def write_cache_file(entry, input, context):
data = []
chunk_axis = None
chunk_axis_index = None
entry.local_path = entry.new_output_path()
with context.new_output(entry.local_path) as outfile:
for _, _, chunk in input.chunks_ingress(context):
if chunk_axis is None:
chunk_axis_index = chunk.getAxisIndex(input.chunk_axis)
chunk_axis = chunk.getAxis(chunk_axis_index)
if chunk_axis.isTime():
outfile.write(chunk, id=input.variable.var_name)
else:
data.append(chunk)
if chunk_axis is not None and not chunk_axis.isTime():
data = MV2.concatenate(data, axis=chunk_axis_index)
outfile.write(data, id=input.variable.var_name)
try:
size = entry.set_size()
except db.IntegrityError:
# Handle case where cache files are written at same time
pass
else:
metrics.WPS_DATA_CACHE_WRITE.inc(size)
def testSubRegion(self):
# positional
s2 = self.var.subRegion((366., 731., 'ccn'), (-42., 42., 'ccn'),
(90., 270., 'con'))
self.assertTrue(numpy.ma.allequal(self.vp, s2))
# squeeze
s2 = self.var.subRegion((731., 731., 'ccn'), (-42., 42., 'ccn'),
(90., 270., 'con'),
squeeze=1)
self.assertEqual(len(s2.shape), 2)
# keyword
s2 = self.var.subRegion(latitude=(-42., 42., 'ccn'),
longitude=(90., 270., 'con'),
time=(366., 731., 'ccn'))
self.assertTrue(numpy.ma.allequal(self.vp, s2))
# Wraparound
u = self.file['u']
u1 = u[:, :, 8:]
u2 = u[:, :, :8]
ucat = MV.concatenate((u1, u2), axis=2)
su = u.subRegion(lon=(90, 450, 'co'))
self.assertTrue(numpy.ma.allequal(ucat, su))
def min_func(data, axes):
for axis in axes:
axis_index = data.getAxisIndex(axis)
if axis_index == -1:
raise WPSError('Unknown axis {!s}', axis)
data = MV2.min(data, axis=axis_index)
return data
def test_axis_segfault():
from cdms2 import MV2
# Contributed by Lawson Hanson
month = 'January'
year = '2012'
miss_value = -9999.0
data = ['34.006348', '28.314002', '29.269668', '33.698551', '34.177242']
Rad_global_month = MV2.zeros([len(data)], MV2.float)
time_day = MV2.zeros([len(data)], MV2.float)
tim = cdms.createAxis(time_day)
tim.designateTime()
tim.id = "time"
tim.units = 'days since 2012-01-01'
for i in range(len(time_day)):
Rad_global_month[i] = data[i]
tim[i] = float(i)
# Create a temporary file
fd, f = tempfile.mkstemp('.nc')
os.close(fd)
try:
out = cdms.open(f, 'w')
rad_total_month = cdms.createVariable(Rad_global_month,
id='accum_swfcdown_' + month,
axis=[tim],
typecode='f')
rad_total_month.setAxisList([tim])
print rad_total_month, tim
print len(rad_total_month), len(tim)
out.write(rad_total_month)
finally:
out.close()
os.remove(f)
def test_axis_segfault():
from cdms2 import MV2
# Contributed by Lawson Hanson
month = 'January'
year = '2012'
miss_value = -9999.0
data = ['34.006348', '28.314002', '29.269668', '33.698551', '34.177242']
Rad_global_month = MV2.zeros([len(data)],MV2.float)
time_day = MV2.zeros([len(data)],MV2.float)
tim = cdms.createAxis(time_day)
tim.designateTime()
tim.id = "time"
tim.units = 'days since 2012-01-01'
for i in range(len(time_day)):
Rad_global_month[i] = data[i]
tim[i] = float(i)
# Create a temporary file
fd, f = tempfile.mkstemp('.nc')
os.close(fd)
try:
out = cdms.open(f,'w')
rad_total_month = cdms.createVariable(Rad_global_month,id = 'accum_swfcdown_'+month,axis=[tim],typecode='f')
rad_total_month.setAxisList([tim])
print rad_total_month,tim
print len(rad_total_month),len(tim)
out.write(rad_total_month)
finally:
out.close()
os.remove(f)
def uncompress_fisccp1( fisccp1, isccp_prs, isccp_tau ):
"""Re-dimensions the input variable FISCCP1, to "un-compress" the 49-element iccp_prstau axis
into the standard two 7-element axes, isccp_prs and isccp_tau (which should be supplied).
The resulting variable, CLISCCP, is returned.
