def doCalTest(self, a, b, c, d, e, f, g, cal=cdtime.MixedCalendar):
x = cdtime.comptime(d, e, f)
units = "days since %d-%d-%d" % (a, b, c)
r = x.torel(units, cal)
self.assertTrue(self.isEqual(r.value, g))
r2 = cdtime.reltime(g, units)
x2 = r2.tocomp(cal)
self.assertTrue(self.cmpYear(x, x2))
units2 = "days since %d-%d-%d" % (d, e, f)
r3 = cdtime.reltime(10.0, units2)
r4 = r3.torel(units)
self.assertTrue(self.isEqual(r4.value, (10.0 + g)))
bb = cdtime.comptime(a, b, c)
x2 = bb.add(g, cdtime.Day, cal)
self.assertTrue(self.cmpYear(x, x2))
x2 = x.sub(g, cdtime.Day, cal)
self.assertTrue(self.cmpYear(bb, x2))
r2 = cdtime.reltime(g, units)
r3 = r2.add(1000.0, cdtime.Day, cal)
self.assertTrue(self.isEqual(r3.value, g + 1000.0))
r3 = r2.sub(1000.0, cdtime.Day, cal)
self.assertTrue(self.isEqual(r3.value, g - 1000.0))
def testSubRegionTimes(self):
# subRegion - time types
s2 = self.var.subRegion(latitude=(-42., 42., 'ccn'),
longitude=(90., 270., 'con'),
time=('2001-1', '2002-1', 'ccn'))
self.assertTrue(numpy.ma.allequal(self.vp, s2))
t1 = cdtime.comptime(2001)
t2 = cdtime.comptime(2002)
s2 = self.var.subRegion(latitude=(-42., 42., 'ccn'),
longitude=(90., 270., 'con'),
time=(t1, t2))
self.assertTrue(numpy.ma.allequal(self.vp, s2))
t1 = cdtime.comptime(2003)
t2 = cdtime.comptime(2004)
with self.assertRaises(cdms2.CDMSError):
s2 = self.var.subRegion(latitude=(-42., 42., 'ccn'),
longitude=(90., 270., 'con'),
time=(t1, t2))
t1 = cdtime.reltime(0, "years since 2001")
t2 = cdtime.reltime(1, "year since 2001")
s2 = self.var.subRegion(latitude=(-42., 42., 'ccn'),
longitude=(90., 270., 'con'),
time=(t1, t2, 'ccn'))
self.assertTrue(numpy.ma.allequal(self.vp, s2))
xx = self.var.subRegion('2000')
self.assertTrue(numpy.ma.allequal(xx, self.var(time="2000")))
def plot_obs_trends(self, dataset, **kwargs):
X = self.OBS[string.upper(dataset)]
west = X.reshaped["west"]
east = X.reshaped["east"]
if "start" not in kwargs.keys():
start = cmip5.start_time(east)
start = cdtime.comptime(start.year, start.month, 1)
else:
start = kwargs.pop("start")
if "stop" not in kwargs.keys():
stop = cmip5.stop_time(east)
stop = cdtime.comptime(stop.year, stop.month, 30)
else:
stop = kwargs.pop("stop")
west = west(time=(start, stop))
east = east(time=(start, stop))
west.getAxis(0).id = "time"
east.getAxis(0).id = "time"
plt.plot(cmip5.get_linear_trends(west).asma(),
label="WEST",
color=get_colors("west"),
**kwargs)
plt.plot(cmip5.get_linear_trends(east).asma(),
label="EAST",
color=get_colors("east"),
**kwargs)
months = [
"JAN", "FEB", "MAR", "APR", "MAY", "JUN", "JUL", "AUG", "SEP",
"OCT", "NOV", "DEC"
]
plt.xticks(np.arange(12), months)
def recup_nfs_sst_seviri_recent(self, URL_SEVIRI_DATA, YYYY, MM, DD, timerange):
""" Recuperation par NFS d un fichier de SST SEVIRI horaire recent (depuis 01/01/2012)"""
import os, subprocess, shutil
import cdtime, glob
from vacumm.misc.atime import strftime
# -- Complement du nom de repertoire avec l'annee et le jour relatif (001-> 365)
DATA_DIR = os.path.join(URL_SEVIRI_DATA, str(YYYY))
# print DATA_DIR
a = cdtime.comptime(YYYY, MM, DD)
a2 = a.torel("days since " + strftime("%Y-%m-%d", cdtime.comptime(YYYY - 1, 12, 31)))
DATA_DIR = os.path.join(DATA_DIR, "%(#)03d" % {"#": a2.value})
# print DATA_DIR
if timerange == "midnight":
filename = "%(#)04d%(##)02d%(####)02d00*" % {"#": YYYY, "##": MM, "####": DD}
else:
filename = "%(#)04d%(##)02d%(####)02d*" % {"#": YYYY, "##": MM, "####": DD}
copy_mode = "nfs"
list_file = glob.glob(os.path.join(DATA_DIR, filename))
# si la liste est vide, on essaie en se connectant a service7
if not list_file:
find_cmd = "ssh service7 \"find %(DATA_DIR)s -name '%(filename)s' \"" % vars()
list_file = subprocess.check_output(find_cmd, shell=True).strip().split()
if list_file:
copy_mode = "scp"
for file_to_read in list_file:
if os.path.isfile(os.path.basename(file_to_read)) == False:
if copy_mode == "nfs":
shutil.copyfile(file_to_read, os.path.basename(file_to_read))
if copy_mode == "scp":
copy_cmd = "scp caparmor-sftp.ifremer.fr:%(file_to_read)s ." % vars()
subprocess.check_call(copy_cmd, shell=True)
def checkDatawc(self, name, value):
checkName(self, name, value)
if isNumber(value):
value = float(value), 0
elif isinstance(value, str):
t = cdtime.s2c(value)
if t != cdtime.comptime(0, 1):
t = t.torel(self.datawc_timeunits, self.datawc_calendar)
value = float(t.value), 1
else:
checkedRaise(
self, value, ValueError, 'The ' + name +
' attribute must be either an integer or a float value or a date/time.'
)
elif type(value) in [
type(cdtime.comptime(1900)),
type(cdtime.reltime(0, 'days since 1900'))
]:
value = value.torel(self.datawc_timeunits,
self.datawc_calendar).value, 1
else:
checkedRaise(
self, value, ValueError, 'The ' + name +
' attribute must be either an integer or a float value or a date/time.'
)
return value
def app_test():
short_run = True
start_time = cdtime.comptime( 1980, 1 )
end_time = cdtime.comptime( 1980, 7 ) if short_run else cdtime.comptime( 1982, 1 )
decomp_mode = DecompositionMode.Temporal
dataset = {}
dataset['path'] = '/Users/tpmaxwel/Data/MERRA_hourly_2D_precip/MERRA_hourly_precip.xml'
dataset[ 'variable' ] = 'prectot'
time_specs = {}
time_specs['start_time'] = str( start_time )
time_specs['end_time'] = str( end_time )
time_specs[ 'period_value' ] = 1
time_specs[ 'period_units' ] = cdtime.Month
grid = {}
grid['lat'] = [ -90, 90 ]
grid[ 'lon' ] = [ -180, 180 ]
task_metadata = {}
task_metadata['time'] = time_specs
task_metadata['grid'] = grid
task_metadata['dataset'] = dataset
testApp = IOTestApp( task_metadata )
testApp.execute( decomp_mode )
def testCal(a, b, c, d, e, f, g, cal=cdtime.MixedCalendar):
x = cdtime.comptime(d, e, f)
units = "days since %d-%d-%d" % (a, b, c)
r = x.torel(units, cal)
if not isEqual(r.value, g):
markError('component => relative failed: %s %s' % ( ` x `, ` r `))
r2 = cdtime.reltime(g, units)
x2 = r2.tocomp(cal)
if not cmpYear(x, x2):
markError('relative => component failed: %s %s' % ( ` r2 `, ` x2 `))
units2 = "days since %d-%d-%d" % (d, e, f)
r3 = cdtime.reltime(10.0, units2)
r4 = r3.torel(units)
if not isEqual(r4.value, (10.0 + g)):
markError('relative => relative: %s %s' % ( ` r3 `, ` r4 `))
bb = cdtime.comptime(a, b, c)
x2 = bb.add(g, cdtime.Day, cal)
if not cmpYear(x, x2):
markError('component add failed: %s %s' % ( ` bb `, ` x2 `))
x2 = x.sub(g, cdtime.Day, cal)
if not cmpYear(bb, x2):
markError('component sub failed: %s %s' % ( ` x `, ` x2 `))
r2 = cdtime.reltime(g, units)
r3 = r2.add(1000.0, cdtime.Day, cal)
if not isEqual(r3.value, g + 1000.0):
markError('relative add failed: %s %s' % ( ` r2 `, ` r3 `))
r3 = r2.sub(1000.0, cdtime.Day, cal)
if not isEqual(r3.value, g - 1000.0):
markError('relative sub failed: %s %s' % ( ` r2 `, ` r3 `))
def proj_aerosols(AA, piControl, H85, start=None, stop=None):
if start is None:
start = cdtime.comptime(1945, 1, 1)
if stop is None:
stop = cdtime.comptime(1984, 12, 31)
data = [H85.reshaped["west"], H85.reshaped["east"]]
nmod, nyears, nmonths = H85.reshaped["west"].shape
P = MV.zeros((nmod, nyears))
msolver = AA.solvers["multi"]
fac = da.get_orientation(msolver)
for i in range(nmod):
to_proj = [H85.reshaped["west"][i], H85.reshaped["east"][i]]
P[i] = msolver.projectField(to_proj)[:, 0] * fac
P.setAxis(0, H85.reshaped["west"].getAxis(0))
timeax = H85.reshaped["west"].getAxis(1)
timeax.id = "time"
P.setAxis(1, timeax)
piCdata = [piControl.reshaped["west"], piControl.reshaped["east"]]
pc = msolver.projectField(piCdata)[:, 0]
Pt = P(time=(start, stop))
nt = len(Pt.getTime())
hslopes = cmip5.get_linear_trends(Pt)
pslopes = da.get_slopes(pc, nt)
def teestTimes(self):
fsc = cdms2.open(os.path.join(cdat_info.get_sampledata_path(),'tas_mo_clim.nc'))
print("Step #0 : Reading data")
s=self.f('tas',longitude=(0,360,'co'))
acok=fsc('climseas',longitude=(0,360,'co'))
print('Test #1 : Test result')
ac=cdutil.times.JAN.climatology(s)
self.assertTrue(MV2.allclose(ac[0],acok[0]))
fsc.close()
a=cdtime.comptime(1980)
b=cdtime.comptime(1980,5)
f = cdms2.open(os.path.join(cdat_info.get_sampledata_path(),'tas_6h.nc'))
s=f('tas',time=(a,b,'co'),squeeze=1)
print("Test #2 : 6hourly AND get")
jans=cdutil.times.JAN(s)
print("Test #3 : climatology 6h")
JFMA=cdutil.times.Seasons('JFMA')
jfma=JFMA.climatology(s)
#Test reorder
print("Test #4 : time not first axis")
jfma=JFMA.climatology(s(order='x...'))
