def getMeans2D(energy_x, energy_y, z, lim):
ind = np.invert(np.isnan(z))
z = z[ind]
energy_x = energy_x[ind]
energy_y = energy_y[ind]
neighborhood = neighbors.NearestNeighbors(n_neighbors=250)
neighborhood.fit(np.transpose(np.matrix([energy_x, energy_y])))
step = 0.5
ener_x = np.arange(0, lim + step, step)
ener_y = np.arange(0, lim + step, step)
sigma_gaussianfit = []
mean_gaussianfit = []
for y in ener_y:
line_mean = []
line_sigma = []
for x in ener_x:
if x + y < lim:
dist, ind = neighborhood.kneighbors(X=[[x, y]])
z_ind = z[ind]
params = gaussian_fit(z_ind, binwidth=0.1, reducedChi2Max=10)
if math.isnan(params[0]):
line_mean.append(np.mean(z_ind))
line_sigma.append(np.sqrt(np.std(z_ind)))
else:
line_mean.append(params[1])
line_sigma.append(params[0])
else:
line_mean.append(math.nan)
line_sigma.append(math.nan)
mean_gaussianfit.append(line_mean)
sigma_gaussianfit.append(line_sigma)
return mean_gaussianfit, sigma_gaussianfit
def getMeans(energy_x, y):
ind = np.invert(np.isnan(y))
y = y[ind]
energy_x = energy_x[ind]
neighborhood = neighbors.NearestNeighbors(n_neighbors=1000)
neighborhood.fit(np.transpose(np.matrix(energy_x)))
step = 0.1
ener = np.arange(min(energy_x), max(energy_x), step)
sigma_gaussianfit = []
mean_gaussianfit = []
means = []
energy = []
reducedChi2 = []
for e in ener:
dist, ind = neighborhood.kneighbors(X=e)
y_ind = y[ind][np.invert(np.isnan(y[ind]))]
params, reduced = gaussian_fit(y_ind,
binwidth=0.1,
giveReducedChi2=True,
reducedChi2Max=10)
if not (math.isnan(params[1])):
means.append(np.mean(y_ind))
sigma_gaussianfit.append(params[0])
mean_gaussianfit.append(params[1])
energy.append(e)
reducedChi2.append(reduced)
return energy, means, mean_gaussianfit, sigma_gaussianfit, reducedChi2