Python scipy.optimize 模块，curve_fit() 实例源码

我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用scipy.optimize.curve_fit()。

项目：Auspex 作者：BBN-Q | 项目源码 | 文件源码

def fit_rabi(xdata, ydata):"""Analyze Rabi amplitude data to find pi-pulse amplitude and phase offset.Arguments:xdata: ndarray of calibration amplitudes. length should be even.ydata: measurement amplitudesReturns:pi_amp: Fitted amplitude of pi pulsedoffset: Fitted mixer offsetfit_pts: Fitted points."""#seed Rabi frequency from largest FFT componentN = len(ydata)yfft = fft(ydata)f_max_ind = np.argmax(np.abs(yfft[1:N//2]))f_0 = 0.5 * max([1, f_max_ind]) / xdata[-1]amp_0 = 0.5*(ydata.max() - ydata.min())offset_0 = np.mean(ydata)phase_0 = 0if ydata[N//2 - 1] > offset_0:amp_0 = -amp_0popt, _ = curve_fit(rabi_model, xdata, ydata, [offset_0, amp_0, f_0, phase_0])f_rabi = np.abs(popt[2])pi_amp = 0.5/f_rabioffset = popt[3]return pi_amp, offset, popt

项目：pyqha 作者：mauropalumbo75 | 项目源码 | 文件源码

def fit_Murn(V,E,guess=[0.0,0.0,900/RY_KBAR,1.15],lm_pars={}):"""This is the function for fitting with the Murnaghan EOS as a function of volume only.The input variable *V* is an 1D array of volumes, *E* are the corresponding energies (or other analogous quantity to be fitted with the Murnaghan EOS.*a*Note: volumes must be in a.u.^3 and energies in Rydberg."""# reasonable initial guesses for EOS parametersif guess[0]==0.0:guess[0] = E[len(E) / 2]if guess[1]==0.0:guess[1] = V[len(V) / 2]a, pcov = curve_fit(E_MurnV, V, E, p0=guess, **lm_pars)chi = 0for i in range(0,len(V)):chi += (E[i]-E_Murn(V[i],a))**2return a, pcov, chi

项目：Auspex 作者：BBN-Q | 项目源码 | 文件源码

def fit_photon_number(xdata, ydata, params):''' Fit number of measurement photons before a Ramsey. See McClure et al., Phys. Rev. App. 2016input params:1 - cavity decay rate kappa (MHz)2 - detuning Delta (MHz)3 - dispersive shift 2Chi (MHz)4 - Ramsey decay time T2* (us)5 - exp(-t_meas/T1) (us), only if starting from |1> (to include relaxation during the 1st msm't)6 - initial qubit state (0/1)'''params = [2*np.pi*p for p in params[:3]] + params[3:] # convert to angular frequenciesdef model_0(t, pa, pb):return (-np.imag(np.exp(-(1/params[3]+params[1]*1j)*t + (pa-pb*params[2]*(1-np.exp(-((params[0] + params[2]*1j)*t)))/(params[0]+params[2]*1j))*1j)))def model(t, pa, pb):return  params[4]*model_0(t, pa, pb) + (1-params[4])*model_0(t, pa+np.pi, pb) if params[5] == 1  else model_0(t, pa, pb)popt, pcov = curve_fit(model, xdata, ydata, p0 = [0, 1])perr = np.sqrt(np.diag(pcov))finer_delays = np.linspace(np.min(xdata), np.max(xdata), 4*len(xdata))fit_curve = model(finer_delays, *popt)return popt[1], perr[1], (finer_delays, fit_curve)

项目：Auspex 作者：BBN-Q | 项目源码 | 文件源码

def find_null_offset(xpts, powers, default=0.0):"""Finds the offset corresponding to the minimum power using a fit to the measured data"""def model(x, a, b, c):return a*(x - b)**2 + cpowers = np.power(10, powers/10.)min_idx = np.argmin(powers)try:fit = curve_fit(model, xpts, powers, p0=[1, xpts[min_idx], powers[min_idx]])except RuntimeError:logger.warning("Mixer null offset fit failed.")return default, np.zeros(len(powers))best_offset = np.real(fit[0][1])best_offset = np.minimum(best_offset, xpts[-1])best_offset = np.maximum(best_offset, xpts[0])xpts_fine = np.linspace(xpts[0],xpts[-1],101)fit_pts = np.array([np.real(model(x, *fit[0])) for x in xpts_fine])if min(fit_pts)<0: fit_pts-=min(fit_pts)-1e-10 #prevent log of a negative numberreturn best_offset, xpts_fine, 10*np.log10(fit_pts)

项目：dataArtist 作者：radjkarl | 项目源码 | 文件源码

def activate(self):if self.pMethod.value() == 'Poisson':v = self.pPlot.value()index = Noneif v != 'All':# index here if specific plot chosenindex = self.pPlot.opts['limits'].index(v) - 1plots = self.display.widget.getData(index)for plot in plots:# fit with curve_fitx, y = plot.x, plot.yif not self.guess:self.guess = _Poisson.initGuess(x, y)parameters, cov_matrix = curve_fit(_Poisson.function, x, y, self.guess)# plot poisson-deviation with fitted parameterx_vals = self._getXVals()y_vals = _Poisson.function(x_vals, *parameters)self.display.addLayer(data=(x_vals, y_vals),filename='X - Poission fitted')

