def project_east_west(self, dataset, experiment, best_fit=True):
X = self.OBS[string.upper(dataset)]
fingerprint = getattr(self, experiment)
westsolver = fingerprint.solvers["west"]
westfac = da.get_orientation(westsolver)
time_plot(westfac * westsolver.projectField(X.reshaped["west"])[:, 0],
label="WEST",
color=get_colors("west"))
eastsolver = fingerprint.solvers["east"]
eastfac = da.get_orientation(eastsolver)
time_plot(eastfac * eastsolver.projectField(X.reshaped["east"])[:, 0],
label="EAST",
color=get_colors("east"))
plt.legend()
plt.ylabel("Projection onto " + fingerprint.experiment +
" fingerprint")
if best_fit:
y = westfac * westsolver.projectField(X.reshaped["west"])[:, 0]
t = cmip5.get_plottable_time(y)
p = np.polyfit(t, y.asma(), 1)
plt.plot(t, np.polyval(p, t), "--", color=get_colors("west"))
y = eastfac * eastsolver.projectField(X.reshaped["east"])[:, 0]
t = cmip5.get_plottable_time(y)
p = np.polyfit(t, y.asma(), 1)
plt.plot(t, np.polyval(p, t), "--", color=get_colors("east"))
def get_crossing_time(region, variable, scenario, month):
vcert = stats.norm.interval(.99)[1]
mfile = get_file(region, variable, scenario, month)
if mfile is None:
crossing_time = None
else:
f = cdms.open(mfile)
data = f(variable + "_SN")
avg = MV.average(data, axis=0)
threshexceed = np.where(np.abs(avg) > vcert)[0]
#If it never exceeds the threshold, return None
if len(threshexceed) == 0:
return (None)
#If it hasn't exceeded the threshold by the last time step, return None
if len(avg) - 1 not in threshexceed:
return (None)
if len(np.where(np.diff(threshexceed) > 1)[0]) > 0:
isnot1 = np.max(np.where(np.diff(threshexceed) > 1)[0]) + 1
else:
isnot1 = 0
staysabove = int(threshexceed[isnot1])
crossing_time = int(cmip5.get_plottable_time(data)[staysabove])
f.close()
return (crossing_time)
def get_slopes(c,yrs,plot = False):
"""
get non-overlapping trends in concatenated control run
"""
trends = np.ma.array([])
start = 0
end = yrs
if plot:
Ntot = float(len(c)/yrs)
while end < len(c):
ctrunc = c[start:end]
#if len(ctrunc.compressed()) == len(ctrunc):
if True:
# Express in "per decade" units. All control runs are in days.
slope0,intercept0 = genutil.statistics.linearregression(ctrunc)
slope = slope0*3650.
if plot:
xax = cmip5.get_plottable_time(ctrunc)
plt.plot(xax,ctrunc.asma(),color = cm.RdYlBu(float(start/yrs)/Ntot))
plt.plot(xax,float(slope0)*ctrunc.getTime()[:]+float(intercept0),color ="k",linewidth = 3)
trends = np.append(trends,slope)
start = end
end += yrs
trends = MV.masked_where(np.isnan(trends),trends)
trends = trends.compressed()
return trends
def time_plot(x,ax=None,**kwargs):
""" plot a cdms time series """
t = cmip5.get_plottable_time(x)
if ax is None:
ax=plt.gca()
ax.plot(t,x.asma(),**kwargs)
def plot_h85_trends(H85):
x = cmip5.get_plottable_time(H85)
nmod = H85.shape[0]
cmap = cm.magma
for i in range(nmod):
if not H85[i].mask[0]:
time_plot(H85[i],
lw=.5,
alpha=.5,
color=cm.magma(float(i) / float(nmod)))
p = np.ma.polyfit(x, H85[i], 1)
plt.plot(x,
np.polyval(p, x),
color=cm.magma(float(i) / float(nmod)))
def project_multi(X, fingerprint, best_fit=True):
multisolver = fingerprint.solvers["multi"]
multifac = da.get_orientation(multisolver)
time_plot(multifac * multisolver.projectField(X)[:, 0],
label="MULTI",
color=get_colors("multi"))
plt.legend()
plt.ylabel("Projection onto " + fingerprint.experiment + " fingerprint")
if best_fit:
y = multifac * multisolver.projectField(X)[:, 0]
t = cmip5.get_plottable_time(y)
p = np.polyfit(t, y.asma(), 1)
plt.plot(t, np.polyval(p, t), "--", color=get_colors("multi"))