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updated doctests + fitting

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master
Per A Brodtkorb 8 years ago
parent f69683428a
commit 9d37eb4f67
3 changed files with 66 additions and 49 deletions
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53
wafo/misc.py
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@ -302,20 +302,20 @@ def rotation_matrix(heading, pitch, roll):
[ 0., 1., 0.], [ 0., 1., 0.],
[ 0., 0., 1.]]) [ 0., 0., 1.]])
>>> np.all(np.abs(rotation_matrix(heading=180, pitch=0, roll=0)- >>> np.allclose(rotation_matrix(heading=180, pitch=0, roll=0),
... np.array([[ -1.000000e+00, -1.224647e-16, 0.000000e+00], ... [[ -1., 0., 0.],
... [ 1.224647e-16, -1.000000e+00, 0.000000e+00], ... [ 0., -1., 0.],
... [ -0.000000e+00, 0.000000e+00, 1.000000e+00]]))<1e-7) ... [ 0., 0., 1.]])
True True
>>> np.all(np.abs(rotation_matrix(heading=0, pitch=180, roll=0)- >>> np.allclose(rotation_matrix(heading=0, pitch=180, roll=0),
... np.array([[ -1.000000e+00, 0.000000e+00, 1.224647e-16], ... [[ -1., 0., 0.],
... [ -0.000000e+00, 1.000000e+00, 0.000000e+00], ... [ 0., 1., 0.],
... [ -1.224647e-16, -0.000000e+00, -1.000000e+00]]))<1e-7) ... [ 0., 0., -1.]])
True True
>>> np.all(np.abs(rotation_matrix(heading=0, pitch=0, roll=180)- >>> np.allclose(rotation_matrix(heading=0, pitch=0, roll=180),
... np.array([[ 1.000000e+00, 0.000000e+00, 0.000000e+00], ... [[ 1., 0., 0.],
... [ 0.000000e+00, -1.000000e+00, -1.224647e-16], ... [ 0., -1., 0.],
... [ -0.000000e+00, 1.224647e-16, -1.000000e+00]]))<1e-7) ... [ 0., 0., -1.]])
True True
''' '''
data = np.diag(np.ones(3)) # No transform if H=P=R=0 data = np.diag(np.ones(3)) # No transform if H=P=R=0
@ -369,14 +369,14 @@ def rotate_2d(x, y, angle_deg):
Examples Examples
-------- --------
>>> rotate_2d(x=1, y=0, angle_deg=0) >>> np.allclose(rotate_2d(x=1, y=0, angle_deg=0), (1.0, 0.0))
(1.0, 0.0) True
>>> rotate_2d(x=1, y=0, angle_deg=90) >>> np.allclose(rotate_2d(x=1, y=0, angle_deg=90), (0, 1.0))
(6.123233995736766e-17, 1.0) True
>>> rotate_2d(x=1, y=0, angle_deg=180) >>> np.allclose(rotate_2d(x=1, y=0, angle_deg=180), (-1.0, 0))
(-1.0, 1.2246467991473532e-16) True
>>> rotate_2d(x=1, y=0, angle_deg=360) >>> np.allclose(rotate_2d(x=1, y=0, angle_deg=360), (1.0, 0))
(1.0, -2.4492935982947064e-16) True
''' '''
angle_rad = angle_deg * pi / 180 angle_rad = angle_deg * pi / 180
ch = cos(angle_rad) ch = cos(angle_rad)
@ -2455,7 +2455,8 @@ def _discretize_linear(fun, a, b, tol=0.005, n=5):
err0 = inf err0 = inf
err = 10000 err = 10000
nmax = 2 ** 20 nmax = 2 ** 20
while (err != err0 and err > tol and n < nmax): num_tries = 0
while (num_tries < 5 and err > tol and n < nmax):
err0 = err err0 = err
x0 = x x0 = x
y0 = y y0 = y
@ -2464,6 +2465,7 @@ def _discretize_linear(fun, a, b, tol=0.005, n=5):
y = fun(x) y = fun(x)
y00 = interp(x, x0, y0) y00 = interp(x, x0, y0)
err = 0.5 * amax(np.abs((y00 - y) / (np.abs(y00 + y) + _TINY))) err = 0.5 * amax(np.abs((y00 - y) / (np.abs(y00 + y) + _TINY)))
num_tries += int(abs (err - err0) <= tol/2)
return x, y return x, y
@ -2479,9 +2481,9 @@ def _discretize_adaptive(fun, a, b, tol=0.005, n=5):
erri = hstack((zeros((n2, 1)), ones((n2, 1)))).ravel() erri = hstack((zeros((n2, 1)), ones((n2, 1)))).ravel()
err = erri.max() err = erri.max()
err0 = inf err0 = inf
# while (err != err0 and err > tol and n < nmax): num_tries = 0
for j in range(50): for j in range(50):
if err != err0 and np.any(erri > tol): if num_tries < 5 and np.any(erri > tol):
err0 = err err0 = err
# find top errors # find top errors
@ -2490,10 +2492,9 @@ def _discretize_adaptive(fun, a, b, tol=0.005, n=5):
y = (vstack(((x[I] + x[I - 1]) / 2, y = (vstack(((x[I] + x[I - 1]) / 2,
(x[I + 1] + x[I]) / 2)).T).ravel() (x[I + 1] + x[I]) / 2)).T).ravel()
fy = fun(y) fy = fun(y)
fy0 = interp(y, x, fx) fy0 = interp(y, x, fx)
erri = 0.5 * (np.abs((fy0 - fy) / (np.abs(fy0 + fy) + _TINY)))
erri = 0.5 * (np.abs((fy0 - fy) / (np.abs(fy0 + fy) + _TINY)))
err = erri.max() err = erri.max()
x = hstack((x, y)) x = hstack((x, y))
@ -2502,7 +2503,7 @@ def _discretize_adaptive(fun, a, b, tol=0.005, n=5):
x = x[I] x = x[I]
erri = hstack((zeros(len(fx)), erri))[I] erri = hstack((zeros(len(fx)), erri))[I]
fx = hstack((fx, fy))[I] fx = hstack((fx, fy))[I]
num_tries += int(abs (err - err0) <= tol/2)
else: else:
break break
else: else:

