(df.index))]\n df['tkintertable-order'] = Series(dat, index=list(df.index))\n df = df.T\n return df\n\n # while tkintertable can't sort rows\n if checkstack('tkinter'):\n df = add_tkt_index(df)\n\n if kwargs.get('df1_always_df'):\n if isinstance(df, Series):\n df = DataFrame(df)\n\n # delete non-appearing conc lines\n lns = None\n if isinstance(getattr(interrogation, 'concordance', None), Concordance):\n try:\n col_crit = interrogation.concordance['m'].map(lambda x: x in list(df.columns))\n ind_crit = interrogation.concordance['c'].map(lambda x: x in list(df.index))\n lns = interrogation.concordance[col_crit]\n lns = lns.loc[ind_crit]\n lns = Concordance(lns)\n except ValueError:\n lns = None\n \n output = Interrogation(results=df, totals=total, query=locs, concordance=lns)\n\n if print_info:\n print('***Done!***\\n========================\\n')\n\n return output\n\n\n\n\n\n"
},
{
"path": "corpkit/env.py",
"content": "\"\"\"\nA corpkit interpreter, with natural language commands.\n\ntodo:\n\n* documentation\n* handling of kwargs tuples etc\n* checking for bugs, tests\n* merge entries with name\n\n\"\"\"\n\nfrom __future__ import print_function\n\nhelp_text = \"\\nThis is a dedicated interpreter for corpkit, a tool for creating, searching\\n\" \\\n \"and visualising corpora. It works through a combination of objects and commands:\\n\\n\" \\\n \"Objects:\\n\\n\" \\\n \" +---------------+-----------------------------------------------+ \\n\"\\\n \" | Object | Contains | \\n\"\\\n \" +===============+===============================================+ \\n\"\\\n \" | `corpus` | Dataset selected for parsing or searching | \\n\"\\\n \" +---------------+-----------------------------------------------+ \\n\"\\\n \" | `results` | Search output | \\n\"\\\n \" +---------------+-----------------------------------------------+ \\n\"\\\n \" | `edited` | Results after sorting, editing or calculating | \\n\"\\\n \" +---------------+-----------------------------------------------+ \\n\"\\\n \" | `concordance` | Concordance lines from search | \\n\"\\\n \" +---------------+-----------------------------------------------+ \\n\"\\\n \" | `features` | General linguistic features of corpus | \\n\"\\\n \" +---------------+-----------------------------------------------+ \\n\"\\\n \" | `wordclasses` | Distribution of word classes in corpus | \\n\"\\\n \" +---------------+-----------------------------------------------+ \\n\"\\\n \" | `postags` | Distribution of POS tags in corpus | \\n\"\\\n \" +---------------+-----------------------------------------------+ \\n\"\\\n \" | `figure` | Plotted data | \\n\"\\\n \" +---------------+-----------------------------------------------+ \\n\"\\\n \" | `query` | Values used to perform search or edit | \\n\"\\\n \" +---------------+-----------------------------------------------+ \"\\\n \"\\n\\nCommand examples:\\n\\n\" \\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | Command | Purpose | Syntax | \\n\"\\\n \" +=================+==============================================================+============================================================================================+ \\n\"\\\n \" | `new` | Make a new project | `new project ` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `set` | Set current corpus | `set ` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `parse` | Parse corpus | `parse corpus with [options]*` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `search` | Search a corpus for linguistic feature, generate concordance | `search corpus for [feature matching pattern]* showing [feature]* with [options]*` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `edit` | Edit results or edited results | `edit result by [skipping subcorpora/entries matching pattern]* with [options]*` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `calculate` | Calculate relative frequencies, keyness, etc. | `calculate result/edited as operation of denominator` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `sort` | Sort results or concordance | `sort result/concordance by value` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `plot` | Visualise result or edited result | `plot result/edited as line chart with [options]*` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `show` | Show any object | `show object` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `annotate` | Add annotations to corpus based on search results | `annotate all with field as and value as m` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `unannotate` | Delete annotation fields from corpus | `unannotate field` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `sample` | Get a random sample of subcorpora or files from a corpus | `sample 5 subcorpora of corpus` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `call` | Name an object (i.e. make a variable) | `call object ''` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `export` | Export result, edited result or concordance to string/file | `export result to string/csv/latex/file ` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `save` | Save data to disk | `save object to ` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `load` | Load data from disk | `load object as result` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `store` | Store something in memory | `store object as ` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `fetch` | Fetch something from memory | `fetch as object` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `help` | Get help on an object or command | `help command/object` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `history` | See previously entered commands | `history` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `ipython` | Enter IPython with objects available | `ipython` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `py` | Execute Python code | `py 'print('hello world')'` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \" | `!` | Run a line of bash shell | `!ls -al data` | \\n\"\\\n \" +-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+ \\n\"\\\n \"\\nMore information:\\n\\nYou can access more specific help by entering corpkit, then by doing 'help ', or by visiting\\n\" \\\n \"http://corpkit.readthedocs.io/en/latest\\n\\n\" \\\n \"For help on viewing results, hit '?' when in the result viewing mode. For concordances, hit 'h'.\\n\\n(Hit 'q' to exit help).\\n\\n\"\n\nfrom corpkit.constants import STRINGTYPE, PYTHON_VERSION, INPUTFUNC\n\nimport os\nimport traceback\nimport pandas as pd\n\ntry:\n import gnureadline as readline\nexcept ImportError:\n import readline\n\nimport atexit\n\nimport corpkit\nfrom corpkit import *\nfrom corpkit.constants import transshow, transobjs\n\nimport warnings\nwarnings.simplefilter(action=\"ignore\", category=UserWarning)\n\n# pandas display setup, though this is rarely used when LESS\n# and tabview are available\nsize = pd.util.terminal.get_terminal_size()\npd.set_option('display.max_rows', 0)\npd.set_option('display.max_columns', 0)\npd.set_option('display.float_format', lambda x: '{:,.3f}'.format(x))\n\n# allow ctrl-r, etc\nreadline.parse_and_bind('set editing-mode vi')\n\n# command completion\nposs = ['testing', 'search', 'concordance']\nfrom corpkit.completer import Completer\n\ntry:\n import gnureadline as readline\nexcept ImportError:\n import readline as readline\n\ncompleter = Completer(poss)\n\nreadline.set_completer(completer.complete)\n\nif 'libedit' in readline.__doc__:\n readline.parse_and_bind(\"bind ^I rl_complete\")\nelse:\n readline.parse_and_bind(\"tab: complete\")\n\n# this code makes it possible to remember history from previous sessions\nhistory_path = os.path.expanduser(\"~/.pyhistory\")\n\ndef save_history(history_path=history_path):\n \"\"\"\n On exit, add previous commands to history file\n \"\"\"\n try:\n import gnureadline as readline\n except ImportError:\n import readline as readline\n try:\n readline.remove_history_item(readline.get_current_history_length() - 1)\n except ValueError:\n pass\n readline.write_history_file(history_path)\n\nif os.path.exists(history_path):\n try:\n readline.set_history_length(1000)\n readline.read_history_file(history_path)\n readline.set_history_length(1000)\n except IOError:\n pass\n\n# bind to exit\natexit.register(save_history)\n\n# tab completion\n\n# ctrl r search\n#readline.parse_and_bind(\"bind ^R em-inc-search-prev\")\n\ndel os, atexit, save_history, history_path\n#del rlcompleter\n\nclass Objects(object):\n \"\"\"\n In here are all of the major variables being used by the interpreter\n This is much nicer than using globals\n \"\"\"\n\n def __init__(self):\n\n # get dict of all possible wordlists\n from corpkit.dictionaries.roles import roles\n from corpkit.dictionaries.wordlists import wordlists\n from corpkit.dictionaries.process_types import processes\n from corpkit.dictionaries.verblist import allverbs\n from collections import defaultdict\n wl = wordlists._asdict()\n try:\n wl.update(roles.__dict__)\n except AttributeError:\n wl.update(roles._asdict())\n wl.update(processes.__dict__)\n wl['anyverb'] = allverbs\n\n self._protected = ['result', 'previous', 'edited', 'corpus', 'concordance',\n 'query', 'features', 'postags', 'wordclasses', 'stored',\n 'figure', 'totals', 'wordlists', 'wordlist', 'matching',\n 'showing', 'excluding', 'not', 'as', 'with', 'and', 'by',\n 'm', 'l', 'r', 'conc', 'keeping', 'skipping', 'entries', 'subcorpora',\n 'merging', 'k', 'sampled',\n '_in_a_project', '_previous_type', '_old_concs',\n '_conc_colours', '_conc_kwargs', '_do_conc',\n '_interactive', '_decimal', '_protected']\n \n # main variables the user might access\n self.result = None\n self.previous = None\n self.edited = None\n self.corpus = None\n self.concordance = None\n self.query = None\n self.features = None\n self.sampled = None\n self.postags = None\n self.wordclasses = None\n self.stored = {}\n self.figure = None\n self.totals = None\n self.wordlists = wl\n self.wordlist = None\n self.named = {}\n self.just = {}\n self.skip = {}\n self.symbolic = False\n self._docker = False\n self.max_cols = None\n self.max_rows = None\n self._comma = False\n\n # system toggles and references to older data\n self._in_a_project = None\n self._previous_type = None\n self._old_concs = []\n self._conc_colours = defaultdict(dict)\n self._conc_kwargs = {'n': 999}\n\n # user settings (more to come?)\n self._do_conc = True\n self._interactive = True\n self._decimal = 3\n self._annotation_unlocked = False\n\n def _get(self, name):\n \"\"\"\n Get an object, item from store or wordlist\n \"\"\"\n if name.startswith('wordlist') or name.startswith('stored'):\n ob, attr = name.split('.', 1) if '.' in name else name.split(':', 1)\n return ob, getattr(self, ob).get(attr)\n feats = ['features', 'postags', 'wordclasses']\n if any(name.startswith(i) for i in feats):\n corpus = self.corpus\n attr = name.split('.', 1)\n if len(attr) == 1:\n return name, getattr(corpus, name)\n else:\n bit = attr[1].upper() if attr[0] == 'postags' else attr[1].title()\n data = getattr(corpus, attr[0])[bit]\n return 'series', data\n if name in self._protected:\n return name, getattr(self, name, None)\n else:\n return self.named.get(name, (None, None))\n\ndef interpreter(debug=False,\n fromscript=False,\n quiet=False,\n python_c_mode=False,\n profile=False,\n loadcurrent=False,\n docker=False):\n\n import os\n\n allig = '\\n Welcome!\\n'\n allig += \" .-._ _ _ _ _ _ _ _ _\\n \" \\\n \".-''-.__.-'00 '-' ' ' ' ' ' ' ' '-.\\n \" \\\n \"'.___ ' . .--_'-' '-' '-' _'-' '._\\n \" \\\n \"V: V 'vv-' '_ '. .' _..' '.'.\\n \"\\\n \" '=.____.=_.--' :_.__.__:_ '. : :\\n \"\\\n \" (((____.-' '-. / : :\\n \"\\\n \" (((-'\\ .' /\\n \"\\\n \" _____..' .'\\n \"\\\n \" '-._____.-'\\n\"\n\n objs = Objects()\n\n if docker:\n objs._docker = docker\n\n # basic way to check that we're currently in a project, because i'm lazy\n def check_in_project():\n import os\n proj_dirs = ['data', 'saved_interrogations', 'exported']\n if all(x in os.listdir('.') for x in proj_dirs):\n return True\n\n cpkt_dirs = ['API-README.md', 'corpkit', 'data', 'rst_docs']\n if all(x in os.listdir('.') for x in cpkt_dirs):\n return True\n\n return False\n\n def generate_outprint():\n \"\"\"\n Text to appear when switching to IPython\n \"\"\"\n s = 'Switched to IPython ... defined variables:\\n\\n\\t'\n s += 'corpus, results, concordance, edited ...\\n\\n\\tType \"quit\" to return to corpkit environment'\n return s\n\n def read_script(fname):\n \"\"\"\n If a sript was passed to corpkit, get lines in it, minus comments\n \"\"\"\n from corpkit.constants import OPENER\n with OPENER(fname, 'r') as fo:\n data = fo.read()\n data = data.splitlines()\n data = [i for i in data if i.strip() and i.strip()[0] != '#']\n \n # turn off concordancing if it's never used in the script\n if 'concordance' not in ' '.join(data):\n objs._do_conc = False\n\n return list(reversed(data))\n\n def helper(tokens):\n \"\"\"\n Give user help if they enter a command alone\n \"\"\"\n func = get_command.get(tokens[0], False)\n\n if not func:\n print('Not recognised: %s' % tokens[0])\n return\n\n print(getattr(func, '__doc__', 'Not written yet, sorry.'))\n\n def switch_to_ipython(args):\n from IPython import embed\n from IPython.terminal.embed import InteractiveShellEmbed\n s = generate_outprint()\n for k, v in objs.__dict__.items():\n if not k.startswith('_'):\n locals()[k] = v\n for k, v in objs.named.items():\n locals()[k] = v\n\n ret = InteractiveShellEmbed(header=s,\n babaii='babaii',\n #colors='Linux',\n exit_msg='Switching back to corpkit environment ...',\n local_ns=locals())\n cc = ret()\n\n def switch_to_jupyter(args):\n comm = [\"jupyter\", \"notebook\"]\n import subprocess\n if args:\n import os\n nbfile = args[-1]\n if not nbfile.endswith('.ipynb'):\n nbfile = nbfile + '.ipynb'\n if os.path.isfile(nbfile):\n comm.append(nbfile)\n subprocess.call(comm) \n\n def switch_to_gui(args):\n import subprocess\n import os\n print('Loading graphical interface ... ')\n subprocess.call([\"python\", \"-m\", 'corpkit.gui', os.getcwd()])\n\n def show_concordance(obj, command, args):\n import pydoc\n # update the showing parameters \n kwargs = process_kwargs(args)\n if kwargs:\n objs._conc_kwargs.update(kwargs)\n \n if obj is None:\n print(\"There's no concordance here right now, sorry.\")\n return\n # retrieve the correct concordances, so we can colour them\n found_the_conc = next((i for i, c in enumerate(objs._old_concs) \\\n if c.equals(obj)), None)\n if found_the_conc is None:\n return\n # if colours have been saved for these lines, try to fill them in \n if objs._conc_colours.get(found_the_conc):\n try:\n lines_to_print = []\n from colorama import Fore, Back, Style, init\n lines = obj.format(print_it=False, **objs._conc_kwargs).splitlines()\n # for each concordance line\n for line in lines:\n # get index as str\n num = line.strip().split(' ', 1)[0]\n # get dict of style and colour for line\n gotnums = objs._conc_colours[found_the_conc].get(num, {})\n highstr = ''\n for sty, col in gotnums.items():\n if col.upper() in ['DIM', 'NORMAL', 'BRIGHT', 'RESET_ALL']:\n thing_to_color = Style\n elif sty == 'Back':\n thing_to_color = Back\n else:\n thing_to_color = Fore\n highstr += getattr(thing_to_color, col.upper())\n highstr += line \n lines_to_print.append(highstr)\n\n if objs._interactive:\n pydoc.pipepager('\\n'.join(lines_to_print), cmd=\"less -X -R -S\")\n else:\n print('\\n'.join(lines_to_print))\n except ImportError:\n formatted = obj.format(print_it=False, **objs._conc_kwargs) \n if objs._interactive:\n pydoc.pipepager(formatted, cmd=\"less -X -R -S\")\n else:\n print(formatted)\n\n else:\n formatted = obj.format(print_it=False, **objs._conc_kwargs)\n if objs._interactive:\n pydoc.pipepager(formatted, cmd=\"less -X -R -S\")\n else:\n print(formatted)\n\n def show_table(obj, objtype=False, start_pos=False):\n \"\"\"\n Print or tabview a Pandas object\n \"\"\"\n if objs._interactive:\n import tabview\n showfunc = tabview.view\n kwa = {'column_width': 10, 'align_right': True}\n else:\n showfunc = print\n kwa = {}\n\n if start_pos:\n kwa['start_pos'] = start_pos\n\n obj = getattr(obj, 'results', obj)\n if isinstance(obj, (pd.DataFrame, pd.Series)):\n df = obj.round(objs._decimal)\n if isinstance(obj, pd.DataFrame):\n df = df.iloc[:objs.max_rows,:objs.max_cols]\n else:\n df = df[:objs.max_rows]\n\n if objs._comma:\n if isinstance(obj, pd.DataFrame):\n df = df.applymap(lambda x: '{:,}'.format(x))\n else:\n df = df.apply(lambda x: '{:,}'.format(x))\n #if showfunc == tabview.view:\n # df = df.astype(str).apply(lambda x: x.str.rjust(len(x.name), ' '))\n showfunc(df, **kwa)\n return\n # not sure what might be here\n elif obj:\n try:\n df = obj.round(objs._decimal)\n except:\n df = obj\n try:\n df = df[:objs.max_rows,:objs.max_cols]\n except:\n pass\n showfunc(df[:objs.max_rows,:objs.max_cols], **kwa)\n return\n #else:\n # if isinstance(objs._get(command)[1], (pd.DataFrame, pd.Series)):\n # showfunc(objs._get(command)[1].round(objs._decimal), **kwa)\n # return\n\n def single_command_print(command):\n \"\"\"\n If the user enters just a single token, show them that token\n This function also processes multiword tokens sometimes, which is inconsistent.\n \"\"\"\n\n helpable = ['calculate', 'plot', 'search', 'fetch', 'store', 'save', 'edit',\n 'export', 'sort', 'load', 'mark', 'del', 'annotate', 'unannotate',\n 'sample', 'call']\n\n if isinstance(command, list) and len(command) == 1 and command[0] in helpable:\n helper(command)\n\n args = []\n if isinstance(command, list):\n args = command[1:]\n command = command[0]\n\n if command in objs.named.keys():\n objtype, obj = objs.named.get(command)\n if isinstance(obj, str):\n print('%s: %s' % (command, obj))\n return\n elif objtype == 'eval':\n print('%s: ' % command, obj)\n else:\n objtype, obj = objs._get(command)\n if not objtype:\n objtype = command\n\n if objtype == 'ls':\n import os\n print('\\n'.join(os.listdir('.')))\n\n if objtype == 'clear':\n try:\n from blessings import Terminal\n terminal = Terminal()\n print(terminal.clear())\n print(terminal.move(0,0))\n except:\n print(chr(27) + \"[2J\")\n\n if objtype == 'history':\n import readline\n for i in range(readline.get_current_history_length()):\n print(readline.get_history_item(i + 1))\n\n if objtype == 'help':\n import pydoc\n pydoc.pipepager(help_text, cmd='less -X -R -S') \n\n if objtype == 'corpus':\n if not hasattr(obj, 'name'):\n print('Corpus not set. use \"set \".')\n return\n else:\n print(obj)\n \n elif objtype == 'python' or objtype == 'ipython':\n switch_to_ipython(args)\n\n elif objtype.startswith('jupyter') or objtype == 'notebook':\n switch_to_jupyter(args)\n\n elif objtype == 'gui':\n switch_to_gui(args)\n \n elif objtype in ['result', 'edited', 'totals', 'previous',\n 'features', 'postags', 'wordclasses', 'series']:\n show_table(obj, objtype)\n\n elif objtype == 'concordance':\n show_concordance(obj, objtype, args)\n elif objtype == 'wordlists':\n show_this([objtype])\n elif objtype == 'wordlist':\n print(objs.wordlist)\n elif objtype.startswith('wordlist'):\n o, l = objtype.split('.', 1) if '.' in objtype else objtype.split(':', 1)\n print(getattr(objs.wordlists, l))\n elif objtype == 'query':\n show_this([objtype])\n else:\n pass\n\n def set_something(tokens):\n \"\"\"\n Set the active corpus, a corpus filter, subcorpus structure, or option:\n\n :Examples:\n\n `set junglebook-parsed`\n `set decimal as 3`\n `set just topic as freedom|democracy`\n `set max_rows as 1000`\n `set papers as corpora`\n\n \"\"\"\n if tokens and tokens[0].startswith('decimal'):\n objs._decimal = int(tokens[2])\n print('Decimal places set to %d.' % objs._decimal) \n return\n\n if tokens and tokens[0] in ['max_cols', 'max_rows']:\n dim = 'rows' if tokens[0].endswith('rows') else 'columns'\n \n if tokens[-1] in ['off', 'all', 'false', 'False', 'None', 'none']:\n setattr(objs, tokens[0], None)\n print(\"Max %s turned off.\" % dim)\n return\n else:\n setattr(objs, tokens[0], int(tokens[-1]))\n print('Max %s set to %s.' % (dim, format(int(tokens[-1]), ',')))\n return\n\n # set subcorpora as attribute of corpus object ... is this ideal?\n if tokens and tokens[0].startswith('subcorp'):\n from corpkit.corpus import Corpus\n if tokens[-1] in ['false', 'none', 'normal', 'off',\n 'folder', 'folders', 'False', 'None']:\n tokens[-1] = False\n objs.symbolic = tokens[-1]\n #objs.corpus = Corpus(objs.corpus, subcorpora=tokens[-1], print_info=False)\n print('Set subcorpora to \"%s\".' % (tokens[-1]))\n return\n\n # setting skipping and just filters\n if tokens and tokens[0] in ['skip', 'just']:\n attr = tokens[0]\n # if the filter is already in the dict\n if isinstance(getattr(objs, tokens[0]), dict) and tokens[1] in getattr(objs, tokens[0]).keys():\n oldd = getattr(objs, tokens[0])\n # delete it\n if tokens[-1] in ['none', 'None', 'off', 'default']:\n oldd.pop(tokens[1], None)\n setattr(objs, tokens[0], oldd)\n # modify it\n else:\n oldd[tokens[1]] = parse_pattern(tokens[-1])\n setattr(objs, tokens[0], oldd)\n # delete the entire filter dict\n elif tokens[-1] in ['none', 'None', 'off', 'default']:\n setattr(objs, attr, False)\n print(\"Reset %s filter.\" % attr)\n return\n # if not in dict, add it\n metfeat = tokens[1]\n crit = tokens[-1]\n if crit.startswith('tag'):\n setattr(objs, attr, {'tags': parse_pattern(crit)})\n else:\n if isinstance(getattr(objs, attr, False), dict):\n d = getattr(objs, attr)\n d[metfeat] = parse_pattern(crit)\n setattr(objs, attr, d)\n else:\n setattr(objs, attr, {metfeat: parse_pattern(crit)})\n print(\"Current %s filter: %s\" % (attr, getattr(objs, attr)))\n return\n\n if not check_in_project():\n print(\"Must be in project to set corpus.\")\n return\n \n from corpkit.other import load\n if not tokens:\n # todo: pick first one if only one\n show_this(['corpora'])\n selected = INPUTFUNC('Pick a corpus by name or number: ')\n selected = selected.strip()\n if not selected:\n return\n elif selected.isdigit():\n dirs = [x for x in os.listdir('data') \\\n if os.path.isdir(os.path.join('data', x))]\n set_something([dirs[int(selected)-1]])\n return\n else:\n set_something([selected])\n return\n\n path = tokens[0]\n loadsaved = len(tokens) > 1 and tokens[1].startswith('load')\n\n kwargs = process_kwargs(tokens, fixjust=True)\n if debug:\n print('KWARGS', kwargs)\n\n if os.path.exists(path) or os.path.exists(os.path.join('data', path)):\n from corpkit.corpus import Corpus, Corpora\n if tokens[-1] == 'corpora':\n objs.corpus = Corpora(path, load_saved=loadsaved, **kwargs)\n else:\n objs.corpus = Corpus(path, load_saved=loadsaved, **kwargs)\n else:\n dirs = [x for x in os.listdir('data') if os.path.isdir(os.path.join('data', x))]\n corpname = dirs[int(tokens[0])-1]\n set_something([corpname] + tokens[1:])\n\n\n def get_something(tokens):\n \"\"\"\n Get a subcorpus or file of a corpus\n\n :Example:\n\n get file 1 from corpus\n\n \"\"\"\n # if the user wants to start at a sentence\n start_pos = False\n six = next((i for i, w in enumerate(tokens) if w.startswith('sent')), -1) + 1\n if six:\n start_pos = int(tokens[six])\n tokens = [t for i, t in enumerate(tokens) if i != six and i != six-1]\n\n target_corpus = 'corpus'\n if len(tokens) > 2:\n target_corpus = tokens[-1]\n corpp = objs._get(target_corpus)[1]\n getwhat = 'files' if tokens[0] == 'file' else 'subcorpora'\n dlist = getattr(corpp, getwhat)\n try:\n ix = int(tokens[1]) - 1\n except:\n ix = tokens[1]\n ob = dlist[ix]\n if objs._interactive and hasattr(ob, 'document'):\n ndf = ob.document\n ndf.columns = ['Word', 'Lemma', 'POS', 'NER', 'Morphology', 'Governor', 'Function', 'Dependents', 'Co-reference']\n ndf.index.names = ['Sentence', 'Token']\n start_pos = next((i for i, l in enumerate(ndf.index.labels[0]) if l == start_pos), 0)\n show_table(ndf, objtype=False, start_pos=start_pos)\n objs.sampled = ob\n print('Sample created.')\n return\n\n def parse_search_related(search_related):\n \"\"\"\n parse the capitalised tokens in\n 'search corpus FOR GOVERNOR-LEMMA MATCHING XYZ AND LEMMA MATCHING .* showing ...'\n \"\"\"\n search = {}\n exclude = {} \n searchmode = 'all'\n cqlmode = False\n if search_related[0] != 'for':\n search_related.insert(0, 'for')\n for i, token in enumerate(search_related):\n if token in ['for', 'and', 'not', 'or']:\n if token == 'or':\n searchmode = 'any'\n if search_related[i+1] == 'not':\n continue\n if search_related[i+1] == 'features':\n search = 'features'\n continue\n if search_related[i+1] == 'postags':\n search = 'postags'\n continue\n if search_related[i+1] == 'wordclasses':\n search = 'wordclasses'\n continue\n k = search_related[i+1]\n if k == 'cql':\n cqlmode = True\n aquery = next((i for i in search_related[i+2:] \\\n if i not in ['matching']), False)\n if aquery:\n search = aquery\n break\n k = process_long_key(k)\n v = search_related[i+3].strip(\"'\")\n v = parse_pattern(v)\n if token == 'not':\n exclude[k] = v\n else:\n search[k] = v\n return search, exclude, searchmode, cqlmode\n\n def process_long_key(srch):\n \"\"\"\n turn governor-lemma into 'gl', etc.\n \"\"\"\n from corpkit.constants import transshow, transobjs\n \n transobjs = {v.lower(): k.replace(' ', '-') for k, v in transobjs.items()}\n transshow = {v.lower(): k.replace(' ', '-') for k, v in transshow.items()}\n showplurals = {k + 's': v for k, v in transshow.items()}\n objsplurals = {k + 's': v for k, v in transobjs.items()}\n \n transshow['distance'] = 'r'\n transshow['ngram'] = 'n'\n transshow['distances'] = 'r'\n transshow['ngrams'] = 'n'\n transshow['wordclass'] = 'x'\n transshow['wordclasses'] = 'x'\n transshow['count'] = 'c'\n\n transshow.update(showplurals)\n transobjs.update(objsplurals)\n\n ngram = srch.startswith('n')\n colls = srch.startswith('b')\n\n if ngram or colls:\n newsrch = srch.split('-', 1)[-1]\n if newsrch == srch:\n srch = 'nw' if ngram else 'bw'\n else:\n srch = newsrch\n\n srch = srch.lower()\n \n if '-' not in srch:\n if srch in transobjs:\n string = transobjs.get(srch) + 'w'\n elif srch in transshow or srch.startswith('t'):\n if not srch.startswith('t'):\n string = 'm' + transshow.get(srch)\n else:\n string = transshow.get(srch, 't')\n else:\n string = srch\n else:\n obj, show = srch.split('-', 1)\n string = '%s%s' % (transobjs.get(obj, obj), transshow.get(show, show))\n if ngram:\n return 'n' + string\n elif colls:\n return 'b' + string\n else:\n return string\n\n def parse_pattern(val):\n \"\"\"\n Parse the token after 'matching'---that is, the search criteria\n \"\"\"\n trans = {'true': True,\n 'false': False,\n 'on': True,\n 'off': False,\n 'none': None}\n\n # this means that if the query is a variable, the variable is returned\n # maybe this is not ideal behaviour.\n if val in objs.named.keys():\n return objs.named[val]\n\n if any(val.startswith(x) for x in ['role', 'process', 'wordlist']) \\\n and any(x in [':', '.'] for x in val):\n lis, attrib = val.split('.', 1) if '.' in val else val.split(':', 1)\n customs = []\n from corpkit.dictionaries import roles, processes, wordlists\n mapped = {'roles': roles,\n 'processes': processes}\n\n if lis.startswith('wordlist'):\n lst = objs.wordlists.get(attrib)\n else:\n lst = getattr(mapped.get(lis), attrib)\n if lst:\n return lst\n else:\n print('Wordlist \"%s\" unrecognised.' % attrib)\n\n if val.isdigit():\n return int(val)\n elif val.startswith('[') and val.endswith(']'):\n val = val.lstrip('[').rstrip(']')\n if ', ' in val:\n return val.strip('\"').strip(\"'\").split(', ')\n elif ',' in val:\n return val.strip('\"').strip(\"'\").split(',')\n elif ' ' in val:\n return val.strip('\"').strip(\"'\").split()\n\n elif val.lower() in trans.keys():\n return trans.get(val.lower())\n # interpret columns\n elif all(i in ['i', 'c', 'f', 's', 'l', 'm', 'r'] for i in val.lower()) and len(val) <= 6:\n return [i for i in val.lower()]\n else:\n if val in dir(__builtins__) + ['any', 'all']:\n return val\n try:\n return eval(val)\n except (SyntaxError, NameError):\n return val\n\n def search_helper(text='search'):\n \"\"\"\n Interactive mode for search, exclude or show\n \"\"\"\n\n if text in ['search', 'exclude']:\n search_or_show = {}\n else:\n search_or_show = []\n while True:\n out = INPUTFUNC('\\n %s (words, lemma, pos, function ... ) > ' % text)\n out = out.lower()\n \n if not out:\n break\n if out.startswith('done'):\n break\n\n\n if out == 'cql':\n cql = INPUTFUNC('\\n CQL query > ')\n return cql.strip()\n if text == 'show':\n out = out.replace(', ', ' ').replace(',', ' ').replace('/', ' ')\n return out.split(' ')\n #search_or_show.append(out)\n #continue\n val = INPUTFUNC('\\n value (regex, wordlist) > ')\n if not val:\n continue\n if val.startswith('done'):\n break\n out = process_long_key(out)\n search_or_show[out] = parse_pattern(val)\n\n return search_or_show\n\n def process_kwargs(tokens, fixjust=False):\n \"\"\"\n Turn the last part of a command into a dict of kwargs\n \"\"\"\n if 'with' in tokens:\n start = tokens.index('with')\n with_related = tokens[start+1:]\n else:\n with_related = []\n withs = {}\n skips = []\n for i, token in enumerate(with_related):\n if i in skips or token == 'and':\n continue\n # this is used when making corpora with filters\n if fixjust:\n if token == 'skip':\n pat = parse_pattern(with_related[i+3])\n withs['skip'] = {with_related[i+1]: pat}\n elif token == 'just':\n pat = parse_pattern(with_related[i+3])\n withs['just'] = {with_related[i+1]: pat}\n skips.append(i+1)\n skips.append(i+2)\n skips.append(i+3)\n if token in ['skip', 'just']:\n continue\n if token == 'not':\n withs[with_related[i+1].lower()] = False\n skips.append(i+1)\n elif '=' not in token:\n if len(with_related) >= i+2 and with_related[i+1] == 'as':\n val = with_related[i+2]\n val = parse_pattern(val)\n withs[token.lower()] = val\n skips.append(i+1)\n skips.append(i+2)\n else:\n withs[token.lower()] = True\n elif '=' in token:\n k, v = token.lower().split('=', 1)\n v = parse_pattern(v)\n withs[k] = v\n return withs\n\n\n def parse_search_args(tokens):\n \"\"\"\n Put all search arguments together---search, exclude, show, and kwargs\n \"\"\"\n tokens = tokens[1:]\n \n # interactive mode\n if not tokens:\n search = search_helper(text='search')\n exclude = search_helper(text='exclude')\n show = search_helper(text='show')\n show = [process_long_key(i) for i in show]\n kwargs = {'search': search, 'show': show, 'exclude': exclude,\n 'cql': isinstance(search, str), 'conc': objs._do_conc}\n return kwargs\n\n search_related = []\n for token in tokens:\n search_related.append(token)\n if token in [',', 'showing', 'with', 'excluding']:\n break\n\n if 'excluding' in tokens:\n start = tokens.index('excluding')\n ex_related = tokens[start+1:]\n end = ex_related.index('showing') if 'showing' in ex_related else False\n if end is False:\n end = ex_related.index('with') if 'with' in ex_related else False\n if end:\n ex_related = ex_related[:end]\n else:\n ex_related = []\n\n if 'showing' in tokens:\n start = tokens.index('showing')\n show_related = tokens[start+1:]\n end = show_related.index('with') if 'with' in show_related else False\n if end:\n show_related = show_related[:end]\n else:\n show_related = []\n\n if 'with' in tokens:\n start = tokens.index('with')\n with_related = tokens[start+1:]\n else:\n with_related = []\n\n search, exclude, searchmode, cqlmode = parse_search_related(search_related)\n if not exclude and ex_related:\n exclude, _, _, _, _ = parse_search_related(ex_related)\n\n show = []\n if show_related:\n for token in show_related:\n token = token.rstrip(',')\n if token == 'and':\n continue\n token = process_long_key(token)\n show.append(token)\n else:\n show = ['w']\n\n withs = process_kwargs(tokens)\n\n kwargs = {'search': search, 'exclude': exclude,\n 'show': show, 'cql': cqlmode, 'conc': objs._do_conc, 'searchmode': searchmode}\n kwargs.update(withs)\n return kwargs\n\n def search_corpus(tokens):\n \"\"\"\n Search a corpus for lexicogrammatical features. You are limited only by your imagination.\n \n :Syntax:\n\n search corpus for [object matching pattern]* showing [things to show]* with [extra options]*\n \n The asterisk marked parts of the query can be recursive and negated\n\n :Examples:\n\n > search corpus for cql matching '[word=\"test\"]' showing word and lemma\n > search corpus for governor-function matching roles:process with coref\n > search corpus for trees matching \"/NN.?/ >># NP\" \n > search corpus for words matching \"^[abcde]\" with preserve_case and case_sensitive\n \"\"\"\n\n corpp = objs._get(tokens[0])[1]\n\n if not corpp:\n print('Corpus not set. use \"set \".')\n return\n\n kwargs = parse_search_args(tokens)\n kwargs['quiet'] = quiet\n \n if 'just_metadata' not in kwargs:\n kwargs['just_metadata'] = objs.just if objs.just else False\n if 'skip_metadata' not in kwargs:\n kwargs['skip_metadata'] = objs.skip if objs.skip else False\n if 'subcorpora' not in kwargs:\n kwargs['subcorpora'] = objs.symbolic if objs.symbolic else False\n\n if debug:\n print(kwargs)\n\n kwargs['show_conc_metadata'] = True\n if corpp.level == 's' and not kwargs.get('subcorpora', False) \\\n and not corpp.symbolic:\n kwargs['files_as_subcorpora'] = True\n\n #objs.corpus.just = objs.just\n #objs.corpus.skip = objs.skip\n #objs.corpus.symbolic = objs.symbolic\n \n sch = kwargs.get('search', False)\n if sch in ['features', 'postags', 'wordclasses']:\n objs.result = getattr(corpp, sch)\n try:\n objs.totals = getattr(corpp, sch).sum(axis=1)\n except:\n objs.totals = None\n #if objs._interactive:\n # show_this([sch])\n else:\n objs.result = corpp.interrogate(**kwargs)\n objs.totals = objs.result.totals\n # this should be done for pos and wordclasses too\n print('')\n return objs.result\n\n def show_this(tokens):\n \"\"\"\n Show any object in a human-readable form\n \"\"\"\n if tokens[0].startswith('file'):\n get_something[tokens]\n return\n if tokens[0] == 'corpora':\n dirs = [x for x in os.listdir('data') if \\\n os.path.isdir(os.path.join('data', x))]\n dirs = ['\\t%d: %s' % (i, x) for i, x in enumerate(dirs, start=1)]\n print ('\\n'.join(dirs))\n elif tokens[0].startswith('store'):\n for k, v in objs.stored.items():\n print(k, v)\n elif tokens[0].startswith('filter'):\n print('Skip:', getattr(objs, 'skip', 'none'))\n print('Just:', getattr(objs, 'just', 'none'))\n elif tokens[0].startswith('subcorp'):\n print('Symbolic structure:', getattr(objs, 'symbolic', 'none'))\n elif tokens[0].startswith('wordlists'):\n if '.' in tokens[0] or ':' in tokens[0]:\n if ':' in tokens[0]:\n _, attr = tokens[0].split(':')\n else:\n _, attr = tokens[0].split('.')\n print(objs.wordlists.get(attr))\n else:\n for k, v in sorted(objs.wordlists.items()):\n showv = str(v[:5])\n if len(v) > 5:\n showv = showv.rstrip('] ') + ' ... ]'\n print('\"%s\": %s' % (k, showv))\n\n elif tokens[0] == 'saved':\n ss = [i for i in os.listdir('saved_interrogations') \\\n if not i.startswith('.')]\n print ('\\t' + '\\n\\t'.join(ss))\n elif tokens[0] == 'query':\n for k, v in sorted(objs.query.items()):\n print('%s: %s' % (str(k), str(v)))\n elif tokens[0] == 'figure':\n if hasattr(objs, 'figure') and objs.figure:\n objs.figure.show()\n else:\n print('Nothing here yet.')\n elif tokens[0] in ['features', 'wordclasses', 'postags']:\n show_table(getattr(objs.corpus, tokens[0]))\n elif objs._get(tokens[0]):\n single_command_print(tokens)\n else:\n print(\"No information about: %s\" % tokens[0])\n\n def get_info(tokens):\n pass\n\n def edit_conc(conc, kwargs, varname=False):\n from corpkit.interrogation import Concordance\n for k, v in kwargs.items():\n if k == 'just_subcorpora':\n objs.concordance = conc[conc['c'].str.contains(v)]\n elif k == 'skip_subcorpora':\n objs.concordance = conc[~conc['c'].str.contains(v)]\n elif k == 'just_entries':\n objs.concordance = conc[conc['m'].str.contains(v)]\n elif k == 'skip_entries':\n objs.concordance = conc[~conc['m'].str.contains(v)]\n objs.concordance = Concordance(objs.concordance)\n \n # should this really happen?\n if varname and varname in objs._protected.keys():\n objs.named[varname] = objs.concordance\n \n return objs.concordance\n #if objs._interactive:\n # show_this(['concordance'])\n\n def edit_something(tokens):\n \"\"\"\n Edit an interrogation or concordance\n\n :Example:\n\n `edit result by skippings subcorpora matching [1,2,3] with keep_top as 5`\n\n \"\"\"\n\n thing_to_edit = get_thing_to_edit(tokens[0])\n\n trans = {'skipping': 'skip',\n 'keeping': 'just',\n 'merging': 'merge',\n 'spanning': 'span'}\n\n recog = ['not', 'matching', 'result', 'entry', 'entries',\n 'results', 'subcorpus', 'subcorpora', 'edited']\n\n # skip, keep, merge\n by_related = []\n if 'by' in tokens:\n start = tokens.index('by')\n by_related = tokens[start+1:]\n\n if not by_related:\n inter = interactive_edit(tokens)\n\n kwargs = {}\n skips = []\n for i, token in enumerate(by_related):\n if i in skips:\n continue\n if token in trans.keys():\n k = trans.get(token) + '_' + by_related[i+1]\n v = next((x for x in by_related[i+1:] if x not in recog), False)\n if not v:\n print('v not found')\n return\n v = parse_pattern(v)\n if token == 'merging':\n newname = next(tokens[i+1] for i, t in enumerate(tokens) if t == 'as')\n v = {newname: v}\n kwargs[k] = v\n #for x in range(i, ind+1):\n # skips.append(x)\n \n # add bools etc\n morekwargs = process_kwargs(tokens)\n for k, v in morekwargs.items():\n if k not in kwargs.keys():\n kwargs[k] = v\n\n if debug:\n print(kwargs)\n\n from corpkit.interrogation import Concordance\n if isinstance(thing_to_edit, Concordance):\n edt = edit_conc(thing_to_edit, kwargs, varnam=tokens[0])\n else:\n edt = thing_to_edit.edit(**kwargs)\n if isinstance(edt, Concordance):\n objs.concordance = edt\n else:\n objs.edited = edt\n if hasattr(objs.edited, 'totals'):\n objs.totals = objs.edited.totals\n return edt\n\n\n def run_command(tokens):\n \"\"\"\n Run command and send output to objs class\n \"\"\"\n\n command = get_command.get(tokens[0], unrecognised) \n out = command(tokens[1:])\n import pydoc\n import tabview\n \n # store the previous thing as previous\n attr = objmap.get(command, False)\n if attr:\n objs.previous = getattr(objs, attr)\n objs._previous_type = attr\n\n if command == search_corpus:\n # can be dataframe in the case of features\n import pandas as pd\n from corpkit.interrogation import Interrogation, Concordance\n if not isinstance(objs.result, (Interrogation, Concordance, pd.DataFrame, pd.Series)):\n return\n objs.result = out\n objs.previous = out\n show_this(['result'])\n objs.query = getattr(out, 'query', None)\n if objs._do_conc and (hasattr(out, 'concordance') and out.concordance is not None):\n objs.concordance = out.concordance\n objs._old_concs.append(objs.concordance)\n else:\n objs.concordance = None\n # find out what is going on here\n objs.edited = False\n\n # either all or no showing should be done here \n elif command in [edit_something, sort_something, calculate_result,\n keep_conc, del_conc]:\n from corpkit.interrogation import Concordance\n if isinstance(out, Concordance):\n objs._old_concs[-1] = objs.concordance\n if objs._interactive:\n show_this(['concordance'] + tokens[1:])\n else:\n if objs._interactive:\n show_this(['edited'])\n \n else:\n if debug:\n print('Done:', repr(out))\n return out\n\n def add_colour_to_conc_df(conc):\n \"\"\"\n Exporting conc lines needs to preserve colouring\n \"\"\"\n colourdict = objs._conc_colours[len(objs._old_concs)-1]\n fores = []\n backs = []\n stys = [] \n for index in list(conc.index):\n line = colourdict.get(str(index))\n if not line:\n fores.append('')\n backs.append('')\n stys.append('')\n else:\n fores.append(line.get('Fore', ''))\n backs.append(line.get('Back', ''))\n stys.append(line.get('Style', ''))\n\n if any(i != '' for i in fores):\n conc['Foreground'] = fores\n if any(i != '' for i in backs):\n conc['Background'] = backs\n if any(i != '' for i in stys):\n conc['Style'] = stys\n return conc\n \n def export_result(tokens):\n \"\"\"\n Send a result, edited result or concordance to file\n\n :Example:\n\n `export result as csv to out.csv`\n `export concordance as latex to concs.tex`\n\n \"\"\"\n import os\n obj = objs._get(tokens[0])[1]\n \n if tokens[0].startswith('conc'):\n obj = add_colour_to_conc_df(obj.copy())\n\n if tokens[0] in ['result', 'edited']:\n obj = getattr(obj, 'results', obj)\n \n if len(tokens) == 1:\n print(obj.to_string())\n return\n tokens = tokens[1:]\n\n if 'as' in tokens:\n formt = tokens[tokens.index('as') + 1][0]\n tokens = tokens[tokens.index('as') + 2:]\n else:\n formt = 'c'\n\n if tokens:\n buf = tokens[-1]\n else:\n buf = None\n \n if buf == formt:\n buf = None\n \n if os.pathsep not in buf:\n buf = os.path.join('exported', buf)\n if formt == 'c':\n obj.to_csv(buf, sep='\\t')\n elif formt == 's':\n obj.to_string(buf)\n elif formt == 'l':\n obj.to_latex(buf)\n\n if buf:\n if os.path.isfile(buf):\n print('Saved to: %s' % buf)\n else:\n print('Problem exporting file.')\n\n def interactive_edit(tokens):\n print('Not done yet')\n pass\n\n def get_thing_to_edit(token):\n thing = objs._get(token)[1]\n if thing is None:\n thing = objs.result\n return thing\n\n def sort_something(tokens):\n \"\"\"\n Sort a result or concordance line\n\n \"\"\"\n\n thing_to_edit = get_thing_to_edit(tokens[0])\n\n recog = ['by', 'with', 'from']\n\n val = next((x for x in tokens[1:] if x not in recog), 'total')\n\n from corpkit.interrogation import Concordance\n if not isinstance(thing_to_edit, Concordance):\n sortedd = thing_to_edit.edit(sort_by=val)\n if sortedd == 'linregress':\n raise ValueError(\"scipy needs to be installed for linear regression sorting.\")\n objs.edited = sortedd\n objs.totals = objs.edited.totals\n return objs.edited\n else:\n if val.startswith('i'):\n sorted_lines = thing_to_edit.sort_index()\n else:\n if val[0] in ['l', 'm', 'r']:\n \n l_or_r = thing_to_edit[val[0]]\n \n if len(val) == 1:\n val = val + '1'\n\n ind = int(val[1:])\n\n val = val[0]\n\n if val == 'l':\n ind = -ind\n else:\n ind = ind - 1\n\n import numpy as np\n\n # bad arg parsing here!\n if 'slashsplit' in tokens:\n splitter = '/'\n else:\n splitter = ' '\n\n to_sort_on = l_or_r.str.split(splitter).tolist()\n if val == 'l':\n # todo: this is broken on l2,l3 etc\n to_sort_on = [i[ind].lower() if i and len(i) >= abs(ind) \\\n else np.nan for i in to_sort_on]\n else:\n to_sort_on = [i[ind].lower() if i and len(i) > abs(ind) \\\n else np.nan for i in to_sort_on]\n thing_to_edit['x'] = to_sort_on\n val = 'x'\n\n elif val in ['scheme', 'color', 'colour']:\n val = 'x'\n num_col = objs._conc_colours[len(objs._old_concs)-1]\n series = []\n # todo: fix this!\n for i in range(len(thing_to_edit)):\n bit = num_col.get(str(i), 'zzzzz')\n if isinstance(bit, dict):\n bit = bit.get('Fore', bit.get('Back', 'zzzzz'))\n series.append(bit)\n thing_to_edit['x'] = series\n\n sorted_lines = thing_to_edit.sort_values(val, axis=0, na_position='last')\n \n if val == 'x':\n sorted_lines = sorted_lines.drop('x', axis=1)\n \n objs.concordance = Concordance(sorted_lines)\n\n # do not add new entry to old concs for sorting :)\n objs._old_concs[-1] = objs.concordance\n if objs._interactive:\n single_command_print('concordance')\n\n def plot_result(tokens):\n \"\"\"\n Visualise an interrogation using matplotlib\n \"\"\"\n \n import matplotlib.pyplot as plt\n kinds = ['line', 'heatmap', 'bar', 'barh', 'pie', 'area']\n kind = next((x for x in tokens if x in kinds), 'line')\n kwargs = process_kwargs(tokens)\n kwargs['tex'] = kwargs.get('tex', False)\n title = kwargs.pop('title', False)\n objs.figure = objs._get(tokens[0])[1].visualise(title=title, kind=kind, **kwargs)\n objs.figure.show()\n return objs.figure\n\n def asciiplot_result(tokens):\n \"\"\"\n Visualise an interrogation with a simple ascii line chart\n \"\"\"\n obj, attr = tokens[0].split(':', 1) if ':' in tokens[0] else tokens[0].split('.', 1)\n to_plot = objs._get(obj)[1]\n # if it said 'asciiplot result.this on axis 1', turn it into\n # asciiplot result.this with axis as 1\n\n if 'on' in tokens and not 'with' in tokens:\n tokens[tokens.index('on')] = 'with'\n if tokens[tokens.index('with')+1] == 'axis':\n if tokens[tokens.index('with')+2] in [0, 1]:\n tokens.insert(tokens.index('with')+2, 'as')\n\n kwargs = process_kwargs(tokens)\n to_plot.asciiplot(attr, **kwargs)\n\n def calculate_result(tokens):\n \"\"\"\n Get relative frequencies, combine results, do keywording\n\n :Syntax:\n\n calculate / as of \n\n :Examples:\n\n > calculate result as percentage of self\n > calculate edited as percentage of features.clauses\n > calculate result as percentage of stored.myname\n\n \"\"\"\n # todo: use real syntax for keyness\n if 'k' in tokens or 'keyness' in tokens:\n operation = 'k'\n else:\n operation = False\n\n dd = {'percentage': '%',\n 'key': 'k',\n 'keyness': 'k'}\n\n calcs = ['k', '%', '+', '/', '-', '*', 'percentage', 'keyness']\n operation = next((i for i in tokens if any(i.startswith(x) for x in calcs)), operation)\n if not operation:\n if tokens[-1].startswith('conc'):\n res = objs.concordance.calculate()\n objs.result = res.results\n objs.totals = res.totals\n if objs._interactive:\n show_this(['result'])\n return\n else:\n print('Bad operation.')\n return\n objtype, denominator = objs._get(tokens[-1])\n \n if tokens[-1] == 'self':\n denominator = 'self'\n\n operation = dd.get(operation, operation)\n\n the_obj = objs._get(tokens[0])[1]\n if not the_obj:\n the_obj = objs._get(tokens[-1])[1]\n\n objs.edited = the_obj.edit(operation, denominator)\n if hasattr(objs.edited, 'totals'):\n objs.totals = objs.edited.totals\n #print('\\nedited:\\n\\n', objs.edited.results, '\\n')\n return objs.edited\n\n def tokenise_corpus(tokens):\n \"\"\"\n Tokenise an unparsed corpus\n\n :Example:\n\n `parse corpus with speaker_segmentation and metadata and multiprocess as 2`\n\n \"\"\"\n typ, to_parse = objs._get(tokens[0])\n if typ != 'corpus':\n print('Command not understood. Use \"set \" and \"parse corpus\"')\n if not to_parse:\n print('Corpus not set. use \"set \" on an unparsed corpus.')\n return\n\n if to_parse.datatype != 'plaintext':\n print('Corpus is not plain text.')\n return\n\n kwargs = process_kwargs(tokens)\n parsed = to_parse.tokenise(**kwargs)\n if parsed:\n setattr(objs, 'corpus', parsed)\n return\n\n def parse_corpus(tokens):\n \"\"\"\n Parse an unparsed corpus\n\n :Example:\n\n `parse corpus with speaker_segmentation and metadata and multiprocess as 2`\n\n \"\"\"\n typ, to_parse = objs._get(tokens[0])\n if typ != 'corpus':\n print('Command not understood. Use \"set \" and \"parse corpus\"')\n if not to_parse:\n print('Corpus not set. use \"set \" on an unparsed corpus.')\n return\n\n if to_parse.datatype != 'plaintext':\n print('Corpus is not plain text.')\n return\n\n kwargs = process_kwargs(tokens)\n parsed = to_parse.parse(**kwargs)\n if parsed:\n setattr(objs, 'corpus', parsed)\n return\n\n def fetch_this(tokens, unbound=False):\n \"\"\"\n Get something from storage\n\n :Example:\n\n `fetch my_result as result`\n \"\"\"\n\n from corpkit.corpus import Corpus\n from corpkit.interrogation import Interrogation, Concordance\n \n from_wl = False\n to_fetch = objs.stored.get(tokens[0], False)\n\n if to_fetch is False:\n to_fetch = objs.wordlists.get(tokens[0], False)\n from_wl = True\n if to_fetch is False:\n print('Not in store or wordlists: %s' % tokens[0])\n return\n\n if unbound:\n return to_fetch\n\n if from_wl:\n objs.wordlist = to_fetch\n showv = to_fetch[:5]\n if len(to_fetch) > 5:\n showv = str(showv).rstrip('] ') + ' ... ]'\n print('%s (%s) fetched as wordlist.' % (repr(tokens[0]), showv))\n return\n\n if len(tokens) < 3:\n if isinstance(to_fetch, Corpus):\n weneed = 'corpus'\n elif isinstance(to_fetch, Interrogation):\n if hasattr(to_fetch, 'denominator'):\n weneed = 'edited'\n else:\n weneed = 'result'\n elif isinstance(to_fetch, Concordance):\n weneed = 'concordance'\n else:\n weneed = tokens[2]\n\n if weneed == 'corpus':\n objs.corpus = to_fetch\n elif weneed.startswith('conc'):\n objs.concordance = to_fetch\n elif weneed == 'result':\n objs.result = to_fetch\n elif weneed == 'edited':\n objs.edited = to_fetch\n\n print('%s fetched as %s.' % (repr(to_fetch), weneed))\n\n def save_this(tokens, passedin=False):\n \"\"\"\n Save a result or concordance\n\n :Example:\n\n `save result as 'non_pronoun_actors'`\n \"\"\"\n if passedin:\n to_save = passedin\n else:\n to_save = objs._get(tokens[0])[1]\n\n if to_save == 'store' and not passedin:\n for k, v in objs.stored.items():\n save_this(['as', k], passedin=v)\n\n if tokens[0] == 'figure' or hasattr(to_save, 'savefig'):\n tokens[-1] = os.path.join('images', tokens[-1])\n to_save.savefig(tokens[-1])\n else:\n to_save.save(tokens[-1])\n\n\n def load_this(tokens):\n \"\"\"\n Load data from file\n \"\"\"\n\n from corpkit.other import load\n if tokens[-1] == 'result':\n objs.result = load(tokens[0])\n if tokens[-1] == 'concordance':\n objs.concordance = load(tokens[0])\n if tokens[-1] == 'edited':\n objs.edited = load(tokens[0])\n if 'all' in tokens:\n from corpkit.other import load_all_results\n loaded = load_all_results()\n for k, v in loaded.items():\n objs.stored[k] = v\n\n def interactive_listmaker():\n done_words = []\n text = 'Enter words separated by spaces, or one per line. (Leave the line blank to end)\\n\\n\\t> '\n while True:\n res = INPUTFUNC(text)\n text = '\\t> '\n if res:\n if ',' in res:\n words = [i.strip(', ') for i in res.split()]\n for w in words:\n done_words.append(w)\n elif ' ' in res.strip():\n words = res.split()\n for w in words:\n done_words.append(w)\n else:\n done_words.append(res.strip())\n else: \n return done_words\n \n def new_thing(tokens):\n \"\"\"\n Start a new project with a name of your choice, then move into it\n \"\"\"\n if tokens[0] == 'project':\n from corpkit.other import new_project\n new_project(tokens[-1])\n os.chdir(tokens[-1])\n if tokens[0] == 'wordlist':\n the_name = next((tokens[i+1] for i, t in enumerate(tokens) if t in ['called', 'named']), None)\n if not the_name:\n print('Syntax: new wordlist named .')\n return\n if objs.wordlists.get(the_name):\n print('\"%s\" already exists in wordlists.' % the_name)\n return\n filename = next((tokens[i+1] for i, t in enumerate(tokens) if t in ['from']), None)\n if filename:\n with open(filename, 'r') as fo:\n words = [i.strip() for i in fo.read().splitlines() if i]\n else:\n words = interactive_listmaker()\n if words:\n objs.wordlists[the_name] = words\n objs.wordlist = words\n print('Wordlist \"%s\" stored.' % the_name)\n\n def get_matching_indices(tokens):\n \"\"\"\n Find concordance indices matching some criteria in tokens\n\n This can be:\n\n - an int as index: \"20\"\n - a pseudo index: \"5-10\"\n - a column and regex: \"l matching 'regex'\"\n - a colour name\n - 'all'\n\n :returns: a `set` of matching indices\n\n \"\"\"\n # what's missing from this list?\n # hard coding in case user doesn't have colorama\n colours = ['red', 'yellow', 'magenta', 'lightmagenta_ex', 'black',\n 'white', 'lightcyan_ex', 'reset', 'green', 'lightyellow_ex',\n 'lightblack_ex', 'lightwhite_ex', 'blue', 'lightblue_ex',\n 'lightgreen_ex', 'cyan', 'lightred_ex']\n cols = []\n token = tokens[0]\n\n # for something like del red\n if token in colours:\n if tokens[-1].lower() == 'back':\n sty = 'Back'\n elif tokens[-1].lower() in ['dim', 'bright', 'normal', 'reset']:\n sty = 'Style'\n else:\n sty = 'Fore'\n colourdict = objs._conc_colours[len(objs._old_concs)-1]\n return set([k for k, v in colourdict.items() if v.get(sty) == token]) \n \n # annotate range of tokens\n if token == 'all':\n return [str(i) for i in list(objs.concordance.index)]\n\n elif '-' in token:\n first, last = token.split('-', 1)\n if not first:\n first = 0\n if not last:\n last = len(objs.concordance.index)\n for n in range(int(first), int(last)+1):\n cols.append(str(n))\n # get by index/indices\n elif token.isdigit():\n cols = [i for i in tokens if i.isdigit()]\n else:\n # regex match only what's shown in the window\n window = objs._conc_kwargs.get('window', 35)\n if token in list(objs.concordance.columns) \\\n and token not in ['l', 'm', 'r']:\n token_bits = [token]\n else:\n token_bits = list(token)\n slic = slice(None, None)\n for bit in token_bits:\n if bit.lower() == 'm':\n slic = slice(None, None)\n elif bit.lower() == 'l':\n slic = slice(-window, None)\n elif bit.lower() == 'r':\n slic = slice(None, window)\n # get slice for left window\n mx = max(objs.concordance[bit.lower()].str.len()) \\\n if bit.lower() == 'l' else 0\n # get the regex criteria\n if 'matching' in tokens:\n rgx = tokens[tokens.index('matching') + 1]\n else:\n rgx = tokens[1]\n rgx = parse_pattern(rgx)\n\n pol = not 'not' in tokens\n \n # get the window size of context, and reduce to just windows\n if isinstance(rgx, list):\n trues = objs.concordance[bit].str.rjust(mx).str[slic].isin(rgx)\n else:\n trues = objs.concordance[bit].str.rjust(mx).str[slic].str.contains(rgx)\n \n if pol:\n mtch = objs.concordance[trues]\n else:\n mtch = objs.concordance[~trues]\n matches = list(mtch.index)\n for ind in matches:\n cols.append(str(ind))\n\n return set(cols)\n\n def parse_anno_with(tokens):\n \"\"\"\n Decide what the text is for an annotation\n \"\"\"\n if tokens[0] == 'tag':\n return tokens[-1]\n elif tokens[0] == 'field':\n tokens = tokens[1:]\n nx = next((i for i, w in enumerate(tokens) if w != 'as'), 0)\n return {tokens[nx]: tokens[-1]}\n\n def unannotate_corpus(tokens):\n \"\"\"\n Remove annotations from a corpus\n\n :Example:\n\n `unannotate character field`\n `unannotate friend tag`\n \"\"\"\n to_remove = tokens[0]\n from corpkit.annotate import annotator\n # right now, you can't delete just a value...\n if tokens[-1] == 'field':\n to_remove = {to_remove: r'.*'}\n if to_remove == 'all' and tokens[-1].startswith('tag'):\n to_remove = {'tags': r'.*'}\n #elif tokens[-1] == 'tag':\n\n annotator(df_or_corpus=objs.corpus,\n annotation=to_remove,\n dry_run=not objs._annotation_unlocked,\n deletemode=True)\n if not objs._annotation_unlocked:\n print(\"\\nIf you really want to delete these annotations from the corpus, \" \\\n \"do `toggle annotation` and run the previous command again.\\n\")\n else:\n print('Deleted %s annotations.' % tokens[0])\n\n def annotate_corpus(tokens):\n \"\"\"\n Add annotations to the corpus\n\n :Example:\n\n `annotate m matching 'ing$' with tag as 'mytag'`\n `annotate m matching 'ing$' with field as 'person' and value as 'daisy'`\n \"\"\"\n\n cols = get_matching_indices(tokens)\n from corpkit.interrogation import Concordance\n from corpkit.annotate import annotator\n df = objs.concordance.ix[[int(i) for i in list(cols)]]\n\n if 'with' in tokens:\n start = tokens.index('with')\n with_related = tokens[start+1:]\n else:\n with_related = []\n annotation = parse_anno_with(with_related)\n \n annotator(df, annotation, dry_run=not objs._annotation_unlocked)\n if not objs._annotation_unlocked:\n print(\"\\nWhen you're ready to actually add annotations to the files, \" \\\n \"do `toggle annotation` and run the previous command again.\\n\")\n else:\n print('Corpus annotated (%d additions).' % len(df.index))\n\n def annotate_conc(tokens):\n \"\"\"\n Annotate concordance lines matching criteria with a colour or style\n\n :Example:\n\n `mark m matching 'ing$' red`\n \"\"\"\n from colorama import Fore, Back, Style, init\n init(autoreset=True)\n cols = get_matching_indices(tokens)\n color = tokens[-1]\n if color in ['dim', 'normal', 'bright']:\n sty = 'Style'\n elif 'back' in tokens or 'background' in tokens:\n sty = 'Back'\n else:\n sty = 'Fore'\n #if tokens[-2].lower() in ['back', 'dim', 'fore', 'normal', 'bright', 'background', 'foreground']:\n # sty = tokens[-2].title().replace('ground', '')\n\n for line in cols:\n if not int(line) in list(objs.concordance.index):\n continue\n # if there is already info for this line number, add present info\n if objs._conc_colours[len(objs._old_concs)-1].get(line):\n objs._conc_colours[len(objs._old_concs)-1][line][sty] = color\n else:\n objs._conc_colours[len(objs._old_concs)-1][line] = {}\n objs._conc_colours[len(objs._old_concs)-1][line][sty] = color\n\n single_command_print(['concordance'] + tokens)\n\n def add_corpus(tokens):\n \"\"\"\n Copy a folder to the ./data directory of a project\n\n :Example:\n\n `add '../../data'`\n \"\"\"\n import shutil\n import os\n the_path = os.path.expanduser(tokens[-1])\n outf = os.path.join(os.getcwd(), 'data', os.path.basename(the_path))\n # if we're using docker things get tricky\n if objs._docker:\n # not working yet\n import subprocess\n from docker import Client\n cli = Client()\n idx = str(subprocess.check_output([\"cat\", \"/etc/hostname\"])).strip('\\n').strip()\n outf = ':'.join([idx, outf])\n os.makedirs(outf)\n os.system(\"docker cp %s/ %s\" % (the_path, outf))\n #cli.copy(the_path, outf)\n print('%s added to %s.' % (tokens[-1], outf))\n return\n shutil.copytree(the_path, outf)\n print('%s added to %s.' % (tokens[-1], outf))\n\n def del_conc(tokens):\n \"\"\"\n Delete concordance lines matching criteria\n\n :Example:\n\n `del m matching 'ing$'`\n \"\"\"\n from corpkit.interrogation import Concordance\n cols = get_matching_indices(tokens)\n cols = [int(i) for i in cols]\n objs.concordance = Concordance(objs.concordance.drop(cols, axis=0))\n return objs.concordance\n\n def keep_conc(tokens):\n \"\"\"\n Keep concordance lines matching criteria\n\n :Example:\n\n `keep m matching 'ing$'`\n \"\"\"\n from corpkit.interrogation import Concordance\n cols = get_matching_indices(tokens)\n cols = [int(i) for i in cols]\n objs.concordance = Concordance(objs.concordance.loc[cols])\n return objs.concordance\n\n def store_this(tokens):\n \"\"\"\n Send a result into storage\n\n :Example:\n\n `store result as 'processes'`\n\n To view the contents of the store, do `show store`.\n\n \"\"\"\n\n to_store = objs._get(tokens[0])[1]\n\n #if not to_store:\n # print('Not storable:' % tokens[0])\n # return\n\n if tokens[1] == 'as':\n name = tokens[2]\n else:\n count = 0\n while str(count) in objs.stored.keys():\n count += 1\n name = str(count)\n if name in objs.stored.keys():\n cont = INPUTFUNC('%s already in store. Replace it? (y/n) ')\n if cont.lower().startswith('n'):\n return\n objs.stored[name] = to_store\n print('Stored: %s' % name)\n\n def toggle_this(tokens):\n \"\"\"\n Toggle a specified option\n\n Currently available:\n\n - toggle conc (does concordancing when searching)\n - toggle interactive (shows results and concordances after running)\n - toggle annotation (allows the user to annotate the real corpus)\n - toggle comma (shows thousands comma in results)\n\n \"\"\"\n if tokens[0].startswith('conc'):\n objs._do_conc = not objs._do_conc\n s = 'on' if objs._do_conc else 'off'\n print('Concordancing turned %s.' % s)\n if tokens[0].startswith('interactive'):\n objs._interactive = not objs._interactive\n s = 'on' if objs._interactive else 'off'\n print('Auto showing of results and concordances turned %s.' % s)\n if tokens[0].startswith('anno'):\n objs._annotation_unlocked = not objs._annotation_unlocked\n s = 'on' if objs._annotation_unlocked else 'off'\n print('Annotation mode %s.' % s)\n if tokens[0].startswith('comma'):\n objs._comma = not objs._comma\n s = 'on' if objs._comma else 'off'\n print('Thousand separator in results turned %s.' % s)\n\n def remove_something(tokens):\n if len(tokens) == 1:\n objs.named.pop(tokens[0])\n else:\n frm = getattr(objs, tokens[-1])\n frm.pop(tokens[0])\n print('Forgot %s.' % tokens[0])\n\n def name_something(tokens):\n \"\"\"\n Basically, make a variable. In reality,\n it is a key-value pair in objs.named\n\n \"\"\"\n # get the object if it exists somewhere\n originally_was, thing = objs._get(tokens[0])\n\n if thing is None or thing is False:\n thing = tokens[0]\n if thing == 'py':\n thing = eval(tokens[1])\n originally_was = 'eval'\n tokens = tokens[1:]\n else:\n originally_was = 'string'\n #print('Nothing stored in %s to name.' % tokens[0])\n #return\n \n # this should just be objects!\n if tokens[0] in objs._protected:\n originally_was = tokens[0]\n \n #elif tokens[0] in objs.named.keys():\n # pass\n \n from corpkit.process import makesafe\n name = makesafe(tokens[-1])\n if name in objs._protected + list(get_command.keys()):\n print('The name \"%s\" is protected, sorry.' % name)\n else:\n objs.named[name] = (originally_was, thing)\n print('%s named \"%s\".' % (tokens[0], name))\n\n def sample_something(tokens):\n \"\"\"\n Make a sample from a corpus\n \n :Example: sample 2 subcorpora of corpus\n \"\"\"\n trans = {'s': 'subcorpora', 'f': 'files'}\n originally_was, thing = objs._get(tokens[-1])\n if '.' in tokens[0]:\n n = float(tokens[0])\n else:\n n = int(tokens[0])\n level = tokens[1].lower()[0]\n samp = thing.sample(n, level)\n objs.sampled = samp\n #todo: proper printing\n names = [i.name for i in getattr(objs.sampled, trans[level])]\n form = ', '.join(names[:3])\n if len(names) > 3:\n form += ' ...'\n print('Sample created: %d %s from %s --- %s' % (n, trans[level],\n thing.name, form))\n #single_command_print('sample')\n\n def run_previous(tokens):\n import shlex\n output = list(reversed(objs.previous))[int(tokens[0]) - 1][0]\n tokens = [i.rstrip(',') for i in shlex.split(output)]\n return run_command(tokens)\n\n def ch_dir(tokens):\n \"\"\"\n Change directory\n\n :Example:\n\n `cd essays`\n\n \"\"\"\n import os\n os.chdir(tokens[0])\n print(os.getcwd())\n get_prompt()\n\n def ls_dir(tokens):\n \"\"\"\n List directory contents\n \n :Example:\n\n `ls data`\n\n \"\"\"\n import os\n nonhidden = [i for i in os.listdir(tokens[0]) if not i.startswith('.')]\n print('\\n'.join(nonhidden))\n\n get_command = {'set': set_something,\n 'get': get_something,\n 'show': show_this,\n 'search': search_corpus,\n 'info': get_info,\n 'parse': parse_corpus,\n 'export': export_result,\n 'redo': run_previous,\n 'mark': annotate_conc,\n 'annotate': annotate_corpus,\n 'unannotate': unannotate_corpus,\n 'del': del_conc,\n 'just': keep_conc,\n 'sort': sort_something,\n 'sample': sample_something,\n 'toggle': toggle_this,\n 'edit': edit_something,\n 'tokenise': tokenise_corpus,\n 'tokenize': tokenise_corpus,\n 'ls': ls_dir,\n 'cd': ch_dir,\n 'plot': plot_result,\n 'asciiplot': asciiplot_result,\n 'help': helper,\n 'store': store_this,\n 'new': new_thing,\n 'fetch': fetch_this,\n 'call': name_something,\n 'remove': remove_something,\n 'save': save_this,\n 'load': load_this,\n 'calculate': calculate_result,\n 'add': add_corpus}\n\n objmap = {search_corpus: 'result',\n edit_something: 'edited',\n calculate_result: 'edited',\n sort_something: 'edited',\n plot_result: 'figure',\n set_something: 'corpus',\n load_this: 'result'}\n\n def unrecognised(*args, **kwargs):\n print('unrecognised!')\n\n import shlex\n\n if debug:\n try:\n objs.corpus = Corpus('jb-parsed')\n except:\n pass\n\n def get_prompt(backslashed=False):\n \"\"\"\n Create the prompt, based on being in project etc.\n\n :param backslashed: is when there is a line continuation\n \"\"\"\n folder = os.path.basename(os.getcwd())\n proj_dirs = ['data', 'saved_interrogations', 'exported']\n objs._in_a_project = check_in_project()\n end = '*' if not objs._in_a_project else ''\n name = getattr(objs.corpus, 'name', 'no-corpus')\n txt = 'corpkit@%s%s:%s> ' % (folder, end, name)\n if not backslashed:\n return txt\n else:\n return '... '.rjust(len(txt))\n\n def nest_on_semicolon(tokens):\n \"\"\"\n Take a tokenised command and make a nested list,\n which accounts for ';' breaks\n \"\"\"\n if ';' not in tokens:\n return [tokens]\n nested = []\n while tokens:\n s_ix = next((i for i, x in enumerate(tokens) if x == ';'), len(tokens))\n nested.append(tokens[:s_ix])\n tokens = tokens[s_ix+1:]\n return nested\n\n if not fromscript and not python_c_mode:\n print(allig)\n\n if not check_in_project():\n print(\"\\nWARNING: You aren't in a project yet. \"\\\n \"Use 'new project named ' to make one and enter it.\\n\"\\\n \"Alternatively, you can `cd` into an existing project now.\\n\")\n\n def do_bash(output):\n \"\"\"\n Run text as shell command\n \"\"\"\n import os\n os.system(output.lstrip('! '))\n\n # backslashed allows line breaks with backslashes ala python.\n # it's a bit of a hack, but seems to work pretty well\n\n backslashed = ''\n\n if fromscript:\n commands = read_script(fromscript)\n objs._interactive = False\n\n if python_c_mode:\n objs._interactive = False\n\n if loadcurrent:\n load_this(['all'])\n\n # the main loop, with exception handling\n while True:\n try:\n if not fromscript and not python_c_mode and not profile:\n output = INPUTFUNC(get_prompt(backslashed))\n \n elif fromscript:\n if not commands:\n break\n output = commands.pop()\n \n elif python_c_mode:\n if 'concordance' not in python_c_mode:\n objs._do_conc = False\n output = python_c_mode\n\n elif profile:\n output = 'quit'\n\n # terminate\n if output.lower() in ['exit', 'quit', 'exit()', 'quit()']:\n break\n\n # do nothing\n if not output:\n output = True\n continue\n \n # append line to previous backslashed line\n if backslashed:\n output = backslashed + output\n backslashed = ''\n \n # add to stack if backslashed or delete stack otherwise\n if output.strip().endswith(\"\\\\\"):\n backslashed += output.rstrip('\\\\')\n continue\n else:\n backslashed = ''\n\n if output.startswith('!'):\n do_bash(output)\n continue\n\n # is this just a terrible idea?\n if output.startswith('py '):\n\n output = output[3:].strip().strip(\"'\").strip('\"')\n \n for k, v in objs.__dict__.items():\n locals()[k] = v\n exec(output, globals(), locals())\n for k, v in locals().items():\n if hasattr(objs, k):\n setattr(objs, k, v)\n continue\n\n # tokenise command with quotations preserved\n tokens = shlex.split(output, comments=True)\n\n nested_tokens = nest_on_semicolon(tokens)\n\n for tokens in nested_tokens:\n if debug:\n print('command', tokens)\n \n # give info if it is an info command\n if len(tokens) == 1 or tokens[0] == 'jupyter':\n if tokens[0] == 'set':\n set_something([])\n else:\n single_command_print(tokens)\n continue\n\n # otherwise, run the command and reset the stack\n out = run_command(tokens)\n backslashed = ''\n\n # terminate if c mode\n if python_c_mode:\n break\n\n # exception handling: interrupt, exit or raise error...\n except KeyboardInterrupt:\n print('\\nEnter ctrl+d, \"exit\" or \"quit\" to quit\\n')\n backslashed = ''\n if python_c_mode:\n import sys\n sys.exit(0)\n\n except EOFError:\n import sys\n #print('\\n\\nBye!\\n')\n sys.exit(0)\n except SystemExit:\n raise\n except Exception:\n if python_c_mode:\n import sys\n print('Error in \"%s\":\\n' % ' '.join(tokens), file=sys.stderr)\n raise\n traceback.print_exc()\n\nif __name__ == '__main__':\n import sys\n import pip\n import importlib\n import traceback\n import os\n\n def install(name, loc):\n \"\"\"\n If we don't have a module, download it\n \"\"\"\n try:\n importlib.import_module(name)\n except ImportError:\n pip.main(['install', loc])\n\n tabview = ('tabview', 'git+https://github.com/interrogator/tabview@93644dd1f410de4e47466ea8083bb628b9ccc471#egg=tabview')\n colorama = ('colorama', 'colorama')\n\n if not any(arg.lower() == 'noinstall' for arg in sys.argv):\n install(*tabview)\n install(*colorama)\n\n if len(sys.argv) > 1 and os.path.isfile(sys.argv[-1]):\n fromscript = sys.argv[-1]\n else:\n fromscript = False\n\n docker = next((i for i in sys.argv if i.startswith('docker=')), False)\n if docker:\n docker = docker.split('=', 1)[-1]\n print(sys.argv)\n\n debug = sys.argv[-1] == 'debug'\n interpreter(debug=debug, fromscript=fromscript, docker=docker)\n"
},
{
"path": "corpkit/gui.py",
"content": "#!/usr/bin/env python\n\n\"\"\"\n# corpkit GUI\n# Daniel McDonald\n\n# This file conains the frontend side of the corpkit gui.\n# You can use py2app or pyinstaller on it to make a .app,\n# or just run it as a script.\n\n# Below is a string that is used to determine when minor\n# updates are available on github for automatic download:\n# DATE-REPLACE\n\n# Tabbed notebook template created by: \n# Patrick T. Cossette \n\"\"\"\n\nfrom __future__ import print_function\n\nimport sys\n# is string used?\nimport string\nimport time\nimport os\n# is threading used?\nimport threading\n\ntry:\n import tkMessageBox as messagebox\n import tkSimpleDialog as simpledialog\n import tkFileDialog as filedialog\nexcept ImportError:\n import tkinter.messagebox\n import tkinter.filedialog\n import tkinter.simpledialog\ntry:\n import Tkinter as tkinter\n from Tkinter import *\n from ttk import Progressbar, Style\n from Tkinter import _setit\nexcept ImportError:\n import tkinter\n from tkinter import * \n from tkinter.ttk import Progressbar, Style\n from tkinter import _setit\n\n# todo: delete from the rest of code\nfrom corpkit.corpus import Corpus\n\n# determine path to gui resources:\npy_script = False\nfrom_py = False\nrd = sys.argv[0]\nif sys.platform == 'darwin':\n key = 'Mod1'\n fext = 'app'\n if '.app' in rd:\n rd = os.path.join(rd.split('.app', 1)[0] + '.app', 'Contents', 'MacOS')\n else:\n import corpkit\n rd = os.path.dirname(corpkit.__file__)\n from_py = True\nelse:\n key = 'Control'\n fext = 'exe'\nif '.py' in rd:\n py_script = True\n rd = os.path.dirname(os.path.join(rd.split('.py', 1)[0]))\n\n########################################################################\n\nclass SplashScreen(object):\n \"\"\"\n A simple splash screen to display before corpkit is loaded.\n \"\"\"\n def __init__(self, tkRoot, imageFilename, minSplashTime=0):\n import os\n # if there is some PIL issue, just don't show GUI\n # todo: this would also need to disable display of previous figures\n self._can_operate = True\n try:\n from PIL import Image\n from PIL import ImageTk\n except ImportError:\n self._can_operate = False\n return\n self._root = tkRoot\n fname = os.path.join(rd, imageFilename)\n if os.path.isfile(fname):\n self._image = ImageTk.PhotoImage(file=fname)\n self._splash = None\n self._minSplashTime = time.time() + minSplashTime\n else:\n self._image = False\n \n def __enter__(self):\n # Remove the app window from the display\n #self._root.withdraw( )\n\n if not self._can_operate:\n return\n\n if not self._image:\n return\n\n # Calculate the geometry to center the splash image\n scrnWt = self._root.winfo_screenwidth()\n scrnHt = self._root.winfo_screenheight()\n \n imgWt = self._image.width()\n imgHt = self._image.height()\n \n imgXPos = (scrnWt / 2) - (imgWt / 2)\n imgYPos = (scrnHt / 2) - (imgHt / 2)\n\n # Create the splash screen \n self._splash = Toplevel()\n self._splash.overrideredirect(1)\n self._splash.geometry('+%d+%d' % (imgXPos, imgYPos))\n\n background_label = Label(self._splash, image=self._image)\n background_label.grid(row=1, column=1, sticky=W)\n\n # this code shows the version number, but it's ugly.\n #import corpkit\n #oldstver = str(corpkit.__version__)\n #txt = 'Loading corpkit v%s ...' % oldstver\n #cnv = Canvas(self._splash, width=200, height=20)\n #cnv.create_text((100, 14), text=txt, font=(\"Helvetica\", 14, \"bold\"))\n #cnv.grid(row=1, column=1, sticky='SW', padx=20, pady=20)\n \n self._splash.lift()\n self._splash.update( )\n \n def __exit__(self, exc_type, exc_value, traceback ):\n # Make sure the minimum splash time has elapsed\n\n if not self._can_operate:\n return\n\n if not self._image:\n return\n\n timeNow = time.time()\n if timeNow < self._minSplashTime:\n time.sleep( self._minSplashTime - timeNow )\n \n # Destroy the splash window\n self._splash.destroy( )\n\n # Display the application window\n #self._root.deiconify( )\n\nclass RedirectText(object):\n \"\"\"Send text to app from stdout, for the log and the status bar\"\"\"\n\n def __init__(self, text_ctrl, log_text, text_widget):\n \"\"\"Constructor\"\"\"\n \n def dumfun():\n \"\"\"to satisfy ipython, sys, which look for a flush method\"\"\"\n pass\n\n self.output = text_ctrl\n self.log = log_text\n self.flush = dumfun\n self.fileno = dumfun\n self.text_widget = text_widget\n \n def write(self, string):\n \"\"\"Add stdout and stderr to log and/or to console\"\"\" \n import re\n # don't show blank lines\n show_reg = re.compile(r'^\\s*$')\n # delete lobal abs paths from traceback\n del_reg = re.compile(r'^/*(Users|usr).*/(site-packages|corpkit/corpkit/)')\n if 'Parsing file' not in string and 'Initialising parser' not in string \\\n and not 'Interrogating subcorpus' in string:\n if not re.match(show_reg, string):\n string = re.sub(del_reg, '', string)\n self.log.append(string.rstrip('\\n'))\n self.text_widget.config(state='normal')\n self.text_widget.delete(1.0, 'end')\n self.text_widget.insert('end', string.rstrip('\\n'))\n\n self.text_widget.config(state='disabled')\n if not re.match(show_reg, string):\n if not string.lstrip().startswith('#') and not string.lstrip().startswith('import'):\n string = re.sub(del_reg, '', string).rstrip('\\n').rstrip()\n string = string.split('\\n')[-1]\n self.output.set(string.lstrip().rstrip('\\n').rstrip())\n self.text_widget.config(state='normal')\n self.text_widget.delete(1.0, 'end')\n self.text_widget.insert('end', string.lstrip().rstrip('\\n').rstrip())\n self.text_widget.config(state='disabled')\n\nclass Label2(Frame):\n \"\"\"a label whose size can be specified in pixels\"\"\"\n def __init__(self, master, width=0, height=0, **kwargs):\n self.width = width\n self.height = height\n \n Frame.__init__(self, master, width=self.width, height=self.height)\n self.label_widget = Text(self, width=1, **kwargs)\n self.label_widget.pack(fill='both', expand=True)\n #self.label_widget.config(state=DISABLED)\n\n def pack(self, *args, **kwargs):\n Frame.pack(self, *args, **kwargs)\n self.pack_propagate(False)\n\n def grid(self, *args, **kwargs):\n Frame.grid(self, *args, **kwargs)\n self.grid_propagate(False)\n\n\nclass HyperlinkManager:\n \"\"\"Hyperlinking for About\"\"\"\n def __init__(self, text):\n self.text=text\n self.text.tag_config(\"hyper\", foreground=\"blue\", underline=1)\n self.text.tag_bind(\"hyper\", \"\", self._enter)\n self.text.tag_bind(\"hyper\", \"\", self._leave)\n self.text.tag_bind(\"hyper\", \"\", self._click)\n self.reset()\n def reset(self):\n self.links = {}\n def add(self, action):\n # add an action to the manager. returns tags to use in\n # associated text widget\n tag = \"hyper-%d\" % len(self.links)\n self.links[tag] = action\n return \"hyper\", tag\n def _enter(self, event):\n self.text.config(cursor=\"hand2\")\n def _leave(self, event):\n self.text.config(cursor=\"\")\n def _click(self, event):\n for tag in self.text.tag_names(CURRENT):\n if tag[:6] == \"hyper-\":\n self.links[tag]()\n return\n\nclass Notebook(Frame):\n \"\"\"Notebook Widget\"\"\"\n def __init__(self, parent, activerelief=RAISED, inactiverelief=FLAT, \n xpad=4, ypad=6, activefg='black', inactivefg='black', debug=False,\n activefc=(\"Helvetica\", 14, \"bold\"), inactivefc=(\"Helvetica\", 14), **kw):\n \"\"\"Construct a Notebook Widget\n\n Notebook(self, parent, activerelief = RAISED, inactiverelief = RIDGE, \n xpad = 4, ypad = 6, activefg = 'black', inactivefg = 'black', **kw) \n \n Valid resource names: background, bd, bg, borderwidth, class,\n colormap, container, cursor, height, highlightbackground,\n highlightcolor, highlightthickness, relief, takefocus, visual, width, activerelief,\n inactiverelief, xpad, ypad.\n\n xpad and ypad are values to be used as ipady and ipadx\n with the Label widgets that make up the tabs. activefg and inactivefg define what\n color the text on the tabs when they are selected, and when they are not\n\n \"\"\"\n self.activefg = activefg\n self.inactivefg = inactivefg\n self.activefc = activefc\n self.inactivefc = inactivefc\n self.deletedTabs = []\n self.xpad = xpad\n self.ypad = ypad\n self.activerelief = activerelief\n self.inactiverelief = inactiverelief\n self.tabVars = {} \n self.tabs = 0\n self.progvar = DoubleVar()\n self.progvar.set(0)\n self.style = Style()\n self.style.theme_use(\"default\")\n self.style.configure(\"TProgressbar\", thickness=15, foreground='#347DBE', background='#347DBE')\n self.kwargs = kw \n self.tabVars = {}\n self.tabs = 0 \n # the notebook, with its tabs, middle, status bars\n self.noteBookFrame = Frame(parent, bg='#c5c5c5') \n self.BFrame = Frame(self.noteBookFrame, bg='#c5c5c5')\n self.statusbar = Frame(self.noteBookFrame, bd=2, height=24, width=kw.get('width'), bg='#F4F4F4')\n self.noteBook = Frame(self.noteBookFrame, relief=RAISED, bd=2, **kw)\n self.noteBook.grid_propagate(0)\n # status bar text and log\n self.status_text=StringVar()\n self.log_stream = []\n\n #self.progspace = Frame(self.statusbar, width=int(kw.get('width') * 0.4))\n #self.progspace.grid(sticky=E)\n #self.statusbar.grid_columnconfigure(2, weight=5)\n self.text = Label2(self.statusbar, #textvariable=self.status_text,\n width=int(kw.get('width') * 0.65), height=24, font=(\"Courier New\", 13))\n self.progbar = Progressbar(self.statusbar, orient='horizontal', \n length=int(kw.get('width') * 0.35),\n mode='determinate', variable=self.progvar, \n style=\"TProgressbar\")\n\n #self.statusbar.grid_columnconfigure(1, weight=2)\n self.statusbar.grid(row=2, column=0)\n\n #self.progbar.pack(anchor=E, fill='x')\n self.text.pack(side=LEFT)\n self.progbar.pack(side=RIGHT, expand=True)\n #self.statusbar.grid_propagate()\n \n # redirect stdout for log\n self.redir = RedirectText(self.status_text, self.log_stream, self.text.label_widget)\n if not debug:\n sys.stdout = self.redir\n sys.stderr = self.redir\n\n Frame.__init__(self)\n self.noteBookFrame.grid()\n self.BFrame.grid(row=0, column=0, columnspan=27, sticky=N) # \", column=13)\" puts the tabs in the middle!\n self.noteBook.grid(row=1, column=0, columnspan=27)\n\n \n \n #self.progbarspace.grid(row=2, column=0, padx=(273, 0), sticky=E)\n\n def change_tab(self, IDNum):\n \"\"\"Internal Function\"\"\"\n \n for i in (a for a in range(0, len(list(self.tabVars.keys())))):\n if not i in self.deletedTabs: \n if i != IDNum:\n self.tabVars[i][1].grid_remove() \n self.tabVars[i][0]['relief'] = self.inactiverelief \n self.tabVars[i][0]['fg'] = self.inactivefg \n self.tabVars[i][0]['font'] = self.inactivefc\n self.tabVars[i][0]['bg'] = '#c5c5c5'\n else:\n self.tabVars[i][1].grid() \n self.tabVars[IDNum][0]['relief'] = self.activerelief \n self.tabVars[i][0]['fg'] = self.activefg\n self.tabVars[i][0]['font'] = self.activefc\n self.tabVars[i][0]['bg'] = 'white' \n\n def add_tab(self, width=2, **kw):\n import tkinter\n \"\"\"Creates a new tab, and returns its corresponding frame\n \"\"\"\n \n temp = self.tabs\n self.tabVars[self.tabs] = [Label(self.BFrame, relief = RIDGE, **kw)]\n self.tabVars[self.tabs][0].bind(\"\", lambda Event:self.change_tab(temp)) \n self.tabVars[self.tabs][0].pack(side = LEFT, ipady=self.ypad, ipadx=self.xpad) \n self.tabVars[self.tabs].append(Frame(self.noteBook, **self.kwargs))\n self.tabVars[self.tabs][1].grid(row=0, column=0)\n self.change_tab(0)\n self.tabs += 1\n return self.tabVars[temp][1]\n\n def destroy_tab(self, tab):\n \"\"\"Delete a tab from the notebook, as well as it's corresponding frame\n \"\"\"\n \n self.iteratedTabs = 0\n for b in list(self.tabVars.values()):\n if b[1] == tab:\n b[0].destroy()\n self.tabs -= 1\n self.deletedTabs.append(self.iteratedTabs)\n break\n self.iteratedTabs += 1\n \n def focus_on(self, tab):\n \"\"\"Locate the IDNum of the given tab and use\n change_tab to give it focus\n \"\"\"\n self.iteratedTabs = 0\n for b in list(self.tabVars.values()):\n if b[1] == tab:\n self.change_tab(self.iteratedTabs)\n break\n self.iteratedTabs += 1\n\ndef corpkit_gui(noupdate=False, loadcurrent=False, debug=False):\n \"\"\"\n The actual code for the application\n\n :param noupdate: prevent auto update checking\n :type noupdate: bool \n\n :param loadcurrent: load this path as the project\n :type loadcurrent: str\n \"\"\"\n\n # make app\n root=Tk()\n #minimise it\n root.withdraw( )\n # generate splash\n with SplashScreen(root, 'loading_image.png', 1.0):\n # set app size\n #root.geometry(\"{0}x{1}+0+0\".format(root.winfo_screenwidth(), root.winfo_screenheight()))\n import warnings\n warnings.filterwarnings(\"ignore\")\n import traceback\n import dateutil\n import sys\n import os\n import corpkit\n from corpkit.process import get_gui_resource_dir, get_fullpath_to_jars\n from tkintertable import TableCanvas, TableModel\n from nltk.draw.table import MultiListbox, Table\n from collections import OrderedDict\n from pandas import Series, DataFrame\n \n # stop warning when insecure download is performed\n # this somehow raised an attribute error for anrej,\n # so we'll allow it to pass ...\n\n import requests\n try:\n requests.packages.urllib3.disable_warnings()\n except AttributeError:\n pass\n\n import locale\n if sys.platform == 'win32':\n try:\n locale.setlocale(locale.LC_ALL, 'english-usa')\n except:\n pass\n else:\n locale.setlocale(locale.LC_ALL, 'en_US.UTF-8')\n\n # unused in the gui, dummy imports for pyinstaller\n #import seaborn\n from hashlib import md5\n import chardet\n import pyparsing\n # a try statement in case not bundling scipy, which\n # tends to bloat the .app\n try:\n from scipy.stats import linregress\n except:\n pass\n\n # compress some things for a small screen ...\n small_screen = root.winfo_screenheight() < 800\n #small_screen = True\n \n ## add tregex and some other bits to path\n paths = ['', 'dictionaries', 'corpkit', 'nltk_data']\n for p in paths:\n fullp = os.path.join(rd, p).rstrip('/')\n if not fullp in sys.path:\n sys.path.append(fullp)\n\n # add nltk data to path\n import nltk\n nltk_data_path = os.path.join(rd, 'nltk_data')\n if nltk_data_path not in nltk.data.path:\n nltk.data.path.append(os.path.join(rd, 'nltk_data'))\n\n # not sure if needed anymore: more path setting \n corpath = os.path.dirname(corpkit.__file__)\n baspat = os.path.dirname(os.path.dirname(corpkit.__file__))\n dicpath = os.path.join(baspat, 'dictionaries')\n os.environ[\"PATH\"] += os.pathsep + corpath + os.pathsep + dicpath\n sys.path.append(corpath)\n sys.path.append(dicpath)\n sys.path.append(baspat)\n\n root.title(\"corpkit\")\n root.imagewatched = StringVar()\n #root.overrideredirect(True)\n #root.resizable(False,False)\n note_height = 600 if small_screen else 660\n note_width = root.winfo_screenwidth()\n if note_width > note_height * 1.62 and not small_screen:\n note_width = note_height * 1.62\n note_width = int(note_width)\n note = Notebook(root, width=note_width, height=note_height,\n activefg='#000000', inactivefg='#585555', debug=debug) #Create a Note book Instance\n note.grid()\n tab0 = note.add_tab(text=\"Build\")\n tab1 = note.add_tab(text=\"Interrogate\")\n tab2 = note.add_tab(text=\"Edit\") \n tab3 = note.add_tab(text=\"Visualise\") \n tab4 = note.add_tab(text=\"Concordance\")\n note.text.update_idletasks()\n\n ################### ################### ################### ###################\n # VARIABLES # # VARIABLES # # VARIABLES # # VARIABLES #\n ################### ################### ################### ###################\n\n # in this section, some recurring, empty variables are defined\n # to do: compress most of the dicts into one\n\n # round up text so we can bind keys to them later\n all_text_widgets = []\n\n # for the build tab (could be cleaned up)\n chosen_f = []\n sentdict = {}\n boxes = []\n buildbits = {}\n most_recent_projects = []\n\n # some variables that will get used throughout the gui\n # a dict of the editor frame names and models\n editor_tables = {}\n currently_in_each_frame = {}\n # for conc sort toggle\n sort_direction = True\n\n subc_sel_vals = []\n subc_sel_vals_build = []\n\n # store every interrogation and conc in this session \n all_interrogations = OrderedDict()\n all_conc = OrderedDict()\n all_images = []\n all_interrogations['None'] = 'None'\n\n # corpus path setter\n\n corpus_fullpath = StringVar()\n corpus_fullpath.set('')\n\n corenlppath = StringVar()\n corenlppath.set(os.path.join(os.path.expanduser(\"~\"), 'corenlp'))\n\n # visualise\n # where to put the current figure and frame\n thefig = []\n oldplotframe = []\n\n # for visualise, this holds a list of subcorpora or entries,\n # so that the title will dynamically change at the right time\n single_entry_or_subcorpus = {}\n \n # conc\n # to do: more consistent use of globals!\n itemcoldict = {}\n current_conc = ['None']\n global conc_saved\n conc_saved = False\n import itertools\n try:\n toggle = itertools.cycle([True, False]).__next__\n except AttributeError:\n toggle = itertools.cycle([True, False]).next\n\n # manage pane: obsolete\n manage_box = {}\n\n # custom lists\n custom_special_dict = {}\n # just the ones on the hd\n saved_special_dict = {}\n\n # not currently using this sort feature---should use in conc though\n import itertools\n try:\n direct = itertools.cycle([0,1]).__next__\n except AttributeError:\n direct = itertools.cycle([0,1]).next\n corpus_names_and_speakers = {}\n \n ################### ################### ################### ###################\n # DICTIONARIES # # DICTIONARIES # # DICTIONARIES # # DICTIONARIES #\n ################### ################### ################### ###################\n\n qd = {'Subjects': r'__ >># @NP',\n 'Processes': r'/VB.?/ >># ( VP >+(VP) (VP !> VP $ NP))',\n 'Modals': r'MD < __',\n 'Participants': r'/(NN|PRP|JJ).?/ >># (/(NP|ADJP)/ $ VP | > VP)',\n 'Entities': r'NP <# NNP',\n 'Any': 'any'}\n\n # concordance colours\n colourdict = {1: '#fbb4ae',\n 2: '#b3cde3',\n 3: '#ccebc5',\n 4: '#decbe4',\n 5: '#fed9a6',\n 6: '#ffffcc',\n 7: '#e5d8bd',\n 8: '#D9DDDB',\n 9: '#000000',\n 0: '#F4F4F4'}\n\n # translate search option for interrogator()\n transdict = {\n 'Get distance from root for regex match': 'a',\n 'Get tag and word of match': 'b',\n 'Count matches': 'c',\n 'Get role of match': 'f',\n 'Get \"role:dependent\", matching governor': 'd',\n 'Get ngrams from tokens': 'j',\n 'Get \"role:governor\", matching dependent': 'g',\n 'Get lemmata matching regex': 'l',\n 'Get tokens by role': 'm',\n 'Get ngrams from trees': 'n',\n 'Get part-of-speech tag': 'p',\n 'Regular expression search': 'r',\n 'Get tokens matching regex': 't',\n 'Get stats': 'v',\n 'Get words': 'w',\n 'Get tokens by regex': 'h',\n 'Get tokens matching list': 'e'}\n\n # translate sort_by for editor\n sort_trans = {'None': False,\n 'Total': 'total',\n 'Inverse total': 'infreq',\n 'Name': 'name',\n 'Increase': 'increase',\n 'Decrease': 'decrease',\n 'Static': 'static',\n 'Turbulent': 'turbulent',\n 'P value': 'p',\n 'Reverse': 'reverse'}\n\n # translate special queries for interrogator()\n spec_quer_translate = {'Participants': 'w',\n 'Any': 'any',\n 'Processes': 'w',\n 'Subjects': 'w',\n 'Entities': 'w'}\n\n # todo: newer method\n from corpkit.constants import transshow, transobjs, LETTERS\n from corpkit.process import make_name_to_query_dict\n\n exist = {'Trees': 't', 'Stats': 'v', 'CQL': 'cql'}\n convert_name_to_query = make_name_to_query_dict(exist)\n\n # these are example queries for each data type\n def_queries = {}\n for i in convert_name_to_query.keys():\n if i.lower().endswith('function'):\n def_queries[i] = r'\\b(amod|nn|advm|vmod|tmod)\\b'\n elif i.lower().endswith('lemma'):\n def_queries[i] = r'\\b(want|desire|need)\\b'\n elif i.lower().endswith('word class'):\n def_queries[i] = r'^(ad)verb$'\n elif i.lower().endswith('index'):\n def_queries[i] = r'[012345]',\n elif i.lower().endswith('stats'):\n def_queries[i] = r'any',\n elif i.lower().endswith('cql'):\n def_queries[i] = r'[pos=\"RB\" & word=\".