def do_something(self):
num, den = 0, 0
try: num / den
except: self.exception('Division by 0 failed !\n%s', self.exception_trace())
self.info('The function emitting this message is: %s', self.func_name())
self.info('The stack trace of the function emitting this message is:\n%s', self.stack_trace())
t = create_time(['1900-01-01 00:00:00', '9999-12-31 23:59:59'])
y = create_lat(range(-90, 90))
x = create_lon(range(-180, 180))
v = cdms2.createVariable(numpy.random.ranf((len(t), len(y), len(x))), axes=[t, y, x], id='data', long_name='random data')
self.info('Time:\n%s', self.describe(t))
self.info('Latitude:\n%s', self.describe(y))
self.info('Longitude:\n%s', self.describe(x, stats=True))
self.info('Variable:\n%s', self.describe(v, stats=True))
def zeros(var, ref='mean', mean=None, getref=True, **kwargs):
"""Get the zeros of a tidal signal
:Returns: A :mod:`cdms2` variable of signs (-1,1) with a time axis
:Usage:
>>> tidal_zeros = zeros(sea_level,ref='demerliac')
>>> print tidal_zeros[0:1]
>>> print tidal_zeros[0:1].getTime().asComponentTime()
"""
# Get anomaly
ref = kwargs.pop('reference', ref)
vara, varref = _get_anomaly_(var, ref=ref, mean=mean)
taxis = vara.getTime()
vara = vara.filled()
longref = hasattr(varref, '__len__')
# Find indices
sign = N.sign(vara)
izeros = N.arange(len(vara) - 1).compress(sign[:-1] != sign[1:])
# Interpolate
units = taxis.units
times = taxis.getValue()
zeros = N.zeros((len(izeros), ))
if getref:
ret = MV2.zeros(len(zeros), id='zeros')
if not longref:
ret[:] = varref
for i, i0 in enumerate(izeros):
dv = vara[i0 + 1] - vara[i0]
zeros[i] = old_div(times[i0] * vara[i0 + 1], dv) - old_div(
times[i0 + 1] * vara[i0], dv)
if getref and longref:
dt = times[i0 + 1] - times[i0]
ret[i] = old_div(var_ref[i0] * vara[i0 + 1], dv) - old_div(
var_ref[i0 + 1] * vara[i0], dv)
# Format
if not getref:
ret = MV2.array(sign[izeros], id='zeros')
ret.units = '1 up and -1 down'
else:
cp_atts(var, ret)
ret.long_name = 'Zeros'
zeros = create_time(zeros, units)
ret.setAxis(0, zeros)
return ret
def _extrema_var_(extrem,units=None,indices=False,**kwargs):
ctime,var,idx = zip(*extrem)
if indices:
mytime = cdms2.createAxis(idx)
mytime.id = 'time_index'
mytime.long_name = 'Time index'
else:
if units is None:
units = 'minutes since %s'%strftime('%Y-%m-%d %H:%M:%S',ctime[0])
mytime = create_time(list(ctime), units)
var = cdms2.createVariable(var,copy=0)
var.setMissing(1.e20)
var.setAxis(0,mytime)
for att,val in kwargs.items():
setattr(var,att,val)
return var
def zeros(var, ref='mean',mean=None, getref=True, **kwargs):
"""Get the zeros of a tidal signal
:Returns: A :mod:`cdms2` variable of signs (-1,1) with a time axis
:Usage:
>>> tidal_zeros = zeros(sea_level,ref='demerliac')
>>> print tidal_zeros[0:1]
>>> print tidal_zeros[0:1].getTime().asComponentTime()
"""
# Get anomaly
ref = kwargs.pop('reference', ref)
vara, varref = _get_anomaly_(var, ref=ref,mean=mean)
taxis = vara.getTime()
vara = vara.filled()
longref = hasattr(varref, '__len__')
# Find indices
sign = N.sign(vara)
izeros = N.arange(len(vara)-1).compress(sign[:-1]!=sign[1:])
# Interpolate
units = taxis.units
times = taxis.getValue()
zeros = N.zeros((len(izeros), ))
if getref:
ret = MV2.zeros(len(zeros), id='zeros')
if not longref:
ret[:] = varref
for i, i0 in enumerate(izeros):
dv = vara[i0+1]-vara[i0]
zeros[i] = times[i0]*vara[i0+1]/dv - times[i0+1]*vara[i0]/dv
if getref and longref:
dt = times[i0+1]-times[i0]
ret[i] = var_ref[i0]*vara[i0+1]/dv - var_ref[i0+1]*vara[i0]/dv
# Format
if not getref:
ret = MV2.array(sign[izeros], id='zeros')
ret.units = '1 up and -1 down'
else:
cp_atts(var, ret)
ret.long_name = 'Zeros'
zeros = create_time(zeros, units)
ret.setAxis(0, zeros)
return ret
def _extrema_var_(extrem, units=None, indices=False, **kwargs):
ctime, var, idx = zip(*extrem)
if indices:
mytime = cdms2.createAxis(idx)
mytime.id = 'time_index'
mytime.long_name = 'Time index'
else:
if units is None:
units = 'minutes since %s' % strftime('%Y-%m-%d %H:%M:%S',
ctime[0])
mytime = create_time(list(ctime), units)
var = cdms2.createVariable(var, copy=0)
var.setMissing(1.e20)
var.setAxis(0, mytime)
for att, val in kwargs.items():
setattr(var, att, val)
return var
strftime, tz_to_tz, now, to_utc, utc_to_paris)
from vacumm.misc.axes import create_time
# We define units
units = 'hours since 2000-01-15 06:00'
