def _extremum_(func,ctime,i0,i,var,spline):
"""Extremum possibly using splines"""
nt = len(var)
if spline and nt >= 4: # and i != 0 and i != (nt-1)
if i == 0:
ifirst, ilast = 0, 4
elif i == nt-1:
ifirst, ilast = nt-4, nt
else:
icenter = i - int(var[i-1] > var[i+1])
ifirst = max(icenter-1, 0)
ilast = ifirst + 4
if ilast > nt:
ilast -= 1
ifirst -= 1
mn_units = 'minutes since %s'%ctime[i0+ifirst]
old_rts = cdms2.createAxis(N.asarray([ct.torel(mn_units).value for ct in ctime[i0+ifirst:i0+ilast]],dtype='d'))
old_var = MV2.array(var[ifirst:ilast], axes=[old_rts], copyaxes=0)
mn_rts = cdms2.createAxis(N.arange(int(old_rts[-1]+1),dtype='d'))
mn_var = interp1d(old_var, mn_rts, method='cubic')
del old_var, old_rts
# mn_var = spline_interpolate(old_rts,var[i-1:i+2],mn_rts)
# mn_var = splev(mn_rts, splrep(old_rts,var[ifirst:ilast]))
mn_i = func(mn_var)
val = mn_var[mn_i]
del mn_var
this_ctime = cdtime.reltime(mn_i,mn_units).tocomp()
else:
this_ctime = ctime[i0+i]
val = var[i]
return val,this_ctime
def _extremum_(func, ctime, i0, i, var, spline):
"""Extremum possibly using splines"""
nt = len(var)
if spline and nt >= 4: # and i != 0 and i != (nt-1)
if i == 0:
ifirst, ilast = 0, 4
elif i == nt - 1:
ifirst, ilast = nt - 4, nt
else:
icenter = i - int(var[i - 1] > var[i + 1])
ifirst = max(icenter - 1, 0)
ilast = ifirst + 4
if ilast > nt:
ilast -= 1
ifirst -= 1
mn_units = 'minutes since %s' % ctime[i0 + ifirst]
old_rts = cdms2.createAxis(
N.asarray([
ct.torel(mn_units).value
for ct in ctime[i0 + ifirst:i0 + ilast]
],
dtype='d'))
old_var = MV2.array(var[ifirst:ilast], axes=[old_rts], copyaxes=0)
mn_rts = cdms2.createAxis(N.arange(int(old_rts[-1] + 1), dtype='d'))
mn_var = interp1d(old_var, mn_rts, method='cubic')
del old_var, old_rts
# mn_var = spline_interpolate(old_rts,var[i-1:i+2],mn_rts)
# mn_var = splev(mn_rts, splrep(old_rts,var[ifirst:ilast]))
mn_i = func(mn_var)
val = mn_var[mn_i]
del mn_var
this_ctime = cdtime.reltime(mn_i, mn_units).tocomp()
else:
this_ctime = ctime[i0 + i]
val = var[i]
return val, this_ctime
# 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' # Plots from matplotlib import rcParams rcParams['font.size'] = 8 import pylab as P from vacumm.misc.plot import yhide, xscale, savefigs, hov2, curve2 from vacumm.misc.color import cmap_jets from genutil import minmax
def plot_layer_mod_on_pro(self,
model,
profiles,
varname,
depth,
select=None,
**kwargs):
'''Get a layer of variable for a specified depth.
