Department of Physics and Astronomy

The Forbes Group

Uncertanties

## Uncertainties¶

Here we discuss the python uncertainties package and demonstrate some of its features.

The primary purpose of the uncertainties package is to represent quantities with correlated errors:

In [1]:
import mmf_setup;mmf_setup.nbinit(quiet=True)
import numpy as np
import uncertainties
from uncertainties import ufloat
x = ufloat(1.0, 0.1)
x

$\newcommand{\vect}[1]{\mathbf{#1}} \newcommand{\uvect}[1]{\hat{#1}} \newcommand{\abs}[1]{\lvert#1\rvert} \newcommand{\norm}[1]{\lVert#1\rVert} \newcommand{\I}{\mathrm{i}} \newcommand{\ket}[1]{\left|#1\right\rangle} \newcommand{\bra}[1]{\left\langle#1\right|} \newcommand{\braket}[1]{\langle#1\rangle} \newcommand{\Braket}[1]{\left\langle#1\right\rangle} \newcommand{\op}[1]{\mathbf{#1}} \newcommand{\mat}[1]{\mathbf{#1}} \newcommand{\d}{\mathrm{d}} \newcommand{\D}[1]{\mathcal{D}[#1]\;} \newcommand{\pdiff}[3][]{\frac{\partial^{#1} #2}{\partial {#3}^{#1}}} \newcommand{\diff}[3][]{\frac{\d^{#1} #2}{\d {#3}^{#1}}} \newcommand{\ddiff}[3][]{\frac{\delta^{#1} #2}{\delta {#3}^{#1}}} \newcommand{\floor}[1]{\left\lfloor#1\right\rfloor} \newcommand{\ceil}[1]{\left\lceil#1\right\rceil} \DeclareMathOperator{\Tr}{Tr} \DeclareMathOperator{\erf}{erf} \DeclareMathOperator{\erfi}{erfi} \DeclareMathOperator{\sech}{sech} \DeclareMathOperator{\sn}{sn} \DeclareMathOperator{\cn}{cn} \DeclareMathOperator{\dn}{dn} \DeclareMathOperator{\sgn}{sgn} \DeclareMathOperator{\order}{O} \DeclareMathOperator{\diag}{diag} \newcommand{\mylabel}[1]{\label{#1}\tag{#1}} \newcommand{\degree}{\circ}$
Out[1]:
1.0+/-0.1

Here $x$=x represents a quantity with nominal value 1.0 and error 0.1 in the sense of one standard deviation. I.e. we assume that the parameter $x$ represents a normally distributed random variable with a Gaussian probability distribution function (PDF)

$$P(x) = \frac{\exp\left(-\frac{(x-\bar{x})^2}{2\sigma^2}\right)} {\sqrt{2\pi\sigma^2}}$$

where $\bar{x} = \braket{x}$ is the mean of the distribution and $\sigma^2$ is the variance.

Base quantities can be combined in such a way that the errors propagate forward using standard error analysis techniques. This propagation of errors assumes that the errors represent 1 standard deviation of normal Gaussian errors and that the errors are small enough for any functional dependence to be well approximated by a linear relationship. For example, we can demonstrate the following simple rules for adding uncorrelated errors:

In [2]:
a = ufloat(1.0, 0.1)
b = ufloat(2.0, 0.3)


In [3]:
a+b, np.sqrt(a.s**2 + b.s**2)

Out[3]:
(3.0+/-0.31622776601683794, 0.31622776601683794)

In [4]:
print(a*b, np.sqrt((a.s/a.n)**2 + (b.s/b.n)**2)*(a*b).n)
print(a/b, np.sqrt((a.s/a.n)**2 + (b.s/b.n)**2)*(a/b).n)

2.0+/-0.4 0.36055512754639896
0.50+/-0.09 0.09013878188659974


Powers: Relative errors add in quadrature weighted by factors of the square of the power.

In [5]:
a**2*b**(-3), np.sqrt((2*a.s/a.n)**2 + (3*b.s/b.n)**2)*(a**2/b**3).n

Out[5]:
(0.125+/-0.06155536126122565, 0.06155536126122565)

## Correlations¶

One great feature is the ability to track correlations. Thus, if we $c=ab$, then the errors in $b$ and $c$ are correlated.

