def roots(coeffs):
'''Find roots of polynomial specified by coefficients
'''
poly = _poly(_asDS(
coeffs, _dnp.float64)._jdataset().data) # static method call fails
return _asDS(poly.findRoots()
) # cannot rely on general wrapper with arrays of stuff
def polyval(p, x):
'''Evaluate polynomial at given points
If p is of length N, this function returns the value:
p[0]*(x**N-1) + p[1]*(x**N-2) + ... + p[N-2]*x + p[N-1]
'''
poly = _poly(_asDS(p, _dnp.float64)._jdataset().data)
d = _asDS(x, _dnp.float, force=True)._jdataset()
return poly.calculateValues([d])
def polyval(p, x):
'''Evaluate polynomial at given points
If p is of length N, this function returns the value:
p[0]*(x**N-1) + p[1]*(x**N-2) + ... + p[N-2]*x + p[N-1]
'''
poly = _poly(_asDS(p, _dnp.float64)._jdataset().data)
d = _asDS(x, _dnp.float, force=True)._jdataset()
return poly.calculateValues([d])
def __init__(self, c_or_r, r=False, variable=None):
if variable:
self.variable = variable
else:
self.variable = 'x'
if r:
self.order = len(c_or_r)
# expansion of factors
self.c = _polycoeff(c_or_r)
self.roots = c_or_r
else:
self.order = len(c_or_r) - 1
self.c = c_or_r
self.roots = None
self._poly = _poly(self.c)
def __init__(self, c_or_r, r=False, variable=None):
if variable:
self.variable = variable
else:
self.variable = 'x'
if r:
self.order = len(c_or_r)
# expansion of factors
self.c = _polycoeff(c_or_r)
self.roots = c_or_r
else:
self.order = len(c_or_r) - 1
self.c = c_or_r
self.roots = None
self._poly = _poly(self.c)
def polyfit(x, y, deg, rcond=None, full=False):
'''Linear least squares polynomial fit
Fit a polynomial p(x) = p[0] * x**deg + ... + p[deg] of order deg to points (x, y).
Arguments:
x -- x-coordinates of sample points
y -- y-coordinates of sample points
deg -- order of fitting polynomial
Returns:
a vector of coefficients p that minimises the squared error and
a fitresult object
'''
poly = _poly(deg)
x = _asIterable(x)
if rcond is None:
rcond = 2e-16 * y.size
_fitter.polyFit(x, y, rcond, poly)
fr = fitresult(poly, x, y)
if full:
return fr.parameters, fr
else:
return fr.parameters
def polyfit(x, y, deg, rcond=None, full=False):
'''Linear least squares polynomial fit
Fit a polynomial p(x) = p[0] * x**deg + ... + p[deg] of order deg to points (x, y).
Arguments:
x -- x-coordinates of sample points
y -- y-coordinates of sample points
deg -- order of fitting polynomial
Returns:
a vector of coefficients p that minimises the squared error and
a fitresult object
'''
poly = _poly(deg)
x = _asIterable(x)
if rcond is None:
rcond = 2e-16*y.size
_fitter.polyFit(x, y, rcond, poly)
fr = fitresult(poly, x, y)
if full:
return fr.parameters, fr
else:
return fr.parameters
def roots(coeffs):
'''Find roots of polynomial specified by coefficients
'''
poly = _poly(_asDS(coeffs, _dnp.float64)._jdataset().data) # static method call fails
return _asDS(poly.findRoots()) # cannot rely on general wrapper with arrays of stuff
