def testMinMax(self):
# Scalar
xouter = MV2.outerproduct(MV2.arange(10), MV2.arange(10))
maxval = MV2.maximum(xouter)
minval = MV2.minimum(xouter)
self.assertEqual(maxval, 81)
self.assertEqual(minval, 0)
# Do an elementwise maximum
xmax = MV2.maximum(self.u_file, self.v_file)
xmin = MV2.minimum(self.u_file, self.v_file)
self.assertTrue(((xmax - self.u_file) >= 0).all())
self.assertTrue(((xmax - self.v_file) >= 0).all())
self.assertTrue(((xmin - self.u_file) <= 0).all())
self.assertTrue(((xmin - self.v_file) <= 0).all())
# Reduce along axes
slicer = [Ellipsis for i in range(len(self.u_file.shape))]
for axis_index, axis_length in enumerate(self.u_file.shape):
amax = MV2.maximum.reduce(self.u_file, axis=axis_index)
amin = MV2.minimum.reduce(self.u_file, axis=axis_index)
s = list(slicer)
for i in range(axis_length):
s[axis_index] = i
ind_slice = self.u_file.subSlice(*s, squeeze=True)
self.assertTrue(((ind_slice - amax) <= 0).all())
self.assertTrue(((ind_slice - amin) >= 0).all())
t1 = MV2.TransientVariable([1., 2.])
t2 = MV2.TransientVariable([1., 10.])
t3 = MV2.minimum.outer(t1, t2)
self.assertTrue(MV2.allequal(t3, [[1, 1], [1, 2]]))
t3 = MV2.maximum.outer(t1, t2)
self.assertTrue(MV2.allequal(t3, [[1, 10], [2, 10]]))
def testContiguousRegridNANIssue(self):
a = MV2.reshape(MV2.sin(MV2.arange(20000)), (2, 1, 100, 100))
lon = cdms2.createAxis(MV2.arange(100) * 3.6)
lon.designateLongitude()
lon.units = "degrees_east"
lon.id = "longitude"
lat = cdms2.createAxis(MV2.arange(100) * 1.8 - 90.)
lat.id = "latitude"
lat.designateLatitude()
lat.units = "degrees_north"
lev = cdms2.createAxis([1000.])
lev.id = "plev"
lev.designateLevel()
lev.units = "hPa"
t = cdms2.createAxis([0, 31.])
t.id = "time"
t.designateTime()
t.units = "days since 2014"
cdutil.setTimeBoundsMonthly(t)
a.setAxisList((t, lev, lat, lon))
a = MV2.masked_less(a, .5)
grd = cdms2.createGaussianGrid(64)
a = a.ascontiguous()
a = a.regrid(grd, regridTool="regrid2")
a = cdutil.averager(a, axis='txy')
self.assertEqual(a[0], 0.7921019540305255)
def test11(self):
bg = True
n = 4
self.M = EzTemplate.Multi(
rows=1,
columns=1,
legend_direction='vertical',
right_margin=.2,
left_margin=.15,
top_margin=.15,
bottom_margin=.15)
t = self.M.get()
leg = t.legend
tt = self.x.gettexttable(leg.texttable)
to = self.x.gettextorientation(leg.textorientation)
OD = EzTemplate.oneD(n=n, template=t)
x = MV2.arange(0, 360)
x = x / 180. * MV2.pi
ax = cdms2.createAxis(x)
self.x.portrait()
for i in range(n):
y = MV2.sin((i + 1) * x)
y.setAxis(0, ax)
yx = self.x.createyxvsx()
yx.linecolor = 241 + i
yx.datawc_y1 = -1.
yx.datawc_y2 = 1.
