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Fixed a bug in LevelCrossings.trdata

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Added plotflag to Plotter_1d and WafoData.
Otherwise small cosmetic fixes
master
per.andreas.brodtkorb 14 years ago
parent 5704928b44
commit 843ddb90c4
7 changed files with 236 additions and 126 deletions
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3
pywafo/src/wafo/__init__.py
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@ -1,7 +1,8 @@
from info import __doc__
import misc
import data
import data
import demos
import kdetools
import objects
import spectrum

96
pywafo/src/wafo/objects.py
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@ -179,7 +179,8 @@ class LevelCrossings(WafoData):
L0 = vstack((L0, r1 * L0 + sqrt(1 - r2 ** 2) * randn(1)))
#%Simulate the process, starting in L0
lfilter = scipy.signal.lfilter
L = lfilter(1, [1, a1, a2], e, lfilter([1, a1, a2], 1, L0))
# TODO: lfilter crashes the example
L = lfilter(ones(1), [1, a1, a2], e, lfilter([1, a1, a2], ones(1), L0))
epsilon = 1.01
min_L = min(L)
@ -322,20 +323,20 @@ class LevelCrossings(WafoData):
>>> int(S.tr.dist2gauss()*100)
141
>>> int(g0emp.dist2gauss()*100)
1544
380995
>>> int(g0.dist2gauss()*100)
340
143
>>> int(g1.dist2gauss()*100)
352
162
>>> int(g2.dist2gauss()*100)
365
120
hold on, trplot(g1,g) # Check the fit
trplot(g2)
See also troptset, dat2tr, trplot, findcross, smooth
g0.plot() # Check the fit.
See also
troptset, dat2tr, trplot, findcross, smooth
NB! the transformated data will be N(0,1)
NB! the transformated data will be N(0,1)
Reference
---------
@ -345,22 +346,14 @@ class LevelCrossings(WafoData):
Marine structures, Design, Construction and Safety,
Vol 10, pp 13--47
'''
# Tested on: Matlab 5.3, 5.2, 5.1
# History:
# by pab 29.12.2000
# based on lc2tr, but the inversion is faster.
# by IR and PJ
if mean is None:
mean = self.mean
if sigma is None:
sigma = self.stdev
opt = DotDict(chkder=True, plotflag=False, csm=0.9, gsm=.05,
param=(-5, 5, 513), delay=2, linextrap=True, ntr=inf, ne=7, cvar=1, gvar=1)
# If just 'defaults' passed in, return the default options in g
param=(-5, 5, 513), delay=2, linextrap=True, ntr=10000, ne=7, gvar=1)
opt.update(options)
param = opt.param
Ne = opt.ne
@ -380,21 +373,11 @@ class LevelCrossings(WafoData):
gvar = opt.gvar * ones(ncr)
else:
gvar = interp1d(linspace(0, 1, ng) , opt.gvar, kind='linear')(linspace(0, 1, ncr))
ng = len(atleast_1d(opt.cvar))
if ng == 1:
cvar = opt.cvar * ones(ncr)
else:
cvar = interp1d(linspace(0, 1, ng), opt.cvar, kind='linear')(linspace(0, 1, ncr))
uu = linspace(*param)
g1 = sigma * uu + mean
g22 = lc2.copy()
if Ner > 0: # Compute correction factors
cor1 = trapz(lc2[0:Ner + 1], lc1[0:Ner + 1])
cor2 = trapz(lc2[-Ner - 1::], lc1[-Ner - 1::])
@ -402,27 +385,11 @@ class LevelCrossings(WafoData):
cor1 = 0
cor2 = 0
lc22 = hstack((0, cumtrapz(lc2, lc1) + cor1))
lc22 = (lc22 + 0.5) / (lc22[-1] + cor2 + 1)
lc11 = (lc1 - mean) / sigma
# find the mode
imin = abs(lc22 - 0.15).argmin()
imax = abs(lc22 - 0.85).argmin()
inde = slice(imin, imax + 1)
lc222 = SmoothSpline(lc11[inde], g22[inde], opt.csm, opt.linextrap, cvar[inde])(lc11[inde])
#tmp = smooth(cros(inde,1),g2(inde,2),opt.csm,cros(inde,1),def,cvar(inde));
imax = lc222.argmax()
u0 = lc22[inde][imax]
#u0 = interp1q(cros(:,2),cros(:,1),.5)
#lc22 = ndtri(lc22) - u0 #
lc22 = invnorm(lc22) - u0
lc22 = invnorm(lc22) #- ymean
g2 = TrData(lc22.copy(), lc1.copy(), mean=mean, sigma=sigma)
g2.setplotter('step')
@ -432,9 +399,9 @@ class LevelCrossings(WafoData):