print("Test 4b: Result ok ?")
self.assertEqual(jfma.getOrder()[0], 'x')
def old_Figure4(D,SM):
axes=[]
axes+=[plt.subplot(221)]
t1981=cdtime.comptime(1981,1,1)
t2017=cdtime.comptime(2017,12,31)
pdsi_time_series(D,t1981,t2017,SM=SM,best_fit=False)
axes+=[plt.subplot(222)]
pdsi_SN_figure(D)
plt.title("(b): PDSI")
plt.legend(fontsize=8)
axes+=[plt.subplot(223)]
noise,signal_gleam,signal_merra2,H85=soil_SN_figure(SM,"30cm")
plt.legend(fontsize=8)
plt.title("(c): Surface soil moisture")
axes+=[plt.subplot(224)]
noise2,signal_gleam2,signal_merra22,H852=soil_SN_figure(SM,"2m")
plt.title("(d): Root zone soil moisture")
plt.legend(fontsize=8)
h,l=axes[1].get_legend_handles_labels()
axes[1].legend(h[:3],l[:3],fontsize=8)
h,l=axes[2].get_legend_handles_labels()
axes[2].legend(h[:2],l[:2],fontsize=8)
h,l=axes[3].get_legend_handles_labels()
axes[3].legend(h[:2],l[:2],fontsize=8)
axes[0].legend(ncol=2,fontsize=8)
def average_vs_projection(data):
"""Show the difference between PDSI regional average and projections"""
#start = cdtime.comptime(1975,8,1)
#stop = cdtime.comptime(2005,12,31)
start = cdtime.comptime(1900,1,1)
stop = cdtime.comptime(1949,12,31)
ax1=plt.subplot(211)
ax2=plt.subplot(212)
for thing in sorted(["MXDA","OWDA","NADA","MADA","ANZDA"])+["ALL"]:
X=getattr(data,thing)
pdsi_av = cdutil.averager(X.obs,axis='xy')(time=(start,stop))
c=colorregions(X.name)
Plotting.time_plot(pdsi_av,lw=3,color=c,label=X.name,ax=ax1)
Plotting.time_plot(X.projection(time=(start,stop)),lw=3,color=c,label=X.name,ax=ax2)
if thing =="ALL":
x=np.arange(1975,2006)
y=X.projection(time=(start,stop))
p=np.polyfit(x,y,1)
ax2.plot(x,np.polyval(p,x),"k--",lw=1)
y=pdsi_av
p=np.polyfit(x,y,1)
ax1.plot(x,np.polyval(p,x),"k--",lw=1)
ax1.set_title("(a): Regional mean PDSI")
ax1.set_ylabel("PDSI")
ax2.set_title("(b): Projection on fingerprint")
# ax2.set_ylim(-22,22)
ax2.set_ylabel("Projection")
ax1.set_ylim(-1.6,1.6)
plt.legend(ncol=4,fontsize=8)
def pad_by_10(X, year1, year2):
"""
Pad an array at the beginning with an artificial 10 year spinup, in which each value is set to the climatology
"""
if type(year1) == type("string"):
year, month, day = year1.split("-")
year1 = cdtime.comptime(int(year), int(month), int(day))
if type(year2) == type("string"):
year, month, day = year2.split("-")
year2 = cdtime.comptime(int(year), int(month), int(day))
tax = X.getTime()
lastten = [year1.sub(x, cdtime.Months) for x in range(121)[1:]][::-1]
dayax = np.array([x.torel(tax.units).value for x in lastten])
tax_new = np.append(dayax, tax)
new_time_axis = cdms.createAxis(tax_new)
new_time_axis.designateTime()
new_time_axis.id = "time"
new_time_axis.units = tax.units
Xnew = MV.zeros((len(tax_new), ) + X.shape[1:])
Xnew[:120] = np.repeat(MV.average(X(time=(year1, year2)),
axis=0).asma()[np.newaxis, :, :],
120,
axis=0)
Xnew[120:] = X
for k in X.attributes.keys():
setattr(Xnew, k, X.attributes[k])
Xnew.setAxisList([new_time_axis] + X.getAxisList()[1:])
return Xnew
def testCal(a,b,c,d,e,f,g,cal=cdtime.MixedCalendar):
x = cdtime.comptime(d,e,f)
units = "days since %d-%d-%d"%(a,b,c)
r = x.torel(units,cal)
if not isEqual(r.value,g):
markError('component => relative failed: %s %s'%(`x`,`r`))
r2 = cdtime.reltime(g, units)
x2 = r2.tocomp(cal)
if not cmpYear(x,x2):
markError('relative => component failed: %s %s'%(`r2`,`x2`))
units2 = "days since %d-%d-%d"%(d,e,f)
r3 = cdtime.reltime(10.0,units2)
r4 = r3.torel(units)
if not isEqual(r4.value,(10.0+g)):
markError('relative => relative: %s %s'%(`r3`,`r4`))
bb = cdtime.comptime(a,b,c)
x2 = bb.add(g,cdtime.Day,cal)
if not cmpYear(x,x2):
markError('component add failed: %s %s'%(`bb`,`x2`))
x2 = x.sub(g,cdtime.Day,cal)
if not cmpYear(bb,x2):
markError('component sub failed: %s %s'%(`x`,`x2`))
r2 = cdtime.reltime(g, units)
r3 = r2.add(1000.0,cdtime.Day,cal)
if not isEqual(r3.value, g+1000.0):
markError('relative add failed: %s %s'%(`r2`,`r3`))
r3 = r2.sub(1000.0,cdtime.Day,cal)
if not isEqual(r3.value, g-1000.0):
markError('relative sub failed: %s %s'%(`r2`,`r3`))
def __init__(self, dataset):
f = cdms.open("DATA/OBS/PROCESSED/" + dataset + ".nc")
self.data = {}
obs_w = f("pr_W")
self.data["west"] = obs_w
stop_time = cmip5.stop_time(obs_w)
if stop_time.month != 12:
stop_time = cdtime.comptime(stop_time.year - 1, 12, 31)
start_time = cmip5.start_time(obs_w)
if start_time.month != 1:
start_time = cdtime.comptime(start_time.year + 1, 1, 1)
obs_w = obs_w(time=(start_time, stop_time))
obs_w = fp.by_month(obs_w)
obs_e = f("pr_CE")
self.data["east"] = obs_e
stop_time = cmip5.stop_time(obs_e)
if stop_time.month != 12:
stop_time = cdtime.comptime(stop_time.year - 1, 12, 31)
start_time = cmip5.start_time(obs_e)
if start_time.month != 1:
start_time = cdtime.comptime(start_time.year + 1, 1, 1)
obs_e = obs_e(time=(start_time, stop_time))
obs_e = fp.by_month(obs_e)
self.reshaped = {}
self.reshaped["east"] = obs_e - MV.average(obs_e, axis=0)
self.reshaped["west"] = obs_w - MV.average(obs_w, axis=0)
self.reshaped["multi"] = [self.reshaped["west"], self.reshaped["east"]]
self.dataset = dataset
def test_xdrange_CdtimeCompDateInput_StringDateOutput(self): ''' Testing for xdrange string date with skipdays input ''' gen = days.xdrange(startdate = cdtime.comptime(2011,6,10),enddate = cdtime.comptime(2011,6,14), stepdays = 2, returnType = 's') checkdates = ['20110610','20110612','20110614'] count = 0 for date in gen: assert date == checkdates[count] count = count + 1
def DA_histogram(self,
experiment,
direction,
start=None,
stop=None,
datasets=None):
fingerprint = getattr(self, experiment)
if start is None:
start = cmip5.start_time(self.gpcp.reshaped["east"])
start = cdtime.comptime(start.year, start.month, 1)
if stop is None:
stop = cmip5.stop_time(self.gpcp.reshaped["east"])
stop = cdtime.comptime(stop.year, stop.month, 30)
#get the h85 projections over the same time period
H85m = self.model_projections(experiment,
direction)(time=(start, stop))
H85 = cmip5.cdms_clone(np.ma.mask_rows(H85m), H85m)
H85_trends = cmip5.get_linear_trends(H85)
#get the piControl projection time series
noise = self.noise_projections(experiment, direction)
L = stop.year - start.year + 1
noise_trends = da.get_slopes(noise, L)
#plot
plt.hist(H85_trends.compressed(),
25,
color=da_colors("h85"),
alpha=.5,
normed=True)
plt.hist(noise_trends,
25,
color=da_colors("piC"),
alpha=.5,
normed=True)
da.fit_normals_to_data(H85_trends,
color=da_colors("h85"),
lw=3,
label="H85")
da.fit_normals_to_data(noise_trends,
color=da_colors("piC"),
lw=3,
label="piControl")
# plt.axvline(obs_trend,label=obs.dataset,color=da_colors(obs.dataset))
#Project the observations
if datasets is None:
datasets = ["gpcp", "cmap", "precl"]
if type(datasets) != type([]):
datasets = [datasets]
for dataset in datasets:
obs_proj = self.obs_projections(experiment, dataset,
direction)(time=(start, stop))
obs_trend = cmip5.get_linear_trends(obs_proj)
plt.axvline(obs_trend, label=dataset, color=da_colors(dataset))
print dataset + "S/N is: " + str(obs_trend / np.std(noise_trends))
def rcp_solver(D,early_start=None,early_stop=None):
if early_start is None:
early_start = cdtime.comptime(2006,1,1)
if early_stop is None:
early_stop=cdtime.comptime(2099,12,31)
earlydata = D.ALL.model(time=(early_start,early_stop))
early_mma=MV.average(cmip5.ensemble2multimodel(earlydata),axis=0)
earlysolver = Eof(early_mma,weights="area")
return earlysolver
def aerosol_solver(D,aerosol_start=None,aerosol_stop=None,include_cru=False):
if aerosol_start is None:
aerosol_start = cdtime.comptime(1950,1,1)
if aerosol_stop is None:
aerosol_stop=cdtime.comptime(1975,1,1)
aerosoldata = D.ALL.model(time=(aerosol_start,aerosol_stop))
aerosol_mma=MV.average(cmip5.ensemble2multimodel(aerosoldata),axis=0)
aerosolsolver = Eof(aerosol_mma,weights="area")
return aerosolsolver
def timestr2comp(self, date):
"""
:func:`timestr2comp`: To convert date from yyyymmdd[hh] formate into
cdtime.comptime formate
Condition :
passing date must be yyyymmdd formate in either int or str
Inputs:
date in yyyymmdd formate or yyyymmddhh formate.
i.e. hour(hh) is optional.
