from mpl_toolkits.basemap import Basemap
from mpl_toolkits.basemap import cm, maskoceans
#from pygeode.plot import plot_v1 as pl
#from pygeode.plot import basemap as bm
## settings
nax = 2 # number of panels
ndom = 2
sf = dict(dpi=150) # print properties
folder = '/home/me/Research/Dynamical Downscaling/figures/' # figure directory
if __name__ == '__main__':
## read data
data = openWRF('ctrl-1',[1982],range(11,12))
print data[ndom-1]
## setup projection
f = pyl.figure(facecolor='white', figsize = (6.25,4.25))
ax = []
for n in xrange(nax):
ax.append(f.add_subplot(1,2,n+1))
f.subplots_adjust(bottom=0.12, left=0.06, right=.97, top=.94, hspace=0.05, wspace=0.05) # hspace, wspace
# setup lambert conformal basemap.
# lat_1 is first standard parallel.
# lat_2 is second standard parallel (defaults to lat_1).
# lon_0,lat_0 is central point.
# rsphere=(6378137.00,6356752.3142) specifies WGS4 ellipsoid
# area_thresh=1000 means don't plot coastline features less
# than 1000 km^2 in area.
#subplots = (1,2)
#figsize = (6.25,4.25)
subplot = (2,2)
figsize = (6.25,6.25)
figtitle = 'Annual Average Precipitation [mm/day]'
axtitles = ['WRF Control','WRF Grell-3','CFSR Climatology', 'NARR Climatology']
margins = dict(bottom=0.025, left=0.065, right=.885, top=.925, hspace=0.05, wspace=0.05)
caxpos = [0.91, 0.05, 0.03, 0.9]
sf = dict(dpi=150) # print properties
folder = '/home/me/Research/Dynamical Downscaling/figures/' # figure directory
if __name__ == '__main__':
# load WRF data
domains = [1]; ndom = len(domains) # WRF domain
way2, = openWRF(exp='2way-ctrl',years=[1983],months=[3], domains=domains)
grell3, = openWRF(exp='grell3-ctrl',years=[1981],months=[2], domains=domains)
ctrl1, = openWRF(exp='ctrl-1',years=[1985],months=[5], domains=domains)
ensA, = openWRF(exp='ens-A-ctrl',years=[1985],months=[5], domains=domains)
WRF = (ctrl1, grell3, )
# load CFSR data
CFSR = openCFSRclim()
# load NARR data
NARR = openNARRclim() # just load whole climatology
# print NARR
## compute data
data = []; lon = []; lat=[] # list of data and coordinate fields to be plotted
# compute average WRF precip
for exp in WRF:
nrec = exp.time.values[-1]+1
from mpl_toolkits.basemap import Basemap
from mpl_toolkits.basemap import cm, maskoceans
#from pygeode.plot import plot_v1 as pl
#from pygeode.plot import basemap as bm
## settings
nax = 2 # number of panels
ndom = 2
sf = dict(dpi=150) # print properties
folder = '/home/me/Research/Dynamical Downscaling/figures/' # figure directory
if __name__ == '__main__':
## read data
data = openWRF('ctrl-1', [1982], range(11, 12))
print data[ndom - 1]
## setup projection
f = pyl.figure(facecolor='white', figsize=(6.25, 4.25))
ax = []
for n in xrange(nax):
ax.append(f.add_subplot(1, 2, n + 1))
