def __myGetAxisWeights(x, i, axisoptions=None):
""" Assume x is an MV2 duh! and get the weights associated with axis index i.
index i can be any of the dimensions in x whose bounds are available.
If bounds are not available then an error is raised.
"""
if x.getAxis(i).isLatitude():
Lataxis = x.getAxis(i)
xgr = cdms2.createGenericGrid(Lataxis[:], numpy.array(range(1), numpy.float), latBounds=Lataxis.getBounds())
xlatwt, xlonwt = xgr.getWeights()
return xlatwt
elif x.getAxis(i).isLongitude():
Lonaxis = x.getAxis(i)
xgr = cdms2.createGenericGrid(numpy.array(range(1), numpy.float), Lonaxis[:], lonBounds=Lonaxis.getBounds())
xlatwt, xlonwt = xgr.getWeights()
return xlonwt
else:
axis_bounds = x.getAxis(i).getBounds()
if axis_bounds is None:
ax=x.getAxis(i)
if axisoptions is not None:
axo=cdms2.orderparse(axisoptions)
if (i in axo ) or (ax.isTime() and 't' in axo) or (ax.isLevel() and 'z' in axo) or ('('+ax.id+')' in axo):
raise AveragerError, 'Bounds not available to compute weights on dimension: '+ax.id
else:
axis_wts=numpy.ones(x.getAxis(i)[:].shape)
else:
raise AveragerError, 'Bounds not available to compute weights for dimension: '+ax.id
else:
axis_wts = abs(axis_bounds[..., 1] - axis_bounds[..., 0])
return axis_wts
def __myGetAxisWeights(x, i, axisoptions=None):
""" Assume x is an MV2 duh! and get the weights associated with axis index i.
index i can be any of the dimensions in x whose bounds are available.
If bounds are not available then an error is raised.
"""
if x.getAxis(i).isLatitude():
Lataxis = x.getAxis(i)
xgr = cdms2.createGenericGrid(Lataxis[:], numpy.array(range(1), numpy.float), latBounds=Lataxis.getBounds())
xlatwt, xlonwt = xgr.getWeights()
return xlatwt
elif x.getAxis(i).isLongitude():
Lonaxis = x.getAxis(i)
xgr = cdms2.createGenericGrid(numpy.array(range(1), numpy.float), Lonaxis[:], lonBounds=Lonaxis.getBounds())
xlatwt, xlonwt = xgr.getWeights()
return xlonwt
else:
axis_bounds = x.getAxis(i).getBounds()
if axis_bounds is None:
ax=x.getAxis(i)
if axisoptions is not None:
axo=cdms2.orderparse(axisoptions)
if (i in axo ) or (ax.isTime() and 't' in axo) or (ax.isLevel() and 'z' in axo) or ('('+ax.id+')' in axo):
raise AveragerError, 'Bounds not available to compute weights on dimension: '+ax.id
else:
axis_wts=numpy.ones(x.getAxis(i)[:].shape)
else:
raise AveragerError, 'Bounds not available to compute weights for dimension: '+ax.id
else:
axis_wts = abs(axis_bounds[..., 1] - axis_bounds[..., 0])
return axis_wts
def _check_axisoptions(x, axisoptions):
"""checks the axis=options to make sure that it is valid"""
axlist = cdms2.orderparse(axisoptions)
if Ellipsis in axlist:
raise AveragerError, 'Error: Ellipsis (...) not allowed in axis options'
try:
cdms2.order2index(x.getAxisList(), axisoptions)
return axlist
except:
raise AveragerError, 'Error: You have specified an invalid axis= option.'
def _check_axisoptions(x, axisoptions):
"""checks the axis=options to make sure that it is valid"""
axlist = cdms2.orderparse(axisoptions)
if Ellipsis in axlist:
raise AveragerError, 'Error: Ellipsis (...) not allowed in axis options'
try:
cdms2.order2index(x.getAxisList(), axisoptions)
return axlist
except:
raise AveragerError, 'Error: You have specified an invalid axis= option.'
def check_order(var, allowed, vertical=None, copy=False, reorder=False,
extended=None, getorder=False):
"""Check that the axis order of a variable is matches
at least one the specifed valid orders
:Params:
- **var**: MV2 array.
- **allowed**: A single order string or a list. It should contain one or
several of these letters:
- ``x``: longitudes,
- ``y``: latitudes,
- ``z``: vertical levels,
- ``t``: time axis,
- ``d``: data values (ignored),
- ``-``: any kind of axis.
