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alive-progress

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Have you ever wondered where your lengthy processing was at, and when would it finish? Do you usually hit RETURN several times to make sure it didn't crash, or the SSH connection didn't freeze? Have you ever thought it'd be awesome to be able to pause some processing without hassle, return to the Python prompt to manually fix some items, then seamlessly resume it? I did...

I've started this new progress bar thinking about all that, behold the alive-progress! 😃

alive-progress demo

Introducing the newest concept in progress bars for Python! alive-progress is in a class of its own, with an array of cool features that set it apart. Here are a few highlights:

Table of contents

This README is always evolving, so do take a more comprehensive look from time to time... You might find great new details in other sections! 😊

<!-- TOC --> <!-- TOC -->

📌 What's new in 3.2 series

After about a year of reassuring stability, the new alive-progress has finally landed!

The main features and improvements are:

And more!

Previous releases

<details> <summary>New in 3.1 series</summary>

A very cool update here! In addition to polishing things up and improving terminal support, now alive-progress supports resuming computations!

When processing huge datasets or things that take a long time, you might either use batches or cache partial results. Then, in case it stops and is restarted, you end up skipping all those already done items very quickly, which makes the alive_bar think you're processing thousands of items per second, which in turn completely ruins the ETA... But not anymore! Just tell bar() that you've skipped items... 👏

You can use it in two ways:

<strong>1.</strong> If you do know where you've stopped:

with alive_bar(120000) as bar:
    bar(60000, skipped=True)
    for i in range(60000, 120000):
        # process item
        bar()

Yep, just call bar(N, skipped=True) once, with the number of items.

<strong>2.</strong> If you do not know or the items are scattered:

with alive_bar(120000) as bar:
    for i in range(120000):
        if done(i):
            bar(skipped=True)
            continue

        # process item
        bar()

Yep, it's as simple as that! Just call bar(skipped=True) when an item is already done, or bar() as usual otherwise. You could also share a single bar(skipped=?) call at the end, with a bool saying whether you did skip that item or not. Cool, huh?

Also in this version:

</details> <details> <summary>New in 3.0 series</summary>

Yep, I could finally get this version out! These are the new goodies:

Highly anticipated fixes:

And last but not least, a more polished layout for you to enjoy your progress! alive-progress 3.0

</details> <details> <summary>New in 2.4 series</summary>

Now, alive_bar supports Dual Line text mode!

If you ever wanted to include longer situational messages within the bar, you probably felt squeezed into one line. You had to shrink the beautifully animated bar or, even worse, remove widgets (!) to be able to see what you needed... <br>Not anymore!! You can now make the bar Dual Line, and put text below it!

Yes, there's a message below the whole bar, and any other print/logging messages scroll above it!

letters = [chr(ord('A') + x) for x in range(26)]
with alive_bar(26, dual_line=True, title='Alphabet') as bar:
    for c in letters:
        bar.text = f'-> Teaching the letter: {c}, please wait...'
        if c in 'HKWZ':
            print(f'fail "{c}", retry later')
        time.sleep(0.3)
        bar()

Output:

on 7: fail "H", retry later
on 10: fail "K", retry later
Alphabet |███████████████████████████▊            | ▃▅▇ 18/26 [69%] in 6s (3.2/s, eta: 3s)
-> Teaching the letter: S, please wait...

There's also a new finalize function parameter in alive_it which enables you to set the title and/or text of the final receipt, and improved logging support which detects customized loggers.

</details> <details> <summary>New in 2.3 series</summary>

This is all about customization; the core widgets can now be changed:

It's incredible that these strings support all Python format features, so you can e.g., {percent:.1%} 😉.

They can be further customized when on the final receipt!

If you've hidden some widgets before, just so they wouldn't appear on the receipt, now you can see them in all their running glory, and hide just the receipt ones! Or the other way around 😜

Another addition, now alive-progress beautifully renders its cool final receipt whenever it is stopped, even if you CTRL+C it prematurely! I don't know why I haven't thought about that before...

Download |██████████████████⚠︎                     | (!) 45/100 [45%] in 4.8s (9.43/s)

And finally, you can choose to disable CTRL+C at all! The default is the safer ctrl_c=True, which does make CTRL-C work as usual. <br>Disable it ctrl_c=False, to make your interactive alive_bar much smoother to use (there are no stack traces if you stop it), and/or if it is at the top-level of your program!

