"""Test :class:`~vacumm.misc.stats.StatAccum` with dumping and loading """
# Imports
from vcmq import MV2, N, code_file_name, StatAccum
from numpy.testing import assert_array_almost_equal
from numpy.random import seed
cdms2.setAutoBounds('off')
# Setup masked data
seed(0)
nt, ny, nx = 20, 3, 3
var1 = MV2.array(N.random.random((nt, ny, nx)))
var1.getAxis(0).designateTime()
var1.getAxis(0).units = 'months since 2000'
var1.getAxis(0).id = 'time'
var1.getAxis(1).id = 'lat'
var1.getAxis(1).designateLatitude()
var1.getAxis(2).id = 'lon'
var1.getAxis(2).designateLongitude()
var1.units = 'm'
var1.id = 'ssh'
var2 = var1.clone()
var2[:] = N.random.random((nt, ny, nx))
var1[3:13, :1, :1] = MV2.masked
var2[5:15, -1:, -1:] = MV2.masked
var2.long_name = 'Sea level'
var2.id = 'sla'
mask = var1.mask|var2.mask # common mask
vmax = var2.max()
bins = N.linspace(-0.1*vmax, 0.9*vmax, 14)
#!/usr/bin/env python
"""Create fake observation netcdf files
"""
from vcmq import (cdms2, MV2, os, create_dep, create_lon, create_lat, N,
masked_polygon, map2)
cdms2.setAutoBounds(False)
# Profiles
ncfile = '../data/obs.profiles.nc'
lons = MV2.array([-5.8, -5.7, -4.6, -2.8], id='lon')
lats = MV2.array([48.1, 47.5, 47.4, 47.3], id='lat')
daxis = create_dep((-100., 1, 5.))
nz = len(daxis)
np = len(lons)
temp_error = N.resize([.2, .5, .3, .8], (nz, np))
sal_error = N.resize([.3, .1, .15, .4], (nz, np))
temp_error = MV2.array(temp_error, id='temp_error')
sal_error = MV2.array(sal_error, id='sal_error')
temp_error[:nz / 2, 2] = MV2.masked
sal_error[:nz / 2, 2] = MV2.masked
temp_error[:3 * nz / 4, 3] = MV2.masked
sal_error[:3 * nz / 4, 3] = MV2.masked
mob = MV2.array([0, 1, 1, 0], id='mobility', fill_value=-1)
paxis = lons.getAxis(0)
paxis.id = 'station'
axes = [daxis, paxis]
f = cdms2.open(ncfile, 'w')
for var in lons, lats, mob, temp_error, sal_error:
var.setAxis(-1, paxis)
"""Test :func:`~vacumm.misc.plot.hov2` with a TZ variable"""
# Imports
from vcmq import N, MV2, create_dep, create_time, hov2, os, rc, code_file_name
# Init data with z 1D
nt = 10
nz = 8
var = N.dot(N.hanning(nt).reshape(nt, 1), N.hanning(nz).reshape(1, nz))
var = MV2.array(var)
time = create_time((0., nt), units="days since 2000")
z1d = create_dep((-nz+1, 1.))
var.setAxis(0, time)
var.setAxis(1, z1d)
z2d = N.resize(z1d, var.shape)
z2d *= N.resize((N.arange(1., nt+1)/nt).reshape(1, nt), (nz, nt)).T
# Plot with z 1D
rc('font', size=8)
kw = dict(show=False, bgcolor='0.5', date_fmt="%a")
hov2(var, subplot=211, **kw)
# Plot with z 2D
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
hov2(var, xaxis=z2d, subplot=212, twin='x', savefig=figfile, close=True, **kw)
# Unittest
result = dict(files=figfile)
# Generate data
# - reference
xr = N.arange(20.) - 25
yr = N.arange(10.) + 43.
xxr, yyr = N.meshgrid(xr, yr)
zzr = (N.sin(xxr*N.pi/6)*N.sin(yyr*N.pi/6) + \
N.exp(-((xxr-7.)**2+(yyr-7.)**2)/4.**2))*100.
