def test_2_points_should_work(self):
"""
Shiftdata should work with 2 points
"""
bm = Basemap(llcrnrlon=0, llcrnrlat=-80, urcrnrlon=360, urcrnrlat=80, projection='mill')
lons_expected = [10, 15, 20]
lonsout = bm.shiftdata(lons_expected[:])
assert_almost_equal(lons_expected, lonsout)
lonsout_expected = bm.shiftdata([10, 361, 362])
lonsout = bm.shiftdata([10, 361])
assert_almost_equal(lonsout_expected[:len(lonsout)], lonsout)
def test_less_than_n_by_3_points_should_work(self):
bm = Basemap(llcrnrlon=0, llcrnrlat=-80, urcrnrlon=360, urcrnrlat=80, projection='mill')
lons_expected = self._get_2d_lons([10, 15, 20])
# nothing should change
lonsout = bm.shiftdata(lons_expected)
assert_almost_equal(lons_expected, lonsout)
# shift n x 3 and n x 2 grids and compare results over overlapping region
lonsin = self._get_2d_lons([10, 361, 362])
lonsout_expected = bm.shiftdata(lonsin[:])[:, :2]
lonsout = bm.shiftdata(lonsin[:, :2])
assert_almost_equal(lonsout_expected, lonsout)
def test_less_than_n_by_3_points_should_work(self):
bm = Basemap(llcrnrlon=0, llcrnrlat=-80, urcrnrlon=360, urcrnrlat=80, projection='mill')
lons_expected = self._get_2d_lons([10, 15, 20])
# nothing should change
lonsout = bm.shiftdata(lons_expected)
assert_almost_equal(lons_expected, lonsout)
# shift n x 3 and n x 2 grids and compare results over overlapping region
lonsin = self._get_2d_lons([10, 361, 362])
lonsout_expected = bm.shiftdata(lonsin[:])[:, :2]
lonsout = bm.shiftdata(lonsin[:, :2])
assert_almost_equal(lonsout_expected, lonsout)
def test_2_points_should_work(self):
"""
Shiftdata should work with 2 points
"""
bm = Basemap(llcrnrlon=0, llcrnrlat=-80, urcrnrlon=360, urcrnrlat=80, projection='mill')
lons_expected = [10, 15, 20]
lonsout = bm.shiftdata(lons_expected[:])
assert_almost_equal(lons_expected, lonsout)
lonsout_expected = bm.shiftdata([10, 361, 362])
lonsout = bm.shiftdata([10, 361])
assert_almost_equal(lonsout_expected[:len(lonsout)], lonsout)
def test_1_point_should_work(self):
bm = Basemap(llcrnrlon=0, llcrnrlat=-80, urcrnrlon=360, urcrnrlat=80, projection='mill')
# should not fail
lonsout = bm.shiftdata([361])
assert_almost_equal(lonsout, [1.0,])
lonsout = bm.shiftdata([10])
assert_almost_equal(lonsout, [10.0,])
lonsin = np.array([361.0])
lonsin.shape = (1, 1)
lonsout = bm.shiftdata(lonsin[:])
assert_almost_equal(lonsout.squeeze(), [1.0,])
def test_1_point_should_work(self):
bm = Basemap(llcrnrlon=0, llcrnrlat=-80, urcrnrlon=360, urcrnrlat=80, projection='mill')
# should not fail
lonsout = bm.shiftdata([361])
assert_almost_equal(lonsout, [1.0,])
lonsout = bm.shiftdata([10])
assert_almost_equal(lonsout, [10.0,])
lonsin = np.array([361.0])
lonsin.shape = (1, 1)
lonsout = bm.shiftdata(lonsin[:])
assert_almost_equal(lonsout.squeeze(), [1.0,])
def plot_global_winds(nt,
fig,
Uin,
Vin,
lon1,
lat1,
datelabel,
fname_out=False):
print nt
fig = plt.figure(figsize=(8, 4.5))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
#nframe = 0; n1 = 390
#nt = 0
pos = ax.get_position()
l, b, w, h = pos.bounds
map = Basemap(projection='cyl',llcrnrlat= -90.,urcrnrlat= 90.,\
resolution='c', llcrnrlon=-180.,urcrnrlon=180.)
map.bluemarble()
map.drawcoastlines()
map.drawcountries()
map.drawcoastlines(color='0.15')
map.drawparallels(np.arange(-90., 91., 30.),
labels=[1, 0, 0, 0],
fontsize=10,
linewidth=0.001,
zorder=0)
map.drawmeridians(np.arange(-180., 180., 30.),
labels=[0, 0, 0, 1],
fontsize=10,
linewidth=0.001,
zorder=0)
clevs = np.arange(0, 50, 5)
lonout, uu = map.shiftdata(lon1, Uin, lon_0=0)
lonout, vv = map.shiftdata(lon1, Vin, lon_0=0)
speed = np.sqrt(uu * uu + vv * vv)
yy = np.arange(0, Y4.shape[0], 4)
xx = np.arange(0, X4.shape[1], 4)
points = np.meshgrid(yy, xx)
vectplot = map.quiver(X4[points],
Y4[points],
uu[points],
vv[points],
color='lightgray')
plt.quiverkey(vectplot,
0.1,
-0.1,
10,
'10 m/s',
fontproperties={'weight': 'bold'},
labelpos='W',
color='0.15') #-- position and reference label
txt = plt.title('10-m Wind ' + str(datelabel),
fontsize=14,
fontweight='bold')
if fname_out:
fig.savefig(fname_out, dpi=100)
def test_shiftdata_on_monotonous_lons(self):
"""
Test that shiftdata with fix_wrap_around keyword added works as before,
when it is True
"""
bm = Basemap(lon_0=0)
lons_in = [120, 140, 160, 180, 200, 220]
lons_out_expect = [-160, -140, 120, 140, 160, 180]
lons_out = bm.shiftdata(lons_in, fix_wrap_around=True)
assert_almost_equal(lons_out, lons_out_expect)
def test_shiftdata_on_monotonous_lons(self):
"""
Test that shiftdata with fix_wrap_around keyword added works as before,
when it is True
"""
bm = Basemap(lon_0=0)
lons_in = [120, 140, 160, 180, 200, 220]
lons_out_expect = [-160, -140, 120, 140, 160, 180]
lons_out = bm.shiftdata(lons_in, fix_wrap_around=True)
assert_almost_equal(lons_out, lons_out_expect)
def test_non_monotonous_longitudes(self):
"""
when called for scatter, the longitudes passed to shiftdata are
not necessarily monotonous...
