我们从Python开源项目中，提取了以下4个代码示例，用于说明如何使用scipy.interpolate.Rbf()。
def __init__(self, A, h, alpha, CL, CD, rho, smoothing=0, k_spline=3): self.A = A self.h = h self.Asp = 2*self.h**2/self.A self.rho = rho # Input lift and drag data self.n = len(alpha) self.alpha = alpha self.CL = CL self.CD = CD self.k_spline = k_spline # -------- Spline interpolation ----------------------------- if len(self.alpha.shape) == 1: self.interpolationMethod = 'spline' self.nrControlParameters = 1 self.CL_spline = interpolate.splrep(self.alpha, self.CL, s=smoothing, k=self.k_spline) self.CD_spline = interpolate.splrep(self.alpha, self.CD, s=smoothing, k=self.k_spline) elif len(self.alpha.shape) == 2: self.interpolationMethod = 'griddata' self.nrControlParameters = 2 self.CL_RbfModel = interpolate.Rbf(self.alpha[:, 0],self.alpha[:, 1], self.CL, smooth=smoothing) self.CD_RbfModel = interpolate.Rbf(self.alpha[:, 0],self.alpha[:, 1], self.CD, smooth=smoothing)
def CLCD(self, alpha): if self.interpolationMethod == 'spline': CL = interpolate.splev(alpha, self.CL_spline) CD = interpolate.splev(alpha, self.CD_spline) elif self.interpolationMethod == 'Rbf': CL = self.CL_RbfModel(alpha[0], alpha[1]) CD = self.CD_RbfModel(alpha[0], alpha[1]) elif self.interpolationMethod == 'griddata': CL = interpolate.griddata(self.alpha, self.CL, alpha) CD = interpolate.griddata(self.alpha, self.CD, alpha) if self.nrControlParameters == 2: return float(CL), float(CD) else: return CL, CD
def populateMissingData(self,approach="Smooth",ilog=None): ''' This function is used to interpolate missing data in the image. ''' if approach == 'Smooth': # first run a median filter over the array, then smooth the result. #xmin,xmax,ymin,ymax,zmin,zmax = self.getRange() mask = np.array(self.flags,dtype=np.bool) z = self.getZImage().asMatrix2D() median = nd.median_filter(z,size=(15,15)) mask = mask.flatten() z = z.flatten() median = median.flatten() z[ mask==False ] = median[ mask==False ] if ilog != None: ilog.log(pv.Image(median.reshape(self.width,self.height)),label="Median") ilog.log(pv.Image(z.reshape(self.width,self.height)),label="ZMedian") mask = mask.flatten() z = z.flatten() median = median.flatten() for i in range(5): tmp = z.copy() smooth = nd.gaussian_filter(z.reshape(self.width,self.height),2.0).flatten() z[ mask==False ] = smooth[ mask==False ] print "Iteration:",i,(z-tmp).max(),(z-tmp).min() ilog.log(pv.Image(z.reshape(self.width,self.height)),label="ZSmooth%02d"%i) ilog.log(pv.Image((z-tmp).reshape(self.width,self.height)),label="ZSmooth%02d"%i) if approach == 'RBF': mask = np.array(self.flags,dtype=np.bool) mask = mask.flatten() x = np.arange(self.width).reshape(self.width,1) x = x*np.ones((1,self.height)) x = x.flatten() y = np.arange(self.height).reshape(1,self.height) y = y*np.ones((self.width,1)) y = y.flatten() z = self.z.copy() z = z.flatten() print "Coords:" print len(mask) print len(x[mask]) print len(y[mask]) print len(z[mask]) # this produces an error. Probably has too much data it.Rbf(x[mask],y[mask],z[mask]) pass
def compute_ck2004_response(self, path, verbose=False): """ Computes Castelli & Kurucz (2004) intensities across the entire range of model atmospheres. @path: path to the directory containing ck2004 SEDs @verbose: switch to determine whether computing progress should be printed on screen Returns: n/a """ models = glob.glob(path+'/*M1.000*') Nmodels = len(models) # Store the length of the filename extensions for parsing: offset = len(models[0])-models[0].rfind('.') Teff, logg, abun = np.empty(Nmodels), np.empty(Nmodels), np.empty(Nmodels) InormE, InormP = np.empty(Nmodels), np.empty(Nmodels) if verbose: print('Computing Castelli & Kurucz (2004) passband intensities for %s:%s. This will take a while.' % (self.pbset, self.pbname)) for i, model in enumerate(models): #~ spc = np.loadtxt(model).T -- waaay slower spc = np.fromfile(model, sep=' ').reshape(-1,2).T Teff[i] = float(model[-17-offset:-12-offset]) logg[i] = float(model[-11-offset:-9-offset])/10 sign = 1. if model[-9-offset]=='P' else -1. abun[i] = sign*float(model[-8-offset:-6-offset])/10 spc[0] /= 1e10 # AA -> m spc[1] *= 1e7 # erg/s/cm^2/A -> W/m^3 wl = spc[0][(spc[0] >= self.ptf_table['wl'][0]) & (spc[0] <= self.ptf_table['wl'][-1])] fl = spc[1][(spc[0] >= self.ptf_table['wl'][0]) & (spc[0] <= self.ptf_table['wl'][-1])] fl *= self.ptf(wl) flP = fl*wl InormE[i] = np.log10(fl.sum()/self.ptf_area*(wl[1]-wl[0])) # energy-weighted intensity InormP[i] = np.log10(flP.sum()/self.ptf_photon_area*(wl[1]-wl[0])) # photon-weighted intensity if verbose: if 100*i % (len(models)) == 0: print('%d%% done.' % (100*i/(len(models)-1))) # Store axes (Teff, logg, abun) and the full grid of Inorm, with # nans where the grid isn't complete. self._ck2004_axes = (np.unique(Teff), np.unique(logg), np.unique(abun)) self._ck2004_energy_grid = np.nan*np.ones((len(self._ck2004_axes[0]), len(self._ck2004_axes[1]), len(self._ck2004_axes[2]), 1)) self._ck2004_photon_grid = np.nan*np.ones((len(self._ck2004_axes[0]), len(self._ck2004_axes[1]), len(self._ck2004_axes[2]), 1)) for i, I0 in enumerate(InormE): self._ck2004_energy_grid[Teff[i] == self._ck2004_axes[0], logg[i] == self._ck2004_axes[1], abun[i] == self._ck2004_axes[2], 0] = I0 for i, I0 in enumerate(InormP): self._ck2004_photon_grid[Teff[i] == self._ck2004_axes[0], logg[i] == self._ck2004_axes[1], abun[i] == self._ck2004_axes[2], 0] = I0 # Tried radial basis functions but they were just terrible. #~ self._log10_Inorm_ck2004 = interpolate.Rbf(self._ck2004_Teff, self._ck2004_logg, self._ck2004_met, self._ck2004_Inorm, function='linear') self.content.append('ck2004') self.atmlist.append('ck2004')
