我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用scipy.interpolate.CubicSpline()。
def __init__(self, streamlines, streamlineData): self.streamlineCoordinates = streamlines self.streamlineData = streamlineData # Get the streamline parameter in a single array. self.parameterisedStreamline = self.streamlineCoordinates[:, 0, 3] # Calculate the velocity along the streamline streamlineVelocity = np.linalg.norm(self.streamlineData[:, 2:4], axis=1) # Fit a cubic spline to the streamline velocity # Need this to calculate velocity derivatives self.parameterisedVelocity = interpolate.CubicSpline(self.parameterisedStreamline, streamlineVelocity, extrapolate=1) # Calculate the first derivative self.parameterisedvelocityPrime = self.parameterisedVelocity.derivative(nu=1) # Calculate the second derivative self.parameterisedvelocityDoublePrime = self.parameterisedVelocity.derivative(nu=2) # Parameterise temperature, pressure and density along the streamline self.parameterisedM = interpolate.interp1d(self.parameterisedStreamline, self.streamlineData[:, 0], kind='linear', fill_value='extrapolate') self.parameterisedT = interpolate.interp1d(self.parameterisedStreamline, self.streamlineData[:, 1], kind='linear', fill_value='extrapolate') self.parameterisedP = interpolate.interp1d(self.parameterisedStreamline, self.streamlineData[:, 5], kind='linear', fill_value='extrapolate') self.parameterisedRho = interpolate.interp1d(self.parameterisedStreamline, self.streamlineData[:, 6], kind='linear', fill_value='extrapolate')
def branch_current(self): """ "branch_current" computes the branch currents :return: * self.ib """ # check is branch voltages are available if self.vb is None: self.branch_voltage() ibranch = np.zeros_like(self.vb) cnt_l = 0 cnt_v = 0 for k, (name, val, voltage) in enumerate(zip(self.names, self.values, self.vb)): if name[0].upper() == 'R': ibranch[k, ...] = voltage / val elif name[0].upper() == 'L': ibranch[k, ...] = self.x[self.node_num + cnt_l, ...] cnt_l += 1 elif name[0].upper() == 'C': if self.analysis[0].lower() == '.op': # the current is zero, hence, do nothing pass elif self.analysis[0].lower() == '.ac': f = float(self.analysis[-1]) Xc = -1.0 / (2 * np.pi * f * val) ibranch[k] = voltage / (Xc * 1j) elif self.analysis[0].lower() == '.tran': from scipy.interpolate import CubicSpline cs = CubicSpline(self.t, voltage) csd = cs.derivative() ibranch[k, ...] = val * csd(self.t) elif name[0].upper() == 'V': ibranch[k, ...] = self.x[self.node_num + len(self.isort[1]) + cnt_v, ...] cnt_v += 1 elif name[0].upper() == 'I': ibranch[k, ...] = val self.ib = ibranch
def __init__(self, Z_map, extrapolate0=False): """ Construct a cubic-spline 3-D E-M transfer function interpolater using the information in *Z_map* returned from :func:`parse_xml` as the function samples. If *extrapolate0*, then 0s are inserted in the transfer function response where extrapolation would occur (this happens when transfer function response is requested at frequencies outside the range provided in the .XML file record). """ self.Z_map = Z_map periods = Z_map.keys() self.f = NP.array([1/x for x in periods[::-1]]) self.omega = 2 * math.pi * self.f self.Zxx_interp = CubicSpline(self.omega, [x[0, 0] for x in Z_map.values()[::-1]], extrapolate=False) self.Zxy_interp = CubicSpline(self.omega, [x[0, 1] for x in Z_map.values()[::-1]], extrapolate=False) self.Zyx_interp = CubicSpline(self.omega, [x[1, 0] for x in Z_map.values()[::-1]], extrapolate=False) self.Zyy_interp = CubicSpline(self.omega, [x[1, 1] for x in Z_map.values()[::-1]], extrapolate=False) self.key_map = {'xx': self.Zxx_interp, 'xy': self.Zxy_interp, 'yx': self.Zyx_interp, 'yy': self.Zyy_interp} self.extrapolate0 = extrapolate0
def __init__(self, ss, qs): # This class receives only normalized path position assert np.allclose(ss[-1], 1) self.cspl = CubicSpline(ss, qs) self.cspld = self.cspl.derivative() self.cspldd = self.cspld.derivative() if np.isscalar(qs[0]): self.dof = 1 else: self.dof = qs[0].shape[0]
def initialisation(self): # Get the streamline parameter in a single array. self.parameterisedStreamline = self.streamlineCoordinates[:, 0, 3] # Calculate the velocity along the streamline streamlineVelocity = np.linalg.norm(self.streamlineData[:, 2:4], axis=1) inverseStreamlineVelocity = 1./streamlineVelocity # Fit a cubic spline to the streamline velocity # Need this to calculate velocity derivatives self.parameterisedVelocity = interpolate.CubicSpline(self.parameterisedStreamline, streamlineVelocity, extrapolate=1) self.parameterisedInverseVelocity = interpolate.CubicSpline(self.parameterisedStreamline, inverseStreamlineVelocity, extrapolate=1) # Calculate the first derivative self.parameterisedInverseVelocityPrime = self.parameterisedInverseVelocity.derivative(nu=1) # Calculate the second derivative self.parameterisedInverseVelocityDoublePrime = self.parameterisedInverseVelocity.derivative(nu=2) return self.parameterisedStreamline, streamlineVelocity, self.parameterisedVelocity, self.parameterisedInverseVelocityPrime, self.parameterisedInverseVelocityDoublePrime
def density_water(temp): """Return the density of water at a given temperature. If given units, the function will automatically convert to Kelvin. If not given units, the function will assume Kelvin. """ ut.check_range([temp, ">0", "Temperature in Kelvin"]) rhointerpolated = interpolate.CubicSpline(WATER_DENSITY_TABLE[0], WATER_DENSITY_TABLE[1]) return rhointerpolated(temp)