*ly$\"]',\n elif i.lower().endswith('pos'):\n def_queries[i] = r'^[NJR]',\n elif i.lower().endswith('index'):\n def_queries[i] = r'[012345]',\n elif i.lower().endswith('distance from root'):\n def_queries[i] = r'[012345]',\n elif i.lower().endswith('trees'):\n def_queries[i] = r'JJ > (NP <<# /NN.?/)'\n else:\n def_queries[i] = r'\\b(m.n|wom.n|child(ren)?)\\b'\n\n ################### ################### ################### ###################\n # FUNCTIONS # # FUNCTIONS # # FUNCTIONS # # FUNCTIONS #\n ################### ################### ################### ###################\n\n # some functions used throughout the gui\n\n def focus_next_window(event):\n \"\"\"tab to next widget\"\"\"\n event.widget.tk_focusNext().focus()\n try:\n event.widget.tk_focusNext().selection_range(0, END)\n except:\n pass\n return \"break\"\n\n def runner(button, command, conc=False):\n \"\"\"\n Runs the command of a button, disabling the button till it is done,\n whether it returns early or not\n \"\"\"\n try:\n if button == interrobut or button == interrobut_conc:\n command(conc)\n else:\n command()\n except Exception as err:\n import traceback\n print(traceback.format_exc())\n note.progvar.set(0)\n button.config(state=NORMAL)\n\n def refresh_images(*args):\n \"\"\"get list of images saved in images folder\"\"\"\n import os\n if os.path.isdir(image_fullpath.get()):\n image_list = sorted([f for f in os.listdir(image_fullpath.get()) if f.endswith('.png')])\n for iname in image_list:\n if iname.replace('.png', '') not in all_images:\n all_images.append(iname.replace('.png', ''))\n else:\n for i in all_images:\n all_images.pop(i)\n #refresh()\n\n # if the dummy variable imagewatched is changed, refresh images\n # this connects to matplotlib's save button, if the modified\n # matplotlib is installed. a better way to do this would be good!\n root.imagewatched.trace(\"w\", refresh_images)\n\n def timestring(input):\n \"\"\"print with time prepended\"\"\"\n from time import localtime, strftime\n thetime = strftime(\"%H:%M:%S\", localtime())\n print('%s: %s' % (thetime, input.lstrip()))\n\n def conmap(cnfg, section):\n \"\"\"helper for load settings\"\"\"\n dict1 = {}\n options = cnfg.options(section)\n # todo: this loops over too many times\n for option in options:\n #try:\n opt = cnfg.get(section, option)\n if opt == '0':\n opt = False\n elif opt == '1':\n opt = True\n elif opt.isdigit():\n opt = int(opt)\n if isinstance(opt, str) and opt.lower() == 'none':\n opt = False\n if not opt:\n opt = 0\n dict1[option] = opt\n return dict1\n\n def convert_pandas_dict_to_ints(dict_obj):\n \"\"\"try to turn pandas as_dict into ints, for tkintertable\n\n the huge try statement is to stop errors when there\n is a single corpus --- need to find source of problem\n earlier, though\"\"\"\n vals = []\n try:\n for a, b in list(dict_obj.items()):\n # c = year, d = count\n for c, d in list(b.items()):\n vals.append(d)\n if all([float(x).is_integer() for x in vals if is_number(x)]):\n for a, b in list(dict_obj.items()):\n for c, d in list(b.items()):\n if is_number(d):\n b[c] = int(d)\n except TypeError:\n pass\n\n return dict_obj\n\n def update_spreadsheet(frame_to_update, df_to_show=None, model=False, \n height=140, width=False, indexwidth=70):\n \"\"\"refresh a spreadsheet\"\"\"\n from collections import OrderedDict\n import pandas\n\n # colours for tkintertable\n kwarg = {'cellbackgr': '#F7F7FA',\n 'grid_color': '#c5c5c5',\n 'entrybackgr': '#F4F4F4',\n 'selectedcolor': 'white',\n 'rowselectedcolor': '#b3cde3',\n 'multipleselectioncolor': '#fbb4ae'}\n if width:\n kwarg['width'] = width\n if model and not df_to_show:\n df_to_show = make_df_from_model(model)\n #if need_make_totals:\n df_to_show = make_df_totals(df_to_show)\n if df_to_show is not None:\n # for abs freq, make total\n model = TableModel()\n df_to_show = pandas.DataFrame(df_to_show, dtype=object)\n #if need_make_totals(df_to_show):\n df_to_show = make_df_totals(df_to_show)\n \n # turn pandas into dict\n raw_data = df_to_show.to_dict()\n \n # convert to int if possible\n raw_data = convert_pandas_dict_to_ints(raw_data)\n \n table = TableCanvas(frame_to_update, model=model, \n showkeynamesinheader=True, \n height=height,\n rowheaderwidth=row_label_width.get(), cellwidth=cell_width.get(), **kwarg)\n table.createTableFrame()\n model = table.model\n model.importDict(raw_data)\n # move columns into correct positions\n for index, name in enumerate(list(df_to_show.index)):\n model.moveColumn(model.getColumnIndex(name), index)\n table.createTableFrame()\n # sort the rows\n if 'tkintertable-order' in list(df_to_show.index):\n table.sortTable(columnName = 'tkintertable-order')\n ind = model.columnNames.index('tkintertable-order')\n try:\n model.deleteColumn(ind)\n except:\n pass\n if 'Total' in list(df_to_show.index):\n table.sortTable(columnName='Total', reverse=True)\n elif len(df_to_show.index) == 1:\n table.sortTable(columnIndex=0, reverse=True)\n\n else:\n #nm = os.path.basename(corpus_fullpath.get().rstrip('/'))\n ind = len(df_to_show.columns) - 1\n table.sortTable(columnIndex = ind, reverse = 1)\n #pass\n\n table.redrawTable()\n editor_tables[frame_to_update] = model\n currently_in_each_frame[frame_to_update] = df_to_show\n return\n\n if model:\n table = TableCanvas(frame_to_update, model=model, \n showkeynamesinheader=True, \n height=height,\n rowheaderwidth=row_label_width.get(), cellwidth=cell_width.get(),\n **kwarg)\n table.createTableFrame()\n try:\n table.sortTable(columnName = 'Total', reverse = direct())\n except:\n direct() \n table.sortTable(reverse = direct())\n table.createTableFrame()\n table.redrawTable()\n else:\n table = TableCanvas(frame_to_update, height=height, cellwidth=cell_width.get(), \n showkeynamesinheader=True, rowheaderwidth=row_label_width.get(), **kwarg)\n table.createTableFrame() # sorts by total freq, ok for now\n table.redrawTable()\n\n from corpkit.cql import remake_special\n\n def ignore():\n \"\"\"turn this on when buttons should do nothing\"\"\"\n return \"break\"\n\n def need_make_totals(df):\n \"\"\"check if a df needs totals\"\"\"\n if len(list(df.index)) < 3:\n return False\n try:\n x = df.iloc[0,0]\n except:\n return False\n # if was_series, basically\n try:\n vals = [i for i in list(df.iloc[0,].values) if is_number(i)]\n except TypeError:\n return False\n if len(vals) == 0:\n return False\n if all([float(x).is_integer() for x in vals]):\n return True\n else:\n return False\n\n def make_df_totals(df):\n \"\"\"make totals for a dataframe\"\"\" \n df = df.drop('Total', errors = 'ignore')\n # add new totals\n df.ix['Total'] = df.drop('tkintertable-order', errors = 'ignore').sum().astype(object)\n return df\n\n def make_df_from_model(model):\n \"\"\"generate df from spreadsheet\"\"\"\n import pandas\n from io import StringIO\n recs = model.getAllCells()\n colnames = model.columnNames\n collabels = model.columnlabels\n row = []\n csv_data = []\n for c in colnames:\n row.append(collabels[c])\n try:\n csv_data.append(','.join([str(s, errors = 'ignore') for s in row]))\n except TypeError:\n csv_data.append(','.join([str(s) for s in row]))\n #csv_data.append('\\n')\n for row in list(recs.keys()):\n rowname = model.getRecName(row)\n try:\n csv_data.append(','.join([str(rowname, errors = 'ignore')] + [str(s, errors = 'ignore') for s in recs[row]]))\n except TypeError:\n csv_data.append(','.join([str(rowname)] + [str(s) for s in recs[row]]))\n\n #csv_data.append('\\n')\n #writer.writerow(recs[row])\n csv = '\\n'.join(csv_data)\n uc = unicode(csv, errors='ignore')\n newdata = pandas.read_csv(StringIO(uc), index_col=0, header=0)\n newdata = pandas.DataFrame(newdata, dtype=object)\n newdata = newdata.T\n newdata = newdata.drop('Total', errors='ignore')\n newdata = add_tkt_index(newdata)\n if need_make_totals(newdata):\n newdata = make_df_totals(newdata)\n return newdata\n\n def color_saved(lb, savepath=False, colour1='#D9DDDB', colour2='white', \n ext='.p', lists=False):\n \"\"\"make saved items in listbox have colour background\n\n lb: listbox to colour\n savepath: where to look for existing files\n colour1, colour2: what to colour foundd and not found\n ext: what to append to filenames when searching for them\n lists: if working with wordlists, things need to be done differently, more colours\"\"\"\n all_items = [lb.get(i) for i in range(len(lb.get(0, END)))]\n\n # define colours for permanent lists in wordlists\n if lists:\n colour3 = '#ffffcc'\n colour4 = '#fed9a6'\n for index, item in enumerate(all_items):\n # check if saved\n if not lists:\n # files or directories with current corpus in name or without\n newn = current_corpus.get() + '-' + urlify(item) + ext\n a = os.path.isfile(os.path.join(savepath, urlify(item) + ext))\n b = os.path.isdir(os.path.join(savepath, urlify(item)))\n c = os.path.isfile(os.path.join(savepath, newn))\n d = os.path.isdir(os.path.join(savepath, newn))\n if any(x for x in [a, b, c, d]):\n issaved = True\n else:\n issaved = False\n # for lists, check if permanently stored\n else:\n issaved = False\n if item in list(saved_special_dict.keys()):\n issaved = True\n if current_corpus.get() + '-' + item in list(saved_special_dict.keys()):\n issaved = True\n if issaved:\n lb.itemconfig(index, {'bg':colour1})\n else:\n lb.itemconfig(index, {'bg':colour2})\n if lists:\n if item in list(predict.keys()):\n\n if item.endswith('_ROLE'):\n lb.itemconfig(index, {'bg':colour3})\n else:\n lb.itemconfig(index, {'bg':colour4})\n lb.selection_clear(0, END)\n\n def paste_into_textwidget(*args):\n \"\"\"paste function for widgets ... doesn't seem to work as expected\"\"\"\n try:\n start = args[0].widget.index(\"sel.first\")\n end = args[0].widget.index(\"sel.last\")\n args[0].widget.delete(start, end)\n except TclError as e:\n # nothing was selected, so paste doesn't need\n # to delete anything\n pass\n # for some reason, this works with the error.\n try:\n args[0].widget.insert(\"insert\", clipboard.rstrip('\\n'))\n except NameError:\n pass\n\n def copy_from_textwidget(*args):\n \"\"\"more commands for textwidgets\"\"\"\n #args[0].widget.clipboard_clear()\n text=args[0].widget.get(\"sel.first\", \"sel.last\").rstrip('\\n')\n args[0].widget.clipboard_append(text)\n\n def cut_from_textwidget(*args):\n \"\"\"more commands for textwidgets\"\"\"\n text=args[0].widget.get(\"sel.first\", \"sel.last\")\n args[0].widget.clipboard_append(text)\n args[0].widget.delete(\"sel.first\", \"sel.last\")\n\n def select_all_text(*args):\n \"\"\"more commands for textwidgets\"\"\"\n try:\n args[0].widget.selection_range(0, END)\n except:\n args[0].widget.tag_add(\"sel\",\"1.0\",\"end\")\n\n\n def make_corpus_name_from_abs(pfp, cfp):\n if pfp in cfp:\n return cfp.replace(pfp.rstrip('/') + '/', '')\n else:\n return cfp\n\n def get_all_corpora():\n import os\n all_corpora = []\n for root, ds, fs in os.walk(corpora_fullpath.get()):\n for d in ds:\n path = os.path.join(root, d)\n relpath = path.replace(corpora_fullpath.get(), '', 1).lstrip('/')\n all_corpora.append(relpath)\n return sorted(all_corpora) \n\n def update_available_corpora(delete=False):\n \"\"\"updates corpora in project, and returns a list of them\"\"\"\n import os\n fp = corpora_fullpath.get()\n all_corpora = get_all_corpora()\n for om in [available_corpora, available_corpora_build]:\n om.config(state=NORMAL)\n om['menu'].delete(0, 'end')\n if not delete:\n for corp in all_corpora:\n if not corp.endswith('parsed') and not corp.endswith('tokenised') and om == available_corpora:\n continue\n om['menu'].add_command(label=corp, command=_setit(current_corpus, corp))\n\n return all_corpora\n\n def refresh():\n \"\"\"refreshes the list of dataframes in the editor and plotter panes\"\"\"\n import os\n # Reset name_of_o_ed_spread and delete all old options\n # get the latest only after first interrogation\n if len(list(all_interrogations.keys())) == 1:\n selected_to_edit.set(list(all_interrogations.keys())[-1])\n dataframe1s['menu'].delete(0, 'end')\n dataframe2s['menu'].delete(0, 'end')\n every_interrogation['menu'].delete(0, 'end')\n #every_interro_listbox.delete(0, 'end')\n #every_image_listbox.delete(0, 'end')\n new_choices = []\n for interro in list(all_interrogations.keys()):\n new_choices.append(interro)\n new_choices = tuple(new_choices)\n dataframe2s['menu'].add_command(label='Self', command=_setit(data2_pick, 'Self'))\n if project_fullpath.get() != '' and project_fullpath.get() != rd:\n dpath = os.path.join(project_fullpath.get(), 'dictionaries')\n if os.path.isdir(dpath):\n dicts = sorted([f.replace('.p', '') for f in os.listdir(dpath) if os.path.isfile(os.path.join(dpath, f)) and f.endswith('.p')])\n for d in dicts:\n dataframe2s['menu'].add_command(label=d, command=_setit(data2_pick, d))\n for choice in new_choices:\n dataframe1s['menu'].add_command(label=choice, command=_setit(selected_to_edit, choice))\n dataframe2s['menu'].add_command(label=choice, command=_setit(data2_pick, choice))\n every_interrogation['menu'].add_command(label=choice, command=_setit(data_to_plot, choice))\n\n refresh_images()\n\n # refresh \n prev_conc_listbox.delete(0, 'end')\n for i in sorted(all_conc.keys()):\n prev_conc_listbox.insert(END, i)\n\n def add_tkt_index(df):\n \"\"\"add order to df for tkintertable\"\"\"\n import pandas\n df = df.T\n df = df.drop('tkintertable-order', errors = 'ignore', axis=1)\n df['tkintertable-order'] = pandas.Series([index for index, data in enumerate(list(df.index))], index = list(df.index))\n df = df.T\n return df\n\n def namer(name_box_text, type_of_data = 'interrogation'):\n \"\"\"returns a name to store interrogation/editor result as\"\"\"\n if name_box_text.lower() == 'untitled' or name_box_text == '':\n c = 0\n the_name = '%s-%s' % (type_of_data, str(c).zfill(2))\n while any(x.startswith(the_name) for x in list(all_interrogations.keys())):\n c += 1\n the_name = '%s-%s' % (type_of_data, str(c).zfill(2))\n else:\n the_name = name_box_text\n return the_name\n\n def show_prev():\n \"\"\"show previous interrogation\"\"\"\n import pandas\n currentname = name_of_interro_spreadsheet.get()\n # get index of current index\n if not currentname:\n prev.configure(state=DISABLED)\n return\n ind = list(all_interrogations.keys()).index(currentname)\n # if it's higher than zero\n if ind > 0:\n if ind == 1:\n prev.configure(state=DISABLED)\n nex.configure(state=NORMAL)\n else:\n if ind + 1 < len(list(all_interrogations.keys())):\n nex.configure(state=NORMAL)\n prev.configure(state=NORMAL)\n\n newname = list(all_interrogations.keys())[ind - 1]\n newdata = all_interrogations[newname]\n name_of_interro_spreadsheet.set(newname)\n i_resultname.set('Interrogation results: %s' % str(name_of_interro_spreadsheet.get()))\n if isinstance(newdata, pandas.DataFrame):\n toshow = newdata\n toshowt = newdata.sum()\n elif hasattr(newdata, 'results') and newdata.results is not None:\n toshow = newdata.results\n if hasattr(newdata, 'totals') and newdata.results is not None:\n toshowt = pandas.DataFrame(newdata.totals, dtype=object)\n update_spreadsheet(interro_results, toshow, height=340)\n update_spreadsheet(interro_totals, toshowt, height=10)\n refresh()\n else:\n prev.configure(state=DISABLED)\n nex.configure(state=NORMAL)\n\n def show_next():\n \"\"\"show next interrogation\"\"\"\n import pandas\n currentname = name_of_interro_spreadsheet.get()\n if currentname:\n ind = list(all_interrogations.keys()).index(currentname)\n else:\n ind = 0\n if ind > 0:\n prev.configure(state=NORMAL)\n if ind + 1 < len(list(all_interrogations.keys())):\n if ind + 2 == len(list(all_interrogations.keys())):\n nex.configure(state=DISABLED)\n prev.configure(state=NORMAL)\n else:\n nex.configure(state=NORMAL)\n newname = list(all_interrogations.keys())[ind + 1]\n newdata = all_interrogations[newname]\n name_of_interro_spreadsheet.set(newname)\n i_resultname.set('Interrogation results: %s' % str(name_of_interro_spreadsheet.get()))\n if isinstance(newdata, pandas.DataFrame):\n toshow = newdata\n toshowt = newdata.sum()\n elif hasattr(newdata, 'results') and newdata.results is not None:\n toshow = newdata.results\n if hasattr(newdata, 'totals') and newdata.results is not None:\n toshowt = newdata.totals\n update_spreadsheet(interro_results, toshow, height=340)\n totals_as_df = pandas.DataFrame(toshowt, dtype=object)\n update_spreadsheet(interro_totals, toshowt, height=10)\n refresh()\n else:\n nex.configure(state=DISABLED)\n prev.configure(state=NORMAL)\n\n def exchange_interro_branch(namedtupname, newdata, branch='results'):\n \"\"\"replaces a namedtuple results/totals with newdata\n --- such a hack, should upgrade to recordtype\"\"\"\n namedtup = all_interrogations[namedtupname]\n the_branch = getattr(namedtup, branch)\n if branch == 'results':\n the_branch.drop(the_branch.index, inplace=True)\n the_branch.drop(the_branch.columns, axis=1, inplace=True)\n for i in list(newdata.columns):\n the_branch[i] = i\n for index, i in enumerate(list(newdata.index)):\n the_branch.loc[i] = newdata.ix[index]\n elif branch == 'totals':\n the_branch.drop(the_branch.index, inplace=True)\n for index, datum in zip(newdata.index, newdata.iloc[:,0].values):\n the_branch.set_value(index, datum)\n all_interrogations[namedtupname] = namedtup\n\n def update_interrogation(table_id, id, is_total=False):\n \"\"\"takes any changes made to spreadsheet and saves to the interrogation\n id: 0 = interrogator\n 1 = old editor window\n 2 = new editor window\"\"\"\n\n model=editor_tables[table_id]\n newdata = make_df_from_model(model)\n if need_make_totals(newdata):\n newdata = make_df_totals(newdata)\n\n if id == 0:\n name_of_interrogation = name_of_interro_spreadsheet.get()\n if id == 1:\n name_of_interrogation = name_of_o_ed_spread.get()\n if id == 2:\n name_of_interrogation = name_of_n_ed_spread.get()\n if not is_total:\n exchange_interro_branch(name_of_interrogation, newdata, branch='results')\n else:\n exchange_interro_branch(name_of_interrogation, newdata, branch='totals')\n\n def update_all_interrogations(pane='interrogate'):\n import pandas\n \"\"\"update all_interrogations within spreadsheet data\n need a very serious cleanup!\"\"\"\n # to do: only if they are there!\n if pane == 'interrogate':\n update_interrogation(interro_results, id=0)\n update_interrogation(interro_totals, id=0, is_total=True)\n if pane == 'edit':\n update_interrogation(o_editor_results, id=1)\n update_interrogation(o_editor_totals, id=1, is_total=True)\n # update new editor sheet if it's there\n if name_of_n_ed_spread.get() != '':\n update_interrogation(n_editor_results, id=2)\n update_interrogation(n_editor_totals, id=2, is_total=True)\n timestring('Updated interrogations with manual data.')\n if pane == 'interrogate':\n the_data = all_interrogations[name_of_interro_spreadsheet.get()]\n tot = pandas.DataFrame(the_data.totals, dtype=object)\n \n if the_data.results is not None:\n update_spreadsheet(interro_results, the_data.results, height=340)\n else:\n update_spreadsheet(interro_results, df_to_show=None, height=340)\n\n update_spreadsheet(interro_totals, tot, height=10)\n if pane == 'edit':\n the_data = all_interrogations[name_of_o_ed_spread.get()]\n there_is_new_data = False\n try:\n newdata = all_interrogations[name_of_n_ed_spread.get()]\n there_is_new_data = True\n except:\n pass\n if the_data.results is not None:\n update_spreadsheet(o_editor_results, the_data.results, height=140)\n update_spreadsheet(o_editor_totals, pandas.DataFrame(the_data.totals, dtype=object), height=10)\n if there_is_new_data:\n if newdata != 'None' and newdata != '':\n if the_data.results is not None:\n update_spreadsheet(n_editor_results, newdata.results, height=140)\n update_spreadsheet(n_editor_totals, pandas.DataFrame(newdata.totals, dtype=object), height=10)\n if name_of_o_ed_spread.get() == name_of_interro_spreadsheet.get():\n the_data = all_interrogations[name_of_interro_spreadsheet.get()]\n tot = pandas.DataFrame(the_data.totals, dtype=object)\n if the_data.results is not None:\n update_spreadsheet(interro_results, the_data.results, height=340)\n update_spreadsheet(interro_totals, tot, height=10)\n \n timestring('Updated spreadsheet display in edit window.')\n\n from corpkit.process import is_number\n\n ################### ################### ################### ###################\n #PREFERENCES POPUP# #PREFERENCES POPUP# #PREFERENCES POPUP# #PREFERENCES POPUP#\n ################### ################### ################### ###################\n\n # make variables with default values\n\n do_auto_update = IntVar()\n do_auto_update.set(1)\n\n do_auto_update_this_session = IntVar()\n do_auto_update_this_session.set(1)\n\n #conc_when_int = IntVar()\n #conc_when_int.set(1)\n\n only_format_match = IntVar()\n only_format_match.set(0)\n\n files_as_subcorpora = IntVar()\n files_as_subcorpora.set(0)\n\n do_concordancing = IntVar()\n do_concordancing.set(1)\n\n show_conc_metadata = IntVar()\n show_conc_metadata.set(1)\n\n #noregex = IntVar()\n #noregex.set(0)\n\n parser_memory = StringVar()\n parser_memory.set(str(2000))\n\n truncate_conc_after = IntVar()\n truncate_conc_after.set(9999)\n\n truncate_spreadsheet_after = IntVar()\n truncate_spreadsheet_after.set(9999)\n\n corenlppath = StringVar()\n corenlppath.set(os.path.join(os.path.expanduser(\"~\"), 'corenlp'))\n\n row_label_width=IntVar()\n row_label_width.set(100)\n\n cell_width=IntVar()\n cell_width.set(50)\n\n p_val = DoubleVar()\n p_val.set(0.05) \n\n # a place for the toplevel entry info\n entryboxes = OrderedDict()\n\n # fill it with null data\n for i in range(10):\n tmp = StringVar()\n tmp.set('')\n entryboxes[i] = tmp\n\n def preferences_popup():\n try:\n global toplevel\n toplevel.destroy()\n except:\n pass\n\n from tkinter import Toplevel\n pref_pop = Toplevel()\n #pref_pop.config(background = '#F4F4F4')\n pref_pop.geometry('+300+100')\n pref_pop.title(\"Preferences\")\n #pref_pop.overrideredirect(1)\n pref_pop.wm_attributes('-topmost', 1)\n Label(pref_pop, text='').grid(row=0, column=0, pady=2)\n \n def quit_coding(*args):\n save_tool_prefs(printout=True)\n pref_pop.destroy()\n\n tmp = Checkbutton(pref_pop, text='Automatically check for updates', variable=do_auto_update, onvalue=1, offvalue=0)\n if do_auto_update.get() == 1:\n tmp.select()\n all_text_widgets.append(tmp)\n tmp.grid(row=0, column=0, sticky=W)\n \n Label(pref_pop, text='Truncate concordance lines').grid(row=1, column=0, sticky=W)\n tmp = Entry(pref_pop, textvariable=truncate_conc_after, width=7)\n all_text_widgets.append(tmp)\n tmp.grid(row=1, column=1, sticky=E)\n\n Label(pref_pop, text='Truncate spreadsheets').grid(row=2, column=0, sticky=W)\n tmp = Entry(pref_pop, textvariable=truncate_spreadsheet_after, width=7)\n all_text_widgets.append(tmp)\n tmp.grid(row=2, column=1, sticky=E)\n\n Label(pref_pop, text='CoreNLP memory allocation (MB)').grid(row=3, column=0, sticky=W)\n tmp = Entry(pref_pop, textvariable=parser_memory, width=7)\n all_text_widgets.append(tmp)\n tmp.grid(row=3, column=1, sticky=E)\n\n Label(pref_pop, text='Spreadsheet cell width').grid(row=4, column=0, sticky=W)\n tmp = Entry(pref_pop, textvariable=cell_width, width=7)\n all_text_widgets.append(tmp)\n tmp.grid(row=4, column=1, sticky=E)\n\n Label(pref_pop, text='Spreadsheet row header width').grid(row=5, column=0, sticky=W)\n tmp = Entry(pref_pop, textvariable=row_label_width, width=7)\n all_text_widgets.append(tmp)\n tmp.grid(row=5, column=1, sticky=E)\n\n Label(pref_pop, text='P value').grid(row=6, column=0, sticky=W)\n tmp = Entry(pref_pop, textvariable=p_val, width=7)\n all_text_widgets.append(tmp)\n tmp.grid(row=6, column=1, sticky=E)\n\n Label(pref_pop, text='CoreNLP path:', justify=LEFT).grid(row=7, column=0, sticky=W, rowspan = 1)\n Button(pref_pop, text='Change', command=set_corenlp_path, width =5).grid(row=7, column=1, sticky=E)\n Label(pref_pop, textvariable=corenlppath, justify=LEFT).grid(row=8, column=0, sticky=W)\n #set_corenlp_path\n\n tmp = Checkbutton(pref_pop, text='Treat files as subcorpora', variable=files_as_subcorpora, onvalue=1, offvalue=0)\n tmp.grid(row=10, column=0, pady=(0,0), sticky=W)\n\n #tmp = Checkbutton(pref_pop, text='Disable regex for plaintext', variable=noregex, onvalue=1, offvalue=0)\n #tmp.grid(row=9, column=1, pady=(0,0), sticky=W)\n\n tmp = Checkbutton(pref_pop, text='Do concordancing', variable=do_concordancing, onvalue=1, offvalue=0)\n tmp.grid(row=10, column=1, pady=(0,0), sticky=W)\n\n tmp = Checkbutton(pref_pop, text='Format concordance context', variable=only_format_match, onvalue=1, offvalue=0)\n tmp.grid(row=11, column=0, pady=(0,0), sticky=W)\n\n tmp = Checkbutton(pref_pop, text='Show concordance metadata', variable=show_conc_metadata, onvalue=1, offvalue=0)\n tmp.grid(row=11, column=1, pady=(0,0), sticky=W)\n\n stopbut = Button(pref_pop, text='Done', command=quit_coding)\n stopbut.grid(row=12, column=0, columnspan=2, pady=15) \n\n pref_pop.bind(\"\", quit_coding)\n pref_pop.bind(\"\", focus_next_window)\n\n ################### ################### ################### ###################\n # INTERROGATE TAB # # INTERROGATE TAB # # INTERROGATE TAB # # INTERROGATE TAB #\n ################### ################### ################### ###################\n\n # hopefully weighting the two columns, not sure if works\n\n interro_opt = Frame(tab1)\n interro_opt.grid(row=0, column=0)\n\n tab1.grid_columnconfigure(2, weight=5)\n\n def do_interrogation(conc=True):\n \"\"\"the main function: calls interrogator()\"\"\"\n import pandas\n from corpkit.interrogator import interrogator\n from corpkit.interrogation import Interrogation, Interrodict\n doing_concondancing = True\n # no pressing while running\n #if not conc:\n interrobut.config(state=DISABLED)\n #else:\n interrobut_conc.config(state=DISABLED)\n recalc_but.config(state=DISABLED)\n # progbar to zero\n note.progvar.set(0)\n for i in list(itemcoldict.keys()):\n del itemcoldict[i]\n \n # spelling conversion?\n #conv = (spl.var).get()\n #if conv == 'Convert spelling' or conv == 'Off':\n # conv = False\n \n # lemmatag: do i need to add as button if trees?\n lemmatag = False\n query = qa.get(1.0, END).replace('\\n', '')\n if not datatype_picked.get() == 'CQL':\n # allow list queries\n if query.startswith('[') and query.endswith(']') and ',' in query:\n query = query.lstrip('[').rstrip(']').replace(\"'\", '').replace('\"', '').replace(' ', '').split(',')\n #elif transdict[searchtype()] in ['e', 's']:\n #query = query.lstrip('[').rstrip(']').replace(\"'\", '').replace('\"', '').replace(' ', '').split(',')\n else:\n # convert special stuff\n query = remake_special(query, customs=custom_special_dict, \n case_sensitive=case_sensitive.get())\n if query is False:\n return\n\n # make name for interrogation\n the_name = namer(nametext.get(), type_of_data='interrogation')\n\n cqlmode = IntVar()\n cqlmode.set(0)\n\n # get the main query\n so = datatype_picked.get()\n\n if so == 'CQL':\n cqlmode.set(1)\n\n selected_option = convert_name_to_query.get(so, so)\n\n if selected_option == '':\n timestring('You need to select a search type.')\n return\n\n queryd = {}\n for k, v in list(additional_criteria.items()):\n # this should already be done\n queryd[k] = v\n queryd[selected_option] = query\n \n # cql mode just takes a string\n if cqlmode.get():\n queryd = query\n\n if selected_option == 'v':\n queryd = 'features'\n doing_concondancing = False\n else:\n doing_concondancing = True\n\n # to do: make this order customisable for the gui too\n poss_returns = [return_function, return_pos, return_lemma, return_token, \\\n return_gov, return_dep, return_tree, return_index, return_distance, \\\n return_count, return_gov_lemma, return_gov_pos, return_gov_func, \\\n return_dep_lemma, return_dep_pos, return_dep_func, \\\n return_ngm_lemma, return_ngm_pos, return_ngm_func, return_ngm]\n must_make = [return_ngm_lemma, return_ngm_pos, return_ngm_func, return_ngm]\n\n to_show = [prenext_pos.get() + i.get() if i in must_make and i.get() else i.get() for i in poss_returns]\n to_show = [i for i in to_show if i and 'Position' not in i]\n\n if not to_show and not selected_option == 'v':\n timestring('Interrogation must return something.')\n return\n \n if 'c' in to_show:\n doing_concondancing = False\n\n if not do_concordancing.get():\n doing_concondancing = False\n\n #if noregex.get() == 1:\n # regex = False\n #else:\n # regex = True\n\n subcc = False\n just_subc = False\n\n met_field_ids = [by_met_listbox.get(i) for i in by_met_listbox.curselection()]\n met_val_ids = [speaker_listbox.get(i) for i in speaker_listbox.curselection()]\n if len(met_field_ids) == 1:\n met_field_ids = met_field_ids[0]\n if len(met_val_ids) == 1:\n met_val_ids = met_val_ids[0]\n if met_field_ids and not met_val_ids:\n if isinstance(met_field_ids, list):\n subcc = met_field_ids\n else:\n if met_field_ids == 'folders':\n subcc = False\n elif met_field_ids == 'files':\n files_as_subcorpora.set(1)\n elif met_field_ids == 'none':\n # todo: no sub mode?\n subcc = False\n else:\n subcc = met_field_ids\n\n elif not met_field_ids:\n subcc = False\n elif met_field_ids and met_val_ids:\n subcc = met_field_ids\n if 'ALL' in met_val_ids:\n pass\n else:\n just_subc = {met_field_ids: met_val_ids}\n\n # default interrogator args: root and note pass the gui itself for updating\n # progress bar and so on.\n interrogator_args = {'search': queryd,\n 'show': to_show,\n 'case_sensitive': bool(case_sensitive.get()),\n 'no_punct': bool(no_punct.get()),\n #'spelling': conv,\n 'root': root,\n 'note': note,\n 'df1_always_df': True,\n 'conc': doing_concondancing,\n 'only_format_match': not bool(only_format_match.get()),\n #'dep_type': depdict.get(kind_of_dep.get(), 'CC-processed'),\n 'nltk_data_path': nltk_data_path,\n #'regex': regex,\n 'coref': coref.get(),\n 'cql': cqlmode.get(),\n 'files_as_subcorpora': bool(files_as_subcorpora.get()),\n 'subcorpora': subcc,\n 'just_metadata': just_subc,\n 'show_conc_metadata': bool(show_conc_metadata.get()),\n 'use_interrodict': True}\n if debug:\n print(interrogator_args)\n\n excludes = {}\n for k, v in list(ex_additional_criteria.items()):\n if k != 'None':\n excludes[k.lower()[0]] = v\n if exclude_op.get() != 'None':\n q = remake_special(exclude_str.get(), return_list=True,\n customs=custom_special_dict, \n case_sensitive=case_sensitive.get())\n if q:\n excludes[exclude_op.get().lower()[0]] = q\n\n if excludes:\n interrogator_args['exclude'] = excludes\n\n try:\n interrogator_args['searchmode'] = anyall.get()\n except:\n pass\n try:\n interrogator_args['excludemode'] = excludemode.get()\n except:\n pass\n \n # translate lemmatag\n tagdict = {'Noun': 'n',\n 'Adjective': 'a',\n 'Verb': 'v',\n 'Adverb': 'r',\n 'None': False,\n '': False,\n 'Off': False}\n\n #interrogator_args['lemmatag'] = tagdict[lemtag.get()]\n\n if corpus_fullpath.get() == '':\n timestring('You need to select a corpus.')\n return\n\n # stats preset is actually a search type\n #if special_queries.get() == 'Stats':\n # selected_option = 'v'\n # interrogator_args['query'] = 'any'\n\n # if ngramming, there are two extra options\n ngm = ngmsize.var.get()\n if ngm != 'Size':\n interrogator_args['gramsize'] = int(ngm)\n clc = collosize.var.get()\n if clc != 'Size':\n interrogator_args['window'] = int(clc)\n\n #if subc_pick.get() == \"Subcorpus\" or subc_pick.get().lower() == 'all' or \\\n # selected_corpus_has_no_subcorpora.get() == 1:\n corp_to_search = corpus_fullpath.get()\n #else:\n # corp_to_search = os.path.join(corpus_fullpath.get(), subc_pick.get())\n\n # do interrogation, return if empty\n if debug:\n print('CORPUS:', corp_to_search)\n interrodata = interrogator(corp_to_search, **interrogator_args)\n if isinstance(interrodata, Interrogation):\n if hasattr(interrodata, 'results') and interrodata.results is not None:\n if interrodata.results.empty:\n timestring('No results found, sorry.')\n return \n\n # make sure we're redirecting stdout again\n if not debug:\n sys.stdout = note.redir\n\n # update spreadsheets\n if not isinstance(interrodata, (Interrogation, Interrodict)):\n update_spreadsheet(interro_results, df_to_show=None, height=340)\n update_spreadsheet(interro_totals, df_to_show=None, height=10) \n return\n\n # make non-dict results into dict, so we can iterate no matter\n # if there were multiple results or not\n interrogation_returned_dict = False\n from collections import OrderedDict\n if isinstance(interrodata, Interrogation):\n dict_of_results = OrderedDict({the_name: interrodata})\n else:\n dict_of_results = interrodata\n interrogation_returned_dict = True\n\n # remove dummy entry from master\n all_interrogations.pop('None', None)\n\n # post-process each result and add to master list\n for nm, r in sorted(dict_of_results.items(), key=lambda x: x[0]):\n # drop over 9999\n # type check probably redundant now\n if r.results is not None:\n large = [n for i, n in enumerate(list(r.results.columns)) if i > truncate_spreadsheet_after.get()]\n r.results.drop(large, axis=1, inplace=True)\n r.results.drop('Total', errors='ignore', inplace=True)\n r.results.drop('Total', errors='ignore', inplace=True, axis=1)\n\n # add interrogation to master list\n if interrogation_returned_dict:\n all_interrogations[the_name + '-' + nm] = r\n all_conc[the_name + '-' + nm] = r.concordance\n dict_of_results[the_name + '-' + nm] = dict_of_results.pop(nm)\n # make multi for conc...\n else:\n all_interrogations[nm] = r\n all_conc[nm] = r.concordance\n\n # show most recent (alphabetically last) interrogation spreadsheet\n recent_interrogation_name = list(dict_of_results.keys())[0]\n recent_interrogation_data = list(dict_of_results.values())[0]\n\n if queryd == {'v': 'any'}:\n conc = False\n if doing_concondancing:\n conc_to_show = recent_interrogation_data.concordance\n if conc_to_show is not None:\n numresults = len(conc_to_show.index)\n if numresults > truncate_conc_after.get() - 1:\n nums = str(numresults)\n if numresults == 9999:\n nums += '+'\n truncate = messagebox.askyesno(\"Long results list\", \n \"%s unique concordance results! Truncate to %s?\" % (nums, str(truncate_conc_after.get())))\n if truncate:\n conc_to_show = conc_to_show.head(truncate_conc_after.get())\n add_conc_lines_to_window(conc_to_show, preserve_colour=False)\n else:\n timestring('No concordance results generated.')\n global conc_saved\n conc_saved = False\n\n name_of_interro_spreadsheet.set(recent_interrogation_name)\n i_resultname.set('Interrogation results: %s' % str(name_of_interro_spreadsheet.get()))\n\n # total in a way that tkintertable likes\n if isinstance(recent_interrogation_data.totals, int):\n recent_interrogation_data.totals = Series(recent_interrogation_data.totals)\n totals_as_df = pandas.DataFrame(recent_interrogation_data.totals, dtype=object)\n\n # update spreadsheets\n if recent_interrogation_data.results is not None:\n update_spreadsheet(interro_results, recent_interrogation_data.results, height=340)\n else:\n update_spreadsheet(interro_results, df_to_show=None, height=340)\n\n update_spreadsheet(interro_totals, totals_as_df, height=10)\n \n ind = list(all_interrogations.keys()).index(name_of_interro_spreadsheet.get())\n if ind == 0:\n prev.configure(state=DISABLED)\n else:\n prev.configure(state=NORMAL)\n\n if ind + 1 == len(list(all_interrogations.keys())):\n nex.configure(state=DISABLED)\n else:\n nex.configure(state=NORMAL)\n refresh()\n\n if recent_interrogation_data.results is not None:\n subs = r.results.index\n else:\n subs = r.totals.index\n\n subc_listbox.delete(0, 'end')\n for e in list(subs):\n if e != 'tkintertable-order':\n subc_listbox.insert(END, e)\n\n #reset name\n nametext.set('untitled')\n \n if interrogation_returned_dict:\n timestring('Interrogation finished, with multiple results.')\n\n interrobut.config(state=NORMAL)\n interrobut_conc.config(state=NORMAL)\n recalc_but.config(state=NORMAL)\n\n class MyOptionMenu(OptionMenu):\n \"\"\"Simple OptionMenu for things that don't change.\"\"\"\n def __init__(self, tab1, status, *options):\n self.var = StringVar(tab1)\n self.var.set(status)\n OptionMenu.__init__(self, tab1, self.var, *options)\n self.config(font=('calibri',(12)),width=20)\n self['menu'].config(font=('calibri',(10)))\n \n def corpus_callback(*args):\n \"\"\"\n On selecting a corpus, set everything appropriately.\n also, disable some kinds of search based on the name\n \"\"\"\n if not current_corpus.get():\n return\n import os\n from os.path import join, isdir, isfile, exists\n corpus_fullpath.set(join(corpora_fullpath.get(), current_corpus.get()))\n fp = corpus_fullpath.get()\n from corpkit.corpus import Corpus\n corpus = Corpus(fp, print_info=False)\n dtype = corpus.datatype\n cols = []\n if dtype == 'conll':\n datatype_picked.set('Word')\n try:\n cols = corpus.metadata['columns']\n except KeyError:\n pass\n\n try:\n subdrs = sorted([d for d in os.listdir(corpus_fullpath.get()) if os.path.isdir(os.path.join(corpus_fullpath.get(),d))])\n except FileNotFoundError:\n subdrs = []\n \n if len(subdrs) == 0:\n charttype.set('bar')\n\n pick_a_datatype['menu'].delete(0, 'end')\n\n path_to_new_unparsed_corpus.set(fp)\n #add_corpus_button.set('Added: \"%s\"' % os.path.basename(fp))\n # why is it setting itself?\n #current_corpus.set(os.path.basename(fp))\n\n from corpkit.process import make_name_to_query_dict\n exist = {'CQL': 'cql'}\n if 'f' in cols:\n exist['Trees'] = 't'\n exist['Stats'] = 'v'\n # todo: only cql for tokenised\n convert_name_to_query = make_name_to_query_dict(exist, cols, dtype)\n\n # allow tokenising/parsing of plaintext\n if not fp.endswith('-parsed') and not fp.endswith('-tokenised'):\n parsebut.config(state=NORMAL)\n tokbut.config(state=NORMAL)\n parse_button_text.set('Parse: %s' % os.path.basename(fp))\n tokenise_button_text.set('Tokenise: %s' % current_corpus.get())\n # disable tokenising and parsing of non plaintxt\n else:\n parsebut.config(state=NORMAL)\n tokbut.config(state=NORMAL)\n parse_button_text.set('Parse corpus')\n tokenise_button_text.set('Tokenise corpus')\n parsebut.config(state=DISABLED)\n tokbut.config(state=DISABLED)\n # no corefs\n if not fp.endswith('-parsed') and not fp.endswith('tokenised'):\n #pick_dep_type.config(state=DISABLED)\n coref_but.config(state=DISABLED)\n #parsebut.config(state=NORMAL)\n #speakcheck_build.config(state=NORMAL)\n interrobut_conc.config(state=DISABLED)\n recalc_but.config(state=DISABLED)\n #sensplitbut.config(state=NORMAL)\n pick_a_datatype.configure(state=DISABLED)\n interrobut.configure(state=DISABLED)\n interrobut_conc.config(state=DISABLED)\n recalc_but.config(state=DISABLED)\n else:\n interrobut_conc.config(state=NORMAL)\n recalc_but.config(state=NORMAL)\n pick_a_datatype.configure(state=NORMAL)\n interrobut.configure(state=NORMAL)\n if datatype_picked.get() not in ['Trees']:\n coref_but.config(state=NORMAL)\n interrobut_conc.config(state=DISABLED)\n recalc_but.config(state=DISABLED)\n for i in sorted(convert_name_to_query):\n # todo: for now --- simplifying gui!\n if i.lower() == 'distance from root' or i.lower().startswith('head'):\n continue\n\n pick_a_datatype['menu'].add_command(label=i, command=_setit(datatype_picked, i))\n #parsebut.config(state=DISABLED)\n #speakcheck_build.config(state=DISABLED)\n datatype_picked.set('Word')\n if not fp.endswith('-tokenised') and not fp.endswith('-parsed'):\n pick_a_datatype['menu'].add_command(label='Word', command=_setit(datatype_picked, 'Word'))\n \n\n else:\n datatype_picked.set('Word')\n \n add_subcorpora_to_build_box(fp)\n\n note.progvar.set(0)\n \n if current_corpus.get() in list(corpus_names_and_speakers.keys()):\n refresh_by_metadata()\n #speakcheck.config(state=NORMAL)\n else:\n pass\n #speakcheck.config(state=DISABLED)\n timestring('Set corpus directory: \"%s\"' % fp)\n editf.set('Edit file: ')\n parse_only = [ck4, ck5, ck6, ck7, ck9, ck10, ck11, ck12, ck13, ck14, ck15, ck16]\n non_parsed = [ck1, ck8]\n if 'l' in cols:\n non_parsed.append(ck2)\n if 'p' in cols:\n non_parsed.append(ck3)\n \n if not current_corpus.get().endswith('-parsed'):\n for but in parse_only:\n desel_and_turn_off(but)\n for but in non_parsed:\n turnon(but) \n else:\n for but in parse_only:\n turnon(but)\n for but in non_parsed:\n turnon(but) \n\n if datatype_picked.get() == 'Trees':\n ck4.config(state=NORMAL)\n else:\n ck4.config(state=DISABLED)\n\n refresh_by_metadata()\n\n Label(interro_opt, text='Corpus/subcorpora:').grid(row=0, column=0, sticky=W)\n current_corpus = StringVar()\n current_corpus.set('Corpus')\n available_corpora = OptionMenu(interro_opt, current_corpus, *tuple(('Select corpus')))\n available_corpora.config(width=30, state=DISABLED, justify=CENTER)\n current_corpus.trace(\"w\", corpus_callback)\n available_corpora.grid(row=0, column=0, columnspan=2, padx=(135,0))\n\n\n available_corpora_build = OptionMenu(tab0, current_corpus, *tuple(('Select corpus')))\n available_corpora_build.config(width=25, justify=CENTER, state=DISABLED)\n available_corpora_build.grid(row=4, column=0, sticky=W)\n\n ex_additional_criteria = {}\n ex_anyall = StringVar()\n ex_anyall.set('any')\n\n ex_objs = OrderedDict()\n # fill it with null data\n for i in range(20):\n tmp = StringVar()\n tmp.set('')\n ex_objs[i] = [None, None, None, tmp]\n\n ex_permref = []\n\n exclude_str = StringVar()\n exclude_str.set('')\n Label(interro_opt, text='Exclude:').grid(row=8, column=0, sticky=W, pady=(0, 10))\n exclude_op = StringVar()\n exclude_op.set('None')\n exclude = OptionMenu(interro_opt, exclude_op, *['None'] + sorted(convert_name_to_query.keys()))\n exclude.config(width=14)\n exclude.grid(row=8, column=0, sticky=W, padx=(60, 0), pady=(0, 10))\n qr = Entry(interro_opt, textvariable=exclude_str, width=18, state=DISABLED)\n qr.grid(row=8, column=0, columnspan=2, sticky=E, padx=(0,40), pady=(0, 10))\n all_text_widgets.append(qr)\n ex_plusbut = Button(interro_opt, text='+', \\\n command=lambda: add_criteria(ex_objs, ex_permref, ex_anyall, ex_additional_criteria, \\\n exclude_op, exclude_str, title = 'Exclude from interrogation'), \\\n state=DISABLED)\n ex_plusbut.grid(row=8, column=1, sticky=E, pady=(0, 10))\n\n #blklst = StringVar()\n #Label(interro_opt, text='Blacklist:').grid(row=12, column=0, sticky=W)\n ##blklst.set(r'^n')\n #blklst.set(r'')\n #bkbx = Entry(interro_opt, textvariable=blklst, width=22)\n #bkbx.grid(row=12, column=0, columnspan=2, sticky=E)\n #all_text_widgets.append(bkbx)\n\n def populate_metavals(evt):\n \"\"\"\n Add the values for a metadata field to the subcorpus box\n \"\"\"\n from corpkit.process import get_corpus_metadata\n try:\n wx = evt.widget\n except:\n wx = evt\n speaker_listbox.configure(state=NORMAL)\n speaker_listbox.delete(0, END)\n indices = wx.curselection()\n if wx.get(indices[0]) != 'none':\n speaker_listbox.insert(END, 'ALL')\n for index in indices:\n value = wx.get(index)\n if value == 'files':\n from corpkit.corpus import Corpus\n corp = Corpus(current_corpus.get(), print_info=False)\n vals = [i.name for i in corp.all_files]\n elif value == 'folders':\n from corpkit.corpus import Corpus\n corp = Corpus(current_corpus.get(), print_info=False)\n vals = [i.name for i in corp.subcorpora]\n elif value == 'none':\n vals = []\n else:\n meta = get_corpus_metadata(corpus_fullpath.get(), generate=True)\n vals = meta['fields'][value]\n #vals = get_speaker_names_from_parsed_corpus(corpus_fullpath.get(), value)\n for v in vals:\n speaker_listbox.insert(END, v)\n\n # lemma tags\n #lemtags = tuple(('Off', 'Noun', 'Verb', 'Adjective', 'Adverb'))\n #lemtag = StringVar(root)\n #lemtag.set('')\n #Label(interro_opt, text='Result word class:').grid(row=13, column=0, columnspan=2, sticky=E, padx=(0, 120))\n #lmt = OptionMenu(interro_opt, lemtag, *lemtags)\n #lmt.config(state=NORMAL, width=10)\n #lmt.grid(row=13, column=1, sticky=E)\n #lemtag.trace(\"w\", d_callback)\n\n def refresh_by_metadata(*args):\n \"\"\"\n Add metadata for a corpus from dotfile to listbox\n \"\"\"\n import os\n if os.path.isdir(corpus_fullpath.get()):\n from corpkit.process import get_corpus_metadata\n ns = get_corpus_metadata(corpus_fullpath.get(), generate=True)\n ns = list(ns.get('fields', {}))\n #ns = corpus_names_and_speakers[os.path.basename(corpus_fullpath.get())]\n else:\n return\n\n speaker_listbox.delete(0, 'end')\n\n # figure out which list we need to add to, and which we should del from\n lbs = []\n delfrom = []\n # todo: this should be, if new corpus, delfrom...\n if True:\n lbs.append(by_met_listbox)\n else:\n delfrom.append(by_met_listbox)\n # add names\n for lb in lbs:\n lb.configure(state=NORMAL)\n lb.delete(0, END)\n from corpkit.corpus import Corpus\n corp = Corpus(current_corpus.get(), print_info=False)\n if corp.level == 'c':\n lb.insert(END, 'folders')\n lb.insert(END, 'files')\n for idz in sorted(ns):\n lb.insert(END, idz)\n lb.insert(END, 'none')\n # or delete names\n for lb in delfrom:\n lb.configure(state=NORMAL)\n lb.delete(0, END)\n lb.configure(state=DISABLED)\n\n by_met_listbox.selection_set(0)\n populate_metavals(by_met_listbox)\n\n # by metadata \n by_meta_scrl = Frame(interro_opt)\n by_meta_scrl.grid(row=1, column=0, rowspan=2, sticky='w', padx=(5,0), pady=(5, 5))\n # scrollbar for the listbox\n by_met_bar = Scrollbar(by_meta_scrl)\n by_met_bar.pack(side=RIGHT, fill=Y)\n # listbox itself\n slist_height = 2 if small_screen else 6\n by_met_listbox = Listbox(by_meta_scrl, selectmode=EXTENDED, width=12, height=slist_height,\n relief=SUNKEN, bg='#F4F4F4',\n yscrollcommand=by_met_bar.set, exportselection=False)\n by_met_listbox.pack()\n by_met_bar.config(command=by_met_listbox.yview)\n xx = by_met_listbox.bind('<>', populate_metavals)\n\n # frame to hold metadata values listbox\n spk_scrl = Frame(interro_opt)\n spk_scrl.grid(row=1, column=0, rowspan=2, columnspan=2, sticky=E, pady=(5,5))\n # scrollbar for the listbox\n spk_sbar = Scrollbar(spk_scrl)\n spk_sbar.pack(side=RIGHT, fill=Y)\n # listbox itself\n speaker_listbox = Listbox(spk_scrl, selectmode=EXTENDED, width=29, height=slist_height,\n relief=SUNKEN, bg='#F4F4F4',\n yscrollcommand=spk_sbar.set, exportselection=False)\n speaker_listbox.pack()\n speaker_listbox.configure(state=DISABLED)\n spk_sbar.config(command=speaker_listbox.yview)\n\n # dep type\n #dep_types = tuple(('Basic', 'Collapsed', 'CC-processed'))\n #kind_of_dep = StringVar(root)\n #kind_of_dep.set('CC-processed')\n #Label(interro_opt, text='Dependency type:').grid(row=16, column=0, sticky=W)\n #pick_dep_type = OptionMenu(interro_opt, kind_of_dep, *dep_types)\n #pick_dep_type.config(state=DISABLED)\n #pick_dep_type.grid(row=16, column=0, sticky=W, padx=(125,0))\n #kind_of_dep.trace(\"w\", d_callback)\n\n coref = IntVar(root)\n coref.set(False)\n coref_but = Checkbutton(interro_opt, text='Count coreferents', variable=coref, onvalue=True, offvalue=False)\n coref_but.grid(row=6, column=1, sticky=E, pady=(5,0)) \n coref_but.config(state=DISABLED)\n\n # query\n entrytext=StringVar()\n\n Label(interro_opt, text='Query:').grid(row=4, column=0, sticky='NW', pady=(5,0))\n entrytext.set(r'\\b(m.n|wom.n|child(ren)?)\\b')\n qa_height = 2 if small_screen else 6\n qa = Text(interro_opt, width=40, height=qa_height, borderwidth=0.5, \n font=(\"Courier New\", 14), undo=True, relief=SUNKEN, wrap=WORD, highlightthickness=0)\n qa.insert(END, entrytext.get())\n qa.grid(row=4, column=0, columnspan=2, sticky=E, pady=(5,5), padx=(0, 4))\n all_text_widgets.append(qa)\n\n additional_criteria = {}\n\n anyall = StringVar()\n anyall.set('all')\n\n objs = OrderedDict()\n # fill it with null data\n for i in range(20):\n tmp = StringVar()\n tmp.set('')\n objs[i] = [None, None, None, tmp]\n\n permref = []\n\n def add_criteria(objs, permref, anyalltoggle, output_dict,\n optvar, enttext, title = \"Additional criteria\"):\n \"\"\"this is a popup for adding additional search criteria.\n\n it's also used for excludes\"\"\"\n if title == 'Additional criteria':\n enttext.set(qa.get(1.0, END).strip('\\n').strip())\n from tkinter import Toplevel\n try:\n more_criteria = permref[0]\n more_criteria.deiconify()\n return\n except:\n pass\n more_criteria = Toplevel()\n more_criteria.geometry('+500+100')\n more_criteria.title(title)\n\n more_criteria.wm_attributes('-topmost', 1)\n\n total = 0\n n_items = []\n \n def quit_q(total, *args):\n \"\"\"exit popup, saving entries\"\"\"\n poss_keys = []\n for index, (option, optvar, entbox, entstring) in enumerate(list(objs.values())[:total]):\n if index == 0:\n enttext.set(entstring.get())\n optvar.set(optvar.get())\n datatype_picked.set(optvar.get())\n if optvar is not None:\n o = convert_name_to_query.get(optvar.get(), optvar.get())\n q = entstring.get().strip()\n q = remake_special(q, customs=custom_special_dict, \n case_sensitive=case_sensitive.get(), return_list=True)\n output_dict[o] = q\n # may not work on mac ...\n if title == 'Additional criteria':\n if len(list(objs.values())[:total]) > 0:\n plusbut.config(bg='#F4F4F4')\n else:\n plusbut.config(bg='white')\n else:\n if len(list(objs.values())[:total]) > 0:\n ex_plusbut.config(bg='#F4F4F4')\n else:\n ex_plusbut.config(bg='white')\n more_criteria.withdraw()\n\n def remove_prev():\n \"\"\"delete last added criteria line\"\"\"\n if len([k for k, v in objs.items() if v[0] is not None]) < 2:\n pass\n else:\n ans = 0\n for k, (a, b, c, d) in reversed(list(objs.items())):\n if a is not None:\n ans = k\n break\n if objs[ans][0] is not None:\n objs[ans][0].destroy()\n optvar = objs[ans][1].get()\n try:\n del output_dict[convert_name_to_query[optvar]]\n except:\n pass\n objs[ans][1] = StringVar()\n if objs[ans][2] is not None:\n objs[ans][2].destroy()\n objs[ans][3] = StringVar()\n objs.pop(ans, None)\n \n\n def clear_q():\n \"\"\"clear the popup\"\"\"\n for optmenu, optvar, entbox, entstring in list(objs.values()):\n if optmenu is not None:\n optvar.set('Word')\n entstring.set('')\n\n def new_item(total, optvar, enttext, init = False):\n \"\"\"add line to popup\"\"\"\n for i in n_items:\n i.destroy()\n for i in n_items:\n n_items.remove(i)\n chosen = StringVar()\n poss = ['None'] + sorted(convert_name_to_query.keys())\n poss = [k for k in poss if not 'distance' in k.lower() and not 'head ' in k.lower()]\n chosen.set('Word')\n opt = OptionMenu(more_criteria, chosen, *poss)\n opt.config(width=16)\n t = total + 1\n opt.grid(row=total, column=0, sticky=W)\n text_str = StringVar()\n text_str.set('')\n text=Entry(more_criteria, textvariable=text_str, width=40, font=(\"Courier New\", 13))\n all_text_widgets.append(text)\n text.grid(row=total, column=1) \n objs[total] = [opt, chosen, text, text_str]\n minuser = Button(more_criteria, text='-', command=remove_prev)\n minuser.grid(row=total + 2, column=0, sticky=W, padx=(38,0))\n plusser = Button(more_criteria, text='+', command=lambda : new_item(t, optvar, enttext))\n plusser.grid(row=total + 2, column=0, sticky=W)\n stopbut = Button(more_criteria, text='Done', command=lambda : quit_q(t))\n stopbut.grid(row=total + 2, column=1, sticky=E)\n clearbut = Button(more_criteria, text='Clear', command=clear_q)\n clearbut.grid(row=total + 2, column=1, sticky=E, padx=(0, 60))\n r1 = Radiobutton(more_criteria, text='Match any', variable=anyalltoggle, value= 'any')\n r1.grid(row=total + 2, column=0, columnspan=2, sticky=E, padx=(0,150))\n r2 = Radiobutton(more_criteria, text='Match all', variable=anyalltoggle, value= 'all')\n r2.grid(row=total + 2, column=0, columnspan=2, sticky=E, padx=(0,250))\n n_items.append(plusser)\n n_items.append(stopbut)\n n_items.append(minuser)\n n_items.append(clearbut)\n n_items.append(r1)\n n_items.append(r2)\n if init:\n text_str.set(enttext.get())\n chosen.set(optvar.get())\n minuser.config(state=DISABLED)\n else:\n minuser.config(state=NORMAL)\n return t\n\n if objs:\n for optmenu, optvar, entbox, entstring in list(objs.values()):\n optmenu.grid()\n entbox.grid()\n\n # make the first button with defaults\n total = new_item(total, optvar, enttext, init = True)\n if more_criteria not in permref:\n permref.append(more_criteria)\n\n plusbut = Button(interro_opt, text='+', \\\n command=lambda: add_criteria(objs, permref, anyall, \\\n additional_criteria, datatype_picked, entrytext), \\\n state=NORMAL)\n plusbut.grid(row=4, column=0, columnspan=1, padx=(25,0), pady=(10,0), sticky='w')\n\n def entry_callback(*args):\n \"\"\"when entry is changed, add it to the textbox\"\"\"\n qa.config(state=NORMAL)\n qa.delete(1.0, END)\n qa.insert(END, entrytext.get())\n entrytext.trace(\"w\", entry_callback)\n\n def onselect(evt):\n \"\"\"when an option is selected, add the example query\n for ngrams, add the special ngram options\"\"\"\n w = evt.widget\n index = int(w.curselection()[0])\n value = w.get(index)\n w.see(index)\n #datatype_chosen_option.set(value)\n #datatype_listbox.select_set(index)\n #datatype_listbox.see(index)\n if qa.get(1.0, END).strip('\\n').strip() in list(def_queries.values()):\n if qa.get(1.0, END).strip('\\n').strip() not in list(qd.values()):\n entrytext.set(def_queries[datatype_picked.get()])\n #try:\n # ngmsize.destroy()\n #except:\n # pass\n #try:\n # split_contract.destroy()\n #except:\n # pass\n\n # boolean interrogation arguments need fixing, right now use 0 and 1\n #lem = IntVar()\n #lbut = Checkbutton(interro_opt, text=\"Lemmatise\", variable=lem, onvalue=True, offvalue=False)\n #lbut.grid(column=0, row=8, sticky=W)\n #phras = IntVar()\n #mwbut = Checkbutton(interro_opt, text=\"Multiword results\", variable=phras, onvalue=True, offvalue=False)\n #mwbut.grid(column=1, row=8, sticky=E)\n #tit_fil = IntVar()\n #tfbut = Checkbutton(interro_opt, text=\"Filter titles\", variable=tit_fil, onvalue=True, offvalue=False)\n #tfbut.grid(row=9, column=0, sticky=W)\n case_sensitive = IntVar()\n tmp = Checkbutton(interro_opt, text=\"Case sensitive\", variable=case_sensitive, onvalue=True, offvalue=False)\n tmp.grid(row=6, column=0, sticky=W, padx=(140,0), pady=(5,0))\n\n no_punct = IntVar()\n tmp = Checkbutton(interro_opt, text=\"Punctuation\", variable=no_punct, onvalue=False, offvalue=True)\n tmp.deselect()\n tmp.grid(row=6, column=0, sticky=W, pady=(5,0))\n\n global ngmsize\n Label(interro_opt, text='N-gram size:').grid(row=5, column=0, sticky=W, padx=(220,0), columnspan=2, pady=(5,0)) \n ngmsize = MyOptionMenu(interro_opt, 'Size','1', '2','3','4','5','6','7','8')\n ngmsize.configure(width=12)\n ngmsize.grid(row=5, column=1, sticky=E, pady=(5,0))\n #ngmsize.config(state=DISABLED)\n\n global collosize\n Label(interro_opt, text='Collocation window:').grid(row=5, column=0, sticky=W, pady=(5,0)) \n collosize = MyOptionMenu(interro_opt, 'Size','1', '2','3','4','5','6','7','8')\n collosize.configure(width=8)\n collosize.grid(row=5, column=0, sticky=W, padx=(140,0), pady=(5,0))\n #collosize.config(state=DISABLED)\n\n #global split_contract\n #split_contract = IntVar(root)\n #split_contract.set(False)\n #split_contract_but = Checkbutton(interro_opt, text='Split contractions', variable=split_contract, onvalue=True, offvalue=False)\n #split_contract_but.grid(row=7, column=1, sticky=E) \n\n #Label(interro_opt, text='Spelling:').grid(row=6, column=1, sticky=E, padx=(0, 75))\n #spl = MyOptionMenu(interro_opt, 'Off','UK','US')\n #spl.configure(width=7)\n #spl.grid(row=6, column=1, sticky=E, padx=(2, 0))\n\n def desel_and_turn_off(but):\n pass\n but.config(state=NORMAL)\n but.deselect()\n but.config(state=DISABLED)\n\n def turnon(but):\n but.config(state=NORMAL)\n\n def callback(*args):\n \"\"\"if the drop down list for data type changes, fill options\"\"\"\n #datatype_listbox.delete(0, 'end')\n chosen = datatype_picked.get()\n #lst = option_dict[chosen]\n #for e in lst:\n # datatype_listbox.insert(END, e)\n notree = [i for i in sorted(convert_name_to_query.keys()) if i != 'Trees']\n\n if chosen == 'Trees':\n for but in [ck5, ck6, ck7, ck9, ck10, ck11, ck12, ck13, ck14, ck15, ck16, \\\n ck17, ck18, ck19, ck20]:\n desel_and_turn_off(but)\n\n for but in [ck1, ck2, ck4, ck4, ck8]:\n turnon(but)\n ck1.select()\n\n #q.config(state=DISABLED)\n #qr.config(state=DISABLED)\n #exclude.config(state=DISABLED)\n #sec_match.config(state=DISABLED)\n plusbut.config(state=DISABLED) \n ex_plusbut.config(state=DISABLED) \n\n elif chosen in notree:\n if current_corpus.get().endswith('-parsed'): \n for but in [ck1, ck2, ck3, ck5, ck6, ck7, ck8, ck9, ck10, \\\n ck11, ck12, ck13, ck14, ck15, ck16, \\\n ck17, ck18, ck19, ck20, \\\n plusbut, ex_plusbut, exclude, qr]:\n turnon(but)\n desel_and_turn_off(ck4)\n\n if chosen == 'Stats':\n nametext.set('features')\n nametexter.config(state=DISABLED)\n else:\n nametexter.config(state=NORMAL)\n nametext.set('untitled')\n\n if chosen == 'Stats':\n for but in [ck2, ck3, ck4, ck5, ck6, ck7, ck8, ck9, ck10, \\\n ck11, ck12, ck13, ck14, ck15, ck16]:\n desel_and_turn_off(but)\n turnon(ck1)\n ck1.select()\n ngmshows = [return_ngm, return_ngm_lemma, return_ngm_func, return_ngm_pos]\n #ngmsize.config(state=NORMAL)\n #collosize.config(state=NORMAL)\n \n #if qa.get(1.0, END).strip('\\n').strip() in def_queries.values() + special_examples.values():\n clean_query = qa.get(1.0, END).strip('\\n').strip()\n acc_for_tups = [i[0] if isinstance(i, tuple) else i for i in list(def_queries.values())]\n if (clean_query not in list(qd.values()) and clean_query in acc_for_tups) \\\n or not clean_query:\n try:\n # for the life of me i don't know why some are appearing as tuples\n found = def_queries.get(chosen, clean_query)\n if isinstance(found, tuple):\n found = found[0]\n entrytext.set(found)\n except:\n pass\n\n datatype_picked = StringVar(root)\n Label(interro_opt, text='Search: ').grid(row=3, column=0, sticky=W, pady=10)\n pick_a_datatype = OptionMenu(interro_opt, datatype_picked, *sorted(convert_name_to_query.keys()))\n pick_a_datatype.configure(width=30, justify=CENTER)\n datatype_picked.set('Word')\n pick_a_datatype.grid(row=3, column=0, columnspan=2, sticky=W, padx=(136,0))\n datatype_picked.trace(\"w\", callback)\n \n # trees, words, functions, governors, dependents, pos, lemma, count\n interro_return_frm = Frame(interro_opt)\n\n Label(interro_return_frm, text=' Return', font=(\"Courier New\", 13, \"bold\")).grid(row=0, column=0, sticky=E)\n interro_return_frm.grid(row=7, column=0, columnspan=2, sticky=W, pady=10, padx=(10,0))\n\n Label(interro_return_frm, text=' Token', font=(\"Courier New\", 13)).grid(row=0, column=1, sticky=E)\n Label(interro_return_frm, text=' Lemma', font=(\"Courier New\", 13)).grid(row=0, column=2, sticky=E)\n Label(interro_return_frm, text=' POS tag', font=(\"Courier New\", 13)).grid(row=0, column=3, sticky=E)\n Label(interro_return_frm, text= 'Function', font=(\"Courier New\", 13)).grid(row=0, column=4, sticky=E)\n Label(interro_return_frm, text=' Match', font=(\"Courier New\", 13)).grid(row=1, column=0, sticky=E)\n Label(interro_return_frm, text=' Governor', font=(\"Courier New\", 13)).grid(row=2, column=0, sticky=E)\n Label(interro_return_frm, text='Dependent', font=(\"Courier New\", 13)).grid(row=3, column=0, sticky=E)\n\n prenext_pos = StringVar(root)\n prenext_pos.set('Position')\n pick_posi_o = ('-5', '-4', '-3', '-2', '-1', '+1', '+2', '+3', '+4', '+5')\n pick_posi_m = OptionMenu(interro_return_frm, prenext_pos, *pick_posi_o)\n pick_posi_m.config(width=8)\n pick_posi_m.grid(row=4, column=0, sticky=E)\n #Label(interro_return_frm, text= 'N-gram', font=(\"Courier New\", 13)).grid(row=4, column=0, sticky=E)\n Label(interro_return_frm, text=' Other', font=(\"Courier New\", 13)).grid(row=5, column=0, sticky=E)\n Label(interro_return_frm, text=' Count', font=(\"Courier New\", 13)).grid(row=5, column=1, sticky=E)\n Label(interro_return_frm, text=' Index', font=(\"Courier New\", 13)).grid(row=5, column=2, sticky=E)\n Label(interro_return_frm, text=' Distance', font=(\"Courier New\", 13)).grid(row=5, column=3, sticky=E)\n Label(interro_return_frm, text=' Tree', font=(\"Courier New\", 13)).grid(row=5, column=4, sticky=E)\n return_token = StringVar()\n return_token.set('')\n ck1 = Checkbutton(interro_return_frm, variable=return_token, onvalue='w', offvalue = '')\n ck1.select()\n ck1.grid(row=1, column=1, sticky=E)\n\n def return_token_callback(*args):\n if datatype_picked.get() == 'Trees':\n if return_token.get():\n for but in [ck3, ck4, ck8]:\n but.config(state=NORMAL)\n but.deselect()\n return_token.trace(\"w\", return_token_callback)\n\n return_lemma = StringVar()\n return_lemma.set('')\n ck2 = Checkbutton(interro_return_frm, anchor=E, variable=return_lemma, onvalue='l', offvalue = '')\n ck2.grid(row=1, column=2, sticky=E)\n\n def return_lemma_callback(*args):\n if datatype_picked.get() == 'Trees':\n if return_lemma.get():\n for but in [ck3, ck4, ck8]:\n but.config(state=NORMAL)\n but.deselect()\n lmt.configure(state=NORMAL)\n else:\n lmt.configure(state=DISABLED)\n return_lemma.trace(\"w\", return_lemma_callback)\n\n return_pos = StringVar()\n return_pos.set('')\n ck3 = Checkbutton(interro_return_frm, variable=return_pos, onvalue='p', offvalue = '')\n ck3.grid(row=1, column=3, sticky=E)\n\n def return_pos_callback(*args):\n if datatype_picked.get() == 'Trees':\n if return_pos.get():\n for but in [ck1, ck2, ck4, ck8]:\n but.config(state=NORMAL)\n but.deselect()\n return_pos.trace(\"w\", return_pos_callback)\n\n return_function = StringVar()\n return_function.set('')\n ck7 = Checkbutton(interro_return_frm, variable=return_function, onvalue='f', offvalue = '')\n ck7.grid(row=1, column=4, sticky=E)\n\n return_tree = StringVar()\n return_tree.set('')\n ck4 = Checkbutton(interro_return_frm, anchor=E, variable=return_tree, onvalue='t', offvalue = '')\n ck4.grid(row=6, column=4, sticky=E)\n\n def return_tree_callback(*args):\n if datatype_picked.get() == 'Trees':\n if return_tree.get():\n for but in [ck1, ck2, ck3, ck8]:\n but.config(state=NORMAL)\n but.deselect()\n return_tree.trace(\"w\", return_tree_callback)\n\n return_tree.trace(\"w\", return_tree_callback)\n\n return_index = StringVar()\n return_index.set('')\n ck5 = Checkbutton(interro_return_frm, anchor=E, variable=return_index, onvalue='i', offvalue = '')\n ck5.grid(row=6, column=2, sticky=E)\n\n return_distance = StringVar()\n return_distance.set('')\n ck6 = Checkbutton(interro_return_frm, anchor=E, variable=return_distance, onvalue='a', offvalue = '')\n ck6.grid(row=6, column=3, sticky=E)\n\n return_count = StringVar()\n return_count.set('')\n ck8 = Checkbutton(interro_return_frm, variable=return_count, onvalue='c', offvalue = '')\n ck8.grid(row=6, column=1, sticky=E)\n\n def countmode(*args):\n ngmshows = [return_ngm, return_ngm_lemma, return_ngm_func, return_ngm_pos]\n ngmbuts = [ck17, ck18, ck19, ck20]\n if any(ngmshow.get() for ngmshow in ngmshows):\n return\n if datatype_picked.get() != 'Trees':\n buttons = [ck1, ck2, ck3, ck4, ck5, ck6, ck7, ck9, \n ck10, ck11, ck12, ck13, ck14, ck15, ck16,\n ck17, ck18, ck19, ck20]\n if return_count.get() == 'c':\n for b in buttons:\n desel_and_turn_off(b)\n ck8.config(state=NORMAL)\n else:\n for b in buttons:\n b.config(state=NORMAL)\n callback()\n else:\n if return_count.get():\n for but in [ck1, ck2, ck3, ck4]:\n but.config(state=NORMAL)\n but.deselect()\n\n return_count.trace(\"w\", countmode)\n\n return_gov = StringVar()\n return_gov.set('')\n ck9 = Checkbutton(interro_return_frm, variable=return_gov, \n onvalue='gw', offvalue = '')\n ck9.grid(row=2, column=1, sticky=E)\n\n return_gov_lemma = StringVar()\n return_gov_lemma.set('')\n ck10 = Checkbutton(interro_return_frm, variable=return_gov_lemma, \n onvalue='gl', offvalue = '')\n ck10.grid(row=2, column=2, sticky=E)\n\n return_gov_pos = StringVar()\n return_gov_pos.set('')\n ck11 = Checkbutton(interro_return_frm, variable=return_gov_pos, \n onvalue='gp', offvalue = '')\n ck11.grid(row=2, column=3, sticky=E)\n\n return_gov_func = StringVar()\n return_gov_func.set('')\n ck12 = Checkbutton(interro_return_frm, variable=return_gov_func, \n onvalue='gf', offvalue = '')\n ck12.grid(row=2, column=4, sticky=E)\n\n return_dep = StringVar()\n return_dep.set('')\n ck13 = Checkbutton(interro_return_frm, variable=return_dep, \n onvalue='dw', offvalue = '')\n ck13.grid(row=3, column=1, sticky=E)\n\n return_dep_lemma = StringVar()\n return_dep_lemma.set('')\n ck14 = Checkbutton(interro_return_frm, variable=return_dep_lemma, \n onvalue='dl', offvalue = '')\n ck14.grid(row=3, column=2, sticky=E)\n\n return_dep_pos = StringVar()\n return_dep_pos.set('')\n ck15 = Checkbutton(interro_return_frm, variable=return_dep_pos, \n onvalue='dp', offvalue = '')\n ck15.grid(row=3, column=3, sticky=E)\n\n return_dep_func = StringVar()\n return_dep_func.set('')\n ck16 = Checkbutton(interro_return_frm, variable=return_dep_func, \n onvalue='df', offvalue = '')\n ck16.grid(row=3, column=4, sticky=E)\n\n return_ngm = StringVar()\n return_ngm.set('')\n ck17 = Checkbutton(interro_return_frm, variable=return_ngm, \n onvalue='w', offvalue = '')\n ck17.grid(row=4, column=1, sticky=E)\n\n return_ngm_lemma = StringVar()\n return_ngm_lemma.set('')\n ck18 = Checkbutton(interro_return_frm, variable=return_ngm_lemma, \n onvalue='l', offvalue = '')\n ck18.grid(row=4, column=2, sticky=E)\n\n return_ngm_pos = StringVar()\n return_ngm_pos.set('')\n ck19 = Checkbutton(interro_return_frm, variable=return_ngm_pos, \n onvalue='p', offvalue = '')\n ck19.grid(row=4, column=3, sticky=E)\n\n return_ngm_func = StringVar()\n return_ngm_func.set('')\n ck20 = Checkbutton(interro_return_frm, variable=return_ngm_func, \n onvalue='f', offvalue = '', state=DISABLED)\n ck20.grid(row=4, column=4, sticky=E)\n\n def q_callback(*args):\n qa.configure(state=NORMAL)\n qr.configure(state=NORMAL)\n\n #queries = tuple(('Off', 'Any', 'Participants', 'Processes', 'Subjects', 'Stats'))\n #special_queries = StringVar(root)\n #special_queries.set('Off')\n #Label(interro_opt, text='Preset:').grid(row=7, column=0, sticky=W)\n #pick_a_query = OptionMenu(interro_opt, special_queries, *queries)\n #pick_a_query.config(width=11, state=DISABLED)\n #pick_a_query.grid(row=7, column=0, padx=(60, 0), columnspan=2, sticky=W)\n #special_queries.trace(\"w\", q_callback)\n\n # Interrogation name\n nametext=StringVar()\n nametext.set('untitled')\n Label(interro_opt, text='Interrogation name:').grid(row=17, column=0, sticky=W)\n nametexter = Entry(interro_opt, textvariable=nametext, width=15)\n nametexter.grid(row=17, column=1, sticky=E)\n all_text_widgets.append(nametexter)\n\n def show_help(kind):\n kindict = {'h': 'http://interrogator.github.io/corpkit/doc_help.html',\n 'q': 'http://interrogator.github.io/corpkit/doc_interrogate.html#trees',\n 't': 'http://interrogator.github.io/corpkit/doc_troubleshooting.html'}\n import webbrowser\n webbrowser.open_new(kindict[kind])\n\n # query help, interrogate button\n #Button(interro_opt, text='Query help', command=query_help).grid(row=14, column=0, sticky=W)\n interrobut = Button(interro_opt, text='Interrogate')\n interrobut.config(command=lambda: runner(interrobut, do_interrogation, conc=True), state=DISABLED)\n interrobut.grid(row=18, column=1, sticky=E)\n\n # name to show above spreadsheet 0\n i_resultname = StringVar()\n\n def change_interro_spread(*args):\n if name_of_interro_spreadsheet.get():\n #savdict.config(state=NORMAL)\n updbut.config(state=NORMAL)\n else:\n #savdict.config(state=DISABLED)\n updbut.config(state=DISABLED)\n\n name_of_interro_spreadsheet = StringVar()\n name_of_interro_spreadsheet.set('')\n name_of_interro_spreadsheet.trace(\"w\", change_interro_spread)\n i_resultname.set('Interrogation results: %s' % str(name_of_interro_spreadsheet.get())) \n \n # make spreadsheet frames for interrogate pane\n\n wdth = int(note_width * 0.50)\n interro_right = Frame(tab1, width=wdth)\n interro_right.grid(row=0, column=1, sticky=N)\n\n interro_results = Frame(interro_right, height=40, width=wdth, borderwidth=2)\n interro_results.grid(column=0, row=0, padx=20, pady=(20,0), sticky='N', columnspan=4)\n\n interro_totals = Frame(interro_right, height=1, width=20, borderwidth=2)\n interro_totals.grid(column=0, row=1, padx=20, columnspan=4)\n\n llab = Label(interro_right, textvariable=i_resultname, \n font=(\"Helvetica\", 13, \"bold\"))\n\n llab.grid(row=0, column=0, sticky='NW', padx=20, pady=0)\n llab.lift()\n \n # show nothing yet \n update_spreadsheet(interro_results, df_to_show=None, height=450, width=wdth)\n update_spreadsheet(interro_totals, df_to_show=None, height=10, width=wdth)\n\n #global prev\n four_interro_under = Frame(interro_right, width=wdth)\n four_interro_under.grid(row=3, column=0, sticky='ew', padx=(20,0))\n prev = Button(four_interro_under, text='Previous', command=show_prev)\n prev.pack(side='left', expand=True)\n #global nex\n nex = Button(four_interro_under, text='Next', command=show_next)\n nex.pack(side='left', expand=True, padx=(0,50))\n if len(list(all_interrogations.keys())) < 2:\n nex.configure(state=DISABLED)\n prev.configure(state=DISABLED)\n\n #savdict = Button(four_interro_under, text='Save as dictionary', command=save_as_dictionary)\n #savdict.config(state=DISABLED)\n #savdict.pack(side='right', expand=True)\n\n updbut = Button(four_interro_under, text='Update interrogation', command=lambda: update_all_interrogations(pane='interrogate'))\n updbut.pack(side='right', expand=True)\n updbut.config(state=DISABLED)\n\n ############## ############## ############## ############## ############## \n # EDITOR TAB # # EDITOR TAB # # EDITOR TAB # # EDITOR TAB # # EDITOR TAB # \n ############## ############## ############## ############## ############## \n\n\n editor_buttons = Frame(tab2)\n editor_buttons.grid(row=0, column=0, sticky='NW')\n\n def do_editing():\n \"\"\"\n What happens when you press edit\n \"\"\"\n edbut.config(state=DISABLED)\n import os\n import pandas as pd\n from corpkit.editor import editor\n \n # translate operation into interrogator input\n operation_text=opp.get()\n if operation_text == 'None' or operation_text == 'Select an operation':\n operation_text=None\n else:\n operation_text=opp.get()[0]\n if opp.get() == u\"\\u00F7\":\n operation_text='/'\n if opp.get() == u\"\\u00D7\":\n operation_text='*'\n if opp.get() == '%-diff':\n operation_text='d'\n if opp.get() == 'rel. dist.':\n operation_text='a'\n\n # translate dataframe2\n data2 = data2_pick.get()\n if data2 == 'None' or data2 == '':\n data2 = False\n elif data2 == 'Self':\n data2 = 'self'\n elif data2 in ['features', 'postags', 'wordclasses']:\n from corpkit.corpus import Corpus\n corp = Corpus(current_corpus.get(), print_info=False)\n data2 = getattr(corp, data2_pick.get())\n #todo: populate results/totals with possibilities for features etc\n elif data2 is not False:\n if df2branch.get() == 'results':\n try:\n data2 = getattr(all_interrogations[data2], df2branch.get())\n except AttributeError:\n timestring('Denominator has no results attribute.') \n return\n elif df2branch.get() == 'totals':\n try:\n data2 = getattr(all_interrogations[data2], df2branch.get())\n except AttributeError:\n timestring('Denominator has no totals attribute.')\n return\n if transpose.get():\n try:\n data2 = data2.T\n except:\n pass\n\n the_data = all_interrogations[name_of_o_ed_spread.get()]\n\n if df1branch.get() == 'results':\n if not hasattr(the_data, 'results'):\n timestring('Interrogation has no results attribute.')\n return\n\n elif df1branch.get() == 'totals':\n data1 = the_data.totals\n\n if (spl_editor.var).get() == 'Off' or (spl_editor.var).get() == 'Convert spelling':\n spel = False\n else:\n spel = (spl_editor.var).get()\n\n\n # editor kwargs\n editor_args = {'operation': operation_text,\n 'dataframe2': data2,\n 'spelling': spel,\n 'sort_by': sort_trans[sort_val.get()],\n 'df1_always_df': True,\n 'root': root,\n 'note': note,\n 'packdir': rd,\n 'p': p_val.get()}\n\n if do_sub.get() == 'Merge':\n editor_args['merge_subcorpora'] = subc_sel_vals\n elif do_sub.get() == 'Keep':\n editor_args['just_subcorpora'] = subc_sel_vals\n elif do_sub.get() == 'Span':\n editor_args['span_subcorpora'] = subc_sel_vals\n elif do_sub.get() == 'Skip':\n editor_args['skip_subcorpora'] = subc_sel_vals\n\n if toreplace_string.get() != '':\n if replacewith_string.get() == '':\n replacetup = toreplace_string.get()\n else:\n replacetup = (toreplace_string.get(), replacewith_string.get())\n editor_args['replace_names'] = replacetup\n\n # special query: add to this list!\n #if special_queries.get() != 'Off':\n #query = spec_quer_translate[special_queries.get()]\n \n entry_do_with = entry_regex.get()\n # allow list queries\n if entry_do_with.startswith('[') and entry_do_with.endswith(']') and ',' in entry_do_with:\n entry_do_with = entry_do_with.lower().lstrip('[').rstrip(']').replace(\"'\", '').replace('\"', '').replace(' ', '').split(',')\n else:\n # convert special stuff\n re.compile(entry_do_with)\n entry_do_with = remake_special(entry_do_with, customs=custom_special_dict, \n case_sensitive=case_sensitive.get(),\n return_list=True)\n if entry_do_with is False:\n return\n\n if do_with_entries.get() == 'Merge':\n editor_args['merge_entries'] = entry_do_with\n nn = newname_var.get()\n if nn == '':\n editor_args['newname'] = False\n elif is_number(nn):\n editor_args['newname'] = int(nn)\n else:\n editor_args['newname'] = nn\n elif do_with_entries.get() == 'Keep':\n editor_args['just_entries'] = entry_do_with\n elif do_with_entries.get() == 'Skip':\n editor_args['skip_entries'] = entry_do_with\n \n if new_subc_name.get() != '':\n editor_args['new_subcorpus_name'] = new_subc_name.get()\n if newname_var.get() != '':\n editor_args['new_subcorpus_name'] = newname_var.get()\n \n if keep_stats_setting.get() == 1:\n editor_args['keep_stats'] = True\n\n if rem_abv_p_set.get() == 1:\n editor_args['remove_above_p'] = True\n\n if just_tot_setting.get() == 1:\n editor_args['just_totals'] = True\n\n if keeptopnum.get() != 'all':\n try:\n numtokeep = int(keeptopnum.get())\n except ValueError:\n timestring('Keep top n results value must be number.')\n \n return\n editor_args['keep_top'] = numtokeep\n\n\n # do editing\n r = the_data.edit(branch=df1branch.get(), **editor_args)\n\n if transpose.get():\n try:\n r.results = r.results.T\n except:\n pass\n try:\n r.totals = r.totals.T\n except:\n pass\n\n \n if isinstance(r, str):\n if r == 'linregress':\n return\n\n if not r:\n timestring('Editing caused an error.')\n return\n\n if len(list(r.results.columns)) == 0:\n timestring('Editing removed all results.')\n return\n\n # drop over 1000?\n # results should now always be dataframes, so this if is redundant\n if isinstance(r.results, pd.DataFrame):\n large = [n for i, n in enumerate(list(r.results.columns)) if i > 9999]\n r.results.drop(large, axis=1, inplace=True)\n\n timestring('Result editing completed successfully.')\n \n # name the edit\n the_name = namer(edit_nametext.get(), type_of_data = 'edited')\n\n # add edit to master dict\n all_interrogations[the_name] = r\n\n # update edited results speadsheet name\n name_of_n_ed_spread.set(list(all_interrogations.keys())[-1])\n editoname.set('Edited results: %s' % str(name_of_n_ed_spread.get()))\n \n # add current subcorpora to editor menu\n for subcl in [subc_listbox]:\n #subcl.configure(state=NORMAL)\n subcl.delete(0, 'end')\n for e in list(r.results.index):\n if e != 'tkintertable-order':\n subcl.insert(END, e)\n #subcl.configure(state=DISABLED)\n\n # update edited spreadsheets\n most_recent = all_interrogations[list(all_interrogations.keys())[-1]]\n if most_recent.results is not None:\n update_spreadsheet(n_editor_results, most_recent.results, height=140)\n update_spreadsheet(n_editor_totals, pd.DataFrame(most_recent.totals, dtype=object), height=10)\n \n # finish up\n refresh()\n # reset some buttons that the user probably wants reset\n opp.set('None')\n data2_pick.set('Self')\n # restore button\n \n\n def df2_callback(*args):\n try:\n thisdata = all_interrogations[data2_pick.get()]\n except KeyError:\n return\n\n if thisdata.results is not None:\n df2box.config(state=NORMAL)\n else:\n df2box.config(state=NORMAL)\n df2branch.set('totals')\n df2box.config(state=DISABLED)\n\n def df_callback(*args):\n \"\"\"show names and spreadsheets for what is selected as result to edit\n also, hide the edited results section\"\"\"\n if selected_to_edit.get() != 'None':\n edbut.config(state=NORMAL)\n name_of_o_ed_spread.set(selected_to_edit.get())\n thisdata = all_interrogations[selected_to_edit.get()]\n resultname.set('Results to edit: %s' % str(name_of_o_ed_spread.get()))\n if thisdata.results is not None:\n update_spreadsheet(o_editor_results, thisdata.results, height=140)\n df1box.config(state=NORMAL)\n else:\n df1box.config(state=NORMAL)\n df1branch.set('totals')\n df1box.config(state=DISABLED)\n update_spreadsheet(o_editor_results, df_to_show=None, height=140)\n if thisdata.totals is not None:\n update_spreadsheet(o_editor_totals, thisdata.totals, height=10)\n #df1box.config(state=NORMAL)\n #else:\n #update_spreadsheet(o_editor_totals, df_to_show=None, height=10)\n #df1box.config(state=NORMAL)\n #df1branch.set('results')\n #df1box.config(state=DISABLED)\n else:\n edbut.config(state=DISABLED)\n name_of_n_ed_spread.set('')\n editoname.set('Edited results: %s' % str(name_of_n_ed_spread.get()))\n update_spreadsheet(n_editor_results, df_to_show=None, height=140)\n update_spreadsheet(n_editor_totals, df_to_show=None, height=10)\n for subcl in [subc_listbox]:\n subcl.configure(state=NORMAL)\n subcl.delete(0, 'end')\n if name_of_o_ed_spread.get() != '':\n if thisdata.results is not None:\n cols = list(thisdata.results.index)\n else:\n cols = list(thisdata.totals.index)\n for e in cols:\n if e != 'tkintertable-order':\n subcl.insert(END, e) \n do_sub.set('Off')\n do_with_entries.set('Off')\n \n # result to edit\n tup = tuple([i for i in list(all_interrogations.keys())]) \n selected_to_edit = StringVar(root)\n selected_to_edit.set('None')\n x = Label(editor_buttons, text='To edit', font=(\"Helvetica\", 13, \"bold\"))\n x.grid(row=0, column=0, sticky=W)\n dataframe1s = OptionMenu(editor_buttons, selected_to_edit, *tup)\n dataframe1s.config(width=25)\n dataframe1s.grid(row=1, column=0, columnspan=2, sticky=W)\n selected_to_edit.trace(\"w\", df_callback)\n\n # DF1 branch selection\n df1branch = StringVar()\n df1branch.set('results')\n df1box = OptionMenu(editor_buttons, df1branch, 'results', 'totals')\n df1box.config(width=11, state=DISABLED)\n df1box.grid(row=1, column=1, sticky=E)\n\n def op_callback(*args):\n if opp.get() != 'None':\n dataframe2s.config(state=NORMAL)\n df2box.config(state=NORMAL)\n if opp.get() == 'keywords' or opp.get() == '%-diff':\n df2branch.set('results')\n elif opp.get() == 'None':\n dataframe2s.config(state=DISABLED)\n df2box.config(state=DISABLED)\n\n # operation for editor\n opp = StringVar(root)\n opp.set('None')\n operations = ('None', '%', u\"\\u00D7\", u\"\\u00F7\", '-', '+', 'combine', 'keywords', '%-diff', 'rel. dist.')\n Label(editor_buttons, text='Operation and denominator', font=(\"Helvetica\", 13, \"bold\")).grid(row=2, column=0, sticky=W, pady=(15,0))\n ops = OptionMenu(editor_buttons, opp, *operations)\n ops.grid(row=3, column=0, sticky=W)\n opp.trace(\"w\", op_callback)\n\n # DF2 option for editor\n tups = tuple(['Self'] + [i for i in list(all_interrogations.keys())])\n data2_pick = StringVar(root)\n data2_pick.set('Self')\n #Label(tab2, text='Denominator:').grid(row=3, column=0, sticky=W)\n dataframe2s = OptionMenu(editor_buttons, data2_pick, *tups)\n dataframe2s.config(state=DISABLED, width=16)\n dataframe2s.grid(row=3, column=0, columnspan=2, sticky='NW', padx=(110,0))\n data2_pick.trace(\"w\", df2_callback)\n\n # DF2 branch selection\n df2branch = StringVar(root)\n df2branch.set('totals')\n df2box = OptionMenu(editor_buttons, df2branch, 'results', 'totals')\n df2box.config(state=DISABLED, width=11)\n df2box.grid(row=3, column=1, sticky=E)\n\n # sort by\n Label(editor_buttons, text='Sort results by', font=(\"Helvetica\", 13, \"bold\")).grid(row=4, column=0, sticky=W, pady=(15,0))\n sort_val = StringVar(root)\n sort_val.set('None')\n poss = ['None', 'Total', 'Inverse total', 'Name','Increase',\n 'Decrease', 'Static', 'Turbulent', 'P value', 'Reverse']\n sorts = OptionMenu(editor_buttons, sort_val, *poss)\n sorts.config(width=11)\n sorts.grid(row=4, column=1, sticky=E, pady=(15,0))\n\n # spelling again\n Label(editor_buttons, text='Spelling:').grid(row=5, column=0, sticky=W, pady=(15,0))\n spl_editor = MyOptionMenu(editor_buttons, 'Off','UK','US')\n spl_editor.grid(row=5, column=1, sticky=E, pady=(15,0))\n spl_editor.configure(width=10)\n\n # keep_top\n Label(editor_buttons, text='Keep top results:').grid(row=6, column=0, sticky=W)\n keeptopnum = StringVar()\n keeptopnum.set('all')\n keeptopbox = Entry(editor_buttons, textvariable=keeptopnum, width=5)\n keeptopbox.grid(column=1, row=6, sticky=E)\n all_text_widgets.append(keeptopbox)\n\n\n # currently broken: just totals button\n just_tot_setting = IntVar()\n just_tot_but = Checkbutton(editor_buttons, text=\"Just totals\", variable=just_tot_setting, state=DISABLED)\n #just_tot_but.select()\n just_tot_but.grid(column=0, row=7, sticky=W)\n\n keep_stats_setting = IntVar()\n keep_stat_but = Checkbutton(editor_buttons, text=\"Keep stats\", variable=keep_stats_setting)\n #keep_stat_but.select()\n keep_stat_but.grid(column=1, row=7, sticky=E)\n\n rem_abv_p_set = IntVar()\n rem_abv_p_but = Checkbutton(editor_buttons, text=\"Remove above p\", variable=rem_abv_p_set)\n #rem_abv_p_but.select()\n rem_abv_p_but.grid(column=0, row=8, sticky=W)\n\n # transpose\n transpose = IntVar()\n trans_but = Checkbutton(editor_buttons, text=\"Transpose\", variable=transpose, onvalue=True, offvalue=False)\n trans_but.grid(column=1, row=8, sticky=E)\n\n # entries + entry field for regex, off, skip, keep, merge\n Label(editor_buttons, text='Edit entries', font=(\"Helvetica\", 13, \"bold\")).grid(row=9, column=0, sticky=W, pady=(15, 0))\n \n # edit entries regex box\n entry_regex = StringVar()\n entry_regex.set(r'.*ing$')\n edit_box = Entry(editor_buttons, textvariable=entry_regex, width=23, state=DISABLED, font=(\"Courier New\", 13))\n edit_box.grid(row=10, column=1, sticky=E)\n all_text_widgets.append(edit_box)\n\n # merge entries newname\n Label(editor_buttons, text='Merge name:').grid(row=11, column=0, sticky=W)\n newname_var = StringVar()\n newname_var.set('')\n mergen = Entry(editor_buttons, textvariable=newname_var, width=23, state=DISABLED, font=(\"Courier New\", 13))\n mergen.grid(row=11, column=1, sticky=E)\n all_text_widgets.append(mergen)\n\n Label(editor_buttons, text='Replace in entry names:').grid(row=12, column=0, sticky=W)\n Label(editor_buttons, text='Replace with:').grid(row=12, column=1, sticky=W)\n toreplace_string = StringVar()\n toreplace_string.set('')\n replacewith_string = StringVar()\n replacewith_string.set('')\n toreplace = Entry(editor_buttons, textvariable=toreplace_string, font=(\"Courier New\", 13))\n toreplace.grid(row=13, column=0, sticky=W)\n all_text_widgets.append(toreplace)\n replacewith = Entry(editor_buttons, textvariable=replacewith_string, font=(\"Courier New\", 13), width=23)\n replacewith.grid(row=13, column=1, sticky=E)\n all_text_widgets.append(replacewith) \n \n def do_w_callback(*args):\n \"\"\"if not merging entries, diable input fields\"\"\"\n if do_with_entries.get() != 'Off':\n edit_box.configure(state=NORMAL)\n else:\n edit_box.configure(state=DISABLED)\n if do_with_entries.get() == 'Merge':\n mergen.configure(state=NORMAL)\n else:\n mergen.configure(state=DISABLED)\n\n # options for editing entries\n do_with_entries = StringVar(root)\n do_with_entries.set('Off')\n edit_ent_op = ('Off', 'Skip', 'Keep', 'Merge')\n ed_op = OptionMenu(editor_buttons, do_with_entries, *edit_ent_op)\n ed_op.grid(row=10, column=0, sticky=W)\n do_with_entries.trace(\"w\", do_w_callback)\n\n def onselect_subc(evt):\n \"\"\"get selected subcorpora: this probably doesn't need to be\n a callback, as they are only needed during do_edit\"\"\"\n for i in subc_sel_vals:\n subc_sel_vals.pop()\n wx = evt.widget\n indices = wx.curselection()\n for index in indices:\n value = wx.get(index)\n if value not in subc_sel_vals:\n subc_sel_vals.append(value)\n\n def do_s_callback(*args):\n \"\"\"hide subcorpora edit options if off\"\"\"\n if do_sub.get() != 'Off':\n pass\n #subc_listbox.configure(state=NORMAL)\n else:\n pass\n #subc_listbox.configure(state=DISABLED)\n if do_sub.get() == 'Merge':\n merge.configure(state=NORMAL)\n else:\n merge.configure(state=DISABLED)\n\n # subcorpora + optionmenu off, skip, keep\n Label(editor_buttons, text='Edit subcorpora', font=(\"Helvetica\", 13, \"bold\")).grid(row=14, column=0, sticky=W, pady=(15,0))\n \n edit_sub_f = Frame(editor_buttons)\n edit_sub_f.grid(row=14, column=1, rowspan = 5, sticky=E, pady=(20,0))\n edsub_scbr = Scrollbar(edit_sub_f)\n edsub_scbr.pack(side=RIGHT, fill=Y)\n subc_listbox = Listbox(edit_sub_f, selectmode = EXTENDED, height=5, relief=SUNKEN, bg='#F4F4F4',\n yscrollcommand=edsub_scbr.set, exportselection=False)\n subc_listbox.pack(fill=BOTH)\n edsub_scbr.config(command=subc_listbox.yview)\n\n xx = subc_listbox.bind('<>', onselect_subc)\n subc_listbox.select_set(0)\n\n # subcorpora edit options\n do_sub = StringVar(root)\n do_sub.set('Off')\n do_with_subc = OptionMenu(editor_buttons, do_sub, *('Off', 'Skip', 'Keep', 'Merge', 'Span'))\n do_with_subc.grid(row=15, column=0, sticky=W)\n do_sub.trace(\"w\", do_s_callback)\n\n # subcorpora merge name \n Label(editor_buttons, text='Merge name:').grid(row=16, column=0, sticky='NW')\n new_subc_name = StringVar()\n new_subc_name.set('')\n merge = Entry(editor_buttons, textvariable=new_subc_name, state=DISABLED, font=(\"Courier New\", 13))\n merge.grid(row=17, column=0, sticky='SW', pady=(0, 10))\n all_text_widgets.append(merge)\n \n # name the edit\n edit_nametext=StringVar()\n edit_nametext.set('untitled')\n Label(editor_buttons, text='Edit name', font=(\"Helvetica\", 13, \"bold\")).grid(row=19, column=0, sticky=W)\n msn = Entry(editor_buttons, textvariable=edit_nametext, width=18)\n msn.grid(row=20, column=0, sticky=W)\n all_text_widgets.append(msn)\n\n # edit button\n edbut = Button(editor_buttons, text='Edit')\n edbut.config(command=lambda: runner(edbut, do_editing), state=DISABLED)\n edbut.grid(row=20, column=1, sticky=E)\n\n def editor_spreadsheet_showing_something(*args):\n \"\"\"if there is anything in an editor window, allow spreadsheet edit button\"\"\"\n if name_of_o_ed_spread.get():\n upd_ed_but.config(state=NORMAL)\n else:\n upd_ed_but.config(state=DISABLED)\n\n\n # show spreadsheets\n e_wdth = int(note_width * 0.55)\n editor_sheets = Frame(tab2)\n editor_sheets.grid(column=1, row=0, sticky='NE')\n resultname = StringVar()\n name_of_o_ed_spread = StringVar()\n name_of_o_ed_spread.set('')\n name_of_o_ed_spread.trace(\"w\", editor_spreadsheet_showing_something)\n resultname.set('Results to edit: %s' % str(name_of_o_ed_spread.get()))\n o_editor_results = Frame(editor_sheets, height=28, width=20)\n o_editor_results.grid(column=1, row=1, rowspan=1, padx=(20, 0), sticky=N)\n Label(editor_sheets, textvariable=resultname, \n font=(\"Helvetica\", 13, \"bold\")).grid(row=0, \n column=1, sticky='NW', padx=(20,0)) \n #Label(editor_sheets, text='Totals to edit:', \n #font=(\"Helvetica\", 13, \"bold\")).grid(row=4, \n #column=1, sticky=W, pady=0)\n o_editor_totals = Frame(editor_sheets, height=1, width=20)\n o_editor_totals.grid(column=1, row=1, rowspan=1, padx=(20,0), sticky=N, pady=(220,0))\n update_spreadsheet(o_editor_results, df_to_show=None, height=160, width=e_wdth)\n update_spreadsheet(o_editor_totals, df_to_show=None, height=10, width=e_wdth)\n editoname = StringVar()\n name_of_n_ed_spread = StringVar()\n name_of_n_ed_spread.set('')\n editoname.set('Edited results: %s' % str(name_of_n_ed_spread.get()))\n Label(editor_sheets, textvariable=editoname, \n font=(\"Helvetica\", 13, \"bold\")).grid(row=1, \n column=1, sticky='NW', padx=(20,0), pady=(290,0)) \n n_editor_results = Frame(editor_sheets, height=28, width=20)\n n_editor_results.grid(column=1, row=1, rowspan=1, sticky=N, padx=(20,0), pady=(310,0))\n #Label(editor_sheets, text='Edited totals:', \n #font=(\"Helvetica\", 13, \"bold\")).grid(row=15, \n #column=1, sticky=W, padx=20, pady=0)\n n_editor_totals = Frame(editor_sheets, height=1, width=20)\n n_editor_totals.grid(column=1, row=1, rowspan=1, padx=(20,0), pady=(500,0))\n update_spreadsheet(n_editor_results, df_to_show=None, height=160, width=e_wdth)\n update_spreadsheet(n_editor_totals, df_to_show=None, height=10, width=e_wdth)\n\n # add button to update\n upd_ed_but = Button(editor_sheets, text='Update interrogation(s)', command=lambda: update_all_interrogations(pane='edit'))\n if not small_screen:\n upd_ed_but.grid(row=1, column=1, sticky=E, padx=(0, 40), pady=(594, 0))\n else:\n upd_ed_but.grid(row=0, column=1, sticky='NE', padx=(20,0))\n upd_ed_but.config(state=DISABLED)\n\n ################# ################# ################# ################# \n # VISUALISE TAB # # VISUALISE TAB # # VISUALISE TAB # # VISUALISE TAB # \n ################# ################# ################# ################# \n\n plot_option_frame = Frame(tab3)\n plot_option_frame.grid(row=0, column=0, sticky='NW')\n\n def do_plotting():\n \"\"\"when you press plot\"\"\"\n plotbut.config(state=DISABLED)\n # junk for showing the plot in tkinter\n for i in oldplotframe:\n i.destroy()\n import matplotlib\n matplotlib.use('TkAgg')\n #from numpy import arange, sin, pi\n from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg\n # implement the default mpl key bindings\n from matplotlib.backend_bases import key_press_handler\n from matplotlib.figure import Figure\n from corpkit.plotter import plotter\n\n if data_to_plot.get() == 'None':\n timestring('No data selected to plot.') \n return\n\n if plotbranch.get() == 'results':\n if all_interrogations[data_to_plot.get()].results is None:\n timestring('No results branch to plot.') \n return\n what_to_plot = all_interrogations[data_to_plot.get()].results\n\n elif plotbranch.get() == 'totals':\n if all_interrogations[data_to_plot.get()].totals is None: \n timestring('No totals branch to plot.')