def meanplots(self):
figpref.current()
pl.close('all')
def pcolor(fld, F, cmin, cmax, oneside=True):
pl.figure(F)
self.pcolor(miv(fld), oneside=oneside)
pl.clim(cmin,cmax)
"""
h = np.where(self.Dchl>0, self.Dchl, np.nan)
pcolor(nanmean(h, axis=0), 1, 0, 2)
h = np.where(self.Dchl<0, self.Dchl, np.nan)
pcolor(-nanmean(h, axis=0), 2, 0, 2)
h = np.where(self.Dsst>0, self.Dsst, np.nan)
pcolor(nanmean(h, axis=0), 3, 0, 0.4)
h = np.where(self.Dsst<0, self.Dsst, np.nan)
pcolor(-nanmean(h, axis=0), 4, 0, 0.4)
"""
pcolor(nanmean(self.Dchl, axis=0), 5, -0.25, 0.25, False)
pl.title(r"Mean change of Chl (mg m$^{-3}$)")
pl.savefig('figs/liege/meanmap_Dchl.pdf',transparent=True)
pcolor(nanmean(self.Dsst, axis=0), 6, -0.25, 0.25, False)
pl.title(r"Mean change of SST ($\degree$C)")
pl.savefig('figs/liege/meanmap_Dsst.pdf',transparent=True)
def movie(self):
import matplotlib as mpl
mpl.rcParams['axes.labelcolor'] = 'white'
pl.close(1)
pl.figure(1, (8, 4.5), facecolor='k')
miv = np.ma.masked_invalid
figpref.current()
jd0 = pl.date2num(dtm(2005, 1, 1))
jd1 = pl.date2num(dtm(2005, 12, 31))
mp = projmaps.Projmap('glob')
x, y = mp(self.llon, self.llat)
for t in np.arange(jd0, jd1):
print pl.num2date(t)
self.load(t)
pl.clf()
pl.subplot(111, axisbg='k')
mp.pcolormesh(x,
y,
miv(np.sqrt(self.u**2 + self.v**2)),
cmap=cm.gist_heat)
pl.clim(0, 1.5)
mp.nice()
pl.title(
'%04i-%02i-%02i' % (pl.num2date(t).year, pl.num2date(t).month,
pl.num2date(t).day),
color='w')
pl.savefig('/Users/bror/oscar/norm/%03i.png' % t,
bbox_inches='tight',
facecolor='k',
dpi=150)
def glob(self):
mat = np.load('globcummat.npz')
figpref.current()
pl.close(1)
pl.figure(1)
pl.plot(*self.cumcum(abs(np.random.rand(500000))), c='g',
lw=1, ls=":", label='Random values, rectangular distr.')
pl.plot(*self.cumcum(abs(np.random.randn(500000))),
c='r', lw=1, ls=":", label='Random values, normal distr.')
pl.plot(*self.cumcum(abs(np.random.lognormal(0,1,500000))), c='b',
lw=1, ls=":", label='Random values log-normal distr.')
pl.plot(np.linspace(0,1,100), np.mean(mat['globmat'],axis=1),
lw=2, alpha=0.5, label="Global")
pl.plot(np.linspace(0,1,100), np.mean(mat['northmat'],axis=1),
lw=2, alpha=0.5, label="Northern hemisphere")
pl.plot(np.linspace(0,1,100), np.mean(mat['southmat'],axis=1),
lw=2, alpha=0.5, label="Southern hemisphere")
pl.ylim(0,1)
pl.legend(loc='lower right')
pl.savefig('figs/liege/glob_glob.pdf',
transparent=True, bbox_inches='tight')
def cumcummap(self):
figpref.current()
h5fld = self.Dchl
tlen, jmat, imat = h5fld.shape
ppp = np.zeros((jmat, imat)) * np.nan
for j in np.arange(jmat):
for i in np.arange(imat):
vec = h5fld[:, j, i]
vec = vec[vec > 0]
if len(vec) > 0: ppp[j, i] = self.cumpos(vec)
pl.close(1)
pl.figure(1)
self.pcolor(miv(ppp), oneside=True)
pl.clim(0.3, 0.8)
pl.title(r'Cumulative Dchl for 10% highest pos obs.')
pl.savefig('figs/liege/cumcummap_pos_Dchl.pdf')
ppp = np.zeros((jmat, imat)) * np.nan
for j in np.arange(jmat):
for i in np.arange(imat):
vec = h5fld[:, j, i]
vec = -vec[vec < 0]
if len(vec) > 0: ppp[j, i] = self.cumpos(vec)
pl.close(2)
pl.figure(2)
self.pcolor(miv(ppp), oneside=True)
pl.clim(0.3, 0.8)
pl.title(r'Cumulative Dchl for 10% highest neg obs.')
pl.savefig('figs/liege/cumcummap_neg_Dchl.pdf')
h5fld = self.Dsst
tlen, jmat, imat = h5fld.shape
ppp = np.zeros((jmat, imat)) * np.nan
for j in np.arange(jmat):
for i in np.arange(imat):
vec = h5fld[:, j, i]
vec = vec[vec > 0]
if len(vec) > 0: ppp[j, i] = self.cumpos(vec)
pl.close(3)
pl.figure(3)
self.pcolor(miv(ppp), oneside=True)
pl.clim(0.3, 0.8)
pl.title(r'Cumulative Dsst for 10% highest pos obs.')
pl.savefig('figs/liege/cumcummap_pos_Dsst.pdf')
ppp = np.zeros((jmat, imat)) * np.nan
for j in np.arange(jmat):
for i in np.arange(imat):
vec = h5fld[:, j, i]
vec = -vec[vec < 0]
if len(vec) > 0: ppp[j, i] = self.cumpos(vec)
pl.close(4)
pl.figure(4)
self.pcolor(miv(ppp), oneside=True)
pl.clim(0.3, 0.8)
pl.title(r'Cumulative Dsst for 10% highest neg obs.')
pl.savefig('figs/liege/cumcummap_neg_Dsst.pdf')
def o2ar(self):
cr = bender_liege.cruises.all_cruises()
figpref.current()
pl.close(1)
pl.figure(1)
pl.plot(*self.cumcum(abs(np.random.rand(500000))),
c='g',
lw=1,
ls=":",
label='Random values, rectangular distr.')
pl.plot(*self.cumcum(abs(np.random.randn(500000))),
c='r',
lw=1,
ls=":",
label='Random values, normal distr.')
pl.plot(*self.cumcum(abs(np.random.lognormal(0, 1, 500000))),
c='b',
lw=1,
ls=":",
label='Random values log-normal distr.')
vec = cr.ncp[cr.ncp > 0]
pl.plot(*self.cumcum(vec), lw=2, alpha=0.5, label="O2Ar based NCP")
pl.ylim(0, 1)
pl.legend(loc='lower right')
pl.savefig('figs/liege/glob_o2ar.pdf',
transparent=True,
bbox_inches='tight')
def heatmap(self, maskvec=None, jd=None, cmap=None, alpha=1, colorbar=True,
clf=True, map_region=None, log=False, kwargs={}):
"""Plot particle positions on a map
mask: Boolean vector determining which particles to plot
ntrac: Particle ID
jd: Julian date to plot, has to be included in jdvec.
cmap: color map to use
alpha: transparancy of pcolormap
clf: Clear figure if True
colorbar: Show colorbar if True (default)
map_region: Use other map_regions than set by config file
kwargs: Other arguments to pcolor (must be a dict).