# Is is well formatted?
print are_valid_units(units)
# -> True
# Same units?
print are_same_units('hours since 2000-1-15 06', units)
# -> True
# Change axis time units
taxis = create_time(N.arange(6.) * 48, units)
# - before
print taxis.units, taxis[0:2]
print taxis.asComponentTime()[0:2]
# -> hours since 2000-01-15 06:00 [ 0. 48.]
# -> [2000-1-15 6:0:0.0, 2000-1-17 6:0:0.0]
# - change
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)
# -*- coding: utf8 -*-
# Lecture du niveau de la mer sur 9 pas de temps à une latitude
import cdms2, MV2
from vacumm.config import data_sample
f = cdms2.open(data_sample('mars3d.xt.xe.nc'))
xe = f('xe', squeeze=1, time=slice(0, 9), lon=(-5, -4.83))
f.close()
xe.long_name = 'Original'
# On crée un trou
xe[3:4, 20:30] = MV2.masked
# Nouvel axe temporel plus précis
from vacumm.misc.axes import create_time
#old_time = xe.getTime()
old_time = create_time((xe.shape[0], ), 'hours since 2000')
xe.setAxis(0, old_time)
dt = (old_time[1] - old_time[0]) / 10.
new_time = create_time((old_time[0], old_time[-1] + dt, dt), old_time.units)
# Interpolation
from vacumm.misc.grid.regridding import interp1d
# - nearest
xe_nea = interp1d(xe, new_time, method='nearest')
xe_nea.long_name = 'Nearest'
# - linear
xe_lin = interp1d(xe, new_time, method='linear')
xe_lin.long_name = 'Linear'
# - cubic
xe_cub = interp1d(xe, new_time, method='cubic')
xe_cub.long_name = 'Cubic'
# -*- coding: utf-8 -*- # Creation d'un jeu de precipitations horaires import MV2, cdms2, numpy as N from vacumm.misc.axes import create_time hours = create_time((12*60, 25.*60, 60), 'minutes since 2000') precip = MV2.sin(N.arange(len(hours))*.2)*10 precip.setAxis(0, hours) precip.units = 'mm' precip.long_name = 'Original' # Nouvel echantillonnage / 2h hours2 = create_time((10, 30., 2), 'hours since 2000') # Regrillage 1D conservatif from vacumm.misc.grid.regridding import regrid1d precip2 = regrid1d(precip, hours2, 'conservative') precip2.long_name = 'Regridded' # Verifications print 'Total precip.:' print '- original =', precip.sum() print '- remapped =', precip2.sum() # > Total precip.: # > - original = 89.957242832779755 # > - remapped = 89.957237 # Plots from vacumm.misc.plot import savefigs from vacumm.misc.plot import bar2 kwplot = dict(color='#00ffff',edgecolor='#55aaaa',
def load(self, restart_file=None, iterindex=None, nowtime=None):
"""Load the current instance from a netcdf file
:Params:
- **restart_file**, optional: Netcdf restart file.
- **iterindex**, optional: If given, the restart file is not loaded if
``iterindex`` is greater or equal to the file's ``iterindex`` attribute.
- **nowtime**, optional: If given, the restart file is not loaded if
``nowtime`` is greater or equal to the file's ``lasttime`` attribute.