zzr -= zzr.mean()
zzr = N.ma.asarray(zzr)
zzr[5:, 10:] = N.ma.masked
# - input at random locations
ij = N.unique((N.random.rand(150) * zzr.size).astype('i'))
xi, yi, zi = xxr.flat[ij], yyr.flat[ij], zzr.flat[ij]
zi = N.ma.resize(zi, (3, zi.size))
# - format
zi = MV2.array(zi, copy=False, id='sst')
taxis = zi.getAxis(0)
taxis.units = 'hours since 2000'
taxis.axis = 'T'
ggo = create_grid(xr, yr)
zzr = MV2.array(zzr)
set_grid(zzr, ggo)
# Call and plot
kw = dict(vmin=zzr.min(),
vmax=zzr.max(),
lon=(xr[0], xr[-1]),
cmap_lum=0.7,
linewidth=.5,
lat=(yr[0], yr[-1]),
show=False,
"""Test the traditionnal CDAT regrid2 regridder""" from vcmq import MV2, create_grid, meshbounds, P, add_grid, N, bounds1d, plot2d, savefigs,code_file_name from regrid2 import Horizontal # Input nx, ny = 6, 4 vari = MV2.array(N.arange(nx*ny*1.).reshape(ny, nx), fill_value=1e20) xi = vari.getAxis(-1) xi[:] *= 2 yi = vari.getAxis(-2) yi[:] *= 3 xi.designateLongitude() yi.designateLatitude() xi.setBounds(bounds1d(xi)) yi.setBounds(bounds1d(yi)) vari[1:2, 2:4] = MV2.masked gridi = vari.getGrid() # Output grido = create_grid(xi[:]+2*2.5, yi[:]+3*1.5) xo = grido.getLongitude() yo = grido.getLatitude() xo.setBounds(bounds1d(xo)) yo.setBounds(bounds1d(yo)) xxob, yyob = meshbounds(xo, yo) # Regridding varo, wo = vari.regrid(grido, tool='regrid2', returnTuple=1)
"""Test :class:`~vacumm.misc.stats.StatAccum` with dumping and loading """
# Imports
from vcmq import MV2, N, code_file_name, StatAccum
from numpy.testing import assert_array_almost_equal
from numpy.random import seed
cdms2.setAutoBounds('off')
# Setup masked data
seed(0)
nt, ny, nx = 20, 3, 3
var1 = MV2.array(N.random.random((nt, ny, nx)))
var1.getAxis(0).designateTime()
var1.getAxis(0).units = 'months since 2000'
var1.getAxis(0).id = 'time'
var1.getAxis(1).id = 'lat'
var1.getAxis(1).designateLatitude()
var1.getAxis(2).id = 'lon'
var1.getAxis(2).designateLongitude()
var1.units = 'm'
var1.id = 'ssh'
var2 = var1.clone()
var2[:] = N.random.random((nt, ny, nx))
var1[3:13, :1, :1] = MV2.masked
var2[5:15, -1:, -1:] = MV2.masked
var2.long_name = 'Sea level'
var2.id = 'sla'
mask = var1.mask | var2.mask # common mask
vmax = var2.max()
bins = N.linspace(-0.1 * vmax, 0.9 * vmax, 14)
xxi[j,:] -= j*0.5
#yyi[j,:] += j
for i in xrange(nxi):
yyi[:,i] += i*0.5
gridi = create_grid2d(xxi,yyi) # input cdms grid
xxib,yyib = meshbounds(xxi,yyi) # coordinates of cell corners
# Output curved grid
nxo = 7
nyo = 7
xxo, yyo = N.meshgrid(N.arange(nxo)+.5, N.arange(nyo)-.5)
grido = create_grid2d(xxo, yyo) # output cdms grid
xxob,yyob = meshbounds(xxo,yyo) # coordinates of cell corners
# Input field
vari = MV2.array(N.arange(nyi*nxi).reshape(nyi,nxi))+10.
