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        "author": "Ryan Heuser",
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        "description": "# llp\n\nLiterary Language Processing (LLP): corpora, models, and tools for the digital humanities.\n\n## Quickstart\n\n1) Install:\n\n```\npip install llp                       # install with pip in terminal\n```\n\n2) Download an existing corpus...\n\n```\nllp status                            # show which corpora/data are available\nllp download ECCO_TCP                 # download a corpus\n```\n\n...or import your own:\n\n```\nllp import                            # use the \"import\" command \\\n  -path_txt mycorpus/txts             # a folder of txt files  (use -path_xml for xml) \\\n  -path_metadata mycorpus/meta.xls    # a metadata csv/tsv/xls about those txt files \\\n  -col_fn filename                    # filename in the metadata corresponding to the .txt filename\n```\n\n...or start a new one:\n\n```\nllp create                            # then follow the interactive prompt\n```\n\n3) Then you can load the corpus in Python:\n\n```python\nimport llp                            # import llp as a python module\ncorpus = llp.load('ECCO_TCP')         # load the corpus by name or ID\n```\n\n...and play with convenient Corpus objects...\n\n```python\ndf = corpus.metadata                  # get corpus metadata as a pandas dataframe\nsmpl=df.query('1740 < year < 1780')   # do a quick query on the metadata\n\ntexts = corpus.texts()                # get a convenient Text object for each text\ntexts_smpl = corpus.texts(smpl.id)    # get Text objects for a specific list of IDs\n```\n\n...and Text objects:\n\n```python\nfor text in texts_smpl:               # loop over Text objects\n    text_meta = text.meta             # get text metadata as dictionary\n    author = text.author              # get common metadata as attributes    \n\n    txt = text.txt                    # get plain text as string\n    xml = text.xml                    # get xml as string\n\n    tokens = text.tokens              # get list of words (incl punct)\n    words  = text.words               # get list of words (excl punct)\n    counts = text.word_counts         # get word counts as dictionary (from JSON if saved)\n    ocracc = text.ocr_accuracy        # get estimate of ocr accuracy\n    \n    spacy_obj = text.spacy            # get a spacy text object\n    nltk_obj = text.nltk              # get an nltk text object\n    blob_obj = text.blob              # get a textblob object\n```\n\n## Corpus magic\n\nEach corpus object can generate data about itself:\n\n```python\ncorpus.save_metadata()                # save metadata from xml files (if possible)\ncorpus.save_plain_text()              # save plain text from xml (if possible)\ncorpus.save_mfw()                     # save list of all words in corpus and their total  count\ncorpus.save_freqs()                   # save counts as JSON files\ncorpus.save_dtm()                     # save a document-term matrix with top N words\n```\n\nYou can also run these commands in the terminal:\n\n```\nllp install my_corpus                 # this is equivalent to python above\nllp install my_corpus -parallel 4     # but can access parallel processing with MPI/Slingshot\nllp install my_corpus dtm             # run a specific step\n```\n\nGenerating this kind of data allows for easier access to things like:\n\n```python\nmfw = corpus.mfw(n=10000)             # get the 10K most frequent words\ndtm = corpus.freqs(words=mfw)         # get a document-term matrix as a pandas dataframe\n```\n\nYou can also build word2vec models:\n\n```python\nw2v_model = corpus.word2vec()         # get an llp word2vec model object\nw2v_model.model()                     # run the modeling process\nw2v_model.save()                      # save the model somewhere\ngensim_model = w2v_model.gensim       # get the original gensim object\n```",
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