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        "author": "Lars G.B. Johnsen",
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        "description": "# Modules\nThis repository only contains python modules for accessing text and pictures at the National Library of Norway.\n\n# nbtext.py\n\n\n\n# Metadata and URNs\n\n### `totals(top=200)`\nReturns a dictionary of the top \u00c2\u00b4top\u00c2\u00b4 words in the digital collection\n\n\n### `urn_from_text(T)`\nReturn a list of URNs as 13 digit serial numbers. \n\n### `metadata(urn=\"\"\"text\"\"\")`\nReturns a list of metada entries for given URN.\n\n### `pure_urn(data)`\nConvert URN-lists with extra data into list of serial numbers. Used to convert different ways of presenting URNs into a list of serial decimal digits. Designed to work with book URNs, and will not work for newspaper URNs.\n```    \n    Args:\n        data: May be a list of URNs, a list of lists with URNs as their\n            initial element, or a string of raw texts containing URNs\n    Returns:\n        List[str]: A list of URNs. Empty list if input is on the wrong\n            format or contains no URNs\n```\n\n\n### `col_agg(df, col='sum')`\nAggregate columns of a panda dataframe.\n\n### `row_agg(df, col='sum')`\nAggregate rows of a panda dataframe.\n\n\n# Access texts as frequency lists\n\n### `get_freq(urn, top=50, cutoff=3)`\nGet frequency list of words for a given URN (as a serial number).\n\n### `get_urn(metadata=None)`\nGet URNSs from metadata specified as a dictionary. Keys specified in quotes are:\n* \"corpus\": \"avis\" or \"bok\"\n* \"author\": wildcard match using % as wildcard.\n* \"title\": wildcard match using %. For newspapers this corresponds to name of paper.\n* \"year\": starting year as number or number as string.\n* \"next\": the next number of years starting from \u00c2\u00b4year\u00c2\u00b4\n*  \"ddk\": Dewy decimal number as wildcard match e.g. \"64%\" \n*   \"gender\": value is \"m\" for male or \"f\" for female\n*  \"subject\": keywords used to annotate text in the national bibliography.\n\n### `get_corpus_text(urns, top = 10000, cutoff=5)`\nFrom a corpus as a list of URNs, get the top `top` words that have a frequency above `cutoff`. Builds on top of `get_freq`.\nReturns a dataframe with URNs as row headers and words as indices.\n``\n    k = dict()\n    for u in urns:\n        k[u] = get_freq(u, top = top, cutoff = cutoff)\n    return pd.DataFrame(k)\n``\n### `get_papers(top=5, cutoff=5, navn='%', yearfrom=1800, yearto=2020, samplesize=100)`\nGet newspapers as frequency lists. Parameter `top` asks for the top ranked words, `cutoff` indicates the lower frequency limit, while `navn` indicates newspaper name as wildcard string.\n\n\n# Collocations and clusters\n\n### `make_a_collocation(word, period=(1990, 2000), before=5, after=5, corpus='avis', samplesize=100, limit=2000)`\nReturn a collocation as dataframe.\n\n\n### `compute_assoc(coll_frame, column, exponent=1.1, refcolumn = 'reference_corpus')`\nCompute an association using PMI.\n``    return pd.DataFrame(coll_frame[column]**exponent/coll_frame.mean(axis=1))``\n\n### `urn_coll(word, urns=[], after=5, before=5, limit=1000)`\nlimit is max number of occurences of word pr. urn. \nFind collocations for word in a set of book URNs. Only books at the moment\n\n### `collocation(word, yearfrom=2010, yearto=2018, before=3, after=3, limit=1000, corpus='avis')`\nCompute a collocation for a given word within indicated period.