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Refactored code

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master
Per A Brodtkorb 7 years ago
parent 06cdca7cf9
commit d16d175190
4 changed files with 95 additions and 98 deletions
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4
setup.cfg
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@ -61,7 +61,7 @@ addopts =
wafo/wave_theory/tests wafo/wave_theory/tests
# wafo/stats/tests # wafo/stats/tests
[pytest] [tool:pytest]
doctest_optionflags = NORMALIZE_WHITESPACE ELLIPSIS doctest_optionflags = NORMALIZE_WHITESPACE ELLIPSIS
# Options for py.test: # Options for py.test:
# Specify command line options as you would do when invoking py.test directly. # Specify command line options as you would do when invoking py.test directly.
@ -75,7 +75,7 @@ addopts =
norecursedirs = norecursedirs =
.* .*
_build build
doc doc
docs docs
tmp* tmp*

2
wafo/fig.py
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@ -902,7 +902,7 @@ cycle = _CycleGenerator()
if __name__ == '__main__': if __name__ == '__main__':
from utilities.testing import test_docstrings from wafo.testing import test_docstrings
import matplotlib import matplotlib
matplotlib.interactive(True) matplotlib.interactive(True)
test_docstrings(__file__) test_docstrings(__file__)

183
wafo/misc.py
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@ -26,9 +26,9 @@ except ImportError:
warnings.warn('c_library not found. Check its compilation.') warnings.warn('c_library not found. Check its compilation.')
clib = None clib = None
floatinfo = np.finfo(float) FLOATINFO = np.finfo(float)
_TINY = floatinfo.tiny _TINY = FLOATINFO.tiny
_EPS = floatinfo.eps _EPS = FLOATINFO.eps
__all__ = ['now', 'spaceline', 'narg_smallest', 'args_flat', 'is_numlike', __all__ = ['now', 'spaceline', 'narg_smallest', 'args_flat', 'is_numlike',
'JITImport', 'DotDict', 'Bunch', 'printf', 'sub_dict_select', 'JITImport', 'DotDict', 'Bunch', 'printf', 'sub_dict_select',
@ -94,7 +94,7 @@ def valarray(shape, value=np.NaN, typecode=None):
return out return out
def piecewise(condlist, funclist, xi=None, fill_value=0.0, args=(), **kw): def piecewise(condlist, funclist, xi=None, fillvalue=0.0, args=(), **kw):
""" """
Evaluate a piecewise-defined function. Evaluate a piecewise-defined function.
@ -121,8 +121,8 @@ def piecewise(condlist, funclist, xi=None, fill_value=0.0, args=(), **kw):
assumed. assumed.
xi : tuple xi : tuple
input arguments to the functions in funclist, i.e., (x0, x1,...., xn) input arguments to the functions in funclist, i.e., (x0, x1,...., xn)
fill_value : scalar fillvalue : scalar
fill value for out of range values. Default 0. fillvalue for out of range values. Default 0.
args : tuple, optional args : tuple, optional
Any further arguments given here passed to the functions Any further arguments given here passed to the functions
upon execution, i.e., if called ``piecewise(..., ..., args=(1, 'a'))``, upon execution, i.e., if called ``piecewise(..., ..., args=(1, 'a'))``,
@ -192,8 +192,8 @@ def piecewise(condlist, funclist, xi=None, fill_value=0.0, args=(), **kw):
return ~np.logical_or.reduce(condlist, axis=0) return ~np.logical_or.reduce(condlist, axis=0)
def check_shapes(condlist, funclist): def check_shapes(condlist, funclist):
nc, nf = len(condlist), len(funclist) num_cond, num_fun = len(condlist), len(funclist)
_assert(nc in [nf - 1, nf], _assert(num_cond in [num_fun - 1, num_fun],
"function list and condition list must be the same length") "function list and condition list must be the same length")
check_shapes(condlist, funclist) check_shapes(condlist, funclist)