项目：Thrifty 作者：swkrueger | 项目源码 | 文件源码

def make_dirichlet(block_len, carrier_len, width=6):def _fit_model(xdata, amplitude, time_offset):xdata = np.array(xdata, dtype=np.float64)dirichlet = _dirichlet_kernel(xdata-time_offset,block_len,carrier_len)return amplitude * np.abs(dirichlet)def _interpolator(fft_mag, peak):xdata = np.array(np.arange(-(width//2), width//2+1))ydata = fft_mag[peak + xdata]initial_guess = (fft_mag[peak], 0)popt, _ = curve_fit(_fit_model, xdata, ydata, p0=initial_guess)_, fit_offset = poptreturn fit_offsetreturn _interpolator

项目：GPfates 作者：Teichlab | 项目源码 | 文件源码

def identify_bifurcation_point(omgp, n_splits=30):''' Linear breakpoint model to infer drastic likelihood decrease'''mix_m = OMGP(omgp.X, omgp.Y, K=omgp.K, kernels=omgp.kern)mix_m.variance = omgp.variancephi = omgp.philog_liks = []t_splits = np.linspace(mix_m.X.min(), mix_m.X.max(), n_splits)for t_s in tqdm(t_splits):mask = mix_m.X > t_sphi_mix = np.ones_like(phi) * 0.5phi_mix[mask[:, 0]] = phi[mask[:, 0]]mix_m.phi = phi_mixlog_liks.append(mix_m.log_likelihood())x = t_splitsy = np.array(log_liks)p, e = optimize.curve_fit(breakpoint_linear, x, y)return p[0]

项目：social-media-pulse 作者：jrmontag | 项目源码 | 文件源码

def optimize(self):"""Applies the chosen optimization technique to the model's evaluate method and the appropriate data structures.  """ # use helper method to possibly narrow data by user-entered window x, y = self.data.get_data_to_fit()# optimize based on model-specific evaluate() optimal_params, covar = sp_optimize.curve_fit( self.model.evaluate,x, y, self.model.parameters )# store the final fit parameters and covariance in the model object for JIT calculationsself.model.parameters = optimal_paramsself.model.covariance = covarreturn optimal_params, covar

项目：pynephoscope 作者：neXyon | 项目源码 | 文件源码

def findStar(self, x, y):x = int(x)y = int(y)roi_size = Configuration.gaussian_roi_sizeroi = self.image[y - roi_size:y + roi_size + 1, x - roi_size:x + roi_size + 1]X, Y = np.meshgrid(range(x - roi_size, x + roi_size + 1), range(y - roi_size, y + roi_size + 1))p0 = (1, x, y, 1, 0, 1)try:popt, _ = optimize.curve_fit(self.gaussBivarFit, (X, Y), roi.ravel(), p0=p0, maxfev=10000)except Exception as e:return 0, (0, 0), np.matrix([[0, 0], [0, 0]]), roiA, x0, y0, v1, v2, v3 = poptcov = np.matrix([[v1, v2 / 2], [v2 / 2, v3]]).Imu = (x0, y0)return A, mu, cov, roi

项目：qmflows-namd 作者：SCM-NV | 项目源码 | 文件源码

def dephasing(f):"""Computes the dephasing time of a given function using optical responseformalisms:S. Mukamel, Principles of Nonlinear Optical Spectroscopy, 1995About the implementation we use the 2nd order cumulant expansion.See also eq. (2) in : Kilina et al. Phys. Rev. Lett., 110, 180404, (2013)To calculate the dephasing time tau we fit the dephasing function to agaussian of the type : exp(-0.5 * (-x / tau) ** 2)"""ts = np.arange(f.shape[0])cumu_ii = np.stack(np.sum(f[0:i]) for i in range(ts.size)) / hbarcumu_i = np.stack(np.sum(cumu_ii[0:i]) for i in range(ts.size)) / hbardeph = np.exp(-cumu_i)np.seterr(over='ignore')popt = curve_fit(gauss_function, ts, deph)[0]xs = np.exp(-0.5 * (-ts / popt[0]) ** 2)deph = np.column_stack((deph, xs))rate = popt[0]return deph, rate

项目：WXMLTilingsHOWTO 作者：maxieds | 项目源码 | 文件源码

def fit_SaddleConnGoldenL_pdf(hist_data, xdata, ydata, hrange): [sl, su] = hrange[fl, fu] = [2.3528, 8.1976]b = (fu - fl) / (su - sl)c = fu - b * su#if len(xdata) > 2 * MAX_FIT_POINTS: # trim to the middle 9000 x values#     idxl, idxu = len(xdata) / 2 - MAX_FIT_POINTS/4, len(xdata) / 2 + MAX_FIT_POINTS/4#     endu = len(xdata) - MAX_FIT_POINTS / 4 - 1#     xdata = list(xdata)[idxl:idxu+1] + list(xdata)[0:MAX_FIT_POINTS/4+1] + list(xdata)[endu:]#     ydata = list(ydata)[idxl:idxu+1] + list(ydata)[0:MAX_FIT_POINTS/4+1] + list(ydata)[endu:]### it appears that scipy has a bug, let's do an adjustment to make it go away:if len(ydata) - len(xdata) == 1:ydata = ydata[:-1]##fit_func_v1 = lambda x, a, b, c, d: a * sc_fitfunc(b*x+c) + dfit_func = lambda x, a, d: fit_func_v1(x, a, b, c, d)p, pcov = curve_fit(fit_func, xdata, ydata)#, bounds = ([0,0], [10,10]))print p, pcovreturn lambda x: fit_func(x, p[0], p[1]), (1 - c) / b, [p[0], b, c, p[1]]##

