def corr(a, b, axis=None):
u"""
相関係数を計算する。
:Arguments:
**a, b** : array_like
入力
**axis** : int, optional
a, b の一方が1次元で、もう一方が多次元の場合に相関を計算する軸。
:Returns:
**r** : array_like
"""
a = np.asarray(a)
b = np.asarray(b)
if axis == None:
if not a.shape == b.shape:
raise ValueError, "input array must have shame shape"
else:
if a.ndim == 1 and b.ndim>1:
a = tools.expand(a, b.ndim, axis=axis)
elif b.ndim == 1 and a.ndim>1:
b = tools.expand(b, a.ndim, axis=axis)
a_anom = a - a.mean(axis=axis)
b_anom = b - b.mean(axis=axis)
xy = (a_anom*b_anom).sum(axis=axis)
xx = np.sqrt((a_anom**2).sum(axis=axis))
yy = np.sqrt((b_anom**2).sum(axis=axis))
r = xy/xx/yy
return r
def corr(a, b, axis=None):
u"""
相関係数を計算する。
:Arguments:
**a, b** : array_like
入力
**axis** : int, optional
a, b の一方が1次元で、もう一方が多次元の場合に相関を計算する軸。
:Returns:
**r** : array_like
"""
a = np.asarray(a)
b = np.asarray(b)
if axis == None:
if not a.shape == b.shape:
raise ValueError, "input array must have shame shape"
else:
if a.ndim == 1 and b.ndim > 1:
a = tools.expand(a, b.ndim, axis=axis)
elif b.ndim == 1 and a.ndim > 1:
b = tools.expand(b, a.ndim, axis=axis)
a_anom = a - a.mean(axis=axis)
b_anom = b - b.mean(axis=axis)
xy = (a_anom * b_anom).sum(axis=axis)
xx = np.sqrt((a_anom**2).sum(axis=axis))
yy = np.sqrt((b_anom**2).sum(axis=axis))
r = xy / xx / yy
return r
def __init__(self,
db,
path,
remote_root,
redirect_stdout=False,
redirect_stderr=False,
finish_up_after=None,
save_interval=None):
self.db = db
self.dbstate = sql.book_dct_postgres_serial(self.db)
if self.dbstate is None:
raise JobError(JobError.NOJOB, 'No job was found to run.')
print "Selected job id=%d in table=%s in db=%s" % (
self.dbstate.id, self.db.tablename, self.db.dbname)
try:
state = expand(self.dbstate)
# The id isn't set by the line above
state["jobman.id"] = self.dbstate.id
if "dbdict" in state:
state.jobman = state.dbdict
experiment = resolve(state.jobman.experiment)
remote_path = os.path.join(remote_root, self.db.dbname,
self.db.tablename, str(self.dbstate.id))
super(DBRSyncChannel,
self).__init__(path, remote_path, experiment, state,
redirect_stdout, redirect_stderr,
finish_up_after, save_interval)
except:
self.dbstate['jobman.status'] = self.ERR_START
raise
def __init__(self, db, path, remote_root,
redirect_stdout=False, redirect_stderr=False,
finish_up_after=None, save_interval=None):
self.db = db
self.dbstate = sql.book_dct_postgres_serial(self.db)
if self.dbstate is None:
raise JobError(JobError.NOJOB,
'No job was found to run.')
print "Selected job id=%d in table=%s in db=%s" % (
self.dbstate.id, self.db.tablename, self.db.dbname)
try:
state = expand(self.dbstate)
# The id isn't set by the line above
state["jobman.id"] = self.dbstate.id
if "dbdict" in state:
state.jobman = state.dbdict
experiment = resolve(state.jobman.experiment)
remote_path = os.path.join(remote_root, self.db.dbname,
self.db.tablename, str(self.dbstate.id))
super(DBRSyncChannel, self).__init__(path, remote_path,
experiment, state,
redirect_stdout,
redirect_stderr,
finish_up_after,
save_interval)
except:
self.dbstate['jobman.status'] = self.ERR_START
raise
def runner_cmdline(options, experiment, *strings):
"""
Start an experiment with parameters given on the command line.
Usage: cmdline [options] <experiment> <parameters>
Run an experiment with parameters provided on the command
line. See the help topics for experiment and parameters for
syntax information.
Example use:
jobman cmdline mymodule.my_experiment \\
stopper::pylearn.stopper.nsteps \\ # use pylearn.stopper.nsteps
stopper.n=10000 \\ # the argument "n" of nsteps is 10000
lr=0.03
you can use the jobman.experiments.example1 as a working
mymodule.my_experiment
"""
parser = getattr(parse, options.parser, None) or resolve(options.parser)
_state = parser(*strings)
state = expand(_state)
state.setdefault('jobman', DD()).experiment = experiment
state.jobman.time = time.ctime()
experiment = resolve(experiment)
if options.workdir and options.dry_run:
raise UsageError('Please use only one of: --workdir, --dry-run.')
if options.workdir and options.workdir_dir:
raise UsageError('Please use only one of: --workdir, --workdir_dir.')