\n`before` is the number of preceeding words, `after` number of words following, `limit`. \n``\ndata =  requests.get(\n        \"https://api.nb.no/ngram/collocation\", \n        params={\n            'word':word,\n            'corpus':corpus, \n            'yearfrom':yearfrom, \n            'before':before,\n            'after':after,\n            'limit':limit,\n            'yearto':yearto}).json()\n    return pd.DataFrame.from_dict(data['freq'], orient='index')\n``\n\n\n\n### `normalize_corpus_dataframe(df)`\nNormalized all values in corpus `df` as a dataframe. Changes `df` in situ, and returns `True`.\n\n### `show_korpus(korpus, start=0, size=4, vstart=0, vsize=20, sortby = '')`\nShow part of a dataframe `korpus`, slicing along columns starting from `start`and numbers by `size` and slicing rows by `vstart`and `vsize`. Sorts by first column by default.\n\n### `aggregate(korpus)`\nMake an aggregated sum of all documents across the corpus, here we use average\n``    return pd.DataFrame(korpus.fillna(0).mean(axis=1))``\n\n### `convert_list_of_freqs_to_dataframe(referanse)`\nThe function get_papers() returns a list of frequencies - convert it and normalize.\n\n### `get_corpus(top=5, cutoff=5, navn='%', corpus='avis', yearfrom=1800, yearto=2020, samplesize=10)`\nFirst version of collecting a corpus using parameters described above for `get_papers` (for newspapers) and `get_corpus` (for books).\n\n\n# Classes\n\n### `class Cluster`\n    def __init__(self, word = '', filename = '', period = (1950,1960) , before = 5, after = 5, corpus='avis', reference = 200, \n                 word_samples=1000):\nSee clustering notebook for example and closer description.\n\n### `class Corpus`\nSee `Corpus` notebook for examples and explanation.\n\n### `Corpus_urn`\nSee example notebook\n\n# Graphs and network analysis\n\n### `make_newspaper_network(key, wordbag, titel='%', yearfrom='1980', yearto='1990', limit=500)`\nSeems not to work at the moment.\n\n### `make_network(urn, wordbag, cutoff=0)`\nMake a graph as `networkx` object from `wordbag` and `urn`. Two words are connected if they occur within same paragraph.\n\n### `make_network_graph(urn, wordbag, cutoff=0)`\nMake a graph as `networkx` object from `wordbag` and `urn`. Two words are connected if they occur within same paragraph.\n\n### `draw_graph_centrality(G, h=15, v=10, fontsize=20, k=0.2, arrows=False, font_color='black', threshold=0.01)` \nDraw a graph using force atlas.\n\n\n# Wordclouds\n\n### `make_cloud(json_text, top=100, background='white', stretch=lambda x: 2**(10*x), width=500, height=500, font_path=None)`*\nCreate a word cloud from a frequency list. First line of code: ``pairs0 = Counter(json_text).most_common(top)``\n\n### `draw_cloud(sky, width=20, height=20, fil='')`\nDraw a word cloud produces by `make_cloud` \n\n### `cloud(pd, column='', top=200, width=1000, height=1000, background='black', file='', stretch=10, font_path=None)`\nMake and draw a cloud from  a pandas dataframe, using `make_cloud` and `draw_cloud`.\n\n# Growth diagrams (sentiment analysis)\n\n### `vekstdiagram(urn, params=None)`\nMake a growth diagram for a given book using a set of words:\nParameters \n\n``\n'words': list of words \n'window': chunk size in the book\n'pr': how many words are skipped before next chunk\n``\n\n### `plot_sammen_vekst(urn, ordlister, window=5000, pr = 100)`\nFor ploting more than one growth diagram. Have a look at example notebook.\n\n# Word relations and n-grams\n\n### `difference(first, second, rf, rs, years=(1980, 2000),smooth=1, corpus='bok')`\nCompute difference of difference (first/second)/(rf/rs) for ngrams.