:Params:
- **varname**: variable to process
- **depth**: output depth(s)
Other params, see: :func:`coloc_mod_on_pro`
'''
self.verbose(
'Plotting layer of colocalized %s on %s\nvarname: %s\ndepth: %s\nselect: %s\nkwargs: %s',
model.__class__.__name__, profiles.__class__.__name__, varname,
depth, select, kwargs)
lons_mod, lats_mod, deps_mod, var_mod = \
self.coloc_mod_on_pro(
model, profiles,
# If varname is a list of possible names, wrap it into a
# tuple of one element as we are requiring only one variable
len(numpy.shape(varname)) and (varname,) or varname,
select, **kwargs)
odep = create_dep(is_iterable(depth) and depth or [depth])
var_mod = var_mod.reorder('-z')
var_mod = interp1d(var_mod, axo=odep, xmap=0,
xmapper=deps_mod) # Required order: ...z
var_mod = var_mod.reorder('z-')
model.verbose('layer: %s', var_mod)
lons_pro, lats_pro, var_pro = profiles.get_layer(varname,
depth,
select=select)
profiles.verbose('layer: %s', var_pro)
# =====
# Tracés
vmin = var_pro.min()
vmax = var_pro.max()
if var_mod.count():
vmin = min(var_mod.min(), vmin)
vmax = max(var_mod.max(), vmax)
else:
self.warning('No model data')
if 'latitude' in select:
lat_min, lat_max = select['latitude'][:2]
else:
la = model.get_latitude()
lat_min, lat_max = min(la), max(la)
if 'longitude' in select:
lon_min, lon_max = select['longitude'][:2]
else:
lo = model.get_longitude()
lon_min, lon_max = min(lo), max(lo)
levels = auto_scale((vmin, vmax))
vmin = levels[0]
vmax = levels[-1]
# Création de la palette
cmap = cmap_magic(levels)
# On trace de champ du modèle moyené sur la période choisie
m = map2(lon=(lon_min, lon_max), lat=(lat_min, lat_max), show=False)
# On trace le modèle en fond
# =====
msca = None
try:
msca = m.map.scatter(lons_mod,
lats_mod,
s=100,
c=var_mod,
vmin=vmin,
vmax=vmax,
cmap=cmap,
label='model')
except:
self.exception('Failed plotting model data')
# =====
# Triangulation du modèle
# import matplotlib.tri as tri
# triang = tri.Triangulation(lons_mod, lats_mod)
# # On trace le modèle en fond
# mod = m.axes.tripcolor(triang, var_mod, vmin=vmin, vmax=vmax, cmap=cmap)
# =====
# On trace les observations avec des points plus petits
psca = None
try:
psca = m.map.scatter(lons_pro,
lats_pro,
s=25,
c=var_pro,
vmin=m.vmin,
vmax=m.vmax,
cmap=m.cmap,
label='profiles')
except:
self.exception('Failed plotting profile data')
# Colorbar
if msca is not None:
colorbar(msca)
elif psca is not None:
colorbar(psca)
def generic(var,
coefs,
units='hours',
get_tide=False,
only_tide=False,
filter_name='Generic',
check_regular=False,
strict=True,
interp_method='cubic',
axis=None):
"""Apply a filter to signal (cdms2 variable) using symetric coefficients. First axis is supposed to be time and regular.
- **var**: Data to filter with first axis supposed to be time.
- **coefs**: Second half of coefficients.
- *strict*: Mask values where at least one value is missing in the filtering interval.
- *filter_name*: Name to give to the filter [default: 'Generic']
%s
"""
# Return?
if only_tide:
get_tide = True
# Coefficients
units = unit_type(units)
sunits = unit_type(units, string_type=True)
coefs = coefs.astype('f')
if coefs[-1] != 0.:
coefs = N.concatenate((coefs, [
0.,
]))
ncoefs = (len(coefs) - 1) * 2 + 1
# Input variable
var = MV2.asarray(var)
check_axes(var)
# Input time axis
if axis is None:
try:
axis = var.getOrder().index('t')
except:
axis = 0
time = var.getAxis(axis)
if check_regular:
assert isregular(time), 'Your time axis seems not regular'
dt = N.diff(time[:]).mean()
if not time.attributes.has_key('units'):
time.units = 'hours since 1970-01-01'
print 'The time axis of your variable has no units. Assuming : ' + time.units
if not time.isTime():
time.designateTime(calendar=cdtime.DefaultCalendar)
# Check time range
time_range = time[-1] - time[0]
coefs_units = sunits + ' ' + ' '.join(time.units.split()[1:])
coefs_range0 = cdtime.r2r(cdtime.reltime(time[0], coefs_units), time.units)
coefs_range1 = coefs_range0.add(ncoefs - 1, units)
coefs_range = coefs_range1.value - coefs_range0.value
coefs_dt = coefs_range0.add(1, units).value - coefs_range0.value
if time_range < coefs_range:
raise Exception, 'Your sample is shorter than the time range of your coefficients'
nt = len(var)
# Remap coefficients to data time axis
tc = var.dtype.char
# print 'dt', dt
# print 'coefs_dt', coefs_dt
if abs((coefs_dt - dt) / dt) > 0.01:
oldx = cdms2.createAxis(N.arange(len(coefs)) * coefs_dt)
old_coefs = MV2.array(coefs, axes=[oldx])
newx = cdms2.createAxis(N.arange(0., max(oldx), dt))
coefs = interp1d(old_coefs, newx, interp_method).filled()
coefs = N.concatenate((coefs[:0:-1], coefs))
coefs /= N.sum(coefs)
ncoefs = len(coefs)
# Filtering using convolution
fvar = convolve1d(var.filled(0.), coefs, axis=axis, mode='constant')
if var.mask is MV2.nomask:
mask = N.zeros(nt)
else:
mask = var.mask.astype('f')
fmask = convolve1d(mask, coefs, axis=axis, mode='constant', cval=1.) != 0.
