def run(self,
V,
cname=None,
bins=10,
range=None,
density=False,
cumulative=False):
"""
generates and stores histogram data for numerical data in V
"""
super(Histogram, self).__init__()
try:
V = sorted(_flatten(list(V)))
except:
raise TypeError('V must be a list-like object')
if len(V) == 0:
raise Exception('V has zero length')
if cname == None:
self.cname = ''
else:
self.cname = cname
values, bin_edges = pystaggrelite3.hist(V,
bins=bins,
range=range,
density=density,
cumulative=cumulative)
self['values'] = values
self['bin_edges'] = bin_edges
if cname == None:
self.cname = ''
else:
self.cname = cname
self.bins = bins
self.range = range
self.density = density
self.cumulative = cumulative
def test(self):
# build the test data
V = []
for i in range(300):
V.append(normalvariate(100., 10.))
# build some weight vectors to test
W1 = [.001 for i in range(300)]
W2 = [1. for i in range(300)]
W2[0] = 10000.
W3 = [-1. for i in range(300)]
W = [W1, W2, W3, None]
# factorially examine the conditions in this DictSet
# see: http://code.google.com/p/dictset/
ds = DictSet({
'bins': [1, 2, 10, 171, 500],
'range': [(0, 100), None],
'density': [True, False],
'weights': [0, 1, 2, 3],
'cumulative': [True, False]
})
for b, r, d, w, c in ds.unique_combinations(
['bins', 'range', 'density', 'weights', 'cumulative']):
print(b, r, d, w, c)
DN, DB = pystaggrelite3.hist(V, b, r, d, W[w], c)
pylab.figure()
RN, RB, patches = pylab.hist(V, b, r, d, W[w], c)
pylab.close()
for d, r in zip(DN, RN):
self.assertAlmostEqual(d, r)
for d, r in zip(DB, RB):
self.assertAlmostEqual(d, r)
def run(self, V, cname=None, bins=10,
range=None, density=False, cumulative=False):
"""
generates and stores histogram data for numerical data in V
"""
super(Histogram, self).__init__()
try:
V = sorted(_flatten(list(V)))
except:
raise TypeError('V must be a list-like object')
if len(V) == 0:
raise Exception('V has zero length')
if cname == None:
self.cname = ''
else:
self.cname = cname
values, bin_edges = pystaggrelite3.hist(V, bins=bins,
range=range, density=density, cumulative=cumulative)
self['values'] = values
self['bin_edges'] = bin_edges
if cname == None:
self.cname = ''
else:
self.cname = cname
self.bins = bins
self.range = range
self.density = density
self.cumulative = cumulative
def test(self):
# build the test data
V=[]
for i in range(300):
V.append(normalvariate(100.,10.))
# build some weight vectors to test
W1=[.001 for i in range(300)]
W2=[1. for i in range(300)]
W2[0]=10000.
W3=[-1. for i in range(300)]
W=[W1, W2, W3, None]
# factorially examine the conditions in this DictSet
# see: http://code.google.com/p/dictset/
ds = DictSet({'bins':[1,2,10,171,500],
'range':[(0,100),None],
'density':[True, False],
'weights':[0, 1, 2, 3],
'cumulative':[True, False]})
for b,r,d,w,c in ds.unique_combinations(
['bins','range','density','weights','cumulative']):
print(b,r,d,w,c)
DN, DB = pystaggrelite3.hist(V, b, r, d, W[w], c)
pylab.figure()
RN, RB, patches = pylab.hist(V, b, r, d, W[w], c)
pylab.close()
for d,r in zip(DN, RN):
self.assertAlmostEqual(d, r)
for d,r in zip(DB, RB):
self.assertAlmostEqual(d, r)