vari[1,1:3] = 100
vari[:2,1:3] = MV2.masked
set_grid(vari, gridi) # set grid and axes
#gridi.setMask(vari.mask)
# Define plot function
figfile = code_file_name(ext=False)+'_%(ifig)i.png'
#'%(tool)s_%(method)s.png'
figfiles = []
rc('font',size=9)
kw = dict(vmin=vari.min(),vmax=vari.max())
# Define logger
logfile = code_file_name(ext='log')
f = open(logfile, 'w')
"""Compare CDAT regridding speed with rectangular and rectangular grids"""
config = {
'esmf':['linear', 'patch', 'conserv'],
'libcf':['linear'],
}
# Imports
from vcmq import MV2, create_grid2d, code_file_name, os, CDATRegridder, N, set_grid, psinfo
from vacumm.misc.grid import rotate_grid
from time import time
# Input
nx = ny = 300
vari = MV2.array(N.arange(nx*ny*1.).reshape(ny, nx))
gridi = create_grid2d(vari.getAxis(1)[:]*50/nx, vari.getAxis(0)[:]*50/nx)
set_grid(vari, gridi)
# Output grid
gridor = create_grid2d(vari.getAxis(1)[:]*0.09*50/nx,
vari.getAxis(0)[:]*0.09*50/nx)
gridoc = rotate_grid(gridi, 30)
# Log
logfile = code_file_name(ext='log')
if os.path.exists(logfile): os.remove(logfile)
f = open(logfile, 'w')
print >>f, 'NY=%(ny)i, NX=%(nx)i'%locals()
# Loop on methods
for tool, methods in config.items():
from vcmq import (N, MV2, code_file_name, os, P, create_lon, create_lat, create_dep,
create_time, lindates, create_axis, reltime, grid2xy,
comptime, set_grid, rotate_grid, add_grid)
# Rectangular xyzt with 1d z data and coords
# - data
lon = create_lon(N.linspace(lon0, lon1, nx))
lat = create_lat(N.linspace(lat0, lat1, ny))
dep = create_dep(N.linspace(dep0, dep1, nz))
time = create_time(lindates(time0, time1, nt))
extra = create_axis(N.arange(ne), id='member')
data = N.resize(lat[:], (ne, nt, nz, nx, ny)) # function of y
data = N.moveaxis(data, -1, -2)
#data = N.arange(nx*ny*nz*nt*ne, dtype='d').reshape(ne, nt, nz, ny, nx)
vi = MV2.array(data,
axes=[extra, time, dep, lat, lon], copy=False,
fill_value=1e20)
N.random.seed(0)
xo = N.random.uniform(lon0, lon1, np)
yo = N.random.uniform(lat0, lat1, np)
zo = N.random.uniform(dep0, dep1, np)
to = comptime(N.random.uniform(reltime(time0, time.units).value,
reltime(time1, time.units).value, np),
time.units)
# Rectangular xyzt with 1d z
vo = grid2xy(vi, xo=xo, yo=yo, zo=zo, to=to, method='linear')
von = grid2xy(vi, xo=xo, yo=yo, zo=zo, to=to, method='nearest')
assert vo.shape==(ne, np)
N.testing.assert_allclose(vo[0], yo)
kwp = dict(vmin=vi.min(), vmax=vi.max())
def generate_pseudo_ensemble(ncpat,
varnames=None,
nrens=50,
enrich=2.,
norms=None,
getmodes=False,
logger=None,
asdicts=False,
anomaly=True,
ncensfile=None,
**kwargs):
"""Generate a static pseudo-ensemble from a single simulation
Parameters
----------
ncpat: string
netcdf file name or pattern
nrens: int
Ensemble size
enrich: float
Enrichment factor
getmodes: bool
Get also EOFs end eigen values
**kwargs:
Extra parameters are passed to :func:`load_model_at_dates`
Return
------
list (or dict) of arrays:
variables with their name as keys
dict: eofs, ev and variance, optional
eofs: list (or dict) of arrays(nmodes, ...), optional
EOFs
ev: array(nmodes), optional
Eigen values
var: array
Variance
"""
# Logger
kwlog = kwfilter(kwargs, 'logger_')
if logger is None:
logger = get_logger(**kwlog)
logger.verbose('Generating pseudo-ensemble')
# Ensembe size
enrich = max(enrich, 1.)