y.id = str(i)
t = OD.get()
self.x.plot(y, t, yx, bg=bg)
self.checkImage('test_EzTemplate_11.png')
def testMaskingFunctions(self):
xouter = MV2.outerproduct(MV2.arange(5.), [1] * 10)
masked = MV2.masked_greater(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[2:], True))
self.assertTrue(MV2.allequal(masked.mask[:2], False))
masked = MV2.masked_greater_equal(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[1:], True))
self.assertTrue(MV2.allequal(masked.mask[:1], False))
masked = MV2.masked_less(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[:1], True))
self.assertTrue(MV2.allequal(masked.mask[1:], False))
masked = MV2.masked_less_equal(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[:2], True))
self.assertTrue(MV2.allequal(masked.mask[2:], False))
masked = MV2.masked_not_equal(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[1], False))
self.assertTrue(MV2.allequal(masked.mask[0], True))
self.assertTrue(MV2.allequal(masked.mask[2:], True))
masked = MV2.masked_equal(xouter, 1)
self.assertTrue(MV2.allequal(masked.mask[1], True))
self.assertTrue(MV2.allequal(masked.mask[0], False))
self.assertTrue(MV2.allequal(masked.mask[2:], False))
masked = MV2.masked_outside(xouter, 1, 3)
self.assertTrue(MV2.allequal(masked.mask[0:1], True))
self.assertTrue(MV2.allequal(masked.mask[1:4], False))
self.assertTrue(MV2.allequal(masked.mask[4:], True))
masked = MV2.masked_where(
MV2.logical_or(MV2.greater(xouter, 3), MV2.less(xouter, 2)),
xouter)
self.assertTrue(MV2.allequal(masked.mask[0:2], True))
self.assertTrue(MV2.allequal(masked.mask[2:4], False))
self.assertTrue(MV2.allequal(masked.mask[4:], True))
def testReshapeMaskedAverage(self):
a = MV2.arange(100)
a = MV2.reshape(a, (10, 10))
self.assertEqual(a.shape, (10, 10))
self.assertEqual(len(a.getAxisList()), 2)
a = MV2.masked_greater(a, 23)
b = MV2.average(a, axis=0)
c = a - b
def testAxisID(self):
a = MV2.ones((12, 12))
ax = cdms2.createAxis(MV2.arange(12))
ax.id = "1234567890" * 26
a.setAxis(0, ax)
with cdms2.open("bad.nc", "w") as f:
f.write(a)
f = cdms2.open("bad.nc")
listdim = f.listdimension()
self.assertEqual(listdim[0], ax.id[0:127])
f.close()
def testAverage(self):
xav = MV2.average(self.ones, axis=1)
self.assertTrue(MV2.allequal(xav, 1))
xav2 = MV2.average(self.u_file)
xav3 = MV2.average(self.u_transient)
xav4, wav4 = MV2.average(
self.u_transient, weights=MV2.ones(
self.u_transient.shape, numpy.float), returned=1)
a = MV2.arange(5)
b = 2 ** a
av, wav = MV2.average(b, weights=a, returned=1)
self.assertEqual(av, 9.8)
self.assertEqual(wav, 10)
import cdms2,MV2,regrid2,cdutil a=MV2.reshape(MV2.sin(MV2.arange(20000)),(2,1,100,100)) lon=cdms2.createAxis(MV2.arange(100)*3.6) lon.designateLongitude() lon.units="degrees_east" lon.id="longitude" lat = cdms2.createAxis(MV2.arange(100)*1.8-90.) lat.id="latitude" lat.designateLatitude() lat.units="degrees_north" lev = cdms2.createAxis([1000.]) lev.id="plev" lev.designateLevel() lev.units="hPa" t=cdms2.createAxis([0,31.]) t.id="time" t.designateTime() t.units="days since 2014" cdutil.setTimeBoundsMonthly(t) a.setAxisList((t,lev,lat,lon)) a=MV2.masked_less(a,.5) grd=cdms2.createGaussianGrid(64) a=a.ascontiguous()
alongxy = hasattr(u, 'long_name') and 'x' in us.long_name.lower()
if alongxy:
uname, vname = 'ux10m', 'vy10m'
else:
uname, vname = 'u10m', 'v10m'
format_var(u, uname, format_axes=format_axes)
format_var(v, vname, format_axes=format_axes)
# Compute
zero = us.filled(1)==0.
zero &= vs.filled(1)==0.
uvsmod = (us**2+vs**2)**-0.25
uvsmod /= N.sqrt(rhoa*cd)
u.assignValue(uvsmod*us)
v.assignValue(uvsmod*vs)
u.assignValue(MV2.where(zero, 0., u))
v.assignValue(MV2.where(zero, 0., v))
del uvsmod
return u, v
if __name__=='__main__':
import MV2
u = MV2.arange(3.)