"""
if maskvec is None:
maskvec = self.ntrac==self.ntrac
if jd is not None:
maskvec = maskvec & (self.jd==jd)
if USE_FIGPREF:
figpref.current()
if clf:
pl.clf()
if cmap is not None:
kwargs['cmap'] = cmap
if log is True:
kwargs['norm'] = LogNorm()
if colorbar is True:
kwargs['colorbar'] = True
mat = self.heatmat(nan=True, maskvec=maskvec, jd=None)
mat[mat==0] = np.nan
self.gcm.pcolor(mat, **kwargs)
if jd: pl.title(pl.num2date(jd).strftime("%Y-%m-%d %H:%M"))
def globtime(self):
mat = np.load('globcummat.npz')
jd1 = pl.datestr2num('2003-01-01')
jd2 = pl.datestr2num('2012-12-31')
jdvec = np.arange(jd1, jd2)
figpref.current()
pl.close(1)
pl.figure(1)
pl.plot_date(jdvec,
mat['northmat'][10, :],
'r-',
alpha=0.5,
label="Northern hemisphere")
pl.plot_date(jdvec,
mat['southmat'][10, :],
'b-',
alpha=0.5,
label="Southern hemisphere")
pl.ylim(0, 1)
pl.legend(loc='lower right')
pl.savefig('figs/liege/glob_time.pdf',
transparent=True,
bbox_inches='tight')
def latslice(self, datadir='/Volumes/keronHD3/globChl/result/'):
from scipy.io import loadmat
figpref.current()
pl.close(1)
pl.figure(1)
pl.plot(*self.cumcum(abs(np.random.rand(500000))),
c='g',
lw=1,
ls=":",
label='Random values, rectangular distr.')
pl.plot(*self.cumcum(abs(np.random.randn(500000))),
c='r',
lw=1,
ls=":",
label='Random values, normal distr.')
pl.plot(*self.cumcum(abs(np.random.lognormal(0, 1, 500000))),
c='b',
lw=1,
ls=":",
label='Random values log-normal distr.')
for lat in [0, 15, 30, 45, 60, 75]:
mat = loadmat(datadir + 'latsliceschl_lat%i.mat' % lat)
pl.plot(*self.cumcum(mat['chl']),
lw=2,
alpha=0.5,
label="Chl, lat=%i" % lat)
pl.ylim(0, 1)
pl.legend(loc='lower right')
pl.savefig('figs/liege/glob_latslice.pdf',
transparent=True,
bbox_inches='tight')
def meanplots(self):
figpref.current()
pl.close('all')
def pcolor(fld, F, cmin, cmax, oneside=True):
pl.figure(F)
self.pcolor(miv(fld), oneside=oneside)
pl.clim(cmin, cmax)
"""
h = np.where(self.Dchl>0, self.Dchl, np.nan)
pcolor(nanmean(h, axis=0), 1, 0, 2)
h = np.where(self.Dchl<0, self.Dchl, np.nan)
pcolor(-nanmean(h, axis=0), 2, 0, 2)
h = np.where(self.Dsst>0, self.Dsst, np.nan)
pcolor(nanmean(h, axis=0), 3, 0, 0.4)
h = np.where(self.Dsst<0, self.Dsst, np.nan)
pcolor(-nanmean(h, axis=0), 4, 0, 0.4)
"""
pcolor(nanmean(self.Dchl, axis=0), 5, -0.25, 0.25, False)
pl.title(r"Mean change of Chl (mg m$^{-3}$)")
pl.savefig('figs/liege/meanmap_Dchl.pdf', transparent=True)
pcolor(nanmean(self.Dsst, axis=0), 6, -0.25, 0.25, False)
pl.title(r"Mean change of SST ($\degree$C)")
pl.savefig('figs/liege/meanmap_Dsst.pdf', transparent=True)
def meantime2plot(self, sst=True, chl=True):
figpref.current()
pl.close(1)
fig = pl.figure(1)
pl.subplots_adjust(hspace=0, right=0.85, left=0.15)
def plot(fldname):
self.histmoeller(fldname)
pl.fill_between(self.jdvec,
self.jdvec * 0,
self.posmean,
facecolor='powderblue')
pl.fill_between(self.jdvec,
self.negmean,
self.jdvec * 0,
facecolor='pink')
pl.plot(self.jdvec, self.negmean + self.posmean, lw=1, c='k')
pl.gca().xaxis.axis_date()
if sst:
ax = pl.subplot(2, 1, 1)
plot('Dsst')
pl.ylabel('Dsst ($\degree$C d$^{-1}$')
pl.setp(ax.get_xticklabels(), visible=False)
if chl:
ax = pl.subplot(2, 1, 2)
plot('Dchl')
pl.ylim(-2, 2)
pl.yticks([-2, -1, 0, 1, 2])
pl.ylabel('Chl (mg m$^{3}$ d$^{-1}$)')
pl.savefig('figs/liege/meantime_both.pdf')
def movie(self):
import matplotlib as mpl
mpl.rcParams['axes.labelcolor'] = 'white'
pl.close(1)
pl.figure(1,(8,4.5),facecolor='k')
miv = np.ma.masked_invalid
figpref.current()
jd0 = pl.date2num(dtm(2005,1,1))
jd1 = pl.date2num(dtm(2005,12,31))
mp = projmaps.Projmap('glob')
x,y = mp(self.llon,self.llat)
for t in np.arange(jd0,jd1):
print pl.num2date(t)
self.load(t)
pl.clf()
pl.subplot(111,axisbg='k')
mp.pcolormesh(x,y,
miv(np.sqrt(self.u**2 +self.v**2)),
cmap=cm.gist_heat)
pl.clim(0,1.5)
mp.nice()
pl.title('%04i-%02i-%02i' % (pl.num2date(t).year,
pl.num2date(t).month,
pl.num2date(t).day),
color='w')
pl.savefig('/Users/bror/oscar/norm/%03i.png' % t,
bbox_inches='tight',facecolor='k',dpi=150)
def meantime2plot(self, sst=True, chl=True):
figpref.current()
pl.close(1)
fig = pl.figure(1)
pl.subplots_adjust(hspace=0,right=0.85, left=0.15)
def plot(fldname):
self.histmoeller(fldname)
pl.fill_between(self.jdvec, self.jdvec*0, self.posmean,
facecolor='powderblue')
pl.fill_between(self.jdvec, self.negmean, self.jdvec*0,
facecolor='pink')
pl.plot(self.jdvec, self.negmean+self.posmean,lw=1,c='k')
pl.gca().xaxis.axis_date()
if sst:
ax = pl.subplot(2,1,1)
plot('Dsst')
pl.ylabel('Dsst ($\degree$C d$^{-1}$')
pl.setp(ax.get_xticklabels(), visible=False)
if chl:
ax = pl.subplot(2,1,2)
plot('Dchl')
pl.ylim(-2,2)
pl.yticks([-2,-1,0,1,2])
pl.ylabel('Chl (mg m$^{3}$ d$^{-1}$)')
pl.savefig('figs/liege/meantime_both.pdf')
def cumcum_theory(self):
figpref.current()
pl.close(1)
pl.figure(1)
pl.plot(*self.cumcum(abs(np.random.rand(500000))), c='g',
lw=2, ls="-", label='Random values, rectangular distr.')