if options.workdir:
workdir = options.workdir
elif options.dry_run or options.workdir_dir:
if options.workdir_dir and not os.path.exists(options.workdir_dir):
os.mkdir(options.workdir_dir)
workdir = tempfile.mkdtemp(dir=options.workdir_dir)
else:
workdir_gen = getattr(workdirgen, options.workdir_gen,
None) or resolve(options.workdir_gen)
workdir = workdir_gen(state)
print "The working directory is:", os.path.join(os.getcwd(), workdir)
channel = StandardChannel(workdir,
experiment,
state,
redirect_stdout=options.redirect
or options.redirect_stdout,
redirect_stderr=options.redirect
or options.redirect_stderr,
finish_up_after=options.finish_up_after or None,
save_interval=options.save_every or None)
channel.catch_sigint = not options.allow_sigint
channel.run(force=options.force)
if options.dry_run:
shutil.rmtree(workdir, ignore_errors=True)
def div(u, v, lon, lat, xdim, ydim, cyclic=True, sphere=True):
ur"""
水平発散を計算する。
:Arguments:
**u, v** : ndarray
ベクトルの東西、南北成分。
**lon, lat** : array_like
緯度と経度
**xdim, ydim** : int
緯度、経度の軸
**cyclic** : bool, optional
経度微分の際に周境界とするかどうか。デフォルトはTrue
**sphere** : bool, optional
球面緯度経度座標かどうか。デフォルトはTrue。Falseにすると直交座標として扱う。
:Returns:
**div** : ndarray
u, v と同じ形状のndarray
.. note::
球面緯度経度座標系における水平発散は次のように定義される。
.. math:: \mbox{\boldmath $\nabla$}\cdot\mbox{\boldmath $v$}
= \frac{1}{a\cos\phi} \left[ \frac{\partial u}{\partial \lambda} +\frac{\partial (v\cos\phi)}{\partial \phi} \right]
= \frac{1}{a\cos\phi}\frac{\partial u}{\partial \lambda} + \frac{1}{a}\frac{\partial v}{\partial \phi} - \frac{v\tan\phi}{a}
ここで、aは地球半径。これを中央差分で次のように計算する。
.. math:: (\mbox{\boldmath $\nabla$}\cdot\mbox{\boldmath $v$})_{i,j}
= \left(\frac{1}{a\cos\phi}\frac{\partial u}{\partial \lambda}\right)_{i,j}
+ \left(\frac{1}{a}\frac{\partial v}{\partial \phi}\right)_{i,j}
- \frac{v_{i,j}\tan\phi_{j}}{a}
緯度、経度微分の項は、:py:func:`dvardx`、 :py:func:`dvardy` を内部で呼ぶ。境界の扱いもこれらに準ずる。
**Examples**
>>> from pymet.grid import div
>>>
"""
u, v = np.array(u), np.array(v)
ndim = u.ndim
out = dvardx(u, lon, lat, xdim, ydim, cyclic=cyclic,
sphere=sphere) + dvardy(v, lat, ydim, sphere=sphere)
if sphere:
out = out - v * tools.expand(np.tan(lat * d2r), ndim, ydim) / a0
out = np.rollaxis(out, ydim, 0)
out[0, ...] = 0.
out[-1, ...] = 0.
out = np.rollaxis(out, 0, ydim + 1)
return out
def runner_cmdline(options, experiment, *strings):
"""
Start an experiment with parameters given on the command line.
Usage: cmdline [options] <experiment> <parameters>
Run an experiment with parameters provided on the command
line. See the help topics for experiment and parameters for
syntax information.
Example use:
jobman cmdline mymodule.my_experiment \\
stopper::pylearn.stopper.nsteps \\ # use pylearn.stopper.nsteps
stopper.n=10000 \\ # the argument "n" of nsteps is 10000
lr=0.03
you can use the jobman.experiments.example1 as a working
mymodule.my_experiment
"""
parser = getattr(parse, options.parser, None) or resolve(options.parser)
_state = parser(*strings)
state = expand(_state)
state.setdefault('jobman', DD()).experiment = experiment
state.jobman.time = time.ctime()
experiment = resolve(experiment)
if options.workdir and options.dry_run:
raise UsageError('Please use only one of: --workdir, --dry-run.')
if options.workdir and options.workdir_dir:
raise UsageError('Please use only one of: --workdir, --workdir_dir.')