"""
lons = [179, 180, 180, 0, 290, 10, 320, -150, 350, -250, 250]
bm = Basemap(lon_0=0)
# before, having several break points would cause the exception,
# inside the shiftdata method called from scatter method.
self.assertRaises(ValueError, bm.shiftdata, lons, fix_wrap_around=True)
lons_new = bm.shiftdata(lons, fix_wrap_around=False)
# Check if the modified longitudes are inside of the projection region
for lon in lons_new:
assert lon >= bm.projparams["lon_0"] - 180
assert lon <= bm.projparams["lon_0"] + 180
def test_non_monotonous_longitudes(self):
"""
when called for scatter, the longitudes passed to shiftdata are
not necessarily monotonous...
"""
lons = [179, 180, 180, 0, 290, 10, 320, -150, 350, -250, 250]
bm = Basemap(lon_0=0)
# before, having several break points would cause the exception,
# inside the shiftdata method called from scatter method.
self.assertRaises(ValueError, bm.shiftdata, lons, fix_wrap_around=True)
lons_new = bm.shiftdata(lons, fix_wrap_around=False)
# Check if the modified longitudes are inside of the projection region
for lon in lons_new:
assert lon >= bm.projparams["lon_0"] - 180
assert lon <= bm.projparams["lon_0"] + 180
def basemap(dat,
lat,
lon,
lat_min=None,
lat_max=None,
lon_min=None,
lon_max=None,
res='c',
proj='mill',
origin=0,
shift=0,
title="",
loc=None,
loc1=None,
loc2=None,
lsmask=False,
ms_lat=None,
ms_lon=None,
lms=0.1,
ms_length=None,
uwinds=None,
vwinds=None,
hatch=None,
hatch_color='none',
gc_lat0=None,
gc_lon0=None,
gc_lat1=None,
gc_lon1=None,
gc_lat2=None,
gc_lon2=None,
gdists=None,
gc_lw=.5,
gc_color=plt.cm.jet,
gc_contours=None,
gc_extend='both',
gc_cbar=False,
font_size=8,
asp=None,
quiverscale=100,
stream=None,
dat2=None,
dat3=None,
dat4=None,
dat5=None,
dat6=None,
area1=None,
area2=None,
area3=None,
area4=None,
area5=None,
area6=None,
area7=None,
area8=None,
area9=None,
area10=None,
area11=None,
area12=None,
area13=None,
contours=None,
contours2=None,
contours3=None,
contours4=None,
contours5=None,
contours6=None,
lcontours=None,
lcd=None,
lclabel=None,
clim=None,
over=None,
extend='neither',
shaderelief=False,
etopo=False,
colors=None,
colors2=None,
colors3=None,
colors4=None,
colors5=None,
colors6=None,
color=plt.cm.gist_stern_r,
colorbar=True,
cbar_title="",
hillshade=False,
scale=.1,
alpha=1,
line_color1=None,
line_color2=None,
line_color3=None,
line_color4=None,
line_color5=None,
line_color6=None,
line_color7=None,
line_color8=None,
line_color9=None,
line_color10=None,
line_color11=None,
line_color12=None,
line_color13=None,
line_style1='solid',
line_style2='solid',
line_style3='solid',
line_style4='solid',
line_style5='solid',
line_style6='solid',
line_style7='solid',
line_style8='solid',
line_style9='solid',
line_style10='solid',
line_style11='solid',
line_style12='solid',
line_style13='solid',
parallels=np.arange(-90., 90., 20.),
meridians=np.arange(0., 360., 20.),
plabels=[1, 0, 0, 0],
mlabels=[0, 0, 0, 1]):
"""plots a pcolormesh of 2-dim. dat on a geographical grid
corresponding to lon and lat, optionally takes fixed colorlimits climl and climr, and saves the figure as title"""
if proj == 'mill' or proj == 'cea' or proj == 'cyl':
if lat_min is None:
m = Basemap(projection=proj,
lat_ts=12,
llcrnrlon=lon.min() + shift,
urcrnrlon=lon.max() + shift,
llcrnrlat=lat.min(),
urcrnrlat=lat.max(),
resolution=res)
else:
m = Basemap(projection=proj,
lat_ts=12,
llcrnrlon=lon_min,
urcrnrlon=lon_max,
llcrnrlat=lat_min,
urcrnrlat=lat_max,
resolution=res)
# m=Basemap(projection = proj, lat_ts=None,llcrnrlon=10, urcrnrlon=190, llcrnrlat=lat.min(), urcrnrlat=lat.max(),resolution = res)
# m=Basemap(projection=proj, lat_0 = 0, lon_0 = shift,resolution = 'c')
elif proj == 'ortho':
m = Basemap(projection='ortho', lat_0=0., lon_0=0., resolution='c')
elif proj == 'moll' or proj == 'hammer' or proj == 'robin':
m = Basemap(projection=proj, lon_0=shift, resolution='c')
elif proj == 'npstere':
m = Basemap(projection=proj,
lat_0=0.,
lon_0=0.,
boundinglat=40,
resolution='c')
# dat, lons = shiftgrid(0., dat, lon)
# print lon
lons, dat = m.shiftdata(lon, dat, lon_0=origin + shift)
if lcd is not None:
lons, lcd = m.shiftdata(lon, lcd, lon_0=origin + shift)
# lons = lon
# print lons
if loc is not None:
xloc, yloc = m(loc[0], loc[1])
m.plot(xloc, yloc, marker='D', color='r', markersize=lms)
if loc1 is not None:
xloc1, yloc1 = m(loc1[0], loc1[1])
m.plot(xloc1, yloc1, marker='D', color='royalblue')
if loc2 is not None:
xloc2, yloc2 = m(loc2[0], loc2[1])
m.plot(xloc2, yloc2, marker='D', color='darkorange')
# dat, lons = addcyclic(dat, lons)
if hatch is not None:
lons, hatch = m.shiftdata(lon, hatch, lon_0=origin + shift)
if area1 is not None:
lons, area1 = m.shiftdata(lon, area1, lon_0=origin + shift)
if area2 is not None:
lons, area2 = m.shiftdata(lon, area2, lon_0=origin + shift)
if area3 is not None:
lons, area3 = m.shiftdata(lon, area3, lon_0=origin + shift)
if area4 is not None:
lons, area4 = m.shiftdata(lon, area4, lon_0=origin + shift)
if area5 is not None:
lons, area5 = m.shiftdata(lon, area5, lon_0=origin + shift)
if area6 is not None:
lons, area6 = m.shiftdata(lon, area6, lon_0=origin + shift)
if area7 is not None:
lons, area7 = m.shiftdata(lon, area7, lon_0=origin + shift)
