z3c.sampledata 0.1.0

Sampledata Generator

This package tries to do the best to support the development of sample
data generators.
A sample data generator is a pluggable tool to create data needed for tests.


Detailed Documentation
**********************

===============================
Pluggable sample data framework
===============================

Creating a good testing environment is the most important way to create
high quality software.

But most of the time it is a pain !

This package tries to do the best to support the development of sample
data generators.
A sample data generator is a pluggable tool to create data needed for tests.


There are several goals to this framework:

- provide the developers with an automatic setup that is close to
real-world use.

- provide the users with an easy setup for evaluation with plausible data

- provide the user a management interface for different sample generators

The framework is pluggable and allows the creators of generator
extensions to provide their own plugins that generate sample data for
those extensions.


Generators
----------

A generator generates sample data.

>>> from zope import interface
>>> from zope import component
>>> from z3c.sampledata.interfaces import ISampleDataPlugin

>>> class GeneratePrincipals(object):
... interface.implements(ISampleDataPlugin)
... dependencies = []
... schema = None
... def generate(self, context, param={}, dataSource=None, seed=None):
... print self.__class__.__name__
... if dataSource is not None:
... for data in dataSource:
... print '- %s'%data['login']
>>> principalPlugin = GeneratePrincipals()

For our tests we provide another generator :

>>> class GenerateSite(object):
... interface.implements(ISampleDataPlugin)
... dependencies = []
... schema = None
... def generate(self, context, param={}, dataSource=None, seed=None):
... if 'sitename' in param:
... print 'This is site %r'%param['sitename']
... else:
... print self.__class__.__name__
... return 'I am from the site'
>>> sitePlugin = GenerateSite()


Generator Manager
-----------------

A generator manager groups a collection of generators.
The manager allows to :

- define dependencies between generators

- define data connections between dependent generators

- provide default configuration data

>>> from z3c.sampledata import Manager
>>> manager = Manager('manager', '')


Generator Plugin
~~~~~~~~~~~~~~~~

For the manager our sample generators must be registered as named utilities.

>>> component.provideUtility(sitePlugin,
... ISampleDataPlugin,'z3c.sampledata.site')
>>> component.provideUtility(principalPlugin,
... ISampleDataPlugin,'z3c.sampledata.principals')


Generating Sample Data
~~~~~~~~~~~~~~~~~~~~~~

Now we can add generators to the manager.

>>> manager.add('z3c.sampledata.principals',
... dependsOn=['z3c.sampledata.site',],
... contextFrom='z3c.sampledata.site')

In addition to the "hardwired" dependencies defined by the dependencies
property in each generator it is possible to add dependencies in the generator
manager.

A manager provides it's generators.

>>> manager.generators.keys()
['z3c.sampledata.principals']

We can tell the manager to generate all samples.
There is no need to add the sample generator 'z3c.sampledata.site', it is added
automatically because of the dependency of 'z3c.sampledata.principals'.

>>> infos = manager.generate(context=None, param={}, seed='something')
GenerateSite
GeneratePrincipals

>>> [info.name for info in infos]
['z3c.sampledata.site', 'z3c.sampledata.principals']


Parameters for the sample generators
------------------------------------

To have more control over the sample generation process it is possible to
setup parameters for the generators.

>>> manager = Manager('manager', '')

>>> manager.add('z3c.sampledata.site',
... param={'sitename':'samplesite'})

>>> manager.add('z3c.sampledata.principals',
... dependsOn=['z3c.sampledata.site',],
... contextFrom='z3c.sampledata.site')

>>> infos = manager.generate(context=None, param={}, seed='something')
This is site 'samplesite'
GeneratePrincipals

It is also possible to overwrite the parameters from the configuration.

>>> infos = manager.generate(context=None,
... param={'z3c.sampledata.site':
... {'sitename':'managers site'}},
... seed='something')
This is site 'managers site'
GeneratePrincipals


Cycles in the generator definition
----------------------------------

>>> manager = Manager('manager', '')
>>> manager.add('z3c.sampledata.principals',
... dependsOn=['z3c.sampledata.site',],
... contextFrom='z3c.sampledata.site')
>>> manager.add('z3c.sampledata.site',
... dependsOn=['z3c.sampledata.principals',])

>>> infos = manager.generate(context=None, param={}, seed='something')
Traceback (most recent call last):
...
CyclicDependencyError: cyclic dependency at 'z3c.sampledata.principals'


A test for a complex dependency.