nt = int(nrens * enrich)
logger.debug(
' enrich={enrich}, nt={nt}, ncpat={ncpat}, varnames={varnames}'.
format(**locals()))
# Read variables
logger.debug('Reading the model at {} dates'.format(nt))
data = load_model_at_regular_dates(ncpat,
varnames=varnames,
nt=nt,
asdict=False,
**kwargs)
single = not isinstance(data, list)
# Norms
if isinstance(norms, dict):
norms = var_prop_dict2list(data, norms)
# Enrichment
witheofs = nrens != nt
if witheofs:
logger.debug('Computing reduced rank ensemble with EOFs analysis')
# Stack packed variables together
stacker = Stacker(data, norms=norms, logger=logger)
meanstate = N.zeros(stacker.ns)
states = N.asfortranarray(stacker.stacked_data.copy())
# Compute EOFs
stddev, svals, svecs, status = f_eofcovar(dim_fields=stacker.ns,
offsets=1,
remove_mstate=0,
do_mv=0,
states=states,
meanstate=meanstate)
if status != 0:
raise SONATError('Error while calling fortran eofcovar routine')
neof = svals.size # computed
neofr = nrens - 1 # retained
svals = svals[:neofr] * N.sqrt(
(neof - 1.) / neof) # to be consistent with total variance
svecs = svecs[:, :neofr]
# Generate ensemble
sens = f_sampleens(svecs, svals, meanstate, flag=0)
# Unstack
ens = stacker.unstack(sens,
format=2,
rescale='norm' if anomaly else True)
if getmodes:
# Modes
mode_axis = create_axis(N.arange(1, neofr + 1, dtype='i'),
id='mode')
eofs = stacker.unstack(svecs,
firstdims=mode_axis,
id='{id}_eof',
rescale=False,
format=1)
svals = MV2.array(svals,
axes=[mode_axis],
id='ev',
attributes={'long_name': 'Eigen values'})
svals.total_variance = float(stacker.ns)
# Variance
vv = stacker.format_arrays([d.var(axis=0) for d in stacker.datas],
id='{id}_variance',
mode=1)
variance = stacker.unmap(vv)
else: # No enrichment -> take the anomaly if requested
logger.debug('Getting the anomaly to build the ensemble')
ens = data
if anomaly:
if single:
ens[:] = ens.asma() - ens.asma().mean(axis=0)
else:
for i, e in enumerate(ens):
ens[i][:] = e.asma() - e.asma().mean(axis=0)
# Finalize
getmodes = getmodes and witheofs
member_axis = create_axis(N.arange(nrens, dtype='i'),
id='member',
long_name='Member')
if single:
ens.setAxis(0, member_axis)
else:
for var in ens:
var.setAxis(0, member_axis)
# Dump to file
if ncensfile:
logger.debug('Dump the ensemble to netcdf')
checkdir(ncensfile)
f = cdms2.open(ncensfile, 'w')
ensvars = list(ens) if not single else [ens]
if getmodes:
if single:
ensvars.append(eofs)
ensvars.append(variance)
else:
ensvars.extend(eofs)
ensvars.extend(variance)
ensvars.append(svals)
for var in ensvars:
f.write(var)
f.close()
logger.created(ncensfile)
# As dicts
if asdicts:
if single:
ens = OrderedDict([(ens.id, ens)])
if getmodes:
eofs = OrderedDict([(eofs.id, eofs)])