Outputs:
It should return the date in cdtime.comptime object type
Usage:
example1:
>>> timestr2comp(20110423)
2011-4-23 0:0:0.0
.. note:: It should return as cdtime.comptype. Here we didnt
pass the hour. i.e only yyyymmdd formate
example2:
>>> timestr2comp(2011082010)
2011-8-20 10:0:0.0
..note:: Here it should return cdtime with hours also.
We passed yyyymmddhh formate. i.e include hh
example3:
>>> timestr2comp(2011082023)
2011-8-20 23:0:0.0
..note:: we cannot pass 24 as hour here. Max 23 hours only.
Written by: Arulalan.T
Date: 23.04.2011
Updated : 21.08.2011
"""
if str(type(date)) == "<type 'comptime'>":
# passed date itself comptime object only
return date
if isinstance(date, int):
date = str(date)
# re match
if self.comppattern.match(date):
# i.e. date is comptime in string formate
# so make it as comptime object
return cdtime.s2c(date)
if self.ymdpattern.match(date):
# i.e date is yyyymmdd string formate
year = int(date[0:4])
month = int(date[4:6])
day = int(date[6:8])
if len(date) == 10:
hour = int(date[-2:])
return cdtime.comptime(year, month, day, hour)
else:
return cdtime.comptime(year, month, day)
else:
raise _TimeUtilityStringError('The given date either comptime \
object or comptime string or yyyymmdd formate only')
def timestr2comp(self, date):
"""
:func:`timestr2comp`: To convert date from yyyymmdd[hh] formate into
cdtime.comptime formate
Condition :
passing date must be yyyymmdd formate in either int or str
Inputs:
date in yyyymmdd formate or yyyymmddhh formate.
i.e. hour(hh) is optional.
Outputs:
It should return the date in cdtime.comptime object type
Usage:
example1:
>>> timestr2comp(20110423)
2011-4-23 0:0:0.0
.. note:: It should return as cdtime.comptype. Here we didnt
pass the hour. i.e only yyyymmdd formate
example2:
>>> timestr2comp(2011082010)
2011-8-20 10:0:0.0
..note:: Here it should return cdtime with hours also.
We passed yyyymmddhh formate. i.e include hh
example3:
>>> timestr2comp(2011082023)
2011-8-20 23:0:0.0
..note:: we cannot pass 24 as hour here. Max 23 hours only.
Written by: Arulalan.T
Date: 23.04.2011
Updated : 21.08.2011
"""
if str(type(date)) == "<type 'comptime'>":
# passed date itself comptime object only
return date
if isinstance(date, int):
date = str(date)
# re match
if self.comppattern.match(date):
# i.e. date is comptime in string formate
# so make it as comptime object
return cdtime.s2c(date)
if self.ymdpattern.match(date):
# i.e date is yyyymmdd string formate
year = int(date[0:4])
month = int(date[4:6])
day = int(date[6:8])
if len(date) == 10:
hour = int(date[-2:])
return cdtime.comptime(year, month, day, hour)
else:
return cdtime.comptime(year, month, day)
else:
raise _TimeUtilityStringError('The given date either comptime \
object or comptime string or yyyymmdd formate only')
def getCompTime( timeString ):
print " >> GetCompTime: ", timeString
timeStringFields = timeString.strip("'").split(' ')
date = timeStringFields[0].split('-')
if len( timeStringFields ) == 1:
return cdtime.comptime( int(date[0]), int(date[1]), float(date[2]) )
else:
time = timeStringFields[1].split(':')
for iT in range(3): time.append(0)
return cdtime.comptime( int(date[0]), int(date[1]), int(date[2]), int(time[0]), int(time[1]), float(time[2]) )
def DA_histogram(fingerprint,
obslist,
h85,
piC,
direction,
start=None,
stop=None):
if type(obslist) == type([]):
obs = obslist[0]
else:
obs = obslist
if start is None:
start = cmip5.start_time(obs.reshaped["east"])
start = cdtime.comptime(start.year, start.month, 1)
if stop is None:
stop = cmip5.stop_time(obs.reshaped["east"])
stop = cdtime.comptime(stop.year, stop.month, 30)
#project the observations onto the fingerprint
obs_proj = obs_projections(fingerprint, obs, direction)(time=(start, stop))
obs_trend = cmip5.get_linear_trends(obs_proj)
#get the h85 projections over the same time period
H85m = model_projections(fingerprint, h85, direction)(time=(start, stop))
H85 = cmip5.cdms_clone(np.ma.mask_rows(H85m), H85m)
H85_trends = cmip5.get_linear_trends(H85)
#get the piControl projection time series
noise = noise_projections(fingerprint, piC, direction)
L = len(obs_proj)
noise_trends = da.get_slopes(noise, L)
#plot
plt.hist(H85_trends.compressed(),
25,
color=da_colors("h85"),
alpha=.5,
normed=True)
plt.hist(noise_trends, 25, color=da_colors("piC"), alpha=.5, normed=True)
da.fit_normals_to_data(H85_trends,
color=da_colors("h85"),
lw=3,
label="H85")
da.fit_normals_to_data(noise_trends,
color=da_colors("piC"),
lw=3,
label="piControl")
plt.axvline(obs_trend, label=obs.dataset, color=da_colors(obs.dataset))
if type(obslist) == type([]):
for obs in obslist[1:]:
obs_proj = obs_projections(fingerprint, obs,
direction)(time=(start, stop))
obs_trend = cmip5.get_linear_trends(obs_proj)
plt.axvline(obs_trend,
label=obs.dataset,
color=da_colors(obs.dataset))
return H85, noise, obs_proj
def test_xdrange_CdtimeCompDateInput_CdtimeCompDateOutput(self): ''' Testing for xdrange string date with skipdays input ''' gen = days.xdrange(startdate = cdtime.comptime(2011,6,10),enddate = cdtime.comptime(2011,6,14), stepdays = 2, returnType = 'c') checkdates = [] checkdates.append(cdtime.comptime(2011,6,10)) checkdates.append(cdtime.comptime(2011,6,12)) checkdates.append(cdtime.comptime(2011,6,14)) count = 0 for date in gen: assert date == checkdates[count] count = count + 1
def read_data_in(
path,
var_in_data,
var_to_consider,
start_time,
end_time,
UnitsAdjust,
LandMask,
debug=False,
):
f = cdms2.open(path)
data_timeseries = f(var_in_data,
time=(start_time, end_time),
latitude=(-90, 90))
cdutil.setTimeBoundsMonthly(data_timeseries)
# missing data check
check_missing_data(data_timeseries)
if UnitsAdjust[0]:
data_timeseries = getattr(MV2, UnitsAdjust[1])(data_timeseries,
UnitsAdjust[2])
if var_to_consider == "ts" and LandMask:
# Replace temperature below -1.8 C to -1.8 C (sea ice)
data_timeseries = sea_ice_adjust(data_timeseries)
# Check available time window and adjust if needed
data_stime = data_timeseries.getTime().asComponentTime()[0]
data_etime = data_timeseries.getTime().asComponentTime()[-1]
data_syear = data_stime.year
data_smonth = data_stime.month
data_eyear = data_etime.year
data_emonth = data_etime.month
if data_smonth > 1:
data_syear = data_syear + 1
if data_emonth < 12:
data_eyear = data_eyear - 1
debug_print(
"data_syear: " + str(data_syear) + " data_eyear: " + str(data_eyear),
debug)
data_timeseries = data_timeseries(time=(
cdtime.comptime(data_syear, 1, 1, 0, 0, 0),
cdtime.comptime(data_eyear, 12, 31, 23, 59, 59),
))
f.close()
return data_timeseries, data_syear, data_eyear
def getCompTime(timeString):
print " >> GetCompTime: ", timeString
timeStringFields = timeString.strip("'").split(' ')
date = timeStringFields[0].split('-')
if len(timeStringFields) == 1:
return cdtime.comptime(int(date[0]), int(date[1]), float(date[2]))
else:
time = timeStringFields[1].split(':')
for iT in range(3):
time.append(0)
return cdtime.comptime(int(date[0]), int(date[1]), int(date[2]),
int(time[0]), int(time[1]), float(time[2]))
def recup_ftp_sst_seviri_recent(self, URL_CERSAT, DATA_DIR, YYYY, MM, DD, ext, usr, pwd, ntry, timerange):
""" Recuperation par FTP d un fichier de SST SEVIRI horaire recent (depuis 01/01/2012)"""
import os, subprocess
from ftplib import FTP
import cdtime
from vacumm.misc.atime import strftime
# --------------- LE FTP FONCTIONNE MAIS LES FICHIERS SEMBLENT CORROMPUS ... PROBLEME DANS LE BZ2
# -- Complement du nom de repertoire avec l'annee et le jour relatif (001-> 365)
DATA_DIR = os.path.join(DATA_DIR, str(YYYY))
print DATA_DIR
a = cdtime.comptime(YYYY, MM, DD)
a2 = a.torel("days since " + strftime("%Y-%m-%d", cdtime.comptime(YYYY - 1, 12, 31)))
DATA_DIR = os.path.join(DATA_DIR, "%(#)03d" % {"#": a2.value})
print DATA_DIR
# connection au CDOCO
ftp = FTP(host=URL_CERSAT, user=usr, passwd=pwd)
# positionnement sur le repertoire "best_estimate"
ftp.cwd(DATA_DIR)
# utile pour ftp.retrbinary
def handleDownload(block):
file.write(block)
if timerange == "midnight":
filename = "%(#)04d%(##)02d%(####)02d00*" % {"#": YYYY, "##": MM, "####": DD}
else:
filename = "%(#)04d%(##)02d%(####)02d*" % {"#": YYYY, "##": MM, "####": DD}
# print filename
list_file = ftp.nlst(filename)
# print list_file
for file_to_read in list_file:
# recuperation fichier si non present dans work/MODEL/MARS
if os.path.isfile(file_to_read) == False:
