Python 'AttributeError: 'function' object has no attribute 'min''

Question

Firstly, apologies for how obvious these two questions seem to be; I'm very very new to this and don't have a clue what I'm doing.

I'm trying to write something to apply the Scipy function for spline interpolation to an array of values. My code currently looks like this:

import numpy as np
import scipy as sp
from scipy.interpolate import interp1d

x=var
x1 = ([0.1,0.3,0.4])
y1 = [0.2,0.5,0.6]

new_length = 25
new_x = np.linspace(x.min(), x.max(), new_length)
new_y = sp.interpolate.interp1d(x, y, kind='cubic')(new_x)

but when it gets to the line

new_x = np.linspace(x.min(), x.max(), new_length)

I get the following error:

AttributeError: 'function' object has no attribute 'min'

and so far googling etc has turned up nothing that I understand. What does this mean and how do I fix it?

Second question: how do I input more than one line of code at once? At the moment, if I try to copy the whole thing and then paste it into PyLab, it only inputs the top line of my code, so I have to paste the whole thing in one line at a time. How do I get round this?

Answer 1

If this line

new_x = np.linspace(x.min(), x.max(), new_length)

is generating the error message

AttributeError: 'function' object has no attribute 'min'

then x is a function, and functions (in general) don't have min attributes, so you can't call some_function.min(). What is x? In your code, you've only defined it as

x=var

I'm not sure what var is. var isn't a default builtin in Python, but if it's a function, then either you've defined it yourself for some reason or you've picked it up from somewhere (say you're using Sage, or you did a star import like from sympy import * or something.)

[Update: since you say you're "using PyLab", probably var is numpy.var which has been imported into scope at startup in IPython. I think you really mean "using IPython in --pylab mode.]

You also define x1 and y1, but then your later code refers to x and y, so it sort of feels like this code is halfway between two functional states.

Now numpy arrays do have a .min() and .max() method, so this:

>>> x = np.array([0.1, 0.3, 0.4, 0.7])
>>> y = np.array([0.2, 0.5, 0.6, 0.9])
>>> new_length = 25
>>> new_x = np.linspace(x.min(), x.max(), new_length)
>>> new_y = sp.interpolate.interp1d(x, y, kind='cubic')(new_x)

would work. Your test data won't because the interpolation needs at least 4 points, and you'd get

ValueError: x and y arrays must have at least 4 entries