def __init__(self, *args, **kwargs):
NumericTestCase.__init__(self, *args, **kwargs)
# Read data from external test data file.
# (In this case, produced by numpy and Python 2.5.)
location = self.get_data_location("support/test_data.zip")
# Now read the data from that file.
zf = zipfile.ZipFile(location, "r")
self.data = pickle.loads(zf.read("data.pkl"))
self.expected = pickle.loads(zf.read("results.pkl"))
zf.close()
def __init__(self, *args, **kwargs):
NumericTestCase.__init__(self, *args, **kwargs)
self.func = stats.pvariance
# Standard test data.
self.data = [4.0, 7.0, 13.0, 16.0]
self.expected = 22.5 # Exact population variance of self.data.
# Test data for exact (uniform distribution) test:
self.uniform_data = range(10000)
self.uniform_expected = (10000**2 - 1)/12
# Expected result calculated by HP-48GX:
self.hp_expected = 88349.2408884
# Scaling factor when you duplicate each data point:
self.scale = 1.0
def __init__(self, *args, **kwargs):
NumericTestCase.__init__(self, *args, **kwargs)
self.xdata = [
1 / 64,
1 / 32,
1 / 16,
1 / 8,
1 / 4,
1 / 2,
1,
3 / 2,
5 / 2,
7 / 2,
9 / 2,
11 / 2,
13 / 2,
15 / 2,
17 / 2,
19 / 2,
]
self.ydata = [
1 / 4,
1 / 2,
3 / 2,
1,
1 / 2,
3 / 2,
1,
5 / 4,
5 / 2,
7 / 4,
9 / 4,
11 / 4,
11 / 4,
7 / 4,
13 / 4,
17 / 4,
]
assert len(self.xdata) == len(self.ydata) == 16
def __init__(self, *args, **kwargs):
NumericTestCase.__init__(self, *args, **kwargs)
self.func = stats.multivar.corr