def buildT2():
nyrs = 10
lat = pyg.regularlat(31)
lon = pyg.regularlon(60)
time = pyg.ModelTime365(values=np.arange(nyrs*365), \
units='days', startdate={'year':2011, 'month':1, 'day':1})
pres = pyg.Pres(np.arange(1000, 0, -50.))
z = 6.6 * pyg.log(1000. / pres)
ts1 = 2 * pyg.sin(2 * np.pi * time / 365.) + 4 * pyg.Var(
(time, ), values=np.random.randn(nyrs * 365))
ts1 = ts1.smooth('time', 20)
ts2 = -5 + 0.6 * time / 365. + 5 * pyg.Var(
(time, ), values=np.random.randn(nyrs * 365))
ts2 = ts2.smooth('time', 20)
T_c = 260. + 40. * pyg.exp(-(
(lat - 10 * np.sin(2 * np.pi * time / 365)) / 45.)**2)
T_wave = 0.05 * lat * pyg.sind(6 * lon - time) # * ts1
T_lapse = -5 * z
Tf = (T_lapse + T_c + T_wave).transpose('time', 'pres', 'lat', 'lon')
Tf.name = 'Temp'
U_c = 40 * pyg.sind(2 * lat)**2 * pyg.sin(2 * np.pi * z / 12)**2
U_wave = 0.08 * lat * pyg.sind(6 * lon - time)
U = (U_c + ts2 * U_wave).transpose('time', 'pres', 'lat', 'lon')
U.name = 'U'
return pyg.Dataset(
[Tf, U],
atts={
'history':
'Synthetic Temperature and Wind data generated by pygeode'
})
def buildT3():
lon = pyg.regularlon(60)
lat = pyg.gausslat(42)
tm = pyg.modeltime365n('1 Jan 2000', 200)
def eps_like(v):
return pyg.Var(v.axes, values=np.random.randn(*v.shape))
X1 = (5. * tm / 2000.) * pyg.cosd(lat)
X2 = pyg.cosd(2 * np.pi * tm / 500.) * pyg.sind(0.5 * lon)
X3 = pyg.sind(2 * np.pi * tm / 120.) * pyg.cosd(3 * lon)**2
X1e = X1 + 0.1 * eps_like(X1)
X2e = X2 + 0.2 * eps_like(X2)
X3e = X3 + 0.2 * eps_like(X3)
Y1 = -1. * pyg.sind(lon) * X1e + eps_like(X1).smooth('lat', 4)
Y2 = -0.2 * X1e + 0.8 * X2e + -0.6 * X3e + 0.1 * eps_like(X1)
X1 = X1.rename('X1')
X2 = X2.rename('X2')
X3 = X3.rename('X3')
X1e = X1e.rename('X1e')
X2e = X2e.rename('X2e')
X3e = X3e.rename('X3e')
Y1 = Y1.rename('Y1')
Y2 = Y2.rename('Y2')
Y3 = Y2.rename('Y3')
return pyg.Dataset(
[X1e, X2e, X3e, Y1, Y2],
atts={'history': 'Synthetic dataset generated by pygeode'})
def buildT2():
nyrs = 10
lat = pyg.regularlat(31)
lon = pyg.regularlon(60)
time = pyg.ModelTime365(values=np.arange(nyrs*365), \
units='days', startdate={'year':2011, 'month':1, 'day':1})
pres = pyg.Pres(np.arange(1000, 0, -50.))
