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Reduced cyclomatic complexity

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master
Per A Brodtkorb 8 years ago
parent 54b1d1d62c
commit d2d3fc1fee
1 changed files with 106 additions and 70 deletions
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176
wafo/polynomial.py
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@ -122,16 +122,23 @@ def polyint(p, m=1, k=None):
msg = "Order of integral must be positive (see polyder)"
raise ValueError(msg)
m = int(m)
_check_order(m)
if k is None:
k = np.zeros(m, float)
k = np.atleast_1d(k)
if len(k) == 1 and m > 1:
k = k[0] * np.ones(m, float)
if len(k) < m:
raise ValueError(
"k must be a scalar or a rank-1 array of length 1 or >m.")
def _check_integration_const(k, m):
if len(k) < m:
msg = "k must be a scalar or a rank-1 array of length 1 or >m."
raise ValueError(msg)
def _init(m, k):
m = int(m)
_check_order(m)
if k is None:
k = np.zeros(m, float)
k = np.atleast_1d(k)
if len(k) == 1 and m > 1:
k = k[0] * np.ones(m, float)
_check_integration_const(k, m)
return m, k
m, k = _init(m, k)
truepoly = isinstance(p, poly1d)
p = np.asarray(p)
if m == 0:
@ -508,18 +515,23 @@ def polyreloc(p, x, y=0.0):
>>> wp.polyval(r,0) # = polyval(p,1)
array([3, 5, 7])
"""
def _reshape(r):
if r.ndim > 1 and r.shape[-1] == 1:
r.shape = (r.size,)
return r
truepoly = isinstance(p, poly1d)
r = np.atleast_1d(p).copy()
n = r.shape[0]
def _relocate_with_horner(p, x, y):
r = np.atleast_1d(p).copy()
n = r.shape[0]
# Relocate polynomial using Horner's algorithm
for ii in range(n, 1, -1):
for i in range(1, ii):
r[i] = r[i] - x * r[i - 1]
r[-1] = r[-1] + y
return _reshape(r)
# Relocate polynomial using Horner's algorithm
for ii in range(n, 1, -1):
for i in range(1, ii):
r[i] = r[i] - x * r[i - 1]
r[-1] = r[-1] + y
if r.ndim > 1 and r.shape[-1] == 1:
r.shape = (r.size,)
truepoly = isinstance(p, poly1d)
r = _relocate_with_horner(p, x, y)
if truepoly:
r = poly1d(r)
return r
@ -1284,21 +1296,26 @@ def chebfit_dct(f, n=(10, ), domain=None):
Approximations for Functions of a Single Independent Variable"
Journal of the ACM (JACM), Vol. 12 , Issue 3, pp 295 - 314
"""
n = np.atleast_1d(n)
if np.any(n > 50):
warnings.warn('CHEBFIT should only be used for n<50')
def _check(n):
if np.any(n > 50):
warnings.warn('CHEBFIT should only be used for n<50')
if hasattr(f, '__call__'):
def _zip(n, domain):
if domain is None:
domain = (-1, 1) * len(n)
domain = np.atleast_2d(domain).reshape((-1, 2))
xi = [map_to_interval(chebroot(ni), d[0], d[1])
for ni, d in zip(n, domain)]
Xi = np.meshgrid(*xi)
ck = f(*Xi)
else:
ck = f
n = ck.shape
return zip(n, np.atleast_2d(domain).reshape((-1, 2)))
def _init_ck(f, n, domain):
n = np.atleast_1d(n)
_check(n)
if hasattr(f, '__call__'):
xi = [map_to_interval(chebroot(ni), d[0], d[1])
for ni, d in _zip(n, domain)]
Xi = np.meshgrid(*xi)
return f(*Xi), n
return f, f.shape
ck, n = _init_ck(f, n, domain)
ndim = len(n)
for i in range(ndim):
@ -1868,6 +1885,25 @@ def padefitlsq(fun, m, k, a=-1, b=1, trace=False, x=None, end_points=True):
warnings.warn('Check the result! Number of function values ' +
'should be at least: {0:d}'.format(npt))
def _init(fun, a, b, x, end_points, npt):
if x is None:
x = map_to_interval(_points(npt, end_points), a, b)
if hasattr(fun, '__call__'):
fs = fun(x)
else:
fs = fun
_check_size(fs, npt)
return x, fs
def _cond_plot1(trace, x, fs):
if trace:
plt.plot(x, fs, '+')
def _cond_plot2(x, fs, x, ys, ix, devmax):
if trace:
print('Iteration=%d, max error=%g' % (ix, devmax))