项目：BigDataML 作者：azheng333 | 项目源码 | 文件源码

def fun():T = [1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968]S = [29.72, 30.61, 31.51, 32.13, 32.34, 32.85, 33.56, 34.20, 34.83]xdata = np.array(T)ydata = np.log(np.array(S))def func(x, a, b):return a + b * x# ??????????????popt, pcov = curve_fit(func, xdata, ydata)plt.figure()plt.plot(xdata, ydata, 'ko', label="Original Noised Data")plt.plot(xdata, func(xdata, *popt), 'r', label="Fitted Curve")plt.show()plt.savefig('test.png')

项目：Optics 作者：danustc | 项目源码 | 文件源码

def psf_zplane(stack, dz, w0, de = 1):'''determine the position of the real focal plane.Don't mistake with psf_slice!'''nz, ny, nx = stack.shapecy, cx = np.unravel_index(np.argmax(gf(stack,2)), (nz,ny,nx))[1:]zrange = (nz-1)*dz*0.5zz = np.linspace(-zrange, zrange, nz)center_z = stack[:,cy-de:cy+de+1,cx-de:cx+de+1]im_z = center_z.mean(axis=2).mean(axis=1)b = np.mean((im_z[0],im_z[-1]))a = im_z.max() - bp0 = (a,0,w0,b)popt = optimize.curve_fit(gaussian, zz, im_z, p0)[0]z_offset = popt[1] # The original version is wrongreturn z_offset, zz

项目：Optics 作者：danustc | 项目源码 | 文件源码

def psf_zplane(stack, dz, w0, de = 1):'''determine the position of the real focal plane.Don't mistake with psf_slice!'''nz, ny, nx = stack.shapecy, cx = np.unravel_index(np.argmax(gf(stack,2)), (nz,ny,nx))[1:]zrange = (nz-1)*dz*0.5zz = np.linspace(-zrange, zrange, nz)center_z = stack[:,cy-de:cy+de+1,cx-de:cx+de+1]im_z = center_z.mean(axis=2).mean(axis=1)b = np.mean((im_z[0],im_z[-1]))a = im_z.max() - bp0 = (a,0,w0,b)popt = optimize.curve_fit(gaussian, zz, im_z, p0)[0]z_offset = popt[1] # The original version is wrongreturn z_offset, zz

项目：Ship 作者：jarlekramer | 项目源码 | 文件源码

def __init__(self, A, h, alpha, CL, CD, x0=0.95):rho = 1025super().__init__(A, h, alpha, CL, CD, rho)self.x0 = x0# Create force functions based on input datafitParams, fitCovariances = curve_fit(liftFunc, self.alpha, self.CL)self.CL_a1 = fitParams[0]fitParams, fitCovariances = curve_fit(dragFunc, self.alpha, self.CD)self.CD_a0 = fitParams[0]self.CD_a2 = fitParams[1]# Max and min alphaself.alpha_min = 0self.alpha_max = 30*np.pi/180

项目：Ship 作者：jarlekramer | 项目源码 | 文件源码

def __init__(self, A, h, alpha, CL, CD, x0=0.95):rho = 1025self.removeBaseDrag = Truesuper().__init__(A, h, alpha, CL, CD, rho)self.x0 = x0# Create force functions based on input datafitParams, fitCovariances = curve_fit(liftFunc, self.alpha, self.CL)self.CL_a1 = fitParams[0]fitParams, fitCovariances = curve_fit(dragFunc, self.alpha, self.CD)self.CD_a0 = fitParams[0]self.CD_a2 = fitParams[1]# Max and min alphaself.alpha_min = 0self.alpha_max = 40*np.pi/180self.alpha_max = 30*np.pi/180

项目：Ship 作者：jarlekramer | 项目源码 | 文件源码

def setDriftData(self, alpha, CL, CDi, CM):self.alpha = alphaself.CL    = CLself.CDi   = CDiself.CM    = CM# Calculate coefficients for liftfitParams, fitCovariances = curve_fit(liftFunc, self.alpha, self.CL)self.CL_a1 = fitParams[0]self.CL_a2 = fitParams[1]# Calculate coefficients for induced dragfitParams, fitCovariances = curve_fit(inducedDragFunc, self.alpha, self.CDi)self.CDi_a2 = fitParams[0]# Spline interpolation for momentself.CMspl = interpolate.splrep(self.alpha, self.CM, s=1e-9)

项目：WellApplication 作者：inkenbrandt | 项目源码 | 文件源码

def ratingCurve(discharge, stage):"""Computes rating curve based on discharge measurements coupled with stagereadings.discharge = array of measured discharges;stage = array of corresponding stage readings;Returns coefficients a, b for the rating curve in the form y = a * x**bhttps://github.com/hydrogeog/hydro/blob/master/hydro/core.py"""from scipy.optimize import curve_fitexp_curve = lambda x, a, b: (a * x ** b)popt, pcov = curve_fit(exp_curve, stage, discharge)a = 0.0b = 0.0for i, j in zip(discharge, stage):a += (i - exp_curve(j, popt[0], popt[1]))**2b += (i - np.mean(discharge))**2r_squ = 1 - a / breturn popt, r_squ