pl.legend(loc='lower right')
pl.ylim(0,1)
pl.savefig('figs/liege/cc_theory_rectdist.pdf',
transparent=True, bbox_inches='tight')
pl.plot(*self.cumcum(abs(np.random.randn(500000))), c='r',
lw=2, ls="-", label='Random values, normal distr.')
pl.legend(loc='lower right')
pl.ylim(0,1)
pl.savefig('figs/liege/cc_theory_normrectdist.pdf',
transparent=True, bbox_inches='tight')
pl.plot(*self.cumcum(abs(np.random.lognormal(0,1,500000))), c='b',
lw=2, ls="-", label='Random values log-normal distr.')
pl.ylim(0,1)
pl.legend(loc='lower right')
pl.savefig('figs/liege/cc_theory_lognormrectdist.pdf',
transparent=True, bbox_inches='tight')
def reset(self):
self.ffmpgF = ("-mbd rd -flags +mv4+aic -trellis 2 " +
" -cmp 2 -subcmp 2 -g 300 -y")
self.oset = " -vcodec libx264 -qscale 1"
self.tdir = tempfile.mkdtemp()
print " === Images for video saved at " + self.tdir
self.n = 0
pl.close('all')
figpref.current()
def reset(self):
self.ffmpgF = ("-mbd rd -flags +mv4+aic -trellis 2 " +
" -cmp 2 -subcmp 2 -g 300 -y")
self.oset = " -vcodec libx264 -qscale 1"
self.tdir = tempfile.mkdtemp()
print " === Images for video saved at " + self.tdir
self.n = 0
pl.close('all')
figpref.current()
def patch(tr):
figpref.current()
pl.figure(1)
sH0,_ = tr.scatter(jd=732371.125+0,s=3,c='g',clf=False)
sH1,_ = tr.scatter(jd=732371.125+1,s=3,c='r',clf=False)
sH2,_ = tr.scatter(jd=732371.125+2,s=3,c='b',clf=False)
pl.legend((sH0,sH1,sH2),('0 Hours','24 Hours','48 Hours') )
pl.savefig('figs/patch_days.png')
def interp(ps,intstart=6353):
import figpref
import projmaps
figpref.current()
ps.c.execute("SELECT distinct(ints) FROM casco__chlor_a " +
" WHERE intstart=%i" % intstart)
ints1,ints2 = ps.c.fetchall()
sql = """SELECT t1.ntrac,t1.val,t2.val,p.x,p.y,p.ints
FROM casco__chlor_a t1
INNER JOIN casco__chlor_a t2 ON t1.ntrac=t2.ntrac
INNER JOIN casco p ON t1.ntrac=p.ntrac
WHERE t1.intstart=%i
AND t2.intstart=%i
AND p.intstart=%i
AND t1.ints=%i
AND t2.ints=%i;
""" % (intstart,intstart,intstart,ints1[0],ints2[0])
ps.c.execute(sql)
res = zip(*ps.c.fetchall())
class trj: pass
if len(res) > 0:
for n,a in enumerate(['ntrac','t1val','t2val','x','y','ints']):
trj.__dict__[a] = np.array(res[n])
mask = (trj.ints.astype(np.float)/100-trj.ints/100)>0.5
trj.ints[mask]=trj.ints[mask]+1
tvec = np.unique(trj.ints)
tvec = tvec[tvec<=ints2[0]]
itvec = tvec-tvec.min()
itvec = itvec.astype(np.float)/itvec.max()
mp = projmaps.Projmap('casco')
xl,yl = mp(ps.llon,ps.llat)
for n,t in enumerate(tvec):
fld = ps.cs.llat * 0
cnt = ps.cs.llat * 0
xvc = (trj.x[trj.ints==t].astype(np.int))
yvc = (trj.y[trj.ints==t].astype(np.int))
val = ( np.log(trj.t1val[trj.ints==t])*(1-itvec[n]) +
np.log(trj.t2val[trj.ints==t])*(itvec[n]) )
for x,y,v in zip(xvc,yvc,val):
fld[y,x] += v
cnt[y,x] += 1
pl.clf()
mp.pcolormesh(xl,yl,miv(fld/cnt))
mp.nice()
jd = (ps.base_iso +
(float(t)-(intstart)/8*3600*24)/3600/24 + intstart/8) + 0.583
pl.title(pl.num2date(jd).strftime("log(Chl) %Y-%m-%d %H:%M"))
pl.clim(-2,2.5)
pl.savefig("interp_%i_%03i.png" % (intstart,n), dpi=100)
def interp(ps,intstart=6353):
import figpref
import projmaps
figpref.current()
ps.c.execute("SELECT distinct(ints) FROM casco__chlor_a " +
" WHERE intstart=%i" % intstart)
ints1,ints2 = ps.c.fetchall()
sql = """SELECT t1.ntrac,t1.val,t2.val,p.x,p.y,p.ints
FROM casco__chlor_a t1
INNER JOIN casco__chlor_a t2 ON t1.ntrac=t2.ntrac
INNER JOIN casco p ON t1.ntrac=p.ntrac
WHERE t1.intstart=%i
AND t2.intstart=%i
AND p.intstart=%i
AND t1.ints=%i
AND t2.ints=%i;
""" % (intstart,intstart,intstart,ints1[0],ints2[0])
ps.c.execute(sql)
res = zip(*ps.c.fetchall())
class trj: pass
if len(res) > 0:
for n,a in enumerate(['ntrac','t1val','t2val','x','y','ints']):
trj.__dict__[a] = np.array(res[n])
mask = (trj.ints.astype(np.float)/100-trj.ints/100)>0.5
trj.ints[mask]=trj.ints[mask]+1
tvec = np.unique(trj.ints)
tvec = tvec[tvec<=ints2[0]]
itvec = tvec-tvec.min()
itvec = itvec.astype(np.float)/itvec.max()
mp = projmaps.Projmap('casco')
xl,yl = mp(ps.llon,ps.llat)
for n,t in enumerate(tvec):
fld = ps.cs.llat * 0
cnt = ps.cs.llat * 0
xvc = (trj.x[trj.ints==t].astype(np.int))
yvc = (trj.y[trj.ints==t].astype(np.int))
val = ( np.log(trj.t1val[trj.ints==t])*(1-itvec[n]) +
np.log(trj.t2val[trj.ints==t])*(itvec[n]) )
for x,y,v in zip(xvc,yvc,val):
