Graphing standard deviations on a Gaussian using Matplotlib

Question

I'm trying to graph the standard deviations on a matplotlib graph, similar to http://en.wikipedia.org/wiki/File:Standard_deviation_diagram.svg

So far, I've managed to draw the curve:

mean = 0
variance = 1
rng = 4.
sigma = sqrt(variance)
x = np.linspace(-rng, rng, (rng * 10) * 2)
plt.plot(x, normpdf(x, mean, sigma))
plt.ylim(0, (rng / 10) + 0.05)

But I'm having difficulty rewriting this R function into Python:

polysection <- function(a, b, col, n=11){
    dx <- seq(a, b, length.out=n)
    polygon(c(a, dx, b), c(0, dnorm(dx), 0), col=col, border=NA)
    # draw a white vertical line on "inside" side to separate each section
    segments(a, 0, a, dnorm(a), col="white")
}

# Build the four left and right portions of this bell curve
for(i in 0:3){
    polysection(   i, i+1,  col=cols[i+1]) # Right side of 0
    polysection(-i-1,  -i,  col=cols[i+1]) # Left right of 0
}

So far, I've got this:

cols = np.array(["#2171B5", "#6BAED6", "#BDD7E7", "#EFF3FF"])

def polysection(a, b, col, n=11):
    dx = np.linspace(a, b, n)
    ax.add_patch(plt.Polygon(
            np.hstack((np.array(a), dx, np.array(b))),
            np.hstack((np.array(0), normpdf(dx, 0, 1), np.array(0)))))
    plt.plot(
        [a, 0],
        [a, normpdf(a, 0, 1)],
        color='blue')

for i in xrange(4):
    polysection(i, i+1, col=cols[i + 1]) 
    polysection(-i - 1, -i, col=cols[i + 1])

But I've got the coordinates in the Polygon() call wrong somehow, as I get:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

Alvaro Fuentes · Accepted Answer · 2014-01-13 18:41:26Z

1

The problem is that the argument to plt.Polygon should be a list of 2D tuples so try this:

xs = list(np.hstack((np.array(a), dx, np.array(b))))
ys = list(np.hstack((np.array(0), mlab.normpdf(dx, 0, 1), np.array(0))))
xy = zip(xs,ys)
ax.add_patch(plt.Polygon(xy))

edited Jan 13, 2014 at 18:41

answered Jan 13, 2014 at 17:40

Alvaro Fuentes

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