v = 2*u
us, vs = wind_stress(u, v)
U, V = ws2w(us, vs)
print u, v
print U, V
ch_units(taxis, 'days since 2000-1-15 06', copy=0)
# - after
print taxis.units, taxis[0:2]
print taxis.asComponentTime()[0:2]
# -> days since 2000-1-15 06 [ 0. 2.]
# -> [2000-1-15 6:0:0.0, 2000-1-17 6:0:0.0]
# Matplotlib times
taxis_mpl = mpl(taxis)
print taxis_mpl[0], taxis_mpl.units
# -> 730134.25 days since 0001
# Change the time units of a variable
import MV2
var = MV2.array(MV2.arange(len(taxis)), dtype='f', axes=[taxis])
ch_units(var, 'hours since 2000-01-15 06')
print var.getTime()[0:2]
# -> [ 0. 48.]
# Change time zone
# - UTC time now
t_utc = now(True)
print strftime('%H:%M', t_utc), t_utc.hour
# -> 15:54 15
# - Paris time
t_paris = utc_to_paris(t_utc)
print strftime('%H:%M', t_paris), t_paris.hour
# -> 17:54 17
# - back to UTC
print tz_to_tz(t_paris, 'Europe/Paris', 'UTC').hour
# %% Make fake data
import MV2, numpy as N
nt = 50
# - reference
ref = MV2.sin(MV2.arange(nt, dtype='f'))*10.
# - modele 1
model1 = ref+N.random.rand(nt)*10.
model1.long_name = 'Model 1'
# - modele 2
model2 = ref/2.+N.random.rand(nt)*15.
model2.long_name = 'Model 2'
# %% Plot
from vacumm.misc.plot import taylor
taylor([model1, model2], ref, figsize=(8, 8), label_size='large', size=15,
labels = ['Good model', None], colors='cyan', title_size=18,
savefigs=__file__, savefigs_pdf=True, show=False, close=True)
# - changement
ch_units(taxis, 'days since 2000-1-15 06', copy=0)
# - apres changement
print taxis.units, taxis[0:2]
print taxis.asComponentTime()[0:2]
# -> days since 2000-1-15 06 [ 0. 2.]
# -> [2000-1-15 6:0:0.0, 2000-1-17 6:0:0.0]
# Le temps matplotlib
taxis_mpl = mpl(taxis)
print taxis_mpl[0], taxis_mpl.units
# -> 730134.25 days since 0001
# Changer les unites de temps d'une variable
import MV2
var = MV2.array(MV2.arange(len(taxis)), dtype='f', axes=[taxis])
ch_units(var, 'hours since 2000-01-15 06')
print var.getTime()[0:2]
# -> [ 0. 48.]
# Changements de fuseau horaire
# - maintenant a l'heure UTC
t_utc = now(True)
print strftime('%H:%M', t_utc), t_utc.hour
# -> 15:54 15
# - heure de paris
t_paris = utc_to_paris(t_utc)
print strftime('%H:%M', t_paris), t_paris.hour
# -> 17:54 17
# - retour en UTC
X = vcs.init()
M = EzTemplate.Multi(rows=1,
columns=1,
legend_direction='vertical',
right_margin=.15)
t = M.get()
leg = t.legend
tt = X.gettexttable(leg.texttable)
to = X.gettextorientation(leg.textorientation)
to.list()
tt.list()
OD = EzTemplate.oneD(n=n, template=t)
x = MV2.arange(0, 360)
x = x / 180. * MV2.pi
ax = cdms2.createAxis(x)
X.portrait()
for i in range(n):
y = MV2.sin((i + 1) * x)
y.setAxis(0, ax)
yx = X.createyxvsx()
yx.linecolor = 241 + i
yx.datawc_y1 = -1.
yx.datawc_y2 = 1.
t = OD.get()
X.plot(y, t, yx, bg=bg)
vcs.test.support.check_plot(X)
def testOuterproduct(self):
xouter = MV2.outerproduct(MV2.arange(3.), MV2.arange(5.))