fld[y,x] += v
cnt[y,x] += 1
pl.clf()
mp.pcolormesh(xl,yl,miv(fld/cnt))
mp.nice()
jd = (ps.base_iso +
(float(t)-(intstart)/8*3600*24)/3600/24 + intstart/8) + 0.583
pl.title(pl.num2date(jd).strftime("log(Chl) %Y-%m-%d %H:%M"))
pl.clim(-2,2.5)
pl.savefig("interp_%i_%03i.png" % (intstart,n), dpi=100)
def contour(self,fld, *args, **kwargs):
"""Make a pcolor-plot of field"""
if USE_FIGPREF: figpref.current()
if not hasattr(self, 'mp'): self.add_mp()
miv = np.ma.masked_invalid
if type(fld) == str:
field = self.__dict__[fld]
else:
field = fld
x,y = self.mp(self.llon, self.llat)
self.mp.contour(x,y,miv(field), *args, **kwargs)
self.mp.nice()
def histmoellerplot(self, fldname="Dchl"):
self.histmoeller(fldname)
figpref.current()
pl.close(1)
fig = pl.figure(1)
pl.subplots_adjust(hspace=0, right=0.85, left=0.15)
def subplot(sp, mat, vec, xvec):
ax = pl.subplot(2, 1, sp, axisbg='0.8')
im = pl.pcolormesh(self.jdvec,
xvec,
miv(mat.T),
rasterized=True,
norm=LogNorm(vmin=1, vmax=10000),
cmap=WRY())
pl.plot(self.jdvec, vec, lw=2)
pl.gca().xaxis.axis_date()
pl.setp(ax, xticklabels=[])
return im
im = subplot(1, self.posmat, self.posmean, self.hpos)
im = subplot(2, self.negmat, self.negmean, -self.hpos)
pl.figtext(0.08,
0.5,
r'%s d$^{-1}$)' % fldname,
rotation='vertical',
size=16,
va='center')
cbar_ax = fig.add_axes([0.87, 0.15, 0.025, 0.7])
cb = fig.colorbar(im, cax=cbar_ax)
cb.set_label('Number of grid cells')
pl.savefig('figs/liege/histmoeller_%s.pdf' % fldname)
pl.close(2)
fig = pl.figure(2)
pl.fill_between(self.jdvec,
self.jdvec * 0,
self.posmean,
facecolor='powderblue')
pl.fill_between(self.jdvec,
self.negmean,
self.jdvec * 0,
facecolor='pink')
pl.plot(self.jdvec, self.negmean + self.posmean, lw=1, c='k')
#pl.title(fldname)
pl.gca().xaxis.axis_date()
pl.savefig('figs/liege/meantime_%s.pdf' % fldname)
def cumcumtime(self):
figpref.current()
h5fld = self.Dsst
tlen,_,_ = h5fld.shape
ppp = []
for t in np.arange(tlen):
vec = h5fld[t,:,:]
vec = vec[vec>0]
if len(vec) > 0: ppp.append(self.cumpos(vec))
#pl.close(1)
#pl.figure(1)
pl.plot(ppp)
def cumcumtime(self):
figpref.current()
h5fld = self.Dsst
tlen, _, _ = h5fld.shape
ppp = []
for t in np.arange(tlen):
vec = h5fld[t, :, :]
vec = vec[vec > 0]
if len(vec) > 0: ppp.append(self.cumpos(vec))
#pl.close(1)
#pl.figure(1)
pl.plot(ppp)
def totmatplot(mat,dt):
"""Plot a histmoeller diagram of totmat"""
xi = np.linspace(np.log(0.001), np.log(10), 101)
figpref.current()
pl.clf()
pl.contourf(np.arange(30),np.exp((xi[1:]+xi[:-1])/2),
miv(mat/mat.max(axis=0)[np.newaxis,:]), np.arange(0,1.1,0.125),
cmap=cm.OrRd)
pl.clim(0,1)
pl.setp(pl.gca(),yscale='log')
cb = pl.colorbar(aspect=40, pad=0.001, ticks=[0,0.25,0.5,0.75,1])
cb.ax.set_ylabel('Relative number of particles')
pl.xlim(0,26)
pl.xlabel("Time (days)")
pl.ylabel(r"Positive change in Chlorophyll (mg l$^{-1}$ d$^{-1}$)")
pl.title("Biological production from particle tracking. Dt=%i hour(s)" % dt)
def cumcum_theory():
figpref.current()
pl.close(1)
pl.figure(1)
#a = np.random.randn(4,4)
a = np.random.lognormal(0, 1, (4, 4))
pl.clf()
pl.pcolormesh(abs(a), cmap=WRY())
pl.yticks([])
pl.xticks([])
pl.savefig('figs/liege/cc_theory_grid.pdf',
transparent=True,
bbox_inches='tight')
b = np.reshape(np.abs(a), (1, 16))
pl.clf()
pl.subplot(8, 1, 1)
pl.pcolormesh(b, cmap=WRY())
pl.yticks([])
pl.xticks([])
pl.savefig('figs/liege/cc_theory_vec.pdf',
transparent=True,
bbox_inches='tight')
c = np.sort(b)[:, ::-1]
pl.clf()
pl.subplot(8, 1, 1)
pl.pcolormesh(c, cmap=WRY())
pl.yticks([])
pl.xticks([])
pl.savefig('figs/liege/cc_theory_sortvec.pdf',
transparent=True,
bbox_inches='tight')
d = np.hstack((0, np.squeeze(np.cumsum(c) / np.sum(c))))
pl.clf()
pl.plot(np.linspace(0, 1, 17), d)
pl.ylim(0, 1)
#pl.xticks([])
pl.savefig('figs/liege/cc_theory_cumcum.pdf',
transparent=True,
bbox_inches='tight')
def glob(self):
mat = np.load('globcummat.npz')
figpref.current()
pl.close(1)
pl.figure(1)
pl.plot(*self.cumcum(abs(np.random.rand(500000))),
c='g',
lw=1,
ls=":",
label='Random values, rectangular distr.')
pl.plot(*self.cumcum(abs(np.random.randn(500000))),
c='r',
lw=1,
ls=":",
label='Random values, normal distr.')
pl.plot(*self.cumcum(abs(np.random.lognormal(0, 1, 500000))),
c='b',
lw=1,
ls=":",
label='Random values log-normal distr.')