项目：WellApplication 作者：inkenbrandt | 项目源码 | 文件源码

def ratingCurve(discharge, stage):"""Computes rating curve based on discharge measurements coupled with stagereadings.discharge = array of measured discharges;stage = array of corresponding stage readings;Returns coefficients a, b for the rating curve in the form y = a * x**b"""from scipy.optimize import curve_fitpopt, pcov = curve_fit(exp_curve, stage, discharge)def r_squ():a = 0.0b = 0.0for i, j in zip(discharge, stage):a += (i - exp_curve(j, popt[0], popt[1]))**2b += (i - np.mean(discharge))**2return 1 - a / breturn popt, r_squ()

项目：dragonboard_testbench 作者：cta-observatory | 项目源码 | 文件源码

def fit(df, cell, plot=False):big_time = df.delta_t.quantile(0.75)p0 = [1.3,-0.38,df.adc_counts[df.delta_t >= big_time].mean(),]try:(a, b, c), cov = curve_fit(f,df['delta_t'],df['adc_counts'],p0=p0,)except RuntimeError:logging.error('Could not fit cell {}'.format(cell))return np.full(4, np.nan)ndf = len(df.index) - 3residuals = df['adc_counts'] - f(df['delta_t'], a, b, c)chisquare = np.sum(residuals**2) / ndfreturn a, b, c, chisquare

项目：ProtScan 作者：gianlucacorrado | 项目源码 | 文件源码

def fit_optimal(blocks, keys):"""Fit the cumulative distribution, by optimization."""values = list()for k in keys:values.extend([max_val for (_, _, max_val) in blocks[k]])hist, bin_edges = np.histogram(values, bins=100, normed=True)bin_centers = np.array([(bin_edges[i - 1] + bin_edges[i]) / 2for i in range(1, len(bin_edges))])cumulative = np.cumsum(hist) / np.sum(hist)popt, _ = optimize.curve_fit(func, bin_centers, cumulative)return popt

项目：atoolbox 作者：liweitianux | 项目源码 | 文件源码

def fit_model(func, xdata, ydata, yerrdata, p0):"""Fit the provided data with the beta model.Arguments:p0: initial values for the parameters of beta modelReturn:(popt, infodict)popt: optimal values for the parametersinfodict:* fvec: the function evaluated at the output parameters* dof: degree of freedom* chisq: chi-squared* perr: one standard deviation errors on the parameters"""popt, pcov = curve_fit(func, xdata, ydata, p0=p0, sigma=yerrdata)# the function evaluated at the output parametersfvec = lambda x: func(x, *popt)# degree of freedomdof = len(xdata) - len(popt) - 1# chi squaredchisq = np.sum(((ydata - fvec(xdata)) / yerrdata) ** 2)# one standard deviation errors on the parametersperr = np.sqrt(np.diag(pcov))infodict = {'fvec': fvec,'dof': dof,'chisq': chisq,'perr': perr}return (popt, infodict)

项目：Auspex 作者：BBN-Q | 项目源码 | 文件源码

def fit_drag(data, DRAG_vec, pulse_vec):"""Fit calibration curves vs DRAG parameter, for variable number of pulses"""num_DRAG = len(DRAG_vec)num_seqs = len(pulse_vec)xopt_vec = np.zeros(num_seqs)perr_vec = np.zeros(num_seqs)popt_mat = np.zeros((4, num_seqs))data = data.reshape(len(data)//num_DRAG, num_DRAG)#first fit sine to lowest n, for the full rangedata_n = data[1, :]T0 = 2*(DRAG_vec[np.argmax(data_n)] - DRAG_vec[np.argmin(data_n)]) #rough estimate of periodp0 = [0, 1, T0, 0]popt, pcov = curve_fit(sinf, DRAG_vec, data_n, p0 = p0)perr_vec[0] = np.sqrt(np.diag(pcov))[0]x_fine = np.linspace(min(DRAG_vec), max(DRAG_vec), 1001)xopt_vec[0] = x_fine[np.argmin(sinf(x_fine, *popt))]popt_mat[:,0] = poptfor ct in range(1, len(pulse_vec)):#quadratic fit for subsequent steps, narrower rangedata_n = data[ct, :]p0 = [1, xopt_vec[ct-1], 0]#recenter for next fitclosest_ind =np.argmin(abs(DRAG_vec - xopt_vec[ct-1]))fit_range = int(np.round(0.5*num_DRAG*pulse_vec[0]/pulse_vec[ct]))curr_DRAG_vec = DRAG_vec[max(0, closest_ind - fit_range) : min(num_DRAG-1, closest_ind + fit_range)]reduced_data_n = data_n[max(0, closest_ind - fit_range) : min(num_DRAG-1, closest_ind + fit_range)]#quadratic fitpopt, pcov = curve_fit(quadf, curr_DRAG_vec, reduced_data_n, p0 = p0)perr_vec[ct] = np.sqrt(np.diag(pcov))[0]x_fine = np.linspace(min(curr_DRAG_vec), max(curr_DRAG_vec), 1001)xopt_vec[ct] = x_fine[np.argmin(quadf(x_fine, *popt))] #why not x0?popt_mat[:3,ct] = poptreturn xopt_vec, perr_vec, popt_mat

项目：Auspex 作者：BBN-Q | 项目源码 | 文件源码

def fit_quad(xdata, ydata):popt, pcov = curve_fit(quadf, xdata, ydata, p0 = [1, min(ydata), 0])perr = np.sqrt(np.diag(pcov))return popt, perr

