stackoverflow.com › questions › 31638508 › plot-piecewise-function-in-python

Some answers are starting to get there... But the points are being connected into a line on the plot. How do we just plot the points?

import matplotlib.pyplot as plt
import numpy as np

def f(x):
 if(x == 2): return 0
 else: return 1

x = np.arange(0., 5., 0.2)

y = []
for i in range(len(x)):
   y.append(f(x[i]))

print x
print y

plt.plot(x,y,c='red', ls='', ms=5, marker='.')
ax = plt.gca()
ax.set_ylim([-1, 2])

plt.show()

Answer from litepresence on Stack Overflow

NumPy

numpy.org › doc › stable › reference › generated › numpy.piecewise.html

numpy.piecewise — NumPy v2.4 Manual

>>> np.piecewise(x, [x < 0, x >= 0], [lambda x: -x, lambda x: x]) array([2.5, 1.5, 0.5, 0.5, 1.5, 2.5]) Apply the same function to a scalar value.

reddit.com › r/learnpython › how to do piecewise functions in python?

r/learnpython on Reddit: How to do piecewise functions in python?

December 11, 2015 -

Hi guys,

I'm using numpy and plotly to graph piecewise functions. I previously used matlab. I need help to essentially translate matlab piecewise functions into something I can use in python. Here's what I had in matlab:

Constant1 = 5;
Constant2 = 10;

Formula1 = @(x,y) x*Constant1;
Formula2 = @(x,y) x*Constant2;

MainFormula = @(x,y) [Formula1(x,y)*(y<=5) + Formula2(x,y)*(y>5)];

So what this would essentially do is make 'MainFormula' equal to 'Formula1' for all values of y less than or equal to 5, and it would make 'MainFormula' equal to 'Formula2' for all values of y greater than 5.

I would really appreciate any help with this.

Thanks

Top answer

1 of 2

def pwise (x): if x < 0: result='less than' elif x==0: result = 'equal' ... #more elif followed by else return result

2 of 2

The direct translation would be this: constant1 = 5 constant2 = 10 formula1 = lambda x: x*constant1 formula2 = lambda x: x*constant2 main_formula = lambda x, y: formula1(x)*(y <= 5) + formula2(x)*(y > 5) Note that I am using python style conventions. All of your variable names could be used as-is, but for style reasons lowercase variable names separated by _ are usually used. Same with the spaces I added in x,y, y<=5, and y>5, they aren't required, just recommended for readability reasons. These style recommendations are found in pep8 , and can be checked automatically by a program of the same name. Further, you could do, for example lambda x, y: x*constant1, it doesn't hurt anything, but it is unnecessary and I think it makes the code less clear. However, assuming you are using numpy arrays (which you should be), it would be easier to do this: def formula(x, y, const1=5, const2=10, sep=5): x1 = x.copy() x1[y <= sep] *= const1 x1[y > sep] *= const2 return x You can then use formula in the exact same way as MainFormula. It will default to using 5 for the first constant and 10 for the second, and 5 for the value of y to use to differentiate the two, but you can change that by passing different values to the function, such as formula(x, y, sep=200), or formula(x, y, 50, 100). If you don't care about losing your original x, you can simplify this further: def formula(x, y, const1=5, const2=10, sep=5): x[y <= sep] *= const1 x[y > sep] *= const2 In this case, whatever array you pass to formula as x will be changed in-place, so nothing is returned. This will be considerably faster than any of the previous approaches when dealing with large datasets. You could even simplify this further by doing the y calculation in the function call if you wanted: def formula(x, isy, const1=5, const2=10): x[isy] *= const1 x[~isy] *= const2 And then call it like this: formula(x, y<=5) The final approach you could do is to handle x separately: def formula(isy, const1=5, const2=10): return isy*const1 + ~isy*const2 or: lambda isy: isy*constant1 + ~isy*constant2 And then call it like this: x1 = x*isy(y <=5)

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