"""
# File
if restart_file is None:
restart_file = self.restart_file
if restart_file is None:
restart_file = self.default_restart_file
self.restart_file = restart_file
f = cdms2.open(restart_file)
# Config
# - check status
if iterindex is not None:
self.iterindex = iterindex
if hasattr(self, 'iterindex') and f.iterindex<=self.iterindex:
return -1
if nowtime is not None:
self.lasttime = comptime(nowtime)
if (hasattr(self, 'lasttime') and f.withtime>0 and self.lasttime
and reltime(f.lasttime, 'hours since 2000').value <=
reltime(self.lasttime, 'hours since 2000').value):
return -1
# - what was initially asked and some more
for sname in self.all_stats + ('sum', 'sqr', 'prod', 'stats'):
for st in 'st':
if not hasattr(f, st+sname): continue
value = getattr(f, st+sname)
setattr(self, st+sname, bool(value))
# - current status
self.iterindex = int(f.iterindex)
self.nitems = int(f.nitems)
if f.withtime==-1:
self.withtime = None
else:
self.withtime = bool(f.withtime)
if f.withtime:
self.lasttime = cdtime.s2c(f.lasttime)
if N.isscalar(f.bin_edges):
self.bins = None
else:
self.bins = N.asarray(f.bin_edges)
self.nbins = self.bins.shape[0]-1
self._baxis = f.getAxis('hbin').clone()
if self.nitems==0: # Still no data
f.close()
return 0
# - already had some data
self.dual = bool(f.dual)
self.ns = int(f.ns)
self.nt = int(f.nt)
self._nts = f.nts.tolist()
self.tstats = bool(f.tstats)
self.sstats = bool(f.sstats)
if not self.withtime:
self._stimes = None
# Spatial statistics
if self.sstats:
# Time axes
if self.withtime:
self._stimes = tuple([[] for i in xrange(self.nitems)])
for i, tt in enumerate(self._stimes):
taxis = f.getAxis('t'+str(i))
tvalues = self._aslist_(taxis[:])
oldid = taxis.stataccum_oldid
for tvals in tvalues:
tx = create_time(tvals, taxis.units, id=oldid)
cp_atts(taxis, tx, id=False, exclude=[oldid])
self._stimes[i].append(tx)
# Count
self._scount = self._load_array_(f, id='scount')
# Other stats
self._sstats = {}
for key in self.single_stats:
if not self.dual: # single var
vid = 's' + key
if vid not in f.variables:
continue
self._sstats[key] = self._load_array_(f, vid),
else: # two vars
for i in xrange(self.nitems):
vid = 's%s%s'%(key, str(i))
if vid not in f.variables:
break
self._sstats.setdefault(key, ())
self._sstats[key] += self._load_array_(f, vid),
for key in self.dual_stats:
vid = 's%s'%key
if vid in f.variables:
self._sstats[key] = self._load_array_(f, vid)
# Temporal statistics
if self.tstats:
# Count
self._tcount = self._load_array_(f, 'tcount')
# Other stats
for key in self._dual_accums+self._single_accums:
tid = 't'+key
if not getattr(self, tid): continue
if key in self._dual_accums:
value = self._load_array_(f, tid)
setattr(self, '_'+tid, value)
else:
value = ()
for i in xrange(self.nitems):
value += self._load_array_(f, tid+str(i)),
setattr(self, '_'+tid, value)
# Templates
# - base arrays
self._tbase = N.zeros(self.ns)
if self.thist:
self._thbase = N.zeros((self.nbins, self.ns), 'l')
# - cdat templates
self._ttemplates = ()
self._thtemplates = None
if self.thist:
self._thtemplates = ()
for i in xrange(self.nitems):
prefix = 'var%i_'%i
for vname in f.variables:
if vname.startswith(prefix) and vname != prefix+'atts': break
ttpl = f(vname)
_rm_id_prefix_(ttpl, 'var%i_'%i, exc=self._baxis)
self._ttemplates += ttpl,
if self.thist:
self._thtemplates += self._template_t2ht_(ttpl),
# Attributes
self._atts = ()
for ivar in xrange(self.nitems):
attrs = f['var%i_atts'%ivar].attributes.copy()
attrs['id'] = attrs['stataccum_id']
del attrs['stataccum_id']
self._atts += attrs,
f.close()
return self.iterindex
# On definit des unites
units = 'hours since 2000-01-15 06:00'
# Le format est-il bon ?
from vacumm.misc.atime import *
print are_good_units(units)
# -> True
# Memes unites ?
print are_same_units('hours since 2000-1-15 06', units)
# -> True
# Changer les unites d'un axe de temps
from vacumm.misc.axes import create_time
import numpy as N
taxis = create_time(N.arange(6.)*48, units)
# - avant changement
print taxis.units, taxis[0:2]
print taxis.asComponentTime()[0:2]
# -> hours since 2000-01-15 06:00 [ 0. 48.]
# -> [2000-1-15 6:0:0.0, 2000-1-17 6:0:0.0]
# - 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)
def coloc_mod_on_pro(self,
model,
profiles,
varnames,
select=None,
method='nearest'):
'''Colocalize model on profile data.