项目：modesolverpy 作者：jtambasco | 项目源码 | 文件源码

def fit_gaussian(x, y, z_2d, save_fits=False):z = z_2dmax_idx = np.unravel_index(z.argmax(), z.shape)max_row = max_idx[0] - 1max_col = max_idx[1] - 1z_max_row = z[max_row, :]z_max_col = z[:, max_col]A = z[max_row, max_col]p_guess_x = (A, x[max_col], 0.1*(x[-1] - x[0]))p_guess_y = (A, y[max_row], 0.1*(y[-1] - y[0]))coeffs_x, var_matrix_x = sciopt.curve_fit(gaussian, x, z_max_row, p_guess_x)coeffs_y, var_matrix_y = sciopt.curve_fit(gaussian, y, z_max_col, p_guess_y)c_x = (x[-1]-x[0])*(max_col+1)/x.size + x[0]c_y = (y[-1]-y[0])*(y.size-(max_row+1))/y.size + y[0]centre = (c_x, c_y)sigma = np.array([coeffs_x[2], coeffs_y[2]])fwhm = 2.355 * sigmasigma_2 = 1.699 * fwhmif save_fits:with open('x_fit.dat', 'w') as fs:for c in np.c_[x, z_max_row, gaussian(x, *coeffs_x)]:s = ','.join([str(v) for v in c])fs.write(s+'\n')with open('y_fit.dat', 'w') as fs:for c in np.c_[y, z_max_col, gaussian(y, *coeffs_y)]:s = ','.join([str(v) for v in c])fs.write(s+'\n')return A, centre, sigma_2

项目：pystudio 作者：satorchi | 项目源码 | 文件源码

def fit_Pbath(T_pts, P_pts):'''find best fit to the P_bath function'''# make sure T_pts and P_pts are 1d arraysnpts=len(T_pts)T=np.array(T_pts).reshape(npts)P=np.array(P_pts).reshape(npts)try:ret=curve_fit(P_bath_function,T,P)except:print('insufficient data for TES %i' % TES)ret=Nonereturn ret

项目：accpy 作者：kramerfelix | 项目源码 | 文件源码

def myfit(x, y, betagamma, qL, orientation, T, yerr=None, krange=[]):if len(krange) != 0:# trim to given rangebooleanrange = [(x >= krange[0]) & (x <= krange[1])]x = x[booleanrange]y = y[booleanrange]if yerr is None:popt, pcov = curve_fit(fun, x, y, sigma=None)else:if len(krange) != 0:yerr = yerr[booleanrange]popt, pcov = curve_fit(fun, x, y, sigma=yerr, absolute_sigma=True)x = linspace(x[0], x[-1], 1e3)fit = fun(x, *popt)perr = sqrt(diag(pcov))A = array([popt[0], perr[0]])/qL**2B = array([popt[1], perr[1]])/qL*orientationC = array([popt[2], perr[2]])eps, bet, alp, gam = twissfromparabola(A, B, C, T)epsn = betagamma*eps*1e6eps *= 1e9string = (r'$\beta=%.2f \pm %.2f$''\n'r'$\alpha=%.2f \pm %.2f$''\n'r'$\gamma=%.2f \pm %.2f$''\n'r'$\epsilon=(%.2f \pm %.2f)\pi\, nm\, rad$''\n'r'$\epsilon^*=(%.2f \pm %.2f)\pi\, \mu m\, rad$'% (bet[0], bet[1], alp[0], alp[1], gam[0], gam[1],eps[0], eps[1], epsn[0], epsn[1]))return x, fit, string

项目：accpy 作者：kramerfelix | 项目源码 | 文件源码

def dofit(xdata, ydata, betagamma, T, dE, D, QL, orientation, yerr=None,krange=[]):# make sure that k is positive k = abs(g)/Brho_0xdata = abs(xdata)if len(krange) != 0:# trim to given rangexdata = xdata[krange]ydata = ydata[krange]yerr = yerr[krange]if yerr is None:popt, pcov = curve_fit(fun, xdata, ydata, sigma=None)else:popt, pcov = curve_fit(fun, xdata, ydata, sigma=yerr, absolute_sigma=True)fit = fun(xdata, *popt)perr = np.sqrt(np.diag(pcov))A = np.array([popt[0], perr[0]])B = np.array([popt[1], perr[1]])C = np.array([popt[2], perr[2]])eps, bet, alp, gam = twissfromparabola(A, B, C, T, dE, D, QL, orientation)epsn = betagamma*eps*1e6eps *= 1e9A *= 1e9B *= 1e9C *= 1e9string = (r'$function:\;y=ax^2+bx+c$''\n'r'$a=(%.2f \pm %.2f)\cdot10^{-9}$''\n'r'$b=(%.2f \pm %.2f)\cdot10^{-9}$''\n'r'$c=(%.2f \pm %.2f)\cdot10^{-9}$''\n'r'$\beta=%.2f \pm %.2f$''\n'r'$\alpha=%.2f \pm %.2f$''\n'r'$\gamma=%.2f \pm %.2f$''\n'r'$\epsilon=(%.2f \pm %.2f)\pi\, nm\, rad$''\n'r'$\epsilon^*=(%.2f \pm %.2f)\pi\, \mu m\, rad$'% (A[0], A[1], B[0], B[1], C[0], C[1],bet[0], bet[1], alp[0], alp[1], gam[0], gam[1],eps[0], eps[1], epsn[0], epsn[1]))return string, fit, xdata

