How to extract points from a graph?


Question

I have a question.

I have plotted a graph using Matplotlib like this:

from matplotlib import pyplot
import numpy
from scipy.interpolate import spline

widths = numpy.array([0, 30, 60, 90, 120, 150, 180])
heights = numpy.array([26, 38.5, 59.5, 82.5, 120.5, 182.5, 319.5])

xnew = numpy.linspace(widths.min(),widths.max(),300)
heights_smooth = spline(widths,heights,xnew)

pyplot.plot(xnew,heights_smooth)
pyplot.show()

Now I want to query a height value using width value as an argument. I cannot seem to find how to do that. Please help! Thanks in advance!

1
6
3/24/2012 10:08:43 AM

Accepted Answer

plot() returns a useful object: [<matplotlib.lines.Line2D object at 0x38c9910>]
From that we can get x- and y-axis values:

import matplotlib.pyplot as plt, numpy as np
...
line2d = plt.plot(xnew,heights_smooth)
xvalues = line2d[0].get_xdata()
yvalues = line2d[0].get_ydata()

Then we can get the index of one of the width values:

idx = np.where(xvalues==xvalues[-2]) # this is 179.3979933110368
# idx is a tuple of array(s) containing index where value was found
# in this case -> (array([298]),)

And the corresponding height:

yvalues[idx]
# -> array([ 315.53469])

To check we can use get_xydata():

>>> xy = line2d[0].get_xydata()
>>> xy[-2]
array([ 179.39799331,  315.53469   ])
7
3/24/2012 11:08:16 AM

Here's another option if you're willing to use a different spline function:

from matplotlib import pyplot
import numpy
from scipy import interpolate

widths = numpy.array([0, 30, 60, 90, 120, 150, 180])
heights = numpy.array([26, 38.5, 59.5, 82.5, 120.5, 182.5, 319.5])

xnew = numpy.linspace(widths.min(),widths.max(),300)
heights_smooth = interpolate.splrep(widths,heights) #Use splrep instead of spline

#Select desired width values
width_vals = [0, 80.5, 38.98743]   

#splev returns the value of your spline evaluated at the width values.    
heights = interpolate.splev(width_vals, heights_smooth)

Then

In[]:  heights
Out[]: array([ 26.        ,  74.1721985 ,  44.47929453])

Or evaluate at a point:

w = 167.2
heights = interpolate.splev(w, heights_smooth)
height = heights.item()

In[]:  height
Out[]: 247.8396196684303

The .item() function is necessary because splev returns an array()


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