def test_ranking_5(self):
"farray.ranking_5"
x = np.array([3.0, 1.0, 2.0])[:, None]
desired = np.array([0.0, 0.0, 0.0])[:, None]
with np.errstate(invalid='ignore'):
actual = ranking(x, axis=1)
assert_almost_equal(actual, desired)
def test_ranking_5(self):
"farray.ranking_5"
x = np.array([3.0, 1.0, 2.0])[:,None]
desired = np.array([0.0, 0.0, 0.0])[:,None]
with np.errstate(invalid='ignore'):
actual = ranking(x, axis=1)
assert_almost_equal(actual, desired)
def test_ranking_17(self):
"farray.ranking_17"
x = np.array([[nan, 1.0, 1.0, 1.0], [1.0, 1.5, 2.0, 2.0],
[2.0, nan, 1.0, 2.0], [2.0, 1.5, 1.0, 2.0]])
desired = np.array([[nan, -1.0, -1.0, -1.0], [-1.0, 0.0, 1.0, 1.0],
[1.0, nan, -1.0, 1.0], [1.0, 0.0, -1.0, 1.0]])
desired *= 8.0 / 13
actual = ranking(x, axis=None)
assert_almost_equal(actual, desired)
def test_ranking_16(self):
"farray.ranking_16"
x = np.array([[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 2.0, 2.0],
[2.0, 2.0, 1.0, 2.0], [2.0, 2.0, 1.0, 2.0]])
desired = np.array([[-1.0, -1.0, -1.0, -1.0], [-1.0, -1.0, 1.0, 1.0],
[1.0, 1.0, -1.0, 1.0], [1.0, 1.0, -1.0, 1.0]])
desired *= 8.0 / 15
actual = ranking(x, axis=None)
assert_almost_equal(actual, desired)
def test_ranking_2(self):
"farray.ranking_2"
x = np.array([[1.0, nan, 2.0, nan, nan], [2.0, 2.0, nan, nan, nan],
[3.0, 3.0, 3.0, 3.0, nan]])
desired = np.array([[-1.0, nan, -1.0, nan, nan],
[0.0, -1.0, nan, nan, nan],
[1.0, 1.0, 1.0, 0.0, nan]])
with np.errstate(invalid='ignore'):
actual = ranking(x)
assert_almost_equal(actual, desired)
def test_ranking_8(self):
"farray.ranking_8"
x = np.array([[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 2.0, 2.0],
[2.0, 2.0, 3.0, 2.0], [2.0, 3.0, 3.0, 3.0]])
desired = np.array([[-2.0 / 3, -2.0 / 3, -1.0, -1.0],
[-2.0 / 3, -2.0 / 3, -1.0 / 3, 0.0],
[2.0 / 3, 1.0 / 3, 2.0 / 3, 0.0],
[2.0 / 3, 1.0, 2.0 / 3, 1.0]])
actual = ranking(x, 0)
assert_almost_equal(actual, desired)
def test_ranking_7(self):
"farray.ranking_7"
x = np.array([[1.0, nan, 1.0, nan, nan], [1.0, 1.0, nan, nan, nan],
[1.0, 2.0, 0.0, 2.0, nan], [1.0, 3.0, 1.0, 1.0, 0.0]])
desired = np.array([[0.0, nan, 0.0, nan, nan],
[0.0, 0.0, nan, nan, nan],
[-1.0 / 3, 2.0 / 3, -1.0, 2.0 / 3, nan],
[0.0, 1.0, 0.0, 0.0, -1.0]])
actual = ranking(x, 1)
assert_almost_equal(actual, desired)
def test_ranking_11(self):
"farray.ranking_11"
x = np.array([[ nan, nan],
[ nan, nan],
[ nan, nan]])
desired = np.array([[ nan, nan],
[ nan, nan],
[ nan, nan]])
actual = ranking(x, axis=0)
assert_almost_equal(actual, desired)
def test_ranking_3(self):
"farray.ranking_3"
x = np.array([[1.0, nan, 2.0, nan, nan], [2.0, 2.0, nan, nan, nan],
[3.0, 3.0, 3.0, 3.0, nan], [4.0, 2.0, 3.0, 1.0, 0.0]])
desired = np.array([[-1.0, nan, 1.0, nan, nan],
[0.0, 0.0, nan, nan, nan],
[0.0, 0.0, 0.0, 0.0, nan],
[1.0, 0.0, 0.5, -0.5, -1.0]])
actual = ranking(x, axis=1)
assert_almost_equal(actual, desired)
def test_ranking_11(self):
"farray.ranking_11"
x = np.array([[ nan, nan],
[ nan, nan],
[ nan, nan]])
desired = np.array([[ nan, nan],
[ nan, nan],
[ nan, nan]])
actual = ranking(x, axis=0)