pl.plot(np.linspace(0, 1, 100),
np.mean(mat['globmat'], axis=1),
lw=2,
alpha=0.5,
label="Global")
pl.plot(np.linspace(0, 1, 100),
np.mean(mat['northmat'], axis=1),
lw=2,
alpha=0.5,
label="Northern hemisphere")
pl.plot(np.linspace(0, 1, 100),
np.mean(mat['southmat'], axis=1),
lw=2,
alpha=0.5,
label="Southern hemisphere")
pl.ylim(0, 1)
pl.legend(loc='lower right')
pl.savefig('figs/liege/glob_glob.pdf',
transparent=True,
bbox_inches='tight')
def totmatplot(mat, dt):
"""Plot a histmoeller diagram of totmat"""
xi = np.linspace(np.log(0.001), np.log(10), 101)
figpref.current()
pl.clf()
pl.contourf(np.arange(30),
np.exp((xi[1:] + xi[:-1]) / 2),
miv(mat / mat.max(axis=0)[np.newaxis, :]),
np.arange(0, 1.1, 0.125),
cmap=cm.OrRd)
pl.clim(0, 1)
pl.setp(pl.gca(), yscale='log')
cb = pl.colorbar(aspect=40, pad=0.001, ticks=[0, 0.25, 0.5, 0.75, 1])
cb.ax.set_ylabel('Relative number of particles')
pl.xlim(0, 26)
pl.xlabel("Time (days)")
pl.ylabel(r"Positive change in Chlorophyll (mg l$^{-1}$ d$^{-1}$)")
pl.title("Biological production from particle tracking. Dt=%i hour(s)" %
dt)
def globtime(self):
mat = np.load('globcummat.npz')
jd1 = pl.datestr2num('2003-01-01')
jd2 = pl.datestr2num('2012-12-31')
jdvec = np.arange(jd1,jd2)
figpref.current()
pl.close(1)
pl.figure(1)
pl.plot_date(jdvec, mat['northmat'][10,:],'r-',
alpha=0.5, label="Northern hemisphere")
pl.plot_date(jdvec, mat['southmat'][10,:],'b-',
alpha=0.5, label="Southern hemisphere")
pl.ylim(0,1)
pl.legend(loc='lower right')
pl.savefig('figs/liege/glob_time.pdf',
transparent=True, bbox_inches='tight')
def freqmaps(self):
figpref.current()
pl.close(1)
pl.figure(1)
h = np.where(self.Dchl > 0.5, 1, 0)
fld = np.sum(h, axis=0).astype(np.float) / np.nansum(
self.Dchl * 0 + 1, 0)
self.pcolor(miv(fld * 100), oneside=True)
pl.clim(0, 25)
pl.title(r'Percent observations where Dchl > 0.5 mg $m^{-3}$')
pl.savefig('figs/liege/freqmap_pos_Dchl.pdf', transparent=True)
pl.close(2)
pl.figure(2)
h = np.where(self.Dchl < -0.5, 1, 0)
fld = np.sum(h, axis=0).astype(np.float) / np.nansum(
self.Dchl * 0 + 1, 0)
self.pcolor(miv(fld * 100), oneside=True)
pl.clim(0, 25)
pl.title('Percent observations where Dchl < -0.5 mg $m^{-3}$')
pl.savefig('figs/liege/freqmap_neg_Dchl.pdf')
figpref.current()
pl.close(3)
pl.figure(3)
h = np.where(self.Dsst > 0.4, 1, 0)
fld = np.sum(h, axis=0).astype(np.float) / np.nansum(
self.Dsst * 0 + 1, 0)
self.pcolor(miv(fld * 100), oneside=True)
pl.clim(0, 25)
pl.title(r'Percent observations where Dsst > 0.4 $\degree$C')
pl.savefig('figs/liege/freqmap_pos_Dsst.pdf')
pl.close(4)
pl.figure(4)
h = np.where(self.Dsst < -0.4, 1, 0)
fld = np.sum(h, axis=0).astype(np.float) / np.nansum(
self.Dsst * 0 + 1, 0)
self.pcolor(miv(fld * 100), oneside=True)
pl.clim(0, 25)
pl.title('Percent observations where Dsst < -0.4 $\degree$C')
pl.savefig('figs/liege/freqmap_neg_Dsst.pdf')
def pcolor(self, fld, title=None, colorbar=False, **kwargs):
"""Make a pcolor-plot of field"""
if USE_FIGPREF: figpref.current()
if not hasattr(self, 'mp'): self.add_mp()
miv = np.ma.masked_invalid
if type(fld) == str:
field = getattr(self, fld)
else:
field = fld
x,y = self.mp(self.llon, self.llat)
self.mp.pcolormesh(x,y,miv(field), **kwargs)
self.mp.nice()
if title is not None:
pl.title(title)
if colorbar:
if type(colorbar) is dict:
self.mp.colorbar(**colorbar)
else:
self.mp.colorbar()
def cumcumtot(self):
figpref.current()
pl.close(1)
pl.figure(1)
pl.plot(*self.cumcum(abs(np.random.rand(500000))), c='g',
lw=1, ls=":", label='Random values, rectangular distr.')
pl.plot(*self.cumcum(abs(np.random.randn(500000))),
c='r', lw=1, ls=":", label='Random values, normal distr.')
pl.plot(*self.cumcum(abs(np.random.lognormal(0,1,500000))), c='b',
lw=1, ls=":", label='Random values log-normal distr.')
vec = self.Dsst[~np.isnan(self.Dsst)]
pl.plot(*self.cumcum(vec[vec>0]), lw=2, alpha=0.5,
label="SST, positive values")
pl.ylim(0,1)
pl.legend(loc='lower right')
pl.savefig('figs/liege/cumcumtot_pos_sst.pdf',
transparent=True, bbox_inches='tight')
pl.plot(*self.cumcum(-vec[vec<0]), lw=2, alpha=0.5,
label="SST, negative values")
pl.ylim(0,1)
pl.legend(loc='lower right')
pl.savefig('figs/liege/cumcumtot_posneg_sst.pdf',
transparent=True, bbox_inches='tight')
vec = self.Dchl[~np.isnan(self.Dchl)]
pl.plot(*self.cumcum(vec[vec>0]), lw=2, alpha=0.5,
label="Chl, positive values")
pl.ylim(0,1)
pl.legend(loc='lower right')
pl.savefig('figs/liege/cumcumtot_posneg_sst_pos_chl.pdf',
transparent=True, bbox_inches='tight')
pl.plot(*self.cumcum(-vec[vec<0]), lw=2, alpha=0.5,
label="Chl, negative values")
pl.ylim(0,1)
pl.legend(loc='lower right')
pl.savefig('figs/liege/cumcumtot_posneg_sst_posneg_chl.pdf',
transparent=True, bbox_inches='tight')
pl.savefig('figs/liege/cumcumtot.pdf')
def tauregmap(self):
if not hasattr(self,'regtaus'): self.calc_halftimes()
if not hasattr(self,'reg'): self.regvec_from_regions()
if not hasattr(self,'lon'): self.ijll()
figpref.current()
pl.close(1)
pl.figure(1)
mask = (self.jd==self.jd.min()) & (self.reg>0)
x,y = self.gcm.mp(self.lon[mask], self.lat[mask])
regs = self.reg[mask]
taus = self.reg[mask].astype(np.float)
for n,t in enumerate(self.regtaus):
taus[regs==n] = t
self.gcm.mp.scatter(x, y, 5, taus*24)
pl.clim(0,30)
cb = pl.colorbar(aspect=30,pad=0.02,ticks=[0,6,12,18,24,30])
cb.ax.set_ylabel(r'$\tau$ (Hours)')
self.gcm.mp.nice()
pl.title('Residence times defined as e-folding half-life ')
pl.savefig('residencetimes.png')
def hr_vs_dy_figs(ntrac=200):
figpref.current()
