def rank_nD(self, data, axis=0):
if not axis in [0, 1]:
if not isinstance(axis, str):
raise 'Ranking error, axis can only be 1 or 2 or name'
else:
nms = data.getAxisIds()
for i in range(len(nms)):
nm = nms[i]
if axis in nm.split('___'):
axis = i
if not axis in [0, 1]:
raise 'Ranking error, axis can only be 1 or 2 or name'
if axis != 0:
data = data(order=(str(axis) + '...'))
a0 = MV2.argsort(data.filled(1.E20), axis=0)
n = a0.shape[0]
b = MV2.zeros(a0.shape, MV2.float)
sh = a0[1].shape
for i in range(n):
I = MV2.ones(sh) * i
c = MV2.array(a0[i].filled(n - 1))
b = genutil.arrayindexing.set(b, c, I)
m = data.mask
if not m is None:
b = MV2.masked_where(m, b)
else:
b = MV2.array(b)
n = MV2.count(b, 0)
n.setAxisList(b.getAxisList()[1:])
b, n = genutil.grower(b, n)
b = 100. * b / (n - 1)
b.setAxisList(data.getAxisList())
if axis != 0:
st = ''
for i in range(axis):
st += str(i + 1)
st += '0...'
data = data(order=st)
b = b(order=st)
return b
def rank_nD(self, data, axis=0):
if axis not in [0, 1]:
if not isinstance(axis, str):
raise 'Ranking error, axis can only be 1 or 2 or name'
else:
nms = data.getAxisIds()
for i in range(len(nms)):
nm = nms[i]
if axis in nm.split('___'):
axis = i
if axis not in [0, 1]:
raise 'Ranking error, axis can only be 1 or 2 or name'
if axis != 0:
data = data(order=(str(axis) + '...'))
a0 = MV2.argsort(data.filled(1.E20), axis=0)
n = a0.shape[0]
b = MV2.zeros(a0.shape, MV2.float)
sh = a0[1].shape
for i in range(n):
Indx = MV2.ones(sh) * i
c = MV2.array(a0[i].filled(n - 1))
b = genutil.arrayindexing.set(b, c, Indx)
m = data.mask
if m is not None:
b = MV2.masked_where(m, b)
else:
b = MV2.array(b)
n = MV2.count(b, 0)
n.setAxisList(b.getAxisList()[1:])
b, n = genutil.grower(b, n)
b = 100. * b / (n - 1)
b.setAxisList(data.getAxisList())
if axis != 0:
st = ''
for i in range(axis):
st += str(i + 1)
st += '0...'
data = data(order=st)
b = b(order=st)
return b
def rank(self, data, axis=0):
if not axis in [0, 1]:
if not isinstance(axis, str):
raise 'Ranking error, axis can only be 1 or 2 or name'
else:
nms = data.getAxisIds()
for i in range(len(nms)):
nm = nms[i]
if axis in nm.split('___'):
axis = i
if not axis in [0, 1]:
raise 'Ranking error, axis can only be 1 or 2 or name'
if data.ndim > 2:
raise "Ranking error, array can only be 2D"
if axis == 1:
data = MV2.transpose(data)
a0 = MV2.argsort(data.filled(1.E20), axis=0)
n = a0.shape[0]
b = MV2.zeros(a0.shape, MV2.float)
sh = a0[1].shape
for i in range(n):
I = MV2.ones(sh) * i
c = MV2.array(a0[i].filled(n - 1))
b = genutil.arrayindexing.set(b, c, I)
m = data.mask
if not m is None:
b = MV2.masked_where(m, b)
else:
b = MV2.array(b)
n = MV2.count(b, 0)
n.setAxis(0, b.getAxis(1))
b, n = genutil.grower(b, n)
b = 100. * b / (n - 1)
b.setAxisList(data.getAxisList())
if axis == 1:
b = MV2.transpose(b)
data = MV2.transpose(data)
return b
def rank(self, data, axis=0):
if axis not in [0, 1]:
if not isinstance(axis, str):
raise 'Ranking error, axis can only be 1 or 2 or name'
else:
nms = data.getAxisIds()
for i in range(len(nms)):
nm = nms[i]
if axis in nm.split('___'):
axis = i
if axis not in [0, 1]:
raise 'Ranking error, axis can only be 1 or 2 or name'
if data.ndim > 2:
raise "Ranking error, array can only be 2D"
if axis == 1:
data = MV2.transpose(data)
a0 = MV2.argsort(data.filled(1.E20), axis=0)
n = a0.shape[0]
b = MV2.zeros(a0.shape, MV2.float)
sh = a0[1].shape
for i in range(n):
Indx = MV2.ones(sh) * i
c = MV2.array(a0[i].filled(n - 1))
b = genutil.arrayindexing.set(b, c, Indx)
m = data.mask
if m is not None:
b = MV2.masked_where(m, b)
else:
b = MV2.array(b)
n = MV2.count(b, 0)
n.setAxis(0, b.getAxis(1))
b, n = genutil.grower(b, n)
b = 100. * b / (n - 1)
b.setAxisList(data.getAxisList())
if axis == 1:
b = MV2.transpose(b)
data = MV2.transpose(data)
return b