In [6]:
c = a*b
print("cov(a, b, c):\n{}\n".format(np.array(uncertainties.covariance_matrix([a, b, c]))))
print("cov(b, c):\n{}\n".format(np.array(uncertainties.covariance_matrix([b, c]))))

cov(a, b, c):
[[0.01 0.   0.02]
[0.   0.09 0.09]
[0.02 0.09 0.13]]

cov(b, c):
[[0.09 0.09]
[0.09 0.13]]



These correlations are described through the covariance matrix $\mat{\Sigma}$ which generalizes the variance $\sigma^2$ of a single variable:

$$\mat{\Sigma} = \braket{\delta\vect{x}\cdot\delta\vect{x}^T}, \qquad [\mat{\Sigma}]_{ij} = \braket{\delta x_i\delta x_j} = \braket{(x_i - \braket{x_i})(x_j-\braket{x_j})} = \braket{x_ix_j}-\braket{x_i}\braket{x_j}.$$

In the same way that for a single variable the interval $(x - \bar{x})^2 < (n\sigma)^2$ describes the $n\sigma$ deviations of a single parameter with 68.3% of the values lying with $1\sigma$, 95.4% lying within $2\sigma$ etc., the distribution of the $N$ correlated parameters is described by the ellipsoid

$$\delta\vect{x}^T\cdot\mat{\Sigma}^{-1}\cdot\delta\vect{x} \leq n^2, \qquad P(\vect{x}) = \frac{\exp\left( -\frac{1}{2}(\vect{x} - \bar{\vect{x}})^T \cdot\mat{\Sigma}^{-1}\cdot (\vect{x} - \bar{\vect{x}})^T\right)}{\sqrt{(2\pi)^N\det{\mat{\Sigma}}}}$$

The matrix $\mat{Q} = \mat{\Sigma}^{-1}$ is sometimes called the precision matrix which is equivalent to the Fisher information matrix in the special case of Gaussian errors.

As shown above, for any two variables, one can plot the corresponding covariance region by extracting the corresponding sub-matrix. Here we demonstrate this covariance region to show the meaning of the errors reported by the uncertainty package:

In [7]:
%pylab inline --no-import-all
import uncertainties
from scipy.stats import norm

a = uncertainties.ufloat(1.0, 0.1)
b = uncertainties.ufloat(2.0, 0.3)
c = a*b

mean = [b.n, c.n]
cov = uncertainties.covariance_matrix([b, c])

# Always use a fixed seed for reproducible data generation.
np.random.seed(1)
Nsamp = 1000
xs, ys = np.random.multivariate_normal(mean=mean, cov=cov, size=Nsamp).T
_x = np.linspace(xs.min(), xs.max(), 100)
_y = np.linspace(ys.min(), ys.max(), 100)

def plot_cov_ellipse(cov, mean, ax, points=100, fill=False, **kw):
d, U = np.linalg.eigh(cov)
thetas = 2*np.pi * np.arange(points)/points
xs, ys = U.dot(np.exp(1j*thetas).view(dtype=float).reshape(points, 2).T
* np.sqrt(d)[:, None])
path = matplotlib.path.Path(list(zip(xs+mean[0], ys+mean[1])), closed=True)

# definitions for the axes
axis_sep = 0.04
hist_size = 0.3
gs = plt.GridSpec(2, 2,
width_ratios=(1,hist_size),
height_ratios=(hist_size,1),
wspace=axis_sep, hspace=axis_sep)

axScatter = plt.subplot(gs[1,0])
axHistx = plt.subplot(gs[0,0], sharex=axScatter)
axHisty = plt.subplot(gs[1,1], sharey=axScatter)

# no labels
axHistx.tick_params(labelbottom=False)
axHisty.tick_params(labelleft=False)

# the scatter plot:
axScatter.scatter(xs, ys, alpha=0.1)
axScatter.set_xlabel('b={}'.format(b))
axScatter.set_ylabel('c={}'.format(c))
plot_cov_ellipse(cov, ax=axScatter, mean=mean)

# demonstrate what ufloat errors are
axScatter.axvspan(b.n-b.s, b.n+b.s, color='y', alpha=0.1)
axScatter.axhspan(c.n-c.s, c.n+c.s, color='y', alpha=0.1)

# histograms
axHistx.hist(xs, bins=50, density=True)
axHistx.plot(_x, norm.pdf(_x, b.n, b.s), '--')
axHisty.hist(ys, bins=50, density=True, orientation='horizontal')
axHisty.plot(norm.pdf(_y, c.n, c.s), _y, '--');

Populating the interactive namespace from numpy and matplotlib


## Example¶

Here we determine the period, phase, and amplitude of a sine wave using a least squares fit. To simulate the errors, we provide Guassian samples of the errors.