>>> class Generator(object):
... interface.implements(ISampleDataPlugin)
... name = 'generator'
... dependencies = []
... schema = None
... def generate(self, context, param={}, dataSource=None, seed=None):
... return 'I am a generator'
>>> component.provideUtility(Generator(), ISampleDataPlugin,'g.1')
>>> component.provideUtility(Generator(), ISampleDataPlugin,'g.2')
>>> component.provideUtility(Generator(), ISampleDataPlugin,'g.3')
>>> manager = Manager('manager', '')
>>> manager.add('g.1')
>>> manager.add('g.2', contextFrom='g.1')
>>> manager.add('g.3', dependsOn=['g.2', 'g.1'], contextFrom='g.1')
>>> infos = manager.generate(context=None, param={}, seed=None)
>>> [info.name for info in infos]
['g.1', 'g.2', 'g.3']


Sample Data Source
------------------

A sample data generator usually gets its sample data from a data source.
Mostly it is necessary to have different data sources for different uses.

As an example, it is always a pain if the sample data for the tests use the
same data as the UI uses later to provide data for the customer to click
around.

>>> manager = Manager('manager', '')

>>> manager.addSource('z3c.datasource.principals',
... data=[{'login':'jukart', 'password':'trakuj'},
... {'login':'srichter', 'password':'rethcirs'}])

>>> manager.add('z3c.sampledata.principals',
... dataSource='z3c.datasource.principals',
... dependsOn=['z3c.sampledata.site',],
... contextFrom='z3c.sampledata.site')

>>> infos = manager.generate(context=None, param={}, seed='something')
GenerateSite
GeneratePrincipals
- jukart
- srichter


It is also possible to use adapters to act as a data source.

>>> manager = Manager('manager', '')

>>> class IPrincipalDataSource(interface.Interface):
... pass

>>> def principalDataFactory(object):
... return [{'login':'jukart', 'password':'trakuj'},
... {'login':'srichter', 'password':'rethcirs'}]

>>> component.provideAdapter(
... factory=principalDataFactory,
... adapts=(ISampleDataPlugin,),
... provides=IPrincipalDataSource,
... name='testprincipals')

>>> manager.addSource('z3c.datasource.principals',
... adapterName='testprincipals',
... adaptTo=IPrincipalDataSource)

>>> manager.add('z3c.sampledata.principals',
... dataSource='z3c.datasource.principals',
... dependsOn=['z3c.sampledata.site',],
... contextFrom='z3c.sampledata.site')

>>> infos = manager.generate(context=None, param={}, seed='something')
GenerateSite
GeneratePrincipals
- jukart
- srichter


How to setup configuration for the generator manager
----------------------------------------------------

Configuration can be done using ZCML.

<configure xmlns="http://namespaces.zope.org/zope">

<configure
xmlns:zcml="http://namespaces.zope.org/zcml"
zcml:condition="have devmode">

<utility
factory=".SampleSite"
provides="z3c.sampledata.interfaces.ISampleDataPlugin"
name="z3c.site"
/>

<utility
factory=".SamplePrincipals"
provides="z3c.sampledata.interfaces.ISampleDataPlugin"
name="z3c.principals"
/>

<SampleManager
name="Site with principals"
>
<generator name="z3c.site" />
<generator
name="z3c.principal"
dependsOn="z3c.site"
contextFrom="z3c.site" />
</SampleManager>

</configure>

</configure>


Data Sources
------------

This package implements the base functionality for data generators.
A data generator is used to provide the raw data for a sample generator.
Raw data can be read from text files in different ways.

>>> from z3c.sampledata.data import DataGenerator
>>> generator = DataGenerator(55)

The generator can read data lines from files.

>>> generator.readLines('testlines.txt')
[u'Line 1', u'Another line']

The generator can read data from CSV files.

>>> generator.readCSV('testlines.csv')
[['Line 1', 'Col 2'], ['Another line', 'Another Col']]

The generator can read a list of files from a path :

>>> import os
>>> generator.files(os.path.dirname(__file__))
['...README.txt', ...]



=======
CHANGES
=======

0.1.0 (2008-02-14)
------------------

- Initial Release