\n\n\n### `relaterte_ord(word, number = 20, score=False)`\nFind related words using eigenvector centrality from networkx. Related words are taken from [NB Ngram](https://www.nb.no/sp_tjenester/beta/ngram_1/galaxies). Note: Works for english and german - add parameter!!\n\n\n### `nb_ngram(terms, corpus='bok', smooth=3, years=(1810, 2010), mode='relative')`\nCollect an ngram as json object from [NB Ngram](https://www.nb.no/sp_tjenester/beta/ngram_1/trends#ngram/query?terms=norway). Terms is string of comma separated ngrams (single words up to trigrams).\n\n### `ngram_conv(ngrams, smooth=1, years=(1810,2013), mode='relative')`\nConvert ngrams to a dataframe.\n\n\n### `make_graph(word)`\nGet graph like in [NB Ngram](https://www.nb.no/sp_tjenester/beta/ngram_1/galaxies)\n\n\n# Concordances\n\n### `get_konk(word, params=None, kind='html')`\nGet a concordance for given word. Params are like `get_urn`. Value is either an HTML-page, a json structure, or a dataframe. Specify `kind` as 'html', 'json' or '' respectively.\n\n### `get_urnkonk(word, params=None, html=True)`\nSame as `get_konk` but from a list of URNs.\n\n\n# Character Analysis and graphs\n\n### `central_characters(graph, n=10)`\nwrapper around `networkx`\n``    res = Counter(nx.degree_centrality(graph)).most_common(n)\n    return res\n``\n### `central_betweenness_characters(graph, n=10)`\nwrapper around `networkx`\n``    res = Counter(nx.betweenness_centrality(graph)).most_common(n)\n    return res\n  ``  \n\n### `check_words(urn, ordbag)`\nFind frequency of words in `ordbag` within a book given by `urn`.\n\n\n# Text complexity\n\n### `sttr(urn, chunk=5000)`\nCompute a standardized type/token-ratio for text identified with urn. The function expects the serial number of a URN for a book. Returns a number.\n\n### `navn(urn)`\nReturns a dictionary of frequency of possible names from URN as serial number.    \n\n# Utilities\n\n### `heatmap(df, color='green')`\nA wrapper for heatmap of df as a Pandas dataframe, like this:\n``return df.fillna(0).style.background_gradient(cmap=sns.light_palette(color, as_cmap=True))``\n\n### `df_combine(array_df)`\nCombine one column dataframes into one dataframe.\n\n### `wildcardsearch(params=None)`\nDefault values: ``params = {'word': '', 'freq_lim': 50, 'limit': 50, 'factor': 2}``\nReturns a dataframe containing matches for `word`. See examples in notebook `wildcardsearch`.\n\n### `sorted_wildcardsearch(params)`\nSame as `wildcardsearch` with results sorted on frequency.\n\n### `combine(clusters)`\nMake new collocation analyses from data in clusters\n\n### `cluster_join(cluster)`\nUsed with serial clusters. Join them together in one dataframe. See example in cluster notebook.\n\n### `serie_cluster(word, start\u00c3\u00a5r, slutt\u00c3\u00a5r, inkrement, before=5, after=5, reference=150, word_samples=500)`\nMake a series of clusters.\n\n#### `save_serie_cluster(tidscluster)`\nSave series to files.\n\n#### `les_serie_cluster(word, start\u00c3\u00a5r, slutt\u00c3\u00a5r, inkrement)`\nRead them\n### `frame(something, name)`\nCreate a dataframe of `something`\n\n\n### `get_urns_from_docx(document)`\nA file in `docx` format may contain a list of URNs.\n\n### `get_urns_from_text(document)`\nURNs from a `.txt`-document.\n\n### `get_urns_from_files(mappe, file_type='txt')`\nExtract URNs from a folder with `.txt` and `.docs` files. Returns a dictionary with filenames as keys, each with a list of URNs.\n\n\n### `check_vals(korpus, vals)`\nA wrapper for dataframes: ``\n    return korpus[korpus.index.isin(vals)].sort_values(by=0, ascending=False)``\n\n\n\n",
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