Pass numpy array as function argument [closed]
Pass numpy array as function argument [closed]
I want to pass a numpy array arr to what_a_function and print the resulting array, and this should be done without using loop. I know it is ugly and unfriendly, but I have to do so. I tried with vectorize but kept failing. May anyone share some pointers please? Thanks!
arr
what_a_function
vectorize
import numpy as np
def what_a_function(x):
return -np.cos(x.all()) * (0.5 < np.sin(x) < 2) + (np.sin(x) <= 0.5) + (x ** 2) * (np.sin(x) >= 2)
a=1
b=5
vfunc = np.vectorize(what_a_function)
arr = np.arange(a,b+0.1,0.1)
print(arr)
print(vfunc(arr))
And it will complain AttributeError: 'float' object has no attribute 'all'.
AttributeError: 'float' object has no attribute 'all'
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vectorize
for
x.all()
Why are you using vectorize?
– juanpa.arrivillaga
Aug 31 at 15:39
The point of
vectorize is to take a function that only works on one value at a time (e.g., a float) and turn it into a function that takes an array of values and automatically loops over them. That means your code should be expecting x to be a float, and using float methods on it, not NumPy array methods. Or, better, it should be using array methods, so you don’t need to use vectorize in the first place.– abarnert
Aug 31 at 15:49
vectorize
x
vectorize
vectorize pass scalar elements of arr to what_a_function. Thus what_a_function must work with a simple numeric x.– hpaulj
Aug 31 at 16:02
vectorize
arr
what_a_function
what_a_function
x
Thanks for all your reponse! I discovered later that it is because I forgot to remove
x.all(). Sorry!– Steven Shi
Sep 2 at 9:00
x.all()
2 Answers
2
That ' super ugly and unfriendly' equation doesn't make sense. What kind of x value is it supposed to evaluate?
x
-np.cos(x.all()) * (0.5 < np.sin(x) < 2) + (np.sin(x) <= 0.5) + (x ** 2) * (np.sin(x) >= 2)
x.all() requires an array (with all method), and returns a boolean (scalar or array), which is a nonsense input for np.cos.
x.all()
all
np.cos
np.sin(x) is ok with a scalar or array, but the 0.5<...<2 only works for a scalar (it's Python that doesn't work for numpy).
np.sin(x)
0.5<...<2
The next np.sin(x)<=.5 will produces a boolean (scalar or array). x**2 will be a numeric value.
np.sin(x)<=.5
x**2
The + and * will sort of work, converting the boolean True/False to 1/0 integers. But logical operators are better.
+
*
If we knew what is was supposed to do, we could probably write it to work directly with a numeric array. np.vectorize is not a good substitute for writing proper array compatible code. As I commented, vectorize passes the array values to the function one by one, as scalars. That's why the all method produces an error (and doesn't make sense). On top of that vectorize is slow.
np.vectorize
vectorize
all
vectorize
A straight forward list comprehension is faster:
np.array([your_function(i) for i in x])
Thanks for your clear and concise answer! It's later discovered that
x,all() is redundant.– Steven Shi
Sep 2 at 9:05
x,all()
The function all() takes list as its argument.
I made a tiny change to the code. I hope it helps!
import numpy as np
def what_a_function(x):
return all([-np.cos(x)]) * (0.5 < np.sin(x) < 2) + (np.sin(x) <= 0.5) + (x ** 2) * (np.sin(x) >= 2)
a=1
b=5
vfunc = np.vectorize(what_a_function)
arr = np.arange(a, b+0.1, 0.1)
print(arr)
print(vfunc(arr))
Output:[1. 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2. 2.1 2.2 2.3 2.4 2.5 2.6 2.7
2.8 2.9 3. 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4. 4.1 4.2 4.3 4.4 4.5
4.6 4.7 4.8 4.9 5. ]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
Please show an example input and output. Also,
vectorizeis basically just a convenience method that isn't going to be faster than aforloop. Since the code fails, it's not clear to me whatx.all()was intended to do.– roganjosh
Aug 31 at 15:38