if options.workdir:
workdir = options.workdir
elif options.dry_run or options.workdir_dir:
if options.workdir_dir and not os.path.exists(options.workdir_dir):
os.mkdir(options.workdir_dir)
workdir = tempfile.mkdtemp(dir=options.workdir_dir)
else:
workdir_gen = getattr(workdirgen, options.workdir_gen,
None) or resolve(options.workdir_gen)
workdir = workdir_gen(state)
print "The working directory is:", os.path.join(os.getcwd(), workdir)
channel = StandardChannel(workdir,
experiment, state,
redirect_stdout=options.redirect or options.redirect_stdout,
redirect_stderr=options.redirect or options.redirect_stderr,
finish_up_after=options.finish_up_after or None,
save_interval=options.save_every or None
)
channel.catch_sigint = not options.allow_sigint
channel.run(force=options.force)
if options.dry_run:
shutil.rmtree(workdir, ignore_errors=True)
def laplacian(var, lon, lat, xdim, ydim, cyclic=True, sphere=True):
ur"""
球面上での2次元ラプラシアンを計算する。
:Arguments:
**var** : ndarray
スカラー場
**lon, lat** : array_like
緯度と経度
**xdim, ydim** : int
緯度、経度の軸
**cyclic** : bool, optional
経度微分の際に周境界とするかどうか。デフォルトはTrue
**sphere** : bool, optional
球面緯度経度座標かどうか。デフォルトはTrue。Falseにすると直交座標として扱う。
:Returns:
**out** : ndarray
varと同じ形状のndarray
.. note::
球面緯度経度座標系における2次元ラプラシアンは次のように定義される。
.. math:: \nabla_{h}^2\Phi
&= \frac{1}{a^2\cos^2\phi} \left[ \frac{\partial^2 \Phi}{\partial \lambda^2} + \cos\phi\frac{\partial}{\partial \phi}
\left( \cos\phi \frac{\partial \Phi}{\partial \phi} \right) \right] \\
&= \frac{1}{a^2\cos^2\phi}\frac{\partial \Phi}{\partial \lambda} + \frac{1}{a^2}\frac{\partial^2 \Phi}{\partial \phi^2}
- \frac{\tan\phi}{a^2}\frac{\partial \Phi}{\partial \phi}
ここで、aは地球半径。これを中央差分で次のように計算する。
.. math:: (\nabla_{h}^2\Phi)_{i,j}
= \left(\frac{1}{a^2\cos^2\phi}\frac{\partial^2 \Phi}{\partial \lambda^2}\right)_{i,j}
- \left(\frac{1}{a^2}\frac{\partial^2 \Phi}{\partial \phi^2}\right)_{i,j}
- \frac{\tan\phi_{j}}{a} \left( \frac{1}{a}\frac{\partial \Phi}{\partial \phi}\right)_{i,j}
緯度、経度微分の項は、:py:func:`d2vardx2`、 :py:func:`d2vardy2` を内部で呼ぶ。境界の扱いもこれらに準ずる。
"""
var = np.asarray(var)
ndim = var.ndim
if sphere:
out = d2vardx2(
var, lon, lat, xdim, ydim, cyclic=cyclic, sphere=sphere
) + d2vardy2(var, lat, ydim, sphere=sphere) - tools.expand(
np.tan(lat * d2r), ndim, ydim) * dvardy(var, lat, ydim) / a0
else:
out = d2vardx2(var, lon, lat, xdim, ydim, cyclic=cyclic,
sphere=sphere) + d2vardy2(var, lat, ydim, sphere=sphere)
return out
def rot(u, v, lon, lat, xdim, ydim, cyclic=True, sphere=True):
ur"""
回転の鉛直成分を計算する。
:Arguments:
**u, v** : ndarray
ベクトルの東西、南北成分。
**lon, lat** : array_like
緯度と経度
**xdim, ydim** : int
緯度、経度の軸
**cyclic** : bool, optional
経度微分の際に周境界とするかどうか。デフォルトはTrue
**sphere** : bool, optional
球面緯度経度座標かどうか。デフォルトはTrue。Falseにすると直交座標として扱う。
:Returns:
**div** : ndarray
u, v と同じ形状のndarray
.. note::
球面緯度経度座標系における回転の鉛直成分は次のように定義される。
.. math:: \mbox{\boldmath $k$}\cdot(\mbox{\boldmath $\nabla$}\times\boldmath{v})
&= \frac{1}{a\cos\phi} \left[ \frac{\partial v}{\partial \lambda} - \frac{\partial (u\cos\phi)}{\partial \phi} \right] \\
&= \frac{1}{a\cos\phi}\frac{\partial v}{\partial \lambda} - \frac{1}{a}\frac{\partial u}{\partial \phi} + \frac{u\tan\phi}{a}
ここで、aは地球半径。これを中央差分で次のように計算する。
.. math:: [\mbox{\boldmath $k$}\cdot(\mbox{\boldmath $\nabla$}\times\boldmath{v})]_{i,j}
= \left(\frac{1}{a\cos\phi}\frac{\partial v}{\partial \lambda}\right)_{i,j}
- \left(\frac{1}{a}\frac{\partial u}{\partial \phi}\right)_{i,j}
+ \frac{u_{i,j}\tan\phi_{j}}{a}
緯度、経度微分の項は、:py:func:`dvardx`、 :py:func:`dvardy` を内部で呼ぶ。境界の扱いもこれらに準ずる。
"""
u, v = np.array(u), np.array(v)
ndim = u.ndim
out = dvardx(v, lon, lat, xdim, ydim, cyclic=cyclic,
sphere=sphere) - dvardy(u, lat, ydim, sphere=sphere)
if sphere:
out = out + u * tools.expand(np.tan(lat * d2r), ndim, ydim) / a0
out = np.rollaxis(out, ydim, 0)
out[0, ...] = 0.
out[-1, ...] = 0.