# to be linear outside the edges or choosing a lower value for csm2.
inds = slice(Ne, ncr - Ne) # indices to points we are smoothing over
scros2 = SmoothSpline(lc11[inds], lc22[inds], opt.gsm, opt.linextrap, gvar[inds])(uu)
slc22 = SmoothSpline(lc11[inds], lc22[inds], opt.gsm, opt.linextrap, gvar[inds])(uu)
g = TrData(scros2, g1, mean=mean, sigma=sigma) #*sa; #multiply with stdev
g = TrData(slc22.copy(), g1.copy(), mean=mean, sigma=sigma) #*sa; #multiply with stdev
if opt.chkder:
for ix in range(5):
@ -739,7 +706,7 @@ class TimeSeries(WafoData):
>>> h = rf.plot()
>>> S = ts.tospecdata()
The default L is set to 68
The default L is set to 325
>>> tp = ts.turning_points()
>>> mm = tp.cycle_pairs()
@ -1215,29 +1182,20 @@ class TimeSeries(WafoData):
>>> g1, g1emp = ts.trdata(method='m', gvar=0.5 ) # Equal weight on all points
>>> g2, g2emp = ts.trdata(method='n', gvar=[3.5, 0.5, 3.5]) # Less weight on the ends
>>> int(S.tr.dist2gauss()*100)
593
141
>>> int(g0emp.dist2gauss()*100)
439
217949
>>> int(g0.dist2gauss()*100)
432
93
>>> int(g1.dist2gauss()*100)
234
66
>>> int(g2.dist2gauss()*100)
437
Hm0 = 7;
S = jonswap([],Hm0); g=ochitr([],[Hm0/4]);
S.tr=g;S.tr(:,2)=g(:,2)*Hm0/4;
xs = spec2sdat(S,2^13);
g0 = dat2tr(xs,[],'plot','iter'); % Monitor the development
g1 = dat2tr(xs,'mnon','gvar', .5 ); % More weight on all points
g2 = dat2tr(xs,'nonl','gvar', [3.5 .5 3.5]); % Less weight on the ends
hold on, trplot(g1,g) % Check the fit
trplot(g2)
84
See also
--------
troptset, lc2tr, cdf2tr, trplot
LevelCrossings.trdata
wafo.transform.models
References
----------
@ -1259,7 +1217,7 @@ class TimeSeries(WafoData):
# 'cvar',1,'gvar',1,'multip',0);
opt = DotDict(chkder=True, plotflag=False, csm=.95, gsm=.05,
param=[-5, 5, 513], delay=2, ntr=inf, linextrap=True, ne=7, cvar=1, gvar=1,
param=[-5, 5, 513], delay=2, ntr=1000, linextrap=True, ne=7, cvar=1, gvar=1,
multip=False, crossdef='uM')
opt.update(**options)

65
pywafo/src/wafo/spectrum/models.py
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@ -97,9 +97,10 @@ def _gengamspec(wn, N=5, M=4):
Examples
--------
>>> import wafo.spectrum.models as wsm
>>> import numpy as np
>>> wn = np.linspace(0,4,5)
>>> _gengamspec(wn, N=6, M=2)
>>> wsm._gengamspec(wn, N=6, M=2)
array([ 0. , 1.16765216, 0.17309961, 0.02305179, 0.00474686])
