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Added doctest for kdetools/demo.py.

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Removed obsolete options from wafo.containers.py
master
Per A Brodtkorb 9 years ago
parent e836d56691
commit 98d5980e6b
2 changed files with 57 additions and 31 deletions
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11
wafo/containers.py
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@ -7,7 +7,7 @@ import numpy as np
from scipy.integrate.quadrature import cumtrapz # @UnresolvedImport
from scipy import interpolate
from scipy import integrate
from _warnings import warn
__all__ = ['PlotData', 'AxisLabels']
@ -71,7 +71,7 @@ class PlotData(object):
'''
def __init__(self, data=None, args=None, *args2, **kwds):
def __init__(self, data=None, args=None, **kwds):
self.data = data
self.args = args
self.date = now()
@ -581,7 +581,11 @@ def plot2d(axis, wdata, plotflag, *args, **kwds):
else:
args1 = tuple((wdata.args,)) + (wdata.data,) + args
pltfun = [None, axis.contour, axis.mesh, axis.surf, axis.waterfal,
# TODO: mesh, surf waterfall plots are missing
mesh = None # axis.mesh
surf = None # axis.surf
waterfall = None # axis.waterfall
pltfun = [None, axis.contour, mesh, surf, waterfall,
axis.pcolor, axis.contour, axis.contour, axis.contour,
axis.contour, axis.contourf][plotflag]
@ -611,6 +615,7 @@ def plot2d(axis, wdata, plotflag, *args, **kwds):
if plotflag == 10:
axis.clabel(h)
plt.colorbar(h)
return h
def test_plotdata():

77
wafo/kdetools/demo.py
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@ -1,10 +1,10 @@
'''
"""
Created on 2. jan. 2017
@author: pab
'''
from __future__ import absolute_import, division
import scipy.stats
"""
from __future__ import absolute_import, division, print_function
import scipy.stats as st
import numpy as np
import warnings
from wafo.plotbackend import plotbackend as plt
@ -25,8 +25,10 @@ def kde_demo1():
observations (solid) and their individual kernels (dashed) for 3
different values of the smoothing parameter, hs.
Example
-------
>>> kde_demo1()
"""
st = scipy.stats
x = np.linspace(-4, 4, 101)
x0 = x / 2.0
data = np.random.normal(loc=0, scale=1.0, size=7)
@ -53,12 +55,15 @@ def kde_demo1():
def kde_demo2():
'''Demonstrate the difference between transformation- and ordinary-KDE.
"""Demonstrate the difference between transformation- and ordinary-KDE.
KDEDEMO2 shows that the transformation KDE is a better estimate for
Rayleigh distributed data around 0 than the ordinary KDE.
'''
st = scipy.stats
Example
-------
>>> kde_demo2()
"""
data = st.rayleigh.rvs(scale=1, size=300)
x = np.linspace(1.5e-2, 5, 55)
@ -84,12 +89,15 @@ def kde_demo2():
def kde_demo3():
'''Demonstrate the difference between transformation and ordinary-KDE in 2D
"""Demonstrate the difference between transformation and ordinary-KDE in 2D
KDEDEMO3 shows that the transformation KDE is a better estimate for
Rayleigh distributed data around 0 than the ordinary KDE.
'''
st = scipy.stats
Example
-------
>>> kde_demo3()
"""
data = st.rayleigh.rvs(scale=1, size=(2, 300))
# x = np.linspace(1.5e-3, 5, 55)
@ -116,14 +124,16 @@ def kde_demo3():
def kde_demo4(N=50):
'''Demonstrate that the improved Sheather-Jones plug-in (hisj) is superior
"""Demonstrate that the improved Sheather-Jones plug-in (hisj) is superior
for 1D multimodal distributions
KDEDEMO4 shows that the improved Sheather-Jones plug-in smoothing is a
better compared to normal reference rules (in this case the hns)
'''
st = scipy.stats
Example
-------
>>> kde_demo4()
"""
data = np.hstack((st.norm.rvs(loc=5, scale=1, size=(N,)),
st.norm.rvs(loc=-5, scale=1, size=(N,))))
@ -147,14 +157,16 @@ def kde_demo4(N=50):
def kde_demo5(N=500):
'''Demonstrate that the improved Sheather-Jones plug-in (hisj) is superior
"""Demonstrate that the improved Sheather-Jones plug-in (hisj) is superior
for 2D multimodal distributions
KDEDEMO5 shows that the improved Sheather-Jones plug-in smoothing is better
compared to normal reference rules (in this case the hns)
'''
st = scipy.stats
Example
-------
>>> kde_demo5()
"""
data = np.hstack((st.norm.rvs(loc=5, scale=1, size=(2, N,)),
st.norm.rvs(loc=-5, scale=1, size=(2, N,))))
kde = KDE(data, kernel=Kernel('gauss', 'hns'))
@ -175,8 +187,12 @@ def kde_demo5(N=500):
plt.plot(data[0], data[1], '.')
def kreg_demo1(hs=None, fast=False, fun='hisj'):
def kreg_demo1(hs=None, fast=True, fun='hisj'):
"""Compare KRegression to KernelReg from statsmodels.nonparametric
Example
-------
>>> kreg_demo1()
"""
N = 100
# ei = np.random.normal(loc=0, scale=0.075, size=(N,))
@ -230,17 +246,16 @@ def kreg_demo1(hs=None, fast=False, fun='hisj'):
plt.plot(x, y2[0], 'm', label='statsmodel')
plt.legend()
plt.show()
# plt.show()
print(kreg.tkde.tkde._inv_hs)
print(kreg.tkde.tkde.hs)
# print(kreg.tkde.tkde._inv_hs)
# print(kreg.tkde.tkde.hs)
def _get_data(n=100, symmetric=False, loc1=1.1, scale1=0.6, scale2=1.0):
"""
Return test data for binomial regression demo.
"""
st = scipy.stats
dist = st.norm
norm1 = scale2 * (dist.pdf(-loc1, loc=-loc1, scale=scale1) +
@ -268,8 +283,12 @@ def _get_data(n=100, symmetric=False, loc1=1.1, scale1=0.6, scale2=1.0):
def check_bkregression():
"""
Check binomial regression
Example
-------
>>> check_bkregression()
"""
plt.ion()
# plt.ion()
k = 0
for _i, n in enumerate([50, 100, 300, 600]):
x, y, fun1 = _get_data(n, symmetric=True, loc1=0.1,
@ -293,13 +312,15 @@ def check_bkregression():
ax.set_yticklabels(ax.get_yticks() * 100.0)
ax.grid(True)
fig.tile(range(0, k))
plt.ioff()
plt.show('hold')
# fig.tile(range(0, k))
# plt.ioff()
# plt.show('hold')
if __name__ == '__main__':
from wafo.testing import test_docstrings
test_docstrings(__file__)
# kde_demo5()
# check_bkregression()
kreg_demo1(hs=0.04, fast=True)
plt.show('hold')
# kreg_demo1(hs=0.04, fast=True)
# plt.show('hold')

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