4
wafo/numba_misc.py
Unescape Escape View File

@ -8,7 +8,7 @@ from numba import jit, float64, int64, int32, int8, void
import numpy as np import numpy as np
@jit(int32(int32[:], int8[:])) @jit(int64(int64[:], int8[:]))
def _findcross(ind, y): def _findcross(ind, y):
ix, dcross, start, v = 0, 0, 0, 0 ix, dcross, start, v = 0, 0, 0, 0
n = len(y) n = len(y)
@ -44,7 +44,7 @@ def _findcross(ind, y):
def findcross(xn): def findcross(xn):
'''Return indices to zero up and downcrossings of a vector '''Return indices to zero up and downcrossings of a vector
''' '''
ind = np.empty(len(xn), dtype=np.int32) ind = np.empty(len(xn), dtype=np.int64)
m = _findcross(ind, xn) m = _findcross(ind, xn)
return ind[:m] return ind[:m]

58
wafo/stats/estimation.py
Unescape Escape View File

@ -329,18 +329,21 @@ class Profile(object):
def _correct_Lmax(self, Lmax, free_par, fix_par): def _correct_Lmax(self, Lmax, free_par, fix_par):
if Lmax > self.Lmax: # foundNewphat = True if Lmax > self.Lmax: # foundNewphat = True
par_old = str(self._par)
dL = self.Lmax - Lmax dL = Lmax - self.Lmax
self.alpha_cross_level -= dL self.alpha_cross_level += dL
self.Lmax = Lmax self.Lmax = Lmax
par = self._par.copy() par = self._par.copy()
par[self.i_free] = free_par par[self.i_free] = free_par
par[self.i_fixed] = fix_par par[self.i_fixed] = fix_par
self.best_par = par self.best_par = par
self._par = par self._par = par
warnings.warn( warnings.warn(
'The fitted parameters does not provide the optimum fit. ' + 'The fitted parameters does not provide the optimum fit. ' +
'Something wrong with fit (par = {})'.format(str(par))) 'Something wrong with fit ' +
'(par = {}, par_old= {}, dl = {})'.format(str(par), par_old,
dL))
def _profile_optimum(self, phatfree0, p_opt): def _profile_optimum(self, phatfree0, p_opt):
phatfree = optimize.fmin(self._profile_fun, phatfree0, args=(p_opt,), phatfree = optimize.fmin(self._profile_fun, phatfree0, args=(p_opt,),
@ -438,17 +441,16 @@ class Profile(object):
def _search_pmin(self, phatfree0, p_min0, p_opt): def _search_pmin(self, phatfree0, p_min0, p_opt):
phatfree = phatfree0.copy() phatfree = phatfree0.copy()
dp = (p_opt - p_min0)/40 dp = np.maximum((p_opt - p_min0)/40, 0.01)*10
if dp < 1e-2:
dp = 0.1
p_min_opt = p_opt - dp
Lmax, phatfree = self._profile_optimum(phatfree, p_opt) Lmax, phatfree = self._profile_optimum(phatfree, p_opt)
for _j in range(50): p_min_opt = p_min0
for j in range(51):
p_min = p_opt - dp p_min = p_opt - dp
Lmax, phatfree = self._profile_optimum(phatfree, p_min) Lmax, phatfree = self._profile_optimum(phatfree, p_min)
# print((dp, p_min, p_min_opt, Lmax))
if np.isnan(Lmax): if np.isnan(Lmax):
dp *= 0.33 dp *= 0.33
elif Lmax < self.alpha_cross_level - self.alpha_Lrange * 5: elif Lmax < self.alpha_cross_level - self.alpha_Lrange*5*(j+1):
p_min_opt = p_min p_min_opt = p_min
dp *= 0.33 dp *= 0.33
elif Lmax < self.alpha_cross_level: elif Lmax < self.alpha_cross_level:
@ -456,6 +458,10 @@ class Profile(object):
break break
else: else:
dp *= 1.67 dp *= 1.67
else:
msg = 'Exceeded max iterations. (p_min0={}, p_min={}, p={})'
warnings.warn(msg.format(p_min0, p_min_opt, p_opt))
# print('search_pmin iterations={}'.format(j))
return p_min_opt return p_min_opt
def _search_pmax(self, phatfree0, p_max0, p_opt): def _search_pmax(self, phatfree0, p_max0, p_opt):