:Return:
``var``, or ``var, order, reordered`` if **reorder** is True.
"""
# Check allowed orders
# - consistency
if not isinstance(allowed, (list, tuple)):
allowed = [allowed]
else:
allowed = list(allowed)
withd = 'd' in allowed[0]
get_rank = lambda o: len(o.replace('d', ''))
rank = get_rank(allowed[0])
for order in allowed:
try:
cdms2.orderparse(order.lower().replace('d', ''))
except:
raise VACUMMError("Wrong allowed order: "+order)
if ('d' in order and not withd) or ('d' not in order and withd):
raise VACUMMError("'d' only partially present in allowed order: %s"%allowed)
if get_rank(order)!=rank:
raise VACUMMError("Inconsistent ranks between allowed orders: %s"%[get_rank(o) for o in allowed])
# - check extended mode
if extended is None: # extended?
re_upper = re.compile('[XYZT]').search
for order in allowed:
if re_upper(order) is not None:
extended = True # force extended mode
break
else:
extended = False
if extended is False: # lower
allowed = [order.lower() for order in allowed]
else: #add tolerance for lower case orders
re_sub = re.compile('[xyzt]').sub
allowed = allowed+[re_sub('-', order) for order in allowed]
# - unique and lower case
_, idx = N.unique(allowed, return_index=True)
idx = N.sort(idx)
allowed = N.array(allowed)[idx].tolist()
# - restrict to vertical or horizontal (1D data only)
if vertical is not None and len(allowed[0])==2:
allowed = [oo for oo in allowed if oo[int(vertical)]=='d']
# Data order
data_cdms_order = get_order(var)
# Loop on allowed orders
from vacumm.misc.grid.misc import get_axis, var2d
reordered = False
for allowed_order in allowed:
# Handle data case
d = allowed_order.find('d')
if d!=-1: allowed_order = allowed_order.replace('d', '') # pure axis
# Check cdms order
allowed_cdms_order = allowed_order.lower() # lower case
if order_match(data_cdms_order, allowed_cdms_order, strict='right'): break # It is already good
# Try to reorder
if reorder:
try:
reordered = cdms2.order2index(var.getAxisList(), allowed_cdms_order)
new_var = var.reorder(allowed_cdms_order)
if allowed_cdms_order[-1]=='x' and len(get_axis(new_var, -1).shape)==2: # 2D axes
del var
var = var2d(new_var,
MV2.transpose(get_axis(new_var, 0)),
MV2.transpose(get_axis(new_var, -1)), copy=0)
set_order(new_var, allowed_cdms_order)
else:
del var
var = new_var
data_cdms_order = get_order(var)
break # No error so it worked and we leave
except:
continue
else:
raise VACUMMError('Wrong type of axes. Possible forms are: %s'%', '.join(allowed))
if not getorder: return var
if d!=-1:
data_cdms_order = cdms2.orderparse(data_cdms_order)
data_cdms_order.insert(d, 'd')
data_cdms_order = ''.join(data_cdms_order)
return var, data_cdms_order, reordered
def __check_weightoptions(x, axisoptions, weightoptions):
"""
Checks the weights=options to make sure that it is valid.
Default: 'weighted'
weight options are one of 'weighted', 'unweighted', an array of weights for each
dimension or a MaskedVariable of the same shape as the data x.
- 'weighted' means use the grid information to generate weights for that
dimension or if multiple axes are passed, generate goes ahead and
generates the full masked area weights.
- 'unweighted' means use equal weights for all the grid points in that axis.
- Also an array of weights (of the same shape as the dimension being
averaged over or same shape as V) can be passed.
"""
#
# Since the _check_axisoptions was passed, the variable x has already been reordered
# to the required order..... check it anyway.
#
#
__DEBUG__ = 0
#
if __DEBUG__: print 'Axis options entering __check_weightoptions = ', axisoptions
#
axisindex = cdms2.order2index(x.getAxisList(), axisoptions)
if __DEBUG__: print 'This should be 0,1,2... etc. is it? ', axisindex
#
axislist = cdms2.orderparse(axisoptions)
if __DEBUG__: print 'axislist = ', axislist
#
if not isinstance(weightoptions, types.ListType):
#
# We have either 1 axis only or multiple axes and one MV2 of weights
#
if MV2.isMaskedVariable(weightoptions):
#
# Weight passed is an MV2. Probably OK
#
try:
# We had required that the weights be of the same rank as the variable to be averaged.
weightoptions = weightoptions(order=axisoptions)
#
# KRISHNA : check this for combinewts!!
#
## if x.shape != weightoptions.shape:
## raise AveragerError, \
## 'Error: Shape of weight array does not match shape of data array'
## # end of if x.shape != weightoptions.shape:
if __DEBUG__: print '... definitely OK'
except:
raise AveragerError, \
'Error: Did not find the axes requested in the Masked variable.'
