smpl.stat

Simplified statistics.

Functions

Chi2(y, f[, sigmas])	Chi2 - Goodness of Fit
R2(y, f)	R2 - Coefficient of determination
average_deviation(y, f)
chi2(y, f[, sigmas])	Chi2 - Goodness of Fit
derivative(func, x0[, dx, n, args, order])	Find the nth derivative of a function at a point.
fft(y)	fft(y,t,sp,freq) = Compute the FFT of y.
fftfreq(n[, d])	Return the Discrete Fourier Transform sample frequencies.
fftshift(x[, axes])	Shift the zero-frequency component to the center of the spectrum.
floor(x, /)	Return the floor of x as an Integral.
get_domain(f[, fmin, fmax, steps])	Return the statistically probed domain of the function f.
get_interesting_domain(f[, min_ch])	Return interesting xmin and xmax of function f.
is_monotone(f[, tmin, tmax, steps])	Test if function f is monotone.
log10(x, /)	Return the base 10 logarithm of x.
mean(n)	Return mean of n with combined error of variance and unvertainties of n.
no_dist(N)	Return N with no uncertainties.
noisy(x[, mean, std])	Add gaussian noise to x.
normal(x[, mean, std])
normalize(ydata)	Return normalized ydata.
novar_mean(n)	Return mean of n with only the uncertainties of n and no variance.
poisson_dist(N)	Return N with added poissonian uncertainties.
r2(y, f)	R2 - Coefficient of determination
round_sig(x[, sig])	Round to sig significant digits.
sfft(x[, n, axis, norm, overwrite_x, ...])	Compute the 1-D discrete Fourier Transform.
trim_domain(f[, fmin, fmax, steps, min_ch, ...])	Get the domain of the function f with the ranges removed where the derivative of f is below min_ch.
unv(arr)	Return the nominal values of the numbers in NumPy array arr.
unv_lambda(f)	Returns a function which applies unv() on the result of f.
usd(arr)	Return the standard deviations of the numbers in NumPy array arr.