Beware: If it is e.g. inside a for-loop, it will just continue to the next iteration, which may or may not be what you want...

for i in range(10):
    with alive_bar(100, ctrl_c=False, title=f'Download {i}') as bar:
        for i in range(100):
            time.sleep(0.02)
            bar()

Output:

Download 0 |████████▊⚠︎                              | (!) 22/100 [22%] in 0.6s (36.40/s)
Download 1 |████████████████▊⚠︎                      | (!) 42/100 [42%] in 1.0s (41.43/s)
Download 2 |██████▍⚠︎                                | (!) 16/100 [16%] in 0.4s (39.29/s)
Download 3 |█████▋⚠︎                                 | (!) 14/100 [14%] in 0.4s (33.68/s)
Download 4 |█████████████▎⚠︎                         | (!) 33/100 [33%] in 0.8s (39.48/s)
Download 5 |███████▎⚠︎                               | (!) 18/100 [18%] in 0.5s (37.69/s)
Download 6 |█████▎⚠︎                                 | (!) 13/100 [13%] in 0.3s (37.28/s)
Download 7 |████████████⚠︎                           | (!) 30/100 [30%] in 0.8s (38.43/s)
Download 8 |██████⚠︎                                 | (!) 15/100 [15%] in 0.4s (36.26/s)
...
</details> <details> <summary>New in 2.2 series</summary>

Some major new features, often requested, have finally landed!

</details> <details> <summary>New in 2.1 series</summary>

YES! Now alive-progress has support for Jupyter Notebooks and also includes a Disabled state! Both were highly sought after, and have finally landed! <br>And better, I've implemented an auto-detection mechanism for jupyter notebooks, so it just works, out of the box, without any changes in your code!!

See for yourself:

alive-progress demo

It seems to work very well, but at this moment, it should be considered experimental. <br>There were instances in which some visual glitches have appeared, like two alive_bar refreshes being concatenated together instead of over one another... And it's something I think I can't possibly work around: it seems Jupyter sometimes refresh the canvas at odd times, which makes it lose some data. Please let me know on the issues if something funnier arises.

</details> <details> <summary>New in 2.0 series</summary>

This is a major breakthrough in alive-progress! <br>I took 1 year developing it, and I'm very proud of what I've accomplished \o/

Since this is a major version change, direct backward compatibility is not guaranteed. If something does not work at first, just check the new imports and functions' signatures, and you should be good to go. All previous features should still work here! 👍

</details>

Using alive-progress

Get it

Just install with pip:

❯ pip install alive-progress

Try it

If you're wondering what styles are builtin, it's showtime! ;)

from alive_progress.styles import showtime

showtime()

Note: Please disregard the path in the animated gif below, the correct one is above. These long gifs are very time-consuming to generate, so I can't make another on every single change. Thanks for your understanding.

alive-progress spinners

I've made these styles just to try all the animation factories I've created, but I think some of them ended up very, very cool! Use them at will, and mix them to your heart's content!

Do you want to see actual alive-progress bars gloriously running in your system before trying them yourself?

❯ python -m alive_progress.tools.demo

alive-progress demo-tool

Awake it

Cool, huh?? Now enter an ipython REPL and try this:

from alive_progress import alive_bar
import time

for x in 1000, 1500, 700, 0:
    with alive_bar(x) as bar:
        for i in range(1000):
            time.sleep(.005)
            bar()

You'll see something like this, with cool animations throughout the process 😜:

|████████████████████████████████████████| 1000/1000 [100%] in 5.8s (171.62/s)
|██████████████████████████▋⚠︎            | (!) 1000/1500 [67%] in 5.8s (172.62/s)
|████████████████████████████████████████✗︎ (!) 1000/700 [143%] in 5.8s (172.06/s)
|████████████████████████████████████████| 1000 in 5.8s (172.45/s)

Nice, huh? Loved it? I knew you would, thank you 😊.

To actually use it, just wrap your normal loop in an alive_bar context manager like this:

with alive_bar(total) as bar:  # declare your expected total
    for item in items:  # <<-- your original loop
        print(item)  # process each item
        bar()  # call `bar()` at the end

And it's alive! 👏

So, in a nutshell: retrieve the items as always, enter the alive_bar context manager with the number of items, and then iterate/process those items, calling bar() at the end! It's that simple! :)

Master it

You can get creative! Since the bar only goes forward when you call bar(), it is independent of the loop! So you can use it to monitor anything you want, like pending transactions, broken items, etc., or even call it more than once in the same iteration! So, in the end, you'll get to know how many of those "special" events there were, including their percentage relative to the total!