f.subplots_adjust(bottom=0.12,
left=0.06,
right=.97,
top=.94,
hspace=0.05,
wspace=0.05) # hspace, wspace
# setup lambert conformal basemap.
# lat_1 is first standard parallel.
from mpl_toolkits.basemap import Basemap
from mpl_toolkits.basemap import cm, maskoceans
#from pygeode.plot import plot_v1 as pl
#from pygeode.plot import basemap as bm
## settings
nax = 2 # number of panels
ndom = 2
sf = dict(dpi=150) # print properties
folder = '/home/me/Research/Dynamical Downscaling/figures/' # figure directory
if __name__ == '__main__':
## read data
data = openWRF('ctrl-1',[1982],list(range(11,12)))
print(data[ndom-1])
## compute data
precip = []; ndays = []
for n in range(ndom):
nrec = data[n].time.values[-1]+1
ndays = data[n].xtime(time=nrec-1).get() /24/60 # xtime is in minutes, need days
dailyrain = data[n].rain(time=nrec-1).get() / ndays
# ndays = ( data[n].xtime(time=nrec-1).get() - data[n].xtime(time=0).get() )/24/60 # xtime is in minutes, need days
# dailyrain = ( data[n].rain(time=nrec-1).get() - data[n].rain(time=0).get() ) / ndays
precip.append(dailyrain.squeeze())
## setup projection
f = pyl.figure(facecolor='white', figsize = (6.25,4.25))
ax = []
for n in range(nax):
margins = dict(bottom=0.025,
left=0.065,
right=.885,
top=.925,
hspace=0.05,
wspace=0.05)
caxpos = [0.91, 0.05, 0.03, 0.9]
sf = dict(dpi=150) # print properties
folder = '/home/me/Research/Dynamical Downscaling/figures/' # figure directory
if __name__ == '__main__':
# load WRF data
domains = [1]
ndom = len(domains) # WRF domain
way2, = openWRF(exp='2way-ctrl', years=[1983], months=[3], domains=domains)
grell3, = openWRF(exp='grell3-ctrl',
years=[1981],
months=[2],
domains=domains)
ctrl1, = openWRF(exp='ctrl-1', years=[1985], months=[5], domains=domains)
ensA, = openWRF(exp='ens-A-ctrl',
years=[1985],
months=[5],
domains=domains)
WRF = (
ctrl1,
grell3,
)
# load CFSR data
CFSR = openCFSRclim()
## figure settings
#subplot = (2,2)
#figsize = (6.25,6.25)
figtitle = 'Annual Average Precipitation [mm/day]'
axtitles = ['WRF Ctrl-1 (GPC)', 'WRF Ctrl-1 (TCS)']
sf = dict(dpi=150) # print properties
folder = '/home/me/Research/Dynamical Downscaling/figures/' # figure directory
if __name__ == '__main__':
# load WRF data
domains = [2]; ndom = len(domains) # WRF domain
# way2, = openWRF(exp='2way-ctrl',years=[1982],months=[9], domains=domains)
# grell3, = openWRF(exp='grell3-ctrl',years=[1980],months=[8], domains=domains)
# ctrl1, = openWRF(exp='ctrl-1',years=[1984],months=[7], domains=domains)
ctrl1, = openWRF(exp='ctrl-1',years=[1980],months=[12], domains=domains)
ctrl2, = openWRF(exp='ctrl-2',years=[1980],months=[12], domains=domains)
WRF = (ctrl1, ctrl2)
# load NARR data
NARR = openNARR() # just load whole climatology
print NARR
## compute data
data = []; lon = []; lat=[] # list of data and coordinate fields to be plotted
# compute average WRF precip
for exp in WRF:
nrec = exp.time.values[-1]+1
ndays = exp.xtime(time=nrec-1).get() /24/60 # xtime is in minutes, need days
dailyrain = exp.rain(time=nrec-1).get().squeeze() / ndays
data.append(dailyrain)
lon.append(exp.lon.get()); lat.append(exp.lat.get())
#subplots = (1,2)
#figsize = (6.25,4.25)
subplot = (2,2)
figsize = (6.25,6.25)
figtitle = 'Annual Average Precipitation [mm/day]'
axtitles = ['WRF 2-way nested','WRF Grell-3','WRF Control', 'NARR Climatology']
margins = dict(bottom=0.025, left=0.065, right=.885, top=.925, hspace=0.05, wspace=0.05)
caxpos = [0.91, 0.05, 0.03, 0.9]
sf = dict(dpi=150) # print properties
folder = '/home/me/Research/Dynamical Downscaling/figures/' # figure directory