if area8 is not None:
lons, area8 = m.shiftdata(lon, area8, lon_0=origin + shift)
if area9 is not None:
lons, area9 = m.shiftdata(lon, area9, lon_0=origin + shift)
if area10 is not None:
lons, area10 = m.shiftdata(lon, area10, lon_0=origin + shift)
if area11 is not None:
lons, area11 = m.shiftdata(lon, area11, lon_0=origin + shift)
if area12 is not None:
lons, area12 = m.shiftdata(lon, area12, lon_0=origin + shift)
if area13 is not None:
lons, area13 = m.shiftdata(lon, area13, lon_0=origin + shift)
# m=Basemap(projection='mill', lat_0 = 0, lon_0 = 160, resolution = 'c')
Lon, Lat = np.meshgrid(lons, lat)
x, y = m(Lon, Lat)
# m.drawcoastlines(linewidth = .3)
if lsmask is True:
m.drawlsmask(land_color='.1', ocean_color='1.', resolution='h')
else:
m.drawcoastlines(linewidth=.3)
m.drawcountries(linewidth=.3)
m.drawmapboundary()
# m.readshapefile('/Users/omarlittle/Desktop/Desktop/PIK/climatenetwork/basemap/10m_physical/ne_10m_coastline', 'scale rank', drawbounds=True)
# m.drawmapbound)
# m.drawcountries(linewidth = .3)
m.drawparallels(parallels,
labels=plabels,
rotation='90',
linewidth=.3,
fontsize=font_size,
family='times new roman')
m.drawmeridians(meridians,
labels=mlabels,
linewidth=.3,
fontsize=font_size,
family='times new roman')
if etopo is True:
m.etopo()
if shaderelief is True:
m.shadedrelief()
if hillshade is True:
m.warpimage(
image=
'/Users/omarlittle/Desktop/Desktop/PIK/climatenetwork/basemap/SR_50M.tif',
scale=scale)
if contours is None:
cs = m.pcolormesh(x, y, dat, cmap=color, alpha=alpha)
if clim is not None:
cs.set_clim(clim)
else:
if colors is None:
cs = m.contourf(x,
y,
dat,
contours,
alpha=alpha,
cmap=color,
extend=extend)
else:
cs = m.contourf(x,
y,
dat,
contours,
alpha=alpha,
colors=colors,
extend=extend)
if contours2 is not None:
csa = m.contourf(x,
y,
dat2,
contours2,
alpha=alpha,
colors=colors2,
extend=extend)
if contours3 is not None:
csb = m.contourf(x,
y,
dat3,
contours3,
alpha=alpha,
colors=colors3,
extend=extend)
if contours4 is not None:
csc = m.contourf(x,
y,
dat4,
contours4,
alpha=alpha,
colors=colors4,
extend=extend)
if contours5 is not None:
csd = m.contourf(x,
y,
dat5,
contours5,
alpha=alpha,
colors=colors5,
extend=extend)
if contours6 is not None:
cse = m.contourf(x,
y,
dat6,
contours6,
alpha=alpha,
colors=colors6,
extend=extend)
if hatch is not None:
# hat = m.contourf(x, y, hatch, [-2,0, 2], colors = 'none', hatches = [None, '////'])
mpl.rcParams['hatch.linewidth'] = 0.1
hat = m.contourf(x,
y,
hatch, [-2, 0, 2],
colors='none',
hatches=[None, '///////'])
if lcontours is not None:
lc = m.contour(x, y, lcd, lcontours, colors='black', linewidths=.2)
if lclabel is True:
plt.clabel(lc, inline=1, fontsize=8, fmt='%d')
if area1 is not None:
cs1 = m.contour(x,
y,
area1, [0],
linestyles=line_style1,
linewidths=1.,
colors=line_color1)
if area2 is not None:
cs2 = m.contour(x,
y,
area2, [0],
linestyles=line_style2,
linewidths=1.,
colors=line_color2)
if area3 is not None:
cs3 = m.contour(x,
y,
area3, [0],
linestyles=line_style3,
linewidths=1.,
colors=line_color3)
if area4 is not None:
cs4 = m.contour(x,
y,
area4, [0],
linestyles=line_style4,
linewidths=1.,
colors=line_color4)
if area5 is not None:
cs5 = m.contour(x,
y,
area5, [0],
linestyles=line_style5,
linewidths=1.,
colors=line_color5)
if area6 is not None:
cs6 = m.contour(x,
y,
area6, [0],
linestyles=line_style6,
linewidths=1.,
colors=line_color6)
if area7 is not None:
cs7 = m.contour(x,
y,
area7, [0],
linestyles=line_style7,
linewidths=1.,
colors=line_color7)
if area8 is not None:
cs8 = m.contour(x,
y,
area8, [0],
linestyles=line_style8,
linewidths=1.,
colors=line_color8)
if area9 is not None:
cs9 = m.contour(x,
y,
area9, [0],
linestyles=line_style9,
linewidths=1.,
colors=line_color9)
if area10 is not None:
cs10 = m.contour(x,
y,
area10, [0],
linestyles=line_style10,
linewidths=.5,
colors=line_color10)
if area11 is not None:
cs11 = m.contour(x,
y,
area11, [0],
linestyles=line_style11,
linewidths=.5,
colors=line_color11)
if area12 is not None:
cs12 = m.contour(x,
y,
area12, [0],
linestyles=line_style12,
linewidths=.5,
colors=line_color12)
if area13 is not None:
cs13 = m.contour(x,
y,
area13, [0],
linestyles=line_style13,
linewidths=.5,
colors=line_color13)
if stream is not None:
print "be careful ..."
lons, stream[0] = m.shiftdata(lon, stream[0], lon_0=origin + shift)
lons, stream[1] = m.shiftdata(lon, stream[1], lon_0=origin + shift)
lons, stream[2] = m.shiftdata(lon, stream[2], lon_0=origin + shift)
xxs, yys = m.makegrid(stream[0].shape[1],
stream[0].shape[0],
returnxy=True)[2:4]
st = m.streamplot(xxs,
yys,
stream[1],
stream[2],
color="w",
cmap=plt.cm.spectral,
arrowsize=.5,
density=3,
linewidth=.5)
FP = FontProperties()
FP.set_family('times new roman')
FP.set_size('%d' % font_size)
if uwinds is not None:
lons, uwinds = m.shiftdata(lon, uwinds, lon_0=origin + shift)
lons, vwinds = m.shiftdata(lon, vwinds, lon_0=origin + shift)
u = uwinds
v = vwinds
#ugrid,newlons = shiftgrid(180.,u,lon,start=False)
#vgrid,newlons = shiftgrid(180.,v,lon,start=False)
# transform vectors to projection grid.
#uproj,vproj,xx,yy = m.transform_vector(ugrid,vgrid,newlons,lat,31,31,returnxy=True,masked=True)