\n return\n\n what_to_plot = all_interrogations[data_to_plot.get()].totals\n\n if single_entry.get() != 'All':\n what_to_plot = what_to_plot[single_entry.get()]\n\n if single_sbcp.get() != 'All':\n what_to_plot = what_to_plot.ix[single_sbcp.get()]\n \n if transpose_vis.get():\n if plotbranch.get() != 'totals':\n what_to_plot = what_to_plot.T\n\n # determine num to plot\n def determine_num_to_plot(num):\n \"\"\"translate num to num_to_plot\"\"\"\n try:\n num = int(num)\n except:\n if num.lower() == 'all':\n num = 'all'\n else:\n num = 7\n number_to_plot.set('7')\n return num\n\n num = determine_num_to_plot(number_to_plot.get())\n\n the_kind = charttype.get()\n if the_kind == 'Type of chart':\n the_kind = 'line'\n\n\n # plotter options\n d = {'num_to_plot': num,\n 'kind': the_kind,\n 'indices': False}\n\n if the_kind == 'heatmap':\n d['robust'] = True\n\n #the_style = \n #if the_style == 'matplotlib':\n #lgd = plt.legend(handles[: the_style = False\n d['style'] = plot_style.get()\n\n # explode option\n if explbox.get() != '' and charttype.get() == 'pie':\n if explbox.get().startswith('[') and explbox.get().endswith(']') and ',' in explbox.get():\n explval = explbox.get().lstrip('[').rstrip(']').replace(\"'\", '').replace('\"', '').replace(' ', '').split(',')\n else:\n explval = explbox.get().strip()\n explval = remake_special(explval, customs=custom_special_dict, \n case_sensitive=case_sensitive.get())\n d['explode'] = explval\n \n # this code is ridiculous\n d['tex'] = bool(texuse.get())\n d['black_and_white'] = bool(bw.get())\n d['reverse_legend'] = bool(rl.get())\n d['subplots'] = bool(sbplt.get())\n if bool(sbplt.get()):\n d['layout'] = (int(lay1.get()), int(lay2.get()))\n\n d['grid'] = bool(gridv.get())\n d['stacked'] = bool(stackd.get())\n d['partial_pie'] = bool(part_pie.get())\n d['filled'] = bool(filledvar.get())\n d['logx'] = bool(log_x.get())\n d['logy'] = bool(log_y.get())\n\n if x_axis_l.get() != '':\n d['x_label'] = x_axis_l.get()\n if x_axis_l.get() == 'None':\n d['x_label'] = False\n if y_axis_l.get() != '':\n d['y_label'] = y_axis_l.get()\n if y_axis_l.get() == 'None':\n d['y_label'] = False\n\n d['cumulative'] = bool(cumul.get())\n\n d['colours'] = chart_cols.get()\n\n legend_loc = legloc.get()\n if legend_loc == 'none':\n d['legend'] = False\n else:\n d['legend_pos'] = legend_loc\n\n if showtot.get() == 'legend + plot':\n d['show_totals'] = 'both'\n else:\n d['show_totals'] = showtot.get()\n\n d['figsize'] = (int(figsiz1.get()), int(figsiz2.get()))\n\n if len(what_to_plot.index) == 1:\n what_to_plot = what_to_plot.ix[what_to_plot.index[0]]\n \n if debug:\n print('Plotter args:', what_to_plot, plotnametext.get(), d)\n \n f = plotter(what_to_plot, plotnametext.get(), **d)\n \n # latex error\n #except RuntimeError as e:\n # s = str(e)\n # print(s)\n # split_report = s.strip().split('Here is the full report generated by LaTeX:')\n # try:\n # if len(split_report) > 0 and split_report[1] != '':\n # timestring('LaTeX error: %s' % split_report[1])\n # except:\n # timestring('LaTeX error: %s' % split_report)\n # else:\n # timestring('No TeX distribution found. Disabling TeX option.')\n # texuse.set(0)\n # tbut.config(state=DISABLED)\n # \n # return\n\n timestring('%s plotted.' % plotnametext.get())\n \n del oldplotframe[:]\n\n def getScrollingCanvas(frame):\n \"\"\"\n Adds a new canvas with scroll bars to the argument frame\n NB: uses grid layout\n return: the newly created canvas\n \"\"\"\n\n frame.grid(column=1, row=0, rowspan = 1, padx=(15, 15), pady=(40, 0), columnspan=3, sticky='NW')\n #frame.rowconfigure(0, weight=9)\n #frame.columnconfigure(0, weight=9)\n fig_frame_height = 440 if small_screen else 500\n canvas = Canvas(frame, width=980, height=fig_frame_height)\n xScrollbar = Scrollbar(frame, orient=HORIZONTAL)\n yScrollbar = Scrollbar(frame)\n xScrollbar.pack(side=BOTTOM,fill=X)\n yScrollbar.pack(side=RIGHT,fill=Y)\n canvas.config(xscrollcommand=xScrollbar.set)\n xScrollbar.config(command=canvas.xview)\n canvas.config(yscrollcommand=yScrollbar.set)\n yScrollbar.config(command=canvas.yview)\n canvas.pack(side=LEFT,expand=True,fill=BOTH)\n return canvas\n\n frame_for_fig = Frame(tab3)\n #frame_for_fig\n scrollC = getScrollingCanvas(frame_for_fig) \n mplCanvas = FigureCanvasTkAgg(f.gcf(), frame_for_fig)\n mplCanvas._tkcanvas.config(highlightthickness=0)\n canvas = mplCanvas.get_tk_widget()\n canvas.pack()\n if frame_for_fig not in boxes:\n boxes.append(frame_for_fig)\n\n scrollC.create_window(0, 0, window=canvas)\n scrollC.config(scrollregion=scrollC.bbox(ALL)) \n\n #hbar=Scrollbar(frame_for_fig,orient=HORIZONTAL)\n #hbar.pack(side=BOTTOM,fill=X)\n #hbar.config(command=canvas.get_tk_widget().xview)\n #vbar=Scrollbar(frame_for_fig,orient=VERTICAL)\n #vbar.pack(side=RIGHT,fill=Y)\n #vbar.config(command=canvas.get_tk_widget().yview)\n ##canvas.config(width=300,height=300)\n #canvas.config(xscrollcommand=hbar.set, yscrollcommand=vbar.set)\n #canvas.pack(side=LEFT,expand=True,fill=BOTH)\n try:\n mplCanvas.show()\n except RuntimeError as e:\n s = str(e)\n print(s)\n split_report = s.strip().split('Here is the full report generated by LaTeX:')\n if len(split_report) > 0 and split_report[1] != '':\n timestring('LaTeX error: %s' % split_report[1])\n else:\n timestring('No TeX distribution found. Disabling TeX option.')\n texuse.set(0)\n tbut.config(state=DISABLED)\n return\n \n oldplotframe.append(mplCanvas.get_tk_widget())\n\n del thefig[:]\n \n toolbar_frame = Frame(tab3, borderwidth=0)\n toolbar_frame.grid(row=0, column=1, columnspan=3, sticky='NW', padx=(400,0), pady=(600,0))\n toolbar_frame.lift()\n\n oldplotframe.append(toolbar_frame)\n toolbar = NavigationToolbar2TkAgg(mplCanvas,toolbar_frame)\n toolbar.update()\n\n thefig.append(f.gcf())\n savedplot.set('Saved image: ')\n\n images = {'the_current_fig': -1}\n\n def move(direction='forward'):\n import os\n try:\n from PIL import Image\n from PIL import ImageTk\n except ImportError:\n timestring(\"You need PIL/Pillow installed to do this.\")\n return\n\n for i in oldplotframe:\n i.destroy()\n del oldplotframe[:]\n\n # maybe sort by date added?\n image_list = [i for i in all_images]\n if len(image_list) == 0:\n timestring('No images found in images folder.')\n return\n \n # figure out where we're up to \n if images['the_current_fig'] != -1:\n ind = image_list.index(images['the_current_fig'])\n else:\n ind = -1\n\n if direction == 'forward':\n newind = ind + 1\n else:\n newind = ind - 1\n\n if newind < 1:\n pbut.configure(state=DISABLED)\n else:\n pbut.configure(state=NORMAL)\n if newind + 1 == len(image_list):\n nbut.configure(state=DISABLED)\n else:\n nbut.configure(state=NORMAL)\n\n imf = image_list[newind]\n if not imf.endswith('.png'):\n imf = imf + '.png'\n image = Image.open(os.path.join(image_fullpath.get(), imf))\n image_to_measure = ImageTk.PhotoImage(image)\n old_height=image_to_measure.height()\n old_width=image_to_measure.width()\n\n def determine_new_dimensions(height, width):\n maxh = 500\n maxw = 1000\n diff = float(height) / float(width)\n if diff > 1:\n # make height max\n newh = maxh\n # figure out level of magnification\n prop = maxh / float(height)\n neww = width * prop\n elif diff < 1:\n neww = maxw\n prop = maxw / float(width)\n newh = height * prop\n elif diff == 1:\n newh = maxh\n neww = maxw\n return (int(neww), int(newh))\n # calculate new dimensions\n newdimensions = determine_new_dimensions(old_height, old_width)\n \n # determine left padding\n padxright = 20\n if newdimensions[0] != 1000:\n padxleft = ((1000 - newdimensions[0]) / 2) + 40\n else:\n padxleft = 40\n padytop = (500 - newdimensions[1]) / 2\n \n def makezero(n):\n if n < 0:\n return 0\n else:\n return n\n \n padxright = makezero(padxright)\n padxleft = makezero(padxleft)\n padytop = makezero(padytop)\n\n image = image.resize(newdimensions)\n image = ImageTk.PhotoImage(image)\n frm = Frame(tab3, height=500, width=1000)\n frm.grid(column=1, row=0, rowspan = 1, padx=(padxleft, padxright), \\\n pady=padytop, columnspan=3)\n gallframe = Label(frm, image = image, justify=CENTER)\n gallframe.pack(anchor='center', fill=BOTH)\n oldplotframe.append(frm)\n images[image_list[newind]] = image\n images['the_current_fig'] = image_list[newind]\n savedplot.set('Saved image: %s' % os.path.splitext(image_list[newind])[0])\n \n timestring('Viewing %s' % os.path.splitext(image_list[newind])[0])\n\n savedplot = StringVar()\n savedplot.set('View saved images: ')\n tmp = Label(tab3, textvariable=savedplot, font=(\"Helvetica\", 13, \"bold\"))\n padding = 555 if small_screen else 616\n tmp.grid(row=0, column=1, padx=(40,0), pady=(padding-50,0), sticky=W)\n pbut = Button(tab3, text='Previous', command=lambda: move(direction='back'))\n pbut.grid(row=0, column=1, padx=(40,0), pady=(padding, 0), sticky=W)\n pbut.config(state=DISABLED)\n nbut = Button(tab3, text='Next', command=lambda: move(direction = 'forward'))\n nbut.grid(row=0, column=1, padx=(160,0), pady=(padding, 0), sticky=W)\n nbut.config(state=DISABLED)\n\n # not in use while using the toolbar instead...\n #def save_current_image():\n # import os\n # # figre out filename\n # filename = namer(plotnametext.get(), type_of_data = 'image') + '.png'\n # import sys\n # defaultextension = '.png' if sys.platform == 'darwin' else ''\n # kwarg = {'defaultextension': defaultextension,\n # #'filetypes': [('all files', '.*'), \n # #('png file', '.png')],\n # 'initialfile': filename}\n # imagedir = image_fullpath.get()\n # if imagedir:\n # kwarg['initialdir'] = imagedir\n # fo = tkFileDialog.asksaveasfilename(**kwarg)\n # if fo is None: # asksaveasfile return `None` if dialog closed with \"cancel\".\n # return\n # thefig[0].savefig(os.path.join(image_fullpath.get(), fo))\n # timestring('%s saved to %s.' % (fo, image_fullpath.get()))\n\n # title tab\n\n Label(plot_option_frame, text='Image title:').grid(row=0, column=0, sticky='W', pady=(10, 0))\n plotnametext=StringVar()\n plotnametext.set('Untitled')\n image_title_entry = Entry(plot_option_frame, textvariable=plotnametext)\n image_title_entry.grid(row=0, column=1, pady=(10, 0))\n all_text_widgets.append(image_title_entry)\n\n def plot_callback(*args):\n \"\"\"enable/disable based on selected dataset for plotting\"\"\"\n if data_to_plot.get() == 'None':\n plotbut.config(state=DISABLED)\n else:\n plotbut.config(state=NORMAL)\n try:\n thisdata = all_interrogations[data_to_plot.get()]\n except KeyError:\n return\n single_entry.set('All')\n single_sbcp.set('All')\n\n subdrs = sorted(set([d for d in os.listdir(corpus_fullpath.get()) \\\n if os.path.isdir(os.path.join(corpus_fullpath.get(),d))]))\n \n single_sbcp_optmenu.config(state=NORMAL)\n single_sbcp_optmenu['menu'].delete(0, 'end')\n single_sbcp_optmenu['menu'].add_command(label='All', command=_setit(single_sbcp, 'All'))\n lst = []\n if len(subdrs) > 0:\n for c in subdrs:\n lst.append(c)\n single_sbcp_optmenu['menu'].add_command(label=c, command=_setit(single_sbcp, c))\n single_entry_or_subcorpus['subcorpora'] = lst\n else:\n single_sbcp_optmenu.config(state=NORMAL)\n single_sbcp_optmenu['menu'].delete(0, 'end')\n single_sbcp_optmenu['menu'].add_command(label='All', command=_setit(single_sbcp, 'All'))\n single_sbcp_optmenu.config(state=DISABLED)\n\n if thisdata.results is not None:\n plotbox.config(state=NORMAL)\n single_ent_optmenu.config(state=NORMAL)\n single_ent_optmenu['menu'].delete(0, 'end')\n single_ent_optmenu['menu'].add_command(label='All', command=_setit(single_entry, 'All'))\n lst = []\n for corp in list(thisdata.results.columns)[:200]:\n lst.append(corp)\n single_ent_optmenu['menu'].add_command(label=corp, command=_setit(single_entry, corp))\n single_entry_or_subcorpus['entries'] = lst\n else:\n single_ent_optmenu.config(state=NORMAL)\n single_ent_optmenu['menu'].delete(0, 'end')\n single_ent_optmenu['menu'].add_command(label='All', command=_setit(single_entry, 'All'))\n single_ent_optmenu.config(state=DISABLED)\n plotbox.config(state=NORMAL)\n plotbranch.set('totals')\n plotbox.config(state=DISABLED)\n\n Label(plot_option_frame, text='Data to plot:').grid(row=1, column=0, sticky=W)\n # select result to plot\n data_to_plot = StringVar(root)\n most_recent = all_interrogations[list(all_interrogations.keys())[-1]]\n data_to_plot.set(most_recent)\n every_interrogation = OptionMenu(plot_option_frame, data_to_plot, *tuple([i for i in list(all_interrogations.keys())]))\n every_interrogation.config(width=20)\n every_interrogation.grid(column=0, row=2, sticky=W, columnspan=2)\n data_to_plot.trace(\"w\", plot_callback)\n Label(plot_option_frame, text='Entry:').grid(row=3, column=0, sticky=W)\n single_entry = StringVar(root)\n single_entry.set('All')\n #most_recent = all_interrogations[all_interrogations.keys()[-1]]\n #single_entry.set(most_recent)\n single_ent_optmenu = OptionMenu(plot_option_frame, single_entry, *tuple(['']))\n single_ent_optmenu.config(width=20, state=DISABLED)\n single_ent_optmenu.grid(column=1, row=3, sticky=E)\n\n def single_entry_plot_callback(*args):\n \"\"\"turn off things if single entry selected\"\"\"\n if single_entry.get() != 'All':\n sbpl_but.config(state=NORMAL)\n sbplt.set(0)\n sbpl_but.config(state=DISABLED)\n num_to_plot_box.config(state=NORMAL)\n number_to_plot.set('1')\n num_to_plot_box.config(state=DISABLED)\n single_sbcp_optmenu.config(state=DISABLED)\n entries = single_entry_or_subcorpus['entries']\n if plotnametext.get() == 'Untitled' or plotnametext.get() in entries:\n plotnametext.set(single_entry.get())\n else:\n plotnametext.set('Untitled')\n sbpl_but.config(state=NORMAL)\n number_to_plot.set('7')\n num_to_plot_box.config(state=NORMAL)\n single_sbcp_optmenu.config(state=NORMAL)\n\n single_entry.trace(\"w\", single_entry_plot_callback)\n\n Label(plot_option_frame, text='Subcorpus:').grid(row=4, column=0, sticky=W)\n single_sbcp = StringVar(root)\n single_sbcp.set('All')\n #most_recent = all_interrogations[all_interrogations.keys()[-1]]\n #single_sbcp.set(most_recent)\n single_sbcp_optmenu = OptionMenu(plot_option_frame, single_sbcp, *tuple(['']))\n single_sbcp_optmenu.config(width=20, state=DISABLED)\n single_sbcp_optmenu.grid(column=1, row=4, sticky=E)\n\n def single_sbcp_plot_callback(*args):\n \"\"\"turn off things if single entry selected\"\"\"\n if single_sbcp.get() != 'All':\n sbpl_but.config(state=NORMAL)\n sbplt.set(0)\n sbpl_but.config(state=DISABLED)\n num_to_plot_box.config(state=NORMAL)\n #number_to_plot.set('1')\n #num_to_plot_box.config(state=DISABLED)\n single_ent_optmenu.config(state=DISABLED)\n charttype.set('bar')\n entries = single_entry_or_subcorpus['subcorpora']\n if plotnametext.get() == 'Untitled' or plotnametext.get() in entries:\n plotnametext.set(single_sbcp.get())\n else:\n plotnametext.set('Untitled')\n sbpl_but.config(state=NORMAL)\n #number_to_plot.set('7')\n num_to_plot_box.config(state=NORMAL)\n single_ent_optmenu.config(state=NORMAL)\n charttype.set('line')\n\n single_sbcp.trace(\"w\", single_sbcp_plot_callback)\n\n # branch selection\n plotbranch = StringVar(root)\n plotbranch.set('results')\n plotbox = OptionMenu(plot_option_frame, plotbranch, 'results', 'totals')\n #plotbox.config(state=DISABLED)\n plotbox.grid(row=2, column=0, sticky=E, columnspan=2)\n\n def plotbranch_callback(*args):\n if plotbranch.get() == 'totals':\n single_sbcp_optmenu.config(state=DISABLED)\n single_ent_optmenu.config(state=DISABLED)\n sbpl_but.config(state=NORMAL)\n sbplt.set(0)\n sbpl_but.config(state=DISABLED)\n trans_but_vis.config(state=NORMAL)\n transpose_vis.set(0)\n trans_but_vis.config(state=DISABLED)\n else:\n single_sbcp_optmenu.config(state=NORMAL)\n single_ent_optmenu.config(state=NORMAL)\n sbpl_but.config(state=NORMAL)\n trans_but_vis.config(state=NORMAL)\n\n plotbranch.trace('w', plotbranch_callback)\n\n # num_to_plot\n Label(plot_option_frame, text='Results to show:').grid(row=5, column=0, sticky=W)\n number_to_plot = StringVar()\n number_to_plot.set('7')\n num_to_plot_box = Entry(plot_option_frame, textvariable=number_to_plot, width=3)\n num_to_plot_box.grid(row=5, column=1, sticky=E)\n all_text_widgets.append(num_to_plot_box)\n\n def pie_callback(*args):\n if charttype.get() == 'pie':\n explbox.config(state=NORMAL)\n ppie_but.config(state=NORMAL)\n else:\n explbox.config(state=DISABLED)\n ppie_but.config(state=DISABLED)\n\n if charttype.get().startswith('bar'):\n #stackbut.config(state=NORMAL)\n filledbut.config(state=NORMAL)\n else:\n #stackbut.config(state=DISABLED)\n filledbut.config(state=DISABLED)\n\n # can't do log y with area according to mpl\n if charttype.get() == 'area':\n logybut.deselect()\n logybut.config(state=DISABLED)\n filledbut.config(state=NORMAL)\n else:\n logybut.config(state=NORMAL)\n filledbut.config(state=DISABLED)\n\n # chart type\n Label(plot_option_frame, text='Kind of chart').grid(row=6, column=0, sticky=W)\n charttype = StringVar(root)\n charttype.set('line')\n kinds_of_chart = ('line', 'bar', 'barh', 'pie', 'area', 'heatmap')\n chart_kind = OptionMenu(plot_option_frame, charttype, *kinds_of_chart)\n chart_kind.config(width=10)\n chart_kind.grid(row=6, column=1, sticky=E)\n charttype.trace(\"w\", pie_callback)\n\n # axes\n Label(plot_option_frame, text='x axis label:').grid(row=7, column=0, sticky=W)\n x_axis_l = StringVar()\n x_axis_l.set('')\n tmp = Entry(plot_option_frame, textvariable=x_axis_l, font=(\"Courier New\", 14), width=18)\n tmp.grid(row=7, column=1, sticky=E)\n all_text_widgets.append(tmp)\n\n Label(plot_option_frame, text='y axis label:').grid(row=8, column=0, sticky=W)\n y_axis_l = StringVar()\n y_axis_l.set('')\n tmp = Entry(plot_option_frame, textvariable=y_axis_l, font=(\"Courier New\", 14), width=18)\n tmp.grid(row=8, column=1, sticky=E)\n all_text_widgets.append(tmp)\n\n tmp = Label(plot_option_frame, text='Explode:')\n if not small_screen:\n tmp.grid(row=9, column=0, sticky=W)\n explval = StringVar()\n explval.set('')\n explbox = Entry(plot_option_frame, textvariable=explval, font=(\"Courier New\", 14), width=18)\n if not small_screen:\n explbox.grid(row=9, column=1, sticky=E)\n all_text_widgets.append(explbox)\n explbox.config(state=DISABLED)\n\n # log options\n log_x = IntVar()\n Checkbutton(plot_option_frame, text=\"Log x axis\", variable=log_x).grid(column=0, row=10, sticky=W)\n log_y = IntVar()\n logybut = Checkbutton(plot_option_frame, text=\"Log y axis\", variable=log_y, width=13)\n logybut.grid(column=1, row=10, sticky=E)\n\n # transpose\n transpose_vis = IntVar()\n trans_but_vis = Checkbutton(plot_option_frame, text=\"Transpose\", variable=transpose_vis, onvalue=True, offvalue=False, width=13)\n trans_but_vis.grid(column=1, row=11, sticky=E)\n\n cumul = IntVar()\n cumulbutton = Checkbutton(plot_option_frame, text=\"Cumulative\", variable=cumul, onvalue=True, offvalue=False)\n cumulbutton.grid(column=0, row=11, sticky=W)\n\n bw = IntVar()\n Checkbutton(plot_option_frame, text=\"Black and white\", variable=bw, onvalue=True, offvalue=False).grid(column=0, row=12, sticky=W)\n texuse = IntVar()\n tbut = Checkbutton(plot_option_frame, text=\"Use TeX\", variable=texuse, onvalue=True, offvalue=False, width=13)\n tbut.grid(column=1, row=12, sticky=E)\n tbut.deselect()\n if not py_script:\n tbut.config(state=DISABLED)\n\n rl = IntVar()\n Checkbutton(plot_option_frame, text=\"Reverse legend\", variable=rl, onvalue=True, offvalue=False).grid(column=0, row=13, sticky=W)\n sbplt = IntVar()\n sbpl_but = Checkbutton(plot_option_frame, text=\"Subplots\", variable=sbplt, onvalue=True, offvalue=False, width=13)\n sbpl_but.grid(column=1, row=13, sticky=E)\n\n def sbplt_callback(*args):\n \"\"\"if subplots are happening, allow layout\"\"\"\n if sbplt.get():\n lay1menu.config(state=NORMAL)\n lay2menu.config(state=NORMAL)\n else:\n lay1menu.config(state=DISABLED)\n lay2menu.config(state=DISABLED)\n\n sbplt.trace(\"w\", sbplt_callback)\n\n\n gridv = IntVar()\n gridbut = Checkbutton(plot_option_frame, text=\"Grid\", variable=gridv, onvalue=True, offvalue=False)\n gridbut.select()\n gridbut.grid(column=0, row=14, sticky=W)\n\n stackd = IntVar()\n stackbut = Checkbutton(plot_option_frame, text=\"Stacked\", variable=stackd, onvalue=True, offvalue=False, width=13)\n stackbut.grid(column=1, row=14, sticky=E)\n #stackbut.config(state=DISABLED)\n\n part_pie = IntVar()\n ppie_but = Checkbutton(plot_option_frame, text=\"Partial pie\", variable=part_pie, onvalue=True, offvalue=False)\n if not small_screen:\n ppie_but.grid(column=0, row=15, sticky=W)\n ppie_but.config(state=DISABLED)\n\n filledvar = IntVar()\n filledbut = Checkbutton(plot_option_frame, text=\"Filled\", variable=filledvar, onvalue=True, offvalue=False, width=13)\n if not small_screen:\n filledbut.grid(column=1, row=15, sticky=E)\n filledbut.config(state=DISABLED)\n\n\n # chart type\n Label(plot_option_frame, text='Colour scheme:').grid(row=16, column=0, sticky=W)\n chart_cols = StringVar(root)\n schemes = tuple(sorted(('Paired', 'Spectral', 'summer', 'Set1', 'Set2', 'Set3', \n 'Dark2', 'prism', 'RdPu', 'YlGnBu', 'RdYlBu', 'gist_stern', 'cool', 'coolwarm',\n 'gray', 'GnBu', 'gist_ncar', 'gist_rainbow', 'Wistia', 'CMRmap', 'bone', \n 'RdYlGn', 'spring', 'terrain', 'PuBu', 'spectral', 'rainbow', 'gist_yarg', \n 'BuGn', 'bwr', 'cubehelix', 'Greens', 'PRGn', 'gist_heat', 'hsv', \n 'Pastel2', 'Pastel1', 'jet', 'gist_earth', 'copper', 'OrRd', 'brg', \n 'gnuplot2', 'BuPu', 'Oranges', 'PiYG', 'YlGn', 'Accent', 'gist_gray', 'flag', \n 'BrBG', 'Reds', 'RdGy', 'PuRd', 'Blues', 'autumn', 'ocean', 'pink', 'binary', \n 'winter', 'gnuplot', 'hot', 'YlOrBr', 'seismic', 'Purples', 'RdBu', 'Greys', \n 'YlOrRd', 'PuOr', 'PuBuGn', 'nipy_spectral', 'afmhot', \n 'viridis', 'magma', 'plasma', 'inferno', 'diverge', 'default')))\n ch_col = OptionMenu(plot_option_frame, chart_cols, *schemes)\n ch_col.config(width=17)\n ch_col.grid(row=16, column=1, sticky=E)\n chart_cols.set('viridis')\n \n # style\n from matplotlib import style\n try:\n stys = tuple(stys.available)\n except:\n stys = tuple(('ggplot', 'fivethirtyeight', 'bmh', 'matplotlib', \\\n 'mpl-white', 'classic', 'seaborn-talk'))\n plot_style = StringVar(root)\n plot_style.set('ggplot')\n Label(plot_option_frame, text='Plot style:').grid(row=17, column=0, sticky=W)\n pick_a_style = OptionMenu(plot_option_frame, plot_style, *stys)\n pick_a_style.config(width=17)\n pick_a_style.grid(row=17, column=1, sticky=E)\n\n def ps_callback(*args):\n if plot_style.get().startswith('seaborn'):\n chart_cols.set('Default')\n ch_col.config(state=DISABLED)\n else:\n ch_col.config(state=NORMAL)\n\n plot_style.trace(\"w\", ps_callback)\n\n # legend pos\n Label(plot_option_frame, text='Legend position:').grid(row=18, column=0, sticky=W)\n legloc = StringVar(root)\n legloc.set('best')\n locs = tuple(('best', 'upper right', 'right', 'lower right', 'lower left', 'upper left', 'middle', 'none'))\n loc_options = OptionMenu(plot_option_frame, legloc, *locs)\n loc_options.config(width=17)\n loc_options.grid(row=18, column=1, sticky=E)\n\n # figure size\n Label(plot_option_frame, text='Figure size:').grid(row=19, column=0, sticky=W)\n figsiz1 = StringVar(root)\n figsiz1.set('10')\n figsizes = tuple(('2', '4', '6', '8', '10', '12', '14', '16', '18'))\n fig1 = OptionMenu(plot_option_frame, figsiz1, *figsizes)\n fig1.configure(width=6)\n fig1.grid(row=19, column=1, sticky=W, padx=(27, 0))\n Label(plot_option_frame, text=\"x\").grid(row=19, column=1, padx=(30, 0))\n figsiz2 = StringVar(root)\n figsiz2.set('4')\n fig2 = OptionMenu(plot_option_frame, figsiz2, *figsizes)\n fig2.configure(width=6)\n fig2.grid(row=19, column=1, sticky=E)\n\n # subplots layout\n Label(plot_option_frame, text='Subplot layout:').grid(row=20, column=0, sticky=W)\n lay1 = StringVar(root)\n lay1.set('3')\n figsizes = tuple([str(i) for i in range(1, 20)])\n lay1menu = OptionMenu(plot_option_frame, lay1, *figsizes)\n lay1menu.configure(width=6)\n lay1menu.grid(row=20, column=1, sticky=W, padx=(27, 0))\n Label(plot_option_frame, text=\"x\").grid(row=20, column=1, padx=(30, 0))\n lay2 = StringVar(root)\n lay2.set('3')\n lay2menu = OptionMenu(plot_option_frame, lay2, *figsizes)\n lay2menu.configure(width=6)\n lay2menu.grid(row=20, column=1, sticky=E)\n lay1menu.config(state=DISABLED)\n lay2menu.config(state=DISABLED)\n\n # show_totals option\n Label(plot_option_frame, text='Show totals: ').grid(row=21, column=0, sticky=W)\n showtot = StringVar(root)\n showtot.set('Off')\n showtot_options = tuple(('Off', 'legend', 'plot', 'legend + plot'))\n show_tot_menu = OptionMenu(plot_option_frame, showtot, *showtot_options)\n show_tot_menu.grid(row=21, column=1, sticky=E)\n\n # plot button\n plotbut = Button(plot_option_frame, text='Plot')\n plotbut.grid(row=22, column=1, sticky=E)\n plotbut.config(command=lambda: runner(plotbut, do_plotting), state=DISABLED)\n\n ################### ################### ################### ###################\n # CONCORDANCE TAB # # CONCORDANCE TAB # # CONCORDANCE TAB # # CONCORDANCE TAB #\n ################### ################### ################### ###################\n\n def add_conc_lines_to_window(data, loading=False, preserve_colour=True):\n import pandas as pd\n import re\n #pd.set_option('display.height', 1000)\n #pd.set_option('display.width', 1000)\n pd.set_option('display.max_colwidth', 200)\n import corpkit\n from corpkit.interrogation import Concordance\n if isinstance(data, Concordance):\n current_conc[0] = data\n elif isinstance(data, pd.core.frame.DataFrame):\n data = Concordance(data)\n current_conc[0] = data\n else:\n current_conc[0] = data.concordance\n data = data.concordance\n if win.get() == 'Window':\n window = 70\n else:\n window = int(win.get())\n\n fnames = show_filenames.get()\n them = show_themes.get()\n spk = show_speaker.get()\n subc = show_subcorpora.get()\n ix = show_index.get()\n\n if not fnames:\n data = data.drop('f', axis=1, errors='ignore')\n if not them:\n data = data.drop('t', axis=1, errors='ignore')\n if not spk:\n data = data.drop('s', axis=1, errors='ignore')\n if not subc:\n data = data.drop('c', axis=1, errors='ignore')\n if not ix:\n data = data.drop('i', axis=1, errors='ignore')\n if them:\n data = data.drop('t', axis=1, errors='ignore')\n themelist = get_list_of_themes(data)\n if any(t != '' for t in themelist):\n data.insert(0, 't', themelist)\n\n # only do left align when long result ...\n # removed because it's no big deal if always left aligned, and this\n # copes when people search for 'root' or something.\n\n def resize_by_window_size(df, window):\n import os\n if 'f' in list(df.columns):\n df['f'] = df['f'].apply(os.path.basename) \n df['l'] = df['l'].str.slice(start=-window, stop=None)\n df['l'] = df['l'].str.rjust(window)\n df['r'] = df['r'].str.slice(start=0, stop=window)\n df['r'] = df['r'].str.ljust(window)\n df['m'] = df['m'].str.ljust(df['m'].str.len().max())\n return df\n\n moddata = resize_by_window_size(data, window)\n lines = moddata.to_string(header=False, index=show_df_index.get()).splitlines()\n #lines = [re.sub('\\s*\\.\\.\\.\\s*$', '', s) for s in lines]\n conclistbox.delete(0, END)\n for line in lines:\n conclistbox.insert(END, line)\n if preserve_colour:\n # itemcoldict has the NUMBER and COLOUR\n index_regex = re.compile(r'^([0-9]+)')\n # make dict for NUMBER:INDEX \n index_dict = {}\n lines = conclistbox.get(0, END)\n for index, line in enumerate(lines):\n index_dict[int(re.search(index_regex, conclistbox.get(index)).group(1))] = index\n todel = []\n for item, colour in list(itemcoldict.items()):\n try:\n conclistbox.itemconfig(index_dict[item], {'bg':colour})\n except KeyError:\n todel.append(item)\n for i in todel:\n del itemcoldict[i]\n\n if loading:\n timestring('Concordances loaded.')\n else:\n timestring('Concordancing done: %d results.' % len(lines))\n\n def delete_conc_lines(*args):\n if type(current_conc[0]) == str:\n return\n items = conclistbox.curselection()\n #current_conc[0].results.drop(current_conc[0].results.iloc[1,].name)\n r = current_conc[0].drop([current_conc[0].iloc[int(n),].name for n in items])\n add_conc_lines_to_window(r)\n if len(items) == 1:\n timestring('%d line removed.' % len(items))\n if len(items) > 1:\n timestring('%d lines removed.' % len(items))\n global conc_saved\n conc_saved = False\n\n def delete_reverse_conc_lines(*args): \n \n if type(current_conc[0]) == str:\n return\n items = [int(i) for i in conclistbox.curselection()]\n r = current_conc[0].iloc[items,]\n add_conc_lines_to_window(r)\n conclistbox.select_set(0, END)\n if len(conclistbox.get(0, END)) - len(items) == 1:\n timestring('%d line removed.' % ((len(conclistbox.get(0, END)) - len(items))))\n if len(conclistbox.get(0, END)) - len(items) > 1:\n timestring('%d lines removed.' % ((len(conclistbox.get(0, END)) - len(items))))\n global conc_saved\n conc_saved = False\n\n def conc_export(data='default'):\n \"\"\"export conc lines to csv\"\"\"\n import os\n import pandas\n \n if type(current_conc[0]) == str:\n timestring('Nothing to export.')\n return\n if in_a_project.get() == 0:\n home = os.path.expanduser(\"~\")\n docpath = os.path.join(home, 'Documents')\n else:\n docpath = project_fullpath.get()\n if data == 'default':\n thedata = current_conc[0]\n thedata = thedata.to_csv(header = False, sep = '\\t')\n else:\n thedata = all_conc[data]\n thedata = thedata.to_csv(header = False, sep = '\\t')\n if sys.platform == 'darwin':\n the_kwargs = {'message': 'Choose a name and place for your exported data.'}\n else:\n the_kwargs = {}\n savepath = filedialog.asksaveasfilename(title='Save file',\n initialdir=exported_fullpath.get(),\n defaultextension='.csv',\n initialfile='data.csv',\n **the_kwargs)\n if savepath == '':\n return\n with open(savepath, \"w\") as fo:\n fo.write(thedata)\n timestring('Concordance lines exported.')\n global conc_saved\n conc_saved = False\n\n def get_list_of_colours(df):\n flipped_colour={v: k for k, v in list(colourdict.items())}\n colours = []\n for i in list(df.index):\n # if the item has been coloured\n if i in list(itemcoldict.keys()):\n itscolour=itemcoldict[i]\n colournumber = flipped_colour[itscolour]\n # append the number of the colour code, with some corrections\n if colournumber == 0:\n colournumber = 10\n if colournumber == 9:\n colournumber = 99\n colours.append(colournumber)\n else:\n colours.append(10)\n return colours\n\n def get_list_of_themes(df):\n flipped_colour={v: k for k, v in list(colourdict.items())}\n themes = []\n for i in list(df.index):\n # if the item has been coloured\n if i in list(itemcoldict.keys()):\n itscolour=itemcoldict[i]\n colournumber = flipped_colour[itscolour]\n theme = entryboxes[list(entryboxes.keys())[colournumber]].get()\n # append the number of the colour code, with some corrections\n if theme is not False and theme != '':\n themes.append(theme)\n else:\n themes.append('')\n else:\n themes.append('')\n if all(i == '' for i in themes):\n timestring('Warning: no scheme defined.')\n return themes\n\n def conc_sort(*args):\n \"\"\"various sorting for conc, by updating dataframe\"\"\"\n import re\n import pandas\n import itertools\n sort_way = True\n if isinstance(current_conc[0], str):\n return\n if prev_sortval[0] == sortval.get():\n # if subcorpus is the same, etc, as well\n sort_way = toggle()\n df = current_conc[0]\n prev_sortval[0] = sortval.get()\n\n # sorting by first column is easy, so we don't need pandas\n if sortval.get() == 'M1':\n low = [l.lower() for l in df['m']]\n df['tosorton'] = low\n elif sortval.get() == 'File':\n low = [l.lower() for l in df['f']]\n df['tosorton'] = low\n elif sortval.get() == 'Colour':\n colist = get_list_of_colours(df)\n df['tosorton'] = colist\n elif sortval.get() == 'Scheme':\n themelist = get_list_of_themes(df)\n #df.insert(1, 't', themelist)\n df.insert(1, 'tosorton', themelist)\n elif sortval.get() == 'Index' or sortval.get() == 'Sort':\n df = df.sort(ascending=sort_way)\n elif sortval.get() == 'Subcorpus':\n sbs = [l.lower() for l in df['c']]\n df['tosorton'] = sbs\n elif sortval.get() == 'Random':\n import pandas\n import numpy as np\n df = df.reindex(np.random.permutation(df.index))\n\n elif sortval.get() == 'Speaker':\n try:\n low = [l.lower() for l in df['s']]\n except:\n timestring('No speaker information to sort by.')\n return\n df['tosorton'] = low\n # if sorting by other columns, however, it gets tough.\n else:\n \n td = {}\n #if 'note' in kwargs.keys():\n # td['note'] = kwargs['note']\n # add_nltk_data_to_nltk_path(**td)\n # tokenise the right part of each line\n # get l or r column\n col = sortval.get()[0].lower()\n tokenised = [s.split() for s in list(df[col].values)]\n if col == 'm':\n repeats = 2\n else:\n repeats = 6\n for line in tokenised:\n for i in range(6 - len(line)):\n if col == 'l':\n line.insert(0, '')\n if col == 'r':\n line.append('')\n\n # get 1-5 and convert it\n num = int(sortval.get().lstrip('LMR'))\n if col == 'l':\n num = -num\n if col == 'r':\n num = num - 1\n\n just_sortword = []\n for l in tokenised: \n if col != 'm':\n just_sortword.append(l[num].lower())\n else:\n # horrible\n if len(l) == 1:\n just_sortword.append(l[0].lower())\n elif len(l) > 1:\n if num == 2:\n just_sortword.append(l[1].lower())\n elif num == -2:\n just_sortword.append(l[-2].lower())\n elif num == -1:\n just_sortword.append(l[-1].lower())\n\n # append list to df\n df['tosorton'] = just_sortword\n\n if sortval.get() not in ['Index', 'Random', 'Sort']:\n df = df.sort(['tosorton'], ascending=sort_way)\n df = df.drop(['tosorton'], axis=1, errors='ignore')\n if show_filenames.get() == 0:\n add_conc_lines_to_window(df.drop('f', axis=1, errors='ignore'))\n else:\n add_conc_lines_to_window(df)\n \n timestring('%d concordance lines sorted.' % len(conclistbox.get(0, END)))\n global conc_saved\n conc_saved = False\n\n def do_inflection(pos='v'):\n global tb\n from corpkit.dictionaries.process_types import get_both_spellings, add_verb_inflections\n \n # get every word\n all_words = [w.strip().lower() for w in tb.get(1.0, END).split()]\n # try to get just selection\n cursel = False\n try:\n lst = [w.strip().lower() for w in tb.get(SEL_FIRST, SEL_LAST).split()]\n cursel = True\n except:\n lst = [w.strip().lower() for w in tb.get(1.0, END).split()]\n lst = get_both_spellings(lst)\n if pos == 'v':\n expanded = add_verb_inflections(lst)\n if pos == 'n':\n from corpkit.inflect import pluralize\n expanded = []\n for w in lst:\n expanded.append(w)\n pl = pluralize(w)\n if pl != w:\n expanded.append(pl)\n if pos == 'a':\n from corpkit.inflect import grade\n expanded = []\n for w in lst:\n expanded.append(w)\n comp = grade(w, suffix = \"er\")\n if comp != w:\n expanded.append(comp)\n supe = grade(w, suffix = \"est\")\n if supe != w:\n expanded.append(supe)\n if cursel:\n expanded = expanded + all_words\n lst = sorted(set(expanded))\n # delete widget text, reinsrt all\n tb.delete(1.0, END)\n for w in lst:\n tb.insert(END, w + '\\n')\n\n def make_dict_from_existing_wordlists():\n from collections import namedtuple\n def convert(dictionary):\n return namedtuple('outputnames', list(dictionary.keys()))(**dictionary)\n all_preset_types = {}\n from corpkit.dictionaries.process_types import processes\n from corpkit.dictionaries.roles import roles\n from corpkit.dictionaries.wordlists import wordlists\n from corpkit.other import as_regex\n customs = convert(custom_special_dict)\n special_qs = [processes, roles, wordlists]\n for kind in special_qs:\n try:\n types = [k for k in list(kind.__dict__.keys())]\n except AttributeError:\n types = [k for k in list(kind._asdict().keys())]\n\n for t in types:\n if kind == roles:\n all_preset_types[t.upper() + '_ROLE'] = kind._asdict()[t]\n else:\n try:\n all_preset_types[t.upper()] = kind.__dict__[t]\n except AttributeError:\n all_preset_types[t.upper()] = kind._asdict()[t]\n return all_preset_types\n \n predict = make_dict_from_existing_wordlists()\n\n for k, v in list(predict.items()):\n custom_special_dict[k.upper()] = v\n\n def store_wordlist():\n global tb\n lst = [w.strip().lower() for w in tb.get(1.0, END).split()]\n global schemename\n if schemename.get() == '':\n timestring('Wordlist needs a name.')\n return\n specname = ''.join([i for i in schemename.get().upper() if i.isalnum() or i == '_'])\n if specname in list(predict.keys()):\n timestring('Name \"%s\" already taken, sorry.' % specname)\n return\n else:\n if specname in list(custom_special_dict.keys()):\n should_continue = messagebox.askyesno(\"Overwrite list\", \n \"Overwrite existing list named '%s'?\" % specname)\n if not should_continue:\n return\n custom_special_dict[specname] = lst\n global cust_spec\n cust_spec.delete(0, END)\n for k, v in sorted(custom_special_dict.items()):\n cust_spec.insert(END, k)\n color_saved(cust_spec, colour1 = '#ccebc5', colour2 = '#fbb4ae', lists = True)\n timestring('LIST:%s stored to custom wordlists.' % specname)\n\n\n parser_opts = StringVar()\n speakseg = IntVar()\n parse_with_metadata = IntVar()\n tokenise_pos = IntVar()\n tokenise_lem = IntVar()\n clicked_done = IntVar()\n clicked_done.set(0)\n\n def parser_options(kind):\n \"\"\"\n A popup with corenlp options, to display before parsing.\n this is a good candidate for 'preferences'\n \"\"\"\n from tkinter import Toplevel\n global poptions\n poptions = Toplevel()\n poptions.title('Parser options')\n from collections import OrderedDict\n popt = OrderedDict()\n if kind == 'parse':\n tups = [('Tokenise', 'tokenize'),\n ('Clean XML', 'cleanxml'),\n ('Sentence splitting', 'ssplit'),\n ('POS tagging', 'pos'),\n ('Lemmatisation', 'lemma'),\n ('Named entity recognition', 'ner'),\n ('Parse', 'parse'),\n ('Referent tracking', 'dcoref')]\n for k, v in tups:\n popt[k] = v\n\n butvar = {}\n butbut = {}\n\n orders = {'tokenize': 0,\n 'cleanxml': 1,\n 'ssplit': 2,\n 'pos': 3,\n 'lemma': 4,\n 'ner': 5,\n 'parse': 6,\n 'dcoref': 7}\n\n for index, (k, v) in enumerate(popt.items()):\n tmp = StringVar()\n but = Checkbutton(poptions, text=k, variable=tmp, onvalue=v, offvalue=False)\n but.grid(sticky=W)\n if k != 'Clean XML':\n but.select()\n else:\n but.deselect()\n butbut[index] = but\n butvar[index] = tmp\n \n if kind == 'tokenise':\n Checkbutton(poptions, text='POS tag', variable=tokenise_pos, onvalue=True, offvalue=False).grid(sticky=W)\n Checkbutton(poptions, text='Lemmatise', variable=tokenise_lem, onvalue=True, offvalue=False).grid(sticky=W)\n Checkbutton(poptions, text='Speaker segmentation', variable=speakseg, onvalue=True, offvalue=False).grid(sticky=W)\n Checkbutton(poptions, text='XML metadata', variable=parse_with_metadata, onvalue=True, offvalue=False).grid(sticky=W)\n\n def optionspicked(*args):\n vals = [i.get() for i in list(butvar.values()) if i.get() is not False and i.get() != 0 and i.get() != '0']\n vals = sorted(vals, key=lambda x:orders[x])\n the_opts = ','.join(vals)\n clicked_done.set(1)\n poptions.destroy()\n parser_opts.set(the_opts)\n\n def qut():\n poptions.destroy()\n\n stopbut = Button(poptions, text='Cancel', command=qut)\n stopbut.grid(row=15, sticky='w', padx=5)\n stopbut = Button(poptions, text='Done', command=optionspicked)\n stopbut.grid(row=15, sticky='e', padx=5)\n\n ############## ############## ############## ############## ############## \n # WORDLISTS # # WORDLISTS # # WORDLISTS # # WORDLISTS # # WORDLISTS # \n ############## ############## ############## ############## ############## \n\n def custom_lists():\n \"\"\"a popup for defining custom wordlists\"\"\"\n from tkinter import Toplevel\n popup = Toplevel()\n popup.title('Custom wordlists')\n popup.wm_attributes('-topmost', 1)\n Label(popup, text='Create wordlist', font=(\"Helvetica\", 13, \"bold\")).grid(column=0, row=0)\n global schemename\n schemename = StringVar()\n schemename.set('')\n scheme_name_field = Entry(popup, textvariable=schemename, justify=CENTER, width=21, font=(\"Courier New\", 13))\n #scheme_name_field.bind('', select_all_text)\n scheme_name_field.grid(column=0, row=5, sticky=W, padx=(7, 0))\n global tb\n custom_words = Frame(popup, width=9, height=40)\n custom_words.grid(row=1, column=0, padx=5)\n cwscrbar = Scrollbar(custom_words)\n cwscrbar.pack(side=RIGHT, fill=Y)\n tb = Text(custom_words, yscrollcommand=cwscrbar.set, relief=SUNKEN,\n bg='#F4F4F4', width=20, height=26, font=(\"Courier New\", 13))\n cwscrbar.config(command=tb.yview)\n bind_textfuncts_to_widgets([tb, scheme_name_field])\n tb.pack(side=LEFT, fill=BOTH)\n tmp = Button(popup, text='Get verb inflections', command=lambda: do_inflection(pos = 'v'), width=17)\n tmp.grid(row=2, column=0, sticky=W, padx=(7, 0))\n tmp = Button(popup, text='Get noun inflections', command=lambda: do_inflection(pos = 'n'), width=17)\n tmp.grid(row=3, column=0, sticky=W, padx=(7, 0)) \n tmp = Button(popup, text='Get adjective forms', command=lambda: do_inflection(pos = 'a'), width=17)\n tmp.grid(row=4, column=0, sticky=W, padx=(7, 0)) \n #Button(text='Inflect as noun', command=lambda: do_inflection(pos = 'n')).grid()\n savebut = Button(popup, text='Store', command=store_wordlist, width=17)\n savebut.grid(row=6, column=0, sticky=W, padx=(7, 0))\n Label(popup, text='Previous wordlists', font=(\"Helvetica\", 13, \"bold\")).grid(column=1, row=0, padx=15)\n other_custom_queries = Frame(popup, width=9, height=30)\n other_custom_queries.grid(row=1, column=1, padx=15)\n pwlscrbar = Scrollbar(other_custom_queries)\n pwlscrbar.pack(side=RIGHT, fill=Y)\n global cust_spec\n cust_spec = Listbox(other_custom_queries, selectmode = EXTENDED, height=24, relief=SUNKEN, bg='#F4F4F4',\n yscrollcommand=pwlscrbar.set, exportselection=False, width=20,\n font=(\"Courier New\", 13))\n pwlscrbar.config(command=cust_spec.yview)\n cust_spec.pack()\n cust_spec.delete(0, END)\n \n def colour_the_custom_queries(*args):\n color_saved(cust_spec, colour1 = '#ccebc5', colour2 = '#fbb4ae', lists = True)\n\n cust_spec.bind('<>', colour_the_custom_queries)\n for k, v in sorted(custom_special_dict.items()):\n cust_spec.insert(END, k)\n\n colour_the_custom_queries()\n\n def remove_this_custom_query():\n global cust_spec\n indexes = cust_spec.curselection()\n for index in indexes:\n name = cust_spec.get(index)\n del custom_special_dict[name]\n cust_spec.delete(0, END)\n for k, v in sorted(custom_special_dict.items()):\n cust_spec.insert(END, k)\n color_saved(cust_spec, colour1 = '#ccebc5', colour2 = '#fbb4ae', lists = True)\n if len(indexes) == 1:\n timestring('%s forgotten.' % name)\n else:\n timestring('%d lists forgotten.' % len(indexes))\n\n def delete_this_custom_query():\n global cust_spec\n indexes = cust_spec.curselection()\n for index in indexes:\n name = cust_spec.get(index)\n if name in list(predict.keys()):\n timestring(\"%s can't be permanently deleted.\" % name)\n return\n del custom_special_dict[name]\n try:\n del saved_special_dict[name]\n except:\n pass\n dump_custom_list_json()\n cust_spec.delete(0, END)\n for k, v in sorted(custom_special_dict.items()):\n cust_spec.insert(END, k)\n color_saved(cust_spec, colour1 = '#ccebc5', colour2 = '#fbb4ae', lists = True)\n \n if len(indexes) == 1:\n timestring('%s permanently deleted.' % name)\n else:\n timestring('%d lists permanently deleted.' % len(indexes))\n\n def show_this_custom_query(*args):\n global cust_spec\n index = cust_spec.curselection()\n if len(index) > 1:\n timestring(\"Can only show one list at a time.\")\n return\n name = cust_spec.get(index)\n tb.delete(1.0, END)\n for i in custom_special_dict[name]:\n tb.insert(END, i + '\\n')\n schemename.set(name)\n\n cust_spec.bind('', show_this_custom_query)\n\n def merge_this_custom_query(*args):\n global cust_spec\n indexes = cust_spec.curselection()\n names = [cust_spec.get(i) for i in indexes]\n tb.delete(1.0, END)\n for name in names:\n for i in custom_special_dict[name]:\n tb.insert(END, i + '\\n')\n schemename.set('Merged')\n\n def add_custom_query_to_json():\n global cust_spec\n indexes = cust_spec.curselection()\n for index in indexes:\n name = cust_spec.get(index)\n saved_special_dict[name] = custom_special_dict[name]\n dump_custom_list_json()\n color_saved(cust_spec, colour1 = '#ccebc5', colour2 = '#fbb4ae', lists = True)\n \n if len(indexes) == 1:\n timestring('%s saved to file.' % name)\n else:\n timestring('%d lists saved to file.' % len(indexes)) \n \n Button(popup, text='View/edit', command=show_this_custom_query, width=17).grid(column=1, row=2, sticky=E, padx=(0, 7))\n Button(popup, text='Merge', command=merge_this_custom_query, width=17).grid(column=1, row=3, sticky=E, padx=(0, 7))\n svb = Button(popup, text='Save', command=add_custom_query_to_json, width=17)\n svb.grid(column=1, row=4, sticky=E, padx=(0, 7))\n if in_a_project.get() == 0:\n svb.config(state=DISABLED)\n else:\n svb.config(state=NORMAL)\n\n Button(popup, text='Remove', command=remove_this_custom_query, width=17).grid(column=1, row=5, sticky=E, padx=(0, 7))\n Button(popup, text='Delete', command=delete_this_custom_query, width=17).grid(column=1, row=6, sticky=E, padx=(0, 7))\n\n def have_unsaved_list():\n \"\"\"finds out if there is an unsaved list\"\"\"\n global tb\n lst = [w.strip().lower() for w in tb.get(1.0, END).split()]\n if any(lst == l for l in list(custom_special_dict.values())):\n return False\n else:\n return True\n\n def quit_listing(*args):\n if have_unsaved_list():\n should_continue = messagebox.askyesno(\"Unsaved data\", \n \"Unsaved list will be forgotten. Continue?\")\n if not should_continue:\n return \n popup.destroy()\n\n stopbut = Button(popup, text='Done', command=quit_listing)\n stopbut.grid(column=0, columnspan=2, row=7, pady=7)\n\n ############## ############## ############## ############## ############## \n # COLSCHEMES # # COLSCHEMES # # COLSCHEMES # # COLSCHEMES # # COLSCHEMES # \n ############## ############## ############## ############## ############## \n\n # a place for the toplevel entry info\n entryboxes = OrderedDict()\n\n # fill it with null data\n for i in range(10):\n tmp = StringVar()\n tmp.set('')\n entryboxes[i] = tmp\n\n def codingschemer():\n try:\n global toplevel\n toplevel.destroy()\n except:\n pass\n\n from tkinter import Toplevel\n toplevel = Toplevel()\n toplevel.geometry('+1089+85')\n toplevel.title(\"Coding scheme\")\n toplevel.wm_attributes('-topmost', 1)\n Label(toplevel, text='').grid(row=0, column=0, pady=2)\n def quit_coding(*args):\n toplevel.destroy()\n #Label(toplevel, text=('When concordancing, you can colour code lines using 0-9 keys. '\\\n # 'If you name the colours here, you can export or save the concordance lines with '\\\n # 'names attached.'), font=('Helvetica', 13, 'italic'), wraplength = 250, justify=LEFT).grid(row=0, column=0, columnspan=2)\n stopbut = Button(toplevel, text='Done', command=quit_coding)\n stopbut.grid(row=12, column=0, columnspan=2, pady=15) \n for index, colour_index in enumerate(colourdict.keys()):\n Label(toplevel, text='Key: %d' % colour_index).grid(row=index + 1, column=0)\n fore = 'black'\n if colour_index == 9:\n fore = 'white'\n tmp = Entry(toplevel, textvariable=entryboxes[index], bg=colourdict[colour_index], fg = fore)\n all_text_widgets.append(tmp)\n if index == 0:\n tmp.focus_set()\n tmp.grid(row=index + 1, column=1, padx=10)\n\n toplevel.bind(\"\", quit_coding)\n toplevel.bind(\"\", focus_next_window)\n\n # conc box needs to be defined up here\n fsize = IntVar()\n fsize.set(12)\n conc_height = 510 if small_screen else 565\n cfrm = Frame(tab4, height=conc_height, width=note_width - 10)\n cfrm.grid(column=0, row=0, sticky='nw')\n cscrollbar = Scrollbar(cfrm)\n cscrollbarx = Scrollbar(cfrm, orient=HORIZONTAL)\n cscrollbar.pack(side=RIGHT, fill=Y)\n cscrollbarx.pack(side=BOTTOM, fill=X)\n conclistbox = Listbox(cfrm, yscrollcommand=cscrollbar.set, relief=SUNKEN, bg='#F4F4F4',\n xscrollcommand=cscrollbarx.set, height=conc_height, \n width=note_width - 10, font=('Courier New', fsize.get()), \n selectmode = EXTENDED)\n conclistbox.pack(fill=BOTH)\n cscrollbar.config(command=conclistbox.yview)\n cscrollbarx.config(command=conclistbox.xview)\n cfrm.pack_propagate(False)\n\n def dec_concfont(*args):\n size = fsize.get()\n fsize.set(size - 1)\n conclistbox.configure(font=('Courier New', fsize.get()))\n\n def inc_concfont(*args):\n size = fsize.get()\n fsize.set(size + 1)\n conclistbox.configure(font=('Courier New', fsize.get()))\n\n def select_all_conclines(*args):\n conclistbox.select_set(0, END)\n\n def color_conc(colour=0, *args):\n import re\n \"\"\"color a conc line\"\"\"\n index_regex = re.compile(r'^([0-9]+)')\n col = colourdict[colour]\n if type(current_conc[0]) == str:\n return\n items = conclistbox.curselection()\n for index in items:\n conclistbox.itemconfig(index, {'bg':col})\n ind = int(re.search(index_regex, conclistbox.get(index)).group(1))\n itemcoldict[ind] = col\n conclistbox.selection_clear(0, END)\n\n conclistbox.bind(\"\", delete_conc_lines)\n conclistbox.bind(\"\", delete_reverse_conc_lines)\n conclistbox.bind(\"\", conc_sort)\n conclistbox.bind(\"<%s-minus>\" % key, dec_concfont)\n conclistbox.bind(\"<%s-equal>\" % key, inc_concfont)\n conclistbox.bind(\"<%s-a>\" % key, select_all_conclines)\n conclistbox.bind(\"<%s-s>\" % key, lambda x: concsave())\n conclistbox.bind(\"<%s-e>\" % key, lambda x: conc_export())\n conclistbox.bind(\"<%s-t>\" % key, lambda x: toggle_filenames())\n conclistbox.bind(\"<%s-A>\" % key, select_all_conclines)\n conclistbox.bind(\"<%s-S>\" % key, lambda x: concsave())\n conclistbox.bind(\"<%s-E>\" % key, lambda x: conc_export())\n conclistbox.bind(\"<%s-T>\" % key, lambda x: toggle_filenames())\n conclistbox.bind(\"0\", lambda x: color_conc(colour=0))\n conclistbox.bind(\"1\", lambda x: color_conc(colour=1))\n conclistbox.bind(\"2\", lambda x: color_conc(colour=2))\n conclistbox.bind(\"3\", lambda x: color_conc(colour=3))\n conclistbox.bind(\"4\", lambda x: color_conc(colour=4))\n conclistbox.bind(\"5\", lambda x: color_conc(colour=5))\n conclistbox.bind(\"6\", lambda x: color_conc(colour=6))\n conclistbox.bind(\"7\", lambda x: color_conc(colour=7))\n conclistbox.bind(\"8\", lambda x: color_conc(colour=8))\n conclistbox.bind(\"9\", lambda x: color_conc(colour=9))\n conclistbox.bind(\"0\", lambda x: color_conc(colour=0))\n\n # these were 'generate' and 'edit', but they look ugly right now. the spaces are nice though.\n #lab = StringVar()\n #lab.set('Concordancing: %s' % os.path.basename(corpus_fullpath.get()))\n #Label(tab4, textvariable=lab, font=(\"Helvetica\", 13, \"bold\")).grid(row=1, column=0, padx=20, pady=10, columnspan=5, sticky=W)\n #Label(tab4, text=' ', font=(\"Helvetica\", 13, \"bold\")).grid(row=1, column=9, columnspan=2)\n\n conc_right_button_frame = Frame(tab4)\n conc_right_button_frame.grid(row=1, column=0, padx=(10,0), sticky='N', pady=(5, 0))\n\n # edit conc lines\n conc_left_buts = Frame(conc_right_button_frame)\n conc_left_buts.grid(row=1, column=0, columnspan=6, sticky='W')\n Button(conc_left_buts, text='Delete selected', command=lambda: delete_conc_lines(), ).grid(row=0, column=0, sticky=W)\n Button(conc_left_buts, text='Just selected', command=lambda: delete_reverse_conc_lines(), ).grid(row=0, column=1)\n #Button(conc_left_buts, text='Sort', command=lambda: conc_sort()).grid(row=0, column=4)\n\n def toggle_filenames(*args):\n if isinstance(current_conc[0], str):\n return\n data = current_conc[0]\n add_conc_lines_to_window(data)\n\n def make_df_matching_screen():\n import re\n if type(current_conc[0]) == str:\n return\n df = current_conc[0]\n\n if show_filenames.get() == 0:\n df = df.drop('f', axis=1, errors = 'ignore')\n if show_themes.get() == 0:\n df = df.drop('t', axis=1, errors = 'ignore')\n\n ix_to_keep = []\n lines = conclistbox.get(0, END)\n reg = re.compile(r'^\\s*([0-9]+)')\n for l in lines:\n s = re.search(reg, l)\n ix_to_keep.append(int(s.group(1)))\n df = df.ix[ix_to_keep]\n df = df.reindex(ix_to_keep)\n return df\n\n def concsave():\n name = simpledialog.askstring('Concordance name', 'Choose a name for your concordance lines:')\n if not name or name == '':\n return\n df = make_df_matching_screen()\n all_conc[name] = df\n global conc_saved\n conc_saved = True\n refresh()\n\n def merge_conclines():\n toget = prev_conc_listbox.curselection()\n should_continue = True\n global conc_saved\n if not conc_saved:\n if type(current_conc[0]) != str and len(toget) > 1:\n should_continue = messagebox.askyesno(\"Unsaved data\", \n \"Unsaved concordance lines will be forgotten. Continue?\")\n else:\n should_continue = True\n\n if not should_continue:\n return\n import pandas\n \n dfs = []\n if toget != ():\n if len(toget) < 2:\n for item in toget:\n nm = prev_conc_listbox.get(item)\n dfs.append(all_conc[nm])\n dfs.append(current_conc[0])\n #timestring('Need multiple concordances to merge.' % name)\n #return\n for item in toget:\n nm = prev_conc_listbox.get(item)\n dfs.append(all_conc[nm])\n else:\n \n timestring('Nothing selected to merge.' % name)\n return\n df = pandas.concat(dfs, ignore_index = True)\n should_drop = messagebox.askyesno(\"Remove duplicates\", \n \"Remove duplicate concordance lines?\")\n if should_drop:\n df = df.drop_duplicates(subset = ['l', 'm', 'r'])\n add_conc_lines_to_window(df)\n\n def load_saved_conc():\n should_continue = True\n global conc_saved\n if not conc_saved:\n if type(current_conc[0]) != str:\n should_continue = messagebox.askyesno(\"Unsaved data\", \n \"Unsaved concordance lines will be forgotten. Continue?\")\n else:\n should_continue = True\n if should_continue:\n toget = prev_conc_listbox.curselection()\n if len(toget) > 1:\n timestring('Only one selection allowed for load.' % name)\n return\n if toget != ():\n nm = prev_conc_listbox.get(toget[0])\n df = all_conc[nm]\n add_conc_lines_to_window(df, loading=True, preserve_colour=False)\n else:\n return\n\n \n fourbuts = Frame(conc_right_button_frame)\n fourbuts.grid(row=1, column=6, columnspan=1, sticky='E')\n Button(fourbuts, text='Store as', command=concsave).grid(row=0, column=0)\n Button(fourbuts, text='Remove', command= lambda: remove_one_or_more(window='conc', kind='concordance')).grid(row=0, column=1)\n Button(fourbuts, text='Merge', command=merge_conclines).grid(row=0, column=2)\n Button(fourbuts, text='Load', command=load_saved_conc).grid(row=0, column=3)\n\n showbuts = Frame(conc_right_button_frame)\n showbuts.grid(row=0, column=0, columnspan=6, sticky='w')\n\n show_filenames = IntVar()\n fnbut = Checkbutton(showbuts, text='Filenames', variable=show_filenames, command=toggle_filenames)\n fnbut.grid(row=0, column=4)\n #fnbut.select()\n show_filenames.trace('w', toggle_filenames)\n\n show_subcorpora = IntVar()\n sbcrp = Checkbutton(showbuts, text='Subcorpora', variable=show_subcorpora, command=toggle_filenames)\n sbcrp.grid(row=0, column=3)\n sbcrp.select()\n show_subcorpora.trace('w', toggle_filenames)\n\n show_themes = IntVar()\n themebut = Checkbutton(showbuts, text='Scheme', variable=show_themes, command=toggle_filenames)\n themebut.grid(row=0, column=1)\n #themebut.select()\n show_themes.trace('w', toggle_filenames)\n\n show_speaker = IntVar()\n showspkbut = Checkbutton(showbuts, text='Speakers', variable=show_speaker, command=toggle_filenames)\n showspkbut.grid(row=0, column=5)\n #showspkbut.select()\n show_speaker.trace('w', toggle_filenames)\n\n show_index = IntVar()\n show_s_w_ix = Checkbutton(showbuts, text='Index', variable=show_index, command=toggle_filenames)\n #show_s_w_ix.select()\n show_s_w_ix.grid(row=0, column=2)\n show_index.trace('w', toggle_filenames)\n\n show_df_index = IntVar()\n indbut = Checkbutton(showbuts, text='#', variable=show_df_index, command=toggle_filenames)\n indbut.grid(row=0, column=0)\n indbut.select()\n # disabling because turning index off can cause problems when sorting, etc\n indbut.config(state=DISABLED)\n show_df_index.trace('w', toggle_filenames)\n \n interrobut_conc = Button(showbuts, text='Re-run')\n interrobut_conc.config(command=lambda: runner(interrobut_conc, do_interrogation, conc = True), state=DISABLED)\n interrobut_conc.grid(row=0, column=6, padx=(5,0))\n\n annotation = False\n txt_var = StringVar()\n txt_var_r = StringVar()\n\n def annotate_corpus():\n \"\"\"\n Allow the user to annotate the corpus\n \"\"\"\n\n anno_trans = {'Middle': 'm',\n 'Scheme': 't',\n 'Index': 'index',\n 'Colour': 'q'}\n\n def allow_text(*args):\n \"\"\"\n If the user wants to add text as value, let him/her\n \"\"\"\n if anno_dec.get() == 'Custom':\n txt_box_r.config(state=NORMAL)\n else:\n txt_box_r.config(state=DISABLED)\n\n def go_action(*args):\n \"\"\"\n Do annotation\n \"\"\"\n from corpkit.corpus import Corpus\n corp = Corpus(current_corpus.get(), print_info=False)\n data = current_conc[0]\n chosen = anno_dec.get()\n\n # add colour and scheme to df\n if chosen == 'Scheme':\n themelist = get_list_of_themes(data)\n if any(t != '' for t in themelist):\n data.insert(0, 't', themelist)\n elif chosen == 'Colour':\n colourlist = get_list_of_colours(data)\n if any(t != '' for t in colourlist):\n data.insert(0, 'q', colourlist)\n\n if chosen == 'Tag':\n annotation = txt_box.get()\n elif chosen == 'Custom':\n field = txt_box.get()\n value = txt_box_r.get()\n annotation = {field: value}\n else:\n field = txt_box.get()\n value = anno_trans.get(chosen, chosen)\n annotation = {field: value}\n\n if debug:\n print('Annotation:', annotation)\n\n corp.annotate(data, annotation, dry_run=False)\n timestring('Annotation done.')\n refresh_by_metadata()\n anno_pop.destroy()\n\n from tkinter import Toplevel\n anno_pop = Toplevel()\n #anno_pop.geometry('+400+40')\n anno_pop.title(\"Annotate corpus\")\n anno_pop.wm_attributes('-topmost', 1)\n\n #Label(anno_pop, text='Annotate with:').grid(row=1, column=0, sticky=W)\n anno_dec = StringVar()\n anno_dec.set('Middle')\n annotype = ('Index', 'Position', 'Speaker', 'Colour', 'Scheme', 'Middle', 'Custom', 'Tag')\n anno_lb = OptionMenu(anno_pop, anno_dec, *annotype)\n anno_lb.grid(row=2, column=1, sticky=E)\n\n Label(anno_pop, text='Field:').grid(row=1, column=0)\n Label(anno_pop, text='Value:').grid(row=1, column=1)\n\n txt_box = Entry(anno_pop, textvariable=txt_var, width=10)\n all_text_widgets.append(txt_box)\n txt_box.grid(row=2, column=0)\n\n txt_box_r = Entry(anno_pop, textvariable=txt_var_r, width=22)\n txt_box_r.config(state=DISABLED)\n all_text_widgets.append(txt_box_r)\n txt_box_r.grid(row=3, columnspan=2)\n\n anno_dec.trace(\"w\", allow_text)\n\n do_anno = Button(anno_pop, text='Annotate', command=go_action)\n do_anno.grid(row=4, columnspan=2)\n\n\n def recalc(*args):\n import pandas as pd\n name = simpledialog.askstring('New name', 'Choose a name for the data:')\n if not name:\n return\n else:\n out = current_conc[0].calculate()\n\n all_interrogations[name] = out\n name_of_interro_spreadsheet.set(name)\n i_resultname.set('Interrogation results: %s' % str(name_of_interro_spreadsheet.get()))\n totals_as_df = pd.DataFrame(out.totals, dtype=object)\n if out.results is not None:\n update_spreadsheet(interro_results, out.results, height=340)\n subs = out.results.index\n else:\n update_spreadsheet(interro_results, df_to_show=None, height=340)\n subs = out.totals.index\n\n update_spreadsheet(interro_totals, totals_as_df, height=10)\n\n ind = list(all_interrogations.keys()).index(name_of_interro_spreadsheet.get())\n if ind == 0:\n prev.configure(state=DISABLED)\n else:\n prev.configure(state=NORMAL)\n\n if ind + 1 == len(list(all_interrogations.keys())):\n nex.configure(state=DISABLED)\n else:\n nex.configure(state=NORMAL)\n refresh()\n\n subc_listbox.delete(0, 'end')\n for e in list(subs):\n if e != 'tkintertable-order':\n subc_listbox.insert(END, e)\n timestring('Calculation done. \"%s\" created.' % name)\n note.change_tab(1)\n\n recalc_but = Button(showbuts, text='Calculate', command=recalc)\n recalc_but.config(command=recalc, state=DISABLED)\n recalc_but.grid(row=0, column=7, padx=(5,0))\n\n win = StringVar()\n win.set('Window')\n wind_size = OptionMenu(conc_left_buts, win, *tuple(('Window', '20', '30', '40', \n '50', '60', '70', '80', '90', '100')))\n wind_size.config(width=10)\n wind_size.grid(row=0, column=5)\n win.trace(\"w\", conc_sort)\n\n # possible sort\n sort_vals = ('Index', 'Subcorpus', 'File', 'Speaker', 'Colour', \n 'Scheme', 'Random', 'L5', 'L4', 'L3', 'L2', 'L1', \n 'M1', 'M2', 'M-2', 'M-1', 'R1', 'R2', 'R3', 'R4', 'R5')\n sortval = StringVar()\n sortval.set('Sort')\n prev_sortval = ['None']\n srtkind = OptionMenu(conc_left_buts, sortval, *sort_vals)\n srtkind.config(width=10)\n srtkind.grid(row=0, column=3)\n sortval.trace(\"w\", conc_sort)\n\n # export to csv\n Button(conc_left_buts, text='Export', command=lambda: conc_export()).grid(row=0, column=6)\n\n # annotate\n Button(conc_left_buts, text='Annotate', command=annotate_corpus).grid(row=0, column=7)\n\n store_label = Label(conc_right_button_frame, text='Stored concordances', font=(\"Helvetica\", 13, \"bold\"))\n\n\n prev_conc = Frame(conc_right_button_frame)\n prev_conc.grid(row=0, column=7, rowspan=3, columnspan=2,\n sticky=E, padx=(10,0), pady=(4,0))\n prevcbar = Scrollbar(prev_conc)\n prevcbar.pack(side=RIGHT, fill=Y)\n prev_conc_lb_size = 20\n prev_conc_listbox = Listbox(prev_conc, selectmode=EXTENDED, width=prev_conc_lb_size,\n height=4, relief=SUNKEN, bg='#F4F4F4',\n yscrollcommand=prevcbar.set, exportselection=False)\n prev_conc_listbox.pack()\n cscrollbar.config(command=prev_conc_listbox.yview)\n \n root.update()\n\n # this laziness is dynamic calculation of how far apart the left and right\n # button sets should be in the conc pane. i don't want to go reframing \n # everything, so instead, we figure out the best distance by math\n\n # width of window - width of left buttons - with of prev conc and 'stored concordances' label (approx)\n padd = note_width - showbuts.winfo_width() - (prev_conc.winfo_width() * 2)\n\n # for now, just a guess!\n if padd < 0:\n padd = 250\n\n store_label.grid(row=0, column=6, sticky=E, padx=(padd,0))\n\n ############## ############## ############## ############## ############## \n # MANAGE TAB # # MANAGE TAB # # MANAGE TAB # # MANAGE 'TAB' # # MANAGE TAB # \n ############## ############## ############## ############## ############## \n\n def make_new_project():\n import os\n from corpkit.other import new_project\n reset_everything()\n name = simpledialog.askstring('New project', 'Choose a name for your project:')\n if not name:\n return\n home = os.path.expanduser(\"~\")\n docpath = os.path.join(home, 'Documents')\n if sys.platform == 'darwin':\n the_kwargs = {'message': 'Choose a directory in which to create your new project'}\n else:\n the_kwargs = {}\n fp = filedialog.askdirectory(title = 'New project location',\n initialdir = docpath,\n **the_kwargs)\n if not fp:\n return\n new_proj_basepath.set('New project: \"%s\"' % name)\n new_project(name = name, loc = fp, root=root)\n project_fullpath.set(os.path.join(fp, name))\n os.chdir(project_fullpath.get())\n image_fullpath.set(os.path.join(project_fullpath.get(), 'images'))\n savedinterro_fullpath.set(os.path.join(project_fullpath.get(), 'saved_interrogations'))\n conc_fullpath.set(os.path.join(project_fullpath.get(), 'saved_concordances'))\n corpora_fullpath.set(os.path.join(project_fullpath.get(), 'data'))\n exported_fullpath.set(os.path.join(project_fullpath.get(), 'exported'))\n log_fullpath.set(os.path.join(project_fullpath.get(), 'logs'))\n addbut.config(state=NORMAL)\n\n open_proj_basepath.set('Loaded: \"%s\"' % name)\n save_config()\n root.title(\"corpkit: %s\" % os.path.basename(project_fullpath.get()))\n #load_project(path = os.path.join(fp, name))\n timestring('Project \"%s\" created.' % name)\n note.focus_on(tab0)\n update_available_corpora()\n\n def get_saved_results(kind='interrogation', add_to=False):\n from corpkit.other import load_all_results\n if kind == 'interrogation':\n datad = savedinterro_fullpath.get()\n elif kind == 'concordance':\n datad = conc_fullpath.get()\n elif kind == 'image':\n datad = image_fullpath.get()\n if datad == '':\n timestring('No project loaded.')\n if kind == 'image':\n image_list = sorted([f for f in os.listdir(image_fullpath.get()) if f.endswith('.png')])\n for iname in image_list:\n if iname.replace('.png', '') not in all_images:\n all_images.append(iname.replace('.png', ''))\n if len(image_list) > 0:\n nbut.config(state=NORMAL)\n else:\n if kind == 'interrogation':\n r = load_all_results(data_dir=datad, root=root, note=note)\n else:\n r = load_all_results(data_dir=datad, root=root, note=note)\n if r is not None:\n for name, loaded in list(r.items()):\n if kind == 'interrogation':\n if isinstance(loaded, dict):\n for subname, subloaded in list(loaded.items()):\n all_interrogations[name + '-' + subname] = subloaded\n else:\n all_interrogations[name] = loaded\n else:\n all_conc[name] = loaded\n if len(list(all_interrogations.keys())) > 0:\n nex.configure(state=NORMAL)\n refresh()\n \n def recentchange(*args):\n \"\"\"if user clicks a recent project, open it\"\"\"\n if recent_project.get() != '':\n project_fullpath.set(recent_project.get())\n load_project(path=project_fullpath.get())\n\n def projchange(*args):\n \"\"\"if user changes projects, add to recent list and save prefs\"\"\"\n if project_fullpath.get() != '' and 'Contents/MacOS' not in project_fullpath.get():\n in_a_project.set(1)\n if project_fullpath.get() not in most_recent_projects:\n most_recent_projects.append(project_fullpath.get())\n save_tool_prefs(printout=False)\n #update_available_corpora()\n else:\n in_a_project.set(0)\n\n # corpus path setter\n savedinterro_fullpath = StringVar()\n savedinterro_fullpath.set('')\n data_basepath = StringVar()\n data_basepath.set('Select data directory')\n in_a_project = IntVar()\n in_a_project.set(0)\n project_fullpath = StringVar()\n project_fullpath.set(rd)\n project_fullpath.trace(\"w\", projchange)\n recent_project = StringVar()\n recent_project.set('')\n recent_project.trace(\"w\", recentchange)\n conc_fullpath = StringVar()\n conc_fullpath.set('')\n exported_fullpath = StringVar()\n exported_fullpath.set('') \n log_fullpath = StringVar()\n import os\n home = os.path.expanduser(\"~\")\n try:\n os.makedirs(os.path.join(home, 'corpkit-logs'))\n except:\n pass\n log_fullpath.set(os.path.join(home, 'corpkit-logs')) \n image_fullpath = StringVar()\n image_fullpath.set('')\n image_basepath = StringVar()\n image_basepath.set('Select image directory')\n corpora_fullpath = StringVar()\n corpora_fullpath.set('')\n\n def imagedir_modified(*args):\n import matplotlib\n matplotlib.rcParams['savefig.directory'] = image_fullpath.get()\n\n image_fullpath.trace(\"w\", imagedir_modified)\n\n def data_getdir():\n import os\n fp = filedialog.askdirectory(title = 'Open data directory')\n if not fp:\n return\n savedinterro_fullpath.set(fp)\n data_basepath.set('Saved data: \"%s\"' % os.path.basename(fp))\n #sel_corpus_button.set('Selected corpus: \"%s\"' % os.path.basename(newc))\n #fs = sorted([d for d in os.listdir(fp) if os.path.isfile(os.path.join(fp, d))])\n timestring('Set data directory: %s' % os.path.basename(fp))\n\n def image_getdir(nodialog = False):\n import os\n fp = filedialog.askdirectory()\n if not fp:\n return\n image_fullpath.set(fp)\n image_basepath.set('Images: \"%s\"' % os.path.basename(fp))\n timestring('Set image directory: %s' % os.path.basename(fp))\n\n def save_one_or_more(kind = 'interrogation'):\n sel_vals = manage_listbox_vals\n if len(sel_vals) == 0:\n timestring('Nothing selected to save.')\n return\n from corpkit.other import save\n import os\n saved = 0\n existing = 0\n # for each filename selected\n for i in sel_vals:\n safename = urlify(i) + '.p'\n # make sure not already there\n if safename not in os.listdir(savedinterro_fullpath.get()):\n if kind == 'interrogation':\n savedata = all_interrogations[i]\n savedata.query.pop('root', None)\n savedata.query.pop('note', None)\n save(savedata, safename, savedir = savedinterro_fullpath.get())\n else:\n savedata = all_conc[i]\n try:\n savedata.query.pop('root', None)\n savedata.query.pop('note', None)\n except:\n pass\n save(savedata, safename, savedir = conc_fullpath.get())\n saved += 1\n else:\n existing += 1\n timestring('%s already exists in %s.' % (urlify(i), os.path.basename(savedinterro_fullpath.get())))\n if saved == 1 and existing == 0:\n timestring('%s saved.' % sel_vals[0])\n else:\n if existing == 0:\n timestring('%d %ss saved.' % (len(sel_vals), kind))\n else:\n timestring('%d %ss saved, %d already existed' % (saved, kind, existing))\n refresh()\n manage_callback()\n\n\n def remove_one_or_more(window=False, kind ='interrogation'):\n sel_vals = manage_listbox_vals\n if window is not False:\n toget = prev_conc_listbox.curselection()\n sel_vals = [prev_conc_listbox.get(toget)]\n if len(sel_vals) == 0:\n timestring('No interrogations selected.')\n return\n for i in sel_vals:\n try:\n if kind == 'interrogation':\n del all_interrogations[i]\n else:\n del all_conc[i]\n except:\n pass\n if len(sel_vals) == 1:\n timestring('%s removed.' % sel_vals[0])\n else:\n timestring('%d interrogations removed.' % len(sel_vals))\n if kind == 'image':\n refresh_images()\n refresh()\n manage_callback()\n\n def del_one_or_more(kind = 'interrogation'):\n sel_vals = manage_listbox_vals\n ext = '.p'\n if kind == 'interrogation':\n p = savedinterro_fullpath.get()\n elif kind == 'image':\n p = image_fullpath.get()\n ext = '.png'\n else:\n p = conc_fullpath.get()\n if len(sel_vals) == 0:\n timestring('No interrogations selected.')\n return\n import os\n result = messagebox.askquestion(\"Are You Sure?\", \"Permanently delete the following files:\\n\\n %s\" % '\\n '.join(sel_vals), icon='warning')\n if result == 'yes':\n for i in sel_vals:\n if kind == 'interrogation':\n del all_interrogations[i]\n os.remove(os.path.join(p, i + ext))\n elif kind == 'concordance':\n del all_conc[i]\n os.remove(os.path.join(p, i + ext))\n else:\n all_images.remove(i)\n os.remove(os.path.join(p, i + ext))\n if len(sel_vals) == 1:\n timestring('%s deleted.' % sel_vals[0])\n else:\n timestring('%d %ss deleted.' % (kind, len(sel_vals)))\n refresh()\n manage_callback()\n\n def urlify(s):\n \"Turn title into filename\"\n import re\n #s = s.lower()\n s = re.sub(r\"[^\\w\\s-]\", '', s)\n s = re.sub(r\"\\s+\", '-', s)\n s = re.sub(r\"-(textbf|emph|textsc|textit)\", '-', s)\n return s\n\n def rename_one_or_more(kind = 'interrogation'):\n ext = '.p'\n sel_vals = manage_listbox_vals\n if kind == 'interrogation':\n p = savedinterro_fullpath.get()\n elif kind == 'image':\n p = image_fullpath.get()\n ext = '.png'\n else:\n p = conc_fullpath.get()\n if len(sel_vals) == 0:\n timestring('No items selected.')\n return\n import os\n permanently = True\n\n if permanently:\n perm_text='permanently '\n else:\n perm_text=''\n for i in sel_vals:\n answer = simpledialog.askstring('Rename', 'Choose a new name for \"%s\":' % i, initialvalue = i)\n if answer is None or answer == '':\n return\n else:\n if kind == 'interrogation':\n all_interrogations[answer] = all_interrogations.pop(i)\n elif kind == 'image':\n ind = all_images.index(i)\n all_images.remove(i)\n all_images.insert(ind, answer)\n else:\n all_conc[answer] = all_conc.pop(i)\n if permanently:\n oldf = os.path.join(p, i + ext)\n if os.path.isfile(oldf):\n newf = os.path.join(p, urlify(answer) + ext)\n os.rename(oldf, newf)\n if kind == 'interrogation':\n if name_of_interro_spreadsheet.get() == i:\n name_of_interro_spreadsheet.set(answer)\n i_resultname.set('Interrogation results: %s' % str(answer))\n #update_spreadsheet(interro_results, all_interrogations[answer].results)\n if name_of_o_ed_spread.get() == i:\n name_of_o_ed_spread.set(answer)\n #update_spreadsheet(o_editor_results, all_interrogations[answer].results)\n if name_of_n_ed_spread.get() == i:\n name_of_n_ed_spread.set(answer)\n #update_spreadsheet(n_editor_results, all_interrogations[answer].results)\n \n if kind == 'image':\n refresh_images()\n\n if len(sel_vals) == 1:\n timestring('%s %srenamed as %s.' % (sel_vals[0], perm_text, answer))\n else:\n timestring('%d items %srenamed.' % (len(sel_vals), perm_text))\n refresh()\n manage_callback()\n\n def export_interrogation(kind = 'interrogation'):\n sel_vals = manage_listbox_vals\n \"\"\"save dataframes and options to file\"\"\"\n import os\n import pandas\n\n fp = False\n\n for i in sel_vals:\n answer = simpledialog.askstring('Export data', 'Choose a save name for \"%s\":' % i, initialvalue = i)\n if answer is None or answer == '':\n return\n if kind != 'interrogation':\n conc_export(data = i)\n else: \n data = all_interrogations[i]\n keys = list(data.__dict__.keys())\n if in_a_project.get() == 0:\n if sys.platform == 'darwin':\n the_kwargs = {'message': 'Choose save directory for exported interrogation'}\n else:\n the_kwargs = {}\n fp = filedialog.askdirectory(title = 'Choose save directory', **the_kwargs)\n if fp == '':\n return\n else:\n fp = project_fullpath.get()\n os.makedirs(os.path.join(exported_fullpath.get(), answer))\n for k in keys:\n if k == 'results':\n if data.results is not None:\n tkdrop = data.results.drop('tkintertable-order', errors = 'ignore')\n tkdrop.to_csv(os.path.join(exported_fullpath.get(), answer, 'results.csv'), sep ='\\t', encoding = 'utf-8')\n if k == 'totals':\n if data.totals is not None:\n tkdrop = data.totals.drop('tkintertable-order', errors = 'ignore')\n tkdrop.to_csv(os.path.join(exported_fullpath.get(), answer, 'totals.csv'), sep ='\\t', encoding = 'utf-8')\n if k == 'query':\n if getattr(data, 'query', None):\n pandas.DataFrame(list(data.query.values()), index = list(data.query.keys())).to_csv(os.path.join(exported_fullpath.get(), answer, 'query.csv'), sep ='\\t', encoding = 'utf-8')\n #if k == 'table':\n # if 'table' in list(data.__dict__.keys()) and data.table:\n # pandas.DataFrame(list(data.query.values()), index = list(data.query.keys())).to_csv(os.path.join(exported_fullpath.get(), answer, 'table.csv'), sep ='\\t', encoding = 'utf-8')\n if fp:\n timestring('Results exported to %s' % (os.path.join(os.path.basename(exported_fullpath.get()), answer)))\n\n def reset_everything():\n # result names\n i_resultname.set('Interrogation results:')\n resultname.set('Results to edit:')\n editoname.set('Edited results:')\n savedplot.set('View saved images: ')\n open_proj_basepath.set('Open project')\n corpus_fullpath.set('')\n current_corpus.set('')\n corpora_fullpath.set('')\n project_fullpath.set(rd)\n #special_queries.set('Off')\n\n # spreadsheets\n update_spreadsheet(interro_results, df_to_show=None, height=340)\n update_spreadsheet(interro_totals, df_to_show=None, height=10)\n update_spreadsheet(o_editor_results, df_to_show=None, height=140)\n update_spreadsheet(o_editor_totals, df_to_show=None, height=10)\n update_spreadsheet(n_editor_results, df_to_show=None, height=140)\n update_spreadsheet(n_editor_totals, df_to_show=None, height=10)\n\n # interrogations\n for e in list(all_interrogations.keys()):\n del all_interrogations[e]\n # another way:\n all_interrogations.clear()\n # subcorpora listbox\n subc_listbox.delete(0, END)\n subc_listbox_build.delete(0, END)\n # concordance\n conclistbox.delete(0, END)\n # every interrogation\n #every_interro_listbox.delete(0, END)\n # every conc\n #ev_conc_listbox.delete(0, END)\n prev_conc_listbox.delete(0, END)\n # images\n #every_image_listbox.delete(0, END)\n every_interrogation['menu'].delete(0, 'end')\n #pick_subcorpora['menu'].delete(0, 'end')\n # speaker listboxes\n speaker_listbox.delete(0, 'end')\n #speaker_listbox_conc.delete(0, 'end')\n # keys\n for e in list(all_conc.keys()):\n del all_conc[e]\n for e in all_images:\n all_images.remove(e)\n #update_available_corpora(delete = True)\n refresh()\n\n def convert_speakdict_to_string(dictionary):\n \"\"\"turn speaker info dict into a string for configparser\"\"\"\n if not dictionary:\n return 'none'\n out = []\n for k, v in list(dictionary.items()):\n out.append('%s:%s' % (k, ','.join([i.replace(',', '').replace(':', '').replace(';', '') for i in v])))\n if not out:\n return 'none'\n else:\n return ';'.join(out)\n\n def parse_speakdict(string):\n \"\"\"turn configparser's speaker info back into a dict\"\"\"\n if string is 'none' or not string:\n return {}\n redict = {}\n corps = string.split(';')\n for c in corps:\n try:\n name, vals = c.split(':')\n except ValueError:\n continue\n vs = vals.split(',')\n redict[name] = vs\n return redict\n\n def load_custom_list_json():\n import json\n f = os.path.join(project_fullpath.get(), 'custom_wordlists.txt')\n if os.path.isfile(f):\n data = json.loads(open(f).read())\n for k, v in data.items():\n if k not in list(custom_special_dict.keys()):\n custom_special_dict[k] = v\n if k not in list(saved_special_dict.keys()):\n saved_special_dict[k] = v\n\n def dump_custom_list_json():\n import json\n f = os.path.join(project_fullpath.get(), 'custom_wordlists.txt')\n with open(f, 'w') as fo:\n fo.write(json.dumps(saved_special_dict))\n\n def load_config():\n \"\"\"use configparser to get project settings\"\"\"\n import os\n try:\n import configparser\n except ImportError:\n import ConfigParser as configparser\n Config = configparser.ConfigParser()\n f = os.path.join(project_fullpath.get(), 'settings.ini')\n Config.read(f)\n # errors here\n plot_style.set(conmap(Config, \"Visualise\")['plot style'])\n texuse.set(conmap(Config, \"Visualise\")['use tex'])\n x_axis_l.set(conmap(Config, \"Visualise\")['x axis title'])\n chart_cols.set(conmap(Config, \"Visualise\")['colour scheme'])\n rel_corpuspath = conmap(Config, \"Interrogate\")['corpus path']\n try:\n files_as_subcorpora.set(conmap(Config, \"Interrogate\")['treat files as subcorpora'])\n except KeyError:\n files_as_subcorpora.set(False)\n if rel_corpuspath:\n current_corpus.get(relcorpuspath)\n #corpus_fullpath.set(corpa)\n \n spk = conmap(Config, \"Interrogate\")['speakers']\n corpora_speakers = parse_speakdict(spk)\n for i, v in list(corpora_speakers.items()):\n corpus_names_and_speakers[i] = v\n fsize.set(conmap(Config, \"Concordance\")['font size'])\n # window setting causes conc_sort to run, causing problems.\n #win.set(conmap(Config, \"Concordance\")['window'])\n #kind_of_dep.set(conmap(Config, 'Interrogate')['dependency type'])\n #conc_kind_of_dep.set(conmap(Config, \"Concordance\")['dependency type'])\n cods = conmap(Config, \"Concordance\")['coding scheme']\n if cods is None:\n for _, val in list(entryboxes.items()):\n val.set('')\n else:\n codsep = cods.split(',')\n for (box, val), cod in zip(list(entryboxes.items()), codsep):\n val.set(cod)\n if corpus_fullpath.get():\n subdrs = [d for d in os.listdir(corpus_fullpath.get()) if os.path.isdir(os.path.join(corpus_fullpath.get(),d))]\n else:\n subdrs = []\n if len(subdrs) == 0:\n charttype.set('bar')\n refresh()\n\n def load_project(path=False):\n import os\n if path is False:\n if sys.platform == 'darwin':\n the_kwargs = {'message': 'Choose project directory'}\n else:\n the_kwargs = {}\n fp = filedialog.askdirectory(title='Open project',\n **the_kwargs)\n else:\n fp = os.path.abspath(path)\n\n if not fp or fp == '':\n return\n reset_everything()\n\n image_fullpath.set(os.path.join(fp, 'images'))\n savedinterro_fullpath.set(os.path.join(fp, 'saved_interrogations'))\n conc_fullpath.set(os.path.join(fp, 'saved_concordances'))\n exported_fullpath.set(os.path.join(fp, 'exported'))\n corpora_fullpath.set(os.path.join(fp, 'data'))\n log_fullpath.set(os.path.join(fp, 'logs'))\n\n if not os.path.isdir(savedinterro_fullpath.get()):\n timestring('Selected folder does not contain corpkit project.') \n return \n\n project_fullpath.set(fp)\n\n f = os.path.join(project_fullpath.get(), 'settings.ini')\n if os.path.isfile(f):\n load_config()\n\n os.chdir(fp)\n list_of_corpora = update_available_corpora()\n addbut.config(state=NORMAL) \n \n get_saved_results(kind='interrogation')\n get_saved_results(kind='concordance')\n get_saved_results(kind='image')\n open_proj_basepath.set('Loaded: \"%s\"' % os.path.basename(fp))\n\n # reset tool:\n root.title(\"corpkit: %s\" % os.path.basename(fp))\n\n # check for parsed corpora\n if not current_corpus.get():\n parsed_corp = [d for d in list_of_corpora if d.endswith('-parsed')]\n # select \n first = False\n if len(parsed_corp) > 0:\n first = parsed_corp[0]\n if first:\n corpus_fullpath.set(os.path.abspath(first))\n name = make_corpus_name_from_abs(project_fullpath.get(), first)\n current_corpus.set(name)\n else:\n corpus_fullpath.set('')\n # no corpora, so go to build...\n note.focus_on(tab0)\n \n if corpus_fullpath.get() != '':\n try:\n subdrs = sorted([d for d in os.listdir(corpus_fullpath.get()) if os.path.isdir(os.path.join(corpus_fullpath.get(),d))])\n except FileNotFoundError:\n subdrs = []\n else:\n subdrs = [] \n\n #lab.set('Concordancing: %s' % corpus_name)\n #pick_subcorpora['menu'].delete(0, 'end')\n\n #if len(subdrs) > 0:\n # pick_subcorpora['menu'].add_command(label='all', command=_setit(subc_pick, 'all'))\n # pick_subcorpora.config(state=NORMAL)\n # for choice in subdrs:\n # pick_subcorpora['menu'].add_command(label=choice, command=_setit(subc_pick, choice))\n #else:\n # pick_subcorpora.config(state=NORMAL)\n # pick_subcorpora['menu'].add_command(label='None', command=_setit(subc_pick, 'None'))\n # pick_subcorpora.config(state=DISABLED)\n timestring('Project \"%s\" opened.' % os.path.basename(fp))\n note.progvar.set(0)\n \n #if corpus_name in list(corpus_names_and_speakers.keys()):\n refresh_by_metadata()\n #speakcheck.config(state=NORMAL)\n #else:\n # pass\n #speakcheck.config(state=DISABLED)\n\n load_custom_list_json()\n\n def view_query(kind=False):\n if len(manage_listbox_vals) == 0:\n return\n if len(manage_listbox_vals) > 1:\n timestring('Can only view one interrogation at a time.')