Load model data corresponding to the selected profiles positions and time.
Returns loaded model longitudes, latitudes, depths and requested variable(s)
:Params:
- **model**: model data :class:`~vacumm.data.misc.dataset.Dataset`
- **profiles**: profile data :class:`~vacumm.data.misc.profile.ProfilesDataset`
- **varnames**: variables to load (ex: ('temp','sal') or (('temp','temperature'),('sal','salinity'))
- **select**: selector
- **method**: coloc method (**nearest** or **interp**)
:Return:
- **lons_mod**: model longitude coordinates, shape: (profile)
- **lats_mod**: model latitude coordinates, shape: (profile)
- **deps_mod**: model depth coordinates, shape: (level,profile)
- **var1**: requested variables, shape: (level,profile)
- ...
- **varN**
.. todo::
- also load and return profile data here
- exclude coords where profile data is masked (no data for specified depth)
- return time coordinates
- return depth and vars with shape (profile,level)
'''
self.verbose(
'Colocalizing %s on %s\nvarnames: %s\nselect: %s\n method: %s',
model.__class__.__name__, profiles.__class__.__name__, varnames,
select, method)
prof_pro = profiles.get_axis('profile', select=select)
if prof_pro is None or not len(prof_pro):
raise Exception('No profiles found, aborting')
lev_pro = profiles.get_axis('level', select=select)
time_pro = profiles.get_variable('time', select=select)
lons_pro = profiles.get_variable('longitude', select=select)
lats_pro = profiles.get_variable('latitude', select=select)
dates = create_time(time_pro).asComponentTime()
self.info('Number of profiles: %s', len(dates))
self.info('Profiles time coverage: %s to %s', dates[0], dates[-1])
# Init model
td = model.get_time_res()
dtmax = (td.days * 86400 + td.seconds, 'seconds')
self.info('Detected model time step: %s', td)
grid_mod = model.get_grid()
xres, yres = resol(grid_mod)
time_mod = model.get_time()
ctime_mod = time_mod.asComponentTime()
self.info('Model time coverage: %s to %s', ctime_mod[0], ctime_mod[-1])
level_mod = model.get_level(select=select)
lons_mod = MV2.zeros((len(prof_pro), )) + MV2.masked
lats_mod = lons_mod.clone()
deps_mod = MV2.zeros((len(level_mod), len(prof_pro))) + MV2.masked
deps_mod.setAxis(1, prof_pro)
lons_mod.id, lats_mod.id, deps_mod.id = 'longitude', 'latitude', 'depth'
# Creation des variables demandees
variables = []
for n in varnames:
v = MV2.zeros((len(level_mod), len(prof_pro))) + MV2.masked
v.setAxis(1, prof_pro)
v.id = is_iterable(n) and n[0] or n
variables.append(v)
cdms2.setAutoBounds(1) # ???
# Boucle temporelle
for ip, date in enumerate(dates):
try:
# Limites spatiales
lon = lons_pro[ip]
lat = lats_pro[ip]
lon_min = lon - 2 * xres
lon_max = lon + 2 * xres
lat_min = lat - 2 * yres
lat_max = lat + 2 * yres
date_interval = (add_time(date, -dtmax[0], dtmax[1]),
add_time(date, dtmax[0], dtmax[1]), 'ccb')
self.info('Colocalizing data for date %s, lon: %s, lat: %s',
date, lon, lat)
# Methode 1 : donnees les plus proches
if method == 'nearest':
sel = dict(time=(date, date, 'ccb'),
longitude=(lon, lon, 'ccb'),
latitude=(lat, lat, 'ccb'))
# Verifier la disponibilite des donnees
if time_mod.mapIntervalExt(sel['time']) is None:
self.warning('Time interval %s not found', sel['time'])
continue
if grid_mod.getLatitude().mapInterval(
sel['latitude']) is None:
self.warning('Latitude coordinate %s not found',
sel['latitude'])
continue
if grid_mod.getLongitude().mapInterval(
sel['longitude']) is None:
self.warning('Longitude coordinate %s not found',
sel['longitude'])
continue
# Load tmp depth to get lon & lat coordinates
#tmp = model.get_depth(select=sel, squeeze=False) # tmp squeezed !!! see sigma ?
