def accumulate24Hourly(data):
"""Returns 12-hourly data accumulated to 24-hours."""
newTimeValues=[]
taxis=data.getTime()
tunits=data.units
print len(data.getTime())
newarray=[]
for i in range((tlen/2)):
p1=data(time=slice(i,i+1))
p2=data(time=slice(i+1,i+2))
accum=p1+p2
newarray.append(accum)
newTimeValues.append(p2.getTime()[0])
array=MA.concatenate(newarray)
array=MA.array(array, 'f', fill_value=data.getMissing())
axes=data.getAxisList()
newTimeAxis=cdms.createAxis(newTimeValues)
newTimeAxis.units=tunits
newTimeAxis.designateTime()
newTimeAxis.id=newTimeAxis.long_name=newTimeAxis.title="time"
newaxes=[newTimeAxis]+axes[1:]
var=cdms.createVariable(array, axes=newaxes, id=data.id)
for att in ("units", "long_name"):
setattr(var, att, getattr(data, att))
return var
def flatten2DTimeData(var, time_var):
"""
Returns a flattened 2D array variable with a recalculated time axies.
"""
data = MV.ravel(var)
missing = var.getMissing()
time_values = time_var._data
time_unit = time_var.units
sampling_rate = var.getAxis(1)
rate = int(sampling_rate.id[3:])
newtime_values = []
for t in time_values:
for i in range(rate):
tvalue = t + ((1./rate)*i)
newtime_values.append(tvalue)
new_time_ax = cdms.createAxis(newtime_values)
new_time_ax.units = time_unit
new_time_ax.designateTime()
new_time_ax.id = new_time_ax.long_name = new_time_ax.standard_name = "time"
atts = var.attributes
newAtts = {}
for att,value in atts.items():
if type(value) in (type((1,1)), type([1,2]), type(Numeric.array([1.]))) and len(value) == 1:
value = value[0]
if type(value) in (type(1), type(1.), type(long(1))):
newAtts[att] = value
elif type(value) == type("string"):
newAtts[att] = value.strip()
# Now create the variable
newvar = cdms.createVariable(data, id=var.id, fill_value=missing, axes=[new_time_ax], attributes=newAtts)
return newvar
def getNewLonAxis(oldLonAxis, startLon=-180):
assert (oldLonAxis.isLongitude())
oldLonVals = oldLonAxis.getData()
newLonVals = (oldLonVals - startLon
) % 360 + startLon # range "startLon" to "startLon + 360"
lonValShift = newLonVals - oldLonVals
nlon = len(newLonVals)
##
## identify shift by jump in longitude values
for i in range(1, nlon):
if newLonVals[i] < newLonVals[i - 1]:
nShift = i
break
else:
nShift = 0
## create array with new longitude values
newLonVals = MA.concatenate((newLonVals[nShift:], newLonVals[:nShift]))
## print oldLonVals
## print newLonVals
newLonAxis = cdms.createAxis(newLonVals)
newLonAxis.designateLongitude()
newLonAxis.replace_external_attributes(oldLonAxis.attributes)
#
return newLonAxis, nShift, lonValShift
def flatten2DTimeData(var, time_var):
"""
Returns a flattened 2D array variable with a recalculated time axies.
"""
data = MV.ravel(var)
missing = var.getMissing()
time_values = time_var._data
time_unit = time_var.units
sampling_rate = var.getAxis(1)
rate = int(sampling_rate.id[3:])
newtime_values = []
for t in time_values:
for i in range(rate):
tvalue = t + ((1. / rate) * i)
newtime_values.append(tvalue)
new_time_ax = cdms.createAxis(newtime_values)
new_time_ax.units = time_unit
new_time_ax.designateTime()
new_time_ax.id = new_time_ax.long_name = new_time_ax.standard_name = "time"
atts = var.attributes
newAtts = {}
for att, value in atts.items():
if type(value) in (type((1, 1)), type([1, 2]), type(Numeric.array(
[1.]))) and len(value) == 1:
value = value[0]
if type(value) in (type(1), type(1.), type(long(1))):
newAtts[att] = value
elif type(value) == type("string"):
newAtts[att] = value.strip()
# Now create the variable
newvar = cdms.createVariable(data,
id=var.id,
fill_value=missing,
axes=[new_time_ax],
attributes=newAtts)
return newvar
def _flatten2DTimeAxis(self, timevar, samplingRate):
"""
Returns a flattened 2D time axis.
"""
if samplingRate!=1:
raise "Cannot yet deal with sub-sampling to non 1Hz sampling rates!"
timevalues=timevar._data
timeunit=timevar.units
newTimeValues=[]
rate=samplingRate
for t in timevalues:
for i in range(rate):
tvalue=t+((1./rate)*i)
newTimeValues.append(tvalue)
newTimeAx=cdms.createAxis(newTimeValues)
newTimeAx.units=timeunit
newTimeAx.designateTime()
newTimeAx.id=newTimeAx.long_name=newTimeAx.standard_name="time"
return newTimeAx
def _flatten2DTimeAxis(self, timevar, samplingRate):
"""
Returns a flattened 2D time axis.
