I am using a np.vectorize-ed function and would like to see the progress of the function with tqdm. However, I have not been able to figure out how to do this.
All the suggestions I have found relate to converting the calculation into a for-loop, or into a pd.DataFrame.
I finally found a method that works to get the tqdm progress bar to update with a np.vectorize function. I wrap the vectorize function using a with
with tqdm(total=len(my_inputs)) as pbar:
my_output = np.vectorize(my_function)(my_inputs)
in my_function() I then add the following lines
global pbar
pbar.update(1)
and voila! I now have a progress bar that updates with each iteration. Only slight performance dip on my code.
Note: when you instantiate the function it might complain that pbar is not yet defined. Simply put a pbar = 0 before you instantiate, and then the function will call the pbar defined by the with
Hope it helps everyone reading here.
To the best of my knowledge,tqdm does not wrap over numpy.vectorize.
To display the progress bar for numpy arrays, numpy.ndenumerate can be used.
Given the inputs and function:
import numpy as np
from tqdm import tqdm
a = np.array([1, 2, 3, 4])
b = 2
def myfunc(a, b):
"Return a-b if a>b, otherwise return a+b"
if a > b:
return a - b
else:
return a + b
Replace this vectorised part below
# using numpy.vectorize
vfunc = np.vectorize(myfunc)
vfunc(a, b)
with this
# using numpy.ndenumerate instead
[myfunc(x,b) for index, x in tqdm(np.ndenumerate(a))]
To see the tqdm progress.
