z3c.csvvocabulary 1.0.0

A package to create vocabularies based on CSV files.

This package provides a very simple vocabulary implementation using CSV files. The advantage of CSV files is that they provide an external point to specify data, which allows a non-developer to adjust the data themselves.

Detailed Documentation

CSV Vocabulary

This package provides a very simple vocabulary implementation using CSV files. The advantage of CSV files is that they provide an external point to specify data, which allows a non-developer to adjust the data themselves.

>>> import z3c.csvvocabulary

>>> import os.path
>>> path = os.path.dirname(z3c.csvvocabulary.__file__)

CSV Vocabulary

The CSV Vocabulary implementation is really just a function that creates a simple vocabulary with titled terms. There is a sample.csv file in the data directory of the testing sub-package, so let’s create a vocabulary from that:

>>> csvfile = os.path.join(path, 'testing', 'data', 'sample.csv')

>>> samples = z3c.csvvocabulary.CSVVocabulary(csvfile)
>>> samples
<zope.schema.vocabulary.SimpleVocabulary object at ...>

>>> sorted([term.value for term in samples])
['value1', 'value2', 'value3', 'value4', 'value5']

Let’s now look at a term:

>>> term1 = samples.getTerm('value1')
>>> term1
<zope.schema.vocabulary.SimpleTerm object at ...>

As you can see, the vocabulary automatically prefixes the value:

>>> term1.value
'value1'

>>> term1.token
'value1'

>>> term1.title
u'sample-value1'

While it looks like the title is the wrong unicode string, it is really an I18n message:

>>> type(term1.title)
<type 'zope.i18nmessageid.message.Message'>

>>> term1.title.default
u'Title 1'

>>> term1.title.domain
'zope'

Of course, it is not always acceptable to make ‘zope’ the domain of the message. You can specify the message factory when initializing the vocabulary:

>>> from zope.i18nmessageid import MessageFactory
>>> exampleDomain = MessageFactory('example')

>>> samples = z3c.csvvocabulary.CSVVocabulary(csvfile, exampleDomain)
>>> term1 = samples.getTerm('value1')
>>> term1.title.domain
'example'

The vocabulary is designed to work with small data sets, typically choices in user interfaces. All terms are created upon initialization, so the vocabulary does not detect updates in the csv file or loads the data when needed. But as I said, this is totally okay.

CSV Message String Extraction

There is a simple function in i18nextract.py that extracts all message strings from the CSV files in a particular sub-tree. Here we just want to make sure that the function completes and some dummy data from the testing package will be used:

>>> basedir = os.path.dirname(z3c.__file__)

>>> catalog = z3c.csvvocabulary.csvStrings(path, basedir)
>>> pprint(catalog)
{u'sample-value1': [('...sample.csv', 1)],
 u'sample-value2': [('...sample.csv', 2)],
 u'sample-value3': [('...sample.csv', 3)],
 u'sample-value4': [('...sample.csv', 4)],
 u'sample-value5': [('...sample.csv', 5)]}