@ -464,14 +470,14 @@ class Profile(object):
dp = (p_max0 - p_opt)/40 dp = (p_max0 - p_opt)/40
if dp < 1e-2: if dp < 1e-2:
dp = 0.1 dp = 0.1
p_max_opt = p_opt + dp
Lmax, phatfree = self._profile_optimum(phatfree, p_opt) Lmax, phatfree = self._profile_optimum(phatfree, p_opt)
for _j in range(50): p_max_opt = p_opt
for j in range(51):
p_max = p_opt + dp p_max = p_opt + dp
Lmax, phatfree = self._profile_optimum(phatfree, p_max) Lmax, phatfree = self._profile_optimum(phatfree, p_max)
if np.isnan(Lmax): if np.isnan(Lmax):
dp *= 0.33 dp *= 0.33
elif Lmax < self.alpha_cross_level - self.alpha_Lrange * 2: elif Lmax < self.alpha_cross_level - self.alpha_Lrange*5*(j+1):
p_max_opt = p_max p_max_opt = p_max
dp *= 0.33 dp *= 0.33
elif Lmax < self.alpha_cross_level: elif Lmax < self.alpha_cross_level:
@ -479,6 +485,10 @@ class Profile(object):
break break
else: else:
dp *= 1.67 dp *= 1.67
else:
msg = 'Exceeded max iterations. (p={}, p_max={}, p_max0 = {})'
warnings.warn(msg.format(p_opt, p_max_opt, p_max0))
# print('search_pmax iterations={}'.format(j))
return p_max_opt return p_max_opt
def _profile_fun(self, free_par, fix_par): def _profile_fun(self, free_par, fix_par):
@ -498,9 +508,8 @@ class Profile(object):
'''Return confidence interval for profiled parameter '''Return confidence interval for profiled parameter
''' '''
if alpha < self.alpha: if alpha < self.alpha:
warnings.warn( msg = 'Might not be able to return bounds with alpha less than {}'
'Might not be able to return bounds with alpha less than %g' % warnings.warn(msg.format(self.alpha))
self.alpha)
cross_level = self.Lmax - 0.5 * chi2isf(alpha, 1) cross_level = self.Lmax - 0.5 * chi2isf(alpha, 1)
ind = findcross(self.data, cross_level) ind = findcross(self.data, cross_level)
n = len(ind) n = len(ind)
@ -511,8 +520,8 @@ class Profile(object):
elif n == 1: elif n == 1:
x0 = ecross(self.args, self.data, ind, cross_level) x0 = ecross(self.args, self.data, ind, cross_level)
isUpcrossing = self.data[ind] > self.data[ind + 1] is_upcrossing = self.data[ind] < self.data[ind + 1]
if isUpcrossing: if is_upcrossing:
bounds = (x0, self.pmax) bounds = (x0, self.pmax)
warnings.warn('Upper bound is larger') warnings.warn('Upper bound is larger')
else: else:
@ -1399,12 +1408,19 @@ class FitDistribution(rv_frozen):
fixstr = 'Fixed: phat[{0:s}] = {1:s} '.format(phatistr, fixstr = 'Fixed: phat[{0:s}] = {1:s} '.format(phatistr,
phatvstr) phatvstr)
subtxt = 'Fit method: {0:s}, Fit p-value: {1:2.2f} {2:s}, phat=[{3:s}]' subtxt = ('Fit method: {0:s}, Fit p-value: {1:2.2f} {2:s}, ' +
'phat=[{3:s}], {4:s}')
par_txt = ('{:1.2g}, '*len(self.par))[:-2].format(*self.par) par_txt = ('{:1.2g}, '*len(self.par))[:-2].format(*self.par)
try: try:
fig.text(0.05, 0.01, subtxt.format(self.method.upper(), LL_txt = 'Lps_max={:2.2g}, Ll_max={:2.2g}'.format(self.LPSmax,
self.pvalue, fixstr, par_txt)) self.LLmax)
except Exception: except Exception:
LL_txt = 'Lps_max={}, Ll_max={}'.format(self.LPSmax, self.LLmax)
try:
fig.text(0.05, 0.01, subtxt.format(self.method.upper(),
self.pvalue, fixstr, par_txt,
LL_txt))
except AttributeError:
pass pass
def plotesf(self, symb1='r-', symb2='b.', axis=None, plot_ci=False): def plotesf(self, symb1='r-', symb2='b.', axis=None, plot_ci=False):

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