tr = Discs('bem', 'bem')
tr.load()
tr.fld2trajs('chlo')
tr.ijll()
mask = tr.ntrac == ntrac
chl = tr.chlo[mask][:200]
jd = tr.jd[mask][:200]
dchl = chl[1:] - chl[:-1]
#plot_date(jd,chl,'-')
#plot(jd[1:], cumsum(dchl)+chl[0])
plot_date(jd[1:], dchl, '-')
plot(jd[1::24], dchl[::24])
pl.figure(1)
pl.clf()
x, y = tr.gcm.mp(tr.lon[mask], tr.lat[mask])
tr.gcm.mp.scatter(x, y)
tr.gcm.mp.nice()
pl.savefig('chlhrdy_map_%i.png' % ntrac)
pl.figure(2)
pl.clf()
pl.subplot(2, 1, 1)
pl.plot_date(jd, chl, '-')
pl.plot(jd[1::24], (cumsum(dchl) + chl[0])[::24], 'o-')
pl.ylabel('Chl (mg C / ml)')
pl.gca().xaxis.set_major_formatter(mf)
pl.subplot(2, 1, 2)
pl.plot_date(jd[1:], dchl, '-')
pl.plot(jd[1::24], dchl[::24], 'o-')
pl.gca().xaxis.set_major_formatter(mf)
pl.ylabel('Change in Chl (mg C / ml)')
pl.xlabel('Time (days)')
pl.savefig('chlhrdy_dchl_%i.png' % ntrac)
def hr_vs_dy_figs(ntrac=200):
figpref.current()
tr = Discs('bem','bem')
tr.load()
tr.fld2trajs('chlo')
tr.ijll()
mask = tr.ntrac == ntrac
chl = tr.chlo[mask][:200]
jd = tr.jd[mask][:200]
dchl = chl[1:]-chl[:-1]
#plot_date(jd,chl,'-')
#plot(jd[1:], cumsum(dchl)+chl[0])
plot_date(jd[1:], dchl,'-')
plot(jd[1::24], dchl[::24])
pl.figure(1)
pl.clf()
x,y = tr.gcm.mp(tr.lon[mask],tr.lat[mask])
tr.gcm.mp.scatter(x,y)
tr.gcm.mp.nice()
pl.savefig('chlhrdy_map_%i.png' % ntrac)
pl.figure(2)
pl.clf()
pl.subplot(2,1,1)
pl.plot_date(jd,chl,'-')
pl.plot(jd[1::24], (cumsum(dchl)+chl[0])[::24],'o-')
pl.ylabel('Chl (mg C / ml)')
pl.gca().xaxis.set_major_formatter(mf)
pl.subplot(2,1,2)
pl.plot_date(jd[1:], dchl,'-')
pl.plot(jd[1::24], dchl[::24],'o-')
pl.gca().xaxis.set_major_formatter(mf)
pl.ylabel('Change in Chl (mg C / ml)')
pl.xlabel('Time (days)')
pl.savefig('chlhrdy_dchl_%i.png' % ntrac)
def cumcum_theory():
figpref.current()
pl.close(1)
pl.figure(1)
#a = np.random.randn(4,4)
a = np.random.lognormal(0,1,(4,4))
pl.clf()
pl.pcolormesh(abs(a), cmap=WRY())
pl.yticks([])
pl.xticks([])
pl.savefig('figs/liege/cc_theory_grid.pdf',
transparent=True, bbox_inches='tight')
b = np.reshape(np.abs(a),(1,16))
pl.clf()
pl.subplot(8,1,1)
pl.pcolormesh(b, cmap=WRY())
pl.yticks([])
pl.xticks([])
pl.savefig('figs/liege/cc_theory_vec.pdf',
transparent=True, bbox_inches='tight')
c = np.sort(b)[:,::-1]
pl.clf()
pl.subplot(8,1,1)
pl.pcolormesh(c, cmap=WRY())
pl.yticks([])
pl.xticks([])
pl.savefig('figs/liege/cc_theory_sortvec.pdf',
transparent=True, bbox_inches='tight')
d = np.hstack((0,np.squeeze(np.cumsum(c)/np.sum(c))))
pl.clf()
pl.plot(np.linspace(0,1,17), d)
pl.ylim(0,1)
#pl.xticks([])
pl.savefig('figs/liege/cc_theory_cumcum.pdf',
transparent=True, bbox_inches='tight')
def freqmaps(self):
figpref.current()
pl.close(1)
pl.figure(1)
h = np.where(self.Dchl>0.5, 1, 0)
fld = np.sum(h, axis=0).astype(np.float) / np.nansum(self.Dchl*0+1,0)
self.pcolor(miv(fld*100), oneside=True)
pl.clim(0,25)
pl.title(r'Percent observations where Dchl > 0.5 mg $m^{-3}$')
pl.savefig('figs/liege/freqmap_pos_Dchl.pdf',transparent=True)
pl.close(2)
pl.figure(2)
h = np.where(self.Dchl<-0.5, 1, 0)
fld = np.sum(h, axis=0).astype(np.float) / np.nansum(self.Dchl*0+1,0)
self.pcolor(-miv(fld*100), oneside="neg")
pl.clim(-25,0)
pl.title('Percent observations where Dchl < -0.5 mg $m^{-3}$')
pl.savefig('figs/liege/freqmap_neg_Dchl.pdf')
figpref.current()
pl.close(3)
pl.figure(3)
h = np.where(self.Dsst>0.4, 1, 0)
fld = np.sum(h, axis=0).astype(np.float) / np.nansum(self.Dsst*0+1,0)
self.pcolor(miv(fld*100), oneside=True)
pl.clim(0,25)
pl.title(r'Percent observations where Dsst > 0.4 $\degree$C')
pl.savefig('figs/liege/freqmap_pos_Dsst.pdf')
pl.close(4)
pl.figure(4)
h = np.where(self.Dsst<-0.4, 1, 0)
fld = np.sum(h, axis=0).astype(np.float) / np.nansum(self.Dsst*0+1,0)
self.pcolor(-miv(fld*100), oneside="neg")
pl.clim(-25,0)
pl.title('Percent observations where Dsst < -0.4 $\degree$C')
pl.savefig('figs/liege/freqmap_neg_Dsst.pdf')
def histmoellerplot(self, fldname="Dchl"):
self.histmoeller(fldname)
figpref.current()
pl.close(1)
fig = pl.figure(1)
pl.subplots_adjust(hspace=0,right=0.85, left=0.15)
def subplot(sp, mat, vec, xvec):
ax = pl.subplot(2,1,sp, axisbg='0.8')
im =pl.pcolormesh(self.jdvec, xvec, miv(mat.T), rasterized=True,
norm=LogNorm(vmin=1,vmax=10000), cmap=WRY())
pl.plot(self.jdvec, vec, lw=2)
pl.gca().xaxis.axis_date()
pl.setp(ax, xticklabels=[])
return im
im = subplot(1, self.posmat, self.posmean, self.hpos)
im = subplot(2, self.negmat, self.negmean, -self.hpos)
pl.figtext(0.08,0.5, r'%s d$^{-1}$)' % fldname,
rotation='vertical', size=16, va='center')
cbar_ax = fig.add_axes([0.87, 0.15, 0.025, 0.7])
cb = fig.colorbar(im, cax=cbar_ax)
cb.set_label('Number of grid cells')
pl.savefig('figs/liege/histmoeller_%s.pdf' % fldname)
pl.close(2)
fig = pl.figure(2)
pl.fill_between(self.jdvec, self.jdvec*0, self.posmean,
facecolor='powderblue')
pl.fill_between(self.jdvec, self.negmean, self.jdvec*0,
facecolor='pink')
pl.plot(self.jdvec, self.negmean+self.posmean,lw=1,c='k')
#pl.title(fldname)
pl.gca().xaxis.axis_date()
pl.savefig('figs/liege/meantime_%s.pdf' % fldname)
def o2ar(self):
cr =bender_liege.cruises.all_cruises()
figpref.current()
pl.close(1)
pl.figure(1)
pl.plot(*self.cumcum(abs(np.random.rand(500000))), c='g',
lw=1, ls=":", label='Random values, rectangular distr.')
pl.plot(*self.cumcum(abs(np.random.randn(500000))),
c='r', lw=1, ls=":", label='Random values, normal distr.')
pl.plot(*self.cumcum(abs(np.random.lognormal(0,1,500000))), c='b',
lw=1, ls=":", label='Random values log-normal distr.')