fvar = MV2.asarray(fvar)
fvar = MV2.masked_where(fmask, fvar, copy=0)
# Output variables
if not only_tide:
fvar.id = var.id + '_cotes'
fvar.name = fvar.id
if var.attributes.has_key('long_name'):
fvar.long_name = 'Tide removed signal from ' + var.long_name.lower(
)
fvar.long_name_fr = 'Signal sans la maree'
if var.attributes.has_key('units'):
fvar.units = var.units
fvar.setAxis(0, time)
fvar.filter_coefficients = coefs
fvar.filter_name = filter_name
res = [
fvar,
]
if get_tide:
tvar = var - fvar
tvar.id = 'tidal_' + var.id
tvar.name = tvar.id
if var.attributes.has_key('long_name'):
tvar.long_name = 'Tidal signal from ' + var.long_name.lower()
if var.attributes.has_key('units'):
tvar.units = var.units
tvar.setAxis(0, time)
tvar.filter_coefficients = coefs
tvar.filter_name = filter_name
if only_tide:
return tvar
res.append(tvar)
if isinstance(res, list) and len(res) == 1:
return res[0]
else:
return res
def generic(var,coefs,units='hours',get_tide=False,only_tide=False,filter_name='Generic',
check_regular=False,strict=True, interp_method='cubic', axis=None):
"""Apply a filter to signal (cdms2 variable) using symetric coefficients. First axis is supposed to be time and regular.
- **var**: Data to filter with first axis supposed to be time.
- **coefs**: Second half of coefficients.
- *strict*: Mask values where at least one value is missing in the filtering interval.
- *filter_name*: Name to give to the filter [default: 'Generic']
%s
"""
# Return?
if only_tide:
get_tide = True
# Coefficients
units = unit_type(units)
sunits = unit_type(units, string_type=True)
coefs = coefs.astype('f')
if coefs[-1] != 0.:
coefs = N.concatenate((coefs,[0.,]))
ncoefs = (len(coefs)-1)*2+1
# Input variable
var = MV2.asarray(var)
check_axes(var)
# Input time axis
if axis is None:
try:
axis = var.getOrder().index('t')
except:
axis = 0
time = var.getAxis(axis)
if check_regular:
assert isregular(time), 'Your time axis seems not regular'
dt = N.diff(time[:]).mean()
if not time.attributes.has_key('units'):
time.units = 'hours since 1970-01-01'
print 'The time axis of your variable has no units. Assuming : '+time.units
if not time.isTime():
time.designateTime(calendar=cdtime.DefaultCalendar)
# Check time range
time_range = time[-1]-time[0]
coefs_units = sunits+' '+' '.join(time.units.split()[1:])
coefs_range0 = cdtime.r2r(cdtime.reltime(time[0],coefs_units),time.units)
coefs_range1 = coefs_range0.add(ncoefs-1,units)
coefs_range = coefs_range1.value - coefs_range0.value
coefs_dt = coefs_range0.add(1,units).value - coefs_range0.value
if time_range < coefs_range:
raise Exception,'Your sample is shorter than the time range of your coefficients'
nt = len(var)
# Remap coefficients to data time axis
tc = var.dtype.char
# print 'dt', dt
# print 'coefs_dt', coefs_dt
if abs((coefs_dt-dt)/dt) > 0.01:
oldx = cdms2.createAxis(N.arange(len(coefs))*coefs_dt)
old_coefs = MV2.array(coefs, axes=[oldx])
newx = cdms2.createAxis(N.arange(0.,max(oldx),dt))
coefs = interp1d(old_coefs, newx, interp_method).filled()
coefs = N.concatenate((coefs[:0:-1],coefs))
coefs /= N.sum(coefs)
ncoefs = len(coefs)
# Filtering using convolution
fvar = convolve1d(var.filled(0.), coefs, axis=axis, mode='constant')
if var.mask is MV2.nomask:
mask = N.zeros(nt)
else:
mask = var.mask.astype('f')
fmask = convolve1d(mask, coefs, axis=axis, mode='constant', cval=1.)!=0.