out = np.rollaxis(out, 0, ydim + 1)
return out
def rhmd(temp, qval, lev, zdim, punit=100., qtyp='q'):
u"""
"""
eps = constants.air_eps
t, q = np.ma.getdata(temp), np.ma.getdata(qval)
mask = np.ma.getmask(temp) | np.ma.getmask(qval)
ndim = t.ndim
if qtyp == 'q':
es = np.exp(19.482 - 4303.4/(t-29.65))*100. # JMA/WMO (Pa)
lev = tools.expand(lev, ndim, axis=zdim)*punit
e = q*lev/(eps+(1-eps)*q)
rh = e/es * 100
else:
raise TypeError, "qtyp '{0}' is incorrect".format(qtyp)
return rh
def absvrt(uwnd, vwnd, lon, lat, xdim, ydim, cyclic=True, sphere=True):
u"""
"""
u, v = np.ma.getdata(uwnd), np.ma.getdata(vwnd)
mask = np.ma.getmask(uwnd) | np.ma.getmask(vwnd)
ndim = u.ndim
vor = rot(u, v, lon, lat, xdim, ydim, cyclic=cyclic, sphere=sphere)
f = tools.expand(constants.earth_f(lat), ndim, axis=ydim)
out = f + vor
out = np.ma.array(out, mask=mask)
out = tools.mrollaxis(out, ydim, 0)
out[0,...] = np.ma.masked
out[-1,...] = np.ma.masked
out = tools.mrollaxis(out, 0, ydim+1)
return out
def pottemp(temp, lev, zdim, punit=100., p0=100000.):
ur"""
温位を計算する。
:Arguments:
**temp** : array_like
気温 [K]
**lev** : array_like
気圧 [punit*Pa]
**zdim** : int
鉛直次元の軸
**punit** : float, optional
**p0** : float, optional
基準気圧。デフォルトは1000hPa。
:Returns:
**out** : ndarray
.. note::
温位は次のように定義される。
.. math:: \theta = T\left( \frac{p_{0}}{p} \right)^{\frac{R_d}{C_p}}
**Referrences**
**Examples**
>>>
"""
temp = np.asarray(temp)
ndim = temp.ndim
p = tools.expand(lev, ndim, axis=zdim)
out = temp * ((p0/p/punit)**kappa)
return out
def stability(temp, lev, zdim, punit=100.):
ur"""
p座標系での静的安定度(Brunt-Vaisala振動数)を計算する。
:Arguments:
**temp** : array_like
気温 [K]
**lev** : array_like
等圧面の気圧
**zdim** : int
鉛直次元の位置
**punit** : float, optional
Paに変換するためのファクター。デフォルトは100.。
:Returns:
**N** : ndarray
静的安定度
.. note::
p面上の静的安定度は次式で定義される。
.. math:: N^2 = g\frac{\partial (\ln\theta)}{\partial z} = -\alpha\frac{\partial(\ln\theta)}{\partial p}
**References**
**Examples**
>>>
"""
temp = np.asarray(temp)
ndim = temp.ndim
p = tools.expand(lev, ndim, axis=zdim)*punit
theta = pottemp(temp, lev, zdim, punit=punit)
alpha = rd*temp/p
N = -alpha * dvardp(np.log(theta), lev, zdim, punit=punit)
return N
def ertelpv(uwnd, vwnd, temp, lon, lat, lev, xdim, ydim, zdim, cyclic=True, punit=100., sphere=True):
ur"""
エルテルのポテンシャル渦度を計算する。
:Arguments:
**uwnd, vwnd** : ndarray
東西風、南北風 [m/s]
**temp** : ndarray
気温 [K]
**lon, lat** : array_like
経度、緯度 [degrees]
**lev** : array_like
気圧 [punit*Pa]
**xdim, ydim, zdim** : int
東西、南北、鉛直次元の軸
**cyclic** : bool, optional
東西境界を周期境界で扱うか。デフォルトはTrue。
**punit** : float, optional
Paに換算するための定数。デフォルトは100、すなわちlevはhPaで与えられると解釈する。
:Returns:
**out** : ndarray
.. note::
p座標系のポテンシャル渦度は次のように定義される。
.. math:: P = -g (f\mathbf{k}+\nabla_{p}\times\mathbf{v})\cdot\nabla_{p}\theta
鉛直風を含む項は他の項に比べ小さいから、次の近似的値を計算している。
.. math:: P \simeq -g\left\{ (f + \zeta)\frac{\partial\theta}{\partial p} - \frac{\partial v}{\partial p} \frac{\partial\theta}{\partial x} + \frac{\partial u}{\partial p}\frac{\partial\theta}{\partial y}\right\}
**Referrences**
Hoskins, B.J., M.E. McIntyre and A.E. Robertson, 1985: On the use and significance of isentropic potential vorticity maps, `QJRMS`, 111, 877-946, <http://onlinelibrary.wiley.com/doi/10.1002/qj.49711147002/abstract>
**Examples**
>>>
"""
u, v, t = np.ma.getdata(uwnd), np.ma.getdata(vwnd), np.ma.getdata(temp)
mask = np.ma.getmask(uwnd) | np.ma.getmask(vwnd) | np.ma.getmask(temp)
ndim = u.ndim
# potential temperature
theta = pottemp(t, lev, zdim, punit=punit)
#
dthdp = dvardp(theta, lev, zdim, punit=punit)
dudp = dvardp(u, lev, zdim, punit=punit)
dvdp = dvardp(v, lev, zdim, punit=punit)
dthdx = dvardx(theta, lon, lat, xdim, ydim, cyclic=cyclic, sphere=sphere)
dthdy = dvardy(theta, lat, ydim, sphere=sphere)
# absolute vorticity
vor = rot(u, v, lon, lat, xdim, ydim, cyclic=cyclic, sphere=sphere)
f = tools.expand(constants.earth_f(lat), ndim, axis=ydim)
avor = f + vor
out = -g * (avor*dthdp - (dthdx*dvdp-dthdy*dudp))
out = np.ma.array(out, mask=mask)
out = tools.mrollaxis(out, ydim, 0)
out[0,...] = np.ma.masked
out[-1,...] = np.ma.masked
out = tools.mrollaxis(out, 0, ydim+1)
return out
def tnflux3d(U, V, T, strm, lon, lat, lev, xdim, ydim, zdim, cyclic=True, limit=100, punit=100.):
ur"""
Takaya & Nakamura (2001) の波活動度フラックスをp面上で計算する。
:Arguments:
**U, V** : ndarray
気候値東西風、南北風 [m/s]
**T** : ndarray
気候値気温 [K]
**strm** : ndarray
流線関数偏差 [m^2/s]
**lon, lat** : array_like
経度、緯度 [degrees]
**lev** : array_like
等圧面の気圧
**xdim, ydim, zdim** : int
東西、南北、鉛直次元の軸
**cyclic** : bool, optional
東西境界を周期境界で扱うか。デフォルトはTrue。
**limit** : float, optional
デフォルトは100.
