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 progressorand import it in python using:
import progressorCompute 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: /
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Release files for progressor 0.1.19
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Source distribution (sdist)
| File | Size | Uploaded | |
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| progressor-0.1.19.tar.gz | 5.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| progressor-0.1.19-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details |
Total release size: 11.2 kB
Release files / progressor-0.1.19.tar.gz
| Download URL | progressor-0.1.19.tar.gz |
|---|---|
| Size | 5.9 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / progressor-0.1.19-py2.py3-none-any.whl
| Download URL | progressor-0.1.19-py2.py3-none-any.whl |
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| Size | 5.2 kB |
| Tags | Python 2 Python 3 |
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