assert_almost_equal(actual, desired)
def test_ranking_2(self):
"farray.ranking_2"
x = np.array([[ 1.0, nan, 2.0, nan, nan],
[ 2.0, 2.0, nan, nan, nan],
[ 3.0, 3.0, 3.0, 3.0, nan]])
desired = np.array([[-1.0, nan, -1.0, nan, nan],
[ 0.0, -1.0, nan, nan, nan],
[ 1.0, 1.0, 1.0, 0.0, nan]])
with np.errstate(invalid='ignore'):
actual = ranking(x)
assert_almost_equal(actual, desired)
def test_ranking_9(self):
"farray.ranking_9"
x = np.array([[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 2.0, 2.0],
[2.0, 2.0, 3.0, 2.0], [2.0, 3.0, 3.0, 3.0]])
x = x.T
desired = np.array([[-2.0 / 3, -2.0 / 3, -1.0, -1.0],
[-2.0 / 3, -2.0 / 3, -1.0 / 3, 0.0],
[2.0 / 3, 1.0 / 3, 2.0 / 3, 0.0],
[2.0 / 3, 1.0, 2.0 / 3, 1.0]])
desired = desired.T
actual = ranking(x, 1)
assert_almost_equal(actual, desired)
def test_ranking_3(self):
"farray.ranking_3"
x = np.array([[ 1.0, nan, 2.0, nan, nan],
[ 2.0, 2.0, nan, nan, nan],
[ 3.0, 3.0, 3.0, 3.0 , nan],
[ 4.0, 2.0, 3.0, 1.0 , 0.0 ]])
desired = np.array([[-1.0, nan, 1.0, nan, nan],
[ 0.0, 0.0, nan, nan, nan],
[ 0.0, 0.0, 0.0, 0.0, nan],
[ 1.0, 0.0, 0.5, -0.5, -1.0]])
actual = ranking(x, axis=1)
assert_almost_equal(actual, desired)
def test_ranking_8(self):
"farray.ranking_8"
x = np.array([[ 1.0, 1.0, 1.0, 1.0],
[ 1.0, 1.0, 2.0, 2.0],
[ 2.0, 2.0, 3.0, 2.0],
[ 2.0, 3.0, 3.0, 3.0]])
desired = np.array([[-2.0/3, -2.0/3, -1.0, -1.0],
[-2.0/3, -2.0/3, -1.0/3, 0.0],
[ 2.0/3, 1.0/3, 2.0/3, 0.0],
[ 2.0/3, 1.0, 2.0/3, 1.0]])
actual = ranking(x, 0)
assert_almost_equal(actual, desired)
def test_ranking_7(self):
"farray.ranking_7"
x = np.array([[ 1.0, nan, 1.0, nan, nan],
[ 1.0, 1.0, nan, nan, nan],
[ 1.0, 2.0, 0.0, 2.0, nan],
[ 1.0, 3.0, 1.0, 1.0, 0.0]])
desired = np.array([[ 0.0, nan , 0.0, nan , nan],
[ 0.0, 0.0 , nan, nan , nan],
[-1.0/3, 2.0/3, -1.0, 2.0/3, nan],
[ 0.0, 1.0 , 0.0, 0.0 , -1.0]])
actual = ranking(x, 1)
assert_almost_equal(actual, desired)
def test_ranking_17(self):
"farray.ranking_17"
x = np.array([[ nan, 1.0, 1.0, 1.0],
[ 1.0, 1.5, 2.0, 2.0],
[ 2.0, nan, 1.0, 2.0],
[ 2.0, 1.5, 1.0, 2.0]])
desired = np.array([[ nan, -1.0, -1.0, -1.0],
[-1.0, 0.0, 1.0, 1.0],
[ 1.0, nan, -1.0, 1.0],
[ 1.0, 0.0, -1.0, 1.0]])
desired *= 8.0 / 13
actual = ranking(x, axis=None)
assert_almost_equal(actual, desired)
def test_ranking_16(self):
"farray.ranking_16"
x = np.array([[ 1.0, 1.0, 1.0, 1.0],
[ 1.0, 1.0, 2.0, 2.0],
[ 2.0, 2.0, 1.0, 2.0],
[ 2.0, 2.0, 1.0, 2.0]])
desired = np.array([[-1.0, -1.0, -1.0, -1.0],
[-1.0, -1.0, 1.0, 1.0],
[ 1.0, 1.0, -1.0, 1.0],
[ 1.0, 1.0, -1.0, 1.0]])
desired *= 8.0 / 15
actual = ranking(x, axis=None)
assert_almost_equal(actual, desired)
def test_ranking_9(self):
"farray.ranking_9"
x = np.array([[ 1.0, 1.0, 1.0, 1.0],
[ 1.0, 1.0, 2.0, 2.0],
[ 2.0, 2.0, 3.0, 2.0],
[ 2.0, 3.0, 3.0, 3.0]])
x = x.T
desired = np.array([[-2.0/3, -2.0/3, -1.0, -1.0],
[-2.0/3, -2.0/3, -1.0/3, 0.0],
[ 2.0/3, 1.0/3, 2.0/3, 0.0],
[ 2.0/3, 1.0, 2.0/3, 1.0]])
desired = desired.T
actual = ranking(x, 1)
assert_almost_equal(actual, desired)
def group_ranking(x, groups, norm='-1,1', axis=0):
"""
Ranking within groups along axis.