# now plot.
# scale =
Q = m.quiver(x[::asp, ::asp],
y[::asp, ::asp],
u[::asp, ::asp],
v[::asp, ::asp],
pivot='mid',
units='width',
scale=quiverscale)
# make quiver key
# qk = plt.quiverkey(Q, 0.85, 0.05, 5, '5 m/s', coordinates = 'figure', labelpos='W', labelcolor = 'black', fontproperties = {'family':'times new roman', 'size':'10', 'style':'normal'})
if gc_lat0 is not None:
for i in xrange(gc_lat1.shape[0]):
if gc_color is None:
gc = m.drawgreatcircle(gc_lon0,
gc_lat0,
gc_lon1[i],
gc_lat1[i],
linewidth=gc_lw,
color=plt.cm.jet(.0001),
alpha=.02)
else:
gc = m.drawgreatcircle(
gc_lon0,
gc_lat0,
gc_lon1[i],
gc_lat1[i],
linewidth=gc_lw,
color=gc_color(int(5 * gdists[i] / gdists.max()) / 5.,
alpha=.02 + gdists[i] / gdists.max()))
if gc_lat2 is not None:
for i in xrange(gc_lat2.shape[0]):
gc = m.drawgreatcircle(gc_lon0,
gc_lat0,
gc_lon2[i],
gc_lat2[i],
linewidth=gc_lw,
color=plt.cm.jet(.9999),
alpha=.3)
if loc is not None:
xloc, yloc = m(loc[0], loc[1])
m.plot(xloc, yloc, marker='D', color='r', markersize=0.5)
if ms_lat is not None:
m.drawmapscale(lat=ms_lat,
lon=ms_lon,
lon0=0.,
lat0=0.,
length=ms_length,
barstyle='fancy')
if colorbar is True:
ticks_font = matplotlib.font_manager.FontProperties(
family='times new roman',
style='normal',
size=font_size,
weight='normal',
stretch='normal')
if gc_cbar == True:
cs_fake = plt.contourf(x,
y,
np.ones_like(dat) * -1e7,
gc_contours,
cmap=gc_color,
extend='neither')
cbar = m.colorbar(cs_fake, extend=extend, orientation='horizontal')
else:
cbar = m.colorbar(cs, extend=extend)
if cbar_title is not None:
cbar.ax.set_ylabel(cbar_title,
fontsize=font_size,
family='times new roman')
for label in cbar.ax.get_yticklabels():
label.set_fontproperties(ticks_font)
if over is not None:
cs.cmap.set_over(over)
plt.title('%s' % title, fontsize=font_size)
return cs
def main():
erainterim_075_folder = "/HOME/data/Validation/ERA-Interim_0.75/Offline_driving_data/3h_Forecast"
vname = "PR"
start_year = 1980
end_year = 2010
season_key = "summer"
season_labels = {season_key: "Summer"}
season_to_months = OrderedDict([
(season_key, [6, 7, 8])
])
# Validate temperature and precip
model_vars = ["TT", "PR"]
obs_vars = ["tmp", "pre"]
obs_paths = [
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc",
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc"
]
model_var_to_obs_var = dict(zip(model_vars, obs_vars))
model_var_to_obs_path = dict(zip(model_vars, obs_paths))
obs_path = model_var_to_obs_path[vname]
cru = CRUDataManager(var_name=model_var_to_obs_var[vname], path=obs_path)
# Calculate seasonal means for CRU
seasonal_clim_fields_cru = cru.get_seasonal_means(season_name_to_months=season_to_months,
start_year=start_year,
end_year=end_year)
# Calculate seasonal mean for erai
flist = get_files_for_season(erainterim_075_folder, start_year=start_year, end_year=end_year, months=season_to_months[season_key])
rpf = MultiRPN(flist)
date_to_field_erai075 = rpf.get_all_time_records_for_name_and_level(varname=vname, level=-1)
# Convert to mm/day
era075 = np.mean([field for field in date_to_field_erai075.values()], axis=0) * 24 * 3600 * 1000
lons_era, lats_era = rpf.get_longitudes_and_latitudes_of_the_last_read_rec()
seasonal_clim_fields_cru_interp = OrderedDict()
# Calculate biases
for season, cru_field in seasonal_clim_fields_cru.items():
seasonal_clim_fields_cru_interp[season] = cru.interpolate_data_to(cru_field,
lons2d=lons_era,
lats2d=lats_era,
nneighbours=1)
# Do the plotting ------------------------------------------------------------------------------
plot_utils.apply_plot_params()
fig = plt.figure()
b = Basemap()
gs = gridspec.GridSpec(nrows=3, ncols=1)
ax = fig.add_subplot(gs[0, 0])
xx, yy = b(cru.lons2d, cru.lats2d)
cs = b.contourf(xx, yy, seasonal_clim_fields_cru[season_key], 20)
b.drawcoastlines(ax=ax)
ax.set_title("CRU")
plt.colorbar(cs, ax=ax)
ax = fig.add_subplot(gs[1, 0])
lons_era[lons_era > 180] -= 360
lons_era, era075 = b.shiftdata(lons_era, datain=era075, lon_0=0)
xx, yy = b(lons_era, lats_era)
# mask oceans in the era plot as well
era075 = maskoceans(lons_era, lats_era, era075)
cs = b.contourf(xx, yy, era075, levels=cs.levels, norm=cs.norm, cmap=cs.cmap, ax=ax)
b.drawcoastlines(ax=ax)
ax.set_title("ERA-Interim 0.75")
plt.colorbar(cs, ax=ax)
# differences
ax = fig.add_subplot(gs[2, 0])
diff = era075 - seasonal_clim_fields_cru_interp[season_key]
delta = np.percentile(np.abs(diff)[~diff.mask], 90)
clevs = np.linspace(-delta, delta, 20)
cs = b.contourf(xx, yy, diff, levels=clevs, cmap="RdBu_r", extend="both")
b.drawcoastlines(ax=ax)
ax.set_title("ERA-Interim 0.75 - CRU")
plt.colorbar(cs, ax=ax)
plt.show()
fig.savefig(os.path.join(img_folder, "erai0.75_vs_cru_precip.png"), bbox_inches="tight")
d_u = g_u.mean(axis=0) - w_u.mean(axis=0)
for lev in range(0, 44):
lev_1 = lev + 1
g_uu = g_u[lev, :, :]
w_uu = w_u[lev, :, :]
d_uu = g_uu - w_uu
############################################################################################################################################
fig = plt.figure(figsize=(8, 8), dpi=100)
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
m = Basemap(projection='mill',
llcrnrlat=lat.min(),
