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 test_issue046():
import pygeode as pyg
import numpy as np
V = pyg.Var((pyg.Lat(32), ), name='Test', values=np.zeros(32) * np.nan)
V.interpolate('lat', pyg.gausslat(32))[:]
def test_serialize(self):
for k, f in formats.items():
fname = '%s.%s' % (testname, k)
f.save(fname, [self.var])
d = f.open(fname)
assertSameVar(d.vardict[self.name], self.var)
os.remove(fname)
tc.__name__ = testname
return tc
ax1 = pyg.StandardTime(values=np.arange(365.), units='days', startdate={'year':2001})
ax2 = pyg.gausslat(32)
ax3 = pyg.Pres(np.arange(0, 100, 10.))
shape = (365, 32, 10)
data = np.random.randn(*shape)
ltwts = ax2.auxarrays['weights']
var = pyg.Var((ax1, ax2, ax3), values=data, name='var')
tv = varTest('1_Simple', var, \
name = 'var', axes = (ax1, ax2, ax3), \
values = data, serialize=True)
sl1 = varTest('slice_simple', var(i_time=(0, 5), i_lat=(0, 5), i_pres=(0, 5)), \
shape = (5, 5, 5), \
axes = (ax1(i_time=(0, 5)), ax2(i_lat=(0, 5)), ax3(i_pres=(0, 5))), \
name = 'var', \
import pygeode as pyg
import numpy as np
import pylab as pyl
from pygeode.formats import netcdf as nc
lat = pyg.gausslat(32)
lon = pyg.Lon(np.arange(0, 360, 360 / 64.))
ln_grid, lt_grid = np.meshgrid(lon.values, lat.values)
T_values = 260. + 40 * np.exp(-(lt_grid / 45.)**2) + 0.05 * lt_grid * np.sin(
3 * ln_grid * np.pi / 180.)
T_c = 260. + 40 * np.exp(-(lt_grid / 45.)**2)
T_wave = 0.05 * lt_grid * np.sin(3 * ln_grid * np.pi / 180.)
T = pyg.Var((lat, lon), name='Temp', values=T_c + T_wave, atts={'units': 'K'})
d = pyg.Dataset(
[T], atts={'history': 'Synthetic Temperature data generated by pygeode'})
nc.save('t_sample.nc', d)
def test_issue046():
import pygeode as pyg
import numpy as np
V = pyg.Var((pyg.Lat(32),), name='Test', values=np.zeros(32) * np.nan)
V.interpolate('lat', pyg.gausslat(32))[:]
""" Cartopy: Use Cartopy Features ============================= """ import pygeode as pyg, numpy as np, pylab as pyl from cartopy import crs as ccrs import cartopy from cartopy.feature.nightshade import Nightshade from datetime import datetime as dt lat = pyg.gausslat(60) lon = pyg.regularlon(120) 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 = dict(central_longitude = 60) #ax = pyg.plot.CartopyAxes(projection = 'NearsidePerspective', prj_args = prj) map = dict(projection = 'NearsidePerspective', prj_args = prj) #map = dict(projection = 'LambertConformal', prj_args = prj) cl = pyg.cldict(0.1, nozero=True) ax = pyg.vcontour(x, map = map, **cl) ax.add_feature(cartopy.feature.OCEAN) ax.add_feature(Nightshade(dt.utcnow())) ax.setp(title = '') pyl.ion() ax.render(2)
"""
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 = []