\n return\n\n global frame_to_the_right\n frame_to_the_right = Frame(manage_pop)\n frame_to_the_right.grid(column=2, row=0, rowspan = 6)\n\n Label(frame_to_the_right, text='Query information', font=(\"Helvetica\", 13, \"bold\")).grid(sticky=W, row=0, column=0, padx=(10,0))\n mlb = Table(frame_to_the_right, ['Option', 'Value'],\n column_weights=[1, 1], height=70, width=30)\n mlb.grid(sticky=N, column=0, row=1)\n for i in mlb._mlb.listboxes:\n i.config(height=29)\n\n mlb.columnconfig('Option', background='#afa')\n mlb.columnconfig('Value', background='#efe')\n\n q_dict = dict(all_interrogations[manage_listbox_vals[0]].query)\n mlb.clear()\n #show_query_vals.delete(0, 'end')\n flipped_trans = {v: k for k, v in list(transdict.items())}\n \n for d in ['dataframe1', 'dataframe2']:\n q_dict.pop(d, None)\n\n for k, v in sorted(q_dict.items()):\n try:\n if isinstance(v, (int, float)) and v == 0:\n v = '0'\n if v is None:\n v == 'None'\n if not v:\n v = 'False'\n if v is True:\n v = 'True'\n # could be bad with threshold etc\n if v == 1:\n v = 'True'\n except:\n pass\n mlb.append([k, v])\n\n if q_dict.get('query'):\n qubox = Text(frame_to_the_right, font=(\"Courier New\", 14), relief=SUNKEN,\n wrap=WORD, width=40, height=5, undo=True)\n qubox.grid(column=0, row=2, rowspan = 1, padx=(10,0))\n qubox.delete(1.0, END)\n qubox.insert(END, q_dict['query'])\n manage_box['qubox'] = qubox\n bind_textfuncts_to_widgets([qubox])\n else:\n try:\n manage_box['qubox'].destroy()\n except:\n pass\n\n manage_listbox_vals = []\n def onselect_manage(evt):\n # remove old vals\n for i in manage_listbox_vals:\n manage_listbox_vals.pop()\n wx = evt.widget\n indices = wx.curselection()\n for index in indices:\n value = wx.get(index)\n if value not in manage_listbox_vals:\n manage_listbox_vals.append(value)\n\n new_proj_basepath = StringVar()\n new_proj_basepath.set('New project')\n open_proj_basepath = StringVar()\n open_proj_basepath.set('Open project')\n\n the_current_kind = StringVar()\n\n def manage_popup():\n from tkinter import Toplevel\n global manage_pop\n manage_pop = Toplevel()\n manage_pop.geometry('+400+40')\n manage_pop.title(\"Manage data: %s\" % os.path.basename(project_fullpath.get()))\n manage_pop.wm_attributes('-topmost', 1)\n\n manage_what = StringVar()\n manage_what.set('Manage: ')\n #Label(manage_pop, textvariable=manage_what).grid(row=0, column=0, sticky='W', padx=(5, 0))\n\n manag_frame = Frame(manage_pop, height=30)\n manag_frame.grid(column=0, row=1, rowspan = 1, columnspan=2, sticky='NW', padx=10)\n manage_scroll = Scrollbar(manag_frame)\n manage_scroll.pack(side=RIGHT, fill=Y)\n manage_listbox = Listbox(manag_frame, selectmode = SINGLE, height=30, width=30, relief=SUNKEN, bg='#F4F4F4',\n yscrollcommand=manage_scroll.set, exportselection=False)\n manage_listbox.pack(fill=BOTH)\n manage_listbox.select_set(0)\n manage_scroll.config(command=manage_listbox.yview) \n xx = manage_listbox.bind('<>', onselect_manage)\n # default: w option\n manage_listbox.select_set(0)\n the_current_kind.set('interrogation')\n #gtsv = StringVar()\n #gtsv.set('Get saved') \n #getbut = Button(manage_pop, textvariable=gtsv, command=lambda: get_saved_results(), width=22)\n #getbut.grid(row=2, column=0, columnspan=2)\n\n manage_type = StringVar()\n manage_type.set('Interrogations')\n\n #Label(manage_pop, text='Save selected: ').grid(sticky=E, row=6, column=1)\n savebut = Button(manage_pop, text='Save', command=lambda: save_one_or_more(kind = the_current_kind.get()))\n savebut.grid(padx=15, sticky=W, column=0, row=3)\n viewbut = Button(manage_pop, text='View', command=lambda: view_query(kind = the_current_kind.get()))\n viewbut.grid(padx=15, sticky=W, column=0, row=4)\n renamebut = Button(manage_pop, text='Rename', command=lambda: rename_one_or_more(kind = the_current_kind.get()))\n renamebut.grid(padx=15, sticky=W, column=0, row=5)\n\n #Checkbutton(manage_pop, text=\"Permanently\", variable=perm, onvalue=True, offvalue=False).grid(column=1, row=16, padx=15, sticky=W)\n exportbut = Button(manage_pop, text='Export', command=lambda: export_interrogation(kind = the_current_kind.get()))\n exportbut.grid(padx=15, sticky=E, column=1, row=3)\n #Label(manage_pop, text='Remove selected: '()).grid(padx=15, sticky=W, row=4, column=0)\n removebut = Button(manage_pop, text='Remove', command= lambda: remove_one_or_more(kind = the_current_kind.get()))\n removebut.grid(padx=15, sticky=E, column=1, row=4)\n #Label(manage_pop, text='Delete selected: '()).grid(padx=15, sticky=E, row=5, column=1)\n deletebut = Button(manage_pop, text='Delete', command=lambda: del_one_or_more(kind = the_current_kind.get()))\n deletebut.grid(padx=15, sticky=E, column=1, row=5)\n\n to_manage = OptionMenu(manage_pop, manage_type, *tuple(('Interrogations', 'Concordances', 'Images')))\n to_manage.config(width=32, justify=CENTER)\n to_manage.grid(row=0, column=0, columnspan=2)\n\n def managed(*args):\n #vals = [i.get() for i in butvar.values() if i.get() is not False and i.get() != 0 and i.get() != '0']\n #vals = sorted(vals, key=lambda x:orders[x])\n #the_opts = ','.join(vals)]\n manage_pop.destroy()\n try:\n del manage_callback\n except:\n pass\n\n global manage_callback\n def manage_callback(*args):\n import os\n \"\"\"show correct listbox, enable disable buttons below\"\"\"\n # set text\n #manage_what.set('Manage %s' % manage_type.get().lower())\n #gtsv.set('Get saved %s' % manage_type.get().lower())\n # set correct action for buttons\n the_current_kind.set(manage_type.get().lower().rstrip('s'))\n #get_saved_results(kind = the_current_kind.get())\n # enable all buttons\n #getbut.config(state=NORMAL)\n #try:\n savebut.config(state=NORMAL)\n viewbut.config(state=NORMAL)\n renamebut.config(state=NORMAL)\n exportbut.config(state=NORMAL)\n removebut.config(state=NORMAL)\n deletebut.config(state=NORMAL)\n manage_listbox.delete(0, 'end')\n if the_current_kind.get() == 'interrogation':\n the_path = savedinterro_fullpath.get()\n the_ext = '.p'\n list_of_entries = list(all_interrogations.keys())\n elif the_current_kind.get() == 'concordance':\n the_path = conc_fullpath.get()\n the_ext = '.p'\n list_of_entries = list(all_conc.keys())\n viewbut.config(state=DISABLED)\n try:\n frame_to_the_right.destroy()\n except:\n pass\n elif the_current_kind.get() == 'image':\n the_path = image_fullpath.get()\n the_ext = '.png'\n refresh_images()\n list_of_entries = all_images\n viewbut.config(state=DISABLED)\n savebut.config(state=DISABLED)\n exportbut.config(state=DISABLED)\n removebut.config(state=DISABLED)\n try:\n frame_to_the_right.destroy()\n except:\n pass\n for datum in list_of_entries:\n manage_listbox.insert(END, datum)\n color_saved(manage_listbox, the_path, '#ccebc5', '#fbb4ae', ext = the_ext)\n\n manage_type.trace(\"w\", manage_callback)\n\n manage_type.set('Interrogations')\n\n ############## ############## ############## ############## ############## \n # BUILD TAB # # BUILD TAB # # BUILD TAB # # BUILD TAB # # BUILD TAB # \n ############## ############## ############## ############## ############## \n \n from corpkit.build import download_large_file, get_corpus_filepaths, \\\n check_jdk, parse_corpus, move_parsed_files, corenlp_exists\n\n def create_tokenised_text():\n from corpkit.corpus import Corpus\n note.progvar.set(0)\n parser_options('tokenise')\n root.wait_window(poptions)\n if not clicked_done.get():\n return\n #tokbut.config(state=DISABLED)\n #tokbut = Button(tab0, textvariable=tokenise_button_text, command=ignore, width=33)\n #tokbut.grid(row=6, column=0, sticky=W)\n unparsed_corpus_path = corpus_fullpath.get()\n #filelist, _ = get_corpus_filepaths(project_fullpath.get(), unparsed_corpus_path)\n corp = Corpus(unparsed_corpus_path, print_info=False)\n parsed = corp.tokenise(postag=tokenise_pos,\n lemmatise=tokenise_lem,\n root=root, \n stdout=sys.stdout, \n note=note,\n nltk_data_path=nltk_data_path,\n speaker_segmentation=speakseg.get(),\n metadata=parse_with_metadata.get())\n #corpus_fullpath.set(outdir)\n outdir = parsed.path\n current_corpus.set(parsed.name)\n subdrs = [d for d in os.listdir(corpus_fullpath.get()) if os.path.isdir(os.path.join(corpus_fullpath.get(),d))]\n if len(subdrs) == 0:\n charttype.set('bar')\n #basepath.set(os.path.basename(outdir))\n #if len([f for f in os.listdir(outdir) if f.endswith('.p')]) > 0:\n timestring('Corpus parsed and ready to interrogate: \"%s\"' % os.path.basename(outdir))\n #else:\n #timestring('Error: no files created in \"%s\"' % os.path.basename(outdir))\n update_available_corpora()\n\n def create_parsed_corpus():\n import os\n import re\n import corpkit\n from corpkit.corpus import Corpus\n from corpkit.process import get_corenlp_path\n\n parser_options('parse')\n root.wait_window(poptions)\n if not clicked_done.get():\n return\n\n unparsed_corpus_path = corpus_fullpath.get()\n unparsed = Corpus(unparsed_corpus_path, print_info=False)\n note.progvar.set(0)\n unparsed_corpus_path = corpus_fullpath.get()\n corenlppath.set(get_corenlp_path(corenlppath.get()))\n if not corenlppath.get() or corenlppath.get() == 'None':\n downstall_nlp = messagebox.askyesno(\"CoreNLP not found.\", \n \"CoreNLP parser not found. Download/install it?\")\n if not downstall_nlp:\n timestring('Cannot parse data without Stanford CoreNLP.')\n return\n jdk = check_jdk()\n if jdk is False:\n downstall_jdk = messagebox.askyesno(\"Java JDK\", \"You need Java JDK 1.8 to use CoreNLP.\\n\\nHit 'yes' to open web browser at download link. Once installed, corpkit should resume automatically\")\n if downstall_jdk:\n import webbrowser\n webbrowser.open_new('http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html')\n import time\n timestring('Waiting for Java JDK 1.8 installation to complete.')\n while jdk is False:\n jdk = check_jdk()\n timestring('Waiting for Java JDK 1.8 installation to complete.')\n time.sleep(5)\n else:\n timestring('Cannot parse data without Java JDK 1.8.')\n return\n\n parsed = unparsed.parse(speaker_segmentation=speakseg.get(),\n proj_path=project_fullpath.get(),\n copula_head=True,\n multiprocess=False,\n corenlppath=corenlppath.get(),\n operations=parser_opts.get(),\n root=root, \n stdout=sys.stdout, \n note=note,\n memory_mb=parser_memory.get(),\n metadata=parse_with_metadata.get())\n if not parsed:\n print('Error during parsing.')\n\n sys.stdout = note.redir\n current_corpus.set(parsed.name)\n\n subdrs = [d for d in os.listdir(corpus_fullpath.get()) if \\\n os.path.isdir(os.path.join(corpus_fullpath.get(), d))]\n if len(subdrs) == 0:\n charttype.set('bar')\n\n update_available_corpora()\n timestring('Corpus parsed and ready to interrogate: \"%s\"' % parsed.name)\n\n parse_button_text=StringVar()\n parse_button_text.set('Create parsed corpus')\n\n tokenise_button_text=StringVar()\n tokenise_button_text.set('Create tokenised corpus')\n\n path_to_new_unparsed_corpus = StringVar()\n path_to_new_unparsed_corpus.set('')\n\n add_corpus = StringVar()\n add_corpus.set('')\n add_corpus_button = StringVar()\n add_corpus_button.set('Add corpus%s' % add_corpus.get())\n\n selected_corpus_has_no_subcorpora = IntVar()\n selected_corpus_has_no_subcorpora.set(0)\n\n def add_subcorpora_to_build_box(path_to_corpus):\n if not path_to_corpus:\n return\n import os\n subc_listbox_build.configure(state=NORMAL)\n subc_listbox_build.delete(0, 'end')\n sub_corpora = [d for d in os.listdir(path_to_corpus) if os.path.isdir(os.path.join(path_to_corpus, d))]\n if len(sub_corpora) == 0:\n selected_corpus_has_no_subcorpora.set(1)\n subc_listbox_build.bind('<>', onselect_subc_build)\n subc_listbox_build.insert(END, 'No subcorpora found.')\n subc_listbox_build.configure(state=DISABLED)\n else:\n selected_corpus_has_no_subcorpora.set(0)\n for e in sub_corpora:\n subc_listbox_build.insert(END, e)\n onselect_subc_build()\n\n def select_corpus():\n \"\"\"selects corpus for viewing/parsing\n ---not used anymore\"\"\"\n from os.path import join as pjoin\n from os.path import basename as bn\n #parse_button_text.set('Parse: \"%s\"' % bn(unparsed_corpus_path))\n tokenise_button_text.set('Tokenise: \"%s\"' % bn(unparsed_corpus_path))\n path_to_new_unparsed_corpus.set(unparsed_corpus_path)\n #add_corpus_button.set('Added: %s' % bn(unparsed_corpus_path))\n where_to_put_corpus = pjoin(project_fullpath.get(), 'data') \n sel_corpus.set(unparsed_corpus_path)\n #sel_corpus_button.set('Selected: \"%s\"' % bn(unparsed_corpus_path))\n parse_button_text.set('Parse: \"%s\"' % bn(unparsed_corpus_path))\n add_subcorpora_to_build_box(unparsed_corpus_path)\n timestring('Selected corpus: \"%s\"' % bn(unparsed_corpus_path))\n\n def getcorpus():\n \"\"\"copy unparsed texts to project folder\"\"\"\n import shutil\n import os\n from corpkit.process import saferead\n home = os.path.expanduser(\"~\")\n docpath = os.path.join(home, 'Documents')\n if sys.platform == 'darwin':\n the_kwargs = {'message': 'Select your corpus of unparsed text files.'}\n else:\n the_kwargs = {}\n fp = filedialog.askdirectory(title = 'Path to unparsed corpus',\n initialdir = docpath,\n **the_kwargs)\n where_to_put_corpus = os.path.join(project_fullpath.get(), 'data')\n newc = os.path.join(where_to_put_corpus, os.path.basename(fp))\n try:\n shutil.copytree(fp, newc)\n timestring('Corpus copied to project folder.')\n except OSError:\n if os.path.basename(fp) == '':\n return\n timestring('\"%s\" already exists in project.' % os.path.basename(fp)) \n return\n from corpkit.build import folderise, can_folderise\n if can_folderise(newc):\n do_folderise = messagebox.askyesno(\"No subcorpora found\", \n \"Your corpus contains multiple files, but no subfolders. \" \\\n \"Would you like to treat each file as a subcorpus?\")\n if do_folderise:\n folderise(newc)\n timestring('Turned files into subcorpora.')\n # encode and rename files\n for (rootdir, d, fs) in os.walk(newc):\n for f in fs:\n fpath = os.path.join(rootdir, f)\n data, enc = saferead(fpath)\n from corpkit.constants import OPENER, PYTHON_VERSION\n with OPENER(fpath, \"w\") as f:\n if PYTHON_VERSION == 2:\n f.write(data.encode('utf-8', errors='ignore'))\n else:\n f.write(data)\n # rename file\n #dname = '-' + os.path.basename(rootdir)\n #newname = fpath.replace('.txt', dname + '.txt')\n #shutil.move(fpath, newname)\n path_to_new_unparsed_corpus.set(newc)\n add_corpus_button.set('Added: \"%s\"' % os.path.basename(fp))\n current_corpus.set(os.path.basename(fp))\n #sel_corpus.set(newc)\n #sel_corpus_button.set('Selected corpus: \"%s\"' % os.path.basename(newc))\n timestring('Corpus copied to project folder.')\n parse_button_text.set('Parse: %s' % os.path.basename(newc))\n tokenise_button_text.set('Tokenise: \"%s\"' % os.path.basename(newc))\n add_subcorpora_to_build_box(newc)\n update_available_corpora()\n timestring('Selected corpus for viewing/parsing: \"%s\"' % os.path.basename(newc))\n \n Label(tab0, text='Project', font=(\"Helvetica\", 13, \"bold\")).grid(sticky=W, row=0, column=0)\n #Label(tab0, text='New project', font=(\"Helvetica\", 13, \"bold\")).grid(sticky=W, row=0, column=0)\n Button(tab0, textvariable=new_proj_basepath, command=make_new_project, width=24).grid(row=1, column=0, sticky=W)\n #Label(tab0, text='Open project: ').grid(row=2, column=0, sticky=W)\n Button(tab0, textvariable=open_proj_basepath, command=load_project, width=24).grid(row=2, column=0, sticky=W)\n #Label(tab0, text='Add corpus to project: ').grid(row=4, column=0, sticky=W)\n addbut = Button(tab0, textvariable=add_corpus_button, width=24, state=DISABLED)\n addbut.grid(row=3, column=0, sticky=W)\n addbut.config(command=lambda: runner(addbut, getcorpus))\n #Label(tab0, text='Corpus to parse: ').grid(row=6, column=0, sticky=W)\n #Button(tab0, textvariable=sel_corpus_button, command=select_corpus, width=24).grid(row=4, column=0, sticky=W)\n #Label(tab0, text='Parse: ').grid(row=8, column=0, sticky=W)\n #speakcheck_build = Checkbutton(tab0, text=\"Speaker segmentation\", variable=speakseg, state=DISABLED)\n #speakcheck_build.grid(column=0, row=5, sticky=W)\n \n parsebut = Button(tab0, textvariable=parse_button_text, width=24, state=DISABLED)\n parsebut.grid(row=5, column=0, sticky=W)\n parsebut.config(command=lambda: runner(parsebut, create_parsed_corpus))\n #Label(tab0, text='Parse: ').grid(row=8, column=0, sticky=W)\n tokbut = Button(tab0, textvariable=tokenise_button_text, width=24, state=DISABLED)\n tokbut.grid(row=6, column=0, sticky=W)\n tokbut.config(command=lambda: runner(tokbut, create_tokenised_text))\n\n def onselect_subc_build(evt = False):\n \"\"\"get selected subcorpus, delete editor, show files in subcorpus\"\"\"\n import os\n if evt:\n # should only be one\n for i in subc_sel_vals_build:\n subc_sel_vals_build.pop()\n wx = evt.widget\n indices = wx.curselection()\n for index in indices:\n value = wx.get(index)\n if value not in subc_sel_vals_build:\n subc_sel_vals_build.append(value)\n # return for false click\n if len(subc_sel_vals_build) == 0 and selected_corpus_has_no_subcorpora.get() == 0:\n return\n\n # destroy editor and canvas if possible\n for ob in list(buildbits.values()):\n try:\n ob.destroy()\n except:\n pass\n\n f_view.configure(state=NORMAL)\n f_view.delete(0, 'end')\n newp = path_to_new_unparsed_corpus.get()\n if selected_corpus_has_no_subcorpora.get() == 0:\n newsub = os.path.join(newp, subc_sel_vals_build[0])\n else:\n newsub = newp\n fs = [f for f in os.listdir(newsub) if f.endswith('.txt') \\\n or f.endswith('.xml') \\\n or f.endswith('.conll') \\\n or f.endswith('.conllu')]\n for e in fs:\n f_view.insert(END, e)\n if selected_corpus_has_no_subcorpora.get() == 0: \n f_in_s.set('Files in subcorpus: %s' % subc_sel_vals_build[0])\n else:\n f_in_s.set('Files in corpus: %s' % os.path.basename(path_to_new_unparsed_corpus.get()))\n\n # a listbox of subcorpora\n Label(tab0, text='Subcorpora', font=(\"Helvetica\", 13, \"bold\")).grid(row=7, column=0, sticky=W)\n height = 21 if small_screen else 24\n build_sub_f = Frame(tab0, width=24, height=height)\n build_sub_f.grid(row=8, column=0, sticky=W, rowspan = 2, padx=(8,0))\n build_sub_sb = Scrollbar(build_sub_f)\n build_sub_sb.pack(side=RIGHT, fill=Y)\n subc_listbox_build = Listbox(build_sub_f, selectmode = SINGLE, height=height, state=DISABLED, relief=SUNKEN, bg='#F4F4F4',\n yscrollcommand=build_sub_sb.set, exportselection=False, width=24)\n subc_listbox_build.pack(fill=BOTH)\n xxy = subc_listbox_build.bind('<>', onselect_subc_build)\n subc_listbox_build.select_set(0)\n build_sub_sb.config(command=subc_listbox_build.yview)\n\n def show_a_tree(evt):\n \"\"\"get selected file and show in file view\"\"\"\n import os\n from nltk import Tree\n from nltk.tree import ParentedTree\n from nltk.draw.util import CanvasFrame\n from nltk.draw import TreeWidget\n\n sbox = buildbits['sentsbox']\n sent = sentdict[int(sbox.curselection()[0])]\n t = ParentedTree.fromstring(sent)\n\n # make a frame attached to tab0\n #cf = CanvasFrame(tab0, width=200, height=200)\n \n cf = Canvas(tab0, width=800, height=400, bd=5)\n buildbits['treecanvas'] = cf\n cf.grid(row=5, column=2, rowspan = 11, padx=(0,0))\n if cf not in boxes:\n boxes.append(cf)\n # draw the tree and send to the frame's canvas\n tc = TreeWidget(cf, t, draggable=1,\n node_font=('helvetica', -10, 'bold'),\n leaf_font=('helvetica', -10, 'italic'),\n roof_fill='white', roof_color='black',\n leaf_color='green4', node_color='blue2')\n\n tc.bind_click_trees(tc.toggle_collapsed)\n\n def select_all_editor(*args):\n \"\"\"not currently using, but might be good for select all\"\"\"\n editor = buildbits['editor']\n editor.tag_add(SEL, \"1.0\", END)\n editor.mark_set(INSERT, \"1.0\")\n editor.see(INSERT)\n return 'break'\n\n def onselect_f(evt):\n \"\"\"get selected file and show in file view\"\"\"\n for box in boxes:\n try:\n box.destroy()\n except:\n pass\n import os\n # should only be one\n for i in chosen_f:\n chosen_f.pop()\n wx = evt.widget\n indices = wx.curselection()\n for index in indices:\n value = wx.get(index)\n if value not in chosen_f:\n chosen_f.append(value)\n\n if len(chosen_f) == 0:\n return\n\n if chosen_f[0].endswith('.txt'):\n newp = path_to_new_unparsed_corpus.get()\n if selected_corpus_has_no_subcorpora.get() == 0:\n fp = os.path.join(newp, subc_sel_vals_build[0], chosen_f[0])\n else:\n fp = os.path.join(newp, chosen_f[0])\n if not os.path.isfile(fp):\n fp = os.path.join(newp, os.path.basename(corpus_fullpath.get()), chosen_f[0])\n from corpkit.constants import OPENER\n with OPENER(fp, 'r', encoding='utf-8') as fo:\n text = fo.read()\n\n # needs a scrollbar\n editor = Text(tab0, height=32)\n bind_textfuncts_to_widgets([editor])\n\n buildbits['editor'] = editor\n editor.grid(row=1, column=2, rowspan=9, pady=(10,0), padx=(20, 0))\n if editor not in boxes:\n boxes.append(editor)\n all_text_widgets.append(editor)\n editor.bind(\"<%s-s>\" % key, savebuttonaction)\n editor.bind(\"<%s-S>\" % key, savebuttonaction)\n editor.config(borderwidth=0,\n font=\"{Lucida Sans Typewriter} 12\",\n #foreground=\"green\",\n #background=\"black\",\n #insertbackground=\"white\", # cursor\n #selectforeground=\"green\", # selection\n #selectbackground=\"#008000\",\n wrap=WORD, # use word wrapping\n width=64,\n undo=True, # Tk 8.4\n ) \n editor.delete(1.0, END)\n editor.insert(END, text)\n editor.mark_set(INSERT, 1.0)\n editf.set('Edit file: %s' % chosen_f[0])\n viewedit = Label(tab0, textvariable=editf, font=(\"Helvetica\", 13, \"bold\"))\n viewedit.grid(row=0, column=2, sticky=W, padx=(20, 0))\n if viewedit not in boxes:\n boxes.append(viewedit)\n filename.set(chosen_f[0])\n fullpath_to_file.set(fp)\n but = Button(tab0, text='Save changes', command=savebuttonaction)\n but.grid(row=9, column=2, sticky='SE')\n buildbits['but'] = but\n if but not in boxes:\n boxes.append(but)\n\n elif chosen_f[0].endswith('.conll') or chosen_f[0].endswith('.conllu'):\n\n import re\n parsematch = re.compile(r'^# parse=(.*)')\n newp = path_to_new_unparsed_corpus.get()\n if selected_corpus_has_no_subcorpora.get() == 0:\n fp = os.path.join(newp, subc_sel_vals_build[0], chosen_f[0])\n else:\n fp = os.path.join(newp, chosen_f[0])\n if not os.path.isfile(fp):\n fp = os.path.join(newp, os.path.basename(corpus_fullpath.get()), chosen_f[0])\n from corpkit.constants import OPENER\n with OPENER(fp, 'r', encoding='utf-8') as fo:\n text = fo.read()\n lines = text.splitlines()\n editf.set('View trees: %s' % chosen_f[0])\n vieweditxml = Label(tab0, textvariable=editf, font=(\"Helvetica\", 13, \"bold\"))\n vieweditxml.grid(row=0, column=2, sticky=W, padx=(20,0))\n buildbits['vieweditxml'] = vieweditxml\n if vieweditxml not in boxes:\n boxes.append(vieweditxml)\n trees = []\n\n def flatten_treestring(tree):\n\n replaces = {'$ ': '$',\n '`` ': '``',\n ' ,': ',',\n ' .': '.',\n \"'' \": \"''\",\n \" n't\": \"n't\",\n \" 're\": \"'re\",\n \" 'm\": \"'m\",\n \" 's\": \"'s\",\n \" 'd\": \"'d\",\n \" 'll\": \"'ll\",\n ' ': ' '}\n import re\n tree = re.sub(r'\\(.*? ', '', tree).replace(')', '')\n for k, v in replaces.items():\n tree = tree.replace(k, v)\n return tree\n\n for l in lines:\n searched = re.search(parsematch, l)\n if searched:\n bracktree = searched.group(1)\n flat = flatten_treestring(bracktree)\n trees.append([bracktree, flat])\n sentsbox = Listbox(tab0, selectmode=SINGLE, width=120, font=(\"Courier New\", 11))\n if sentsbox not in boxes:\n boxes.append(sentsbox)\n buildbits['sentsbox'] = sentsbox\n sentsbox.grid(row=1, column=2, rowspan=4, padx=(20,0))\n sentsbox.delete(0, END)\n for i in list(sentdict.keys()):\n del sentdict[i]\n for i, (t, f) in enumerate(trees):\n cutshort = f[:80] + '...'\n sentsbox.insert(END, '%d: %s' % (i + 1, f))\n sentdict[i] = t\n xxyyz = sentsbox.bind('<>', show_a_tree)\n \n f_in_s = StringVar()\n f_in_s.set('Files in subcorpus ')\n\n # a listbox of files\n Label(tab0, textvariable=f_in_s, font=(\"Helvetica\", 13, \"bold\")).grid(row=0, column=1, sticky='NW', padx=(30, 0))\n height = 31 if small_screen else 36\n build_f_box = Frame(tab0, height=height)\n build_f_box.grid(row=1, column=1, rowspan = 9, padx=(20, 0), pady=(10, 0))\n build_f_sb = Scrollbar(build_f_box)\n build_f_sb.pack(side=RIGHT, fill=Y)\n f_view = Listbox(build_f_box, selectmode = EXTENDED, height=height, state=DISABLED, relief=SUNKEN, bg='#F4F4F4',\n exportselection=False, yscrollcommand=build_f_sb.set)\n f_view.pack(fill=BOTH)\n xxyy = f_view.bind('<>', onselect_f)\n f_view.select_set(0)\n build_f_sb.config(command=f_view.yview) \n\n editf = StringVar()\n editf.set('Edit file: ')\n\n def savebuttonaction(*args):\n from corpkit.constants import OPENER, PYTHON_VERSION\n editor = buildbits['editor']\n text = editor.get(1.0, END)\n with OPENER(fullpath_to_file.get(), \"w\") as fo:\n if PYTHON_VERSION == 2:\n fo.write(text.rstrip().encode(\"utf-8\"))\n fo.write(\"\\n\")\n else:\n fo.write(text.rstrip() + '\\n')\n timestring('%s saved.' % filename.get())\n\n filename = StringVar()\n filename.set('')\n fullpath_to_file = StringVar()\n fullpath_to_file.set('')\n\n ############ ############ ############ ############ ############ \n # MENU BAR # # MENU BAR # # MENU BAR # # MENU BAR # # MENU BAR # \n ############ ############ ############ ############ ############ \n\n realquit = IntVar()\n realquit.set(0)\n\n def clear_all():\n import os\n import sys\n python = sys.executable\n os.execl(python, python, * sys.argv)\n\n def get_tool_pref_file():\n \"\"\"get the location of the tool preferences files\"\"\"\n return os.path.join(rd, 'tool_settings.ini')\n\n def save_tool_prefs(printout=True):\n \"\"\"save any preferences to tool preferences\"\"\"\n try:\n import configparser\n except:\n import ConfigParser as configparser\n import os\n Config = configparser.ConfigParser()\n settingsfile = get_tool_pref_file()\n if settingsfile is None:\n timestring('No settings file found.')\n return\n\n # parsing for ints is causing errors?\n Config.add_section('Projects')\n Config.set('Projects','most recent', ';'.join(most_recent_projects[-5:]).lstrip(';'))\n Config.add_section('CoreNLP')\n Config.set('CoreNLP','Parser path', corenlppath.get())\n Config.set('CoreNLP','Memory allocation', str(parser_memory.get()))\n Config.add_section('Appearance')\n Config.set('Appearance','Spreadsheet row header width', str(row_label_width.get()))\n Config.set('Appearance','Spreadsheet cell width', str(cell_width.get()))\n Config.add_section('Other')\n Config.set('Other','Truncate concordance lines', str(truncate_conc_after.get()))\n Config.set('Other','Truncate spreadsheets', str(truncate_spreadsheet_after.get()))\n Config.set('Other','Automatic update check', str(do_auto_update.get()))\n Config.set('Other','do concordancing', str(do_concordancing.get()))\n Config.set('Other','Only format middle concordance column', str(only_format_match.get()))\n Config.set('Other','p value', str(p_val.get()))\n cfgfile = open(settingsfile ,'w')\n Config.write(cfgfile)\n\n #cell_width.get()\n #row_label_width.get()\n #truncate_conc_after.get()\n #truncate_spreadsheet_after.get()\n #do_auto_update.get()\n\n if printout:\n timestring('Tool preferences saved.')\n\n def load_tool_prefs():\n \"\"\"load preferences\"\"\"\n import os\n try:\n import configparser\n except:\n import ConfigParser as configparser\n settingsfile = get_tool_pref_file()\n if settingsfile is None:\n timestring('No settings file found.')\n return\n if not os.path.isfile(settingsfile):\n timestring('No settings file found at %s' % settingsfile)\n return\n\n def tryer(config, var, section, name):\n \"\"\"attempt to load a value, fail gracefully if not there\"\"\"\n try:\n if config.has_option(section, name):\n bit = conmap(config, section).get(name, False)\n if name in ['memory allocation', 'truncate spreadsheets', \n 'truncate concordance lines', 'p value']:\n bit = int(bit)\n else:\n bit = bool(bit)\n\n var.set(bit)\n except:\n pass\n\n Config = configparser.ConfigParser()\n Config.read(settingsfile)\n tryer(Config, parser_memory, \"CoreNLP\", \"memory allocation\")\n #tryer(Config, row_label_width, \"Appearance\", 'spreadsheet row header width')\n #tryer(Config, cell_width, \"Appearance\", 'spreadsheet cell width')\n tryer(Config, do_auto_update, \"Other\", 'automatic update check')\n #tryer(Config, conc_when_int, \"Other\", 'concordance when interrogating')\n tryer(Config, only_format_match, \"Other\", 'only format middle concordance column')\n tryer(Config, do_concordancing, \"Other\", 'do concordancing')\n #tryer(Config, noregex, \"Other\", 'disable regular expressions for plaintext search')\n tryer(Config, truncate_conc_after, \"Other\", 'truncate concordance lines')\n tryer(Config, truncate_spreadsheet_after, \"Other\", 'truncate spreadsheets')\n tryer(Config, p_val, \"Other\", 'p value')\n try:\n parspath = conmap(Config, \"CoreNLP\")['parser path']\n except:\n parspath = 'default'\n try:\n mostrec = conmap(Config, \"Projects\")['most recent'].lstrip(';').split(';')\n for i in mostrec:\n most_recent_projects.append(i)\n except:\n pass\n if parspath == 'default' or parspath == '':\n corenlppath.set(os.path.join(os.path.expanduser(\"~\"), 'corenlp'))\n else:\n corenlppath.set(parspath)\n timestring('Tool preferences loaded.')\n\n def save_config():\n try:\n import configparser\n except:\n import ConfigParser as configparser\n import os\n if any(v != '' for v in list(entryboxes.values())):\n codscheme = ','.join([i.get().replace(',', '') for i in list(entryboxes.values())])\n else:\n codscheme = None\n Config = configparser.ConfigParser()\n cfgfile = open(os.path.join(project_fullpath.get(), 'settings.ini') ,'w')\n Config.add_section('Build')\n Config.add_section('Interrogate')\n relcorpuspath = corpus_fullpath.get().replace(project_fullpath.get(), '').lstrip('/')\n Config.set('Interrogate','Corpus path', relcorpuspath)\n Config.set('Interrogate','Speakers', convert_speakdict_to_string(corpus_names_and_speakers))\n #Config.set('Interrogate','dependency type', kind_of_dep.get())\n Config.set('Interrogate','Treat files as subcorpora', str(files_as_subcorpora.get()))\n\n Config.add_section('Edit')\n Config.add_section('Visualise')\n Config.set('Visualise','Plot style', plot_style.get())\n Config.set('Visualise','Use TeX', str(texuse.get()))\n Config.set('Visualise','x axis title', x_axis_l.get())\n Config.set('Visualise','Colour scheme', chart_cols.get())\n Config.add_section('Concordance')\n Config.set('Concordance','font size', str(fsize.get()))\n #Config.set('Concordance','dependency type', conc_kind_of_dep.get())\n Config.set('Concordance','coding scheme', codscheme)\n if win.get() == 'Window':\n window = 70\n else:\n window = int(win.get())\n Config.set('Concordance','window', str(window))\n Config.add_section('Manage')\n Config.set('Manage','Project path',project_fullpath.get())\n\n Config.write(cfgfile)\n timestring('Project settings saved to settings.ini.')\n\n def quitfunc():\n if in_a_project.get() == 1:\n save_ask = messagebox.askyesno(\"Save settings\", \n \"Save settings before quitting?\")\n if save_ask:\n save_config()\n save_tool_prefs()\n realquit.set(1)\n root.quit()\n\n root.protocol(\"WM_DELETE_WINDOW\", quitfunc)\n\n def restart(newpath=False):\n \"\"\"restarts corpkit .py or gui, designed for version updates\"\"\"\n import sys\n import os\n import subprocess\n import inspect\n timestring('Restarting ... ')\n # get path to current script\n if newpath is False:\n newpath = inspect.getfile(inspect.currentframe())\n if sys.platform == \"win32\":\n if newpath.endswith('.py'):\n timestring('Not yet supported, sorry.')\n return\n os.startfile(newpath)\n else:\n opener = \"open\" if sys.platform == \"darwin\" else \"xdg-open\"\n if newpath.endswith('.py'):\n opener = 'python'\n if 'daniel/Work/corpkit' in newpath:\n opener = '/Users/daniel/virtenvs/ssled/bin/python'\n cmd = [opener, newpath]\n else:\n if sys.platform == \"darwin\":\n cmd = [opener, '-n', newpath]\n else:\n cmd = [opener, newpath]\n #os.system('%s %s' % (opener, newpath))\n #subprocess.Popen(cmd)\n from time import sleep\n sleep(1)\n #reload(inspect.getfile(inspect.currentframe()))\n subprocess.Popen(cmd)\n try:\n the_splash.__exit__()\n except:\n pass\n root.quit()\n sys.exit()\n\n def untar(fname, extractto):\n \"\"\"untar a file\"\"\"\n import tarfile\n tar = tarfile.open(fname)\n tar.extractall(extractto)\n tar.close()\n\n def update_corpkit(stver):\n \"\"\"get new corpkit, delete this one, open it up\"\"\"\n import sys\n import os\n import inspect\n import corpkit\n from corpkit.build import download_large_file\n # get path to this script\n corpath = rd\n #corpath = inspect.getfile(inspect.currentframe())\n \n # check we're using executable version, because .py users can\n # use github to update\n extens = '.%s' % fext\n if extens not in corpath and sys.platform != 'darwin':\n timestring(\"Get it from GitHub: https://www.github.com/interrogator/corpkit\")\n return\n\n # split on .app or .exe, then re-add .app\n apppath = corpath.split(extens , 1)[0] + extens\n appdir = os.path.dirname(apppath)\n\n # get new version and the abs path of the download dir and the tar file\n url = 'https://raw.githubusercontent.com/interrogator/corpkit-app/master/corpkit-%s.tar.gz' % stver\n \n path_to_app_parent = sys.argv[0]\n if sys.platform == 'darwin':\n if '.app' in path_to_app_parent:\n path_to_app_parent = os.path.dirname(path_to_app_parent.split('.app', 1)[0])\n else:\n # WINDOWS SUPPORT\n pass\n if '.py' in path_to_app_parent:\n py_script = True\n path_to_app_parent = os.path.dirname(os.path.join(path_to_app_parent.split('.py', 1)[0]))\n\n downloaded_dir, corpkittarfile = download_large_file(path_to_app_parent, \\\n url, root=root, note=note, actually_download = True)\n \n timestring('Extracting update ...')\n \n # why not extract to actual dir?\n untar(corpkittarfile, downloaded_dir)\n \n timestring('Applying update ...')\n \n # delete the tar\n #os.remove(corpkittarfile)\n # get whatever the new app is called\n newappfname = [f for f in os.listdir(downloaded_dir) if f.endswith(fext)][0]\n absnewapp = os.path.join(downloaded_dir, newappfname)\n # get the executable in the path\n restart_now = messagebox.askyesno(\"Update and restart\",\n \"Restart now?\\n\\nThis will delete the current version of corpkit.\")\n import shutil\n if restart_now:\n # remove this very app, but not script, just in case\n if '.py' not in apppath:\n if sys.platform == 'darwin':\n shutil.rmtree(apppath)\n # if windows, it's not a dir\n else:\n os.remove(apppath)\n # move new version\n if sys.platform == 'darwin':\n shutil.copytree(absnewapp, os.path.join(appdir, newappfname))\n # if windows, it's not a dir\n else:\n shutil.copy(absnewapp, os.path.join(appdir, newappfname))\n # delete donwnloaded file and dir\n shutil.rmtree(downloaded_dir)\n restart(os.path.join(appdir, newappfname))\n # shitty way to do this. what is the standard way of downloading and not installing?\n else:\n if sys.platform == 'darwin':\n try:\n shutil.copytree(absnewapp, os.path.join(appdir, newappfname))\n except OSError:\n shutil.copytree(absnewapp, os.path.join(appdir, newappfname + '-new'))\n else:\n try:\n shutil.copy(absnewapp, os.path.join(appdir, newappfname))\n except OSError:\n shutil.copy(absnewapp, os.path.join(appdir, newappfname + '-new')) \n timestring('New version in %s' % os.path.join(appdir, newappfname + '-new'))\n return\n\n def make_float_from_version(ver):\n \"\"\"take a version string and turn it into a comparable float\"\"\"\n ver = str(ver)\n ndots_to_delete = ver.count('.') - 1\n return float(ver[::-1].replace('.', '', ndots_to_delete)[::-1])\n\n def modification_date(filename):\n \"\"\"get datetime of file modification\"\"\"\n import os\n import datetime\n t = os.path.getmtime(filename)\n return datetime.datetime.fromtimestamp(t)\n\n def check_updates(showfalse=True, lateprint=False, auto=False):\n \"\"\"check for updates, minor and major.\"\"\"\n import os\n import re\n import datetime\n from dateutil.parser import parse\n import sys\n import shutil\n \n if noupdate:\n return\n \n # weird hacky way to not repeat request\n if do_auto_update.get() == 0 and auto is True:\n return\n\n if do_auto_update_this_session.get() is False and auto is True:\n return\n\n # cancel auto if manual\n if auto is False:\n do_auto_update_this_session.set(0)\n\n # get version as float\n try:\n oldstver = open(os.path.join(rd, 'VERSION.txt'), 'r').read().strip()\n except:\n import corpkit\n oldstver = str(corpkit.__version__)\n ver = make_float_from_version(oldstver)\n\n # check for major update\n try:\n response = requests.get('https://www.github.com/interrogator/corpkit-app', verify=False)\n html = response.text\n except:\n if showfalse:\n messagebox.showinfo(\n \"No connection to remote server\",\n \"Could not connect to remote server.\")\n return\n reg = re.compile('title=.corpkit-([0-9\\.]+)\\.tar\\.gz')\n \n # get version number as string\n stver = str(re.search(reg, html).group(1))\n vnum = make_float_from_version(stver)\n \n # check for major update\n #if 2 == 2:\n if vnum > ver:\n timestring('Update found: corpkit %s' % stver)\n download_update = messagebox.askyesno(\"Update available\",\n \"Update available: corpkit %s\\n\\n Download now?\" % stver)\n if download_update:\n update_corpkit(stver)\n return\n else:\n timestring('Update found: corpkit %s. Not downloaded.' % stver)\n return\n \n # check for minor update\n else:\n import sys\n timereg = re.compile(r'# (.*)<.updated>')\n\n #if '.py' in sys.argv[0] and sys.platform == 'darwin':\n #oldd = open(os.path.join(rd, 'gui.py'), 'r').read()\n #elif '.app' in sys.argv[0]:\n oldd = open(os.path.join(rd, 'gui.py'), 'r').read()\n\n dateline = next(l for l in oldd.split('\\n') if l.startswith('# '))\n dat = re.search(timereg, dateline).group(1)\n try:\n olddate = parse(dat)\n except:\n olddate = modification_date(sys.argv[0])\n\n try:\n script_response = requests.get('https://raw.githubusercontent.com/interrogator/corpkit-app/master/gui.py', verify=False)\n newscript = script_response.text\n dateline = next(l for l in newscript.split('\\n') if l.startswith('# '))\n\n except:\n if showfalse:\n messagebox.showinfo(\n \"No connection to remote server\",\n \"Could not connect to remote server.\")\n return\n\n # parse the date part\n try:\n dat = re.search(timereg, dateline).group(1)\n newdate = parse(dat)\n except:\n if showfalse:\n messagebox.showinfo(\n \"Error checking for update.\",\n \"Error checking for update.\")\n return\n # testing code\n #if 2 == 2:\n if newdate > olddate:\n timestring('Minor update found: corpkit %s' % stver)\n download_update = messagebox.askyesno(\"Minor update available\",\n \"Minor update available: corpkit %s\\n\\n Download and apply now?\" % stver)\n if download_update:\n url = 'https://raw.githubusercontent.com/interrogator/corpkit-app/master/corpkit-%s' % oldstver\n \n # update script\n if not sys.argv[0].endswith('gui.py'):\n script_url = 'https://raw.githubusercontent.com/interrogator/corpkit-app/master/gui.py'\n response = requests.get(script_url, verify=False)\n with open(os.path.join(rd, 'gui.py'), \"w\") as fo:\n fo.write(response.text)\n else:\n timestring(\"Can't replace developer copy, sorry.\")\n return\n\n dir_containing_ex, execut = download_large_file(project_fullpath.get(), \n url = url, root=root, note=note)\n\n # make sure we can execute the new script\n import os\n os.chmod(execut, 0o777)\n\n if not sys.argv[0].endswith('gui.py'):\n os.remove(os.path.join(rd, 'corpkit-%s' % oldstver))\n shutil.move(execut, os.path.join(rd, 'corpkit-%s' % oldstver))\n shutil.rmtree(dir_containing_ex)\n else:\n timestring(\"Can't replace developer copy, sorry.\")\n return\n #import inspect\n #sys.argv[0]\n #extens = '.%s' % fext\n #if extens not in corpath and sys.platform != 'darwin':\n # timestring(\"Get it from GitHub: https://www.github.com/interrogator/corpkit\")\n # return\n ## split on .app or .exe, then re-add .app\n #apppath = corpath.split(extens , 1)[0] + extens\n restart(sys.argv[0].split('.app', 1)[0] + '.app')\n return\n else:\n timestring('Minor update found: corpkit %s, %s. Not downloaded.' % (stver, dat.replace('T', ', ')))\n return\n\n if showfalse:\n messagebox.showinfo(\n \"Up to date!\",\n \"corpkit (version %s) up to date!\" % oldstver)\n timestring('corpkit (version %s) up to date.' % oldstver)\n return\n\n\n def start_update_check():\n if noupdate:\n return\n try:\n check_updates(showfalse=False, lateprint=True, auto=True)\n except:\n filemenu.entryconfig(\"Check for updates\", state=\"disabled\")\n\n def unmax():\n \"\"\"stop it being always on top\"\"\"\n root.attributes('-topmost', False)\n\n root.after(1000, unmax)\n \n if not '.py' in sys.argv[0]:\n root.after(10000, start_update_check)\n\n def set_corenlp_path():\n if sys.platform == 'darwin':\n the_kwargs = {'message': 'Select folder containing the CoreNLP parser.'}\n else:\n the_kwargs = {}\n fp = filedialog.askdirectory(title='CoreNLP path',\n initialdir=os.path.expanduser(\"~\"),\n **the_kwargs)\n if fp and fp != '':\n corenlppath.set(fp)\n if not get_fullpath_to_jars(corenlppath):\n recog = messagebox.showwarning(title='CoreNLP not found', \n message=\"CoreNLP not found in %s.\" % fp )\n timestring(\"CoreNLP not found in %s.\" % fp )\n else:\n save_tool_prefs()\n\n def config_menu(*args):\n import os\n fp = corpora_fullpath.get()\n recentmenu.delete(0, END)\n if len(most_recent_projects) == 0:\n filemenu.entryconfig(\"Open recent project\", state=\"disabled\")\n if len(most_recent_projects) == 1 and most_recent_projects[0] == '':\n filemenu.entryconfig(\"Open recent project\", state=\"disabled\")\n else:\n filemenu.entryconfig(\"Open recent project\", state=\"normal\") \n for c in list(set(most_recent_projects[::-1][:5])):\n if c:\n lab = os.path.join(os.path.basename(os.path.dirname(c)), os.path.basename(c))\n recentmenu.add_radiobutton(label=lab, variable=recent_project, value = c)\n if os.path.isdir(fp):\n all_corpora = get_all_corpora()\n if len(all_corpora) > 0:\n filemenu.entryconfig(\"Select corpus\", state=\"normal\")\n selectmenu.delete(0, END)\n for c in all_corpora:\n selectmenu.add_radiobutton(label=c, variable=current_corpus, value = c)\n else:\n filemenu.entryconfig(\"Select corpus\", state=\"disabled\")\n else:\n filemenu.entryconfig(\"Select corpus\", state=\"disabled\")\n #filemenu.entryconfig(\"Manage project\", state=\"disabled\")\n if in_a_project.get() == 0:\n filemenu.entryconfig(\"Save project settings\", state=\"disabled\")\n filemenu.entryconfig(\"Load project settings\", state=\"disabled\")\n filemenu.entryconfig(\"Manage project\", state=\"disabled\")\n #filemenu.entryconfig(\"Set CoreNLP path\", state=\"disabled\")\n else:\n filemenu.entryconfig(\"Save project settings\", state=\"normal\")\n filemenu.entryconfig(\"Load project settings\", state=\"normal\")\n filemenu.entryconfig(\"Manage project\", state=\"normal\")\n\n #filemenu.entryconfig(\"Set CoreNLP path\", state=\"normal\")\n \n menubar = Menu(root)\n selectmenu = Menu(root)\n recentmenu = Menu(root)\n\n if sys.platform == 'darwin':\n filemenu = Menu(menubar, tearoff=0, name='apple', postcommand=config_menu)\n else:\n filemenu = Menu(menubar, tearoff=0, postcommand=config_menu)\n\n filemenu.add_command(label=\"New project\", command=make_new_project)\n filemenu.add_command(label=\"Open project\", command=load_project)\n filemenu.add_cascade(label=\"Open recent project\", menu=recentmenu)\n filemenu.add_cascade(label=\"Select corpus\", menu=selectmenu)\n\n filemenu.add_separator()\n filemenu.add_command(label=\"Save project settings\", command=save_config)\n filemenu.add_command(label=\"Load project settings\", command=load_config)\n filemenu.add_separator()\n filemenu.add_command(label=\"Save tool preferences\", command=save_tool_prefs)\n filemenu.add_separator()\n filemenu.add_command(label=\"Manage project\", command=manage_popup)\n filemenu.add_separator()\n #filemenu.add_command(label=\"Coding scheme print\", command=print_entryboxes)\n \n # broken on deployed version ... path to self stuff\n #filemenu.add_separator()\n\n filemenu.add_command(label=\"Check for updates\", command=check_updates)\n #filemenu.entryconfig(\"Check for updates\", state=\"disabled\")\n #filemenu.add_separator()\n #filemenu.add_command(label=\"Restart tool\", command=restart)\n filemenu.add_separator()\n #filemenu.add_command(label=\"Exit\", command=quitfunc)\n \n menubar.add_cascade(label=\"File\", menu=filemenu)\n if sys.platform == 'darwin':\n windowmenu = Menu(menubar, name='window')\n menubar.add_cascade(menu=windowmenu, label='Window')\n else:\n sysmenu = Menu(menubar, name='system')\n menubar.add_cascade(menu=sysmenu)\n\n def schemesshow(*args):\n \"\"\"only edit schemes once in project\"\"\"\n import os\n if project_fullpath.get() == '':\n schemenu.entryconfig(\"Wordlists\", state=\"disabled\")\n schemenu.entryconfig(\"Coding scheme\", state=\"disabled\")\n else:\n schemenu.entryconfig(\"Wordlists\", state=\"normal\")\n schemenu.entryconfig(\"Coding scheme\", state=\"normal\")\n\n schemenu = Menu(menubar, tearoff=0, postcommand=schemesshow)\n menubar.add_cascade(label=\"Schemes\", menu=schemenu)\n schemenu.add_command(label=\"Coding scheme\", command=codingschemer)\n schemenu.add_command(label=\"Wordlists\", command=custom_lists)\n\n # prefrences section\n if sys.platform == 'darwin':\n root.createcommand('tk::mac::ShowPreferences', preferences_popup)\n\n def about_box():\n \"\"\"About message with current corpkit version\"\"\"\n import os\n try:\n oldstver = str(open(os.path.join(rd, 'VERSION.txt'), 'r').read().strip())\n except:\n import corpkit\n oldstver = str(corpkit.__version__)\n\n messagebox.showinfo('About', 'corpkit %s\\n\\ninterrogator.github.io/corpkit\\ngithub.com/interrogator/corpkit\\npypi.python.org/pypi/corpkit\\n\\n' \\\n 'Creator: Daniel McDonald\\nmcdonaldd@unimelb.edu.au' % oldstver)\n\n def show_log():\n \"\"\"save log text as txt file and open it\"\"\"\n import os\n the_input = '\\n'.join([x for x in note.log_stream])\n #the_input = note.text.get(\"1.0\",END)\n c = 0\n logpath = os.path.join(log_fullpath.get(), 'log-%s.txt' % str(c).zfill(2))\n while os.path.isfile(logpath):\n logpath = os.path.join(log_fullpath.get(), 'log-%s.txt' % str(c).zfill(2))\n c += 1\n with open(logpath, \"w\") as fo:\n fo.write(the_input)\n prnt = os.path.join('logs', os.path.basename(logpath))\n timestring('Log saved to \"%s\".' % prnt)\n import sys\n \n if sys.platform == \"win32\":\n os.startfile(logpath)\n else:\n opener = \"open\" if sys.platform == \"darwin\" else \"xdg-open\"\n import subprocess\n subprocess.call(['open', logpath])\n\n def bind_textfuncts_to_widgets(lst):\n \"\"\"add basic cut copy paste to text entry widgets\"\"\"\n for i in lst:\n i.bind(\"<%s-a>\" % key, select_all_text)\n i.bind(\"<%s-A>\" % key, select_all_text)\n i.bind(\"<%s-v>\" % key, paste_into_textwidget)\n i.bind(\"<%s-V>\" % key, paste_into_textwidget)\n i.bind(\"<%s-x>\" % key, cut_from_textwidget)\n i.bind(\"<%s-X>\" % key, cut_from_textwidget)\n i.bind(\"<%s-c>\" % key, copy_from_textwidget)\n i.bind(\"<%s-C>\" % key, copy_from_textwidget)\n try:\n i.config(undo = True)\n except:\n pass\n\n # load preferences\n load_tool_prefs()\n\n helpmenu = Menu(menubar, tearoff=0)\n helpmenu.add_command(label=\"Help\", command=lambda: show_help('h'))\n helpmenu.add_command(label=\"Query writing\", command=lambda: show_help('q'))\n helpmenu.add_command(label=\"Troubleshooting\", command=lambda: show_help('t'))\n helpmenu.add_command(label=\"Save log\", command=show_log)\n #helpmenu.add_command(label=\"Set CoreNLP path\", command=set_corenlp_path)\n helpmenu.add_separator()\n helpmenu.add_command(label=\"About\", command=about_box)\n menubar.add_cascade(label=\"Help\", menu=helpmenu)\n\n if sys.platform == 'darwin':\n import corpkit\n import subprocess\n ver = corpkit.__version__\n corpath = os.path.dirname(corpkit.__file__)\n if not corpath.startswith('/Library/Python') and not 'corpkit/corpkit/corpkit' in corpath:\n try:\n subprocess.call('''/usr/bin/osascript -e 'tell app \"Finder\" to set frontmost of process \"corpkit-%s\" to true' ''' % ver, shell = True)\n except:\n pass\n \n root.config(menu=menubar)\n note.focus_on(tab1)\n \n if loadcurrent:\n load_project(loadcurrent)\n\n root.deiconify()\n root.lift()\n try:\n root._splash.__exit__()\n except:\n pass\n root.wm_state('normal')\n #root.resizable(TRUE,TRUE)\n\n # overwrite quitting behaviour, prompt to save settings\n root.createcommand('exit', quitfunc)\n \n root.mainloop()\n\nif __name__ == \"__main__\":\n # the traceback is mostly for debugging pyinstaller errors\n import sys\n import traceback\n import os\n\n lc = sys.argv[-1] if os.path.isdir(sys.argv[-1]) else False\n \n\n #if lc and sys.argv[-1] == '.':\n # lc = os.path.basename(os.getcwd())\n # os.chdir('..')\n\n debugmode = 'debug' in list(sys.argv)\n\n def install(name, loc):\n \"\"\"if we don't have a module, download it\"\"\"\n try:\n import importlib\n importlib.import_module(name)\n except ImportError:\n import pip\n pip.main(['install', loc])\n\n tkintertablecode = ('tkintertable', 'git+https://github.com/interrogator/tkintertable.git')\n pilcode = ('PIL', 'http://effbot.org/media/downloads/Imaging-1.1.7.tar.gz')\n\n if not any(arg.lower() == 'noinstall' for arg in sys.argv):\n install(*tkintertablecode)\n from corpkit.constants import PYTHON_VERSION\n if PYTHON_VERSION == 2:\n install(*pilcode)\n\n try:\n if lc:\n corpkit_gui(loadcurrent=lc, debug=debugmode)\n else:\n corpkit_gui(debug=debugmode)\n except:\n exc_type, exc_value, exc_traceback=sys.exc_info()\n print(\"*** print_tb:\")\n print(traceback.print_tb(exc_traceback, limit=1, file=sys.stdout))\n print(\"*** print_exception:\")\n print(traceback.print_exception(exc_type, exc_value, exc_traceback,\n limit=2, file=sys.stdout))\n print(\"*** print_exc:\")\n print(traceback.print_exc())\n print(\"*** format_exc, first and last line:\")\n formatted_lines = traceback.format_exc()\n print(formatted_lines)\n print(\"*** format_exception:\")\n print('\\n'.join(traceback.format_exception(exc_type, exc_value,\n exc_traceback)))\n print(\"*** extract_tb:\")\n print('\\n'.join([str(i) for i in traceback.extract_tb(exc_traceback)]))\n print(\"*** format_tb:\")\n print(traceback.format_tb(exc_traceback))\n print(\"*** tb_lineno:\", exc_traceback.tb_lineno)\n\n"
},
{
"path": "corpkit/inflect.py",
"content": "#### PATTERN | EN | INFLECT ########################################################################\n# -*- coding: utf-8 -*-\n# Copyright (c) 2010 University of Antwerp, Belgium\n# Author: Tom De Smedt \n# License: BSD (see LICENSE.txt for details).\n\n####################################################################################################\n# Regular expressions-based rules for English word inflection:\n# - pluralization and singularization of nouns and adjectives,\n# - conjugation of verbs,\n# - comparative and superlative of adjectives.\n\n# Accuracy (measured on CELEX English morphology word forms):\n# 95% for pluralize()\n# 96% for singularize()\n# 95% for Verbs.find_lemma() (for regular verbs)\n# 96% for Verbs.find_lexeme() (for regular verbs)\n\n#################################################################################\n# Modified from original to work as standalone for adjective and noun inflection.\n#################################################################################\n\nimport os\nimport sys\nimport re\n\ntry:\n MODULE = os.path.dirname(os.path.realpath(__file__))\nexcept:\n MODULE = \"\"\n \nsys.path.insert(0, os.path.join(MODULE, \"..\", \"..\", \"..\", \"..\"))\n\n#from pattern.text import Verbs as _Verbs\n#from pattern.text import (\n# INFINITIVE, PRESENT, PAST, FUTURE,\n# FIRST, SECOND, THIRD,\n# SINGULAR, PLURAL, SG, PL,\n# PROGRESSIVE,\n# PARTICIPLE\n#)\n\nsys.path.pop(0)\n\nVERB, NOUN, ADJECTIVE, ADVERB = \"VB\", \"NN\", \"JJ\", \"RB\"\n\nVOWELS = \"aeiouy\"\nre_vowel = re.compile(r\"a|e|i|o|u|y\", re.I)\nis_vowel = lambda ch: ch in VOWELS\n\n#### ARTICLE #######################################################################################\n# Based on the Ruby Linguistics module by Michael Granger:\n# http://www.deveiate.org/projects/Linguistics/wiki/English\n\nRE_ARTICLE = [(re.compile(x[0]), x[1]) for x in (\n (\"euler|hour(?!i)|heir|honest|hono\", \"an\"), # exceptions: an hour, an honor\n # Abbreviations:\n # strings of capitals starting with a vowel-sound consonant followed by another consonant,\n # which are not likely to be real words.\n (r\"(?!FJO|[HLMNS]Y.|RY[EO]|SQU|(F[LR]?|[HL]|MN?|N|RH?|S[CHKLMNPTVW]?|X(YL)?)[AEIOU])[FHLMNRSX][A-Z]\", \"an\"),\n (r\"^[aefhilmnorsx][.-]\" , \"an\"),\n (r\"^[a-z][.-]\" , \"a\" ),\n (r\"^[^aeiouy]\" , \"a\" ), # consonants: a bear\n (r\"^e[uw]\" , \"a\" ), # -eu like \"you\": a european\n (r\"^onc?e\" , \"a\" ), # -o like \"wa\" : a one-liner\n (r\"uni([^nmd]|mo)\" , \"a\" ), # -u like \"you\": a university\n (r\"^u[bcfhjkqrst][aeiou]\", \"a\" ), # -u like \"you\": a uterus\n (r\"^[aeiou]\" , \"an\"), # vowels: an owl\n (r\"y(b[lor]|cl[ea]|fere|gg|p[ios]|rou|tt)\", \"an\"), # y like \"i\": an yclept, a year\n (r\"\" , \"a\" ) # guess \"a\"\n)]\n\ndef definite_article(word):\n return \"the\"\n\ndef indefinite_article(word):\n \"\"\" Returns the indefinite article for a given word.\n For example: indefinite_article(\"university\") => \"a\" university.\n \"\"\"\n word = word.split(\" \")[0]\n for rule, article in RE_ARTICLE:\n if rule.search(word) is not None:\n return article\n\nDEFINITE, INDEFINITE = \\\n \"definite\", \"indefinite\"\n\ndef article(word, function=INDEFINITE):\n \"\"\" Returns the indefinite (a or an) or definite (the) article for the given word.\n \"\"\"\n return function == DEFINITE and definite_article(word) or indefinite_article(word)\n\n_article = article\n\ndef referenced(word, article=INDEFINITE):\n \"\"\" Returns a string with the article + the word.\n \"\"\"\n return \"%s %s\" % (_article(word, article), word)\n\n#print referenced(\"hour\") \n#print referenced(\"FBI\")\n#print referenced(\"bear\")\n#print referenced(\"one-liner\")\n#print referenced(\"european\")\n#print referenced(\"university\")\n#print referenced(\"uterus\")\n#print referenced(\"owl\")\n#print referenced(\"yclept\")\n#print referenced(\"year\")\n\n#### PLURALIZE #####################################################################################\n# Based on \"An Algorithmic Approach to English Pluralization\" by Damian Conway:\n# http://www.csse.monash.edu.au/~damian/papers/HTML/Plurals.html\n\n# Prepositions are used in forms like \"mother-in-law\" and \"man at arms\".\nplural_prepositions = set((\n \"about\" , \"before\" , \"during\", \"of\" , \"till\" ,\n \"above\" , \"behind\" , \"except\", \"off\" , \"to\" ,\n \"across\" , \"below\" , \"for\" , \"on\" , \"under\",\n \"after\" , \"beneath\", \"from\" , \"onto\" , \"until\",\n \"among\" , \"beside\" , \"in\" , \"out\" , \"unto\" ,\n \"around\" , \"besides\", \"into\" , \"over\" , \"upon\" ,\n \"at\" , \"between\", \"near\" , \"since\", \"with\" ,\n \"athwart\", \"betwixt\", \n \"beyond\", \n \"but\", \n \"by\"))\n\n# Inflection rules that are either:\n# - general,\n# - apply to a certain category of words,\n# - apply to a certain category of words only in classical mode,\n# - apply only in classical mode.\n# Each rule is a (suffix, inflection, category, classic)-tuple.\nplural_rules = [\n # 0) Indefinite articles and demonstratives.\n (( r\"^a$|^an$\", \"some\" , None, False),\n ( r\"^this$\", \"these\" , None, False),\n ( r\"^that$\", \"those\" , None, False),\n ( r\"^any$\", \"all\" , None, False)\n ), # 1) Possessive adjectives.\n (( r\"^my$\", \"our\" , None, False),\n ( r\"^your$\", \"your\" , None, False),\n ( r\"^thy$\", \"your\" , None, False),\n (r\"^her$|^his$\", \"their\" , None, False),\n ( r\"^its$\", \"their\" , None, False),\n ( r\"^their$\", \"their\" , None, False)\n ), # 2) Possessive pronouns.\n (( r\"^mine$\", \"ours\" , None, False),\n ( r\"^yours$\", \"yours\" , None, False),\n ( r\"^thine$\", \"yours\" , None, False),\n (r\"^her$|^his$\", \"theirs\" , None, False),\n ( r\"^its$\", \"theirs\" , None, False),\n ( r\"^their$\", \"theirs\" , None, False)\n ), # 3) Personal pronouns.\n (( r\"^I$\", \"we\" , None, False),\n ( r\"^me$\", \"us\" , None, False),\n ( r\"^myself$\", \"ourselves\" , None, False),\n ( r\"^you$\", \"you\" , None, False),\n (r\"^thou$|^thee$\", \"ye\" , None, False),\n ( r\"^yourself$\", \"yourself\" , None, False),\n ( r\"^thyself$\", \"yourself\" , None, False), \n ( r\"^she$|^he$\", \"they\" , None, False),\n (r\"^it$|^they$\", \"they\" , None, False),\n (r\"^her$|^him$\", \"them\" , None, False),\n (r\"^it$|^them$\", \"them\" , None, False),\n ( r\"^herself$\", \"themselves\" , None, False),\n ( r\"^himself$\", \"themselves\" , None, False),\n ( r\"^itself$\", \"themselves\" , None, False),\n ( r\"^themself$\", \"themselves\" , None, False),\n ( r\"^oneself$\", \"oneselves\" , None, False)\n ), # 4) Words that do not inflect.\n (( r\"$\", \"\" , \"uninflected\", False),\n ( r\"$\", \"\" , \"uncountable\", False),\n ( r\"s$\", \"s\" , \"s-singular\" , False),\n ( r\"fish$\", \"fish\" , None, False),\n (r\"([- ])bass$\", \"\\\\1bass\" , None, False),\n ( r\"ois$\", \"ois\" , None, False),\n ( r\"sheep$\", \"sheep\" , None, False),\n ( r\"deer$\", \"deer\" , None, False),\n ( r\"pox$\", \"pox\" , None, False),\n (r\"([A-Z].*)ese$\", \"\\\\1ese\" , None, False),\n ( r\"itis$\", \"itis\" , None, False),\n (r\"(fruct|gluc|galact|lact|ket|malt|rib|sacchar|cellul)ose$\", \"\\\\1ose\", None, False)\n ), # 5) Irregular plural forms (e.g., mongoose, oxen).\n (( r\"atlas$\", \"atlantes\" , None, True ),\n ( r\"atlas$\", \"atlases\" , None, False),\n ( r\"beef$\", \"beeves\" , None, True ),\n ( r\"brother$\", \"brethren\" , None, True ),\n ( r\"child$\", \"children\" , None, False),\n ( r\"corpus$\", \"corpora\" , None, True ),\n ( r\"corpus$\", \"corpuses\" , None, False),\n ( r\"^cow$\", \"kine\" , None, True ),\n ( r\"ephemeris$\", \"ephemerides\", None, False),\n ( r\"ganglion$\", \"ganglia\" , None, True ),\n ( r\"genie$\", \"genii\" , None, True ),\n ( r\"genus$\", \"genera\" , None, False),\n ( r\"graffito$\", \"graffiti\" , None, False),\n ( r\"loaf$\", \"loaves\" , None, False),\n ( r\"money$\", \"monies\" , None, True ),\n ( r\"mongoose$\", \"mongooses\" , None, False),\n ( r\"mythos$\", \"mythoi\" , None, False),\n ( r\"octopus$\", \"octopodes\" , None, True ),\n ( r\"opus$\", \"opera\" , None, True ),\n ( r\"opus$\", \"opuses\" , None, False),\n ( r\"^ox$\", \"oxen\" , None, False),\n ( r\"penis$\", \"penes\" , None, True ),\n ( r\"penis$\", \"penises\" , None, False),\n ( r\"soliloquy$\", \"soliloquies\", None, False),\n ( r\"testis$\", \"testes\" , None, False),\n ( r\"trilby$\", \"trilbys\" , None, False),\n ( r\"turf$\", \"turves\" , None, True ),\n ( r\"numen$\", \"numena\" , None, False),\n ( r\"occiput$\", \"occipita\" , None, True )\n ), # 6) Irregular inflections for common suffixes (e.g., synopses, mice, men).\n (( r\"man$\", \"men\" , None, False),\n ( r\"person$\", \"people\" , None, False),\n (r\"([lm])ouse$\", \"\\\\1ice\" , None, False),\n ( r\"tooth$\", \"teeth\" , None, False),\n ( r\"goose$\", \"geese\" , None, False),\n ( r\"foot$\", \"feet\" , None, False),\n ( r\"zoon$\", \"zoa\" , None, False),\n ( r\"([csx])is$\", \"\\\\1es\" , None, False)\n ), # 7) Fully assimilated classical inflections \n # (e.g., vertebrae, codices).\n (( r\"ex$\", \"ices\" , \"ex-ices\" , False),\n ( r\"ex$\", \"ices\" , \"ex-ices*\", True ), # * = classical mode\n ( r\"um$\", \"a\" , \"um-a\" , False),\n ( r\"um$\", \"a\" , \"um-a*\", True ),\n ( r\"on$\", \"a\" , \"on-a\" , False),\n ( r\"a$\", \"ae\" , \"a-ae\" , False),\n ( r\"a$\", \"ae\" , \"a-ae*\", True )\n ), # 8) Classical variants of modern inflections \n # (e.g., stigmata, soprani).\n (( r\"trix$\", \"trices\" , None, True),\n ( r\"eau$\", \"eaux\" , None, True),\n ( r\"ieu$\", \"ieu\" , None, True),\n ( r\"([iay])nx$\", \"\\\\1nges\" , None, True),\n ( r\"en$\", \"ina\" , \"en-ina*\", True),\n ( r\"a$\", \"ata\" , \"a-ata*\", True),\n ( r\"is$\", \"ides\" , \"is-ides*\", True),\n ( r\"us$\", \"i\" , \"us-i*\", True),\n ( r\"us$\", \"us \" , \"us-us*\", True),\n ( r\"o$\", \"i\" , \"o-i*\", True),\n ( r\"$\", \"i\" , \"-i*\", True),\n ( r\"$\", \"im\" , \"-im*\", True)\n ), # 9) -ch, -sh and -ss take -es in the plural \n # (e.g., churches, classes).\n (( r\"([cs])h$\", \"\\\\1hes\" , None, False),\n ( r\"ss$\", \"sses\" , None, False),\n ( r\"x$\", \"xes\" , None, False)\n ), # 10) -f or -fe sometimes take -ves in the plural \n # (e.g, lives, wolves).\n (( r\"([aeo]l)f$\", \"\\\\1ves\" , None, False),\n ( r\"([^d]ea)f$\", \"\\\\1ves\" , None, False),\n ( r\"arf$\", \"arves\" , None, False),\n (r\"([nlw]i)fe$\", \"\\\\1ves\" , None, False),\n ), # 11) -y takes -ys if preceded by a vowel, -ies otherwise \n # (e.g., storeys, Marys, stories).\n ((r\"([aeiou])y$\", \"\\\\1ys\" , None, False),\n (r\"([A-Z].*)y$\", \"\\\\1ys\" , None, False),\n ( r\"y$\", \"ies\" , None, False)\n ), # 12) -o sometimes takes -os, -oes otherwise.\n # -o is preceded by a vowel takes -os \n # (e.g., lassos, potatoes, bamboos).\n (( r\"o$\", \"os\", \"o-os\", False),\n (r\"([aeiou])o$\", \"\\\\1os\" , None, False),\n ( r\"o$\", \"oes\" , None, False)\n ), # 13) Miltary stuff \n # (e.g., Major Generals).\n (( r\"l$\", \"ls\", \"general-generals\", False),\n ), # 14) Assume that the plural takes -s \n # (cats, programmes, ...).\n (( r\"$\", \"s\" , None, False),)\n]\n\n# For performance, compile the regular expressions once:\nplural_rules = [[(re.compile(r[0]), r[1], r[2], r[3]) for r in grp] for grp in plural_rules]\n\n# Suffix categories.\nplural_categories = {\n \"uninflected\": [ \n \"bison\" , \"debris\" , \"headquarters\" , \"news\" , \"swine\" ,\n \"bream\" , \"diabetes\" , \"herpes\" , \"pincers\" , \"trout\" ,\n \"breeches\" , \"djinn\" , \"high-jinks\" , \"pliers\" , \"tuna\" ,\n \"britches\" , \"eland\" , \"homework\" , \"proceedings\", \"whiting\" ,\n \"carp\" , \"elk\" , \"innings\" , \"rabies\" , \"wildebeest\"\n \"chassis\" , \"flounder\" , \"jackanapes\" , \"salmon\" ,\n \"clippers\" , \"gallows\" , \"mackerel\" , \"scissors\" , \n \"cod\" , \"graffiti\" , \"measles\" , \"series\" , \n \"contretemps\", \"mews\" , \"shears\" , \n \"corps\" , \"mumps\" , \"species\"\n ],\n \"uncountable\": [\n \"advice\" , \"fruit\" , \"ketchup\" , \"meat\" , \"sand\" ,\n \"bread\" , \"furniture\" , \"knowledge\" , \"mustard\" , \"software\" ,\n \"butter\" , \"garbage\" , \"love\" , \"news\" , \"understanding\",\n \"cheese\" , \"gravel\" , \"luggage\" , \"progress\" , \"water\"\n \"electricity\", \"happiness\" , \"mathematics\" , \"research\" , \n \"equipment\" , \"information\", \"mayonnaise\" , \"rice\"\n ],\n \"s-singular\": [\n \"acropolis\" , \"caddis\" , \"dais\" , \"glottis\" , \"pathos\" ,\n \"aegis\" , \"cannabis\" , \"digitalis\" , \"ibis\" , \"pelvis\" ,\n \"alias\" , \"canvas\" , \"epidermis\" , \"lens\" , \"polis\" ,\n \"asbestos\" , \"chaos\" , \"ethos\" , \"mantis\" , \"rhinoceros\" ,\n \"bathos\" , \"cosmos\" , \"gas\" , \"marquis\" , \"sassafras\" ,\n \"bias\" , \"glottis\" , \"metropolis\" , \"trellis\"\n ],\n \"ex-ices\": [\n \"codex\" , \"murex\" , \"silex\"\n ],\n \"ex-ices*\": [\n \"apex\" , \"index\" , \"pontifex\" , \"vertex\" , \n \"cortex\" , \"latex\" , \"simplex\" , \"vortex\"\n ],\n \"um-a\": [\n \"agendum\" , \"candelabrum\", \"desideratum\" , \"extremum\" , \"stratum\" ,\n \"bacterium\" , \"datum\" , \"erratum\" , \"ovum\"\n ],\n \"um-a*\": [\n \"aquarium\" , \"emporium\" , \"maximum\" , \"optimum\" , \"stadium\" ,\n \"compendium\" , \"enconium\" , \"medium\" , \"phylum\" , \"trapezium\" ,\n \"consortium\" , \"gymnasium\" , \"memorandum\" , \"quantum\" , \"ultimatum\" ,\n \"cranium\" , \"honorarium\" , \"millenium\" , \"rostrum\" , \"vacuum\" ,\n \"curriculum\" , \"interregnum\", \"minimum\" , \"spectrum\" , \"velum\" ,\n \"dictum\" , \"lustrum\" , \"momentum\" , \"speculum\"\n ],\n \"on-a\": [\n \"aphelion\" , \"hyperbaton\" , \"perihelion\" ,\n \"asyndeton\" , \"noumenon\" , \"phenomenon\" , \n \"criterion\" , \"organon\" , \"prolegomenon\"\n ],\n \"a-ae\": [\n \"alga\" , \"alumna\" , \"vertebra\"\n ],\n \"a-ae*\": [\n \"abscissa\" , \"aurora\" , \"hyperbola\" , \"nebula\" , \n \"amoeba\" , \"formula\" , \"lacuna\" , \"nova\" ,\n \"antenna\" , \"hydra\" , \"medusa\" , \"parabola\"\n ],\n \"en-ina*\": [\n \"foramen\" , \"lumen\" , \"stamen\"\n ],\n \"a-ata*\": [\n \"anathema\" , \"dogma\" , \"gumma\" , \"miasma\" , \"stigma\" ,\n \"bema\" , \"drama\" , \"lemma\" , \"schema\" , \"stoma\" ,\n \"carcinoma\" , \"edema\" , \"lymphoma\" , \"oedema\" , \"trauma\" ,\n \"charisma\" , \"enema\" , \"magma\" , \"sarcoma\" ,\n \"diploma\" , \"enigma\" , \"melisma\" , \"soma\" ,\n ],\n \"is-ides*\": [\n \"clitoris\" , \"iris\"\n ],\n \"us-i*\": [\n \"focus\" , \"nimbus\" , \"succubus\" ,\n \"fungus\" , \"nucleolus\" , \"torus\" , \n \"genius\" , \"radius\" , \"umbilicus\" , \n \"incubus\" , \"stylus\" , \"uterus\"\n ],\n \"us-us*\": [\n \"apparatus\" , \"hiatus\" , \"plexus\" , \"status\"\n \"cantus\" , \"impetus\" , \"prospectus\" ,\n \"coitus\" , \"nexus\" , \"sinus\" , \n ],\n \"o-i*\": [\n \"alto\" , \"canto\" , \"crescendo\" , \"soprano\" ,\n \"basso\" , \"contralto\" , \"solo\" , \"tempo\"\n ],\n \"-i*\": [\n \"afreet\" , \"afrit\" , \"efreet\"\n ],\n \"-im*\": [\n \"cherub\" , \"goy\" , \"seraph\"\n ],\n \"o-os\": [\n \"albino\" , \"dynamo\" , \"guano\" , \"lumbago\" , \"photo\" ,\n \"archipelago\", \"embryo\" , \"inferno\" , \"magneto\" , \"pro\" ,\n \"armadillo\" , \"fiasco\" , \"jumbo\" , \"manifesto\" , \"quarto\" ,\n \"commando\" , \"generalissimo\", \"medico\" , \"rhino\" ,\n \"ditto\" , \"ghetto\" , \"lingo\" , \"octavo\" , \"stylo\"\n ],\n \"general-generals\": [\n \"Adjutant\" , \"Brigadier\" , \"Lieutenant\" , \"Major\" , \"Quartermaster\", \n \"adjutant\" , \"brigadier\" , \"lieutenant\" , \"major\" , \"quartermaster\"\n ]\n}\n\ndef pluralize(word, pos=NOUN, custom={}, classical=True):\n \"\"\" Returns the plural of a given word, e.g., child => children.\n Handles nouns and adjectives, using classical inflection by default\n (i.e., where \"matrix\" pluralizes to \"matrices\" and not \"matrixes\").\n The custom dictionary is for user-defined replacements.\n \"\"\"\n if word in custom:\n return custom[word]\n # Recurse genitives.\n # Remove the apostrophe and any trailing -s, \n # form the plural of the resultant noun, and then append an apostrophe (dog's => dogs').\n if word.endswith((\"'\", \"'s\")):\n w = word.rstrip(\"'s\")\n w = pluralize(w, pos, custom, classical)\n if w.endswith(\"s\"):\n return w + \"'\"\n else:\n return w + \"'s\"\n # Recurse compound words\n # (e.g., Postmasters General, mothers-in-law, Roman deities). \n w = word.replace(\"-\", \" \").split(\" \")\n if len(w) > 1:\n if w[1] == \"general\" or \\\n w[1] == \"General\" and \\\n w[0] not in plural_categories[\"general-generals\"]:\n return word.replace(w[0], pluralize(w[0], pos, custom, classical))\n elif w[1] in plural_prepositions:\n return word.replace(w[0], pluralize(w[0], pos, custom, classical))\n else:\n return word.replace(w[-1], pluralize(w[-1], pos, custom, classical))\n # Only a very few number of adjectives inflect.\n n = list(range(len(plural_rules)))\n if pos.startswith(ADJECTIVE):\n n = [0, 1]\n # Apply pluralization rules.\n for i in n:\n for suffix, inflection, category, classic in plural_rules[i]:\n # A general rule, or a classic rule in classical mode.\n if category is None:\n if not classic or (classic and classical):\n if suffix.search(word) is not None:\n return suffix.sub(inflection, word)\n # A rule pertaining to a specific category of words.\n if category is not None:\n if word in plural_categories[category] and (not classic or (classic and classical)):\n if suffix.search(word) is not None:\n return suffix.sub(inflection, word)\n return word\n\n#print pluralize(\"part-of-speech\")\n#print pluralize(\"child\")\n#print pluralize(\"dog's\")\n#print pluralize(\"wolf\")\n#print pluralize(\"bear\")\n#print pluralize(\"kitchen knife\")\n#print pluralize(\"octopus\", classical=True)\n#print pluralize(\"matrix\", classical=True)\n#print pluralize(\"matrix\", classical=False)\n#print pluralize(\"my\", pos=ADJECTIVE)\n\n#### SINGULARIZE ###################################################################################\n# Adapted from Bermi Ferrer's Inflector for Python:\n# http://www.bermi.org/inflector/\n\n# Copyright (c) 2006 Bermi Ferrer Martinez\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software to deal in this software without restriction, including\n# without limitation the rights to use, copy, modify, merge, publish,\n# distribute, sublicense, and/or sell copies of this software, and to permit\n# persons to whom this software is furnished to do so, subject to the following\n# condition:\n#\n# THIS SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THIS SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THIS SOFTWARE.\n\nsingular_rules = [\n (r'(?i)(.)ae$' , '\\\\1a' ),\n (r'(?i)(.)itis$' , '\\\\1itis' ),\n (r'(?i)(.)eaux$' , '\\\\1eau' ),\n (r'(?i)(quiz)zes$' , '\\\\1' ),\n (r'(?i)(matr)ices$' , '\\\\1ix' ),\n (r'(?i)(ap|vert|ind)ices$', '\\\\1ex' ),\n (r'(?i)^(ox)en' , '\\\\1' ),\n (r'(?i)(alias|status)es$' , '\\\\1' ),\n (r'(?i)([octop|vir])i$' , '\\\\1us' ),\n (r'(?i)(cris|ax|test)es$' , '\\\\1is' ),\n (r'(?i)(shoe)s$' , '\\\\1' ),\n (r'(?i)(o)es$' , '\\\\1' ),\n (r'(?i)(bus)es$' , '\\\\1' ),\n (r'(?i)([m|l])ice$' , '\\\\1ouse' ),\n (r'(?i)(x|ch|ss|sh)es$' , '\\\\1' ),\n (r'(?i)(m)ovies$' , '\\\\1ovie' ),\n (r'(?i)(.)ombies$' , '\\\\1ombie'),\n (r'(?i)(s)eries$' , '\\\\1eries'),\n (r'(?i)([^aeiouy]|qu)ies$', '\\\\1y' ),\n # -f, -fe sometimes take -ves in the plural \n # (e.g., lives, wolves).\n (r\"([aeo]l)ves$\" , \"\\\\1f\" ),\n (r\"([^d]ea)ves$\" , \"\\\\1f\" ),\n (r\"arves$\" , \"arf\" ),\n (r\"erves$\" , \"erve\" ),\n (r\"([nlw]i)ves$\" , \"\\\\1fe\" ),\n (r'(?i)([lr])ves$' , '\\\\1f' ),\n (r\"([aeo])ves$\" , \"\\\\1ve\" ),\n (r'(?i)(sive)s$' , '\\\\1' ),\n (r'(?i)(tive)s$' , '\\\\1' ),\n (r'(?i)(hive)s$' , '\\\\1' ),\n (r'(?i)([^f])ves$' , '\\\\1fe' ),\n # -ses suffixes.\n (r'(?i)(^analy)ses$' , '\\\\1sis' ),\n (r'(?i)((a)naly|(b)a|(d)iagno|(p)arenthe|(p)rogno|(s)ynop|(t)he)ses$', '\\\\1\\\\2sis'),\n (r'(?i)(.)opses$' , '\\\\1opsis'),\n (r'(?i)(.)yses$' , '\\\\1ysis' ),\n (r'(?i)(h|d|r|o|n|b|cl|p)oses$', '\\\\1ose'),\n (r'(?i)(fruct|gluc|galact|lact|ket|malt|rib|sacchar|cellul)ose$', '\\\\1ose'),\n (r'(?i)(.)oses$' , '\\\\1osis' ),\n # -a\n (r'(?i)([ti])a$' , '\\\\1um' ),\n (r'(?i)(n)ews$' , '\\\\1ews' ),\n (r'(?i)s$' , '' ),\n]\n\n# For performance, compile the regular expressions only once:\nsingular_rules = [(re.compile(r[0]), r[1]) for r in singular_rules]\n\nsingular_uninflected = set((\n \"bison\" , \"debris\" , \"headquarters\", \"pincers\" , \"trout\" ,\n \"bream\" , \"diabetes\" , \"herpes\" , \"pliers\" , \"tuna\" ,\n \"breeches\" , \"djinn\" , \"high-jinks\" , \"proceedings\", \"whiting\" ,\n \"britches\" , \"eland\" , \"homework\" , \"rabies\" , \"wildebeest\"\n \"carp\" , \"elk\" , \"innings\" , \"salmon\" , \n \"chassis\" , \"flounder\" , \"jackanapes\" , \"scissors\" , \n \"christmas\" , \"gallows\" , \"mackerel\" , \"series\" , \n \"clippers\" , \"georgia\" , \"measles\" , \"shears\" , \n \"cod\" , \"graffiti\" , \"mews\" , \"species\" , \n \"contretemps\", \"mumps\" , \"swine\" , \n \"corps\" , \"news\" , \"swiss\" , \n))\nsingular_uncountable = set((\n \"advice\" , \"equipment\", \"happiness\" , \"luggage\" , \"news\" , \"software\" ,\n \"bread\" , \"fruit\" , \"information\" , \"mathematics\", \"progress\" , \"understanding\",\n \"butter\" , \"furniture\", \"ketchup\" , \"mayonnaise\" , \"research\" , \"water\"\n \"cheese\" , \"garbage\" , \"knowledge\" , \"meat\" , \"rice\" , \n \"electricity\", \"gravel\" , \"love\" , \"mustard\" , \"sand\" , \n))\nsingular_ie = set((\n \"alergie\" , \"cutie\" , \"hoagie\" , \"newbie\" , \"softie\" , \"veggie\" , \n \"auntie\" , \"doggie\" , \"hottie\" , \"nightie\" , \"sortie\" , \"weenie\" , \n \"beanie\" , \"eyrie\" , \"indie\" , \"oldie\" , \"stoolie\" , \"yuppie\" , \n \"birdie\" , \"freebie\" , \"junkie\" , \"^pie\" , \"sweetie\" , \"zombie\"\n \"bogie\" , \"goonie\" , \"laddie\" , \"pixie\" , \"techie\" , \n \"bombie\" , \"groupie\" , \"laramie\" , \"quickie\" , \"^tie\" , \n \"collie\" , \"hankie\" , \"lingerie\" , \"reverie\" , \"toughie\" , \n \"cookie\" , \"hippie\" , \"meanie\" , \"rookie\" , \"valkyrie\" , \n))\nsingular_irregular = {\n \"atlantes\": \"atlas\", \n \"atlases\": \"atlas\", \n \"axes\": \"axe\",\n \"beeves\": \"beef\", \n \"brethren\": \"brother\", \n \"children\": \"child\",\n \"children\": \"child\", \n \"corpora\": \"corpus\", \n \"corpuses\": \"corpus\", \n \"ephemerides\": \"ephemeris\", \n \"feet\": \"foot\",\n \"ganglia\": \"ganglion\", \n \"geese\": \"goose\",\n \"genera\": \"genus\", \n \"genii\": \"genie\", \n \"graffiti\": \"graffito\", \n \"helves\": \"helve\",\n \"kine\": \"cow\", \n \"leaves\": \"leaf\",\n \"loaves\": \"loaf\", \n \"men\": \"man\",\n \"mongooses\": \"mongoose\", \n \"monies\": \"money\", \n \"moves\": \"move\",\n \"mythoi\": \"mythos\", \n \"numena\": \"numen\", \n \"occipita\": \"occiput\", \n \"octopodes\": \"octopus\", \n \"opera\": \"opus\", \n \"opuses\": \"opus\", \n \"our\": \"my\",\n \"oxen\": \"ox\", \n \"penes\": \"penis\", \n \"penises\": \"penis\", \n \"people\": \"person\",\n \"sexes\": \"sex\",\n \"soliloquies\": \"soliloquy\", \n \"teeth\": \"tooth\",\n \"testes\": \"testis\", \n \"trilbys\": \"trilby\", \n \"turves\": \"turf\", \n \"zoa\": \"zoon\",\n}\n\ndef singularize(word, pos=NOUN, custom={}):\n \"\"\" Returns the singular of a given word.\n \"\"\"\n if word in custom:\n return custom[word]\n # Recurse compound words (e.g. mothers-in-law). \n if \"-\" in word:\n w = word.split(\"-\")\n if len(w) > 1 and w[1] in plural_prepositions:\n return singularize(w[0], pos, custom)+\"-\"+\"-\".join(w[1:])\n # dogs' => dog's\n if word.endswith(\"'\"):\n return singularize(word[:-1]) + \"'s\"\n w = word.lower()\n for x in singular_uninflected:\n if x.endswith(w):\n return word\n for x in singular_uncountable:\n if x.endswith(w):\n return word\n for x in singular_ie:\n if w.endswith(x+\"s\"):\n return w\n for x in singular_irregular:\n if w.endswith(x):\n return re.sub('(?i)'+x+'$', singular_irregular[x], word)\n for suffix, inflection in singular_rules:\n m = suffix.search(word)\n g = m and m.groups() or [] \n if m:\n for k in range(len(g)):\n if g[k] is None:\n inflection = inflection.replace('\\\\' + str(k + 1), '')\n return suffix.sub(inflection, word)\n return word\n\n#### COMPARATIVE & SUPERLATIVE #####################################################################\n\nVOWELS = \"aeiouy\"\n\ngrade_irregular = {\n \"bad\": ( \"worse\", \"worst\"),\n \"far\": (\"further\", \"farthest\"),\n \"good\": ( \"better\", \"best\"), \n \"hind\": ( \"hinder\", \"hindmost\"),\n \"ill\": ( \"worse\", \"worst\"),\n \"less\": ( \"lesser\", \"least\"),\n \"little\": ( \"less\", \"least\"),\n \"many\": ( \"more\", \"most\"),\n \"much\": ( \"more\", \"most\"),\n \"well\": ( \"better\", \"best\")\n}\n\ngrade_uninflected = [\"giant\", \"glib\", \"hurt\", \"known\", \"madly\"]\n\nCOMPARATIVE = \"er\"\nSUPERLATIVE = \"est\"\n\ndef _count_syllables(word):\n \"\"\" Returns the estimated number of syllables in the word by counting vowel-groups.\n \"\"\"\n n = 0\n p = False # True if the previous character was a vowel.\n for ch in word.endswith(\"e\") and word[:-1] or word:\n v = ch in VOWELS\n n += int(v and not p)\n p = v\n return n\n\ndef grade(adjective, suffix=COMPARATIVE):\n \"\"\" Returns the comparative or superlative form of the given adjective.\n \"\"\"\n n = _count_syllables(adjective) \n if adjective in grade_irregular:\n # A number of adjectives inflect irregularly.\n return grade_irregular[adjective][suffix != COMPARATIVE]\n elif adjective in grade_uninflected:\n # A number of adjectives don't inflect at all.\n return \"%s %s\" % (suffix == COMPARATIVE and \"more\" or \"most\", adjective)\n elif n <= 2 and adjective.endswith(\"e\"):\n # With one syllable and ending with an e: larger, wiser.\n suffix = suffix.lstrip(\"e\")\n elif n == 1 and len(adjective) >= 3 \\\n and adjective[-1] not in VOWELS and adjective[-2] in VOWELS and adjective[-3] not in VOWELS:\n # With one syllable ending with consonant-vowel-consonant: bigger, thinner.\n if not adjective.endswith((\"w\")): # Exceptions: lower, newer.\n suffix = adjective[-1] + suffix\n elif n == 1:\n # With one syllable ending with more consonants or vowels: briefer.\n pass\n elif n == 2 and adjective.endswith(\"y\"):\n # With two syllables ending with a y: funnier, hairier.\n adjective = adjective[:-1] + \"i\"\n elif n == 2 and adjective[-2:] in (\"er\", \"le\", \"ow\"):\n # With two syllables and specific suffixes: gentler, narrower.\n pass\n else:\n # With three or more syllables: more generous, more important.\n return \"%s %s\" % (suffix==COMPARATIVE and \"more\" or \"most\", adjective)\n return adjective + suffix\n\ndef comparative(adjective):\n return grade(adjective, COMPARATIVE)\n\ndef superlative(adjective):\n return grade(adjective, SUPERLATIVE)\n\n#### ATTRIBUTIVE & PREDICATIVE #####################################################################\n\ndef attributive(adjective):\n return adjective\n\ndef predicative(adjective):\n return adjective\n"
},
{
"path": "corpkit/interpreter_tests.cki",
"content": "# this code is written in corpkit interpreter language.\n# it is used to test that the interpreter works properly\n\nset test-speak-parsed\nsearch corpus for words matching '^[abcd]' showing pos and lemma\nsearch corpus for governor-function matching 'root' showing lemma with preserve_case\nsearch corpus for words matching any showing lemma\nedit result by keeping entries matching wordlists.anyverb\nexport edited as csv to res.csv\nexport concordance as csv to conc.csv\nsave result as anylemma\n"
},
{
"path": "corpkit/interrogation.py",