@ -212,7 +212,7 @@ def piecewise(condlist, funclist, xi=None, fill_value=0.0, args=(), **kw):
dtype = np.result_type(*arrays) dtype = np.result_type(*arrays)
shape = arrays[0].shape shape = arrays[0].shape
out = valarray(shape, fill_value, dtype) out = valarray(shape, fillvalue, dtype)
for cond, func in zip(condlist, funclist): for cond, func in zip(condlist, funclist):
if cond.any(): if cond.any():
if isinstance(func, collections.Callable): if isinstance(func, collections.Callable):
@ -333,19 +333,19 @@ def rotation_matrix(heading, pitch, roll):
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
if heading != 0 or pitch != 0 or roll != 0: if heading != 0 or pitch != 0 or roll != 0:
deg2rad = np.pi / 180 deg2rad = np.pi / 180
H = heading * deg2rad rheading = heading * deg2rad
P = pitch * deg2rad rpitch = pitch * deg2rad
R = roll * deg2rad # Convert to radians rroll = roll * deg2rad # Convert to radians
data.put(0, cos(H) * cos(P)) data.put(0, cos(rheading) * cos(rpitch))
data.put(1, cos(H) * sin(P) * sin(R) - sin(H) * cos(R)) data.put(1, cos(rheading) * sin(rpitch) * sin(rroll) - sin(rheading) * cos(rroll))
data.put(2, cos(H) * sin(P) * cos(R) + sin(H) * sin(R)) data.put(2, cos(rheading) * sin(rpitch) * cos(rroll) + sin(rheading) * sin(rroll))
data.put(3, sin(H) * cos(P)) data.put(3, sin(rheading) * cos(rpitch))
data.put(4, sin(H) * sin(P) * sin(R) + cos(H) * cos(R)) data.put(4, sin(rheading) * sin(rpitch) * sin(rroll) + cos(rheading) * cos(rroll))
data.put(5, sin(H) * sin(P) * cos(R) - cos(H) * sin(R)) data.put(5, sin(rheading) * sin(rpitch) * cos(rroll) - cos(rheading) * sin(rroll))
data.put(6, -sin(P)) data.put(6, -sin(rpitch))
data.put(7, cos(P) * sin(R)) data.put(7, cos(rpitch) * sin(rroll))
data.put(8, cos(P) * cos(R)) data.put(8, cos(rpitch) * cos(rroll))
return data return data
@ -369,10 +369,10 @@ def rotate(x, y, z, heading=0, pitch=0, roll=0):
True True
""" """
rot_param = rotation_matrix(heading, pitch, roll).ravel() rot_param = rotation_matrix(heading, pitch, roll).ravel()
X = x * rot_param[0] + y * rot_param[1] + z * rot_param[2] x_out = x * rot_param[0] + y * rot_param[1] + z * rot_param[2]
Y = x * rot_param[3] + y * rot_param[4] + z * rot_param[5] y_out = x * rot_param[3] + y * rot_param[4] + z * rot_param[5]
Z = x * rot_param[6] + y * rot_param[7] + z * rot_param[8] z_out = x * rot_param[6] + y * rot_param[7] + z * rot_param[8]
return X, Y, Z return x_out, y_out, z_out
def rotate_2d(x, y, angle_deg): def rotate_2d(x, y, angle_deg):
@ -391,9 +391,9 @@ def rotate_2d(x, y, angle_deg):
True True
''' '''
angle_rad = angle_deg * pi / 180 angle_rad = angle_deg * pi / 180
ch = cos(angle_rad) cos_a = cos(angle_rad)
sh = sin(angle_rad) sin_a = sin(angle_rad)
return ch * x - sh * y, sh * x + ch * y return cos_a * x - sin_a * y, sin_a * x + cos_a * y
def now(show_seconds=True): def now(show_seconds=True):
@ -446,13 +446,9 @@ def spaceline(start_point, stop_point, num=10):
[ 3. , 0. , 0. ]]) [ 3. , 0. , 0. ]])
''' '''
num = int(num) num = int(num)
e1, e2 = np.atleast_1d(start_point, stop_point) start, stop = np.atleast_1d(start_point, stop_point)
e2m1 = e2 - e1 delta = (stop - start) / float(num - 1)
length = np.sqrt((e2m1 ** 2).sum()) return np.array([start + n * delta for n in range(num)])
# length = sqrt((E2[0]-E1(1))^2 + (E2(2)-E1(2))^2 + (E2(3)-E1(3))^2)
C = e2m1 / length
delta = length / float(num - 1)
return np.array([e1 + n * delta * C for n in range(num)])
def narg_smallest(arr, n=1): def narg_smallest(arr, n=1):
@ -521,7 +517,7 @@ def args_flat(*args):
''' '''
nargin = len(args) nargin = len(args)
_assert(nargin in [1, 3], 'Number of arguments must be 1 or 3!') _assert(nargin in [1, 3], 'Number of arguments must be 1 or 3!')
