def taylorGraph(y0, y1, y2, y3):
""" Generate taylor diagram
Parameters:
-----------
y0 : reference dataset
y1...y3 : aproximated dataset by models
Returns:
--------
taylor Graph plot
"""
data = y0 #CONAE
#print data
refstd = data.std(ddof=1) # Reference standard deviation
# Models
m1 = y1 # MLR
m2 = y2 # MLP
m3 = y3 # MARS
# Compute stddev and correlation coefficient of models
samples = np.array([ [m.std(ddof=1), np.corrcoef(data, m)[0,1]]
for m in (m1, m2)])
#samples = np.array([ [sklearn.metrics.r2_score(data, m)**2, np.corrcoef(data, m)[0,1]]
#for m in (m1,m2)])
#samples = np.array([ [statistics.RMSE(data,m), np.corrcoef(data, m)[0,1]]
#for m in (m1,m2)])
#print "AQUI!!" + str(samples)
fig = plt.figure(10, facecolor="white")
#ax1 = fig.add_subplot(1,1,1, xlabel='X', ylabel='Y')
# Taylor diagram
dia = TaylorDiagram(refstd, fig=fig, rect=111, label="Conae")
colors = plt.matplotlib.cm.jet(np.linspace(0,1,len(samples)))
model = [ "MLR", "MLP", "MARS"]
color = ["green", "blue", "red" ]
markers = ["s", "o", "-"]
# Add samples to Taylor diagram
for i,(stddev,corrcoef) in enumerate(samples):
dia.add_sample(stddev, corrcoef, marker=markers[i],markersize=7, ls='', c=color[i],
label= str(model[i]))
# Add RMS contours, and label them
contours = dia.add_contours(colors='0.5')
plt.clabel(contours, inline=1, fontsize=10)
# Add a figure legend
fig.legend(dia.samplePoints,
[ p.get_label() for p in dia.samplePoints ],
numpoints=1, prop=dict(size='small'), loc='upper right')
plt.show()
def taylorplot(self, dpi=72):
'''
Plots the Taylor diagram for this matchup.
Args:
- *dpi* (optional): the figure's DPI (default: 72).
Returns: a matplotlib Figure object and a matplotlib Axes object
'''
#Create the figure
fig = plt.figure()
fig.set_dpi(dpi)
#Create the TaylorDiagram object
dia = TaylorDiagram(self.Ref.std(), fig=fig, label="Reference")
#Calculate the correlation coefficient
corr = self.correlation()
#Plot the model point
dia.add_sample(self.Model.std(), corr, marker='s')
# Add RMS contours, and label them
contours = dia.add_contours(colors='0.5')
plt.clabel(contours, inline=1, fontsize=10)
return fig, dia.ax
def main():
server,dovalidation, station, startdate, enddate, obstyle, expname, oper,tickint = readConfig()
vobsm = readObs(server,station, startdate, enddate,obstyle)
if dovalidation["experiment"] :readExpr(expname,station,startdate,enddate,vobsm)
if dovalidation["operational"] :readOper(server,oper,station,startdate,enddate,vobsm)
stick = getDateRange(startdate,enddate)
itick = range(len(stick))
iitick=itick[::tickint]
fig=plt.figure(num=1,figsize=(12.0,6.0),dpi=300,facecolor='w',edgecolor='k')
ax = fig.add_axes([0.08, 0.08, 0.8, 0.8], axisbg = '1.0')
ax.set_xlabel('Time',fontsize='20',weight='bold')
ax.set_ylabel ('SSH(m)',fontsize='20',weight='bold')
for im in range(len(vssh)):
if im == 0:
ax.plot(vssh[im],'o-',color=ccolor[im],ms=2)
else:
ax.plot(vssh[im],linestyle=cstyle[im],color=ccolor[im],lw=0.75)
print clegend
print ccolor
ax.legend(clegend, numpoints=1, prop=dict(size='small'),loc="best")
ax.grid()
ax.set_title(station,fontsize="30")
ax.xaxis.set_ticks(itick[::tickint])
ax.set_xlim(iitick[0],iitick[-1])
ax.xaxis.set_ticklabels(stick[::tickint],rotation="vertical",fontsize="15")
print "Creating figure for station", station, " ssh"+station+'.png'
plt.savefig("ssh"+station+'.png', bbox_inches='tight',dpi=300,facecolor='w',edgecolor='w',orientation='portrait')
plt.close(1)
TaylorDiagram(RMSVEC=rms, RMSDVEC=rmsd, CORVEC=cor,COLORVEC=ccolor,LABELVEC=clegend, station=station, info=startdate+"-"+enddate)
rects = dict(monclim=121, interannual=122)
legcol = dict(monclim=[0.4, 0.7], interannual=[0.9, 0.7])
fig = PLT.figure(figsize=(15, 8))
