def plot_regions(regions, ptype='nh', colors='k', latlim=None, drawgrid=False):
""" plot multiple region boundaries on one map
regions: tuple of region names
default color = black
colors can be a tuple of color specifications.
index will wrap if not enough
"""
dummy = con.get_t63landmask()
lat = con.get_t63lat()
lon = con.get_t63lon()
lons,lats = np.meshgrid(lon,lat)
dummy = dummy*np.nan
plt.figure()
bm,pc = kemmap(dummy,lat,lon,ptype=ptype,latlim=latlim,suppcb=1,
drawgrid=drawgrid, lmask=True)
for ii,reg in enumerate(regions):
if len(colors)==1: clr=colors
else:
if ii==len(colors): ii=0 # wrap index
clr=colors[ii]
add_regionpolym(reg,bm, ec=clr)
def plot_region(regname,ptype='nh',axis=None,latlim=None,limsdict=None):
""" plot_region(regname,ptype='nh',axis=None,latlim=None,limsdict=None):
Given a region name, plot it for reference.
latlims is unused right now
if passing limsdict (to override regname), set regname='other'
"""
if regname=='other':
reglims=limsdict
else:
reglims = con.get_regionlims(regname)
latlims = reglims['latlims']
lonlims = reglims['lonlims']
dummy = con.get_t63landmask()
lat = con.get_t63lat()
lon = con.get_t63lon()
lons,lats = np.meshgrid(lon,lat)
reglatsbool = np.logical_and(lat>latlims[0],lat<latlims[1])
reglonsbool = np.logical_and(lon>lonlims[0],lon<lonlims[1])
# mask everything but the region of interest
regmask = np.logical_or(
np.logical_or(lats<latlims[0],lats>latlims[1]),
np.logical_or(lons<lonlims[0],lons>lonlims[1]))
dummym = ma.masked_where(regmask,dummy)
plt.figure()
kemmap(dummym,lat,lon,ptype=ptype,axis=axis,latlim=latlim,suppcb=1,
cmin=-11,cmax=2,cmap='blue2blue_w10',drawgrid=True)
def calc_areaweight_mean_ds(ds,dim=None, mask=None, model='CanESM2'):
""" Calculates area-weighted mean of the input Dataset (or DataArray?? test)
If the input ds is a subregion, this should still work. (tested with polcap60)
mask: ['land' | 'ocean'] (mask='land' means mask OUT land)
Lakes are not included in land or ocean.
@@this is hard-coded to CanESM2. @
return area-weighted values in a Dataset (or DataArray?? test)
"""
aweights = get_areaweights_ds(ds)
if mask!=None:
import constants as con
if model=='CanESM2':
lmask = xr.DataArray(con.get_t63landmask()[:,:-1],
dims=('lat','lon'),
coords=[ds.lat,ds.lon],name='lmask')
#print lmask
else:
raise Exception('No landmask defined for model ' + model)
if mask=='land': mval = -1;
elif mask=='ocean': mval=0
#awgtsub = aweights.values
#awgtsub[lmask!=mval] = np.nan
#awgtsub = awgtsub / np.float(np.nansum(awgtsub))# normalize weights
#aweights.values = awgtsub
#print ds.where(lmask!=mval).values.shape
#ds.where(lmask!=mval)
#dssub = ds.values
#dssub[lmask!=mval] = np.nan
#ds.values = dssub
if dim!=None:
if mask!=None:
aw=aweights.where(lmask!=mval) / aweights.where(lmask!=mval).sum()
return (aw * ds.where(lmask!=mval)).sum(dim=dim)
else:
return (aweights * ds).sum(dim=dim)
else:
if mask!=None:
aw=aweights.where(lmask!=mval) / aweights.where(lmask!=mval).sum()
return (aw * ds.where(lmask!=mval)).sum()
else:
return (aweights * ds).sum()
def plot_allregions(ptype='nh'):
""" plot_allregions(ptype='nh'): plot all defined regions
"""
regdict = con.get_regiondict()
nreg = len(regdict)
rem = np.mod(nreg,2)
rows = 2
if rem==0:
cols = nreg/rows
else:
cols = nreg/rows + rem # rem will always be 1 if dividing by 2
if cols>8:
rows = 3
cols = nreg/rows + np.mod(nreg,rows)/2
print 'nrows: ' + str(rows) + ' ncols: ' + str(cols) # @@@
lat = con.get_t63lat()
lon = con.get_t63lon()
fig,spax = plt.subplots(rows,cols)
fig.set_size_inches(cols*2,rows*3)
for aii,ax in enumerate(spax.flat):
dummy = con.get_t63landmask() # dummy data
regkey = regdict.keys()[aii]
limsdict = regdict[regkey]
# mask the dummy data
dummym,dmask = cutl.mask_region(dummy,lat,lon,regkey,limsdict=limsdict)
kemmap(dummym,lat,lon,ptype=ptype,axis=ax,suppcb=1,cmin=-11,cmax=2,cmap='blue2blue_w10')
ax.set_title(regkey)
ax.set_xlabel(str(limsdict['latlims']) + ',' +str(limsdict['lonlims']) )
if aii==nreg-1: break # get out of loop if done with regions
cansicp,cansicpstd = cutl.climatologize(cnc.getNCvar(fcansicp,'GT'))
hurrsicc = cnc.getNCvar(fhurrsic,'SST')+273
#hurrsicc.resize(hadsicc.shape) # can't resize like this. ??