See also
@ -232,7 +233,8 @@ class Bretschneider(ModelSpectrum):
Examples
--------
>>> S = Bretschneider(Hm0=6.5,Tp=10)
>>> import wafo.spectrum.models as wsm
>>> S = wsm.Bretschneider(Hm0=6.5,Tp=10)
>>> S((0,1,2,3))
array([ 0. , 1.69350993, 0.06352698, 0.00844783])
@ -289,21 +291,22 @@ def jonswap_peakfact(Hm0, Tp):
Examples
--------
>>> import wafo.spectrum.models as wsm
>>> import pylab as plb
>>> Tp,Hs = plb.meshgrid(range(4,8),range(2,6))
>>> gam = jonswap_peakfact(Hs,Tp)
>>> gam = wsm.jonswap_peakfact(Hs,Tp)
>>> Hm0 = plb.linspace(1,20)
>>> Tp = Hm0
>>> [T,H] = plb.meshgrid(Tp,Hm0)
>>> gam = jonswap_peakfact(H,T)
>>> gam = wsm.jonswap_peakfact(H,T)
>>> v = plb.arange(0,8)
>>> h = plb.contourf(Tp,Hm0,gam,v);h=plb.colorbar()
>>> Hm0 = plb.arange(1,11)
>>> Tp = plb.linspace(2,16)
>>> T,H = plb.meshgrid(Tp,Hm0)
>>> gam = jonswap_peakfact(H,T)
>>> gam = wsm.jonswap_peakfact(H,T)
>>> h = plb.plot(Tp,gam.T)
>>> h = plb.xlabel('Tp [s]')
>>> h = plb.ylabel('Peakedness parameter')
@ -356,8 +359,9 @@ def jonswap_seastate(u10, fetch=150000., method='lewis', g=9.81, output='dict'):
Example
--------
>>> import wafo.spectrum.models as wsm
>>> fetch = 10000; u10 = 10
>>> ss = jonswap_seastate(u10, fetch, output='dict')
>>> ss = wsm.jonswap_seastate(u10, fetch, output='dict')
>>> ss
{'Ag': 0.016257903375341734,
'Hm0': 0.51083679198275533,
@ -365,13 +369,13 @@ def jonswap_seastate(u10, fetch=150000., method='lewis', g=9.81, output='dict'):
'gamma': 2.4824142635861119,
'sigmaA': 0.075317331395172021,
'sigmaB': 0.091912084512251344}
>>> S = Jonswap(**ss)
>>> S = wsm.Jonswap(**ss)
>>> S.Hm0
0.51083679198275533
# Alternatively
>>> ss1 = jonswap_seastate(u10, fetch, output='list')
>>> S1 = Jonswap(*ss1)
>>> ss1 = wsm.jonswap_seastate(u10, fetch, output='list')
>>> S1 = wsm.Jonswap(*ss1)
>>> S1.Hm0
0.51083679198275533
@ -484,12 +488,14 @@ class Jonswap(ModelSpectrum):
Examples
---------
>>> import pylab as plb
>>> S = Jonswap(Hm0=7, Tp=11,gamma=1)
>>> import wafo.spectrum.models as wsm
>>> S = wsm.Jonswap(Hm0=7, Tp=11,gamma=1)
>>> w = plb.linspace(0,5)
>>> h = plb.plot(w,S(w))
>>> S2 = Bretschneider(Hm0=7, Tp=11)
>>> assert(all(abs(S(w)-S2(w))<1.e-7),'JONSWAP with gamma=1 differs from Bretscneider, should be equal!')