urcrnrlat=lat.max(),
llcrnrlon=lon.min(),
urcrnrlon=lon.max(),
resolution='l')
lons, data = m.shiftdata(lon, datain=g_uu, lon_0=None)
x, y = m(lon, lat)
nice_cmap = plt.get_cmap('ocean_r')
nice_cmap = plt.get_cmap('RdYlGn')
#nice_cmap= plt.get_cmap(mymap)
#clevs=[-15,-10,-5,-0,5,10,15,20,25,30,35,40,45,50,55,60,70]
clevs = [
-9, -6, -3, -0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42,
45, 48, 51
]
#cs = (m.contourf(x,y,data,leves=clevs,cmap=cmap,norm=norml,extend='both'))
cs = (m.contourf(x, y, data, clevs, cmap=nice_cmap, extended='both'))
(m.readshapefile('/home/Data/shapeFiles/uae/ARE_adm1',
'uae',
f = nf.Dataset(files, mode='r')
lat = (np.squeeze(f.variables['XLAT'][:]))
lat1 = np.vstack(lat[:, 0])
lon = (np.squeeze(f.variables['XLONG'][:]))
lon1 = np.vstack(lon[0, :])
temp = np.squeeze(f.variables['T2'][:]) - 273.15
fig = plt.figure(figsize=(8, 8), dpi=100)
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
m = Basemap(projection='mill',
llcrnrlat=lat.min(),
urcrnrlat=lat.max(),
llcrnrlon=lon.min(),
urcrnrlon=lon.max(),
resolution='l')
lons, data = m.shiftdata(lon, datain=temp, lon_0=None)
x, y = m(lon, lat)
#Z=maskoceans(lons,lat,data,inlands=True, resolution='c', grid=2.5)
clevs = [1, 5, 10, 15, 18, 21, 22, 23, 24, 25, 26, 27, 28]
mymap = mcolors.ListedColormap(['white','lime','limegreen','greenyellow','yellow','gold','orange','indianred','firebrick', \
'darkred','lightskyblue','deepskyblue','royalblue','blue'])
# colors1 = plt.get_cmap('ocean_r') ; colors1=colors1(np.linspace(0., 1, 128))
# colors2 = plt.get_cmap('RdYlGn') ; colors2=colors2(np.linspace(0.,1 , 128))
# colors = np.vstack(( colors1,colors2))
# mymap = mcolors.LinearSegmentedColormap.from_list('my_colormap', colors)
nice_cmap = plt.get_cmap(mymap)
colors = nice_cmap([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
cmap, norm = from_levels_and_colors(clevs, colors, extend='both')
# #cs=m.pcolormesh(x,y,data, cmap=cmap, norm=norm)
def map_nwp_tracks_per_storm(date, outfile):
#lat1 = -2
#lat2 = -40
#lon1=0
#lon2=60
#lat1 = 0
#lat2 = -30
#lon1=20
#lon2=60
lat1 = -10
lat2 = -30
lon1 = 18
lon2 = 55
fig = plt.figure(figsize=(6, 3))
ax = fig.add_axes([0.05, 0.1, 0.9, 0.96])
m = Basemap(llcrnrlon=lon1,
llcrnrlat=lat2,
urcrnrlon=lon2,
urcrnrlat=lat1,
projection='mill',
resolution='l')
RSMClats = [-40, 0, 0, -40]
RSMClons = [30, 30, 90, 90]
m.drawcoastlines(linewidth=0.5, color='k') #darkgray
m.drawcountries(linewidth=0.5, color='k') #darkgray
m.fillcontinents(color='silver', alpha=0.25)
hurricane = get_hurricane_symbol()
eps_data = np.genfromtxt(datadir + date +
"_idai_ECMWF_eps_3way_matched_reformatted.txt",
dtype=float,
skip_header=1,
usecols=np.arange(0, 11))
#tid = track id (column 1) in the eps reformatted files
#1 = analysis track, 2 = best track, 3 = control, 4 - 53 = ensemble members 1-50
#total length of the file, to loop over and find the individual tracks
#probably a better way to do this, but it's friday
e = range(4, 54)
e.append(3)
e.append(2)
e.append(1)
NP = len(eps_data[:, 0])
print e
for tid in e:
if tid == 1:
c = 'k'
ls = '-'
lw = 0.7
elif tid == 2:
c = 'None'
ls = '--'
lw = 0.75
elif tid == 3:
c = 'None' #'#FF9F1C'
ls = '-'
lw = 0.75
elif tid >= 4:
c = 'lightblue'
ls = '-'
lw = 0.5
else:
print "something went wrong with the tid"
#tid = str(tid).zfill(4)
#print float(tid)
xyi = []
for i in range(NP):
#print eps_data[i,0]
if eps_data[i, 0] == float(tid):
xyi.append(i)
#print xyi
xs = []
ys = []
for i in xyi:
xs.append(eps_data[i, 7])
ys.append(eps_data[i, 8])
if tid == 1:
no_points = len(xs)
points = np.array([xs, ys]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
#colors = cm.twilight(np.linspace(0.5,1,no_points))
cmap = LinearSegmentedColormap.from_list(
'mycmap', ['lightgrey', 'black', 'dimgrey'])
#cmap = LinearSegmentedColormap.from_list('mycmap',['plum','darkmagenta','plum'])
colors = cmap(np.linspace(0, 1, no_points))
for p, cl in zip(range(no_points - 1), colors):
#put these here so as to not plot the analysis track before the forecast date, just from the fcst date onwards
xarr = []
yarr = []
if p < 61: #61 is when it's no longer a TC, 37 is when it became a TC (10th March), I think
z = 10
elif p > 61: #
z = 9
if p > 37:
xarr.append(
segments[p][0][0]
) #shift these back an indent if plotting the *whole* analysis track
xarr.append(segments[p][1][0])
yarr.append(segments[p][0][1])
yarr.append(segments[p][1][1])
x, y = m(xarr, yarr)
m.plot(x, y, linewidth=1.5, color=cl, zorder=z) #zorder=z
pp = np.linspace(0, 92, 24)
#colors2=cm.twilight(np.linspace(0.5,1,24))
colors2 = cmap(np.linspace(0, 1, 24))
print pp
for p, cl in zip(pp, colors2):
p = int(p)
if p < 61:
z = 10
elif p > 61:
z = 9
if p < 37:
xx, yy = m(xs[p], ys[p])
#m.scatter(xx,yy,marker='o', edgecolors=cl,facecolors=cl, s=8,linewidth=1,zorder=z)
elif 37 < p < 61:
xx, yy = m(xs[p], ys[p])
m.scatter(xx,
yy,
marker=hurricane,
edgecolors=cl,
facecolors='None',
s=100,
linewidth=1.25,
zorder=z)
elif p > 61:
xx, yy = m(xs[p], ys[p])
m.scatter(xx,
yy,
marker='o',
edgecolors=cl,
facecolors='None',
s=8,
linewidth=1,
zorder=z)
#xx,yy=m(xs[0],ys[0])