**punit** : float, optional
等圧面の気圧levをPaに変換するファクター。デフォルトは100.、すなわちhPaからPaへ変換。
:Returns:
**tnx, tny, tnz** : MaskedArray
フラックスの東西、南北、鉛直成分
.. note::
p座標系のwave-activityフラックスは次で定義される。
.. math:: \mathbf{W}=\frac{1}{2|\mathbf{U}|}
\left( \begin{array}{c}
U({\Psi'}_{x}^{2}-\Psi'{\Psi'}_{xx}) + V({\Psi'}_{x}{\Psi'}_{y}-\Psi'{\Psi'}_{xy})\\
U({\Psi'}_{x}{\Psi'}_{y}-\Psi'{\Psi'}_{xy}) + V({\Psi'}_{x}^{2}-\Psi'{\Psi'}_{xx}) \\
\frac{f^2}{S^2}[U({\Psi'}_{x}{\Psi'}_{p}-\Psi'{\Psi'}_{xp}) + V({\Psi'}_{y}{\Psi'}_{p}-\Psi'{\Psi'}_{yp})]
\end{array} \right)
ここでSは静的安定度。
**References**
Takaya, K and H. Nakamura, 2001: A formulation of a phase-independent wave-activity flux for stationary
and migratory quasigeostrophic eddies on a zonally varying basic flow, `JAS`, 58, 608-627.
<http://journals.ametsoc.org/doi/abs/10.1175/1520-0469%282001%29058%3C0608%3AAFOAPI%3E2.0.CO%3B2>
**Examples**
>>>
"""
U, V, T = np.asarray(U), np.asarray(V), np.asarray(T)
ndim=U.ndim
S = stability(T, lev, zdim, punit=punit)
f = tools.expand(constants.const_f(lat), ndim, axis=ydim)
dstrmdx = dvardx(strm, lon, lat, xdim, ydim, cyclic=cyclic)
dstrmdy = dvardy(strm, lat, ydim)
dstrmdp = dvardp(strm, lev, zdim, punit=punit)
d2strmdx2 = d2vardx2(strm, lon, lat, xdim, ydim, cyclic=cyclic)
d2strmdy2 = d2vardy2(strm, lat, ydim)
d2strmdxdy = dvardy(dstrmdx, lat, ydim)
d2strmdxdp = dvardx(dstrmdp, lon, lat, xdim, ydim, cyclic=True)
d2strmdydp = dvardy(dstrmdp, lat, ydim)
tnx = U * (dstrmdx**2 - strm*d2strmdx2) + V * (dstrmdx*dstrmdy - strm*d2strmdxdy)
tny = U * (dstrmdx*dstrmdy - strm*d2strmdxdy) + V * (dstrmdy**2 - strm*d2strmdy2)
tnz = f**2/S**2 * ( U*(dstrmdx*dstrmdp - strm*d2strmdxdp) - V*(dstrmdy*dstrmdp - strm*d2strmdydp) )
tnx = 0.5*tnx/np.abs(U + 1j*V)
tny = 0.5*tny/np.abs(U + 1j*V)
# 水平成分の大きさがlimit以上のグリッドと東風領域をマスクする。
tnxy = np.sqrt(tnx**2 + tny**2)
tnx = np.ma.asarray(tnx)
tny = np.ma.asarray(tny)
tnz = np.ma.asarray(tnz)
tnx[(U<0) | (tnxy>limit)] = np.ma.masked
tny[(U<0) | (tnxy>limit)] = np.ma.masked
tnz[(U<0) | (tnxy>limit)] = np.ma.masked
return tnx, tny, tnz
def eof(data, tdim=0, lat=None, ydim=None):
"""
EOF解析。
:Arguments:
**data** : ndarray
入力する2次元以上のデータ配列。
**tdim** : int, optional
入力データの時間次元の軸。デフォルトは0、すなわち先頭。
**lat** : array_like, optional
指定すると緯度に応じた面積重みをつける。
**ydim** : int, optional
latを指定した場合の緯度次元の軸
:Returns:
**EOFs** : ndarray
m番目のEOFモードの空間構造。形状(M,Xn),Xnは空間方向のデータ数、M=min(Xn,Tn)
**PCs** : ndarray
m番目のEOFモードの時系列。形状(M,Tn),Tnは時間方向のデータ数。M=min(Xn,Tn)
**lambdas** : 1darray
m番目のEOFモードの寄与率[%]。長さM。
.. note::
計算の際にはSVD解析を用いる。
**Referrences**
**Examples**
>>>
"""
data = np.asarray(data)
ndim = data.ndim
dtype = data.dtype
if ndim<2:
raise ValueError, "input data must have more than two dimendin."
#緯度重みを考慮
if lat!=None and ydim!=None:
factor = tools.expand(np.sqrt(np.cos(PI/180.*lat)), ndim, axis=ydim)
data = data * factor
#時間軸を先頭に
data = np.rollaxis(data, tdim, 0)
data, oldshape = tools.unshape(data)
tn, xn = data.shape
#SVD
A, Lh, E = linalg.svd(data, full_matrices=False)
lambdas = Lh*Lh/tn
EOFs = E
PCs = np.transpose(A*Lh)
#規格化
EOFs = EOFs * np.sqrt(lambdas)[:,NA]
PCs = PCs / np.sqrt(lambdas)[:,NA]
#寄与率
lambdas = lambdas / lambdas.sum() * 100.