Displaying messages

While inside an alive_bar context, you can effortlessly display messages tightly integrated with the current progress bar being displayed! It won't break in any way and will even enrich your message!

Awesome right? And all of these work just the same in a terminal or in a Jupyter notebook!

alive-progress printing messages

Auto-iterating

You now have a quicker way to monitor anything! Here, the items are automatically tracked for you! <br>Behold the alive_it => the alive_bar iterator adapter!

Simply wrap your items with it, and loop over them as usual! <br>The bar will just work; it's that simple!

from alive_progress import alive_it

for item in alive_it(items):  # <<-- wrapped items
    print(item)  # process each item

HOW COOL IS THAT?! 😜

All alive_bar parameters apply but total, which is smarter (if not supplied, it will be auto-inferred from your data using len or length_hint), and manual that does not make sense here.

Note there isn't any bar handle at all in there. But what if you do want it, e.g. to set text messages or retrieve the current progress? <br>You can interact with the internal alive_bar by just assigning alive_it to a variable like this:

bar = alive_it(items)  # <<-- bar with wrapped items
for item in bar:  # <<-- iterate on bar
    print(item)  # process each item
    bar.text(f'ok: {item}')  # WOW, it works!

Note that this is a slightly special bar, which does not support bar(), since the iterator adapter tracks items automatically for you. Also, it supports finalize, which enables you to set the title and/or text of the final receipt:

alive_it(items, finalize=lambda bar: bar.text('Success!'))
...

In a nutshell:

Modes of operation

Auto and Unknown: Counter

The default modes are auto and unknown, which use internally a counter to track the progress. They count the number of items processed, and use it to update the progress bar accordingly.

The total argument is optional. If you do provide it, the bar enters in auto mode. In this mode, the progress of the operation is automatically tracked, and all the widgets alive-progress has to offer are available: precise bar, spinner, percentage, counter, throughput, and ETA.

If you don't provide total, the bar enters in unknown mode. In this mode, the progress is indeterminable, and therefore the ETA, so the whole progress bar is continuously animated. The widgets available are: animated bar, spinner, counter, and throughput.

The cool spinner runs completely independently of the animated bar, both running their own animations concurrently, rendering a unique show in your terminal! 😜

Last but not least, the auto mode has a unique ability: mark items as skipped, making the throughput and ETA much more accurate! More on that later.

Manual: Percentage

The manual mode, manually activated by the manual=True argument, uses internally a percentage to track the progress. It enables you to get complete control of the bar position. It's usually used to monitor processes that only feed you a percentage of completion, or to generate some random special effects.

You can use it directly with alive_bar or via config_handler, and it allows you to send percentages to the bar() handler! For example, to set it to 15% completion, just call bar(0.15) — which is 15 / 100.

You can also provide total here. If you do, alive-progress will automatically infer an internal counter, and will thus be able to offer you all the same widgets available in auto mode!

If you don't provide total, you'll at least get rough versions of the throughput and ETA widgets, computed as "%/s" (percentage per second) and until 100%, respectively. Neither of them are very accurate, but they are better than nothing.

Widgets available

When total is provided all is cool:

modecounterpercentagethroughputETAover/underflow
auto✅ (user tick)✅ (inferred)✅✅✅
manual✅ (inferred)✅ (user set)✅✅✅

When it isn't, some compromises have to be made:

modecounterpercentagethroughputETAover/underflow
unknown✅ (user tick)❌✅❌❌
manual❌✅ (user set)⚠️ (simpler)⚠️ (rough)✅

But it's actually simple to understand: you do not need to think about which mode you should use!

That's it! It will just work the best it can! 👏 \o/

The different bar() handlers

The bar() handlers support either relative or absolute semantics, depending on the mode:

Both modes enable you to get creative! Since you can just make the bar go instantly to whatever position you want, you can:

You can call bar() as many times as you want! The terminal refresh rate will always be asynchronously computed according to the current throughput and progress, so you won't risk spamming the terminal with more updates than needed.

In any case, to retrieve the current counter/percentage, just call: bar.current:

Finally, the bar() handler leverages the auto mode unique ability: just call bar(skipped=True) or bar(N, skipped=True) to use it. When skipped is set to=True, the associated item(s) are excluded from throughput calculations, preventing skipped items from inaccurately affecting the ETA.