"content": "\"\"\"\ncorpkit: `Int`errogation and Interrogation-like classes\n\"\"\"\n\nfrom __future__ import print_function\n\nfrom collections import OrderedDict\nimport pandas as pd\nfrom corpkit.process import classname\n\nclass Interrogation(object):\n \"\"\"\n Stores results of a corpus interrogation, before or after editing. The main \n attribute, :py:attr:`~corpkit.interrogation.Interrogation.results`, is a \n Pandas object, which can be edited or plotted.\n \"\"\"\n\n def __init__(self, results=None, totals=None, query=None, concordance=None):\n \"\"\"Initialise the class\"\"\"\n self.results = results\n \"\"\"pandas `DataFrame` containing counts for each subcorpus\"\"\"\n self.totals = totals\n \"\"\"pandas `Series` containing summed results\"\"\"\n self.query = query\n \"\"\"`dict` containing values that generated the result\"\"\"\n self.concordance = concordance\n \"\"\"pandas `DataFrame` containing concordance lines, if concordance lines were requested.\"\"\"\n\n def __str__(self):\n if self.query.get('corpus'):\n prst = getattr(self.query['corpus'], 'name', self.query['corpus'])\n else:\n try:\n prst = self.query['interrogation'].query['corpus'].name\n except:\n prst = 'edited'\n st = 'Corpus interrogation: %s\\n\\n' % (prst)\n return st\n\n def __repr__(self):\n try:\n return \"<%s instance: %d total>\" % (classname(self), self.totals.sum())\n except AttributeError:\n return \"<%s instance: %d total>\" % (classname(self), self.totals)\n\n def edit(self, *args, **kwargs):\n \"\"\"\n Manipulate results of interrogations.\n\n There are a few overall kinds of edit, most of which can be combined \n into a single function call. It's useful to keep in mind that many are \n basic wrappers around `pandas` operations---if you're comfortable with \n `pandas` syntax, it may be faster at times to use its syntax instead.\n\n :Basic mathematical operations:\n\n First, you can do basic maths on results, optionally passing in some \n data to serve as the denominator. Very commonly, you'll want to get \n relative frequencies:\n\n :Example: \n\n >>> data = corpus.interrogate({W: r'^t'})\n >>> rel = data.edit('%', SELF)\n >>> rel.results\n .. to that the then ... toilet tolerant tolerate ton\n 01 18.50 14.65 14.44 6.20 ... 0.00 0.00 0.11 0.00\n 02 24.10 14.34 13.73 8.80 ... 0.00 0.00 0.00 0.00\n 03 17.31 18.01 9.97 7.62 ... 0.00 0.00 0.00 0.00\n\n For the operation, there are a number of possible values, each of \n which is to be passed in as a `str`:\n\n `+`, `-`, `/`, `*`, `%`: self explanatory\n\n `k`: calculate keywords\n\n `a`: get distance metric\n \n `SELF` is a very useful shorthand denominator. When used, all editing \n is performed on the data. The totals are then extracted from the edited \n data, and used as denominator. If this is not the desired behaviour, \n however, a more specific `interrogation.results` or \n `interrogation.totals` attribute can be used.\n\n In the example above, `SELF` (or `'self'`) is equivalent to:\n\n :Example:\n\n >>> rel = data.edit('%', data.totals)\n\n :Keeping and skipping data:\n\n There are four keyword arguments that can be used to keep or skip rows \n or columns in the data:\n\n * `just_entries`\n * `just_subcorpora`\n * `skip_entries`\n * `skip_subcorpora`\n\n Each can accept different input types:\n\n * `str`: treated as regular expression to match\n * `list`: \n\n * of integers: indices to match\n * of strings: entries/subcorpora to match\n\n :Example:\n\n >>> data.edit(just_entries=r'^fr', \n ... skip_entries=['free','freedom'],\n ... skip_subcorpora=r'[0-9]')\n\n :Merging data:\n\n There are also keyword arguments for merging entries and subcorpora:\n\n * `merge_entries`\n * `merge_subcorpora`\n\n These take a `dict`, with the new name as key and the criteria as \n value. The criteria can be a str (regex) or wordlist.\n\n :Example:\n \n >>> from dictionaries.wordlists import wordlists\n >>> mer = {'Articles': ['the', 'an', 'a'], 'Modals': wordlists.modals}\n >>> data.edit(merge_entries=mer)\n\n :Sorting:\n\n The `sort_by` keyword argument takes a `str`, which represents the way \n the result columns should be ordered.\n\n * `increase`: highest to lowest slope value\n * `decrease`: lowest to highest slope value\n * `turbulent`: most change in y axis values\n * `static`: least change in y axis values\n * `total/most`: largest number first\n * `infreq/least`: smallest number first\n * `name`: alphabetically\n\n :Example:\n\n >>> data.edit(sort_by='increase')\n\n :Editing text:\n \n Column labels, corresponding to individual interrogation results, can \n also be edited with `replace_names`.\n\n :param replace_names: Edit result names, then merge duplicate entries\n :type replace_names: `str`/`list of tuples`/`dict`\n\n If `replace_names` is a string, it is treated as a regex to delete from \n each name. If `replace_names` is a dict, the value is the regex, and \n the key is the replacement text. Using a list of tuples in the form \n `(find, replacement)` allows duplicate substitution values.\n\n :Example:\n\n >>> data.edit(replace_names={r'object': r'[di]obj'})\n\n :param replace_subcorpus_names: Edit subcorpus names, then merge duplicates.\n The same as `replace_names`, but on the other axis.\n :type replace_subcorpus_names: `str`/`list of tuples`/`dict`\n\n :Other options:\n\n There are many other miscellaneous options.\n\n :param keep_stats: Keep/drop stats values from dataframe after sorting\n :type keep_stats: `bool`\n \n :param keep_top: After sorting, remove all but the top *keep_top* results\n :type keep_top: `int`\n \n :param just_totals: Sum each column and work with sums\n :type just_totals: `bool`\n \n :param threshold: When using results list as dataframe 2, drop values \n occurring fewer than n times. If not keywording, you \n can use:\n \n `'high'`: `denominator total / 2500`\n \n `'medium'`: `denominator total / 5000`\n \n `'low'`: `denominator total / 10000`\n \n If keywording, there are smaller default thresholds\n\n :type threshold: `int`/`bool`\n\n :param span_subcorpora: If subcorpora are numerically named, span all \n from *int* to *int2*, inclusive\n :type span_subcorpora: `tuple` -- `(int, int2)`\n\n :param projection: multiply results in subcorpus by n\n :type projection: tuple -- `(subcorpus_name, n)`\n :param remove_above_p: Delete any result over `p`\n :type remove_above_p: `bool`\n\n :param p: set the p value\n :type p: `float`\n \n :param revert_year: When doing linear regression on years, turn annual \n subcorpora into 1, 2 ...\n :type revert_year: `bool`\n \n :param print_info: Print stuff to console showing what's being edited\n :type print_info: `bool`\n \n :param spelling: Convert/normalise spelling:\n :type spelling: `str` -- `'US'`/`'UK'`\n\n :Keywording options:\n\n If the operation is `k`, you're calculating keywords. In this case,\n some other keyword arguments have an effect:\n\n :param keyword_measure: what measure to use to calculate keywords:\n\n `ll`: log-likelihood\n `pd': percentage difference\n\n type keyword_measure: `str`\n \n :param selfdrop: When keywording, try to remove target corpus from \n reference corpus\n :type selfdrop: `bool`\n \n :param calc_all: When keywording, calculate words that appear in either \n corpus\n :type calc_all: `bool`\n\n :returns: :class:`corpkit.interrogation.Interrogation`\n \"\"\"\n from corpkit.editor import editor\n return editor(self, *args, **kwargs)\n\n def sort(self, way, **kwargs):\n from corpkit.editor import editor\n return editor(self, sort_by=way, **kwargs)\n\n def visualise(self,\n title='',\n x_label=None,\n y_label=None,\n style='ggplot',\n figsize=(8, 4),\n save=False,\n legend_pos='best',\n reverse_legend='guess',\n num_to_plot=7,\n tex='try',\n colours='Accent',\n cumulative=False,\n pie_legend=True,\n rot=False,\n partial_pie=False,\n show_totals=False,\n transparent=False,\n output_format='png',\n interactive=False,\n black_and_white=False,\n show_p_val=False,\n indices=False,\n transpose=False,\n **kwargs\n ):\n \"\"\"Visualise corpus interrogations using `matplotlib`.\n\n :Example:\n\n >>> data.visualise('An example plot', kind='bar', save=True)\n \n\n :param title: A title for the plot\n :type title: `str`\n :param x_label: A label for the x axis\n :type x_label: `str`\n :param y_label: A label for the y axis\n :type y_label: `str`\n :param kind: The kind of chart to make\n :type kind: `str` (`'line'`/`'bar'`/`'barh'`/`'pie'`/`'area'`/`'heatmap'`)\n :param style: Visual theme of plot\n :type style: `str` ('ggplot'/'bmh'/'fivethirtyeight'/'seaborn-talk'/etc)\n :param figsize: Size of plot\n :type figsize: `tuple` -- `(int, int)`\n :param save: If `bool`, save with *title* as name; if `str`, use `str` as name\n :type save: `bool`/`str`\n :param legend_pos: Where to place legend\n :type legend_pos: `str` ('upper right'/'outside right'/etc)\n :param reverse_legend: Reverse the order of the legend\n :type reverse_legend: `bool`\n :param num_to_plot: How many columns to plot\n :type num_to_plot: `int`/'all'\n :param tex: Use TeX to draw plot text\n :type tex: `bool`\n :param colours: Colourmap for lines/bars/slices\n :type colours: `str`\n :param cumulative: Plot values cumulatively\n :type cumulative: `bool`\n :param pie_legend: Show a legend for pie chart\n :type pie_legend: `bool`\n :param partial_pie: Allow plotting of pie slices only\n :type partial_pie: `bool`\n :param show_totals: Print sums in plot where possible\n :type show_totals: `str` -- 'legend'/'plot'/'both'\n :param transparent: Transparent .png background\n :type transparent: `bool`\n :param output_format: File format for saved image\n :type output_format: `str` -- 'png'/'pdf'\n :param black_and_white: Create black and white line styles\n :type black_and_white: `bool`\n :param show_p_val: Attempt to print p values in legend if contained in df\n :type show_p_val: `bool`\n :param indices: To use when plotting \"distance from root\"\n :type indices: `bool`\n :param stacked: When making bar chart, stack bars on top of one another\n :type stacked: `str`\n :param filled: For area and bar charts, make every column sum to 100\n :type filled: `str`\n :param legend: Show a legend\n :type legend: `bool`\n :param rot: Rotate x axis ticks by *rot* degrees\n :type rot: `int`\n :param subplots: Plot each column separately\n :type subplots: `bool`\n :param layout: Grid shape to use when *subplots* is True\n :type layout: `tuple` -- `(int, int)`\n :param interactive: Experimental interactive options\n :type interactive: `list` -- `[1, 2, 3]`\n :returns: matplotlib figure\n \"\"\"\n from corpkit.plotter import plotter\n branch = kwargs.pop('branch', 'results')\n if branch.lower().startswith('r'):\n to_plot = self.results\n elif branch.lower().startswith('t'):\n to_plot = self.totals\n return plotter(to_plot,\n title=title,\n x_label=x_label,\n y_label=y_label,\n style=style,\n figsize=figsize,\n save=save,\n legend_pos=legend_pos,\n reverse_legend=reverse_legend,\n num_to_plot=num_to_plot,\n tex=tex,\n rot=rot,\n colours=colours,\n cumulative=cumulative,\n pie_legend=pie_legend,\n partial_pie=partial_pie,\n show_totals=show_totals,\n transparent=transparent,\n output_format=output_format,\n interactive=interactive,\n black_and_white=black_and_white,\n show_p_val=show_p_val,\n indices=indices,\n transpose=transpose,\n **kwargs\n )\n\n def multiplot(self, leftdict={}, rightdict={}, **kwargs):\n from corpkit.plotter import multiplotter\n return multiplotter(self, leftdict=leftdict, rightdict=rightdict, **kwargs)\n\n def language_model(self, name, *args, **kwargs):\n \"\"\"\n Make a language model from an Interrogation. This is usually done \n directly on a :class:`corpkit.corpus.Corpus` object with the \n :func:`~corpkit.corpus.Corpus.make_language_model` method.\n \"\"\"\n from corpkit.model import _make_model_from_interro\n multi = self.multiindex()\n order = len(self.query['show'])\n return _make_model_from_interro(multi, name, order=order, *args, **kwargs)\n\n def save(self, savename, savedir='saved_interrogations', **kwargs):\n \"\"\"\n Save an interrogation as pickle to ``savedir``.\n\n :Example:\n \n >>> o = corpus.interrogate(W, 'any')\n ### create ./saved_interrogations/savename.p\n >>> o.save('savename')\n \n :param savename: A name for the saved file\n :type savename: `str`\n \n :param savedir: Relative path to directory in which to save file\n :type savedir: `str`\n \n :param print_info: Show/hide stdout\n :type print_info: `bool`\n \n :returns: None\n \"\"\"\n from corpkit.other import save\n save(self, savename, savedir=savedir, **kwargs)\n\n def quickview(self, n=25):\n \"\"\"view top n results as painlessly as possible.\n\n :Example:\n \n >>> data.quickview(n=5)\n 0: to (n=2227)\n 1: that (n=2026)\n 2: the (n=1302)\n 3: then (n=857)\n 4: think (n=676)\n\n :param n: Show top *n* results\n :type n: `int`\n :returns: `None`\n \"\"\"\n from corpkit.other import quickview\n quickview(self, n=n)\n\n def tabview(self, **kwargs):\n import tabview\n tabview.view(self.results, **kwargs)\n\n def asciiplot(self,\n row_or_col_name,\n axis=0,\n colours=True,\n num_to_plot=100,\n line_length=120,\n min_graph_length=50,\n separator_length=4,\n multivalue=False,\n human_readable='si',\n graphsymbol='*',\n float_format='{:,.2f}',\n **kwargs):\n \"\"\"\n A very quick ascii chart for result\n \"\"\"\n from ascii_graph import Pyasciigraph\n from ascii_graph.colors import Gre, Yel, Red, Blu\n from ascii_graph.colordata import vcolor\n from ascii_graph.colordata import hcolor\n import pydoc\n\n graph = Pyasciigraph(\n line_length=line_length,\n min_graph_length=min_graph_length,\n separator_length=separator_length,\n multivalue=multivalue,\n human_readable=human_readable,\n graphsymbol=graphsymbol\n )\n if axis == 0:\n dat = self.results.T[row_or_col_name]\n else:\n dat = self.results[row_or_col_name]\n data = list(zip(dat.index, dat.values))[:num_to_plot]\n if colours:\n pattern = [Gre, Yel, Red]\n data = vcolor(data, pattern)\n\n out = []\n for line in graph.graph(label=None, data=data, float_format=float_format):\n out.append(line)\n pydoc.pipepager('\\n'.join(out), cmd='less -X -R -S')\n\n def rel(self, denominator='self', **kwargs):\n return self.edit('%', denominator, **kwargs)\n\n def keyness(self, measure='ll', denominator='self', **kwargs):\n return self.edit('k', denominator, **kwargs)\n\n def multiindex(self, indexnames=None):\n \"\"\"Create a `pandas.MultiIndex` object from slash-separated results.\n\n :Example:\n\n >>> data = corpus.interrogate({W: 'st$'}, show=[L, F])\n >>> data.results\n .. just/advmod almost/advmod last/amod \n 01 79 12 6 \n 02 105 6 7 \n 03 86 10 1 \n >>> data.multiindex().results\n Lemma just almost last first most \n Function advmod advmod amod amod advmod \n 0 79 12 6 2 3 \n 1 105 6 7 1 3 \n 2 86 10 1 3 0 \n\n :param indexnames: provide custom names for the new index, or leave blank to guess.\n :type indexnames: `list` of strings\n\n :returns: :class:`corpkit.interrogation.Interrogation`, with \n `pandas.MultiIndex` as \n :py:attr:`~corpkit.interrogation.Interrogation.results` attribute\n \"\"\"\n\n from corpkit.other import make_multi\n return make_multi(self, indexnames=indexnames)\n\n def topwords(self, datatype='n', n=10, df=False, sort=True, precision=2):\n \"\"\"Show top n results in each corpus alongside absolute or relative frequencies.\n\n :param datatype: show abs/rel frequencies, or keyness\n :type datatype: `str` (n/k/%)\n :param n: number of result to show\n :type n: `int`\n :param df: return a DataFrame\n :type df: `bool`\n :param sort: Sort results, or show as is\n :type sort: `bool`\n :param precision: float precision to show\n :type precision: `int`\n\n :Example:\n\n >>> data.topwords(n=5)\n 1987 % 1988 % 1989 % 1990 %\n health 25.70 health 15.25 health 19.64 credit 9.22\n security 6.48 cancer 10.85 security 7.91 health 8.31\n cancer 6.19 heart 6.31 cancer 6.55 downside 5.46\n flight 4.45 breast 4.29 credit 4.08 inflation 3.37\n safety 3.49 security 3.94 safety 3.26 cancer 3.12\n\n :returns: None\n \"\"\"\n from corpkit.other import topwords\n if df:\n return topwords(self, datatype=datatype, n=n, df=True,\n sort=sort, precision=precision)\n else:\n topwords(self, datatype=datatype, n=n,\n sort=sort, precision=precision)\n\n\n\n def perplexity(self):\n \"\"\"\n Pythonification of the formal definition of perplexity.\n\n input: a sequence of chances (any iterable will do)\n output: perplexity value.\n\n from https://github.com/zeffii/NLP_class_notes\n \"\"\"\n\n def _perplex(chances):\n import math\n chances = [i for i in chances if i] \n N = len(chances)\n product = 1\n for chance in chances:\n product *= chance\n return math.pow(product, -1/N)\n\n return self.results.apply(_perplex, axis=1)\n\n def entropy(self):\n \"\"\"\n entropy(pos.edit(merge_entries=mergetags, sort_by='total').results.T\n \"\"\"\n from scipy.stats import entropy\n import pandas as pd\n escores = entropy(self.rel().results.T)\n ser = pd.Series(escores, index=self.results.index)\n ser.name = 'Entropy'\n return ser\n\n def shannon(self):\n from corpkit.stats import shannon\n return shannon(self)\n\nclass Concordance(pd.core.frame.DataFrame):\n \"\"\"\n A class for concordance lines, with methods for saving, formatting and editing.\n \"\"\"\n \n def __init__(self, data):\n\n super(Concordance, self).__init__(data)\n self.concordance = data\n\n def format(self, kind='string', n=100, window=35,\n print_it=True, columns='all', metadata=True, **kwargs):\n \"\"\"\n Print concordance lines nicely, to string, LaTeX or CSV\n\n :param kind: output format: `string`/`latex`/`csv`\n :type kind: `str`\n :param n: Print first `n` lines only\n :type n: `int`/`'all'`\n :param window: how many characters to show to left and right\n :type window: `int`\n :param columns: which columns to show\n :type columns: `list`\n\n :Example:\n\n >>> lines = corpus.concordance({T: r'/NN.?/ >># NP'}, show=L)\n ### show 25 characters either side, 4 lines, just text columns\n >>> lines.format(window=25, n=4, columns=[L,M,R])\n 0 we 're in tucson , then up north to flagst\n 1 e 're in tucson , then up north to flagstaff , then we we\n 2 tucson , then up north to flagstaff , then we went through th\n 3 through the grand canyon area and then phoenix and i sp\n\n :returns: None\n \"\"\"\n from corpkit.other import concprinter\n if print_it:\n print(concprinter(self, kind=kind, n=n, window=window,\n columns=columns, return_it=True, metadata=metadata, **kwargs))\n else:\n return concprinter(self, kind=kind, n=n, window=window,\n columns=columns, return_it=True, metadata=metadata, **kwargs)\n\n def calculate(self):\n \"\"\"Make new Interrogation object from (modified) concordance lines\"\"\"\n from corpkit.process import interrogation_from_conclines\n return interrogation_from_conclines(self)\n\n def shuffle(self, inplace=False):\n \"\"\"Shuffle concordance lines\n\n :param inplace: Modify current object, or create a new one\n :type inplace: `bool`\n\n :Example:\n\n >>> lines[:4].shuffle()\n 3 01 1-01.txt.conll through the grand canyon area and then phoenix and i sp\n 1 01 1-01.txt.conll e 're in tucson , then up north to flagstaff , then we we\n 0 01 1-01.txt.conll we 're in tucson , then up north to flagst\n 2 01 1-01.txt.conll tucson , then up north to flagstaff , then we went through th\n\n \"\"\"\n import random\n index = list(self.index)\n random.shuffle(index)\n shuffled = self.ix[index]\n shuffled.reset_index()\n if inplace:\n self = shuffled\n else:\n return shuffled\n\n def edit(self, *args, **kwargs):\n \"\"\"\n Delete or keep rows by subcorpus or by middle column text.\n\n >>> skipped = conc.edit(skip_entries=r'to_?match')\n \"\"\"\n\n from corpkit.editor import editor\n return editor(self, *args, **kwargs)\n\n def __str__(self):\n return self.format(print_it=False)\n\n def __repr__(self):\n return self.format(print_it=False)\n\n def less(self, **kwargs):\n import pydoc\n pydoc.pipepager(self.format(print_it=False, **kwargs), cmd='less -X -R -S')\n\nclass Interrodict(OrderedDict):\n \"\"\"\n A class for interrogations that do not fit in a single-indexed DataFrame.\n\n Individual interrogations can be looked up via dict keys, indexes or attributes:\n\n :Example:\n\n >>> out_data['WSJ'].results\n >>> out_data.WSJ.results\n >>> out_data[3].results\n\n Methods for saving, editing, etc. are similar to \n :class:`corpkit.corpus.Interrogation`. Additional methods are available for \n collapsing into single (multi-indexed) DataFrames.\n\n This class is now deprecated, in favour of a multiindexed DataFrame.\n \"\"\"\n \n def __init__(self, data):\n from corpkit.process import makesafe\n if isinstance(data, list):\n data = OrderedDict(data)\n # attribute access\n for k, v in data.items():\n setattr(self, makesafe(k), v)\n self.query = None\n super(Interrodict, self).__init__(data)\n\n def __getitem__(self, key):\n \"\"\"allow slicing, indexing\"\"\"\n from corpkit.process import makesafe\n # allow slicing\n if isinstance(key, slice):\n n = OrderedDict()\n for ii in range(*key.indices(len(self))):\n n[self.keys()[ii]] = self[ii]\n return Interrodict(n)\n # allow integer index\n elif isinstance(key, int):\n return next(v for i, (k, v) in enumerate(self.items()) if i == key)\n #return self.subcorpora.__getitem__(makesafe(self.subcorpora[key]))\n # dict key access\n else:\n try:\n return OrderedDict.__getitem__(self, key)\n except:\n from corpkit.process import is_number\n if is_number(key):\n return self.__getattribute__('c' + key)\n\n def __setitem__(self, key, value):\n from corpkit.process import makesafe\n setattr(self, makesafe(key), value)\n super(Interrodict, self).__setitem__(key, value)\n \n def __repr__(self):\n return \"<%s instance: %d items>\" % (classname(self), len(self))\n\n def __str__(self):\n return \"<%s instance: %d items>\" % (classname(self), len(self))\n\n def edit(self, *args, **kwargs):\n \"\"\"Edit each value with :func:`~corpkit.interrogation.Interrogation.edit`.\n\n See :func:`~corpkit.interrogation.Interrogation.edit` for possible arguments.\n\n :returns: A :class:`corpkit.interrogation.Interrodict`\n \"\"\"\n\n from corpkit.editor import editor\n return editor(self, *args, **kwargs)\n\n def multiindex(self, indexnames=False):\n\n \"\"\"Create a `pandas.MultiIndex` version of results.\n\n :Example:\n\n >>> d = corpora.interrogate({F: 'compound', GL: '^risk'}, show=L)\n >>> d.keys()\n ['CHT', 'WAP', 'WSJ']\n >>> d['CHT'].results\n .... health cancer security credit flight safety heart\n 1987 87 25 28 13 7 6 4\n 1988 72 24 20 15 7 4 9\n 1989 137 61 23 10 5 5 6\n >>> d.multiindex().results\n ... health cancer credit security downside \n Corpus Subcorpus \n CHT 1987 87 25 13 28 20 \n 1988 72 24 15 20 12 \n 1989 137 61 10 23 10 \n WAP 1987 83 44 8 44 10 \n 1988 83 27 13 40 6 \n 1989 95 77 18 25 12 \n WSJ 1987 52 27 33 4 21 \n 1988 39 11 37 9 22 \n 1989 55 47 43 9 24 \n\n :returns: A :class:`corpkit.interrogation.Interrogation`\n \"\"\"\n\n import pandas as pd\n import numpy as np\n from itertools import product\n from corpkit.interrogation import Interrodict, Interrogation\n\n query = self.query\n\n def trav(dct, parents={}, level=0, colset=set(), results=list(), conc_results=list(), myparname=[]):\n \"\"\"\n Traverse the Interrodict and flatten it out\n \"\"\"\n from collections import defaultdict\n columns = False\n if hasattr(dct, 'items'):\n parents[level] = list(dct.keys())\n level += 1\n for k, v in list(dct.items()):\n pars = myparname + [k]\n # the below is only for python3\n #pars = [*myparname, k]\n trav(v, parents=parents, level=level,\n results=results, myparname=pars)\n else:\n if parents.get(level):\n parents[level] |= set(dct.results.index)\n else:\n parents[level] = set(dct.results.index)\n\n if not dct.results.empty:\n if dct.concordance is not None:\n conc_results.append(dct.concordance)\n for n, ser in dct.results.iterrows():\n ser.name = tuple(myparname + [ser.name])\n #ser.name = (*myparname, ser.name)\n results.append(ser)\n for c in list(dct.results.columns):\n colset.add(c)\n level += 1\n return results, conc_results\n\n data, conc = trav(self)\n index = [i.name for i in data]\n if conc:\n conc = pd.concat(conc)\n else:\n conc = None\n\n # todo: better default for speakers?\n if isinstance(self.query, dict) and self.query.get('subcorpora'):\n nms = {'names': self.query['subcorpora']}\n elif indexnames:\n nms = {'names': indexnames}\n else:\n nms = {} \n ix = pd.MultiIndex.from_tuples(index, **nms)\n df = pd.DataFrame(data, index=ix)\n df = df.fillna(0).astype(int)\n df = df[df.sum().sort_values(ascending=False).index]\n totals = df.sum(axis=1)\n return Interrogation(results=df, totals=totals, query=query, concordance=conc)\n\n def save(self, savename, savedir='saved_interrogations', **kwargs):\n \"\"\"\n Save an interrogation as pickle to `savedir`.\n\n :param savename: A name for the saved file\n :type savename: `str`\n \n :param savedir: Relative path to directory in which to save file\n :type savedir: `str`\n \n :param print_info: Show/hide stdout\n :type print_info: `bool`\n \n :Example: \n\n >>> o = corpus.interrogate(W, 'any')\n ### create ``saved_interrogations/savename.p``\n >>> o.save('savename')\n\n :returns: None\n \"\"\"\n from corpkit.other import save\n save(self, savename, savedir=savedir, **kwargs)\n\n def collapse(self, axis='y'):\n \"\"\"\n Collapse Interrodict on an axis or along interrogation name.\n\n :param axis: collapse along x, y or name axis\n :type axis: `str`: x/y/n\n\n :Example:\n\n .. code-block:: python\n\n >>> d = corpora.interrogate({F: 'compound', GL: r'^risk'}, show=L)\n \n >>> d.keys()\n ['CHT', 'WAP', 'WSJ']\n \n >>> d['CHT'].results\n .... health cancer security credit flight safety heart\n 1987 87 25 28 13 7 6 4\n 1988 72 24 20 15 7 4 9\n 1989 137 61 23 10 5 5 6\n \n >>> d.collapse().results\n ... health cancer credit security\n CHT 3174 1156 566 697\n WAP 2799 933 582 1127\n WSJ 1812 680 2009 537\n \n >>> d.collapse(axis='x').results\n ... 1987 1988 1989\n CHT 384 328 464\n WAP 389 355 435\n WSJ 428 410 473\n \n >>> d.collapse(axis='key').results\n ... health cancer credit security\n 1987 282 127 65 93\n 1988 277 100 70 107\n 1989 379 253 83 91\n \n :returns: A :class:`corpkit.interrogation.Interrogation`\n \"\"\"\n # join on keys ... probably shouldn't transpose like this though!\n if axis.lower()[0] not in ['x', 'y']:\n df = self.values()[0].results\n others = [i.results.T for i in list(self.values())[1:]]\n try:\n df = df.T.join(others).T\n except ValueError:\n for i in self.values()[1:]:\n df = df.add(i.results, fill_value=0)\n\n df = df.fillna(0)\n else:\n out = []\n for corpus_name, interro in self.items():\n if axis.lower().startswith('y'):\n ax = 0\n elif axis.lower().startswith('x'):\n ax = 1\n data = interro.results.sum(axis=ax)\n data.name = corpus_name\n out.append(data)\n # concatenate and transpose\n df = pd.concat(out, axis=1).T\n # turn NaN to 0, sort\n df = df.fillna(0)\n \n #make interrogation object from df\n if not axis.lower().startswith('x'):\n df = df.edit(sort_by='total', print_info=False)\n else:\n df = df.edit(print_info=False)\n # make sure everything is int, not float\n for col in list(df.results.columns):\n df.results[col] = df.results[col].astype(int)\n return df\n\n def topwords(self, datatype='n', n=10, df=False, sort=True, precision=2):\n \"\"\"Show top n results in each corpus alongside absolute or relative frequencies.\n\n :param datatype: show abs/rel frequencies, or keyness\n :type datatype: `str` (n/k/%)\n :param n: number of result to show\n :type n: `int`\n :param df: return a DataFrame\n :type df: `bool`\n :param sort: Sort results, or show as is\n :type sort: `bool`\n :param precision: float precision to show\n :type precision: `int`\n :Example:\n\n >>> data.topwords(n=5)\n TBT % UST % WAP % WSJ %\n health 25.70 health 15.25 health 19.64 credit 9.22\n security 6.48 cancer 10.85 security 7.91 health 8.31\n cancer 6.19 heart 6.31 cancer 6.55 downside 5.46\n flight 4.45 breast 4.29 credit 4.08 inflation 3.37\n safety 3.49 security 3.94 safety 3.26 cancer 3.12\n\n :returns: None\n \"\"\"\n from corpkit.other import topwords\n if df:\n return topwords(self, datatype=datatype, n=n, df=True,\n sort=sort, precision=precision)\n else:\n topwords(self, datatype=datatype, n=n,\n sort=sort, precision=precision)\n\n def visualise(self, shape='auto', truncate=8, **kwargs):\n \"\"\"\n Attempt to visualise Interrodict by using subplots\n\n :param shape: Layout for the subplots (e.g. `(2, 2)`)\n :type shape: tuple\n\n :param truncate: Only process the first `n` items in the \n class:`corpkit.interrogation.Interrodict`\n :type truncate: `int`\n\n :param kwargs: specifications to pass to :func:`~corpkit.plotter.plotter`\n :type kwargs: keyword arguments\n \"\"\"\n import matplotlib.pyplot as plt\n if shape == 'auto':\n shape = (int(len(self) / 2), 2)\n if truncate:\n self = self[:truncate]\n f, axes = plt.subplots(*shape)\n for (name, interro), ax in zip(self.items(), axes.flatten()):\n if kwargs.get('name_format'):\n name = name_format.format(name)\n interro.visualise(name, ax=ax, **kwargs)\n return plt\n\n def copy(self):\n from corpkit.interrogation import Interrodict\n copied = {}\n for k, v in self.items():\n copied[k] = v\n return Interrodict(copied)\n\n def flip(self, truncate=30, transpose=True, repeat=False, *args, **kwargs):\n \"\"\"\n Change the dimensions of :class:`corpkit.interrogation.Interrodict`,\n making column names into keys.\n\n :param truncate: Get first `n` columns\n :type truncate: `int`/`'all'`\n\n :param transpose: Flip rows and columns:\n :type transpose: `bool`\n\n :param repeat: Flip twice, to move columns into key position\n :type repeat: `bool`\n\n :param kwargs: Arguments to pass to the \n :func:`~corpkit.interrogation.Interrogation.edit`\n method\n\n :returns: :class:`corpkit.interrogation.Interrodict`\n \"\"\"\n import pandas as pd\n from corpkit.interrogation import Interrodict\n\n # copy interrodict\n copied = self.copy()\n\n # first, flip x axis and keys\n words = list(copied.collapse().results.columns)\n if truncate != 'all':\n words = words[:truncate]\n\n data = {}\n for word in words:\n wordata = []\n for k, v in copied.items():\n try:\n point = v.results[word]\n except KeyError:\n ser = [0] * len(v.results.index)\n point = pd.Series(ser, index=v.results.index)\n point.name = k\n wordata.append(point)\n df = pd.concat(wordata, axis=1)\n if transpose:\n df = df.T\n df = df.edit(*args, **kwargs)\n # divide each newspaper separately\n data[word] = df\n idi = Interrodict(data)\n if repeat:\n return idi.flip(truncate=truncate, transpose=False, repeat=False)\n else:\n return idi\n\n def get_totals(self):\n \"\"\"\n Helper function to concatenate all totals\n \"\"\"\n lst = []\n # for each interrogation name and data\n for k, v in self.items():\n # get the totals\n tot = v.totals\n # name the totals with the corpus name\n tot.name = k\n # add to a list\n lst.append(tot)\n # turn the list into a dataframe\n return pd.concat(lst, axis=1)\n"
},
{
"path": "corpkit/interrogator.py",
"content": "\"\"\"\ncorpkit: Interrogate a parsed corpus\n\"\"\"\n\nfrom __future__ import print_function\nfrom corpkit.constants import STRINGTYPE, PYTHON_VERSION, INPUTFUNC\n\ndef interrogator(corpus, \n search='w', \n query='any',\n show='w',\n exclude=False,\n excludemode='any',\n searchmode='all',\n case_sensitive=False,\n save=False,\n subcorpora=False,\n just_metadata=False,\n skip_metadata=False,\n preserve_case=False,\n lemmatag=False,\n files_as_subcorpora=False,\n only_unique=False,\n only_format_match=True,\n multiprocess=False,\n spelling=False,\n regex_nonword_filter=r'[A-Za-z0-9]',\n gramsize=1,\n conc=False,\n maxconc=9999,\n window=None,\n no_closed=False,\n no_punct=True,\n discard=False,\n **kwargs):\n \"\"\"\n Interrogate corpus, corpora, subcorpus and file objects.\n See corpkit.interrogation.interrogate() for docstring\n \"\"\"\n \n conc = kwargs.get('do_concordancing', conc)\n quiet = kwargs.get('quiet', False)\n coref = kwargs.pop('coref', False)\n show_conc_metadata = kwargs.pop('show_conc_metadata', False)\n fsi_index = kwargs.pop('fsi_index', True)\n dep_type = kwargs.pop('dep_type', 'collapsed-ccprocessed-dependencies')\n\n nosubmode = subcorpora is None\n #todo: temporary\n #if getattr(corpus, '_dlist', False):\n # subcorpora = 'file'\n\n # store kwargs and locs\n locs = locals().copy()\n locs.update(kwargs)\n locs.pop('kwargs', None)\n\n import codecs\n import signal\n import os\n from time import localtime, strftime\n from collections import Counter\n\n import pandas as pd\n from pandas import DataFrame, Series\n\n from corpkit.interrogation import Interrogation, Interrodict\n from corpkit.corpus import Datalist, Corpora, Corpus, File, Subcorpus\n from corpkit.process import (tregex_engine, get_deps, unsplitter, sanitise_dict, \n animator, filtermaker, fix_search,\n pat_format, auto_usecols, format_tregex,\n make_conc_lines_from_whole_mid)\n from corpkit.other import as_regex\n from corpkit.dictionaries.process_types import Wordlist\n from corpkit.build import check_jdk\n from corpkit.conll import pipeline\n from corpkit.process import delete_files_and_subcorpora\n \n have_java = check_jdk()\n\n # remake corpus without bad files and folders \n corpus, skip_metadata, just_metadata = delete_files_and_subcorpora(corpus, skip_metadata, just_metadata)\n\n # so you can do corpus.interrogate('features/postags/wordclasses/lexicon')\n if search == 'features':\n search = 'v'\n query = 'any'\n if search in ['postags', 'wordclasses']:\n query = 'any'\n preserve_case = True\n show = 'p' if search == 'postags' else 'x'\n # use tregex if simple because it's faster\n # but use dependencies otherwise\n search = 't' if not subcorpora and not just_metadata and not skip_metadata and have_java else {'w': 'any'}\n if search == 'lexicon':\n search = 't' if not subcorpora and not just_metadata and not skip_metadata and have_java else {'w': 'any'}\n query = 'any'\n show = ['w']\n\n if not kwargs.get('cql') and isinstance(search, STRINGTYPE) and len(search) > 3:\n raise ValueError('search argument not recognised.')\n\n import re\n if regex_nonword_filter:\n is_a_word = re.compile(regex_nonword_filter)\n else:\n is_a_word = re.compile(r'.*')\n\n from traitlets import TraitError\n\n # convert cql-style queries---pop for the sake of multiprocessing\n cql = kwargs.pop('cql', None)\n if cql:\n from corpkit.cql import to_corpkit\n search, exclude = to_corpkit(search)\n\n def signal_handler(signal, _):\n \"\"\"\n Allow pausing and restarting whn not in GUI\n \"\"\"\n if root:\n return \n import signal\n import sys\n from time import localtime, strftime\n signal.signal(signal.SIGINT, original_sigint)\n thetime = strftime(\"%H:%M:%S\", localtime())\n INPUTFUNC('\\n\\n%s: Paused. Press any key to resume, or ctrl+c to quit.\\n' % thetime)\n time = strftime(\"%H:%M:%S\", localtime())\n print('%s: Interrogation resumed.\\n' % time)\n signal.signal(signal.SIGINT, signal_handler)\n\n def add_adj_for_ngram(show, gramsize):\n \"\"\"\n If there's a gramsize of more than 1, remake show\n for ngramming\n \"\"\"\n if gramsize == 1:\n return show\n out = []\n for i in show:\n out.append(i)\n for i in range(1, gramsize):\n for bit in show:\n out.append('+%d%s' % (i, bit))\n return out\n\n def fix_show_bit(show_bit):\n \"\"\"\n Take a single search/show_bit type, return match\n \"\"\"\n ends = ['w', 'l', 'i', 'n', 'f', 'p', 'x', 's', 'a', 'e', 'c']\n starts = ['d', 'g', 'm', 'b', 'h', '+', '-', 'r', 'c']\n show_bit = show_bit.lstrip('n')\n show_bit = show_bit.lstrip('b')\n show_bit = list(show_bit)\n if show_bit[-1] not in ends:\n show_bit.append('w')\n if show_bit[0] not in starts:\n show_bit.insert(0, 'm')\n return ''.join(show_bit)\n\n def fix_show(show, gramsize):\n \"\"\"\n Lowercase anything in show and turn into list\n \"\"\"\n if isinstance(show, list):\n show = [i.lower() for i in show]\n elif isinstance(show, STRINGTYPE):\n show = show.lower()\n show = [show]\n show = [fix_show_bit(i) for i in show]\n return add_adj_for_ngram(show, gramsize)\n\n def is_multiquery(corpus, search, query, outname):\n \"\"\"\n Determine if multiprocessing is needed/possibe, and \n do some retyping if need be as well\n \"\"\"\n is_mul = False\n from collections import OrderedDict\n from corpkit.dictionaries.process_types import Wordlist\n \n if isinstance(query, Wordlist):\n query = list(query)\n\n if subcorpora and multiprocess:\n is_mul = 'subcorpora'\n\n if isinstance(subcorpora, (list, tuple)):\n is_mul = 'subcorpora'\n\n if isinstance(query, (dict, OrderedDict)):\n is_mul = 'namedqueriessingle'\n \n if isinstance(search, dict):\n if all(isinstance(i, dict) for i in list(search.values())):\n is_mul = 'namedqueriesmultiple'\n return is_mul, corpus, search, query\n\n def ispunct(s):\n import string\n return all(c in string.punctuation for c in s)\n\n def uniquify(conc_lines):\n \"\"\"get unique concordance lines\"\"\"\n from collections import OrderedDict\n unique_lines = []\n checking = []\n for index, (_, speakr, start, middle, end) in enumerate(conc_lines):\n joined = ' '.join([speakr, start, 'MIDDLEHERE:', middle, ':MIDDLEHERE', end])\n if joined not in checking:\n unique_lines.append(conc_lines[index])\n checking.append(joined)\n return unique_lines\n\n def compiler(pattern):\n \"\"\"\n Compile regex or fail gracefully\n \"\"\"\n if hasattr(pattern, 'pattern'):\n return pattern\n import re\n try:\n if case_sensitive:\n comped = re.compile(pattern)\n else:\n comped = re.compile(pattern, re.IGNORECASE)\n return comped\n except:\n import traceback\n import sys\n from time import localtime, strftime\n exc_type, exc_value, exc_traceback = sys.exc_info()\n lst = traceback.format_exception(exc_type, exc_value, exc_traceback)\n error_message = lst[-1]\n thetime = strftime(\"%H:%M:%S\", localtime())\n print('%s: Query %s' % (thetime, error_message))\n if root:\n return 'Bad query'\n else:\n raise ValueError('%s: Query %s' % (thetime, error_message))\n\n def determine_search_func(show):\n \"\"\"Figure out what search function we're using\"\"\"\n\n simple_tregex_mode = False\n statsmode = False\n tree_to_text = False\n search_trees = False\n \n simp_crit = all(not i for i in [kwargs.get('tgrep'),\n files_as_subcorpora,\n subcorpora,\n just_metadata,\n skip_metadata])\n\n if search.get('t') and simp_crit:\n if have_java:\n simple_tregex_mode = True\n else:\n search_trees = 'tgrep'\n optiontext = 'Searching parse trees'\n\n elif datatype == 'conll':\n \n if any(i.endswith('t') for i in search.keys()):\n if have_java and not kwargs.get('tgrep'):\n search_trees = 'tregex'\n else:\n search_trees = 'tgrep'\n optiontext = 'Searching parse trees'\n elif any(i.endswith('v') for i in search.keys()):\n # either of these searchers now seems to work\n #seacher = get_stats_conll\n statsmode = True\n optiontext = 'General statistics'\n elif any(i.endswith('r') for i in search.keys()):\n optiontext = 'Distance from root'\n else:\n optiontext = 'Querying CONLL data'\n\n return optiontext, simple_tregex_mode, statsmode, tree_to_text, search_trees\n\n def get_tregex_values(show):\n \"\"\"If using Tregex, set appropriate values\n\n - Check for valid query\n - Make 'any' query\n - Make list query\n \"\"\"\n\n translated_option = 't'\n if isinstance(search['t'], Wordlist):\n search['t'] = list(search['t'])\n q = tregex_engine(corpus=False,\n query=search.get('t'),\n options=['-t'],\n check_query=True,\n root=root,\n preserve_case=preserve_case\n )\n\n # so many of these bad fixing loops!\n nshow = []\n for i in show:\n if i == 'm':\n nshow.append('w')\n else:\n nshow.append(i.lstrip('m'))\n show = nshow\n\n if q is False:\n if root:\n return 'Bad query', None\n else:\n return 'Bad query', None\n\n if isinstance(search['t'], list):\n regex = as_regex(search['t'], boundaries='line', case_sensitive=case_sensitive)\n else:\n regex = ''\n\n # listquery, anyquery, translated_option\n treg_dict = {'p': [r'__ < (/%s/ !< __)' % regex, r'__ < (/.?[A-Za-z0-9].?/ !< __)', 'u'],\n 'pl': [r'__ < (/%s/ !< __)' % regex, r'__ < (/.?[A-Za-z0-9].?/ !< __)', 'u'],\n 'x': [r'__ < (/%s/ !< __)' % regex, r'__ < (/.?[A-Za-z0-9].?/ !< __)', 'u'],\n 't': [r'__ < (/%s/ !< __)' % regex, r'__ < (/.?[A-Za-z0-9].?/ !< __)', 'o'],\n 'w': [r'/%s/ !< __' % regex, r'/.?[A-Za-z0-9].?/ !< __', 't'],\n 'c': [r'/%s/ !< __' % regex, r'/.?[A-Za-z0-9].?/ !< __', 'C'],\n 'l': [r'/%s/ !< __' % regex, r'/.?[A-Za-z0-9].?/ !< __', 't'],\n 'u': [r'/%s/ !< __' % regex, r'/.?[A-Za-z0-9].?/ !< __', 'v']\n }\n\n newshow = []\n\n listq, anyq, translated_option = treg_dict.get(show[0][-1].lower())\n newshow.append(translated_option)\n for item in show[1:]:\n _, _, noption = treg_dict.get(item.lower())\n newshow.append(noption)\n\n if isinstance(search['t'], list):\n search['t'] = listq\n elif search['t'] == 'any': \n search['t'] = anyq\n return search['t'], newshow\n\n def correct_spelling(a_string):\n \"\"\"correct spelling within a string\"\"\"\n if not spelling:\n return a_string\n from corpkit.dictionaries.word_transforms import usa_convert\n if spelling.lower() == 'uk':\n usa_convert = {v: k for k, v in list(usa_convert.items())}\n bits = a_string.split('/')\n for index, i in enumerate(bits):\n converted = usa_convert.get(i.lower(), i)\n if i.islower() or preserve_case is False:\n converted = converted.lower()\n elif i.isupper() and preserve_case:\n converted = converted.upper()\n elif i.istitle() and preserve_case:\n converted = converted.title()\n bits[index] = converted\n r = '/'.join(bits)\n return r\n\n def make_search_iterable(corpus):\n \"\"\"determine how to structure the corpus for interrogation\"\"\"\n # skip file definitions if they are not needed\n if getattr(corpus, '_dlist', False):\n\n return {(i.name, i.path): [i] for i in list(corpus.files)}\n #return {('Sample', 'Sample'): list(corpus.files)}\n\n if simple_tregex_mode:\n if corpus.level in ['s', 'f', 'd']:\n return {(corpus.name, corpus.path): False}\n else:\n return {(os.path.basename(i), os.path.join(corpus.path, i)): False\n for i in os.listdir(corpus.path)\n if os.path.isdir(os.path.join(corpus.path, i))}\n\n if isinstance(corpus, Datalist):\n to_iterate_over = {}\n # it could be files or subcorpus objects\n if corpus[0].level in ['s', 'd']:\n if files_as_subcorpora:\n for subc in corpus:\n for f in subc.files:\n to_iterate_over[(f.name, f.path)] = [f]\n else:\n for subc in corpus:\n to_iterate_over[(subc.name, subc.path)] = subc.files\n elif corpus[0].level == 'f':\n for f in corpus:\n to_iterate_over[(f.name, f.path)] = [f]\n elif corpus.singlefile:\n to_iterate_over = {(corpus.name, corpus.path): [corpus]}\n elif not hasattr(corpus, 'subcorpora') or not corpus.subcorpora:\n # just files in a directory\n if files_as_subcorpora:\n to_iterate_over = {}\n for f in corpus.files:\n to_iterate_over[(f.name, f.path)] = [f]\n else:\n to_iterate_over = {(corpus.name, corpus.path): corpus.files}\n else:\n to_iterate_over = {}\n if files_as_subcorpora:\n # don't know if possible: has subcorpora but also .files\n if hasattr(corpus, 'files') and corpus.files is not None:\n for f in corpus.files:\n to_iterate_over[(f.name, f.path)] = [f]\n # has subcorpora with files in those\n elif hasattr(corpus, 'files') and corpus.files is None:\n for subc in corpus.subcorpora:\n for f in subc.files:\n to_iterate_over[(f.name, f.path)] = [f]\n else:\n if corpus[0].level == 's':\n for subcorpus in corpus:\n to_iterate_over[(subcorpus.name, subcorpus.path)] = subcorpus.files\n elif corpus[0].level == 'f':\n for f in corpus:\n to_iterate_over[(f.name, f.path)] = [f]\n else:\n for subcorpus in corpus.subcorpora:\n to_iterate_over[(subcorpus.name, subcorpus.path)] = subcorpus.files\n return to_iterate_over\n\n def welcome_printer(return_it=False):\n \"\"\"Print welcome message\"\"\"\n if no_conc:\n message = 'Interrogating'\n else:\n message = 'Interrogating and concordancing'\n if only_conc:\n message = 'Concordancing'\n if kwargs.get('printstatus', True):\n thetime = strftime(\"%H:%M:%S\", localtime())\n from corpkit.process import dictformat\n sformat = dictformat(search)\n welcome = ('\\n%s: %s %s ...\\n %s\\n ' \\\n 'Query: %s\\n %s corpus ... \\n' % \\\n (thetime, message, cname, optiontext, sformat, message))\n if return_it:\n return welcome\n else:\n print(welcome)\n\n def goodbye_printer(return_it=False, only_conc=False):\n \"\"\"Say goodbye before exiting\"\"\"\n if not kwargs.get('printstatus', True):\n return\n thetime = strftime(\"%H:%M:%S\", localtime())\n if only_conc:\n finalstring = '\\n\\n%s: Concordancing finished! %s results.' % (thetime, format(len(conc_df), ','))\n else:\n finalstring = '\\n\\n%s: Interrogation finished!' % thetime\n if countmode:\n finalstring += ' %s matches.' % format(tot, ',')\n else:\n finalstring += ' %s unique results, %s total occurrences.' % (format(numentries, ','), format(total_total, ','))\n if return_it:\n return finalstring\n else:\n print(finalstring)\n\n def get_conc_colnames(corpus,\n fsi_index=False,\n simple_tregex_mode=False):\n \n fields = []\n base = 'c f s l m r'\n \n if simple_tregex_mode:\n base = base.replace('f ', '')\n\n if fsi_index and not simple_tregex_mode:\n base = 'i ' + base\n \n if PYTHON_VERSION == 2:\n base = base.encode('utf-8').split()\n else:\n base = base.split() \n\n if show_conc_metadata:\n from corpkit.build import get_all_metadata_fields\n meta = get_all_metadata_fields(corpus.path)\n\n if isinstance(show_conc_metadata, list):\n meta = [i for i in meta if i in show_conc_metadata]\n #elif show_conc_metadata is True:\n # pass\n for i in sorted(meta):\n if i in ['speaker', 'sent_id', 'parse']:\n continue\n if PYTHON_VERSION == 2:\n base.append(i.encode('utf-8'))\n else:\n base.append(i)\n return base\n\n def make_conc_obj_from_conclines(conc_results, fsi_index=False):\n \"\"\"\n Turn conclines into DataFrame\n \"\"\"\n from corpkit.interrogation import Concordance\n #fsi_place = 2 if fsi_index else 0\n\n all_conc_lines = []\n for sc_name, resu in sorted(conc_results.items()):\n if only_unique:\n unique_results = uniquify(resu)\n else:\n unique_results = resu\n #make into series\n for lin in unique_results:\n #spkr = str(spkr, errors = 'ignore')\n #if not subcorpora:\n # lin[fsi_place] = lin[fsi_place]\n #lin.insert(fsi_place, sc_name)\n\n if len(lin) < len(conc_col_names):\n diff = len(conc_col_names) - len(lin)\n lin.extend(['none'] * diff)\n\n all_conc_lines.append(Series(lin, index=conc_col_names))\n\n try:\n conc_df = pd.concat(all_conc_lines, axis=1).T\n except ValueError:\n return\n \n if all(x == '' for x in list(conc_df['s'].values)) or \\\n all(x == 'none' for x in list(conc_df['s'].values)):\n conc_df.drop('s', axis=1, inplace=True)\n\n locs['corpus'] = corpus.name\n\n if maxconc:\n conc_df = Concordance(conc_df[:maxconc])\n else:\n conc_df = Concordance(conc_df)\n try:\n conc_df.query = locs\n except AttributeError:\n pass\n return conc_df\n\n def lowercase_result(res):\n \"\"\" \n Take any result and do spelling/lowercasing if need be\n\n todo: remove lowercase and change name\n \"\"\"\n if not res or statsmode:\n return res\n # this is likely broken, but spelling in interrogate is deprecated anyway\n if spelling:\n res = [correct_spelling(r) for r in res]\n return res\n\n def postprocess_concline(line, fsi_index=False, conc=False):\n # todo: are these right?\n if not conc:\n return line\n subc, star, en = 0, 2, 5\n if fsi_index:\n subc, star, en = 2, 4, 7\n if not preserve_case:\n line[star:en] = [str(x).lower() for x in line[star:en]]\n if spelling:\n line[star:en] = [correct_spelling(str(b)) for b in line[star:en]]\n return line\n\n def make_progress_bar():\n \"\"\"generate a progress bar\"\"\"\n\n if simple_tregex_mode:\n total_files = len(list(to_iterate_over.keys()))\n else:\n total_files = sum(len(x) for x in list(to_iterate_over.values()))\n\n par_args = {'printstatus': kwargs.get('printstatus', True),\n 'root': root, \n 'note': note,\n 'quiet': quiet,\n 'length': total_files,\n 'startnum': kwargs.get('startnum'),\n 'denom': kwargs.get('denominator', 1)}\n\n term = None\n if kwargs.get('paralleling', None) is not None:\n from blessings import Terminal\n term = Terminal()\n par_args['terminal'] = term\n par_args['linenum'] = kwargs.get('paralleling')\n\n if in_notebook:\n par_args['welcome_message'] = welcome_message\n\n outn = kwargs.get('outname', '')\n if outn:\n outn = getattr(outn, 'name', outn)\n outn = outn + ': '\n\n tstr = '%s%d/%d' % (outn, current_iter, total_files)\n p = animator(None, None, init=True, tot_string=tstr, **par_args)\n tstr = '%s%d/%d' % (outn, current_iter + 1, total_files)\n animator(p, current_iter, tstr, **par_args)\n return p, outn, total_files, par_args\n\n # find out if using gui\n root = kwargs.get('root')\n note = kwargs.get('note')\n language_model = kwargs.get('language_model')\n\n # set up pause method\n original_sigint = signal.getsignal(signal.SIGINT)\n if kwargs.get('paralleling', None) is None:\n if not root:\n original_sigint = signal.getsignal(signal.SIGINT)\n signal.signal(signal.SIGINT, signal_handler)\n\n # find out about concordancing\n only_conc = False\n no_conc = False\n if conc is False:\n no_conc = True\n if isinstance(conc, str) and conc.lower() == 'only':\n only_conc = True\n no_conc = False\n numconc = 0\n\n # wipe non essential class attributes to not bloat query attrib\n if isinstance(corpus, Corpus):\n import copy\n corpus = copy.copy(corpus)\n for k, v in corpus.__dict__.items():\n if isinstance(v, (Interrogation, Interrodict)):\n corpus.__dict__.pop(k, None)\n\n # convert path to corpus object\n if not isinstance(corpus, (Corpus, Corpora, Subcorpus, File, Datalist)):\n if not multiprocess and not kwargs.get('outname'):\n corpus = Corpus(corpus, print_info=False)\n\n # figure out how the user has entered the query and show, and normalise\n from corpkit.process import searchfixer\n search = searchfixer(search, query)\n show = fix_show(show, gramsize)\n locs['show'] = show\n\n # instantiate lemmatiser if need be\n lem_instance = False\n if any(i.endswith('l') for i in show) and isinstance(search, dict) and search.get('t'):\n from nltk.stem.wordnet import WordNetLemmatizer\n lem_instance = WordNetLemmatizer()\n\n # do multiprocessing if need be\n im, corpus, search, query, = is_multiquery(corpus, search, query, \n kwargs.get('outname', False))\n\n # figure out if we can multiprocess the corpus\n if hasattr(corpus, '__iter__') and im:\n corpus = Corpus(corpus, print_info=False)\n if hasattr(corpus, '__iter__') and not im:\n im = 'datalist'\n if isinstance(corpus, Corpora):\n im = 'multiplecorpora'\n\n # split corpus if the user wants multiprocessing but no other iterable\n if not im and multiprocess:\n im = 'datalist'\n if getattr(corpus, 'subcorpora', False):\n corpus = corpus[:]\n else:\n corpus = corpus.files\n\n search = fix_search(search, case_sensitive=case_sensitive, root=root)\n exclude = fix_search(exclude, case_sensitive=case_sensitive, root=root)\n\n # if it's already been through pmultiquery, don't do it again\n locs['search'] = search\n locs['exclude'] = exclude\n locs['query'] = query\n locs['corpus'] = corpus\n locs['multiprocess'] = multiprocess\n locs['print_info'] = kwargs.get('printstatus', True)\n locs['multiple'] = im\n locs['subcorpora'] = subcorpora\n locs['nosubmode'] = nosubmode\n\n # send to multiprocess function\n if im:\n signal.signal(signal.SIGINT, original_sigint)\n from corpkit.multiprocess import pmultiquery\n return pmultiquery(**locs)\n\n # get corpus metadata\n cname = corpus.name\n if isinstance(save, STRINGTYPE):\n savename = corpus.name + '-' + save\n if save is True:\n raise ValueError('save must be str, not bool.')\n\n\n datatype = getattr(corpus, 'datatype', 'conll')\n singlefile = getattr(corpus, 'singlefile', False)\n level = getattr(corpus, 'level', 'c')\n \n # store all results in here\n from collections import defaultdict\n results = defaultdict(Counter)\n count_results = defaultdict(list)\n conc_results = defaultdict(list)\n\n # check if just counting, turn off conc if so\n countmode = 'c' in show or 'mc' in show\n if countmode:\n no_conc = True\n only_conc = False\n # where we are at in interrogation\n current_iter = 0\n\n # multiprocessing progress bar\n denom = kwargs.get('denominator', 1)\n startnum = kwargs.get('startnum', 0)\n\n # Determine the search function to be used #\n optiontext, simple_tregex_mode, statsmode, tree_to_text, search_trees = determine_search_func(show)\n \n # no conc for statsmode\n if statsmode:\n no_conc = True\n only_conc = False\n conc = False\n\n # Set some Tregex-related values\n translated_option = False\n if search.get('t'):\n query, translated_option = get_tregex_values(show)\n if query == 'Bad query' and translated_option is None:\n if root:\n return 'Bad query'\n else:\n return\n # more tregex options\n if tree_to_text:\n treg_q = r'ROOT << __'\n op = ['-o', '-t', '-w', '-f']\n elif simple_tregex_mode:\n treg_q = search['t']\n op = ['-%s' % i for i in translated_option] + ['-o', '-f']\n\n # make iterable object for corpus interrogation\n to_iterate_over = make_search_iterable(corpus)\n\n try:\n nam = get_ipython().__class__.__name__\n if nam == 'ZMQInteractiveShell':\n in_notebook = True\n else:\n in_notebook = False\n except TraitError:\n in_notebook = False\n except ImportError:\n in_notebook = False\n # caused in newest ipython\n except AttributeError:\n in_notebook = False\n except NameError:\n in_notebook = False\n\n lemtag = False\n if search.get('t'):\n from corpkit.process import gettag\n lemtag = gettag(search.get('t'), lemmatag)\n\n usecols = auto_usecols(search, exclude, show, kwargs.pop('usecols', None), coref=coref)\n\n # print welcome message\n welcome_message = welcome_printer(return_it=in_notebook)\n\n # create a progress bar\n p, outn, total_files, par_args = make_progress_bar()\n\n if conc:\n conc_col_names = get_conc_colnames(corpus,\n fsi_index=fsi_index,\n simple_tregex_mode=False)\n\n \n\n # Iterate over data, doing interrogations\n for (subcorpus_name, subcorpus_path), files in sorted(to_iterate_over.items()):\n if nosubmode:\n subcorpus_name = 'Total'\n\n # results for subcorpus go here\n #conc_results[subcorpus_name] = []\n #count_results[subcorpus_name] = []\n #results[subcorpus_name] = Counter()\n\n # get either everything (tree_to_text) or the search['t'] query\n if tree_to_text or simple_tregex_mode:\n result = tregex_engine(query=treg_q,\n options=op,\n corpus=subcorpus_path,\n root=root,\n preserve_case=preserve_case)\n\n # format search results with slashes etc\n if not countmode and not tree_to_text:\n result = format_tregex(result, show, translated_option=translated_option,\n exclude=exclude, excludemode=excludemode, lemtag=lemtag,\n lem_instance=lem_instance, countmode=countmode, speaker_data=False)\n\n # if concordancing, do the query again with 'whole' sent and fname\n if not no_conc:\n ops = ['-w'] + op\n #ops = [i for i in ops if i != '-n']\n whole_result = tregex_engine(query=search['t'],\n options=ops,\n corpus=subcorpus_path,\n root=root,\n preserve_case=preserve_case\n )\n\n # format match too depending on option\n if not only_format_match:\n wholeresult = format_tregex(whole_result, show, translated_option=translated_option,\n exclude=exclude, excludemode=excludemode, lemtag=lemtag,\n lem_instance=lem_instance, countmode=countmode, speaker_data=False, whole=True)\n\n # make conc lines from conc results\n conc_result = make_conc_lines_from_whole_mid(whole_result, result, show=show)\n for lin in conc_result:\n if maxconc is False or numconc < maxconc:\n conc_results[subcorpus_name].append(lin)\n numconc += 1\n\n # add matches to ongoing counts\n if countmode:\n count_results[subcorpus_name] += [result] \n else:\n if result:\n results[subcorpus_name] += Counter([i[-1] for i in result])\n else:\n results[subcorpus_name] += Counter()\n\n # update progress bar\n current_iter += 1\n tstr = '%s%d/%d' % (outn, current_iter + 1, total_files)\n animator(p, current_iter, tstr, **par_args)\n continue\n\n # todo: move this\n kwargs.pop('by_metadata', None)\n \n # conll querying goes by file, not subcorpus\n for f in files:\n slow_treg_speaker_guess = kwargs.get('outname', '') if kwargs.get('multispeaker') else ''\n filepath, corefs = f.path, coref\n res, conc_res = pipeline(filepath, search=search, show=show,\n dep_type=dep_type,\n exclude=exclude,\n excludemode=excludemode,\n searchmode=searchmode,\n case_sensitive=case_sensitive,\n conc=conc,\n only_format_match=only_format_match,\n speaker=slow_treg_speaker_guess,\n gramsize=gramsize,\n no_punct=no_punct,\n no_closed=no_closed,\n window=window,\n filename=f.path,\n coref=corefs,\n countmode=countmode,\n maxconc=(maxconc, numconc),\n is_a_word=is_a_word,\n by_metadata=subcorpora,\n show_conc_metadata=show_conc_metadata,\n just_metadata=just_metadata,\n skip_metadata=skip_metadata,\n fsi_index=fsi_index,\n category=subcorpus_name,\n translated_option=translated_option,\n statsmode=statsmode,\n preserve_case=preserve_case,\n usecols=usecols,\n search_trees=search_trees,\n lem_instance=lem_instance,\n lemtag=lemtag,\n **kwargs)\n\n if res is None and conc_res is None:\n current_iter += 1\n tstr = '%s%d/%d' % (outn, current_iter + 1, total_files)\n animator(p, current_iter, tstr, **par_args)\n continue\n\n # deal with symbolic structures---that is, rather than adding\n # results by subcorpora, add them by metadata value\n # todo: sorting?\n if subcorpora:\n for (k, v), concl in zip(res.items(), conc_res.values()): \n v = lowercase_result(v)\n results[k] += Counter(v)\n for line in concl:\n if maxconc is False or numconc < maxconc:\n line = postprocess_concline(line,\n fsi_index=fsi_index, conc=conc)\n conc_results[k].append(line)\n numconc += 1\n \n current_iter += 1\n tstr = '%s%d/%d' % (outn, current_iter + 1, total_files)\n animator(p, current_iter, tstr, **par_args)\n continue\n\n # garbage collection needed?\n sents = None\n corefs = None\n \n if res == 'Bad query':\n return 'Bad query'\n\n if countmode:\n count_results[subcorpus_name] += [res]\n\n else:\n # add filename and do lowercasing for conc\n if not no_conc:\n for line in conc_res:\n line = postprocess_concline(line,\n fsi_index=fsi_index, conc=conc)\n if maxconc is False or numconc < maxconc:\n conc_results[subcorpus_name].append(line)\n numconc += 1\n\n # do lowercasing and spelling\n if not only_conc:\n res = lowercase_result(res)\n # discard removes low results, helping with \n # curse of dimensionality\n countres = Counter(res)\n if isinstance(discard, float):\n countres.most_common()\n nkeep = len(counter) - len(counter) * discard\n countres = Counter({k: v for i, (k, v) in enumerate(countres.most_common()) if i <= nkeep})\n elif isinstance(discard, int):\n countres = Counter({k: v for k, v in countres.most_common() if v >= discard})\n results[subcorpus_name] += countres\n #else:\n #results[subcorpus_name] += res\n\n # update progress bar\n current_iter += 1\n tstr = '%s%d/%d' % (outn, current_iter + 1, total_files)\n animator(p, current_iter, tstr, **par_args)\n\n # Get concordances into DataFrame, return if just conc\n if not no_conc:\n # fail on this line with typeerror if no results?\n conc_df = make_conc_obj_from_conclines(conc_results, fsi_index=fsi_index)\n if only_conc and conc_df is None:\n return\n elif only_conc:\n locs = sanitise_dict(locs)\n try:\n conc_df.query = locs\n except AttributeError:\n return conc_df\n if save and not kwargs.get('outname'):\n if conc_df is not None:\n conc_df.save(savename)\n goodbye_printer(only_conc=True)\n if not root:\n signal.signal(signal.SIGINT, original_sigint) \n return conc_df\n else:\n conc_df = None\n\n # Get interrogation into DataFrame\n if countmode:\n df = Series({k: sum(v) for k, v in sorted(count_results.items())})\n tot = df.sum()\n else:\n the_big_dict = {}\n unique_results = set(item for sublist in list(results.values()) for item in sublist)\n sortres = sorted(results.items(), key=lambda x: x[0])\n for word in unique_results:\n the_big_dict[word] = [subcorp_result[word] for _, subcorp_result in sortres]\n # turn master dict into dataframe, sorted\n df = DataFrame(the_big_dict, index=sorted(results.keys()))\n\n # for ngrams, remove hapaxes\n #if show_ngram or show_collocates:\n # if not language_model:\n # df = df[[i for i in list(df.columns) if df[i].sum() > 1]]\n\n numentries = len(df.columns)\n tot = df.sum(axis=1)\n total_total = df.sum().sum()\n\n # turn df into series if all conditions met\n conds = [countmode,\n files_as_subcorpora,\n subcorpora,\n kwargs.get('df1_always_df', False)]\n\n anyxs = [level == 's',\n singlefile,\n nosubmode]\n\n if all(not x for x in conds) and any(x for x in anyxs):\n df = Series(df.ix[0])\n df.sort_values(ascending=False, inplace=True)\n tot = df.sum()\n numentries = len(df.index)\n total_total = tot\n\n # turn data into DF for GUI if need be\n if isinstance(df, Series) and kwargs.get('df1_always_df', False):\n total_total = df.sum()\n df = DataFrame(df)\n tot = Series(total_total, index=['Total'])\n\n # if we're doing files as subcorpora, we can remove the extension etc\n if isinstance(df, DataFrame) and files_as_subcorpora:\n df.index = df.index.str.replace(r'(?:-[0-9][0-9][0-9]|)\\.txt\\.conll.*', '')\n df = df.groupby(level=0,sort=True).sum()\n\n if conc_df is not None and conc_df is not False:\n # removed 'f' from here for now\n for col in ['c']:\n for pat in ['.txt', '.conll', '.conllu']:\n conc_df[col] = conc_df[col].str.replace(pat, '')\n conc_df[col] = conc_df[col].str.replace(r'-[0-9][0-9][0-9]$', '')\n\n #df.index = df.index.str.replace('w', 'this')\n\n # make interrogation object\n locs['corpus'] = corpus.path\n locs = sanitise_dict(locs)\n if nosubmode and isinstance(df, pd.DataFrame):\n df = df.sum()\n interro = Interrogation(results=df, totals=tot, query=locs, concordance=conc_df)\n\n # save it\n if save and not kwargs.get('outname'):\n print('\\n')\n interro.save(savename)\n \n goodbye = goodbye_printer(return_it=in_notebook)\n if in_notebook:\n try:\n p.children[2].value = goodbye.replace('\\n', '')\n except AttributeError:\n pass\n if not root:\n signal.signal(signal.SIGINT, original_sigint)\n return interro\n"
},
{
"path": "corpkit/keys.py",
"content": "\"\"\"corpkit: simple keyworder\"\"\"\n\nfrom __future__ import print_function\nfrom corpkit.constants import STRINGTYPE, PYTHON_VERSION\n\ndef keywords(target_corpus,\n reference_corpus='bnc.p',\n threshold=False,\n selfdrop=True,\n calc_all=True,\n measure='ll',\n sort_by=False,\n print_info=False,\n **kwargs):\n \"\"\"Feed this function some target_corpus and get its keywords\"\"\"\n \n from pandas import DataFrame, Series\n from collections import Counter\n from corpkit.interrogation import Interrogation\n\n def data_to_dict(target_corpus):\n \"\"\"turn Series/DataFrame into Counter\"\"\"\n if isinstance(target_corpus, Interrogation):\n if hasattr(target_corpus, 'results'):\n target_corpus = target_corpus.results\n else:\n target_corpus = target_corpus.totals\n if isinstance(target_corpus, Series):\n return Counter(target_corpus.to_dict())\n elif isinstance(target_corpus, DataFrame):\n return Counter(target_corpus.sum().to_dict())\n else:\n return Counter(target_corpus)\n\n def log_likelihood_measure(word_in_ref, word_in_target, ref_sum, target_sum):\n \"\"\"calc log likelihood keyness\"\"\"\n import math\n neg = (word_in_target / float(target_sum)) < (word_in_ref / float(ref_sum))\n\n E1 = float(ref_sum)*((float(word_in_ref)+float(word_in_target)) / \\\n (float(ref_sum)+float(target_sum)))\n E2 = float(target_sum)*((float(word_in_ref)+float(word_in_target)) / \\\n (float(ref_sum)+float(target_sum)))\n \n if word_in_ref == 0:\n logaE1 = 0\n else:\n logaE1 = math.log(word_in_ref/E1)\n if word_in_target == 0:\n logaE2 = 0\n else:\n logaE2 = math.log(word_in_target/E2)\n score = float(2* ((word_in_ref*logaE1)+(word_in_target*logaE2)))\n if neg:\n score = -score\n return score\n\n def perc_diff_measure(word_in_ref, word_in_target, ref_sum, target_sum):\n \"\"\"calculate using perc diff measure\"\"\"\n\n norm_target = float(word_in_target) / target_sum\n norm_ref = float(word_in_ref) / ref_sum\n # Gabrielatos and Marchi (2012) do it this way!\n if norm_ref == 0:\n norm_ref = 0.00000000000000000000000001\n return ((norm_target - norm_ref) * 100.0) / norm_ref\n\n def set_threshold(threshold):\n \"\"\"define a threshold\"\"\"\n if threshold is False:\n return 0\n if threshold is True:\n threshold = 'm'\n if isinstance(threshold, STRINGTYPE):\n if threshold.startswith('l'):\n denominator = 800\n if threshold.startswith('m'):\n denominator = 400\n if threshold.startswith('h'):\n denominator = 100\n totwords = sum(loaded_ref_corpus.values())\n return float(totwords) / float(denominator)\n else:\n return threshold\n\n def calc_keywords(target_corpus, reference_corpus):\n \"\"\"\n get keywords in target corpus compared to reference corpus\n this should probably become some kind of row-wise df.apply method\n \"\"\"\n # get total num words in ref corpus\n key_scores = {}\n ref_sum = sum(reference_corpus.values())\n if isinstance(target_corpus, dict):\n target_sum = sum(target_corpus.values())\n if isinstance(target_corpus, Series):\n target_sum = target_corpus.sum()\n\n # get words to calculate\n if calc_all:\n wordlist = list(set(list(target_corpus.keys()) + list(reference_corpus.keys())))\n else:\n wordlist = list(target_corpus.keys())\n wordlist = [(word, reference_corpus[word]) for word in wordlist]\n for w, s in wordlist:\n if s < threshold:\n global skipped\n skipped += 1\n continue\n word_in_ref = reference_corpus.get(w, 0)\n word_in_target = target_corpus.get(w, 0)\n if kwargs.get('only_words_in_both_corpora'):\n if word_in_ref == 0:\n continue\n score = measure_func(word_in_ref, word_in_target, ref_sum, target_sum)\n key_scores[w] = score\n return key_scores\n\n # load string ref corp \n if isinstance(reference_corpus, STRINGTYPE):\n from corpkit.other import load\n ldr = kwargs.get('loaddir', 'dictionaries')\n reference_corpus = load(reference_corpus, loaddir=ldr)\n\n # if a corpus interrogation, assume we want results \n if isinstance(target_corpus, Interrogation):\n reference_corpus = reference_corpus.results\n\n # turn data into dict\n loaded_ref_corpus = data_to_dict(reference_corpus)\n\n df = target_corpus\n index_names = list(df.index)\n results = {}\n threshold = set_threshold(threshold)\n global skipped\n skipped = 0\n\n # figure out which measure we're using\n if measure == 'll':\n measure_func = log_likelihood_measure\n elif measure == 'pd':\n measure_func = perc_diff_measure\n else:\n raise NotImplementedError(\"Only 'll' and 'pd' measures defined so far.\")\n\n for subcorpus in index_names:\n if selfdrop:\n ref_calc = loaded_ref_corpus - Counter(reference_corpus.ix[subcorpus].to_dict())\n else:\n ref_calc = loaded_ref_corpus\n results[subcorpus] = calc_keywords(df.ix[subcorpus], ref_calc)\n\n if print_info:\n print('Skipped %d entries under threshold (%d)\\n' % (skipped, threshold))\n\n df = DataFrame(results).T\n df.sort_index()\n if not sort_by:\n df = df[list(df.sum().sort_values(ascending=False).index)]\n return df\n"
},
{
"path": "corpkit/layouts.py",
"content": "\"\"\"\nThis file contains a dictionary of matplotlib subplot layouts\n\nThey are used during multiplotting. Sooner or later they should be accessible\nwith something other than integer keys, as this is hard to comprehend...\n\"\"\"\n\nlayouts = {1: [(1, 2, 1),\n (1, 2, 2)],\n 2: [(1, 2, 1),\n (1, 3, 2),\n (1, 3, 3)],\n 3: [(1, 2, 1),\n (3, 2, 2),\n (3, 2, 4),\n (3, 2, 6)],\n 3.5: [(2, 1, 1),\n (2, 3, 4),\n (2, 3, 5),\n (2, 3, 6)],\n 4: [(1, 2, 1),\n (2, 4, 3),\n (2, 4, 4),\n (2, 4, 7),\n (2, 4, 8)],\n 5: [(3, 3, (1, 5)),\n (3, 3, 3),\n (3, 3, 6),\n (3, 3, 7),\n (3, 3, 8),\n (3, 3, 9)],\n 6: [(1, 2, 1),\n (3, 4, 3),\n (3, 4, 4),\n (3, 4, 7),\n (3, 4, 8),\n (3, 4, 11),\n (3, 4, 12)],\n 6.5: [(4, 2, (6, 8)),\n (4, 2, 1),\n (4, 2, 2),\n (4, 2, 3),\n (4, 2, 4),\n (4, 2, 5),\n (4, 2, 7)], \n 9: [(1, 5, (1, 2)),\n (3, 5, 3),\n (3, 5, 4),\n (3, 5, 5),\n (3, 5, 8),\n (3, 5, 9),\n (3, 5, 10),\n (3, 5, 13),\n (3, 5, 14),\n (3, 5, 15)],\n 9.5: [(1, 6, (1, 3)),\n (3, 6, 4),\n (3, 6, 5),\n (3, 6, 6),\n (3, 6, 10),\n (3, 6, 11),\n (3, 6, 12),\n (3, 6, 16),\n (3, 6, 17),\n (3, 6, 18)],\n 12: [(4, 1, 1),\n (4, 4, 5),\n (4, 4, 6),\n (4, 4, 7),\n (4, 4, 8),\n (4, 4, 9),\n (4, 4, 10),\n (4, 4, 11),\n (4, 4, 12),\n (4, 4, 13),\n (4, 4, 14),\n (4, 4, 15),\n (4, 4, 16)],\n 12.5: [(4, 4, (1, 6)),\n (4, 4, 3),\n (4, 4, 4),\n (4, 4, 7),\n (4, 4, 8),\n (4, 4, 9),\n (4, 4, 10),\n (4, 4, 11),\n (4, 4, 12),\n (4, 4, 13),\n (4, 4, 14),\n (4, 4, 15),\n (4, 4, 16)],\n 14: [(6, 3, (1, 5)),\n (6, 3, 3),\n (6, 3, 6),\n (6, 3, 7),\n (6, 3, 8),\n (6, 3, 9),\n (6, 3, 10),\n (6, 3, 11),\n (6, 3, 12),\n (6, 3, 13),\n (6, 3, 14),\n (6, 3, 15),\n (6, 3, 16),\n (6, 3, 17),\n (6, 3, 18)]\n }\n"
},
{
"path": "corpkit/lazyprop.py",
"content": "# this file duplicates lazyprop many times because i can't work out how to\n# automatically add the right docstring...\n\ndef lazyprop(fn):\n \"\"\"Lazy loading class attributes, with hard-coded docstrings for now...\"\"\"\n attr_name = '_lazy_' + fn.__name__\n if fn.__name__ == 'subcorpora':\n @property\n def _lazyprop(self):\n \"\"\"A list-like object containing a corpus' subcorpora.\n\n :Example:\n\n >>> corpus.subcorpora\n \n\n \"\"\"\n if not hasattr(self, attr_name):\n setattr(self, attr_name, fn(self))\n return getattr(self, attr_name)\n elif fn.__name__ == 'files':\n @property\n def _lazyprop(self):\n \"\"\"A list-like object containing the files in a folder.\n\n :Example:\n\n >>> corpus.subcorpora[0].files\n \n\n \"\"\"\n if not hasattr(self, attr_name):\n setattr(self, attr_name, fn(self))\n return getattr(self, attr_name)\n elif fn.__name__ == 'features':\n @property\n def _lazyprop(self):\n \"\"\"\n Generate and show basic stats from the corpus, including number of sentences, clauses, process types, etc.\n\n :Example:\n \n >>> corpus.features\n .. Characters Tokens Words Closed class words Open class words Clauses\n 01 26873 8513 7308 4809 3704 2212 \n 02 25844 7933 6920 4313 3620 2270 \n 03 18376 5683 4877 3067 2616 1640 \n 04 20066 6354 5366 3587 2767 1775\n \n \"\"\"\n if not hasattr(self, attr_name):\n setattr(self, attr_name, fn(self))\n return getattr(self, attr_name)\n elif fn.__name__ == 'document':\n @property\n def _lazyprop(self):\n \"\"\"Return a DataFrame representation of a parsed file\"\"\"\n if not hasattr(self, attr_name):\n setattr(self, attr_name, fn(self))\n return getattr(self, attr_name)\n elif fn.__name__ == 'relational':\n @property\n def _lazyprop(self):\n \"\"\"List of relational processes\"\"\"\n if not hasattr(self, attr_name):\n setattr(self, attr_name, fn(self))\n return getattr(self, attr_name)\n elif fn.__name__ == 'verbal':\n @property\n def _lazyprop(self):\n \"\"\"List of verbal processes\"\"\"\n if not hasattr(self, attr_name):\n setattr(self, attr_name, fn(self))\n return getattr(self, attr_name)\n elif fn.__name__ == 'mental':\n @property\n def _lazyprop(self):\n \"\"\"List of mental processes\"\"\"\n if not hasattr(self, attr_name):\n setattr(self, attr_name, fn(self))\n return getattr(self, attr_name)\n else:\n @property\n def _lazyprop(self):\n \"\"\"Lazy-loaded data.\n \"\"\"\n if not hasattr(self, attr_name):\n setattr(self, attr_name, fn(self))\n return getattr(self, attr_name)\n return _lazyprop"
},
{
"path": "corpkit/make.py",
"content": "from __future__ import print_function\n\nfrom corpkit.constants import INPUTFUNC, PYTHON_VERSION\ndef make_corpus(unparsed_corpus_path,\n project_path=None,\n parse=True,\n tokenise=False,\n postag=False,\n lemmatise=False,\n corenlppath=False,\n nltk_data_path=False,\n operations=False,\n speaker_segmentation=False,\n root=False,\n multiprocess=False,\n split_texts=400,\n outname=False,\n metadata=False,\n restart=False,\n coref=True,\n lang='en',\n **kwargs):\n \"\"\"\n Create a parsed version of unparsed_corpus using CoreNLP or NLTK's tokeniser\n :param unparsed_corpus_path: path to corpus containing text files, \n or subdirs containing text files\n :type unparsed_corpus_path: str\n \n :param project_path: path to corpkit project\n :type project_path: str\n\n :param parse: Do parsing?\n :type parse: bool\n \n :param tokenise: Do tokenising?\n :type tokenise: bool\n \n :param corenlppath: folder containing corenlp jar files\n :type corenlppath: str\n \n :param nltk_data_path: path to tokeniser if tokenising\n :type nltk_data_path: str\n \n :param operations: which kinds of annotations to do\n :type operations: str\n \n :param speaker_segmentation: add speaker name to parser output if your corpus is script-like:\n :type speaker_segmentation: bool\n :returns: list of paths to created corpora\n \"\"\"\n\n import sys\n import os\n from os.path import join, isfile, isdir, basename, splitext, exists\n import shutil\n import codecs\n from corpkit.build import folderise, can_folderise\n from corpkit.process import saferead, make_dotfile\n\n from corpkit.build import (get_corpus_filepaths, \n check_jdk, \n rename_all_files,\n make_no_id_corpus, parse_corpus, move_parsed_files)\n from corpkit.constants import REPEAT_PARSE_ATTEMPTS, OPENER, PYTHON_VERSION\n\n if parse is True and tokenise is True:\n raise ValueError('Select either parse or tokenise, not both.')\n \n if project_path is None:\n project_path = os.getcwd()\n\n\n fileparse = isfile(unparsed_corpus_path)\n if fileparse:\n copier = shutil.copyfile\n else:\n copier = shutil.copytree\n\n # raise error if no tokeniser\n #if tokenise:\n # if outname:\n # newpath = os.path.join(os.path.dirname(unparsed_corpus_path), outname)\n # else:\n # newpath = unparsed_corpus_path + '-tokenised'\n # if isdir(newpath):\n # shutil.rmtree(newpath)\n # import nltk\n # if nltk_data_path:\n # if nltk_data_path not in nltk.data.path:\n # nltk.data.path.append(nltk_data_path)\n # try:\n # from nltk import word_tokenize as tokenise\n # except:\n # print('\\nTokeniser not found. Pass in its path as keyword arg \"nltk_data_path = \".\\n')\n # raise\n\n if sys.platform == \"darwin\":\n if not check_jdk():\n print(\"Get the latest Java from http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html\")\n\n cop_head = kwargs.get('copula_head', True)\n note = kwargs.get('note', False)\n stdout = kwargs.get('stdout', False)\n\n # make absolute path to corpus\n unparsed_corpus_path = os.path.abspath(unparsed_corpus_path)\n\n # move it into project\n if fileparse:\n datapath = project_path\n else:\n datapath = join(project_path, 'data')\n \n if isdir(datapath):\n newp = join(datapath, basename(unparsed_corpus_path))\n else:\n os.makedirs(datapath)\n if fileparse:\n noext = splitext(unparsed_corpus_path)[0]\n newp = join(datapath, basename(noext))\n else:\n newp = join(datapath, basename(unparsed_corpus_path))\n\n if exists(newp):\n pass\n else:\n copier(unparsed_corpus_path, newp)\n \n unparsed_corpus_path = newp\n\n # ask to folderise?\n check_do_folderise = False\n do_folderise = kwargs.get('folderise', None)\n if can_folderise(unparsed_corpus_path):\n import __main__ as main\n if do_folderise is None and not hasattr(main, '__file__'):\n check_do_folderise = INPUTFUNC(\"Your corpus has multiple files, but no subcorpora. \"\\\n \"Would you like each file to be treated as a subcorpus? (y/n) \")\n check_do_folderise = check_do_folderise.lower().startswith('y')\n if check_do_folderise or do_folderise:\n folderise(unparsed_corpus_path)\n \n # this is bad!\n if join('data', 'data') in unparsed_corpus_path:\n unparsed_corpus_path = unparsed_corpus_path.replace(join('data', 'data'), 'data')\n\n def chunks(l, n):\n for i in range(0, len(l), n):\n yield l[i:i+n]\n\n if (parse or tokenise) and coref:\n \n # this loop shortens files containing more than 500 lines,\n # for corenlp memory's sake. maybe user needs a warning or\n # something in case s/he is doing coref?\n\n # try block because it isn't necessarily essential that this works.\n # just delete generated files if it fails part way\n split_f_names = []\n try:\n for rootx, dirs, fs in os.walk(unparsed_corpus_path):\n for f in fs:\n # skip hidden files\n if f.startswith('.'):\n continue\n fp = join(rootx, f)\n data, enc = saferead(fp)\n data = data.splitlines()\n if len(data) > split_texts:\n chk = chunks(data, split_texts)\n for index, c in enumerate(chk):\n newname = fp.replace('.txt', '-%s.txt' % str(index + 1).zfill(3))\n split_f_names.append(newname)\n data = '\\n'.join(c) + '\\n'\n # does this work?\n with OPENER(newname, 'w') as fo: \n if PYTHON_VERSION == 2:\n data = data.encode('utf-8', errors='ignore')\n fo.write(data)\n os.remove(fp)\n else:\n pass\n except (KeyboardInterrupt, SystemExit):\n raise\n except:\n for f in split_f_names:\n os.remove(f)\n pass\n\n if outname:\n newpath = os.path.join(os.path.dirname(unparsed_corpus_path), outname)\n else:\n newpath = unparsed_corpus_path + '-parsed'\n if restart:\n restart = newpath\n if speaker_segmentation or metadata:\n if isdir(newpath) and not root:\n import __main__ as main\n if not restart and not hasattr(main, '__file__'):\n ans = INPUTFUNC('\\n Path exists: %s. Do you want to overwrite? (y/n)\\n' %newpath)\n if ans.lower().strip()[0] == 'y':\n shutil.rmtree(newpath)\n else:\n return\n elif isdir(newpath) and root:\n raise OSError('Path exists: %s' % newpath)\n if speaker_segmentation:\n print('Processing speaker IDs ...')\n make_no_id_corpus(unparsed_corpus_path,\n unparsed_corpus_path + '-stripped',\n metadata_mode=metadata,\n speaker_segmentation=speaker_segmentation)\n to_parse = unparsed_corpus_path + '-stripped'\n else:\n to_parse = unparsed_corpus_path\n\n if not fileparse:\n print('Making list of files ... ')\n\n # now we enter a while loop while not all files are parsed\n #todo: these file lists are not necessary when not parsing\n\n if outname:\n newparsed = os.path.join(project_path, 'data', outname)\n else:\n basecp = os.path.basename(to_parse)\n newparsed = os.path.join(project_path, 'data', '%s-parsed' % basecp)\n newparsed = newparsed.replace('-stripped-', '-')\n\n while REPEAT_PARSE_ATTEMPTS:\n\n if not parse:\n break\n\n if not fileparse:\n pp = os.path.dirname(unparsed_corpus_path)\n # if restart mode, the filepaths won't include those already parsed...\n filelist, fs = get_corpus_filepaths(projpath=pp, \n corpuspath=to_parse,\n restart=restart,\n out_ext=kwargs.get('output_format'))\n\n else:\n filelist = unparsed_corpus_path.replace('.txt', '-filelist.txt')\n with open(filelist, 'w') as fo:\n fo.write(unparsed_corpus_path + '\\n')\n\n # split up filelists\n if multiprocess is not False:\n\n if multiprocess is True:\n import multiprocessing\n multiprocess = multiprocessing.cpu_count()\n\n from joblib import Parallel, delayed\n # split old file into n parts\n if os.path.isfile(filelist):\n data, enc = saferead(filelist)\n fs = [i for i in data.splitlines() if i]\n else:\n fs = []\n # if there's nothing here, we're done\n if not fs:\n # double dutch\n REPEAT_PARSE_ATTEMPTS = 0\n break\n if len(fs) <= multiprocess:\n multiprocess = len(fs)\n # make generator with list of lists\n divl = int(len(fs) / multiprocess)\n filelists = []\n if not divl:\n filelists.append(filelist)\n else:\n fgen = chunks(fs, divl)\n \n # for each list, make new file\n from corpkit.constants import OPENER\n for index, flist in enumerate(fgen):\n as_str = '\\n'.join(flist) + '\\n'\n new_fpath = filelist.replace('.txt', '-%s.txt' % str(index).zfill(4))\n filelists.append(new_fpath)\n with OPENER(new_fpath, 'w', encoding='utf-8') as fo:\n try:\n fo.write(as_str.encode('utf-8'))\n except TypeError:\n fo.write(as_str)\n\n try:\n os.remove(filelist)\n except:\n pass\n\n ds = []\n for listpath in filelists:\n d = {'proj_path': project_path, \n 'corpuspath': to_parse,\n 'filelist': listpath,\n 'corenlppath': corenlppath,\n 'nltk_data_path': nltk_data_path,\n 'operations': operations,\n 'copula_head': cop_head,\n 'multiprocessing': True,\n 'root': root,\n 'note': note,\n 'stdout': stdout,\n 'outname': outname,\n 'coref': coref,\n 'output_format': kwargs.get('output_format', 'xml')\n }\n ds.append(d)\n\n res = Parallel(n_jobs=multiprocess)(delayed(parse_corpus)(**x) for x in ds)\n if len(res) > 0:\n newparsed = res[0]\n else:\n return\n if all(r is False for r in res):\n return\n\n for i in filelists:\n try:\n os.remove(i)\n except:\n pass\n\n else:\n newparsed = parse_corpus(proj_path=project_path, \n corpuspath=to_parse,\n filelist=filelist,\n corenlppath=corenlppath,\n nltk_data_path=nltk_data_path,\n operations=operations,\n copula_head=cop_head,\n root=root,\n note=note,\n stdout=stdout,\n fileparse=fileparse,\n outname=outname,\n output_format=kwargs.get('output_format', 'conll'))\n\n if not restart:\n REPEAT_PARSE_ATTEMPTS = 0\n else:\n REPEAT_PARSE_ATTEMPTS -= 1\n print('Repeating parsing due to missing files. '\\\n '%d iterations remaining.' % REPEAT_PARSE_ATTEMPTS)\n\n if parse and not newparsed:\n return \n\n if parse and all(not x for x in newparsed):\n print('Error after parsing.')\n return\n\n if parse and fileparse:\n # cleanup mistakes :)\n if isfile(splitext(unparsed_corpus_path)[0]):\n os.remove(splitext(unparsed_corpus_path)[0])\n if isfile(unparsed_corpus_path.replace('.txt', '-filelist.txt')):\n os.remove(unparsed_corpus_path.replace('.txt', '-filelist.txt'))\n return unparsed_corpus_path + '.conll'\n\n if parse:\n move_parsed_files(project_path, to_parse, newparsed,\n ext=kwargs.get('output_format', 'conll'), restart=restart)\n\n from corpkit.conll import convert_json_to_conll\n coref = False\n if operations is False:\n coref = True\n elif 'coref' in operations or 'dcoref' in operations:\n coref = True\n\n convert_json_to_conll(newparsed, speaker_segmentation=speaker_segmentation,\n coref=coref, metadata=metadata)\n\n try:\n os.remove(filelist)\n except:\n pass\n\n if not parse and tokenise:\n #todo: outname\n newparsed = to_parse.replace('-stripped', '-tokenised')\n from corpkit.tokenise import plaintext_to_conll\n newparsed = plaintext_to_conll(to_parse,\n postag=postag,\n lemmatise=lemmatise,\n lang=lang,\n metadata=metadata,\n nltk_data_path=nltk_data_path,\n speaker_segmentation=speaker_segmentation,\n outpath=newparsed)\n\n if outname:\n if not os.path.isdir(outname):\n outname = os.path.join('data', os.path.basename(outdir))\n import shutil\n shutil.copytree(newparsed, outname)\n newparsed = outname\n if newparsed is False:\n return\n else:\n make_dotfile(newparsed)\n return newparsed\n\n rename_all_files(newparsed)\n print('Generating corpus metadata...')\n make_dotfile(newparsed)\n print('Done!\\n')\n return newparsed"
},
{
"path": "corpkit/model.py",
"content": "\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport math\nimport os\nfrom nltk import ngrams, sent_tokenize, word_tokenize\n\nfrom corpkit.constants import PYTHON_VERSION, STRINGTYPE\n\nclass LanguageModel(object):\n def __init__(self, order, alpha, data):\n \"\"\"\n :param data: a pandas series with multiindex\n \"\"\"\n self.order = order\n self.alpha = alpha\n if order > 1:\n self.backoff = LanguageModel(order - 1, alpha, data)\n self.lexicon = None\n else:\n self.backoff = None\n self.n = 0\n from collections import Counter\n self.counts = Counter()\n lexicon = set()\n # below, we make a counter object from the series\n # it should be fine for simpler show values, but\n # has some theoretical issues when the model is of\n # mixed type, such as L, GL, GF, because the backoff\n # involves getting just the first ORDER words ...\n for index, count in data.iteritems():\n #gramm = index.split('/')\n # order == 1 still gives us a tuple for now!\n cut_index = index[:order]\n self.counts[cut_index] += count\n if order == 1:\n lexicon.add(cut_index)\n self.n += 1\n self.v = len(lexicon)\n\n def _logprob(self, ngram):\n return math.log(self._prob(ngram))\n \n def _prob(self, ngram):\n if self.backoff is not None:\n freq = self.counts[ngram]\n backoff_freq = self.backoff.counts[ngram[1:]]\n if freq == 0:\n return self.alpha * self.backoff._prob(ngram[1:])\n else:\n return freq / backoff_freq\n else:\n # laplace smoothing to handle unknown unigrams\n return (self.counts[ngram] + 1) / (self.n + self.v)\n\nclass MultiModel(dict):\n \n def __init__(self, data, order, name='', **kwargs):\n import os\n from corpkit.other import load\n if isinstance(data, STRINGTYPE):\n name = data\n if not name.endswith('.p'):\n name = name + '.p'\n self.name = name\n self.order = order\n self.kwargs = kwargs\n if os.path.isfile(self.name):\n data = load(self.name, loaddir='models')\n else:\n pth = os.path.join('models', self.name)\n if os.path.isfile(pth):\n data = load(self.name, loaddir='models')\n super(MultiModel, self).__init__(data)\n\n def score(self, data, **kwargs):\n \"\"\"\n Score text against a model\n \"\"\"\n # get data into a list of ngram tuples\n from collections import Counter, OrderedDict\n import pandas as pd\n from corpkit.corpus import Subcorpus, File\n # get subcorpus\n if isinstance(data, Subcorpus):\n counts = self[data.name].counts\n elif isinstance(data, STRINGTYPE) and data in self.keys():\n counts = self[data].counts \n #get file\n elif isinstance(data, File) or (isinstance(data, STRINGTYPE) and \\\n os.path.isfile(data)):\n counts = self._turn_file_obj_into_counts(data)\n # get text\n #elif isinstance(data, STRINGTYPE) and not \\\n # os.path.exists(data):\n # dat = []\n # #sents = sent_tokenize(data)\n # #for sent in sents:\n # # dat.append('-spl-it-'.join(word_tokenize(sent)))\n # counted_sents = Counter(dat)\n # counts = LanguageModel(self.order, kwargs.get('alpha', 0.4), counted_sents).counts\n tempscores = {}\n for name, model in self.items():\n tempscores[name] = self._score_counts_against_model(counts, model)\n ser = pd.Series(tempscores)\n ser = ser.sort_values(ascending=False)\n ser['Corpus'] = ser.pop('Corpus')\n return ser\n\n def _score_counts_against_model(self, counts, model):\n grams = []\n for k, v in counts.items():\n for _ in range(v):\n grams.append(k)\n slogprob = 0\n for gram in grams:\n lb = model._logprob(gram)\n slogprob += lb\n return slogprob / len(counts)\n\n def _turn_file_obj_into_counts(self, data, *args, **kwargs):\n if data.datatype != 'parse':\n data = data.parse(**kwargs)\n kwgs = self.kwargs\n # add kwargs\n res = data.interrogate(**kwgs)\n model = _make_model_from_interro(res, self.name, order=self.order, \n nosave=True, singlemod=True, *args, **kwargs)\n return model.counts\n\n def score_subcorpora(self):\n import pandas as pd\n data = []\n for subname in self.keys():\n ser = pd.Series(self.score(subname))\n ser.name = subname\n data.append(ser)\n df = pd.concat(data, axis=1)\n return df[sorted(df.columns)]\n\ndef _make_model_from_interro(self, name, order, **kwargs):\n import os\n from pandas import DataFrame, Series\n from corpkit.other import load\n nosave = kwargs.get('nosave')\n singlemod = kwargs.get('singlemod')\n if not nosave:\n if not name.endswith('.p'):\n name = name + '.p'\n pth = os.path.join('models', name)\n if os.path.isfile(pth):\n return load(name, loaddir='models')\n scores = {}\n if not hasattr(self, 'results'):\n raise ValueError('Need results attribute to make language model.')\n # determine what we iterate over\n if not singlemod:\n to_iter_over = [(nm, self.results.ix[nm][self.results.ix[nm] > 0]) \\\n for nm in list(self.results.index)]\n else:\n if isinstance(self.results, Series):\n to_iter_over = [(name, self.results)]\n else:\n to_iter_over = [(name, self.results.sum())]\n try:\n tot = self.results.sum()[self.results.sum() > 0]\n to_iter_over.append(('Corpus', tot))\n except:\n pass\n for subname, subc in list(to_iter_over):\n # get name for file\n model = _train(subc, subname, name, order=order, **kwargs)\n scores[subname] = model\n if singlemod:\n return scores.values()[0]\n mm = MultiModel(scores, order=order, name=name, **kwargs)\n if not os.path.isfile(os.path.join('models', name)):\n from corpkit.other import save\n save(scores, name, savedir='models')\n print('Done!\\n')\n return mm\n\n #scores[subc.name] = score_text_with_model(trained)\n #return sorted(scores.items(), key=lambda x: x[1], reverse=True)\n\ndef _train(data, name, corpusname, order=3, **kwargs):\n alpha = kwargs.get('alpha', 0.4)\n print('Making model: %s ... ' % name.replace('.p', ''))\n lm = LanguageModel(order, alpha, data)\n return lm\n\n"
},
{
"path": "corpkit/multiprocess.py",
"content": "\"\"\"corpkit: multiprocessing of interrogations\"\"\"\n\nfrom __future__ import print_function\n\ndef pmultiquery(corpus, \n search,\n show='words',\n query='any', \n sort_by='total', \n save=False,\n multiprocess='default', \n root=False,\n note=False,\n print_info=True,\n subcorpora=False,\n **kwargs\n ):\n \"\"\"\n - Parallel process multiple queries or corpora.\n - This function is used by corpkit.interrogator.interrogator()\n - for multiprocessing.\n - There's no reason to call this function yourself.\n \"\"\"\n import os\n from pandas import DataFrame, Series\n import pandas as pd\n import collections\n from collections import namedtuple, OrderedDict\n from time import strftime, localtime\n import corpkit\n from corpkit.interrogator import interrogator\n from corpkit.interrogation import Interrogation, Interrodict\n from corpkit.process import canpickle\n try:\n from joblib import Parallel, delayed\n except ImportError:\n pass\n import multiprocessing\n\n locs = locals()\n for k, v in kwargs.items():\n locs[k] = v\n in_notebook = locs.get('in_notebook')\n\n def best_num_parallel(num_cores, num_queries):\n \"\"\"decide how many parallel processes to run\n\n the idea, more or less, is to balance the load when possible\"\"\"\n import corpkit\n if num_queries <= num_cores:\n return num_queries\n if num_queries > num_cores:\n if (num_queries / num_cores) == num_cores:\n return int(num_cores)\n if num_queries % num_cores == 0:\n try:\n return max([int(num_queries / n) for n in range(2, num_cores) \\\n if int(num_queries / n) <= num_cores]) \n except ValueError:\n return num_cores\n else:\n import math\n if (float(math.sqrt(num_queries))).is_integer():\n square_root = math.sqrt(num_queries)\n if square_root <= num_queries / num_cores: \n return int(square_root) \n return num_cores\n\n num_cores = multiprocessing.cpu_count()\n\n # what is our iterable? ...\n multiple = kwargs.get('multiple', False)\n mult_corp_are_subs = False\n if hasattr(corpus, '__iter__'):\n if all(getattr(x, 'level', False) == 's' for x in corpus):\n mult_corp_are_subs = True\n\n non_first_sub = None\n if subcorpora:\n non_first_sub = subcorpora[1:] if isinstance(subcorpora, list) else None\n subval = subcorpora if not non_first_sub else subcorpora[0]\n #print(subcorpora, non_first_sub, subval)\n if subcorpora is True:\n import re\n subcorpora = re.compile(r'.*')\n else:\n # strange travis error happened here\n subcorpora = corpus.metadata['fields'][subval]\n if len(subcorpora) == 0:\n print('No %s metadata found.' % str(subval))\n return\n\n mapcores = {'datalist': [corpus, 'corpus'],\n 'multiplecorpora': [corpus, 'corpus'],\n 'namedqueriessingle': [query, 'query'],\n 'namedqueriesmultiple': [search, 'search'],\n 'subcorpora': [subcorpora, 'subcorpora']}\n\n # a is a dummy, just to produce default one\n toiter, itsname = mapcores.get(multiple, [False, False])\n if isinstance(toiter, dict):\n toiter = toiter.items()\n denom = len(toiter)\n num_cores = best_num_parallel(num_cores, denom)\n\n # todo: code below makes no sense\n vals = ['eachspeaker', 'multiplespeaker', 'namedqueriesmultiple']\n if multiple == 'multiplecorpora' and any(x is True for x in vals):\n from corpkit.corpus import Corpus, Corpora\n if isinstance(corpus, Corpora):\n multiprocess = False\n else:\n corpus = Corpus(corpus)\n\n if isinstance(multiprocess, int):\n num_cores = multiprocess\n if multiprocess is False:\n num_cores = 1\n\n # make sure saves are right type\n if save is True:\n raise ValueError('save must be string when multiprocessing.')\n\n # make a list of dicts to pass to interrogator,\n # with the iterable unique in every one\n locs['printstatus'] = False\n locs['multiprocess'] = False\n locs['df1_always_df'] = False\n locs['files_as_subcorpora'] = False\n locs['corpus'] = corpus\n\n if multiple == 'multiplespeaker':\n locs['multispeaker'] = True\n\n if isinstance(non_first_sub, list) and len(non_first_sub) == 1:\n non_first_sub = non_first_sub[0]\n\n # make the default query\n locs = {k: v for k, v in locs.items() if canpickle(v)}\n # make a new dict for every iteration\n ds = [dict(**locs) for i in range(denom)]\n for index, (d, bit) in enumerate(zip(ds, toiter)):\n d['paralleling'] = index\n if multiple in ['namedqueriessingle', 'namedqueriesmultiple']:\n d[itsname] = bit[1]\n d['outname'] = bit[0]\n elif multiple in ['multiplecorpora', 'datalist']:\n d['outname'] = bit.name.replace('-parsed', '')\n d[itsname] = bit\n elif multiple in ['subcorpora']:\n d[itsname] = bit\n jmd = {subval: bit}\n # put this earlier\n j2 = kwargs.get('just_metadata', False)\n if not j2:\n j2 = {}\n jmd.update(j2)\n \n d['just_metadata'] = jmd\n d['outname'] = bit\n d['by_metadata'] = False\n d['subcorpora'] = non_first_sub\n if non_first_sub:\n d['print_info'] = False\n\n # message printer should be a function...\n if kwargs.get('conc') is False:\n message = 'Interrogating'\n elif kwargs.get('conc') is True:\n message = 'Interrogating and concordancing'\n elif kwargs.get('conc').lower() == 'only':\n message = 'Concordancing'\n\n time = strftime(\"%H:%M:%S\", localtime())\n from corpkit.process import dictformat\n \n if print_info:\n\n # proper printing for plurals\n # in truth this needs to be revised, it's horrible.\n sformat = dictformat(search, query)\n\n if num_cores == 1:\n add_es = ''\n else:\n add_es = 'es'\n if multiple in ['multiplecorpora', 'datalist']:\n corplist = \"\\n \".join([i.name for i in list(corpus)[:20]])\n if len(corpus) > 20:\n corplist += '\\n ... and %d more ...\\n' % (len(corpus) - 20)\n print((\"\\n%s: Beginning %d corpus interrogations (in %d parallel process%s):\\n %s\" \\\n \"\\n Query: %s\\n %s corpus ... \\n\" % (time, len(corpus), num_cores, add_es, corplist, sformat, message)))\n\n elif multiple == 'namedqueriessingle':\n print((\"\\n%s: Beginning %d corpus interrogations (in %d parallel process%s): %s\" \\\n \"\\n Queries: %s\\n %s corpus ... \\n\" % (time, len(query), num_cores, add_es, corpus.name, sformat, message) ))\n\n elif multiple == 'namedqueriesmultiple':\n print((\"\\n%s: Beginning %d corpus interrogations (in %d parallel process%s): %s\" \\\n \"\\n Queries: %s\\n %s corpus ... \\n\" % (time, len(list(search.keys())), num_cores, add_es, corpus.name, sformat, message)))\n\n elif multiple in ['eachspeaker', 'multiplespeaker']:\n print((\"\\n%s: Beginning %d parallel corpus interrogation%s: %s\" \\\n \"\\n Query: %s\\n %s corpus ... \\n\" % (time, num_cores, add_es.lstrip('e'), corpus.name, sformat, message) ))\n elif multiple in ['subcorpora']:\n print((\"\\n%s: Beginning %d parallel corpus interrogation%s: %s\" \\\n \"\\n Query: %s\\n %s corpus ... \\n\" % (time, num_cores, add_es.lstrip('e'), corpus.name, sformat, message) ))\n\n # run in parallel, get either a list of tuples (non-c option)\n # or a dataframe (c option)\n #import sys\n #reload(sys)\n #stdout=sys.stdout\n failed = False\n terminal = False\n used_joblib = False\n #ds = ds[::-1]\n #todo: the number of blank lines to print can be way wrong\n if not root and print_info:\n from blessings import Terminal\n terminal = Terminal()\n print('\\n' * (len(ds) - 2))\n for dobj in ds:\n linenum = dobj['paralleling']\n # this try handles nosetest problems in sublime text\n try:\n with terminal.location(0, terminal.height - (linenum + 1)):\n # this is a really bad idea.\n thetime = strftime(\"%H:%M:%S\", localtime())\n num_spaces = 26 - len(dobj['outname'])\n print('%s: QUEUED: %s' % (thetime, dobj['outname']))\n except:\n pass\n\n if not root and multiprocess:\n try:\n res = Parallel(n_jobs=num_cores)(delayed(interrogator)(**x) for x in ds)\n used_joblib = True\n except:\n failed = True\n print('Multiprocessing failed.')\n raise\n if not res:\n failed = True\n else:\n res = []\n for index, d in enumerate(ds):\n d['startnum'] = (100 / denom) * index\n res.append(interrogator(**d))\n try:\n res = sorted([i for i in res if i])\n except:\n pass\n\n # remove unpicklable bits from query\n from types import ModuleType, FunctionType, BuiltinMethodType, BuiltinFunctionType\n badtypes = (ModuleType, FunctionType, BuiltinFunctionType, BuiltinMethodType)\n qlocs = {k: v for k, v in locs.items() if not isinstance(v, badtypes)}\n\n if hasattr(qlocs.get('corpus', False), 'name'):\n qlocs['corpus'] = qlocs['corpus'].path\n else:\n qlocs['corpus'] = list([i.path for i in qlocs.get('corpus', [])])\n\n # return just a concordance\n from corpkit.interrogation import Concordance\n if kwargs.get('conc') == 'only':\n concs = pd.concat([x for x in res])\n thetime = strftime(\"%H:%M:%S\", localtime())\n concs = concs.reset_index(drop=True)\n if kwargs.get('maxconc'):\n concs = concs[:kwargs.get('maxconc')]\n lines = Concordance(concs)\n \n if save:\n lines.save(save, print_info=print_info)\n\n if print_info:\n print('\\n\\n%s: Finished! %d results.\\n\\n' % (thetime, format(len(concs.index), ',')))\n\n return lines\n\n # return interrodict (to become multiindex)\n if isinstance(res[0], Interrodict) or not all(isinstance(i.results, Series) for i in res):\n out = OrderedDict()\n for interrog, d in zip(res, ds):\n for unpicklable in ['note', 'root']:\n interrog.query.pop(unpicklable, None)\n try:\n out[interrog.query['outname']] = interrog\n except KeyError:\n out[d['outname']] = interrog\n\n idict = Interrodict(out)\n \n if print_info:\n thetime = strftime(\"%H:%M:%S\", localtime())\n print(\"\\n\\n%s: Finished! Output is multi-indexed.\" % thetime)\n idict.query = qlocs\n\n if save:\n idict.save(save, print_info=print_info)\n\n return idict\n\n # make query and total branch, save, return\n # todo: standardise this so we don't have to guess transposes\n # \n else:\n if multiple == 'multiplecorpora' and not mult_corp_are_subs:\n sers = [i.results for i in res]\n out = DataFrame(sers, index=[i.query['outname'] for i in res])\n out = out.reindex_axis(sorted(out.columns), axis=1) # sort cols\n out = out.fillna(0) # nan to zero\n out = out.astype(int) # float to int\n out = out.T \n else:\n # make a series from counts\n if all(len(i.results) == 1 for i in res):\n out = pd.concat([r.results for r in res])\n out = out.sort_index()\n else:\n try:\n out = pd.concat([r.results for r in res], axis=1)\n out = out.T\n out.index = [i.query['outname'] for i in res]\n except ValueError:\n return None\n # format like normal\n # this sorts subcorpora, which are cls\n out = out[sorted(list(out.columns))]\n # puts subcorpora in the right place\n if not mult_corp_are_subs and multiple != 'subcorpora':\n out = out.T\n if multiple == 'subcorpora':\n out = out.sort_index()\n out = out.fillna(0) # nan to zero\n out = out.astype(int)\n if 'c' in show and mult_corp_are_subs:\n out = out.sum()\n out.index = sorted(list(out.index))\n\n # sort by total\n if isinstance(out, DataFrame):\n\n out = out[list(out.sum().sort_values(ascending=False).index)]\n\n # really need to figure out the deal with tranposing!\n if all(x.endswith('.xml') for x in list(out.columns)) \\\n or all(x.endswith('.txt') for x in list(out.columns)) \\\n or all(x.endswith('.conll') for x in list(out.columns)):\n out = out.T\n \n if kwargs.get('nosubmode'):\n out = out.sum()\n \n from corpkit.interrogation import Interrogation\n tt = out.sum(axis=1) if isinstance(out, DataFrame) else out.sum()\n out = Interrogation(results=out, totals=tt, query=qlocs)\n\n if hasattr(out, 'columns') and len(out.columns) == 1:\n out = out.sort_index() \n\n if kwargs.get('conc') is True:\n try:\n concs = pd.concat([x.concordance for x in res], ignore_index=True)\n concs = concs.sort_values(by='c')\n concs = concs.reset_index(drop=True)\n if kwargs.get('maxconc'):\n concs = concs[:kwargs.get('maxconc')]\n out.concordance = Concordance(concs)\n except ValueError:\n out.concordance = None\n\n thetime = strftime(\"%H:%M:%S\", localtime())\n if terminal:\n print(terminal.move(terminal.height-1, 0))\n if print_info:\n if terminal:\n print(terminal.move(terminal.height-1, 0))\n if hasattr(out.results, 'columns'):\n print('%s: Interrogation finished! %s unique results, %s total.' % (thetime, format(len(out.results.columns), ','), format(out.totals.sum(), ',')))\n else:\n print('%s: Interrogation finished! %s matches.' % (thetime, format(tt, ',')))\n if save:\n out.save(save, print_info = print_info)\n\n if list(out.results.index) == ['0'] and not kwargs.get('df1_always_df'):\n out.results = out.results.ix[0].sort_index()\n return out\n"
