def test_2d_wo_missing(self):
# Test corrcoef on 1 2D variable w/o missing values
x = self.data.reshape(3, 4)
assert_almost_equal(np.corrcoef(x), corrcoef(x))
assert_almost_equal(np.corrcoef(x, rowvar=False), corrcoef(x, rowvar=False))
with catch_warn_mae():
warnings.simplefilter("ignore")
assert_almost_equal(np.corrcoef(x, rowvar=False, bias=True), corrcoef(x, rowvar=False, bias=True))
def test_1d_wo_missing(self):
# Test cov on 1D variable w/o missing values
x = self.data
assert_almost_equal(np.corrcoef(x), corrcoef(x))
assert_almost_equal(np.corrcoef(x, rowvar=False),
corrcoef(x, rowvar=False))
assert_almost_equal(np.corrcoef(x, rowvar=False, bias=True),
corrcoef(x, rowvar=False, bias=True))
def test_2d_wo_missing(self):
# Test corrcoef on 1 2D variable w/o missing values
x = self.data.reshape(3, 4)
assert_almost_equal(np.corrcoef(x), corrcoef(x))
assert_almost_equal(np.corrcoef(x, rowvar=False),
corrcoef(x, rowvar=False))
assert_almost_equal(np.corrcoef(x, rowvar=False, bias=True),
corrcoef(x, rowvar=False, bias=True))
def test_1d_w_missing(self):
# Test corrcoef 1 1D variable w/missing values
x = self.data
x[-1] = masked
x -= x.mean()
nx = x.compressed()
assert_almost_equal(np.corrcoef(nx), corrcoef(x))
assert_almost_equal(np.corrcoef(nx, rowvar=False),
corrcoef(x, rowvar=False))
with catch_warn_mae():
warnings.simplefilter("ignore")
assert_almost_equal(np.corrcoef(nx, rowvar=False, bias=True),
corrcoef(x, rowvar=False, bias=True))
try:
corrcoef(x, allow_masked=False)
except ValueError:
pass
# 2 1D variables w/ missing values
nx = x[1:-1]
assert_almost_equal(np.corrcoef(nx, nx[::-1]), corrcoef(x, x[::-1]))
assert_almost_equal(np.corrcoef(nx, nx[::-1], rowvar=False),
corrcoef(x, x[::-1], rowvar=False))
with catch_warn_mae():
warnings.simplefilter("ignore")
# ddof and bias have no or negligible effect on the function
assert_almost_equal(np.corrcoef(nx, nx[::-1]),
corrcoef(x, x[::-1], bias=1))
assert_almost_equal(np.corrcoef(nx, nx[::-1]),
corrcoef(x, x[::-1], ddof=2))
def test_ddof(self):
# ddof raises DeprecationWarning
x, y = self.data, self.data2
expected = np.corrcoef(x)
expected2 = np.corrcoef(x, y)
with catch_warn_mae():
warnings.simplefilter("always")
assert_warns(DeprecationWarning, corrcoef, x, ddof=-1)
warnings.simplefilter("ignore")
# ddof has no or negligible effect on the function
assert_almost_equal(np.corrcoef(x, ddof=0), corrcoef(x, ddof=0))
assert_almost_equal(corrcoef(x, ddof=-1), expected)
assert_almost_equal(corrcoef(x, y, ddof=-1), expected2)
assert_almost_equal(corrcoef(x, ddof=3), expected)
assert_almost_equal(corrcoef(x, y, ddof=3), expected2)
def test_2d_w_missing(self):
# Test corrcoef on 2D variable w/ missing value
x = self.data
x[-1] = masked
x = x.reshape(3, 4)
test = corrcoef(x)
control = np.corrcoef(x)
assert_almost_equal(test[:-1, :-1], control[:-1, :-1])
with catch_warn_mae():
warnings.simplefilter("ignore")
# ddof and bias have no or negligible effect on the function
assert_almost_equal(corrcoef(x, ddof=-2)[:-1, :-1], control[:-1, :-1])
assert_almost_equal(corrcoef(x, ddof=3)[:-1, :-1], control[:-1, :-1])
assert_almost_equal(corrcoef(x, bias=1)[:-1, :-1], control[:-1, :-1])
def test_2d_with_missing(self):
# Test corrcoef on 2D variable w/ missing value
x = self.data
x[-1] = masked
x = x.reshape(3, 4)
test = corrcoef(x)
control = np.corrcoef(x)
assert_almost_equal(test[:-1, :-1], control[:-1, :-1])
with suppress_warnings() as sup:
sup.filter(DeprecationWarning, "bias and ddof have no effect")
# ddof and bias have no or negligible effect on the function
assert_almost_equal(
corrcoef(x, ddof=-2)[:-1, :-1], control[:-1, :-1])
assert_almost_equal(
corrcoef(x, ddof=3)[:-1, :-1], control[:-1, :-1])
assert_almost_equal(
corrcoef(x, bias=1)[:-1, :-1], control[:-1, :-1])
def test_2d_w_missing(self):
# Test corrcoef on 2D variable w/ missing value
x = self.data
x[-1] = masked
x = x.reshape(3, 4)
test = corrcoef(x)
control = np.corrcoef(x)
assert_almost_equal(test[:-1, :-1], control[:-1, :-1])
with catch_warn_mae():
warnings.simplefilter("ignore")
# ddof and bias have no or negligible effect on the function
assert_almost_equal(
corrcoef(x, ddof=-2)[:-1, :-1], control[:-1, :-1])
assert_almost_equal(
corrcoef(x, ddof=3)[:-1, :-1], control[:-1, :-1])
assert_almost_equal(