Maintaining an open source project is hard and time-consuming, and I've put much ❤️ and effort into this.

If you've appreciated my work, you can back me up with a donation! Thank you 😊

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Styles

The showtime exhibit has an optional argument to choose which show to present, Show.SPINNERS (default), Show.BARS or Show.THEMES, do take a look at them! ;)

from alive_progress.styles import showtime, Show

showtime(Show.BARS)
showtime(Show.THEMES)

Note: Please disregard the path in the animated gif below, the correct one is above. These long gifs are very time-consuming to generate, so I can't make another on every single change. Thanks for your understanding.

alive-progress bars

And the themes one (📌 new in 2.0):

alive-progress themes

The showtime exhibit also accepts some customization options:

For example to get a marine show, you can showtime(pattern='boat|fish|crab'):

alive-progress filtered spinners

You can also access these shows with the shorthands show_bars(), show_spinners(), and show_themes()!

There's also a small utility called print_chars(), to help find that cool character to put in your customized spinners and bars, or to determine if your terminal does support Unicode characters.

Configuration

There are several options to customize both appearance and behavior! <br>All of them can be set both directly in the alive_bar or globally in the config_handler!

These are the options - default values in brackets:

And there's also one that can only be set locally in the alive_bar context:

To set them locally, just send them as keyword arguments to alive_bar:

with alive_bar(total, title='Processing', length=20, bar='halloween') as bar:
    ...

To use them globally, send them to config_handler, and any alive_bar created after that will include those options! And you can mix and match them, local options always have precedence over global ones:

from alive_progress import config_handler

config_handler.set_global(length=20, spinner='wait')

with alive_bar(total, bar='blocks', spinner='twirls') as bar:
    # the length is 20, the bar is 'blocks' and the spinner is 'twirls'.
    ...

Create your own animations

Yes, you can assemble your own spinners! And it's easy! <br>I've created a plethora of special effects, so you can just mix and match them any way you want! There are frames, scrolling, bouncing, sequential, alongside, and delayed spinners! Get creative! 😍

Intro: How do they work?

The spinners' animations are engineered by very advanced generator expressions, deep within several layers of meta factories, factories and generators 🤯!

These generators are capable of multiple different animation cycles according to the spinner behavior, e.g. a bouncing spinner can run one cycle to smoothly bring a subject into the scene, then repeatedly reposition it until the other side, then make it smoothly disappear off the scene => and this is all only one cycle! Then it can be followed by another cycle to make it all again but backwards! And bouncing spinners also accept different and alternating patterns in both the right and left directions, which makes them generate the cartesian product of all the combinations, possibly producing dozens of different cycles until they start repeating them!! 🤯

And there's more, I think one of the most impressive achievements I got in this animation system (besides the spinner compiler itself)... They only yield more animation frames until the current cycle is not exhausted, then they halt themselves! Yep, the next cycle does not start just yet! This behavior creates natural breaks in exactly the correct spots, where the animations are not disrupted, so I can smoothly link with whatever other animation I want!! <br>This has all kinds of cool implications: the cycles can have different frame counts, different screen lengths, they do not need to be synchronized, they can create long different sequences by themselves, they can cooperate to play cycles in sequence or alongside, and I can amaze you displaying several totally distinct animations at the same time without any interferences whatsoever!

It's almost like they were... alive!! 😄 <br>==> Yes, that's where this project's name came from! 😉

A Spinner Compiler, really?

Now, these generators of cycles and frames are fully consumed ahead of time by the Spinner Compiler! This is a very cool new processor that I made inside the Cell Architecture effort, to make all these animations work even in the presence of wide chars or complex grapheme clusters! It was very hard to make these clusters gradually enter and exit frames, smoothly, while keeping them from breaking the Unicode encoding and especially maintain their original lengths in all frames! Yes, several chars in sequence can represent another completely different symbol, so they cannot ever be split! They have to enter and exit the frame always together, all at once, or the grapheme won't show up at all (an Emoji for instance)!! Enter the Spinner Compiler......

This has made possible some incredible things!! Since this Compiler generates the whole spinner frame data beforehand:

So, I can just collect all that ready to play animations and be done with it, no runtime overhead at all!! 👏

Also, with the complete frame data compiled and persisted, I could create several commands to refactor that data, like changing shapes, replacing chars, adding visual pauses (frame repetitions), generating bouncing effects on-demand over any content, and even transposing cycles with frames!!

