about-time 4.2.2
Easily measure timing and throughput of code blocks, with beautiful human friendly representations.
License: MIT
Requires Python: <4,>=3.8
Keywords: track, tracker, time, code, blocks, monitor, statistics, analytics

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# about-time

### A cool helper for tracking time and throughput of code blocks, with beautiful human friendly renditions.

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## What does it do?

Did you ever need to measure the duration of an operation? Yeah, this is easy.

But how to:
- measure the duration of two or more blocks at the same time, including the whole duration?
- instrument a code to cleanly retrieve durations in one line, to log or send to time series databases?
- easily see human friendly durations in *s* (seconds), *ms* (milliseconds), *µs* (microseconds) and even *ns* (nanoseconds)?
- easily see human friendly counts with SI prefixes like *k*, *M*, *G*, *T*, etc?
- measure the actual throughput of a block? (this is way harder, since it needs to measure both duration and number of iterations)
- easily see human friendly throughputs in "/second", "/minute", "/hour" or even "/day", including SI prefixes?

Yes, it can get tricky! More interesting details about [duration](https://github.com/rsalmei/about-time#the-human-duration-magic) and [throughput](https://github.com/rsalmei/about-time#the-human-throughput-magic).
<br>If you'd tried to do it without these magic, it would probably get messy and immensely pollute the code being instrumented.

I have the solution, behold!

```python
from about_time import about_time


def some_func():
    import time
    time.sleep(85e-3)
    return True


def main():
    with about_time() as t1:  # <-- use it like a context manager!

        t2 = about_time(some_func)  # <-- use it with any callable!!

        t3 = about_time(x * 2 for x in range(56789))  # <-- use it with any iterable or generator!!!
        data = [x for x in t3]  # then just iterate!

    print(f'total: {t1.duration_human}')
    print(f'  some_func: {t2.duration_human} -> result: {t2.result}')
    print(f'  generator: {t3.duration_human} -> {t3.count_human} elements, throughput: {t3.throughput_human}')
```

This `main()` function prints:
```
total: 95.6ms
  some_func: 89.7ms -> result: True
  generator: 5.79ms -> 56.8k elements, throughput: 9.81M/s
```

How cool is that? 😲👏

You can also get the duration in seconds if needed:
```
In [7]: t1.duration
Out[7]: 0.09556673200064251
```
But `95.6ms` is way better, isn't it? The same with `count` and `throughput`!

So, `about_time` measures code blocks, both time and throughput, and converts them to beautiful human friendly representations! 👏

## Get it

Just install with pip:

```bash
❯ pip install about-time
```

## Use it

There are three modes of operation: context manager, callable and throughput. Let's dive in.

### 1. Use it like a context manager:

```python
from about_time import about_time

with about_time() as t:
    expensive()  # the code to be measured...

print(f'The whole block took: {t.duration_human}')
```

This way you can nicely wrap any amount of code.

> In this mode, there are the basic fields `duration` and `duration_human`.

### 2. Use it with any callable:

```python
from about_time import about_time

t = about_time(some_func)

print(f'The whole block took: {t.duration_human}')
print(f'And the result was: {t.result}')

```

This way you have a nice one liner, and do not need to increase the indent of your code.

> In this mode, there is an additional field `result`, with the return of the callable.

If the callable have params, you can use a `lambda` or (📌 new) simply send them:

```python
def add(n, m):
    return n + m


t = about_time(add, 1, 41)
# or:
t = about_time(add, n=1, m=41)
# or even:
t = about_time(lambda: add(1, 41))

```

### 3. Use it with any iterable or generator:

```python
from about_time import about_time

t = about_time(iterable)
for item in t:
    print(item)  # process item.

print(f'The whole block took: {t.duration_human}')
print(f'It was detected {t.count_human} elements')
print(f'The throughput was: {t.throughput_human}')
```

This way `about_time` also extracts the number of iterations, and with the measured duration it calculates the throughput of the whole loop! It's especially useful with generators, which do not have length.

> In this mode, there are the additional fields `count`, `count_human`, `throughput` and `throughput_human`.

Cool tricks under the hood:
- you can use it even with generator expressions, anything that is iterable to python!
- you can consume it not only in a `for` loop, but also in { list | dict | set } comprehensions, `map()`s, `filter()`s, `sum()`s, `max()`s, `list()`s, etc, thus any function that expects an iterator! 👏
- the timer only starts when the first element is queried, so you can initialize whatever you need before entering the loop! 👏
- the `count`/`count_human` and `throughput`/`throughput_human` fields are updated in **real time**, so you can use them even inside the loop!

## Features:

According to the SI standard, there are 1000 bytes in a `kilobyte`.
<br>There is another standard called IEC that has 1024 bytes in a `kibibyte`, but this is only useful when measuring things that are naturally a power of two, e.g. a stick of RAM.

Be careful to not render IEC quantities with SI scaling, which would be incorrect. But I still support it, if you really want to ;)

By default, this will use SI, `1000` divisor, and `no space` between values and scales/units. SI uses prefixes: `k`, `M`, `G`, `T`, `P`, `E`, `Z`, and `Y`.