variance = OrderedDict([(variance.id, variance)])
else:
ens = OrderedDict([(var.id, var) for var in ens])
if getmodes:
eofs = OrderedDict([(var.id, var) for var in eofs])
variance = OrderedDict([(var.id, var) for var in variance])
# Return
if not getmodes:
return ens
return ens, dict(eofs=eofs, eigenvalues=svals, variance=variance)
"""Test :func:`~vacumm.misc.atime.interp_clim`"""
from vcmq import (code_file_name, interp_clim, MV2, N, create_time, lindates,
curve, strftime)
# Original clim
N.random.seed(0)
s = N.resize(N.sin(N.linspace(0, 1, 13)[:12] * 2 * N.pi), (2, 12)).T
clim = MV2.array(s, fill_value=1e20)
p = curve(clim[:, 0],
'o-',
show=False,
subplot=211,
title='Original climatology',
xmin=-.5,
xmax=11.5,
xticks=range(12),
xticklabels=[strftime('%b', '2000-%i' % i) for i in range(1, 13)])
# Target times
times = lindates('2000-01-01', '2001-12-31', 5, 'day')
# Interpolations
for i, method in enumerate((
'linear',
'cubic',
)):
climo = interp_clim(clim, times, method=method)
c = curve(climo[:, 0],
'o-',
color='gr'[i],
show=False,
vari = MV2.asarray(
N.ma.resize(depi1d[:], (nt, ny, nx, nzi)).transpose([0, 3, 1, 2]))
vari.setAxis(1, depi1d)
varol1 = regrid1d(vari, depo1d, method='linear')
varol2 = regrid1d(vari, depo1d, method='linear', iaxi=0, iaxo=0, axi=depi1d)
result.append(('assertEqual', [(varol1 - varol2).std(), 0]))
varoc = regrid1d(vari, depo1d, method='cellave')
myplot(vari, depi1d, varol1, varoc, depo1d, code_file_name(ext='_0.png'))
# 4d->1d
depi1d = N.arange(-4500., 1, 500)
nzi = depi1d.shape[0]
depi4d = N.resize(N.resize(depi1d, (nx, ny, nzi)).T, (nt, nzi, ny, nx))
depi4d += 500 * (N.random.random(depi4d.shape) - 0.5)
depo1d = create_dep(N.arange(-4000., 1, 333.33))
vari = MV2.array(depi4d, fill_value=1e20)
vari.getAxis(1).designateLevel()
depi4d = MV2.asarray(depi4d)
depi4d.getAxis(1).designateLevel()
varol1 = regrid1d(vari, depo1d, method='linear', axi=depi4d)
varol2 = regrid1d(vari, depo1d, method='linear', iaxi=1, axi=depi4d)
result.append(('assertEqual', [(varol1 - varol2).std(), 0]))
varoc = regrid1d(vari, depo1d, method='cellave', iaxi=1, axi=depi4d)
myplot(vari, depi4d, varol1, varoc, depo1d, code_file_name(ext='_1.png'))
# 4d->4d
depi1d = N.arange(-4500., 1, 500)
nzi = depi1d.shape[0]
depi4d = N.resize(N.resize(depi1d, (nx, ny, nzi)).T, (nt, nzi, ny, nx))
depi4d += 500 * (N.random.random(depi4d.shape) - 0.5)
depo1d = create_dep(N.arange(-4000., 1, 333.33))
"""Test the class :class:`~vacumm.misc.grid.regridding.CurvedInterpolator`"""
from vacumm.misc.grid.misc import rotate_grid
from vacumm.misc.grid.regridding import CurvedInterpolator
from vcmq import P, N, set_grid, plot2d, MV2, add_grid, code_file_name, os
# Curved grid
nxy = 10
lon = N.arange(nxy*1.)
lat = N.arange(nxy*1.)
