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pep8

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Per A Brodtkorb 8 years ago
parent 5e6cfbe5ee
commit d10324085a
1 changed files with 36 additions and 25 deletions
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wafo/interpolate.py
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@ -1,7 +1,6 @@
#!/usr/bin/env python
from __future__ import absolute_import, division
import numpy as np
import scipy.signal
# import scipy.sparse.linalg # @UnusedImport
import scipy.sparse as sparse
from numpy import ones, zeros, prod, sin, diff, pi, inf, vstack, linspace
@ -92,7 +91,7 @@ def savitzky_golay(y, window_size, order, deriv=0):
half_window = (window_size - 1) // 2
# precompute coefficients
b = np.mat([[k ** i for i in order_range]
for k in range(-half_window, half_window + 1)])
for k in range(-half_window, half_window + 1)])
m = np.linalg.pinv(b).A[deriv]
# pad the signal at the extremes with
# values taken from the signal itself
@ -106,12 +105,15 @@ def _get_turnpoint(xvals):
turnpoint = 0
last = len(xvals)
if xvals[0] < xvals[1]: # x is increasing?
compare = lambda a, b: a < b
def compare(a, b):
return a < b
else: # no, x is decreasing
compare = lambda a, b: a > b
def compare(a, b):
return a > b
for i in range(1, last): # yes
if compare(xvals[i], xvals[i - 1]): # search where x starts to fall or rise
for i in range(1, last): # yes
# search where x starts to fall or rise
if compare(xvals[i], xvals[i - 1]):
turnpoint = i
break
@ -245,44 +247,52 @@ def sgolay2d(z, window_size, order, derivative=None):
Z = np.zeros((new_shape))
# top band
band = z[0, :]
Z[:half_size, half_size:-half_size] = band - np.abs(np.flipud(z[1:half_size + 1, :]) - band)
Z[:half_size, half_size:-half_size] = band - \
np.abs(np.flipud(z[1:half_size + 1, :]) - band)
# bottom band
band = z[-1, :]
Z[-half_size:, half_size:-half_size] = band + np.abs(np.flipud(z[-half_size - 1:-1, :]) - band)
Z[-half_size:, half_size:-half_size] = band + \
np.abs(np.flipud(z[-half_size - 1:-1, :]) - band)
# left band
band = np.tile(z[:, 0].reshape(-1, 1), [1, half_size])
Z[half_size:-half_size, :half_size] = band - np.abs(np.fliplr(z[:, 1:half_size + 1]) - band)
Z[half_size:-half_size, :half_size] = band - \
np.abs(np.fliplr(z[:, 1:half_size + 1]) - band)
# right band
band = np.tile(z[:, -1].reshape(-1, 1), [1, half_size])
Z[half_size:-half_size, -half_size:] = band + np.abs(np.fliplr(z[:, -half_size - 1:-1]) - band)
Z[half_size:-half_size, -half_size:] = band + \
np.abs(np.fliplr(z[:, -half_size - 1:-1]) - band)
# central band
Z[half_size:-half_size, half_size:-half_size] = z
# top left corner
band = z[0, 0]
Z[:half_size, :half_size] = band - \
np.abs(np.flipud(np.fliplr(z[1:half_size + 1, 1:half_size + 1])) - band)
np.abs(
np.flipud(np.fliplr(z[1:half_size + 1, 1:half_size + 1])) - band)
# bottom right corner
band = z[-1, -1]
Z[-half_size:, -half_size:] = band + \
np.abs(np.flipud(np.fliplr(z[-half_size - 1:-1, -half_size - 1:-1])) - band)
np.abs(
np.flipud(np.fliplr(z[-half_size - 1:-1, -half_size - 1:-1])) - band)