elif len(axislist) == 1:
#
# Only one axis to reduce over....'
# figure out if it is an array (numpy or numpy.ma) or 'equal' or 'generate' or something else
#
if __DEBUG__: print 'I have only 1 axis and 1 option'
weightoptions = __check_each_weight_option(x, axislist[0], axisindex[0], weightoptions)
else:
#
# More than 1 axis to reduce over, and not an MV2 of the same dimensionality as x!
#
if weightoptions in ['generate','weighted']:
weightoptions = area_weights(x,axisoptions)
else:
# Cannot do this because 'generate' was not passed with multiple axes.
raise AveragerError, 'Error: Multiple axes passed without weights to match'
# end of if weightoptions == 'generate':
else:
#
# I have a list of weightoptions i.e more than 1 axis in axisoption
#
#
# We have multiple axes to deal with each with a weight....
#
for i in range(len(axislist)):
weightoptions[i] = __check_each_weight_option(x, axislist[i], axisindex[i], weightoptions[i])
# end of for i in range(len(axislist)):
#
if len(axislist) < len(weightoptions):
for j in range(len(axislist), len(weightoptions), 1):
weightoptions[j] = __check_each_weight_option(x, None, j, weightoptions[j])
# end of if len(axislist) < len(weightoptions):
# end of if not isinstance(weightoptions, types.ListType):
#
if __DEBUG__: print 'Successful with __check_weightoptions'
#
return weightoptions
def __check_weightoptions(x, axisoptions, weightoptions):
"""
Checks the weights=options to make sure that it is valid.
Default: 'weighted'
weight options are one of 'weighted', 'unweighted', an array of weights for each
dimension or a MaskedVariable of the same shape as the data x.
- 'weighted' means use the grid information to generate weights for that
dimension or if multiple axes are passed, generate goes ahead and
generates the full masked area weights.
- 'unweighted' means use equal weights for all the grid points in that axis.
- Also an array of weights (of the same shape as the dimension being
averaged over or same shape as V) can be passed.
"""
#
# Since the _check_axisoptions was passed, the variable x has already been reordered
# to the required order..... check it anyway.
#
#
__DEBUG__ = 0
#
if __DEBUG__:
print 'Axis options entering __check_weightoptions = ', axisoptions
#
axisindex = cdms2.order2index(x.getAxisList(), axisoptions)
if __DEBUG__: print 'This should be 0,1,2... etc. is it? ', axisindex
#
axislist = cdms2.orderparse(axisoptions)
if __DEBUG__: print 'axislist = ', axislist
#
if not isinstance(weightoptions, types.ListType):
#
# We have either 1 axis only or multiple axes and one MV2 of weights
#
if MV2.isMaskedVariable(weightoptions):
#
# Weight passed is an MV2. Probably OK
#
try:
# We had required that the weights be of the same rank as the variable to be averaged.
weightoptions = weightoptions(order=axisoptions)
#
# KRISHNA : check this for combinewts!!
#
## if x.shape != weightoptions.shape:
## raise AveragerError, \
## 'Error: Shape of weight array does not match shape of data array'
## # end of if x.shape != weightoptions.shape:
if __DEBUG__: print '... definitely OK'
except:
raise AveragerError, \
'Error: Did not find the axes requested in the Masked variable.'