>>> S2 = wsm.Bretschneider(Hm0=7, Tp=11)
>>> all(abs(S(w)-S2(w))<1.e-7)
True
>>> plb.close('all')
See also
@ -652,10 +658,10 @@ def phi1(wi, h, g=9.81):
Example:
-------
Transform a JONSWAP spectrum to a spectrum for waterdepth = 30 m
>>> S = Jonswap()
>>> import wafo.spectrum.models as wsm
>>> S = wsm.Jonswap()
>>> w = range(3.0)
>>> S(w)*phi1(w,30.0)
>>> S(w)*wsm.phi1(w,30.0)
array([ 0. , 1.0358056 , 0.03796281])
@ -723,9 +729,10 @@ class Tmaspec(Jonswap):
Example
--------
>>> import wafo.spectrum.models as wsm
>>> import pylab as plb
>>> w = plb.linspace(0,2.5)
>>> S = Tmaspec(h=10,gamma=1) # Bretschneider spectrum Hm0=7, Tp=11
>>> S = wsm.Tmaspec(h=10,gamma=1) # Bretschneider spectrum Hm0=7, Tp=11
>>> o=plb.plot(w,S(w))
>>> o=plb.plot(w,S(w,h=21))
>>> o=plb.plot(w,S(w,h=42))
@ -812,9 +819,10 @@ class Torsethaugen(ModelSpectrum):
Example
-------
>>> import wafo.spectrum.models as wsm
>>> import pylab as plb
>>> w = plb.linspace(0,4)
>>> S = Torsethaugen(Hm0=6, Tp=8)
>>> S = wsm.Torsethaugen(Hm0=6, Tp=8)
>>> h=plb.plot(w,S(w),w,S.wind(w),w,S.swell(w))
See also
@ -1034,7 +1042,8 @@ class McCormick(Bretschneider):
Example:
--------
>>> S = McCormick(Hm0=6.5,Tp=10)
>>> import wafo.spectrum.models as wsm
>>> S = wsm.McCormick(Hm0=6.5,Tp=10)
>>> S(range(4))
array([ 0. , 1.87865908, 0.15050447, 0.02994663])
@ -1107,7 +1116,8 @@ class OchiHubble(ModelSpectrum):
Examples
--------
>>> S = OchiHubble(par=2)
>>> import wafo.spectrum.models as wsm
>>> S = wsm.OchiHubble(par=2)
>>> S(range(4))
array([ 0. , 0.90155636, 0.04185445, 0.00583207])
@ -1239,7 +1249,8 @@ class Wallop(Bretschneider):
Example:
--------
>>> S = Wallop(Hm0=6.5, Tp=10)
>>> import wafo.spectrum.models as wsm
>>> S = wsm.Wallop(Hm0=6.5, Tp=10)
>>> S(range(4))
array([ 0.00000000e+00, 9.36921871e-01, 2.76991078e-03,
7.72996150e-05])
@ -1360,8 +1371,9 @@ class Spreading(object):
Examples
--------
>>> import wafo.spectrum.models as wsm
>>> import pylab as plb
>>> D = Spreading('cos2s',s_a=10.0)
>>> D = wsm.Spreading('cos2s',s_a=10.0)
>>> w = plb.linspace(0,3,257)
>>> theta = plb.linspace(-pi,pi,129)
@ -1369,13 +1381,13 @@ class Spreading(object):
# Make frequency dependent direction
>>> theta0 = lambda w: w*plb.pi/6.0
>>> D2 = Spreading('cos2s',theta0=theta0)
>>> D2 = wsm.Spreading('cos2s',theta0=theta0)
>>> t = plb.contour(D2(theta,w)[0])
# Plot all spreading functions
>>> alltypes = ('cos2s','box','mises','poisson','sech2','wrap_norm')
>>> for ix in range(len(alltypes)):
... D3 = Spreading(alltypes[ix])
... D3 = wsm.Spreading(alltypes[ix])
... t = plb.figure(ix)
... t = plb.contour(D3(theta,w)[0])
... t = plb.title(alltypes[ix])
@ -1927,10 +1939,11 @@ class Spreading(object):
Example
-------
>>> S = Jonswap().tospecdata()
>>> D = Spreading('cos2s')
>>> import wafo.spectrum.models as wsm
>>> S = wsm.Jonswap().tospecdata()
>>> D = wsm.Spreading('cos2s')
>>> SD = D.tospecdata2d(S)
>>> SD.plot()
>>> h = SD.plot()
See also spreading, rotspec, jonswap, torsethaugen
'''

5
pywafo/src/wafo/stats/estimation.py
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@ -837,9 +837,10 @@ class FitDistribution(rv_frozen):
plotbackend.subplot(2, 2, 2)
self.plotepdf()
plotbackend.subplot(2, 2, 3)
self.plotresprb()
plotbackend.subplot(2, 2, 4)
self.plotresq()
plotbackend.subplot(2, 2, 4)
self.plotresprb()
fixstr = ''
if not self.par_fix == None:
numfix = len(self.i_fixed)

5
pywafo/src/wafo/transform/core.py
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@ -145,15 +145,16 @@ class TrData(WafoData, TrCommon):
-------
Construct a linear transformation model
>>> import numpy as np
>>> import wafo.transorm as wt
>>> sigma = 5; mean = 1
>>> u = np.linspace(-5,5); x = sigma*u+mean; y = u
>>> g = TrData(y,x)
>>> g = wt.TrData(y,x)
>>> g.mean
array([ 1.])
