def teestTimes2(self):
a=MV2.masked_array(MV2.array([0,0,0,0,0,0,0,0,0,0,0,0]),[0,1,1,1,1,1,1,1,1,1,1,0])
bounds=numpy.ma.array(
[[0,31],
[31,59],
[59,90],
[90,120],
[120,151],
[151,181],
[181,212],
[212,243],
[243,273],
[273,304],
[304,334],
[334,365]]
)
ax=a.getAxis(0)
ax.setBounds(bounds)
#print cdutil.times.centroid(a,[-10,30])
print(('Centroid Normal:',cdutil.times.centroid(a,[0,365])))
print(('Centroid Cyclic:',cdutil.times.cyclicalcentroid(a,[0,365])))
djf=cdutil.DJF(self.tas_mo)
djf=cdutil.DJF(self.tas_mo,criteriaarg=[.8,0.0001])
djf=cdutil.ANNUALCYCLE.climatology(self.tas_mo)
djf=cdutil.YEAR.departures(self.tas_mo)
def testAnnualSeasonalAverage(self):
f = cdms2.open(self.filename, "r")
# Read in the raw data EXCLUDING a leap year
obs_timeseries1 = f('obs', time=slice(0, 48)) # 1900.1. to 1903.12.
# Read in the raw data INCLUDING a leap year
obs_timeseries2 = f('obs', time=slice(
0, 60)) # 1900.1. to 1904.12., 1904 is year lear
### Truncate first Jan, Feb and last Dec before get Annual cycle anomaly ... (to have fair DJF seasonal mean later)
obs_timeseries1 = obs_timeseries1[2:-1]
obs_timeseries2 = obs_timeseries2[2:-1]
### Set monthly time bounds ...
cdutil.setTimeBoundsMonthly(obs_timeseries1)
cdutil.setTimeBoundsMonthly(obs_timeseries2)
#### Removing Annual cycle ...
obs_timeseries_ano1 = cdutil.ANNUALCYCLE.departures(obs_timeseries1)
obs_timeseries_ano2 = cdutil.ANNUALCYCLE.departures(obs_timeseries2)
#### Calculate time average ...
obs_timeseries_ano_timeave1 = cdutil.averager(
obs_timeseries_ano1, axis='t') ## This should be zero and it does
obs_timeseries_ano_timeave2 = cdutil.averager(
obs_timeseries_ano2,
axis='t') ## This should be zero BUT it does NOT
#### SEASONAL MEAN TEST ####
obs_timeseries_ano1_DJF = cdutil.DJF(obs_timeseries_ano1,
criteriaarg=[0.95, None])
obs_timeseries_ano2_DJF = cdutil.DJF(obs_timeseries_ano2,
criteriaarg=[0.95, None])
obs_timeseries_ano1_JJA = cdutil.JJA(obs_timeseries_ano1,
criteriaarg=[0.95, None])
obs_timeseries_ano2_JJA = cdutil.JJA(obs_timeseries_ano2,
criteriaarg=[0.95, None])
#### Calculate time average ...
obs_timeseries_ano1_DJF_timeave = cdutil.averager(
obs_timeseries_ano1_DJF,
axis='t') ## This should be zero and it does
obs_timeseries_ano2_DJF_timeave = cdutil.averager(
obs_timeseries_ano2_DJF,
axis='t') ## This should be zero BUT it does NOT
obs_timeseries_ano1_JJA_timeave = cdutil.averager(
obs_timeseries_ano1_JJA,
axis='t') ## This should be zero and it does
obs_timeseries_ano2_JJA_timeave = cdutil.averager(
obs_timeseries_ano2_JJA,
axis='t') ## This should be zero and it does
numpy.testing.assert_almost_equal(obs_timeseries_ano_timeave2,
obs_timeseries_ano_timeave1, 10)
numpy.testing.assert_almost_equal(obs_timeseries_ano1_JJA_timeave,
obs_timeseries_ano2_JJA_timeave, 10)
numpy.testing.assert_almost_equal(obs_timeseries_ano1_DJF_timeave,
obs_timeseries_ano2_DJF_timeave, 10)
def write_merra2(surf=None, root=None):
if surf is None:
surf, root = merra2()
cdutil.setTimeBoundsMonthly(surf)
cdutil.setTimeBoundsMonthly(root)
fw = cdms.open(
"../DROUGHT_ATLAS/OBSERVATIONS/MERRA2_soilmoisture_summerseason.nc",
"w")
djf_surf = cdutil.DJF(surf, criteriaarg=(1, None))[1:]
jja_surf = cdutil.DJF(surf, criteriaarg=(1, None))[1:]
ss_surf = summerseason_GLEAM(jja_surf, djf_surf)
ss_surf.id = "smsurf"
fw.write(ss_surf)
djf_root = cdutil.DJF(root, criteriaarg=(1, None))[1:]
jja_root = cdutil.DJF(root, criteriaarg=(1, None))[1:]
ss_root = summerseason_GLEAM(jja_root, djf_root)
ss_root.id = "smroot"
fw.write(ss_root)
fw.close()
# Adapted for numpy/ma/cdms2 by convertcdms.py
import cdms2, cdutil, numpy.ma, MV2, os, sys, cdat_info
cdms2.setAutoBounds('on')
# centroid test
a = MV2.masked_array(MV2.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0])
bounds = numpy.ma.array([[0, 31], [31, 59], [59, 90], [90, 120], [120, 151],
[151, 181], [181, 212], [212, 243], [243, 273],
[273, 304], [304, 334], [334, 365]])
ax = a.getAxis(0)
ax.setBounds(bounds)
#print cdutil.times.centroid(a,[-10,30])
print 'Centroid Normal:', cdutil.times.centroid(a, [0, 365])
print 'Centroid Cyclic:', cdutil.times.cyclicalcentroid(a, [0, 365])
f = cdms2.open(os.path.join(cdat_info.get_sampledata_path(), 'tas_mo.nc'))
s = f('tas')
cdutil.setTimeBoundsMonthly(s)
djf = cdutil.DJF(s)
djf = cdutil.DJF(s, criteriaarg=[.8, 0.0001])
djf = cdutil.ANNUALCYCLE.climatology(s)
djf = cdutil.YEAR.departures(s)
# Set time period ---
start_year = 1980
end_year = 2000
start_time = cdtime.comptime(start_year)
end_time = cdtime.comptime(end_year)
# Load variable ---
d = f('psl',
time=(start_time, end_time),
longitude=(-180, 180),
latitude=(20, 90)) # Provide proper variable name
d = MV2.divide(d, 100.) # Pa to hPa
d.units = 'hPa'
# Get DJF seasonal mean time series ---
d_DJF = cdutil.DJF(d)
# EOF (take only first variance mode...) ---
solver = Eof(d_DJF, weights='area')
eof = solver.eofsAsCovariance(neofs=1)
pc = solver.pcs(npcs=1, pcscaling=1) # pcscaling=1: scaled to unit variance
# (divided by the square-root of their eigenvalue)
frac = solver.varianceFraction()
# Sign control if needed ---
eof = eof * -1
pc = pc * -1
#===========================================================================================================
# Plot
#-----------------------------------------------------------------------------------------------------------
