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@ -2,7 +2,6 @@
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Misc
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'''
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from __future__ import absolute_import, division
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import collections
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import sys
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import fractions
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import numpy as np
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@ -17,9 +16,9 @@ from scipy.special import gammaln
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from scipy.integrate import trapz, simps
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import warnings
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from time import strftime, gmtime
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from numdifftools.extrapolation import dea3
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from numdifftools.extrapolation import dea3 # @UnusedImport
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from .plotbackend import plotbackend
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from collections import OrderedDict
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from collections import Callable
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try:
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from . import c_library as clib # @UnresolvedImport
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except ImportError:
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@ -54,7 +53,7 @@ def valarray(shape, value=np.NaN, typecode=None):
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return out
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def piecewise(xi, condlist, funclist, fill_value=0.0, args=(), **kw):
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def piecewise(condlist, funclist, xi=None, fill_value=0.0, args=(), **kw):
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"""
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Evaluate a piecewise-defined function.
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@ -126,20 +125,20 @@ def piecewise(xi, condlist, funclist, fill_value=0.0, args=(), **kw):
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Define the sigma function, which is -1 for ``x < 0`` and +1 for ``x >= 0``.
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>>> x = np.linspace(-2.5, 2.5, 6)
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>>> piecewise(x, [x < 0, x >= 0], [-1, 1])
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>>> piecewise([x < 0, x >= 0], [-1, 1])
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array([-1., -1., -1., 1., 1., 1.])
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Define the absolute value, which is ``-x`` for ``x <0`` and ``x`` for
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``x >= 0``.
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>>> piecewise((x,), [x < 0, x >= 0], [lambda x: -x, lambda x: x])
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>>> piecewise([x < 0, x >= 0], [lambda x: -x, lambda x: x], xi=(x,))
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array([ 2.5, 1.5, 0.5, 0.5, 1.5, 2.5])
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Define the absolute value, which is ``-x*y`` for ``x*y <0`` and ``x*y`` for
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``x*y >= 0``
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>>> X, Y = np.meshgrid(x, x)
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>>> piecewise((X, Y), [X * Y < 0, ],
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... [lambda x, y: -x * y, lambda x, y: x * y])
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>>> piecewise([X * Y < 0, ], [lambda x, y: -x * y, lambda x, y: x * y],
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... xi=(X, Y))
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array([[ 6.25, 3.75, 1.25, 1.25, 3.75, 6.25],
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[ 3.75, 2.25, 0.75, 0.75, 2.25, 3.75],
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[ 1.25, 0.75, 0.25, 0.25, 0.75, 1.25],
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@ -157,23 +156,27 @@ def piecewise(xi, condlist, funclist, fill_value=0.0, args=(), **kw):
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" must be the same length")
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check_shapes(condlist, funclist)
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if not isinstance(xi, tuple):
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xi = (xi,)
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condlist = np.broadcast_arrays(*condlist)
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if len(condlist) == len(funclist)-1:
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condlist.append(otherwise_condition(condlist))
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arrays = np.broadcast_arrays(*xi)
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dtype = np.result_type(*arrays)
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if xi is None:
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arrays = ()
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dtype = np.result_type(*funclist)
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else:
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if not isinstance(xi, tuple):
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xi = (xi,)
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arrays = np.broadcast_arrays(*xi)
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dtype = np.result_type(*arrays)
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out = valarray(arrays[0].shape, fill_value, dtype)
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out = valarray(condlist[0].shape, fill_value, dtype)
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for cond, func in zip(condlist, funclist):
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if isinstance(func, collections.Callable):
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if isinstance(func, Callable):
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temp = tuple(np.extract(cond, arr) for arr in arrays) + args
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np.place(out, cond, func(*temp, **kw))
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else: # func is a scalar value
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np.place(out, cond, func)
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else: # func is a scalar value or array
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np.putmask(out, cond, func)
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return out
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@ -1634,6 +1637,7 @@ def findoutliers(x, zcrit=0.0, dcrit=None, ddcrit=None, verbose=False):
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>>> ddcrit = 9.81/2*dt*dt
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>>> zcrit = 0
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>>> [inds, indg] = wm.findoutliers(xx[:,1],zcrit,dcrit,ddcrit,verbose=True)
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Found 0 missing points
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Found 0 spurious positive jumps of Dx
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Found 0 spurious negative jumps of Dx