项目：accpy 作者：kramerfelix | 项目源码 | 文件源码

def parabolafit(xdata, ydata, init, sigma):popt, pcov = curve_fit(fun, xdata, ydata, p0=init, sigma=sigma)perr = np.sqrt(np.diag(pcov))x = np.linspace(min(xdata), max(xdata), 1e3)fit = fun(x, *popt)A = np.array([popt[0], perr[0]])B = np.array([popt[1], perr[1]])C = np.array([popt[2], perr[2]])return x, fit, A, B, C

项目：georges 作者：chernals | 项目源码 | 文件源码

def bpm_fit(a, p):if a is None:return Nonex = a['channel']['data'][p][:, 1]y = a['channel']['data'][p][:, 2]ar = np.trapz(y / np.sum(y) * len(y), x)mean = np.mean(x * y / np.sum(y) * len(y))rms = np.std(x * y / np.sum(y) * len(y))popt, pcov = curve_fit(gaussian, x, y, p0=[ar, mean, rms])return [popt, np.sqrt(pcov.diagonal())]

项目：georges 作者：chernals | 项目源码 | 文件源码

def variquad_fit(x, y):popt, pcov = curve_fit(quadratic, x, y, p0=[0, 0, 1])return [popt, np.sqrt(pcov.diagonal())]

项目：nmmn 作者：rsnemmen | 项目源码 | 文件源码

def _peakdetect_parabole_fitter(raw_peaks, x_axis, y_axis, points):"""Performs the actual parabole fitting for the peakdetect_parabole function.keyword arguments:raw_peaks -- A list of either the maximium or the minimum peaks, as givenby the peakdetect_zero_crossing function, with index used as x-axisx_axis -- A numpy list of all the x valuesy_axis -- A numpy list of all the y valuespoints -- How many points around the peak should be used during curvefitting, must be odd.return -- A list giving all the peaks and the fitted waveform, format:[[x, y, [fitted_x, fitted_y]]]"""func = lambda x, k, tau, m: k * ((x - tau) ** 2) + mfitted_peaks = []for peak in raw_peaks:index = peak[0]x_data = x_axis[index - points // 2: index + points // 2 + 1]y_data = y_axis[index - points // 2: index + points // 2 + 1]# get a first approximation of tau (peak position in time)tau = x_axis[index]# get a first approximation of peak amplitudem = peak[1]# build list of approximations# k = -m as first approximation?p0 = (-m, tau, m)popt, pcov = curve_fit(func, x_data, y_data, p0)# retrieve tau and m i.e x and y value of peakx, y = popt[1:3]# create a high resolution data set for the fitted waveformx2 = np.linspace(x_data[0], x_data[-1], points * 10)y2 = func(x2, *popt)fitted_peaks.append([x, y, [x2, y2]])return fitted_peaks

项目：frc_rekt 作者：jaustinpage | 项目源码 | 文件源码

def _fit_func_factory(a=None, b=None, c=None, d=None, e=None):# Specific with correctionif a and b and c and d and e:def func(x):"""Specific Function."""return a * ((b * (x + c))**d) + e# Specific without correctionelif a and b and c and d:def func(x):"""Specific Function."""return a * ((b * (x + c))**d)# Generic, which we have scipy.optimize.curve_fit run on# scipy.optimize.curve_fit will then give us a, b, c, d,# however, scipy.optimize has touble with e. We correct e "by hand"# at the end.else:def func(x, a, b, c, d):"""Unparameterized Function."""return a * ((b * (x + c))**d)return func

项目：frc_rekt 作者：jaustinpage | 项目源码 | 文件源码

def _generate_func_fit(func_factory, x, y):popt, pcov = optimize.curve_fit(func_factory(), x, y)logging.debug(popt)logging.debug(pcov)# Static shift to have the end condition be niceunshifted_func = func_factory(*popt)end_diff = y.iloc[-1] - unshifted_func(x.iloc[-1])logging.debug("end diff: %s", end_diff)popt_shifted = np.append(popt, end_diff)return func_factory(*popt_shifted)

项目：CAAPR 作者：Stargrazer82301 | 项目源码 | 文件源码

def fit(self, lambdav, fluxv, sigmav):if np.all(np.asarray(fluxv)>0):popt, pcov = curve_fit(self._bound_greybody, lambdav, fluxv, p0=(17,1e7), sigma=sigmav,absolute_sigma=False, maxfev=5000)return poptelse:return (0,0)# ----------------------------------------------------------------------

项目：CAAPR 作者：Stargrazer82301 | 项目源码 | 文件源码

def fit(self, lambdav, fluxv, sigmav):if np.all(np.asarray(fluxv)>0):popt, pcov = curve_fit(self._bound_greybody, lambdav, fluxv, p0=(17,1e7), sigma=sigmav,absolute_sigma=False, maxfev=5000)return poptelse:return (0,0)# ----------------------------------------------------------------------

项目：Orbit-Fitting 作者：jacob-i-skinner | 项目源码 | 文件源码

def initialGuessNoE(lower, upper, JDp, RVp):'''Make a guess of the values of the elements, assuming it is circular.Parameters----------lower : list(ndim)Lower bounds of the orbital elements.upper : list(ndim)Upper bounds of the orbital elements.JDp : listTimes of observation of the primary component.RVp : listRadial velocities of the primary component.Returns-------(K, T, P, y) : listOutput from curve_fit.'''from scipy.optimize import curve_fit# This is a simple harmonic function.def alteredNoERV(x, K, T, P, y):return K*cos((2*pi/P)*(x-T))+yreturn curve_fit(alteredNoERV, JDp, np.asarray(RVp), bounds=(lower, upper))[0]