# 形状を戻す
eofshape = list(oldshape)
eofshape.pop(tdim)
eofshape.insert(0, len(lambdas))
EOFs = EOFs.reshape(eofshape) #(M,Tn,...)
print eofshape
#緯度重みを考慮
if lat!=None and ydim!=None:
factor = tools.expand(np.sqrt(np.cos(PI/180.*lat)), ndim, axis=ydim)
EOFs = EOFs / factor
return EOFs, PCs, lambdas
def eof(data, tdim=0, lat=None, ydim=None):
"""
EOF解析。
:Arguments:
**data** : ndarray
入力する2次元以上のデータ配列。
**tdim** : int, optional
入力データの時間次元の軸。デフォルトは0、すなわち先頭。
**lat** : array_like, optional
指定すると緯度に応じた面積重みをつける。
**ydim** : int, optional
latを指定した場合の緯度次元の軸
:Returns:
**EOFs** : ndarray
m番目のEOFモードの空間構造。形状(M,Xn),Xnは空間方向のデータ数、M=min(Xn,Tn)
**PCs** : ndarray
m番目のEOFモードの時系列。形状(M,Tn),Tnは時間方向のデータ数。M=min(Xn,Tn)
**lambdas** : 1darray
m番目のEOFモードの寄与率[%]。長さM。
.. note::
計算の際にはSVD解析を用いる。
**Referrences**
**Examples**
>>>
"""
data = np.asarray(data)
ndim = data.ndim
dtype = data.dtype
if ndim < 2:
raise ValueError, "input data must have more than two dimendin."
#緯度重みを考慮
if lat != None and ydim != None:
factor = tools.expand(np.sqrt(np.cos(PI / 180. * lat)),
ndim,
axis=ydim)
data = data * factor
#時間軸を先頭に
data = np.rollaxis(data, tdim, 0)
data, oldshape = tools.unshape(data)
tn, xn = data.shape
#SVD
A, Lh, E = linalg.svd(data, full_matrices=False)
lambdas = Lh * Lh / tn
EOFs = E
PCs = np.transpose(A * Lh)
#規格化
EOFs = EOFs * np.sqrt(lambdas)[:, NA]
PCs = PCs / np.sqrt(lambdas)[:, NA]
#寄与率
lambdas = lambdas / lambdas.sum() * 100.
# 形状を戻す
eofshape = list(oldshape)
eofshape.pop(tdim)
eofshape.insert(0, len(lambdas))
EOFs = EOFs.reshape(eofshape) #(M,Tn,...)
print eofshape
#緯度重みを考慮
if lat != None and ydim != None:
factor = tools.expand(np.sqrt(np.cos(PI / 180. * lat)),
ndim,
axis=ydim)
EOFs = EOFs / factor
return EOFs, PCs, lambdas
def dvardx(var, lon, lat, xdim, ydim, cyclic=True, sphere=True):
ur"""
経度方向のx微分を中央差分で計算。
:Arguments:
**var** : ndarray
微分を計算する領域の格子点の値
**lon** : array_like
経度, もしくはx座標
**lat** : array_like
緯度, もしくはy座標
**xdim, ydim**: int
入力配列の経度、緯度次元のインデックス
**cyclic** : bool, optional
東西の境界を周期境界として扱うかどうか。False の場合は周期境界を用いずに前方、後方差分
で計算する。デフォルトは True。
**sphere** : bool, optional
球面緯度経度座標かどうか。デフォルトはTrue。Falseにすると直交座標として扱う。
:Returns:
**result** : ndarray
varと同じ形状。
.. note::
球面緯度経度座標系におけるx微分は、次のように定義される。
.. math:: \frac{\partial \Phi}{\partial x} = \frac{1}{a\cos\phi}\frac{\partial \Phi}{\partial \lambda}
ここで、aは地球半径。これを中央差分で次のように計算する。
.. math:: \left( \frac{\partial \Phi}{\partial x} \right)_{i,j}
= \frac{1}{a\cos\phi_{j}}\frac{\Phi_{i+1,j} - \Phi_{i-1,j}}{\lambda_{i+1} - \lambda_{i-1}}
cyclic=Falseの場合は、両端は前方、後方差分を用いて、
.. math:: \left( \frac{\partial \Phi}{\partial x} \right)_{0,j}
= \frac{1}{a\cos\phi_{j}}\frac{\Phi_{1,j} - \Phi_{0,j}}{\lambda_{1} - \lambda_{0}}
\hspace{3em}
\left( \frac{\partial \Phi}{\partial x} \right)_{N-1,j}
= \frac{1}{a\cos\phi_{j}}\frac{\Phi_{N-1,j} - \Phi_{N-2,j}}{\lambda_{N-1} - \lambda_{N-2}}
で計算する。sphere=Falseの場合は、
.. math:: \left( \frac{\partial \Phi}{\partial x} \right)_{i,j}
= \frac{\Phi_{i+1,j} - \Phi_{i-1,j}}{x_{i+1} - x_{i-1}}
**Examples**
>>> var.shape
(24, 73, 144)
>>> lon = np.arange(0, 360, 2.5)
>>> lat = np.arange(-90, 90.1, 2.5)
>>> result = dvardx(var, lon, lat, 2, 1, cyclic=True)
>>> result.shape
(24, 73, 144)
領域が全球でない場合。
>>> var.shape
(24, 73, 72)
>>> lon = np.arange(0, 180, 2.5)
>>> lat = np.arange(-90, 90.1, 2.5)
>>> result = dvardx(var, lon, lat, 2, 1, cyclic=False)
>>> result.shape
(24, 73, 72)
"""
var = np.array(var)
ndim = var.ndim
var = np.rollaxis(var, xdim, ndim)
if cyclic and sphere:
dvar = np.concatenate(\
((var[...,1]-var[...,-1])[...,NA],\
(var[...,2:]-var[...,:-2]),\
(var[...,0]-var[...,-2])[...,NA]), axis=-1)
dx = np.r_[(lon[1]+360-lon[-1]),\
(lon[2:]-lon[:-2] ),\
(lon[0]+360-lon[-2] )]
else:
dvar = np.concatenate(\
((var[...,1]-var[...,0])[...,NA],\
(var[...,2:]-var[...,:-2]),\
(var[...,-1]-var[...,-2])[...,NA]), axis=-1)
dx = np.r_[(lon[1]-lon[0] ),\
(lon[2:]-lon[:-2] ),\
(lon[-1]-lon[-2])]
dvar = np.rollaxis(dvar, ndim - 1, xdim)
if sphere:
dx = a0 * PI / 180. * tools.expand(dx, ndim, xdim) * tools.expand(
np.cos(lat * d2r), ndim, ydim)
else:
dx = tools.expand(dx, ndim, xdim)
out = dvar / dx
return out
def setup(self):
# Extract a single experiment from the table that is not
# already running. set self.experiment and self.state
super(DBRSyncChannel, self).setup()