>>> g.sigma
array([ 5.])
>>> g = TrData(y,x,mean=1,sigma=5)
>>> g = wt.TrData(y,x,mean=1,sigma=5)
>>> g.mean
1
>>> g.sigma

30
pywafo/src/wafo/transform/models.py
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@ -26,19 +26,20 @@ from core import TrCommon, TrData
__all__ = ['TrHermite', 'TrLinear', 'TrOchi']
_example = '''
>>> import numpy as np
>>> import wafo.spectrum.models as sm
>>> import wafo.transform.models as tm
>>> std = 7./4
>>> g = <generic>(sigma=std, ysigma=std)
>>> g = tm.<generic>(sigma=std, ysigma=std)
Simulate a Transformed Gaussian process:
>>> import numpy as np
>>> import wafo.spectrum.models as sm
>>> Sj = sm.Jonswap(Hm0=4*std, Tp=11)
>>> w = np.linspace(0,4,256)
>>> S = Sj.tospecdata(w) #Make spectrum object from numerical values
>>> S = Sj.tospecdata(w) # Make spectrum object from numerical values
>>> ys = S.sim(ns=15000) # Simulated in the Gaussian world
>>> me, va, sk, ku = S.stats_nl(moments='mvsk')
>>> g2 = <generic>(mean=me, var=va, skew=sk, kurt=ku, ysigma=std)
>>> g2 = tm.<generic>(mean=me, var=va, skew=sk, kurt=ku, ysigma=std)
>>> xs = g2.gauss2dat(ys[:,1:]) # Transformed to the real world
'''
class TrCommon2(TrCommon):
@ -111,7 +112,9 @@ class TrHermite(TrCommon2):
--------
""" + _example.replace('<generic>', 'TrHermite') + """
>>> g.dist2gauss()
3.9858776379926808
0.88230868748851499
>>> g2.dist2gauss()
1.1411663205144991
See also
--------
@ -337,6 +340,8 @@ class TrLinear(TrCommon2):
""" + _example.replace('<generic>', 'TrLinear') + """
>>> g.dist2gauss()
0.0
>>> g2.dist2gauss()
3.8594770921678001e-31
See also
--------
@ -399,7 +404,9 @@ class TrOchi(TrCommon2):
-------
""" + _example.replace('<generic>', 'TrOchi') + """
>>> g.dist2gauss()
5.9322684525265501
1.410698801056657
>>> g2.dist2gauss()
1.988807188766706
See also
--------
@ -486,7 +493,8 @@ class TrOchi(TrCommon2):
mean = self.mean
sigma = self.sigma
xn = atleast_1d(x)
xn = (xn - mean) / sigma
shape0 = xn.shape
xn = (xn.ravel() - mean) / sigma
igp, = where(0 <= xn)
igm, = where(xn < 0)
@ -498,6 +506,7 @@ class TrOchi(TrCommon2):
if gb != 0:
np.put(g, igm, (-expm1(-gb * xn[igm])) / gb)
g.shape = shape0
return (g - mean2) * self.ysigma / sigma2 + self.ymean
def _gauss2dat(self, y, *yi):
@ -509,7 +518,7 @@ class TrOchi(TrCommon2):
sigma = self.sigma
yn = (atleast_1d(y) - self.ymean) / self.ysigma
xn = sigma2 * yn + mean2
xn = sigma2 * yn.ravel() + mean2
igp, = where(0 <= xn)
igm, = where(xn < 0)
@ -519,7 +528,8 @@ class TrOchi(TrCommon2):
if gb != 0:
np.put(xn, igm, -log1p(-gb * xn[igm]) / gb)
xn.shape = yn.shape
return sigma * xn + mean
def main():

158
pywafo/src/wafo/wafodata.py
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@ -2,6 +2,7 @@ import warnings
from plotbackend import plotbackend
from time import gmtime, strftime
import numpy as np
from scipy.integrate.quadrature import cumtrapz #@UnresolvedImport
