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 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 sn_at_time(self,start_time,L,overlapping=True):
if not hasattr(self,"P"):
self.model_projections()
stop_time=start_time.add(L,cdtime.Years)
modslopes = cmip5.get_linear_trends(self.P(time=(start_time,stop_time)))
if overlapping:
noiseterm = bootstrap_slopes(self.noise,L)
else:
noiseterm = da.get_slopes(self.noise,L)/365.
return modslopes,noiseterm
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 sn_at_time(self, start_time, L, depth, overlapping=True):
self.project_piControl_on_solver(depth)
self.model_projections(depth)
stop_time = start_time.add(L, cdtime.Years)
modslopes = cmip5.get_linear_trends(self.P[depth](time=(start_time,
stop_time)))
if overlapping:
noiseterm = b.bootstrap_slopes(self.noise[depth], L)
else:
noiseterm = da.get_slopes(self.noise[depth], L) / 365.
return modslopes, noiseterm
def signal_to_noise_map(drought_atlas,start_year=1100,modern_start=1979):
preindustrial = drought_atlas[start_year:1850]
modern = drought_atlas[modern_start:]
modern_times = modern.shape[0]
nt,nlat,nlon=drought_atlas.shape
SN = MV.zeros(drought_atlas.shape[1:])+1.e20
signals = cmip5.get_linear_trends(modern)
for i in range(nlat):
for j in range(nlon):
if not drought_atlas.mask[-1,i,j]:
noise = da.get_slopes(preindustrial[:,i,j],modern_times)/365. #get_slopes assumes time units are days; these are years
width=np.ma.std(noise)
signal = signals[i,j]
SN[i,j]=signal/width
return SN
def sn_at_time(self,
start_time,
L,
overlapping=True,
noisestart=None,
solver=None):
if noisestart is None:
noisestart = cmip5.start_time(self.obs)
noisestop = cmip5.stop_time(self.get_noise(solver=solver))
stop_time = start_time.add(L, cdtime.Years)
modslopes = cmip5.get_linear_trends(
self.get_forced(solver=solver)(time=(start_time, stop_time)))
if overlapping:
noiseterm = bootstrap_slopes(
self.get_noise(solver=solver)(time=(noisestart, noisestop)), L)
else:
noiseterm = da.get_slopes(
self.get_noise(solver=solver)(time=(noisestart, noisestop)),
L) / 365.
return modslopes, noiseterm
def TOE(OnePct, piControl, H85, start=None):
#Calculate the time of emergence:
data = [H85.reshaped["west"], H85.reshaped["east"]]
nmod, nyears, nmonths = H85.reshaped["west"].shape
P = MV.zeros((nmod, nyears))
msolver = OnePct.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]
if start is None:
start = cdtime.comptime(2000, 1, 1)
stop = start.add(1, cdtime.Years)
final_year = cdtime.comptime(2099, 12, 31)
tl = final_year.year - start.year + 1
NOISE = MV.zeros(tl)
SIGNAL = MV.zeros((nmod, tl))
i = 0
while stop.cmp(final_year) < 0:
modelproj = P(time=(start, stop))
L = modelproj.shape[1]
slopes = da.get_slopes(pc, L)
SIGNAL[:, i] = cmip5.get_linear_trends(modelproj)
NOISE[i] = np.ma.std(slopes)
stop = stop.add(1, cdtime.Years)
i += 1
return SIGNAL, NOISE
def average_histogram(obslist,
h85,
piC,
direction,
start=None,
stop=None,
months="JJ"):
if months is "JJ":
mmean = lambda x: MV.average(x[:, 5:7], axis=1)
bigmmean = lambda X: MV.average(X[:, :, 5:7], axis=2)
elif months is "SO":
mmean = lambda x: MV.average(x[:, 8:10], axis=1)
bigmmean = lambda X: MV.average(X[:, :, 8:10], axis=2)
elif months is "JJA":
mmean = lambda x: MV.average(x[:, 5:8], axis=1)
bigmmean = lambda X: MV.average(X[:, :, 5:8], axis=2)
elif months is "Jun":
mmean = lambda x: x[:, 5]
bigmmean = lambda X: MV.average(X[:, :, 5])
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["west"])
stop = cdtime.comptime(stop.year, stop.month, 30)
#calculate the trend in the observations
obs_avg = mmean(obs.reshaped[direction](time=(start, stop)))
obs_trend = cmip5.get_linear_trends(obs_avg)
#get the h85 trends over the same time period
H85m = bigmmean(h85.reshaped[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 = mmean(piC.reshaped[direction])
L = len(obs_avg)
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_avg = mmean(obs.reshaped[direction](time=(start, stop)))
obs_trend = cmip5.get_linear_trends(obs_avg)
plt.axvline(obs_trend,
label=obs.dataset,
color=da_colors(obs.dataset))
plt.xlabel("S/N")
plt.ylabel("Frequency")
plt.legend(loc=0)
def average_histogram(self,
direction,
start=None,
stop=None,
months="JJ",
datasets=None):
if months is "JJ":
mmean = lambda x: MV.average(x[:, 5:7], axis=1)
bigmmean = lambda X: MV.average(X[:, :, 5:7], axis=2)
elif months is "SO":
mmean = lambda x: MV.average(x[:, 8:10], axis=1)
bigmmean = lambda X: MV.average(X[:, :, 8:10], axis=2)
elif months is "JJA":
mmean = lambda x: MV.average(x[:, 5:8], axis=1)
bigmmean = lambda X: MV.average(X[:, :, 5:8], axis=2)
elif months is "JAS":
mmean = lambda x: MV.average(x[:, 6:9], axis=1)
bigmmean = lambda X: MV.average(X[:, :, 6:9], axis=2)
elif months is "Jun":
mmean = lambda x: x[:, 5]
bigmmean = lambda X: MV.average(X[:, :, 5])
elif months is "YEAR":
mmean = lambda x: MV.average(x, axis=1)
bigmmean = lambda X: MV.average(X, axis=2)
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 trends over the same time period
H85m = bigmmean(self.h85.reshaped[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 = mmean(self.piC.reshaped[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")
#calculate the trend in the observations
if datasets is None:
datasets = ["gpcp", "cmap", "precl"]
if type(datasets) != type([]):
datasets = [datasets]
for dataset in datasets:
X = self.OBS[string.upper(dataset)]
obs_avg = mmean(X.reshaped[direction](time=(start, stop)))
obs_trend = cmip5.get_linear_trends(obs_avg)
plt.axvline(obs_trend, label=dataset, color=da_colors(dataset))
plt.xlabel("S/N")
plt.ylabel("Frequency")
plt.legend(loc=0)