self.assertEqual(xouter.shape, (3, 5))
for i in range(3):
self.assertTrue(MV2.allequal(xouter[i], i * xouter[1]))
import os, sys, numpy, cdms2, MV2, vcs, testing.regression as regression x = regression.init() t = cdms2.createAxis(numpy.arange(120)) t.designateTime() t.id = "time" t.units = "months since 2014" data = MV2.arange(120,0,-1) data.id = "data" data.setAxis(0,t) x = regression.init() x.plot(data,bg=1) fnm = 'test_vcs_monotonic_decreasing_yxvsx_default.png' regression.run(x, fnm)
def testAxisConvert(self):
gm = vcs.create1d()
gm.yaxisconvert = "log10"
data = MV2.arange(10)
data_pow = MV2.power(10, data)
data_pow.id = "test"
self.x.plot(data_pow, gm, bg=self.bg)
self.checkImage("test_vcs_yaxisconvert_log10.png")
self.x.clear()
gm.flip = True
gm.yaxisconvert = "linear"
gm.xaxisconvert = "log10"
self.x.plot(data_pow, gm, bg=self.bg)
self.checkImage("test_vcs_xaxisconvert_log10_flip.png")
self.x.clear()
gm.flip = False
gm.xaxisconvert = "linear"
gm.yaxisconvert = "ln"
data_exp = MV2.exp(data)
data_exp.id = "test"
self.x.plot(data_exp, gm, bg=self.bg)
self.checkImage("test_vcs_yaxisconvert_ln.png")
self.x.clear()
gm.flip = False
gm.xaxisconvert = "area_wt"
gm.yaxisconvert = "linear"
data2 = self.clt("clt",
time=slice(0, 1),
longitude=slice(23, 24),
squeeze=1)
self.x.plot(data2, gm, bg=self.bg)
self.checkImage("test_vcs_xaxisconvert_areawt.png")
self.x.clear()
gm.flip = True
gm.yaxisconvert = "area_wt"
gm.xaxisconvert = "linear"
self.x.plot(data2, gm, bg=self.bg)
self.checkImage("test_vcs_yaxisconvert_areawt_flip.png")
self.x.clear()
gm.flip = False
gm.xaxisconvert = "log10"
gm.yaxisconvert = "ln"
self.x.plot(data_pow, data_exp, gm, bg=self.bg)
self.checkImage("test_vcs_axisconvert_log10_ln.png")
self.x.clear()
#gm.flip = True
data2[:] = MV2.power(10, MV2.arange(1, data2.shape[0] + 1))
data2 = data2[:10]
gm.xaxisconvert = "area_wt"
gm.yaxisconvert = "log10"
self.x.plot(data2, gm, bg=self.bg)
self.checkImage("test_vcs_axisconvert_area_log10.png")
self.x.clear()
gm.flip = True
gm.yaxisconvert = "area_wt"
gm.xaxisconvert = "log10"
self.x.plot(data2, gm, bg=self.bg)
self.checkImage("test_vcs_axisconvert_area_log10_flip.png")
self.x.clear()
gm.flip = False
gm.xaxisconvert = "area_wt"
gm.yaxisconvert = "log10"
data_axis = MV2.array(data2.getAxis(0)[:])
data_axis.id = "test"
self.x.plot(data_axis, data2, gm, bg=self.bg)
self.checkImage("test_vcs_axisconvert_area_log10_2arrays.png")
with self.assertRaises(RuntimeError):
self.x.clear()
gm.flip = True
self.x.plot(data_axis, data2, gm, bg=self.bg)
self.x.clear()
gm.flip = False
gm.xaxisconvert = "area_wt"
gm.yaxisconvert = "log10"
gm.xticlabels1 = {-90: "90S", -80: "80S", -70: "70S", -60: "60S"}
gm.xticlabels2 = {-85: "85S", -75: "75S", -65: "65S", -55: "55S"}
mtics = {}
def func(x):
mtics[x] = ""
map(func, range(-90, -40, 10))
gm.xmtics2 = mtics
mtics = {}
map(func, range(-85, -40, 10))
gm.xmtics1 = mtics
gm.yticlabels1 = {100: "hun"}
gm.yticlabels2 = {1000: "thou"}
