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"""A polynomial fit of degree="""
from vedo import np, precision, Text3D, DashedLine, show
from vedo.pyplot import plot, fit, histogram
# np.random.seed(0)
n = 25 # nr of data points to generate
deg = 3 # degree of the fitting polynomial
# Generate some noisy data points
x = np.linspace(0, 12, n)
y = (x-6)**3 /50 + 6 # the "truth" is a cubic function!
xerrs = np.linspace(0.4, 1.0, n) # make last points less precise
yerrs = np.linspace(1.0, 0.4, n) # make first points less precise
noise = np.random.randn(n)
# Plot the noisy points with their error bars
fig1 = plot(
x, y+noise,
".k",
title=__doc__+str(deg),
xerrors=xerrs,
yerrors=yerrs,
aspect=4/5,
xlim=(-3,15),
ylim=(-3,15),
padding=0,
)
fig1 += DashedLine(x, y, c='r')
# Fit points and evaluate, with a bootstrap and Monte-Carlo technique,
# the correct errors and error bands. Return a Line object:
pfit = fit(
[x, y+noise],
deg=deg, # degree of the polynomial
niter=500, # nr. of MC iterations to compute error bands
nstd=2, # nr. of std deviations to display
xerrors=xerrs, # optional array of errors on x
yerrors=yerrs, # optional array of errors on y
vrange=(-3,15), # specify the domain of fit
)
fig1 += [pfit, pfit.error_band, pfit.error_lines] # add these objects to Figure
# Add some text too
txt = "fit coefficients:\n " + precision(pfit.coefficients, 2) \
+ "\n:pm" + precision(pfit.coefficient_errors, 2) \
+ "\n:Chi^2_:nu = " + precision(pfit.reduced_chi2, 3)
fig1 += Text3D(txt, s=0.42, font='VictorMono').pos(4,-2).c('k')
# Create a 2D histo to show the correlation of fit parameters
fig2 = histogram(
pfit.monte_carlo_coefficients[:,0],
pfit.monte_carlo_coefficients[:,1],
title="parameters correlation",
xtitle='coeff_0', ytitle='coeff_1',
cmap='ocean_r',
scalarbar=True,
)
show(fig1, fig2, N=2, sharecam=False, zoom='tight').close()
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