项目：imgProcessor 作者：radjkarl | 项目源码 | 文件源码

def scaleSignal(img, fitParams=None,backgroundToZero=False, reference=None):'''scale the image between...backgroundToZero=True -> 0 (average background) and 1 (maximum signal)backgroundToZero=False -> signal+-3stdreference -> reference image -- scale image to fit this onereturns:scaled image'''img = imread(img)if reference is not None:#         def fn(ii, m,n):#             return ii*m+n#         curve_fit(fn, img[::10,::10], ref[::10,::10])low, high = signalRange(img, fitParams)low2, high2 = signalRange(reference)img = np.asfarray(img)ampl = (high2 - low2) / (high - low)img -= lowimg *= amplimg += low2return imgelse:offs, div = scaleParams(img, fitParams, backgroundToZero)img = np.asfarray(img) - offsimg /= divprint('offset: %s, divident: %s' % (offs, div))return img

项目：imgProcessor 作者：radjkarl | 项目源码 | 文件源码

def scaleParamsFromReference(img, reference):# saving startup time:from scipy.optimize import curve_fitdef ff(arr):arr = imread(arr, 'gray')if arr.size > 300000:arr = arr[::10, ::10]m = np.nanmean(arr)s = np.nanstd(arr)r = m - 3 * s, m + 3 * sb = (r[1] - r[0]) / 5return arr, r, bimg, imgr, imgb = ff(img)reference, refr, refb = ff(reference)nbins = np.clip(15, max(imgb, refb), 50)refh = np.histogram(reference, bins=nbins, range=refr)[0].astype(np.float32)imgh = np.histogram(img, bins=nbins, range=imgr)[0].astype(np.float32)import pylab as pltplt.figure(1)plt.plot(refh)plt.figure(2)plt.plot(imgh)plt.show()def fn(x, offs, div):return (x - offs) / divparams, fitCovariances = curve_fit(fn, refh, imgh, p0=(0, 1))perr = np.sqrt(np.diag(fitCovariances))print('error scaling to reference image: %s' % perr[0])# if perr[0] < 0.1:return params[0], params[1]

项目：imgProcessor 作者：radjkarl | 项目源码 | 文件源码

def _fit(x, y):popt, _ = curve_fit(function, x, y, check_finite=False)return popt

项目：CElegansBehaviour 作者：ChristophKirst | 项目源码 | 文件源码

def g(x,a,b):return a * np.exp(b * ()popt, pcov = opt.curve_fit(f, xdata, w, p0 = (0.1, 10));plt.figure(1); plt.clf();
plt.plot(xdata, w)
plt.plot(xdata, f(xdata, *popt), 'r')
plt.title(str(popt))

项目：Wind-Turbine-Design 作者：doublerob7 | 项目源码 | 文件源码

def sigmoid(x, x0, k):import numpy as npimport pylabfrom scipy.optimize import curve_fity = 1 / (1 + np.exp(-k*(x-x0)))return y

项目：scrap 作者：BruceJohnJennerLawso | 项目源码 | 文件源码

def __init__(self, data, mleValue, fitParameters=True, a=None, b=None):super(uniformModel, self).__init__(data)self.MLE = mleValueif(None in [a,b]):fitParameters = Trueif(fitParameters):self.b = max(self.getDataSet())self.a = min(self.getDataSet())def uniDist(x, a, b):return scipy.where((a<=x) & (x<=b), 1.0/float(b-a), 0.0)try:self.n, self.bins, patches = plt.hist(self.getDataSet(), self.getDatasetSize()/10, normed=1, facecolor='blue', alpha = 0.55)popt,pcov = curve_fit(uniDist,self.bins[:-1], self.n, p0=[self.a, self.b])##plt.plot(bins[:-1], gaus(bins[:-1],*popt),'c-',label="Gaussian Curve with params\na=%f\nx0=%f\nsigma=%f" % (popt[0], popt[1], popt[2]), alpha=0.5)print "Fitted uniform distribution pdf curve to data with params a %f, b %f" % (popt[0], popt[1])self.a = popt[0]self.b = popt[1]##self.sigma = popt[2]self.fitted = Trueexcept RuntimeError:print "Unable to fit data to uniform distribution pdf curve"raiseexcept Warning:raise RuntimeErrorelse:self.a = aself.b = b

项目：scrap 作者：BruceJohnJennerLawso | 项目源码 | 文件源码

def __init__(self, data, mleValue, fitParameters=True, mean=None, sigma=None):super(normalModel, self).__init__(data)self.MLE = mleValueif(None in [mean, sigma]):fitParameters=Trueif(fitParameters):mean = Mean(self.getDataSet())      sigma = standardDeviation(self.getDataSet())try:def normDist(x, x0, sigma):output = 1.0/sqrt(2*np.pi*(sigma**2))output *= exp(-0.5*((x - x0)/sigma)**2)return outputself.n, self.bins, patches = plt.hist(self.getDataSet(), self.getDatasetSize()/10, normed=1, facecolor='blue', alpha = 0.55)popt,pcov = curve_fit(normDist,self.bins[:-1], self.n, p0=[mean, sigma])##plt.plot(bins[:-1], gaus(bins[:-1],*popt),'c-',label="Gaussian Curve with params\na=%f\nx0=%f\nsigma=%f" % (popt[0], popt[1], popt[2]), alpha=0.5)print "Fitted gaussian curve to data with params x0 %f, sigma %f" % (popt[0], popt[1])self.x0 = popt[0]self.sigma = popt[1]self.fitted = Trueexcept RuntimeError:print "Unable to fit data to normal curve, runtime error"raiseexcept Warning:raise RuntimeErrorelse:self.x0 = meanself.sigma = sigma