gm.ymtics1 = {10: "", 1000: ""}
gm.ymtics2 = {10000: "", 1000: ""}
gm.list()
tmpl = vcs.createtemplate()
for num in ["1", "2"]:
for loc in ["x", "y"]:
for attr in ["mintic", "tic"]:
filled_attr = "{}{}{}".format(loc, attr, num)
print("PRIO FOR ATT:", filled_attr)
obj = getattr(tmpl, filled_attr)
obj.priority = 1
obj.list()
tmpl.xlabel1.priority = 1
tmpl.xlabel2.priority = 1
tmpl.ylabel1.priority = 1
tmpl.ylabel2.priority = 1
self.x.plot(data_axis, data2, gm, tmpl, bg=self.bg)
self.checkImage("test_vcs_axisconvert_area_log10_user_labels.png")
def testWhere(self):
xouter = MV2.outerproduct(MV2.arange(3), MV2.arange(3))
xwhere = MV2.where(MV2.greater(xouter, 3), xouter, -123)
self.assertEqual(xwhere[-1, -1], 4)
xwhere.mask = xwhere == 4
self.assertTrue(MV2.allequal(xwhere, -123))
# Construction des jeux de donnees
import MV2, numpy as N
nt = 50
# - reference
ref = MV2.sin(MV2.arange(nt, dtype="f")) * 10.0
# - modele 1
model1 = ref + N.random.rand(nt) * 10.0
model1.long_name = "Model 1"
# - modele 2
model2 = ref / 2.0 + N.random.rand(nt) * 15.0
model2.long_name = "Model 2"
# Plot
from vacumm.misc.plot import taylor
taylor(
[model1, model2],
ref,
figsize=(8, 8),
label_size="large",
size=15,
labels=["Good model", None],
colors="cyan",
title_size=18,
savefigs=__file__,
savefigs_pdf=True,
show=False,
)
import vcs.test.support
bg= vcs.test.support.bg
n = 4
X=vcs.init()
M = EzTemplate.Multi(rows=1,columns=1,legend_direction='vertical',right_margin=.15)
t=M.get()
leg = t.legend
tt=X.gettexttable(leg.texttable)
to=X.gettextorientation(leg.textorientation)
to.list()
tt.list()
OD = EzTemplate.oneD(n=n,template=t)
x = MV2.arange(0,360)
x = x/180.*MV2.pi
ax = cdms2.createAxis(x)
X.portrait()
for i in range(n):
y = MV2.sin((i+1)*x)
y.setAxis(0,ax)
yx = X.createyxvsx()
yx.linecolor=241+i
yx.datawc_y1=-1.
yx.datawc_y2=1.
t = OD.get()
X.plot(y,t,yx,bg=bg)
vcs.test.support.check_plot(X)
def testCount(self):
xouter = MV2.outerproduct(MV2.arange(5.), [1] * 10)
masked = MV2.masked_outside(xouter, 1, 3)
self.assertEqual(MV2.count(masked), 30)
self.assertTrue(MV2.allequal(MV2.count(masked, 0), 3))
self.assertTrue((MV2.count(masked, 1) == [0, 10, 10, 10, 0]).all())
x4 = 1.0+ud
x5 = uf-1
x6 = ud*uf
x7 = ud/x2
x8=1/uf
x9 = 3*ud
x10=uf**3
x13 = MV2.add.reduce(uf)
x14 = MV2.add.reduce(ud)
x15 = x9.astype(numpy.float32)
if not x15.dtype.char==numpy.sctype2char(numpy.float32): markError('astype error')
## arrayrange(start, stop=None, step=1, typecode=None, axis=None, attributes=None, id=None)
## Just like range() except it returns a variable whose type can be specfied
## by the keyword argument typecode. The axis of the result variable may be specified.
xarange = MV2.arange(16., axis=ulat)
## masked_array(a, mask=None, fill_value=None, axes=None, attributes=None, id=None)
## masked_array(a, mask=None) =
## array(a, mask=mask, copy=0, fill_value=fill_value)
## Use fill_value(a) if None.
xmarray = MV2.masked_array(ud)