But how can you see these effects? Does the effect you created look good? Or is it not working as you thought? YES, now you can see all generated cycles and frames analytically, in a very beautiful rendition!! <br>I love what I've achieved here 😊, it's probably THE most beautiful tool I've ever created... Behold the check tool!!

alive-progress check tool

It's awesome if I say so myself, isn't it? And a very complex piece of software I'm proud of, take a look at its code if you'd like.

And the check tool is much more powerful! For instance, you can see the codepoints of the frames!!! And maybe have a glimpse of why this version was so, so very hard and complex to make...

alive-progress check tool

In red, you see the grapheme clusters, that occupy one or two "logical positions", regardless of their actual sizes... These are the "Cells" of the new Cell Architecture... <br>Look how awesome an Emoji Flag is represented:

alive-progress check tool

The flag seems to move so smoothly because it uses "half-characters"! Since it is a wide char, alive-progress knows it will be rendered with "two visible chars", and the animations consider this, but compose with spaces, which occupy only one. When one uses mixed backgrounds, the situation is much more complex...

Spinner Factories

The types of factories I've created are:

For more details please look at their docstrings, which are very complete.

Bar Factories

Customizing bars is nowhere near that involved. Let's say they are "immediate", passive objects. They do not support animations, i.e. they will always generate the same rendition given the same parameters. Remember spinners are infinite generators, capable of generating long and complex sequences.

Well, bars also have a meta factory, use closures to store the styling parameters, and receive additional operating parameters, but then the actual factory can't generate any content by itself. It still needs an extra parameter, a floating-point number between 0 and 1, which is the percentage to render itself.

alive_bar calculates this percentage automatically based on the counter and total, but you can send it yourself when in the manual mode!

Bars also do not have a Bar Compiler, but they do provide the check tool!! 🎉

alive-progress check tool

You can even mix and match wide chars and normal chars just like in spinners! (and everything keeps perfectly aligned 😅)

alive-progress check tool

Use the check tools to your heart's content!! They have even more goodies awaiting you, even real-time animations!

Create the wildest and coolest animations you can and send them to me! <br>I'm thinking about creating some kind of contrib package, with user-contributed spinners and bars!

Wow, if you've read everything till here, you should now have a sound knowledge about using alive-progress! 👏 <br>But brace yourself because there is even more, exciting stuff lies ahead!

Maintaining an open source project is hard and time-consuming, and I've put much ❤️ and effort into this.

If you've appreciated my work, you can back me up with a donation! Thank you 😊

<img align="right" src="https://cdn.buymeacoffee.com/buttons/default-orange.png" width="217px" height="51x"> <img align="right" alt="Donate with PayPal button" src="https://www.paypalobjects.com/en_US/i/btn/btn_donate_LG.gif">

Advanced

The Pause Mechanism

Oh, you want to pause it altogether, I hear? This is an amazing novel concept, not found anywhere AFAIK. <br>With this you get to act on some items manually, at will, right in the middle of an ongoing processing!! <br>YES, you can return to the prompt and fix, change, submit things, and the bar will just "remember" where it was...

Suppose you need to reconcile payment transactions (been there, done that). You need to iterate over thousands of them, detect somehow the faulty ones, and fix them. This fix is not simple nor deterministic, you need to study each one to understand what to do. They could be missing a recipient, or have the wrong amount, or not be synced with the server, etc., it's hard to even imagine all possibilities.

Typically, you would have to let the detection process run until completion, appending to a list each inconsistency it finds and waiting, potentially a long time, until you can finally start fixing them... You could of course mitigate that by processing in chunks, or printing them and acting via another shell, etc., but those have their own shortcomings... 😓 <br>Now, there's a better way! Simply pause the actual detection process for a while! Then you just have to wait till the next fault is found, and act in near real-time!