#m.scatter(xx,yy,marker=hurricane, edgecolors=c, facecolors='None',s=25,linewidth=0.6,zorder=10)
else:
#continue
x, y = m.shiftdata(xs, ys)
#if tid == 3.:
#print xs
#print ys
#print x,y
m.plot(x, y, linewidth=lw, color=c, linestyle=ls, latlon=True)
#if tid == 1.:
#xx,yy=m(xs[0],ys[0])
#m.scatter(xx,yy,marker=hurricane, edgecolors=c, facecolors='None',s=25,linewidth=0.6,zorder=10)
#if tid == 3:
#xx,yy=m(xs[0],ys[0])
#m.scatter(xx,yy,marker=hurricane, edgecolors=c, facecolors='None',s=25,linewidth=0.6,zorder=10)
mean_file = datadir + date + "_idai_ECMWF_mean_3way_matched_reformatted.txt"
if os.path.isfile(mean_file):
mean_data = np.genfromtxt(mean_file,
dtype=float,
skip_header=1,
usecols=np.arange(0, 11))
x, y = m.shiftdata(mean_data[:, 7], mean_data[:, 8])
m.plot(x,
y,
linewidth=0.75,
color='mediumblue',
latlon=True,
linestyle='-',
zorder=9)
#xx,yy=m(mean_data[0,7],mean_data[0,8])
#m.scatter(xx,yy,marker=hurricane, edgecolors='darkturquoise', facecolors='None',s=25,linewidth=0.6,zorder=10)
det_file = datadir + date + "_idai_ECMWF_det_3way_matched_reformatted.txt"
if os.path.isfile(det_file):
det_data = np.genfromtxt(det_file,
dtype=float,
skip_header=1,
usecols=np.arange(0, 11))
NP = len(det_data[:, 0])
#tid = str(3).zfill(4)
tid = 3
xyi = []
for i in range(NP):
if det_data[i, 0] == float(tid):
xyi.append(i)
xs = []
ys = []
for i in xyi:
xs.append(det_data[i, 7])
ys.append(det_data[i, 8])
x, y = m.shiftdata(xs, ys)
m.plot(x,
y,
linewidth=0.75,
color='#F71735',
latlon=True,
linestyle='-',
zorder=9)
#if len(xs)>0:
#xx,yy=m(xs[0],ys[0])
#m.scatter(xx,yy,marker=hurricane, edgecolors='#F71735', facecolors='None',s=25,linewidth=0.6,zorder=10)
#else:
#continue
title = "Cyclone Idai ECMWF Forecast\n" + str(date)
ib = plt.Line2D((0, 1), (0, 0), color='k', linestyle='--', linewidth=0.5)
an = plt.Line2D((0, 1), (0, 0), color='k', linestyle='-', linewidth=0.5)
det = plt.Line2D((0, 1), (0, 0), color='#F71735', linewidth=0.5)
ctrl = plt.Line2D((0, 1), (0, 0), color='#FF9F1C', linewidth=0.5)
mean = plt.Line2D((0, 1), (0, 0), color='medium', linewidth=0.5)
eps = plt.Line2D((0, 1), (0, 0),
color='lightblue',
linewidth=0.5,
linestyle=':')
#legend = ax.legend((an, det, ctrl, mean, eps), ['Analysis Track', 'Deterministic', 'Control', 'Ensemble Mean', 'Ensembles'],title=title, fontsize=5, loc='lower left')
#plt.setp(legend.get_title(), fontsize='5')
#legend._legend_box.align = "left"
#save and close the plot
fig.subplots_adjust(wspace=0)
plt.savefig(outfile, bbox_inches='tight', pad_inches=0.05, dpi=500)
plt.close()
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
map = Basemap(projection='sinu',
lat_0=0, lon_0=0)
lons = np.arange(30, 390, 30)
lats = np.arange(0, 100, 10)
data = np.indices((lats.shape[0], lons.shape[0]))
data = data[0] + data[1]
print data
print lons
lons, data = map.shiftdata(lons, datain = data, lon_0=0)
print lons
llons, llats = np.meshgrid(lons, lats)
x, y = map(llons, llats)
map.contourf(x, y, data)
map.drawcoastlines()
plt.show()
gsi_f=nf.Dataset(gsi_file, mode='r')
lat=(np.squeeze(gsi_f.variables['XLAT'][:])) ; lat1=np.vstack(lat[:,0]) ;
lon=(np.squeeze(gsi_f.variables['XLONG'][:])) ; lon1=np.vstack(lon[0,:])
wrf_f=nf.Dataset(wrf_file, mode='r')
g_qv=np.squeeze(gsi_f.variables['QVAPOR'][:]) ;
w_qv=np.squeeze(wrf_f.variables['QVAPOR'][:]) ;
d_qv=g_qv.mean(axis=0)-w_qv.mean(axis=0)
for lev in range(0,44):
lev_1=lev+1
g_qvv=g_qv[lev,:,:] ; w_qvv=w_qv[lev,:,:] ; d_qvv=g_qvv-w_qvv ;
############################################################################################################################################
fig=plt.figure(figsize=(8,8),dpi=100); ax = fig.add_axes([0.1,0.1,0.8,0.8]) ;
m = Basemap(projection='mill',llcrnrlat=lat.min(),urcrnrlat=lat.max(),llcrnrlon=lon.min(),urcrnrlon=lon.max(),resolution='l')
lons, data = m.shiftdata(lon, datain=g_qvv, lon_0=None)
x, y=m(lon,lat)
nice_cmap=plt.get_cmap('ocean_r')
nice_cmap=plt.get_cmap('RdYlGn')
#nice_cmap= plt.get_cmap(mymap)
clevs=[0.0, 0.001,0.0015,0.0020,0.0025,0.0030,0.0035,0.0040,0.0045,0.0050,0.0055,0.0060, \
0.0065,0.0070,0.0075,0.0080, 0.0085, 0.0090, 0.0095, 0.010, 0.0105,0.0110,0.0115,0.0120,0.0125]
clevs=[0.0, 0.001,0.0020,0.0030,0.0040,0.0050,0.0060,0.0070,0.0080,0.0090,0.010,0.0110,0.0120,0.0130]
#cs = (m.contourf(x,y,data,leves=clevs,cmap=cmap,norm=norml,extend='both'))
cs = (m.contourf(x,y,data,clevs,cmap=nice_cmap,extended='both'))
(m.readshapefile('/home/Data/shapeFiles/uae/ARE_adm1','uae',drawbounds=True, zorder=None, linewidth=1.0, color='k', antialiased=1, ax=None, default_encoding='utf-8'))
m.drawparallels(np.arange(20.,30.,5.), labels=[1,0,0,0])
def main():
erainterim_075_folder = "/HOME/data/Validation/ERA-Interim_0.75/Offline_driving_data/3h_Forecast"
vname = "PR"
start_year = 1980
end_year = 2010
season_key = "summer"
season_labels = {season_key: "Summer"}
season_to_months = OrderedDict([(season_key, [6, 7, 8])])
# Validate temperature and precip
model_vars = ["TT", "PR"]
obs_vars = ["tmp", "pre"]
obs_paths = [
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc",
"/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc",
]
model_var_to_obs_var = dict(zip(model_vars, obs_vars))