## masked_object(data, value, copy=1, savespace=0)
## Create array masked where exactly data equal to value
## masked_values(data, value, rtol=1.0000000000000001e-05, atol=1e-08, copy=1, savespace=0, axes=None,
## attributes=None, id=None)
## masked_values(data, value, rtol=1.e-5, atol=1.e-8)
## Create a masked array; mask is None if possible.
## May share data values with original array, but not recommended.
# Adapted for numpy/ma/cdms2 by convertcdms.py
import vcs_legacy,cdms2 as cdms,MV2 as MV,sys,support,os
bg=support.bg
f=cdms.open(os.path.join(cdms.__path__[0],'..','..','..','..','sample_data','clt.nc'))
s=f('clt',slice(0,5),latitude=(0.,0.,'cob'),squeeze=1)
nt=s.shape[0]
ax=MV.arange(nt,typecode='d')
bounds=MV.zeros((nt,2),typecode='d')
for i in range(nt):
bounds[i,0]=ax[i]
bounds[i,1]=ax[i]+1.
ax=cdms.createAxis(ax)
s.setAxis(0,ax)
x=vcs_legacy.init()
x.plot(s,bg=bg)
support.check_plot(x)
y=vcs_legacy.init()
y.plot(s,ybounds=bounds,bg=bg) # instead of being halfway, bounds are from node to node
support.check_plot(y)
# -*- coding: utf8 -*- from vacumm.misc.grid.regridding import regrid2d import MV2 from vacumm.misc.grid import create_grid from vacumm.misc.plot import map2 as map import pylab as P gmax=40 ggi = create_grid((0., gmax, 1.), (0., gmax, 1.)) ggo = create_grid((0.01, gmax, .5), (0.01, gmax, .5)) vari = MV2.reshape(MV2.arange(ggi.size(), dtype='f'), ggi.shape) vari.setGrid(ggi) varo = regrid2d(vari, ggo, 'bilinear') map(varo, clabel_glow=True, resolution=None, show=False, savefigs=__file__)
import MV2
from markError import clearError, markError, reportError
clearError()
print 'Test 15: reshape and mask and average ...',
a = MV2.arange(100)
try:
failed = False
a.shape = (10, 10)
except:
failed = True
a = MV2.reshape(a, (10, 10))
if failed is False:
markError('shape should not have worked (protected attribute)')
if len(a.getAxisList()) != 2: markError('reshape did not produce 2 axes')
a = MV2.masked_greater(a, 23)
b = MV2.average(a, axis=0)
c = a - b
reportError()
x5 = uf - 1
x6 = ud * uf
x7 = ud / x2
x8 = 1 / uf
x9 = 3 * ud
x10 = uf ** 3
x13 = MV2.add.reduce(uf)
x14 = MV2.add.reduce(ud)
x15 = x9.astype(numpy.float32)
if not x15.dtype.char == numpy.sctype2char(numpy.float32):
markError("astype error")
## arrayrange(start, stop=None, step=1, typecode=None, axis=None, attributes=None, id=None)
## Just like range() except it returns a variable whose type can be specfied
## by the keyword argument typecode. The axis of the result variable may be specified.
xarange = MV2.arange(16.0, axis=ulat)
## masked_array(a, mask=None, fill_value=None, axes=None, attributes=None, id=None)
## masked_array(a, mask=None) =
## array(a, mask=mask, copy=0, fill_value=fill_value)
## Use fill_value(a) if None.
xmarray = MV2.masked_array(ud)