gridi = rotate_grid((lon, lat), 30)
xxi = gridi.getLongitude()[:].filled()
yyi = gridi.getLatitude()[:].filled()
vari = MV2.array(yyi)
set_grid(vari, gridi)
kw = dict(vmin=vari.min(), vmax=vari.max())
P.figure(figsize=(10, 3.5))
P.subplot(131, aspect=1)
P.contourf(xxi, yyi, vari.asma(), **kw)
add_grid(gridi, edges=False, centers=-1)
xylims = (xxi.min(), xxi.max(), yyi.min(), yyi.max())
P.axis(xylims)
P.title('Curved grid')
# Interpolate to grid
xg, yg = N.meshgrid(N.arange(-3.5, 14.5), N.arange(-3.5, 14.5))
nxyg = xg.shape
cig = CurvedInterpolator(gridi, (xg, yg), g2g=True)
varog = cig(vari)
P.subplot(132, aspect=1)
vari = MV2.asarray(N.ma.resize(depi1d[:], (nt, ny, nx, nzi)).transpose([0, 3, 1, 2]))
vari.setAxis(1, depi1d)
varol1 = regrid1d(vari, depo1d, method='linear')
varol2 = regrid1d(vari, depo1d, method='linear', iaxi=0, iaxo=0, axi=depi1d)
result.append(('assertEqual', [(varol1-varol2).std(), 0]))
varoc = regrid1d(vari, depo1d, method='cellave')
myplot(vari, depi1d, varol1, varoc, depo1d, code_file_name(ext='_0.png'))
# 4d->1d
depi1d = N.arange(-4500., 1, 500)
nzi = depi1d.shape[0]
depi4d = N.resize(N.resize(depi1d, (nx, ny, nzi)).T, (nt, nzi, ny, nx))
depi4d += 500*(N.random.random(depi4d.shape)-0.5)
depo1d = create_dep(N.arange(-4000., 1, 333.33))
vari = MV2.array(depi4d, fill_value=1e20)
vari.getAxis(1).designateLevel()
depi4d = MV2.asarray(depi4d)
depi4d.getAxis(1).designateLevel()
varol1 = regrid1d(vari, depo1d, method='linear', axi=depi4d)
varol2 = regrid1d(vari, depo1d, method='linear', iaxi=1, axi=depi4d)
result.append(('assertEqual', [(varol1-varol2).std(), 0]))
varoc = regrid1d(vari, depo1d, method='cellave', iaxi=1, axi=depi4d)
myplot(vari, depi4d, varol1, varoc, depo1d, code_file_name(ext='_1.png'))
# 4d->4d
depi1d = N.arange(-4500., 1, 500)
nzi = depi1d.shape[0]
depi4d = N.resize(N.resize(depi1d, (nx, ny, nzi)).T, (nt, nzi, ny, nx))
depi4d += 500*(N.random.random(depi4d.shape)-0.5)
def create_Dthnc(self, fileout, TimeSeries):
if '2D' in fileout:
self.i23d = 2
else:
self.i23d = 3
# create file
if self.i23d == 3:
Nlev = self.zz.shape[1]
else:
Nlev = 1
time_Series, nc = create_ncTH(
fileout, len(self.llon), Nlev, self.ivs,
np.round((TimeSeries - TimeSeries[0]) * 24 * 3600))
for n in range(0, len(TimeSeries)):
tin = create_time(np.ones(len(self.llon) * Nlev) *
(TimeSeries[n] + 1),
units='days since 1-1-1')
total = np.zeros(shape=(self.ivs, len(self.llon), Nlev))
# get tide
if self.tidal:
var = self.HC.keys()
for i, v in enumerate(sorted(var)):
# horizontal interpolation
tmp = get_tide(self.constidx, self.tfreq, self.HC[v],
np.array(TimeSeries[n]), self.lat0)
if self.i23d > 2: # vertical interpolation
tmp = vertical_extrapolation(tmp, self.zz, z0=self.z0)
total[i, :, :] = total[i, :, :] + tmp
if self.residual:
var = self.res_vars
for i, v in enumerate(sorted(var)):
arri = self.res_file[v][:]
if self.i23d > 2:
dep = create_depth(arri.getAxis(1)[:])
extra = create_axis(N.arange(1), id='member')