#np.append(hurrsicc,hurrsicc[:,:,0])#,axis=2) # could not get append to work
#hurrsicc[:,:,len(lon)-1] = hurrsicc[:,:,0] # add a wraparound. nope.
#hurrsicc = np.flipud(hurrsicc) # the lats are flipped compared to hadisst and nsidc
hurrsicp = cnc.getNCvar(fhurrsicp,'SST')+273
#hurrsicc.resize(hadsicc.shape) # other datasets have extra lon.
#hurrsicp[:,:,len(lon)-1] = hurrsicp[:,:,0] # add a wraparound
#hurrsicp = np.flipud(hurrsicp) # the lats are flipped compared to hadisst and nsidc
# MASK out land & inland lakes
lsmask=con.get_t63landmask()
lsmask = np.tile(lsmask,(12,1,1))
## if flipmask:
## lsmask=np.flipud(lsmask)
hadsicc = ma.masked_where(lsmask!=0,hadsicc) # 0 is ocean
hadsicp = ma.masked_where(lsmask!=0,hadsicp) # 0 is ocean
hadsicc = hadsicc[:,:,0:-1]
hadsicp = hadsicp[:,:,0:-1]
#hurrsicc = ma.masked_where(lsmask!=0,hurrsicc) # 0 is ocean # @@ BOGUS
cansicc = ma.masked_where(lsmask!=0,cansicc) # 0 is ocean
cansicp = ma.masked_where(lsmask!=0,cansicp) # 0 is ocean
cansicc = cansicc[:,:,0:-1]
cansicp = cansicp[:,:,0:-1]
hadsicd = hadsicp-hadsicc
axis=0)
if sia==1:
fldc = cutl.calc_seaicearea(fldc,lat,lon)
fldp = cutl.calc_seaicearea(fldp,lat,lon)
# Prepare the data here: @@
# @@ from hists script:
areas = cutl.calc_cellareas(lat,lon,repeat=fldc.shape)
areas = areas[:,lat>latlim,:]
fldcorig=copy.copy(fldc)
fldporig=copy.copy(fldp)
fldc = fldc[:,lat>latlim,:]
fldp = fldp[:,lat>latlim,:]
if maskland:
lmask = con.get_t63landmask(repeat=fldc.shape)
areas = ma.masked_where(lmask[:,lat>latlim,:]==-1,areas)
fldc = ma.masked_where(lmask[:,lat>latlim,:]==-1,fldc)
fldp = ma.masked_where(lmask[:,lat>latlim,:]==-1,fldp)
mtype='ocn'
elif maskocean:
lmask = con.get_t63landmask(repeat=fldc.shape)
areas = ma.masked_where(lmask[:,lat>latlim,:]!=-1,areas)
fldc = ma.masked_where(lmask[:,lat>latlim,:]!=-1,fldc)
fldp = ma.masked_where(lmask[:,lat>latlim,:]!=-1,fldp)
mtype='lnd'
totarea = np.sum(np.sum(areas,axis=2),axis=1)
totarea = np.tile(totarea,(fldc.shape[1],fldc.shape[2],1))
totarea = np.transpose(totarea,(2,0,1))
weights = areas/totarea
def kemmap(fld, lat, lon,title='',units='',cmap='blue2red_w20',ptype='sq',
cmin='',cmax='',axis=None, suppcb=0,lmask=0,flipmask=0,latlim=None,drawgrid=False,
round=True,lcol='0.7',coastres='c',coastwidth=1,area_thresh=10000,panellab=None):
""" returns bm,pc (Basemap,Pcolor handle)
"""
if cmap =='' or cmap==None:
cmap='blue2red_w20'
incmap = plt.cm.get_cmap(cmap)
# default Basemap dictionary
if ptype == 'sq':
mapparams = dict(projection='robin',lon_0=180,lat_0=0,
resolution=coastres,area_thresh=area_thresh)
elif ptype == 'sqshift':
mapparams = dict(projection='robin',lon_0=0,lat_0=0,
resolution=coastres,area_thresh=area_thresh)
elif ptype == 'nh' or ptype=='nheur' or ptype=='nhkug':
if ptype=='nheur':
lon0 = 90.