__all__ = ['WafoData', 'AxisLabels']
@ -66,19 +67,21 @@ class WafoData(object):
self.data = data
self.args = args
self.date = now()
self.plotter = None
self.plotter = kwds.pop('plotter', None)
self.children = None
self.plot_args_children = kwds.get('plot_args_children',[])
self.plot_kwds_children = kwds.get('plot_kwds_children',{})
self.plot_args = kwds.get('plot_args',[])
self.plot_kwds = kwds.get('plot_kwds',{})
self.plot_args_children = kwds.pop('plot_args_children',[])
self.plot_kwds_children = kwds.pop('plot_kwds_children',{})
self.plot_args = kwds.pop('plot_args',[])
self.plot_kwds = kwds.pop('plot_kwds',{})
self.labels = AxisLabels(**kwds)
self.setplotter()
if not self.plotter:
self.setplotter()
def plot(self, *args, **kwds):
tmp = None
if self.children != None:
plotflag = kwds.get('plotflag', None)
if not plotflag and self.children != None:
plotbackend.hold('on')
tmp = []
child_args = args if len(args) else tuple(self.plot_args_children)
@ -106,12 +109,10 @@ class WafoData(object):
newcopy.__dict__.update(self.__dict__)
return newcopy
def setplotter(self, plotmethod=None):
'''
Set plotter based on the data type data_1d, data_2d, data_3d or data_nd
'''
if isinstance(self.args, (list, tuple)): # Multidimensional data
ndim = len(self.args)
if ndim < 2:
@ -140,10 +141,6 @@ class AxisLabels:
newcopy = empty_copy(self)
newcopy.__dict__.update(self.__dict__)
return newcopy
#lbkwds = self.labels.__dict__.copy()
#labels = AxisLabels(**lbkwds)
#return labels
def labelfig(self):
try:
h1 = plotbackend.title(self.title)
@ -178,7 +175,6 @@ class Plotter_1d(object):
plotmethod = 'plot'
self.plotbackend = plotbackend
try:
#self.plotfun = plotbackend.__dict__[plotmethod]
self.plotfun = getattr(plotbackend, plotmethod)
except:
pass
@ -190,9 +186,136 @@ class Plotter_1d(object):
args1 = tuple((wdata.args)) + (wdata.data,) + args
else:
args1 = tuple((wdata.args,)) + (wdata.data,) + args
h1 = self.plotfun(*args1, **kwds)
plotflag = kwds.pop('plotflag', None)
if plotflag:
h1 = self._plot(plotflag, *args1, **kwds)
else:
h1 = self.plotfun(*args1, **kwds)
h2 = wdata.labels.labelfig()
return h1, h2
def _plot(self, plotflag, *args1, **kwds):
x = args1[0]
data = transformdata(x,args1[1], plotflag)
dataCI = ()
h1 = plot1d(x,data, dataCI, plotflag, *args1[2:], **kwds)
return h1
def plot1d(args,data,dataCI,plotflag,*varargin, **kwds):
plottype = np.mod(plotflag,10)
if plottype==0: # % No plotting
return []
elif plottype==1:
H = plotbackend.plot(args,data,*varargin, **kwds)
elif plottype==2:
H = plotbackend.step(args,data,*varargin, **kwds)
elif plottype==3:
H = plotbackend.stem(args,data,*varargin, **kwds)
elif plottype==4:
H = plotbackend.errorbar(args,data,dataCI[:,0]-data,dataCI[:,1]-data,*varargin, **kwds)
elif plottype==5:
H = plotbackend.bar(args,data,*varargin, **kwds)
elif plottype==6:
level = 0
if np.isfinite(level):