In [8]:
%pylab inline --no-import-all
import numpy as np
import uncertainties
from uncertainties import ufloat
from uncertainties import unumpy as unp

np.random.seed(2)  # Always use a seed so you can reproduce your results

def f(t, A, w, phi, np=np):
return A*np.sin(w*t + phi)

A = 1.0
w = 2*np.pi
phi = 0.1

N = 50  # Sample size
ts = np.linspace(0, 5.0, N)
fs = f(ts, A, w, phi)
f_std_dev = 0.1  # Error in data
dfs = np.random.normal(0, f_std_dev, size=N)
plt.errorbar(ts, fs+dfs, f_std_dev, fmt='+')
plt.plot(ts, fs, '-');

Populating the interactive namespace from numpy and matplotlib

In [9]:
from scipy.optimize import curve_fit

params_guess = (1.0, 2*np.pi, 1.0)
params, pcov = curve_fit(f, ts, fs + dfs, sigma=f_std_dev*np.ones(len(ts)),
p0=params_guess, absolute_sigma=True)

A_, w_, phi_ = uncertainties.correlated_values(
params, covariance_mat=pcov, tags=['A', 'w', 'phi'])

print(A_, w_, phi_)
A__ = ufloat(A_.n, A_.s)
w__ = ufloat(w_.n, w_.s)
phi__ = ufloat(phi_.n, phi_.s)

1.009+/-0.020 6.269+/-0.013 0.13+/-0.04

In [10]:
fs = f(ts, A, w, phi)
fs_ = f(ts, A_, w_, phi_, np=unp)
fs__ = f(ts, A__, w__, phi__, np=unp)
errs = unp.nominal_values(fs_ - fs)
plt.axhspan(-f_std_dev, f_std_dev, color='y', alpha=0.1)
plt.fill_between(ts, errs - unp.std_devs(fs__), errs + unp.std_devs(fs__),
label='uncorrelated', alpha=1.0)
plt.fill_between(ts, errs - unp.std_devs(fs_), errs + unp.std_devs(fs_),
label='correlated', alpha=1.0)
plt.ylabel('error')
plt.xlabel('t')
plt.legend(loc='best');


Above we demonstrate the difference between correlated and uncorrelated errors in the model parameters.

Here we use the Cholesky decomposition of the covariance matrix $\mat{C}$=pcov to generate correlated random values for the parameters. We check with a histogram that these are indeed correctly generated:

In [11]:
from scipy.stats import norm

M = 4
plt.figure(figsize=(6, 2))
C = np.linalg.cholesky(pcov)
labels = [r'$A$', r'$\omega$', r'$\phi$']
dA, dw, dphi = C.dot(np.random.normal(size=(3, N*M)))
for _n, (X, dX, label) in enumerate(
zip([A_, w_, phi_],
[dA, dw, dphi],
labels)):
_x = np.linspace(X.n-4*X.s, X.n + 4*X.s, 50)
plt.subplot(131+_n)
plt.plot(_x, norm.pdf(_x, X.n, X.s))
plt.hist(X.n + dX, density=True)
plt.xlabel(label)

In [12]:
import pandas as pd
labels = [r'$A$', r'$\omega$', r'$\phi$']
data = np.transpose([A_.n + dA, w_.n + dw, phi_.n + dphi])
data_frame = pd.DataFrame(data, columns=labels)
scatter_axes = pd.plotting.scatter_matrix(data_frame, diagonal="kde")

plt.tight_layout()

for _n, (X, dX) in enumerate(
zip([A_, w_, phi_],
[dA, dw, dphi])):
_x = np.linspace(X.n-4*X.s, X.n + 4*X.s, 50)
scatter_axes[_n, _n].plot(_x, norm.pdf(_x, X.n, X.s), '--')
plt.savefig('Correlations.svg')


## To Do¶

As an exercise, use such randomly generated data to check that the parameter estimates are correct.