# rajouter test sur date du fichier ... telecharge que si plus recent ...
file = open(file_to_read, "wb")
ftp.retrbinary("RETR " + file_to_read, handleDownload)
subprocess.call(["bunzip2", "-f", file_to_read])
else:
print "Pas de downloading : %(file_to_read)s existe deja" % vars()
ftp.quit()
def makeCalendar(timeStart,timeEnd,calendarStep='months',monthStart=1,monthEnd=12,dayStep=1):
"""
Documentation for makeCalendar():
-----
The makeCalendar() function creates a time calendar for given dates
Author: Paul J. Durack : [email protected]
Inputs:
-----
| **timeStart** - string start time (e.g. '2001' or '2001-1-1 0:0:0.0')
| **timeEnd** - string end time
| **calendarStep <optional>** - string either 'months' or 'days'
| **monthStart <optional>** - int
| **monthEnd <optional>** - int
| **dayStep <optional>** - int
Returns:
-----
| **time** - cdms2 transient axis
Usage:
-----
>>> from durolib import makeCalendar
>>> time = makeCalendar('2001','2014',calendarStep='month')
Notes:
-----
* PJD 30 Apr 2015 - Fixed 'off by one' error with partial years
* TODO: Update to take full date identifier '2001-1-1 0:0:0.0', not just year
* Issues with the daily calendar creation - likely require tweaks to cdutil.times.setAxisTimeBoundsDaily (range doesn't accept fractions, only ints)
* Consider reviewing calendar assignment in /work/durack1/Shared/obs_data/AQUARIUS/read_AQ_SSS.py
"""
# First check inputs
if calendarStep not in ['days','months',]:
print '** makeCalendar error: calendarStep unknown, exiting..'
return
if not isinstance(timeStart,str) or not isinstance(timeEnd,str):
print '** makeCalendar error: timeStart or timeEnd invalid, exiting..'
return
if not (int(monthStart) in range(1,13) and int(monthEnd) in range(1,13)):
print '** makeCalendar error: monthStart or monthEnd invalid, exiting..'
return
try:
timeStartTest = cdt.comptime(int(timeStart))
timeEndTest = cdt.comptime(int(timeEnd))
except SystemExit,err:
print '** makeCalendar error: timeStart invalid - ',err
return
def __init__(self,cfg=None):
import os, cdtime
Satellite.__init__(self)
SCRIPT_DIR=os.getcwd()
self.SCRIPT_DIR = SCRIPT_DIR
#-- retrocompatibilite
if cfg is None:
config = ConfigParser.RawConfigParser()
config.read(os.path.join(SCRIPT_DIR,'config.cfg'))
andeb = config.getint('Time Period', 'andeb')
anfin = config.getint('Time Period', 'anfin')
mdeb = config.getint('Time Period', 'mdeb')
mfin = config.getint('Time Period', 'mfin')
jdeb = config.getint('Time Period', 'jdeb')
jfin = config.getint('Time Period', 'jfin')
hdeb = config.getint('Time Period', 'hdeb')
hfin = config.getint('Time Period', 'hfin')
self.WORKDIR = config.get('Env', 'workdir')
else:
andeb = cfg['Time Period']['andeb']
anfin = cfg['Time Period']['anfin']
mdeb = cfg['Time Period']['mdeb']
mfin = cfg['Time Period']['mfin']
jdeb = cfg['Time Period']['jdeb']
jfin = cfg['Time Period']['jfin']
hdeb = cfg['Time Period']['hdeb']
hfin = cfg['Time Period']['hfin']
self.WORKDIR = cfg['Env']['workdir']
#print "%(andeb)s-%(mdeb)s-%(jdeb)s %(hdeb)s"%vars()
#print "%(anfin)s-%(mfin)s-%(jfin)s %(hfin)s"%vars()
# Conversion en "Component Time"
self.ctdeb=cdtime.comptime(andeb,mdeb,jdeb,hdeb,0,0)
self.ctfin=cdtime.comptime(anfin,mfin,jfin,hfin,0,0)
DIR_SST=os.path.join(self.WORKDIR,'SST') # repertoire de stockage des donnees
if os.path.isdir(DIR_SST)==False:
os.mkdir(DIR_SST)
self.WORKDIR=os.path.join(self.WORKDIR,'SST')
self.name="Satellite Sea surface Temperature"
self.shortname="SST"
self.units=" "
def __init__(self, cfg=None):
import os, cdtime
Satellite.__init__(self)
SCRIPT_DIR = os.getcwd()
self.SCRIPT_DIR = SCRIPT_DIR
#-- retrocompatibilite
if cfg is None:
config = ConfigParser.RawConfigParser()
config.read(os.path.join(SCRIPT_DIR, 'config.cfg'))
andeb = config.getint('Time Period', 'andeb')
anfin = config.getint('Time Period', 'anfin')
mdeb = config.getint('Time Period', 'mdeb')
mfin = config.getint('Time Period', 'mfin')
jdeb = config.getint('Time Period', 'jdeb')
jfin = config.getint('Time Period', 'jfin')
hdeb = config.getint('Time Period', 'hdeb')
hfin = config.getint('Time Period', 'hfin')
self.WORKDIR = config.get('Env', 'workdir')
else:
andeb = cfg['Time Period']['andeb']
anfin = cfg['Time Period']['anfin']
mdeb = cfg['Time Period']['mdeb']
mfin = cfg['Time Period']['mfin']
jdeb = cfg['Time Period']['jdeb']
jfin = cfg['Time Period']['jfin']
hdeb = cfg['Time Period']['hdeb']
hfin = cfg['Time Period']['hfin']
self.WORKDIR = cfg['Env']['workdir']
#print "%(andeb)s-%(mdeb)s-%(jdeb)s %(hdeb)s"%vars()
#print "%(anfin)s-%(mfin)s-%(jfin)s %(hfin)s"%vars()
# Conversion en "Component Time"
self.ctdeb = cdtime.comptime(andeb, mdeb, jdeb, hdeb, 0, 0)
self.ctfin = cdtime.comptime(anfin, mfin, jfin, hfin, 0, 0)
DIR_SST = os.path.join(self.WORKDIR,
'SST') # repertoire de stockage des donnees
if os.path.isdir(DIR_SST) == False:
os.mkdir(DIR_SST)
self.WORKDIR = os.path.join(self.WORKDIR, 'SST')
self.name = "Satellite Sea surface Temperature"
self.shortname = "SST"
self.units = " "
def test_xdrange_StringDate_CalendarInput_CdtimeCompDateOutput(self): ''' Testing for xdrange string date with skipdays input ''' gen = days.xdrange(startdate = '20120226',enddate = '20120301', calendarName = cdtime.NoLeapCalendar,returnType = 'c') checkdates = [] checkdates.append(cdtime.comptime(2012,2,26)) checkdates.append(cdtime.comptime(2012,2,27)) checkdates.append(cdtime.comptime(2012,2,28)) # 2012,2,29 not included, because we are passing NoLeapCalendar option checkdates.append(cdtime.comptime(2012,3,1)) count = 0 for date in gen: assert date == checkdates[count] count = count + 1
def NatureRevisions_Figure3(D,start_year=2019):
plt.subplot(311)
Plotting.time_plot(D.ALL.get_noise(),color=get_dataset_color("tree"),label="Pre-industrial Noise",lw=1)
Plotting.time_plot(D.ALL.projection(time=('1850-1-1','1975-12-31')),c="k",lw=1)
plt.ylabel("Projection (temporal amplitude)",fontsize=8)
plt.xlabel("Year")
plt.legend()
plt.title("(a)")#: Global Drought Atlas Projection Onto Fingerprint",fontsize=8)
plt.subplot(312)
t1900 = cdtime.comptime(1900,7,1)
times = np.arange(1,201)
likely=stats.norm.interval(.66)[1]
vlikely=stats.norm.interval(.90)[1]
certain=stats.norm.interval(.99)[1]
plt.axhline(likely,lw=1,color=cm.Reds(.33),label="Likely",alpha=.5)
plt.axhline(vlikely,lw=1,color=cm.Reds(.66),label="Very Likely",alpha=.5)
plt.axhline(certain,lw=1,color=cm.Reds(.99),label="Virtually Certain",alpha=.5)
D.ALL.time_of_emergence(t1900,times=times,color="k",lw=3)
pdsi_SN_figure(D,cdtime.comptime(1900,1,1),use_dai=True)
plt.title("(b)")#: Model-predicted time of emergence",fontsize=8)
plt.subplot(313)
hist_start = cdtime.comptime(1900,1,1)
times = np.arange(10,2100-start_year)
for X in ["ALL","ANZDA","MADA","MXDA","NADA","OWDA"]:
getattr(D,X).time_of_emergence(cdtime.comptime(start_year,1,1),times=times,color=colorregions(X),uncertainty="shade")
certain=stats.norm.interval(.99)[1]
plt.axhline(certain,lw=1,color=cm.Reds(.99),label="Virtually Certain", alpha=.5)
plt.title("(c)")#: Time of Emergence (assuming "+str(start_year)+" start)",fontsize=8)
ax1,ax2,ax3=plt.gcf().axes
ax2.set_xlim(1900,2055)
ax2.set_ylim(-3,7)
for ax in [ax1,ax2,ax3]:
ax.set_xlabel(ax.get_xlabel(),fontsize=8)
ax.set_ylabel(ax.get_ylabel(),fontsize=8)
ax3.set_xlim(start_year+10,start_year+55)
ax2.legend(fontsize=6)
ax3.legend(fontsize=6)
ax1.legend(fontsize=6)
for ax in plt.gcf().axes:
plt.setp(ax.get_xticklabels(),fontsize=6)
plt.setp(ax.get_yticklabels(),fontsize=6)
plt.setp(ax.xaxis.get_label(),fontsize=6)
plt.setp(ax.yaxis.get_label(),fontsize=6)
def recup_nfs_sst_seviri_recent(self, URL_SEVIRI_DATA, YYYY, MM, DD,
timerange):
""" Recuperation par NFS d un fichier de SST SEVIRI horaire recent (depuis 01/01/2012)"""
import os, subprocess, shutil
import cdtime, glob
from vacumm.misc.atime import strftime
# -- Complement du nom de repertoire avec l'annee et le jour relatif (001-> 365)
DATA_DIR = os.path.join(URL_SEVIRI_DATA, str(YYYY))
#print DATA_DIR
a = cdtime.comptime(YYYY, MM, DD)
a2 = a.torel('days since ' +
strftime('%Y-%m-%d', cdtime.comptime(YYYY - 1, 12, 31)))
DATA_DIR = os.path.join(DATA_DIR, '%(#)03d' % {'#': a2.value})
#print DATA_DIR
if timerange == 'midnight':
filename = '%(#)04d%(##)02d%(####)02d00*' % {
'#': YYYY,
'##': MM,
'####': DD
}
else:
filename = '%(#)04d%(##)02d%(####)02d*' % {
'#': YYYY,
'##': MM,
'####': DD
}
copy_mode = 'nfs'
list_file = glob.glob(os.path.join(DATA_DIR, filename))
# si la liste est vide, on essaie en se connectant a service7
if not list_file:
find_cmd = 'ssh service7 "find %(DATA_DIR)s -name \'%(filename)s\' "' % vars(
)
list_file = subprocess.check_output(find_cmd,
shell=True).strip().split()
if list_file:
copy_mode = 'scp'
for file_to_read in list_file:
if os.path.isfile(os.path.basename(file_to_read)) == False:
if copy_mode == 'nfs':
shutil.copyfile(file_to_read,
os.path.basename(file_to_read))
if copy_mode == 'scp':
copy_cmd = 'scp caparmor-sftp.ifremer.fr:%(file_to_read)s .' % vars(
)
subprocess.check_call(copy_cmd, shell=True)
def pdsi_SN_figure(D,start_time=None,stop_time=None,use_dai=True):
noise_cru=[]
noise_dai=[]
noise_tree=[]
noise=[]
signal_cru=[]
signal_dai=[]
signal_tree=[]
if start_time is None:
start_time=cdtime.comptime(1981,1,1)
if stop_time is None:
stop_time=cdtime.comptime(2017,12,31)
stop_cru=cdtime.comptime(2017,12,31)
stop_dai=cdtime.comptime(2014,12,31)
start_cru = cdtime.comptime(1901,1,1)
start_dai=cdtime.comptime(1901,1,1)
pcru=D.ALL.project_cru_on_solver(start=start_cru)
pdai=D.ALL.project_dai_on_solver(start=start_dai)
start_tree=cdtime.comptime(1400,1,1)
stop_tree=cdtime.comptime(1975,12,31)
nt=stop_cru.year-start_time.year
nmodel=D.ALL.P.shape[0]
H85=np.ma.zeros((nmodel,nt))
t=start_time.add(1,cdtime.Years)
i=0
cru_time=[]
tree_time=[]
dai_time=[]
while t.cmp(stop_time)<0:
L=t.year-start_time.year+1
modslopes,noiseterm = D.ALL.sn_at_time(start_time,L)
H85[:,i] = modslopes
noise+=[np.std(noiseterm)]
if (t.cmp(stop_cru)<=0) and (t.cmp(start_cru)>0):
signal_cru += [float(cmip5.get_linear_trends(pcru(time=(start_time,t))))]
cru_time+=[t.year]
noise_cru += [np.std(noiseterm)]
if (t.cmp(stop_dai)<=0) and (t.cmp(start_dai)>0):
signal_dai += [float(cmip5.get_linear_trends(pdai(time=(start_time,t))))]
dai_time+=[t.year]
noise_dai += [np.std(noiseterm)]
if t.cmp(stop_tree)<=0:
signal_tree += [float(cmip5.get_linear_trends(D.ALL.projection(time=(start_time,t))))]
tree_time +=[t.year]
noise_tree += [np.std(noiseterm)]
t=t.add(1,cdtime.Years)
i+=1
timex=np.arange(start_time.year+1,start_time.year+1+nt)
#for i in range(nmodel):
# plt.plot(timex,H85[i]/np.array(noise),c="k",lw=1,alpha=.2)
plt.plot(cru_time,np.array(signal_cru)/np.array(noise_cru),label="CRU",color=get_dataset_color("cru"),lw=3)
if use_dai:
plt.plot(dai_time,np.array(signal_dai)/np.array(noise_dai),label="Dai",color=get_dataset_color("dai"),lw=3)
plt.plot(tree_time,np.array(signal_tree)/np.array(noise_tree),label="Tree Rings",color=get_dataset_color("tree"),lw=3)
def rappatrie(self, cfg=None):
""" Rappatrie par ftp les donnees SEVIRI SST """
import os, subprocess, cdtime
# ----------------------------------------------------
print ""
print "---------- RECUPERATION FICHIERS SEVIRI ----------"
print ""
# ----------------------------------------------------
# -------------------------------------------------------------
# ------- recuperation des donnees : generalites (site ftp, etc)
if cfg is None:
config = ConfigParser.RawConfigParser()
config.read(os.path.join(self.SCRIPT_DIR, "config.cfg"))
try:
if cfg is None:
timerange = config.get("Seviri SST", "timerange")
else:
timerange = cfg["Seviri SST"]["timerange"]
# timerange = 'midnight' pour donnees a minuit seulement
# timerange = 'all' pour donnees a minuit seulement
except ConfigParser.NoOptionError:
print "No Time Range"
timerange = "all" # Par defaut, lecture de toutes les heures
if self.ctdeb >= cdtime.comptime(2012, 01, 01, 0, 0, 0):
print timerange
print "Data moved to /home5/taveeg/cache/project/osi-saf/data/sst/l3c/seviri/osi-saf/"
EXT_SEVIRI = ".nc"
if cfg is None:
URL_SEVIRI_DATA = config.get("Seviri SST", "recent_dir")
URL_CERSAT = config.get("Seviri SST", "url_cersat_rt")
DATA_DIR = config.get("Seviri SST", "data_dir_rt")
# URL_SEVIRI_DATA=os.path.join(URL_CERSAT,DATA_DIR)
usr = config.get("Seviri SST", "user")
pwd = config.get("Seviri SST", "pwd")
else:
URL_SEVIRI_DATA = cfg["Seviri SST"]["recent_dir"]
URL_CERSAT = cfg["Seviri SST"]["url_cersat_rt"]
DATA_DIR = cfg["Seviri SST"]["data_dir_rt"]
# URL_SEVIRI_DATA=os.path.join(URL_CERSAT,DATA_DIR)
usr = cfg["Seviri SST"]["user"]
pwd = cfg["Seviri SST"]["pwd"]
# -- recuperation des donnees par NFS
# ----------------------------------------------------
ctest = self.ctdeb
while ctest <= self.ctfin:
# Pour le FTP fichiers telecharges corrompus ... pb BZ2
# Seviri.recup_ftp_sst_seviri_recent(self,URL_CERSAT,DATA_DIR,ctest.year,ctest.month,ctest.day, EXT_SEVIRI, usr,pwd, '9')
Seviri.recup_nfs_sst_seviri_recent(self, URL_SEVIRI_DATA, ctest.year, ctest.month, ctest.day, timerange)
# On incremente le temps "test" de 1 jour
ctest = ctest.add(1, cdtime.Days)
def SignalToNoise(D,fortalk=False):
if fortalk:
plt.figure()
else:
plt.subplot(211)
time_of_emergence_figure(D,noisestart=cmip5.start_time(D.ALL.obs))
plt.title("(a): Time of emergence for PDSI signal")
plt.xlim(1985,2050)
plt.legend(ncol=2)
if fortalk:
plt.figure()
ax=plt.subplot(111)
else:
ax=plt.subplot(212)
start_time = cdtime.comptime(1981,1,1)
ALL_SM = soil.SoilMoisture(D.ALL.obs.mask[0])
ALL_SM.time_of_emergence(start_time,"30cm",ax=ax,color=cm.Set1(1/2.))
ALL_SM.time_of_emergence(start_time,"2m",ax=ax,color=cm.Set1(2/2.))
plt.xlim(1985,2050)
plt.title("(b): Times of emergence for soil moisture metrics")
#noisefigure(D)
plt.legend()
plt.title("(b): Preindustrial \"noise\" terms")
def var_pdf_daily(var, season, years):
"Calculate diurnal cycle climatology of each variable"
if season == 'JJA':
mo0 = 6
if season == 'SON':
mo0 = 9
if season == 'DJF':
mo0 = 12
if season == 'MAM':
mo0 = 3
var_da_year = np.empty([len(years),90])*np.nan
for iy,year in enumerate(years):
t1 = cdtime.comptime(year,mo0,0o1)
t2 = t1.add(90,cdtime.Days)
# try:
var_yr = var(time=(t1,t2,'co'))
var_da_year[iy,:]= var_yr
if var.id == 'tas':
var_da_year[iy,:] = var_da_year[iy,:]-273.15
if var.id == 'pr':
var_da_year[iy,:] = var_da_year[iy,:]*3600.*24.