tmp = model.get_variable(varnames[0],
select=sel,
squeeze=False)
lons_mod[ip] = tmp.getLongitude()[0]
lats_mod[ip] = tmp.getLatitude()[0]
deps_mod[:, ip] = model.get_depth(select=sel, squeeze=True)
for iv, vn in enumerate(varnames):
variables[iv][:, ip] = model.get_variable(vn,
select=sel,
squeeze=True)
# Methode 2 : interpolation
elif method == 'interp':
sel = dict(time=date_interval,
longitude=(lon_min, lon_max),
latitude=(lat_min, lat_max))
if time_mod.mapIntervalExt(sel['time']) is None:
self.warning('Time interval %s not found', sel['time'])
continue
if grid_mod.getLatitude().mapInterval(
sel['latitude']) is None:
self.warning('Latitude coordinate %s not found',
sel['latitude'])
continue
if grid_mod.getLongitude().mapInterval(
sel['longitude']) is None:
self.warning('Longitude coordinate %s not found',
sel['longitude'])
continue
# Lectures
order = 'tzyx'
# lon & lat du profile car interp sur cette position
lons_mod[ip], lats_mod[ip] = lon, lat
deps_mod_tzyx = model.get_depth(select=sel,
order=order,
squeeze=True)
tmp_tzyx = []
for iv, vn in enumerate(varnames):
tmp_tzyx.append(
model.get_variable(vn,
select=sel,
order=order,
squeeze=True))
# Interpolations temporelles
mctime = tmp_tzyx[0].getTime()
mrtime = mctime.asRelativeTime()
d0 = date.torel(mctime.units).value - mrtime[0].value
d1 = mrtime[1].value - date.torel(mctime.units).value
f0 = d0 / (d0 + d1)
f1 = d1 / (d0 + d1)
deps_mod_zyx = f0 * deps_mod_tzyx[0] + f1 * deps_mod_tzyx[1]
tmp_zyx = []
for iv, vn in enumerate(varnames):
tmp_zyx.append(f0 * tmp_tzyx[iv][0] +
f1 * tmp_tzyx[iv][1])
del tmp_tzyx
# Interpolations spatiales
deps_mod[:, ip] = numpy.squeeze(
grid2xy(deps_mod_zyx,
numpy.array([lon]),
numpy.array([lat]),
method='nat'))
for iv, vn in enumerate(varnames):
variables[iv][:, ip] = numpy.squeeze(
grid2xy(tmp_zyx[iv],
numpy.array([lon]),
numpy.array([lat]),
method='nat'))
del tmp_zyx
else:
raise ValueError('Invalid colocation method: %s' %
(method))
except:
self.exception('Failed to colocalize data for date %s', date)
for v in [deps_mod] + variables:
v.getAxis(0).id = 'level'
v.getAxis(0).designateLevel()
data = tuple([lons_mod, lats_mod, deps_mod] + variables)
self.verbose('Colocalized data:\n %s',
'\n '.join(self.describe(o) for o in data))
return data
def load(self, restart_file=None, iterindex=None, nowtime=None):
"""Load the current instance from a netcdf file
:Params:
- **restart_file**, optional: Netcdf restart file.
- **iterindex**, optional: If given, the restart file is not loaded if
``iterindex`` is greater or equal to the file's ``iterindex`` attribute.
- **nowtime**, optional: If given, the restart file is not loaded if
``nowtime`` is greater or equal to the file's ``lasttime`` attribute.
