python-dwca-reader 0.3.0

Major limitations

• It sometimes assumes the file has been produced by GBIF’s IPT. For example, only zip compression is curently supported, even tough the Darwin Core Archive allows other compression formats.

• No write support.

Installation

Quite simply:

$ pip install python-dwca-reader

Example use

1. Basic use, access to metadata and “Core lines”

from dwca import DwCAReader
from dwca.darwincore.utils import qualname as qn

# Let's open our archive...
# Using the with statement ensure that resources will be properly freed/cleaned after use.
with DwCAReader('my_archive.zip') as dwca:
    # We can now interact with the 'dwca' object

    # We can read scientific metadata (EML) through a BeautifulSoup object in the 'metadata' attribute
    # See BeautifulSoup 4 documentation: http://www.crummy.com/software/BeautifulSoup/bs4/doc
    print dwca.metadata.prettify()

    # We can get inspect archive to discover what is the Core Type (Occurrence, Taxon, ...):
    print "Core type is: %s" % dwca.core_rowtype
    # => Core type is: http://rs.tdwg.org/dwc/terms/Occurrence

    # Check if a Darwin Core term in present in the core file
    if dwca.core_contains_term('http://rs.tdwg.org/dwc/terms/locality'):
        print "This archive contains the 'locality' term in its core file."
    else:
        print "Locality term is not present."

    # Using full qualnames for DarwincCore terms (such as 'http://rs.tdwg.org/dwc/terms/country') is verbose...
    # The qualname() helper function make life easy for common terms.
    # (here, it has been imported as 'qn'):
    qn('locality')
    # => u'http://rs.tdwg.org/dwc/terms/locality'

    # Combined with previous examples, this can be used to things more clear:
    # For example:
    if dwca.core_contains_term(qn('locality')):
        pass

    # Or:
    if dwca.core_rowtype == qn('Occurrence'):
        pass

    # Finally, let's iterate over the archive lines and get the data:
    for line in dwca.each_line():
        # line is an instance of DwCALine

        # Print can be used for debugging purposes...
        print line

        # => --
        # => Rowtype: http://rs.tdwg.org/dwc/terms/Occurrence
        # => Source: Core file
        # => Line ID:
        # => Data: {u'http://rs.tdwg.org/dwc/terms/basisOfRecord': u'Observation', u'http://rs.tdwg.org/dwc/terms/family': # => u'Tetraodontidae', u'http://rs.tdwg.org/dwc/terms/locality': u'Borneo', u'http://rs.tdwg.#
        # => org/dwc/terms/scientificName': u'tetraodon fluviatilis'}
        # => --

        # You can get the value of a specific Darwin Core term through
        # the "data" dict:
        print "Locality for this line is: %s" % line.data[qn('locality')]
        # => Locality for this line is: Mumbai

    # Alternatively, we can get a list of core lines instead of using each_line():
    lines = dwca.lines

    # Or retrieve a specific line by its id:
    occurrence_number_three = dwca.get_line(3)

2. Use of Darwin Core Archives using extensions (star schema)

from dwca import DwCAReader
from dwca.darwincore.utils import qualname as qn

with DwCAReader('archive_with_vernacularnames_extension.zip') as dwca:
    # Let's ask the archive what kind of extensions are in use:
    print dwca.extensions_rowtype
    # => [u'http://rs.gbif.org/terms/1.0/VernacularName']

    # For convenience
    core_lines = dwca.lines

    # a) Data access
    # Extension lines are accessible as a list of DwcALine instances in the 'extensions' attribute:
    for e in core_lines[0].extensions:
        # Display all extensions line that refers to the first Core line
        print e

    # b) We can now see in a given archive, a DwcALine can come from multiple sources...
    # Se we can ask it where it's from:
    print core_lines[0].from_core
    # => True
    print core_lines[0].extensions[0].from_extension
    # => True

    # ... and what its rowtype is:
    print core_lines[0].rowtype
    # => http://rs.tdwg.org/dwc/terms/Taxon

3. Another example with multiple extensions (no new API here):

from dwca import DwCAReader
from dwca.darwincore.utils import qualname as qn

with DwCAReader('multiext_archive.zip') as dwca:
    lines = dwca.lines
    ostrich = lines[0]

    print "You'll find below all extensions line reffering to Ostrich"
    print "There should be 3 verncaular names and 2 taxon description"
    for ext in ostrich.extensions:
        print ext

    print "We can then simply filter by type..."
    for ext in ostrich.extensions:
        if ext.rowtype == 'http://rs.gbif.org/terms/1.0/VernacularName':
            print ext

    print "We can also use list comprehensions for this:"
    description_ext = [e for e in ostrich.extensions if
                   e.rowtype == 'http://rs.gbif.org/terms/1.0/Description']

    for ext in description_ext:
        print ext

4. GBIF Data Portal exports

The new version of the GBIF Data Portal (to be released later this year) will allow users to export searched occurrences as a zip file. The file format is actually a slightly augmented version of Darwin Core Archive that can also be read with this library in two different ways:

• As a standard DwC-A file (see example above). In this case you won’t have access to the additional, non-standard data.

• Via the specific GBIFResultsReader, see example below:

from dwca import GBIFResultsReader

with GBIFResultsReader('results.zip') as results:
    # GBIFResultsReader being a subclass of DwCAReader, all previously described features will work the same.
    #
    # But there's more:
    #
    # 1) GBIF Portal downloads include citation and IP rights information about the resultset. They can be accessed via specific attributes:

    results.citations
    # => "Please cite this data as follows, and pay attention to the rights documented in the rights.txt: ..."

    results.rights
    # => "Dataset: [Name and license of source datasets for this resultset]"

    # 2) In addition to the dataset-wide metadata (EML) file, these archives also include the source metadata for all datasets whose lines are part of the resultset.

    # 2.1) At the archive level, they can be accessed as a dict:
    results.source_metadata
    # {'dataset1_UUID': <dataset1 EML (BeautifulSoup instance)>,
    #  'dataset2_UUID': <dataset2 EML (BeautifulSoup instance)>, ...}

    # 2.2 From a DwCALine instance, we can get back to the metadata of its source dataset:
    first_line = results.line[0]
    first_line.source_metadata
    => <Source dataset EML (BeautifulSoup instance)>

Run the test suite

$ pip install nose
$ nosetests

Test coverage can easily be obtained after installing coverage.py

$ nosetests --with-coverage --cover-erase --cover-package=dwca
.....................
Name                    Stmts   Miss  Cover   Missing
-----------------------------------------------------
dwca                        0      0   100%
dwca.darwincore             0      0   100%
dwca.darwincore.terms       1      0   100%
dwca.darwincore.utils       3      0   100%
dwca.dwca                 130     16    88%   23-45
dwca.utils                  5      1    80%   12
-----------------------------------------------------
TOTAL                     139     17    88%
----------------------------------------------------------------------
Ran 21 tests in 0.830s

OK