corrcoef(x, bias=1)[:-1, :-1], control[:-1, :-1])
def test_2d_w_missing(self):
# Test corrcoef on 2D variable w/ missing value
x = self.data
x[-1] = masked
x = x.reshape(3, 4)
test = corrcoef(x)
control = np.corrcoef(x)
assert_almost_equal(test[:-1, :-1], control[:-1, :-1])
def test_2d_w_missing(self):
# Test corrcoef on 2D variable w/ missing value
x = self.data
x[-1] = masked
x = x.reshape(3, 4)
test = corrcoef(x)
control = np.corrcoef(x)
assert_almost_equal(test[:-1, :-1], control[:-1, :-1])
def test_bias(self):
x, y = self.data, self.data2
expected = np.corrcoef(x)
# bias raises DeprecationWarning
with catch_warn_mae():
warnings.simplefilter("always")
assert_warns(DeprecationWarning, corrcoef, x, y, True, False)
assert_warns(DeprecationWarning, corrcoef, x, y, True, True)
assert_warns(DeprecationWarning, corrcoef, x, bias=False)
warnings.simplefilter("ignore")
# bias has no or negligible effect on the function
assert_almost_equal(corrcoef(x, bias=1), expected)
def test_bias(self):
x, y = self.data, self.data2
expected = np.corrcoef(x)
# bias raises DeprecationWarning
with suppress_warnings() as sup:
warnings.simplefilter("always")
assert_warns(DeprecationWarning, corrcoef, x, y, True, False)
assert_warns(DeprecationWarning, corrcoef, x, y, True, True)
assert_warns(DeprecationWarning, corrcoef, x, bias=False)
sup.filter(DeprecationWarning, "bias and ddof have no effect")
# bias has no or negligible effect on the function
assert_almost_equal(corrcoef(x, bias=1), expected)
def test_bias(self):
x, y = self.data, self.data2
expected = np.corrcoef(x)
# bias raises DeprecationWarning
with catch_warn_mae():
warnings.simplefilter("always")
assert_warns(DeprecationWarning, corrcoef, x, y, True, False)
assert_warns(DeprecationWarning, corrcoef, x, y, True, True)
assert_warns(DeprecationWarning, corrcoef, x, bias=False)
warnings.simplefilter("ignore")
# bias has no or negligible effect on the function
assert_almost_equal(corrcoef(x, bias=1), expected)
def G_reg1d(xx, yy, ww=None):
"""
description needed
ww: weights
"""
rtn = []
ab = polyfit(xx, yy, 1, w=ww)
rtn.append(ab[0])
rtn.append(ab[1])
rtn.append(std(yy) / std(xx))
rtn.append(mean(yy) - rtn[2] * mean(xx))
r = corrcoef(xx, yy)
rr = r[0, 1] * r[0, 1]
rtn.append(rr)
return rtn
def G_reg1d(xx, yy, ww=None):
'''
计算斜率和截距
ww: weights
'''
rtn = []
ab = polyfit(xx, yy, 1, w=ww)
rtn.append(ab[0])
rtn.append(ab[1])
rtn.append(std(yy) / std(xx))
rtn.append(mean(yy) - rtn[2] * mean(xx))
r = corrcoef(xx, yy)
rr = r[0, 1] * r[0, 1]
rtn.append(rr)
return rtn
def test_1d_w_missing(self):
"Test corrcoef 1 1D variable w/missing values"
x = self.data
x[-1] = masked
x -= x.mean()
nx = x.compressed()
assert_almost_equal(np.corrcoef(nx), corrcoef(x))
assert_almost_equal(np.corrcoef(nx, rowvar=False),
corrcoef(x, rowvar=False))
assert_almost_equal(np.corrcoef(nx, rowvar=False, bias=True),
corrcoef(x, rowvar=False, bias=True))
#
try:
corrcoef(x, allow_masked=False)
except ValueError:
pass
#
# 2 1D variables w/ missing values
nx = x[1:-1]
assert_almost_equal(np.corrcoef(nx, nx[::-1]), corrcoef(x, x[::-1]))
assert_almost_equal(np.corrcoef(nx, nx[::-1], rowvar=False),
corrcoef(x, x[::-1], rowvar=False))
assert_almost_equal(np.corrcoef(nx, nx[::-1], rowvar=False, bias=True),
corrcoef(x, x[::-1], rowvar=False, bias=True))
def test_1d_w_missing(self):
# Test corrcoef 1 1D variable w/missing values
x = self.data
x[-1] = masked
x -= x.mean()
nx = x.compressed()
assert_almost_equal(np.corrcoef(nx), corrcoef(x))
assert_almost_equal(np.corrcoef(nx, rowvar=False),
corrcoef(x, rowvar=False))
assert_almost_equal(np.corrcoef(nx, rowvar=False, bias=True),
corrcoef(x, rowvar=False, bias=True))
#
try:
corrcoef(x, allow_masked=False)
except ValueError:
pass
#
# 2 1D variables w/ missing values
nx = x[1:-1]
assert_almost_equal(np.corrcoef(nx, nx[::-1]), corrcoef(x, x[::-1]))
assert_almost_equal(np.corrcoef(nx, nx[::-1], rowvar=False),
corrcoef(x, x[::-1], rowvar=False))
assert_almost_equal(np.corrcoef(nx, nx[::-1], rowvar=False, bias=True),
corrcoef(x, x[::-1], rowvar=False, bias=True))
def test_ddof(self):
"Test ddof keyword"
x = self.data
assert_almost_equal(np.corrcoef(x, ddof=0), corrcoef(x, ddof=0))
def test_ddof(self):
# Test ddof keyword
x = self.data
assert_almost_equal(np.corrcoef(x, ddof=0), corrcoef(x, ddof=0))
