def describe(obj, stats=None, format=pprint.pformat):
'''Return the object decription depending on its type.
Usefull with numpy and cdms variables/axes
:Params:
- **obj**: The object to describe.
- **stats**: If True, include numerical information like min, max, mean, count, ...
:Return:
- The object's summary string
'''
try:
import cdms2, MV2, numpy
from cdms2.avariable import AbstractVariable
from cdms2.axis import AbstractAxis
if isinstance(obj, configobj.ConfigObj):
obj = obj.dict()
if not isinstance(obj, (cdms2.avariable.AbstractVariable,
cdms2.axis.AbstractAxis, numpy.ndarray)):
return format(obj)
otype = obj.__class__.__name__ # type(obj)
sh, sz = MV2.shape(obj), MV2.size(obj)
mi = ma = av = co = None
if stats and sz:
try:
if hasattr(obj, 'typecode') and obj.typecode() not in ('c', ):
mi, ma, av, co = MV2.min(obj), MV2.max(obj), MV2.average(
obj), MV2.count(obj)
except:
logger.exception('Error getting statistics of object %s',
type(obj))
if isinstance(obj, AbstractVariable):
return '%s: %s, shape: (%s), order: %s%s' % (
otype, obj.id, ','.join(
'%s=%s' % (a.id, a.shape[0])
for a in obj.getAxisList()), obj.getOrder(),
stats and ', min: %s, max: %s, avg: %s, count: %s' %
(mi, ma, av, co) or '')
elif isinstance(obj, AbstractAxis):
return '%s: %s, size: %s%s' % (
otype, obj.id, sz,
stats and ', min: %s, max: %s, avg: %s, count: %s' %
(mi, ma, av, co) or '')
else: #if isinstance(obj, numpy.ndarray):
return '%s: shape: %s%s' % (
otype, sh, stats and ', min: %s, max: %s, avg: %s, count: %s' %
(mi, ma, av, co) or '')
except Exception as e:
logger.exception('Error getting description of object %s', type(obj))
return '%s (error getting description)' % (type(obj))
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(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 _get(self):
if 'relative' in self.portrait_types.keys():
d = self.portrait_types['relative']
vals = d[1]
real_value = getattr(self, d[0])
real = self.__get()
setattr(self, d[0], vals[0])
a0 = self.__get()
sh = list(a0.shape)
sh.insert(0, 1)
a0 = MV2.reshape(a0, sh)
for v in vals[1:]:
setattr(self, d[0], v)
tmp = self.__get()
tmp = MV2.reshape(tmp, sh)
a0 = MV2.concatenate((a0, tmp))
a0 = MV2.sort(a0, 0).filled()
real2 = real.filled()
a0 = MV2.reshape(a0, (a0.shape[0], sh[1] * sh[2]))
real2 = MV2.reshape(real2, (sh[1] * sh[2], ))
a0 = MV2.transpose(a0)
indices = []
for i in range(len(real2)):
indices.append(MV2.searchsorted(a0[i], real2[i]))
indices = MV2.array(indices)
indices = MV2.reshape(indices, (sh[1], sh[2]))
if not ((real.mask is None) or (real.mask is MV2.nomask)):
indices = MV2.masked_where(real.mask, indices)
a = MV2.masked_equal(a0, 1.e20)
a = MV2.count(a, 1)
a = MV2.reshape(a, indices.shape)
indices = indices / a * 100
setattr(self, d[0], real_value)
indices.setAxisList(real.getAxisList())
## print indices.shape
return indices
else:
return self.__get()
## masked_less(x, value) = x masked where x < value
xl = MV2.masked_less(xouter, 160)
## masked_less_equal(x, value)
## masked_less_equal(x, value) = x masked where x <= value
## masked_not_equal(x, value)
## masked_not_equal(x, value) = x masked where x != value
## masked_outside(x, v1, v2)
## x with mask of all values of x that are outside [v1,v2]
xmo = MV2.masked_outside(xouter, 120, 160)
## count(a, axis=None)
## Count of the non-masked elements in a, or along a certain axis.
xcount = MV2.count(xmo, 0)
xcount2 = MV2.count(xmo, 1)
## masked_where(condition, x, copy=1)
## Return x as an array masked where condition is true.
## Also masked where x or condition masked.
xmwhere = MV2.masked_where(
MV2.logical_and(MV2.greater(xouter, 120), MV2.less(xouter, 160)), xouter)
## maximum(a, b=None)
## returns maximum element of a single array, or elementwise
maxval = MV2.maximum(xouter)
xmax = MV2.maximum(ud, vd)
xmax = MV2.maximum.reduce(ud)
xmax = MV2.maximum.reduce(vd)
xmax2 = numpy.ma.maximum.reduce(vd.subSlice(), axis=0)
b137 = MV2.zeros(137 + 1, typecode=MV2.float32)
f = open('L137.dta')
lines = f.readlines()
f.close()
for i, line in enumerate(lines):
tmp = line.split()
a137[i] = float(tmp[0])
b137[i] = float(tmp[1])
ph137 = a137 + b137 * ps
tmp = MV2.where(ph137 > ph[nlev], 1.e20, ph137)
tmp = MV2.masked_values(tmp, 1.e20)
nlev0 = MV2.count(tmp)
tmp = ph137[0:nlev0]
tmp = tmp[::-1]
nlev2 = nlev + 1 + len(tmp)
ph2 = MV2.zeros(nlev2, typecode=MV2.float)
ph2[0:nlev + 1] = ph[:]
ph2[nlev + 1:] = tmp[:]
a = MV2.zeros(nlev2, typecode=MV2.float)
b = MV2.zeros(nlev2, typecode=MV2.float)
tmp = a137[0:nlev0]
a[nlev + 1:] = tmp[::-1]
def testCount(self):
xouter = MV2.outerproduct(MV2.arange(5.), [1] * 10)
masked = MV2.masked_outside(xouter, 1, 3)
self.assertEqual(MV2.count(masked), 30)
self.assertTrue(MV2.allequal(MV2.count(masked, 0), 3))
self.assertTrue((MV2.count(masked, 1) == [0, 10, 10, 10, 0]).all())