# except:
# print str(year) +' not Available!'
# var_da_year[iy,:] = np.nan
var_da = np.reshape(var_da_year, (90*len(years)))
return var_da
def forTable(D):
aerosol_start = cdtime.comptime(1950,1,1)
aerosol_stop = cdtime.comptime(1975,12,31)
hist_start = cdtime.comptime(1900,1,1)
hist_stop = cdtime.comptime(1949,12,31)
with open("../DroughtAtlasPaper/FIGS/ForPaper/SI/Table1.csv","w") as fw:
csvwriter=csv.writer(fw,delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
d={}
d
for X in ["ANZDA","MADA","MXDA","NADA","OWDA","NHDA","NoNADA","NoMEX"]:
towrite=getattr(D,X).obs_SN(hist_start,stop_time=hist_stop,plot=False)+getattr(D,X).obs_SN(aerosol_start,stop_time=aerosol_stop,plot=False)
towrite2 = [X]+(map(str,towrite))
csvwriter.writerow(towrite2)
def read_Marshall(infile, metric, time_start):
"""Read the data from http://www.antarctica.ac.uk/met/gjma/sam.html"""
SAM_index = pandas.read_csv(infile,
skiprows=2,
delim_whitespace=True,
header=0,
index_col=0,
names=range(1, 13))
output_data = []
output_times = []
for year, data in SAM_index.iterrows():
for month in data.keys():
if not numpy.isnan(data[month]):
output_data.append(data[month])
ct = cdtime.comptime(year, month, 15)
rt = ct.torel(time_start).value
output_times.append(rt)
output_atts = {
'id': 'SAM',
'standard_name': 'SAM',
'long_name': 'Southern Annular Mode index',
'units': '',
'notes': 'SAM index (Marshall, 2003)'
}
history_dict = {
'history':
datetime.now().strftime("%a %b %d %H:%M:%S %Y") +
': downloaded from http://www.antarctica.ac.uk/met/gjma/sam.html \n'
}
return numpy.array(output_data), output_times, output_atts, history_dict
def two_times_from_one(t):
"""Input is a time representation, either as the long int used in the cdscan
script, or a string in the format "2010-08-25 15:26:00", or as a cdtime comptime
(component time) object.
Output is the same time, both as a long _and_ as a comptime."""
if t == 0:
t = 0L
if isinstance(t, str):
t = cdtime.s2c(t)
if (isinstance(t, long) or isinstance(t, int)) and t > 1000000000L:
tl = t
year = tl / 1000000000L
rem = tl % 1000000000L
month = rem / 10000000L
rem = rem % 10000000L
day = rem / 100000
allsecs = rem % 100000
sec = allsecs % 60
allmins = allsecs / 60
min = allmins % 60
hour = allmins / 60
tc = cdtime.comptime(year, month, day, hour, min, sec)
else:
# I'd like to check that t is type comptime, but although Python
# prints the type as <type 'comptime'> it won't recognize as a type
# comptime or anything similar. Note that cdtime.comptime is a C
# function available from Python.
tc = t
tl = tc.year * 1000000000L
tl += tc.month * 10000000L
tl += tc.day * 100000
tl += tc.hour * 3600
tl += tc.minute * 60
tl += tc.second.__int__()
return tl, tc
def checkDatawc(self,name,value):
checkName(self,name,value)
if isNumber(value):
value = float(value), 0
elif isinstance(value,str):
t=cdtime.s2c(value)
if t!=cdtime.comptime(0,1):
t=t.torel(self.datawc_timeunits,self.datawc_calendar)
value = float(t.value), 1
else:
raise ValueError, 'The '+name+' attribute must be either an integer or a float value or a date/time.'
elif type(value) in [type(cdtime.comptime(1900)),type(cdtime.reltime(0,'days since 1900'))]:
value = value.torel(self.datawc_timeunits,self.datawc_calendar).value, 1
else:
raise ValueError, 'The '+name+' attribute must be either an integer or a float value or a date/time.'
return value
def createObsRainfallData(today):
outdir = os.path.join(outpath, today)
if not os.path.exists(outdir): os.makedirs(outdir)
os.chdir(outdir)
cDay = datetime.datetime.strptime(today, "%Y%m%d")
tDay = cDay.strftime('%d%m%Y')
mDay = cDay.strftime('%d%b%Y').lower()
infile = '/gpfs4/home/akmitra/tmp/mrgdr/GPM%s.grd' % tDay
if not os.path.isfile(infile): return
ctlfilename = 'IMDGPM_%s.ctl' % today
f = open(ctlfilename, 'w')
f.write(ctltemplate % (tDay, mDay))
f.close()
f = cdms2.open(ctlfilename)
rain = f('arf25', latitude=(5, 40), longitude=(65, 110))
rain.id = 'rainfall'
rain.comments = 'IMD GPM Merged Rainfall on ' + today
tunits = 'seconds since 1970-01-01 00:00:00.0'
csec = cdtime.comptime(cDay.year, cDay.month, cDay.day,
3).torel(tunits).value
rtime = cdms2.createAxis(numpy.array([csec]),
id='time') #60*24*3 sec for 3hr
rtime.units = tunits
rtime.designateTime()
rain.setAxis(0, rtime)
rnc = 'rf_%s.nc' % today
f2 = cdms2.open(rnc, 'w')
f2.write(rain)
f2.close()
f.close()
cmd = 'rm ' + ctlfilename
subprocess.call(cmd, shell=True)
grads_cmd = """'sdfopen %s'
'define rainfall=rainfall'
'set sdfwrite -3dt -rt RFG_%s.nc'
'sdfwrite rainfall'
'quit'
""" % (rnc, today)
f3 = open('rainfall.gs', 'w')
f3.write(grads_cmd)
f3.close()
cmd = grads + ' -blc rainfall.gs'
subprocess.call(cmd, shell=True)
cmd = 'rm rainfall.gs %s ' % rnc
subprocess.call(cmd, shell=True)
cmd = 'mv RFG_%s.nc IMD_GPM_Obs_Rainfall_%s.nc ' % (today, today)
subprocess.call(cmd, shell=True)
def concatenate_this(piC,modaxis=0,compressed=False):
if not ("time" in piC.getAxisIds()):
print("Need a time axis to concatenate along")
raise TypeError
if compressed:
axlist = piC.getAxisList()
todel=[]
modax = piC.getAxis(modaxis)
allfiles=eval(modax.models)
if len(allfiles)!=piC.shape[modaxis]:
allfiles =[x+".xml" for x in allfiles[0].split(".xml")[:-1]]
allfiles=np.array(allfiles)
#assume model axis is 0 for this
for i in range(len(allfiles)):
if len(piC[i].compressed()) != len(piC[i]):
todel+=[i]
piC = MV.array(np.delete(piC,todel,axis=modaxis))
allfiles = np.delete(allfiles,todel).tolist()
newmodax=cmip5.make_model_axis(allfiles)
piC.setAxisList([newmodax]+axlist[1:])
naxes = len(piC.shape)
timeaxis = piC.getAxisIds().index("time")
dimensions=piC.shape
nmodc = dimensions[modaxis]
ntc = dimensions[timeaxis]
newdim = (nmodc*ntc,)
units = 'days since 0001-1-1'
start = cdtime.comptime(1,1,1)
tax = cdms.createAxis(np.arange(0,nmodc*ntc*365,365)+15.5)
#tax = cdms.createAxis([start.add(i,cdtime.Months).torel(units).value for i in range(ntc*nmodc)])
tax.units = units
tax.id = "time"
tax.designateTime()
newaxes = [tax]
if len(dimensions)>2:
for i in range(len(dimensions)):
if (i != timeaxis) and (i!= modaxis):
newdim+=(dimensions[i],)
newaxes+=[piC.getAxis(i)]
piC_concatenate = piC.reshape(newdim)
piC_concatenate.setAxisList(newaxes)
return piC_concatenate
def two_times_from_one( t ):
"""Input is a time representation, either as the long int used in the cdscan
script, or a string in the format "2010-08-25 15:26:00", or as a cdtime comptime
(component time) object.
Output is the same time, both as a long _and_ as a comptime."""
if t==0:
t = 0L
if isinstance(t,str):
t = cdtime.s2c(t)
if (isinstance(t,long) or isinstance(t,int)) and t>1000000000L :
tl = t
year = tl / 1000000000L
rem = tl % 1000000000L
month = rem / 10000000L
rem = rem % 10000000L
day = rem / 100000
allsecs = rem % 100000
sec = allsecs%60
allmins = allsecs/60
min = allmins%60
hour = allmins/60
tc = cdtime.comptime(year,month,day,hour,min,sec)
else:
# I'd like to check that t is type comptime, but although Python
# prints the type as <type 'comptime'> it won't recognize as a type
# comptime or anything similar. Note that cdtime.comptime is a C
# function available from Python.
tc = t
tl = tc.year * 1000000000L
tl += tc.month * 10000000L
tl += tc.day * 100000
tl += tc.hour * 3600
tl += tc.minute *60
tl += tc.second.__int__()
return tl,tc
def post(self,fetched,slab,axes,specifications,confined_by,aux,axismap):
''' Post processing retouches the bounds and later will deal with the mask'''
import cdms2 as cdms
fetched=cdms.createVariable(fetched,copy=1)
faxes=fetched.getAxisList()
a=None
for i in range(len(faxes)):
if confined_by[i] is self:
newaxvals=[]
bounds=[]
a=None
sh=list(fetched.shape)
sh[i]=1
for l in self.aux[i]:
try:
tmp=fetched(**{faxes[i].id:(l,l)})
ax=tmp.getAxis(i)
#print ax
newaxvals.append(ax[0])
if ax.getBounds()!=None:
bounds.append(ax.getBounds()[0])
else:
bounds=None
except Exception,err:
#print 'err:',err,'match:',self.match
if self.match==1:
raise Exception,'Error axis value :'+str(l)+' was requested but is not present in slab\n(more missing might exists)'
elif self.match==0:
tmp=MV2.ones(sh,typecode=MV2.float)
tmp=MV2.masked_equal(tmp,1)
if type(l)==type(cdtime.comptime(1999)) or type(l)==type(cdtime.reltime(0,'days since 1999')) or type(l)==type(''):
if type(l)!=type(''):
newaxvals.append(l.torel(faxes[i].units).value)
else:
newaxvals.append(cdtime.s2r(l,faxes[i].units).value)
else:
newaxvals.append(l)
if bounds is not None:
bounds.append([ax[-1]-1.,ax[-1]+1])
else:
tmp=None
if not tmp is None:
if a is None:
a=tmp
elif not tmp is None:
a=MV2.concatenate((a,tmp),i)
if bounds is not None:
newax=cdms.createAxis(numpy.array(newaxvals),bounds=numpy.array(bounds),id=ax.id)
else:
newax=cdms.createAxis(numpy.array(newaxvals),id=ax.id)
for att in faxes[i].attributes.keys():
setattr(newax,att,faxes[i].attributes.get(att))
for j in range(len(fetched.shape)):
if j==i:
a.setAxis(i,newax)
else:
a.setAxis(j,faxes[j])
fetched=a.astype(fetched.dtype.char)
faxes=fetched.getAxisList()
def post(self,fetched,slab,axes,specifications,confined_by,aux,axismap):
''' Post processing retouches the bounds and later will deal with the mask'''
import cdms2 as cdms
fetched=cdms.createVariable(fetched,copy=1)
faxes=fetched.getAxisList()
a=None
for i in range(len(faxes)):
if confined_by[i] is self:
newaxvals=[]
bounds=[]
a=None
sh=list(fetched.shape)
sh[i]=1
for l in self.aux[i]:
try:
tmp=fetched(**{faxes[i].id:(l,l)})
ax=tmp.getAxis(i)
#print ax
newaxvals.append(ax[0])
if ax.getBounds()!=None:
bounds.append(ax.getBounds()[0])
else:
bounds=None
except Exception,err:
#print 'err:',err,'match:',self.match
if self.match==1:
raise Exception,'Error axis value :'+str(l)+' was requested but is not present in slab\n(more missing might exists)'
elif self.match==0:
tmp=MV2.ones(sh,typecode=MV2.float)
tmp=MV2.masked_equal(tmp,1)
if type(l)==type(cdtime.comptime(1999)) or type(l)==type(cdtime.reltime(0,'days since 1999')) or type(l)==type(''):
if type(l)!=type(''):
newaxvals.append(l.torel(faxes[i].units).value)
else:
newaxvals.append(cdtime.s2r(l,faxes[i].units).value)
else:
newaxvals.append(l)
if bounds is not None:
bounds.append([ax[-1]-1.,ax[-1]+1])
else:
tmp=None
if not tmp is None:
if a is None:
a=tmp
elif not tmp is None:
a=MV2.concatenate((a,tmp),i)
if bounds is not None:
newax=cdms.createAxis(numpy.array(newaxvals),bounds=numpy.array(bounds),id=ax.id)
else:
newax=cdms.createAxis(numpy.array(newaxvals),id=ax.id)
for att in faxes[i].attributes.keys():
setattr(newax,att,faxes[i].attributes.get(att))
for j in range(len(fetched.shape)):
if j==i:
a.setAxis(i,newax)
else:
a.setAxis(j,faxes[j])
fetched=a.astype(fetched.dtype.char)
faxes=fetched.getAxisList()
def getCompTime(cls, str_time):
try:
if str_time:
itime = [
int(float(tok)) for tok in str_time.replace("-", " ").replace(":", " ").replace(",", " ").split()
]
return cdtime.comptime(*itime)
except Exception, err:
print >>sys.stderr, "Error parsing time string '%s': %s" % (str_time, str(err))
def __init__(self):
import cdtime
SCRIPT_DIR = os.getcwd()
#print SCRIPT_DIR
#os.chdir('../')
#BASENAME = os.getcwd()
#print BASENAME
#os.chdir(SCRIPT_DIR) # back to the script_dir
#self.WORKDIR = os.path.join(BASENAME, 'work')
# repertoire de travail
#print self.WORKDIR
self.SCRIPT_DIR = SCRIPT_DIR
# Lecture de la periode de validation
config = ConfigParser.RawConfigParser()
config.read(os.path.join(SCRIPT_DIR, 'config.cfg'))
andeb = config.getint('Time Period', 'andeb')
anfin = config.getint('Time Period', 'anfin')
mdeb = config.getint('Time Period', 'mdeb')
mfin = config.getint('Time Period', 'mfin')
jdeb = config.getint('Time Period', 'jdeb')
jfin = config.getint('Time Period', 'jfin')
hdeb = config.getint('Time Period', 'hdeb')
hfin = config.getint('Time Period', 'hfin')
self.WORKDIR = config.get('Env', 'workdir')
# Conversion en "Component Time"
self.ctdeb = cdtime.comptime(andeb, mdeb, jdeb, hdeb, 0, 0)
self.ctfin = cdtime.comptime(anfin, mfin, jfin, hfin, 0, 0)
DIR_REC = os.path.join(self.WORKDIR, 'RECOPESCA') # repertoire de stockage des donnees
if os.path.isdir(DIR_REC) == False:
os.chdir(self.WORKDIR)
os.mkdir('RECOPESCA')
self.WORKDIR = os.path.join(self.WORKDIR, 'RECOPESCA')
self.name = "RECOPESCA data"
self.shortname = "Recopesca"
#dictionnaire contient les profils (cle : date)
self.map_profiles = {}
def add_variable( self, varId, variable, **args ):
if self.time <> None:
data_start = self.time['start'].split('-')
part_time_step = self.time.get('step',1)
part_time_units = get_cdtime_units( self.time['units'] )
data_start_ct = cdtime.comptime( *[int(tok) for tok in data_start] )
partition_start_ct = data_start_ct.add( self.pIndex*part_time_step, part_time_units )
partition_end_ct = partition_start_ct.add( part_time_step, part_time_units )
wpsLog.info( 'Domain[%d]: addVariable: %s -> %s' % (self.pIndex, str(partition_start_ct), str(partition_end_ct) ))
part_variable = variable( time=( partition_start_ct, partition_end_ct, 'co') )
self.variables[varId] = part_variable
def checkTimeUnits(self, name, value):
checkName(self, name, value)
if not isinstance(value, str):
raise ValueError, "time units must be a string"
a = cdtime.reltime(1, "days since 1900")
try:
a.torel(value)
except:
raise ValueError, value + " is invalid time units"
sp = value.split("since")[1]
b = cdtime.s2c(sp)
if b == cdtime.comptime(0, 1):
raise ValueError, sp + " is invalid date"
return value
def checkTimeUnits(self,name,value):
checkName(self,name,value)
if not isinstance(value,str):
raise ValueError, 'time units must be a string'
a=cdtime.reltime(1,'days since 1900')
try:
a.torel(value)
except:
raise ValueError, value+' is invalid time units'
sp=value.split('since')[1]
b=cdtime.s2c(sp)
if b==cdtime.comptime(0,1):
raise ValueError, sp+' is invalid date'
return value
def parse(value):
parts = value.split(" ")
if len(parts) == 1:
# It's just a date
date = value
time = "0:0:0"
else:
date, time = parts
# Parse date
date_parts = date.split("-")
num_date = [int(d) for d in date_parts if d != '']
num_date += [0 for _ in range(3 - len(num_date))]
year, month, day = num_date
time_parts = time.split(":")
num_time = [int(t) for t in time_parts if t != '']
num_time += [0 for _ in range(3 - len(num_time))]
hour, minute, second = num_time
# Check if the units match up with the specificity
if time_increment[0:6] == "second":
if 0 in (year, month, day, hour, minute, second):
return None
elif time_increment[0:6] == "minute":
if 0 in (year, month, day, hour, minute):
return None
elif time_increment[0:4] == "hour":
if 0 in (year, month, day, hour):
return None
elif time_increment[0:3] == "day" or time_increment[0:4] == "week":
if 0 in (year, month, day):
return None
elif time_increment[0:5] == "month" or time_increment[0:6] == "season":
if 0 in (year, month):
return None
elif time_increment[0:4] == "year":
if 0 in (year):
return None
try:
comptime = cdtime.comptime(year, month, day, hour, minute, second)
reltime = comptime.torel(units, calendar)
return reltime.value
except:
return None
def generateTime(matchobj,timespecs):
iyr = 0
imo = 1
idy = 1
ihr = 0
imi = 0
ise = 0
yrspec,mospec,dyspec,hrspec,mispec,sespec = timespecs
if yrspec:
pat,name,dimtype,pos = _specifierMap[yrspec]
yrstr = matchobj.group(name)
iyr = string.atoi(yrstr)
# Map 2-digit year to [1950,2049)
if yrspec in ('%y','%ey'):
if iyr<50:
iyr = iyr+2000
else:
iyr = iyr+1900
if mospec:
pat,name,dimtype,pos = _specifierMap[mospec]
mostr = matchobj.group(name)
if mospec in ('%G','%eG'):
imo = _monthMapUpper[mostr]
elif mospec in ('%g','%eg'):
imo = _monthMapLower[mostr]
elif mospec in ('%m','%em','%n','%en'):
imo = string.atoi(mostr)
if dyspec:
pat,name,dimtype,pos = _specifierMap[dyspec]
dystr = matchobj.group(name)
idy = string.atoi(dystr)
if hrspec:
pat,name,dimtype,pos = _specifierMap[hrspec]
hrstr = matchobj.group(name)
ihr = string.atoi(hrstr)
if mispec:
pat,name,dimtype,pos = _specifierMap[mispec]
mistr = matchobj.group(name)
imi = string.atoi(mistr)
if sespec:
pat,name,dimtype,pos = _specifierMap[sespec]
sestr = matchobj.group(name)
ise = string.atoi(sestr)
return cdtime.comptime(iyr,imo,idy,ihr,imi,ise)
error = True
if error == False:
raise Exception,"Error it should have failed here!"
s=f('ta',slice(0,1),genutil.picker(level=levels,match=0))
if s.shape[1]!=3:
raise "Error did not return 3 levels!"
if (s.getLevel()[:]!=levels).any():
raise Exception,"Error did not retrieve the right levels!"
print "folowing plot should show all missing values, since 800 does not exisits!"