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Found 0 spurious positive jumps of D^2x
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@ -1907,19 +1911,18 @@ def getshipchar(value=None, property="max_deadweight", # @ReservedAssignment
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---------
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>>> import wafo.misc as wm
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>>> sc = wm.getshipchar(10,'service_speed')
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>>> for key in sorted(sc): key, sc[key]
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('beam', 29.0)
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('beamSTD', 2.9000000000000004)
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('draught', 9.6)
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('draughtSTD', 2.112)
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('length', 216.0)
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('lengthSTD', 2.011309883194276)
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('max_deadweight', 30969.0)
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('max_deadweightSTD', 3096.9)
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('propeller_diameter', 6.761165385916601)
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('propeller_diameterSTD', 0.20267047566705432)
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('service_speed', 10.0)
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('service_speedSTD', 0)
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>>> sc == {'service_speedSTD': 0,
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... 'lengthSTD': 2.0113098831942762,
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... 'draught': 9.5999999999999996,
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... 'propeller_diameterSTD': 0.20267047566705432,
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... 'max_deadweightSTD': 3096.9000000000001,
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... 'beam': 29.0, 'length': 216.0,
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... 'beamSTD': 2.9000000000000004,
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... 'service_speed': 10.0,
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... 'draughtSTD': 2.1120000000000001,
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... 'max_deadweight': 30969.0,
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... 'propeller_diameter': 6.761165385916601}
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True
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Other units: 1 ft = 0.3048 m and 1 knot = 0.5144 m/s
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@ -1936,7 +1939,7 @@ def getshipchar(value=None, property="max_deadweight", # @ReservedAssignment
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raise ValueError('Only on keyword')
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property = names[0] # @ReservedAssignment
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value = kwds[property]
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value = np.atleast_1d(value)
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value = np.array(value)
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valid_props = dict(l='length', b='beam', d='draught', m='max_deadweigth',
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s='service_speed', p='propeller_diameter')
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prop = valid_props[property[0]]
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@ -1970,14 +1973,14 @@ def getshipchar(value=None, property="max_deadweight", # @ReservedAssignment
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max_deadweight = np.round(max_deadweight)
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max_deadweightSTD = 0.1 * max_deadweight
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shipchar = OrderedDict(beam=beam, beamSTD=beam_err,
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draught=draught, draughtSTD=draught_err,
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length=length, lengthSTD=length_err,
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max_deadweight=max_deadweight,
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max_deadweightSTD=max_deadweightSTD,
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propeller_diameter=p_diam,
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propeller_diameterSTD=p_diam_err,
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service_speed=speed, service_speedSTD=speed_err)
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shipchar = dict(beam=beam, beamSTD=beam_err,
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draught=draught, draughtSTD=draught_err,
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length=length, lengthSTD=length_err,
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max_deadweight=max_deadweight,
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max_deadweightSTD=max_deadweightSTD,
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propeller_diameter=p_diam,
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propeller_diameterSTD=p_diam_err,
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service_speed=speed, service_speedSTD=speed_err)
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shipchar[propertySTD] = 0
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return shipchar
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@ -2559,10 +2562,10 @@ def good_bins(data=None, range=None, num_bins=None, # @ReservedAssignment
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return m * 10 ** e
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if data is not None:
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x = np.atleast_1d(data)
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data = np.atleast_1d(data)
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mn, mx = _default_range(range, x)
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num_bins = _default_bins(num_bins, x)
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mn, mx = _default_range(range, data)
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num_bins = _default_bins(num_bins, data)
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d = _default_step(mn, mx, num_bins)
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mn = (np.floor(mn / d) - loose) * d - odd * d / 2
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mx = (np.ceil(mx / d) + loose) * d + odd * d / 2
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@ -2723,7 +2726,7 @@ def fourier(data, t=None, period=None, m=None, n=None, method='trapz'):
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>>> T = 2*np.pi
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>>> t = np.linspace(0,4*T)
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>>> x = np.sin(t)
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>>> a, b = wm.fourier(x, t, T=T, m=5)
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>>> a, b = wm.fourier(x, t, period=T, m=5)
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>>> np.abs(a.ravel())<1e-12
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array([ True, True, True, True, True], dtype=bool)
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>>> np.abs(b.ravel()-np.array([ 0., 4., 0., 0., 0.]))<1e-12
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Per A Brodtkorb
9 years ago