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 test_scalar_variable_read():
import pygeode as pyg
# Create a scalar variable and test reading from it
v = pyg.Var((), name='scalar', values=10.)
assert v[()] == 10.
# Write to netcdf
pyg.save('test_issue_108.nc', v)
def test_issue053():
import pygeode as pyg
import numpy as np
l = pyg.regularlat(30)
t = pyg.ModelTime365(values=np.arange(100),
units='days',
startdate=dict(year=1, month=1))
v = pyg.Var((t, l), name='Test', values=np.ones((100, 30)))
v.plotatts['scalefactor'] = 2.
v.plotatts['plottitle'] = 'V'
a = l * v
b = t + v
assert a.plotatts == v.plotatts
assert b.plotatts == v.plotatts
def eps_like(v):
return pyg.Var(v.axes, values=np.random.randn(*v.shape))
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))[:]
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', \
values = data[:5, :5, :5])
sl2 = varTest('slice_stride', var(i_time=(1, -1, 4)), \
values = data[1:-1:4, :, :])
sl3 = varTest('slice_negative_stride', var(i_time=(2, -5, -3)), \
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)