deffit_gaussian(x_data, y_data):""" Fit a gaussian to data """# Setup fit# Start values need to be ballpark correct!params = Parameters() params.add('A', value=-2000) params.add('mu', value=6562.801) params.add('sigma', value=1) out = minimize(residual, params, args=(x_data...
# 需要导入模块: from diffpy.srfit.fitbase import FitRecipe [as 别名]# 或者: from diffpy.srfit.fitbase.FitRecipe importclearFitHooks[as 别名]def_makeRecipe(self, x, y, dy):'''Make a FitRecipe for fitting a Gaussian curve to data. '''profile = Profile() profile.setObservedProfile(x,...
print "maximum is at x=", np.where(ysum==max) ##fit gaussian #fit only part of my data in the chosen range [glo:ghi] x=wavelen_pix[glo:ghi] y=ysum[glo:ghi] def func(x, a, x0, sigma): return a*np.exp(-(x-x0)**2/float((2*sigma**2))) sig=np.std(ysum[500:100...
def fit_gaussian(x, w=None, eps=None): """ Fits and returns a gaussian to a (possibly weighted) dataset using maximum likelihood. :param x: data matrix, rows are datapoints :param w: weights for each datapoint; if None, no weighting happens :param eps: a constant number to add to...
fit 1D (multiple) data including: spectra, surface brightness profiles, light curves, general ASCII arrays fit 2D images/surfaces in Poisson/Gaussian regime build complex model expressions import and use your own models use appropriate statistics for modeling Poisson or Gaussian data ...
参考:[Bayesian] “我是bayesian我怕谁”系列 - Gaussian Process 牛津讲义:An Introduction to Fitting Gaussian Processes to Data 博客:Fitting Gaussian Process Models in Python ###3.1 决策树回归### fromsklearnimporttree model_DecisionTreeRegressor=tree.DecisionTreeRegressor() Ref...
To illustrate the PyAutoFit API, we use an illustrative toy model of fitting a one-dimensional Gaussian to noisy 1D data. Here's the data (black) and the model (red) we'll fit: We define our model, a 1D Gaussian by writing a Python class using the format below: class Gaussian: def...
def fit_gaussian(x, y, yerr, p0): """ Fit a Gaussian to the data """ try: popt, pcov = curve_fit(gaussian, x, y, sigma=yerr, p0=p0, absolute_sigma=True) except RuntimeError: return [0],[0] return popt, pcov Example #22Source...
twoD_Gaussian的输出必须是1D。您可以在最后一行的结尾加上.ravel()，如下所示：
kde = stats.kde.gaussian_kde(samples_mean) x = np.linspace(bins[0], bins[-1], 100) pl.plot(x, kde(x), label="kde") # 拟合正态分布 mean, std = stats.norm.fit(samples_mean) pl.plot(x, stats.norm(mean,std).pdf(x), alpha=0.8, label="normal fitting") ...