arri2 = np.tile(arri, [1, 1, 1, 1, 1])
arri3 = MV2.array(arri2,
axes=[
extra,
arri.getAxis(0), dep,
arri.getAxis(2),
arri.getAxis(3)
],
copy=False,
fill_value=1e20)
zi = arri.getAxis(1)[:]
if np.mean(zi) > 0:
zi = zi * -1
tb = grid2xy(arri3,
xo=np.tile(self.llon,
[Nlev, 1]).T.flatten(),
yo=np.tile(self.llat,
[Nlev, 1]).T.flatten(),
zo=self.zz.flatten(),
method='linear',
to=tin,
zi=zi)
else:
tb = grid2xy(arri,
xo=self.llon,
yo=self.llat,
method='linear',
to=tin)
if np.any(tb.mask == True):
bad = tb.mask == True
if len(bad.shape) > 1:
bad = bad[0, :]
tin_bad = create_time(np.ones(len(bad)) *
(TimeSeries[n] + 1),
units='days since 1-1-1')
if self.i23d > 2:
llon = np.tile(self.llon, [Nlev, 1]).T.flatten()
llat = np.tile(self.llat, [Nlev, 1]).T.flatten()
zz = self.zz.flatten()
zi = arri.getAxis(1)[:]
if np.mean(zi) > 0:
zi = zi * -1
tb[0, bad] = grid2xy(arri3,
xo=llon[bad],
yo=llat[bad],
zo=zz[bad],
method='nearest',
to=tin_bad,
zi=zi)
else:
tb[bad] = grid2xy(
arri,
xo=np.array(self.llon)[bad].tolist(),
yo=np.array(self.llat)[bad].tolist(),
method='nearest',
to=tin_bad)
if np.any(tb.mask == True):
print('probleme')
total[i, :, :] = total[i, :, :] + np.reshape(
tb, (len(self.llon), Nlev))
total = np.transpose(total, (1, 2, 0))
if np.isnan(total).any():
import pdb
pdb.set_trace()
if n % 100 == 0:
self.logger.info(
'For timestep=%.f, max=%.4f, min=%.4f , max abs diff=%.4f'
% (TimeSeries[n], total.max(), total.min(),
abs(np.diff(total, n=1, axis=0)).max()))
time_Series[n, :, :, :] = total
nc.close()
# Add text
m.add_text(0.1, 0.1, 'My text', color='r', shadow=True, weight='bold')
m.add_text(-2, 50.5, 'My text with transform', transform='data', ha='center',
bbox=dict(facecolor='y', alpha=.3))
m.add_lon_label(-8, 47.5, -8, va='center', ha='center', transform='data', fmt='%g')
m.add_lat_label(-8, 47, 47, va='center', ha='center', transform='data')
m.add_time_label(.1, .9, '2000')
# Add place
m.add_place(-2, 49, 'My place', text_family='monospace', text_size=15)
# Add lines
m.add_line([-8, 46, -1, 50], color='b')
m.add_lines([-8, -1, -4, -8], [45, 45, 47, 45], color='r', linewidth=2, shadow=True)
m.add_box([-8, 46, -1, 50], color='c')
m.savefig(figfile%0)
m.show()
m.close()
# Test with dates
var = MV2.array([3, 6])
time = create_time(['2000', '2005'], units='years since 2000')
var.setAxis(0, time)
c = curve2(var, show=False)
c.add_point('2001', 4, marker='+', size=100, linewidth=2)
c.savefig(figfile%1)
m.close()
"""Test :func:`~vacumm.misc.grid.regridding.extend1d` and :func:`~vacumm.misc.grid.regridding.extend2d`"""
from vcmq import N, create_grid2d, P, rc, plot2d, MV2, set_grid, savefigs, code_file_name
from vacumm.misc.grid.regridding import extend1d, extend2d
# Input data
nxi = 4
nyi = 3
xxi, yyi = N.meshgrid(N.arange(nxi) + .25, N.arange(nyi) - .25)
for j in xrange(nyi):
xxi[j, :] -= j * 0.5
for i in xrange(nxi):
yyi[:, i] += i * 0.5
gridi = create_grid2d(xxi, yyi) # input cdms grid
vari = MV2.array(N.arange(nyi * nxi).reshape(nyi, nxi)) + 10.