vec = cr.ncp[cr.ncp>0]
pl.plot(*self.cumcum(vec), lw=2, alpha=0.5,
label="O2Ar based NCP")
pl.ylim(0,1)
pl.legend(loc='lower right')
pl.savefig('figs/liege/glob_o2ar.pdf',
transparent=True, bbox_inches='tight')
def cumcum_theory(self):
figpref.current()
pl.close(1)
pl.figure(1)
pl.plot(*self.cumcum(abs(np.random.rand(500000))),
c='g',
lw=2,
ls="-",
label='Random values, rectangular distr.')
pl.legend(loc='lower right')
pl.ylim(0, 1)
pl.savefig('figs/liege/cc_theory_rectdist.pdf',
transparent=True,
bbox_inches='tight')
pl.plot(*self.cumcum(abs(np.random.randn(500000))),
c='r',
lw=2,
ls="-",
label='Random values, normal distr.')
pl.legend(loc='lower right')
pl.ylim(0, 1)
pl.savefig('figs/liege/cc_theory_normrectdist.pdf',
transparent=True,
bbox_inches='tight')
pl.plot(*self.cumcum(abs(np.random.lognormal(0, 1, 500000))),
c='b',
lw=2,
ls="-",
label='Random values log-normal distr.')
pl.ylim(0, 1)
pl.legend(loc='lower right')
pl.savefig('figs/liege/cc_theory_lognormrectdist.pdf',
transparent=True,
bbox_inches='tight')
def latslice(self, datadir='/Volumes/keronHD3/globChl/result/'):
from scipy.io import loadmat
figpref.current()
pl.close(1)
pl.figure(1)
pl.plot(*self.cumcum(abs(np.random.rand(500000))), c='g',
lw=1, ls=":", label='Random values, rectangular distr.')
pl.plot(*self.cumcum(abs(np.random.randn(500000))),
c='r', lw=1, ls=":", label='Random values, normal distr.')
pl.plot(*self.cumcum(abs(np.random.lognormal(0,1,500000))), c='b',
lw=1, ls=":", label='Random values log-normal distr.')
for lat in [0,15,30,45,60,75]:
mat = loadmat(datadir + 'latsliceschl_lat%i.mat' % lat)
pl.plot(*self.cumcum(mat['chl']), lw=2, alpha=0.5,
label="Chl, lat=%i" % lat)
pl.ylim(0,1)
pl.legend(loc='lower right')
pl.savefig('figs/liege/glob_latslice.pdf',
transparent=True, bbox_inches='tight')
def scatter(self,mask=None, ntrac=None, jd=None, k1=None, k2=None,
c="g", alpha=1, lc='-w', lw='0.5', clf=True, coord="latlon",
land="nice", map_region=None):
"""Plot particle positions on a map
mask: Boolean vector determining which particles to plot
ntrac: Particle ID
jd: Julian date to plot, has to be included in jdvec.
k1: only plot particle deeper than k1
k2: only plot particle shallower than k1
c: color of particles (string or array)
alpha: transparancy of particles
clf: Clear figure if True
coord: Use lat-lon coordinates if set to "latlon" (default),
i-j coordinates if set to "ij"
land: Use landmask from basemap if set to "nice" (default),
landmask from model if set to "model".
map_region: Use other map_regions than set by config file
"""
if mask is None:
mask = self.ntrac==self.ntrac
if jd is not None:
mask = mask & (self.jd==jd)
if ntrac is not None:
mask = mask & (self.ntrac==ntrac)
if (type(c) != str) & (len(c) == len(mask)):
c = c[mask]
if USE_FIGPREF:
figpref.current()
if clf:
pl.clf()
"""
if coord is "latlon":
x,y = self.mp(self.lon[mask],self.lat[mask])
scH = self.mp.scatter(x, y, 5, c, lw=0, alpha=alpha)
else:
scH = pl.scatter(self.x[mask],self.y[mask], 5, c,lw=0, alpha=alpha)
"""
if coord is "latlon":
if (not hasattr(self,'mp')) & (coord=="latlon"):
self.add_mp(map_region)
if (not hasattr(self,'lon')) & (coord=="latlon"):
self.ijll()
xvec,yvec = self.mp(self.lon[mask], self.lat[mask])
scatter = self.mp.scatter
else:
xvec,yvec = self.x[mask],self.y[mask]
scatter = pl.scatter
scH = mp.scatter(xvec, yvec, 5, c, lw=0, alpha=alpha)
if land is "nice":
land = self.gcm.mp.nice()
elif coord is "latlon":
self.mp.pcolormesh(self.mpxll,
self.mpyll,
np.ma.masked_equal(self.landmask, True),
cmap=GR_CMAP)
else:
pl.pcolormesh(np.arange(self.gcm.i1, self.gcm.i2+1),
np.arange(self.gcm.j1, self.gcm.j2+1),
np.ma.masked_equal(self.landmask, True),
cmap=GR_CMAP)
pl.xlim(0, self.imt)
pl.ylim(0, self.jmt)
if (ntrac is not None) & (coord is "latlon"):
self.mp.plot(x,y,lc,lw=lw)
"""
xl,yl = self.mp(
[self.llon[0,0], self.llon[0,-1], self.llon[-1,-1],
self.llon[-1,0],self.llon[0,0]],
[self.llat[0,0], self.llat[0,-1], self.llat[-1,-1],
self.llat[-1,0], self.llat[0,0]]
)
#self.mp.plot(xl,yl,'0.5')
"""
if jd: pl.title(pl.num2date(jd).strftime("%Y-%m-%d %H:%M"))
return scH
def cumcummap(self):
figpref.current()
h5fld = self.Dchl
tlen,jmat,imat = h5fld.shape
ppp = np.zeros((jmat,imat)) * np.nan
for j in np.arange(jmat):
for i in np.arange(imat):
vec = h5fld[:,j,i]
vec = vec[vec>0]
if len(vec) > 0: ppp[j,i] = self.cumpos(vec)
pl.close(1)
pl.figure(1)
self.pcolor(miv(ppp), oneside=True)
pl.clim(0.3,0.8)
pl.title(r'Cumulative Dchl for 10% highest pos obs.')
pl.savefig('figs/liege/cumcummap_pos_Dchl.pdf')
ppp = np.zeros((jmat,imat)) * np.nan
for j in np.arange(jmat):
for i in np.arange(imat):
vec = h5fld[:,j,i]
vec = -vec[vec<0]
if len(vec) > 0: ppp[j,i] = self.cumpos(vec)
pl.close(2)
pl.figure(2)
self.pcolor(miv(ppp), oneside=True)
pl.clim(0.3,0.8)
pl.title(r'Cumulative Dchl for 10% highest neg obs.')
pl.savefig('figs/liege/cumcummap_neg_Dchl.pdf')
h5fld = self.Dsst
tlen,jmat,imat = h5fld.shape
ppp = np.zeros((jmat,imat)) * np.nan
for j in np.arange(jmat):
for i in np.arange(imat):
vec = h5fld[:,j,i]
vec = vec[vec>0]
if len(vec) > 0: ppp[j,i] = self.cumpos(vec)
pl.close(3)
pl.figure(3)
self.pcolor(miv(ppp), oneside=True)
pl.clim(0.3,0.8)
pl.title(r'Cumulative Dsst for 10% highest pos obs.')