These are the optional features:
- `iec` => use IEC instead of SI: `Ki`, `Mi`, `Gi`, `Ti`, `Pi`, `Ei`, `Zi`, `Yi` (implies `1024`);
- `1024` => use `1024` divisor — if `iec` is not enabled, use prefixes: `K`, `M`, `G`, `T`, `P`, `E`, `Z`, and `Y` (note the upper 'K');
- `space` => include a space between values and scales/units everywhere: `48 B` instead of `48B`, `15.6 µs` instead of `15.6µs`, and `12.4 kB/s` instead of `12.4kB/s`.

To change them, just use the properties:

```python
from about_time import FEATURES

FEATURES.feature_1024
FEATURES.feature_iec
FEATURES.feature_space
```

For example, to enable spaces between scales/units:
```python
from about_time import FEATURES

FEATURES.feature_space = True
```

## The human duration magic

I've used just one key concept in designing the human duration features: cleanliness.
> `3.44s` is more meaningful than `3.43584783784s`, and `14.1us` is much nicer than `.0000141233333s`.

So what I do is: round values to at most two decimal places (three significant digits), and find the best scale unit to represent them, minimizing resulting values smaller than `1`. The search for the best unit considers even the rounding been applied!
> `0.000999999` does not end up as `999.99us` (truncate) nor `1000.0us` (bad unit), but is auto-upgraded to the next unit `1.0ms`!

The `duration_human` units change seamlessly from nanoseconds to hours.
- values smaller than 60 seconds are always rendered as "num.D[D]unit", with one or two decimals;
- from 1 minute onward it changes to "H:MM:SS".

It feels much more humane, humm? ;)

Some examples:

| duration (float seconds) | duration_human |
|:------------------------:|:--------------:|
|       .00000000185       |    '1.85ns'    |
|      .000000999996       |    '1.00µs'    |
|          .00001          |    '10.0µs'    |
|         .0000156         |    '15.6µs'    |
|           .01            |    '10.0ms'    |
|      .0141233333333      |    '14.1ms'    |
|         .1099999         |    '110ms'     |
|         .1599999         |    '160ms'     |
|          .8015           |    '802ms'     |
|         3.434999         |    '3.43s'     |
|          59.999          |   '0:01:00'    |
|           68.5           |   '0:01:08'    |
|         125.825          |   '0:02:05'    |
|         4488.395         |   '1:14:48'    |

## The human throughput magic

I've made the `throughput_human` with a similar logic. It is funny how much trickier "throughput" is to the human brain!
> If something took `1165263 seconds` to handle `123 items`, how fast did it go? It's not obvious...

It doesn't help even if we divide the duration by the number of items, `9473 seconds/item`, which still does not mean much. How fast was that? We can't say.
<br>How many items did we do per time unit?
> Oh, we just need to invert it, so `0,000105555569858 items/second`, there it is! 😂

To make some sense of it we need to multiply that by 3600 (seconds in an hour) to get `0.38/h`, which is much better, and again by 24 (hours in a day) to finally get `9.12/d`!! Now we know how fast that process was! \o/ As you see, it's not easy at all.

The `throughput_human` unit changes seamlessly from per-second, per-minute, per-hour, and per-day.
<br>It also automatically inserts SI-prefixes, like k, M, and G. 👍

| duration (float seconds) | number of elements | throughput_human |
|:------------------------:|:------------------:|:----------------:|
|           1\.            |         10         |     '10.0/s'     |
|           1\.            |        2500        |    '2.50k/s'     |
|           1\.            |      1825000       |    '1.82M/s'     |
|           2\.            |         1          |     '30.0/m'     |
|           2\.            |         10         |     '5.00/s'     |
|    1.981981981981982     |         11         |     '5.55/s'     |
|          100\.           |         10         |     '6.00/m'     |
|          1600\.          |         3          |     '6.75/h'     |
|           .99            |         1          |     '1.01/s'     |
|        1165263\.         |        123         |     '9.12/d'     |

## Accuracy

`about_time` supports all versions of python, but in pythons >= `3.3` it performs even better, with much higher resolution and smaller propagation of errors, thanks to the new `time.perf_counter`. In older versions, it uses `time.time` as usual.

## Changelog highlights:
- 4.2.2: fix optional precision parameter not being actually optional; add `py.typed` in the distribution to satisfy mypy and other type checking tools; update build system and github workflow; drop Python 3.7 and include 3.12, 3.13, and 3.14.
- 4.2.1: makes fixed precision actually gain more resolution, when going from a default 1 to 2 decimals
- 4.2.0: support for fixed precision, useful when one needs output without varying lengths; official Python 3.11 support
- 4.1.0: enable to cache features within closures, to improve performance for https://github.com/rsalmei/alive-progress
- 4.0.0: new version, modeled after my Rust implementation in https://crates.io/crates/human-repr; includes new global features, new objects for each operation, and especially, new simpler human friendly representations; supports Python 3.7+
- 3.3.0: new interfaces for count_human and throughput_human; support more common Kbyte for base 2 (1024), leaving IEC one as an alternate
- 3.2.2: support IEC kibibyte standard for base 2 (1024)
- 3.2.1: support divisor in throughput_human
- 3.2.0: both durations and throughputs now use 3 significant digits; throughputs now include SI-prefixes
- 3.1.1: make `duration_human()` and `throughput_human()` available for external use
- 3.1.0: include support for parameters in callable mode; official support for python 3.8, 3.9 and 3.10
- 3.0.0: greatly improved the counter/throughput mode, with a single argument and working in real time
- 2.0.0: feature complete, addition of callable and throughput modes
- 1.0.0: first public release, context manager mode

## License

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


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