if (nargin == 1): # pos if nargin == 1: # pos
pos = np.atleast_2d(args[0]) pos = np.atleast_2d(args[0])
_assert((pos.shape[1] == 3) and (pos.ndim == 2), _assert((pos.shape[1] == 3) and (pos.ndim == 2),
'POS array must be of shape N x 3!') 'POS array must be of shape N x 3!')
@ -790,22 +786,22 @@ def detrendma(x, L):
_assert(0 < L, 'L must be positive') _assert(0 < L, 'L must be positive')
_assert(L == np.round(L), 'L must be an integer') _assert(L == np.round(L), 'L must be an integer')
x1 = np.atleast_1d(x) x_1 = np.atleast_1d(x)
if x1.shape[0] == 1: if x_1.shape[0] == 1:
x1 = x1.ravel() x_1 = x_1.ravel()
n = x1.shape[0] n = x_1.shape[0]
if n < 2 * L + 1: # only able to remove the mean if n < 2 * L + 1: # only able to remove the mean
return x1 - x1.mean(axis=0) return x_1 - x_1.mean(axis=0)
mn = x1[:2 * L + 1].mean(axis=0) mean = x_1[:2 * L + 1].mean(axis=0)
y = np.empty_like(x1) y = np.empty_like(x_1)
y[:L + 1] = x1[:L + 1] - mn y[:L + 1] = x_1[:L + 1] - mean
ix = np.r_[L + 1:(n - L)] i = np.r_[L + 1:(n - L)]
trend = ((x1[ix + L] - x1[ix - L]) / (2 * L)).cumsum(axis=0) + mn trend = ((x_1[i + L] - x_1[i - L]) / (2 * L)).cumsum(axis=0) + mean
y[ix] = x1[ix] - trend y[i] = x_1[i] - trend
y[n - L::] = x1[n - L::] - trend[-1] y[n - L::] = x_1[n - L::] - trend[-1]
return y return y
@ -860,13 +856,13 @@ def ecross(t, f, ind, v=0):
(f[ind + 1] - f[ind])) (f[ind + 1] - f[ind]))
def _findcross(xn, method='clib'): def _findcross(x, method='clib'):
'''Return indices to zero up and downcrossings of a vector '''Return indices to zero up and downcrossings of a vector
''' '''
if clib is not None and method == 'clib': if clib is not None and method == 'clib':
ind, m = clib.findcross(xn, 0.0) ind, m = clib.findcross(x, 0.0)
return ind[:int(m)] return ind[:int(m)]
return numba_misc.findcross(xn) return numba_misc.findcross(x)
def findcross(x, v=0.0, kind=None, method='clib'): def findcross(x, v=0.0, kind=None, method='clib'):
@ -993,8 +989,7 @@ def findextrema(x):
findcross findcross
crossdef crossdef
''' '''
xn = atleast_1d(x).ravel() return findcross(diff(x), 0.0) + 1
return findcross(diff(xn), 0.0) + 1
def findpeaks(data, n=2, min_h=None, min_p=0.0): def findpeaks(data, n=2, min_h=None, min_p=0.0):
@ -1003,14 +998,18 @@ def findpeaks(data, n=2, min_h=None, min_p=0.0):
Parameters Parameters
---------- ----------
data = matrix or vector data : matrix or vector
n = The n highest peaks are found (if exist). (default 2) n :
min_h = The threshold in the rainflowfilter (default 0.05*range(S(:))). The n highest peaks are found (if exist). (default 2)
min_h :
The threshold in the rainflowfilter (default 0.05*range(S(:))).
A zero value will return all the peaks of S. A zero value will return all the peaks of S.
min_p = 0..1, Only the peaks that are higher than min_p : 0..1
min_p*max(max(S)) min_p*(the largest peak in S) Only the peaks that are higher than min_p*max(max(S))
are returned (default 0). min_p*(the largest peak in S) are returned (default 0).