z = 6.6 * pyg.log(1000./pres)
ts1 = 2*pyg.sin(2*np.pi*time/365.) + 4*pyg.Var((time,), values=np.random.randn(nyrs*365))
ts1 = ts1.smooth('time', 20)
ts2 = -5 + 0.6*time/365. + 5*pyg.Var((time,), values=np.random.randn(nyrs*365))
ts2 = ts2.smooth('time', 20)
T_c = 260. + 40. * pyg.exp(-((lat - 10*np.sin(2*np.pi*time/365))/45.)**2)
T_wave = 0.05 * lat * pyg.sind(6*lon - time)# * ts1
T_lapse = -5*z
Tf = (T_lapse + T_c + T_wave).transpose('time', 'pres', 'lat', 'lon')
Tf.name = 'Temp'
U_c = 40 * pyg.sind(2*lat)**2 * pyg.sin(2*np.pi * z / 12)**2
U_wave = 0.08 * lat * pyg.sind(6*lon - time)
U = (U_c + ts2*U_wave).transpose('time', 'pres', 'lat', 'lon')
U.name = 'U'
return pyg.Dataset([Tf, U], atts={'history':'Synthetic Temperature and Wind data generated by pygeode'})
def buildT1():
lat = pyg.regularlat(31)
lon = pyg.regularlon(60)
T_c = 260. + 40. * pyg.exp(-(lat/45.)**2)
T_wave = 0.05 * lat * pyg.sind(6*lon)
T = T_c + T_wave
T.name = 'Temp'
T.units = 'K'
return pyg.Dataset([T], atts={'history':'Synthetic Temperature data generated by pygeode'})
"""
Specify contour levels
=======================
Use :func:`clfdict()` to create a set of contour levels and contour lines to plot.
"""
import pygeode as pyg, numpy as np
import pylab as pyl
pyl.ioff()
lat = pyg.regularlat(60)
lon = pyg.regularlon(120)
z = pyg.sin(2 * np.pi * lat /
180.)**10 + pyg.cos(10 +
(2 * np.pi / 180.)**2 * lat * lon) * pyg.cos(
2 * np.pi * lat / 180.)
ax = pyg.plot.AxesWrapper()
contour_dict = pyg.clfdict(min=-1.2,
axes=ax,
cdelt=0.4,
ndiv=3,
nf=2,
nl=1,
extend='both',
cmap='RdGy')
pyg.vcontour(z, **contour_dict)
ax.setp(title='Using helper function to set up contour levels')
"""
Plot lines using showlines
==========================
Compute numerical integrals using :func:`Var.integrate` and use :func:`showlines` to make plot
"""
import pygeode as pyg, numpy as np
import pylab as pyl
# Use longitudes as horizontal axis
x = pyg.regularlon(45)
lam = np.pi * x / 180.
pyl.ioff()
# Example 1: f = sin(x)
# F = int_0^x f dx' = 1 - cos(x)
# Different integration methods
f = pyg.sin(lam)
Fr = f.integrate('lon', dx = lam, type='rectr')
Fl = f.integrate('lon', dx = lam, type='rectl')
Ft = f.integrate('lon', dx = lam, type='trapz')
ax1 = pyg.showlines([1 - pyg.cosd(x), Fr, Fl, Ft],
fmts = ['k+', '_', '_', 'x'],
labels = [r'$1 - \cos x$', 'rectr', 'rectl', 'trapz'], fig=3)
# Set panel title and axes labels
"""
Cartopy: Test regional projections
=============================================
"""
import pygeode as pyg, numpy as np, pylab as pyl
from cartopy import crs as ccrs
import cartopy
lat = pyg.gausslat(40)
lon = pyg.regularlon(80, origin=-180)
x = pyg.sin(2 * np.pi * lon / 180.) * pyg.exp(-(lat - 30)**2 / (2 * 10**2))
y = pyg.sin(2 * np.pi * lon / 180.) * pyg.exp(-(lat + 40)**2 / (2 * 10**2))
pyl.ioff()
prj_grid = ['PlateCarree', 'Mercator', 'Miller', 'TransverseMercator']
prj_reg = ['AlbersEqualArea', 'EquidistantConic', 'LambertConformal', \
'LambertCylindrical', 'RotatedPole']
prj_glob = ['AzimuthalEquidistant', 'Mollweide', 'Orthographic', \
'Stereographic', 'Robinson', 'Sinusoidal', \
'Geostationary', 'LambertAzimuthalEqualArea', 'EckertIII']
prj = dict(central_longitude=60.)
gridlines = dict(draw_labels=False,
xlocs=range(0, 361, 30),
ylocs=range(-80, 81, 20)),
map = dict(gridline=gridlines)
i = 0
axr = []