set_grid(vari, gridi) # set grid and axes
# Extend and plot
rc('font', size=9)
P.figure(figsize=(6, 6))
kw = dict(xmin=xxi.min() - 3,
xmax=xxi.max() + 3,
ymin=yyi.min() - 3,
ymax=yyi.max() + 3,
show=False,
xhide='auto',
yhide='auto')
# - original
plot2d(vari, title='Original', subplot=(2, 2, 1), **kw)
# - extend1d
for i, (axis, ext, mode) in enumerate([(-1, (2, 2), 'same'),
"""Test :func:`~vacumm.misc.plot.section2` with a Z- variable"""
# Imports
from vcmq import N, MV2, cdms2, create_dep, rc, section2, code_file_name, os
# Init data with z 1D
nz = 8
nd = 10
var = N.dot(N.hanning(nz).reshape(nz, 1), N.hanning(nd).reshape(1, nd))
var = MV2.array(var)
d = cdms2.createAxis(N.arange(nd))
d.units = 'km'
d.long_name = 'Distance'
z1d = create_dep((-nz + 1, 1.))
var.setAxis(0, z1d)
var.setAxis(1, d)
z2d = N.resize(z1d[:].reshape(1, nz), (nd, nz)).T
z2d *= N.arange(1., nd + 1) / nd
# Plot with z 1D
rc('font', size=8)
kw = dict(show=False, bgcolor='0.5')
section2(var, subplot=211, **kw)
# Plot with z 2D
figfile = code_file_name(ext='png')
if os.path.exists(figfile): os.remove(figfile)
section2(var, yaxis=z2d, subplot=212, savefig=figfile, close=True, **kw)
# Result
result = dict(files=figfile)
-8,
va='center',
ha='center',
transform='data',
fmt='%g')
m.add_lat_label(-8, 47, 47, va='center', ha='center', transform='data')
m.add_time_label(.1, .9, '2000')
# Add place
m.add_place(-2, 49, 'My place', text_family='monospace', text_size=15)
# Add lines
m.add_line([-8, 46, -1, 50], color='b')
m.add_lines([-8, -1, -4, -8], [45, 45, 47, 45],
color='r',
linewidth=2,
shadow=True)
m.add_box([-8, 46, -1, 50], color='c')
m.savefig(figfile % 0)
m.close()
# Test with dates
var = MV2.array([3, 6])
time = create_time(['2000', '2005'], units='years since 2000')
var.setAxis(0, time)
c = curve2(var, show=False)
c.add_point('2001', 4, marker='+', size=100, linewidth=2)
c.savefig(figfile % 1)
m.close()
"""Test the traditionnal CDAT regrid2 regridder""" from vcmq import MV2, create_grid, meshbounds, P, add_grid, N, bounds1d, plot2d, savefigs, code_file_name from regrid2 import Horizontal # Input nx, ny = 6, 4 vari = MV2.array(N.arange(nx * ny * 1.).reshape(ny, nx), fill_value=1e20) xi = vari.getAxis(-1) xi[:] *= 2 yi = vari.getAxis(-2) yi[:] *= 3 xi.designateLongitude() yi.designateLatitude() xi.setBounds(bounds1d(xi)) yi.setBounds(bounds1d(yi)) vari[1:2, 2:4] = MV2.masked gridi = vari.getGrid() # Output grido = create_grid(xi[:] + 2 * 2.5, yi[:] + 3 * 1.5) xo = grido.getLongitude() yo = grido.getLatitude() xo.setBounds(bounds1d(xo)) yo.setBounds(bounds1d(yo)) xxob, yyob = meshbounds(xo, yo) # Regridding varo, wo = vari.regrid(grido, tool='regrid2', returnTuple=1) # Plot