Parameters
----------
x : ndarray
Data to be ranked.
groups : list
List of group membership of each element along axis=0.
norm : str
A string that specifies the normalization:
'0,N-1' Zero to N-1 ranking
'-1,1' Scale zero to N-1 ranking to be between -1 and 1
'gaussian' Rank data then scale to a Gaussian distribution
axis : int, {default: 0}
axis along which the ranking is calculated
Returns
-------
idx : ndarray
The ranked data. The dtype of the output is always np.float even if
the dtype of the input is int.
Notes
----
If there is only one non-NaN value within a group along the axis=0, then
that value is set to the midpoint of the specified normalization method.
For '0,N-1' normalization, note that N is the number of element in the
group even in there are NaNs.
"""
# Find set of unique groups
ugroups = unique_group(groups)
# Convert groups to a numpy array
groups = np.asarray(groups)
# Loop through unique groups and normalize
xnorm = np.nan * np.zeros(x.shape)
for group in ugroups:
idx = groups == group
idxall = [slice(None)] * x.ndim
idxall[axis] = idx
xnorm[idxall] = ranking(x[idxall], axis=axis, norm=norm)
return xnorm
def group_ranking(x, groups, norm='-1,1', axis=0):
"""
Ranking within groups along axis.
Parameters
----------
x : ndarray
Data to be ranked.
groups : list
List of group membership of each element along axis=0.
norm : str
A string that specifies the normalization:
'0,N-1' Zero to N-1 ranking
'-1,1' Scale zero to N-1 ranking to be between -1 and 1
'gaussian' Rank data then scale to a Gaussian distribution
axis : int, {default: 0}
axis along which the ranking is calculated
Returns
-------
idx : ndarray
The ranked data. The dtype of the output is always np.float even if
the dtype of the input is int.
Notes
----
If there is only one non-NaN value within a group along the axis=0, then
that value is set to the midpoint of the specified normalization method.
For '0,N-1' normalization, note that N is the number of element in the
group even in there are NaNs.
"""
# Find set of unique groups
ugroups = unique_group(groups)
# Convert groups to a numpy array
groups = np.asarray(groups)
# Loop through unique groups and normalize
xnorm = np.nan * np.zeros(x.shape)
for group in ugroups:
idx = groups == group
idxall = [slice(None)] * x.ndim
idxall[axis] = idx
xnorm[idxall] = ranking(x[idxall], axis=axis, norm=norm)
return xnorm
def test_ranking_15(self):
"farray.ranking_15"
x = np.array([ -np.inf, nan, 1.0, np.inf])
desired = np.array([-1.0, nan, 0.0, 1.0])
actual = ranking(x, axis=None)
assert_almost_equal(actual, desired)
def test_ranking_13(self):
"farray.ranking_13"
x = np.array([ 1.0, np.inf, 2.0])
desired = np.array([-1.0, 1.0, 0.0])
actual = ranking(x, axis=0)
assert_almost_equal(actual, desired)
def test_ranking_4(self):
"farray.ranking_4"
x = np.array([3.0, 1.0, 2.0])[:, None]
desired = np.array([1.0, -1.0, 0.0])[:, None]
actual = ranking(x, axis=0)
assert_almost_equal(actual, desired)
def test_ranking_13(self):
"farray.ranking_13"
x = np.array([1.0, np.inf, 2.0])
desired = np.array([-1.0, 1.0, 0.0])
actual = ranking(x, axis=0)
assert_almost_equal(actual, desired)
def test_ranking_4(self):
"farray.ranking_4"
x = np.array([3.0, 1.0, 2.0])[:,None]
desired = np.array([1.0,-1.0, 0.0])[:,None]
actual = ranking(x, axis=0)
assert_almost_equal(actual, desired)
def test_ranking_15(self):
"farray.ranking_15"
x = np.array([-np.inf, nan, 1.0, np.inf])
desired = np.array([-1.0, nan, 0.0, 1.0])
actual = ranking(x, axis=None)
assert_almost_equal(actual, desired)
