def test_error(self):
"""
Error if chebfun is called with another type.
"""
class C(object):
pass
with self.assertRaises(TypeError):
chebfun(C())
def test_error(self):
"""
Error if chebfun is called with another type.
"""
class C(object):
pass
with self.assertRaises(TypeError):
chebfun(C())
def __chebydiff__(x, dt, params, options=None):
"""
Fit the timeseries with chebyshev polynomials, and differentiate this model.
:param x: (np.array of floats, 1xN) time series to differentiate
:param dt: (float) time step
:param params: (list) [N] : (int) order of the polynomial
:param options:
:return: x_hat : estimated (smoothed) x
dxdt_hat : estimated derivative of x
"""
if isinstance(params, list):
n = params[0]
else:
n = params
mean = np.mean(x)
x = x - mean
def f(y):
t = np.linspace(-1, 1, len(x))
return np.interp(y, t, x)
# Chebychev polynomial
poly = pychebfun.chebfun(f, N=n, domain=[-1, 1])
ts = np.linspace(poly.domain()[0], poly.domain()[-1], len(x))
x_hat = poly(ts) + mean
dxdt_hat = poly.differentiate()(ts) * (2 / len(x)) / dt
return x_hat, dxdt_hat
def __setstate__(self, dict):
self.lower = dict["lower"]
self.upper = dict["upper"]
self.name = dict["name"]
self.interp_points = dict["interp_points"]
if 'interp_err_typ' not in dict:
self.interp_err_typ = chebfun(self.getTypical, domain=[self.lower, self.upper], N=self.interp_points)
dict['interp_err_typ'] = self.interp_err_typ
self.__dict__ = dict # make dict our attribute dictionary
def mergeSegments(f, i, j):
if i is j:
return f.segments[i]
else:
# Range of the new segment
a = f.segments[i].a
b = f.segments[j].b
interp = cheb.chebfun(f, domain=[a, b], N=100)
tmp = pacal.segments.InterpolatedSegment(a, b, interp)
return tmp
def __chebydiff__(x, dt, params, options={}):
'''
Fit the timeseries with chebyshev polynomials, and differentiate this model.
Inputs
------
x : (np.array of floats, 1xN) time series to differentiate
dt : (float) time step
Parameters
----------
params : (list) [N] : (int) order of the polynomial
options : (dict) {}
Outputs
-------
x_hat : estimated (smoothed) x
dxdt_hat : estimated derivative of x
'''
if type(params) is list:
N = params[0]
else:
N = params
mean = np.mean(x)
x = x - mean
def f(y):
t = np.linspace(-1, 1, len(x))
return np.interp(y, t, x)
# Chebychev polynomial
poly = pychebfun.chebfun(f, N=N, domain=[-1, 1])
ts = np.linspace(poly.domain()[0], poly.domain()[-1], len(x))
x_hat = poly(ts) + mean
dxdt_hat = poly.differentiate()(ts) * (2 / len(x)) / dt
return x_hat, dxdt_hat
def test_from_scalar(self):
val = np.random.rand()
cr = chebfun(val)
ce = Chebfun.from_data([val])
tools.assert_close(cr, ce)
def test_from_values(self):
values = np.random.randn(10)
cr = chebfun(values)
ce = Chebfun.from_data(values)
tools.assert_close(cr, ce)
def test_from_chebfun(self):
ce = Chebfun.from_function(tools.f)
cr = chebfun(ce)
tools.assert_close(cr, ce)
def test_from_chebcoeffs(self):
coeffs = np.random.randn(10)
cr = chebfun(chebcoeff=coeffs)
ce = Chebfun.from_coeff(coeffs)
tools.assert_close(cr, ce)
def __init__(self, lower, upper, interp_points):
self.lower=lower
self.upper=upper
self.interp_points=interp_points
self.name="TypErrorInterpolator"
self.interp_err_typ=chebfun(self.getTypical, domain=[self.lower, self.upper], N=self.interp_points)
def test_from_scalar(self):
val = np.random.rand()
cr = chebfun(val)
ce = Chebfun.from_data([val])
tools.assert_close(cr, ce)
def test_from_values(self):
values = np.random.randn(10)
cr = chebfun(values)
ce = Chebfun.from_data(values)
tools.assert_close(cr, ce)
def test_from_chebfun(self):
ce = Chebfun.from_function(tools.f)
cr = chebfun(ce)
tools.assert_close(cr, ce)
def test_from_chebcoeffs(self):
coeffs = np.random.randn(10)
cr = chebfun(chebcoeff=coeffs)
ce = Chebfun.from_coeff(coeffs)
tools.assert_close(cr, ce)