"""
# File
if restart_file is None:
restart_file = self.restart_file
if restart_file is None:
restart_file = self.default_restart_file
self.restart_file = restart_file
f = cdms2.open(restart_file)
# Config
# - check status
if iterindex is not None:
self.iterindex = iterindex
if hasattr(self, 'iterindex') and f.iterindex<=self.iterindex:
return -1
if nowtime is not None:
self.lasttime = comptime(nowtime)
if (hasattr(self, 'lasttime') and f.withtime>0 and self.lasttime
and comptime(f.lasttime)<=comptime(self.lasttime)):
return -1
# - what was initially asked and some more
for sname in self.all_stats + ('sum', 'sqr', 'prod', 'stats'):
for st in 'st':
if not hasattr(f, st+sname): continue
value = getattr(f, st+sname)
setattr(self, st+sname, bool(value))
# - current status
self.iterindex = int(f.iterindex)
self.nitems = int(f.nitems)
if f.withtime==-1:
self.withtime = None
else:
self.withtime = bool(f.withtime)
if f.withtime:
self.lasttime = cdtime.s2c(f.lasttime)
if N.isscalar(f.bin_edges):
self.bins = None
else:
self.bins = N.asarray(f.bin_edges)
self.nbins = self.bins.shape[0]-1
self._baxis = f.getAxis('hbin').clone()
if self.nitems==0: # Still no data
f.close()
return 0
# - already had some data
self.dual = bool(f.dual)
self.ns = int(f.ns)
self.nt = int(f.nt)
self._nts = f.nts.tolist()
self.tstats = bool(f.tstats)
self.sstats = bool(f.sstats)
if not self.withtime:
self._stimes = None
# Spatial statistics
if self.sstats:
# Time axes
if self.withtime:
self._stimes = tuple([[] for i in xrange(self.nitems)])
for i, tt in enumerate(self._stimes):
taxis = f.getAxis('t'+str(i))
tvalues = self._aslist_(taxis[:])
oldid = taxis.stataccum_oldid
for tvals in tvalues:
tx = create_time(tvals, taxis.units, id=oldid)
cp_atts(taxis, tx, id=False, exclude=[oldid])
self._stimes[i].append(tx)
# Count
self._scount = self._load_array_(f, id='scount')
# Other stats
self._sstats = {}
for key in self.single_stats:
self._sstats[key] = ()
for i in xrange(self.nitems):
self._sstats[key] += self._load_array_(f, 's%s%s'%(key, str(i))),
for key in self.dual_stats:
self._sstats[key] = self._load_array_(f, 's%s'%key)
# Temporal statistics
if self.tstats:
# Count
self._tcount = self._load_array_(f, 'tcount')
# Other stats
for key in self._dual_accums+self._single_accums:
tid = 't'+key
if not getattr(self, tid): continue
if key in self._dual_accums:
value = self._load_array_(f, tid)
setattr(self, '_'+tid, value)
else:
value = ()
for i in xrange(self.nitems):
value += self._load_array_(f, tid+str(i)),
setattr(self, '_'+tid, value)
# Templates
# - base arrays
self._tbase = N.zeros(self.ns)
if self.thist:
self._thbase = N.zeros((self.nbins, self.ns), 'l')
# - cdat templates
self._ttemplates = ()
if self.thist:
self._thtemplates = ()
for i in xrange(self.nitems):
prefix = 'var%i_'%i
for vname in f.variables:
if vname.startswith(prefix): break
ttpl = f(vname)
_rm_id_prefix_(ttpl, 'var%i_'%i, exc=self._baxis)
self._ttemplates += ttpl,
if self.thist:
self._thtemplates += self._template_t2ht_(ttpl),
# Attributes
self._atts = ()
for ivar in xrange(self.nitems):
attrs = f['var%i_atts'%ivar].attributes.copy()
attrs['id'] = attrs['stataccum_id']
del attrs['stataccum_id']
self._atts += attrs,
f.close()
return self.iterindex
# -*- coding: utf8 -*-
# Lecture du niveau de la mer sur 9 pas de temps à une latitude
import cdms2, MV2
from vacumm.config import data_sample
f =cdms2.open(data_sample('mars3d.xt.xe.nc'))
xe = f('xe', squeeze=1, time=slice(0, 9), lon=(-5, -4.83))
f.close()
xe.long_name = 'Original'
# On crée un trou
xe[3:4, 20:30] = MV2.masked
# Nouvel axe temporel plus précis
from vacumm.misc.axes import create_time
#old_time = xe.getTime()
old_time=create_time((xe.shape[0], ), 'hours since 2000')
xe.setAxis(0, old_time)
dt = (old_time[1]-old_time[0])/10.
new_time = create_time((old_time[0], old_time[-1]+dt, dt), old_time.units)
# Interpolation
from vacumm.misc.grid.regridding import interp1d
# - nearest
xe_nea = interp1d(xe, new_time, method='nearest')
xe_nea.long_name = 'Nearest'
# - linear
xe_lin = interp1d(xe, new_time, method='linear')
xe_lin.long_name = 'Linear'
# - cubic
xe_cub = interp1d(xe, new_time, method='cubic')
xe_cub.long_name = 'Cubic'
# -*- coding: utf-8 -*- # Creation d'un jeu de precipitations horaires import MV2, cdms2, numpy as N from vacumm.misc.axes import create_time hours = create_time((12*60, 25.*60, 60), 'minutes since 2000') precip = MV2.sin(N.arange(len(hours))*.2)*10 precip.setAxis(0, hours) precip.units = 'mm' precip.long_name = 'Original' # Nouvel echantillonnage / 2h hours2 = create_time((10, 30., 2), 'hours since 2000') # Regrillage 1D conservatif from vacumm.misc.grid.regridding import regrid1d precip2 = regrid1d(precip, hours2, 'conservative') precip2.long_name = 'Regridded' # Verifications print 'Total precip.:' print '- original =', precip.sum() print '- remapped =', precip2.sum() # > Total precip.: # > - original = 89.957242832779755 # > - remapped = 89.957237 # Plots from vacumm.misc.plot import savefigs from vacumm.misc.plot import bar2 kwplot = dict(color='#00ffff',edgecolor='#55aaaa',
def coloc_mod_on_pro(self, model, profiles, varnames, select=None, method='nearest'):
'''Colocalize model on profile data.