"""
# Charles Doutriaux told me how to use reshape, setAxisList, etc. Any errors are mine. (JfP)
axes = list(fisccp1.getAxisList())
alen = [len(ax) for ax in axes]
aid = [ax.id for ax in axes]
iprstau = aid.index('isccp_prstau')
csh = alen[0:iprstau]+[len(isccp_prs),len(isccp_tau)]+alen[iprstau+1:]
clisccp = MV2.reshape(fisccp1,csh)
axes.pop(iprstau)
axes.insert(iprstau,isccp_tau)
axes.insert(iprstau,isccp_prs)
clisccp.setAxisList(axes)
return clisccp
args = parser.parse_args()
d = cdms2.open(args.ncfile)
try:
# Remove singleton dimensions (time or level in surface fields)
ncvar = d.variables[args.ncvarname](squeeze=1)
except KeyError:
print "\nError: variable %s not in %s" % (args.ncvarname, args.ncfile)
sys.exit(1)
f = umfile.UMFile(args.target, "r+")
# Set new missing value to match the UM missing value
arr = MV2.array(ncvar[:])
arr.setMissing(f.missval_r)
arr = MV2.filled(arr).astype(f.float)
# Loop over all the fields
replaced = False
for k in range(f.fixhd[FH_LookupSize2]):
ilookup = f.ilookup[k]
lbegin = ilookup[LBEGIN] # lbegin is offset from start
if lbegin == -99:
break
if ilookup[ITEM_CODE] == args.varcode:
print "Replacing field", k, ilookup[ITEM_CODE]
if not (ilookup[LBROW], ilookup[LBNPT]) == arr.shape:
print "\nError: array shape mismatch"
print "UM field shape", (ilookup[LBROW], ilookup[LBNPT])
print 'Cannot find the tutorials data, please pass path as first argument.'
print 'e.g.: python getting_started.py ../cdat_tutorial_data'
sys.exit()
TEMPDIR = './'
# quick example for using xmgrace
import cdms2 as cdms
from cdms2 import MV2 as MV
# preliminary work, retrieve data and make a zonal mean, 2 different year
f = cdms.open(TESTDIR + 'tas.rnl_ncep.nc')
tim = f.getAxis('time')
s1 = f('tas', time=(tim[0], tim[11]))
# Compute time mean
s1 = MV.average(s1)
# compute zonal mean
z1 = MV.average(s1, 1)
# Now the last year
s2 = f('tas', time=(tim[-12], tim[-1]))
# Compute time mean
s2 = MV.average(s2)
# compute zonal mean
z2 = MV.average(s2, 1)
# Now computes the difference
z3 = z2 - z1
# Now the real grace thing, plot the 2 on one graph and the diff on another graph
from genutil import xmgrace # first we need to import xmgrace module
markError('subRegion squeeze option failed')
# mf 20010308 subRegion - extended wrap
fw = cdms2.open(os.path.join(pth, 'ps.wrap.test.0E.nc'))
ps = fw.getVariable('ps')
ps1 = ps[:, :, 36:]
ps2 = ps[:, :, :37]
s2 = numpy.ma.concatenate((ps1, ps2), axis=2)
s2w = fw('ps', longitude=(-180, 180, 'ccn'))
if not numpy.ma.allequal(s2, s2w): markError('subRegion extended wrap')
varlist = fw.getVariables(spatial=1)
u = f['u']
u1 = u[:, :, 8:]
u2 = u[:, :, :8]
ucat = MV.concatenate((u1, u2), axis=2)
su = u.subRegion(lon=(90, 450, 'co'))
if not numpy.ma.allequal(ucat, su): markError('subRegion wrap, test 2')
# negative strides
fc = cdms2.Cdunif.CdunifFile(os.path.join(pth, 'ps.wrap.test.0E.nc'))
psc = fc.variables['ps']
psb = psc[:]
s3c = psb[0, ::-1]
s4c = psb[0, ::-2]
s3 = fw('ps', latitude=(90, -90))
if not numpy.ma.allequal(s3, s3c): markError('Reverse interval failed')
s4 = ps.getSlice(':', (None, None, -1))
# s4 = ps.subRegion(latitude=slice(None,None,-1))
if not numpy.ma.allequal(s4, s3c): markError('Negative stride failed')
print 'Cannot find the tutorials data, please pass path as first argument.'