self.state.jobman.sql.host_name = socket.gethostname()
def state_del(state, keys):
# Delete from the state the following key if present
for key in keys:
if hasattr(state, key):
del state[key]
# put jobs scheduler info into the state
condor_slot = os.getenv("_CONDOR_SLOT")
sge_task_id = os.getenv('SGE_TASK_ID')
pbs_task_id = os.getenv('PBS_JOBID')
if condor_slot:
self.state.jobman.sql.condor_slot = condor_slot
job_ad_file = os.getenv("_CONDOR_JOB_AD", None)
if job_ad_file:
f = open(job_ad_file)
try:
for line in f.readlines():
if line.startswith('GlobalJobId = '):
self.state.jobman.sql.condor_global_job_id = line.split(
'=')[1].strip()[1:-1]
elif line.startswith('Out = '):
self.state.jobman.sql.condor_stdout = line.split(
'=')[1].strip()[1:-1]
elif line.startswith('Err = '):
self.state.jobman.sql.condor_stderr = line.split(
'=')[1].strip()[1:-1]
elif line.startswith('OrigIwd = '):
self.state.jobman.sql.condor_origiwd = line.split(
'=')[1].strip()[1:-1]
finally:
f.close()
elif sge_task_id:
self.state.jobman.sql.sge_task_id = sge_task_id
self.state.jobman.sql.job_id = os.getenv('JOB_ID')
self.state.jobman.sql.sge_stdout = os.getenv('SGE_STDOUT_PATH')
self.state.jobman.sql.sge_stderr = os.getenv('SGE_STDERR_PATH')
elif pbs_task_id:
self.state.jobman.sql.pbs_task_id = pbs_task_id
self.state.jobman.sql.pbs_queue = os.getenv('PBS_QUEUE')
self.state.jobman.sql.pbs_arrayid = os.getenv('PBS_ARRAYID')
self.state.jobman.sql.pbs_num_ppn = os.getenv('PBS_NUM_PPN')
# delete old jobs scheduler info into the state
# this is needed in case we move a job to a different system.
# to know where it is running now.
key_to_del = []
if not condor_slot:
key_to_del.extend(['jobman.sql.condor_global_job_id',
'jobman.sql.condor_stdout',
'jobman.sql.condor_stderr',
'jobman.sql.condor_origiwd',
'jobman.sql.condor_slot'])
if not sge_task_id:
key_to_del.extend(['jobman.sql.sge_task_id',
'jobman.sql.job_id',
'jobman.sql.sge_stdout',
'self.state.jobman.sql.sge_stderr'])
if not pbs_task_id:
key_to_del.extend(['jobman.sql.pbs_task_id',
'jobman.sql.pbs_queue',
'jobman.sql.pbs_arrayid',
'jobman.sql.pbs_num_ppn'])
flattened_state = flatten(self.state)
deleted = False
for k in key_to_del:
if k in flattened_state:
del flattened_state[k]
deleted = True
if deleted:
self.state = expand(flattened_state)
self.state.jobman.sql.start_time = time.time()
self.state.jobman.sql.host_workdir = self.path
self.dbstate.update(flatten(self.state))
def setup(self):
# Extract a single experiment from the table that is not
# already running. set self.experiment and self.state
super(DBRSyncChannel, self).setup()
self.state.jobman.sql.host_name = socket.gethostname()
def state_del(state, keys):
# Delete from the state the following key if present
for key in keys:
if hasattr(state, key):
del state[key]
#put jobs scheduler info into the state
condor_slot = os.getenv("_CONDOR_SLOT")
sge_task_id = os.getenv('SGE_TASK_ID')
pbs_task_id = os.getenv('PBS_JOBID')
if condor_slot:
self.state.jobman.sql.condor_slot = condor_slot
job_ad_file = os.getenv("_CONDOR_JOB_AD", None)
if job_ad_file:
f = open(job_ad_file)
try:
for line in f.readlines():
if line.startswith('GlobalJobId = '):
self.state.jobman.sql.condor_global_job_id = line.split(
'=')[1].strip()[1:-1]
elif line.startswith('Out = '):
self.state.jobman.sql.condor_stdout = line.split(
'=')[1].strip()[1:-1]
elif line.startswith('Err = '):
self.state.jobman.sql.condor_stderr = line.split(
'=')[1].strip()[1:-1]
elif line.startswith('OrigIwd = '):
self.state.jobman.sql.condor_origiwd = line.split(
'=')[1].strip()[1:-1]
finally:
f.close()
elif sge_task_id:
self.state.jobman.sql.sge_task_id = sge_task_id
self.state.jobman.sql.job_id = os.getenv('JOB_ID')
self.state.jobman.sql.sge_stdout = os.getenv('SGE_STDOUT_PATH')
self.state.jobman.sql.sge_stderr = os.getenv('SGE_STDERR_PATH')
elif pbs_task_id:
self.state.jobman.sql.pbs_task_id = pbs_task_id
self.state.jobman.sql.pbs_queue = os.getenv('PBS_QUEUE')
self.state.jobman.sql.pbs_arrayid = os.getenv('PBS_ARRAYID')
self.state.jobman.sql.pbs_num_ppn = os.getenv('PBS_NUM_PPN')
#delete old jobs scheduler info into the state
#this is needed in case we move a job to a different system.