},
{
"path": "corpkit/new_project",
"content": "#!/usr/bin/env python\n\nfrom __future__ import print_function\n\n\"\"\"\nA script to create a new corpkit project\n\"\"\"\n\nimport sys\nfrom corpkit.other import new_project\nif len(sys.argv) == 1:\n raise ValueError('Please specify name of new project.')\nelse:\n name = sys.argv[1]\n new_project(name)\n\n"
},
{
"path": "corpkit/noseinstall.py",
"content": "import os\nfrom nose.tools import assert_equals\nfrom corpkit import *\n\nunparsed_path = 'data/test'\nparsed_path = 'data/test-parsed'\n\ndef test_import():\n import corpkit\n from dictionaries.wordlists import wordlists\n from corpkit import Corpus\n assert_equals(wordlists.articles, ['a', 'an', 'the', 'teh'])\n"
},
{
"path": "corpkit/nosetests.py",
"content": "\"\"\"\nThis file contains tests for the corpkit API, to be run by Nose.\n\nThere are fast and slow tests. Slow tests include those that test the parser.\nThese should be run before anything goes into master. Fast tests corpus\ninterrogations, edits, concordances and so on. These are done on commit.\n\nThe tests don't cover everything in the module yet.\n\nTo run all tests:\n\n nosetests corpkit/nosetests.py\n\nJust fast tests:\n\n nosetests corpkit/nosetests.py -a '!slow'\n\n\"\"\"\n\nimport os\nimport nose\nfrom nose.tools import assert_equals\nimport corpkit\nfrom corpus import Corpus\n\nunparsed_path = 'data/test'\nparsed_path = 'data/test-plain-parsed'\nspeak_path = 'data/test-speak-parsed'\ntok_path = 'data/test-tokenised'\n\ndef test_import():\n import corpkit\n from dictionaries.wordlists import wordlists\n from corpus import Corpus\n assert_equals(wordlists.articles, ['a', 'an', 'the', 'teh'])\n\ndef test_corpus_class():\n \"\"\"Test that Corpus can be created\"\"\"\n unparsed = Corpus(unparsed_path)\n assert_equals(os.path.basename(unparsed_path), unparsed.name)\n \ndef test_parse():\n \"\"\"Test CoreNLP parsing\"\"\"\n import shutil\n unparsed = Corpus(unparsed_path)\n try:\n shutil.rmtree(parsed_path)\n except:\n pass\n parsed = unparsed.parse(metadata=True)\n fnames = []\n for subc in parsed.subcorpora:\n for f in subc.files:\n fnames.append(f.name)\n shutil.move(parsed.path, parsed_path)\n assert_equals(fnames, ['intro.txt.conll', 'body.txt.conll'])\n\ndef test_tokenise():\n \"\"\"Test the tokeniser, lemmatiser and POS tagger\"\"\"\n unparsed = Corpus(unparsed_path)\n try:\n shutil.rmtree(tok_path)\n except:\n pass\n tok = unparsed.tokenise(speaker_segmentation=True, lemmatise=True, postag=True, metadata=True)\n\n df = tok[0][0].document\n assert_equals(tok.name, 'test-tokenised')\n assert_equals(len(tok.subcorpora), 2)\n #assert_equals(list(df.columns), ['w', 'l', 'p'])\n assert_equals(list(df.index.names), ['s', 'i'])\n\ndef test_speak_parse():\n \"\"\"Test CoreNLP parsing with speaker segmentation\"\"\"\n import shutil\n unparsed = Corpus(unparsed_path)\n try:\n shutil.rmtree(speak_path)\n except:\n pass\n parsed = unparsed.parse(speaker_segmentation=True, metadata=True)\n fnames = []\n for subc in parsed.subcorpora:\n for f in subc.files:\n fnames.append(f.name)\n shutil.move(parsed.path, speak_path)\n assert_equals(fnames, ['intro.txt.conll', 'body.txt.conll'])\n\n# this is how you define a slow test\ntest_parse.slow = 1\ntest_speak_parse.slow = 1\n\ndef test_interro1():\n \"\"\"Testing interrogation 1\"\"\"\n corp = Corpus(parsed_path)\n data = corp.interrogate('t', r'__ < /JJ.?/')\n assert_equals(data.results.shape, (2, 6))\n\ndef test_interro2():\n \"\"\"Testing interrogation 2\"\"\"\n corp = Corpus(parsed_path)\n data = corp.interrogate({'t': r'__ < /JJ.?/'})\n assert_equals(data.results.shape, (2, 6))\n\ndef test_interro3():\n \"\"\"Testing interrogation 3\"\"\"\n corp = Corpus(parsed_path)\n data = corp.interrogate({'w': r'^c'}, exclude={'l': r'check'}, show=['l', 'f'])\n st = {'can/aux',\n 'computational/amod',\n 'concordancing/appos',\n 'corpkit/nmod:poss',\n 'corpus/compound',\n 'case/nmod:in',\n 'continuum/nmod:on',\n 'conduit/nmod:as',\n 'concordancing/nmod:like',\n 'corpus/nsubjpass',\n }\n assert_equals(set(list(data.results.columns)), st)\n\n# skipping this for now, as who cares about tokens\n#def test_interro4():\n# \"\"\"Testing interrogation 4\"\"\"\n# corp = Corpus('data/test-stripped-tokenised')\n# data = corp.interrogate({'w': 'any'}, show='nw')\n# d = {'and interrogating': {'first': 0, 'second': 2},\n# 'concordancing and': {'first': 0, 'second': 2}}\n# assert_equals(data.results.to_dict(), d)\n \ndef test_interro_multiindex_tregex_justspeakers():\n \"\"\"Testing interrogation 6\"\"\"\n import pandas as pd\n corp = Corpus(speak_path)\n data = corp.interrogate('t', r'__ < /JJ.?/', subcorpora='speaker')\n assert_equals(all(data.results.index), \n all(pd.MultiIndex(levels=[['ANONYMOUS', 'NEWCOMER',\n 'TESTER', 'UNIDENTIFIED'], ['first', 'second']],\n labels=[[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]])))\n\ntest_interro_multiindex_tregex_justspeakers.slow = 1\n\ndef test_conc():\n \"\"\"Testing concordancer\"\"\"\n corp = Corpus(parsed_path)\n data = corp.concordance({'f': 'amod'})\n assert_equals(data.ix[0]['m'], 'small')\n\n# this syntax isn't recognised by tgrep, so we'll skip it in tests\ndef test_edit():\n \"\"\"Testing edit function\"\"\"\n corp = Corpus(parsed_path)\n data = corp.interrogate({'t': r'__ !< __'})\n data = data.edit('%', 'self')\n assert_equals(data.results.iloc[0,0], 10.204081632653061)\n #assert_equals(data.results.iloc[0,0], 11.627906976744185)\n\ntest_edit.slow = 1\n\ndef test_tok1_interro():\n \"\"\"\n Check that indexes can be shown\n \"\"\"\n corpus = Corpus(tok_path)\n res = corpus.interrogate(show=['s', 'i', 'l'])\n sortd = res.results[sorted(res.results.columns)]\n three = ['0/0/corpus', '0/0/this', '0/1/linguistics']\n assert_equals(list(sortd.columns)[:3], three)\n assert_equals(sortd.sum().sum(), 77)\n\ndef test_tok2_interro():\n \"\"\"\n Check a tokenised corpus interrogation\n \"\"\"\n corpus = Corpus(tok_path)\n res = corpus.interrogate(show=['w', 'l', 'p'], conc=True)\n assert_equals(res.results['is/be/vbz']['second'], 1)\n assert_equals(res.results['is/be/vbz']['first'], 2)\n assert_equals(str(res.results.ix[0].dtype), 'int64')\n flo = res.edit('%', 'self').results.iat[0,0].round(2)\n assert_equals(flo, 5.71)\n assert_equals(res.concordance.m.iloc[-1], 'work/work/nn')\n attributes = ['query', 'results', 'totals', 'visualise', 'edit', 'concordance']\n allat = all(getattr(res, i) is not None for i in attributes)\n assert_equals(allat, True)\n\ndef document_check():\n \"\"\"\n Check that the document lazy attribute works\n \"\"\"\n corpus = Corpus(speak_path)\n df = corpus[0][0].document\n fir = ['This', 'this', 'DT', 'O', 3, 'det', '0', '1']\n assert_equals(list(df.ix[1,1], fir))\n kys = list(df._metadata[5].keys())\n lst = ['year', 'test', 'parse', 'speaker', 'num']\n assert_equals(kys, lst)\n assert_equals(corpus.files, None)\n assert_equals(corpus.datatype, 'conll')\n\ndef test_conc_edit():\n \"\"\"\n Make sure we can edit concordance lines\n \"\"\"\n corpus = Corpus(speak_path)\n res = corpus.interrogate(show=['l','gl'], conc=True)\n assert_equals(len(res.concordance), 77)\n noling = len(res.concordance.edit(skip_entries='ling'))\n assert_equals(noling, 69)\n\ndef test_symbolic_subcorpora():\n \"\"\"\n Check that we can make speaker into subcorpora\n \"\"\"\n corpus = Corpus(speak_path, subcorpora='speaker')\n res = corpus.interrogate({'l': r'^[abcde]'})\n spks = ['ANONYMOUS', 'NEWCOMER', 'TESTER', 'UNIDENTIFIED']\n assert_equals(list(res.results.index), spks)\n\ndef test_symbolic_multiindex():\n \"\"\"\n Check that we can make a named multiindex\n \"\"\"\n subval = ['speaker', 'test']\n corpus = Corpus(speak_path, subcorpora=subval)\n res = corpus.interrogate({'l': r'^[abcde]'})\n test_poss = {'none', 'off', 'on'}\n assert_equals(set(res.results.index.levels[1]), test_poss)\n assert_equals(list(res.results.index.names), subval)\n\ndef check_skip_filt():\n \"\"\"\n Check that we can make a skip filter\n \"\"\"\n corpus = Corpus(speak_path, skip={'speaker': 'UNIDENTIFIED'})\n res = corpus.interrogate()\n assert_equals(len(res.results.columns), 47)\n\ndef check_just_filt():\n \"\"\"\n Check that we can make a just filter\n \"\"\"\n corpus = Corpus(speak_path, just={'speaker': 'UNIDENTIFIED'})\n res = corpus.interrogate()\n assert_equals(len(res.results.columns), 22)\n\ndef test_interpreter():\n \"\"\"\n Test for errors in interpreter functionality\n \"\"\"\n import os\n try:\n os.remove('saved_interrogations/test-speak-parsed-anylemma.p')\n except:\n pass\n from corpkit.env import interpreter\n try:\n os.makedirs('exported')\n except:\n pass\n try:\n os.makedirs('saved_interrogations')\n except:\n pass\n # this will make some data in exported/\n out = interpreter(fromscript='corpkit/interpreter_tests.cki')\n assert_equals(out, None)\n \ndef check_interpreter_res_csv():\n \"\"\"\n Interpreter check made exported/res.csv---check it\n \"\"\"\n import pandas as pd\n df = pd.read_csv('exported/res.csv', sep='\\t', index_col=0)\n assert_equals(df.mean()['a'], 1.5)\n\ndef check_interpreter_conc_csv():\n \"\"\"\n Interpreter check made exported/conc.csv---check it\n \"\"\"\n import pandas as pd\n import shutil\n df = pd.read_csv('exported/conc.csv', sep='\\t', index_col=0)\n shutil.rmtree('exported')\n assert_equals(df.shape, (77, 7))\n\ndef check_interpreter_saved_interro():\n \"\"\"\n Interpreter made a pickled result. Check it\n \"\"\"\n import pandas as pd\n import shutil \n from corpkit import load\n dat = load('test-speak-parsed-anylemma')\n shutil.rmtree('saved_interrogations')\n assert hasattr(dat, 'results')\n assert hasattr(dat, 'totals')\n assert hasattr(dat, 'query')\n assert('concordancing' in dat.results)\n rel = dat.results.T / dat.totals\n assert_equals(rel.ix[0].sum().round(2), 0.19)\n\n# test to write:\n# annotation\n# unannotation\n# language model\n# tgrep\n# features, postags, wordclasses, dotfiles\n"
},
{
"path": "corpkit/other.py",
"content": "from __future__ import print_function\n\n\"\"\"\nIn here are functions used internally by corpkit, but also\nmight be called by the user from time to time\n\"\"\"\nfrom corpkit.constants import STRINGTYPE, PYTHON_VERSION, INPUTFUNC\n\ndef quickview(results, n=25):\n \"\"\"\n View top n results as painlessly as possible.\n\n :param results: Interrogation data\n :type results: :class:``corpkit.interrogation.Interrogation``\n :param n: Show top *n* results\n :type n: int\n :returns: None\n \"\"\"\n\n import corpkit\n import pandas as pd\n import numpy as np\n import os\n import corpkit\n from corpkit.interrogation import Interrogation\n\n # handle dictionaries too:\n dictpath = 'dictionaries'\n savedpath = 'saved_interrogations'\n\n # too lazy to code this properly for every possible data type:\n if n == 'all':\n n = 9999\n\n dtype = corpkit.interrogation.Interrogation\n\n if isinstance(results, STRINGTYPE):\n if os.path.isfile(os.path.join(dictpath, results)):\n from corpkit.other import load\n results = load(results, loaddir=dictpath)\n\n elif os.path.isfile(os.path.join(savedpath, results)):\n from corpkit.other import load\n results = load(results)\n else:\n raise OSError('File \"%s\" not found.' % os.path.abspath(results))\n\n elif isinstance(results, Interrogation):\n if getattr(results, 'results'):\n datatype = results.results.iloc[0,0].dtype\n if datatype == 'int64':\n option = 't'\n else:\n option = '%'\n rq = results.query.get('operation', False)\n if rq:\n rq = rq.lower()\n if rq.startswith('k'):\n option = 'k'\n if rq.startswith('%'):\n option = '%'\n if rq.startswith('/'):\n option = '/'\n try:\n the_list = list(results.results.columns)[:n]\n except:\n the_list = list(results.results.index)[:n]\n else:\n print(results.totals)\n return\n else:\n raise ValueError('Results not recognised.')\n\n # get longest word length for justification\n longest = max([len(i) for i in the_list])\n\n for index, entry in enumerate(the_list):\n if option == 't':\n if isinstance(results, Interrogation):\n if hasattr(results, 'results'):\n to_get_from = results.results\n tot = to_get_from[entry].sum()\n else:\n to_get_from = results.totals\n tot = to_get_from[entry]\n print('%s: %s (n=%d)' %(str(index).rjust(3), entry.ljust(longest), tot))\n elif option == '%' or option == '/':\n if isinstance(results, Interrogation):\n to_get_from = results.totals\n tot = to_get_from[entry]\n totstr = \"%.3f\" % tot\n print('%s: %s (%s%%)' % (str(index).rjust(3), entry.ljust(longest), totstr)) \n elif dtype == corpkit.interrogation.Results:\n print('%s: %s (%s)' %(str(index).rjust(3), entry.ljust(longest), option))\n elif dtype == corpkit.interrogation.Totals:\n tot = results[entry]\n totstr = \"%.3f\" % tot\n print('%s: %s (%s%%)' % (str(index).rjust(3), entry.ljust(longest), totstr)) \n elif option == 'k':\n print('%s: %s (l/l)' %(str(index).rjust(3), entry.ljust(longest)))\n else:\n print('%s: %s' %(str(index).rjust(3), entry.ljust(longest)))\n\ndef concprinter(dataframe, kind='string', n=100,\n window=35, columns='all', metadata=True, **kwargs):\n \"\"\"\n Print conc lines nicely, to string, latex or csv\n\n :param df: concordance lines from :class:``corpkit.corpus.Concordance``\n :type df: pd.DataFame \n :param kind: output format\n :type kind: str ('string'/'latex'/'csv')\n :param n: Print first n lines only\n :type n: int/'all'\n :returns: None\n \"\"\"\n import corpkit\n\n df = dataframe.copy().fillna('')\n\n if n > len(df):\n n = len(df)\n if not kind.startswith('l') and kind.startswith('c') and kind.startswith('s'):\n raise ValueError('kind argument must start with \"l\" (latex), \"c\" (csv) or \"s\" (string).')\n import pandas as pd\n\n # shitty thing to hardcode\n pd.set_option('display.max_colwidth', -1)\n\n if isinstance(n, int):\n to_show = df.head(n)\n elif n is False:\n to_show = df\n elif n == 'all':\n to_show = df\n else:\n raise ValueError('n argument \"%s\" not recognised.' % str(n))\n\n def resize_by_window_size(df, window):\n df.is_copy = False\n if isinstance(window, int):\n df['l'] = df['l'].str.slice(start=-window, stop=None)\n df['l'] = df['l'].str.rjust(window)\n df['r'] = df['r'].str.slice(start=0, stop=window)\n df['r'] = df['r'].str.ljust(window)\n df['m'] = df['m'].str.ljust(df['m'].str.len().max())\n else:\n df['l'] = df['l'].str.slice(start=-window[0], stop=None)\n df['l'] = df['l'].str.rjust(window[0])\n df['r'] = df['r'].str.slice(start=0, stop=window[-1])\n df['r'] = df['r'].str.ljust(window[-1])\n df['m'] = df['m'].str.ljust(df['m'].str.len().max()) \n return df\n\n to_show.is_copy = False\n if window:\n to_show = resize_by_window_size(to_show, window)\n\n # if showing metadata to the right of lmr, add it here\n cnames = list(to_show.columns)\n ind = cnames.index('r')\n if columns == 'all':\n columns = cnames[:ind+1]\n if metadata is True:\n after_right = cnames[ind+1:]\n columns = columns + after_right\n elif isinstance(metadata, list):\n columns = columns + metadata\n\n to_show = to_show[columns]\n\n if kind.startswith('s'):\n functi = pd.DataFrame.to_string\n if kind.startswith('l'):\n functi = pd.DataFrame.to_latex\n if kind.startswith('c'):\n functi = pd.DataFrame.to_csv\n kwargs['sep'] = ','\n if kind.startswith('t'):\n functi = pd.DataFrame.to_csv\n kwargs['sep'] = '\\t'\n\n # automatically basename subcorpus for show\n if 'c' in list(to_show.columns):\n import os\n to_show['c'] = to_show['c'].apply(os.path.basename)\n\n if 'f' in list(to_show.columns):\n import os\n to_show['f'] = to_show['f'].apply(os.path.basename)\n\n return_it = kwargs.pop('return_it', False)\n print_it = kwargs.pop('print_it', True)\n\n if return_it:\n return functi(to_show, header=kwargs.get('header', False), **kwargs)\n else:\n print('\\n')\n print(functi(to_show, header=kwargs.get('header', False), **kwargs))\n print('\\n')\n\ndef save(interrogation, savename, savedir='saved_interrogations', **kwargs):\n \"\"\"\n Save an interrogation as pickle to *savedir*.\n\n >>> interro_interrogator(corpus, 'words', 'any')\n >>> save(interro, 'savename')\n\n will create ``./saved_interrogations/savename.p``\n\n :param interrogation: Corpus interrogation to save\n :type interrogation: corpkit interogation/edited result\n \n :param savename: A name for the saved file\n :type savename: str\n \n :param savedir: Relative path to directory in which to save file\n :type savedir: str\n \n :param print_info: Show/hide stdout\n :type print_info: bool\n \n :returns: None\n \"\"\"\n\n try:\n import cPickle as pickle\n except ImportError:\n import pickle as pickle\n import os\n from time import localtime, strftime\n import corpkit\n from corpkit.process import makesafe, sanitise_dict\n\n from corpkit.interrogation import Interrogation\n from corpkit.corpus import Corpus, Datalist\n\n print_info = kwargs.get('print_info', True)\n\n def make_filename(interrogation, savename):\n \"\"\"create a filename\"\"\"\n if '/' in savename:\n return savename\n\n firstpart = ''\n if savename.endswith('.p'):\n savename = savename[:-2] \n savename = makesafe(savename, drop_datatype=False, hyphens_ok=True)\n if not savename.endswith('.p'):\n savename = savename + '.p'\n if hasattr(interrogation, 'query') and isinstance(interrogation.query, dict):\n corpus = interrogation.query.get('corpus', False)\n if corpus:\n if isinstance(corpus, STRINGTYPE):\n firstpart = corpus\n else:\n if isinstance(corpus, Datalist):\n firstpart = Corpus(corpus).name\n if hasattr(corpus, 'name'):\n firstpart = corpus.name\n else:\n firstpart = ''\n \n firstpart = os.path.basename(firstpart)\n\n if firstpart:\n return firstpart + '-' + savename\n else:\n return savename\n\n savename = make_filename(interrogation, savename)\n\n # delete unpicklable parts of query\n if hasattr(interrogation, 'query') and isinstance(interrogation.query, dict):\n iq = interrogation.query\n if iq:\n from types import ModuleType, FunctionType, BuiltinMethodType, BuiltinFunctionType\n interrogation.query = {k: v for k, v in iq.items() if not isinstance(v, ModuleType) \\\n and not isinstance(v, FunctionType) \\\n and not isinstance(v, BuiltinFunctionType) \\\n and not isinstance(v, BuiltinMethodType)}\n else:\n iq = {}\n\n if savedir and not '/' in savename:\n if not os.path.exists(savedir):\n os.makedirs(savedir)\n fullpath = os.path.join(savedir, savename)\n else:\n fullpath = savename\n\n while os.path.isfile(fullpath):\n selection = INPUTFUNC((\"\\nSave error: %s already exists in %s.\\n\\n\" \\\n \"Type 'o' to overwrite, or enter a new name: \" % (savename, savedir)))\n\n if selection == 'o' or selection == 'O':\n os.remove(fullpath)\n else:\n selection = selection.replace('.p', '')\n if not selection.endswith('.p'):\n selection = selection + '.p'\n fullpath = os.path.join(savedir, selection)\n\n if hasattr(interrogation, 'query'):\n interrogation.query = sanitise_dict(interrogation.query)\n\n with open(fullpath, 'wb') as fo:\n pickle.dump(interrogation, fo)\n \n time = strftime(\"%H:%M:%S\", localtime())\n if print_info:\n print('\\n%s: Data saved: %s\\n' % (time, fullpath))\n\ndef load(savename, loaddir='saved_interrogations'):\n \"\"\"\n Load saved data into memory:\n\n >>> loaded = load('interro')\n\n will load ``./saved_interrogations/interro.p`` as loaded\n\n :param savename: Filename with or without extension\n :type savename: str\n \n :param loaddir: Relative path to the directory containg *savename*\n :type loaddir: str\n \n :param only_concs: Set to True if loading concordance lines\n :type only_concs: bool\n\n :returns: loaded data\n \"\"\" \n try:\n import cPickle as pickle\n except ImportError:\n import pickle as pickle\n import os\n if not savename.endswith('.p'):\n savename = savename + '.p'\n\n if loaddir:\n if '/' not in savename:\n fullpath = os.path.join(loaddir, savename)\n else:\n fullpath = savename\n else:\n fullpath = savename\n\n with open(fullpath, 'rb') as fo:\n data = pickle.load(fo)\n return data\n\ndef loader(savedir='saved_interrogations'):\n \"\"\"Show a list of data that can be loaded, and then load by user input of index\"\"\"\n import glob\n import os\n import corpkit\n from corpkit.other import load\n fs = [i for i in glob.glob(r'%s/*' % savedir) if not os.path.basename(i).startswith('.')]\n string_to_show = '\\nFiles in %s:\\n' % savedir\n most_digits = max([len(str(i)) for i, j in enumerate(fs)])\n for index, fname in enumerate(fs):\n string_to_show += str(index).rjust(most_digits) + ':\\t' + os.path.basename(fname) + '\\n'\n print(string_to_show)\n INPUTFUNC('Enter index of item to load: ')\n if ' ' in index or '=' in index:\n if '=' in index:\n index = index.replace(' = ', ' ')\n index = index.replace('=', ' ')\n varname, ind = index.split(' ', 1)\n globals()[varname] = load(os.path.basename(fs[int(ind)]))\n print(\"%s = %s. Don't do this again.\" % (varname, os.path.basename(fs[int(ind)])))\n return\n try:\n index = int(index)\n except:\n raise ValueError('Selection not recognised.')\n return load(os.path.basename(fs[index]))\n\ndef new_project(name, loc='.', **kwargs):\n \"\"\"Make a new project in ``loc``.\n\n :param name: A name for the project\n :type name: str\n :param loc: Relative path to directory in which project will be made\n :type loc: str\n\n :returns: None\n \"\"\"\n import corpkit\n import os\n import shutil\n import stat\n import platform\n from time import strftime, localtime\n\n root = kwargs.get('root', False)\n\n path_to_corpkit = os.path.dirname(corpkit.__file__)\n thepath, corpkitname = os.path.split(path_to_corpkit)\n \n # make project directory\n fullpath = os.path.join(loc, name)\n try:\n os.makedirs(fullpath)\n except:\n if root:\n thetime = strftime(\"%H:%M:%S\", localtime())\n print('%s: Directory already exists: \"%s\"' %( thetime, fullpath))\n return\n else:\n raise\n # make other directories\n dirs_to_make = ['data', 'images', 'saved_interrogations', \\\n 'saved_concordances', 'dictionaries', 'exported', 'logs']\n #subdirs_to_make = ['dictionaries', 'saved_interrogations']\n for directory in dirs_to_make:\n os.makedirs(os.path.join(fullpath, directory))\n #for subdir in subdirs_to_make:\n #os.makedirs(os.path.join(fullpath, 'data', subdir))\n # copy the bnc dictionary to dictionaries\n\n def resource_path(relative):\n import os\n return os.path.join(os.environ.get(\"_MEIPASS2\",os.path.abspath(\".\")),relative)\n\n corpath = os.path.dirname(corpkit.__file__)\n if root:\n corpath = corpath.replace('/lib/python2.7/site-packages.zip/corpkit', '')\n baspat = os.path.dirname(corpath)\n dicpath = os.path.join(corpath, 'dictionaries')\n try:\n shutil.copy(os.path.join(dicpath, 'bnc.p'), os.path.join(fullpath, 'dictionaries'))\n except:\n # find out why bnc not found!\n if root:\n try:\n shutil.copy(resource_path(os.path.join('dictionaries', 'bnc.p')), os.path.join(fullpath, 'dictionaries'))\n except:\n pass\n\n if not root:\n thetime = strftime(\"%H:%M:%S\", localtime())\n print('\\n%s: New project created: \"%s\"\\n' % (thetime, name))\n \ndef load_all_results(data_dir='saved_interrogations', **kwargs):\n \"\"\"\n Load every saved interrogation in data_dir into a dict:\n\n >>> r = load_all_results()\n\n :param data_dir: path to saved data\n :type data_dir: str\n\n :returns: dict with filenames as keys\n \"\"\"\n import os\n from time import localtime, strftime\n from other import load\n from process import makesafe\n\n root = kwargs.get('root', False)\n note = kwargs.get('note', False) \n \n datafiles = [f for f in os.listdir(data_dir) if os.path.isfile(os.path.join(data_dir, f)) \\\n and f.endswith('.p')]\n\n # just load first n (for testing)\n if kwargs.get('n', False):\n datafiles = datafiles[:kwargs['n']]\n\n output = {}\n\n l = 0\n for index, f in enumerate(datafiles): \n try:\n loadname = f.replace('.p', '')\n output[loadname] = load(f, loaddir = data_dir)\n time = strftime(\"%H:%M:%S\", localtime())\n print('%s: %s loaded as %s.' % (time, f, makesafe(loadname)))\n l += 1\n except:\n time = strftime(\"%H:%M:%S\", localtime())\n print('%s: %s failed to load. Try using load to find out the matter.' % (time, f))\n if note and len(datafiles) > 3:\n note.progvar.set((index + 1) * 100.0 / len(datafiles))\n if root:\n root.update()\n time = strftime(\"%H:%M:%S\", localtime())\n print('%s: %d interrogations loaded from %s.' % (time, l, os.path.basename(data_dir)))\n from interrogation import Interrodict\n return Interrodict(output)\n\ndef texify(series, n=20, colname='Keyness', toptail=False, sort=False):\n \"\"\"turn a series into a latex table\"\"\"\n import corpkit\n import pandas as pd\n if sort:\n df = pd.DataFrame(series.order(ascending=False))\n else:\n df = pd.DataFrame(series)\n df.columns = [colname]\n if not toptail:\n return df.head(n).to_latex()\n else:\n comb = pd.concat([df.head(n), df.tail(n)])\n longest_word = max([len(w) for w in list(comb.index)])\n tex = ''.join(comb.to_latex()).split('\\n')\n linelin = len(tex[0])\n try:\n newline = (' ' * (linelin / 2)) + ' &'\n newline_len = len(newline)\n newline = newline + (' ' * (newline_len - 1)) + r'\\\\'\n newline = newline.replace(r' \\\\', r'... \\\\')\n newline = newline.replace(r' ', r'... ', 1)\n except:\n newline = r'... & ... \\\\'\n tex = tex[:n+4] + [newline] + tex[n+4:]\n tex = '\\n'.join(tex)\n return tex\n\ndef as_regex(lst, boundaries='w', case_sensitive=False, inverse=False, compile=False):\n \"\"\"Turns a wordlist into an uncompiled regular expression\n\n :param lst: A wordlist to convert\n :type lst: list\n\n :param boundaries:\n :type boundaries: str -- 'word'/'line'/'space'; tuple -- (leftboundary, rightboundary)\n \n :param case_sensitive: Make regular expression case sensitive\n :type case_sensitive: bool\n \n :param inverse: Make regular expression inverse matching\n :type inverse: bool\n\n :returns: regular expression as string\n \"\"\"\n import corpkit\n\n import re\n if case_sensitive:\n case = r''\n else:\n case = r'(?i)'\n if not boundaries:\n boundary1 = r''\n boundary2 = r''\n elif isinstance(boundaries, (tuple, list)):\n boundary1 = boundaries[0]\n boundary2 = boundaries[1]\n else:\n if boundaries.startswith('w') or boundaries.startswith('W'):\n boundary1 = r'\\b'\n boundary2 = r'\\b'\n elif boundaries.startswith('l') or boundaries.startswith('L'):\n boundary1 = r'^'\n boundary2 = r'$'\n elif boundaries.startswith('s') or boundaries.startswith('S'):\n boundary1 = r'\\s'\n boundary2 = r'\\s'\n else:\n raise ValueError('Boundaries not recognised. Use a tuple for custom start and end boundaries.')\n if inverse:\n inverser1 = r'(?!'\n inverser2 = r')'\n else:\n inverser1 = r''\n inverser2 = r''\n\n if inverse:\n joinbit = r'%s|%s' % (boundary2, boundary1)\n as_string = case + inverser1 + r'(?:' + boundary1 + joinbit.join(sorted(list(set([re.escape(w) for w in lst])))) + boundary2 + r')' + inverser2\n else:\n as_string = case + boundary1 + inverser1 + r'(?:' + r'|'.join(sorted(list(set([re.escape(w) for w in lst])))) + r')' + inverser2 + boundary2\n if compile:\n return re.compile(as_string)\n else:\n return as_string\n\ndef make_multi(interrogation, indexnames=None):\n \"\"\"\n make pd.multiindex version of an interrogation (for pandas geeks)\n\n :param interrogation: a corpkit interrogation\n :type interrogation: a corpkit interrogation, pd.DataFrame or pd.Series\n\n :param indexnames: pass in a list of names for the multiindex;\n leave as None to get them if possible from interrogation\n use False to explicitly not get them\n :type indexnames: list of strings/None/False\n :returns: pd.DataFrame with multiindex\"\"\"\n\n # get proper names for index if possible\n from corpkit.constants import transshow, transobjs\n\n import numpy as np\n import pandas as pd\n\n # if it's an interrodict, we want to make it into a single df\n import corpkit\n from corpkit.interrogation import Interrodict, Interrogation\n \n seriesmode = False\n \n if isinstance(interrogation, (Interrodict, dict)):\n import pandas as pd\n import numpy as np\n\n flat = [[], [], []]\n \n for name, data in list(interrogation.items()):\n for subcorpus in list(data.results.index):\n # make multiindex\n flat[0].append(name)\n flat[1].append(subcorpus)\n # add results\n flat[2].append(data.results.ix[subcorpus])\n\n flat[0] = np.array(flat[0])\n flat[1] = np.array(flat[1])\n\n df = pd.DataFrame(flat[2], index=flat[:2])\n\n if indexnames is None:\n indexnames = ['Corpus', 'Subcorpus']\n \n df.index.names = indexnames\n df = df.fillna(0)\n df = df.T\n df[('Total', 'Total')] = df.sum(axis=1)\n df = df.sort_values(by=('Total', 'Total'), \n ascending=False).drop(('Total', 'Total'),\n axis=1).T\n try:\n df = df.astype(int)\n except:\n pass\n return Interrogation(df, df.sum(axis=1), getattr(interrogation, 'query', None))\n # determine datatype, get df and cols\n rows=False\n if isinstance(interrogation, pd.core.frame.DataFrame):\n df = interrogation\n cols = list(interrogation.columns)\n rows = list(interrogation.index)\n elif isinstance(interrogation, pd.core.series.Series):\n cols = list(interrogation.index)\n seriesmode = True\n df = pd.DataFrame(interrogation).T\n elif isinstance(interrogation, Interrogation):\n df = interrogation.results\n if isinstance(df, pd.core.series.Series):\n cols = list(df.index)\n seriesmode = True\n df = pd.DataFrame(df).T\n else:\n cols = list(df.columns)\n rows = list(df.index)\n\n # set indexnames if we have them\n if indexnames is not False:\n if interrogation.query.get('show'):\n indexnames = []\n ends = ['w', 'l', 'i', 'n', 'f', 'p', 'x', 's']\n for showval in interrogation.query['show']:\n if len(showval) == 1:\n if showval in ends:\n showval = 'm' + showval\n else:\n showval = showval + 'w'\n a = transobjs.get(showval[0], showval[0])\n b = transshow.get(showval[-1], showval[-1])\n indexstring = '%s %s' % (a, b.lower())\n indexnames.append(indexstring)\n else:\n indexnames = False\n\n # split column names on slash\n for index, i in enumerate(cols):\n cols[index] = i.split('/')\n\n # make numpy arrays\n arrays = []\n for i in range(len(cols[0])):\n arrays.append(np.array([x[i] for x in cols]))\n \n # make output df, add names if we have them\n newdf = pd.DataFrame(df.T.as_matrix(), index=arrays).T\n if indexnames:\n newdf.columns.names = indexnames\n\n if rows:\n newdf.index = rows\n \n pd.set_option('display.multi_sparse', False)\n totals = newdf.sum(axis=1)\n query = interrogation.query\n conco = getattr(interrogation, 'concordance', None)\n return Interrogation(newdf, totals, query, conco)\n\ndef topwords(self, datatype='n', n=10, df=False, sort=True, precision=2):\n\n \"\"\"Show top n results in each corpus alongside absolute or relative frequencies.\n\n :param relative: show abs/rel frequencies\n :type relative: bool\n :param n: number of result to show\n :type n: int\n :param df: return a DataFrame instead of a string\n :type df: bool\n :param sort: sort or leave as is\n :type sort: bool, 'reverse'\n :param precision: float precision\n :type precision: int\n\n :Example:\n\n >>> data.topwords(n = 5)\n TBT % UST % WAP % WSJ %\n health 25.70 health 15.25 health 19.64 credit 9.22\n security 6.48 cancer 10.85 security 7.91 health 8.31\n cancer 6.19 heart 6.31 cancer 6.55 downside 5.46\n flight 4.45 breast 4.29 credit 4.08 inflation 3.37\n safety 3.49 security 3.94 safety 3.26 cancer 3.12\n\n :returns: None\n \"\"\"\n import corpkit\n from corpkit.interrogation import Interrogation, Interrodict\n import pandas as pd\n pd.set_option('display.float_format', lambda x: format(x, '.%df' % precision))\n strings = []\n if sort == 'reverse':\n ascend = True\n sort = True\n else:\n ascend = False\n\n if datatype.lower().startswith('n'):\n operation = 'n'\n if datatype.lower().startswith('k'):\n operation = 'k'\n else:\n operation = '%'\n if isinstance(self, corpkit.interrogation.Interrodict):\n to_iterate = self.items()\n else:\n if sort is True:\n to_iterate = [(x, self.results.ix[x].sort_values(ascending=ascend)) \\\n for x in list(self.results.index)]\n else:\n to_iterate = [(x, self.results.ix[x]) for x in list(self.results.index)]\n for name, data in to_iterate:\n if isinstance(self, corpkit.interrogation.Interrodict):\n if sort is True:\n data = data.results.sum().sort_values(ascending=ascend)\n else:\n data = data.results.sum()\n # todo: if already float, don't do this operation...\n if operation == '%':\n data = data * 100.0 / data.sum()\n if operation == 'n':\n data = data.astype(float)\n if df:\n data.index.name = name\n df1 = pd.DataFrame({'Result': list(data.index)[:n], operation: list(data)[:n]})\n df1 = df1[['Result', operation]]\n strings.append(df1)\n #ser1 = pd.Series(list(data.index), index = range(len(data)))[:n]\n #ser2 = pd.Series(list(data), index = range(len(data)))[:n]\n #ser1.name = 'Result'\n #ser2.name = operation\n #strings.append(ser1)\n #strings.append(ser2)\n else:\n as_str = data[:n].to_string(header=False)\n linelen = len(as_str.splitlines()[1])\n strings.append(name.ljust(linelen - 1) + '%s\\n' % operation + as_str)\n\n\n # strings is a list of series as strings\n if df:\n dataframe = pd.concat(strings, axis=1, keys=[i for i, _ in to_iterate])\n return dataframe\n output = ''\n for tup in zip(*[i.splitlines() for i in strings]):\n output += ' '.join(tup) + '\\n'\n print(output)\n"
},
{
"path": "corpkit/parse",
"content": "#!/usr/bin/env python\n\nfrom __future__ import print_function\n\n\"\"\"\nA script to parse using corpkit\n\n:Example:\n\n$ parse junglebook --speaker-segmentation True\n\n\"\"\"\n\nimport sys\nfrom corpkit.corpus import Corpus\n\nif len(sys.argv) == 1:\n raise ValueError('Please specify a corpus to parse.')\n\ntrans = {'true': True,\n 'false': False,\n 'none': None}\n\ncorp = Corpus(sys.argv[1])\nkwargs = {}\nargs = sys.argv[2:]\nfor index, item in enumerate(args):\n if item.startswith('-'):\n item = item.lstrip('-').lower().replace('-', '_')\n if '=' in item:\n val = item.split('=', 1)[1]\n else:\n val = args[index+1]\n if val.isdigit():\n val = int(val)\n if isinstance(val, str):\n val = trans.get(val.lower(), val)\n kwargs[item] = val\n\nparsed = corp.parse(**kwargs)\n\n\n"
},
{
"path": "corpkit/plotter.py",
"content": "from __future__ import print_function\nfrom corpkit.constants import STRINGTYPE, PYTHON_VERSION\n\ndef plotter(df,\n title=False,\n kind='line',\n x_label=None,\n y_label=None,\n style='ggplot',\n figsize=(8, 4),\n save=False,\n legend_pos='best',\n reverse_legend='guess',\n num_to_plot=6,\n tex='try',\n colours='default',\n cumulative=False,\n pie_legend=True,\n partial_pie=False,\n show_totals=False,\n transparent=False,\n output_format='png',\n interactive=False,\n black_and_white=False,\n show_p_val=False,\n indices=False,\n transpose=False,\n rot=False,\n **kwargs):\n \"\"\"Visualise corpus interrogations.\n :param title: A title for the plot\n :type title: str\n :param df: Data to be plotted\n :type df: Pandas DataFrame\n :param x_label: A label for the x axis\n :type x_label: str\n :param y_label: A label for the y axis\n :type y_label: str\n :param kind: The kind of chart to make\n :type kind: str ('line'/'bar'/'barh'/'pie'/'area')\n :param style: Visual theme of plot\n :type style: str ('ggplot'/'bmh'/'fivethirtyeight'/'seaborn-talk'/etc)\n :param figsize: Size of plot\n :type figsize: tuple (int, int)\n :param save: If bool, save with *title* as name; if str, use str as name\n :type save: bool/str\n :param legend_pos: Where to place legend\n :type legend_pos: str ('upper right'/'outside right'/etc)\n :param reverse_legend: Reverse the order of the legend\n :type reverse_legend: bool\n :param num_to_plot: How many columns to plot\n :type num_to_plot: int/'all'\n :param tex: Use TeX to draw plot text\n :type tex: bool\n :param colours: Colourmap for lines/bars/slices\n :type colours: str\n :param cumulative: Plot values cumulatively\n :type cumulative: bool\n :param pie_legend: Show a legend for pie chart\n :type pie_legend: bool\n :param partial_pie: Allow plotting of pie slices only\n :type partial_pie: bool\n :param show_totals: Print sums in plot where possible\n :type show_totals: str -- 'legend'/'plot'/'both'\n :param transparent: Transparent .png background\n :type transparent: bool\n :param output_format: File format for saved image\n :type output_format: str -- 'png'/'pdf'\n :param black_and_white: Create black and white line styles\n :type black_and_white: bool\n :param show_p_val: Attempt to print p values in legend if contained in df\n :type show_p_val: bool\n :param indices: To use when plotting \"distance from root\"\n :type indices: bool\n :param stacked: When making bar chart, stack bars on top of one another\n :type stacked: str\n :param filled: For area and bar charts, make every column sum to 100\n :type filled: str\n :param legend: Show a legend\n :type legend: bool\n :param rot: Rotate x axis ticks by *rot* degrees\n :type rot: int\n :param subplots: Plot each column separately\n :type subplots: bool\n :param layout: Grid shape to use when *subplots* is True\n :type layout: tuple -- (int, int)\n :param interactive: Experimental interactive options\n :type interactive: list -- [1, 2, 3]\n :returns: matplotlib figure\n \"\"\"\n import corpkit\n import os\n try:\n from IPython.utils.shimmodule import ShimWarning\n import warnings\n warnings.simplefilter('ignore', ShimWarning)\n except:\n pass\n\n kwargs['rot'] = rot\n\n xtickspan = kwargs.pop('xtickspan', False)\n\n # prefer seaborn plotting\n try:\n import seaborn as sns\n except (ImportError, AttributeError):\n pass\n\n import matplotlib as mpl\n from matplotlib import rc\n\n if interactive:\n import matplotlib.pyplot as plt, mpld3\n else:\n import matplotlib.pyplot as plt\n\n import matplotlib.ticker as ticker\n \n import pandas\n from pandas import DataFrame, Series, MultiIndex\n\n from time import localtime, strftime\n from process import checkstack\n\n if interactive:\n import mpld3\n import collections\n from mpld3 import plugins, utils\n from plugins import InteractiveLegendPlugin, HighlightLines\n\n have_mpldc = False\n try:\n from mpldatacursor import datacursor, HighlightingDataCursor\n have_mpldc = True\n except ImportError:\n pass\n\n # if the data was multiindexed, the default is a little different!\n from corpkit.interrogation import Interrogation\n if isinstance(df.index, MultiIndex):\n import matplotlib.pyplot as nplt\n shape = kwargs.get('shape', 'auto')\n truncate = kwargs.get('truncate', 8)\n if shape == 'auto':\n shape = (int(len(df.index.levels[0]) / 2), 2)\n f, axes = nplt.subplots(*shape)\n for i, ((name, data), ax) in enumerate(zip(df.groupby(level=0), axes.flatten())):\n data = data.loc[name]\n if isinstance(truncate, int) and i > truncate:\n continue\n if kwargs.get('name_format'):\n name = kwargs.get('name_format').format(name)\n data = Interrogation(results=data, totals=data.sum(axis=1), query=None)\n data.visualise(title=name,\n ax=ax,\n kind=kind,\n x_label=x_label,\n y_label=y_label,\n style=style,\n figsize=figsize,\n save=save,\n legend_pos=legend_pos,\n reverse_legend=reverse_legend,\n num_to_plot=num_to_plot,\n tex=tex,\n colours=colours,\n cumulative=cumulative,\n pie_legend=pie_legend,\n partial_pie=partial_pie,\n show_totals=show_totals,\n transparent=transparent,\n output_format=output_format,\n interactive=interactive,\n black_and_white=black_and_white,\n show_p_val=show_p_val,\n indices=indices,\n transpose=transpose,\n rot=rot)\n return nplt\n\n def copy(self):\n from corpkit.interrogation import Interrodict\n copied = {}\n for k, v in self.items():\n copied[k] = v\n return Interrodict(copied)\n\n # check what environment we're in\n tk = checkstack('tkinter')\n running_python_tex = checkstack('pythontex')\n running_spider = checkstack('spyder')\n\n if not title:\n title = ''\n\n def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):\n \"\"\"remove extreme values from colourmap --- no pure white\"\"\"\n import matplotlib.colors as colors\n import numpy as np\n new_cmap = colors.LinearSegmentedColormap.from_list(\n 'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),\n cmap(np.linspace(minval, maxval, n)))\n return new_cmap\n\n def get_savename(imagefolder, save = False, title = False, ext = 'png'):\n \"\"\"Come up with the savename for the image.\"\"\"\n import os\n from corpkit.process import urlify\n\n # name as \n if not ext.startswith('.'):\n ext = '.' + ext\n if isinstance(save, STRINGTYPE):\n savename = os.path.join(imagefolder, (urlify(save) + ext))\n #this 'else' is redundant now that title is obligatory\n else:\n if title:\n filename = urlify(title) + ext\n savename = os.path.join(imagefolder, filename)\n\n # remove duplicated ext\n if savename.endswith('%s%s' % (ext, ext)):\n savename = savename.replace('%s%s' % (ext, ext), ext, 1)\n return savename\n\n def rename_data_with_total(dataframe, was_series = False, using_tex = False, absolutes = True):\n \"\"\"adds totals (abs, rel, keyness) to entry name strings\"\"\"\n if was_series:\n where_the_words_are = dataframe.index\n else:\n where_the_words_are = dataframe.columns\n the_labs = []\n for w in list(where_the_words_are):\n if not absolutes:\n if was_series:\n perc = dataframe.T[w][0]\n else:\n the_labs.append(w)\n continue\n if using_tex:\n the_labs.append('%s (%.2f\\%%)' % (w, perc))\n else:\n the_labs.append('%s (%.2f %%)' % (w, perc))\n else:\n if was_series:\n score = dataframe.T[w].sum()\n else:\n score = dataframe[w].sum()\n if using_tex:\n the_labs.append('%s (n=%d)' % (w, score))\n else:\n the_labs.append('%s (n=%d)' % (w, score))\n if not was_series:\n dataframe.columns = the_labs\n else:\n vals = list(dataframe[list(dataframe.columns)[0]].values)\n dataframe = pandas.DataFrame(vals, index=the_labs)\n dataframe.columns = ['Total']\n return dataframe\n\n def auto_explode(dataframe, tinput, was_series = False, num_to_plot = 7):\n \"\"\"give me a list of strings and i'll output explode option\"\"\"\n output = [0 for s in range(num_to_plot)]\n\n if was_series:\n l = list(dataframe.index)\n else:\n l = list(dataframe.columns)\n\n if isinstance(tinput, (STRINGTYPE, int)):\n tinput = [tinput]\n if isinstance(tinput, list):\n for i in tinput:\n if isinstance(i, STRINGTYPE):\n index = l.index(i)\n else:\n index = i\n output[index] = 0.1\n return output\n\n # get a few options from kwargs\n sbplt = kwargs.get('subplots', False)\n show_grid = kwargs.pop('grid', True)\n the_rotation = kwargs.get('rot', False)\n dragmode = kwargs.pop('draggable', False)\n leg_frame = kwargs.pop('legend_frame', True)\n leg_alpha = kwargs.pop('legend_alpha', 0.8)\n # auto set num to plot based on layout\n lo = kwargs.get('layout', None)\n if lo:\n num_to_plot = lo[0] * lo[1]\n\n # todo: get this dynamically instead.\n styles = ['dark_background', 'bmh', 'grayscale', 'ggplot', 'fivethirtyeight', 'matplotlib', False, 'mpl-white']\n #if style not in styles:\n #raise ValueError('Style %s not found. Use %s' % (str(style), ', '.join(styles)))\n\n if style == 'mpl-white':\n try:\n sns.set_style(\"whitegrid\")\n except:\n pass\n style = 'matplotlib'\n\n if kwargs.get('savepath'):\n mpl.rcParams['savefig.directory'] = kwargs.get('savepath')\n kwargs.pop('savepath', None)\n\n mpl.rcParams['savefig.bbox'] = 'tight'\n mpl.rcParams.update({'figure.autolayout': True})\n\n # try to use tex\n # TO DO:\n # make some font kwargs here\n using_tex = False\n mpl.rcParams['font.family'] = 'sans-serif'\n mpl.rcParams['text.latex.unicode'] = True\n \n if tex == 'try' or tex is True:\n try:\n rc('text', usetex=True)\n rc('font', **{'family': 'serif', 'serif': ['Computer Modern']})\n using_tex = True\n except:\n matplotlib.rc('font', family='sans-serif') \n matplotlib.rc('font', serif='Helvetica Neue') \n matplotlib.rc('text', usetex='false') \n rc('text', usetex=False)\n else:\n rc('text', usetex=False) \n\n if interactive:\n using_tex = False \n\n if show_totals is False:\n show_totals = 'none'\n\n # find out what kind of plot we're making, and enable\n # or disable interactive values if need be\n kwargs['kind'] = kind.lower()\n\n if interactive:\n if kwargs['kind'].startswith('bar'):\n interactive_types = [3]\n elif kwargs['kind'] == 'area':\n interactive_types = [2, 3]\n elif kwargs['kind'] == 'line':\n interactive_types = [2, 3]\n elif kwargs['kind'] == 'pie':\n interactive_types = None\n warnings.warn('Interactive plotting not yet available for pie plots.')\n else:\n interactive_types = [None]\n if interactive is False:\n interactive_types = [None]\n\n # find out if pie mode, add autopct format\n piemode = False\n if kind == 'pie':\n piemode = True\n # always the best spot for pie\n #if legend_pos == 'best':\n #legend_pos = 'lower left'\n if show_totals.endswith('plot') or show_totals.endswith('both'):\n kwargs['pctdistance'] = 0.6\n if using_tex:\n kwargs['autopct'] = r'%1.1f\\%%'\n else:\n kwargs['autopct'] = '%1.1f%%'\n\n # copy data, make series into df\n dataframe = df.copy()\n if kind == 'heatmap':\n try:\n dataframe = dataframe.T\n except:\n pass\n was_series = False\n if isinstance(dataframe, Series):\n was_series = True\n if not cumulative:\n dataframe = DataFrame(dataframe)\n else:\n dataframe = DataFrame(dataframe.cumsum())\n else:\n # don't know if this is much good.\n if transpose:\n dataframe = dataframe.T\n if cumulative:\n dataframe = DataFrame(dataframe.cumsum())\n if len(list(dataframe.columns)) == 1:\n was_series = True\n \n # attempt to convert x axis to ints:\n #try:\n # dataframe.index = [int(i) for i in list(dataframe.index)]\n #except:\n # pass\n\n # remove totals and tkinter order\n if not was_series:\n for name, ax in zip(['Total'] * 2 + ['tkintertable-order'] * 2, [0, 1, 0, 1]):\n try:\n dataframe = dataframe.drop(name, axis=ax, errors='ignore')\n except:\n pass\n \n try:\n dataframe = dataframe.drop('tkintertable-order', errors='ignore')\n except:\n pass\n try:\n dataframe = dataframe.drop('tkintertable-order', axis=1, errors='ignore')\n except:\n pass\n\n # look at columns to see if all can be ints, in which case, set up figure\n # for depnumming\n if not was_series:\n if indices == 'guess':\n def isint(x):\n try:\n a = float(x)\n b = int(a)\n except (ValueError, OverflowError):\n return False\n else:\n return a == b\n\n if all([isint(x) is True for x in list(dataframe.columns)]):\n indices = True\n else:\n indices = False\n\n # if depnumming, plot all, transpose, and rename axes\n if indices is True:\n num_to_plot = 'all'\n dataframe = dataframe.T\n if y_label is None:\n y_label = 'Percentage of all matches'\n if x_label is None:\n x_label = ''\n\n # set backend?\n output_formats = ['svgz', 'ps', 'emf', 'rgba', 'raw', 'pdf', 'svg', 'eps', 'png', 'pgf']\n if output_format not in output_formats:\n raise ValueError('%s output format not recognised. Must be: %s' % (output_format, ', '.join(output_formats)))\n \n # don't know if these are necessary\n if 'pdf' in output_format:\n plt.switch_backend(output_format) \n if 'pgf' in output_format:\n plt.switch_backend(output_format)\n\n if num_to_plot == 'all':\n if was_series:\n if not piemode:\n num_to_plot = len(dataframe)\n else:\n num_to_plot = len(dataframe)\n else:\n if not piemode:\n num_to_plot = len(list(dataframe.columns))\n else:\n num_to_plot = len(dataframe.index)\n\n # explode pie, or remove if not piemode\n if piemode and not sbplt and kwargs.get('explode'):\n kwargs['explode'] = auto_explode(dataframe, \n kwargs['explode'], \n was_series=was_series, \n num_to_plot=num_to_plot)\n else:\n kwargs.pop('explode', None)\n\n legend = kwargs.get('legend', True)\n\n #cut data short\n plotting_a_totals_column = False\n if was_series:\n if list(dataframe.columns)[0] != 'Total':\n try:\n can_be_ints = [int(x) for x in list(dataframe.index)]\n num_to_plot = len(dataframe)\n except:\n dataframe = dataframe[:num_to_plot]\n elif list(dataframe.columns)[0] == 'Total':\n plotting_a_totals_column = True\n if not 'legend' in kwargs:\n legend = False\n num_to_plot = len(dataframe)\n else:\n if transpose:\n dataframe = dataframe.head(num_to_plot)\n else:\n dataframe = dataframe.T.head(num_to_plot).T\n\n # remove stats fields, put p in entry text, etc.\n statfields = ['slope', 'intercept', 'r', 'p', 'stderr']\n try:\n dataframe = dataframe.drop(statfields, axis=1, errors='ignore')\n except:\n pass \n try:\n dataframe.ix['p']\n there_are_p_vals = True\n except:\n there_are_p_vals = False\n if show_p_val:\n if there_are_p_vals:\n newnames = []\n for col in list(dataframe.columns):\n pval = dataframe[col]['p']\n\n def p_string_formatter(val):\n if val < 0.001:\n if not using_tex:\n return 'p < 0.001'\n else:\n return r'p $<$ 0.001'\n else:\n return 'p = %s' % format(val, '.3f')\n\n pstr = p_string_formatter(pval)\n newname = '%s (%s)' % (col, pstr)\n newnames.append(newname)\n dataframe.columns = newnames\n dataframe.drop(statfields, axis=0, inplace = True, errors='ignore')\n else:\n warnings.warn('No p-values calculated to show.\\n\\nUse keep_stats kwarg while editing to generate these values.')\n else:\n if there_are_p_vals:\n dataframe.drop(statfields, axis=0, inplace=True, errors='ignore')\n\n # make and set y label\n absolutes = True\n if isinstance(dataframe, DataFrame):\n try:\n if not all([s.is_integer() for s in dataframe.iloc[0,:].values]):\n absolutes = False\n except:\n pass\n else:\n if not all([s.is_integer() for s in dataframe.values]): \n absolutes = False\n\n ##########################################\n ################ COLOURS #################\n ##########################################\n\n # set defaults, with nothing for heatmap yet\n if colours is True or colours == 'default' or colours == 'Default':\n if kind != 'heatmap':\n colours = 'viridis'\n else:\n colours = 'default'\n \n # assume it's a single color, unless string denoting map\n cmap_or_c = 'color'\n if isinstance(colours, str):\n cmap_or_c = 'colormap'\n from matplotlib.colors import LinearSegmentedColormap\n if isinstance(colours, LinearSegmentedColormap):\n cmap_or_c = 'colormap'\n\n # for heatmaps, it's always a colormap\n if kind == 'heatmap':\n cmap_or_c = 'cmap'\n # if it's a defaulty string, set accordingly\n if isinstance(colours, str):\n if colours.lower().startswith('diverg'):\n colours = sns.diverging_palette(10, 133, as_cmap=True)\n\n # if default not set, do diverge for any df with a number < 0\n elif colours.lower() == 'default':\n mn = dataframe.min()\n if isinstance(mn, Series):\n mn = mn.min()\n if mn < 0:\n colours = sns.diverging_palette(10, 133, as_cmap=True)\n else:\n colours = sns.light_palette(\"green\", as_cmap=True)\n\n if 'seaborn' not in style:\n kwargs[cmap_or_c] = colours\n #if not was_series:\n # if kind in ['pie', 'line', 'area']:\n # if colours and not plotting_a_totals_column:\n # kwargs[cmap_or_c] = colours\n # else:\n # if colours:\n # kwargs[cmap_or_c] = colours\n #if piemode:\n # if num_to_plot > 0:\n # kwargs[cmap_or_c] = colours\n # else:\n # if num_to_plot > 0:\n # kwargs[cmap_or_c] = colours\n \n # multicoloured bar charts\n #if colours and cmap_or_c == 'colormap':\n # if kind.startswith('bar'):\n # if len(list(dataframe.columns)) == 1:\n # if not black_and_white:\n # import numpy as np\n # the_range = np.linspace(0, 1, num_to_plot)\n # middle = len(the_range) / 2\n # try:\n # cmap = plt.get_cmap(colours)\n # kwargs[cmap_or_c] = [cmap(n) for n in the_range][middle]\n # except ValueError:\n # kwargs[cmap_or_c] = colours\n # # make a bar width ... ? ...\n # #kwargs['width'] = (figsize[0] / float(num_to_plot)) / 1.5\n \n\n # reversing legend option\n if reverse_legend is True:\n rev_leg = True\n elif reverse_legend is False:\n rev_leg = False\n\n # show legend or don't, guess whether to reverse based on kind\n if kind in ['bar', 'barh', 'area', 'line', 'pie']:\n if was_series:\n legend = False\n if kind == 'pie':\n if pie_legend:\n legend = True\n else:\n legend = False\n if kind in ['barh', 'area']:\n if reverse_legend == 'guess':\n rev_leg = True\n if not 'rev_leg' in locals():\n rev_leg = False\n\n # the default legend placement\n if legend_pos is True:\n legend_pos = 'best'\n\n # cut dataframe if just_totals\n try:\n tst = dataframe['Combined total']\n dataframe = dataframe.head(num_to_plot)\n except:\n pass\n\n # no title for subplots because ugly,\n if title and not sbplt:\n kwargs['title'] = title\n \n # no interactive subplots yet:\n if sbplt and interactive:\n import warnings\n interactive = False\n warnings.warn('No interactive subplots yet, sorry.')\n return\n \n # not using pandas for labels or legend anymore.\n #kwargs['labels'] = None\n #kwargs['legend'] = False\n\n if legend:\n if num_to_plot > 6:\n if not kwargs.get('ncol'):\n kwargs['ncol'] = num_to_plot // 7\n # kwarg options go in leg_options\n leg_options = {'framealpha': leg_alpha,\n 'shadow': kwargs.get('shadow', False),\n 'ncol': kwargs.pop('ncol', 1)} \n\n # determine legend position based on this dict\n if legend_pos:\n possible = {'best': 0, 'upper right': 1, 'upper left': 2, 'lower left': 3, 'lower right': 4, \n 'right': 5, 'center left': 6, 'center right': 7, 'lower center': 8, 'upper center': 9, \n 'center': 10, 'o r': 2, 'outside right': 2, 'outside upper right': 2, \n 'outside center right': 'center left', 'outside lower right': 'lower left'}\n\n if isinstance(legend_pos, int):\n the_loc = legend_pos\n elif isinstance(legend_pos, str):\n try:\n the_loc = possible[legend_pos]\n except KeyError:\n raise KeyError('legend_pos value must be one of:\\n%s\\n or an int between 0-10.' %', '.join(list(possible.keys())))\n leg_options['loc'] = the_loc\n #weirdness needed for outside plot\n if legend_pos in ['o r', 'outside right', 'outside upper right']:\n leg_options['bbox_to_anchor'] = (1.02, 1)\n if legend_pos == 'outside center right':\n leg_options['bbox_to_anchor'] = (1.02, 0.5)\n if legend_pos == 'outside lower right':\n leg_options['loc'] == 'upper right'\n leg_options['bbox_to_anchor'] = (0.5, 0.5)\n \n # a bit of distance between legend and plot for outside legends\n if isinstance(legend_pos, str):\n if legend_pos.startswith('o'):\n leg_options['borderaxespad'] = 1\n\n if not piemode:\n if show_totals.endswith('both') or show_totals.endswith('legend'):\n dataframe = rename_data_with_total(dataframe, \n was_series = was_series, \n using_tex = using_tex, \n absolutes = absolutes)\n else:\n if pie_legend:\n if show_totals.endswith('both') or show_totals.endswith('legend'):\n dataframe = rename_data_with_total(dataframe, \n was_series = was_series, \n using_tex = using_tex, \n absolutes = absolutes)\n\n if piemode:\n if partial_pie:\n dataframe = dataframe / 100.0\n\n # some pie things\n if piemode:\n if not sbplt:\n kwargs['y'] = list(dataframe.columns)[0]\n \n def filler(df):\n pby = df.T.copy()\n for i in list(pby.columns):\n tot = pby[i].sum()\n pby[i] = pby[i] * 100.0 / tot\n return pby.T\n\n areamode = False\n if kind == 'area':\n areamode = True\n\n if legend is False:\n kwargs['legend'] = False\n\n # line highlighting option for interactive!\n if interactive:\n if 2 in interactive_types:\n if kind == 'line':\n kwargs['marker'] = ','\n if not piemode:\n kwargs['alpha'] = 0.1\n \n # convert dates --- works only in my current case!\n #if plotting_a_totals_column or not was_series:\n # try:\n # can_it_be_int = int(list(dataframe.index)[0])\n # can_be_int = True\n # except:\n # can_be_int = False\n # if can_be_int:\n # if 1500 < int(list(dataframe.index)[0]):\n # if 2050 > int(list(dataframe.index)[0]):\n # n = pandas.PeriodIndex([d for d in list(dataframe.index)], freq='A')\n # dataframe = dataframe.set_index(n)\n\n if kwargs.get('filled'):\n if areamode or kind.startswith('bar'):\n dataframe = filler(dataframe)\n kwargs.pop('filled', None)\n\n MARKERSIZE = 4\n COLORMAP = {\n 0: {'marker': None, 'dash': (None,None)},\n 1: {'marker': None, 'dash': [5,5]},\n 2: {'marker': \"o\", 'dash': (None,None)},\n 3: {'marker': None, 'dash': [1,3]},\n 4: {'marker': \"s\", 'dash': [5,2,5,2,5,10]},\n 5: {'marker': None, 'dash': [5,3,1,2,1,10]},\n 6: {'marker': 'o', 'dash': (None,None)},\n 7: {'marker': None, 'dash': [5,3,1,3]},\n 8: {'marker': \"1\", 'dash': [1,3]},\n 9: {'marker': \"*\", 'dash': [5,5]},\n 10: {'marker': \"2\", 'dash': [5,2,5,2,5,10]},\n 11: {'marker': \"s\", 'dash': (None,None)}\n }\n\n HATCHES = {\n 0: {'color': '#dfdfdf', 'hatch':\"/\"},\n 1: {'color': '#6f6f6f', 'hatch':\"\\\\\"},\n 2: {'color': 'b', 'hatch':\"|\"},\n 3: {'color': '#dfdfdf', 'hatch':\"-\"},\n 4: {'color': '#6f6f6f', 'hatch':\"+\"},\n 5: {'color': 'b', 'hatch':\"x\"}\n }\n\n if black_and_white:\n if kind == 'line':\n kwargs['linewidth'] = 1\n\n cmap = plt.get_cmap('Greys')\n new_cmap = truncate_colormap(cmap, 0.25, 0.95)\n if kind == 'bar':\n # darker if just one entry\n if len(dataframe.columns) == 1:\n new_cmap = truncate_colormap(cmap, 0.70, 0.90)\n kwargs[cmap_or_c] = new_cmap\n\n # remove things from kwargs if heatmap\n if kind == 'heatmap':\n hmargs = {'annot': kwargs.pop('annot', True),\n cmap_or_c: kwargs.pop(cmap_or_c, None),\n 'fmt': kwargs.pop('fmt', \".2f\"),\n 'cbar': kwargs.pop('cbar', False)}\n\n for i in ['vmin', 'vmax', 'linewidths', 'linecolor',\n 'robust', 'center', 'cbar_kws', 'cbar_ax',\n 'square', 'mask', 'norm']:\n if i in kwargs.keys():\n hmargs[i] = kwargs.pop(i, None)\n\n class dummy_context_mgr():\n \"\"\"a fake context for plotting without style\n perhaps made obsolete by 'classic' style in new mpl\"\"\"\n def __enter__(self):\n return None\n def __exit__(self, one, two, three):\n return False\n\n with plt.style.context((style)) if style != 'matplotlib' else dummy_context_mgr():\n\n kwargs.pop('filled', None)\n\n if not sbplt:\n # check if negative values, no stacked if so\n if areamode:\n if not kwargs.get('ax'):\n kwargs['legend'] = False\n if dataframe.applymap(lambda x: x < 0.0).any().any():\n kwargs['stacked'] = False\n rev_leg = False\n if kind != 'heatmap':\n # turn off pie labels at the last minute\n if kind == 'pie' and pie_legend:\n kwargs['labels'] = None\n kwargs['autopct'] = '%.2f'\n if kind == 'pie':\n kwargs.pop('color', None)\n ax = dataframe.plot(figsize=figsize, **kwargs)\n else:\n fg = plt.figure(figsize=figsize)\n if title:\n plt.title(title)\n ax = kwargs.get('ax', plt.axes())\n tmp = sns.heatmap(dataframe, ax=ax, **hmargs)\n ax.set_title(title)\n for item in tmp.get_yticklabels():\n item.set_rotation(0)\n plt.close(fg)\n\n if areamode and not kwargs.get('ax'):\n handles, labels = plt.gca().get_legend_handles_labels()\n del handles\n del labels\n\n if x_label:\n ax.set_xlabel(x_label)\n if y_label:\n ax.set_ylabel(y_label)\n\n else:\n if not kwargs.get('layout'):\n plt.gcf().set_tight_layout(False)\n\n if kind != 'heatmap':\n ax = dataframe.plot(figsize=figsize, **kwargs)\n else:\n plt.figure(figsize=figsize)\n if title:\n plt.title(title)\n ax = plt.axes()\n sns.heatmap(dataframe, ax=ax, **hmargs)\n plt.xticks(rotation=0)\n plt.yticks(rotation=0)\n\n def rotate_degrees(rotation, labels):\n if rotation is None:\n if max(labels, key=len) > 6:\n return 45\n else:\n return 0\n elif rotation is False:\n return 0\n elif rotation is True:\n return 45\n else:\n return rotation\n \n if sbplt:\n if 'layout' not in kwargs:\n axes = [l for l in ax]\n else:\n axes = []\n cols = [l for l in ax]\n for col in cols:\n for bit in col:\n axes.append(bit)\n for index, a in enumerate(axes):\n if xtickspan is not False:\n a.xaxis.set_major_locator(ticker.MultipleLocator(xtickspan))\n labels = [item.get_text() for item in a.get_xticklabels()]\n rotation = rotate_degrees(the_rotation, labels) \n try:\n if the_rotation == 0:\n ax.set_xticklabels(labels, rotation=rotation, ha='center')\n else:\n ax.set_xticklabels(labels, rotation=rotation, ha='right')\n except AttributeError:\n pass\n else:\n if kind == 'heatmap':\n labels = [item.get_text() for item in ax.get_xticklabels()]\n rotation = rotate_degrees(the_rotation, labels)\n if the_rotation == 0:\n ax.set_xticklabels(labels, rotation=rotation, ha='center')\n else:\n ax.set_xticklabels(labels, rotation=rotation, ha='right')\n\n if transparent:\n plt.gcf().patch.set_facecolor('white')\n plt.gcf().patch.set_alpha(0)\n\n if black_and_white:\n if kind == 'line':\n # white background\n # change everything to black and white with interesting dashes and markers\n c = 0\n for line in ax.get_lines():\n line.set_color('black')\n #line.set_width(1)\n line.set_dashes(COLORMAP[c]['dash'])\n line.set_marker(COLORMAP[c]['marker'])\n line.set_markersize(MARKERSIZE)\n c += 1\n if c == len(list(COLORMAP.keys())):\n c = 0\n\n # draw legend with proper placement etc\n if legend:\n if not piemode and not sbplt and kind != 'heatmap':\n if 3 not in interactive_types:\n handles, labels = plt.gca().get_legend_handles_labels()\n # area doubles the handles and labels. this removes half:\n #if areamode:\n # handles = handles[-len(handles) / 2:]\n # labels = labels[-len(labels) / 2:]\n if rev_leg:\n handles = handles[::-1]\n labels = labels[::-1]\n if kwargs.get('ax'):\n lgd = plt.gca().legend(handles, labels, **leg_options)\n ax.get_legend().draw_frame(leg_frame)\n else:\n lgd = plt.legend(handles, labels, **leg_options)\n lgd.draw_frame(leg_frame)\n\n if interactive:\n # 1 = highlight lines\n # 2 = line labels\n # 3 = legend switches\n ax = plt.gca()\n # fails for piemode\n lines = ax.lines\n handles, labels = plt.gca().get_legend_handles_labels()\n if 1 in interactive_types:\n plugins.connect(plt.gcf(), HighlightLines(lines))\n\n if 3 in interactive_types:\n plugins.connect(plt.gcf(), InteractiveLegendPlugin(lines, labels, alpha_unsel=0.0))\n\n for i, l in enumerate(lines):\n y_vals = l.get_ydata()\n x_vals = l.get_xdata()\n x_vals = [str(x) for x in x_vals]\n if absolutes:\n ls = ['%s (%s: %d)' % (labels[i], x_val, y_val) for x_val, y_val in zip(x_vals, y_vals)]\n else:\n ls = ['%s (%s: %.2f%%)' % (labels[i], x_val, y_val) for x_val, y_val in zip(x_vals, y_vals)]\n if 2 in interactive_types:\n #if 'kind' in kwargs and kwargs['kind'] == 'area':\n tooltip_line = mpld3.plugins.LineLabelTooltip(lines[i], labels[i])\n mpld3.plugins.connect(plt.gcf(), tooltip_line)\n #else:\n if kind == 'line':\n tooltip_point = mpld3.plugins.PointLabelTooltip(l, labels = ls)\n mpld3.plugins.connect(plt.gcf(), tooltip_point)\n \n if piemode:\n if not sbplt:\n plt.axis('equal')\n ax.get_xaxis().set_visible(False)\n ax.get_yaxis().set_visible(False)\n\n # add x label\n # this could be revised now!\n # if time series period, it's year for now\n if isinstance(dataframe.index, pandas.tseries.period.PeriodIndex):\n x_label = 'Year'\n\n y_l = False\n if not absolutes:\n y_l = 'Percentage'\n else:\n y_l = 'Absolute frequency'\n\n # hacky: turn legend into subplot titles :)\n if sbplt:\n # title the big plot\n #plt.gca().suptitle(title, fontsize = 16)\n #plt.subplots_adjust(top=0.9)\n # get all axes\n if 'layout' not in kwargs:\n axes = [l for index, l in enumerate(ax)]\n else:\n axes = []\n cols = [l for index, l in enumerate(ax)]\n for col in cols:\n for bit in col:\n axes.append(bit)\n \n # set subplot titles\n for index, a in enumerate(axes):\n try:\n titletext = list(dataframe.columns)[index]\n except:\n pass\n a.set_title(titletext)\n try:\n a.legend_.remove()\n except:\n pass\n #try:\n # from matplotlib.ticker import MaxNLocator\n # from corpkit.process import is_number\n # indx = list(dataframe.index)\n # if all([is_number(qq) for qq in indx]):\n # ax.get_xaxis().set_major_locator(MaxNLocator(integer=True))\n #except:\n # pass\n # remove axis labels for pie plots\n if piemode:\n a.axes.get_xaxis().set_visible(False)\n a.axes.get_yaxis().set_visible(False)\n a.axis('equal')\n\n a.grid(b=show_grid)\n \n # add sums to bar graphs and pie graphs\n # doubled right now, no matter\n\n if not sbplt:\n \n # show grid\n ax.grid(b=show_grid)\n\n if kind.startswith('bar'):\n width = ax.containers[0][0].get_width()\n\n if was_series:\n the_y_limit = plt.ylim()[1]\n if show_totals.endswith('plot') or show_totals.endswith('both'):\n # make plot a bit higher if putting these totals on it\n plt.ylim([0,the_y_limit * 1.05])\n for i, label in enumerate(list(dataframe.index)):\n if len(dataframe.ix[label]) == 1:\n score = dataframe.ix[label][0]\n else:\n if absolutes:\n score = dataframe.ix[label].sum()\n else:\n #import warnings\n #warnings.warn(\"It's not possible to determine total percentage from individual percentages.\")\n continue\n if not absolutes:\n plt.annotate('%.2f' % score, (i, score), ha = 'center', va = 'bottom')\n else:\n plt.annotate(score, (i, score), ha = 'center', va = 'bottom')\n else:\n the_y_limit = plt.ylim()[1]\n if show_totals.endswith('plot') or show_totals.endswith('both'):\n for i, label in enumerate(list(dataframe.columns)):\n if len(dataframe[label]) == 1:\n score = dataframe[label][0]\n else:\n if absolutes:\n score = dataframe[label].sum()\n else:\n #import warnings\n #warnings.warn(\"It's not possible to determine total percentage from individual percentages.\")\n continue\n if not absolutes:\n plt.annotate('%.2f' % score, (i, score), ha='center', va='bottom')\n else:\n plt.annotate(score, (i, score), ha='center', va='bottom') \n\n if not kwargs.get('layout') and not sbplt and not kwargs.get('ax'):\n plt.tight_layout()\n if kwargs.get('ax'):\n try:\n plt.gcf().set_tight_layout(False)\n except:\n pass\n try:\n plt.set_tight_layout(False)\n except:\n pass\n\n if save:\n if running_python_tex:\n imagefolder = '../images'\n else:\n imagefolder = 'images'\n\n savename = get_savename(imagefolder, save=save, title=title, ext=output_format)\n\n if not os.path.isdir(imagefolder):\n os.makedirs(imagefolder)\n\n # save image and get on with our lives\n if legend_pos.startswith('o') and not sbplt:\n plt.gcf().savefig(savename, dpi=150, bbox_extra_artists=(lgd,), \n bbox_inches='tight', format=output_format)\n else:\n plt.gcf().savefig(savename, dpi=150, format=output_format)\n time = strftime(\"%H:%M:%S\", localtime())\n if os.path.isfile(savename):\n print('\\n' + time + \": \" + savename + \" created.\")\n else:\n raise ValueError(\"Error making %s.\" % savename)\n\n if dragmode:\n plt.legend().draggable()\n\n if sbplt:\n plt.subplots_adjust(right=.8)\n plt.subplots_adjust(left=.1)\n\n # add DataCursor to notebook backend if possible\n if have_mpldc:\n if kind == 'line':\n HighlightingDataCursor(plt.gca().get_lines(), highlight_width=4, highlight_color = False,\n formatter=lambda **kwargs: '%s: %s' % (kwargs['label'], \"{0:.3f}\".format(kwargs['y'])))\n else:\n datacursor(formatter=lambda **kwargs: '%s: %s' % (kwargs['label'], \"{0:.3f}\".format(kwargs['height'])))\n\n #if not interactive and not running_python_tex and not running_spider \\\n # and not tk:\n # plt.gcf().show()\n # return plt\n #elif running_spider or tk:\n # return plt\n\n if interactive:\n plt.subplots_adjust(right=.8)\n plt.subplots_adjust(left=.1)\n try:\n ax.legend_.remove()\n except:\n pass\n return mpld3.display()\n else:\n return plt\n\n\ndef multiplotter(df, leftdict={},rightdict={}, **kwargs):\n \"\"\"\n Plot a big chart and its subplots together\n :param leftdict: a dict of arguments for the big plot\n :type leftdict: dict\n :param rightdict: a dict of arguments for the small plot\n :type rightdict: dict\n \n \"\"\"\n from corpkit.interrogation import Interrogation\n import matplotlib.pyplot as plt\n axes = []\n\n if isinstance(df, Interrogation):\n df = df.results\n\n df2 = rightdict.pop('data', df.copy())\n if isinstance(df2, Interrogation):\n df2 = df2.results\n\n # add more cool layouts here\n # and figure out a nice way to access them other than numbers...\n from corpkit.layouts import layouts\n\n if isinstance(kwargs.get('layout'), list):\n layout = kwargs.get('layout')\n else:\n lay = kwargs.get('layout', 1)\n layout = layouts.get(lay)\n\n kinda = leftdict.pop('kind', 'area')\n kindb = rightdict.pop('kind', 'line')\n tpa = leftdict.pop('transpose', False)\n tpb = rightdict.pop('transpose', False)\n numtoplot = leftdict.pop('num_to_plot', len(layout) - 1)\n ntpb = rightdict.pop('num_to_plot', 'all')\n sharex = rightdict.pop('sharex', True)\n sharey = rightdict.pop('sharey', False)\n if kindb == 'pie':\n piecol = rightdict.pop('colours', 'default')\n coloursb = rightdict.pop('colours', 'default')\n fig = plt.figure()\n \n for i, (nrows, ncols, plot_number) in enumerate(layout, start=-1):\n if tpb:\n df2 = df2.T\n if i >= len(df2.columns):\n continue\n ax = fig.add_subplot(nrows, ncols, plot_number)\n if i == -1:\n df.visualise(kind=kinda, ax=ax, transpose=tpa,\n num_to_plot=numtoplot, **leftdict)\n if kinda in ['bar', 'hist', 'barh']:\n import matplotlib as mpl\n colmap = []\n rects = [r for r in ax.get_children() if isinstance(r, mpl.patches.Rectangle)]\n for r in rects:\n if r._facecolor not in colmap:\n colmap.append(r._facecolor)\n try:\n colmap = {list(df.columns)[i]: x for i, x in enumerate(colmap[:len(df.columns)])}\n except AttributeError:\n colmap = {list(df.index)[i]: x for i, x in enumerate(colmap[:len(df.index)])}\n else:\n try:\n colmap = {list(df.columns)[i]: l.get_color() for i, l in enumerate(ax.get_lines())}\n except AttributeError:\n colmap = {list(df.index)[i]: l.get_color() for i, l in enumerate(ax.get_lines())}\n else:\n if colmap and kindb != 'pie':\n try:\n name = df2.iloc[:, i].name\n coloursb = colmap[name]\n except IndexError:\n coloursb = 'gray'\n except KeyError:\n coloursb = 'gray'\n\n if kindb == 'pie':\n rightdict['colours'] = piecol\n else:\n rightdict['colours'] = coloursb\n\n df2.iloc[:, i].visualise(kind=kindb, ax=ax, sharex=sharex, sharey=sharey,\n num_to_plot=ntpb, **rightdict)\n ax.set_title(df2.iloc[:, i].name)\n \n wspace = kwargs.get('wspace', .2)\n hspace = kwargs.get('hspace', .5)\n\n fig.subplots_adjust(bottom=0.025, left=0.025, top=0.975, right=0.975,\n wspace=wspace, hspace=hspace)\n\n size = kwargs.get('figsize', (10, 4))\n fig.set_size_inches(size)\n try:\n plt.set_size_inches(size)\n except:\n pass\n if kwargs.get('save'):\n import os\n from corpkit.process import urlify\n savepath = os.path.join('images', urlify(kwargs['save']) + '.png')\n fig.savefig(savepath, dpi=150)\n return plt\n "
},
{
"path": "corpkit/plugins.py",
