| Name | valmix JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | Adjust numerical values from a terminal user interface. |
| upload_time | 2025-07-14 09:00:38 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
multiprocessing
value
tui
user
interface
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Valmix
<img align="right" width=400 src="https://github.com/stephane-caron/valmix/assets/1189580/280c02b9-46a4-4a61-bb42-3befd1c59879">
[](https://github.com/stephane-caron/valmix/actions)
[](https://stephane-caron.github.io/valmix/)
[](https://coveralls.io/github/stephane-caron/valmix?branch=main)
[](https://pypi.org/project/valmix/)
Valmix ("value mixer") gives a systematic way to tune Python program parameters from your terminal (similar to `alsamixer` for Linux users familiar with it). Wrap your parameters in `multiprocessing` values, pass them to both your program and `valmix.run()`, and a terminal user interface will appear 🪔 allowing you to modify parameters in real time while your program is running.
Code is shorter than words in [Usage](#usage) below :wink:
## Installation
### From conda-forge
```console
conda install -c conda-forge valmix
```
### From PyPI
```console
pip install valmix
```
## Usage
Suppose you have a Python program with parameters you want to tune:
```py
def main(kp: float, kd: float):
pass # your code here
```
Valmix gives a systematic way to tune these parameters from the command line. First, wrap your parameters in `multiprocessing.Value`s:
```py
import multiprocessing as mp
kp = mp.Value("f", 10.0)
kd = mp.Value("f", 1.0)
```
Next, update your program to read from the multiprocessing values. For example:
```py
import numpy as np
import time
def main(kp: mp.Value, kd: mp.Value):
with open("/tmp/output", "w") as output:
for _ in range(100):
u = np.clip(kp.value * 1.0 + kd.value * 0.1, 5.0, 20.0)
output.write(f"{u}\n")
output.flush()
time.sleep(1.0)
```
Finally, run your program and Valmix together, specifying the tuning range for each value:
```py
main_process = mp.Process(target=main, args=(kp, kd))
main_process.start()
valmix.run(
{
"kp": (kp, np.arange(0.0, 20.0, 0.5)),
"kd": (kd, np.arange(0.0, 10.0, 0.5)),
}
)
```
This will fire up a terminal user interface (TUI) where you can tune `kp` and `kd` while the program runs in the background:
Useful for instance to [tune robot behaviors](https://github.com/upkie/upkie/blob/main/examples/wheeled_balancing.py) in real-time 😉
## See also
Related software:
- [Textual](https://textual.textualize.io/): terminal user interface (TUI) framework for Python, used to build this tool.
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"description": "# Valmix\n\n<img align=\"right\" width=400 src=\"https://github.com/stephane-caron/valmix/assets/1189580/280c02b9-46a4-4a61-bb42-3befd1c59879\">\n\n[](https://github.com/stephane-caron/valmix/actions)\n[](https://stephane-caron.github.io/valmix/)\n[](https://coveralls.io/github/stephane-caron/valmix?branch=main)\n[](https://pypi.org/project/valmix/)\n\nValmix (\"value mixer\") gives a systematic way to tune Python program parameters from your terminal (similar to `alsamixer` for Linux users familiar with it). Wrap your parameters in `multiprocessing` values, pass them to both your program and `valmix.run()`, and a terminal user interface will appear \ud83e\ude94 allowing you to modify parameters in real time while your program is running.\n\nCode is shorter than words in [Usage](#usage) below :wink:\n\n## Installation\n\n### From conda-forge\n\n```console\nconda install -c conda-forge valmix\n```\n\n### From PyPI\n\n```console\npip install valmix\n```\n\n## Usage\n\nSuppose you have a Python program with parameters you want to tune:\n\n```py\ndef main(kp: float, kd: float):\n pass # your code here\n```\n\nValmix gives a systematic way to tune these parameters from the command line. First, wrap your parameters in `multiprocessing.Value`s:\n\n```py\nimport multiprocessing as mp\n\nkp = mp.Value(\"f\", 10.0)\nkd = mp.Value(\"f\", 1.0)\n```\n\nNext, update your program to read from the multiprocessing values. For example:\n\n```py\nimport numpy as np\nimport time\n\ndef main(kp: mp.Value, kd: mp.Value):\n with open(\"/tmp/output\", \"w\") as output:\n for _ in range(100):\n u = np.clip(kp.value * 1.0 + kd.value * 0.1, 5.0, 20.0)\n output.write(f\"{u}\\n\")\n output.flush()\n time.sleep(1.0)\n\n```\n\nFinally, run your program and Valmix together, specifying the tuning range for each value:\n\n```py\nmain_process = mp.Process(target=main, args=(kp, kd))\nmain_process.start()\n\nvalmix.run(\n {\n \"kp\": (kp, np.arange(0.0, 20.0, 0.5)),\n \"kd\": (kd, np.arange(0.0, 10.0, 0.5)),\n }\n)\n```\n\nThis will fire up a terminal user interface (TUI) where you can tune `kp` and `kd` while the program runs in the background:\n\n\n\nUseful for instance to [tune robot behaviors](https://github.com/upkie/upkie/blob/main/examples/wheeled_balancing.py) in real-time \ud83d\ude09\n\n## See also\n\nRelated software:\n\n- [Textual](https://textual.textualize.io/): terminal user interface (TUI) framework for Python, used to build this tool.\n",
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