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={}): 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)