Load model data corresponding to the selected profiles positions and time.
Returns loaded model longitudes, latitudes, depths and requested variable(s)
:Params:
- **model**: model data :class:`~vacumm.data.misc.dataset.Dataset`
- **profiles**: profile data :class:`~vacumm.data.misc.profile.ProfilesDataset`
- **varnames**: variables to load (ex: ('temp','sal') or (('temp','temperature'),('sal','salinity'))
- **select**: selector
- **method**: coloc method (**nearest** or **interp**)
:Return:
- **lons_mod**: model longitude coordinates, shape: (profile)
- **lats_mod**: model latitude coordinates, shape: (profile)
- **deps_mod**: model depth coordinates, shape: (level,profile)
- **var1**: requested variables, shape: (level,profile)
- ...
- **varN**
.. todo::
- also load and return profile data here
- exclude coords where profile data is masked (no data for specified depth)
- return time coordinates
- return depth and vars with shape (profile,level)
'''
self.verbose('Colocalizing %s on %s\nvarnames: %s\nselect: %s\n method: %s', model.__class__.__name__, profiles.__class__.__name__, varnames, select, method)
prof_pro = profiles.get_axis('profile', select=select)
if prof_pro is None or not len(prof_pro):
raise Exception('No profiles found, aborting')
lev_pro = profiles.get_axis('level', select=select)
time_pro = profiles.get_variable('time', select=select)
lons_pro = profiles.get_variable('longitude', select=select)
lats_pro = profiles.get_variable('latitude', select=select)
dates = create_time(time_pro).asComponentTime()
self.info('Number of profiles: %s', len(dates))
self.info('Profiles time coverage: %s to %s', dates[0], dates[-1])
# Init model
td = model.get_time_res()
dtmax = (td.days*86400+td.seconds, 'seconds')
self.info('Detected model time step: %s', td)
grid_mod = model.get_grid()
xres, yres = resol(grid_mod)
time_mod = model.get_time()
ctime_mod = time_mod.asComponentTime()
self.info('Model time coverage: %s to %s', ctime_mod[0], ctime_mod[-1])
level_mod = model.get_level(select=select)
lons_mod = MV2.zeros((len(prof_pro),))+MV2.masked
lats_mod = lons_mod.clone()
deps_mod = MV2.zeros((len(level_mod), len(prof_pro)))+MV2.masked
deps_mod.setAxis(1, prof_pro)
lons_mod.id, lats_mod.id, deps_mod.id = 'longitude', 'latitude', 'depth'
# Creation des variables demandees
variables = []
for n in varnames:
v = MV2.zeros((len(level_mod), len(prof_pro)))+MV2.masked
v.setAxis(1, prof_pro)
v.id = is_iterable(n) and n[0] or n
variables.append(v)
cdms2.setAutoBounds(1) # ???
# Boucle temporelle
for ip, date in enumerate(dates):
try:
# Limites spatiales
lon = lons_pro[ip]
lat = lats_pro[ip]
lon_min = lon-2*xres
lon_max = lon+2*xres
lat_min = lat-2*yres
lat_max = lat+2*yres
date_interval = (add_time(date, - dtmax[0], dtmax[1]), add_time(date, dtmax[0], dtmax[1]), 'ccb')
self.info('Colocalizing data for date %s, lon: %s, lat: %s', date, lon, lat)
# Methode 1 : donnees les plus proches
if method == 'nearest':
sel = dict(time=(date, date, 'ccb'), longitude=(lon, lon, 'ccb'), latitude=(lat, lat, 'ccb'))
# Verifier la disponibilite des donnees
if time_mod.mapIntervalExt(sel['time']) is None:
self.warning('Time interval %s not found', sel['time'])
continue
if grid_mod.getLatitude().mapInterval(sel['latitude']) is None:
self.warning('Latitude coordinate %s not found', sel['latitude'])
continue
if grid_mod.getLongitude().mapInterval(sel['longitude']) is None:
self.warning('Longitude coordinate %s not found', sel['longitude'])
continue
# Load tmp depth to get lon & lat coordinates
#tmp = model.get_depth(select=sel, squeeze=False) # tmp squeezed !!! see sigma ?