elif ptype=='nhkug':
lon0 = 180.
else:
lon0=0.
if latlim != None: # try 'round=True' !@@@
mapparams = dict(projection='npstere',boundinglat=latlim,lon_0=lon0,
resolution=coastres,area_thresh=area_thresh)
if round==True:
mapparams['round'] = True
else:
# try mill, hammer, merc
mapparams = dict(projection='ortho',lon_0=lon0,lat_0=89.5,\
resolution=coastres,area_thresh=area_thresh) #llcrnrlon='-180',llcrnrlat='45',urcrnrlon='180',urcrnrlat='90'
# I thought the above corner limits would work to zoom on NH but I'm getting
# AttributeError: 'Basemap' object has no attribute '_height'
# 5/12/14 -- don't know why. same goes for lat_0=0.
# 10/13/2015: changed lat_0 to 89.5 from 90. b/c .eps figure file wouldn't save
elif ptype == 'sh':
if latlim != None: # try 'round=True' !@@@
mapparams = dict(projection='spstere',boundinglat=latlim,lon_0=0,
resolution=coastres,area_thresh=area_thresh)
if round==True:
mapparams['round'] = True
else:
mapparams = dict(projection='ortho',lon_0=0.,lat_0=-89.5,
resolution=coastres,area_thresh=area_thresh)
# same error if add: llcrnrlon='-180',llcrnrlat='-90',urcrnrlon='180',urcrnrlat='-45'
elif ptype == 'eastere': # Eurasia stere projection
#mapparams = dict(width=2500000,height=2700000,resolution='i',projection='laea',\
# lat_ts=62.5,lat_0=62.5,lon_0=77.0)
mapparams = dict(llcrnrlon=40.,llcrnrlat=10.,urcrnrlon=160.,urcrnrlat=50.,
resolution=coastres,area_thresh=area_thresh,projection='stere',lat_0=45.,lon_0=80.)
#mapparams = dict(width=3000000,height=3000000,resolution='c',projection='laea',\
# lat_0=55.,lon_0=80.)# can't get width/height big enough -- errors
elif ptype == 'eabksstere': # Eurasia + Barents Kara attempt
mapparams = dict(llcrnrlon=40.,llcrnrlat=10.,urcrnrlon=175.,urcrnrlat=60.,
resolution=coastres,area_thresh=area_thresh,projection='stere',lat_0=45.,lon_0=80.)
elif ptype == 'ealamb': # Lambert azimuthal equal-area
mapparams = dict(llcrnrlon=40.,llcrnrlat=10.,urcrnrlon=160.,urcrnrlat=50.,
resolution=coastres,area_thresh=area_thresh,projection='laea',lat_0=45.,lon_0=80.)
elif ptype == 'eabkslamb': # Lambert azimuthal equal-area
mapparams = dict(llcrnrlon=40.,llcrnrlat=10.,urcrnrlon=175.,urcrnrlat=60.,
resolution=coastres,area_thresh=area_thresh,projection='laea',lat_0=45.,lon_0=80.)
elif ptype == 'nastere': # North America stere projection
mapparams = dict(llcrnrlon=220.,llcrnrlat=20.,urcrnrlon=320.,urcrnrlat=50.,
resolution=coastres,area_thresh=area_thresh,projection='stere',lat_0=45.,lon_0=230.)