H = plotbackend.fill_between(args,data,level,*varargin, **kwds);
else:
H = plotbackend.fill_between(args,data,*varargin, **kwds);
scale = plotscale(plotflag);
logXscale = any(scale=='x');
logYscale = any(scale=='y');
logZscale = any(scale=='z');
ax = plotbackend.gca()
if logXscale:
plotbackend.setp(ax,xscale='log')
if logYscale:
plotbackend.setp(ax,yscale='log')
if logZscale:
plotbackend.setp(ax,zscale='log')
transFlag = np.mod(plotflag//10,10)
logScale = logXscale or logYscale or logZscale
if logScale or (transFlag ==5 and not logScale):
ax = list(plotbackend.axis())
fmax1 = data.max()
if transFlag==5 and not logScale:
ax[3] = 11*np.log10(fmax1)
ax[2] = ax[3]-40
else:
ax[3] = 1.15*fmax1;
ax[2] = ax[3]*1e-4;
plotbackend.axis(ax)
if dataCI and plottype < 3:
plotbackend.hold('on')
plot1d(args,dataCI,(),plotflag,'r--');
return H
def plotscale(plotflag):
'''
Return plotscale from plotflag
CALL scale = plotscale(plotflag)
plotflag = integer defining plotscale.
Let scaleId = floor(plotflag/100).
If scaleId < 8 then:
0 'linear' : Linear scale on all axes.
1 'xlog' : Log scale on x-axis.
2 'ylog' : Log scale on y-axis.
3 'xylog' : Log scale on xy-axis.
4 'zlog' : Log scale on z-axis.
5 'xzlog' : Log scale on xz-axis.
6 'yzlog' : Log scale on yz-axis.
7 'xyzlog' : Log scale on xyz-axis.
otherwise
if (mod(scaleId,10)>0) : Log scale on x-axis.
if (mod(floor(scaleId/10),10)>0) : Log scale on y-axis.
if (mod(floor(scaleId/100),10)>0) : Log scale on z-axis.
scale = string defining plotscale valid options are:
'linear', 'xlog', 'ylog', 'xylog', 'zlog', 'xzlog',
'yzlog', 'xyzlog'
Example
plotscale(100) % xlog
plotscale(200) % xlog
plotscale(1000) % ylog
See also plotscale
'''
scaleId = plotflag//100
if scaleId>7:
logXscaleId = np.mod(scaleId,10)>0
logYscaleId = (np.mod(scaleId//10, 10)>0)*2
logZscaleId = (np.mod(scaleId//100, 10)>0)*4
scaleId = logYscaleId +logXscaleId+logZscaleId
scales = ['linear','xlog','ylog','xylog','zlog','xzlog','yzlog','xyzlog']
return scales[scaleId]
def transformdata(x,f,plotflag):
transFlag = np.mod(plotflag//10,10)
if transFlag==0:
data = f
elif transFlag==1:
data = 1-f
elif transFlag==2:
data = cumtrapz(f,x)
elif transFlag==3:
data = 1-cumtrapz(f,x)
if transFlag in (4,5):
if transFlag==4:
data = -np.log1p(-cumtrapz(f,x))
else:
if any(f<0):
raise ValueError('Invalid plotflag: Data or dataCI is negative, but must be positive')
data = 10*np.log10(f)
return data
class Plotter_2d(Plotter_1d):
"""
@ -201,6 +324,7 @@ class Plotter_2d(Plotter_1d):
plotmethod : string
defining type of plot. Options are:
contour (default)
contourf
mesh
surf
"""
@ -209,7 +333,9 @@ class Plotter_2d(Plotter_1d):
if plotmethod is None:
plotmethod = 'contour'
super(Plotter_2d, self).__init__(plotmethod)
#self.plotfun = plotbackend.__dict__[plotmethod]
def _plot(self,*args,**kwds):
pass
def main():

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