model_var_to_obs_path = dict(zip(model_vars, obs_paths))
obs_path = model_var_to_obs_path[vname]
cru = CRUDataManager(var_name=model_var_to_obs_var[vname], path=obs_path)
# Calculate seasonal means for CRU
seasonal_clim_fields_cru = cru.get_seasonal_means(
season_name_to_months=season_to_months, start_year=start_year, end_year=end_year
)
# Calculate seasonal mean for erai
flist = get_files_for_season(
erainterim_075_folder, start_year=start_year, end_year=end_year, months=season_to_months[season_key]
)
rpf = MultiRPN(flist)
date_to_field_erai075 = rpf.get_all_time_records_for_name_and_level(varname=vname, level=-1)
# Convert to mm/day
era075 = np.mean([field for field in date_to_field_erai075.values()], axis=0) * 24 * 3600 * 1000
lons_era, lats_era = rpf.get_longitudes_and_latitudes_of_the_last_read_rec()
seasonal_clim_fields_cru_interp = OrderedDict()
# Calculate biases
for season, cru_field in seasonal_clim_fields_cru.items():
seasonal_clim_fields_cru_interp[season] = cru.interpolate_data_to(
cru_field, lons2d=lons_era, lats2d=lats_era, nneighbours=1
)
# Do the plotting ------------------------------------------------------------------------------
plot_utils.apply_plot_params()
fig = plt.figure()
b = Basemap()
gs = gridspec.GridSpec(nrows=3, ncols=1)
ax = fig.add_subplot(gs[0, 0])
xx, yy = b(cru.lons2d, cru.lats2d)
cs = b.contourf(xx, yy, seasonal_clim_fields_cru[season_key], 20)
b.drawcoastlines(ax=ax)
ax.set_title("CRU")
plt.colorbar(cs, ax=ax)
ax = fig.add_subplot(gs[1, 0])
lons_era[lons_era > 180] -= 360
lons_era, era075 = b.shiftdata(lons_era, datain=era075, lon_0=0)
xx, yy = b(lons_era, lats_era)
# mask oceans in the era plot as well
era075 = maskoceans(lons_era, lats_era, era075)
cs = b.contourf(xx, yy, era075, levels=cs.levels, norm=cs.norm, cmap=cs.cmap, ax=ax)
b.drawcoastlines(ax=ax)
ax.set_title("ERA-Interim 0.75")
plt.colorbar(cs, ax=ax)
# differences
ax = fig.add_subplot(gs[2, 0])
diff = era075 - seasonal_clim_fields_cru_interp[season_key]
delta = np.percentile(np.abs(diff)[~diff.mask], 90)
clevs = np.linspace(-delta, delta, 20)
cs = b.contourf(xx, yy, diff, levels=clevs, cmap="RdBu_r", extend="both")
b.drawcoastlines(ax=ax)
ax.set_title("ERA-Interim 0.75 - CRU")
plt.colorbar(cs, ax=ax)
plt.show()
fig.savefig(os.path.join(img_folder, "erai0.75_vs_cru_precip.png"), bbox_inches="tight")
def imshow_Pakistan_hatch(data,
lons_sig_arr,
lats_sig_arr,
vmin=None,
vmax=None,
norm=None,
cmap=None,
ticks=None,
title=None,
fig_name=None):
fig = plt.figure()
M = Basemap(resolution='l',
llcrnrlat=minlat,
urcrnrlat=maxlat,
llcrnrlon=minlon,
urcrnrlon=maxlon)
M.fillcontinents(color='#D6D5D5')
M.drawmapboundary(fill_color='#80cdc1')
M.drawcountries(color='white', linewidth=1.25)
lons_map, lats_map = M(lon - 0.25, lat + 0.25)
cs = M.pcolormesh(lons_map,
lats_map,
data[::-1],
vmin=vmin,
vmax=vmax,
norm=norm,
cmap=cmap,
zorder=4)
lons_sig_arr, lats_sig_arr = M.shiftdata(lons_sig_arr, lats_sig_arr)
M.scatter(lons_sig_arr,
lats_sig_arr,
marker='.',
s=50,
color='coral',
latlon=True,
zorder=6)
M.scatter(67.3695, 24.7854, marker='*', s=200, color='#343837',
zorder=5) # Location of powerplant A
M.scatter(70.1736, 24.4338, marker='*', s=200, color='#343837',
zorder=5) # Location of powerplant B
M.scatter(70.3151, 24.8408, marker='*', s=200, color='#343837',
zorder=5) # Location of powerplant C
M.scatter(70.3151, 24.8408, marker='*', s=200, color='#343837',
zorder=5) # Location of powerplant D
M.scatter(73.2359, 30.7196, marker='*', s=200, color='#343837',
zorder=5) # Location of powerplant E
M.scatter(70.1790, 24.6970, marker='*', s=200, color='#343837',
zorder=5) # Location of powerplant F
M.scatter(66.6944, 24.9057, marker='*', s=200, color='#343837',
zorder=5) # Location of powerplant G
M.plot(lonlat[:, 0], lonlat[:, 1], lw=1.25, color='k', alpha=0.7, zorder=7)
M.drawcoastlines()
cbar = M.colorbar(cs,
ticks=ticks,
location='right',
pad="5%",
size="3%",
extend='both')
fig.text(0.14,
0.32,
'Iran',
size=18,
va="baseline",
ha="left",
fontweight='medium',
color='grey',
fontstyle='italic')
fig.text(0.2,
0.6,
'Afghanistan',
size=18,
va="baseline",
ha="left",
fontweight='medium',
color='grey',
fontstyle='italic')
fig.text(0.6,
0.3,
'India',
size=18,
va="baseline",
ha="left",
fontweight='medium',
color='grey',
fontstyle='italic')
fig.text(0.15,
0.15,
'Arabian Sea',
size=12,
va="baseline",
ha="left",
fontweight='medium',
color='white',
fontstyle='italic')
fig.text(0.13,
0.8,
model.upper(),
size=22,
va="baseline",
ha="left",
fontweight='medium',
color='#343837')
#plt.title(title)
plt.savefig('%s/%s_%s.pdf' % (dir_fig, fig_name, model))
plt.show()
fig, ax = plt.subplots(1, 1, figsize=(10, 8))
print(np.nanmin(lon))
map = Basemap(ax=ax,
llcrnrlon=np.nanmin(lon) + 1,
urcrnrlon=np.nanmax(lon) - 1,
llcrnrlat=np.nanmin(lat) + 1,
urcrnrlat=np.nanmax(lat) - 1,
projection="cyl",
fix_aspect=False,
resolution='l')
map.fillcontinents(color="black", lake_color="black")
map.drawparallels(np.arange(-90, 90, 20), labels=[1, 0, 0, 0])
map.drawmeridians(np.arange(-180, 180, 20), labels=[1, 0, 0, 1])
LON, LAT = map.shiftdata(lon, lat)