else:
print "Incorrect map ptype. Choose sq,nh,nheur,nhkug,sh,nastere,eastere,eabksstere,ealamb,eabkslamb"
return -1
# default pcolormesh dictionary
if cmin =='':
#pcparams = dict(shading='gouraud',latlon=True,cmap=incmap)
pcparams = dict(latlon=True,cmap=incmap)
else:
cmlen=float(incmap.N) # or: from __future__ import division
if cmlen>200:
cmlen = float(20) # ie. if using a built in colormap that is continuous, want smaller # of colors
incr = (cmax-cmin) /cmlen
conts = np.arange(cmin,cmax+incr,incr)
#pcparams = dict(shading='gouraud',latlon=True,cmap=incmap,vmin=cmin,vmax=cmax)
pcparams = dict(latlon=True,cmap=incmap,vmin=cmin,vmax=cmax)
if ptype not in ('eastere','eabksstere','nastere','ealamb','eabkslamb'):
pcparams['levels']=conts
pcparams['extend']='both'
if axis != None: # if an axis is given, add to dict for basemap
mapparams['ax'] = axis
if ptype in ('eastere','eabksstere','nastere','ealamb','eabkslamb'):
pcparams['shading']='flat' #'gouraud' # @@ gouraud pdf files fail/crash. flat and interp are same??
""" m = Basemap(projection='ortho',lon_0=lon_0,lat_0=lat_0,resolution='l',\
llcrnrx=0.,llcrnry=0.,urcrnrx=m1.urcrnrx/2.,urcrnry=m1.urcrnry/2.)
"""
bm = Basemap(**mapparams)
#if np.mod(lon.shape[0],2) == 0:
if np.mod(len(lon),2) == 0:
# add cyclic lon
fld,lon = mpltk.basemap.addcyclic(fld,lon)
if lmask==1: # the landmask has an extra lon already
# add land mask
lsmask=con.get_t63landmask()
if flipmask:
lsmask=np.flipud(lsmask)
#@@@fld = ma.masked_where(lsmask!=0,fld) # 0 is ocean
lons, lats = np.meshgrid(lon,lat)
# pc = bm.pcolormesh(lons,lats,fld,**pcparams)
if cmin=='':
if ptype in ('eastere','eabksstere','nastere','ealamb','eabkslamb'):
# for some reason these projections don't plot colors correctly so have to use pcolormesh()@@@
pc=bm.pcolormesh(lons,lats,fld,**pcparams)
else:
pc = bm.contourf(lons,lats,fld,20,**pcparams) # 20 auto levels
else:
if ptype in ('eastere','eabksstere','nastere','ealamb','eabkslamb'):
pc=bm.pcolormesh(lons,lats,fld,**pcparams)
else:
#print '@@@@@@@@@@@ ' + str(pcparams)
pc = bm.contourf(lons,lats,fld,**pcparams)
if drawgrid:
bm.drawparallels(np.arange(-90.,90.,20.),labels=[1,0,0,0])
bm.drawmeridians(np.arange(0.,360.,30.),labels=[0,0,0,1])
bm.drawcoastlines(color=lcol,linewidth=coastwidth)
#bm.drawmapboundary(fill_color='#99ffff')
if lmask:
#@@@bm.drawmapboundary(fill_color='0.7')
if float(lcol)>0.7:
bm.drawcoastlines(color='.5',linewidth=coastwidth)
else:
bm.drawcoastlines(color='.3',linewidth=coastwidth)
bm.fillcontinents(color=lcol)
# I think drawlsmask puts the mask on the bottom
#bm.drawlsmask(land_color='0.7',lsmask=con.get_t63landmask(),ax=axis)
if axis!=None:
axis.set_title(title,fontsize=10)
if panellab!=None:
axis.annotate(panellab,xy=(0.01,1.02),
xycoords='axes fraction',fontsize=18,fontweight='bold')
else:
plt.title(title,fontsize=10)
if panellab!=None:
plt.annotate(panellab,xy=(0.01,1.02),
xycoords='axes fraction',fontsize=18,fontweight='bold')
# add colorbar.
if suppcb == 0:
cbar = bm.colorbar(pc,location='bottom',pad="5%")
cbar.set_label(units)
return bm,pc