def __init__(self, docstring, config={}):
NumpyDocString.__init__(self, docstring, config=config)
self.load_config(config)
def __init__(self, docstring, config={}, warn=None):
self.use_plots = config.get('use_plots', False)
NumpyDocString.__init__(self, docstring, config=config, warn=warn)
def __init__(self, docstring, config=None):
config = {} if config is None else config
self.use_plots = config.get('use_plots', False)
NumpyDocString.__init__(self, docstring, config=config)
def __init__(self, docstring, config={}):
self.use_plots = config.get("use_plots", False)
NumpyDocString.__init__(self, docstring, config=config)
def __init__(self, docstring, config={}):
NumpyDocString.__init__(self, docstring, config=config)
self.load_config(config)
def __init__(self, docstring, config={}):
self.use_plots = config.get('use_plots', False)
NumpyDocString.__init__(self, docstring, config=config)
>>> cov = [[1,0],[1,0]]
>>> x = multivariate_normal(mean,cov,(3,3))
>>> print x.shape
(3, 3, 2)
The following is probably true, given that 0.6 is roughly twice the
standard deviation:
>>> print list( (x[0,0,:] - mean) < 0.6 )
[True, True]
.. index:: random
:refguide: random;distributions, random;gauss
'''
doc = NumpyDocString(doc_txt)
def test_signature():
assert doc['Signature'].startswith('numpy.multivariate_normal(')
assert doc['Signature'].endswith('spam=None)')
def test_summary():
assert doc['Summary'][0].startswith('Draw values')
assert doc['Summary'][-1].endswith('covariance.')
def test_extended_summary():
assert doc['Extended Summary'][0].startswith('The multivariate normal')
def test_parameters():
assert_equal(len(doc['Parameters']), 3)