## masked_object(data, value, copy=1, savespace=0)
## Create array masked where exactly data equal to value
## masked_values(data, value, rtol=1.0000000000000001e-05, atol=1e-08, copy=1, savespace=0, axes=None,
## attributes=None, id=None)
## masked_values(data, value, rtol=1.e-5, atol=1.e-8)
## Create a masked array; mask is None if possible.
## May share data values with original array, but not recommended.
def testArange(self):
test_range = MV2.arange(16, axis=self.u_lat)
self.assertEqual(len(test_range), 16)
self.assertIsNotNone(test_range.getLatitude())
import MV2
from markError import clearError,markError,reportError
print 'Test 15: reshape and mask and average ...',
a=MV2.arange(100)
try:
failed = False
a.shape=(10,10)
except:
failed = True
a = MV2.reshape(a,(10,10))
if failed is True: markError('shape should not have worked (protected attribute)')
if len(a.getAxisList())!=2: markError('reshape did not produce 2 axes')
a=MV2.masked_greater(a,23)
b=MV2.average(a,axis=0)
c=a-b
import os, sys, numpy, cdms2, MV2, vcs, vcs.testing.regression as regression x = regression.init() t = cdms2.createAxis(numpy.arange(120)) t.designateTime() t.id = "time" t.units = "months since 2014" data = MV2.arange(120,0,-1) data.id = "data" data.setAxis(0,t) x = regression.init() x.plot(data,bg=1) fnm = 'test_vcs_monotonic_decreasing_yxvsx_default.png' regression.run(x, fnm)
# -*- coding: utf8 -*-
from vacumm.misc.grid.regridding import regrid2d
import MV2
from vacumm.misc.grid import create_grid
from vacumm.misc.plot import map2 as map
import pylab as P
gmax=40
ggi = create_grid((0., gmax, 1.), (0., gmax, 1.))
ggo = create_grid((0.01, gmax, .5), (0.01, gmax, .5))
vari = MV2.reshape(MV2.arange(ggi.size(), dtype='f'), ggi.shape)
vari.setGrid(ggi)
varo = regrid2d(vari, ggo, 'bilinear')
map(varo, clabel_glow=True, resolution=None, show=False, savefigs=__file__,
close=True)
x5 = uf - 1
x6 = ud * uf
x7 = ud / x2
x8 = 1 / uf
x9 = 3 * ud
x10 = uf**3
x13 = MV2.add.reduce(uf)
x14 = MV2.add.reduce(ud)
x15 = x9.astype(numpy.float32)
if not x15.dtype.char == numpy.sctype2char(numpy.float32):
markError('astype error')
## arrayrange(start, stop=None, step=1, typecode=None, axis=None, attributes=None, id=None)
## Just like range() except it returns a variable whose type can be specfied
## by the keyword argument typecode. The axis of the result variable may be specified.
xarange = MV2.arange(16., axis=ulat)
## masked_array(a, mask=None, fill_value=None, axes=None, attributes=None, id=None)
## masked_array(a, mask=None) =
## array(a, mask=mask, copy=0, fill_value=fill_value)
## Use fill_value(a) if None.
xmarray = MV2.masked_array(ud)
## masked_object(data, value, copy=1, savespace=0)
## Create array masked where exactly data equal to value
## masked_values(data, value, rtol=1.0000000000000001e-05, atol=1e-08, copy=1, savespace=0, axes=None,
## attributes=None, id=None)
## masked_values(data, value, rtol=1.e-5, atol=1.e-8)
## Create a masked array; mask is None if possible.
## May share data values with original array, but not recommended.
# models=['GISS-E2-R','bcc-csm1-1','MPI-ESM-P','CCSM4','MIROC-ESM','FGOALS-gl','IPSL-CM5A-LR']
# models=['MPI-ESM-P','CCSM4','MIROC-ESM','FGOALS-gl','IPSL-CM5A-LR']
# models=['FGOALS-gl']
# models=['MPI-ESM-P']
# models=['GISS-E2-R','bcc-csm1-1']
models = os.listdir("/big_disk_1/pcmdi/ipcc/data/hist_rcp85/atm/mo/ts")
rips = ["r1i1p", "r2i1p", "r3i1p", "r4i1p", "r5i1p", "r6i1p", "r7i1p", "r8i1p", "r9i1p", "r10i1p", "r11i1p", "r12i1p"]
# rips=['r2i1p','r3i1p']
# rips=['r3i1p','r4i1p','r5i1p','r6i1p','r7i1p','r8i1p']
models.remove("PET0.ESMF_LogFile")
models.remove("ts_hist_rcp85_all_runs_rmclimo0009_removed_lrmIcepoub.nc")
# models=['giss_e2_r','bnu_esm','miroc_esm']
models = MV.arange(1, 101, 1)
models = list(models)
models = [0]
# oexpt='past1000' ### because!
oexpt = "hist_rcp85" ### because!
# oexpt='historicalNat' ### because!
rips = ["1"]
for m in models:
for rip in rips:
varss = [var3]
mmm = m
pp = "1"