x=vcs.init()
x.plot(s[0,-1],bg=bg)
vcs.test.support.check_plot(x)
levels = [1000,700,850]
s3=f('ta',genutil.picker(time=['1987-7','1988-1',cdtime.comptime(1988,3)],level=[1000,700,850]))
if s3.shape!=(3, 3, 73, 144):
raise Exception,"Did not retrieve the right slab"
t1= cdtime.componenttime(1987,7)
t2= cdtime.componenttime(1988,1)
t3= cdtime.componenttime(1988,3)
if s3.getTime().asComponentTime()!=[t1,t2,t3]:
raise Exception,"Error did not get the right times"
test = s3.getLevel()[:]!=levels
if test.any():
raise Exception,"Error did not get the right levels"
import vcs,cdms2,os,sys,cdtime
import testing.regression as regression
f=cdms2.open(os.path.join(vcs.sample_data,"clt.nc"))
s=f("clt",squeeze=1)
x=regression.init()
x.plot(s,bg=1,time=cdtime.comptime(2015))
fnm = os.path.split(__file__)[1][:-3]+".png"
regression.run(x, fnm)
print "Step #0 : Reading data"
s=f(var,longitude=(0,360,'co'))
acok=fsc('climseas',longitude=(0,360,'co'))
print 'Test #1 : Test result'
ac=times.JAN.climatology(s)
assert(MV2.allclose(ac[0],acok[0]))
f.close()
fsc.close()
a=cdtime.comptime(1980)
b=cdtime.comptime(1980,5)
f = cdms2.open(os.path.join(sys.prefix,'sample_data','tas_6h.nc'))
s=f(var,time=(a,b,'co'),squeeze=1)
print "Test #2 : 6hourly AND get"
jans=times.JAN(s)
print "Test #3 : climatology 6h"
JFMA=times.Seasons('JFMA')
jfma=JFMA.climatology(s)
#Test reorder
print "Test #4 : time not first axis"
# Adapted for numpy/ma/cdms2 by convertcdms.py
import cdms2 as cdms
import vcs
import cdtime
import support
import os
bg = support.bg
t0 = cdtime.comptime(1987, 8)
t1 = cdtime.comptime(1987, 12)
f = cdms.open(os.path.join(vcs.sample_data, 'ta_ncep_87-6-88-4.nc'))
s = f(
'ta', latitude=slice(
5, 6), level=slice(
0, 1), longitude=slice(
6, 7), squeeze=1)
s2 = s()
t2 = s2.getTime()
t2.units = 'months since 1949-2'
x = vcs.init()
y = vcs.init()
b = x.createyxvsx('new2')
b.datawc_x1 = t0
b.datawc_x2 = t1
x.plot(s, b, bg=bg)
support.check_plot(x)
y.plot(s2, b, bg=bg)
# # Example 1: # We compute the spatial rms difference between 2 fields. The 2 fields # chosen here are the surface air temperature fields in the NCEP/NCAR # reanalysis for the 1960-1969 and 1980-1989 periods. # #******************************************************************************* # # First of all let us define our 2 periods of interest # import cdtime # # Period 1 with start at 1980 and end at 1985 # a1 = cdtime.comptime(1980) b1 = cdtime.comptime(1985) # # By default, the definition above sets the time at January 1st of the years # specified. # # Similarly, Period 2 will start at 1990 and end at 1995 # a2 = cdtime.comptime(1990) b2 = cdtime.comptime(1995) # # Let us retrieve data for surface air temperature (tas) # for each of these 2 periods we just defined. # ncep = os.path.join(sys.prefix, 'sample_data', 'tas_mo.nc')
def specify(self,slab,axes,specification,confined_by,aux):
''' First part: confine the slab within a Domain wide enough to do the exact in post'''
import string,copy
from numpy.ma import minimum,maximum
# myconfined is for later, we can't confine a dimension twice with an argument plus a keyword or 2 keywords
myconfined=[None]*len(axes)
self.aux=copy.copy(specification)
# First look at the arguments (i.e not keywords) and confine the dimensions
# in the order of the arguments
for i in range(len(self.args)):
if confined_by[i] is None : # Check it hasn't been confined by somebody else
myconfined[i]=1 # dim confined by argument list
confined_by[i]=self # for cdms I want to confine this dimension
self.aux[i]=specs=list(self.args[i]) # How do we want to confine this dim ?
if type(specs)==type(slice(0)):
specification[i]=specs # If it's a slicing nothing to do
else: # But if it's not...
if specs[0] is None:
tmp=axes[i].getBounds()
if tmp is None:
raise ValueError, 'Region error, axis:'+axes[i].id+' has no bounds'
specs[0]=minimum(minimum(tmp[0],tmp[-1]))
if specs[1] is None:
tmp=axes[i].getBounds()
if tmp is None:
raise ValueError, 'Region error, axis:'+axes[i].id+' has no bounds'
specs[1]=maximum(maximum(tmp[0],tmp[-1]))
if axes[i].isTime(): # Time is as always "Special"
import cdtime
tc=type(cdtime.comptime(0)) # component time type
tr=type(cdtime.reltime(0,'months since 0')) # relative time type
t=type(specs[0]) # my first spec type
if t==type(''): #if my first spec is passed as a string
specs[0]=cdtime.s2r(specs[0],axes[i].units)
elif t==tc or t==tr: #if my first spec is passed as a cdtime object
specs[0]=cdtime.torel(specs[0],axes[i].units)
else: # If not it has to be that the users knows the time values in the axis
pass
t=type(specs[1]) # my second spec type
if t==type(''): #if my second spec is passed as a string
specs[1]=cdtime.s2r(specs[1],axes[i].units)
elif t==tc or t==tr: #if my second spec is passed as a cdtime object
specs[1]=cdtime.torel(specs[1],axes[i].units)
sp=[specs[0],specs[1],'oob'] # Now retrieve the values wide enough for the exact specification[i]=sp # sets the specifications
else:
return 1
for kw in self.kargs.keys():
axis=None
for i in range(len(axes)):
if axes[i].id==kw : axis=i
if axis is None:
if kw=='time' :
for i in range(len(axes)):
if axes[i].isTime() : axis=i
elif kw=='level' :
for i in range(len(axes)):
if axes[i].isLevel() : axis=i
elif kw=='longitude' :
for i in range(len(axes)):
if axes[i].isLongitude() : axis=i
elif kw=='latitude' :
for i in range(len(axes)):
if axes[i].isLatitude() : axis=i
elif not kw in ['exact','atol','rtol']: # keyword not a recognised keyword or dimension name
raise 'Error, keyword: '+kw+' not recognized'
# At this point, if axis is None:
# we are dealing with a keyword for the selector
# so we'll skip it
if not axis is None :
if confined_by[axis] is None:
confined_by[axis]=self
myconfined[axis]=1
self.aux[axis]=specs=list(self.kargs[kw])
if type(specs)!=type(slice(0)):
if specs[0] is None:
tmp=axes[axis].getBounds()
if tmp is None:
raise ValueError, 'Region error, axis:'+axes[axis].id+' has no bounds'
specs[0]=minimum(minimum(tmp[0],tmp[-1]))
if specs[1] is None:
tmp=axes[axis].getBounds()
if tmp is None:
raise ValueError, 'Region error, axis:'+axes[axis].id+' has no bounds'
specs[1]=maximum(maximum(tmp[0],tmp[-1]))
if axes[axis].isTime():
import cdtime
tc=type(cdtime.comptime(0))
tr=type(cdtime.reltime(0,'months since 0'))
t=type(specs[0])
if t==type(''):
specs[0]=cdtime.s2r(specs[0],axes[i].units)
elif t==tc or t==tr:
specs[0]=cdtime.torel(specs[0],axes[i].units)
t=type(specs[1])
if t==type(''):
specs[1]=cdtime.s2r(specs[1],axes[i].units)
elif t==tc or t==tr:
specs[1]=cdtime.torel(specs[1],axes[i].units)
sp=[specs[0],specs[1],'oob']
specification[axis]=sp
else:
specification[axis]=specs
else:
if myconfined[axis]==1:
raise 'Error you are attempting to set the axis: '+str(axes[axis].id)+' more than once'
else:
return 1
return 0
import cdtime
from eofs.cdms import Eof
import vcs
import string
#===========================================================================================================
# DATA
#-----------------------------------------------------------------------------------------------------------
# Open file ---
data_path = '/clim_obs/obs/ocn/mo/tos/UKMETOFFICE-HadISST-v1-1/130122_HadISST_sst.nc' ## Put your file here
f = cdms.open(data_path)
# 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('sst',time=(start_time,end_time),longitude=(0,360),latitude=(-90,90)) # Provide proper variable name
# Reomove annual cycle ---
d_anom = cdutil.ANNUALCYCLE.departures(d)
# EOF (take only first variance mode...) ---
solver = Eof(d_anom, 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()
# if var and var.code == ilookup[ITEM_CODE]:
# Then this is another level of the same variable
# Perhaps should just pass the full lookup array?
# Should this return a unique list of names ???
npts = ilookup[LBNPT]
nrows = ilookup[LBROW]
if not timeunits:
# Should be hidden as a function somewhere
timeunits = "days since %4.4d-%2.2d-%2.2d %2.2d:%2.2d" % (ilookup[LBYR],
ilookup[LBMON], ilookup[LBDAT], ilookup[LBHR], ilookup[LBMIN])
# Different variables may be saved with different steps
# Really only need to do this the first time variable is seen.
end = cdtime.comptime(ilookup[LBYRD], ilookup[LBMOND],
ilookup[LBDATD], ilookup[LBHRD], ilookup[LBMIND])
period = end.torelative(timeunits)
step = period.value/ilookup[LBROW]
if verbose:
print "TIME STEP (days)", step
# Step will probably be some integer number of minutes
step = round(1440*step,4)
if verbose:
print "TIME STEP (mins)", step
# ilookup[LBCODE] is 31320 for Gregorian timeseries, 31323 for other calendar
# rlookup[51] is level, -1 for single or special levels
f.wordseek(lbegin) # Offset rather than absolute seek?
s = f.wordread(npts*nrows)
# Where is this format for grid point values defined?
# Added by routine extra_ts_info