Metadata-Version: 1.2
Name: corpussearch
Version: 0.0.10
Summary: Tools for loading and analyzing large text corpora.
Home-page: https://github.com/TOPOI-DH/corpussearch/
Author: Malte Vogl
Author-email: mvogl@mpiwg-berlin.mpg.de
License: GPLv3
Project-URL: Home, https://github.com/TOPOI-DH/corpussearch/
Project-URL: Tracker, https://github.com/TOPOI-DH/corpussearch/issues
Project-URL: Download, https://github.com/TOPOI-DH/corpussearch/archive/0.0.10.tar.gz
Description-Content-Type: UNKNOWN
Description: CorpusSearch
        ============
        
        A tool to load and search in text corpora.
        
        The tool provides routines to search in large corpora in pandas
        dataframe format, where rows contain textual information on the level of
        sentences or paragraphs. Dataframes can be single or multilevel indexed
        and loaded from URL, DOI,
        `citable <http://www.edition-topoi.org/publishing_with_us/citable>`__ or
        local files. Accepted file formats are pickle, excel, json and csv.
        
        This package is designed to work with Jupyter Notebooks as well as in
        the IPython command line. If used in a Notebook, the user has access to
        a search GUI.
        
        Installation
        ============
        
        The package can be installed via ``pip``:
        
        ::
        
              pip install corpussearch
        
        Since the package is under active development, the most recent version
        is always on Github, and can be installed by
        
        ::
        
              pip install git+https://github.com/TOPOI-DH/corpussearch.git
        
        Basic usage
        ===========
        
        Import the package
        
        .. code:: python
        
            from corpussearch.base import CorpusTextSearch
        
        The class is instantiated by providing the path to the source file.
        Excepted formats are pickled dataframes, CSV, JSON or Excel files.
        
        Standard parameters assume pickled, multi-indexed dataframes, where the
        main text is contained in a column 'text'. For other sources change
        parameters accordingly.
        
        Loading data
        ------------
        
        Using a pre-pickled dataframe:
        
        .. code:: python
        
              search = CorpusTextSearch('./path/to/dataframe/file.pickle')
        
        Using data in excel format:
        
        .. code:: python
        
              search = CorpusTextSearch(
                  pathDF='./path/to/excel/file.xlsx'
                  dataType='excel',
                  dataIndex='single'
              )
        
        Loading data in excel format from a DOI:
        
        .. code:: python
        
              search = CorpusTextSearch(
                  pathDF='10.17171/1-6-90'
                  pathType='DOI',
                  dataType='excel',
                  dataIndex='single'
              )
        
        Search for text and/or parts
        ----------------------------
        
        A reduction to a specific part and page number is obtained by chaining
        the desired reductions ``.reduce(key,value)``, where ``key`` can be
        either a level of the multi index, or a column name. To obtain the
        resulting dataframe, ``.results()`` is added.
        
        .. code:: python
        
              result = search.reduce('part','part_name').reduce('page','page_number').results()
        
        To restart a search, e.g. within another part, use
        
        .. code:: python
        
              search.resetSearch()
        
        Additional search logic can be used with ``.logicReduce()``. The method
        accepts a list of reductions chained with logical AND,OR, or NOT. For
        example,
        
        .. code:: python
        
              search.logicReduce([('part','Part1'),&,('page','10'),|,('text','TEST')]).result()
        
        will return the entries of a dataframe where part is Part1 and page
        number is 10, or the text string contains TEST.
        
        GUI usage
        =========
        
        **Attention:** *Work in progress*
        
        Import the GUI part of the package into a Jupyter Notebook
        
        .. code:: python
        
            from corpussearch.gui import CorpusGUI
        
        Instantiate with path to source file, as above.
        
        .. code:: python
        
              gui = CorpusGUI('./path/to/dataframe/file.pickle')
        
        and display the interface
        
        .. code:: python
        
              gui.displayGUI()
        
        A basic word search returns all results where the search word is
        contained in the main column, e.g. 'text'. Search values can contain
        regular expressions, e.g. ``\d{2,4}\s[A-Z]``. For search in parts other
        then the main column, fuzzy searches are possible if the number of
        unique values on that level is less than ``maxValues``. This routine
        uses ``difflib`` to compare the search string to possible values on that
        level. This can help if the actual string formating is not well known,
        but could possibly lead to undesired results.
        
        Results are displayed in the sentence output boxes, where the right box
        contains meta-information derived from the non-main columns or
        multi-index levels.
        
        To navigate between results use the 'previous' and 'next' buttons.
        
        Additional search logic
        -----------------------
        
        To chain search terms, use the 'more'-button. This opens additional
        search fields. Possible logic operations are 'AND', 'OR', and 'NOT'.
        Each logic operation is between two consecutive search pairs
        (part,value). The logic operates in a linear fashion, from the first
        triple downwards, e.g. for the search (('text','NAME') &
        ('part','PART1') \| ('page','PAGE4')) each tuple (key,value) yields a
        boolean vector v, such that the search becomes (v1 & v2 \| v3).
        Evaluation continues for the pair vtemp = (v1 & v2), and finally vres=
        (vtemp \| v3). The resulting boolean vector is used to reduce the full
        data to the dataframe containing the search result.
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Text Processing :: General
Classifier: Topic :: Text Processing :: Indexing
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3