elif len(axislist) == 1:
#
# Only one axis to reduce over....'
# figure out if it is an array (numpy or numpy.ma) or 'equal' or 'generate' or something else
#
if __DEBUG__: print 'I have only 1 axis and 1 option'
weightoptions = __check_each_weight_option(x, axislist[0],
axisindex[0],
weightoptions)
else:
#
# More than 1 axis to reduce over, and not an MV2 of the same dimensionality as x!
#
if weightoptions in ['generate', 'weighted']:
weightoptions = area_weights(x, axisoptions)
else:
# Cannot do this because 'generate' was not passed with multiple axes.
raise AveragerError, 'Error: Multiple axes passed without weights to match'
# end of if weightoptions == 'generate':
else:
#
# I have a list of weightoptions i.e more than 1 axis in axisoption
#
#
# We have multiple axes to deal with each with a weight....
#
for i in range(len(axislist)):
weightoptions[i] = __check_each_weight_option(
x, axislist[i], axisindex[i], weightoptions[i])
# end of for i in range(len(axislist)):
#
if len(axislist) < len(weightoptions):
for j in range(len(axislist), len(weightoptions), 1):
weightoptions[j] = __check_each_weight_option(
x, None, j, weightoptions[j])
# end of if len(axislist) < len(weightoptions):
# end of if not isinstance(weightoptions, types.ListType):
#
if __DEBUG__: print 'Successful with __check_weightoptions'
#
return weightoptions
def check_order(var,
allowed,
vertical=None,
copy=False,
reorder=False,
extended=None,
getorder=False):
"""Check that the axis order of a variable is matches
at least one the specifed valid orders
:Params:
- **var**: MV2 array.
- **allowed**: A single order string or a list. It should contain one or
several of these letters:
- ``x``: longitudes,
- ``y``: latitudes,
- ``z``: vertical levels,
- ``t``: time axis,
- ``d``: data values (ignored),
- ``-``: any kind of axis.
:Return:
``var``, or ``var, order, reordered`` if **reorder** is True.
"""
# Check allowed orders
# - consistency
if not isinstance(allowed, (list, tuple)):
allowed = [allowed]
else:
allowed = list(allowed)
withd = 'd' in allowed[0]
get_rank = lambda o: len(o.replace('d', ''))
rank = get_rank(allowed[0])
for order in allowed:
try:
cdms2.orderparse(order.lower().replace('d', ''))
except:
raise VACUMMError("Wrong allowed order: " + order)
if ('d' in order and not withd) or ('d' not in order and withd):
raise VACUMMError(
"'d' only partially present in allowed order: %s" % allowed)
if get_rank(order) != rank:
raise VACUMMError("Inconsistent ranks between allowed orders: %s" %
[get_rank(o) for o in allowed])
# - check extended mode
if extended is None: # extended?
re_upper = re.compile('[XYZT]').search
for order in allowed:
if re_upper(order) is not None:
extended = True # force extended mode
break
else:
extended = False
if extended is False: # lower
allowed = [order.lower() for order in allowed]
else: #add tolerance for lower case orders
re_sub = re.compile('[xyzt]').sub
allowed = allowed + [re_sub('-', order) for order in allowed]
# - unique and lower case
_, idx = N.unique(allowed, return_index=True)
idx = N.sort(idx)
allowed = N.array(allowed)[idx].tolist()
# - restrict to vertical or horizontal (1D data only)
if vertical is not None and len(allowed[0]) == 2:
allowed = [oo for oo in allowed if oo[int(vertical)] == 'd']
# Data order
data_cdms_order = get_order(var)
# Loop on allowed orders
from vacumm.misc.grid.misc import get_axis, var2d
reordered = False
for allowed_order in allowed:
# Handle data case
d = allowed_order.find('d')
if d != -1: allowed_order = allowed_order.replace('d', '') # pure axis
# Check cdms order
allowed_cdms_order = allowed_order.lower() # lower case
if order_match(data_cdms_order, allowed_cdms_order, strict='right'):
break # It is already good
# Try to reorder
if reorder:
try:
reordered = cdms2.order2index(var.getAxisList(),
allowed_cdms_order)
new_var = var.reorder(allowed_cdms_order)
if allowed_cdms_order[-1] == 'x' and len(
get_axis(new_var, -1).shape) == 2: # 2D axes
del var
var = var2d(new_var,
MV2.transpose(get_axis(new_var, 0)),
MV2.transpose(get_axis(new_var, -1)),
copy=0)
set_order(new_var, allowed_cdms_order)
else:
del var
var = new_var
data_cdms_order = get_order(var)
break # No error so it worked and we leave
except:
continue
else:
raise VACUMMError('Wrong type of axes. Possible forms are: %s' %
', '.join(allowed))
if not getorder: return var
if d != -1:
data_cdms_order = cdms2.orderparse(data_cdms_order)
data_cdms_order.insert(d, 'd')
data_cdms_order = ''.join(data_cdms_order)
return var, data_cdms_order, reordered