Returns Returns
-------
ix = ix =
linear index to peaks of S linear index to peaks of S
@ -1031,53 +1030,53 @@ def findpeaks(data, n=2, min_h=None, min_p=0.0):
-------- --------
findtp findtp
''' '''
S = np.atleast_1d(data) data1 = np.atleast_1d(data)
smax = S.max() dmax = data1.max()
if min_h is None: if min_h is None:
smin = S.min() dmin = data1.min()
min_h = 0.05 * (smax - smin) min_h = 0.05 * (dmax - dmin)
ndim = S.ndim ndim = data1.ndim
S = np.atleast_2d(S) data1 = np.atleast_2d(data1)
nrows, mcols = S.shape nrows, mcols = data1.shape
# Finding turningpoints of the spectrum # Finding turningpoints of the spectrum
# Returning only those with rainflowcycle heights greater than h_min # Returning only those with rainflowcycle heights greater than h_min
indP = [] # indices to peaks ind_p = [] # indices to peaks
ind = [] ind = []
for iy in range(nrows): # find all peaks for iy in range(nrows): # find all peaks
TuP = findtp(S[iy], min_h) tp = findtp(data1[iy], min_h)
if len(TuP): if len(tp):
ind = TuP[1::2] # extract indices to maxima only ind = tp[1::2] # extract indices to maxima only
else: # did not find any , try maximum else: # did not find any , try maximum
ind = np.atleast_1d(S[iy].argmax()) ind = np.atleast_1d(data1[iy].argmax())
if ndim > 1: if ndim > 1:
if iy == 0: if iy == 0:
ind2 = np.flatnonzero(S[iy, ind] > S[iy + 1, ind]) ind2 = np.flatnonzero(data1[iy, ind] > data1[iy + 1, ind])
elif iy == nrows - 1: elif iy == nrows - 1:
ind2 = np.flatnonzero(S[iy, ind] > S[iy - 1, ind]) ind2 = np.flatnonzero(data1[iy, ind] > data1[iy - 1, ind])
else: else:
ind2 = np.flatnonzero((S[iy, ind] > S[iy - 1, ind]) & ind2 = np.flatnonzero((data1[iy, ind] > data1[iy - 1, ind]) &
(S[iy, ind] > S[iy + 1, ind])) (data1[iy, ind] > data1[iy + 1, ind]))
if len(ind2): if len(ind2):
indP.append((ind[ind2] + iy * mcols)) ind_p.append((ind[ind2] + iy * mcols))
if ndim > 1: if ndim > 1:
ind = np.hstack(indP) if len(indP) else [] ind = np.hstack(ind_p) if len(ind_p) else []
if len(ind) == 0: if len(ind) == 0:
return [] return []
peaks = S.take(ind) peaks = data1.take(ind)
ind2 = peaks.argsort()[::-1] ind2 = peaks.argsort()[::-1]
# keeping only the Np most significant peak frequencies. # keeping only the Np most significant peaks.
nmax = min(n, len(ind)) nmax = min(n, len(ind))
ind = ind[ind2[:nmax]] ind = ind[ind2[:nmax]]
if (min_p > 0): if (min_p > 0):
# Keeping only peaks larger than min_p percent relative to the maximum # Keeping only peaks larger than min_p percent relative to the maximum
# peak # peak
ind = ind[(S.take(ind) > min_p * smax)] ind = ind[(data1.take(ind) > min_p * dmax)]
return ind return ind
@ -2252,7 +2251,7 @@ def trangood(x, f, min_n=None, min_x=None, max_x=None, max_n=inf):
xn = xo[-1] xn = xo[-1]
x0 = xo[0] x0 = xo[0]
L = float(xn - x0) L = float(xn - x0)
if ((nf < min_n) or (max_n < nf) or np.any(np.abs(ddx) > 10 * _EPS * (L))): if nf < min_n or max_n < nf or np.any(np.abs(ddx) > 10 * _EPS * L):