tmp = model.get_variable(varnames[0], select=sel, squeeze=False)
lons_mod[ip] = tmp.getLongitude()[0]
lats_mod[ip] = tmp.getLatitude()[0]
deps_mod[:, ip] = model.get_depth(select=sel, squeeze=True)
for iv,vn in enumerate(varnames):
variables[iv][:,ip] = model.get_variable(vn, select=sel, squeeze=True)
# Methode 2 : interpolation
elif method == 'interp':
sel = dict(time=date_interval, longitude=(lon_min, lon_max), latitude=(lat_min, lat_max))
if time_mod.mapIntervalExt(sel['time']) is None:
self.warning('Time interval %s not found', sel['time'])
continue
if grid_mod.getLatitude().mapInterval(sel['latitude']) is None:
self.warning('Latitude coordinate %s not found', sel['latitude'])
continue
if grid_mod.getLongitude().mapInterval(sel['longitude']) is None:
self.warning('Longitude coordinate %s not found', sel['longitude'])
continue
# Lectures
order = 'tzyx'
# lon & lat du profile car interp sur cette position
lons_mod[ip], lats_mod[ip] = lon, lat
deps_mod_tzyx = model.get_depth(select=sel, order=order, squeeze=True)
tmp_tzyx = []
for iv,vn in enumerate(varnames):
tmp_tzyx.append(model.get_variable(vn, select=sel, order=order, squeeze=True))
# Interpolations temporelles
mctime = tmp_tzyx[0].getTime()
mrtime = mctime.asRelativeTime()
d0 = date.torel(mctime.units).value - mrtime[0].value
d1 = mrtime[1].value - date.torel(mctime.units).value
f0 = d0 / (d0 + d1)
f1 = d1 / (d0 + d1)
deps_mod_zyx = f0 * deps_mod_tzyx[0] + f1 * deps_mod_tzyx[1]
tmp_zyx = []
for iv,vn in enumerate(varnames):
tmp_zyx.append(f0 * tmp_tzyx[iv][0] + f1 * tmp_tzyx[iv][1])
del tmp_tzyx
# Interpolations spatiales
deps_mod[:,ip] = numpy.squeeze(grid2xy(deps_mod_zyx, numpy.array([lon]), numpy.array([lat]), method='nat'))
for iv,vn in enumerate(varnames):
variables[iv][:,ip] = numpy.squeeze(grid2xy(tmp_zyx[iv], numpy.array([lon]), numpy.array([lat]), method='nat'))
del tmp_zyx
else:
raise ValueError('Invalid colocation method: %s'%(method))
except:
self.exception('Failed to colocalize data for date %s', date)
for v in [deps_mod] + variables:
v.getAxis(0).id = 'level'
v.getAxis(0).designateLevel()
data = tuple([lons_mod, lats_mod, deps_mod] + variables)
self.verbose('Colocalized data:\n %s', '\n '.join(self.describe(o) for o in data))
return data
# Comversions
ct = comptime('2000-01')
print ct.day
# -> 1
print comptime(['2000', '2001'])
# -> [2000-1-1 0:0:0.0, 2001-1-1 0:0:0.0]
print numtime(ct)
# -> 730120.0
print date2num(Datetime(2000, 1, 1))
# -> 730120.0
print strtime([730120.0, cdtime.reltime(1, 'years since 1999')])
['2000-1-1 0:0:0.0', '2000-1-1 0:0:0.0']
# Axes de temps
taxis = create_time((0, 3.), 'days since 2000-01-01')
print datetime(taxis)
# -> [datetime.datetime(2000, 1, 1, 0, 0),
# datetime.datetime(2000, 1, 2, 0, 0),
# datetime.datetime(2000, 1, 3, 0, 0)]
# Additions/soustractions
print add(ct, 2, 'days')
# -> 2000-1-3 0:0:0.0
print add(taxis, 1, 'month')[:]
# -> [ 31. 32. 33.]
# Zones
print utc_to_paris('2000-01-01 12:00')
# -> 2000-1-1 13:0:0.0
print utc_to_paris('2000-06-01 12:00')