# top right corner
band = Z[half_size, -half_size:]
Z[:half_size, -half_size:] = band - \
np.abs(np.flipud(Z[half_size + 1:2 * half_size + 1, -half_size:]) - band)
np.abs(
np.flipud(Z[half_size + 1:2 * half_size + 1, -half_size:]) - band)
# bottom left corner
band = Z[-half_size:, half_size].reshape(-1, 1)
Z[-half_size:, :half_size] = band - \
np.abs(np.fliplr(Z[-half_size:, half_size + 1:2 * half_size + 1]) - band)
np.abs(
np.fliplr(Z[-half_size:, half_size + 1:2 * half_size + 1]) - band)
# solve system and convolve
sgn = {None:1}.get(derivative , -1)
dims = {None: (0,), 'col': (1,), 'row': (2,), 'both':(1, 2)}[derivative]
sgn = {None: 1}.get(derivative, -1)
dims = {None: (0,), 'col': (1,), 'row': (2,), 'both': (1, 2)}[derivative]
res = tuple(fftconvolve(Z, sgn * np.linalg.pinv(A)[i].reshape((window_size, -1)), mode='valid')
for i in dims)
if len(dims)>1:
if len(dims) > 1:
return res
return res[0]
@ -597,7 +607,8 @@ class SmoothSpline(PPform):
di = di.T
ci = ci.T
ai = ai.T
coefs = vstack([val.ravel() for val in [di, ci, bi, ai] if val.size>0])
coefs = vstack([val.ravel()
for val in [di, ci, bi, ai] if val.size > 0])
return coefs
def _compute_coefs(self, xx, yy, p=None, var=1):
@ -643,7 +654,7 @@ class SmoothSpline(PPform):
QQ = (6 * (1 - p)) * (QDQ) + p * R
return QQ
def _compute_u(self,QQ, p, dydx, n):
def _compute_u(self, QQ, p, dydx, n):
# Make sure it uses symmetric matrix solver
ddydx = diff(dydx, axis=0)
# sp.linalg.use_solver(useUmfpack=True)
@ -715,7 +726,7 @@ def _catmull_rom_slope(x, y, dx, dydx, *args):
def _cardinal_slope(x, y, dx, dydx, tension):
yp = (1-tension) * _catmull_rom_slope(x, y, dx, dydx)
yp = (1 - tension) * _catmull_rom_slope(x, y, dx, dydx)
return yp
@ -978,6 +989,7 @@ class StinemanInterp(object):
--------
slopes, Pchip
'''
def __init__(self, x, y, yp=None, method='parabola', monotone=False):
if yp is None:
yp = slopes(x, y, method, monotone=monotone)
@ -1171,7 +1183,7 @@ def interp3(x, y, z, v, xi, yi, zi, method='cubic'):
def somefunc(x, y, z):
return x**2 + y**2 - z**2 + x*y*z
return x**2 + y**2 - z**2 + x * y * z
def test_interp3():
@ -1214,7 +1226,6 @@ def test_smoothing_spline():
plt.plot(x, y, x1, y1, '.', x1, dy1, 'ro', x1, y01, 'r-')
plt.show('hold')
pass
# tck = interpolate.splrep(x, y, s=len(x))
@ -1232,7 +1243,7 @@ def compare_methods():
# Initialize the interpolator slopes
# Create the pchip slopes
m = slopes(x, y, method='parabola', monotone=True)
m = slopes(x, y, method='parabola', monotone=True)
m1 = slopes(x, y, method='parabola', monotone=False)
m2 = slopes(x, y, method='catmul', monotone=False)
m3 = pchip_slopes(x, y)
@ -1290,8 +1301,8 @@ def demo_monoticity():
yvec5 = Pchip(x, y, m3)(xvec) # @UnusedVariable
# Plot the results
plt.plot(x, y, 'ro', label='Data')
plt.plot(xvec, yvec, 'b', label='Pchip')
plt.plot(x, y, 'ro', label='Data')
plt.plot(xvec, yvec, 'b', label='Pchip')
plt.plot(xvec, yvec2, 'k', label='interp1d')
plt.plot(xvec, yvec3, 'g', label='CHS')
plt.plot(xvec, yvec4, 'm', label='Stineman')

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