# pab 07.01.2001: Always choose the stepsize df so that # pab 07.01.2001: Always choose the stepsize df so that
# it is an exactly representable number. # it is an exactly representable number.
# This is important when calculating numerical derivatives and is # This is important when calculating numerical derivatives and is
@ -2268,13 +2267,13 @@ def trangood(x, f, min_n=None, min_x=None, max_x=None, max_n=inf):
dx = xo[1] - xo[0] dx = xo[1] - xo[0]
# Extrapolate linearly outside the range of ff # Extrapolate linearly outside the range of ff
if (min_x < xo[0]): if min_x < xo[0]:
x1 = dx * arange(floor((min_x - xo[0]) / dx), -2) x1 = dx * arange(floor((min_x - xo[0]) / dx), -2)
f2 = fo[0] + x1 * (fo[1] - fo[0]) / (xo[1] - xo[0]) f2 = fo[0] + x1 * (fo[1] - fo[0]) / (xo[1] - xo[0])
fo = hstack((f2, fo)) fo = hstack((f2, fo))
xo = hstack((x1 + xo[0], xo)) xo = hstack((x1 + xo[0], xo))
if (max_x > xo[-1]): if max_x > xo[-1]:
x1 = dx * arange(1, ceil((max_x - xo[-1]) / dx) + 1) x1 = dx * arange(1, ceil((max_x - xo[-1]) / dx) + 1)
f2 = f[-1] + x1 * (f[-1] - f[-2]) / (xo[-1] - xo[-2]) f2 = f[-1] + x1 * (f[-1] - f[-2]) / (xo[-1] - xo[-2])
fo = hstack((fo, f2)) fo = hstack((fo, f2))
@ -2413,8 +2412,8 @@ def tranproc(x, f, x0, *xi):
return y return y
def good_bins(data=None, range=None, num_bins=None, # @ReservedAssignment # pylint: disable=redefined-builtin
num_data=None, odd=False, loose=True): def good_bins(data=None, range=None, num_bins=None, odd=False, loose=True): # @ReservedAssignment
''' Return good bins for histogram ''' Return good bins for histogram
Parameters Parameters
@ -2486,8 +2485,8 @@ def _make_bars(limits, bin_):
return xx, yy return xx, yy
def _histogram(data, bins=None, range=None, normed=False, weights=None, # pylint: disable=redefined-builtin
density=None): def _histogram(data, bins=None, range=None, normed=False, weights=None, density=None): # @ReservedAssignment
""" """
Example Example
------- -------
@ -2619,7 +2618,7 @@ def num2pistr(x, n=3, numerator_max=10, denominator_max=10):
return fmt.format(x) return fmt.format(x)
def fourier(data, t=None, period=None, m=None, n=None, method='trapz'): def fourier(data, t=None, period=None, m=None, method='trapz'):
''' '''
Returns Fourier coefficients. Returns Fourier coefficients.
@ -2628,13 +2627,11 @@ def fourier(data, t=None, period=None, m=None, n=None, method='trapz'):
data : array-like data : array-like
vector or matrix of row vectors with data points shape p x n. vector or matrix of row vectors with data points shape p x n.
t : array-like t : array-like
vector with n values indexed from 1 to N. vector with n values indexed from 1 to n.
period : real scalar, (default T = t[-1]-t[0]) period : real scalar, (default T = t[-1]-t[0])
primitive period of signal, i.e., smallest period. primitive period of signal, i.e., smallest period.
m : scalar integer m : scalar integer
defines no of harmonics desired (default M = N) defines no of harmonics desired (default m = n)
n : scalar integer
no of data points (default len(t))
method : string method : string
integration method used integration method used

2
wafo/tests/test_misc.py
Unescape Escape View File

@ -548,7 +548,7 @@ class TestPiecewise(TestCase):
X, Y = np.meshgrid(x, x) X, Y = np.meshgrid(x, x)
vals = piecewise([X * Y < -0.5, X * Y > 0.5], vals = piecewise([X * Y < -0.5, X * Y > 0.5],
[lambda x, y: -x * y, lambda x, y: x * y], (X, Y), [lambda x, y: -x * y, lambda x, y: x * y], (X, Y),
fill_value=np.nan) fillvalue=np.nan)
nan = np.nan nan = np.nan
assert_array_equal(vals, [[4., 2., nan, 2., 4.], assert_array_equal(vals, [[4., 2., nan, 2., 4.],
[2., 1., nan, 1., 2.], [2., 1., nan, 1., 2.],

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