# cleantimer
Track progress of long-running scripts, without cluttering your code with log statements.
cleantimer is a minimal wrapper around a couple of my favorite packages for timing scripts - [contexttimer](https://pypi.org/project/contexttimer/) and [tqdm](https://pypi.org/project/tqdm/). It merges their most useful features in a clean API based simply on the way I've found I like to use them. Hopefully you find it simply useful. 😊
## Installation
`pip install cleantimer`
### Import:
`from cleantimer import CTimer`
## Use cases
### A basic timer with a message for what you're timing:
```Python
with CTimer("Waking up"):
sleep(4)
```
```
Waking up (3:22PM)...done. (4.0s)
```
### Print with varying precision:
```Python
with CTimer("Waking up", 3):
sleep(4.123456)
```
```
Waking up (3:22PM)...done. (4.123s)
```
### Sub-timers
```Python
with CTimer("Making breakfast") as timer:
sleep(2)
with timer.child("cooking eggs") as eggtimer:
sleep(3)
with timer.child("pouring juice"):
sleep(1)
```
```
Making breakfast (3:22PM)...
cooking eggs (3:22PM)...done. (3.0s)
pouring juice (3:23PM)...done. (1.0s)
done. (6.0s)
```
### Progress meter on a Pandas apply
```Python
df = pd.DataFrame({"A": list(range(10000))})
def times2(row): return row["A"] * 2
with CTimer("Computing doubles") as timer:
df["2A"] = timer.progress_apply(df, times2)
```
```
Computing doubles (3:22PM)...
: 100% ██████████████████████████ 10000/10000 [00:07<00:00, 135869it/s]
done. (7.4s)
```
### Segmented progress meter
```Python
df = pd.DataFrame({"A": list(range(10000)), "type": [1]*5000 + [2]*5000})
def times2(row): return row["A"] * 2
with CTimer("Computing doubles") as timer:
df["2A"] = timer.progress_apply(df, times2, split_col="type", message="part {}")
```
```
Computing doubles (3:22PM)...
part 1: 100% ██████████████████████████ 5000/5000 [00:07<00:00, 135869it/s]
part 2: 100% ██████████████████████████ 5000/5000 [00:07<00:00, 122854it/s]
done. (8.2s)
```
Raw data
{
"_id": null,
"home_page": "https://github.com/alecglen/cleantimer",
"name": "cleantimer",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": "",
"keywords": "time,timer,progress",
"author": "Alec Ostrander",
"author_email": "",
"download_url": "https://files.pythonhosted.org/packages/f1/b7/1211ff034d28971b8c7365a5fafe78d83232e653888a192620df55e7fdb8/cleantimer-0.0.2.tar.gz",
"platform": null,
"description": "# cleantimer\n\nTrack progress of long-running scripts, without cluttering your code with log statements.\n\ncleantimer is a minimal wrapper around a couple of my favorite packages for timing scripts - [contexttimer](https://pypi.org/project/contexttimer/) and [tqdm](https://pypi.org/project/tqdm/). It merges their most useful features in a clean API based simply on the way I've found I like to use them. Hopefully you find it simply useful. \ud83d\ude0a\n\n## Installation\n\n`pip install cleantimer`\n\n### Import:\n\n`from cleantimer import CTimer`\n\n## Use cases\n\n### A basic timer with a message for what you're timing:\n\n```Python\nwith CTimer(\"Waking up\"):\n sleep(4)\n```\n\n```\nWaking up (3:22PM)...done. (4.0s)\n```\n\n### Print with varying precision:\n\n```Python\nwith CTimer(\"Waking up\", 3):\n sleep(4.123456)\n```\n\n```\nWaking up (3:22PM)...done. (4.123s)\n```\n\n### Sub-timers\n\n```Python\nwith CTimer(\"Making breakfast\") as timer:\n sleep(2)\n with timer.child(\"cooking eggs\") as eggtimer:\n sleep(3)\n with timer.child(\"pouring juice\"):\n sleep(1)\n```\n\n```\nMaking breakfast (3:22PM)...\n cooking eggs (3:22PM)...done. (3.0s)\n pouring juice (3:23PM)...done. (1.0s)\ndone. (6.0s)\n```\n\n### Progress meter on a Pandas apply\n\n```Python\ndf = pd.DataFrame({\"A\": list(range(10000))})\ndef times2(row): return row[\"A\"] * 2\n\nwith CTimer(\"Computing doubles\") as timer:\n df[\"2A\"] = timer.progress_apply(df, times2)\n```\n\n```\nComputing doubles (3:22PM)...\n : 100% \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 10000/10000 [00:07<00:00, 135869it/s]\ndone. (7.4s)\n```\n\n### Segmented progress meter\n\n```Python\ndf = pd.DataFrame({\"A\": list(range(10000)), \"type\": [1]*5000 + [2]*5000})\ndef times2(row): return row[\"A\"] * 2\n\nwith CTimer(\"Computing doubles\") as timer:\n df[\"2A\"] = timer.progress_apply(df, times2, split_col=\"type\", message=\"part {}\")\n```\n\n```\nComputing doubles (3:22PM)...\n part 1: 100% \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 5000/5000 [00:07<00:00, 135869it/s]\n part 2: 100% \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 5000/5000 [00:07<00:00, 122854it/s]\ndone. (8.2s)\n```\n",
"bugtrack_url": null,
"license": "GPLv3",
"summary": "Track progress of long-running scripts, without cluttering your code with log statements.",
"version": "0.0.2",
"project_urls": {
"Homepage": "https://github.com/alecglen/cleantimer"
},
"split_keywords": [
"time",
"timer",
"progress"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "27aad3c9d95f48303f951b5210b3042dcb6d6fffd5270867c2e02c0a5f9b68fc",
"md5": "a2f9d98482817712db61fdf54644976e",
"sha256": "d692774600faff7b1eac56eb77ed722d288651c05ac1afc4d0b6b6aabdf9848d"
},
"downloads": -1,
"filename": "cleantimer-0.0.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "a2f9d98482817712db61fdf54644976e",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6",
"size": 16116,
"upload_time": "2023-10-29T03:07:32",
"upload_time_iso_8601": "2023-10-29T03:07:32.846096Z",
"url": "https://files.pythonhosted.org/packages/27/aa/d3c9d95f48303f951b5210b3042dcb6d6fffd5270867c2e02c0a5f9b68fc/cleantimer-0.0.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "f1b71211ff034d28971b8c7365a5fafe78d83232e653888a192620df55e7fdb8",
"md5": "0e6f62945138449281c932bec6bce136",
"sha256": "91e0a8e1708a2ef19eae3085b3dfe9d1831309648f07d783bc9aaedb37072f3a"
},
"downloads": -1,
"filename": "cleantimer-0.0.2.tar.gz",
"has_sig": false,
"md5_digest": "0e6f62945138449281c932bec6bce136",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6",
"size": 17182,
"upload_time": "2023-10-29T03:07:34",
"upload_time_iso_8601": "2023-10-29T03:07:34.357421Z",
"url": "https://files.pythonhosted.org/packages/f1/b7/1211ff034d28971b8c7365a5fafe78d83232e653888a192620df55e7fdb8/cleantimer-0.0.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-10-29 03:07:34",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "alecglen",
"github_project": "cleantimer",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "cleantimer"
}
JSON
