Basically I have two arrays, one containing the values of the x-axis and the second containing the values of the y-axis. The problem is, when I do
plt.semilogy(out_samp,error_mc)
I get this
Which doesn’t make any sense. That is because the plot functions plots everything as it encounters in the x array, not caring whether it’s sorted in ascending order or not. How can I sort these two arrays so that the x array is sorted by increasing value and the y axis sorted in the same way so that the points are the same but the plot is connected so that it doesn’t make this mess?
Thank you in advance!
Answers:
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Method 1
It is easier to zip, sort and unzip the two lists of data.
Example:
xs = [...] ys = [...] xs, ys = zip(*sorted(zip(xs, ys))) plot(xs, ys)
See the zip documentation here: https://docs.python.org/3.5/library/functions.html#zip
Method 2
Sort by the value of x-axis before plotting. Here is an MWE.
import itertools x = [3, 5, 6, 1, 2] y = [6, 7, 8, 9, 10] lists = sorted(itertools.izip(*[x, y])) new_x, new_y = list(itertools.izip(*lists)) # import operator # new_x = map(operator.itemgetter(0), lists) # [1, 2, 3, 5, 6] # new_y = map(operator.itemgetter(1), lists) # [9, 10, 6, 7, 8] # Plot import matplotlib.pylab as plt plt.plot(new_x, new_y) plt.show()
For small data, zip (as mentioned by other answerers) is enough.
new_x, new_y = zip(*sorted(zip(x, y)))
The result,
Method 3
An alternative to sort the lists would be to use NumPy arrays and use np.sort() for sorting. The advantage with using arrays would be a vectorized operation while computing a function like y=f(x). Following is an example of plotting a normal distribution:
Without using sorted data
mu, sigma = 0, 0.1 x = np.random.normal(mu, sigma, 200) f = 1/(sigma * np.sqrt(2 * np.pi)) *np.exp( - (x - mu)**2 / (2 * sigma**2) ) plt.plot(x,f, '-bo', ms = 2)
Output 1
With using np.sort() This allows straightforwardly using sorted array x while computing the normal distribution.
mu, sigma = 0, 0.1 x = np.sort(np.random.normal(mu, sigma, 200)) # or use x = np.random.normal(mu, sigma, 200).sort() f = 1/(sigma * np.sqrt(2 * np.pi)) *np.exp( - (x - mu)**2 / (2 * sigma**2) ) plt.plot(x,f, '-bo', ms = 2)
Alternatively if you already have both x and y data unsorted, you may use numpy.argsort to sort them a posteriori
mu, sigma = 0, 0.1 x = np.random.normal(mu, sigma, 200) f = 1/(sigma * np.sqrt(2 * np.pi)) *np.exp( - (x - mu)**2 / (2 * sigma**2) ) plt.plot(np.sort(x), f[np.argsort(x)], '-bo', ms = 2)
Notice that the code above uses sort() twice: first with np.sort(x) and then with f[np.argsort(x)]. The total sort() invocations can be reduced to one:
# once you have your x and f... indices = np.argsort(x) plt.plot(x[indices], f[indices], '-bo', ms = 2)
In both cases the output is
Output 2
Method 4
just do this
list=zip(*sorted(zip(*(x,y)))) plt.plot(*list)
sorted function will sort according to the 1st argument i.e x values
Method 5
I think you need to sort one array and the other array should also get sorted based on the first array. I got this solution from some other stack overflow question. Most probably this should be your solution.
out_samp,error_mc=zip(*sorted(zip(out_samp,error_mc)))
Now plot those two values, you get a correct graph.
Method 6
You can convert your arrays to numpy arrays, then use argsort on the first array, take the the array and sort both arrays with the argsort array.
All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0