项目：scrap 作者：BruceJohnJennerLawso | 项目源码 | 文件源码

def __init__(self, data, mleValue, fitParameters=True, mean=None):super(exponentialModel, self).__init__(data)self.MLE = mleValueif(None in [mean]):fitParameters = Trueif(fitParameters):mean = Mean(self.getDataSet())      try:def expDist(x, x0):return (exp(-(x/x0))/x0)self.n, self.bins, patches = plt.hist(self.getDataSet(), self.getDatasetSize()/10, normed=1, facecolor='blue', alpha = 0.55)popt,pcov = curve_fit(expDist,self.bins[:-1], self.n, p0=[mean])##plt.plot(bins[:-1], gaus(bins[:-1],*popt),'c-',label="Gaussian Curve with params\na=%f\nx0=%f\nsigma=%f" % (popt[0], popt[1], popt[2]), alpha=0.5)print "Fitted gaussian curve to data with params x0 %f" % (popt[0])self.x0 = popt[0]##self.sigma = popt[2]self.fitted = Trueexcept RuntimeError:print "Unable to fit data to exponential curve"raiseexcept Warning:raise RuntimeErrorelse:self.x0 = mean

项目：PyCS 作者：COSMOGRAIL | 项目源码 | 文件源码

def calcslope(self, fmin=1./1000.0, fmax=1./2.0):"""Measures the slope of the PS, between fmin and fmax.All info about this slope is sored into the dictionnary self.slope.Should this depend on self.flux ?? No, only the constructor depends on this !This is just fitting the powerspectrum as given by the constructor."""if fmin == None:fmin = self.f[1]if fmax == None:fmax = self.f[-1]reg = np.logical_and(self.f <= fmax, self.f >= fmin)fitx = np.log10(self.f[reg]).flatten()fity = np.log10(self.p[reg]).flatten()if not np.all(np.isfinite(fity)):print "Skipping calcsclope for flat function !"returnself.slope = {}def func(x, m, h):return m*x + hpopt = spopt.curve_fit(func, fitx, fity, p0=[0.0, 0.0])[0]sepfit = func(fitx, popt[0], popt[1]) # we evaluate the linear fit.self.slope["slope"] = popt[0]self.slope["f"] = 10.0**fitxself.slope["p"] = 10.0**sepfitself.slope["fmin"] = fminself.slope["fmax"] = fmax

项目：upho 作者：yuzie007 | 项目源码 | 文件源码

def _create_initial_peak_position(self, frequencies, sf, prec=1e-12):position = frequencies[np.argmax(sf)]# "curve_fit" does not work well for extremely small initial guess.# To avoid this problem, "position" is rounded.# See also "http://stackoverflow.com/questions/15624070"if abs(position) < prec:position = 0.0return position

项目：pyrl 作者：frsong | 项目源码 | 文件源码

def fit_psychometric(xdata, ydata, func=None, p0=None):"""Fit a psychometric function."""if func is None:func = 'cdf_gaussian'if p0 is None:if func == 'cdf_gaussian':p0 = [np.mean(xdata), np.std(xdata)]elif func == 'cdf_gaussian_with_guessing':p0 = [np.mean(xdata), np.std(xdata), 0.1]else:raise ValueError("[ pycog.fittools.fit_psychometric ] Need initial guess p0.")if isinstance(func, str):func = fit_functions[func]popt_list, pcov_list = curve_fit(func, xdata, ydata, p0=p0)# Return parameters with namesargs = inspect.getargspec(func).argspopt = OrderedDict()for name, value in zip(args[1:], popt_list):popt[name] = valuereturn popt, func

项目：Dart_EnvGIS 作者：geojames | 项目源码 | 文件源码

def expfit(x, p1, p2):return p1 * np.exp(p2*x)# popt, pcov = curve_fit(func, xdata, ydata)
#   popt = optimized parameters matrix (in the order they ar in your func)
#   pcov = covariance stats
#   p0 = (p1,p2,...) - initial guesses at the coeff, optional

项目：OilLibrary 作者：NOAA-ORR-ERD | 项目源码 | 文件源码

def normalized_cut_values(self, N=10):f_res, f_asph, _estimated = self.inert_fractions()cuts = list(self.culled_cuts())if len(cuts) == 0:if self.record.api is not None:oil_api = self.record.apielse:oil_rho = self.density_at_temp(288.15)oil_api = est.api_from_density(oil_rho)BP_i = est.cut_temps_from_api(oil_api)fevap_i = np.cumsum(est.fmasses_flat_dist(f_res, f_asph))else:BP_i, fevap_i = zip(*[(c.vapor_temp_k, c.fraction) for c in cuts])popt, _pcov = curve_fit(_linear_curve, BP_i, fevap_i)f_cutoff = _linear_curve(732.0, *popt)  # center of asymptote (< 739)popt = popt.tolist() + [f_cutoff]fevap_i = np.linspace(0.0, 1.0 - f_res - f_asph, (N * 2) + 1)[1:]T_i = _inverse_linear_curve(fevap_i, *popt)fevap_i = fevap_i.reshape(-1, 2)[:, 1]T_i = T_i.reshape(-1, 2)[:, 0]above_zero = T_i > 0.0T_i = T_i[above_zero]fevap_i = fevap_i[above_zero]return T_i, fevap_i

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