plt.plot(x, fs, x, ee + fs)
NFAC = 8
BIG = 1e30
MAXIT = 5
@ -1877,17 +1913,9 @@ def padefitlsq(fun, m, k, a=-1, b=1, trace=False, x=None, end_points=True):
# Number of points where function is evaluated, i.e. fineness of mesh
npt = NFAC * ncof
if x is None:
x = map_to_interval(_points(npt, end_points), a, b)
x, fs = _init(fun, a, b, x, end_points, npt)
if hasattr(fun, '__call__'):
fs = fun(x)
else:
fs = fun
_check_size(fs, npt)
if trace:
plt.plot(x, fs, '+')
_cond_plot1(trace, x, fs)
wt = np.ones((npt))
ee = np.ones((npt))
@ -1924,10 +1952,7 @@ def padefitlsq(fun, m, k, a=-1, b=1, trace=False, x=None, end_points=True):
smallest_devmax = devmax
c1 = cof[m::-1]
c2 = cof[ncof:m:-1].tolist() + [1, ]
if trace:
print('Iteration=%d, max error=%g' % (ix, devmax))
plt.plot(x, fs, x, ee + fs)
_cond_plot2(x, fs, x, ee + fs, ix, devmax)
return poly1d(c1), poly1d(c2)
@ -2050,14 +2075,17 @@ def chebvandernd(deg, *xi):
--------
chebvander, chebvalnd, chebfitnd
"""
def _check_deg(ideg, is_valid, ndim):
if np.any(is_valid != 1):
raise ValueError("degrees must be non-negative integers")
if len(ideg) != ndim:
msg = 'length of deg must be the same as number of dimensions'
raise ValueError(msg)
ideg = [int(d) for d in deg]
is_valid = np.array([di == d and di >= 0 for di, d in zip(ideg, deg)])
if np.any(is_valid != 1):
raise ValueError("degrees must be non-negative integers")
ndim = len(xi)
if len(ideg) != ndim:
msg = 'length of deg must be the same as number of dimensions'
raise ValueError(msg)
_check_deg(ideg, is_valid, ndim)
xi = np.array(xi, copy=0) + 0.0
chebvander = np.polynomial.chebyshev.chebvander
@ -2161,7 +2189,9 @@ def chebfitnd(xi, f, deg, rcond=None, full=False, w=None):
Examples
--------
"""
def _check_shapes(z, ndims, sizes):
def _check_shapes(z, xi):
ndims = np.array([np.ndim(x) for x in xi])
sizes = np.array([np.size(x) for x in xi])
ndim = len(ndims)
if np.any(ndims != ndim) or z.ndim != ndim:
msg = "expected {0:d}D array for x1, x2,...,xn and f".format(ndim)
@ -2173,32 +2203,38 @@ def chebfitnd(xi, f, deg, rcond=None, full=False, w=None):
if n != len(w):
raise TypeError("expected x and w to have same length")
def _scale(lhs):
if issubclass(lhs.dtype.type, np.complexfloating):
scl = np.sqrt((np.square(lhs.real) +
np.square(lhs.imag)).sum(axis=0))
else:
scl = np.sqrt(np.square(lhs).sum(axis=0))
scl[scl == 0] = 1
return scl
def _init(xi, z, w, degrees, order):
lhs = chebvandernd(degrees, *xi).reshape((-1, order))
rhs = z.ravel()
if w is not None:
w = np.asarray(w).ravel() + 0.0
_check_size(w, len(lhs))
lhs = lhs * w
rhs = rhs * w
scl = _scale(lhs)
return lhs, scl, rhs
# xi = np.array(xi, copy=0) + 0.0
z = np.array(f)
_check_shapes(z, xi)
degrees = np.asarray(deg, dtype=int)
orders = degrees + 1
order = np.product(orders)
ndims = np.array([x.ndim for x in xi])
sizes = np.array([x.size for x in xi])
_check_shapes(z, ndims, sizes)
lhs = chebvandernd(degrees, *xi).reshape((-1, order))
rhs = z.ravel()
if w is not None:
w = np.asarray(w).ravel() + 0.0
_check_size(w, len(lhs))
lhs = lhs * w
rhs = rhs * w
lhs, rhs, scl = _init(xi, z, w, degrees, order)
if rcond is None:
rcond = xi[0].size * np.finfo(x.dtype).eps
if issubclass(lhs.dtype.type, np.complexfloating):
scl = np.sqrt((np.square(lhs.real) + np.square(lhs.imag)).sum(axis=0))
else:
scl = np.sqrt(np.square(lhs).sum(axis=0))
scl[scl == 0] = 1
rcond = xi[0].size * np.finfo(xi[0].dtype).eps
# Solve the least squares problem.
c, resids, rank, s = np.linalg.lstsq(lhs/scl, rhs, rcond)

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