#to know where it is running now.
key_to_del = []
if not condor_slot:
key_to_del.extend([
'jobman.sql.condor_global_job_id', 'jobman.sql.condor_stdout',
'jobman.sql.condor_stderr', 'jobman.sql.condor_origiwd',
'jobman.sql.condor_slot'
])
if not sge_task_id:
key_to_del.extend([
'jobman.sql.sge_task_id', 'jobman.sql.job_id',
'jobman.sql.sge_stdout', 'self.state.jobman.sql.sge_stderr'
])
if not pbs_task_id:
key_to_del.extend([
'jobman.sql.pbs_task_id', 'jobman.sql.pbs_queue',
'jobman.sql.pbs_arrayid', 'jobman.sql.pbs_num_ppn'
])
flattened_state = flatten(self.state)
deleted = False
for k in key_to_del:
if k in flattened_state:
del flattened_state[k]
deleted = True
if deleted:
self.state = expand(flattened_state)
self.state.jobman.sql.start_time = time.time()
self.state.jobman.sql.host_workdir = self.path
self.dbstate.update(flatten(self.state))
def d2vardx2(var, lon, lat, xdim, ydim, cyclic=True, sphere=True):
ur"""
経度方向の2階x微分を中央差分で計算。
:Arguments:
**var** : ndarray
微分を計算する領域の格子点の値
**lon** : array_like
経度、もしくはx座標
**lat** : array_like
緯度、もしくはy座標
**xdim, ydim** : int
経度、緯度次元のインデックス。
**cyclic** : bool, optional
経度方向にはサイクリックとするかどうか。デフォルトではTrue。Falseの場合は東西端はゼロとする。
**sphere** : bool, optional
球面緯度経度座標かどうか。デフォルトはTrue。Falseにすると直交座標として扱う。
:Returns:
**result** : ndarray
varと同じ形状をもつ。
.. note::
球面緯度経度座標系におけるx2回微分は、次のように定義される。
.. math:: \frac{\partial^2 \Phi}{\partial x^2} = \frac{1}{a^2\cos^2\phi}\frac{\partial^2 \Phi}{\partial \lambda^2}
ここで、aは地球半径。これを中央差分で次のように計算する。
.. math:: \left( \frac{\partial^2 \Phi}{\partial x^2} \right)_{i,j}
= \frac{4}{a^2\cos^2\phi_{j}}\frac{\Phi_{i+1,j} - 2\Phi_{i,j} + \Phi_{i-1,j}}{\lambda_{i+1} - \lambda_{i-1}}
cyclic=Falseの場合は、両端はゼロとする。
**Examples**
>>>
>>>
"""
var = np.array(var)
ndim = var.ndim
#roll lon dim axis to last
var = np.rollaxis(var, xdim, ndim)
if cyclic and sphere:
dvar = np.concatenate(\
((var[...,1]-2*var[...,0]+var[...,-1])[...,NA],\
(var[...,2:]-2*var[...,1:-1]+var[...,:-2]),\
(var[...,0]-2*var[...,-1]+var[...,-2])[...,NA]),\
axis=-1)
dx = np.r_[(lon[1]+360-lon[-1]),\
(lon[2:]-lon[:-2] ),\
(lon[0]+360-lon[-2] )]
else: #edge is zero
dvar = np.concatenate(\
((var[...,0]-var[...,0])[...,NA],\
(var[...,2:]-2*var[...,1:-1]+var[...,:-2]),\
(var[...,0]-var[...,0])[...,NA]),\
axis=-1)
dx = np.r_[(lon[1]-lon[0] ),\
(lon[2:]-lon[:-2] ),\
(lon[-1]-lon[-2])]
dvar = np.rollaxis(dvar, ndim - 1, xdim)
if sphere:
dx2 = a0**2 * (PI / 180.)**2 * tools.expand(
dx**2, ndim, xdim) * tools.expand(
np.cos(lat * d2r)**2, ndim, ydim)
else:
dx2 = tools.expand(dx**2, ndim, xdim)
out = 4. * dvar / dx2
#reroll lon dim axis to original dim
out = np.rollaxis(out, ndim - 1, xdim)
return out
