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Description:

progressor 0.1.19

A visually appealing progress bar for long lasting computations. It also
computes the remaining estimated time for the task by ad-hoc learning of
the completion so far. For this reason scikit-learn and numpy
are required.
You can install progressor via
pip install progressor
and import it in python using:
import progressor
Compute a task as follows:
from __future__ import print_function
import time

res = [ 0 ]

def task(elem):
time.sleep(0.01)
res[0] += elem

progressor.progress_list(range(1000), task, prefix="sleep list")
print(res[0])
or in a range:
def task_range(cur_ix, length):
task(cur_ix)

progressor.progress(0, 1000, task_range, prefix="sleep range")
print(res[0])
or while reading a file:
with progressor.IOWrapper(open(datafile, "r"), prefix="loading data", out=sys.stdout) as f_in:
data = f_in.read()
The output looks roughly like this:
sleep list: |████████████▌ | 62.30% (T 7.492s ETA 6.791s)
If no estimate of the progress towards completion can be made use:
def repeat(num):
while True:
yield num

progressor.progress_indef(repeat(1), task, prefix="sleep indefinitely")
which produces output like this:
sleep indefinitely: /