To use the pause mechanism you just have to write a function, so the code can yield the items you want to interact with. You most probably already use one in your code, but in the ipython shell or another REPL you probably don't. So just wrap your debug code in a function, then enter within a bar.pause() context!!

def reconcile_transactions():
    qs = Transaction.objects.filter()  # django example, or in sqlalchemy: session.query(Transaction).filter()
    with alive_bar(qs.count()) as bar:
        for transaction in qs:
            if faulty(transaction):
                with bar.pause():
                    yield transaction
            bar()

That's it! It's that simple! \o/ <br>Now run gen = reconcile_transactions() to instantiate the generator, and whenever you want the next faulty transaction, just call next(gen, None)! I love it... <br>The alive-progress bar will start and run as usual, but as soon as any inconsistency is found, the bar will pause itself, turning off the refresh thread and remembering its exact state, and yield the transaction to you directly on the prompt! It's almost magic! 😃

In [11]: gen = reconcile_transactions()

In [12]: next(gen, None)
|█████████████████████                   | 105/200 [52%] in 5s (18.8/s, eta: 4s)
Out[12]: Transaction<#123>

You can then inspect the transaction with the usual _ shortcut of ipython (or just directly assign it with t = next(gen, None)), and you're all set to fix it! <br>When you're done, just reactivate the bar with the same next call as before!! The bar reappears, turns everything back on, and continues like it had never stopped!! Ok, it is magic 😜

In [21]: next(gen, None)
|█████████████████████                   | ▁▃▅ 106/200 [52%] in 5s (18.8/s, eta: 4s)

Rinse and repeat till the final receipt appears, and there'll be no faulty transactions anymore. 😄

Loop-less use

So, you need to monitor a fixed operation, without any loops, right? <br>It'll work for sure! Here is a naive example (we'll do better in a moment):

with alive_bar(4) as bar:
    corpus = read_file(file)
    bar()  # file was read, tokenizing
    tokens = tokenize(corpus)
    bar()  # tokens generated, processing
    data = process(tokens)
    bar()  # process finished, sending response
    resp = send(data)
    bar()  # we're done! four bar calls with `total=4`

It's naive because it assumes all steps take the same amount of time, but actually, each one may take a very different time to complete. Think read_file and tokenize may be extremely fast, which makes the percentage skyrocket to 50%, then stopping for a long time in the process step... You get the point, it can ruin the user experience and create a very misleading ETA.

To improve upon that you need to distribute the steps' percentages accordingly! Since you told alive_bar there were four steps, when the first one was completed it understood 1/4 or 25% of the whole processing was complete... Thus, you need to measure how long your steps actually take and use the manual mode to increase the bar percentage by the right amount at each step!

You can use my other open source project about-time to easily measure these durations! Just try to simulate with some representative inputs, to get better results. Something like:

from about_time import about_time

with about_time() as t_total:  # this about_time will measure the whole time of the block.
    with about_time() as t1:  # the other four will get the relative timings within the whole.
        corpus = read_file(file)  # `about_time` supports several calling conventions, including one-liners.
    with about_time() as t2:  # see its documentation for more details.
        tokens = tokenize(corpus)
    with about_time() as t3:
        data = process(tokens)
    with about_time() as t4:
        resp = send(data)

print(f'percentage1 = {t1.duration / t_total.duration}')
print(f'percentage2 = {t2.duration / t_total.duration}')
print(f'percentage3 = {t3.duration / t_total.duration}')
print(f'percentage4 = {t4.duration / t_total.duration}')

There you go! Now you know the relative timings of all the steps, and can use them to improve your original code! Just get the cumulative timings and put them within a manual mode alive_bar!

For example, if the timings you found were 10%, 30%, 20%, and 40%, you'd use 0.1, 0.4, 0.6, and 1.0 (the last one should always be 1.0):

with alive_bar(4, manual=True) as bar:
    corpus = read_big_file()
    bar(0.1)  # 10%
    tokens = tokenize(corpus)
    bar(0.4)  # 30% + 10% from previous steps
    data = process(tokens)
    bar(0.6)  # 20% + 40% from previous steps
    resp = send(data)
    bar(1.)  # always 1. in the last step

That's it! The user experience and ETA should be greatly improved now.

FPS Calibration

Yes, you can calibrate the spinner speed!