if __name__ == '__main__':
# load WRF data
domains = [1]; ndom = len(domains) # WRF domain
way2, = openWRF(exp='2way-ctrl',years=[1982],months=[9], domains=domains)
grell3, = openWRF(exp='grell3-ctrl',years=[1980],months=[8], domains=domains)
ctrl1, = openWRF(exp='ctrl-1',years=[1984],months=[7], domains=domains)
WRF = (way2, grell3, ctrl1)
# load NARR data
NARR = openNARR() # just load whole climatology
print NARR
## compute data
data = []; lon = []; lat=[] # list of data and coordinate fields to be plotted
# compute average WRF precip
for exp in WRF:
nrec = exp.time.values[-1]+1
ndays = exp.xtime(time=nrec-1).get() /24/60 # xtime is in minutes, need days
dailyrain = exp.rain(time=nrec-1).get().squeeze() / ndays
data.append(dailyrain)
#subplot = (2,2)
#figsize = (6.25,6.25)
figtitle = 'Annual Average Precipitation [mm/day]'
axtitles = ['WRF Ctrl-1 (GPC)', 'WRF Ctrl-1 (TCS)']
sf = dict(dpi=150) # print properties
folder = '/home/me/Research/Dynamical Downscaling/figures/' # figure directory
if __name__ == '__main__':
# load WRF data
domains = [2]
ndom = len(domains) # WRF domain
# way2, = openWRF(exp='2way-ctrl',years=[1982],months=[9], domains=domains)
# grell3, = openWRF(exp='grell3-ctrl',years=[1980],months=[8], domains=domains)
# ctrl1, = openWRF(exp='ctrl-1',years=[1984],months=[7], domains=domains)
ctrl1, = openWRF(exp='ctrl-1', years=[1980], months=[12], domains=domains)
ctrl2, = openWRF(exp='ctrl-2', years=[1980], months=[12], domains=domains)
WRF = (ctrl1, ctrl2)
# load NARR data
NARR = openNARR() # just load whole climatology
print NARR
## compute data
data = []
lon = []
lat = [] # list of data and coordinate fields to be plotted
# compute average WRF precip
for exp in WRF:
nrec = exp.time.values[-1] + 1
ndays = exp.xtime(time=nrec -
1).get() / 24 / 60 # xtime is in minutes, need days
margins = dict(bottom=0.025,
left=0.065,
right=.885,
top=.925,
hspace=0.05,
wspace=0.05)
caxpos = [0.91, 0.05, 0.03, 0.9]
sf = dict(dpi=150) # print properties
folder = '/home/me/Research/Dynamical Downscaling/figures/' # figure directory
if __name__ == '__main__':
# load WRF data
domains = [1]
ndom = len(domains) # WRF domain
way2, = openWRF(exp='2way-ctrl', years=[1982], months=[9], domains=domains)
grell3, = openWRF(exp='grell3-ctrl',
years=[1980],
months=[8],
domains=domains)
ctrl1, = openWRF(exp='ctrl-1', years=[1984], months=[7], domains=domains)
WRF = (way2, grell3, ctrl1)
# load NARR data
NARR = openNARR() # just load whole climatology
print NARR
## compute data
data = []
lon = []
lat = [] # list of data and coordinate fields to be plotted
# compute average WRF precip
from myPlots.plots import surfacePlot
from mpl_toolkits.basemap import Basemap
from mpl_toolkits.basemap import cm, maskoceans
#from pygeode.plot import plot_v1 as pl
#from pygeode.plot import basemap as bm
## settings
sf = dict(dpi=150) # print properties
folder = '/home/me/Research/Dynamical Downscaling/figures/' # figure directory
if __name__ == '__main__':
## read data
data1, data2 = openWRF('ctrl-1',[1981],[1,2,12])
print data1
## compute data
# precip1 = data1.rain(time=)
# precip2 =
## setup projection
f = pyl.figure(facecolor='white', figsize = (6.25,4.25))
ax = f.add_subplot(1,1,1)
f.subplots_adjust(bottom=0.05, left=0.05, right=.93, top=.93) # hspace, wspace
# setup lambert conformal basemap.
# lat_1 is first standard parallel.
# lat_2 is second standard parallel (defaults to lat_1).
# lon_0,lat_0 is central point.
# rsphere=(6378137.00,6356752.3142) specifies WGS4 ellipsoid
# area_thresh=1000 means don't plot coastline features less