pl.savefig('figs/liege/cumcummap_pos_Dsst.pdf')
ppp = np.zeros((jmat,imat)) * np.nan
for j in np.arange(jmat):
for i in np.arange(imat):
vec = h5fld[:,j,i]
vec = -vec[vec<0]
if len(vec) > 0: ppp[j,i] = self.cumpos(vec)
pl.close(4)
pl.figure(4)
self.pcolor(miv(ppp), oneside=True)
pl.clim(0.3,0.8)
pl.title(r'Cumulative Dsst for 10% highest neg obs.')
pl.savefig('figs/liege/cumcummap_neg_Dsst.pdf')
def scatter(self,
mask=None,
ntrac=None,
jd=None,
k1=None,
k2=None,
c="g",
clf=True,
coord="latlon",
land="nice",
map_region=None):
"""Plot particle positions on a map
mask: Boolean vector inidcating which particles to plot
ntrac: Particle ID
jd: Julian date to plot, has to be included in jdvec.
k1: only plot particle deeper than k1
k2: only plot particle shallower than k1
c: color of particles
clf: Clear figure if True
coord: Use lat-lon coordinates if set to "latlon" (default),
i-j coordinates if set to "ij"
land: Use landmask from basemap if set to "nice" (default),
landmask from model if set to "model".
map_region: Use other map_regions than set by config file
"""
if (not hasattr(self, 'mp')) & (coord == "latlon"):
self.add_mp(map_region)
if (not hasattr(self, 'lon')) & (coord == "latlon"):
self.ijll()
if mask is None:
mask = self.ntrac == self.ntrac
if jd is not None:
mask = mask & (self.jd == jd)
if ntrac is not None:
mask = mask & (self.ntrac == ntrac)
figpref.current()
if clf: pl.clf()
if coord is "latlon":
x, y = self.mp(self.lon[mask], self.lat[mask])
scH = self.mp.scatter(x, y, 5, c)
else:
scH = pl.scatter(self.x[mask], self.y[mask], 5, c)
if land is "nice":
land = self.gcm.mp.nice()
elif coord is "latlon":
self.mp.pcolormesh(self.mpxll,
self.mpyll,
np.ma.masked_equal(self.landmask, False),
cmap=GrGr())
else:
pl.pcolormesh(np.arange(self.gcm.i1, self.gcm.i2 + 1),
np.arange(self.gcm.j1, self.gcm.j2 + 1),
np.ma.masked_equal(self.landmask, False),
cmap=GrGr())
if (ntrac is not None) & (coord is "latlon"):
self.mp.plot(x, y, '-w', lw=0.5)
"""
xl,yl = self.mp(
[self.llon[0,0], self.llon[0,-1], self.llon[-1,-1],
self.llon[-1,0],self.llon[0,0]],
[self.llat[0,0], self.llat[0,-1], self.llat[-1,-1],
self.llat[-1,0], self.llat[0,0]]
)
#self.mp.plot(xl,yl,'0.5')
"""
if jd: pl.title(pl.num2date(jd).strftime("%Y-%m-%d %H:%M"))
return scH
def cumcumtot(self):
figpref.current()
pl.close(1)
pl.figure(1)
pl.plot(*self.cumcum(abs(np.random.rand(500000))),
c='g',
lw=1,
ls=":",
label='Random values, rectangular distr.')
pl.plot(*self.cumcum(abs(np.random.randn(500000))),
c='r',
lw=1,
ls=":",
label='Random values, normal distr.')
pl.plot(*self.cumcum(abs(np.random.lognormal(0, 1, 500000))),
c='b',
lw=1,
ls=":",
label='Random values log-normal distr.')
vec = self.Dsst[~np.isnan(self.Dsst)]
pl.plot(*self.cumcum(vec[vec > 0]),
lw=2,
alpha=0.5,
label="SST, positive values")
pl.ylim(0, 1)
pl.legend(loc='lower right')
pl.savefig('figs/liege/cumcumtot_pos_sst.pdf',
transparent=True,
bbox_inches='tight')
pl.plot(*self.cumcum(-vec[vec < 0]),
lw=2,
alpha=0.5,
label="SST, negative values")
pl.ylim(0, 1)
pl.legend(loc='lower right')
pl.savefig('figs/liege/cumcumtot_posneg_sst.pdf',
transparent=True,
bbox_inches='tight')
vec = self.Dchl[~np.isnan(self.Dchl)]
pl.plot(*self.cumcum(vec[vec > 0]),
lw=2,
alpha=0.5,
label="Chl, positive values")
pl.ylim(0, 1)
pl.legend(loc='lower right')
pl.savefig('figs/liege/cumcumtot_posneg_sst_pos_chl.pdf',
transparent=True,
bbox_inches='tight')
pl.plot(*self.cumcum(-vec[vec < 0]),
lw=2,
alpha=0.5,
label="Chl, negative values")
pl.ylim(0, 1)
pl.legend(loc='lower right')
pl.savefig('figs/liege/cumcumtot_posneg_sst_posneg_chl.pdf',
transparent=True,
bbox_inches='tight')
pl.savefig('figs/liege/cumcumtot.pdf')
def scatter(self,mask=None, ntrac=None, jd=None, k1=None, k2=None,
c="g", clf=True, coord="latlon", land="nice", map_region=None):
"""Plot particle positions on a map
mask: Boolean vector inidcating which particles to plot
ntrac: Particle ID
jd: Julian date to plot, has to be included in jdvec.
k1: only plot particle deeper than k1
k2: only plot particle shallower than k1
c: color of particles
clf: Clear figure if True
coord: Use lat-lon coordinates if set to "latlon" (default),
i-j coordinates if set to "ij"
land: Use landmask from basemap if set to "nice" (default),
landmask from model if set to "model".
map_region: Use other map_regions than set by config file
"""
if (not hasattr(self,'mp')) & (coord=="latlon"):
self.add_mp(map_region)
if (not hasattr(self,'lon')) & (coord=="latlon"):
self.ijll()
if mask is None:
mask = self.ntrac==self.ntrac
if jd is not None:
mask = mask & (self.jd==jd)
if ntrac is not None:
mask = mask & (self.ntrac==ntrac)
figpref.current()
if clf: pl.clf()
if coord is "latlon":
x,y = self.mp(self.lon[mask],self.lat[mask])
scH = self.mp.scatter(x, y, 5, c)
else:
scH = pl.scatter(self.x[mask], self.y[mask], 5, c)
if land is "nice":
land = self.gcm.mp.nice()
elif coord is "latlon":
self.mp.pcolormesh(self.mpxll,
self.mpyll,
np.ma.masked_equal(self.landmask, False),
cmap=GrGr())
else:
pl.pcolormesh(np.arange(self.gcm.i1, self.gcm.i2+1),
np.arange(self.gcm.j1, self.gcm.j2+1),
np.ma.masked_equal(self.landmask, False),
cmap=GrGr())
if (ntrac is not None) & (coord is "latlon"):
self.mp.plot(x,y,'-w',lw=0.5)
"""
xl,yl = self.mp(
[self.llon[0,0], self.llon[0,-1], self.llon[-1,-1],
self.llon[-1,0],self.llon[0,0]],
[self.llat[0,0], self.llat[0,-1], self.llat[-1,-1],
self.llat[-1,0], self.llat[0,0]]
)
#self.mp.plot(xl,yl,'0.5')
"""
if jd: pl.title(pl.num2date(jd).strftime("%Y-%m-%d %H:%M"))
return scH