"""
if samplingRate != 1:
raise "Cannot yet deal with sub-sampling to non 1Hz sampling rates!"
timevalues = timevar._data
timeunit = timevar.units
newTimeValues = []
rate = samplingRate
for t in timevalues:
for i in range(rate):
tvalue = t + ((1.0 / rate) * i)
newTimeValues.append(tvalue)
newTimeAx = cdms.createAxis(newTimeValues)
newTimeAx.units = timeunit
newTimeAx.designateTime()
newTimeAx.id = newTimeAx.long_name = newTimeAx.standard_name = "time"
return newTimeAx
def writeOutput(infile, var, lat, lon, dav, dmax, dmin, sdupper, sdlower, location):
"""
Writes an output file of the variables generated.
"""
location=location.replace(" ","_").lower()
f=cdms.open(infile)
mapit={"apcp":("rainfall","l/m^2"), "tmpk":("temperature","K")}
varname=mapit[var][0]
units=mapit[var][1]
datetime=os.path.split(infile)[-1].split(".")[1]
outfile="%s_%s_%s.nc" % (datetime, location, varname)
outpath=os.path.split(infile)[0]
outfile=os.path.join(outpath, outfile)
fout=cdms.open(outfile, "w")
latax=cdms.createAxis([lat])
latax.units="degrees_north"
latax.id=latax.standard_name=latax.long_name="latitude"
lonax=cdms.createAxis([lon])
lonax.units="degrees_east"
lonax.id=lonax.standard_name=lonax.long_name="longitude"
tax=f[var].getTime() #f(var, level=slice(0,1), lat=slice(0,1), lon=slice(0,1)).getTime()
timeax=cdms.createAxis(Numeric.array(tax[0:tlen],'d'))
timeax.designateTime()
timeax.units=tax.units
#timeax.id=timeax.standard_name=timeax.long_name="time"
timeax.id="time"
timeax.title=tax.title
timeax.delta_t=tax.delta_t
timeax.init_time=tax.init_time
timeax.actual_range=tax.actual_range
del timeax.axis
del timeax.calendar
metadata=f[var]
fv=metadata.missing_value
newshape=(len(timeax), len(latax), len(lonax))
maxFound=20. # Set as our max value if not greater
for v in ((dav, "average"), (dmax, "maximum"), (dmin, "minimum"), \
(sdupper, "plus_std_dev"), (sdlower, "minus_std_dev"), ("always10", "always10")):
if type(v[0])==type("jlj") and v[0]=="always10":
print "Creating always equal to 10 variable."
always10=MA.zeros(newshape, 'f')+10.
#print always10.shape, dav.shape, type(dav)
newvar=cdms.createVariable(always10, axes=[timeax, latax, lonax], id=v[1], fill_value=fv)
newvar.longname="always10"
else:
data=v[0]
name=varname+"_"+v[1]
if not type(data)==type([1,2]):
data=data(squeeze=1)
data=MA.resize(data, newshape)
newvar=cdms.createVariable(data, axes=[timeax, latax, lonax], id=name, fill_value=fv)
newvar.long_name="%s - %s" % (varname.title(), v[1].replace("_", " "))
newvar.units=metadata.units
(dummy,vmax)=vcs.minmax(newvar)
if vmax>maxFound:
maxFound=vmax
fout.write(newvar)
fout.sync()
del newvar
fout.close()
return (outfile, varname, datetime, maxFound)
file_xml = os.path.join(sys.prefix,'sample_data/'+var+'_'+model[i]+'.xml')
#a=cdms.open('/pcmdi/AMIP3/amip/mo/'+var+'/'+model[i]+'/'+var+'_'+model[i]+'.xml')
a=cdms.open(file_xml)
data=a(var,time=(start_time,end_time),squeeze=1)
a.close()
ac=cdutil.ANNUALCYCLE.climatology(data(time=(start_time, end_time, 'cob')))
data_an=cdutil.ANNUALCYCLE.departures(data,ref=ac)
print i,model[i],data.shape, data_an.shape
glan[i,:]=cdutil.averager(data_an,axis='xy')
#
# setup metadata and write out to a netcdf file
#
tim=data.getTime()
runs=Numeric.arange(0,len(model))
runs=cdms.createAxis(runs,id='models')
glan=cdms.createVariable(glan,axes=(runs,tim),id='global_'+var+'_anomalies')
#
print 'start write file..'
q=cdms.open('global_anomalies.nc','w')
q.model_designation=model_description
q.write(glan)
q.close()
#
# a simple plot
#
x=vcs.init()
x.setcolormap('default')
x.plot(glan)
#
print ""