print 'e.g.: python getting_started.py ../cdat_tutorial_data'
sys.exit()
TEMPDIR = './'
# quick example for using xmgrace
import cdms2 as cdms
from cdms2 import MV2 as MV
# preliminary work, retrieve data and make a zonal mean, 2 different year
f = cdms.open(TESTDIR + 'tas.rnl_ncep.nc')
tim = f.getAxis('time')
s1 = f('tas', time=(tim[0], tim[11]))
# Compute time mean
s1 = MV.average(s1)
# compute zonal mean
z1 = MV.average(s1, 1)
# Now the last year
s2 = f('tas', time=(tim[-12], tim[-1]))
# Compute time mean
s2 = MV.average(s2)
# compute zonal mean
z2 = MV.average(s2, 1)
# Now computes the difference
z3 = z2 - z1
# Now the real grace thing, plot the 2 on one graph and the diff on
# another graph
from genutil import xmgrace # first we need to import xmgrace module
parser.add_argument('target', help='UM File to change')
args = parser.parse_args()
d = cdms2.open(args.ncfile)
try:
# Remove singleton dimensions (time or level in surface fields)
ncvar = d.variables[args.ncvarname](squeeze=1)
except KeyError:
print "\nError: variable %s not in %s" % (args.ncvarname, args.ncfile)
sys.exit(1)
f = umfile.UMFile(args.target, "r+")
# Set new missing value to match the UM missing value
arr = MV2.array(ncvar[:])
arr.setMissing(f.missval_r)
arr = MV2.filled(arr).astype(f.float)
# Loop over all the fields
replaced = False
for k in range(f.fixhd[FH_LookupSize2]):
ilookup = f.ilookup[k]
lbegin = ilookup[LBEGIN] # lbegin is offset from start
if lbegin == -99:
break
if ilookup[ITEM_CODE] == args.varcode:
print "Replacing field", k, ilookup[ITEM_CODE]
if not (ilookup[LBROW], ilookup[LBNPT]) == arr.shape:
print "\nError: array shape mismatch"
print "UM field shape", (ilookup[LBROW], ilookup[LBNPT])
markError('subRegion squeeze option failed')
# mf 20010308 subRegion - extended wrap
fw = cdms2.open(os.path.join(get_sample_data_dir(), 'ps.wrap.test.0E.nc'))
ps = fw.getVariable('ps')
ps1 = ps[:,:,36:]
ps2 = ps[:,:,:37]
s2 = numpy.ma.concatenate((ps1,ps2),axis=2)
s2w = fw('ps',longitude=(-180,180,'ccn'))
if not numpy.ma.allequal(s2,s2w): markError('subRegion extended wrap')
varlist = fw.getVariables(spatial=1)
u = f['u']
u1 = u[:,:,8:]
u2 = u[:,:,:8]
ucat = MV.concatenate((u1,u2),axis=2)
su = u.subRegion(lon=(90,450,'co'))
if not numpy.ma.allequal(ucat,su): markError('subRegion wrap, test 2')
# negative strides
fc = cdms2.Cdunif.CdunifFile(os.path.join(get_sample_data_dir(),'ps.wrap.test.0E.nc'))
psc = fc.variables['ps']
psb = psc[:]
s3c = psb[0,::-1]
s4c = psb[0,::-2]
s3 = fw('ps',latitude=(90,-90))
if not numpy.ma.allequal(s3,s3c): markError('Reverse interval failed')
s4 = ps.getSlice(':',(None,None,-1))
# s4 = ps.subRegion(latitude=slice(None,None,-1))
if not numpy.ma.allequal(s4,s3c): markError('Negative stride failed')
def process(self, operation, num_inputs, output_file, process):
grid = None
gridder = operation.get_parameter('gridder')
start = datetime.datetime.now()
axes = operation.get_parameter('axes', True)
axes = axes.values[0]
self.log('Starting to process inputs')
result_list = []
if len(operation.inputs) == 1 or num_inputs == 1:
collections = [
file_manager.DataSetCollection.from_variables(operation.inputs)
]
else:
collections = [
file_manager.DataSetCollection.from_variables([x])
for x in operation.inputs
]
with file_manager.FileManager(collections) as fm:
output_list = []
var_name = fm.get_variable_name()
with contextlib.nested(*[x for x in fm.collections]):
over_temporal = fm.collections[0].datasets[0].get_time().id == axes
for meta in fm.partitions(operation.domain, axes, num_inputs):
data_list = []
axis_index = None
for item in meta:
ds, chunk = item
if axis_index is None:
axis_index = ds.get_variable().getAxisIndex(axes)
if gridder is not None:
if grid is None:
grid = self.generate_grid(gridder, ds.spatial, chunk)
if not over_temporal:
chunk = chunk.regrid(grid, regridTool=gridder.tool, regridMethod=gridder.method)
data_list.append(chunk)
if len(data_list) == 0:
break
if len(data_list) > 1:
result_data = process(*data_list)
else:
result_data = process(*data_list, axis=axis_index)
self.log('Process output shape {}'.format(result_data.shape))
if over_temporal:
result_list.append(result_data)
else:
output_file.write(result_data, id=var_name)
if over_temporal:
data = MV.concatenate(result_list)
if grid is not None:
data = data.regrid(grid, regridTool=gridder.tool, regridMethod=gridder.method)
output_file.write(data, id=var_name)
stop = datetime.datetime.now()
final_shape = output_file[var_name].shape
self.log('Finish retrieving all files, final shape "{}", elapsed time {}', final_shape, stop-start, percent=100)
return var_name
albcs = np.array(fkernel['albcs'])
fd1 = cdms2.open(os.path.join(data_dir, fdata1))
fd2 = cdms2.open(os.path.join(data_dir, fdata2))
hist1 = fd1['histogram']
hist1 = hist1[:, ::-1, :, :, :]
hist2 = fd2['histogram']
hist2 = hist2[:, ::-1, :, :, :]
lon = fd1['lon']
change = hist2 - hist1
[a, b, c, d, e] = change.shape
dpctau = MV2.masked_array(1e20 * np.ones((12, b, c, d, e), 'float64'))
for mm in xrange(12):
mm_val = change[mm::12, :, :, :, :]
dpctau[mm, :, :, :, :] = MV2.average(mm_val, 0)
tmp = dpctau.swapaxes(1, 2)
dpctau = tmp
f_albedo = 'albedo_clim_xalll_uacdg.nc'
falb = cdms2.open(os.path.join(data_dir, f_albedo))
albedo = np.array(falb['rsuscs'])
f_dtas = 'dtas_clim_glob_xalll_uacdg.nc'
fdtas = cdms2.open(os.path.join(data_dir, f_dtas))
dtas = fdtas['tas']
albcs = np.array(fkernel['albcs'])
fd1 = cdms2.open(os.path.join(data_dir, fdata1))
fd2 = cdms2.open(os.path.join(data_dir, fdata2))
hist1 = fd1['histogram']
hist1 = hist1[:, ::-1, :, :, :]
hist2 = fd2['histogram']
hist2 = hist2[:, ::-1, :, :, :]
lon = fd1['lon']
e = len(lon)
print a, b, c, d, e
change = hist2 - hist1
dpctau = MV2.masked_array(1e20 * np.ones((12, b, c, d, e), 'float64'))
for mm in xrange(12):
mm_val = change[mm::12, :, :, :, :]
dpctau[mm, :, :, :, :] = MV2.average(mm_val, 0)
tmp = dpctau.swapaxes(1, 2)
dpctau = tmp
f_albedo = 'albedo_clim_xallf_xalua.nc'
falb = cdms2.open(os.path.join(data_dir, f_albedo))
albedo = np.array(falb['rsuscs'])
f_dtas = 'dtas_clim_glob_xallf_xalua.nc'
fdtas = cdms2.open(os.path.join(data_dir, f_dtas))
dtas = fdtas['tas']
LWkernel = np.ma.masked_invalid(LWkernel)
def concat(self, contexts):
""" Concatenate data chunks.
Args:
context (OperationContext): Current context.
Returns:
Updated context.
"""
context = OperationContext.merge_ingress(contexts)
context.output_path = context.gen_public_path()
nbytes = 0
start = datetime.datetime.now()
with context.new_output(context.output_path) as outfile:
for index, input in enumerate(context.sorted_inputs()):
data = []
chunk_axis = None
chunk_axis_index = None
# Skip file if not mapped
if input.mapped is None:
logger.info('Skipping %r', input.filename)
continue
for file_path, _, chunk in input.chunks(input_index=index,
context=context):
logger.info('Chunk shape %r %r', file_path, chunk.shape)
if chunk_axis is None:
chunk_axis_index = chunk.getAxisIndex(input.chunk_axis)
chunk_axis = chunk.getAxis(chunk_axis_index)
# We can write chunks along the temporal axis immediately
# otherwise we need to collect them to concatenate over
# an axis
if chunk_axis.isTime():
logger.info('Writing temporal chunk %r', chunk.shape)
if context.units is not None:
chunk.getTime().toRelativeTime(str(context.units))
if context.is_regrid:
chunk = regrid_chunk(context, chunk, input.mapped)
outfile.write(chunk, id=str(input.variable.var_name))
else:
logger.info('Gathering spatial chunk')
data.append(chunk)
nbytes += chunk.nbytes
# Concatenate chunks along an axis
if chunk_axis is not None and not chunk_axis.isTime():
data = MV2.concatenate(data, axis=chunk_axis_index)
if context.is_regrid:
chunk = regrid_chunk(context, chunk, input.mapped)
outfile.write(data, id=str(input.variable.var_name))
nbytes += chunk.nbytes
elapsed = datetime.datetime.now() - start
self.status('Processed {!r} bytes in {!r} seconds', nbytes,
elapsed.total_seconds())
return context