"content": "import mpld3\nimport collections\nfrom mpld3 import plugins, utils\n\nclass HighlightLines(plugins.PluginBase):\n\n \"\"\"A plugin to highlight lines on hover\"\"\"\n JAVASCRIPT = \"\"\"\n mpld3.register_plugin(\"linehighlight\", LineHighlightPlugin);\n LineHighlightPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n LineHighlightPlugin.prototype.constructor = LineHighlightPlugin;\n LineHighlightPlugin.prototype.requiredProps = [\"line_ids\"];\n LineHighlightPlugin.prototype.defaultProps = {alpha_bg:0.0, alpha_fg:1.0}\n function LineHighlightPlugin(fig, props){\n mpld3.Plugin.call(this, fig, props);\n };\n LineHighlightPlugin.prototype.draw = function(){\n for(var i=0; ilink', '')\n return HTML('' % url)\n\ndef add_corpkit_to_path():\n import sys\n import os\n import inspect\n corpath = inspect.getfile(inspect.currentframe())\n baspat = os.path.dirname(corpath)\n dicpath = os.path.join(baspat, 'dictionaries')\n for p in [corpath, baspat, dicpath]:\n if p not in sys.path:\n sys.path.append(p)\n if p not in os.environ[\"PATH\"].split(':'): \n os.environ[\"PATH\"] += os.pathsep + p\n\ndef add_nltk_data_to_nltk_path(**kwargs):\n import nltk\n import os\n npat = nltk.__file__\n nltkpath = os.path.dirname(npat)\n if nltkpath not in nltk.data.path:\n nltk.data.path.append(nltkpath)\n if 'note' in list(kwargs.keys()):\n path_within_gui = os.path.join(nltkpath.split('/lib/python2.7')[0], 'nltk_data')\n if path_within_gui not in nltk.data.path:\n nltk.data.path.append(path_within_gui)\n if path_within_gui.replace('/nltk/', '/', 1) not in nltk.data.path:\n nltk.data.path.append(path_within_gui.replace('/nltk/', '/', 1))\n\ndef get_gui_resource_dir():\n import inspect\n import os\n import sys\n if sys.platform == 'darwin':\n fext = 'app'\n else:\n fext = 'exe'\n corpath = corpath = __file__\n extens = '.%s' % fext\n apppath = corpath.split(extens , 1)\n resource_path = ''\n # if not an .app\n if len(apppath) == 1:\n resource_path = os.path.dirname(corpath)\n else:\n apppath = apppath[0] + extens\n appdir = os.path.dirname(apppath)\n if sys.platform == 'darwin':\n #resource_path = os.path.join(apppath, 'Contents', 'Resources')\n resource_path = os.path.join(apppath, 'Contents', 'MacOS')\n else:\n resource_path = appdir\n return resource_path\n\ndef get_fullpath_to_jars(path_var):\n \"\"\"when corenlp is needed, this sets corenlppath as the path to jar files,\n or returns false if not found\"\"\"\n import os\n important_files = ['stanford-corenlp-3.5.2-javadoc.jar', 'stanford-corenlp-3.5.2-models.jar',\n 'stanford-corenlp-3.5.2-sources.jar', 'stanford-corenlp-3.5.2.jar']\n # if user selected file in parser dir rather than dir,\n # get the containing dir\n path_var_str = path_var.get()\n\n if os.path.isfile(path_var_str):\n path_var_str = os.path.dirname(path_var_str.rstrip('/'))\n # if the user selected the subdir:\n if all(os.path.isfile(os.path.join(path_var_str, f)) for f in important_files):\n path_var.set(path_var_str)\n return True\n\n # if the user selected the parent dir:\n if os.path.isdir(path_var_str):\n # get subdirs containing the jar\n try:\n find_install = [d for d in os.listdir(path_var_str) \\\n if os.path.isdir(os.path.join(path_var_str, d)) \\\n and os.path.isfile(os.path.join(path_var_str, d, 'jollyday.jar'))]\n except OSError:\n pass\n if len(find_install) > 0:\n path_var.set(os.path.join(path_var_str, find_install[0]))\n return True\n\n # need to fix this duplicated code\n try:\n home = os.path.expanduser(\"~\")\n try_dir = os.path.join(home, 'corenlp')\n if os.path.isdir(try_dir):\n path_var_str = try_dir\n # get subdirs containing the jar\n try:\n find_install = [d for d in os.listdir(path_var_str) \\\n if os.path.isdir(os.path.join(path_var_str, d)) \\\n and os.path.isfile(os.path.join(path_var_str, d, 'jollyday.jar'))]\n except OSError:\n pass\n if len(find_install) > 0:\n path_var.set(os.path.join(path_var_str, find_install[0]))\n return True\n except:\n pass\n return False\n\ndef determine_datatype(path):\n \"\"\"\n Determine if plaintext, tokens or parsed XML\n \"\"\"\n import os\n from collections import Counter\n exts = []\n if not os.path.isdir(path) and not os.path.isfile(path):\n raise ValueError(\"Corpus path '%s' doesn't exist.\" % path)\n singlefile = False\n if os.path.isfile(path):\n singlefile = True\n if '.' in path:\n exts = [os.path.splitext(path)[1]]\n else:\n exts = ['.txt']\n else:\n for (root, dirs, fs) in os.walk(path):\n for f in fs:\n if '.' in f:\n ext = os.path.splitext(f)[1]\n exts.append(ext)\n counted = Counter(exts)\n counted.pop('', None)\n try:\n mc = counted.most_common(1)[0][0]\n except IndexError:\n mc = '.txt'\n \n lookup = {'.txt': 'plaintext',\n '.conll': 'conll',\n '.conllu': 'conll'}\n\n return lookup.get(mc, 'plaintext'), singlefile\n\ndef filtermaker(the_filter, case_sensitive=False, **kwargs):\n \"\"\"\n Create a search/exclude value\n \"\"\"\n import re\n from corpkit.dictionaries.process_types import Wordlist\n from time import localtime, strftime\n root = kwargs.get('root')\n if isinstance(the_filter, (list, Wordlist)):\n from corpkit.other import as_regex\n the_filter = as_regex(the_filter, case_sensitive=case_sensitive)\n try:\n output = re.compile(the_filter)\n is_valid = True\n except:\n is_valid = False\n if root:\n import traceback\n import sys\n exc_type, exc_value, exc_traceback = sys.exc_info()\n lst = traceback.format_exception(exc_type, exc_value, exc_traceback)\n error_message = lst[-1]\n thetime = strftime(\"%H:%M:%S\", localtime())\n print('%s: Filter %s' % (thetime, error_message))\n return 'Bad query'\n \n while not is_valid:\n if root:\n time = strftime(\"%H:%M:%S\", localtime())\n print(the_filter)\n print('%s: Invalid regular expression.' % time)\n return False\n time = strftime(\"%H:%M:%S\", localtime())\n selection = INPUTFUNC('\\n%s: filter regular expression \" %s \" contains an error. You can either:\\n\\n' \\\n ' a) rewrite it now\\n' \\\n ' b) exit\\n\\nYour selection: ' % (time, the_filter))\n if 'a' in selection:\n the_filter = INPUTFUNC('\\nNew regular expression: ')\n try:\n output = re.compile(r'\\b' + the_filter + r'\\b')\n is_valid = True\n except re.error:\n is_valid = False\n elif 'b' in selection:\n print('')\n return False\n return output\n\ndef searchfixer(search, query, datatype=False):\n \"\"\"\n Normalise query/search value\n \"\"\"\n if isinstance(search, STRINGTYPE) and isinstance(query, dict):\n return search\n if isinstance(search, STRINGTYPE):\n srch = search[0].lower()\n if not srch.startswith('t') and not srch.lower().startswith('n'):\n if query == 'any':\n query = r'.*'\n search = {search: query}\n return search\n\ndef is_number(s):\n \"\"\"\n Check if str can be can be made into float/int\n \"\"\"\n try:\n float(s) # for int, long and float\n return True\n except ValueError:\n try:\n complex(s) # for complex\n return True\n except ValueError:\n return False\n except TypeError:\n return False\n\ndef animator(progbar,\n count,\n tot_string=False,\n linenum=False,\n terminal=False,\n init=False,\n length=False,\n quiet=False,\n **kwargs\n ):\n \"\"\"\n Animates progress bar in unique position in terminal\n Multiple progress bars not supported in jupyter yet.\n \"\"\"\n\n # if ipython?\n welcome_message = kwargs.pop('welcome_message', '')\n if welcome_message:\n welcome_message = welcome_message.replace('Interrogating corpus ... \\n', '')\n welcome_message = welcome_message.replace('Concordancing corpus ... \\n', '')\n welcome_message = welcome_message.replace('\\n', ' ').replace(' ' * 17, ' ' * 17)\n else:\n welcome_message = ''\n if init:\n from traitlets import TraitError\n try:\n from ipywidgets import IntProgress, HTML, VBox\n from IPython.display import display\n progress = IntProgress(min=0, max=length, value=1)\n using_notebook = True\n progress.bar_style = 'info'\n label = HTML()\n label.font_family = 'monospace'\n gblabel = HTML()\n gblabel.font_family = 'monospace'\n box = VBox(children=[label, progress, gblabel])\n display(box)\n return box\n except TraitError:\n pass\n except ImportError:\n pass\n # newest ipython error\n except AttributeError:\n pass\n if not init:\n try:\n from ipywidgets.widgets.widget_box import Box\n if isinstance(progbar, Box):\n label, progress, goodbye = progbar.children\n progress.value = count\n if count == length:\n progress.bar_style = 'success'\n else:\n label.value = '%s\\nInterrogating: %s ...' % (welcome_message, tot_string)\n return\n except:\n pass\n\n # add startnum\n start_at = kwargs.get('startnum', 0)\n if start_at is None:\n start_at = 0.0\n denominator = kwargs.get('denom', 1)\n if kwargs.get('note'):\n if count is None:\n perc_done = 0.0\n else:\n perc_done = (count * 100.0 / float(length)) / float(denominator)\n kwargs['note'].progvar.set(start_at + perc_done)\n kwargs['root'].update()\n return\n\n if init:\n from corpkit.textprogressbar import TextProgressBar\n return TextProgressBar(length, dirname=tot_string)\n # this try is for sublime text nosetests, which don't take terminal object\n try:\n with terminal.location(0, terminal.height - (linenum + 1)):\n if tot_string:\n progbar.animate(count, tot_string, quiet=quiet)\n else:\n progbar.animate(count, quiet=quiet)\n # typeerror for nose\n except:\n if tot_string:\n progbar.animate(count, tot_string, quiet=quiet)\n else:\n progbar.animate(count, quiet=quiet)\n\n\ndef parse_just_speakers(just_speakers, corpus):\n if just_speakers is True:\n just_speakers = ['each']\n if just_speakers is False or just_speakers is None:\n return False\n if isinstance(just_speakers, STRINGTYPE):\n just_speakers = [just_speakers]\n if isinstance(just_speakers, list):\n if just_speakers == ['each']:\n from corpkit.build import get_speaker_names_from_parsed_corpus\n just_speakers = get_speaker_names_from_parsed_corpus(corpus)\n return just_speakers\n\n\ndef get_deps(sentence, dep_type):\n if dep_type == 'basic-dependencies':\n return sentence.basic_dependencies\n if dep_type == 'collapsed-dependencies':\n return sentence.collapsed_dependencies\n if dep_type == 'collapsed-ccprocessed-dependencies':\n return sentence.collapsed_ccprocessed_dependencies\n\ndef timestring(input):\n \"\"\"print with time prepended\"\"\"\n from time import localtime, strftime\n thetime = strftime(\"%H:%M:%S\", localtime())\n print('%s: %s' % (thetime, input.lstrip()))\n\ndef makesafe(variabletext, drop_datatype=True, hyphens_ok=False):\n import re\n from corpkit.process import is_number\n if hyphens_ok:\n variable_safe_r = re.compile(r'[^A-Za-z0-9_-]+', re.UNICODE)\n else:\n variable_safe_r = re.compile(r'[^A-Za-z0-9_]+', re.UNICODE)\n try:\n txt = variabletext.name.split('.')[0]\n except AttributeError:\n txt = variabletext.split('.')[0]\n if drop_datatype:\n txt = txt.replace('-parsed', '')\n txt = txt.replace(' ', '_')\n if not hyphens_ok:\n txt = txt.replace('-', '_')\n variable_safe = re.sub(variable_safe_r, '', txt)\n if is_number(variable_safe):\n variable_safe = 'c' + variable_safe\n return variable_safe\n\ndef interrogation_from_conclines(newdata):\n \"\"\"\n Make new interrogation result from its conc lines\n \"\"\"\n from collections import Counter\n from pandas import DataFrame\n from corpkit.editor import editor\n results = {}\n conc = newdata\n subcorpora = list(set(conc['c']))\n for subcorpus in subcorpora:\n counted = Counter(list(conc[conc['c'] == subcorpus]['m']))\n results[subcorpus] = counted\n\n the_big_dict = {}\n unique_results = set([item for sublist in list(results.values()) for item in sublist])\n for word in unique_results:\n the_big_dict[word] = [subcorp_result[word] for name, subcorp_result \\\n in sorted(results.items(), key=lambda x: x[0])]\n # turn master dict into dataframe, sorted\n df = DataFrame(the_big_dict, index=sorted(results.keys())) \n df = editor(df, sort_by='total', print_info=False)\n df.concordance = conc\n return df\n\ndef checkstack(the_string):\n \"\"\"checks for pytex\"\"\"\n import inspect\n thestack = []\n for bit in inspect.stack():\n for b in bit:\n thestack.append(str(b))\n as_string = ' '.join(thestack)\n return as_string.lower().count(the_string) > 1\n\ndef check_tex(have_ipython=True):\n \"\"\"\n See if tex is available\n \"\"\"\n import os\n if have_ipython:\n checktex_command = 'which latex'\n o = get_ipython().getoutput(checktex_command)[0]\n have_tex = not o.startswith('which: no latex in')\n else:\n import subprocess\n FNULL = open(os.devnull, 'w')\n checktex_command = [\"which\", \"latex\"]\n try:\n o = subprocess.check_output(checktex_command, stderr=FNULL)\n have_tex = True\n except subprocess.CalledProcessError:\n have_tex = False\n return have_tex\n\n\n \ndef get_corenlp_path(corenlppath):\n \"\"\"\n Find a working CoreNLP path.\n Return a dir containing jars\n \"\"\"\n\n import os\n import sys\n import re\n import glob\n \n cnlp_regex = re.compile(r'stanford-corenlp-[0-9\\.]+\\.jar')\n\n # if something has been passed in, find that first\n if corenlppath:\n # if it's a file, get the parent dir\n if os.path.isfile(corenlppath):\n corenlppath = os.path.dirname(corenlppath)\n if any(re.search(cnlp_regex, f) for f in os.listdir(corenlppath)):\n return corenlppath\n # if it's a dir, check if dir contains jar\n elif os.path.isdir(corenlppath):\n if any(re.search(cnlp_regex, f) for f in os.listdir(corenlppath)):\n return corenlppath\n # if it doesn't contain jar, get subdir by glob\n globpath = os.path.join(corenlppath, 'stanford-corenlp*')\n poss = [i for i in glob.glob(globpath) if os.path.isdir(i)]\n if poss:\n poss = poss[-1]\n if any(re.search(cnlp_regex, f) for f in os.listdir(poss)):\n return poss\n\n # put possisble paths into list\n pths = ['.', 'corenlp',\n os.path.expanduser(\"~\"),\n os.path.join(os.path.expanduser(\"~\"), 'corenlp')]\n if isinstance(corenlppath, STRINGTYPE):\n pths.append(corenlppath)\n possible_paths = os.getenv('PATH').split(os.pathsep) + sys.path + pths\n # remove empty strings\n possible_paths = set([i for i in possible_paths if os.path.isdir(i)])\n\n # check each possible path\n for path in possible_paths:\n if any(re.search(cnlp_regex, f) for f in os.listdir(path)):\n return path\n # check if it's a parent\n for path in possible_paths:\n globpath = os.path.join(path, 'stanford-corenlp*')\n cnlp_dirs = [d for d in glob.glob(globpath)\n if os.path.isdir(d)]\n for cnlp_dir in cnlp_dirs:\n if any(re.search(cnlp_regex, f) for f in os.listdir(cnlp_dir)):\n return cnlp_dir\n return\n\ndef unsplitter(data):\n \"\"\"unsplit contractions and apostophes from tokenised text\"\"\"\n \n\n if isinstance(data, STRINGTYPE):\n replaces = [(\"$ \", \"$\"),\n (\"`` \", \"``\"),\n (\" ,\", \",\"),\n (\" .\", \".\"),\n (\"'' \", \"''\"),\n (\" n't\", \"n't\"),\n (\" 're\", \"'re\"),\n (\" 'm\", \"'m\"),\n (\" 's\", \"'s\"),\n (\" 'd\", \"'d\"),\n (\" 'll\", \"'ll\"),\n (' ', ' ')\n ]\n for find, replace in replaces:\n data = data.replace(find, replace)\n return data\n else:\n unsplit = []\n for index, t in enumerate(data):\n if index == 0 or index == len(data) - 1:\n unsplit.append(t)\n continue\n if \"'\" in t and not t.endswith(\"'\"):\n rejoined = ''.join([data[index - 1], t])\n unsplit.append(rejoined)\n else:\n if not \"'\" in data[index + 1]:\n unsplit.append(t)\n return unsplit\n\ndef classname(cls):\n \"\"\"Create the class name str for __repr__\"\"\"\n return '.'.join([cls.__class__.__module__, cls.__class__.__name__])\n\n\ndef format_middle(tree, show, df=False, sent_id=False, ixs=False):\n tree_vals = {}\n if 't' in show or 'mt' in show:\n tre = str(tree).replace('/', '-slash')\n # todo?\n\n if 'mw' in show:\n tree_vals['mw'] = [i.replace('/', '-slash-') for i in tree.leaves()]\n if 'ml' in show:\n print(df)\n tree_vals['ml'] = [df.loc[sent_id, i]['l'] for i in ixs]\n if 'mp' in show:\n tree_vals['mp'] = [y for x, y in tree.pos()]\n if 'mx' in show:\n from corpkit.dictionaries import taglemma\n tree_vals['mx'] = [taglemma.get(y.lower(), y) for x, y in tree.pos()]\n\n output = []\n zipped = zip(*[tree_vals[i] for i in show])\n\n for tup in zipped:\n output.append('/'.join(tup))\n # now we have [a/a, nicer/nice, day/day]\n return ' '.join(output)\n\ndef format_conc(tups, show, df=False, sent_id=False, root=False, ixs=False):\n \"\"\"\n Prepare start or end of tgrepped conc lines\n \"\"\"\n tree_vals = {}\n if 't' in show or 'mt' in show:\n tre = str(root).replace('/', '-slash')\n # todo?\n if 'mw' in show:\n tree_vals['mw'] = [w.replace('/', '-slash-') for w, p, i in tups]\n if 'ml' in show:\n tree_vals['ml'] = [df.loc[sent_id, i]['l'] for i in ixs]\n if 'mp' in show:\n tree_vals['mp'] = [p.replace('/', '-slash-') for w, p, i in tups]\n if 'mx' in show:\n from corpkit.dictionaries import taglemma\n tree_vals['mx'] = [taglemma.get(p.lower(), p) for w, p, i in tups]\n if 'ms' in show:\n tree_vals['ms'] = [str(sent_id) for i in tups]\n if 'mi' in show:\n tree_vals['mi'] = [str(i) for w, p, i in tups]\n\n output = []\n zipped = zip(*[tree_vals[i] for i in show])\n\n for tup in zipped:\n output.append('/'.join(tup))\n # now we have [a/a, nicer/nice, day/day]\n return ' '.join(output)\n\ndef show_tree_as_per_option(show, tree, sent=False, df=False,\n sent_id=False, conc=False,\n only_format_match=True):\n \"\"\"\n Turn a ParentedTree into shown output\n\n :returns: tok_id, metcat, start, middle, end\n \"\"\"\n # make a list of all indices\n start = False\n end = False\n metcat = False\n \n # here, we need to get the indexes of the first and last\n # token in the match, when the tree is flattened.\n # i wonder if there is an easier way!\n all_leaf_positions = tree.root().treepositions(order='leaves')\n match_position = tree.treeposition()\n ixs = [e for e, i in enumerate(all_leaf_positions, start=1) \\\n if i[:len(match_position)] == match_position]\n\n # get the data from the left and right\n if conc:\n start = [(w, p, i) for i, (w, p) in enumerate(tree.root().pos(), start=1)\n if i < ixs[0]]\n end = [(w, p, i) for i, (w, p) in enumerate(tree.root().pos(), start=1)\n if i > ixs[-1]]\n\n middle = format_middle(tree, show, df=df, sent_id=sent_id, ixs=ixs)\n\n if conc:\n if not only_format_match:\n start_ixes = list(range(1, len(start)+1))\n end_ixes = list(range(ixs[-1]+1, len(end)+1))\n start = format_conc(start, show, df=df, sent_id=sent_id, root=tree.root(), ixs=start_ixes)\n end = format_conc(end, show, df=df, sent_id=sent_id, root=tree.root(), ixs=end_ixes)\n else:\n start = ' '.join([w for w, p, i in start])\n end = ' '.join([w for w, p, i in end])\n\n return ixs[0], start, middle, end\n\ndef tgrep(parse_string, search):\n \"\"\"\n Uses tgrep to search a parse tree string\n\n :param parse_string: A bracketed tree\n :type parse_string: `str`\n\n :param search: A search query\n :type search: `str` -- Tgrep query\n \"\"\"\n from nltk.tree import ParentedTree\n from nltk.tgrep import tgrep_nodes, tgrep_positions\n pt = ParentedTree.fromstring(parse_string)\n ptrees = [i for i in list(tgrep_nodes(search, [pt])) if i]\n return [item for sublist in ptrees for item in sublist]\n\ndef canpickle(obj):\n \"\"\"\n Determine if object can be pickled\n\n :returns: `bool`\n\n \"\"\"\n import os\n try:\n from cPickle import UnpickleableError as unpick_error\n import cPickle as pickle\n from cPickle import PicklingError as unpick_error_2\n except ImportError:\n import pickle\n from pickle import UnpicklingError as unpick_error\n from pickle import PicklingError as unpick_error_2\n\n mode = 'w' if PYTHON_VERSION == 2 else 'wb'\n with open(os.devnull, mode) as fo:\n try:\n pickle.dump(obj, fo)\n return True\n except (unpick_error, TypeError, unpick_error_2, AttributeError) as err:\n return False\n\ndef sanitise_dict(d):\n \"\"\"\n Make a dict that works as query attribute---remove nesting and unpicklable\n \"\"\"\n if not isinstance(d, dict):\n return\n newd = {}\n if d.get('kwargs') and isinstance(d['kwargs'], dict):\n for k, v in d['kwargs'].items():\n if canpickle(v) and not isinstance(v, type):\n newd[k] = v\n for k, v in d.items():\n if canpickle(v) and not isinstance(v, type):\n newd[k] = v\n return newd\n\ndef saferead(path):\n \"\"\"\n Read a file with detect encoding\n \n :returns: text and its encoding\n \"\"\"\n import chardet\n import sys\n if sys.version_info.major == 3:\n enc = 'utf-8'\n try:\n with open(path, 'r', encoding=enc) as fo:\n data = fo.read()\n except:\n with open(path, 'rb') as fo:\n data = fo.read().decode('utf-8', errors='ignore')\n return data, enc\n else:\n with open(path, 'r') as fo:\n data = fo.read()\n try:\n enc = 'utf-8'\n data = data.decode(enc)\n except UnicodeDecodeError:\n enc = chardet.detect(data)['encoding']\n data = data.decode(enc, errors='ignore')\n return data, enc\n\ndef urlify(s):\n \"\"\"\n Turn plot title into filename for saving\n \"\"\"\n import re\n s = s.lower()\n s = re.sub(r\"[^\\w\\s-]\", '', s)\n s = re.sub(r\"\\s+\", '-', s)\n s = re.sub(r\"-(textbf|emph|textsc|textit)\", '-', s)\n return s\n\ndef gui():\n \"\"\"\n Run the graphical interface with the current directory loaded\n \"\"\"\n import os\n from corpkit.gui import corpkit_gui\n current = os.getcwd()\n corpkit_gui(noupdate=True, loadcurrent=current)\n\ndef dictformat(d, query=False):\n \"\"\"\n Format a dict search query for printing\n\n :returns: `str`\n \"\"\"\n from corpkit.constants import transshow, transobjs\n if isinstance(d, STRINGTYPE) and isinstance(query, dict):\n newd = {}\n for k, v in query.items():\n newd[k] = fix_search({d: v})\n return dictformat(newd)\n if query:\n sformat = dictformat(query)\n return sformat\n else:\n return d\n if all(isinstance(i, dict) for i in d.values()):\n sformat = '\\n'\n for k, v in d.items():\n sformat += ' ' + k + ':'\n sformat += dictformat(v)\n return sformat\n if len(d) == 1 and d.get('v'):\n return 'Features'\n sformat = '\\n'\n for k, v in d.items():\n adj = ''\n if k[0] in ['-', '+']:\n adj = ' ' + k[:2]\n k = k[2:]\n if k == 't':\n dratt = ''\n else:\n dratt = transshow.get(k[-1], k[-1])\n if len(k) > 1:\n drole = transobjs.get(k[0], k[0])\n else:\n drole = ''\n if k == 't':\n drole = 'Trees'\n vform = getattr(v, 'pattern', v)\n sformat += ' %s %s %s: %s\\n' % (adj, drole, dratt.lower(), vform)\n return sformat\n\n\ndef fix_search(search, case_sensitive=False, root=False):\n \"\"\"\n If search has nested dicts, translate them\n \"\"\"\n ends = ['w', 'l', 'i', 'n', 'f', 'p', 'x', 's', 'c']\n \n # handle the possibility of nesting queries\n nestq = False\n if isinstance(search, dict):\n if all(isinstance(v, dict) for v in search.values()):\n nestq = True\n for v in search.values():\n if not all(x.islower() and x.isalpha() and len(x) < 4 for x in v.keys()):\n nestq = False\n if nestq:\n newd = {}\n for k, v in search.items():\n newd[k] = fix_search(v)\n return newd\n\n newsearch = {}\n\n if not search:\n return\n if isinstance(search, STRINGTYPE):\n return search\n if search.get('t'):\n return search\n trees = 't' in search.keys()\n for srch, pat in search.items():\n if len(srch) == 1 and srch in ends:\n if trees:\n pass\n else:\n srch = 'm%s' % srch\n if len(srch) == 3 and srch[0] in ['+', '-']:\n srch = list(srch)\n if srch[-1] in ends:\n srch.insert(2, 'm')\n else:\n srch.append('w')\n srch = ''.join(srch)\n if isinstance(pat, dict):\n for k, v in list(pat.items()):\n if k != 'w':\n newsearch[srch + k] = pat_format(v, case_sensitive=case_sensitive, root=root)\n else:\n newsearch[srch] = pat_format(v, case_sensitive=case_sensitive, root=root)\n else:\n newsearch[srch] = pat_format(pat, case_sensitive=case_sensitive)\n return newsearch\n\ndef pat_format(pat, case_sensitive=False, root=False):\n \"\"\"\n Coerce or compile search value\n \"\"\"\n from corpkit.dictionaries.process_types import Wordlist\n import re\n if pat == 'any':\n return re.compile(r'.*')\n if isinstance(pat, Wordlist):\n pat = list(pat)\n if isinstance(pat, list):\n if all(isinstance(x, int) for x in pat):\n pat = [str(x) for x in pat]\n pat = filtermaker(pat, case_sensitive=case_sensitive, root=root)\n else:\n if isinstance(pat, int):\n return pat\n if isinstance(pat, re._pattern_type):\n return pat\n if case_sensitive:\n pat = re.compile(pat)\n else:\n pat = re.compile(pat, re.IGNORECASE)\n return pat\n\ndef make_name_to_query_dict(existing={}, cols=False, dtype=False):\n \"\"\"\n Make or add to dict of longhand and shorthand search bits\n \"\"\"\n from corpkit.constants import transshow, transobjs\n if dtype and dtype != 'conll':\n return {'None': 'None'}\n \n for l, o in transobjs.items():\n if cols and l in ['g', 'd'] and l not in cols:\n continue\n if o == 'Match':\n o = ''\n else:\n o = o + ' '\n for m, p in sorted(transshow.items()):\n if cols:\n fixed = m.replace('x', 'p')\n if fixed in ['n', 't', 'a']:\n pass\n else:\n if fixed not in cols:\n continue\n if m in ['n', 't']:\n continue\n if p != 'POS' and o != '' and o != 'NER':\n p = p.lower()\n existing['%s%s' % (o, p)] = '%s%s' % (l, m)\n return existing\n\ndef auto_usecols(search, exclude, show, usecols, coref=False):\n \"\"\"\n Figure out if we can speed up conll parsing based on search,\n exclude and show. the return value is passed to pandas.read_csv\n so that only relevant columns are parsed.\n\n If usecols isn't None, the user has specified needed cols.\n\n todo: coref\n \"\"\"\n if usecols:\n return usecols\n from corpkit.constants import CONLL_COLUMNS\n\n # get all objs from search, exclude and show\n needed = []\n\n for i in search.keys():\n if 'g' in i or i == 'g':\n needed.append('d')\n elif 'd' in i or i == 'd':\n needed.append('g')\n elif 'h' in i or i == 'h':\n needed.append('c')\n elif 'r' in i or i == 'r':\n needed.append('c')\n # features mode:\n elif i == 'v':\n needed.append('f')\n needed.append('w')\n needed.append('p')\n continue\n needed.append(i)\n if isinstance(exclude, dict):\n for i in exclude.keys():\n needed.append(i)\n if isinstance(show, list):\n for i in show:\n if i.endswith('a'):\n needed.append('g')\n if i.startswith('r') or i.startswith('h'):\n needed.append('c')\n i = i.replace('a', 'f').replace('x', 'p')\n needed.append(i)\n else:\n needed.append(show)\n\n if coref:\n needed.append('c')\n # get the obj and attr from these\n stcols = []\n for i in needed:\n stcols.append(i[-1])\n try:\n stcols.append(i[-2])\n except:\n pass\n # word class is pos\n #stcols = ['p' if i == 'x' else i for i in stcols]\n # we always get word right now, but could remove '2' in the future\n # we add one to the index because conll_columns does not have 's', yet it\n # is there in the df\n out = [0, 1, 2]\n for n, c in enumerate(CONLL_COLUMNS, start=1):\n if c in stcols and n not in out:\n out.append(n)\n return out\n\ndef format_tregex(results,\n show=False,\n lemtag=False,\n exclude=False,\n excludemode=False,\n translated_option=False,\n lem_instance=False,\n whole=False,\n speaker_data=False,\n countmode=False):\n \"\"\"\n Format tregex by show list\n \"\"\"\n import re\n\n if countmode:\n return results\n\n if not results:\n return\n\n done = []\n\n fnames, snames, results = zip(*results)\n\n # this needs to be standardised!\n new_show = [x.lstrip('m') for x in show]\n new_show = ['w' if not x else x for x in new_show]\n\n if 'l' in new_show or 'x' in new_show:\n from corpkit.process import lemmatiser\n lemmata = lemmatiser(results, tag=lemtag,\n lem_instance=lem_instance,\n translated_option=translated_option)\n else:\n lemmata = [None for i in results]\n for word, lemma in list(zip(results, lemmata)):\n bits = []\n if exclude and exclude.get('w'):\n if len(list(exclude.keys())) == 1 or excludemode == 'any':\n if re.search(exclude.get('w'), word):\n continue\n if len(list(exclude.keys())) == 1 or excludemode == 'any':\n if re.search(exclude.get('l'), lemma):\n continue\n if len(list(exclude.keys())) == 1 or excludemode == 'any':\n if re.search(exclude.get('p'), word):\n continue\n if len(list(exclude.keys())) == 1 or excludemode == 'any':\n if re.search(exclude.get('x'), lemma):\n continue\n if exclude and excludemode == 'all':\n num_to_cause_exclude = len(list(exclude.keys()))\n current_num = 0\n if exclude.get('w'):\n if re.search(exclude.get('w'), word):\n current_num += 1\n if exclude.get('l'):\n if re.search(exclude.get('l'), lemma):\n current_num += 1\n if exclude.get('p'):\n if re.search(exclude.get('p'), word):\n current_num += 1\n if exclude.get('x'):\n if re.search(exclude.get('x'), lemma):\n current_num += 1 \n if current_num == num_to_cause_exclude:\n continue \n\n for i in new_show:\n if i == 't':\n bits.append(word)\n if i == 'l':\n bits.append(lemma)\n elif i == 'w':\n bits.append(word)\n elif i == 'p':\n bits.append(word)\n elif i == 'x':\n bits.append(lemma)\n joined = '/'.join(bits)\n done.append(joined)\n\n done = list(zip(fnames, snames, done))\n \n return done\n\ndef make_conc_lines_from_whole_mid(wholes,\n middle_column_result,\n show=False,\n category=False,\n filename=False):\n \"\"\"\n Create concordance line output from tregex output\n \"\"\"\n import re\n import os\n if not wholes and not middle_column_result:\n return []\n\n conc_lines = []\n unique_wholes = []\n unique_middle_column_result = []\n duplicates = []\n\n word_index = show.index('w') if 'w' in show else 0\n\n metcat = category if category else ''\n\n for (f, sk, whole), mid in list(zip(wholes, middle_column_result)):\n mid = mid[-1]\n joined = '-join-'.join([f, sk, whole, mid])\n if joined not in duplicates:\n duplicates.append(joined)\n unique_wholes.append([f, sk, whole])\n unique_middle_column_result.append(mid)\n\n # split into start, middle and end, dealing with multiple occurrences\n # this fails when multiple show values are given, because they are slash separated...\n for (f, sk, whole), mid in list(zip(unique_wholes, unique_middle_column_result)):\n mid = mid.split('/')[word_index]\n reg = re.compile(r'([^a-zA-Z0-9-]|^)(' + re.escape(mid) + r')([^a-zA-Z0-9-]|$)', \\\n re.IGNORECASE | re.UNICODE)\n offsets = [(m.start(), m.end()) for m in re.finditer(reg, whole)]\n for offstart, offend in offsets:\n start, middle, end = whole[0:offstart].strip(), whole[offstart:offend].strip(), \\\n whole[offend:].strip()\n # lin = [ix, category, fname, sname, start, mid, end]\n conc_lines.append(['_,_', metcat, os.path.basename(f), sk, start, middle, end])\n return conc_lines\n\ndef gettag(query, lemmatag=False):\n \"\"\"\n Find tag for WordNet lemmatisation\n \"\"\"\n if lemmatag:\n return lemmatag\n\n tagdict = {'n': 'n',\n 'j': 'a',\n 'v': 'v',\n 'a': 'r'}\n\n # in case someone compiles the tregex query\n query = getattr(query, 'pattern', query)\n qr = query.replace(r'\\w', '').replace(r'\\s', '').replace(r'\\b', '')\n firstletter = next((c for c in qr if c.isalpha()), 'n')\n return tagdict.get(firstletter.lower(), 'n')\n\ndef lemmatiser(list_of_words, tag, translated_option,\n lem_instance=False, preserve_case=False):\n \"\"\"\n Take a list of unicode words and a tag and return a lemmatised list\n \"\"\"\n from corpkit.dictionaries.word_transforms import wordlist, taglemma\n if not lem_instance:\n from nltk.stem.wordnet import WordNetLemmatizer\n lem_instance = WordNetLemmatizer()\n output = []\n for word in list_of_words:\n if 'u' in translated_option:\n word = taglemma.get(word.lower(), 'Other')\n else:\n word = wordlist.get(word, lem_instance.lemmatize(word, tag))\n output.append(word)\n return output\n\ndef get_first_df(corpus):\n \"\"\"\n Get the first document in a corpus, so that we can\n check what columns it has\n \"\"\"\n # genius code below\n from corpkit.corpus import Corpus\n if not isinstance(corpus, Corpus):\n corpus = Corpus(corpus, print_info=False)\n if corpus.subcorpora:\n return corpus.subcorpora[0].files[0].document\n elif corpus.files:\n return corpus.files[0].document\n else:\n return corpus.document\n\ndef make_dotfile(path, return_json=False, data_dict=False):\n \"\"\"\n Generate a dotfile in the data directory containing corpus information\n Right now, this information is the metadata fields and their values\n \"\"\"\n path = getattr(path, 'path', path)\n import os\n import json\n from corpkit.build import get_all_metadata_fields, get_speaker_names_from_parsed_corpus\n from corpkit.constants import OPENER\n if data_dict:\n json_data = data_dict\n else:\n json_data = {'fields': {}}\n fields = get_all_metadata_fields(path, include_speakers=True)\n for field in fields:\n vals = get_speaker_names_from_parsed_corpus(path, field)\n json_data['fields'][field] = vals\n df = get_first_df(path)\n json_data['columns'] = ['s', 'i'] + list(df.columns)\n dotname = '.%s.json' % os.path.basename(path)\n with OPENER(os.path.join('data', dotname), 'w', encoding='utf-8') as fo:\n fo.write(json.dumps(json_data))\n if return_json:\n return json_data\n \ndef get_corpus_metadata(path, generate=False):\n \"\"\"\n Return a dict containing corpus metadata, or None if not done yet\n \"\"\"\n import os\n import json\n from corpkit.corpus import Corpus\n from corpkit.constants import OPENER\n\n if not isinstance(path, Corpus):\n corpus = Corpus(path, print_info=False)\n else:\n corpus = path\n\n if not corpus.datatype == 'conll':\n return {}\n\n path = getattr(path, 'path', path)\n dotname = '.%s.json' % os.path.basename(path)\n dotpath = os.path.join('data', dotname)\n if not os.path.isfile(dotpath):\n if generate:\n return make_dotfile(path, return_json=True)\n else:\n return\n else:\n with OPENER(dotpath, 'r') as fo:\n data = fo.read()\n if data:\n return json.loads(data)\n else:\n return make_dotfile(path, return_json=True)\n\ndef make_df_json_name(typ, subcorpora=False):\n if subcorpora:\n if isinstance(subcorpora, list):\n subcorpora = '-'.join(subcorpora)\n return '%s-%s' % (typ, subcorpora)\n else:\n return typ\n\ndef add_df_to_dotfile(path, df, typ='features', subcorpora=False):\n \"\"\"\n Add some Pandas data to corpus metadata\n \"\"\"\n path = getattr(path, 'path', path)\n name = make_df_json_name(typ, subcorpora)\n md = get_corpus_metadata(path, generate=True)\n if name not in md:\n md[name] = df.astype(object).to_dict()\n make_dotfile(path, data_dict=md)\n\ndef delete_files_and_subcorpora(corpus, skip_metadata, just_metadata):\n \"\"\"\n Remake a Corpus object without some files or folders\n \"\"\"\n\n import re\n from corpkit.corpus import Corpus, Subcorpus\n \n # we use type here because subcorpora don't have subcorpora, but Subcorpus\n # inherits from Corpus\n if not type(corpus) == Corpus:\n return corpus, skip_metadata, just_metadata\n \n if not skip_metadata and not just_metadata:\n return corpus, skip_metadata, just_metadata\n\n sd = skip_metadata.pop('folders', None) if isinstance(skip_metadata, dict) else None\n sf = skip_metadata.pop('files', None) if isinstance(skip_metadata, dict) else None\n jd = just_metadata.pop('folders', None) if isinstance(just_metadata, dict) else None\n jf = just_metadata.pop('files', None) if isinstance(just_metadata, dict) else None\n sd = str(sd) if isinstance(sd, (int, float)) else sd\n sf = str(sf) if isinstance(sf, (int, float)) else sf\n jd = str(jd) if isinstance(jd, (int, float)) else jd\n jf = str(jf) if isinstance(jf, (int, float)) else jf\n\n if not any([sd, sf, jd, jf]):\n return corpus, skip_metadata, just_metadata\n\n # now, the real processing begins\n # the code has to be this way, sorry.\n\n if sd not in [None, False, {}]:\n todel = set()\n if isinstance(sd, list):\n for i, sub in enumerate(corpus.subcorpora):\n if sub.name in sd:\n todel.add(i)\n else:\n for i, sub in enumerate(corpus.subcorpora):\n if re.search(sd, sub.name, re.IGNORECASE):\n todel.add(i)\n\n for i in sorted(todel, reverse=True):\n del corpus.subcorpora[i]\n\n if sf not in [None, False, {}]:\n if not getattr(corpus, 'subcorpora', False):\n todel = set()\n if isinstance(sf, list):\n for i, sub in enumerate(corpus.files):\n if sub.name in sf:\n todel.add(i)\n else:\n for i, sub in enumerate(corpus.files):\n if re.search(sf, sub.name, re.IGNORECASE):\n todel.add(i)\n\n for i in sorted(todel, reverse=True):\n del corpus.files[i]\n\n else:\n for sc in corpus.subcorpora:\n todel = set()\n if isinstance(sf, list):\n for i, sub in enumerate(corpus.files):\n if sub.name in sf:\n todel.add(i)\n\n else:\n for i, sub in enumerate(sc.files):\n if re.search(sf, sub.name, re.IGNORECASE):\n todel.add(i)\n\n for i in sorted(todel, reverse=True):\n del sc.files[i]\n\n if jd not in [None, False, {}]:\n todel = set()\n if isinstance(jd, list):\n for i, sub in enumerate(corpus.subcorpora):\n if sub.name not in jd:\n todel.add(i)\n else:\n for i, sub in enumerate(corpus.subcorpora):\n if not re.search(jd, sub.name, re.IGNORECASE):\n todel.add(i)\n\n for i in sorted(todel, reverse=True):\n del corpus.subcorpora[i]\n\n if jf not in [None, False, {}]:\n if not getattr(corpus, 'subcorpora', False):\n todel = set()\n if isinstance(jf, list):\n for i, sub in enumerate(corpus.files):\n if sub.name not in jf:\n todel.add(i)\n else:\n for i, sub in enumerate(corpus.files):\n if not re.search(jf, sub.name, re.IGNORECASE):\n todel.add(i)\n\n for i in sorted(todel, reverse=True):\n del corpus.files[i]\n\n else:\n for sc in corpus.subcorpora:\n todel = set()\n if isinstance(jf, list):\n for i, sub in enumerate(corpus.files):\n if sub.name not in jf:\n todel.add(i)\n\n else:\n for i, sub in enumerate(sc.files):\n if not re.search(jf, sub.name, re.IGNORECASE):\n todel.add(i)\n\n for i in sorted(todel, reverse=True):\n del sc.files[i]\n\n return corpus, skip_metadata, just_metadata\n"
},
{
"path": "corpkit/stats.py",
"content": "\"\"\"\nscikit-learn stuff\n\"\"\"\n\ndef tfidf(self, search={'w': 'any'}, show=['w'], **kwargs):\n \"\"\"\n Generate TF-IDF vector representation of corpus\n using interrogate method\n \"\"\"\n\n from sklearn.feature_extraction.text import TfidfVectorizer\n\n def tokeniser(s):\n return [s]\n\n vectoriser = TfidfVectorizer(input='content', tokenizer=tokeniser)\n\n matrices = {}\n\n res = self.interrogate(search=search,\n show=show,\n **kwargs).results\n\n def dupe_string(line):\n \"\"\"\n turn {'corpus': 3, 'linguistics': 2} into\n 'corpus corpus corpus linguistics linguistics'\n \"\"\"\n return ''.join([(w + ' ') * line[w] \\\n for w in line.index])\n\n ser = res.apply(dupe_string, axis=1)\n\n vec = vectoriser.fit_transform(ser.values)\n return vectoriser, vec\n #vec = vectoriser.transform(ser.values)\n #return vec\n \ndef translate_show_for_surprisal(show, gramsize):\n \"\"\"\n Translate a simple show query into \n \"\"\"\n return show\n\ndef surprisal(self,\n search={'w', 'any'},\n exclude=False,\n show=['w'],\n gramsize=1,\n subcorpora='default',\n **kwargs):\n \"\"\"\n A method to show surprisal for tokens (and averages for sents?)\n\n It involves two parts. First, we generate a model over the whole corpus\n Then, we score each sent by it\n\n It should then be possible to annotate the corpus with this info.\n \"\"\"\n model = self.interrogate(search=search, exclude=exclude, show=show,\n subcorpora=subcorpora, **kwargs)\n \n\ndef shannon(self):\n from corpkit.interrogation import Interrogation\n \n def to_apply(ser):\n data = []\n import numpy as np\n import pandas as pd\n for word in ser.index:\n if not ser[word]:\n probability = np.nan\n self_information = np.nan\n else:\n probability = ser[word] / float(1.0 * len(ser)) \n self_information = np.log2(1.0/probability)\n data.append(self_information)\n return pd.Series(data, index=ser.index)\n \n res = getattr(self, 'results', self)\n appl = res.apply(to_apply, axis=1)\n ents = appl.sum(axis=1) / appl.shape[1]\n ents.name = 'Entropy'\n return Interrogation(results=appl, totals=ents)\n"
},
{
"path": "corpkit/textprogressbar.py",
"content": "#!/usr/bin/python\n\nfrom __future__ import print_function\n\nclass TextProgressBar:\n \"\"\"a text progress bar for CLI operations\n no need for user to call\"\"\"\n from time import localtime, strftime\n \n def __init__(self, iterations, dirname=False, quiet=False):\n from time import localtime, strftime\n self.iterations = iterations\n self.prog_bar = '[]'\n self.fill_char = '*'\n self.width = 60\n self.quiet = quiet\n self.__update_amount(0, dirname=dirname)\n self.animate = self.animate_ipython\n\n def animate_ipython(self, iter, dirname=None, quiet=False):\n from time import localtime, strftime\n import sys\n if not self.quiet:\n print(str(self) + '\\r', end='')\n try:\n sys.stdout.flush()\n except:\n pass\n self.update_iteration(iter + 1, dirname)\n\n def update_iteration(self, elapsed_iter, dirname=None):\n from time import localtime, strftime\n num = float(self.iterations)\n if num != 0:\n self.__update_amount((elapsed_iter / float(self.iterations)) * 100.0, dirname)\n else:\n self.__update_amount(0 * 100.0, dirname)\n self.prog_bar += ' ' # + ' %d of %s complete' % (elapsed_iter, self.iterations)\n\n def __update_amount(self, new_amount, dirname=None):\n from time import localtime, strftime\n percent_done = int(round((new_amount / 100.0) * 100.0, 2))\n all_full = self.width - 2\n num_hashes = int(round((percent_done / 100.0) * all_full))\n time = strftime(\"%H:%M:%S\", localtime())\n self.prog_bar = time + ': [' + self.fill_char * num_hashes + ' ' * (all_full - num_hashes) + ']'\n \n if dirname:\n pct_string = '%d%% ' % percent_done + '(' + dirname + ')'\n else:\n pct_string = '%d%%' % percent_done # could pass dirname here!\n # find out where the index of the first space in the middle string\n index_of_space = next((i for i, j in enumerate(pct_string) if j.isspace()), 0)\n \n # put middle string in centre, adjust so that spaces are always aligned\n pct_place = int(len(self.prog_bar) / float(2)) - index_of_space\n #if len(pct_string) < 20:\n #pct_string = ' ' * (20 - len(pct_string)) + pct_string\n self.prog_bar = self.prog_bar[0:pct_place] + \\\n (pct_string + self.prog_bar[pct_place + len(pct_string):])\n\n def __str__(self):\n return str(self.prog_bar)"
},
{
"path": "corpkit/tokenise.py",
"content": "from __future__ import print_function\n\n\"\"\"\nTokenise, POS tag and lemmatise a corpus, returning CONLL-U data\n\"\"\"\n\ndef nested_list_to_pandas(toks):\n \"\"\"\n Turn sent/word tokens into Series\n \"\"\"\n import pandas as pd\n index = []\n words = []\n for si, sent in enumerate(toks, start=1):\n for wi, w in enumerate(sent, start=1):\n index.append((si, wi))\n words.append(w)\n ix = pd.MultiIndex.from_tuples(index)\n ser = pd.Series(words, index=ix)\n ser.name = 'w'\n return ser\n\ndef pos_tag_series(ser, tagger):\n \"\"\"\n Create a POS tag Series from token Series\n \"\"\"\n import nltk\n import pandas as pd\n tags = [i[-1] for i in nltk.pos_tag(ser.values)]\n tagser = pd.Series(tags, index=ser.index)\n tagser.name = 'p'\n return tagser\n\ndef lemmatise_series(words, postags, lemmatiser):\n \"\"\"\n Create a lemma Series from token+postag Series\n \"\"\"\n import nltk\n import pandas as pd\n tups = zip(words.values, postags.values)\n lemmata = []\n tag_convert = {'j': 'a'}\n \n for word, tag in tups:\n tag = tag_convert.get(tag[0].lower(), tag[0].lower())\n if tag in ['n', 'a', 'v', 'r']:\n lem = lemmatiser.lemmatize(word, tag)\n else:\n lem = word\n lemmata.append(lem)\n\n lems = pd.Series(lemmata, index=words.index)\n lems.name = 'l'\n return lems\n \ndef write_df_to_conll(df, newf, plain=False, stripped=False,\n metadata=False, speaker_segmentation=False, offsets=False):\n \"\"\"\n Turn a DF into CONLL-U text, and write to file, newf\n \"\"\"\n import os\n from corpkit.constants import OPENER, PYTHON_VERSION\n from corpkit.conll import get_speaker_from_offsets\n outstring = ''\n sent_ixs = set(df.index.labels[0])\n for si in sent_ixs:\n si = si + 1\n #outstring += '# sent_id %d\\n' % si\n if metadata:\n metad = get_speaker_from_offsets(stripped,\n plain,\n offsets[si - 1],\n metadata_mode=metadata,\n speaker_segmentation=speaker_segmentation)\n \n for k, v in sorted(metad.items()):\n outstring += '# %s=%s\\n' % (k, v)\n\n sent = df.loc[si]\n\n csv = sent.to_csv(None, sep='\\t', header=False)\n outstring += csv + '\\n'\n try:\n os.makedirs(os.path.dirname(newf))\n except OSError:\n pass\n with OPENER(newf, 'w') as newf:\n if PYTHON_VERSION == 2:\n outstring = outstring.encode('utf-8', errors='ignore')\n newf.write(outstring)\n\ndef new_fname(oldpath, inpath):\n \"\"\"\n Determine output filename\n \"\"\"\n import os\n newf, ext = os.path.splitext(oldpath)\n newf = newf + '.conll'\n if '-stripped' in newf:\n return newf.replace('-stripped', '-tokenised')\n else:\n return newf.replace(inpath, inpath + '-tokenised')\n\ndef process_meta(data, speaker_segmentation, metadata):\n import re\n from corpkit.constants import MAX_SPEAKERNAME_SIZE\n meta_offsets = []\n if metadata and speaker_segmentation:\n reg = re.compile(r'(^.{,%d}?:\\s| )')\n elif metadata and not speaker_segmentation:\n reg = re.compile(r' ')\n elif not metadata and not speaker_segmentation:\n reg = re.compile(r'^.{,%d}?:\\s' % MAX_SPEAKERNAME_SIZE)\n\n #splt = re.split(reg, data)\n if speaker_segmentation or metadata:\n for i in re.finditer(reg, data):\n meta_offsets.append((i.start(), i.end()))\n for s, e in reversed(meta_offsets):\n data = data[:s] + data[e:]\n return data, meta_offsets\n\ndef plaintext_to_conll(inpath,\n postag=False,\n lemmatise=False,\n lang='en',\n metadata=False,\n outpath=False,\n nltk_data_path=False,\n speaker_segmentation=False):\n \"\"\"\n Take a plaintext corpus and sent/word tokenise.\n\n :param inpath: The corpus to read in\n :param postag: do POS tagging?\n :param lemmatise: do lemmatisation?\n :param lang: choose language for pos/lemmatiser (not implemented yet)\n :param metadata: add metadata to conll (not implemented yet)\n :param outpath: custom name for the resulting corpus\n :param speaker_segmentation: did the corpus has speaker names?\n \"\"\"\n \n import nltk\n import shutil\n import pandas as pd\n from corpkit.process import saferead\n\n from corpkit.build import get_filepaths\n fps = get_filepaths(inpath, 'txt')\n\n # IN THE SECTIONS BELOW, WE COULD ADD MULTILINGUAL\n # ANNOTATORS, PROVIDED THEY BEHAVE AS THE NLTK ONES DO\n\n # SENT TOKENISERS\n from nltk.tokenize.punkt import PunktSentenceTokenizer\n stoker = PunktSentenceTokenizer()\n s_tokers = {'en': stoker}\n sent_tokenizer = s_tokers.get(lang, stoker)\n\n # WORD TOKENISERS\n tokenisers = {'en': nltk.word_tokenize}\n tokeniser = tokenisers.get(lang, nltk.word_tokenize)\n\n # LEMMATISERS\n if lemmatise:\n from nltk.stem.wordnet import WordNetLemmatizer\n lmtzr = WordNetLemmatizer()\n lemmatisers = {'en': lmtzr}\n lemmatiser = lemmatisers.get(lang, lmtzr)\n\n # POS TAGGERS\n if postag:\n # nltk.download('averaged_perceptron_tagger')\n postaggers = {'en': nltk.pos_tag}\n tagger = postaggers.get(lang, nltk.pos_tag)\n\n # iterate over files, make df of each, convert this\n # to conll and sent to new filename\n for f in fps:\n for_df = []\n data, enc = saferead(f)\n plain, enc = saferead(f.replace('-stripped', ''))\n #orig_data = data\n #data, offsets = process_meta(data, speaker_segmentation, metadata)\n #nest = []\n sents = sent_tokenizer.tokenize(data)\n soffs = sent_tokenizer.span_tokenize(data)\n toks = [tokeniser(sent) for sent in sents]\n ser = nested_list_to_pandas(toks)\n for_df.append(ser)\n if postag or lemmatise:\n postags = pos_tag_series(ser, tagger)\n if lemmatise:\n lemma = lemmatise_series(ser, postags, lemmatiser)\n for_df.append(lemma)\n for_df.append(postags)\n else:\n if postag:\n for_df.append(postags)\n df = pd.concat(for_df, axis=1)\n fo = new_fname(f, inpath)\n write_df_to_conll(df, fo,\n metadata=metadata,\n plain=plain,\n stripped=data,\n speaker_segmentation=speaker_segmentation,\n offsets=soffs)\n nsent = len(set(df.index.labels[0]))\n print('%s created (%d sentences)' % (fo, nsent))\n\n if '-stripped' in inpath:\n return inpath.replace('-stripped', '-tokenised')\n else:\n return inpath + '-tokenised'\n"
},
{
"path": "corpkit/tregex.sh",
"content": "#!/bin/sh\nscriptdir=`dirname $0`\n\njava -mx100m -cp \"$scriptdir/stanford-tregex.jar:\" edu.stanford.nlp.trees.tregex.TregexPattern \"$@\"\n"
},
{
"path": "data/corpus-filelist.txt",
"content": "/Users/daniel/Work/corpkit/corpkit/data/test-stripped/first/intro.txt\n/Users/daniel/Work/corpkit/corpkit/data/test-stripped/second/body.txt"
},
{
"path": "data/test/first/intro.txt",
"content": "TESTER: This small corpus is used in corpkit's tests. Not a lot of data is required. \nANONYMOUS: Here, we're testing the speaker_segmentation option. \nTESTER: Right you are. The next file, however, won't have speaker IDs. \n"
},
{
"path": "data/test/second/body.txt",
"content": "Corpus linguistics and computational linguistics, like concordancing and interrogating, are situated on a vast continuum.\nIn both cases, Python can act as a fantastic conduit. \nNEWCOMER: Just checking I'm getting noticed during parsing, concordancing and interrogating. This corpus linguistics is hard work! \n"
},
{
"path": "data/test-plain-parsed/first/intro.txt.conll",
"content": "# parse=(ROOT (FRAG (NP (NN TESTER)) (: :) (S (NP (DT This) (JJ small) (NN corpus)) (VP (VBZ is) (VP (VBN used) (PP (IN in) (NP (NP (NN corpkit) (POS 's)) (NNS tests)))))) (. .)))\n# speaker=none\n# test=on\n# year=2004\n1\tTESTER\ttester\tNN\tO\t_\t0\tROOT\t2,7,12\t_\n2\t:\t:\t:\tO\t_\t1\tpunct\t0\t_\n3\tThis\tthis\tDT\tO\t_\t5\tdet\t0\t_\n4\tsmall\tsmall\tJJ\tO\t_\t5\tamod\t0\t_\n5\tcorpus\tcorpus\tNN\tO\t_\t7\tnsubjpass\t3,4\t_\n6\tis\tbe\tVBZ\tO\t_\t7\tauxpass\t0\t_\n7\tused\tuse\tVBN\tO\t_\t1\tappos\t5,6,11\t_\n8\tin\tin\tIN\tO\t_\t11\tcase\t0\t_\n9\tcorpkit\tcorpkit\tNN\tO\t_\t11\tnmod:poss\t10\t_\n10\t's\t's\tPOS\tO\t_\t9\tcase\t0\t_\n11\ttests\ttest\tNNS\tO\t_\t7\tnmod:in\t8,9\t_\n12\t.\t.\t.\tO\t_\t1\tpunct\t0\t_\n\n# parse=(ROOT (S (NP (NP (RB Not) (DT a) (NN lot)) (PP (IN of) (NP (NNS data)))) (VP (VBZ is) (VP (VBN required))) (. .)))\n# speaker=none\n# test=on\n# year=2004\n1\tNot\tnot\tRB\tO\t_\t3\tneg\t0\t_\n2\ta\ta\tDT\tO\t_\t5\tdet:qmod\t3,4\t_\n3\tlot\tlot\tNN\tO\t_\t2\tmwe\t1\t_\n4\tof\tof\tIN\tO\t_\t2\tmwe\t0\t_\n5\tdata\tdatum\tNNS\tO\t_\t7\tnsubjpass\t2\t_\n6\tis\tbe\tVBZ\tO\t_\t7\tauxpass\t0\t_\n7\trequired\trequire\tVBN\tO\t_\t0\tROOT\t5,6,8\t_\n8\t.\t.\t.\tO\t_\t7\tpunct\t0\t_\n\n# parse=(ROOT (NP (NP (NNP ANONYMOUS)) (: :) (S (ADVP (RB Here)) (, ,) (NP (PRP we)) (VP (VBP 're) (VP (VBG testing) (NP (DT the) (NN speaker_segmentation) (NN option))))) (. .)))\n# speaker=none\n# test=on\n# year=2005\n1\tANONYMOUS\tANONYMOUS\tNNP\tO\t_\t0\tROOT\t2,7,11\t_\n2\t:\t:\t:\tO\t_\t1\tpunct\t0\t_\n3\tHere\there\tRB\tO\t_\t7\tadvmod\t0\t_\n4\t,\t,\t,\tO\t_\t7\tpunct\t0\t_\n5\twe\twe\tPRP\tO\t_\t7\tnsubj\t0\t_\n6\t're\tbe\tVBP\tO\t_\t7\taux\t0\t_\n7\ttesting\ttest\tVBG\tO\t_\t1\tparataxis\t3,4,5,6,10\t_\n8\tthe\tthe\tDT\tO\t_\t10\tdet\t0\t_\n9\tspeaker_segmentation\tspeaker_segmentation\tNN\tO\t_\t10\tcompound\t0\t_\n10\toption\toption\tNN\tO\t_\t7\tdobj\t8,9\t_\n11\t.\t.\t.\tO\t_\t1\tpunct\t0\t_\n\n# parse=(ROOT (NP (NP (NN TESTER)) (: :) (NP (NP (NNP Right)) (SBAR (S (NP (PRP you)) (VP (VBP are))))) (. .)))\n# speaker=none\n# test=on\n# year=2004\n1\tTESTER\ttester\tNN\tO\t_\t0\tROOT\t2,3,6\t_\n2\t:\t:\t:\tO\t_\t1\tpunct\t0\t_\n3\tRight\tRight\tNNP\tO\t_\t1\tdep\t5\t_\n4\tyou\tyou\tPRP\tO\t_\t5\tnsubj\t0\t_\n5\tare\tbe\tVBP\tO\t_\t3\tacl:relcl\t4\t_\n6\t.\t.\t.\tO\t_\t1\tpunct\t0\t_\n\n# parse=(ROOT (S (NP (DT The) (JJ next) (NN file)) (, ,) (ADVP (RB however)) (, ,) (VP (MD wo) (RB n't) (VP (VB have) (NP (NN speaker) (NNS IDs)))) (. .)))\n# speaker=none\n# test=on\n# year=2004\n1\tThe\tthe\tDT\tO\t_\t3\tdet\t0\t_\n2\tnext\tnext\tJJ\tO\t_\t3\tamod\t0\t_\n3\tfile\tfile\tNN\tO\t_\t9\tnsubj\t1,2\t_\n4\t,\t,\t,\tO\t_\t9\tpunct\t0\t_\n5\thowever\thowever\tRB\tO\t_\t9\tadvmod\t0\t_\n6\t,\t,\t,\tO\t_\t9\tpunct\t0\t_\n7\two\twill\tMD\tO\t_\t9\taux\t0\t_\n8\tn't\tnot\tRB\tO\t_\t9\tneg\t0\t_\n9\thave\thave\tVB\tO\t_\t0\tROOT\t3,4,5,6,7,8,11,12\t_\n10\tspeaker\tspeaker\tNN\tO\t_\t11\tcompound\t0\t_\n11\tIDs\tid\tNNS\tO\t_\t9\tdobj\t10\t_\n12\t.\t.\t.\tO\t_\t9\tpunct\t0\t_\n\n"
},
{
"path": "data/test-plain-parsed/second/body.txt.conll",
"content": "# parse=(ROOT (S (NP (NP (NNP Corpus) (NNS linguistics)) (CC and) (NP (JJ computational) (NNS linguistics))) (, ,) (PP (IN like) (NP (NP (NN concordancing)) (CC and) (NP (VBG interrogating)))) (, ,) (VP (VBP are) (VP (VBN situated) (PP (IN on) (NP (DT a) (JJ vast) (NN continuum))))) (. .)))\n# speaker=none\n1\tCorpus\tCorpus\tNNP\tO\t_\t2\tcompound\t0\t_\n2\tlinguistics\tlinguistics\tNNS\tO\t_\t13\tnsubjpass\t1,3,5\t_\n3\tand\tand\tCC\tO\t_\t2\tcc\t0\t_\n4\tcomputational\tcomputational\tJJ\tO\t_\t5\tamod\t0\t_\n5\tlinguistics\tlinguistics\tNNS\tO\t_\t2\tconj:and\t4\t_\n6\t,\t,\t,\tO\t_\t13\tpunct\t0\t_\n7\tlike\tlike\tIN\tO\t_\t8\tcase\t0\t_\n8\tconcordancing\tconcordancing\tNN\tO\t_\t13\tnmod:like\t7,9,10\t_\n9\tand\tand\tCC\tO\t_\t8\tcc\t0\t_\n10\tinterrogating\tinterrogate\tVBG\tO\t_\t8\tconj:and\t0\t_\n11\t,\t,\t,\tO\t_\t13\tpunct\t0\t_\n12\tare\tbe\tVBP\tO\t_\t13\tauxpass\t0\t_\n13\tsituated\tsituate\tVBN\tO\t_\t0\tROOT\t2,5,6,8,10,11,12,17,18\t_\n14\ton\ton\tIN\tO\t_\t17\tcase\t0\t_\n15\ta\ta\tDT\tO\t_\t17\tdet\t0\t_\n16\tvast\tvast\tJJ\tO\t_\t17\tamod\t0\t_\n17\tcontinuum\tcontinuum\tNN\tO\t_\t13\tnmod:on\t14,15,16\t_\n18\t.\t.\t.\tO\t_\t13\tpunct\t0\t_\n\n# parse=(ROOT (S (PP (IN In) (NP (DT both) (NNS cases))) (, ,) (NP (NNP Python)) (VP (MD can) (VP (VB act) (PP (IN as) (NP (DT a) (JJ fantastic) (NN conduit))))) (. .)))\n# speaker=none\n# test=off\n# year=2002\n1\tIn\tin\tIN\tO\t_\t3\tcase\t0\t_\n2\tboth\tboth\tDT\tO\t_\t3\tdet\t0\t_\n3\tcases\tcase\tNNS\tO\t_\t7\tnmod:in\t1,2\t_\n4\t,\t,\t,\tO\t_\t7\tpunct\t0\t_\n5\tPython\tPython\tNNP\tPERSON\t_\t7\tnsubj\t0\t_\n6\tcan\tcan\tMD\tO\t_\t7\taux\t0\t_\n7\tact\tact\tVB\tO\t_\t0\tROOT\t3,4,5,6,11,12\t_\n8\tas\tas\tIN\tO\t_\t11\tcase\t0\t_\n9\ta\ta\tDT\tO\t_\t11\tdet\t0\t_\n10\tfantastic\tfantastic\tJJ\tO\t_\t11\tamod\t0\t_\n11\tconduit\tconduit\tNN\tO\t_\t7\tnmod:as\t8,9,10\t_\n12\t.\t.\t.\tO\t_\t7\tpunct\t0\t_\n\n# parse=(ROOT (X (NP (NN NEWCOMER)) (: :) (FRAG (S (ADVP (RB Just)) (VP (VBG checking) (SBAR (S (NP (PRP I)) (VP (VBP 'm) (VP (VP (VBG getting) (S (VP (VBN noticed) (PP (IN during) (NP (NP (NN parsing)) (, ,) (NP (NN concordancing))))))) (CC and) (VP (VBG interrogating))))))))) (. .)))\n# speaker=none\n# test=on\n# year=2005\n1\tNEWCOMER\tnewcomer\tNN\tO\t_\t0\tROOT\t2,4,15\t_\n2\t:\t:\t:\tO\t_\t1\tpunct\t0\t_\n3\tJust\tjust\tRB\tO\t_\t4\tadvmod\t0\t_\n4\tchecking\tcheck\tVBG\tO\t_\t1\tdep\t3,7,14\t_\n5\tI\tI\tPRP\tO\t_\t7\tnsubj\t0\t_\n6\t'm\tbe\tVBP\tO\t_\t7\taux\t0\t_\n7\tgetting\tget\tVBG\tO\t_\t4\tccomp\t5,6,8,13,14\t_\n8\tnoticed\tnotice\tVBN\tO\t_\t7\tdep\t10\t_\n9\tduring\tduring\tIN\tO\t_\t10\tcase\t0\t_\n10\tparsing\tparsing\tNN\tO\t_\t8\tnmod:during\t9,11,12\t_\n11\t,\t,\t,\tO\t_\t10\tpunct\t0\t_\n12\tconcordancing\tconcordancing\tNN\tO\t_\t10\tappos\t0\t_\n13\tand\tand\tCC\tO\t_\t7\tcc\t0\t_\n14\tinterrogating\tinterrogate\tVBG\tO\t_\t4\tccomp\t5\t_\n15\t.\t.\t.\tO\t_\t1\tpunct\t0\t_\n\n# parse=(ROOT (S (NP (DT This) (NN corpus) (NNS linguistics)) (VP (VBZ is) (NP (JJ hard) (NN work))) (. !)))\n# speaker=none\n# test=on\n# year=2005\n1\tThis\tthis\tDT\tO\t_\t3\tdet\t0\t_\n2\tcorpus\tcorpus\tNN\tO\t_\t3\tcompound\t0\t_\n3\tlinguistics\tlinguistics\tNNS\tO\t_\t4\tnsubj\t1,2\t_\n4\tis\tbe\tVBZ\tO\t_\t0\tROOT\t3,6,7\t_\n5\thard\thard\tJJ\tO\t_\t6\tamod\t0\t_\n6\twork\twork\tNN\tO\t_\t4\txcomp\t5\t_\n7\t!\t!\t.\tO\t_\t4\tpunct\t0\t_\n\n"
},
{
"path": "data/test-speak-parsed/first/intro.txt.conll",
"content": "# parse=(ROOT (S (NP (DT This) (JJ small) (NN corpus)) (VP (VBZ is) (VP (VBN used) (PP (IN in) (NP (NP (NN corpkit) (POS 's)) (NNS tests))))) (. .)))\n# speaker=TESTER\n# test=on\n# year=2004\n1\tThis\tthis\tDT\tO\t_\t3\tdet\t0\t_\n2\tsmall\tsmall\tJJ\tO\t_\t3\tamod\t0\t_\n3\tcorpus\tcorpus\tNN\tO\t_\t5\tnsubjpass\t1,2\t_\n4\tis\tbe\tVBZ\tO\t_\t5\tauxpass\t0\t_\n5\tused\tuse\tVBN\tO\t_\t0\tROOT\t3,4,9,10\t_\n6\tin\tin\tIN\tO\t_\t9\tcase\t0\t_\n7\tcorpkit\tcorpkit\tNN\tO\t_\t9\tnmod:poss\t8\t_\n8\t's\t's\tPOS\tO\t_\t7\tcase\t0\t_\n9\ttests\ttest\tNNS\tO\t_\t5\tnmod:in\t6,7\t_\n10\t.\t.\t.\tO\t_\t5\tpunct\t0\t_\n\n# parse=(ROOT (S (NP (NP (RB Not) (DT a) (NN lot)) (PP (IN of) (NP (NNS data)))) (VP (VBZ is) (VP (VBN required))) (. .)))\n# speaker=TESTER\n# test=on\n# year=2004\n1\tNot\tnot\tRB\tO\t_\t3\tneg\t0\t_\n2\ta\ta\tDT\tO\t_\t5\tdet:qmod\t3,4\t_\n3\tlot\tlot\tNN\tO\t_\t2\tmwe\t1\t_\n4\tof\tof\tIN\tO\t_\t2\tmwe\t0\t_\n5\tdata\tdatum\tNNS\tO\t_\t7\tnsubjpass\t2\t_\n6\tis\tbe\tVBZ\tO\t_\t7\tauxpass\t0\t_\n7\trequired\trequire\tVBN\tO\t_\t0\tROOT\t5,6,8\t_\n8\t.\t.\t.\tO\t_\t7\tpunct\t0\t_\n\n# parse=(ROOT (S (ADVP (RB Here)) (, ,) (NP (PRP we)) (VP (VBP 're) (VP (VBG testing) (NP (DT the) (NN speaker_segmentation) (NN option)))) (. .)))\n# speaker=ANONYMOUS\n# test=on\n# year=2005\n1\tHere\there\tRB\tO\t_\t5\tadvmod\t0\t_\n2\t,\t,\t,\tO\t_\t5\tpunct\t0\t_\n3\twe\twe\tPRP\tO\t_\t5\tnsubj\t0\t_\n4\t're\tbe\tVBP\tO\t_\t5\taux\t0\t_\n5\ttesting\ttest\tVBG\tO\t_\t0\tROOT\t1,2,3,4,8,9\t_\n6\tthe\tthe\tDT\tO\t_\t8\tdet\t0\t_\n7\tspeaker_segmentation\tspeaker_segmentation\tNN\tO\t_\t8\tcompound\t0\t_\n8\toption\toption\tNN\tO\t_\t5\tdobj\t6,7\t_\n9\t.\t.\t.\tO\t_\t5\tpunct\t0\t_\n\n# parse=(ROOT (S (ADVP (RB Right)) (NP (PRP you)) (VP (VBP are)) (. .)))\n# speaker=TESTER\n# test=on\n# year=2004\n1\tRight\tright\tRB\tO\t_\t3\tadvmod\t0\t_\n2\tyou\tyou\tPRP\tO\t_\t3\tnsubj\t0\t_\n3\tare\tbe\tVBP\tO\t_\t0\tROOT\t1,2,4\t_\n4\t.\t.\t.\tO\t_\t3\tpunct\t0\t_\n\n# parse=(ROOT (S (NP (DT The) (JJ next) (NN file)) (, ,) (ADVP (RB however)) (, ,) (VP (MD wo) (RB n't) (VP (VB have) (NP (NN speaker) (NNS IDs)))) (. .)))\n# speaker=TESTER\n# test=on\n# year=2004\n1\tThe\tthe\tDT\tO\t_\t3\tdet\t0\t_\n2\tnext\tnext\tJJ\tO\t_\t3\tamod\t0\t_\n3\tfile\tfile\tNN\tO\t_\t9\tnsubj\t1,2\t_\n4\t,\t,\t,\tO\t_\t9\tpunct\t0\t_\n5\thowever\thowever\tRB\tO\t_\t9\tadvmod\t0\t_\n6\t,\t,\t,\tO\t_\t9\tpunct\t0\t_\n7\two\twill\tMD\tO\t_\t9\taux\t0\t_\n8\tn't\tnot\tRB\tO\t_\t9\tneg\t0\t_\n9\thave\thave\tVB\tO\t_\t0\tROOT\t3,4,5,6,7,8,11,12\t_\n10\tspeaker\tspeaker\tNN\tO\t_\t11\tcompound\t0\t_\n11\tIDs\tid\tNNS\tO\t_\t9\tdobj\t10\t_\n12\t.\t.\t.\tO\t_\t9\tpunct\t0\t_\n\n"
},
{
"path": "data/test-speak-parsed/second/body.txt.conll",
"content": "# parse=(ROOT (S (NP (NP (NNP Corpus) (NNS linguistics)) (CC and) (NP (JJ computational) (NNS linguistics))) (, ,) (PP (IN like) (NP (NP (NN concordancing)) (CC and) (NP (VBG interrogating)))) (, ,) (VP (VBP are) (VP (VBN situated) (PP (IN on) (NP (DT a) (JJ vast) (NN continuum))))) (. .)))\n# speaker=UNIDENTIFIED\n1\tCorpus\tCorpus\tNNP\tO\t_\t2\tcompound\t0\t_\n2\tlinguistics\tlinguistics\tNNS\tO\t_\t13\tnsubjpass\t1,3,5\t_\n3\tand\tand\tCC\tO\t_\t2\tcc\t0\t_\n4\tcomputational\tcomputational\tJJ\tO\t_\t5\tamod\t0\t_\n5\tlinguistics\tlinguistics\tNNS\tO\t_\t2\tconj:and\t4\t_\n6\t,\t,\t,\tO\t_\t13\tpunct\t0\t_\n7\tlike\tlike\tIN\tO\t_\t8\tcase\t0\t_\n8\tconcordancing\tconcordancing\tNN\tO\t_\t13\tnmod:like\t7,9,10\t_\n9\tand\tand\tCC\tO\t_\t8\tcc\t0\t_\n10\tinterrogating\tinterrogate\tVBG\tO\t_\t8\tconj:and\t0\t_\n11\t,\t,\t,\tO\t_\t13\tpunct\t0\t_\n12\tare\tbe\tVBP\tO\t_\t13\tauxpass\t0\t_\n13\tsituated\tsituate\tVBN\tO\t_\t0\tROOT\t2,5,6,8,10,11,12,17,18\t_\n14\ton\ton\tIN\tO\t_\t17\tcase\t0\t_\n15\ta\ta\tDT\tO\t_\t17\tdet\t0\t_\n16\tvast\tvast\tJJ\tO\t_\t17\tamod\t0\t_\n17\tcontinuum\tcontinuum\tNN\tO\t_\t13\tnmod:on\t14,15,16\t_\n18\t.\t.\t.\tO\t_\t13\tpunct\t0\t_\n\n# parse=(ROOT (S (PP (IN In) (NP (DT both) (NNS cases))) (, ,) (NP (NNP Python)) (VP (MD can) (VP (VB act) (PP (IN as) (NP (DT a) (JJ fantastic) (NN conduit))))) (. .)))\n# speaker=UNIDENTIFIED\n# test=off\n# year=2002\n1\tIn\tin\tIN\tO\t_\t3\tcase\t0\t_\n2\tboth\tboth\tDT\tO\t_\t3\tdet\t0\t_\n3\tcases\tcase\tNNS\tO\t_\t7\tnmod:in\t1,2\t_\n4\t,\t,\t,\tO\t_\t7\tpunct\t0\t_\n5\tPython\tPython\tNNP\tPERSON\t_\t7\tnsubj\t0\t_\n6\tcan\tcan\tMD\tO\t_\t7\taux\t0\t_\n7\tact\tact\tVB\tO\t_\t0\tROOT\t3,4,5,6,11,12\t_\n8\tas\tas\tIN\tO\t_\t11\tcase\t0\t_\n9\ta\ta\tDT\tO\t_\t11\tdet\t0\t_\n10\tfantastic\tfantastic\tJJ\tO\t_\t11\tamod\t0\t_\n11\tconduit\tconduit\tNN\tO\t_\t7\tnmod:as\t8,9,10\t_\n12\t.\t.\t.\tO\t_\t7\tpunct\t0\t_\n\n# parse=(ROOT (S (S (VP (ADVP (RB Just)) (VBG checking) (NP (PRP I)))) (VP (VBP 'm) (VP (VP (VBG getting) (S (VP (VBN noticed) (PP (IN during) (NP (NP (NN parsing)) (, ,) (NP (NN concordancing))))))) (CC and) (VP (VBG interrogating)))) (. .)))\n# speaker=NEWCOMER\n# test=on\n# year=2005\n1\tJust\tjust\tRB\tO\t_\t2\tadvmod\t0\t_\n2\tchecking\tcheck\tVBG\tO\t_\t5\tcsubj\t1,3\t_\n3\tI\tI\tPRP\tO\t_\t2\tdobj\t0\t_\n4\t'm\tbe\tVBP\tO\t_\t5\taux\t0\t_\n5\tgetting\tget\tVBG\tO\t_\t0\tROOT\t2,4,6,11,12,13\t_\n6\tnoticed\tnotice\tVBN\tO\t_\t5\tdep\t8\t_\n7\tduring\tduring\tIN\tO\t_\t8\tcase\t0\t_\n8\tparsing\tparsing\tNN\tO\t_\t6\tnmod:during\t7,9,10\t_\n9\t,\t,\t,\tO\t_\t8\tpunct\t0\t_\n10\tconcordancing\tconcordancing\tNN\tO\t_\t8\tappos\t0\t_\n11\tand\tand\tCC\tO\t_\t5\tcc\t0\t_\n12\tinterrogating\tinterrogate\tVBG\tO\t_\t5\tconj:and\t2\t_\n13\t.\t.\t.\tO\t_\t5\tpunct\t0\t_\n\n# parse=(ROOT (S (NP (DT This) (NN corpus) (NNS linguistics)) (VP (VBZ is) (NP (JJ hard) (NN work))) (. !)))\n# speaker=NEWCOMER\n# test=on\n# year=2005\n1\tThis\tthis\tDT\tO\t_\t3\tdet\t0\t_\n2\tcorpus\tcorpus\tNN\tO\t_\t3\tcompound\t0\t_\n3\tlinguistics\tlinguistics\tNNS\tO\t_\t4\tnsubj\t1,2\t_\n4\tis\tbe\tVBZ\tO\t_\t0\tROOT\t3,6,7\t_\n5\thard\thard\tJJ\tO\t_\t6\tamod\t0\t_\n6\twork\twork\tNN\tO\t_\t4\txcomp\t5\t_\n7\t!\t!\t.\tO\t_\t4\tpunct\t0\t_\n\n"
},
{
"path": "data/test-stripped/first/intro.txt",
"content": "This small corpus is used in corpkit's tests. Not a lot of data is required. \nHere, we're testing the speaker_segmentation option. \nRight you are. The next file, however, won't have speaker IDs. "
},
{
"path": "data/test-stripped/second/body.txt",
"content": "Corpus linguistics and computational linguistics, like concordancing and interrogating, are situated on a vast continuum.\nIn both cases, Python can act as a fantastic conduit. \nJust checking I'm getting noticed during parsing, concordancing and interrogating. This corpus linguistics is hard work! "
},
{
"path": "index.rst",
"content": ".. corpkit documentation master file, created by\n sphinx-quickstart on Thu Nov 5 11:43:02 2015.\n You can adapt this file completely to your liking, but it should at least\n contain the root `toctree` directive.\n\n========================================\ncorpkit documentation\n========================================\n\n*corpkit* is a Python-based tool for doing more sophisticated corpus linguistics. It exists as a graphical interface, a Python API, and a natural language interpreter. The API and interpreter are documented here.\n\nWith *corpkit*, you can create parsed, structured and metadata-annotated corpora, and then search them for complex lexicogrammatical patterns. Search results can be quickly edited, sorted and visualised, saved and loaded within projects, or exported to formats that can be handled by other tools. In fact, you can easily work with any dataset in `CONLL U`_ format, including the freely available, multilingual `Universal Dependencies Treebanks`_. \n\nConcordancing is extended to allow the user to query and display grammatical features alongside tokens. Keywording can be restricted to certain word classes or positions within the clause. If your corpus contains multiple documents or subcorpora, you can identify keywords in each, compared to the corpus as a whole.\n\n*corpkit* leverages `Stanford CoreNLP`_, NLTK_ and pattern_ for the linguistic heavy lifting, and pandas_ and matplotlib_ for storing, editing and visualising interrogation results. Multiprocessing is available via joblib_, and Python 2 and 3 are both supported.\n\n.. rubric:: API example\n\nHere's a basic workflow, using a corpus of news articles published between 1987 and 2014, structured like this:\n\n.. code-block:: none\n \n ./data/NYT:\n\n ├───1987\n │ ├───NYT-1987-01-01-01.txt\n │ ├───NYT-1987-01-02-01.txt\n │ ...\n │\n ├───1988\n │ ├───NYT-1988-01-01-01.txt\n │ ├───NYT-1988-01-02-01.txt\n │ ...\n ...\n\n\nBelow, this corpus is made into a `Corpus` object, parsed with *Stanford CoreNLP*, and interrogated for a lexicogrammatical feature. Absolute frequencies are turned into relative frequencies, and results sorted by trajectory. The edited data is then plotted.\n\n.. code-block:: python\n\n >>> from corpkit import *\n >>> from corpkit.dictionaries import processes\n \n ### parse corpus of NYT articles containing annual subcorpora\n >>> unparsed = Corpus('data/NYT')\n >>> parsed = unparsed.parse()\n \n ### query: nominal nsubjs that have verbal process as governor lemma\n >>> crit = {F: r'^nsubj$',\n ... GL: processes.verbal.lemmata,\n ... P: r'^N'}\n\n ### interrogate corpus, outputting lemma forms\n >>> sayers = parsed.interrogate(crit, show=L)\n >>> sayers.quickview(10)\n\n 0: official (n=4348)\n 1: expert (n=2057)\n 2: analyst (n=1369)\n 3: report (n=1103)\n 4: company (n=1070)\n 5: which (n=1043)\n 6: researcher (n=987)\n 7: study (n=901)\n 8: critic (n=826)\n 9: person (n=802)\n\n ### get relative frequency and sort by increasing\n >>> rel_say = sayers.edit('%', SELF, sort_by='increase')\n\n ### plot via matplotlib, using tex if possible\n >>> rel_say.visualise('Sayers, increasing', kind='area',\n ... y_label='Percentage of all sayers')\n\nOutput:\n\n.. figure:: images/sayers-increasing.png\n\n.. rubric:: Installation\n\nVia pip:\n\n.. code-block:: bash\n\n $ pip install corpkit\n\nvia Git:\n\n.. code-block:: bash\n\n $ git clone https://www.github.com/interrogator/corpkit\n $ cd corpkit\n $ python setup.py install\n\nParsing and interrogation of parse trees will also require *Stanford CoreNLP*. *corpkit* can download and install it for you automatically.\n\n.. rubric:: Graphical interface\n\nMuch of corpkit's command line functionality is also available in the *corpkit GUI*. After installation, it can be started from the command line with:\n\n.. code-block:: bash\n\n $ python -m corpkit.gui\n\n\nIf you're working on a project from within Python, you can open it graphically with:\n\n.. code-block:: python\n\n >>> from corpkit import gui\n >>> gui()\n\nAlternatively, the GUI is available (alongside documentation) as a standalone OSX app here_.\n\n.. rubric:: Interpreter\n\n*corpkit* also has its own interpreter, a bit like the `Corpus Workbench`_. You can open it with:\n\n.. code-block:: bash\n\n $ corpkit\n # or, alternatively:\n $ python -m corpkit.env\n\nAnd then start working with natural language commands:\n\n.. code-block:: bash\n\n > set junglebook as corpus\n > parse junglebook with outname as jb\n > set jb as corpus\n > search corpus for governor-lemma matching processes:verbal showing pos and lemma\n > calculate result as percentage of self\n > plot result as line chart with title as 'Example figure'\n\nFrom the interpreter, you can enter ``ipython``, ``jupyter notebook`` or ``gui`` to switch between interfaces, preserving the local namespace and data where possible.\n\nInformation about the syntax is available at the :ref:`interpreter-page`.\n\n.. toctree::\n :maxdepth: 1\n :caption: API\n\n rst_docs/API/corpkit.building.rst\n rst_docs/API/corpkit.interrogating.rst\n rst_docs/API/corpkit.concordancing.rst\n rst_docs/API/corpkit.editing.rst\n rst_docs/API/corpkit.visualising.rst\n rst_docs/API/corpkit.langmodel.rst\n rst_docs/API/corpkit.managing.rst\n \n.. toctree::\n :maxdepth: 1\n :caption: Interpreter\n\n rst_docs/interpreter/corpkit.interpreter.overview.rst\n rst_docs/interpreter/corpkit.interpreter.setup.rst\n rst_docs/interpreter/corpkit.interpreter.making.rst\n rst_docs/interpreter/corpkit.interpreter.interrogating.rst\n rst_docs/interpreter/corpkit.interpreter.concordancing.rst\n rst_docs/interpreter/corpkit.interpreter.annotating.rst\n rst_docs/interpreter/corpkit.interpreter.editing.rst\n rst_docs/interpreter/corpkit.interpreter.visualising.rst\n rst_docs/interpreter/corpkit.interpreter.managing.rst\n\n.. toctree::\n :maxdepth: 2\n :caption: API reference\n\n rst_docs/API-ref/corpkit.corpus.rst\n rst_docs/API-ref/corpkit.interrogation.rst\n rst_docs/API-ref/corpkit.other.rst\n rst_docs/API-ref/corpkit.dictionaries.rst\n\n.. toctree::\n :maxdepth: 2\n :caption: External links\n :hidden:\n\n Graphical interface \n GitHub \n\n.. rubric:: Cite\n\nIf you'd like to cite *corpkit*, you can use:\n\n.. code-block:: none\n\n McDonald, D. (2015). corpkit: a toolkit for corpus linguistics. Retrieved from\n https://www.github.com/interrogator/corpkit. DOI: http://doi.org/10.5281/zenodo.28361\n\n\n.. _Stanford CoreNLP: http://stanfordnlp.github.io/CoreNLP/\n.. _NLTK: http://www.nltk.org/\n.. _pandas: http://pandas.pydata.org/\n.. _here: http://interrogator.github.io/corpkit/\n.. _pattern: http://www.clips.ua.ac.be/pages/pattern-en/\n.. _matplotlib: http://matplotlib.org/\n.. _joblib: http://pythonhosted.org/joblib/\n.. _Corpus Workbench: http://cwb.sourceforge.net/\n.. _CONLL U: http://universaldependencies.org/format.html\n.. _Universal Dependencies Treebanks: https://github.com/UniversalDependencies"
},
{
"path": "make.bat",
"content": "@ECHO OFF\r\n\r\nREM Command file for Sphinx documentation\r\n\r\nif \"%SPHINXBUILD%\" == \"\" (\r\n\tset SPHINXBUILD=sphinx-build\r\n)\r\nset BUILDDIR=_build\r\nset ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% .\r\nset I18NSPHINXOPTS=%SPHINXOPTS% .\r\nif NOT \"%PAPER%\" == \"\" (\r\n\tset ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS%\r\n\tset I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS%\r\n)\r\n\r\nif \"%1\" == \"\" goto help\r\n\r\nif \"%1\" == \"help\" (\r\n\t:help\r\n\techo.Please use `make ^` where ^ is one of\r\n\techo. html to make standalone HTML files\r\n\techo. dirhtml to make HTML files named index.html in directories\r\n\techo. singlehtml to make a single large HTML file\r\n\techo. pickle to make pickle files\r\n\techo. json to make JSON files\r\n\techo. htmlhelp to make HTML files and a HTML help project\r\n\techo. qthelp to make HTML files and a qthelp project\r\n\techo. devhelp to make HTML files and a Devhelp project\r\n\techo. epub to make an epub\r\n\techo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter\r\n\techo. text to make text files\r\n\techo. man to make manual pages\r\n\techo. texinfo to make Texinfo files\r\n\techo. gettext to make PO message catalogs\r\n\techo. changes to make an overview over all changed/added/deprecated items\r\n\techo. xml to make Docutils-native XML files\r\n\techo. pseudoxml to make pseudoxml-XML files for display purposes\r\n\techo. linkcheck to check all external links for integrity\r\n\techo. doctest to run all doctests embedded in the documentation if enabled\r\n\techo. coverage to run coverage check of the documentation if enabled\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"clean\" (\r\n\tfor /d %%i in (%BUILDDIR%\\*) do rmdir /q /s %%i\r\n\tdel /q /s %BUILDDIR%\\*\r\n\tgoto end\r\n)\r\n\r\n\r\nREM Check if sphinx-build is available and fallback to Python version if any\r\n%SPHINXBUILD% 2> nul\r\nif errorlevel 9009 goto sphinx_python\r\ngoto sphinx_ok\r\n\r\n:sphinx_python\r\n\r\nset SPHINXBUILD=python -m sphinx.__init__\r\n%SPHINXBUILD% 2> nul\r\nif errorlevel 9009 (\r\n\techo.\r\n\techo.The 'sphinx-build' command was not found. Make sure you have Sphinx\r\n\techo.installed, then set the SPHINXBUILD environment variable to point\r\n\techo.to the full path of the 'sphinx-build' executable. Alternatively you\r\n\techo.may add the Sphinx directory to PATH.\r\n\techo.\r\n\techo.If you don't have Sphinx installed, grab it from\r\n\techo.http://sphinx-doc.org/\r\n\texit /b 1\r\n)\r\n\r\n:sphinx_ok\r\n\r\n\r\nif \"%1\" == \"html\" (\r\n\t%SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.Build finished. The HTML pages are in %BUILDDIR%/html.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"dirhtml\" (\r\n\t%SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"singlehtml\" (\r\n\t%SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"pickle\" (\r\n\t%SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.Build finished; now you can process the pickle files.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"json\" (\r\n\t%SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.Build finished; now you can process the JSON files.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"htmlhelp\" (\r\n\t%SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.Build finished; now you can run HTML Help Workshop with the ^\r\n.hhp project file in %BUILDDIR%/htmlhelp.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"qthelp\" (\r\n\t%SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.Build finished; now you can run \"qcollectiongenerator\" with the ^\r\n.qhcp project file in %BUILDDIR%/qthelp, like this:\r\n\techo.^> qcollectiongenerator %BUILDDIR%\\qthelp\\corpkit.qhcp\r\n\techo.To view the help file:\r\n\techo.^> assistant -collectionFile %BUILDDIR%\\qthelp\\corpkit.ghc\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"devhelp\" (\r\n\t%SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.Build finished.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"epub\" (\r\n\t%SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.Build finished. The epub file is in %BUILDDIR%/epub.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"latex\" (\r\n\t%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.Build finished; the LaTeX files are in %BUILDDIR%/latex.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"latexpdf\" (\r\n\t%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex\r\n\tcd %BUILDDIR%/latex\r\n\tmake all-pdf\r\n\tcd %~dp0\r\n\techo.\r\n\techo.Build finished; the PDF files are in %BUILDDIR%/latex.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"latexpdfja\" (\r\n\t%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex\r\n\tcd %BUILDDIR%/latex\r\n\tmake all-pdf-ja\r\n\tcd %~dp0\r\n\techo.\r\n\techo.Build finished; the PDF files are in %BUILDDIR%/latex.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"text\" (\r\n\t%SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.Build finished. The text files are in %BUILDDIR%/text.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"man\" (\r\n\t%SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.Build finished. The manual pages are in %BUILDDIR%/man.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"texinfo\" (\r\n\t%SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"gettext\" (\r\n\t%SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.Build finished. The message catalogs are in %BUILDDIR%/locale.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"changes\" (\r\n\t%SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.The overview file is in %BUILDDIR%/changes.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"linkcheck\" (\r\n\t%SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.Link check complete; look for any errors in the above output ^\r\nor in %BUILDDIR%/linkcheck/output.txt.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"doctest\" (\r\n\t%SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.Testing of doctests in the sources finished, look at the ^\r\nresults in %BUILDDIR%/doctest/output.txt.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"coverage\" (\r\n\t%SPHINXBUILD% -b coverage %ALLSPHINXOPTS% %BUILDDIR%/coverage\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.Testing of coverage in the sources finished, look at the ^\r\nresults in %BUILDDIR%/coverage/python.txt.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"xml\" (\r\n\t%SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.Build finished. The XML files are in %BUILDDIR%/xml.\r\n\tgoto end\r\n)\r\n\r\nif \"%1\" == \"pseudoxml\" (\r\n\t%SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml\r\n\tif errorlevel 1 exit /b 1\r\n\techo.\r\n\techo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml.\r\n\tgoto end\r\n)\r\n\r\n:end\r\n"
},
{
"path": "meta.yaml",
"content": "package:\n name: corpkit\n version: \"2.3.8\"\n\nsource:\n fn: corpkit-2.3.8.tar.gz\n url: https://pypi.python.org/packages/d1/20/ee1ebd50a3c067d60a863a2afaaa2ed02f012858c005706f08c4540317d3/corpkit-2.3.8.tar.gz\n md5: 4bf94ac616419b51f8b28ea6a12447ee\n# patches:\n # List any patch files here\n # - fix.patch\n\nbuild:\n # noarch_python: True\n # preserve_egg_dir: True\n entry_points:\n # Put any entry points (scripts to be generated automatically) here. The\n # syntax is module:function. For example\n #\n - gui = corpkit:gui\n - corpkit = corpkit:env\n #\n # Would create an entry point called corpkit that calls corpkit.main()\n\n\n # If this is a new build for the same version, increment the build\n # number. If you do not include this key, it defaults to 0.\n number: 1\n\nrequirements:\n build:\n - python\n - setuptools\n - matplotlib >=1.4.3\n - nltk >=3.0.0\n - joblib\n - pandas >=0.18.1\n - requests\n - tabview >=1.4.0\n - chardet\n - blessings >=1.6\n - traitlets >=4.1.0\n\n run:\n - python\n - matplotlib >=1.4.3\n - nltk >=3.0.0\n - joblib\n - pandas >=0.18.1\n - requests\n - tabview >=1.4.0\n - chardet\n - blessings >=1.6\n - traitlets >=4.1.0\n\ntest:\n # Python imports\n imports:\n - corpkit\n - corpkit.download\n\n # commands:\n # You can put test commands to be run here. Use this to test that the\n # entry points work.\n\n\n # You can also put a file called run_test.py in the recipe that will be run\n # at test time.\n\n # requires:\n # Put any additional test requirements here. For example\n # - nose\n\nabout:\n home: http://github.com/interrogator/corpkit\n license: MIT\n summary: 'A toolkit for working with linguistic corpora'\n license_family: MIT\n\n# See\n# http://docs.continuum.io/conda/build.html for\n# more information about meta.yaml\n"
},
{
"path": "requirements.txt",
"content": "git+https://github.com/interrogator/tkintertable.git#egg=tkintertable-1.2\ngit+https://github.com/interrogator/tabview#egg=tabview-1.4.0\nmatplotlib >= 1.5.1\nnltk >= 3.0.0\njoblib\npandas >= 0.16.1\nrequests\nchardet\nblessings>=1.6\ntraitlets>=4.1.0\n"
},
{
"path": "rst_docs/API/corpkit.building.rst",
"content": "Creating projects and building corpora\n=======================================\n\nDoing corpus linguistics involves building and interrogating corpora, and exploring interrogation results. ``corpkit`` helps with all of these things. This page will explain how to create a new project and build a corpus.\n\n.. contents::\n :local:\n\nCreating a new project\n-----------------------\n\nThe simplest way to begin using corpkit is to import it and to create a new project. Projects are simply folders containing subfolders where corpora, saved results, images and dictionaries will be stored. The simplest way is to do it is to use the ``new_project`` command in `bash`, passing in the name you'd like for the project as the only argument:\n\n.. code-block:: bash\n\n $ new_project psyc\n # move there:\n $ cd psyc\n # now, enter python and begin ...\n\nOr, from Python:\n\n.. code-block:: python\n\n >>> import corpkit\n >>> corpkit.new_project('psyc')\n ### move there:\n >>> import os\n >>> os.chdir('psyc')\n >>> os.listdir('.')\n \n ['data',\n 'dictionaries',\n 'exported',\n 'images',\n 'logs',\n 'saved_concordances',\n 'saved_interrogations']\n\nAdding a corpus\n----------------\n\nNow that we have a project, we need to add some plain-text data to the `data` folder. At the very least, this is simply a text file. Better than this is a folder containing a number of text files. Best, however, is a folder containing subfolders, with each subfolder containing one or more text files. These subfolders represent subcorpora. \n\nYou can add your corpus to the `data` folder from the command line, or using Finder/Explorer if you prefer.\n\n.. code-block:: bash\n\n $ cp -R /Users/me/Documents/transcripts ./data\n\nOr, in `Python`, using `shutil`:\n\n.. code-block:: python\n\n >>> import shutil\n >>> shutil.copytree('/Users/me/Documents/transcripts', './data')\n\nIf you've been using `bash` so far, this is the moment when you'd enter `Python` and ``import corpkit``.\n\nCreating a Corpus object\n-------------------------\n\nOnce we have a corpus of text files, we need to turn it into a `Corpus` object.\n\n.. code-block:: python\n\n >>> from corpkit import Corpus\n ### you can leave out the 'data' if it's in there\n >>> unparsed = Corpus('data/transcripts')\n >>> unparsed\n \n\n\n\nPre-processing the data\n----------------------------------\n\nA `Corpus` object can only be interrogated if tokenisation or parsing has been performed. For this, :class:`corpkit.corpus.Corpus` objects have :func:`~corpkit.corpus.Corpus.tokenise` and :func:`~corpkit.corpus.Corpus.parse` methods. Tokenising is faster, simpler, and will work for more languages. As shown below, you can also elect to POS tag and lemmatise the data:\n\n.. code-block:: python\n\n > corpus = unparsed.tokenise(postags=True, lemmatisation=True)\n # switch either to false to disable---but lemmatisation requires pos\n\nParsing relies on Stanford CoreNLP's parser, and therefore, you must have the parser and Java installed. ``corpkit`` will look around in your PATH for the parser, but you can also pass in its location manually with (e.g.) ``corenlppath='users/you/corenlp'``. If it can't be found, you'll be asked if you want to download and install it automatically. Parsing has sensible defaults, and can be run with:\n\n.. code-block:: python\n\n >>> corpus = unparsed.parse()\n\n.. note::\n\n Remember that parsing is a computationally intensive task, and can take a long time!\n\n``corpkit`` can also work with speaker IDs. If lines in your file contain capitalised alphanumeric names, followed by a colon (as per the example below), these IDs can be stripped out and turned into metadata features in the parsed dataset.\n\n.. code-block:: none\n\n JOHN: Why did they change the signs above all the bins?\n SPEAKER23: I know why. But I'm not telling.\n\nTo use this option, use the ``speaker_segmentation`` keyword argument:\n\n.. code-block:: python\n\n >>> corpus = unparsed.parse(speaker_segmentation=True)\n\nTokenising or parsing creates a corpus that is structurally identical to the original, but with annotations in `CONLL-U` formatted files in place of the original ``.txt`` files. When parsing, there are also methods for multiprocessing, memory allocation and so on:\n\n+--------------------------+------------+---------------------------------------+\n| `parse()` argument | Type | Purpose |\n+==========================+============+=======================================+\n| `corenlppath` | `str` | Path to CoreNLP |\n+--------------------------+------------+---------------------------------------+\n+--------------------------+------------+---------------------------------------+\n| `operations` | `str` | `List of annotations`_ |\n+--------------------------+------------+---------------------------------------+\n| `copula_head` | `bool` | Make copula head of dependency parse |\n+--------------------------+------------+---------------------------------------+\n| `speaker_segmentation` | `bool` | Do speaker segmentation |\n+--------------------------+------------+---------------------------------------+\n| `memory_mb` | `int` | Amount of memory to allocate |\n+--------------------------+------------+---------------------------------------+\n| `multiprocess` | `int/bool` | Process in `n` parallel jobs |\n+--------------------------+------------+---------------------------------------+\n| `outname` | `str` | Custom name for parsed corpus |\n+--------------------------+------------+---------------------------------------+\n\nYou can run parsing operations from the command line:\n\n.. code-block:: bash\n\n $ parse mycorpus --multiprocess 4 --outname MyData\n\nManipulating a parsed corpus\n-----------------------------\n\nOnce you have a parsed corpus, you're ready to analyse it. :class:`corpkit.corpus.Corpus` objects can be navigated in a number of ways. *CoreNLP XML* is used to navigte the internal structure of `CONLL-U` files within the corpus.\n\n.. code-block:: python\n\n >>> corpus[:3] # access first three subcorpora\n >>> corpus.subcorpora.chapter1 # access subcorpus called chapter1\n >>> f = corpus[5][20] # access 21st file in 6th subcorpus\n >>> f.document.sentences[0].parse_string # get parse tree for first sentence\n >>> f.document.sentences.tokens[0].word # get first word\n\n\nCounting key features\n-----------------------\n\nBefore constructing your own queries, you may want to use some predefined attributes for counting key features in the corpus. \n\n.. code-block:: python\n\n >>> corpus.features\n\nOutput: \n\n.. code-block:: none\n\n S Characters Tokens Words Closed class Open class Clauses Sentences Unmod. declarative Passives Mental processes Relational processes Mod. declarative Interrogative Verbal processes Imperative Open interrogative Closed interrogative \n 01 4380658 1258606 1092113 643779 614827 277103 68267 35981 16842 11570 11082 3691 5012 2962 615 787 813 \n 02 3185042 922243 800046 471883 450360 209448 51575 26149 10324 8952 8407 3103 3407 2578 540 547 461 \n 03 3157277 917822 795517 471578 446244 209990 51860 26383 9711 9163 8590 3438 3392 2572 583 556 452 \n 04 3261922 948272 820193 486065 462207 216739 53995 27073 9697 9553 9037 3770 3702 2665 652 669 530 \n 05 3164919 921098 796430 473446 447652 210165 52227 26137 9543 8958 8663 3622 3523 2738 633 571 467 \n 06 3187420 928350 797652 480843 447507 209895 52171 25096 8917 9011 8820 3913 3637 2722 686 553 480 \n 07 3080956 900110 771319 466254 433856 202868 50071 24077 8618 8616 8547 3623 3343 2676 615 515 434 \n 08 3356241 972652 833135 502913 469739 218382 52637 25285 9921 9230 9562 3963 3497 2831 692 603 442 \n 09 2908221 840803 725108 434839 405964 191851 47050 21807 8354 8413 8720 3876 3147 2582 675 554 455 \n 10 2868652 815101 708918 421403 393698 185677 43474 20763 8640 8067 8947 4333 3181 2727 584 596 424\n\nThis can take a while, as it counts a number of complex features. Once it's done, however, it saves automatically, so you don't need to do it again. There are also ``postags``, ``wordclasses`` and ``lexicon`` attributes, which behave similarly:\n\n.. code-block:: python\n\n >>> corpus.postags\n >>> corpus.wordclasses\n >>> corpus.lexicon\n\nThese results can be useful when generating relative frequencies later on. Right now, however, you're probably interested in searching the corpus yourself, however. Hit `Next` to learn about that.\n\n.. _List of annotations: http://nlp.stanford.edu/index.shtml\n"
},
{
"path": "rst_docs/API/corpkit.concordancing.rst",
"content": "\nConcordancing\n==============\n\nConcordancing is the task of getting an aligned list of *keywords in context*. Here's a very basic example, using *Industrial Society and Its Future* as a corpus:\n\n.. code-block:: python\n\n >>> tech = corpus.concordance({W: r'techn*'})\n >>> tech.format(n=10, columns=[L, M, R])\n\n\n 0 The continued development of technology will worsen the situation \n 1 vernments but the economic and technological basis of the present society \n 2 They want to make him study technical subjects become an executive o\n 3 program to acquire some petty technical skill then come to work on tim\n 4 rom nature are consequences of technological progress \n 5 n them and modern agricultural technology has made it possible for the e\n 6 -LRB- Also technology exacerbates the effects of cro\n 7 changes very rapidly owing to technological change \n 8 they enthusiastically support technological progress and economic growth \n 9 e rapid drastic changes in the technology and the economy of a society w\n\nGenerating a concordance\n-------------------------\n\nWhen using *corpkit*, any interrogation is also optionally a concordance. If you use the ``do_concordancing`` keyword argument, your interrogation will have a ``concordance`` attribute containing concordance lines. Like interrogation results, concordances are stored as *Pandas DataFrames*. ``maxconc`` controls the number of lines produced.\n\n.. code-block:: python\n\n >>> withconc = corp.interrogate({L: ['man', 'woman', 'person']},\n ... show=[W,P],\n ... do_concordancing=True,\n ... maxconc=500)\n\n 0 T Asian/JJ a/DT disabled/JJ person/nn or/cc a/dt woman/nn origin\n 1 led/JJ person/NN or/CC a/DT woman/nn originally/rb had/vbd no/d\n 2 woman/NN or/CC disabled/JJ person/nn but/cc a/dt minority/nn of\n 3 n/JJ immigrant/JJ abused/JJ woman/nn or/cc disabled/jj person/n\n 4 ing/VBG weak/JJ -LRB-/-LRB- women/nns -rrb-/-rrb- defeated/vbn -\n\nIf you like, you can use ``only_format_match=True`` to keep the left and right context simple:\n\n.. code-block:: python\n\n >>> withconc = corp.interrogate({L: ['man', 'woman', 'person']},\n ... show=[W,P],\n ... only_format_match=True,\n ... do_concordancing=True,\n ... maxconc=500)\n\n 0 African an Asian a disabled person/nn or a woman originally had \n 1 sian a disabled person or a woman/nn originally had no derogato\n 2 nt abused woman or disabled person/nn but a minority of activist\n 3 ller Asian immigrant abused woman/nn or disabled person but a m\n 4 n image of being weak -LRB- women/nns -rrb- defeated -lrb- ameri\n\n\nIf you don't want or need the interrogation data, you can use the :func:`~corpkit.corpus.Corpus.concordance` method:\n\n.. code-block:: python\n\n >>> conc = corpus.concordance(T, r'/JJ.?/ > (NP <<# /man/)')\n\nDisplaying concordance lines\n------------------------------\n\nHow concordance lines will be displayed really depends on your interpreter and environment. For the most part, though, you'll want to use the :func:`~corpkit.interrogation.Concordance.format` method.\n\n.. code-block:: python\n\n >>> lines.format(kind='s',\n ... n=100,\n ... window=50,\n ... columns=[L, M, R])\n\n``kind='c'/'l'/'s'`` allows you to print as CSV, LaTeX, or simple string. ``n`` controls the number of results shown. ``window`` controls how much context to show in the left and right columns. ``columns`` accepts a list of column names to show.\n\nPandas' set_option_ can be used to customise some visualisation defaults.\n\nWorking with concordance lines\n-------------------------------\n\nYou can edit concordance lines using the :func:`~corpkit.interrogation.Concordance.edit` method. You can use this method to keep or remove entries or subcorpora matching regular expressions or lists. Keep in mind that because concordance lines are DataFrames, you can use Pandas' dedicated methods for working with text data.\n\n.. code-block:: python\n\n ### get just uk variants of words with variant spellings\n >>> from corpkit.dictionaries import usa_convert\n >>> concs = result.concordance.edit(just_entries=usa_convert.keys())\n\n\nConcordance objects can be saved just like any other ``corpkit`` object:\n\n.. code-block:: python\n\n >>> concs.save('adj_modifying_man')\n\nYou can also easily turn them into CSV data, or into LaTeX:\n\n.. code-block:: python\n\n ### pandas methods\n >>> concs.to_csv()\n >>> concs.to_latex()\n\n ### corpkit method: csv and latex\n >>> concs.format('c', window=20, n=10)\n >>> concs.format('l', window=20, n=10)\n\nThe *calculate* method\n------------------------\n\nYou might have begun to notice that interrogating and concordancing aren't really very different tasks. If we drop the left and right context, and move the data around, we have all the data we get from an interrogation.\n\nFor this reason, you can use the :func:`~corpkit.interrogation.Concordance.calculate` method to generate an :class:`corpus.interrogation.Interrogation` object containing a frequency count of the middle column of the concordance as the ``results`` attribute.\n\nTherefore, one method for ensuring accuracy is to:\n\n 1. Run an interrogation, using ``do_concordance=True`` \n 2. Remove false positives from the concordance result using :func:`~corpkit.interrogation.Concordance.edit`\n 3. Use the :func:`~corpkit.interrogation.Concordance.calculate` method to regenerate the overall frequencies\n 4. Edit, visualise or export the data\n\nIf you'd like to randomise the order of your results, you can use ``lines.shuffle()``\n\n.. _set_option: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.set_option.html"
},
{
"path": "rst_docs/API/corpkit.editing.rst",
"content": ".. _editing-page:\n\nEditing results\n=====================\n\nCorpus interrogation is the task of getting frequency counts for a lexicogrammatical phenomenon in a corpus. Simple absolute frequencies, however, are of limited use. The :func:`~corpkit.interrogation.Interrogation.edit` method allows us to do complex things with our results, including:\n\n.. contents::\n :local:\n\nEach of these will be covered in the sections below. Keep in mind that because results are stored as DataFrames, you can also use Pandas/Numpy/Scipy to manipulate your data in ways not covered here.\n\nKeeping or deleting results and subcorpora\n-------------------------------------------\n\nOne of the simplest kinds of editing is removing or keeping results or subcorpora. This is done using keyword arguments: `skip_subcorpora`, `just_subcorpora`, `skip_entries`, `just_entries`. The value for each can be:\n\n 1. A string (treated as a regular expression to match)\n 2. A list (a list of words to match)\n 3. An integer (treated as an index to match)\n\n.. code-block:: python\n\n >>> criteria = r'ing$'\n >>> result.edit(just_entries=criteria)\n\n.. code-block:: python\n\n >>> criteria = ['everything', 'nothing', 'anything']\n >>> result.edit(skip_entries=criteria)\n\n\n.. code-block:: python\n\n >>> result.edit(just_subcorpora=['Chapter_10', 'Chapter_11'])\n\nYou can also span subcorpora, using a tuple of ``(first_subcorpus, second_subcorpus)``. This works for numerical and non-numerical subcorpus names:\n\n.. code-block:: python\n\n >>> just_span = result.edit(span_subcorpora=(3, 10))\n\nEditing result names\n--------------------\n\nYou can use the ``replace_names`` keyword argument to edit the text of each result. If you pass in a string, it is treated as a regular expression to delete from every result:\n\n.. code-block:: python\n\n >>> ingdel = result.edit(replace_names=r'ing$')\n\nYou can also pass in a ``dict`` with the structure of ``{newname: criteria}``:\n\n.. code-block:: python\n\n >>> rep = {'-ing words': r'ing$', '-ed words': r'ed$'}\n >>> replaced = result.edit(replace_names=rep)\n\nIf you wanted to see how commonly words start with a particular letter, you could do something creative:\n\n.. code-block:: python\n\n >>> from string import lowercase\n >>> crit = {k.upper() + ' words': r'(?i)^%s.*' % k for k in lowercase}\n >>> firstletter = result.edit(replace_names=crit, sort_by='total')\n\nSpelling normalisation\n-----------------------\n\nWhen results are single words, you can normalise to UK/US spelling:\n\n.. code-block:: python\n\n >>> spelled = result.edit(spelling='UK')\n\nYou can also perform this step when interrogating a corpus.\n\nGenerating relative frequencies\n---------------------------------\n\nBecause subcorpora often vary in size, it is very common to want to create relative frequency versions of results. The best way to do this is to pass in an ``operation`` and a ``denominator``. The ``operation`` is simply a string denoting a mathematical operation: '+', '-', '*', '/', '%'. The last two of these can be used to get relative frequencies and percentage.\n\nDenominator is what the result will be divided by. Quite often, you can use the string ``'self'``. This means, after all other editing (deleting entries, subcorpora, etc.), use the totals of the result being edited as the denominator. When doing no other editing operations, the two lines below are equivalent:\n\n.. code-block:: python\n\n >>> rel = result.edit('%', 'self')\n >>> rel = result.edit('%', result.totals)\n\nThe best denominator, however, may not simply be the totals for the results being edited. You may instead want to relativise by the total number of words:\n\n.. code-block:: python\n\n >>> rel = result.edit('%', corpus.features.Words)\n\nOr by some other result you have generated:\n\n.. code-block:: python\n\n >>> words_with_oo = corpus.interrogate(W, 'oo')\n >>> rel = result.edit('%', words_with_oo.totals)\n\nThere is a more complex kind of relative frequency making, where a ``.results`` attribute is used as the denominator. In the example below, we calculate the percentage of the time each verb occurs as the `root` of the parse.\n\n.. code-block:: python\n\n >>> verbs = corpus.interrogate(P, r'^vb', show=L)\n >>> roots = corpus.interrogate(F, 'root', show=L)\n >>> relv = verbs.edit('%', roots.results)\n\nKeywording\n---------------------------------\n\n``corpkit`` treats keywording as an editing task, rather than an interrogation task. This makes it easy to get key nouns, or key Agents, or key grammatical features. To do keywording, use the ``K`` operation:\n\n.. code-block:: python\n\n >>> from corpkit import *\n ### * imports predefined global variables like K and SELF\n >>> keywords = result.edit(K, SELF)\n\nThis finds out which words are key in each subcorpus, compared to the corpus as a whole. You can compare subcorpora directly as well. Below, we compare the ``plays`` subcorpus to the ``novels`` subcorpus.\n\n. code-block:: python\n\n >>> from corpkit import *\n >>> keywords = result.edit(K, result.ix['novels'], just_subcorpora='plays')\n\nYou could also pass in word frequency counts from some other source. A wordlist of the *British National Corpus* is included:\n\n.. code-block:: python\n\n >>> keywords = result.edit(K, 'bnc')\n\nThe default keywording metric is *log-likelihood*. If you'd like to use *percentage difference*, you can do:\n\n.. code-block:: python\n\n >>> keywords = result.edit(K, 'bnc', keyword_measure='pd')\n\nSorting\n---------------------------------\n\nYou can sort results using the ``sort_by`` keyword. Possible values are:\n\n * `'name'` (alphabetical)\n * `'total'` (most common first)\n * `'infreq'` (inverse total)\n * `'increase'` (most increasing)\n * `'decrease'` (most decreasing)\n * `'turbulent'` (by most change)\n * `'static'` (by least change)\n * `'p'` (by p value)\n * `'slope'` (by slope)\n * `'intercept'` (by intercept)\n * `'r'` (by correlation coefficient)\n * `'stderr'` (by standard error of the estimate)\n * `''` by total in \n\n.. code-block:: python\n\n >>> inc = result.edit(sort_by='increase', keep_stats=False)\n\nMany of these rely on Scipy's ``linregress`` function. If you want to keep the generated statistics, use ``keep_stats=True``. \n\nCalculating trends, P values\n---------------------------------\n\n``keep_stats=True`` will cause slopes, p values and stderr to be calculated for each result.\n\nSaving results\n----------------\n\nYou can save edited results to disk.\n\n.. code-block:: python\n\n >>> edited.save('savename')\n\nExporting results\n------------------\n\nYou can generate CSV data very easily using Pandas:\n\n.. code-block:: python\n\n >>> result.results.to_csv()\n\nNext step\n----------\n\nOnce you've edited data, it's ready to visualise. Hit next to learn how to use the :func:`~corpkit.interrogation.Interrogation.visualise` method."