map.plot(LON, LAT, latlon=True)
u = floats.u.to_masked_array()
v = floats.v.to_masked_array()
U = np.sqrt(u**2 + v**2)
xbins = np.arange(-80, 0, 0.5)
ybins = np.arange(15, 65, 0.5)
H = stats.binned_statistic_2d(lon,
lat,
None,
bins=[xbins, ybins],
statistic="count")
M = stats.binned_statistic_2d(lon,
# convert the sliced data to a pandas dataframe
df = hur_lev.to_dataframe()
# this is a simple way to rotate the coordinates of longitudes from 0-360 to -180-180
new_lons = ((ds.lon.data + 180) % 360) - 180
lons_greenwich = ((lons_pacific + 180) % 360) - 180
# see also:
# basemap shiftdata
from mpl_toolkits.basemap import Basemap, addcyclic, shiftgrid
m = Basemap( epsg=4326 )
new_arr = np.rollaxis( hur_lev.values.T, -1 )
test = m.shiftdata( xds.lon.data, hur_lev.values[1,...][:].T, lon_0=0.0 )
llons, llats = np.meshgrid( test[0], ds.lat.data )
x, y = m(llons, llats)
# m.contourf(x, y, test[1])
# m.drawcoastlines()
m.imshow( test[1] )
plt.savefig('test.png')
scp [email protected]:/home/malindgren/Documents/hur/test.png ~/Documents
# from frank
gdalwarp -multi -overwrite -of NetCDF -t_srs WGS84 hur_Amon_GFDL-CM3_historical_r1i1p1_186001-186412.nc test.nc -wo SOURCE_EXTRA=1000 -wo NUM_THREADS=ALL_CPUS --config CENTER_LONG 0
# test file
d_t = g_t.mean(axis=0) - w_t.mean(axis=0)
for lev in range(0, 44):
lev_1 = lev + 1
g_tt = g_t[lev, :, :]
w_tt = w_t[lev, :, :]
d_tt = g_tt - w_tt
############################################################################################################################################
fig = plt.figure(figsize=(8, 8), dpi=100)
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
m = Basemap(projection='mill',
llcrnrlat=lat.min(),
urcrnrlat=lat.max(),
llcrnrlon=lon.min(),
urcrnrlon=lon.max(),
resolution='l')
lons, data = m.shiftdata(lon, datain=g_tt, lon_0=None)
x, y = m(lon, lat)
nice_cmap = plt.get_cmap('ocean_r')
nice_cmap = plt.get_cmap('RdYlGn')
#nice_cmap= plt.get_cmap(mymap)
clevs = [
-25, -20, -15, -10, -5, 0, 5, 10, 25, 50, 75, 100, 125, 150, 175, 200,
225
]
#cs = (m.contourf(x,y,data,leves=clevs,cmap=cmap,norm=norml,extend='both'))
cs = (m.contourf(x, y, data, clevs, cmap=nice_cmap, extended='both'))
(m.readshapefile('/home/Data/shapeFiles/uae/ARE_adm1',
'uae',
drawbounds=True,
from mpl_toolkits.basemap import Basemap from mpl_toolkits.basemap import addcyclic import matplotlib.pyplot as plt import numpy as np map = Basemap(projection='sinu', lat_0=0, lon_0=0) lons = np.arange(30, 390, 30) lats = np.arange(0, 100, 10) data = np.indices((lats.shape[0], lons.shape[0])) data = data[0] + data[1] data, lons = addcyclic(data, lons) lons, data = map.shiftdata(lons, datain=data, lon_0=0) llons, llats = np.meshgrid(lons, lats) x, y = map(llons, llats) map.contourf(x, y, data) map.drawcoastlines() plt.show()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
lat0 = 5
lon0 = 60
#m = Basemap(projection='ortho',lat_0=lat0,lon_0=lon0, resolution='c',area_thresh = 1000.)
m = Basemap(projection='mill',
llcrnrlat=-90,
urcrnrlat=90,
llcrnrlon=-180,
urcrnrlon=180,
resolution='l')
m = Basemap(projection='kav7', lon_0=0, resolution=None)
#m = Basemap(projection='ortho',lon_0=lon0,lat_0=lat0,resolution='l', \
# llcrnrx=0.,llcrnry=0.,urcrnrx=m1.urcrnrx/3,urcrnry=m1.urcrnry/3)
lons, data = m.shiftdata(lon, datain=temp[0, :, :], lon_0=None)
x, y = m(lons, lat)
Z = maskoceans(lons, lat, data, inlands=True, resolution='c', grid=2.5)
clevs = [-50, -40, -20, -10, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45]
cmap = plt.get_cmap('gist_rainbow')
#nice_cmap= plt.get_cmap('ocean_r')
#colors = nice_cmap([0,8, 16, 40, 60, 80,100,120,140])
#colors = nice_cmap([0,10,20,40,60,80,100,130,160])
#levels = [0, 5, 10, 15, 25,30,35,40] ;
#cmap, norm = from_levels_and_colors(levels, colors, extend='both')
#cs=m.pcolormesh(x,y,Z, cmap=cmap, norm=norm)
cs = (m.contourf(x, y, Z, clevs, cmap=plt.cm.jet))
#m.warpimage(image='venuscyl4.jpg')
#(m.readshapefile('ncfiles/subdiv/India_subdiv','ind',drawbounds=True, zorder=None, linewidth=1.0, color='k', antialiased=1, ax=None, default_encoding='utf-8'))
d_vv = g_vv - w_vv
g_uu = g_u[lev, :, :]
w_uu = w_u[lev, :, :]
d_uu = g_uu - w_uu
##############################################################################################################################
fig = plt.figure(figsize=(8, 8), dpi=100)
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
m = Basemap(projection='mill',
llcrnrlat=lat.min(),
urcrnrlat=lat.max(),
llcrnrlon=lon.min(),
urcrnrlon=lon.max(),
resolution='l')
lons, data = m.shiftdata(lon, datain=g_wss, lon_0=None)
x, y = m(lons, lat)
nice_cmap = plt.get_cmap('ocean_r')
nice_cmap = plt.get_cmap('RdYlGn')
#nice_cmap= plt.get_cmap(mymap)
#clevs=[-15,-10,-5,-0,5,10,15,20,25,30,35,40,45,50,55,60,70]
clevs = [-9, -6, -3, -0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39]
skip = (slice(None, None, 5), slice(None, None, 5))
# #cs = (m.contourf(x,y,data,leves=clevs,cmap=cmap,norm=norml,extend='both'))
# kw = dict( color='black', alpha=0.75)
# m.quiver(x,y,g_uu,g_vv,width=0.0003, scale=500)
# plt.show()
cs = (m.contourf(x, y, data, clevs, cmap=nice_cmap, extended='both'))
