Question

I want to insert some arrows into a plot of some exponential distributions:

import pylab as pl
import numpy as np


def gauss2d(x,sigma):
    return (1/np.sqrt(2*np.pi*sigma ))*np.exp(-1/2*(x/sigma)**2 )

def draw_arrow(zero, sigma, function):
    startx = zero
    print startx,function(sigma, sigma)
    arr = pl.Arrow(startx,function(startx+sigma, sigma), sigma,0,fc="k",ec="k")
    ax = pl.gca()
    ax.add_patch(arr)

def plot_gauss2d():
    x = np.mgrid[115:135:100j]
    #x=np.array(zip(range(5)),dtype=float)
    sigma = 1
    off=1.0
    pl.plot(x,gauss2d(x-126.21,3.56), 'b-')
    draw_arrow(126.21, 3.56, gauss2d)
    pl.plot(x,gauss2d(x-126.71,4.57), 'b-')
    pl.plot(x,gauss2d(x-120.64,3.5), 'b-')
    pl.ylabel('frequency')
    pl.xlabel('ppm of N')
    pl.title
    pl.show()

def main():
    plot_gauss2d()                                                             

if __name__ == "__main__":
    main()

Somehow I can’t seem to get the arrow right. What I essentially would like to have is something like this:

what I simply cannot figure out is how to set the arrow straight to where I want it to be. It should mark the point of the standard deviation in the correct height. The whole thing should of course produce multiple exponential curves.

Answer 1

The problem with arrow is that it uses the figure coordinate as compared to the data coordinates. Hence, as @Paul have suggested, you can use annotate, as

import pylab as pl
import numpy as np


def gauss2d(x,sigma):
    return (1/np.sqrt(2*np.pi*sigma ))*np.exp(-1/2*(x/sigma)**2 )

def markParameters(m,s):
    p1=gauss2d(s,s)
    p2=gauss2d(0,s)

    pl.annotate("", xy=(m-s, p1), xycoords='data', xytext=(m+s, p1), textcoords='data', arrowprops=dict(arrowstyle="<->", connectionstyle="arc3"),)
    pl.text(m,p1,'sigma',horizontalalignment='center',verticalalignment='top')
    pl.annotate("", xy=(m, 0), xycoords='data', xytext=(m, p2), textcoords='data', arrowprops=dict(arrowstyle="<->", connectionstyle="arc3"),)
    pl.text(m,p2*0.75,'mean',horizontalalignment='right',verticalalignment='center',rotation=90)

def plot_gauss2d():
    x = np.mgrid[115:135:100j]
    #x=np.array(zip(range(5)),dtype=float)
    m,s=126,3.56

    pl.plot(x,gauss2d(x-m,s), 'b-')
    markParameters(m,s)

    pl.ylabel('frequency')
    pl.xlabel('ppm of N')
    pl.title
    pl.show()

def main():
    plot_gauss2d()

if __name__ == "__main__":
    main()