},
{
"path": "rst_docs/API/corpkit.interrogating.rst",
"content": "Interrogating corpora\n=====================\n\nOnce you've built a corpus, you can search it for linguistic phenomena. This is done with the :func:`~corpkit.corpus.Corpus.interrogate` method.\n\n.. contents::\n :local:\n\nIntroduction\n--------------\n\nInterrogations can be performed on any :class:`corpkit.corpus.Corpus` object, but also, on :class:`corpkit.corpus.Subcorpus` objects, :class:`corpkit.corpus.File` objects and :class:`corpkit.corpus.Datalist` objects (slices of ``Corpus`` objects). You can search plaintext corpora, tokenised corpora or fully parsed corpora using the same method. We'll focus on parsed corpora in this guide.\n\n.. code-block:: python\n \n >>> from corpkit import *\n ### words matching 'woman', 'women', 'man', 'men'\n >>> query = {W: r'/(^wo)m.n/'}\n ### interrogate corpus\n >>> corpus.interrogate(query)\n ### interrogate parts of corpus\n >>> corpus[2:4].interrogate(query)\n >>> corpus.files[:10].interrogate(query)\n ### if you have a subcorpus called 'abstract':\n >>> corpus.subcorpora.abstract.interrogate(query)\n\nCorpus interrogations will output a :class:`corpkit.interrogation.Interrogation` object, which stores a DataFrame of results, a Series of totals, a ``dict`` of values used in the query, and, optionally, a set of concordance lines. Let's search for proper nouns in *The Great Gatsby* and see what we get:\n\n.. code-block:: python\n\n >>> corp = Corpus('gatsby-parsed')\n ### turn on concordancing:\n >>> propnoun = corp.interrogate({P: '^NNP'}, do_concordancing=True)\n >>> propnoun.results\n\n gatsby tom daisy mr. wilson jordan new baker york miss \n chapter1 12 32 29 4 0 2 10 21 6 19 \n chapter2 1 30 6 8 26 0 6 0 6 0 \n chapter3 28 0 1 8 0 22 5 6 5 1 \n chapter4 38 10 15 25 1 9 5 8 4 7 \n chapter5 36 3 26 4 0 0 1 1 1 1 \n chapter6 37 21 19 11 0 1 4 0 3 4 \n chapter7 63 87 60 9 27 35 9 2 5 1 \n chapter8 21 3 19 1 19 1 0 1 0 0 \n chapter9 27 5 9 14 4 3 4 1 4 1 \n\n >>> propnoun.totals\n\n chapter1 232\n chapter2 252\n chapter3 171\n chapter4 428\n chapter5 128\n chapter6 219\n chapter7 438\n chapter8 139\n chapter9 208\n dtype: int64\n\n >>> propnoun.query\n\n {'case_sensitive': False,\n 'corpus': 'gatsby-parsed',\n 'dep_type': 'collapsed-ccprocessed-dependencies',\n 'do_concordancing': True,\n 'exclude': False,\n 'excludemode': 'any',\n 'files_as_subcorpora': True,\n 'gramsize': 1,\n ...}\n\n >>> propnoun.concordance # (sample)\n\n 54 chapter1 They had spent a year in france for no particular reason and then d\n 55 chapter1 n't believe it I had no sight into daisy 's heart but i felt that tom would \n 56 chapter1 into Daisy 's heart but I felt that tom would drift on forever seeking a li\n 57 chapter1 This was a permanent move said daisy over the telephone but i did n't be\n 58 chapter1 windy evening I drove over to East egg to see two old friends whom i scarc\n 59 chapter1 warm windy evening I drove over to east egg to see two old friends whom i s\n 60 chapter1 d a cheerful red and white Georgian colonial mansion overlooking the bay \n 61 chapter1 pen to the warm windy afternoon and tom buchanan in riding clothes was stan\n 62 chapter1 to the warm windy afternoon and Tom buchanan in riding clothes was standing with\n\nCool, eh? We'll focus on what to do with these attributes later. Right now, we need to learn how to generate them.\n\nSearch types\n---------------------\n\nParsed corpora contain many different kinds of things we might like to search. There are word forms, lemma forms, POS tags, word classes, indices, and constituency and (three different) dependency grammar annotations. For this reason, the search query is a ``dict`` object passed to the ``interrogate()`` method, whose keys specify what to search, and whose values specify a query. The simplest ones are given in the table below.\n\n.. note::\n\n Single capital letter variables in code examples represent lowercase strings ``(W = 'w')``. These variables are made available by doing ``from corpkit import *``. They are used here for readability.\n\n+--------+-----------------------+\n| Search | Gloss |\n+========+=======================+\n| W | Word |\n+--------+-----------------------+\n| L | Lemma |\n+--------+-----------------------+\n| F | Function |\n+--------+-----------------------+\n| P | POS tag |\n+--------+-----------------------+\n| X | Word class |\n+--------+-----------------------+\n| E | NER tag |\n+--------+-----------------------+\n| A | Distance from root |\n+--------+-----------------------+\n| I | Index in sentence |\n+--------+-----------------------+\n| S | Sentence index |\n+--------+-----------------------+\n| R | Coref representative |\n+--------+-----------------------+\n\n\nBecause it comes first, and because it's always needed, you can pass it in like an argument, rather than a keyword argument.\n\n.. code-block:: python\n\n ### get variants of the verb 'be'\n >>> corpus.interrogate({L: 'be'})\n ### get words in 'nsubj' position\n >>> corpus.interrogate({F: 'nsubj'})\n\nMultiple key/value pairs can be supplied. By default, all must match for the result to be counted, though this can be changed with ``searchmode=ANY`` or ``searchmode=ALL``:\n\n.. code-block:: python\n\n >>> goverb = {P: r'^v', L: r'^go'}\n ### get all variants of 'go' as verb\n >>> corpus.interrogate(goverb, searchmode=ALL)\n ### get all verbs and any word starting with 'go':\n >>> corpus.interrogate(goverb, searchmode=ANY)\n\nGrammatical searching\n----------------------\n\nIn the examples above, we match attributes of tokens. The great thing about parsed data, is that we can search for relationships between words. So, other possible search keys are:\n\n+--------+------------------------+\n| Search | Gloss |\n+========+========================+\n| G | Governor |\n+--------+------------------------+\n| D | Dependent |\n+--------+------------------------+\n| H | Coreference head |\n+--------+------------------------+\n| T | Syntax tree |\n+--------+------------------------+\n| A1 | Token 1 place to left |\n+--------+------------------------+\n| Z1 | Token 1 place to right |\n+--------+------------------------+\n\n.. code-block:: python\n\n >>> q = {G: r'^b'}\n ### return any token with governor word starting with 'b'\n >>> corpus.interrogate(q)\n\n`Governor`, `Dependent` and `Left/Right` can be combined with the earlier table, allowing a large array of search types:\n\n+--------------------+-------+----------+-----------+------------+-----------------+\n| | Match | Governor | Dependent | Coref head | Left/right |\n+====================+=======+==========+===========+============+=================+\n| Word | W | G | D | H | A1/Z1 |\n+--------------------+-------+----------+-----------+------------+-----------------+\n| Lemma | L | GL | DL | HL | A1L/Z1L |\n+--------------------+-------+----------+-----------+------------+-----------------+\n| Function | F | GF | DF | HF | A1F/Z1F |\n+--------------------+-------+----------+-----------+------------+-----------------+\n| POS tag | P | GP | DP | HP | A1P/Z1P |\n+--------------------+-------+----------+-----------+------------+-----------------+\n| Word class | X | GX | DX | HX | A1X/Z1X |\n+--------------------+-------+----------+-----------+------------+-----------------+\n| Distance from root | A | GA | DA | HA | A1A/Z1A |\n+--------------------+-------+----------+-----------+------------+-----------------+\n| Index | I | GI | DI | HI | A1I/Z1I |\n+--------------------+-------+----------+-----------+------------+-----------------+\n| Sentence index | S | GS | DS | HS | A1S/Z1S |\n+--------------------+-------+----------+-----------+------------+-----------------+\n\nSyntax tree searching can't be combined with other options. We'll return to them in a minute, however.\n\nExcluding results\n---------------------\n\nYou may also wish to exclude particular phenomena from the results. The ``exclude`` argument takes a ``dict`` in the same form a ``search``. By default, if any key/value pair in the ``exclude`` argument matches, it will be excluded. This is controlled by ``excludemode=ANY`` or ``excludemode=ALL``.\n\n.. code-block:: python\n\n >>> from corpkit.dictionaries import wordlists\n ### get any noun, but exclude closed class words\n >>> corpus.interrogate({P: r'^n'}, exclude={W: wordlists.closedclass})\n ### when there's only one search criterion, you can also write:\n >>> corpus.interrogate(P, r'^n', exclude={W: wordlists.closedclass})\n\nIn many cases, rather than using ``exclude``, you could also remove results later, during editing.\n\nWhat to show\n---------------------\n\nUp till now, all searches have simply returned words. The final major argument of the ``interrogate`` method is ``show``, which dictates what is returned from a search. Words are the default value. You can use any of the search values as a show value. ``show`` can be either a single string or a list of strings. If a list is provided, each value is returned with forward slashes as delimiters.\n\n.. code-block:: python\n\n >>> example = corpus.interrogate({W: r'fr?iends?'}, show=[W, L, P])\n >>> list(example.results)\n\n ['friend/friend/nn', 'friends/friend/nns', 'fiend/fiend/nn', 'fiends/fiend/nns', ... ]\n\nUnigrams are generated by default. To get n-grams, pass in an n value as ``gramsize``:\n\n.. code-block:: python\n\n >>> example = corpus.interrogate({W: r'wom[ae]n]'}, show=N, gramsize=2)\n >>> list(example.results)\n\n ['a/woman', 'the/woman', 'the/women', 'women/are', ... ]\n\n\nSo, this leaves us with a huge array of possible things to show, all of which can be combined if need be:\n\n+--------------------+-------+----------+-----------+------------+-------------+-------------+\n| | Match | Governor | Dependent | Coref Head | 1L position | 1R position |\n+====================+=======+==========+===========+============+=============+=============+\n| Word | W | G | D | H | A1 | Z1 |\n+--------------------+-------+----------+-----------+------------+-------------+-------------+\n| Lemma | L | GL | DL | HL | A1L | Z1L |\n+--------------------+-------+----------+-----------+------------+-------------+-------------+\n| Function | F | GF | DF | HF | A1F | Z1F |\n+--------------------+-------+----------+-----------+------------+-------------+-------------+\n| POS tag | P | GP | DP | HP | A1P | Z1P |\n+--------------------+-------+----------+-----------+------------+-------------+-------------+\n| Word class | X | GX | DX | HX | A1X | Z1X |\n+--------------------+-------+----------+-----------+------------+-------------+-------------+\n| Distance from root | A | GA | DA | HA | A1A | Z1R |\n+--------------------+-------+----------+-----------+------------+-------------+-------------+\n| Index | I | GI | DI | HI | A1I | Z1I |\n+--------------------+-------+----------+-----------+------------+-------------+-------------+\n| Sentence index | S | GS | DS | HS | A1S | Z1S |\n+--------------------+-------+----------+-----------+------------+-------------+-------------+\n\nOne further extra show value is ``'c'`` (count), which simply counts occurrences of a phenomenon. Rather than returning a DataFrame of results, it will result in a single Series. It cannot be combined with other values.\n\nWorking with trees\n---------------------\n\nIf you have elected to search trees, by default, searching will be done with Java, using Tregex. If you don't have Java, or if you pass in ``tgrep=True``, searching will the more limited Tgrep2 syntax. Here, we'll concentrate on Tregex.\n\nTregex is a language for searching syntax trees like this one:\n\n.. figure:: https://raw.githubusercontent.com/interrogator/sfl_corpling/master/images/const-grammar.png\n\nTo write a Tregex query, you specify *words and/or tags* you want to match, in combination with *operators* that link them together. First, let's understand the Tregex syntax.\n\nTo match any adjective, you can simply write:\n\n.. code-block:: none\n\n JJ\n\nwith `JJ` representing adjective as per the `Penn Treebank tagset`_. If you want to get NPs containing adjectives, you might use:\n\n.. code-block:: none\n\n NP < JJ\n \nwhere `<` means `with a child/immediately below`. These operators can be reversed: If we wanted to show the adjectives within NPs only, we could use:\n\n.. code-block:: none\n\n JJ > NP\n\nIt's good to remember that **the output will always be the left-most part of your query**.\n\nIf you only want to match Subject NPs, you can use bracketting, and the `$` operator, which means *sister/directly to the left/right of*:\n\n.. code-block:: none\n\n JJ > (NP $ VP)\n\nIn this way, you build more complex queries, which can extent all the way from a sentence's *root* to particular tokens. The query below, for example, finds adjectives modifying `book`:\n\n.. code-block:: none\n\n JJ > (NP <<# /book/)\n\nNotice that here, we have a different kind of operator. The `<<` operator means that the node on the right does not need to be a child, but can be a descendant. the `#` means `head`—that is, in SFL, it matches the `Thing` in a Nominal Group.\n\nIf we wanted to also match `magazine` or `newspaper`, there are a few different approaches. One way would be to use `|` as an operator meaning `or`:\n\n.. code-block:: none\n\n JJ > (NP ( <<# /book/ | <<# /magazine/ | <<# /newspaper/))\n\nThis can be cumbersome, however. Instead, we could use a regular expression:\n\n.. code-block:: none\n\n JJ > (NP <<# /^(book|newspaper|magazine)s*$/)\n\nThough it is beyond the scope of this guide to teach Regular Expressions, it is important to note that Regular Expressions are extremely powerful ways of searching text, and are invaluable for any linguist interested in digital datasets.\n\nDetailed documentation for Tregex usage (with more complex queries and operators) can be found here_.\n\nTree `show` values\n-------------------\n\nThough you can use the same Tregex query for tree searches, the output changes depending on what you select as the ``show`` value. For the following sentence:\n\n.. code-block:: none\n\n These are prosperous times.\n\nyou could write a query:\n\n.. code-block:: python\n\n r'JJ < __'\n\nWhich would return:\n\n+------+----------+----------------------+\n| Show | Gloss | Output |\n+======+==========+======================+\n| W | Word | `prosperous` |\n+------+----------+----------------------+\n| T | Tree | `(JJ prosperous)` |\n+------+----------+----------------------+\n| p | POS tag | `JJ` |\n+------+----------+----------------------+\n| C | Count | `1` (added to total) |\n+------+----------+----------------------+\n\nWorking with dependencies\n--------------------------\n\nWhen working with dependencies, you can use any of the long list of search and `show` values. It's possible to construct very elaborate queries:\n\n.. code-block:: python\n\n >>> from corpkit.dictionaries import process_types, roles\n ### nominal nsubj with verbal process as governor\n >>> crit = {F: r'^nsubj$',\n ... GL: processes.verbal.lemmata,\n ... GF: roles.event,\n ... P: r'^N'}\n ### interrogate corpus, outputting the nsubj lemma\n >>> sayers = parsed.interrogate(crit, show=L)\n\nWorking with metadata\n----------------------------------\n\nIf you've used speaker segmentation and/or metadata addition when building your corpus, you can tell the :func:`~corpkit.corpus.Corpus.interrogate` method to use these values as subcorpora, or restrict searches to particular values. The code below will limit searches to sentences spoken by Jason and Martin, or exclude them from the search:\n\n.. code-block:: python\n\n >>> corpus.interrogate(query, just_metadata={'speaker': ['JASON', 'MARTIN']})\n >>> corpus.interrogate(query, skip_metadata={'speaker': ['JASON', 'MARTIN']})\n\nIf you wanted to compare Jason and Martin's contributions in the corpus as a whole, you could treat them as subcorpora:\n\n.. code-block:: python\n\n >>> corpus.interrogate(query, subcorpora='speaker',\n ... just_metadata={'speaker': ['JASON', 'MARTIN']})\n\nThe method above, however, will make an interrogation with two subcorpora, `'JASON'` AND ``MARTIN``. You can pass a list in as the ``subcorpora`` keyword argument to generate a multiindex:\n\n.. code-block:: python\n\n >>> corpus.interrogate(query, subcorpora=['folder', 'speaker'],\n just_metadata={'speaker': ['JASON', 'MARTIN']})\n\nWorking with coreferences\n--------------------------\n\nOne major challenge in corpus linguistics is the fact that pronouns stand in for other words. Parsing provides coreference resolution, which maps pronouns to the things they denote. You can enable this kind of parsing by specifying the `dcoref` annotator:\n\n.. code-block:: python\n\n >>> corpus = Corpus('example.txt')\n >>> ops = 'tokenize,ssplit,pos,lemma,parse,ner,dcoref'\n >>> parsed = corpus.interrogate(operations=ops)\n ### print a plaintext representation of the parsed corpus\n >>> print(parsed.plain)\n\n.. code-block:: none\n\n 0. Clinton supported the independence of Kosovo\n 1. He authorized the use of force.\n\nIf you have done this, you can use `coref=True` while interrogating to allow coreferent forms to be counted alongside query matches. For example, if you wanted to find all the processes Clinton is engaged in, you could do:\n\n.. code-block:: python\n\n >>> from corpkit.dictionaries import roles\n >>> query = {W: 'clinton', GF: roles.process}\n >>> res = parsed.interrogate(query, show=L, coref=True)\n >>> res.results.columns\n\nThis matches both `Clinton` and `he`, and thus gives us:\n\n.. code-block:: python\n\n ['support', 'authorize']\n\n\nMultiprocessing\n---------------------\n\nInterrogating the corpus can be slow. To speed it up, you can pass an integer as the ``multiprocess`` keyword argument, which tells the ``interrogate()`` method how many processes to create.\n\n.. code-block:: python\n\n >>> corpus.interrogate({T: r'__ > MD'}, multiprocess=4)\n\n.. note::\n\n Too many parallel processes may slow your computer down. If you pass in ``multiprocessing=True``, the number of processes will equal the number of cores on your machine. This is usually a fairly sensible number.\n\nN-grams\n---------------------\n\nN-gramming can be generated by making ``gramsize`` > 1:\n\n.. code-block:: python\n\n >>> corpus.interrogate({W: 'father'}, show='L', gramsize=3)\n\nCollocation\n------------\n\nCollocations can be shown by making using ``window``:\n\n.. code-block:: python\n\n >>> corpus.interrogate({W: 'father'}, show='L', window=6)\n\nSaving interrogations\n----------------------\n\n.. code-block:: python\n\n >>> interro.save('savename')\n\nInterrogation savenames will be prefaced with the name of the corpus interrogated.\n\nYou can also quicksave interrogations:\n\n.. code-block:: python\n\n >>> corpus.interrogate(T, r'/NN.?/', save='savename')\n\nExporting interrogations\n-------------------------\n\nIf you want to quickly export a result to CSV, LaTeX, etc., you can use Pandas' DataFrame methods:\n\n.. code-block:: python\n\n >>> print(nouns.results.to_csv())\n >>> print(nouns.results.to_latex())\n\nOther options\n---------------\n\n:func:`~corpkit.corpus.Corpus.interrogate` takes a number of other arguments, each of which is documented in the API documentation.\n\nIf you're done interrogating, you can head to the page on :ref:`editing-page` to learn how to transform raw frequency counts into something more meaningful. Or, hit `Next` to learn about concordancing.\n\n.. _here: http://nlp.stanford.edu/~manning/courses/ling289/Tregex.htm\n.. _Penn Treebank tagset: https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html"
},
{
"path": "rst_docs/API/corpkit.langmodel.rst",
"content": "Using language models \n======================\n\n.. warning::\n\n Language modelling is currently deprecated, while the tool is updated to use `CONLL` formatted data, rather than `CoreNLP XML`. Sorry!\n\n\nLanguage models are probability distributions over sequences of words. They are common in a number of natural language processing tasks. In corpus linguistics, they can be used to judge the similarity between texts.\n\n*corpkit*'s :func:`~corpkit.corpus.Corpus.make_language_model` method makes it very easy to generate a language model:\n\n.. code-block:: python\n\n >>> corpus = Corpus('threads')\n # save as models/savename.p\n >>> lm = corpus.make_language_model('savename')\n\nOne simple thing you can do with a language model is pass in a string of text:\n\n.. code-block:: python\n\n >>> text = (\"We can compare an arbitrary string against the models \"\\\n ... \"created for each subcorpus, in order to find out how \"\\\n ... \"similar the text is to the texts in each subcorpus... \")\n # get scores for each subcorpus, and the corpus as a whole\n >>> lm.score(text)\n\n 01 -4.894732\n 04 -4.986471\n 02 -5.060964\n 03 -5.096785\n 05 -5.106083\n 07 -5.226934\n 06 -5.338614\n 08 -5.829444\n 09 -5.874777\n 10 -6.351399\n Corpus -5.285553\n\nYou can also pass in :class:`corpkit.corpus.Subcorpus` objects, subcorpus names or :class:`corpkit.corpus.File` instances.\n\nCustomising models\n--------------------\n\nUnder the hood, *corpkit* interrogates the corpus using some special parameters, then builds a model from the results. This means that you can pass in arbitrary arguments for the :func:`~corpkit.corpus.Corpus.interrogate` method:\n\n.. code-block:: python\n\n >>> lm = corpus.make_language_model('lemma_model',\n ... show=L,\n ... just_speakers='MAHSA',\n ... multiprocess=2)\n\n\nCompare subcorpora\n-------------------\n\nYou can find out which subcorpora are similar using the :func:`~corpkit.model.MultiModel.score` method:\n\n.. code-block:: python\n\n >>> lm.score('1996')\n\nOr get a complete *DataFrame* of values using :func:`~corpkit.model.MultiModel.score_subcorpora`:\n\n.. code-block:: python\n\n >>> df = lm.score_subcorpora()\n\n\nAdvanced stuff\n----------------\n\n.. note::\n\n Coming soon\n"
},
{
"path": "rst_docs/API/corpkit.managing.rst",
"content": "Managing projects\n=================\n\n``corpkit`` has a few other bits and pieces designed to make life easier when doing corpus linguistic work. This includes methods for loading saved data, for working with multiple corpora at the same time, and for switching between command line and graphical interfaces. Those things are covered here.\n\n.. contents::\n :local:\n\nLoading saved data\n-------------------\n\nWhen you're starting a new session, you probably don't want to start totally from scratch. It's handy to be able to load your previous work. You can load data in a few ways.\n\nFirst, you can use ``corpkit.load()``, using the name of the filename you'd like to load. By default, ``corpkit`` looks in the ``saved_interrogations`` directory, but you can pass in an absolute path instead if you like.\n\n.. code-block:: python\n\n >>> import corpkit\n >>> nouns = corpkit.load('nouns')\n\nSecond, you can use ``corpkit.loader()``, which provides a list of items to load, and asks the user for input:\n\n.. code-block:: python\n\n >>> nouns = corpkit.loader()\n\nThird, when instantiating a ``Corpus`` object, you can add ``load_saved=True`` keyword argument to load any saved data belonging to this corpus as an attribute.\n\n.. code-block:: python\n\n >>> corpus = Corpus('data/psyc-parsed', load_saved=True)\n\nA final alternative approach stores all interrogations within an :class:`corpkit.interrogation.Interrodict` object object:\n\n.. code-block:: python\n\n >>> r = corpkit.load_all_results()\n\nManaging multiple corpora\n--------------------\n\n``corpkit`` can handle one further level of abstraction for both Corpus and Interrogations. :class:`corpkit.corpus.Corpora` models a collection of :class:`corpkit.corpus.Corpus` objects. To create one, pass in a directory containing corpora, or a list of paths/``Corpus`` objects:\n\n.. code-block:: python\n\n >>> from corpkit import Corpora\n >>> corpora = Corpora('data')\n\nIndividual corpora can be accessed as attributes, by index, or as keys:\n\n.. code-block:: python\n\n >>> corpora.first\n >>> corpora[0]\n >>> corpora['first']\n\nYou can use the :func:`~corpkit.corpus.Corpora.interrogate` method to search them, using the same arguments as you would for :func:`~corpkit.corpus.Corpus.interrogate`.\n\nInterrogating these objects often returns an :class:`corpkit.interrogation.Interrodict` object, which models a collection of DataFrames.\n\nEditing can be performed with :func:`~corpkit.interrogation.Interrodict.edit`. The editor will iterate over each DataFrame in turn, generally returning another ``Interrodict``.\n\n.. note::\n \n There is no ``visualise()`` method for Interrodict objects.\n\n:func:`~corpkit.interrogation.Interrodict.multiindex` can turn an ``Interrodict`` into a `Pandas MultiIndex`:\n\n.. code-block:: python\n\n >>> multiple_res.multiindex()\n\n:func:`~corpkit.interrogation.Interrodict.collapse` will collapse one dimension of the ``Interrodict``. You can collapse the x axis (``'x'``), the y axis (``'y'``), or the Interrodict keys (``'k'``). In the example below, an ``Interrodict`` is collapsed along each axis in turn.\n\n.. code-block:: python\n\n >>> d = corpora.interrogate({F: 'compound', GL: r'^risk'}, show=L)\n >>> d.keys()\n \n ['CHT', 'WAP', 'WSJ']\n \n >>> d['CHT'].results\n\n .... health cancer security credit flight safety heart\n 1987 87 25 28 13 7 6 4\n 1988 72 24 20 15 7 4 9\n 1989 137 61 23 10 5 5 6\n \n >>> d.collapse(axis=Y).results\n\n ... health cancer credit security\n CHT 3174 1156 566 697\n WAP 2799 933 582 1127\n WSJ 1812 680 2009 537\n \n >>> d.collapse(axis=X).results\n\n ... 1987 1988 1989\n CHT 384 328 464\n WAP 389 355 435\n WSJ 428 410 473\n \n >>> d.collapse(axis=K).results\n\n ... health cancer credit security\n 1987 282 127 65 93\n 1988 277 100 70 107\n 1989 379 253 83 91\n\n:func:`~corpkit.interrogation.Interrodict.topwords` quickly shows the top results from every interrogation in the ``Interrodict``.\n\n.. code-block:: python\n\n >>> data.topwords(n=5)\n\nOutput:\n\n.. code-block:: none\n\n TBT % UST % WAP % WSJ %\n health 25.70 health 15.25 health 19.64 credit 9.22\n security 6.48 cancer 10.85 security 7.91 health 8.31\n cancer 6.19 heart 6.31 cancer 6.55 downside 5.46\n flight 4.45 breast 4.29 credit 4.08 inflation 3.37\n safety 3.49 security 3.94 safety 3.26 cancer 3.12\n\n\nUsing the GUI\n-------------\n\n``corpkit`` is also designed to work as a GUI. It can be started in ``bash`` with:\n\n.. code-block:: bash\n\n $ python -m corpkit.gui\n\nThe GUI can understand any projects you have defined. If you open it, you can simply select your project via ``Open Project`` and resume work in a graphical environment."
},
{
"path": "rst_docs/API/corpkit.visualising.rst",
"content": "Visualising results\n=====================\n\nOne thing missing in a lot of corpus linguistic tools is the ability to produce high-quality visualisations of corpus data. ``corpkit`` uses the :class:`corpkit.interrogation.Interrogation.visualise` method to do this.\n\n.. contents::\n :local:\n\n.. note::\n\n Most of the keyword arguments from Pandas' plot_ method are available. See their documentation for more information.\n\nBasics\n---------------------\n\n``visualise()`` is a method of all :class:`corpkit.interrogation.Interrogation` objects. If you use `from corpkit import *`, it is also monkey-patched to Pandas objects.\n\n.. note::\n\n If you're using a *Jupyter Notebook*, make sure you use ``%matplotlib inline`` or ``%matplotlib notebook`` to set the appropriate backend.\n\nA common workflow is to interrogate a corpus, relative results, and visualise:\n\n.. code-block:: python\n\n >>> from corpkit import *\n >>> corpus = Corpus('data/P-parsed', load_saved=True)\n >>> counts = corpus.interrogate({T: r'MD < __'})\n >>> reldat = counts.edit('%', SELF)\n >>> reldat.visualise('Modals', kind='line', num_to_plot=ALL).show()\n ### the visualise method can also attach to the df:\n >>> reldat.results.visualise(...).show()\n\nThe current behaviour of ``visualise()`` is to return the ``pyplot`` module. This allows you to edit figures further before showing them. Therefore, there are two ways to show the figure: \n\n.. code-block:: python\n\n >>> data.visualise().show()\n\n.. code-block:: python\n\n >>> plt = data.visualise()\n >>> plt.show()\n\nPlot type\n---------------------\n\nThe visualise method allows ``line``, ``bar``, horizontal bar (``barh``), ``area``, and ``pie`` charts. Those with `seaborn` can also use ``'heatmap'`` (docs_). Just pass in the type as a string with the ``kind`` keyword argument. Arguments such as ``robust=True`` can then be used.\n\n.. code-block:: python\n\n >>> data.visualise(kind='heatmap', robust=True, figsize=(4,12),\n ... x_label='Subcorpus', y_label='Event').show()\n\n.. figure:: ../../images/event-heatmap.png\n :width: 50%\n :target: ../../images/event-heatmap.png\n :align: center\n\n Heatmap example\n\nStacked area/line plots can be made with ``stacked=True``. You can also use ``filled=True`` to attempt to make all values sum to 100. Cumulative plotting can be done with ``cumulative=True``. Below is an area plot beside an area plot where ``filled=True``. Both use the ``vidiris`` colour scheme.\n\n.. image:: ../../images/area.png\n :width: 45%\n :target: ../../images/area.png\n :align: left\n\n.. image:: ../../images/area-filled.png\n :width: 45%\n :target: ../../images/area-filled.png\n :align: right\n \nPlot style\n---------------------\n\nYou can select from a number of styles, such as ``ggplot``, ``fivethirtyeight``, ``bmh``, and ``classic``. If you have `seaborn` installed (and you should), then you can also select from `seaborn` styles (``seaborn-paper``, ``seaborn-dark``, etc.).\n\nFigure and font size\n---------------------\n\nYou can pass in a tuple of ``(width, height)`` to control the size of the figure. You can also pass an integer as ``fontsize``.\n\nTitle and labels\n---------------------\n\nYou can label your plot with `title`, `x_label` and `y_label`:\n\n.. code-block:: python\n\n >>> data.visualise('Modals', x_label='Subcorpus', y_label='Relative frequency')\n\nSubplots\n---------------------\n\n``subplots=True`` makes a separate plot for every entry in the data. If using it, you'll probably also want to use ``layout=(rows,columns)`` to specify how you'd like the plots arranged.\n\n.. code-block:: python\n\n >>> data.visualise(subplots=True, layout=(2,3)).show()\n\n.. figure:: ../../images/subplots.png\n :width: 60%\n :target: ../../images/subplots.png\n :align: center\n\n Line charts using subplots and layout specification\n\n\nTeX\n---------------------\n\nIf you have LaTeX installed, you can use ``tex=True`` to render text with LaTeX. By default, ``visualise()`` tries to use LaTeX if it can.\n\nLegend\n---------------------\n\nYou can turn the legend off with ``legend=False``. Legend placement can be controlled with ``legend_pos``, which can be:\n\n.. table:: \n :column-dividers: single double double single\n\n+---------------------+----------------------------+----------------------+\n| Margin | Figure | Margin |\n+=====================+=============+==============+======================+\n| outside upper left | upper left | upper right | outside upper right |\n+---------------------+-------------+--------------+----------------------+\n| outside center left | center left | center right | outside center right |\n+---------------------+-------------+--------------+----------------------+\n| outside lower left | lower left | lower right | outside lower right |\n+---------------------+-------------+--------------+----------------------+\n\nThe default value, ``'best'``, tries to find the best place automatically (without leaving the figure boundaries).\n\nIf you pass in ``draggable=True``, you should be able to drag the legend around the figure.\n\nColours\n---------------------\n\nYou can use the ``colours`` keyword argument to pass in:\n\n 1. A colour name recognised by *matplotlib*\n 2. A hex colour string\n 3. A colourmap object\n\nThere is an extra argument, ``black_and_white``, which can be set to ``True`` to make greyscale plots. Unlike ``colours``, it also updates line styles.\n\nSaving figures\n---------------------\n\nTo save a figure to a project's `images` directory, you can use the ``save`` argument. ``output_format='png'/'pdf'`` can be used to change the file format.\n\n.. code-block:: python\n\n >>> data.visualise(save='name', output_format='png')\n\nOther options\n--------------------\n\nThere are a number of further keyword arguments for customising figures:\n\n+--------------------+------------+---------------------------------+\n| Argument | Type | Action |\n+====================+============+=================================+\n| `grid` | `bool` | Show grid in background |\n+--------------------+------------+---------------------------------+\n| `rot` | `int` | Rotate x axis labels n degrees |\n+--------------------+------------+---------------------------------+\n| `shadow` | `bool` | Shadows for some parts of plot |\n+--------------------+------------+---------------------------------+\n| `ncol` | `int` | n columns for legend entries |\n+--------------------+------------+---------------------------------+\n| `explode` | `list` | Explode these entries in pie |\n+--------------------+------------+---------------------------------+\n| `partial_pie` | `bool` | Allow plotting of pie slices |\n+--------------------+------------+---------------------------------+\n| `legend_frame` | `bool` | Show frame around legend |\n+--------------------+------------+---------------------------------+\n| `legend_alpha` | `float` | Opacity of legend |\n+--------------------+------------+---------------------------------+\n| `reverse_legend` | `bool` | Reverse legend entry order |\n+--------------------+------------+---------------------------------+\n| `transpose` | `bool` | Flip axes of DataFrame |\n+--------------------+------------+---------------------------------+\n| `logx/logy` | `bool` | Log scales |\n+--------------------+------------+---------------------------------+\n| `show_p_val` | `bool` | Try to show p value in legend |\n+--------------------+------------+---------------------------------+\n| `interactive` | `bool` | Experimental mpld3_ use |\n+--------------------+------------+---------------------------------+\n\nA number of these and other options for customising figures are also described in the :class:`corpkit.interrogation.Interrogation.visualise` method documentation.\n\nMultiplotting\n---------------\n\nThe :class:`corpkit.interrogation.Interrogation` also comes with a :class:`corpkit.interrogation.Interrogation.multiplot` method, which can be used to show two different kinds of chart within the same figure.\n\nThe first two arguments for the function are two `dict` objects, which configure the larger and smaller plots.\n\nFor the second dictionary, you may pass in a `data` argument, which is an :class:`corpkit.interrogation.Interrogation` or similar, and will be used as separate data for the subplots. This is useful, for example, if you want your main plot to show absolute frequencies, and your subplots to show relative frequencies.\n\nThere is also `layout`, which you can use to choose an overall grid design. You can also pass in a list of tuples if you like, to use your own layout. Below is a complete example, focussing on objects in risk processes:\n\n.. code-block:: python\n\n >>> from corpkit import *\n >>> from corpkit.dictionaries import * \n ### parse a collection of text files\n >>> corpora = Corus('data/news')\n ### make dependency parse query: get get 'object' of risk process\n >>> query = {F: roles.participant2, GL: r'\\brisk', GF: roles.process} \n ### interrogate corpus, return lemma form, no coreference\n >>> result = corpus.interrogate(query, show=[L], coref=False) \n ### generate relative frequencies, skip closed class, and sort\n >>> inc = result.edit('%', SELF,\n >>> sort_by='increase',\n >>> skip_entries=wordlists.closedclass) \n ### visualise as area and line charts combined\n >>> inc.multiplot({'title': 'Objects of risk processes, increasing',\n >>> 'kind': 'area',\n >>> 'x_label': 'Year',\n >>> 'y_label': 'Percentage of all results'},\n >>> {'kind': 'line'}, layout=5)\n\n.. figure:: ../../images/inc-risk-obj.png\n :width: 50%\n :target: ../../images/inc-risk-obj.png\n :align: center\n\n `multiplot` example\n\n\n.. _plot: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html\n.. _docs: https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.heatmap.html\n.. _mpld3: http://mpld3.github.io/"
},
{
"path": "rst_docs/API-ref/corpkit.corpus.rst",
"content": "Corpus classes\n=====================\n\nMuch of *corpkit*'s functionality comes from the ability to work with ``Corpus`` and ``Corpus``-like objects, which have methods for parsing, tokenising, interrogating and concordancing.\n\n`Corpus`\n---------------------\n\n.. autoclass:: corpkit.corpus.Corpus\n :members:\n :undoc-members:\n :show-inheritance:\n\n`Corpora`\n---------------------\n\n.. autoclass:: corpkit.corpus.Corpora\n :members:\n :undoc-members:\n :show-inheritance:\n\n`Subcorpus`\n---------------------\n\n.. autoclass:: corpkit.corpus.Subcorpus\n :members:\n :undoc-members:\n :show-inheritance:\n\n`File`\n---------------------\n\n.. autoclass:: corpkit.corpus.File\n :members:\n :undoc-members:\n :show-inheritance:\n\n`Datalist`\n---------------------\n\n.. autoclass:: corpkit.corpus.Datalist\n :members:\n :undoc-members:\n :show-inheritance:\n\n"
},
{
"path": "rst_docs/API-ref/corpkit.dictionaries.rst",
"content": "Wordlists\n============================\n\nClosed class word types\n-------------------------------------------\n\nVarious wordlists, mostly for subtypes of closed class words\n\n.. autodata:: corpkit.dictionaries.wordlists.wordlists\n\nSystemic functional process types\n-------------------------------------------\n\nInflected verbforms for systemic process types.\n\n.. data:: corpkit.dictionaries.process_types.processes\n\nStopwords\n-------------------------------------------\n\nA list of arbitrary stopwords.\n\n.. data:: corpkit.dictionaries.stopwords.stopwords\n\nSystemic/dependency label conversion\n-------------------------------------------\n\nSystemic-functional to dependency role translation.\n\n.. autodata:: corpkit.dictionaries.roles.roles\n\nBNC reference corpus\n-------------------------------------------\n\nBNC word frequency list.\n\n.. data:: corpkit.dictionaries.bnc.bnc\n\nSpelling conversion\n-------------------------------------------\n\nA dict with U.S. English spellings as keys, U.K. spellings as values.\n\n.. data:: corpkit.dictionaries.word_transforms.usa_convert\n"
},
{
"path": "rst_docs/API-ref/corpkit.interrogation.rst",
"content": "Interrogation classes\n============================\n\nOnce you have searched a ``Corpus`` object, you'll want to be able to edit, visualise and store results. Remember that upon importing *corpkit*, any ``pandas.DataFrame`` or ``pandas.Series`` object is monkey-patched with ``save``, ``edit`` and ``visualise`` methods.\n\n`Interrogation`\n---------------------\n.. autoclass:: corpkit.interrogation.Interrogation\n :members:\n :undoc-members:\n :show-inheritance:\n\n`Interrodict`\n---------------------\n.. autoclass:: corpkit.interrogation.Interrodict\n :members:\n :undoc-members:\n :show-inheritance:\n\n`Concordance`\n---------------------\n.. autoclass:: corpkit.interrogation.Concordance\n :members:\n :undoc-members:\n :show-inheritance:"
},
{
"path": "rst_docs/API-ref/corpkit.other.rst",
"content": "Functions\n====================\n\n*corpkit* contains a small set of standalone functions.\n\n`as_regex`\n--------------------\n.. autofunction:: corpkit.other.as_regex\n\n`load`\n--------------------\n.. autofunction:: corpkit.other.load\n\n`load_all_results`\n--------------------\n.. autofunction:: corpkit.other.load_all_results\n\n`new_project`\n--------------------\n.. autofunction:: corpkit.other.new_project\n"
},
{
"path": "rst_docs/interpreter/corpkit.interpreter.annotating.rst",
"content": "Annotating your corpus\n========================\n\nAnother thing you might like to do is add metadata or annotations to your corpus. This can be done by simply editing corpus files, which are stored in a human-readable format. You can also automate annotation, however.\n\nTo do annotation, you first run a ``search`` command and generate a ``concordance``. After deleting any false positives from the ``concordance``, you can use the ``annotate`` command to annotate each sentence for which a concordance line exists.\n\n``annotate` works a lot like the ``mark``, ``keep``, and ``del`` commands to begin with, but has some special syntax at the end, which controls whether you annotate using *tags*, or *fields and values*.\n\nTagging sentences\n-------------------\n\nThe first way of annotating is to add a **tag** to one or more sentences:\n\n.. code-block:: shell\n\n > search corpus for pos matching NNP and word matching 'daisy'\n > annotate m matching '^daisy$' with tag 'has_daisy'\n\nYou can use `all` to annotate every single concordance line:\n\n.. code-block:: shell\n\n > search corpus for governor-function matching nsubjpass \\\n ... showing governor-lemma and lemma\n > annotate all with tag 'passive'\n\nIf you try to run this code, you actually get a `dry run`, showing you what would be modified in your corpus. Once you're happy with it, you can do ``toggle annotation`` to turn file writing on, and then run the previous line again (use the up arrow to get it!).\n\nCreating fields and values\n-----------------------------\n\nMore complex than adding tags is adding **fields** and **values**. This creates a new metadata category with multiple possible realisations. Below, we tag an sentence sentences based on their containing certain kinds of processes\n\n.. code-block:: shell\n\n > search corpus for function matching roles.process showing lemma\n > mark m matching processes.verbal red\n # annotate by colour\n > annotate red with field as process \\\n ... and value as 'verbal'\n # annotate without colouring first\n > annotate m matching processes.mental with field as process \\\n ... and value as 'mental'\n\nYou can also use ``m`` as the value, which passes in the text from the middle column of the concordance.\n\n.. code-block:: shell\n\n > search corpus for pos matching NNP showing word\n > annotate m matching [gatsby, daisy, tom] \\\n ... with field as character and value as m\n\nThe moment these values have been added to your text, you can do really powerful things with them. You can, for example, use them as subcorpora, or use them as filters for the sentences being processed.\n\n.. code-block:: shell\n\n > set subcorpora as process\n > set skip character as 'gatsby'\n > set skip passive tag\n\nNow, the subcorpora will be the different processes (*verbal*, *mental* and *none*), and any sentence annotated as containing the ``gatsby`` ``character``, or the ``passive`` ``tag``, will be ignored.\n\nRemoving annotations\n-----------------------\n\nTo remove a ``tag`` or a ``field`` across the dataset, the commands are very simple. Note that again, you need to ``toggle annotation`` to actually alter any files.\n\n.. code-block:: shell\n\n > unannotate character field\n > unannotate typo tag\n > unannotate all tags\n"
},
{
"path": "rst_docs/interpreter/corpkit.interpreter.concordancing.rst",
"content": "Concordancing\n===============\n\nBy default, every search also produces concordance lines. You can view them by typing ``concordance``. This opens an interactive display, which can be scrolled and searched---hit ``h`` to get help on possible commands.\n\nCustomising appearance\n---------------------------------------------\n\nThe first thing you might want to do is adjust how concordance lines are displayed:\n\n.. code-block:: shell\n\n # hide subcorpus name, speaker name\n > show concordance with columns as lmr\n # enlarge window\n > show concordance with columns as lmr and window as 60\n # limit number of results to 100\n > show concordance with columns as lmr and window as 60 and n as 100\n\nThe values you enter here are persistant---the window size, number of lines, etc. will remain the same until you shut down the interpreter or provide new values.\n\nSorting\n--------\n\nSorting can be by column, or by word.\n\n.. code-block:: shell\n\n # middle column, first word\n > sort concordance by M1\n # left column, last word\n > sort concordance by L1\n # right column, third word\n > sort concordance by R3\n # by index (original order)\n > sort concordance by index\n\nColouring\n-----------\n\nOne nice feature is that concordances can be coloured. This can be done through either indexing or regular expression matches. Note that ``background`` can be added to colour the background instead of the foreground, and ``dim``/``bright`` can be used to adjust text brightness. This means that you can code lines for multiple phenomena. Background highlighting could mark the argument structure, foreground highlighting could mark the mood type, and bright and dim could be used to mark exemplars or false positives.\n\n.. note::\n\n If you're using Python 2, you may find that colouring concordance lines causes some interference with `readline`, making it difficult to select or search previous commands. This is a limitation of readline in Python 2. Use Python 3 instead!\n\n.. code-block:: shell\n\n # colour by index\n > mark 10 blue\n > mark -10 background red\n > mark 10-15 cyan\n > mark 15- magenta\n # reset all\n > mark - reset\n\n.. code-block:: shell\n\n # regular expression methods: specify column(s) to search\n > mark m '^PRP.*' yellow\n > mark r 'be(ing)' background green\n > mark lm 'JJR$' dim\n # reset via regex\n > mark m '.*' reset\n\nYou can then sort by colour with `sort concordance by scheme`. If you export the concordances to a file (`export concordance as csv to conc.csv`), colour information will be added in additional columns.\n\nEditing\n--------\n\nTo edit concordance lines, you can use the same syntax as you would use to edit results:\n\n.. code-block:: shell\n\n > edit concordance by skipping subcorpora matching '[123]$'\n > edit concordance by keeping entries matching 'PRP'\n\nPerhaps faster is the use of `del` and `keep`. For these, specify the column and the criteria using the same methods as you would for colouring:\n\n.. code-block:: shell\n\n > del m matching 'this'\n > keep l matching '^I\\s'\n > del 10-20\n\nRecalculating results from concordance lines\n---------------------------------------------\n\nIf you've deleted some concordance lines, you can update the ``result`` object to reflect these changes with `calculate result from concordance`.\n\n\nWorking with metadata\n------------------------\n\nYou can use ``show_conc_metadata`` when interrogating or concordancing to collect and display metadata alongside concordance results:\n\n.. code-block:: shell\n\n > search corpus for words matching any with show_conc_metadata\n > concordance\n \n\n"
},
{
"path": "rst_docs/interpreter/corpkit.interpreter.editing.rst",
"content": "Editing results\n================\n\nOnce you have generated a `result` object via the `search` command, you can edit the result in a number of ways. You can delete, merge or otherwise alter entries or subcorpora; you can do statistics, and you can sort results.\n\nEditing, calculating and sorting each create a new object, called `edited`. This means that if you make a mistake, you still have access to the original `result` object, without needing to run the search again.\n\nThe edit command\n------------------\n\nWhen using the `edit` command, the main things you'll want to do is skip, keep, span or merge results or subcorpora.\n\n.. code-block:: bash\n\n > edit result by keeping subcorpora matching '[01234]'\n > edit result by skipping entries matching wordlists.closedclass\n # merge has a slightly different syntax, because you need\n # to specify the name to merge under\n > edit result by merging entries matching 'be|have' as 'aux'\n\n.. note::\n\n The syntax above works for concordance lines too, if you change `result` to `concordance`. Merging is not possible.\n\nDoing basic statistics \n------------------------\n\nThe `calculate` command allows you to turn the absolute frequencies into relative frequencies, keyness scores, etc.\n\n.. code-block:: bash\n\n > calculate result as percentage of self\n > calculate edited as percentage of features.clauses\n > calculate result as keyness of self\n\nIf you want to run more complicated operations on the results, you might like to use the `ipython` command to enter an IPython session, and then manipulate the Pandas objects directly.\n\nSorting results\n------------------\n\nThe `sort` command allows you to change the search result order.\n\nPossible values are `total`, `name`, `infreq`, `increase`, `decrease`, `static`, `turbulent`.\n\n.. code-block:: bash\n\n > sort result by total\n # requires scipy\n > sort edited by increase\n"
},
{
"path": "rst_docs/interpreter/corpkit.interpreter.interrogating.rst",
"content": "Interrogating corpora\n=======================\n\nThe most powerful thing about *corpkit* is its ability to search parsed corpora for very complex constituency, dependency or token level features.\n\nBefore we begin, make sure you've ``set`` the corpus as the thing to search:\n\n.. code-block:: bash\n\n > set nyt-parsed as corpus\n # you could also try just typing `set` ...\n\n.. note::\n \n By default, when using the interpreter, searching also produces concordance lines. If you don't need them, you can use ``toggle conc`` to switch them off, or on again. This can dramatically speed up processing time.\n\nSearch examples\n--------------------\n\n.. code-block:: bash\n\n > search corpus ### interactive search helper\n > search corpus for words matching \".*\"\n > search corpus for words matching \"^[A-M]\" showing lemma and word with case_sensitive\n > search corpus for cql matching '[pos=\"DT\"] [pos=\"NN\"]' showing pos and word with coref\n > search corpus for function matching roles.process showing dependent-lemma\n > search corpus for governor-lemma matching processes.verbal showing governor-lemma, lemma\n > search corpus for words matching any and not words matching wordlists.closedclass\n > search corpus for trees matching '/NN.?/ >># NP'\n > search corpus for pos matching NNP showing ngram-word and pos with gramsize as 3\n > etc.\n\nUnder the surface, what you are doing is selecting a `Corpus` object to search, and then generating arguments for the :func:`~corpkit.corpus.Corpus.interrogate` method. These arguments, in order, are:\n\n1. `search` criteria\n2. `exclude` criteria\n3. `show` values\n4. Keyword arguments\n\nHere is a syntax example that might help you see how the command gets parsed. Note that there are two ways of setting `exclude` criteria.\n\n.. code-block:: bash\n\n > search corpus \\ # select object\n ... for words matching 'ing$' and \\ # search criterion\n ... not lemma matching 'being' and \\ # exclude criterion\n ... pos matching 'NN' \\ # seach criterion\n ... excluding words matching wordlists.closedclass \\ # exclude criterion\n ... showing lemma and pos and function \\ # show values\n ... with preserve_case and \\ # boolean keyword arg\n ... not no_punct and \\ # bool keyword arg\n ... excludemode as 'all' # keyword arg\n\n\n\nWorking with metadata\n----------------------\n\nBy default, *corpkit* treats folders within your corpus as subcorpora. If you want to treat files, rather than folders, as subcorpora, you can do:\n\n.. code-block:: bash\n\n > search corpus for words matching 'ing$' with subcorpora as files\n\nIf you have metadata in your corpus, you can use the metadata value as the subcorpora:\n\n.. code-block:: bash\n\n > search corpus for words matching 'ing$' with subcorpora as speaker\n\nIf you don't want to keep specifying the subcorpus structure every time you search a corpus, you have a couple of choices. First, you can set the default subcorpus value with for the session with ``set subcorpora``. This applies the filter globally, to whatever corpus you search:\n\n.. code-block:: bash\n\n # use speaker metadata as subcorpora\n > set subcorpora as speaker\n # ignore folders, use files as subcorpora\n > set subcorpora as files\n\nYou can also define metadata filters, which skip sentences matching a metadata feature, or which keep only sentences matching a metadata feature:\n\n.. code-block:: bash\n\n # if you have a `year` metadata field, skip this decade\n > set skip year as '^201'\n # if you want only this decade:\n > set keep year as '^201'\n\nIf you want to set subcorpora and filters for a corpus, rather than globally, you can do this by passing in the values when you select the corpus:\n\n.. code-block:: bash\n\n > set mydata-parsed as corpus with year as subcorpora and \\\n ... just year as '^201' and skip speaker as 'chomsky'\n # forget filters for this corpus:\n > set mydata-parsed\n\nSampling a corpus\n------------------\n\nSometimes, your corpus is too big to search quickly. If this is the case, you can use the ``sample`` command to create a randomise sample of the corpus data:\n\n.. code-block:: bash\n\n > sample 3 subcorpora of corpus\n > sample 100 files of corpus\n\nIf you pass in a float, it will try to get a proportional amount of data: ``sample 0.33 subcorpora of corpus`` will return a third of the subcorpora in the corpus.\n\nA sampled corpus becomes an object called ``sampled``. You can then refer to it when searching:\n\n.. code-block:: bash\n\n > search sampled for words matching '^[abcde]'\n\nGlobal metadata filters and subcorpus declarations will be observed when searching this corpus as well.\n"
},
{
"path": "rst_docs/interpreter/corpkit.interpreter.making.rst",
"content": "Making projects and corpora\n============================\n\nThe first two things you need to do when using *corpkit* are to create a project, and to create (and optionally parse) a corpus. These steps can all be accomplished quickly using shell commands. They can also be done using the interpreter, however.\n\nOnce you're in *corpkit*, the command below will create a new project called `iran-news`, and move you into it.\n\n.. code-block:: bash\n\n > new project named iran-news\n\nAdding a corpus\n----------------\n\nAdding a corpus simply copies it to the project's data directory. The syntax is simple:\n\n.. code-block:: bash\n\n > add '../../my_corpus'\n\nParsing a corpus\n-----------------\n\nTo parse a text file, folder of text files, or folder of folder of text files, you first ``set`` the corpus, and then use the ``parse`` command:\n\n.. code-block:: bash\n\n > set my_corpus as corpus\n > parse corpus\n\nTokenising, POS tagging and lemmatising\n-----------------------------------------\n\nIf you don't want/need full parses, or if you aren't working with English, you might want to use the ``tokenise`` method.\n\n.. code-block:: bash\n\n > set abstracts as corpus\n > tokenise corpus\n\nPOS tagging and lemmatisation are switched on by default, but you could also disable them:\n\n.. code-block:: bash\n\n > tokenise corpus with postag as false and lemmatise as false\n\nWorking with metadata\n-------------------------\n\nParsing/tokenising can be made way cooler when your data has some metadata in it. The metadata will be transferred over to the parsed version of the corpus, and then you can search or filter by metadata features, use metadata values as symbolic subcorpora, or display metadata alongside concordances.\n\nMetadata should take the form of an XML tag at the end of a line, which could be a sentence or a paragraph:\n\n.. code-block:: xml\n\n I hope everyone is hanging in with this blasted heat. As we all know being hot, sticky,\n stressed and irritated can bring on a mood swing super fast. So please make sure your \n all takeing your meds and try to stay out of the heat. \n\nThen, parse with metadata:\n\n.. code-block:: bash\n\n > parse corpus with metadata\n\nThe parser output will look something like:\n\n.. code-block:: none\n\n # sent_id 1\n # parse=(ROOT (S (NP (PRP I)) (VP (VBP hope) (SBAR (S (NP (NN everyone)) (VP (VBZ is) (VP (VBG hanging) (PP (IN in) (IN with) (NP (DT this) (VBN blasted) (NN heat)))))))) (. .)))\n # speaker=Emz45\n # totalposts=5063\n # threadlength=1\n # currentposts=4051\n # stage=10\n # date=2011-07-13\n # year=2011\n # postnum=0\n 1 1 I I PRP O 2 nsubj 0 1\n 1 2 hope hope VBP O 0 ROOT 1,5,11 _\n 1 3 everyone everyone NN O 5 nsubj 0 _\n 1 4 is be VBZ O 5 aux 0 _\n 1 5 hanging hang VBG O 2 ccomp 3,4,10 _\n 1 6 in in IN O 10 case 0 _\n 1 7 with with IN O 10 case 0 _\n 1 8 this this DT O 10 det 0 2\n 1 9 blasted blast VBN O 10 amod 0 2\n 1 10 heat heat NN O 5 nmod:with 6,7,8,9 2*\n 1 11 . . . O 2 punct 0 _\n\n\nViewing corpus data\n--------------------\n\nYou can interactively work with the parser output.\n\n.. code-block:: bash\n\n > get file of corpus\n\nOr, if your corpus has subcorpora:\n\n.. code-block:: bash\n\n > get subcorpus of corpus\n > get file of sampled\n\nThis view can be surprisingly powerful: sorting by lemma, POS or dependency function can show you some recurring lexicogrammatical patterns in a file without the need for searching.\n\n\nThe next page will show you how to search the corpus you've built, and to work with metadata if you've added it.\n\n\n"
},
{
"path": "rst_docs/interpreter/corpkit.interpreter.managing.rst",
"content": "Settings and management\n========================\n\nThe interpreter can do a number of other useful things. They are outlined here.\n\nManaging data\n---------------\n\nYou should be able to store most of the objects you create in memory using the ``store`` command:\n\n.. code-block:: bash\n\n > store result as 'good_result'\n > show store\n > fetch 'good_result' as result\n\nA more permanent solution is to use `save` and `load`:\n\n.. code-block:: bash\n\n > save result as 'good_result'\n > ls saved_interrogations\n > load 'good_result' as result\n\nAn alternative approach is to create variables using the ``call`` command:\n\n.. code-block:: bash\n\n > search corpus for words matching any\n > call result anyword\n > calculate anyword as percentage of self\n\nA variable can also be a simple string, which you can then add into searches:\n\n.. code-block:: bash\n\n > call '/NN.?/ >># NP' headnoun\n > search corpus for trees matching headnoun\n\nTo forget a variable, just do `remove `.\n\nToggles and settings\n---------------------\n\n* Using ``toggle interactive``, You can switch between interactive mode, where results and concordances are shown in a way that you can manipulate directly, and non-interactive mode, where results and concordances are simply printed to the console.\n* Using ``toggle conc``, you can tell *corpkit* not to produce concordances. This can be much faster, especially when there are a lot of results.\n* ``toggle comma`` will display thousands separators in results\n* ``toggle annotation`` is used to switch from dry-run to actual modification of corpus files when annotating\n\n* You can set the number of decimals displayed when viewing results with ``set decimal to ``\n* ``set max_rows to `` and ``set max_cols to `` limit the amount of data loaded into results lists. This can speed up interactive viewing.\n\n\nSwitching to IPython\n---------------------\n\nWhen the interpreter constrains you, you can switch to IPython with ``ipython``. Your objects are available there under the same name. When you're done there, do ``quit`` to return to the *corpkit* interpreter.\n\nRunning scripts\n-----------------\n\nYou can also write and run scripts. If you make a file, ``participants.cki``, containing:\n\n.. code-block:: bash\n \n #!/usr/bin/env corpkit\n\n set mydata-parsed as corpus\n search corpus for function matching roles.participant showing lemma\n export result as csv to part.csv\n\n\nYou can run it from the terminal with:\n\n.. code-block:: bash\n\n corpkit participants.cki\n # or, directly, if there's a shebang and chmod +x:\n ./participants.cki\n\n\nwhich will leave you with a CSV file at ``exported/part.csv``. This approach can be handy if you need to pipe ``stdout`` or ``stderr``, or if you want to call *corpkit* within a shell script.\n\nFinally, just like Python, you can use the ``-c`` flag to pass code in on the command line:\n\n.. code-block:: bash\n\n corpkit -c \"set 2 ; search corpus for features ; export result as csv to feat.csv\"\n\n.. note::\n\n When running a script, interactivity will automatically be switched off, and concordancing disabled if the script does not appear to need it.\n\n"
},
{
"path": "rst_docs/interpreter/corpkit.interpreter.overview.rst",
"content": ".. _interpreter-page:\n\nOverview\n=======================\n\n*corpkit* comes with a dedicated interpreter, which receives commands in a natural language syntax like these:\n\n.. code-block:: bash\n\n > set mydata as corpus\n > search corpus for pos matching 'JJ.*'\n > call result 'adjectives'\n > edit adjectives by skipping subcorpora matching 'books'\n > plot edited as line chart with title as 'Adjectives'\n\nIt's a little less powerful than the full Python API, but it is easier to use, especially if you don't know Python. You can also switch instantly from the interpreter to the full API, so you only need the API for the really tricky stuff.\n\nThe syntax of the interpreter is based around *objects*, which you do things to, and *commands*, which are actions performed upon the objects. The example below uses the `search` command on a `corpus` object, which produces new objects, called `result`, `concordance`, `totals` and `query`. As you can see, very complex searches can be performed using an English-like syntax:\n\n.. code-block:: bash\n\n > search corpus for lemma matching '^t' and pos matching 'VB' \\\n ... excluding words matching 'try' \\\n ... showing word and dependent-word \\\n ... with preserve_case\n > result\n\nThis shows us results for each subcorpus:\n\n.. code-block:: none\n\n . I/think I/thought and/turned me/told and/took I/told ...\n chapter1 5 3 2 2 1 3 ...\n chapter2 7 2 5 3 0 2 ...\n chapter3 5 5 4 4 1 0 ...\n chapter4 3 7 1 0 3 1 ...\n chapter5 7 7 2 1 4 2 ...\n chapter6 2 0 0 2 1 0 ...\n chapter7 6 2 6 1 1 3 ...\n chapter8 3 1 2 2 1 1 ...\n chapter9 5 7 1 4 6 3 ...\n\n\nObjects\n---------\n\nThe most common objects you'll be using are:\n\n+---------------+-----------------------------------------------+\n| Object | Contains |\n+===============+===============================================+\n| `corpus` | Dataset selected for parsing or searching |\n+---------------+-----------------------------------------------+\n| `result` | Search output |\n+---------------+-----------------------------------------------+\n| `edited` | Results after sorting, editing or calculating |\n+---------------+-----------------------------------------------+\n| `concordance` | Concordance lines from search |\n+---------------+-----------------------------------------------+\n| `features` | General linguistic features of corpus |\n+---------------+-----------------------------------------------+\n| `wordclasses` | Distribution of word classes in corpus |\n+---------------+-----------------------------------------------+\n| `postags` | Distribution of POS tags in corpus |\n+---------------+-----------------------------------------------+\n| `lexicon` | Distribution of lexis in the corpus |\n+---------------+-----------------------------------------------+\n| `figure` | Plotted data |\n+---------------+-----------------------------------------------+\n| `query` | Values used to perform search or edit |\n+---------------+-----------------------------------------------+\n| `previous` | Object created before last |\n+---------------+-----------------------------------------------+\n| `sampled` | A sampled corpus |\n+---------------+-----------------------------------------------+\n| `wordlists` | A list of wordlists for searching, editing |\n+---------------+-----------------------------------------------+\n\nWhen you start the interpreter, these are all empty. You'll need to run commands in order to fill them with data. You can also create your own object names using the ``call`` command.\n\nCommands \n-----------\n\nYou do things to the objects via commands. Each command has its own syntax, designed to be as similar to natural language as possible. Below is a table of common commands, an explanation of their purpose, and an example of their syntax\n\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| Command | Purpose | Syntax |\n+=================+==============================================================+============================================================================================+\n| `new` | Make a new project | `new project ` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `set` | Set current corpus | `set ` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `parse` | Parse corpus | `parse corpus with [options]*` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `search` | Search a corpus for linguistic feature, generate concordance | `search corpus for [feature matching pattern]* showing [feature]* with [options]*` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `edit` | Edit results or edited results | `edit result by [skipping subcorpora/entries matching pattern]* with [options]*` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `calculate` | Calculate relative frequencies, keyness, etc. | `calculate result/edited as operation of denominator` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `sort` | Sort results or concordance | `sort result/concordance by value` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `plot` | Visualise result or edited result | `plot result/edited as line chart with [options]*` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `show` | Show any object | `show object` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `annotate` | Add annotations to corpus based on search results | `annotate all with field as and value as m` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `unannotate` | Delete annotation fields from corpus | `unannotate field` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `sample` | Get a random sample of subcorpora or files from a corpus | `sample 5 subcorpora of corpus` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `call` | Name an object (i.e. make a variable) | `call object ''` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `export` | Export result, edited result or concordance to string/file | `export result to string/csv/latex/file ` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `save` | Save data to disk | `save object to ` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `load` | Load data from disk | `load object as result` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `store` | Store something in memory | `store object as ` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `fetch` | Fetch something from memory | `fetch as object` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `help` | Get help on an object or command | `help command/object` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `history` | See previously entered commands | `history` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `ipython` | Enter IPython with objects available | `ipython` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `py` | Execute Python code | `py 'print(\"hello world\")'` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n| `!` | Run a line of bash shell | `!ls -al data` |\n+-----------------+--------------------------------------------------------------+--------------------------------------------------------------------------------------------+\n\nIn square brackets with asterisks are recursive parts of the syntax, which often also accept `not` operators. `` denotes places where you can choose an identifier, filename, etc.\n\nIn the pages that follow, the syntax is provided for the most common commands. You can also type the name of the command with no arguments into the interpreter, in order to show usage examples.\n\nPrompt features\n-------------------\n\n* You can use `history`, `clear`, `ls` and `cd` commands as you would in the shell\n* You can execute arbitrary bash commands by beginning the line with an exclamation point (e.g. ``!rm data/*``)\n* You can use semicolons to put multiple commands on a line (currently needs a space **before and after** the semicolon)\n* There is no piping or output redirection (yet), but you can use the `export` and `save` commands to export results\n* You can use backslashes to continue writing on the next line\n* You can write scripts and pass them to the *corpkit* interpreter\n\nThe below is therefore a possible (but terrible) way to write code in *corpkit*:\n\n.. code-block:: bash\n\n > !du -h data ; set mycorp ; search corpus for words \\\n ... matching any \\\n ... excluding wordlists.closedclass \\\n ... showing lemma and pos ; concordance\n"
},
{
"path": "rst_docs/interpreter/corpkit.interpreter.setup.rst",
"content": "Setup\n==============================\n\n.. contents::\n :local:\n\nDependencies\n-------------\n\nTo use the interpreter, you'll need *corpkit* installed. To use all features of the interpreter, you will also need *readline* and *IPython*.\n\nAccessing\n--------------------\n\nWith *corpkit* installed, you can start the interpreter in a couple of ways:\n\n.. code-block:: bash\n\n $ corpkit\n # or\n $ python -m corpkit.env\n\nYou can start it from a Python session, too:\n\n.. code-block:: python\n\n >>> from corpkit import env\n >>> env()\n\nThe prompt\n------------\n\nWhen using the interpreter, the prompt (the text to the left of where you type your command) displays the directory you are in (with an asterisk if it does not appear to be a *corpkit* project) and the currently active corpus, if any:\n\n.. code-block:: none\n\n corpkit@junglebook:no-corpus> \n\nWhen you see it, *corpkit* is ready to accept commands!\n\n"
},
{
"path": "rst_docs/interpreter/corpkit.interpreter.visualising.rst",
"content": "\nPlotting\n=========\n\nYou can plot results and edited results using the `plot` method, which interfaces with *matplotlib*.\n\n.. code-block:: bash\n\n > plot edited as bar chart with title as 'Example plot' and x_label as 'Subcorpus'\n > plot edited as area chart with stacked and colours as Paired\n > plot edited with style as seaborn-talk # defaults to line chart\n\nThere are many possible arguments for customising the figure. The table below shows some of them. \n\n.. code-block:: bash\n\n > plot edited as bar chart with rot as 45 and logy and \\\n ... legend_alpha as 0.8 and show_p_val and not grid\n\n+--------------------+------------+---------------------------------+\n| Argument | Type | Action |\n+====================+============+=================================+\n| `grid` | `bool` | Show grid in background |\n+--------------------+------------+---------------------------------+\n| `rot` | `int` | Rotate x axis labels n degrees |\n+--------------------+------------+---------------------------------+\n| `shadow` | `bool` | Shadows for some parts of plot |\n+--------------------+------------+---------------------------------+\n| `ncol` | `int` | n columns for legend entries |\n+--------------------+------------+---------------------------------+\n| `explode` | `list` | Explode these entries in pie |\n+--------------------+------------+---------------------------------+\n| `partial_pie` | `bool` | Allow plotting of pie slices |\n+--------------------+------------+---------------------------------+\n| `legend_frame` | `bool` | Show frame around legend |\n+--------------------+------------+---------------------------------+\n| `legend_alpha` | `float` | Opacity of legend |\n+--------------------+------------+---------------------------------+\n| `reverse_legend` | `bool` | Reverse legend entry order |\n+--------------------+------------+---------------------------------+\n| `transpose` | `bool` | Flip axes of DataFrame |\n+--------------------+------------+---------------------------------+\n| `logx/logy` | `bool` | Log scales |\n+--------------------+------------+---------------------------------+\n| `show_p_val` | `bool` | Try to show p value in legend |\n+--------------------+------------+---------------------------------+\n\n.. note::\n\n If you want to set a boolean value, you can just say ``and value`` or ``and not value``. If you like, however, you could write it more fully as ``with value as true/false`` as well."
},
{
"path": "setup.cfg",
"content": "[metadata]\nname = corpkit\ndescription-file = README.md\ndescription = A toolkit for working with parsed corpora\nurl = http://github.com/interrogator/corpkit\nauthor = Daniel McDonald\nauthor-email = mcdonaldd@unimelb.edu.au\n\n[bdist_wheel]\nuniversal = 1"
},
{
"path": "setup.py",
"content": "import setuptools\nfrom setuptools import setup, find_packages\nfrom setuptools.command.install import install\nimport os\nfrom os.path import isfile, isdir, join, dirname\n\nclass CustomInstallCommand(install):\n \"\"\"\n Customized setuptools install command, which installs\n some NLTK data automatically\n \"\"\"\n def run(self):\n from setuptools.command.install import install\n install.run(self)\n # since nltk may have just been installed\n # we need to update our PYTHONPATH\n import site\n try:\n reload(site)\n except NameError:\n pass\n # Now we can import nltk\n import nltk\n # install these three resources and add them to path\n install_d = {'tokenizers': 'punkt',\n 'corpora': 'wordnet',\n 'taggers': 'averaged_perceptron_tagger'}\n \n path_to_nltk_f = nltk.__file__\n nltkpath = dirname(path_to_nltk_f)\n for path, name in install_d.items():\n pat = join(nltkpath, path)\n if not isfile(join(pat, '%s.zip' % name)) \\\n and not isdir(join(pat, name)):\n nltk.download(name, download_dir=nltkpath)\n\n nltk.data.path.append(nltkpath)\n\nsetup(name='corpkit',\n version='2.3.8',\n description='A toolkit for working with linguistic corpora',\n url='http://github.com/interrogator/corpkit',\n author='Daniel McDonald',\n packages=['corpkit', 'corpkit.download'],\n scripts=['corpkit/new_project', 'corpkit/parse',\n 'corpkit/corpkit', 'corpkit/corpkit.1'],\n package_dir={'corpkit': 'corpkit'},\n package_data={'corpkit': ['*.jar', 'corpkit/*.jar', '*.sh', 'corpkit/*.sh', \n '*.ipynb', 'corpkit/*.ipynb', '*.p', 'dictionaries/*.p',\n '*.py', 'dictionaries/*.py']},\n author_email='mcdonaldd@unimelb.edu.au',\n license='MIT',\n cmdclass={'install': CustomInstallCommand,},\n keywords=['corpus', 'linguistics', 'nlp'],\n install_requires=[\"matplotlib>=1.4.3\",\n \"nltk>=3.0.0\",\n \"joblib\",\n \"pandas>=0.18.1\",\n #\"mpld3>=0.2\", \n \"requests\",\n \"tabview>=1.4.0\",\n \"chardet\",\n \"blessings>=1.6\",\n \"traitlets>=4.1.0\"],\n dependency_links=['git+https://github.com/interrogator/tabview.git#egg=tabview-1.4.0',\n 'git+https://github.com/interrogator/tkintertable.git#egg=tkintertable-1.2'])\n \n"
},
{
"path": "talks/IDL_seminar.tex",
"content": "\\documentclass{beamer} % print frames\n%\\documentclass[notes=only]{beamer} % only notes\n%\\documentclass{beamer} % only frames\n\n\\newcounter{savedenum}\n\\newcommand*{\\saveenum}{\\setcounter{savedenum}{\\theenumi}}\n\\newcommand*{\\resume}{\\setcounter{enumi}{\\thesavedenum}}\n\\usepackage{multienum}\n\\usepackage[notocbib]{apacite}\n\t\\renewcommand{\\APACrefatitle}[2]{#1}\n\t\\renewcommand{\\APACrefbtitle}[2]{#1}\n\t\\renewcommand{\\APACrefbtitle}[2]{\\Bem{#1}}\n\t\\renewcommand{\\APACrefaetitle}[2]{[#2]}\n\t\\renewcommand{\\APACrefbetitle}[2]{[#2]}\n\\usepackage{textpos}\n\\usefonttheme{serif}\n%\\usepackage{graphicx}\n\\usetheme{Boadilla}\n\\usepackage{hyperref}\n\\usepackage[UKenglish]{babel}\n\\usepackage{lingmacros}\n\\setcounter{tocdepth}{1} \n\\usecolortheme{orchid}\n\\title[University of Melbourne]{Exploring the influence of medium and culture on language use in an online support group}\n\\author[Daniel McDonald]{~\\\\Daniel McDonald~\\\\~\\\\~\\\\\\footnotesize}\n\n\\date{IDL, 20/3/2015}\n\n\\begin{document}\n\n\\addtobeamertemplate{frametitle}{}{%\n\\begin{textblock*}{100mm}(.775\\textwidth,-.5cm)\n\\includegraphics[scale=.235]{../images/unimelblong}\n\\end{textblock*}}\n\n\\frame{\\titlepage}\n\n\\begin{frame}\n\\frametitle{Context}\n\\begin{itemize}\n\\item Show you all what my PhD research is about\n\\item Introduce \\emph{Systemic Functional Linguistics} (SFL)\n\\item Discuss some of the problems \\emph{SFL} might help \\emph{us} solve\n\\item Discuss some of the problems \\emph{you} might help \\emph{me} solve\n\\end{itemize}\n\\end{frame}\n\n \\begin{frame}\n \\frametitle{Case study: my thesis}\n \\begin{itemize}\n \\item I'm researching longitudinal linguistic change in a 12 year old bipolar disorder forum\n \\item 60,000 posts\n \\item 5821 unique usernames\n \\item A few veterans with over 1000 posts, most users have 1--5\n \\item Rules: no asking for diagnosis, stay `anonymous'\n \\item Normative biomedical ideology \\cite{vayreda_social_2009}\n \\item People come for information and/or social support\n \\item Veterans answer questions, welcome new members, speak as `we'\n \\end{itemize}\n \\end{frame}\n\n \\begin{frame}\n \\frametitle{The forum}\n \\centering\n \\includegraphics[scale=.5]{../images/forum.png}\n \\end{frame}\n\n\\begin{frame}\n \t\\frametitle{Thesis methodology: the easy part}\n \t\n \t\\begin{itemize}\n \t\\item \\emph{Wget} to spider the forum\n \t\\item Extract text from HTML with \\emph{Beautiful Soup}\n \t\\item Spelling normalisation via \\emph{PyEnchant} \n \t\\item Parse posts for constituency and dependency grammar with \\emph{Stanford CoreNLP}\n \t\\item Create subcorpora based on post count\n \t\\item Build \\texttt{corpkit}, some tools for searching parse trees, manipulating and plotting results: \\texttt{pip install corpkit} (Alpha!)\n \t\\item Develop an \\emph{IPython Notebook} interface for working with the corpus\\slash toolkit\n \t\\item \\url{https://github.com/interrogator/corpkit}, \\url{https://github.com/interrogator/sfl_corpling}\n \t\\end{itemize}\n \\end{frame}\n\n\n \\begin{frame}\n \t\\frametitle{Analysing 8m words: the hard part}\n\n\n \tHow do we actually make sense of this huge collection of data?\n\n\n \\end{frame}\n \n \n\n \\begin{frame}\n \\frametitle{ \\frametitle{An example: \\emph{Katlin09}}}\n\n Katlin09's early posts look like this:\n\n \\centering \\includegraphics[width=0.9\\textwidth]{../images/kat1.png}\n \\end{frame}\n\n \\begin{frame}\n \\frametitle{Katlin09}\n \t\n \t... and here's one of her later posts...\n\n \\centering \\includegraphics[width=0.9\\textwidth]{../images/kat2.png}\n \\end{frame}\n\n\n \\begin{frame}\n \t\\frametitle{What's happening with her language use?}\n \t\n \t\\begin{itemize}\n \t\\item Has her language use changed over time?\n \t\\item Does this same change happen to other members?\n \t\\item What \\emph{causes} the change?\n \t\\item If many things cause the change, how can we distinguish what forces are acting and when?\n \t\\end{itemize}\n\n \tTo answer these kinds of questions, we need a \\emph{theory of language}\n \\end{frame}\n \n\\begin{frame}\n \\frametitle{\\emph{Systemic Functional Linguistics} (SFL)}\n\n\\citeA{halliday_introduction_2004} conceptualise language as:\n\\begin{enumerate}\n \\item Systemic: a sign-\\emph{system}, from which users select realisations\n \\item Functional: structured to achieve meaningful social goals\n \\item Constitutive of context: context is mapped onto abstractions of grammatical systems (mood, transitivity and theme)\n \\begin{itemize}\n \\item \\textbf{Context is in text.}\n \\end{itemize}\n \\end{enumerate} \n\n\\end{frame}\n\n\\begin{frame}\n \\frametitle{Overview of SFL}\n \\centering\n \\includegraphics[width=0.90\\textwidth]{../images/egginsfixed.jpg}\n\\end{frame}\n\n\\begin{frame}\n\\frametitle{SFL: Introduction}\nSFL argues that we can break communication into \\emph{strata}\n\\begin{itemize}\n\\item Context:\n\\begin{itemize}\n \t\\item Genre\n \t\\item Register\n\\end{itemize}\n\\item Language:\n\\begin{itemize} \t\n \t\\item Discourse-semantic\n \t\\item Lexicogrammatical\n \t\\item Phonological/typographical\n \\end{itemize}\n\\end{itemize}\n\\end{frame}\n \n\\begin{frame}\n\\frametitle{SFL: \\emph{Metafunctions}}\nIn SFL, language/images are communicating \\emph{up to} three different \\emph{kinds} of meaning simultaneously:\n\\begin{itemize}\n\t\\item Interpersonal meaning: roles and relationships between people\n\t\\item Ideational meaning: what happens to whom, under what circumstances?\n\t\\item Textual meaning: the role that language is playing in the interaction\n\\end{itemize}\n \\end{frame}\n\n \\begin{frame}\n \\frametitle{Overview of SFL}\n \\centering\n \\includegraphics[width=0.90\\textwidth]{../images/egginsfixed.jpg}\n\\end{frame}\n\n \\begin{frame}\n \\frametitle{Metafunctions in the lexicogrammar}\n Each function is accomplished through different linguistic subsystems at the level of lexicogrammar.\n \\begin{itemize}\n \t\\item \\textbf{Interpersonal meanings} are made by the \\textbf{mood system}: \\emph{imperative, interrogative, declarative, modality}\n \t\\item \\textbf{Ideational meanings} are made by the \\textbf{transitivity system}: \\emph{predicates and their arguments}\n \t\\item \\textbf{Textual meanings} are made by the \\textbf{thematic system}: \\emph{linking texts together with \\emph{and}, \\emph{but}, \\emph{so} ... }\n \\end{itemize}\n \\end{frame}\n \n \\begin{frame}\n \\centering\n \\includegraphics[width=0.60\\textwidth]{../images/system.png}\n \\end{frame}\n\n\\begin{frame}\n\\frametitle{Power and mood choice}\n\\begin{itemize}\n\t\\item When there's a power disparity, some interpersonal meanings are less available to the one with less power.\n\t\\item A good example is the mood system (Questions, statements, commands):\n\\begin{itemize}\n\t\\item Me: Would you tell me the answer? / I think you should tell me the answer. / Tell me the readings!\n\t\\item You: Can you speak slower? / We're having trouble understanding you / \\underline{~~~~~~~~~~~} ?\n\\end{itemize}\n\\end{itemize}\n\\end{frame}\n\n\n\\begin{frame}\n\\frametitle{SFL and multimodality}\n\\begin{itemize}\n\\item Multimodality: the (simultaneous) use of different channels/modes to communicate\n\\item Inherent to online communication (page layout, at the very least)\n\\item We can look at images as also accomplishing the three metafunctions...\n\\end{itemize}\n\\end{frame}\n \n \\begin{frame}\n \\centering\n \\includegraphics[width=0.50\\textwidth]{../images/ikea.jpg}\n \\end{frame}\n\n\n\\begin{frame}\n\\frametitle{SFL and genre}\n\\begin{itemize}\n\t\\item In SFL, a genre is a staged, goal-oriented social process\n\t\\item Culturally recognised sets of role relationships, things being spoken about, and modes of communication\n\t\\item We can actually figure this stuff out from looking at the lexicogrammar\n\t\\item Example: \n\t\\end{itemize}\n\t~~~~~~~~~~~~~~\\emph{Submissions must contain 8--10 references.}\n\\end{frame}\n\n \\begin{frame}\n \\centering\n \\includegraphics[width=0.50\\textwidth]{../images/ikea.jpg}\n \\end{frame}\n\n\\begin{frame}\n\t\\frametitle{IPython Notebook ...}\n\t\n\\end{frame}\n\n\n\\begin{frame}\n\t\\frametitle{Summary of findings}\n\t\n\t\\begin{itemize}\n\t\\item More imperatives\n\t\\item More \\emph{I would}\n\t\\item More jargon\n\t\\item More `metadiscourse'\n\t\\item etc.\n\t\\end{itemize}\n\\end{frame}\n\n\n\\begin{frame}\n\t\\frametitle{What's causing the change?}\n\t\n\tTwo contradictory hypotheses:\n\n\t\\begin{enumerate}\n\t\\item \\textbf{Socialisation:} new members learn from veterans and implement the linguistic repertoire they've learned\n\t\\item \\textbf{Genre awareness:} new members know they are newcomers, and act like it\n\t\\end{enumerate}\n\t... and what role does the technology itself play? \n\\end{frame}\n\n\n\\begin{frame}\n\t\\frametitle{Reasons for jargonisation?}\n\n\tWe can use SFL's idea of metafunctions as an explanation for why a certain kind of language change occurs.\n\t\n\t\\begin{itemize}\n\t\\item \\textbf{Interpersonal change:} jargon demonstrates expertise, belonging\n\t\\item \\textbf{Experiential change:} jargon distinguishes between hypertechnical things\n\t\\item \\textbf{Textual change:} shorter words are easier to type\n\t\\end{itemize}\n\n\tThe important thing to remember is that different kinds of meaning are made \\emph{simultaneously.}\n\\end{frame}\n\n\n\\begin{frame}\n\t\\frametitle{Brainstorming ...}\n\n\tWhat \\emph{interpersonal}, \\emph{experiential} and \\emph{textual} meanings can we make by:\n\t\n\t\\begin{itemize}\n\t\\item Using imperatives: \\emph{do this}, \\emph{go there}\n\t\\item Vague language: \\emph{things will get better}, \\emph{things are alright}\n\t\\end{itemize}\n\\end{frame}\n\n\n\\begin{frame}\n\t\\frametitle{Where to next?}\n\t\\begin{itemize}\n\t\\item More awareness of multimodality, hardware influence, site architecture\n\t\\item Can we predict the influence of the three metafunctions?\n\t\\item Can we \\emph{measure} the influence?\n\t\\item How does it play out in new technology? \\emph{Reddit? iPhones?}\n\t\\item A framework for doing SFL with mediated communication?\n\t\\end{itemize}\n\\end{frame}\n\n\n\\begin{frame}\n\t\\frametitle{Help!}\n\t\n\t~~~~~~~~~~~Thoughts?\n\n\\end{frame}\n\n\n\n\n\n\n\n\\begin{frame}[t,allowframebreaks]\n\\frametitle{References}\n\\bibliographystyle{apacite}\n\\bibliography{../references/libwin.bib}\n\\end{frame}\n\n\\end{document}\n"
}
]
![](\"https://raw.githubusercontent.com/interrogator/risk/master/images/md2.png\")
\n![](\"https://raw.githubusercontent.com/interrogator/risk/master/images/risk-of-noun.png\")
\n![](\"https://raw.githubusercontent.com/interrogator/risk/master/images/sayers-increasing.png\")
\n![](\"https://raw.githubusercontent.com/interrogator/risk/master/images/ann_he_she.png\")
\n![](\"https://raw.githubusercontent.com/interrogator/risk/master/images/terror-as-participant-in-the-emphnyt.png\")
\n![](\"https://raw.githubusercontent.com/interrogator/risk/master/images/keyness-of-politicians-in-the-emphnyt.png\")
\n![](\"https://raw.githubusercontent.com/interrogator/risk/master/images/risk_processes-2.png\")
\n![](\"https://raw.githubusercontent.com/interrogator/risk/master/images/types-of-riskers.png\")
\n![](\"https://raw.githubusercontent.com/interrogator/risk/master/images/risk-and-power-2.png\")
\n![](\"https://raw.githubusercontent.com/interrogator/risk/master/images/pie-chart-of-common-modals-in-the-nyt2.png\")
\n![](\"https://raw.githubusercontent.com/interrogator/risk/master/images/modals-use-by-decade.png\")
\n![](\"https://raw.githubusercontent.com/interrogator/risk/master/images/an-ocean-of-modals2.png\")
\n![](\"https://raw.githubusercontent.com/interrogator/corpkit/master/images/actors.png\")
\n\nThere are also some [more detailed API examples over here](https://github.com/interrogator/corpkit/blob/master/API-README.md). This document is fairly thorough, but now deprecated, because the official docs are now over at [ReadTheDocs](http://corpkit.readthedocs.io/en/latest/).\n\n## Example figures\n\n![](\"https://raw.githubusercontent.com/interrogator/corpkit/master/images/inc-proc.png\")
![](\"https://raw.githubusercontent.com/interrogator/corpkit/master/images/best-derived.png\")
\n![](\"https://raw.githubusercontent.com/interrogator/corpkit/master/images/symlog-part2.png\")
![](\"https://raw.githubusercontent.com/interrogator/corpkit/master/images/process-types-for-part-types.png\")
\n![](\"https://raw.githubusercontent.com/interrogator/corpkit/master/images/risk-and-power-2.png\")
![](\"https://raw.githubusercontent.com/interrogator/corpkit/master/images/mood-role-risk.png\")
\n