fig.suptitle("Mixed layer depth", size='x-large')
#for season in ['winter','spring','summer','autumn']:
id_df = 0
for intvl in ['monclim']:
print(rects[intvl], stdrefs[intvl])
dia = TaylorDiagram(stdrefs[intvl],
fig=fig,
rect=rects[intvl],
label='deBoyer et al. (2004)',
srange=(0, 2.5))
#dia.ax.plot(x95,y95,color='k')
#dia.ax.plot(x99,y99,color='k')
# Add samples to Taylor diagram
for mip in range(2):
id_df = id_df + 1
i = 0
for index, row in mldstats[id_df - 1].iterrows():
print(row[0], row[1])
if (index != 'Reference'):
stddev = row[1]
#print(index)
#print(row[0],row[1])
if (index == 'Reference'):
stdrefs['monclim'] = row[1]
print(stdrefs)
rects = dict(interannual=224, monclim=224)
legcol = dict(interannual=[0.825, 0.05], monclim=[0.825, 0.05])
#legcol = dict(interannual=[1.00,0.26],
# monclim=[1.00,0.26])
id_df = 0
dia = TaylorDiagram(stdrefs[intvl],
fig=fig,
rect=rects[intvl],
label='PCMDI-SST')
for mip in range(2):
id_df = id_df + 1
i = 0
for index, row in sststats[id_df - 1].iterrows():
print(row[0], row[1])
if (index != 'Reference'):
stddev = row[1]
corrcoef = row[0]
name = index + '-omip' + str(mip + 1)
dia.add_sample(
stddev,
corrcoef,
marker=markers[i],
if args.make_figures:
# the lines below activate TaylorPlots but it is disabled for now
fig = plt.figure(figsize=(11, 7)) #width,height
i = 1
axes = {}
axes_taylor = {}
colors = ['r', 'g', 'b', 'm', 'y', 'purple', 'orange', 'sienna', 'navy']
symbols = ['*', 'x', '+']
dias = {}
i = 1
for varkey in ['h', 'theta', 'q']:
dias[varkey] = TaylorDiagram(1.,
srange=[0.0, 1.7],
fig=fig,
rect=(230 + i + 3),
label='Reference')
axes[varkey] = fig.add_subplot(2, 3, i)
for ikey, key in enumerate(args.experiments.strip(' ').split(' ')[:1]):
icolor = 0
for ikoeppen, koeppen in koeppenlookuptable.iterrows():
print(ikoeppen, ':', koeppen)
kgc_select = (
c4gldata[key].frames['stats']['records_all_stations_ini']
['KGCname'] == koeppen['KGCID'])
koeppen_mod = c4gldata[key].frames['stats'][
'records_all_stations_mod_stats']['d' + varkey +
'dt'][kgc_select]
#y99 = [0.0, 3.45]
x95 = [0.05, 13.9] # For Prcp, this is for 95th level (r = 0.195)
y95 = [0.0, 71.0]
x99 = [0.05, 19.0] # For Prcp, this is for 99th level (r = 0.254)
y99 = [0.0, 70.0]
rects = dict(winter=221, spring=222, summer=223, autumn=224)
fig = PLT.figure(figsize=(11, 8))
fig.suptitle("Precipitations", size='x-large')
for season in ['winter', 'spring', 'summer', 'autumn']:
dia = TaylorDiagram(stdrefs[season],
fig=fig,
rect=rects[season],
label='Reference')
dia.ax.plot(x95, y95, color='k')
dia.ax.plot(x99, y99, color='k')
# Add samples to Taylor diagram
for i, (stddev, corrcoef, name) in enumerate(samples[season]):
dia.add_sample(
stddev,
corrcoef,
marker='$%d$' % (i + 1),
ms=10,
ls='',
#mfc='k', mec='k', # B&W
mfc=colors[i],
x95 = [0.05, 13.9] # For Prcp, this is for 95th level (r = 0.195)
y95 = [0.0, 71.0]
x99 = [0.05, 19.0] # For Prcp, this is for 99th level (r = 0.254)
y99 = [0.0, 70.0]
rects = dict(winter=221,
spring=222,
summer=223,
autumn=224)
fig = PLT.figure(figsize=(11,8))
fig.suptitle("Precipitations", size='x-large')
for season in ['winter','spring','summer','autumn']:
dia = TaylorDiagram(stdrefs[season], fig=fig, rect=rects[season],
label='Reference')
dia.ax.plot(x95,y95,color='k')
dia.ax.plot(x99,y99,color='k')
# Add samples to Taylor diagram
for i,(stddev,corrcoef,name) in enumerate(samples[season]):
dia.add_sample(stddev, corrcoef,
marker='$%d$' % (i+1), ms=10, ls='',
#mfc='k', mec='k', # B&W
mfc=colors[i], mec=colors[i], # Colors
label=name)
# Add RMS contours, and label them
contours = dia.add_contours(levels=5, colors='0.5') # 5 levels
dia.ax.clabel(contours, inline=1, fontsize=10, fmt='%.1f')
