How to extract points from a graph?

Question

I have a question.

I plotted using Matplotlib as follows:

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 the height value using the width value as an argument. I can't seem to find how to do this. Please help! Thanks in advance!

Answer 1

plot() returns a useful object: [<matplotlib.lines.Line2D object at 0x38c9910>]
From here we can get the values ​​of the x and y axes:

 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 widths:

 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 verify that we can use get_xydata() :

 >>> xy = line2d[0].get_xydata() >>> xy[-2] array([ 179.39799331, 315.53469 ]) 

Answer 2

You can select an array in a list:

 >>> heights[list(widths).index(30)] 38.5 

for the interpolated result:

 s = xnew[56] print s, heights_smooth[list(xnew).index(s)] 33.7123745819, 40.9547542163 

Since xnew is an ordered list, you can use the bisect module to find the nearest width value for the requested width, and then find the corresponding height in the same way:

 .... import bisect pyplot.plot(xnew,heights_smooth) #33.1222 is a queried value which does not exist in xnew. index_of_nearest_width = bisect.bisect_left(xnew, 33.1222) width_val = xnew[index_of_closest_width] print width_val, heights_smooth[list(xnew).index(width_val)] #prints the nearest width to 33.1222 then the corresponding height. 33.7123745819 40.9547542163 

Answer 3

Here is another option if you want to use another 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:

 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 array()