The alive-progress bars have cool visual feedback of the current throughput, so you can actually see how fast your processing is, as the spinner runs faster or slower with it. <br>For this to happen, I've put together and implemented a few fps curves to empirically find which one gave the best feel of speed:

<p align="center"><img alt="alive-progress fps curves" src="https://raw.githubusercontent.com/rsalmei/alive-progress/main/img/alive-bar_fps.png" width="80%" height="80%"></p> <p align="center">(interactive version [here](https://www.desmos.com/calculator/ema05elsux))</p>

The graph shows the logarithmic (red), parabolic (blue) and linear (green) curves, these are the ones I started with. It was not an easy task, I've made dozens of tests, and never found one that really inspired that feel of speed I was looking for. The best one seemed to be the logarithmic one, but it reacted poorly with small numbers. I know I could make it work with a few twists for those small numbers, so I experimented a lot and adjusted the logarithmic curve (dotted orange) until I finally found the behavior I expected! It is the one that seemed to provide the best all-around perceived speed changes throughout the whole spectrum from a few to billions... That is the curve I've settled with, and it's the one used in all modes and conditions. In the future and if someone would find it useful, that curve could be configurable.

Well, the default alive-progress calibration is 1,000,000 in bounded modes, i.e., it takes 1 million iterations per second for the bar to refresh itself at 60 frames per second. In the manual unbounded mode, it is 1.0 (100%). Both enable a vast operating range and generally work quite well.

For example, take a look at the effect these very different calibrations have, running the very same code at the very same speed! Notice the feel the spinner passes to the user, is this processing going slow or going fast? And remember that isn't only the spinner refreshing but the whole line, complete with the bar rendition and all widgets, so everything gets smoother or sluggish:

alive-progress calibration

So, if your processing hardly gets to 20 items per second, and you think alive-progress is rendering sluggish, you could increase that sense of speed by calibrating it to let's say 40, and it will be running waaaay faster... It is better to always leave some headroom and calibrate it to something between 50% and 100% more, and then tweak it from there to find the one you like the most! :)

Forcing animations on PyCharm, Jupyter, etc.

Do these astonishing alive-progress animations refuse to display?

PyCharm is awesome, I love it! But I'll never understand why they've disabled emulating a terminal by default... If you do use PyCharm's output console, please enable this on all your Run Configurations:

<p align="center"><img alt="alive-progress in pycharm" src="https://raw.githubusercontent.com/rsalmei/alive-progress/main/img/pycharm-terminal.png"></p>

I even recommend you go into File > New Projects Setup > Run Configuration Templates, select Python, and also enable it there, so any new ones you create will already have this set.

In addition to that, some terminals report themselves as "non-interactive", like when running out of a real terminal (PyCharm and Jupyter for example), in shell pipelines (cat file.txt | python program.py), or in background processes (not connected to a tty).

When alive-progress finds itself in a non-interactive terminal, it automatically disables all kinds of animations, printing only the final receipt. This is made in order to avoid both messing up the pipeline output and spamming your log file with thousands of alive-progress refreshes.

So, when you know it's safe, you can force them to see alive-progress in all its glory! Here is the force_tty argument:

with alive_bar(1000, force_tty=True) as bar:
    for i in range(1000):
        time.sleep(.01)
        bar()

The values accepted are:

You can also set it system-wide using config_handler, so you don't need to pass it manually anymore.

Do note that PyCharm's console and Jupyter notebooks are heavily instrumented and thus have much more overhead, so the outcome may not be as fluid as you would expect. On top of that, Jupyter notebooks do not support ANSI Escape Codes, so I had to develop some workarounds to emulate functions like "clear the line" and "clear from cursor"... To see the fluid and smooth alive_bar animations as I intended, always prefer a full-fledged terminal.

Interesting facts

To do

<details> <summary>Noteworthy features already done ✅</summary> </details> <details> <summary>Changelog highlights</summary>

<br>Complete here.

</details>

Python End of Life notice

alive_progress will always try to keep up with Python, so starting from version 2.0, I'll drop support for all Python versions which enter EoL. See their schedule here.

But don't worry if you can't migrate just yet: alive_progress versions are perennial, so just keep using the one that works for you and you're good. <br>I just strongly recommend setting older alive_progress packages in a requirements.txt file with the following formats. These will always fetch the latest build releases previous to a given version, so, if I ever release bug fixes, you'll get them too.

For Python 2.7 and 3.5

❯ pip install -U "alive_progress<2"

For Python 3.6

❯ pip install -U "alive_progress<2.2"

For Python 3.7 and 3.8

❯ pip install -U "alive_progress<3.2"

License

This software is licensed under the MIT License. See the LICENSE file in the top distribution directory for the full license text.

Maintaining an open source project is hard and time-consuming, and I've put much ❤️ and effort into this.

If you've appreciated my work, you can back me up with a donation! Thank you 😊

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