fvar = MV2.asarray(fvar)
fvar = MV2.masked_where(fmask, fvar, copy=0)
# Output variables
if not only_tide:
fvar.id = var.id+'_cotes'
fvar.name = fvar.id
if var.attributes.has_key('long_name'):
fvar.long_name = 'Tide removed signal from '+var.long_name.lower()
fvar.long_name_fr = 'Signal sans la maree'
if var.attributes.has_key('units'):
fvar.units = var.units
fvar.setAxis(0,time)
fvar.filter_coefficients = coefs
fvar.filter_name = filter_name
res = [fvar,]
if get_tide:
tvar = var-fvar
tvar.id = 'tidal_'+var.id
tvar.name = tvar.id
if var.attributes.has_key('long_name'):
tvar.long_name = 'Tidal signal from '+var.long_name.lower()
if var.attributes.has_key('units'):
tvar.units = var.units
tvar.setAxis(0,time)
tvar.filter_coefficients = coefs
tvar.filter_name = filter_name
if only_tide:
return tvar
res.append(tvar)
if isinstance(res, list) and len(res) == 1:
return res[0]
else:
return res
# 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' # Plots from matplotlib import rcParams ; rcParams['font.size'] = 8 import pylab as P from vacumm.misc.plot import yhide, xscale, savefigs, hov2, curve2 from vacumm.misc.color import cmap_jets from genutil import minmax vmin, vmax = minmax(xe, xe_lin)
def plot_layer_mod_on_pro(self, model, profiles, varname, depth, select=None, **kwargs):
'''Get a layer of variable for a specified depth.
:Params:
- **varname**: variable to process
- **depth**: output depth(s)
Other params, see: :func:`coloc_mod_on_pro`
'''
self.verbose('Plotting layer of colocalized %s on %s\nvarname: %s\ndepth: %s\nselect: %s\nkwargs: %s',
model.__class__.__name__, profiles.__class__.__name__, varname, depth, select, kwargs)
lons_mod, lats_mod, deps_mod, var_mod = \
self.coloc_mod_on_pro(
model, profiles,
# If varname is a list of possible names, wrap it into a
# tuple of one element as we are requiring only one variable
len(numpy.shape(varname)) and (varname,) or varname,
select, **kwargs)
odep = create_dep(is_iterable(depth) and depth or [depth])
var_mod = var_mod.reorder('-z')
var_mod = interp1d(var_mod, axo=odep, xmap=0, xmapper=deps_mod) # Required order: ...z
var_mod = var_mod.reorder('z-')
model.verbose('layer: %s', var_mod)
lons_pro, lats_pro, var_pro = profiles.get_layer(varname, depth, select=select)
profiles.verbose('layer: %s', var_pro)
# =====
# Tracés
vmin = var_pro.min()
vmax = var_pro.max()
if var_mod.count():
vmin = min(var_mod.min(), vmin)
vmax = max(var_mod.max(), vmax)
else:
self.warning('No model data')
if 'latitude' in select:
lat_min, lat_max = select['latitude'][:2]
else:
la = model.get_latitude()
lat_min, lat_max = min(la), max(la)
if 'longitude' in select:
lon_min, lon_max = select['longitude'][:2]
else:
lo = model.get_longitude()
lon_min, lon_max = min(lo), max(lo)
levels = auto_scale((vmin, vmax))
vmin = levels[0]
vmax = levels[-1]
# Création de la palette
cmap = cmap_magic(levels)
# On trace de champ du modèle moyené sur la période choisie
m = map2(lon=(lon_min, lon_max), lat=(lat_min, lat_max), show=False)
# On trace le modèle en fond
# =====
msca = None
try: msca = m.map.scatter(lons_mod, lats_mod, s=100, c=var_mod, vmin=vmin, vmax=vmax, cmap=cmap, label='model')
except: self.exception('Failed plotting model data')
# =====
# Triangulation du modèle
# import matplotlib.tri as tri
# triang = tri.Triangulation(lons_mod, lats_mod)
# # On trace le modèle en fond
# mod = m.axes.tripcolor(triang, var_mod, vmin=vmin, vmax=vmax, cmap=cmap)
# =====
# On trace les observations avec des points plus petits
psca = None
try: psca = m.map.scatter(lons_pro, lats_pro, s=25, c=var_pro, vmin=m.vmin, vmax=m.vmax, cmap=m.cmap, label='profiles')
except: self.exception('Failed plotting profile data')
# Colorbar
if msca is not None:
colorbar(msca)
elif psca is not None:
colorbar(psca)
